Education, poverty and inequality in South Africa 1

Transcription

1 Education, poverty and inequality in South Africa 1 Paper to the conference of the Centre for the Study of African Economies on Economic growth and poverty in Africa Oxford, March 2002 by Servaas van der Berg University of Stellenbosch Abstract: South African poverty and inequality are strongly rooted in the labour market. Despite the continuing relevance of race, labour market race discrimination has declined as cause of inequality compared to other factors also often correlated with race (e.g. education and location). Moreover, if cognisance is taken of large differentials in educational quality, the residual earnings differentials ascribable to labour market race discrimination may well be small. This emphasises the need to concentrate on the one factor amenable to policy, education. The paper uses census and survey data to show that quantitative educational attainment differentials in South Africa (years of education) have been substantially reduced. Qualitative differentials remain larger, though. Despite massive resource shifts to black schools, overall matriculation results actually deteriorated in the post-apartheid period. Thus the school system contributes little to supporting the upward mobility of poor children in the labour market. The persistence of former racial inequalities is reflected in extremely poor pass rates in mainly black schools (the majority of schools), but with high standard deviations. Regressions of matriculation pass rates from school level data show that racial composition of schools remains a major explanatory factor besides socio-economic background (as measured by school fees set by school governing bodies) and educational inputs (measured by teacherpupil ratios and teacher salaries as proxy for qualifications and experience). The remarkable differentials in performance amongst black schools cannot be accounted for by socioeconomic background or teaching resources, pointing to the importance of school management and to the malfunctioning of large parts of the school system being largely a problem of x-inefficiency rather than allocative efficiency. The optimistic policy interpretation is that additional fiscal resources can play only a limited role, so addressing the issue need not be costly; the pessimistic view is that good administration and management are even more scarce than fiscal resources. 1. Introduction Education enhances the earnings potential of the poor, both in competing for jobs and earnings and as a source of growth and employment in itself. As Kanbur (1998: 20) puts it, The distribution of physical and human capital emerges from the theoretical and empirical 1 This paper draws on some of the author s ongoing research (only some of it published), inter alia Van der Berg 2001a; 2001b; 2001c; van der Berg et al

2 2 literature as the key to distributional consequences of growth, and as the determinant of growth itself. South Africa is a country with a notoriously skew distribution of income and consequently high poverty levels for an upper-middle-income developing country. A comparison with Latin America, a continent sharing South Africa s middle-income status and high inequality, is instructive. A large part of Latin American inequality relates to the difference between the top 10 percent of the population and the rest (Inter-American Development Bank (IADB) 1998: 1), but this applies even more to South Africa: The per capita income of the richest decile exceeds that of the second richest decile by 60% in the USA, by 160% in Latin America, and by 208% in South Africa 2. And like in Latin America, in South Africa, too, much of this gap between the top 10 percent and the rest reflects the slow and unequal progress in improving the level and quality of schooling (IADB 1998: 2). This paper is concerned with this human capital inequality, described by Simkins (1998:4, 8-11) as one of apartheid s footprints in the sand of poverty and inequality. The premise is that South African poverty and inequality are strongly rooted in the labour market, thus the origins of labour market earnings differentials require attention. Despite the continuing relevance of race for identifying the poor and analysing labour market outcomes, labour market race discrimination has declined as cause of inequality compared to other factors also often correlated with race, such as education, location (urban/rural), family size and composition. Moreover, the educational returns literature needs to be interpreted in the South African context even more circumspectly than usual, given considerable variations in educational quality amongst schools and over time usually ignored in earnings functions that may account for much of the residual earnings differentials usually ascribed to labour market discrimination. This emphasises the need to concentrate on the one factor amenable to policy intervention, education, to reduce earnings inequality through more equal educational attainment and quality. This paper explores some of the distributional consequences of education, using census and survey data to show that quantitative educational attainment differentials in South Africa (years of education) have been substantially reduced. Qualitative differentials remain larger, 2 The South African data are based on expenditures, not income; incomes may even show greater inequality.

3 3 though. The next section (Section 2) looks at the relation between education, inequality and poverty in the South African context, whilst also presenting an overview of the educational situation in South Africa. Despite massive resource shifts to black schools, overall matriculation results actually deteriorated in the post-apartheid period. Thus the case will be made that the school system contributes little to supporting the upward mobility of poor children in the labour market. After a brief discussion of resource allocation in education, Section 3 applies regression analysis to school level data to show that racial composition of schools remains a major explanatory factor of matriculation pass rates, besides socioeconomic background (as measured by school fees set by school governing bodies) and educational inputs (measured by teacher-pupil ratios and teacher salaries as proxy for qualifications and experience). The conclusions follow in Section Education, inequality and poverty in South Africa 2.1 Racial inequalities in educational attainment Schooling inequality between races as reflected in years of education completed (educational attainment) is still large (Table 1), but has been substantially reduced in the past decades. For instance, Lam (1999) shows the decline in inequality in years of education completed between two birth cohorts separated by 30 years, reproduced in Table 2. Schooling variance declined even amongst blacks, while schooling inequality between the different races also greatly declined. Blacks in the cohort born in 1920 had a mean backlog of 8.0 years of education compared to whites; those born in 1950 still a 6.0 year backlog, the 1960 cohort a 4.6 year backlog, and the 1970s cohort a backlog that had been reduced to only 3.2 years. Table 1: Years of education completed of adult population (20 years or more) by race and location, 1996 Urban Rural Total Black Coloured Indian White Total Source: Census 1996

4 4 Table 2: Educational inequality for two SA cohorts, 1995 Cohorts Cohorts Mean Standard deviation Coefficient of variation Gini Source: Lam 1999: Table Educational inequality and labour market earnings In South Africa, an expanding literature shows educational inequality to be a determining factor in earnings distribution. In a recent study, Bhorat and Leibbrandt (2001) show that education affects the propensity of blacks to participate in the labour force, their probability of being employed and their earnings, with returns to secondary education being particularly high. Work by inter alia Moll (2000), Mwabu & Schultz (1996), Fallon & Lucas (1998) and Hofmeyr (1998) broadly confirms the importance of education for blacks, the largest but also poorest race group. Between 1980 and 1993, South African earnings inequality decreased between race groups, whilst it increased within race groups (Moll 2000). The net result was to leave overall earnings inequality largely unchanged, as Table 3 shows. Improved black educational attainment probably played only a minor role. Moll rather ascribes widening within group earnings inequality to the removal of labour market discrimination, with some blacks benefiting from new opportunities for upward occupational mobility, while the earnings premium formerly enjoyed by even poorly educated whites declined once they lost the protection they had historically enjoyed. Thus even though education was not directly responsible for changes in earnings, its distribution determined who could benefit from the new opportunities for blacks in the labour market.

5 5 Table 3: Inequality of monthly earnings by race, 1980 & Gini coefficient Coefficient of variation L-statistic * L-statistic: Blacks L-statistic: Indians L-statistic: Coloureds L-statistic: Whites Within-group inequality Between group inequality * The L-statistic (mean logarithmic deviation) is an additively decomposable measure of inequality that ranges from 0 (complete equality) to infinity. If utility has a logarithmic form, L measures the difference between maximum social welfare with a given income (the ideal state of distribution) and the actual social welfare (Moll 1998: 4). It is calculated as the mean of the natural logarithms of earnings, minus the natural logarithm of mean earnings. Source: Mol 1998: Tables 1 & 2 Research has shown the importance of education not only for earnings but also for labour force participation and employment. Ferreira and Litchfield (1998: 32) report that between one-quarter and one-third of income differentials between households in Chile can be ascribed to differences in the educational attainment of the household head; in South Africa this proportion is lower (about 16% in 1995), yet still very important. Figure 1 provides an alternative presentation of the influence of level of education of the household head on household poverty. For levels of education lower than matriculation (completion of secondary education), there is clear poverty dominance, with more education of the household head always being associated with less household level poverty. But at higher education levels (matric or more), where poverty is far less prevalent, other factors (e.g. race, location, household size and composition, or education of other household members) intervene to reduce the role of educational attainment of the household head in household-level poverty.

6 6 Fig 1: Cumulative density curves of per capita earnings by education of household head, Quality differentials in education One should not forget, though, that the quality of education still varies considerably. This is again not unique to South Africa: In some Latin American countries, "the poor receive an inferior quality of schooling (IADB 1998: 53), with the result that individuals from the lower deciles receive a primary education whose quality (measured in terms of income generation capacity) is 35 percent lower than that of the next decile above. (IADB 1998: 54). Although the old dividing lines of race have blurred in education, with many black pupils now attending formerly white schools, data for 1977 for 7 provinces summarised in Table 4 shows that most black pupils (96%) were still in schools which were predominantly (more than 70%) black. About 5% of the pupils in mainly white schools were black, whilst in mixed schools (where no race group constituted more than 70% of pupils), 40% were black. (Van der Berg 2001b). But there is great quality diversity in mainly black schools, and as a group most formerly black schools still perform much worse than white schools, as reflected in matric pass rates (a topic discussed in more detail in Section 3.2). Judging by the high matriculation failure rates, lenient promotion policies in black schools may cause educational attainment at levels below matriculation to give an inflated impression of educational standards reached, as reflected in cognitive levels mastered.

7 7 Table 4: Schools, pupil-teacher ratios and pupils by school "race type" in seven provinces, 1997 (Eastern Cape and Mpumalanga not included) Mainly black schools Mainly coloured schools Mainly Indian schools Mainly white schools Mainly "other" schools Mixed schools Total Schools Pupil/Teacher ratio Pupils: Of whom: Black Coloured Indian White Other (race specified) Note: A school is classified in a particular race type of more than 70% of pupils are from that particular race groups. Schools where not race dominated in this way were classified mixed. Whilst other schools are those (mainly in the Western Cape) who refused to divulge race information in the survey by the national Department of Education.. Quality differentials are also reflected in the quality of the matriculation itself, in terms of the standard at which matric is passed as well as the subject choice. Few mainly black schools provide an adequate background in Mathematics or Science. Only 45% of all matriculation candidates wrote Mathematics in 1997 (with a marked male bias); only 21% passed it, and most only attempted Standard Grade Mathematics, a standard far below what is conventional in developed countries. Even in the Western Cape, the province with the best matriculation results, only 24% of matriculation candidates attempted Mathematics at the Higher Grade, and only 20% passed it. Nationally, the proportions who wrote and passed Science were even lower at 25% and 16% respectively. Only 50% and 42% of teachers teaching Mathematics and Science have studied these subjects beyond secondary school level (Edusource 1999: 5). Another indication of inequality in educational output at higher standards can be gleaned from data for the Western Cape. As Western Cape pass rates are almost uniformly high (almost 80% of all candidates pass matric), differences in pass rates between schools are relatively low, as Table 5 shows. However, if more onerous levels of school performance (university

8 8 exemptions or percentage A-aggregates achieved 3 ) are evaluated, inequality increases considerably. Table 5: Inequality of three measures of educational outcomes at matriculation level between schools in the Western Cape, 1997 Passes University A-aggregate Exemptions Mean 80.6% 23.0% 2.6% Standard deviation 22.5% 22.0% 5.0% Coefficient of variation Gini coefficient Source: Own calculations from Western Cape Education Department data Fig. 2 shows literacy and numeracy test scores for 1993 for blacks and whites aged 12 to 18, where questions have been set at approximately Grade 7 (age 12) level (see also Fuller et al. 1995). Not even the performance of whites is very encouraging, but what is particularly alarming is that blacks perform far worse on both tests despite the fact that educational levels attained by blacks and whites differ relatively little at this age. The unsatisfactory black performance in matric appears to be partly the delayed effect of lower cognitive achievement levels at earlier ages. Although blacks aged 12 to 18 in 1995 had attained on average 78% of white years of education, in 1993 their literacy scores were 55% of white levels, and their numeracy scores lagged even further at 46% of white levels. 3 University exemptions is a measure of the quality of the matriculation pass obtained; such an exemption has usually acted as entrance barrier to universities. A-aggregates are average distinction levels, obtained by a very small proportion of candidates.

9 9 Fig. 2: Literacy and numeracy test scores of black and white teenagers (12-18 years), 1993 (scores converted to percentages; ratios relative to white scores) Literacy and numeracy scores of black and white teenagers (12-18 years), 1993 (scores on 8 point scale converted to percentages; ratios relative to white scores) 100% 80% 60% 40% 20% Black White 69.5% 38.3% 27.7% 60.7% 55.1% 45.6% 78.0% 0% Literacy score Numeracy score Literacy ratio Numeracy ratio Years education completed: Ratio In a previous study (Van der Berg et al. 2002), the same 1993 data from the Living Standards and Development Survey were used to try to explain combined numeracy and literacy test scores of teenagers. The regressions (see Appendix) showed that educational attainment does matter for numeracy and literacy performance, but some other factors also have an influence, including race (Indians considerable outperform the rest, whilst blacks do about 10% worse than whites and coloureds of similar education and socio-economic background), location, parent education, and economic status (as measured by per capita household expenditure). The regressions explain 39% of the variation in test scores, whereas educational attainment alone can only account for 21%. Clearly, pupil home background influences not only how well students progress through school, but also the quality of how well they learn, as measured by these test scores. Both their educational attainment and their educational quality influence how well they later fare in the labour market. The regressions imply that black teenagers have a backlog in test scores compared to whites and coloureds of similar educational levels equivalent to almost three years of education, and of six years compared to Indians, even when standardising for home language. 4 This illustrates the inability of the former black school system to provide the educational quality required to integrate most school leavers into a 4 Using the same data for a different purpose, Case and Deaton s found an even far bigger backlog of black teenagers, equivalent to almost 10 years of education completed: (F)our additional years generate one additional correct answer on the tests (Case & Deaton, 1999: 26; cf. also Table 7).

10 10 modern economy the yardstick by which, from an economic viewpoint, the educational system must be measured. Considering these quality differentials, racial wage differentials for persons with similar education and experience may result less from labour market discrimination, as is usually presumed in earnings functions, than from pre-labour market discrimination in school quality. Measured wage discrimination had declined from the 1970s. As early as 1989, black wages were barely 15% lower than those of whites in a similar job grade and of the same gender, a substantial reduction from the 43% differential of 1976 and the 22% of 1985 (McGrath 1990:97), i.e. there was progress towards the "rate for the job" (though this says nothing about differential access to particular jobs). Using other data sources, Moll (1998:1 & Table 10) came to a similar conclusion: total discrimination fell from 20% of the Black wage in 1980 to 12% in Work by Chamberlain (2001) confirms that a substantial proportion of the unexplained residual in earnings between race groups may be due to differentials in educational quality rather than conventionally measured labour market discrimination. 2.4 Educational inequalities amongst blacks Amongst blacks, educational inequality largely follows the lines of income: more affluent households are better able to support their children through school, implying increasing stratification within black society. Children from the top two black deciles progress considerably better through the school system than their poorer counterparts and only at age 15 start falling behind whites. Case & Deaton (1999: 21) conclude that private resources were a major factor determining differential black educational outcomes under apartheid. Pupils in better-off Black households do better in their education, and we find no parallel for Whites. That the education of Blacks but not Whites is constrained by financial resources is further supported by the fact that many Blacks who are not in school (but not Whites) report lack of resources as the reason. (Case & Deaton 1999: 28). Furthermore, greater recent access to formerly white schools for more affluent blacks may have accentuated qualitative educational differentials amongst blacks. Data from the 1996 census show mean earnings of full-time employed black workers for whom the educational level of a parent is known (i.e. children of the head of household still resident in the household) to be substantially higher where the household head has at least matriculated. But is this perhaps solely due to more educated parents having more educated

11 11 children, i.e. to differential attainment? Table 6 shows mean income of such children who have completed at least matric. Differentials remain substantial: In some way the better education of the parent (household head) translates into higher earnings for children even compared to other young workers who also have matriculated, but where the parent had less education. However, it is not clear whether this measures the quality of education, or some other non-observed aspect of human capital transmitted from parents to children. Such premia do decline, though, to about 9% in cases where the children have graduated. Table 6: Mean monthly earnings of full-time employed black children of head of household by education and whether head of household has matriculated, 1996 Mean monthly income by education Matric Matric + diploma or certificate Matric + other nondegree Degree Head of household matriculated R1 731 R2 658 R2 849 R3 388 Head of household not matriculated R1 380 R2 285 R2 164 R3 104 Premium for head being matriculated 25.4% 16.3% 31.6% 9.1% Note: Cases where the worker reported being in full-time employment but reporting no income were excluded. This had only a very minor effect on the premia. Source: Own calculations from Census % Sample. 2.5 Earnings and the demand for skills Educational differentials in association with the demand for labour determine returns to education. 5 Little is known about the evolution over time of South African returns to education, not even to speak about what these would have been in the absence of apartheidbased labour market interventions. Bhorat & Hodge (1999) have shown that South African labour demand patterns reflect a growing demand for higher skilled labour and declining demand for low-skilled workers. International experience also indicates that without an acceleration in the availability of such skills, educational premia are likely to remain high. In the USA, only an enormous expansion in secondary schooling after 1910 made possible a reduction in the returns to education until the 1950s, wherafter returns again rose as skills demand outstripped their supply because of skill-biased technological change (Goldin & Katz 1999: 25; for more recent evidence, see also Murphy & Welch 1994). Thus reducing 5 Returns to education result from the interaction between the supply and demand for human capital, and the latter is related to the economic growth path, which is itself changing. Moreover, insofar as education itself may determine growth, there is an endogeneity problem that cannot be resolved in forecasting the evolution of earnings.

12 12 labour market inequality would require substantial improvement in the supply of skills through more and better quality education. The needs of the economy in terms of the type rather than the level of educational output should also be considered, and may be an unobserved variable that affects the returns to education (e.g. the importance of Mathematics for further training). 3. Implications for education policy: More resources, or better resource use? The foregoing all points to the importance of improving the educational attainment and quality of education (as measured by cognitive tests) of the poor in order to reduce earnings inequality and poverty. In terms of resource use in education, two alternative routes are possible: applying more resources, or using resources better. 3.1 Fiscal resources for education South Africa allocates, by international standards, a large share of its national resources to education; its public education spending ratio of about 7 per cent of GDP is amongst the highest in the world (without even considering the skills training levy). Moreover, education spending has already increased rapidly. Shifting substantially more fiscal resources to education does not appear to be a viable proposition. Moreover, larger financial flows to education in the past five years did not in fact increase real resources for education commensurately, as fiscal resource shifts were overshadowed by wage increases for teachers; the pupil/teacher ratio declined only marginally from 33.7 to 32.7 from 1996 to 2000 (South Africa 2001: 35). In contrast to the international experience 6, teacher salaries outpaced the growth of per capita GDP. Cutbacks in educational personnel in some richer provinces were barely matched by increases in personnel in educationally more poorly endowed provinces, despite much larger fiscal shifts. As Donaldson perceptively remarked some years earlier, "...the constraint at work is not (only) finance, but the limited real resources available to the economy. Competent teachers, nurses, doctors and community workers are scarce, as is the capacity to produce books, medical supplies, and building materials. So the growth and improved distribution of social services must be viewed as the growth and improved distribution of the inputs required for delivering these services." (Donaldson 1993: 147) 6 Lee and Barro (1997: 17/18) provide some evidence of the relative decline of teachers wages in international context: The ratios of estimated real salaries of primary school teachers to per capita GDP have typically declined over time; from 1965 to 1990, the value dropped from 2.5 to 2.2 in the OECD, from 4.9 to 3.6

13 13 The strong bargaining power of teacher unions allowed them to raise their real salaries substantially. Unlike their public sector contemporaries, black teachers did not benefit from the wave of black advancement following democratisation, as there were few opportunities for promotion within teaching 7, thus their frustrations were often vented in wage bargaining. Thus fiscal resources for education increasingly had to be directed to personnel spending, leaving a growing dearth of non-personnel spending: from 1995/6 to 1997/8, real personnel expenditure increased by 20% while non-personnel expenditure in schools declined by 17% (South Africa 1998: 27). Yet despite more aggregate spending, both the number of successful matriculants and university exemption remained stagnant or even declined, whilst the school-age population was growing, implying that declining proportions of the school-completing age were attaining matriculation or exemptions. As the growth in pupil numbers still exceeds the growth rate of the economy due to continued, though declining, growth in the school-age population and longer retention of pupils in the school system, a government team investigating the medium term expenditure framework concluded that there will be a major funding problem in education in coming years, unless! more funds are allocated to education which they regard as fiscally infeasible, and which internationally has been shown not always to improve educational outcomes (Gupta et al. 1999: 4);! pupil-teacher ratios rise even further which is unacceptable to government, teacher unions and parents alike;! teacher salaries decline in real terms which is strongly opposed by the teacher unions;! some combination of the above occurs (South Africa 1998). Thus there is limited scope for substantially increasing fiscal resources per pupil in coming decades, so the question of resources largely revolves around issues of resource allocation versus resource efficiency. If the malaise of the South African educational system lies in x- inefficiency rather than in allocative inefficiency, reallocating resources between levels of education would bring little gain, and it is not even clear which level of education most requires additional resources. There is perhaps a stronger case for shifting more financial in the overall group of developing countries, and from 7.4 to 1.7 in the CPEs (centrally planned economies). 7 De Villiers (1996: 288-9) reports that more than 90 per cent of teachers will not receive more than one promotion in a lifetime of teaching

14 14 resources to non-personnel teaching resources; personnel spending is so dominant that even a small relative shift could greatly increase the availability of classroom resources. 3.2 Efficiency in educational resource use However, the clearest need appears to be in utilising existing resources better, even in their present application. The major inefficiencies are in what used to be the black school system, by far the largest part of the system, where the quality of learning in schools is often abysmal. The Culture of Learning, Teaching, and Service (COLTS) campaign launched in 1996 was the first more or less official recognition of the fact that efficiency and work effort problems, rather than funding by itself, were at the heart of the problems in the education sector (South Africa 1998: 35). Former President Mandela, and later also the new President and the new Education Minister, publicly put the blame for mediocre education results on lack of teacher discipline. Such inefficiencies result from a typical principal-agent problem. Educational outputs are notoriously difficult to monitor, as is teacher effort (input), thus low teacher productivity is hard to overcome through incentive schemes. The educational authorities have responded by attempting to shift monitoring to the parent community as the final principal. Unfortunately, however, this policy is least successful in schools where parents themselves have had little formal education and therefore are hesitant to confront teachers or ignorant about what can be expected of teachers precisely those schools where failure rates are greatest. Moreover, lines of authority are also not always clear and school principals often find it difficult to act against undisciplined teachers or pupils. As always when there is a principal-agent problem, one avenue for improvement is through providing more information. The education authorities have a paucity of information to analyse the educational situation and their policy options. Presently, the only measure of educational output available to them is matriculation results, but these still do not identify the roots of the problem (nor are they properly analysed at the school level). It has been shown above that literacy and numeracy levels amongst many blacks are already far below par as early as age 12. Allocating resources based on matriculation results cannot adequately address a problem which requires much earlier intervention. Where to direct resources cannot be decided without information on the qualitative performance of different parts of the school system. This requires large scale and continued efforts at measuring cognitive achievement at

15 15 different levels, in order to better understand the relationship between home background of pupils, educational inputs, and enhanced cognitive achievement. Moreover, identifying schools performing badly in order to take remedial action requires a better understanding of how schools perform and the causes thereof. Against this background, school level data may shed light on whether matriculation results vary systematically with socio-economic background and with educational inputs. 8 The policy issue is whether educational resource shifts can effectively overcome differentials flowing from socio-economic background or accumulated educational backlogs from the previous dispensation. The data consists of matriculation pass rates for 1999 and 2000 for secondary schools who had matriculation candidates in the latter year, matched to data on 1997 school resources. Matching the data records was not always possible, as schools were identified in different ways in the 1997 data set than in the examination data published by the national Department of Education. The matching process could not be completed for one of the seven provinces covered in the 1997 data set, leaving data for only six of the nine provinces. Although the final sample did include a large proportion of schools in all of the six provinces covered, it is unclear whether missing observations were non-random. The sample size of almost covers about half of all schools, matriculation candidates and matriculation passes, and about 70% of those in the provinces included. Matriculation class size in schools included in the sample was above average for the six provinces included, while the 56% pass rate in the sample differs little from the actual average in these provinces (58%). Despite variations in the sampling proportions across provinces (the Free State, Northern Cape and Northern Province were better presented than average) largely due to the problems of matching the two data sets, it was decided not to re-weight the data at the provincial level. Thus the results apply to a fairly but not fully representative sample of about half of all schools and matriculation candidates nationally. In addition to the variables in the 1997 data set, the examination data set contains pass rates for 1999 and 2000, as well as the number of candidates who wrote the examination in Moreover, progress to matric favours the more affluent populations, and the quality of matric, in terms of grades attained, subject choice and cognitive outcomes, may further differ systematically to the detriment of the poor.

16 16 The correlation (r=0.85) between the pass rates for 1999 and 2000 indicates moderate but not great stability in pass rates at the school level. The average pass rate was used in the analysis to reduce the effect of year to year variations, especially for small schools, and the number of candidates who wrote in 2000 was used to weight each observation (number of candidates was unavailable for 1999). The 1997 racial composition of schools provides a fair approximation of their matric class in 1999 and 2000, and 1997 school fees and educational resources were taken as proxy for socio-economic status and availability of educational resources over the school career. Table 7 shows that pass rates in schools still differ substantially between socio-economic groups (proxied by school fees), school race types and provinces. The performance in the poorer schools is generally weak, rising from below 44% in the poorest to 96% in the richest group of schools, and there is far more variation in the performance in poor schools. Noticeable is that the standard deviation is much lower in the top two school fee groups pass rates in more affluent schools are almost uniformly high.

17 17 Table 7: Number of schools, mean and standard deviation of average matriculation pass rates in sample by school fee group, race type and province Number of schools Mean Standard deviation Total sample: % 27.3% School fee group: <R % 23.4% R20-R % 22.2% R50-R % 22.2% R100-R % 23.2% R200-R % 15.0% R % 8.6% School "race type": Black % 20.3% Coloured % 14.2% Other % 13.9% Indian % 12.3% White % 3.8% Mixed % 21.5% Province: Free State % 29.5% Gauteng % 29.5% Kwazulu-Natal % 25.5% Northern Cape % 25.6% Northern Province % 20.2% Western Cape % 19.7% Source: Own calculations It is again useful to distinguish schools by race type. 9 Not only are mainly black schools worse off in terms of mean matriculation passes (43%), but their pass rates vary considerably, even though standard deviations are smaller when schools are sorted into race types, as experiences are more uniform within each category. The highest standard deviations occur in mainly black schools and in schools not dominated by a single race group such schools have a quite varied experience, unlike mainly white schools, amongst whom the lowest recorded pass rate was 68%. Table 8 shows that mainly black schools, by far the majority, perform abysmally. Most such schools have pass rates in the range 20-60%, and ten percent have even lower pass rates, whilst only 3 out of 179 mainly white schools have pass rates below 80%. 9 As in Table 6, schools with more than 70% of a specific race group were classified as of that race type.

18 18 Table 8: Frequency distribution of schools by race type and pass rate range, Range of average Black Coloured Indian Mixed Other White Total pass rate Below 20% % % % % Total Below 20% 9.8% 0.0% 0.0% 1.5% 1.0% 0.0% 7.6% 20-39% 35.3% 2.2% 0.0% 5.3% 1.0% 0.0% 27.4% 40-59% 31.7% 16.7% 4.8% 16.0% 5.2% 0.0% 26.4% 60-79% 16.2% 41.3% 38.1% 21.8% 11.3% 1.7% 17.1% % 6.9% 39.9% 57.1% 55.3% 81.4% 98.3% 21.5% Total 100% 100% 100% 100% 100% 100% 100% At provincial level, the large differences in pass rates between the best performing province (the Western Cape with a mean pass rate of 82%) and the worst in the sample (the Northern Province, with a 45% pass rate) are well known. Notably, performances within provinces are more varied than that within race type, as reflected in the standard deviations. A priori, one would expect matriculation pass rates to be better: The higher the school fee, because of the additional resources this brings and because it reflects socio-economic status. The lower the pupil/teacher ratio, a measure of class size. The higher teacher salaries (which partly reflect teacher qualifications and seniority). In schools where black pupils are not numerically dominant black schools are still most likely to reflect the ravages of the previous education dispensation and are most likely to have continued problems with the culture of learning. Racial composition still reflects past privilege. In historically better-endowed provinces such as Gauteng and the Western Cape, with good provincial education management, mainly metropolitan pupils, and who did not need to incorporate former homeland structures.

19 19 Broadly speaking, regression results were consistent with these expectations, although provincial dummies were not very informative. 10 Regression 1 in Table 9 shows that the matriculation pass rates of schools were associated with the socio-economic background as measured by school fees, teaching resources (both the pupil-teacher ratio and the average teacher salary in a school, a measure of the quality of teachers), provincial location, but also, disturbingly, by the race category of schools. The exceedingly high coefficient of determination for a cross-sectional regression of this nature shows that well over half (57%) of the variation in pass rates is explained by the equation. Moreover, apart from some provincial dummies, all variables are highly significant, and the signs for all non-provincial variables are as expected. The coefficient of the dummy for mainly white schools indicates that, holding constant the level of school fees, pupil/teacher ratios, teacher salary level and province, a school containing mainly white pupils has a matriculation pass rate 33 percentage points higher than a similar school with mainly black pupils (black schools is the omitted or reference value). This is a highly disturbing, but not surprising, finding the educational system still is unable to overcome the effect of past patterns of education, dysfunctional management structures and dismal functioning of black schools. Although extremely large, this differential needs to be put into historical context: In 1994, the last year for which racial data were collected on matriculation pass rates, the black pass rate was 49% as against the 97% of whites. Table 6 showed that pass rates in dominantly black schools in this sample were 43% as against 97% in dominantly white schools, a differential of 54 percentage points which, as the race type dummy showed, remains very large after standardising for school fees, educational resources and province. Kwazulu-Natal and the Western Cape both perform significantly better (5 and 8 percentage points respectively) than similar schools in the Northern Province (the reference province). Other provincial dummies are not significant. 10 A full log-transformation of the basic model did not improve results. Robust regression tests show the results to be insensitive to the presence of outliers.

20 20 Table 9: Regressions of matriculation pass rates by school in six provinces, (tvalues shown below coefficients) Dependent variable: Pass rate (average 1999 & 2000) Regression 1: All schools Regression 2: Mainly black schools Regression 3: Other schools School fees per pupil (Rand per *** *** *** annum) Pupil-teacher ratio *** ** *** Average teacher salary *** *** *** Mainly coloured school (dummy) *** Mainly Indian school (dummy *** Mixed school (dummy) *** Mainly white school (dummy) *** Mainly other school (dummy) *** Kwazulu (dummy) *** *** Free State (dummy) * * Northern Cape (dummy) Gauteng (dummy) Western Cape (dummy) *** *** Constant * N R R 2 -adjusted Standard error * indicates.10 level of significance ** indicates.01 level of significance *** indicates.001 level of significance Replacing school fees by their natural logarithm (implying that their effect is proportional), or replacing the pupil-teacher ratio and the teacher salary with a single variable, the teacher cost per pupil 11, had little effect on the results and the other coefficients remained relatively 11 The coefficient for school fees is five times as large as that for teacher cost per pupil. This shows that school fees do more than only augment fiscal resources, for two reasons: School fees are likely to be spent more effectively than other school expenditures, being controlled by school governing bodies, who are closer to schools real needs. School fees are a good proxy for economic status. Where school choice exists in practice (as in metropolitan

21 21 unaffected, implying that the underlying logic of the model was not greatly affected by the particular specification used. Regressions 2 and 3 did not use school race type as explanatory variables, but instead regressions were applied to mainly black schools and to non-black schools separately, which still left the samples at a healthy and 662 schools respectively. One should not read too much into the magnitude of coefficients in a model which is not fully specified, thus comparison between the regressions should not be interpreted too finely. With this caveat, the results from these two regressions can be summarised as follows: Again, both school fees and educational resources had a significantly positive influence on matriculation pass rates in both regressions. Surprisingly, the coefficient for school fees is almost three times as large in Regression 2 as in Regression 3 economic status is a very important determinant of matriculation pass rates in black schools, pointing to strong differentiation within dominantly black parts of the school system. This is consistent with an interpretation that largely rural black schools, where communities are generally poorer, perform much worse than black schools in urban communities, where parents are less poor and often more educated than their rural counterparts. Also surprising is that the coefficient for class size (pupils per teacher) is much smaller in mainly black schools, precisely those schools in which the historically high pupil-teacher ratio has usually been seen as particularly detrimental. The coefficient for teacher quality (as measured by salary) is also somewhat lower for black schools. These results do not support the widely held view that, at the margin, resource shifts from other schools to blacks schools would improve overall educational performance, at least not without additional attention to the efficiency of resource use in these schools. Importantly, although school resources and socio-economic background matter, as the coefficient of determination of 0.08 indicates, the model leaves the very large variation in pass rates in black schools largely unexplained. In contrast, almost half the much smaller variation in results in other schools is explained by these variables. areas or large towns), parents mostly choose the best school they can afford for their children. Where distance limits school choice, as in rural areas, school fees are set at a level commensurate with what the overwhelming majority of parents can afford. Thus school fees in both cases reflect the economic status of most parents.

22 22 An interesting result concerns the provincial dummies: Kwazulu-Natal s black schools fare significantly better than expected, whilst for other schools the Western Cape significantly outperforms most provinces. The Free State, interestingly, has significantly lower pass rates in its mainly black schools and higher pass rates in other schools. Thus race (or what lies behind this variable) still appears to be the principal factor determining differential matriculation pass rates, followed by economic status (school fees). Even massive shifts in school level resources since the transition to democracy still leave mainly black schools performing much worse than white schools, and the regressions indicate that further shifts of educational resources may have a very limited effect. That black schools fare systematically worse in terms of pass rates cannot be accounted for by resources or the socioeconomic status of pupils alone. More educational resources for black schools can make only a small contribution to improving educational outcomes and therefore their long term position in the labour market. The pupils who have furthest to catch up are those in rural areas, where socio-economic status, including education of parents, is weakest, and where good teachers are hard to come by. Although resources matter, greater resource inputs alone cannot much improve this situation, without a fundamental reorganisation in how schools function. The quarter of black schools which bettered the total sample s average pass rate of 56% have only slightly better pupil-teacher ratios (32.5 as against 34.0) and slightly better remunerated teachers (R versus R82 387) than the rest, but socio-economic status differs more, as reflected in schools fees of R92 versus R36 per annum. But even this is not a large difference. Above and below average performance in black schools cannot be explained by differences in resources, and only to a limited extent by differences in socio-economic status. Moreover, the greatest improvement in educational chances for black children probably arise for those who can afford to send their children to historically white schools or to private schools, where pass rates are far greater. Here too, private resources, as reflected in school fees, matter. But differences in public educational resources play only a small role in the performance of black schools. Again, factors associated with the functioning of the school as a productive unit rather than the availability of resources appear to be crucial to proper performance, particularly in poor communities. Thus this analysis supports the contention that school efficiency, and most likely particularly management, requires most attention to overcome the legacies of past inequalities in education.

23 23 4. Conclusion Despite the lasting influence of apartheid, educational access is no longer a major problem in South Africa, as more than 90% of children of all race groups remain at school until attaining matric or reaching age 16. The racial gaps in educational attainment (years of education completed) have also been substantially reduced over the past decades. However, there are severe problems with the quality of education of a large part of the South African school system, as reflected in cognitive tests of numeracy and literacy and also shown by matriculation results. The deficient performance of particularly mainly black schools is a source of concern, as this shows that reduced earnings inequality may well be more difficult than rising educational attainment at lower school levels would indicate. Only limited scope remains for additional resource outlays to redress this malfunctioning of the major part of the school system. Moreover, the evidence shows that more resources is not the solution to bad educational performance, as some of the worst performing schools are well-resourced, whilst some schools perform excellently with limited resources. This paper has shown that the persistence of former racial inequalities is reflected in extremely low pass rates in mainly black schools (the majority of schools), but with high standard deviations, pointing to a varied experience. Regressions show that only a small portion of the remarkable differentials in performance amongst poor black schools can be accounted for by socio-economic background or teaching resources, pointing to the importance of school management. Thus it appears as if the malfunctioning of the bulk of the school system is largely a problem of x-inefficiency rather than allocative efficiency. The optimistic policy interpretation is that additional fiscal resources can play only a limited role, so addressing the issue need not be costly; the pessimistic view is that good administration and management are even more scarce than fiscal resources. Given strong economic growth, both poverty and racial inequality may be strongly reduced in the coming decades, and the trend towards increased inequality within racial groups may be arrested. But from the perspective of distribution and even as a factor in growth itself, an improvement in the quality of education amongst South Africa s poor is likely to be very rewarding. This requires urgent attention at the highest levels to the functioning of the worstperforming schools, to ensure continued upward mobility of the largest part of the workforce and to prevent growth itself becoming endangered.