Education, poverty and inequality in South Africa1

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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 teacher- pupil ratios and teacher salaries as proxy for qualifications and experience). The remarkable differentials in performance amongst black schools cannot be accounted for by socio- economic 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 authors ongoing research (only some of it published), inter alia Van der Berg 2001a; 2001b; 2001c; van der Berg et al. 2002.

Transcript of Education, poverty and inequality in South Africa1

Page 1: Education, poverty and inequality in South Africa1

Education, poverty and inequality in South Africa1

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 teacher-pupil ratios and teacher salaries as proxy for qualifications and experience). The remarkable differentials in performance amongst black schools cannot be accounted for by socio-economic 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. 2002.

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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 Africa2. 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.

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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 socio-

economic 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 4.

2. 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 9.0 7.9 8.5 Coloured 8.9 6.8 8.6 Indian 10.4 9.7 10.4 White 11.9 11.7 11.9 Total 9.7 8.1 9.2 Source: Census 1996

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Table 2: Educational inequality for two SA cohorts, 1995

Cohorts 55-59 Cohorts 25-29 Mean 5.77 9.05 Standard deviation 4.51 3.60 Coefficient of variation 0.78 0.40 Gini 0.44 0.21

Source: Lam 1999: Table 2

2.2 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.

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Table 3: Inequality of monthly earnings by race, 1980 & 1993

1980 1993Gini coefficient 0.52 0.51Coefficient of variation 1.19 1.12L-statistic * 0.49 0.50L-statistic: Blacks 0.14 0.28L-statistic: Indians 0.21 0.28L-statistic: Coloureds 0.25 0.35L-statistic: Whites 0.21 0.28Within-group inequality 0.17 0.29Between group inequality 0.32 0.21

* 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.

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Fig 1: Cumulative density curves of per capita earnings by education of household head, 1995

2.3 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 400 000 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.

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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 13 234 1 015 127 660 260 779 16 075Pupil/Teacher

ratio 35.1 31.8 25.7 25.6 31.4 28.5 33.4

Pupils: 5 897 116 538 393 99 122 400 105 178 254 488 511 7 601 501Of whom: Black 5 876 133 24 172 15 023 21 603 4 197 073 6 134 008 Coloured 10 848 512 718 1 339 11 024 12 70 156 606 097 Indian 4 658 800 82 604 3 557 0 111 592 203 211 White 3 696 413 133 363 346 1 104 211 471 800 Other (race

specified) 1 781 290 23 575 178 237 5 479 186 385

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

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exemptions or percentage A-aggregates achieved3) 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 Exemptions

A-aggregate

Mean 80.6% 23.0% 2.6% Standard deviation 22.5% 22.0% 5.0% Coefficient of variation 0.28 0.96 1.94 Gini coefficient 0.15 0.56 0.80 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.

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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)

38.3%

27.7%

55.1%

45.6%

78.0%

69.5%

60.7%

0%

20%

40%

60%

80%

100%

Literacy score Numeracy score Literacy ratio Numeracy ratio Years educationcompleted: Ratio

BlackWhite

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).

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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 1993. 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

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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 non-

degree

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 1996 10% 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 apartheid-

based 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.

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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 experience6, 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

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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 teaching7, 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

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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

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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 2 800

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 2000.

8 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.

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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.

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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: 2 768 55.5% 27.3% School fee group:

<R20 651 43.9% 23.4% R20-R49 1 177 47.8% 22.2% R50-R99 496 54.1% 22.2% R100-R199 82 69.7% 23.2% R200-R999 243 91.9% 15.0% R1000+ 119 97.2% 8.6%

School "race type": Black 2 106 43.3% 20.3% Coloured 138 75.5% 14.2% Other 97 91.8% 13.9% Indian 42 80.5% 12.3% White 179 97.3% 3.8% Mixed 206 79.0% 21.5%

Province: Free State 289 49.1% 29.5% Gauteng 367 62.2% 29.5% Kwazulu-Natal 784 55.7% 25.5% Northern Cape 87 66.2% 25.6% Northern Province 1 002 43.9% 20.2% Western Cape 239 81.8% 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�.

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Table 8: Frequency distribution of schools by �race type� and pass rate range, 1999-2000

Range of average pass rate

Black

Coloured

Indian

Mixed

Other

White

Total

Below 20% 207 3 1 211 20-39% 743 3 11 1 758 40-59% 668 23 2 33 5 731 60-79% 342 57 16 45 11 3 474 80-100% 146 55 24 114 79 176 594 Total 2 106 138 42 206 97 179 2 768 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% 80-100% 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.

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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.

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Table 9: Regressions of matriculation pass rates by school in six provinces, 1999-2000 (t-values 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)

.0129566*** .0341672*** .0114441***

12.188 9.353 12.735

Pupil-teacher ratio -.1353492*** -.1187243** -.3745764*** -3.397 -2.724 -3.360

Average teacher salary .0004347*** .0004122*** .0009125*** 8.072 6.357 8.962

Mainly coloured school (dummy) 21.92395*** - - 10.952

Mainly Indian school (dummy 24.18364*** - - 9.549

Mixed school (dummy) 20.52412*** - - 13.034

Mainly white school (dummy) 32.95964*** - - 17.584

Mainly other school (dummy) 29.14448*** - - 12.763

Kwazulu (dummy) 5.35849*** 5.8295*** -1.061254 5.425 5.137 -0.329

Free State (dummy) -1.605378 -2.724809* 7.582651* -1.317 -1.978 2.151

Northern Cape (dummy) 3.454839 1.813976 5.108072 1.420 0.455 1.368

Gauteng (dummy) -.9128545 -1.866107 1.339477 -0.852 -1.468 0.423

Western Cape (dummy) 8.418335*** 3.10358 10.51278*** 4.758 1.012 3.285

Constant 9.945317* 10.49816 -4.127132 2.048 1.818 -0.393

N 2 768 2 106 662 R2 .5719 .0862 .4214 R2-adjusted .5698 .0827 .4143 Standard error 17.908 19.396 13.259

* 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 pupil11, 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

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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 2 106 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.

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• 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 socio-

economic 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 (R83 900 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.

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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 worst-

performing schools, to ensure continued upward mobility of the largest part of the workforce

and to prevent growth itself becoming endangered.

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Appendix: Regressions of test scores of South African teenagers 13-18 on literacy and

numeracy tests, 1993 Regression 1: Test score of teenagers (out of 14) regressed on years of education completed, a

metropolitan dummy, two race dummies (one for black, one for Indian), parent education, and the natural log of per capita household expenditure

Regression 2: Test score of teenagers (out of 14) regressed on years of education completed, a metropolitan dummy, two race dummies (one for black, one for Indian), parent education, the natural log of per capita household expenditure, and whether the home language was English (some test questions were in English)

(t-values shown below the coefficients in small font) Dependent variable: Test score out of 14 Regression 1: Regression 2 Years of education completed .3957*** .3968*** 9.52 9.65

Metro .54677** .5502** 2.32 2.36

Race dummy: Black -1.5015*** -1.1641*** -5.28 -3.99

Race dummy: Indian 2.5689*** 1.4319** 3.90 2.04

Parent Education .0994*** .09769*** 3.35 3.33

Natural logarithm of household expenditure per capita .5600*** .4834*** 3.61 3.13

English home language - 1.6814*** - 4.29

Constant .0311 .0565 0.04 0.06

n 819 819 R2 .382 .396 R2-adjusted .377 .390 Standard error 2.69 2.67

* indicates .10 level of significance ** indicates .05 level of significance

*** indicates .01 level of significance