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South Africa’s economics of education: A stocktaking and an agenda for the way forward
Martin Gustafsson & Thabo Mabogoane
This paper reviews some of the existing economics of education literature from the perspective of
South Africa’s education policymaking needs. It also puts forward a suggested research agenda for
future work. The review is arranged according to five areas of research: rates of return, production
functions, teacher incentives, benefit incidence analysis and cross-country comparisons.
Production functions, especially if translated to cost-effectiveness models, can point to
important policy solutions. Teacher incentives is a policy area that is in need of a better
theoretical and empirical basis. Rates of return are difficult for policymakers to interpret, but
suggest a need for a qualification below the Grade 12 level. While benefit incidence analysis
can demonstrate large improvements in the equity of public financing, cross-country
comparisons reveal that not only is the distribution of schooling outcomes particularly unequal,
on average it is well below what the country’s level of development would predict.
Keywords: economics of education; rates of return; production functions; teacher incentives;
benefit incidence analysis
JEL classification: H52; I21; I28
1. Introduction
Psacharopoulos, arguably one of the founders of the current economics of education
tradition, observes that ‘[i]n the field of education, perhaps more than in any other
sector of the economy, politics are substituted for analysis’ (1996:343). This problem
in the education sector is conceivably brought about by three factors: an absence of
relevant analysis, analysts who are unsuccessful in communicating their findings to
the policymakers, or policymakers who resist paying attention to the analysts. This
paper examines the first two factors in the South African context.
The paper takes stock of the economics of education literature that is influencing, or
should influence, South Africa’s education policymaking, through reference to a few
key texts, though by no means all the available literature. Gaps in the literature are
identified on the basis of assumptions about what policymakers need. The bias is
towards a utilitarian view of the literature: it should inform policymaking and
development in rather explicit ways. Less policy-oriented and more academic pursuits
in the economics of education field are undoubtedly important, but they are not the
subject of this paper. The discussion of the literature is organised according to five
models or areas of research: rates of return, production functions, teacher incentives,
Respectively, Economist Researcher, Social Policy Research Group, Department of Economics, University of Stellenbosch; and Economist and Manager, The Presidency, Pretoria, South Africa. Corresponding author: [email protected]
Development Southern Africa Vol. 29, No. 3, September 2012
ISSN 0376-835X print/ISSN 1470-3637 online/12/030351-14 # 2012 Development Bank of Southern Africa http://dx.doi.org/10.1080/0376835X.2012.706033
benefit incidence analysis and cross-country comparisons. The paper concludes with a
tentative research agenda for the economics of education in South Africa.
2. Rates of return
The unconditional relationship between earnings and years of schooling in South Africa
points to an average increase in earnings of around 22% for every additional year of
schooling completed in the range of two to 11 years of schooling, and a large increase of
around 125% associated with the difference between 11 and 12 years of schooling, in other
words with having attained Grade 12. (This finding is based on the authors’ analysis of the
Statistics South Africa 2005 Income and Expenditure Survey data, focusing on anyone
who reported earning an income.) This kind of unconditional analysis suffers from two
weaknesses. Firstly, the net benefits are not clear, because the cost, both private and social,
of completing more years of schooling is not taken into account. Secondly, other factors
such as years of experience, gender and (in particular in the case of South Africa) race,
which may play a separate role in determining income, are ignored. Two distinct methods
are commonly used to overcome these two weaknesses, though it is rare to find both
weaknesses addressed within the same analysis. Herein lies some of the confusion that
surrounds rates of return to education. A further problem is the fact that the policy
implications of rates of return analyses are often not explored, or they are explored in a
manner that is too rudimentary to be helpful to policymakers (Glewwe, 1996:283).
The first of the two methods, which has been called the ‘elaborate method’
(Psacharopoulos, 1981:322), uses the same internal rate of return calculation that would
be used to calculate the return on a non-education investment. This method considers
both income benefits associated with more education, and the private and public costs
of education. Psacharopoulos & Patrinos (2004) argue that a cross-country comparison
of annual rates of return, where these rates are based on the elaborate method, reveals
two clear patterns. Firstly, primary schooling yields better returns than secondary
schooling, which in turn yields better returns than tertiary education. Secondly, rates of
return are higher the less developed a country is. Education rates of return for South
Africa published by Psacharopoulos & Patrinos (2004:125) using the elaborate method
are too old and unrepresentative to be useful for South African education policymakers.
They are based on 1980 data for African residents of Durban (Psacharopoulos,
1993:41). It would seem that no subsequent rates of return estimates using the elaborate
method have been published for South Africa.
The second method, generally referred to as the Mincerian approach, uses an earnings
function to examine the relative effects of years of schooling and years of experience
on earnings (Mincer, 1974:130). It considers the cost of formal schooling only in
terms of the opportunity cost of income forfeited, not in terms of the direct private
and public costs of schooling.
Strictly speaking, only the elaborate method yields proper rates of return values, though
Mincerian beta coefficients for years of schooling are commonly also described as rates
of return – Psacharopoulos & Patrinos prefer the term ‘wage effects’ (2004:116). The
argument that the Mincerian approach produces rate of return-like statistics is sound,
but it is important to explain to policymakers that this approach produces values that
are often much lower than those produced by the elaborate approach.
There have been numerous rates of return analyses applying the Mincerian approach to
South African data. Keswell & Poswell (2004) present their own data analysis, plus a
352 M Gustafsson & T Mabogoane
meta-analysis of analyses carried out in previously published South African texts.
(Though Keswell & Poswell refer to some of their models as ‘non-Mincerian’
[2004:841], they are essentially non-linear versions of the Mincerian approach.)
Authors using the Mincerian approach typically add demographic variables not
included in Mincer’s original model (1974). Keswell & Poswell include race, gender
and rurality in their 2004 analysis. Their main finding is that the returns to schooling
increase with years of schooling, but only beyond 11 years, and that before Grade 12
each additional year of schooling yields almost no income returns. Put differently, the
rate of return curve viewed from the years of schooling axis is convex. This is in stark
contrast to the finding by Psacharopoulos & Patrinos (2004) that returns are highest at
the primary level and, implicitly, that their shape is concave. It is important to view
this not as a fundamental dispute between economists over the effects of education,
but rather as a natural outcome of using different methods and attaching different
meanings to the term ‘rate of return’. Crucially, the Mincerian approach does not take
into account the high direct cost to the household of tertiary education.
From an education planning perspective, it is useful to view the typical Mincerian
analysis in the light of labour market signals produced by education qualifications.
The very sharp increase in the returns to schooling at the point where 12 years of
schooling are completed, as seen in Keswell & Poswell’s (2004) sharp ‘take-off’ in
the rate of return at or one year after Grade 12, is to a large degree associated with the
possession of a Grade 12 certificate. This certificate, which is the only standardised
qualification issued in the South African schooling system, may put pressure on the
system to improve the skills and knowledge of pupils to an exceptional degree in the
one or two grades preceding Grade 12, but at least part of the income advantage of
having successfully completed Grade 12 (as opposed to Grade 11) must flow from
one’s possession of a crucial and widely recognised means for signalling to employers
the value of one’s human capital. Assuming that qualifications do influence earnings
in a manner independent of actual education, an obvious question for the policymaker
is how changing the system of qualifications, for instance by introducing a Grade 9
certificate (something that has been on the South African policy agenda for a while),
might influence the profile of returns to years of schooling. Figure 1 illustrates how
crucial a policy question this is. It shows that the recent trend has been for about 60%
of young South African adults to have no qualification at all, and that there is no
evidence (at least not in the graph) of a downward trend in this statistic.
There is a challenge, not just in South Africa, to make the policy implications of patterns seen
in rates of return analyses clearer. One part of this challenge is to examine how the structure
of the country’s qualifications system affects decisions and earnings in the labour market.
Glewwe’s study of data from Ghana (1996), using a relatively rare combination of
variables covering years of schooling, innate ability, knowledge acquired through schooling
and qualifications held, approximates the type of analysis that policymakers ideally require.
Here, the separate effect of qualifications is measured. The analysis also highlights what
should be obvious, but is often not, namely that what pupils actually learn, rather than years
spent at school, is what lies behind productivity and improvements in the earnings of
individuals. Du Rand et al. (2011) reach similar conclusions in a study that is constrained
by missing data. Inadequate data in South Africa on the competencies of individuals in
the labour market remains a hurdle for better human resources planning in the country.
Finally, rates of return analyses can also be used to compare income returns to different
types of education at the same level. Psacharopoulos (1993:48) provides a comparison of
South Africa’s economics of education 353
the rates of return for academic and vocational secondary schooling, and finds that when
the higher costs of vocational schooling are taken into account, the returns to academic
schooling are higher. What might the situation be in South Africa, where recent years
have seen substantial growth in the budgets and enrolments of pre-tertiary vocational
schooling (the ‘FET colleges’), 1
and the introduction of a new vocational curriculum,
but also concerns that students enter vocational colleges when they are too old (partly
due to the absence of a lower level basic schooling qualification)? No published rates
of return analysis seem to exist, though unpublished and exploratory analysis
conducted by one of the authors of this paper suggests that when controlling for race
and gender, and taking into account private and public costs, historically vocational
training has yielded better rates of return than ordinary schooling. This suggests that
South Africa, like many other countries (Bennell, 1996), does not conform to the
global pattern described by Psacharopoulos (1993).
3. Production functions
While rates of return analyses are crucial in attempting to explain the external efficiency
of education systems, production functions assume the same role with respect to the
internal efficiency of education institutions. Production functions essentially aim to
Figure 1: Highest qualification held by age (2007)
Note: ‘Certificate (Gr 12+)’ refers to Grade 12 Senior Certificate with a university entrance level. ‘No qualification (Gr 12)’ refers to someone who attended but did not
pass Grade 12, and therefore has no national school qualification. ‘No qualification
(,Gr 12)’ refers to someone who did not attend Grade 12. An examination of the
NIDS Wave 1 dataset (SALDRU, 2009) reveals the same percentage of unqualified
adults as the 2007 Stats SA Community Survey dataset used here.
Source: Community Survey dataset (Stats SA, 2007).
1 FET colleges are institutions that may accept school leavers in the Grades 9 to 12 range. They
were previously known as technical colleges.
354 M Gustafsson & T Mabogoane
identify which education inputs, such as teacher qualifications, teaching materials,
teaching time, and so on, have the largest effect on outcomes as measured by pupils’
test scores. Economists have shown keen interest in exploring this model though, for a
number of reasons discussed below, the reception by policymakers has been mixed.
Perhaps the most important reason to be sceptical about the use of a single production
function study to inform policy is that the data used for the analysis were in most
cases not compiled specifically for this kind of analysis. For this reason, Hanushek, a
prolific analyst in this area, laments the fact that most production function analysis is
‘opportunistic’ (2002:12). In particular, the ideal of test scores from two points in
time, allowing for a value-added approach, and data at the level of pupils (including
socioeconomic data) and not just the school, are often not realised. Despite these
problems, identifying what inputs emerge as important across several production
function studies through meta-analyses, such as that produced by Hanushek (2002) for
the US, has come to be regarded as a valuable process that can indeed inform policy.
Of course, this solution presupposes the existence of a critical mass of studies from
the country concerned.
For South Africa, a pioneering production function study is that conducted by Case and
Deaton (1999:1078), who use pupil-level data from the 1991 to 1993 period. More
recently, a number of studies have used pupil-level data to examine the production of
learning results in primary schools, including Gustafsson (2007), Van der Berg (2008),
Taylor & Yu (2009) and Spaull (2011). Studies focusing on secondary schooling have
relied on school-level data: Crouch & Mabogoane (1998b), Van der Berg & Burger
(2003), and Bhorat & Oosthuizen (2006). Curiously, no one appears to have used the
opportunity offered by the 2003 TIMSS data (Mullis et al., 2004) to produce a pupil-
level production function at the secondary level. A similar opportunity will arise when
the 2007 TIMSS data are released. The South African findings regarding school inputs
have been fairly intuitive ones. For instance, libraries, teacher housing in rural areas,
and better teacher knowledge all advance pupil performance. The South African work
has also confirmed what studies from elsewhere have found, namely that socioeconomic
status, in particular the level of education of the pupil’s parents, plays a large role, in
fact much larger than is commonly believed. In most countries, socioeconomic status
has a somewhat greater effect on the performance differences between schools than all
school resource factors combined (OECD, 2007:171).
Arguably the biggest influence that production function studies around the world have
had on policy relates to one factor that they have consistently said does not play a role
in improving pupil performance, namely class size. This finding, obviously an
important one from a budgetary perspective, has been taken seriously by
policymakers. The question of class size illustrates the importance of distinguishing
statistical significance from policy significance in an analysis. Certain models do in
fact find class size to be a statistically significant predictor of pupil performance (see
for instance Taylor & Yu, 2009), but even then, when coefficients are translated into
financial values, it is nearly always found that reducing class sizes would be among
the most costly of all the available policy interventions implied by the model, relative
to the desired performance improvement. This translation of a production function
into a cost-effectiveness model, by bringing in actual unit costs, is seldom done in the
academic literature and is arguably one important reason why policymakers find
production functions difficult to interpret. Pritchett & Filmer (1997) and Mingat et al.
(2003:56) explain the methodology required for this translation.
South Africa’s economics of education 355
On the matter of class size, there is an important South African caveat. South Africa’s
class sizes are exceptionally large even by developing country standards. To illustrate,
16% of the country’s Grade 8 pupils in 2008 were in classes exceeding 55 pupils
(Gustafsson & Patel, 2008:25). In countries such as Botswana, Malaysia and Egypt
the figure is considerably lower. Data from other years and other grades confirm this
pattern. One wonders whether the orthodoxy on class size, partly resulting from
production function findings, has perhaps created a blind spot in education
policymaking, considering how little policy attention has been devoted to excessively
large classes. Case & Deaton (1999) do in fact argue strongly that in South Africa, at
least in the early 1990s, over-sized classes were a noteworthy predictor of poor pupil
performance. However, Crouch & Mabogoane’s criticism (1998a) with respect to an
earlier draft of the article still applies to Case & Deaton (1999): the results are
insufficiently robust, with respect to R 2
values and t statistics, to support the policy
conclusions. More focused attention on the effects of extremely large class sizes, with
good data, in the South African production function work may reveal that South
Africa’s exceptional situation allows for the identification of critical thresholds
beyond which pupil performance is affected by class size.
A very practical application of the production function technique is demonstrated by
Crouch & Mabogoane (1998a). In preparing a list of top performing schools, with
respect to the Grade 12 examinations, for the Sunday Times newspaper they used both
a traditional approach of simply taking the best results, and a more socioeconomically
sensitive approach where they compare actual results to the expected results emerging
from a production function. In the list using the first approach, only one historically
black public school appeared in a list of the 10 best performing schools. In the list
using the second approach, nine of the 10 best performing schools were historically
black public schools. The second approach clearly helps the education administration
to identify and praise schools that succeed in overcoming contextual and historical
difficulties, and it helps to identify schools that should be the subject of more
qualitative case studies aimed at finding out what makes these schools so successful
and efficient. (In 2009 the Sunday Times again published a list of top schools, but this
time only the traditional approach was used, resulting in only one historically black
public school appearing in the top 10.)
A discussion of production functions can probably not avoid including a reference to
multi-level modelling, also known as hierarchical linear modelling. This method, used
for instance by Gustafsson (2007), has become popular, but has arguably made
explaining production function findings even more difficult than when using the
ordinary least squares method. Johnes (2004:647) finds multi-level modelling
‘computationally intractable’.
Much of the challenge with respect to production function analysis lies in working
towards data collections from schools that are better suited to this kind of analysis.
Crouch & Mabogoane (1998a) emphasise the need for better variables on school
management in order to reduce the residual, or unexplained, part of South African
production functions. One variable that is rare yet of great potential importance in a
production function is a direct measure of teacher knowledge. Such a variable has
become available, for the first time in South Africa, with the release of the 2007
SACMEQ (Southern and Eastern Africa Consortium for Monitoring Educational
Quality) dataset, which includes teachers’ scores in subject knowledge tests (Spaull,
2011). Two recent data collections with test scores from two points in time, the
356 M Gustafsson & T Mabogoane
National School Effectiveness Study and a cross-country study covering just over 100
schools on either side of the South Africa – Botswana border, are currently being
analysed and are likely to add immense value to the South African policy discourse.
The advantage that the two-point data has over cross-sectional analysis is that it
overcomes some of the endogeneity problems of the omitted variable variety – for
example, the problem that test scores from just one point in time reflect pupils’ innate
ability and learning acquired from other sources, for instance other teachers. The
additional information results in better estimates of the magnitude of the effect of the
various inputs in the schooling process.
What is striking in South Africa is that the government’s sample-based Systemic
Evaluation testing programme, which produces a dataset that is exceptionally well
suited for production function analysis focusing on South African policy questions,
has barely been used for this purpose because researchers have not been able to access
the dataset. This is unlike the situation in Brazil or the US, whose equivalent SAEB
(System for the Evaluation of Basic Education) and NAEP (National Assessment of
Educational Progress) datasets, respectively, are widely available to research
institutions. The institutional problems that make data inaccessible in South Africa
should be resolved in the interests of a greater volume and variety of analysis that can
inform difficult education policy decisions.
Lastly, a word of warning by Hoenack (1996:332) on the influence of production
function work on education policymaking deserves mention. Underlying this work is a
quest for the right ‘recipe’ for effective schooling, so that this can be expressed in the
right policies and budgets. This whole approach obviously reflects a rather top-down
paradigm. An alternative paradigm states that the education authorities should simply
insist on good learning outcomes, and monitor such outcomes, and let schools
themselves find the right recipes. Both arguments have merit, depending partly on the
kind of school one is focusing on. Clearly, production functions should not reinforce
an overly top-down policy agenda.
4. Teacher incentives
Recent developments in the policy on teacher incentives in South Africa have been
turbulent. A much publicised wage agreement with unions in 2008 stipulated the
introduction of salary notch increases every second year for outstanding teachers,
where the evaluation of teachers would be based on a mix of schools-based and
external inputs. This element of the agreement was dropped in 2009 due to
insufficient support from unions.
Economic analysis of teacher incentives has been conducted in two key areas. The first
looks at the core teacher salary as an incentive for joining the teaching profession and
staying there. Because teacher pay is to a large degree determined through a political
process, and not in an open market, research into how it is determined becomes
especially important. In economic terms, the determination of teacher pay in South
Africa displays elements of both monopoly (there is in a sense one supplier, the
teaching force represented by unions) and monopsony (there is largely just one buyer
of teaching services, the state). The political nature of the wage negotiation process
makes the risk high that teacher pay will be substantially higher or lower than it
would have been if it had been more market-driven. In order to determine whether it
is too high or too low, economists typically perform a conditional wage comparison
South Africa’s economics of education 357
between teachers and other professionals in the economy using household data. The
analysis is subject to a number of complexities, including how to define the group of
professionals against which teachers are compared, how ‘teacher’ should be defined
and how teacher productivity should be dealt with. Teacher productivity relative to
that of other professionals is virtually impossible to assess empirically using the
household data that are typically used for this kind of work. Yet it is important to at
least acknowledge this dynamic.
There have been at least four South African conditional wage comparisons dealing with
teachers: Crouch (2001), Gustafsson & Patel (2008), Armstrong (2009) and Van der Berg
& Burger (2010). The findings of the four are broadly similar. Crouch (2001) finds
teacher pay to be more or less comparable to that of other professions, and argues that
if there is an under-supply of teachers in South Africa, this is due more to an
insufficient provisioning of teacher training than to a problem of poor pay dissuading
prospective teachers. However, the pay scales are said to be insufficiently generous
for older teachers, increasing the possibility that older teachers will leave the
profession. Gustafsson & Patel (2008:18) explain that the administered nature of
teacher pay tends to result in a situation where the actual spread of pay over years of
experience does not match the official salary scales, because when the official scales
change, they are not implemented retroactively, meaning that teachers’ actual pay will
to a large degree be a reflection of previously existing official scales. Like Crouch
(2001) and Van der Berg & Burger (2010), they find that older teachers are under-
paid, but conclude that more generous increments linked to years of experience
introduced in 2008 will over the years eliminate this problem (2009 policy
developments have, however, removed some but not all of this deferred generosity).
Gustafsson & Patel (2008) emphasise that the employer needs to signal the existence
of future incentives to prospective teachers in an active manner, since they are not
visible if the prospective teacher simply looks at what older teachers are currently
earning.
The second key area of analysis looks at incentives paid to those who perform
exceptionally well. Despite such incentives being a highly topical policy issue, there
has been little work of an academic nature in South Africa on this subject. There are
arguably two things that should inform the policy process. One is better data on
teachers’ perceptions, for instance with respect to their pay, their sense of professional
identity and how to improve pupil performance. To some extent such data are
collected in programmes such as the Systemic Evaluation, which includes teacher
questionnaires and allows for linking to pupil performance. But for a more in-depth
understanding of the necessary performance incentives for teachers, the other thing
that is probably required is a dedicated and nationally representative teacher opinion
survey, perhaps along the lines of the OECD’s TALIS programme (OECD, n.d.) In
the absence of such data, the policy process ends up relying too heavily on the
assumption that teacher unions are able to represent adequately what incentivises
teachers, an assumption that will clearly not always be a sound basis for policy.
Furthermore, the policymaking process would benefit from a systematic stocktaking of
lessons from abroad, and an understanding of the relevant theory, in order to
counteract fairly pervasive misperceptions in the policy debates. One misperception
which has arguably brought with it large costs in recent years is the notion that
performance-linked incentives for individual teachers are easily realisable. The
international evidence suggests that the nature of schools requires incentives to be
358 M Gustafsson & T Mabogoane
largely group-based if they are to be practical. The notion that teachers will respond in
predictable ways to incentives, even well-designed and group-based ones, must also be
interrogated.
An important trend within economics of education has been the increasing emphasis on
better understanding of cause and effect in policy areas, such as teacher incentives, where
misunderstandings are common (63% of articles with the word ‘causality’ in the whole
text in the Economics of Education Review are from 2005 or later, against 28% for the
term ‘rate[s] of return’). We can see the beginnings of a critical stock of literature on
the effects of performance-linked teacher incentives in developing countries, which
tend to experience effects that differ from those in developed countries (see literature
review provided by Bruns et al., 2011). Some of this literature draws from randomised
controlled trials of different teacher incentive approaches at a local level. This method
stands out for the degree of certainty it can offer with regard to how teachers will
react to incentives. Even here, however, the policymaker should exercise caution. As
Woessman (2011) argues, small-scale experiments cannot gauge the effect of new
incentive programmes on long-term teacher recruitment and retention trends.
5. Benefit incidence analysis
A society where it is widely felt that people’s opportunities in life are unfairly distributed
cannot be a healthy society. In the economics literature, recent empirical evidence
indicates that in developing countries where inequality is extreme, economic growth is
retarded (Barro, 2000). As the foregoing discussion has suggested, social and income
inequalities are perpetuated by unsound education policies that fail to educate the
poor. Part of the solution lies in ensuring that sufficient public spending goes towards
schooling for the poor, while another part lies in ensuring that improved spending on
the poor is translated into better learning outcomes. Benefit incidence analysis focuses
on the first of these two challenges.
Benefit incidence analysis typically uses a combination of unit cost figures from public
accounts and data on the use of public services from household surveys to draw
conclusions such as that 35% of public spending goes towards the poorest 20% of the
population, and that public spending is thus pro-poor. Comparisons between countries
using the same approach can reveal important patterns. For instance, Davoodi et al.
(2003:21) argue that sub-Saharan Africa has been particularly unsuccessful at
targeting public education spending towards the poor. For example, at the secondary
level on average only 7.4% of spending goes towards the poorest 20% of the population.
In South Africa, Van der Berg (2005, 2009) has calculated benefit incidence patterns for
a range of public services, including education. This analysis indicates that public
spending in 2006 on primary and secondary schooling was clearly pro-poor, and well
targeted by international standards (Van der Berg, 2009:13). It also reveals that
between 1993 and 2006 there was a clear trend towards better targeting of the poor
(Van der Berg, 2005:8, 2009:14). The public funding of tertiary education reveals a
different pattern. As in virtually all countries, in South Africa this funding is pro-rich,
though in South Africa it is even more so than elsewhere. Van der Berg (2009)
explains that this finding could be influenced by an important data problem, namely
that in the household data poor students living away from their families are likely to
appear artificially better off than their families of origin actually are.
South Africa’s economics of education 359
Van der Berg (2009) points to a few common areas of misunderstanding. The standard
benefit incidence approach of examining the breakdown of public education spending by
quintile of the population means that the age pyramid of different segments of the
population will influence the results. Above all, if a larger proportion of the poor are
young, which is the case in most countries, then a pro-poor pattern will emerge even
if the state spends an equal amount on each pupil. In fact, if just public spending per
enrolled pupil is considered, then public spending is slightly pro-rich, and not pro-
poor as seen in the typical benefit incidence analysis (Gustafsson & Patel, 2006). It is
obviously important for education policymakers to recognise the differences between
the two approaches.
The finding that publicly funded education inputs, at least at school level, are more or less
equitably distributed is important information for policymakers as it provides evidence
that ambitious post-1994 policies to correct the grossly distorted spending patterns of
the apartheid era have paid off. There is thus ample empirical justification for the
current and rather strong policy shift away from education inputs and towards
education outcomes. Clearly there are education input issues that must still be
resolved, but devoting the bulk of the policy attention to outcomes appears completely
justified and necessary.
Substantial public funding for the poor in South Africa would suggest that private inputs
are low. The evidence suggests that this is the case. The analysis by Gustafsson & Patel
indicates that overall around 8% of the funding of public schools was private in 2005,
though for the poorest two quintiles it was around 2% (2006:71). Kattan & Burnett
(2004) find that private inputs into public primary schooling in many other developing
countries are at a substantially higher level: 21% in China, 30% in Ghana and 43% in
India. They moreover find that policy analysis of private inputs into public primary
schooling is often confounded by definitional problems. Many countries that claim to
have abolished ‘fees’ in fact still demand that parents pay, for instance for textbooks,
because textbooks are not regarded as a part of ‘fees’. In South Africa, at least
anecdotally, the newly declared ‘no fee’ schools for the poor have in many cases
simply renamed what were previously ‘fees’ as ‘voluntary donations’. Though private
contributions to schooling in South Africa may not be large, at least by international
standards, they receive considerable policy attention and warrant better analysis. For
this, Statistics South Africa’s Income and Expenditure Survey data would be useful
(Stats SA, 2005). But apart from clarifying the numbers, it is necessary to gain a
better idea of the reasons why even in poor communities parents often encourage
private contributions to the school fund. It is unlikely that insufficient public funding
is the only reason; there are probably also reasons relating to the way schools are
viewed and used by their communities.
6. Cross-country comparisons
Even simple cross-country comparisons can be very informative for policymakers. Often
these comparisons can correct misperceptions or at least nuance existing perceptions
within one country. In the case of South Africa, they have indicated that although the
top decile of pupils, in terms of test results, perform exceedingly well compared to the
remainder of pupils, they in fact do not perform well compared to the top decile of
other middle income countries (see for instance Mullis et al., 2004:34). Despite the
common perception in South Africa that too many pupils drop out of school before
360 M Gustafsson & T Mabogoane
completing 12 years, a cross-country analysis reveals that in terms of secondary school
completion South Africa is slightly above the average for similarly developed countries
(Gustafsson & Morduchowicz, 2008:28).
The increasing availability of internationally comparable data on quality of education has
allowed for new insights into education and country development. Above all, these data
have permitted economists to adapt traditional cross-country growth models so that they
now include educational quality, and not just years of schooling. This adaptation has
profound implications for education policy. In particular, it implies that focusing
exclusively on increasing enrolments in developing countries, without explicitly
considering improvements in pupil performance, can be misguided (Hanushek &
Woessman, 2009).
Two areas of exploration with respect to cross-country analysis should interest education
policymakers. Firstly, as summarised recently by Stiglitz et al. (2009), there are
important debates about what country development indicators policymakers should be
focusing on. The traditional economic growth or GDP per capita indicators are
inadequate on their own and excessive focus on them can be dangerous, for instance if
income inequality is ignored. Development models using non-traditional dependent
variables such as happiness or freedom from poverty are still rare, partly due to data
availability problems. One potentially valuable data source that has received little
attention in this regard is the World Values Survey (WVSA, n.d.), in which South
Africa has participated for three waves in the last two decades.
The second area of exploration is cross-country models designed to predict not a
development indicator such as GDP growth, but educational quality as measured by
standardised test scores. In such models critical explanatory variables would cover
education policy choices made by particular countries. What is currently clear from
the literature is that a policy intervention that does not appear to be strongly
associated with better educational quality is higher spending per pupil (Hanushek &
Woessman, 2007:60). In what is likely to become an important strand in the literature,
Bishop (1997) points to the importance of having standardised examinations, while
more recently Woessman (2011) finds evidence in the cross-country data of the value
of performance-linked teacher incentives for improving learning outcomes. In this
type of analysis the direction of causality must be a concern. For instance, do
standardised examinations produce good educational performance, or is it rather that
countries that perform well educationally are willing to have more standardised
examinations? Here the policymaker should look for evidence of a proper examination
of cause and effect of the kind provided by Hanushek & Woessman (2009), who use
an instrumental variables approach for testing causation.
7. Conclusion
The five areas of research considered here do not of course cover the whole economics of
education field. Two other important areas could have been included, had space
permitted. One is the estimation of ideal unit costs in, say, primary schooling for
countries at a particular level of development. Psacharopoulos (1996), in a paper
which, like this one, explores a possible economics of education agenda, emphasises
the need for work in this area. Another is teacher supply and demand analysis. Here
too, little work has been done in South Africa. Crouch (2001) suggests a simple
model for South Africa but, as he himself admits, it is merely exploratory.
South Africa’s economics of education 361
An economics of education research agenda for South Africa, based on the discussion in
the preceding sections, might look as follows.
Rates of return: Here a clearer sense of what the typical rates of return analyses mean for
policymakers is important. Perhaps some atypical rates of return analyses that took into
account the qualifications that people have obtained would throw new light on what
should be done with the qualifications structure of the schooling system. Rates of
return comparisons of general and vocational education could be an important
empirical input when considering the current policy shift towards higher enrolments in
FET colleges.
Production functions: Considering that these models are most informative for
policymakers when there are many of them, it is important to build on the stock of
existing local literature and to perform periodic meta-analyses. Policymakers are best
served by production function analyses that incorporate a final cost-effectiveness step
so that the meaning of the coefficients can be understood by non-economists. Class
size thresholds are a matter that seems to deserve more focused attention. Ensuring
that data are structured in a way that facilitates good production function analysis
should be a priority.
Teacher incentives: Periodic comparisons of teacher pay with other professional pay will
always be necessary. With regard to performance-linked teacher incentives, there is
much work to do. What can be done without new data is to apply the knowledge that
has been gained in other countries, with respect to theory, empirical evidence and
policy design, to the South African context in order to clarify key issues around which
there is still too much confusion. New data on the behaviour and preferences of South
Africa’s teachers would greatly assist in determining what kinds of incentives will
work best.
Benefit incidence analysis: Here it is necessary to build on the existing stock of standard
benefit incidence analyses to monitor the progressivity of public spending into the future.
Further interrogation of the socioeconomic status of tertiary students might produce
revisions of the country’s very regressive distribution of public expenditure on tertiary
education. Using the existing data, the incidence of private spending on education
could be better investigated.
Cross-country comparisons: Exploring the relationship between education and non-
traditional country development indicators (other than growth or per capita income)
represents an exciting research frontier. The same can be said of cross-country models
that explore how the education policy choices that countries make can affect
education outcomes.
Acknowledgements
Both authors are lecturers on the economics of education course offered to education
planners studying for the Professional Certificate in Education Finance, Economics
and Planning at the University of the Witwatersrand. This initiative, funded by GIZ
(German Society for International Cooperation), is targeted at sub-Saharan Africa.
This article is partly informed by what has appeared important to practitioners
attending this course. An earlier working paper version of this article can be found
online: http://ideas.repec.org/p/sza/wpaper/wpapers105.html
362 M Gustafsson & T Mabogoane
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