ArticlePDF Available

Abstract and Figures

The current study examined differences in race relations as a predictor of life satisfaction among South African adults. We analysed data from the South African Social Attitudes Survey 2017 (n = 3 135; female = 61%; black = 61% , coloured/mixed-race = 16%, Indian South Africans = 11%, and white South Africans = 11%; mean age = 43 years, SD = 17.22 years). Linear regression models indicated that positive racial interaction predicted life satisfaction for black Africans, coloured/mixed-race, and the total sample in general. For the black Africans, education, household income, and living standard predicted life satisfaction, while age and household income predicted life satisfaction for the coloured/mixed-race group. Living standard predicted life satisfaction for South African Indians, and age and education predicted life satisfaction for white South Africans. These results support the importance of positive relations and diversity as salient sources of life satisfaction in a society transforming from a history of racial segregation
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
Journal of Psychology in Africa
ISSN: (Print) (Online) Journal homepage:
Racial relations and life satisfaction among South
Africans: Results from the 2017 South African
Social Attitudes Survey (SASAS)
Adekunle Adedeji, Erhabor S. Idemudia, Obasanjo Afolabi Bolarinwa &
Franka Metzner
To cite this article: Adekunle Adedeji, Erhabor S. Idemudia, Obasanjo Afolabi Bolarinwa & Franka
Metzner (2021) Racial relations and life satisfaction among South Africans: Results from the 2017
South African Social Attitudes Survey (SASAS), Journal of Psychology in Africa, 31:5, 522-528,
DOI: 10.1080/14330237.2021.1978183
To link to this article:
Published online: 30 Oct 2021.
Submit your article to this journal
View related articles
View Crossmark data
Journal of Psychology in Africa is co-published by NISC (Pty) Ltd and Informa UK Limited (trading as Taylor & Francis Group)
Journal of Psychology in Africa, 2021
Vol. 31, No. 5, 522–528,
© 2021 Africa Scholarship Development Enterprize
Positive racial relations presume an appreciation of
mutual habitation (Diène & UN Commission on Human
Right, 2006). Accordingly, race relations characterised
by equity and friendliness are essential for populations’
well-being (Patchen, 1999; Schulz et al., 2002; Valdez &
Golash-Boza, 2017), particularly in post-colonial country
settings such as South Africa (Staples, 1976). Despite the
progress made in many economic and social development
dimensions since post-apartheid in 1994, South Africa
remains one of the world’s most racially unequal countries
(United Nations Human Development Report, 2009).
Recent evidence suggests that racial minorities have low
life satisfaction due to depraved mental health, physical
health, environment, and socioeconomic circumstances
(Angner, 2010).
Globally, there is evidence of interracial strife
(Kirmanoğlu & Başlevent, 2014), with chronic
discrimination against minority race members by the
numerical majority group (Taylor & Turner, 2002).
South Africa, otherwise known as the “Rainbow Nation”
(a metaphor for a multiracial and ethnic country), has a
history of interracial conicts (Buqa, 2015; Olzak &
Olivier, 1998). There is evidence that experience of racial
discrimination is a risk factor for poor mental and physical
health outcomes (Hackett et al., 2020; Paradies et al.,
2015; Williams, 2018; Williams et al., 2019). However,
in post-apartheid South Africa, the racial majority (black
South Africans) are minorities economically (Møller,
2007) – thereby creating a unique pattern where the
numerical majority may have lower satisfaction with their
lives; resulting in more decient interracial interactions
(Ebrahim et al., 2013).
Socioeconomic stand and racial relation in
post-apartheid South Africa
According to Botha and colleagues (2018) and Mafini
(2017), some socioeconomic and demographic factors
moderate social relations’ effect on life satisfaction. A
study conducted by Davids and Gaibie (2011) identified
eight significant determinants of quality of life in post-
apartheid South Africa: race, gender, age, geographic
location, education level, living standard measure (LSM),
satisfaction with basic services, and fear of crime. In
addition, Patel and colleagues (2009) and Hino and
colleagues (2018) considered race a crucial determinant in
experiencing the desired quality of life in post-apartheid
South Africa. Studies have identified racial bias in social
and economic opportunities in post-colonial society
(Branson & Wittenberg, 2007; Leibbrandt et al., 2012;
Seekings, 2008). These historic inequalities may translate
into hostile interracial relationships (Vincent, 2008).
Goal of the study
This study examined the relationship between racial group
membership and life satisfaction among South Africans.
Our research questions were:
How does racial membership explain life satisfaction
among South African adults?
To what extent do the quality of interracial
relationships and socioeconomic factors explain life
satisfaction among South African adults?
Sources of data
We accessed the South African Social Attitudes Survey
data (SASAS, 2017). As indicated in Table 1, the sample
characteristics were as follows: females = (61%); blacks
Racial relations and life satisfaction among South Africans: Results from the 2017 South
African Social Attitudes Survey (SASAS)
Adekunle Adedeji1* , Erhabor S. Idemudia1, Obasanjo Afolabi Bolarinwa2 and Franka Metzner3
1Faculty of Humanities, North-West University, Mafikeng, South Africa
2Department of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
3Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
The current study examined differences in race relations as a predictor of life satisfaction among South African adults. We
analysed data from the South African Social Attitudes Survey 2017 (n = 3 135; female = 61%; black = 61% , coloured/
mixed-race = 16%, Indian South Africans = 11%, and white South Africans = 11%; mean age = 43 years, SD = 17.22
years). Linear regression models indicated that positive racial interaction predicted life satisfaction for black Africans,
coloured/mixed-race, and the total sample in general. For the black Africans, education, household income, and living
standard predicted life satisfaction, while age and household income predicted life satisfaction for the coloured/mixed-race
group. Living standard predicted life satisfaction for South African Indians, and age and education predicted life satisfaction
for white South Africans. These results support the importance of positive relations and diversity as salient sources of life
satisfaction in a society transforming from a history of racial segregation.
Keywords: black African, discrimination, life satisfaction, racial relations, representative survey, South African Social
Attitudes Survey
Racial relations and life satisfaction among South Africans 523
(61%), coloured/mixed-race (16%), Indian (11%), and
white (11%); mean age = 43 years (SD = 17.22 years).
Most of the black African participants (59%) reported
moderate (middle) living standards. However, for
coloured/mixed-race participants, a majority of 51%
reported high living standard. In comparison, 78% and
87% of Indians and whites, respectively, reported a high
living standard.
Life astisfaction
The Personal Well-being Index (PWI: International Well-
being Group, 2013) is a subjective measure of life quality.
The PWI summarises individual perceptions of living
standards, health, achievements in life, relationships,
safety, community connectedness, future, and security.
Participants rate their satisfaction with areas of their life
using a scale ranging from 0 = not at all satisfied with
life, to 10 = absolute satisfaction with life. For the current
sample, we observed a Cronbach’s alpha of 0.96 for scores
from the PWI.
Racial relations
The Subjective Evaluation of Interactions (Sigelman et al.,
1996) measures perceptions of interracial groups’ equality
and friendliness. Participants rate how much they agree
with the statements using a 5-point Likert scale, ranging
from 1 = totally agree, to 5 = totally disagree. In the present
study, the Cronbach’s alpha reliability of scores was 0.84
Statistical analyses
A one-way repeated measures ANOVA was conducted to
compare the effect of race on life satisfaction among black
Africans, coloured/mixed-race, Indian, and white South
Africans. Pearson’s correlations were applied to explore
the bivariate relationships between life satisfaction, racial
relations, socioeconomic, and demographic features. We
performed multiple linear regression analyses to predict
life satisfaction by racial interaction and racial group
membership. In doing so, we considered the influence
of sociodemographic variables and living standard to
determine which factors to include as control variable. We
conducted a literature review to identify likely covariants
for minorities life satisfaction (Cheng et al., 2021). We
kept the tests for statistical significance at p < 0.05 for all
Bivariate analysis
As indicated in Table 2, the aggregated life satisfaction
scores showed a moderate positive correlation
with the interracial relations in terms of equal
interactions (r = 0.233; p < 0.01) and friendly contacts
(r = 0.244; p < 0.01). Life satisfaction was weakly and
positively associated with age (r = 0.049; p < 0.05)
and education (r = 0.116; p < 0.01). Lastly, household
income (r = 0.331; p < 0.01) and living standard
(r = 0.404; p < 0.01) had a moderate association with life
satisfaction scores.
Table 1. Socioeconomic and demographic characteristics of participants (South African Social Attitudes Survey 2017, n = 3 135)
Black Africans Coloured Indian White Total
Male 735 38.3 167 32.9 151 42.2 171 49.0 1224 39.0
Female 1186 61.7 340 67.1 207 57.8 178 51.0 1911 61.0
Marital status
Married 486 25.3 225 44.4 206 57.5 221 63.3 1138 36.3
Separated from spouse / partner 64 3.3 14 2.8 11 3.1 4 1.1 93 3.0
Divorced 43 2.2 29 5.7 14 3.9 12 3.4 98 3.1
Widowed 168 8.7 46 9.1 62 17.3 42 12.0 318 10.1
Never married but engaged 86 4.5 17 3.4 2 0.6 11 3.2 116 3.7
Never married and not engaged 1032 53.7 171 33.7 56 15.6 52 14.9 1311 41.8
(Refused to answer) 15 0.8 4 0.8 3 0.8 3 0.9 25 0.8
(Do not know) 3 0.2 1 0.3 4 0.1
(No answer) 24 1.2 1 0.2 4 1.1 3 0.9 32 1.0
Highest education level
Primary 402 20.9 100 19.7 61 17.0 9 2.6 572 18.2
Some secondary, excl. Matric 704 36.6 222 43.8 103 28.8 78 22.3 1107 35.3
Matric or equivalent 596 31.0 130 25.6 136 38.0 162 46.4 1024 32.7
Tertiary education 178 9.3 50 9.9 50 14.0 96 27.5 374 11.9
(Other/Do not know) 13 0.7 4 0.8 3 0.8 2 0.6 22 0.7
(No answer) 28 1.5 1 0.2 5 1.4 2 0.6 36 1.1
Living standard
Low 110 5.7 3 0.6 – – 113 3.6
Middle 1128 58.7 217 42.8 49 13.7 18 5.2 1412 45.0
High 481 25.0 259 51.1 280 78.2 304 87.1 1324 42.2
(No answer) 202 10.5 28 5.5 29 8.1 27 7.7 286 9.1
Adedeji et al.
Similarly, racial relations had a positive association
with education, household income, and living standard.
Equal racial relations showed a week positive correlation
with education (r = 0.045; p < 0.05), household income
(r = 0.092; p < 0.05), and with living standard measure
(r = 0.145; p < 0.01). Additionally, friendly contact
showed a weak positive association with education
(r = 0.056; p < 0.05), income (r = 0.116; p < 0.01), and
with living standard measure (r = 0.143; p < 0.01).
Interracial relation
Data on racial relations shows that about 60% of black
African participants reported equal interaction with other
races. About 70% of coloured/mixed-race, 74% of Indian
participants, and 72% of white participants also report
equal interaction with other racial groups. Similarly, 59%
of black African participants, 75% of coloured/mixed-race,
73% of Indian, and 70% of white participants reported
friendly contact with other racial groups.
Life satisfaction
As indicated in Table 3, we observed a significant effect
of race on life satisfaction at the p < 0.05 level for all four
race group (F(3, 3131) = 100.006, p < 0.001). Six paired-
samples t-tests using the Scheffe test indicated significant
differences (p < 0.001) between mean life satisfaction
score for black Africans (M = 60.3, SD = 15.8) and
coloured/mixed-race participants (M = 67.3, SD = 15.2);
black Africans and Indians (M = 71.2, SD = 15.7); black
and white Africans (M = 71.7, SD = 12.9); coloured/
mixed-race participants and Indians; and coloured/mixed-
race and white participants. However, as depicted in
Figure 1, the mean life satisfaction score for Indians South
Africans did not significantly differ from the mean life
satisfaction score for Whites (p = 0.986).
Predicting life satisfaction from racial group
The results of the linear regression models presented in
Table 4, indicated that life satisfaction was significantly
associated with racial relations (adjusted R2 = 0.07)
for equal interactions (β = 0.115; p < 0.001), and
friendly contacts (β = 0.159; p < 0.001). Participants’
sociodemographic factors household income (β = 0.105;
p < 0.001), and living standard (β = 0.304; p < 0.001)
predicted life satisfaction (adjusted R2 = 0.21)
Black African racial group
For black African participants, we observed significant
influences of race (equal interactions β = 0.112; p = 0.009,
Table 2. Correlation Matrix1 (South African Social Attitudes Survey 2017, n = 3 135)
Racial relations
Age Education Household
Interact as
Life satisfaction 1
Racial relations Interact as equal 0.233** 1
Friendly contact 0.244** 0.746** 1
Age 0.049* –0.026 –0.012 1
Education 0.116** 0.045* 0.056* –0.224** 1
Household income 0.331** 0.092** 0.116** 0.018 0.251** 1
Living standard measure 0.404** 0.145** 0.143** 0.059** 0.308** 0.673** 1
Note. **Correlation is significant at the 0.01 level (2-tailed); * Correlation is significant at the 0.05 level (2-tailed);
1Listwise n = 2 065
Table 3. Descriptive distribution of life satisfaction score by race and Scheffe Multiple Comparisons ANOVA test (South African
Social Attitudes Survey 2017, n = 3 135)
Race (I) N (I) Mean (I) SD (I) SE (I) Race (J)
SE Sig.
95% Confidence
1 921 60.3024 15.76750 0.35975 Coloured −6.98552* 0.76789 < 0.001 −9.1333 −4.8377
Indian −10.92381* 0.88534 < 0.001 −13.4001 −8.4475
White −11.36804* 0.89491 < 0.001 −13.8711 −8.8650
Coloured /
507 67.2880 15.24477 0.67704 Black African 6.98552* 0.76789 < 0.001 4.8377 9.1333
Indian −3.93829* 1.06171 0.003 −6.9079 −0.9687
White −4.38252* 1.06970 0.001 −7.3745 −1.3906
Indian 358 71.2263 15.68605 0.82903 Black African 10.92381* 0.88534 < 0.001 8.4475 13.4001
Coloured 3.93829* 1.06171 0.003 0.9687 6.9079
White −0.44423 1.15691 0.986 −3.6801 2.7916
White 349 71.6705 12.88757 0.68986 Black African 11.36804* 0.89491 < 0.001 8.8650 13.8711
Coloured 4.38252* 1.06970 0.001 1.3906 7.3745
Indian 0.44423 1.15691 0.986 −2.7916 3.6801
Total 3 135 63.9451 16.09182 0.28740
Note. *The mean difference is significant at the 0.05 level; Dependent Variable: Life Satisfaction
Racial relations and life satisfaction among South Africans 525
and friendly contacts β = 0.180; p < 0.001, R2 = 0.08).
The sociodemographic variables of household income
(β = 0.095; p = 0.002) and living standard (β = 0.265;
p < 0.001) predicted life satisfaction (adjusted R2 = 0.17).
Coloured/mixed-race group
For the coloured/mixed-race participants, we observed a
significant association between equal interaction and life
satisfaction score (β = 0.158; p = 0.016) , and also by age
(β = 0.145; p = 0.008), and household income (β = 0.154;
p < 0.022; adjusted R2 = 0.21)
Indian racial group
The linear regression models suggested that life
satisfaction was not significantly associated with racial
relations for the Indian racial group. Moreover, only living
standard (β = 0.174; p = 0.036) returned significant results
(adjusted R2 = 0.03), indicating low explanatory power in
this racial grouping.
White racial group
For the white racial group, life satisfaction was not
significantly associated with racial relations qualities.
However, participants’ age (β = 0.191; p = 0.021) and
educational attainment (β = .189; p = 0.032) showed
significant association with life satisfaction and account
for 11% variance in life satisfaction (adjusted R
= 0.11;
p = 0.001).
Intergroup comparison
A comparison of results from the regression models reveals
that only black African participants reported a significant
association between both measures of racial relations
and life satisfaction. Coloured/mixed-race participants
show that only interaction as equal produced a significant
association with life satisfaction. Following Cohen (2013),
however, the predictive effects of interaction as equal as a
measure of interracial relation were stronger for coloured/
mixed-race participants (β = 0.158; p = 0.016) than for
black African participants (β = 0.112; p < 0.01).
The current study examined how racial and socioeconomic
factors predict life satisfaction among a representative
sample of South African adults. We observed significant
differences in life satisfaction based on racial membership.
Black South African participants reported the lowest
life satisfaction, and white South Africans the highest
life satisfaction. The lower life satisfaction and lower
socioeconomic status among the black adults are likely
explained by South Africans’ historical segregation
(Gradín, 2019). This segregation denied many black South
Africans well-paid employment participation and have
impoverished intergenerationally (Leibbrandt et al., 2010).
These findings are consistent with those of previous studies
which reported low socioeconomic status to be associated
with lower satisfaction with life (Hiscock et al., 2014;
Tan et al., 2020; Tull, 2013). The disadvantaged group’s
inability to meet daily needs would negatively affect both
the quality of, and satisfaction with, life (Adedeji et al.,
2019; Tan et al., 2020).
For black Africans, racial relations and life satisfaction
were associated with higher household income, education,
and living standard. Similarly, for the coloured/mixed-
race group, the positive association between better racial
relations and higher life satisfaction scores was explained
by older age and higher household income. People with
higher socio-economic status have more opportunities for
equitable outgroup relationships compared to those with
lower socio-economic status (Li et al., 2020; Adedeji et
al., 2021). Similarly, intergroup interactions act as bridging
mechanism across the socioeconomic gap (Boyce et al.,
2010; Frijters et al., 2004; Salinas-Jiménez et al., 2011;
Wolbring et al., 2013), thereby reduces racial discord and
facilitates socioeconomic integration of disadvantaged
groups (Akindès, 2018; Bangane, 1991).
Figure 1. Life Satisfaction mean score distribution by race (South African Social Attitudes Survey 2017, n = 3 135)
Black Africans Coloured/Mixed Race Indian White
Adedeji et al.
Table 4. Multiple regression models exploring racial relations and life satisfaction (South African Social Attitudes Survey 2017, n = 3 135)
Black African Coloured Indian White Total
Bβp B βp B βp B βp B βp
Model 1
Constant 44.767 < 0.001 62.874 < 0.001 59.817 < 0.001 67.356 < 0.001 47.355 < 0.001
as equals 1.582 0.112 0.009 2.408 0.158 0.016 0.904 0.046 0.581 2.014 0.134 0.327 1.784 0.115 < 0.001
contacts 2.558 0.180 < 0.001 −1.389 −0.072 0.268 2.166 0.109 0.195 −0.580 −0.041 0.765 2.516 0.159 < 0.001
Adjusted R20.075 0.011 0.012 −0.003 0.07
ΔF (df1, df2)
ΔF (2, 1313) = 54.519
p < 0.001
ΔF (2, 344) = 3.012
p = 0.050
ΔF (2, 236) = 2.445
p = 0.89
ΔF (2, 139) = 0.774
p = 0.463
ΔF (2, 2041) = 71.50
p < 0.001
Model 2
Constant 46.355 < 0.001 48.573 < 0.001 50.507 < 0.001 39.144 < 0.001 44.185 < 0.001
as equals 1.231 0.087 0.033 2.351 0.155 0.019 0.826 0.042 0.612 3.183 0.212 0.106 1.380 0.089 0.003
contacts 2.209 0.156 < 0.001 −0.990 −0.052 0.422 2.360 0.118 0.156 −1.049 −0.074 0.568 1.954 0.123 < 0.001
Age −0.044 −0.046 0.078 0.137 0.145 0.008 0.070 0.072 0.297 0.139 0.191 0.021 0.030 0.031 0.124
Education −0.020 −0.010 0.725 −0.145 −0.057 0.318 0.022 0.006 0.934 0.504 0.189 0.032 −0.019 −0.008 0.716
income 0.518 0.095 0.002 0.878 0.154 0.022 0.033 0.005 0.950 0.603 0.128 0.170 0.546 0.105 < 0.001
Living standard
measure 5.619 0.265 < 0.001 1.782 0.086 0.221 4.421 0.174 0.036 2.721 0.152 0.095 5.620 0.304 < 0.001
Adjusted R20.177 0.061 0.029 0.113 0.21
ΔF (df1,
df2), p-value
ΔF (4, 1309) = 48.044
p < 0.001
ΔF (4, 340) = 4.742
p < 0.001
ΔF (4, 232) = 2.166
p = 0.047
ΔF (4, 135) = 3.999
p < 0.001
ΔF (4, 2037) = 89.63
p < 0.001
Change in R2 0.11 0.06 0.033 0.140 0.14
Change in F, p-value 41.49, p < 0.001 5.53, p < 0.001 2.005, p = 0.093 5.560, p < 0.001, 92.30, p < 0.001
Note. Model 1: Evaluate the predictive effect of racial relations, as in interactions as equals and friendly contacts, on life satisfaction. Model 2: Assess the added predictive effect of age, education,
household income, and living standard on the impact of interracial relations, as in interactions as equals and friendly contacts, on life satisfaction
Racial relations and life satisfaction among South Africans 527
For the Indian South African participants, a higher
living standard was signicant for higher life satisfaction.
Indian South Africans have signicant business ownership
(Bhowan & Tewari, 1997) aording them a better life
(Ntloedibe & Ngqinani, 2020). Older white South Africans
have cumulative social and economic wealth which would
enhance their social well-being (Cutler & Lleras-Muney,
2006; Schillinger et al., 2006).
Limitations of the study and suggestions for further
The cross-sectional design data limits the generalisability
of the results. A longitudinal study on interracial relations
and advancement in socioeconomic status – as South
Africa move further away from apartheid – will provide
better insight into the relationship between interracial
relations, socioeconomic status, and life satisfaction.
Further analyses examining intergenerational differences
(apartheid and post-apartheid) will provide a better insight
into race as a predictor of well-being. Furthermore, a
comparative study of the effects of racial relations between
countries with varying race will advance knowledge on
sociocultural determinants of well-being.
The results show a significant difference in life satisfaction
based on race. Black South African participants were
the least satisfied with their life, followed by coloured/
mixed-race, and Indian South Africans. For the Indian
South African participants, a higher living standard was
more important for better life satisfaction. Older age and
higher education attainment were significant, with better
life satisfaction for white South African participants. These
results suggest the lingering effects of race-based privilege
and disadvantages in post-apartheid in South Africa.
Author notes
This article was written as part of a Feodor Lynen Research
Fellowship funded by the Alexander von Humboldt Foundation.
The authors have no conflict or competing interest. Data
included in this report are available on request. All procedures
were by institutional and national research committee’s ethical
standards and comparable ethical standards. Informed consent
was obtained from all individual participants included in the
The authors acknowledged the Human Science Research
Council (HSRC), South Africa, for granting us access to the
South African Social Attitudes Survey 2017 dataset. The author
also acknowledges the Faculty of Humanities, North-West
University, South Africa.
Adekunle Adedeji –
Adedeji, A., & Bullinger, M. (2019). Subjective integration and
quality of life of Sub-Saharan African migrants in Germany.
Public Health, 174, 134–144.
Adedeji, A., Silva, N., & Bullinger, M. (2021). Silva, N., &
Bullinger, M. (2019). Cognitive and structural social capital
as predictors of quality of life for sub-Saharan African
migrants in Germany. Applied Research in Quality of Life,
16, 1003–1017.
Angner, E. (2010). Subjective well-being. Journal of
Socio-Economics, 39(3), 361–368.
Akindès, F. (2018). Understanding Côte d’Ivoire’s “Microbes”:
The political economy of a youth gang. In J. E. Salahub, M.
Gottsbacher, & J. de Boer (Eds.), Social theories of urban
violence in the global south: Towards safe and inclusive
cities (pp. 161–182). Routledge.
Bangane, W. T. (1999). The unemployment problem in South
Africa with specific reference to the Lekoa Vaal Triangle
Metropolitan area (LVTMA) [Master’s thesis], The
Rands Afrikaans University.
Bhowan, K., & Tewari, D. D. (1997). Indian Entrepreneurs in
South Africa: Challenges and Achievements during the
Apartheid Era. The Journal of Entrepreneurship, 6(2),
Botha, F., Booysen, F., & Wouters, E. (2018). Satisfaction with
family life in South Africa: The role of socioeconomic status.
Journal of Happiness Studies, 19(8), 2339–2372. https://doi.
Boyce, C. J., Brown, G. D., & Moore, S. C. (2010). Money
and happiness: Rank of income, not income, affects life
satisfaction. Psychological Science, 21(4), 471–475. https://
Branson, N., & Wittenberg, M. (2007). The measurement of
employment status in South Africa using cohort analysis,
1994-2004. The South African Journal of Economics, 75(2),
Buqa, W. (2015). Storying Ubuntu as a rainbow nation. Verbum et
Ecclesia, 36(2), 1–8.
Cheng, A., Leung, Y., & Brodaty, H. (2021). A systematic
review of the associations, mediators and moderators
of life satisfaction, positive affect and happiness in
near-centenarians and centenarians. Aging & Mental Health,
Cohen, J. (2013). Statistical Power Analysis for the
Behavioral Sciences. Taylor & Francis. https://doi.
Cutler, D. M., & Lleras-Muney, A. (2006). Education and
Health: Evaluating Theories and Evidence (No. w12352).
National Bureau of Economic Research. https://doi.
Davids, Y. D., & Gaibie, F. (2011). Quality of life in
post-apartheid South Africa. Politikon: South African Journal
of Political Studies, 38(2), 231–256.
Diène, D., & UN. Commission on Human Right. (2006). Report
of the Special Rapporteur on Contemporary Forms of
Racism, Racial Discrimination, Xenophobia and Related
Intolerance. Retrieved from
Ebrahim, A., Botha, F., & Snowball, J. (2013). Determinants
of life satisfaction among race groups in South Africa.
Development Southern Africa, 30(2), 168–185. https://doi.
Frijters, P., Haisken-DeNew, J. P., & Shields, M. A. (2004).
Money does matter! Evidence from increasing real income
and life satisfaction in East Germany following reunification.
The American Economic Review, 94(3), 730–740. https://doi.
Hackett, R. A., Ronaldson, A., Bhui, K., Steptoe, A., & Jackson,
S. E. (2020). Racial discrimination and health: A prospective
study of ethnic minorities in the United Kingdom. BMC
Public Health, 20(1), 1652.
Adedeji et al.
Hino, H., Leibbrandt, M., Machema, R., Shifa, M., & Soudien,
C. (2018). Identity, inequality and social contestation
in the Post-Apartheid South Africa. Retrieved from
Hiscock, R., Bi, J., Liu, M., Asikainen, A., Dobbie, F., Bauld, L.,
Mudu, P., Martuzzi, M., & Sabel, C. (2014). Socioeconomic
inequalities and well-being in England, Finland and China:
Rosemary Hiscock. European Journal of Public Health,
24(suppl_2), cku161–cku005.
International Well-being Group. (2013). Personal Well-being
Index (5th Ed.). Melbourne: Australian Centre on Quality
of Life, Deakin University.
Kirmanoğlu, H., & Başlevent, C. (2014). Life satisfaction of
ethnic minority members: An examination of interactions
with immigration, discrimination, and citizenship. Social
Indicators Research, 116(1), 173–184. https://doi.
Leibbrandt, M., Woolard, I., McEwen, H., & Koep, C. (2010).
Employment and inequality outcomes in South Africa.
Retrieved from
Leibbrandt, M., Finn, A., & Woolard, I. (2012). Describing and
decomposing post-apartheid income inequality in South
Africa. Development Southern Africa, 29(1), 19–34. https://
Li, J., Wang, J., Li, J., Qian, S., Jia, R., Wang, Y., Liang, J.,
& Xu, Y. (2020). How do socioeconomic status relate to
social relationships among adolescents: A school-based
study in East China. BMC Pediatrics, 20(1), 271. https://doi.
Mafini, C. (2017). Economic Factors and Life Satisfaction:
Trends from South African Communities. Acta
Universitatis Danubius. Œconomica, 13(3), 155-168.
Møller, V. (2007). Satisfied and dissatisfied South Africans:
Results from the General Household Survey in international
comparison. Social Indicators Research, 81(2), 389–415.
Ntloedibe, M., & Ngqinani, P. (2020). South Africa:
Understanding the Living Standards Measure Segmentation
in South Africa. Retrieved from
Olzak, S., & Olivier, J. L. (1998). Racial Conflict and Protest in
South Africa and the United States. European Sociological
Review, 14(3), 255–278.
Paradies, Y., Ben, J., Denson, N., Elias, A., Priest, N., Pieterse,
A., Gupta, A., Kelaher, M., & Gee, G. (2015). Racism
as a Determinant of Health: A Systematic Review and
Meta-Analysis. PLoS One, 10(9), e0138511. https://doi.
Patchen, M. (1999). Diversity and unity: Relations between racial
and ethnic group. Nelson-Hall Publishers.
Patel, C. J., Ramgoon, S., & Paruk, Z. (2009). Exploring religion,
race and gender as factors in the life satisfaction and religiosity
of young South African adults. South African Journal of
Psychology. Suid-Afrikaanse Tydskrif vir Sielkunde, 39(3),
Roberts, B., Struwig, J., & Human Sciences Research Council.
(2020). South African Social Attitudes Survey (SASAS)
2017: Questionnaire 1 - All provinces (2.0) [Data set].
HSRC - Human Science Research Council SA. https://doi.
Salinas-Jiménez, M. del M., Artés, J., & Salinas-Jiménez, J.
(2011). Education as a Positional Good: A Life Satisfaction
Approach. Social Indicators Research, 103(3), 409–426.
Schillinger, D., Barton, L. R., Karter, A. J., Wang, F., &
Adler, N. (2006). Does Literacy Mediate the Relationship
between Education and Health Outcomes? A Study of
a Low-Income Population with Diabetes. Public Health
Reports (Washington, D.C.), 121(3), 245–254. https://doi.
Schulz, A. J., Williams, D. R., Israel, B. A., & Lempert, L.
B. (2002). Racial and Spatial Relations as Fundamental
Determinants of Health in Detroit. The Milbank Quarterly,
80(4), 677–707.
Seekings, J. (2008). The continuing salience of race:
Discrimination and diversity in South Africa. Journal of
Contemporary African Studies, 26(1), 1–25. https://doi.
Sigelman, L., Bledsoe, T., Welch, S., & Combs, M. W. (1996).
Making Contact? Black-White Social Interaction in an Urban
Setting. American Journal of Sociology, 101(5), 1306–1332.
Staples, R. (1976). RACE AND COLONIALISM: The Domestic
Case in Theory and Practice. The Black Scholar, 7(9), 37–49.
Tan, J. J. X., Kraus, M. W., Carpenter, N. C., & Adler, N. E.
(2020). The association between objective and subjective
socioeconomic status and subjective well-being: A
meta-analytic review. Psychological Bulletin, 146(11),
Taylor, J., & Turner, R. J. (2002). Perceived discrimination,
social stress, and depression in the transition to adulthood:
Racial contrasts. Social Psychology Quarterly, 65, 213–225.
Tull, E. L. (2013). Inequality and Health: Stress Mediates
the Relationship Between Subjective SES and Well-being.
Retrieved from
United Nations Human Development Report. (2009). Economy
and inequality Gini index. Retrieved from http://hdrstats.
Valdez, Z., & Golash-Boza, T. (2017). US racial and ethnic
relations in the twenty-first century. Ethnic and Racial
Studies, 40(13), 2181–2209.
Vincent, L. (2008). The limitations of ‘interracial contact’: Stories
from young South Africa. Ethnic and Racial Studies, 31(8),
Williams, D. R. (2018). Stress and the Mental Health of
Populations of Color: Advancing Our Understanding of
Race-related Stressors. Journal of Health and Social Behavior,
59(4), 466–485.
Williams, D. R., Lawrence, J. A., Davis, B. A., & Vu, C.
(2019). Understanding how discrimination can affect health.
Health Services Research, 54(S2), 1374–1388. https://doi.
Wolbring, T., Keuschnigg, M., & Negele, E. (2013). Needs,
comparisons, and adaptation: The importance of relative
income for life satisfaction. European Sociological Review,
29(1), 86–104.
... These racial classifications remain relevant for evaluating South Africa's social wellbeing performance and as a measure of social equality. The cultural and racial discord in South Africa is attributed to unequal access to socioeconomic resources among the different racial groups [5,22]. The diverse cultural attributes and the clear racial divide [22] make South Africa ideal for exploring different features and correlates of microaggression. ...
Full-text available
Despite the increasing interest in exploring microaggression in the humanitarian context, there remains uncertainty on its mechanism for affecting life outcomes. There is a lack of studies on ethnic and racial minorities in non-western countries. The current research explores dimensions and manifestations of microaggression and how they affect wellbeing in a multicultural setting. The study uses a qualitative approach with 15 focus group discussions (FGDs) and 66 participants conducted in 4 provinces of South Africa: Gauteng (k = 6), NorthWest (k = 3), KwaZulu-Natal (k = 3), and Western Cape (k = 3). The recorded FGDs were transcribed using the intelligent verbatim technique. The transcripts were then analysed using a phenomenological approach. Data analysis was done stepwise using the deductive coding technique. Results show that par-ticipants' perception of the dimensions of microaggression varies depending on the manifestation as verbal, behavioural, or systemic. Furthermore, variations in patterns and reactions to dimensions of microaggression were linked with participants' racial identity. It further confirms that experiencing discrimination is associated with poorer wellbeing. Connectedness to the ingroup provides stability and certainty in multi-group societies due to the group rivalry that pervades such societies.
Full-text available
Background Racism has been linked with poor health in studies in the United States. Little is known about prospective associations between racial discrimination and health outcomes in the United Kingdom (UK). Methods Data were from 4883 ethnic minority (i.e. non-white) participants in the UK Household Longitudinal Study. Perceived discrimination in the last 12 months on the basis of ethnicity or nationality was reported in 2009/10. Psychological distress, mental functioning, life satisfaction, self-rated health, physical functioning and reports of limiting longstanding illness were assessed in 2009/10 and 2011/12. Linear and logistic regression analyses adjusted for age, sex, income, education and ethnicity. Prospective analyses also adjusted for baseline status on the outcome being evaluated. Results Racial discrimination was reported by 998 (20.4%) of the sample. Cross-sectionally, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness (odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.49; 2.13) and fair/poor self-rated health (OR = 1.50; 95% CI 1.24; 1.82) than those who did not report racial discrimination. Racial discrimination was associated with greater psychological distress (B = 1.11, 95% CI 0.88; 1.34), poorer mental functioning (B = − 3.61; 95% CI -4.29; − 2.93), poorer physical functioning (B = − 0.86; 95% CI -1.50; − 0.27), and lower life satisfaction (B = − 0.40, 95% CI -0.52; − 0.27). Prospectively, those who reported racial discrimination had a greater likelihood on average of limiting longstanding illness (OR = 1.31, 95% CI 1.01; 1.69) and fair/poor self-rated health (OR = 1.30; 95% CI 1.00; 1.69), than those who did not report racial discrimination. Racial discrimination was associated increased psychological distress (B = 0.52, 95% CI 0.20; 0.85) and poorer mental functioning (B = − 1.77; 95% CI -2.70; − 0.83) over two-year follow-up, adjusting for baseline scores. Conclusions UK adults belonging to ethnic minority groups who perceive racial discrimination experience poorer mental and physical health than those who do not. These results highlight the need for effective interventions to combat racial discrimination in order to reduce inequalities in health.
Full-text available
Background: A great number of studies have concentrated on the influence of socioeconomic status with health outcomes, but little on how socioeconomic status affects social relationship in adolescents' families, peers and schools. This study aimed to clarify more detailed information on the connection between social relationships and different dimensions of socioeconomic status. Methods: A school-based cross-sectional study was performed by 13-18 adolescents enrolled in East China from September, 2018 to May, 2019, which recruited 6902 students from junior and senior high schools and used the stratified random sampling method. Parent-child relationship (cohesion, expressiveness, conflict), peer relationship (interpersonal relationship, communication and interaction, social emotion) and student-teacher relationship (intimacy, support, satisfaction, conflict) were investigated. Besides, objective socioeconomic status (parental education and occupation, assessed by the adolescent) and subjective socioeconomic status (self-evaluation of family social class) were measured. More detailed information was used to clarify the link between social relationships and different dimensions of socioeconomic status. Results: All five indicators of socioeconomic status were slightly positively correlated with the quality of social relationships (r ranged from 0.036 to 0.189, all p < 0.001), except that maternal education was not correlated with the conflict dimension of parent-child relationship. Standardized regression coefficients indicated that paternal education (β = 0.08) and occupation (β = 0.07) were the predictors of parent-child relationship. And peer relationship model revealed that the corresponding effect size was slightly stronger for subjective socioeconomic status (β = 0.10), whereas the maternal education had a slightly stronger correlation with student-teacher relationship (β = 0.07) relative to other indicators. Conclusions: Adolescents with lower socioeconomic status had poorer social relationships compared to those with higher socioeconomic status. These findings have important public health implications for health policy makers to make sound decisions on resources allocation and services planning in improving adolescents' social relationships and promoting health outcomes.
Full-text available
Background: To provide an overview of the empirical research linking self-reports of racial discrimination to health status and health service utilization. Methods: A review of literature reviews and meta-analyses published from January 2013 to 2019 was conducted using PubMed, PsycINFO, Sociological Abstracts, and Web of Science. Articles were considered for inclusion using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework. Results: Twenty-nine studies met the criteria for review. Both domestic and international studies find that experiences of discrimination reported by adults are adversely related to mental health and indicators of physical health, including preclinical indicators of disease, health behaviors, utilization of care, and adherence to medical regimens. Emerging evidence also suggests that discrimination can affect the health of children and adolescents and that at least some of its adverse effects may be ameliorated by the presence of psychosocial resources. Conclusions: Increasing evidence indicates that racial discrimination is an emerging risk factor for disease and a contributor to racial disparities in health. Attention is needed to strengthen research gaps and to advance our understanding of the optimal interventions that can reduce the negative effects of discrimination.
Objective: Results from studies investigating life satisfaction, positive affect and happiness of near-centenarians (95+) and centenarians are inconsistent. This is the first systematic review to summarise the extant literature on the subjective well-being of this unique age group. Method: Seven electronic databases (PubMed, MEDLINE, EMBASE, PsycINFO, CINAHL, Web of Science and the Cochrane database for systematic reviews) were systematically searched. Subjective well-being was defined as life satisfaction, positive affect and happiness. A narrative synthesis of relevant articles was undertaken. Results: Of 28 studies eligible for inclusion in this review, 20 predominantly examined life satisfaction, 11 positive affect and 4 happiness. Sex and other demographic variables were not significant predictors of subjective well-being. In contrast, greater perceived health was significantly associated with higher levels of life satisfaction and positive affect. Fatigue and visual impairment were significantly correlated with lower levels of life satisfaction and positive affect. However, there was considerable heterogeneity in the findings on physical, cognitive and social associations, mediators and moderators. Conclusion: The large discrepancy of results in the literature may be explained by methodological differences between studies. Centenarian research needs a clearer definition of life satisfaction, positive affect and happiness as their operationalisation is inconsistent. An international consortium of centenarian studies could facilitate cross-cultural comparisons on subjective well-being. Future research should be directed towards interventions that promote subjective well-being in the oldest-old.
This meta-analysis tested if the links between socioeconomic standing (SES) and subjective well-being (SWB) differ by whether SES is assessed objectively or subjectively. The associations between measures of objective SES (i.e., income and educational attainment), subjective SES (i.e., the MacArthur ladder SES and perceived SES), and SWB (i.e., happiness and life satisfaction) were synthesized across 354 studies, totaling 2,352,095 participants. Overall, the objective SES and subjective SES measures were moderately associated (r = .32). The subjective SES-SWB association (r = .22) was larger than the objective SES-SWB association (r = .16). The income-SWB association (r = .23) was comparable to the ladder SES-SWB association (r = .22) but larger than the perceived SES-SWB association (r = .196). The education-SWB association (r = .12) was smaller than the associations with both measures of subjective SES. The subjective SES-SWB association was partially explained by common method variance. The subjective SES-SWB association, particularly with the ladder SES measure, also mediated the objective SES-SWB association. In moderation analyses, the objective SES-SWB associations strengthened as samples increased in wealth and population density. The subjective SES-SWB associations strengthened as samples increased in population density, decreased in income inequality and decreased in relative social mobility. The role of common method variance, social comparisons and other processes in explaining the SES-SWB links are discussed.
This article presents empirical evidence on the quality of life of migrants’ from SSA in Germany, exploring its association with subjective integration and the influence of some socioeconomic and demographic characteristics. A cross-sectional study design using quantitative data from 518 SSA migrants’ collected across the 16 Germany federal states were analysed in this study. Association between participants’ quality of life, measured by the four domains of the WHOQOL-BREF, subjective integration and socio-demographic characteristics were evaluated using Pearson product-moment correlations. Stepwise multiple linear regressions were performed to explore the contribution of predictor variables on the quality of life domain Participants’ age averaged 32.5 years (SD 7.93). The sample reported a low QoL score with a mean score of 64.3 (SD 14.4, Range 70.2). Multiple linear regression revealed that subjective integration, age, education, and gender had significant associations and explained up to 27% of the variance in the quality of life domain scores. The findings of this study support the conclusion that subjective integration positively and significantly associates with physical health, psychological health, social relationships, and environmental domain of Sub-Saharan African migrants’ quality of life in Germany.
This article provides an overview of research on race-related stressors that can affect the mental health of socially disadvantaged racial and ethnic populations. It begins by reviewing the research on self-reported discrimination and mental health. Although discrimination is the most studied aspect of racism, racism can also affect mental health through structural/institutional mechanisms and racism that is deeply embedded in the larger culture. Key priorities for research include more systematic attention to stress proliferation processes due to institutional racism, the assessment of stressful experiences linked to natural or manmade environmental crises, documenting and understanding the health effects of hostility against immigrants and people of color, cataloguing and quantifying protective resources, and enhancing our understanding of the complex association between physical and mental health.