race differences in educational attainment of youth aged 7–18 in post-apartheid south africa: the...
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Race differences in educational attainmentof youth aged 7–18 in post-apartheidSouth Africa: The role of family structure,resources and school qualityTim B. Heatona, Acheampong Yaw Amoatengb & Mikaela Dufura
a Department of Sociology Brigham Young University, Provo, Utah, U.S.A. ,b School of Research and Postgraduate Studies North-West University(Mafikeng Campus)Published online: 25 Apr 2014.
To cite this article: Tim B. Heaton, Acheampong Yaw Amoateng & Mikaela Dufur (2014) Race differencesin educational attainment of youth aged 7–18 in post-apartheid South Africa: The role of familystructure, resources and school quality, South African Review of Sociology, 45:1, 101-121, DOI:10.1080/21528586.2014.887917
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South African Review of Sociology VOL 45 • NO 1 • 2014ISSN 2152-8586/Online 2072-1978© South African Sociological Association pp 101–121DOI: 10.1080/21528586.2014.887917
RACE DIFFERENCES IN EDUCATIONAL ATTAINMENT OF YOUTH AGED 7–18 IN POST-APARTHEID SOUTH AFRICA: THE ROLE OF FAMILY STRUCTURE, RESOURCES AND SCHOOL QUALITY
Race differences in educational attainment of youth aged 7–18
Tim B. HeatonDepartment of Sociology
Brigham Young University, Provo, Utah, [email protected]
Acheampong Yaw AmoatengSchool of Research and Postgraduate Studies
North-West University (Mafikeng Campus)[email protected]
Mikaela DufurDepartment of Sociology
Brigham Young University, Provo, Utah, [email protected]
ABSTRACTThe present study uses seven waves of the General Household Survey by Statistics South Africa to examine the role of family structure, resources and school quality in educational inequalities in post-apartheid South Africa. Indians/Asians and whites have an education advantage over Africans and coloureds, and maintain that advantage as they age. About half of the disadvantage of Africans and coloureds is because of family background; very little is because of school characteristics that can be measured in this study. Findings show that the family institution is central in understanding racial inequalities in educational outcomes in South Africa.
Keywords: education, racial inequality, family background
INTRODUCTIONWhile discrimination was always present in South African society, after the National Party swept to power in 1948 such actions became more overt and purposive, especially in the sphere of education. From the 1950s to the mid-1990s, no other social institution evidenced the government’s racial philosophy of apartheid more clearly than the
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education system. Schools were required both to teach and to practise apartheid, making them both an instrument for and a victim of racism. Following the political transition in 1994, the state took steps to address racial inequality (Asmal 2005; Hartshorne 1992). While many interventions have been directed at erasing inequality from the educational system, stark differences in educational resources, practices and outcomes across wealthier and poorer schools remain common (Van der Berg 2007; Spaull 2012; Van der Berg and Burger 2002; Taylor et al. 2003).
This paper adds to research on the persistent inequalities in education in South Africa by examining trends in racial inequality in the post-apartheid period. We extend previous inquiries to examine differences across four major racial designations in South Africa: students who are white, black African, Indian/Asian, or Coloured1. In particular, we focus on increasing disparities in educational attainment as children move through primary and secondary education. Using national surveys taken from 2002 to 2011, we address four key questions. First, how much racial inequality in education exists for children aged 7 to 18? Second, how much of this inequality is due to differences in household structure, household resources, and school quality? Third, have inequalities narrowed recently? Finally, do changes in family and school contexts account for any trends in inequality during this period?
THE EDUCATION CONTEXT IN SOUTH AFRICAIn addition to its role as transmitter of social values, the education system assumed economic importance in the twentieth century as it prepared young black Africans for low-wage labour. However, the system also protected the privileged white minority from competition. Essentially, the 1953 Bantu Education Act served the interests of white supremacy by denying black people access to the same educational opportunities and resources enjoyed by white South Africans. It denigrated black African people’s history, culture and identity through the promotion of myths and racial stereotypes in its curricula and textbooks (Hartshorne 1992).
As a result of such policies, glaring discrepancies in education expanded among racial groups in South Africa. For instance, as anti-apartheid protests grew in the mid-1970s, teacher-pupil ratios in primary schools averaged 1:18 in white schools, 1:24 in Indian/Asian schools, 1:27 in coloured schools and 1:39 in black African schools. Secondary school pass rates for black African pupils in the nationwide, standardized high school graduation examinations were less than one-half the pass rate for whites. A study by the Human Sciences Research Council (2005) found that almost 20% of teachers are absent on Mondays and Fridays, rates that increase to one-third at month end. As a result of these absentee rates teachers in black African schools teach an average of 3.5 hours a day, compared with about 6.5 hours a day in former white schools. Moreover, these poor standards in black African schools have been exacerbated by a large number of under-qualified or unqualified teachers who teach in overcrowded and ill-equipped classrooms, with severely limited resources (Mgibisa 2009). The new
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democratic government has been striving to reconfigure the country’s education system since the political transition in 1994. A major policy shift is reflected in the South African Schools Act of 1996, which makes education compulsory for all South Africans from the age of seven (grade 1) to age 15, or the completion of grade 9. Thus, in South Africa, all children enter school, and the majority complete grade 9 with little gender disparity (Lloyd and Hewett 2009; Lam, Ardington and Leibbrandt 2008; Department of Education 2006b; Anderson, Case and Lam 2001).
Even though the democratic government has persistently pursued policies to ensure educational access and equality, the apartheid legacy remains in areas such as educational financing, administration and the delivery of services (e.g. Hartley and Omarjee 2008; Lam et al. 2008; Van der Berg 2007; Simkins 1998). Geographic divisions along racial lines that have their history in apartheid-era policies remain in place even after the political transition (Yamamauchi 2011). On the whole, students’ levels of achievement are not commensurate with the financial investment the state has made in the education sector (Department of Education 2006a; Taylor, Muller and Vinjevold 2003; Chisholm 2004).
While racial differences in access have been substantially reduced as a result of racial integration in schools, there still remains considerable variation in school quality across student race, with black African schools generally performing more poorly relative to their white counterparts (Van der Berg 2007; Van der Berg, Wood and Le Roux 2002; Fuller, Pillay and Sirur 1995). For instance, predominantly black African schools demonstrate average pass rates of 43%, but predominantly white schools have pass rates of 97% (Van der Berg 2007). Efforts to improve access and equity have led to an increase in unqualified teachers, but privileged schools have maintained teacher quality with private funding (Chisholm 2004). Inequality is also evident in measures of how much students actually learn (Gilmour and Soudien 2009). Spaull (2012) states that there are, in essence, two different school systems in South Africa, one for the privileged and one for the disadvantaged, and preponderance of evidence suggests that these levels of disadvantage are still linked to race. In short, defining education as a universal right has not led to racial equality in South Africa, perhaps because other mechanisms linked to the legacy of apartheid perpetuate advantage and disadvantage (Christie 2010).
It is against this background that we examine educational inequality in post-apartheid South Africa. Much prior research is based on people who were enrolled or had completed school during the apartheid era. This study begins with a time period when new enrolees were born in the post-apartheid period. Prior research also focuses on completed education, making it difficult to determine where in the education process inequalities emerge. We focus on educational attainment for children aged 7 through 18, allowing us to identify more precise points where inequality emerges or converges. We also attempt to distinguish between different sources of inequality including household structure, household socioeconomic resources and school quality.
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EFFECTS OF HOUSEHOLD STRUCTURE, RESOURCES AND SCHOOL QUALITY ON EDUCATION
Effects of household structure and family resources in western societies A long tradition of both cross-sectional and longitudinal research on wealthy, Western societies suggests that the number of parents available to children has strong effects on educational outcomes. Children raised by two biological, married parents score higher on both mathematics and reading tests than children in other family types (Sun and Li 2011; Formby and Cherlin 2007; Cavanaugh, Schiller and Riegle-Crumb 2006; Hofferth 2006; McLanahan and Sandefur 1994). Similar patterns exist for self-reported grades, educational expectations, high school completion, and enrolment in post-secondary schooling (Sun and Li 2009; Heard 2007; Sun 2003; Ermisch and Francesconi 2001). Family structures with multiple adults or with children living away from both parents, such as children being raised by grandparents or in institutional care, have high levels of heterogeneity and mixed outcomes concerning child academic achievement (Hayslip and Kaminski 2005).
The presence and number of siblings also have an effect on child outcomes. Patterns of larger sibships being associated with lower academic outcomes are likely related both to dividing resources more finely across multiple siblings (Downey 2001; 1995; Kuo and Hauser 1997) and to selectivity issues concerning which parents are likely to have large families (Guo and Van Wey 1999a; 1999b).
One of the reasons household structure issues are so influential on child educational outcomes is because of the ways such structures affect the acquisition and distribution of key resources that encourage academic growth and achievement. For example, children in single-parent families tend to have fewer financial resources than do children in two-parent families (McLanahan and Percheski 2008; Avellar and Smock 2005; McLanahan and Sandefur 1994). In turn, poverty and low socioeconomic status, including parental education, are associated with lower academic achievement (Duncan and Brooks-Gunn 1997; Duncan, Brooks-Gunn and Klebanov 1994; Garrett, Ng’andu and Ferron 1994, Huston et al. 2001). The relationship between SES (spell this out – ND) and academic achievement appears to have its roots in the way socioeconomic disadvantage affects both children’s physical environment and health (Guo and Harris 2000) and cognitive stimulation at home (Votruba-Drzal 2003; Parcel and Menaghan 1994).
Effects of household structure and family resources in South AfricaAlthough the above findings were generated largely from populations in Western countries, similar patterns concerning household structure and family resources exist in
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South Africa. In a study of black Africans in South Africa, Anderson (2000) found that family structure was highly correlated with educational outcomes. The strongest effects were seen for children living with neither of their genetic parents, who were less likely to be enrolled in school, had completed fewer grades, were older for their grade if enrolled, and had less money spent on their school fees and related transportation costs, than children living with both biological parents (see also Case and Ardington 2006; Cherian 1989; 1994). While Africans aged 8 to 18 from female-headed households were more likely to be enrolled in school than those from male-headed households, children from female-headed households experience less total educational mobility than do those from male-headed households (Nimubona and Vencatachellum 2007). Among Coloureds and Asians, the odds of dropping out were 1.3 and 1.5 times greater for children in female-headed households as for those of children living in male-headed households. Effects of family structure on educational outcomes may be especially sensitive in South Africa because of the dramatic impact of HIV and AIDS. A substantial number of children have only one parent or are orphaned, with children lacking mothers at particularly high risk of school dropout (Nyamukapa and Gregson 2005).
Unlike the effects of parental structures, however, the patterns found in Western populations concerning family size may not hold for South Africans. For instance, Sibanda (2004) found that children living in large households were less likely to drop out of primary and secondary schools than children living with three or fewer members. While the finding about the positive association between sibship size and children’s schooling may be contrary to the situation that prevails in Western societies, it corroborates studies that have been done in other sub-Saharan countries (Chernichovsky 1985; Gomes 1984).
Studies of South African populations report similar findings to Western populations concerning family resources and socioeconomic status. For example, Sibanda (2004) found that children from households that received remittances from the government were half as likely to drop out of primary school as children from households with no remittances. Schools that can require fees, increasing the amount of capital they have to improve educational offerings, show improved achievement, but poorer families have fewer resources with which to pay such fees, or must make choices between paying fees for better schools or paying for other pressing needs (Van der Berg 2007; Yamauchi 2011).
Resources in the form of education exert positive influences, as well; children from households where the head has more education attain more education themselves (Case and Deaton 1999; Nimubona and Vencatachellum 2007). Similarly, in a longitudinal study, Gingsburg et al. (2010) reported a significant negative relationship between household socioeconomic status and the probability of repeating a grade, while higher levels of maternal education reduced the probability of a grade repeat for males.
The complicated ways family structures and resources are intertwined are apparent in the effects of family size and resource availability. While family size has often been linked to resource dilution in studies of Western families, the effects of extreme poverty
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might help to explain the distinct effect of sibship size in sub-Saharan samples. In Western samples, extreme poverty often leads to “adultification” of children, where young people are expected to provide significant labour that brings necessary income to the family, thus diverting attention from academic pursuits (Dodson and Dickert 2004). However, the positive effect of household size on children’s schooling in some African societies has been attributed to the possibility of adult family members substituting for child labour (Sibanda 2004), suggesting that larger families actually reduce this “adultification” effect of poverty in Africa.
Effects of school quality and resources in Western societies While resources and structures at the family level are important predictors of child educational attainment and achievement, interventions at the family level are often difficult and expensive, resulting in interventions targeted at schools. Debate continues, however, as to whether such policies are effective in increasing academic outcomes. One difficulty in assessing the role school quality plays in promoting academic achievement is in how school quality is measured across studies. In this paper, we focus on average student achievement as well as the basic financial, human and social resources available to children at their schools as the appropriate measure of school quality. While measures of classroom interactions and interventions are not available in the data we use here, previous research on South African schools suggests that useful programmes and interventions are most available in wealthy schools that were limited to white enrolment prior to the end of apartheid and least available in African schools (Van der Berg 2007; Spaull 2012; Van der Berg and Burger 2002; Taylor et al. 2003). Below we discuss the effects basic school resources have on academic achievement.
Basic school facilities play an important role in student achievement. Research on schools in Western democracies shows that students in schools with poor ventilation, temperature control and lighting experience increased absenteeism and off-task behaviours, thus reducing student learning (Schneider 2002; Smedje and Norback 1999; Myhrvoid et al. 1996). Human and social resources are also important for student achievement (Grubb 2009). Such resources, in the form of well-trained teachers who invest in their students, are related to positive academic achievement and growth (Parcel, Dufur and Zito 2010; Dufur, Parcel and McKune 2008; Hoffmann and Dufur 2008; Parcel and Dufur 2001).
In addition, the concept of resource dilution can be extended from families to schools. Although definitions of “small” vary widely across studies (Grubb 2009; 2008; Jepsen and Rivkin 2009; Sims 2009), most research indicates that children and adolescents in smaller schools or classrooms, where resources are shared among fewer students, do better on standardized tests and are at lower risk of dropping out (Lee and Smith 1997; Farber 1998).
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Effects of school quality and resources in South Africa In South Africa, despite almost two decades of democratic governance during which the disparate school administrations were collapsed into a single ministry (Moll 1998), many schools are still racially homogeneous, with differing quality and resources (Fiske and Ladd 2004; Van der Berg 2007; Yamauchi 2011). Patterns of how these differences in resources affect educational outcomes are similar to those found in Western samples. In their study of the impact of infrastructure on educational outcomes in South Africa, Oosthuizen and Bhorat (2006) found that better facilities were correlated with superior school performance. Only 11% of black African children lived in communities where the primary school had a library, compared with almost all Indian/Asian and white children. Moreover, only 39% of African children lived in communities where there was a secondary school with a science laboratory compared with nearly all Indian/Asian and white children (Case and Deaton 1999).
Examinations of school social resources also mirror Western patterns. Regional student/teacher ratios in South Africa have also been found to affect the probability of enrolment in school and the highest grade completed (Case and Deaton 1999; Case and Yogo 1999). Beyond the age of 13, black African children in districts with fewer pupils per teacher advanced more quickly. These patterns are even more pronounced when considering teachers who are paid through private means. Students in schools at the bottom end of the performance distribution were highly unlikely to have non-state paid teachers. In this study, while the average decile one school had over 340 students for every privately paid teacher, the ratio dropped to one privately paid teacher for every 59 students in decile ten schools (Oosthuizen and Bhorat 2006). While some research on South African schools suggests that the effect of increasing social resources by reducing class sizes in poorer (generally black African) schools would be relatively small, those interventions would be in a positive direction (Van der Berg 2007; Fiske and Ladd 2004; Soudien 2010). Both privately paid and government-paid teachers also demonstrate sharp differences in qualifications and experiences in predominantly white and predominantly African schools, with commensurate differences in pay. Van der Berg (2001) estimates that teacher costs per pupil in 1997 in mainly black African schools (schools with at least 70 per cent of students being African) was R2 137 compared with R3 792 in mainly white schools. Teachers in poorer schools are also more likely to demonstrate outdated pedagogy and technique related to lower student achievement (Crouch and Mabogoane 2001; Fiske and Ladd 2004; Van der Berg 2007; Spaull 2012; Van der Berg and Burger 2002; Taylor et al. 2003; Lee and Ross 2005).
In spite of government attempts at reducing inequality in educational outcomes, evidence exist suggesting racial patterns in child access to basic resources in both homes and schools (Spaull 2012). Our analysis examines how racial gaps in educational achievement change across time in post-apartheid South Africa and takes into account the important roles that family structure, family resources and school quality can have on educational inequality (Anderson et al. 2001; Van der Berg and Burger 2002).
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DATA AND ANALYSIS
The sample The data for the study come from the seven waves (2002–2011) of the General Household Survey (GHS)2 (Statistics South Africa 2011). The GHS is a multi-stage stratified random sample, which is drawn using Probability Proportional to Size (PPS) principles. It is drawn from the master sample, which Statistics South Africa uses to draw samples for its regular household surveys. The master sample is drawn from the database of enumeration areas (EAs) established during the demarcation phase of the 1996 Population Census. A new sample is drawn each year. We pool these seven cross-sectional surveys.
As part of the master sample, small EAs consisting of fewer than 100 households are combined with adjacent EAs to form primary sampling units (PSUs) of at least 100 households, to allow for repeated sampling of dwelling units within each PSU. The sampling procedure for the master sample involves explicit stratification by province and within each province, by urban and non-urban areas. Within each stratum, the sample is allocated disproportionately. A PPS sample of PSUs was drawn in each stratum, with the size measured as the number of households in the PSU.
Approximately 3 000 PSUs are selected for each wave of the survey. In each selected PSU a systematic sample of ten dwelling units is drawn, resulting in approximately 26 000 dwelling units per survey. The person file collects background demographic information such as age, sex, population group, school attendance, level of education, disability status and health, while the household file contains information such as dwelling type, ownership of the dwelling and other household assets, water, sanitation, environmental issues, transport and expenditures3.
MEASURES AND STATISTICAL ANALYSISOur focus is on racial inequality in schooling. To assess recent changes we focus on youth aged 7 through 18 years. This creates a sample of children born after the apartheid government had fallen (the so-called born-frees) and their policies had been replaced with policies favouring equality. Our outcome of interest is years of schooling completed. The variable is coded from 0 for no education to 14 for post-secondary education. Completion of secondary school is coded 13. Most youth in the sample (95%) fall in the range from no schooling to completing secondary school. A few students reported to have a certificate or diploma at less than grade 12 were given a code of 12 (<1%), and students with a grade 12 certificate or diploma coded 13 (2%). Race differences in highest level of education are shown in Figure 1.
Independent variables include race coded as dummy variables for coloured (11% of the sample), Indian/Asian (2%), and white (4%), with African (83%) as the implicit
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category. We exclude the small percentage listing some other race group from the analysis. Patterns of labour migration, mortality and residential norms lead to a complex household structure in South Africa. We measure key elements of this complexity with three variables. First, we include variables tapping parental availability. The first, which is a set of dummies, includes the presence of both a father and mother (the implicit category), a father with no partner, a mother head with no partner, or a household with neither parent. To measure sibshipsize, we include the number of household members aged 17 or younger. These are the members in the household who compete for parental resources with respect to direct and indirect costs of schooling. Substantial differences in family structure exist across the four racial groups. Over one-third of black African children of school age live in female-headed households and another third have neither parent in the household. Coloured children also have disadvantaged household structures. These comparisons can be found in Table 1.
Household educational resources are measured by education for the male and female head and/or partner. We use head and partner instead of mother and father because of the relatively large percentage of children who do not live with a parent (30%). We use multiple imputations to deal with households that have missing data on male head and partner’s education. We use an index of household possessions, source of income and expenditures to measure household economic resources. The household index includes the quality of roof and walls (coded from 1 for very weak to five for very good), dichotomous variables for electricity, land phone line and cell phone, source of income (coded 3 for salary or business income, 2 for other sources and 1 for no income), and household expenditures (coded from 1 for below 400 Rand to 8 for over 10 000 Rand). Cronbach’s Alpha for this index is .627. The index is used because household income was not included in most years of the survey. The index is the simple sum of these items (see Filmer and Scott 2011 for a justification of using indexes when income data are incomplete). Children from white households had access to the most resources, with Asian children not far behind. Coloured and African children scored much lower on this index. Similar racial patterns appear for household head education, regardless of the sex of the head (Table 1).
School quality is measured as a neighbourhood variable by aggregating the responses in each primary sampling unit. This index is the average of the sum of people in the primary sampling unit who report the school has problems because of lack of books, poor teaching, lack of teachers, poor facilities, high fees and large classes. Cronbach’s Alpha for this index is .585. Reliabilities for these two indexes are admittedly low but the alphas would be even lower if any items were removed and correlations among component variables high enough to present multicollinearity problems were we to include them as separate variables. Interestingly, the racial pattern for the index of school quality deviates slightly from the patterns for family structure and household resources: While white children attend the highest quality schools and African children the lowest quality, Indian/Asian children’s schools have more problems than do coloured children’s schools (Table 1).
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Table 1: Race differences in mean values of independent variables including household structure, resources and school quality
Population group
Mother only
Father only
neither parent
children age 0-17
index of household resources
male head edu-cation
female head edu-cation
index of school quality
African/Black
.370 .036 .338 2.73 15.2 7.18 6.84 .429
Coloured .311 .033 .196 2.26 16.6 8.47 8.23 .370Indian/Asian .118 .028 .061 1.868 20.0 11.40 10.82 .466White .159 .030 .045 1.77 21.44 12.56 12.37 .280Total .351 .035 .306 2.63 15.69 7.86 7.30 .418
We ask a series of increasingly complex questions about the racial gap in education. First, does an education gap across racial groups exist? Second, how much of the gap is due to household and school environments? Third, how does the gap change as children age? Fourth, is there a trend in the magnitude of the gap across survey years? Finally, are the trends by age and time period explained by household and school characteristics?
These questions are addressed with a series of regression models. The first model includes all the dummy variables for race. The second model adds household and school characteristics to model 1. The third model includes dummy variables for race and interactions between age and race. The fourth model adds interactions between survey year and race to model 3. The interactions in Model 3 and Model 4 show adjusted racial difference in the increase in education as children age and across the years of the survey after the average effects of age and time are removed. The final model (Model 5) adds school and household characteristics.
RESULTSFigure 1 shows average years of schooling completed by age and race. Indian/Asians already have an education advantage by age 7 and maintain that advantage as they age. Other groups have similar attainment at age 7, but the gap increases as children age so that by age 18, coloureds and especially black Africans experience substantial disadvantage.
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Figure 1: Years of schooling completed by age and race
Coefficients from the regression models are shown in Table 2. The first model simply shows the overall gap in highest level of education averaged across all ages and all years of the survey. The constant term in this model shows the average education for black Africans to be a little over ten years by age 18 (b=10.275). Coloureds are a third of a year above Africans (b=.311), whites are nearly a full year above black Africans and the Indian/Asian group excels with a value 1.1 years above Africans (b=.840). These differences are not surprising given the inequalities documented by other research and are reported for reference as other variables are added to the analysis.
The second model adds controls for family and school characteristics. Coefficients for these variables generally fit with prior research. One important exception is that children in female-headed households are par with children in two parent families (the comparison group). Children in households without a mother have a substantial
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disadvantage. Greater education of the household head and partner is associated with higher educational attainment, and the female partner’s education matters somewhat more than the education of the male. Presence of more children under age 17 is associated with lower educational attainment. The index of household economic status is positively associated with children’s education. The index of school quality surprisingly has a positive relationship with education. It is possible that as children go farther in school, they find more problems. It is also possible that the measure of school quality does not reflect objective differences in quality of teachers of adequacy of resources. The measure is based on self-reports and is not highly correlated with race of household socioeconomic status.
Of particular interest is the shift in coefficients for race variables after family and school characteristics are taken into account. The coefficients for coloureds and Indian/Asians drop by nearly 40 per cent (from .378 to .242 for Coloureds and from 1.107 to .682 for Indian/Asians), suggesting that a substantial share of their educational advantage over black Africans is because they live in favourable family contexts. The coefficient for whites drops by nearly 60 per cent, suggesting that a major part of their educational advantage is because they live in favourable family contexts.
The next model examines the growing racial gap in education for older children. The coefficient for age in this model shows the pattern for black Africans. Their education only increases by .8 years for each year of age. Results for interactions between age and each of the other groups show that the age graded slope is somewhat higher for coloureds, and substantially higher for whites and the Indian/Asian group. Indeed, the coefficients for these two groups (i.e. whites and Indian/Asians) indicate that the gap between them and black Africans increased by about .15 years for each year of age. This is consistent with Figure 1 that shows a gap of nearly two years by the time children reach age 18. Once age is taken into account, the coefficients for Indian/Asian and White also indicate that black Africans have nearly a two-year education disadvantage by age 18.
Model 4 explores trends across surveys in the average race difference. A dummy variable for surveys after 2008 is added to take into account the lower average education in these surveys. It is unclear whether this drop is due to change in the sampling frame or coding of education. Coefficients for survey year (reflecting the trend for Africans since they are the implicit comparison) and the interactions of year and race indicate a narrowing of the educational gap between coloureds and black Africans of about .038 per year, while the gaps between black Africans and both whites and Indian/Asians are narrowing by about .07. While these trends suggest a decline in inequality, this decline is small relative to the overall differences in the coefficients for race. The magnitude of the coefficients imply that it would take 20 years to eliminate the education difference between Africans and coloureds (.775/.038=20.4), 31 years for the black African/Asian gap and 27 years for the black African/white gap. Of course, these calculations are not so much a prediction as a means of assessing the magnitude of the trends.
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Table 2: Regression of Education on Race, Age, Year, Family background and School Quality.
Variables 1 2 3 4 5
Constant 10.275* 9.927* 10.203* 10.339* 9.583*
Coloured .378* .242* .626* .775* .548*
Indian/Asian 1.107* .682* 1.876* 2.231* 1.693*
White .840* .352* 1.668* 1.923* 1.192*
Age (-18) .827* .824* .814* .815* .813*
Male -.362* -.359*
Mother only -.035* .015*
Father only -.135* -.058*
Neither Mother nor father -.152* -.079*
Male head education .020* .023*
Female head education .029* .039*
Children aged 0-17 -.059* -.026*
Household Index .021* .043*
Index of school quality .207* -.013
Age*Coloured .046* .047* .046*
Age*Indian/Asian .153* .150* .155*
Age*White .159* .155* .157*
Year .047* .048*
Year dummy (after 2009) -.924* -1.091
Year*Coloured -.038* -.027*
Year*Indian/Asian -.071* -.057*
Year*White -.072* -.053*
R2 (from OLS regression) .770 .799 .772 .784 .814
The final model adds household and school variables to the model with trends across survey. Interactions with age and race are basically the same. The interactions between year and race become somewhat smaller. These shifts suggest that the household circumstances of black Africans are improving, and without this improvement black Africans would actually be doing worse over time. On the other hand, part of the relative position of whites may be that their household circumstances are not improving as much as is the case for other groups. Otherwise, adding household variables does not have much of an impact on the other coefficients.
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CONCLUSIONSAs a result of the deliberate policy of the apartheid government to create an unequal society through the manipulation of the education system, subsequent governments since the inception of democracy in South Africa have made concerted efforts to undo the damage done to the education system by ensuring both access and equality in education. But despite these policy interventions, data from post-apartheid South Africa demonstrate that substantial educational inequality still exists. In describing the persistence of low educational standards in black African schools due to apartheid educational inequities, Fataar (2010: 43) notes:
This situation has not changed during the post-apartheid years …. The routine didactic transfer of prescribed content, repetition, rote learning and memorization made up the pedagogical platform of these schools.
While this inequality is not so evident at ages when children begin school, it becomes increasingly evident at older ages. This pattern suggests that black African and coloured children either drop out or repeat grades to the extent that by age 18 they have a two-year education disadvantage compared with white and Indian/Asian children. In fact, one report states that of the 920 716 pupils in Grade 11 in 2007, only 64 per cent went on to take their Senior Certificate examination in 2008 (Mgibisa 2009). About half of this disadvantage is because of family background. Very little is because of school characteristics that can be measured in this study.
Even though in South Africa race, schooling and education, separately and in relation to one another pose fundamental challenges for the society, the importance of the family institution is central to understanding racial inequalities in educational outcomes in the country. Institutionalized family inequities enacted under apartheid continue to have resonant effects long after the discriminatory laws have been dismantled. For instance, the family, especially among black Africans and coloureds, was severely fractured during the apartheid era. The black African family was subjected to disorganization through such mechanisms as the migratory labour system and the Group Areas Act, which either singly or jointly discouraged family life to varying degrees. Among the social problems that have been associated with dysfunctional family life in the black African communities are school dropout, grade repetition, child abuse and neglect, marital breakdown, family violence, mental health problems, drug and alcohol abuse, especially teenage pregnancy and parenthood, violence, and crime especially, among the youth in these disadvantaged communities.
While these discriminatory policies are no longer in place and families do not have formal or legal obstacles to their stability, change is slow. The history of apartheid-induced family dysfunction may actually help to explain why female-headed households do not differ significantly from families with two parents in place: female-headed households were common enough in the apartheid era and that social support and structures may
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already be in place to address the deficits inherent in such family types.One lingering effect of apartheid policies that cannot be addressed quickly is the
low educational attainment of the parents of the black African and coloured children we study here. It is possible that as attainment gaps shrink, even slowly, the effects of previous discriminatory policies will weaken across time and generations. On the other hand, part of the relative position of whites may be that their household circumstances are not improving as much as is the case for other groups. Thus, even though there is some indication that inequality may be narrowing, it is not enough to eliminate inequality in the near future as long as other factors of social inequalities such as unemployment and poverty are not addressed simultaneously. Specifically, these results imply the need for more effective education policy that should address both issues of racial inequality and disadvantaged family backgrounds.
One path for future research is a more thorough examination of the roles schools play. Schools serving different racial groups have vastly different levels of resources, suggesting that schools might play a key part in how inequality in educational opportunities and achievement increases over time. However, our multivariate analyses show little support for the role of school quality. This could in part be because the measures of school quality in the data are insufficient to tap actual educational processes through which stratification is reproduced; the school quality variable used here has poor reliability. For example, research examining in-classroom pedagogy and practices has found substantial differences in the training and abilities of teachers in wealthy South African schools and poor ones, a distinction that is likely linked to race (Crouch and Mabogoane 2011; Fiske and Ladd 2004; Van der Berg 2007; Spaull 2012; Van der Berg and Burger 2002; Taylor et al. 2003; Soudien 2010). The data we use here do not extend to classroom-level variables, but we suspect that a lack of both physical and social resources in schools would likely contribute to the racial gap we find here. Similarly, measures of educational outcomes are also limited to education completed and say nothing about student skills, readiness, or level of proficiency (Department of Education 2001; Fiske and Ladd 2004; Van der Berg 2007; Yamauchi 2011; Spaull 2012). Additional studies with more detailed school data are needed to untangle the extent to which schools make a unique contribution to education gaps or merely reflect inequality driven by family background.
The reality of the apartheid legacy is that in South Africa, race and socio-economic status are entwined (O’Gorman 2004; Pearce 2004). To the extent that black Africans bear the brunt of the effects of the so-called triple challenge of poverty, unemployment and inequality in the new South Africa, the stated state policy goals of equalizing educational outcomes among the country’s youth will continue to be elusive. Given the slow pace of change in family context, it appears that racial inequality will persist until effective school based policies are implemented.
NOTES
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1 In this paper, we use the nomenclature and spellings used in the General Household Survey: White, African, Indian/Asian, and Coloured. Although current law does not always use these distinctions, we use them as basic comparison groups here for comparisons to data from the apartheid era and because many contemporary South Africans still use these labels for themselves and others.
2 Stats SA revised the population model to produce mid-year population estimates during 2008 in the light of the findings of the Community Survey 2007 and new HIV/AIDS and mortality data. The new data have been used to adjust the benchmarking for all previous datasets. Weighting and benchmarking were also adjusted for the provincial boundaries that came into effect in December 2006. The data for the GHS 2002 to GHS 2009 as presented in this release are therefore comparable.
3 With the exception of the 2009 survey for which data collection was spread over a three-month period, data collection for all waves of the survey was done only during July (Statistics South Africa 2010).
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BIOGRAPHICAL NOTESTim B. Heaton holds a Camilla Kimball chair in the Department of Sociology at Brigham Young University. His research focuses on demographic trends in the family. Research in the United States and Indonesia has focused on trends in and determinants of marital dissolution. He has examined the relationship between family characteristics and children’s health in Latin America and Africa.
Acheampong Yaw Amoateng is a Research Professor of Sociology and Family Studies in the School of Research and Postgraduate Studies, North-West University (Mafikeng Campus). His areas of research interest include adolescent and youth development, family formation and dissolution patterns and ‘interracial’ marriages. He is currently working on a project on political participation in sub-Saharan Africa looking specifically at gender and ethnicity.
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Mikaela Dufur is an Associate Professor in the Department of Sociology at Brigham Young University. The bulk of her research examines social resources available to children and youth in their families and at their schools. She also studies issues of race and gender in sport. Her research has been published in outlets such as Social Forces, Journal of Marriage and Family, and Journal of Health and Social Behavior.
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