International Journal of Social Economics
The rich or the poor: who gains from public education spending in Ghana?
Mawuli Gaddah Alistair Munro Peter Quartey
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Mawuli Gaddah Alistair Munro Peter Quartey , (2015),"The rich or the poor: who gains from public
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The rich or the poor: who gains
from public education
spending in Ghana?
Mawuli Gaddah
Real Sector Division, Ministry of Finance, Accra, Ghana
Alistair Munro
Department of Economics, National Graduate Institute for Policy Studies (GRIPS),
Tokyo, Japan, and
Peter Quartey
Institute of Statistical, Social and Economic Research,
University of Ghana, Accra, Ghana
Abstract
Purpose – The purpose of this paper is to examine the incidence of public education subsidies in
Ghana. Since the late 1990s, Ghana’s government has increasingly recognized human capital as key to
alleviating poverty and income inequality, causing dramatic increases of government expenditures
to the education sector. At the same time user fees have been introduced in higher education while
basic education is being made progressively free. The question then is, whether these spending
increases have been effective in reaching the poor and to what extent? What factors influence the
poor’s participation in the public school system?
Design/methodology/approach – The authors address the key issues by employing both the standard
benefit incidence methods and the willingness-to-pay method.
Findings – The results give a clear evidence of progressivity with consistent ordering: pre-schooling
and primary schooling are the most progressive, followed by secondary, and then tertiary. Own price
and income elasticities are higher for private schools than public schools and for secondary than
basic schools.
Practical implications – Given the liquidity constraints African governments face yet there is the
need to improve the human capacity of the countries, this study offers solution to how to optimally
allocate the educational budget.
Originality/value – The use of policy simulations to ascertain the incidence of public spending on
education is innovative as far as previous studies in Africa is concerned.
Keywords Development, Efficiency, Education
Paper type Research paper
1. Introduction
Human capital development is widely recognized as an important requirement for
achieving sustained economic growth and rising incomes, particularly in developing
countries (World Bank, 1995). Its role was critical in the outstanding economic
transformation of Japan, Taiwan, Hong Kong, South Korea, and other fast-growing
economies (Becker, 1995). Empirical evidence suggests that each additional year of
schooling is associated with a 6-10 per cent increase in earnings in developing countries
(Duflo, 2001), providing further support for educational investment as an effective
approach to poverty reduction both by encouraging economic growth and as a method
of redistribution to the poor (Besley and Burgess, 2003).
International Journal of Social
Economics
Vol. 42 No. 2, 2015
pp. 112-131
©EmeraldGroup Publishing Limited
0306-8293
DOI 10.1108/IJSE-11-2013-0269
Received 29 November 2013
Revised 22 January 2014
Accepted 22 January 2014
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0306-8293.htm
JEL Classification — H22, H52, H53
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In Ghana, the government has identified investments in human capital (education
and health) as an important means of achieving broad-based growth resulting in
effective poverty reduction (Government of Ghana (GOG), 2005; Canagarajah and Ye,
2001). In line with this, public education expenditures have increased consistently,
reaching about 20 per cent of total expenditures (about 5.0 per cent of GDP) and 74.0
per cent of social spending in 2005 (Osei et al., 2007, p. 10)[1]. The question is, how are
public education subsidies benefiting poorer households in Ghana? Demery et al. (1995)
conducted benefit incidence analysis (BIA) in Ghana using the Ghana Living Standard
Survey and found that, the richest quintile receives the largest share of the benefits of
public education subsidies compared to the poorest quintile. Nearly 20 years later, the
question is, whether the distributional pattern of education benefits has changed? Have
the recent pending increases improved access to, and choice of public schools by poorer
households? Who then are the actual beneficiaries of subsidized education services?
And what factors determine the choice of education services?
The purpose of this study is to examine distribution, coverage, utilization as well as
the benefit incidence of public education spending in Ghana, considering all levels of
education - pre-schooling, primary, secondary, and tertiary. We address thee above
questions using a combination of BIA and willingness-to-pay approach to analyze the
welfare impact of public education expenditures[2]. We use a nested multinomial logit
(NMNL) model to estimate the demand for education services and then use the
compensating variations (CVs) derived from those estimates to value education services to
households. The demand estimates also enable us to examine the factors influencing
households’ utilization of these services and to conduct some policy simulations. We also
compare the results from the static analysis to those of Demery et al. (1995) to see how the
distribution of the education benefits has changed overtime.
The remainder of the paper proceeds as follows: Section 2 provides a brief overview of
the education sector in Ghana. The review of previous research is considered in Section 3
while Section 4 explains the methodology and data. The results and discussions are
presented in Section 5 while Section 6 presents the concluding remarks.
2. A brief overview of the education system
Ghana’s education system was established to reflect a standard British-style education.
This system was once regarded as one of the most developed in West Africa (Demery
et al., 1995). The general economic decline of the early 1980s severely affected the
education system and by the mid-1980s, the system was already in sharp decline.
The education budget as a share of GNP had declined by 5.0 percentage points between
1975 and 1983 (6.4-1.4 per cent) with primary education spending per capita falling by
61 per cent over the same period (Demery et al., 1995). Besides budgetary issues, the
education system also suffered from acute shortage of educational materials including
teachers, textbooks, and instructional materials throughout the country’s schools
(Akyeampong et al., 2007). The problem was exacerbated by poor conditions of service
(low salaries) which caused the exodus of trained teachers for greener pastures
elsewhere, particularly to Nigeria, where the oil boom has increased the demand for
professionals including teachers.
Against this background, a radical educational reform was launched in 1987 as part
of the overall economic reform programme (ERP), which sought, among others, to
expand and create a more equitable access at all levels of education; to change the
structure of the school system, reducing the length of pre-tertiary education from 17 to 12
years while increasing contact hours between teachers and pupils[3]. The deteriorations
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that characterized the sector in the 1970s were halted while the infrastructure base was
improved. The number of basic schools also witnessed a significant increase – rising
from 12,997 in 1980 to 18,374 in 2000 – while attendance and completion rates improved
(Akyeampong et al., 2007). Total government expenditure on education had more than
doubled under the ERP, moving from 1.4 per cent of GDP in 1983 to 3.8 per cent in 1992
(Demery et al., 1995). Total education expenditures (actual) both as a share of the national
discretionary budget and GDP have also increased consistently reaching 31.0 and
6.0 per cent, respectively, in 2006 (Ministry of Education Science and Sports (MOESS),
2008); the lion’s share going to basic education.
Financing education has remained central in Ghana’s education policy[4]. Rate of return
studies have shown that in Sub-Saharan Africa, both the social and private rate of
return are highest in primary education and lowest in higher education (Psacharopoulos,
1994; World Bank, 1995). At the same time, benefit incidence studies have shown that
public expenditures on primary education are welfare improving compared to those on
higher education. The implication of these findings is straight forward: cut subsidies
to higher education and introduce cost recovery through user fees, raise subsidies to basic
education and abolish tuition fees. In the mid-1990s and consistent with the above policy
implications, Ghana’s education system witnessed two main policy reforms: cost recovery
measures were introduced in higher education to be achieved through increased school
fees, facilities user fees, and withdrawal of subsidies while “free and compulsory universal
basic education” (FCUBE) was introduced in basic education (to be achieved by 2005).
The cost recovery measure was initially opposed by tertiary education students with
demonstrations and disruption of academic calendars, forcing fees to remain consistently
low with little upward adjustments. The new policy is to allow qualified applicants who do
not get a place in the regular admission but can afford the full fees to enrol. The FCUBE
initially covered only the tuition fee. However, the cost of sending a child to school goes far
beyond tuition. Other costs such as cost of uniform, books, travel, tariffs for structural
works, parent’ teacher association fees – used as supplements to the government subsidies
to the schools (Canagarajah and Ye, 2001) – all impose substantial burden on households
(Aryeetey and Goldstein, 2000). The programme led to steady but slower increase in school
enrolments, yet failed to reduce the opportunity cost of schooling to households
(Akyeampong, 2009). In order to make the FCUBE fully operational, the government, in
2005, initiated the capitation grant concept, abolishing fees being charged in basic schools
by providing each school with a little grant for each child enroled[5]. The school lunch and
the free uniform programmes have also been launched.
The recent proliferation of private universities also marked a significant feature of the
reforms. These institutions, mainly religious based, offer a limited number of professional
courses – accountancy, marketing, economics, banking and finance, and computer
science – tailored towards the labour market. They target mainly working class students
and run programmes in the evenings and on part-time bases, employing mostly part-time
teachers. They derived revenue from tuition and boarding fees (Larocque, 2001). The
traditional universities have come under constant attacks from employers in recent years
for failing to produce graduates that meet the changing labour market standards. Thus, the
emergence of the private institutions should be seen as a welcome development since they
are more focused on the job market requirements than just providing a general education.
3. Public spending, education, and poverty
The impact of public spending on educational outcome has been a major subject of
research for years, with mixed results. The general consensus is that public spending
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alone is insufficient for achieving improved educational outcomes of the poor. The issue
of targeting is equally important (Martinez-Vazquez, 2001). Meanwhile, Yuki (2003)
has compiled studies that examined the incidence of public spending on education in a
cross-section of developing countries. For those that focus on Africa, the poorest
quintile shares in total education subsidies were 16.4 per cent in Ghana in 1992 (21.8 per
cent primary, 14.9 per cent secondary, 6.0 per cent tertiary), 19.9 per cent in South
Africa in 1993 (25.8 per cent primary, 18.8 per cent secondary, 6.1 per cent higher),
19.4 per cent in Cote d’Ivoire in 1995 (28.8 per cent primary, 11.2 per cent secondary),
17.0 per cent in Kenya in 1992 (21.8 per cent primary, 6.4 per cent secondary,
2.0 per cent higher), 16.0 per cent in Malawi in 1995 (20.0 per cent primary, 9.0 per cent
secondary, 1.0 per cent higher), 13.0 per cent in Tanzania in 1994 (20.0 per cent primary,
7.6 per cent secondary, 0.0 per cent higher), and 8.3 per cent in Madagascar in 1994
(17.2 per cent primary, 2.0 per cent secondary, 0.0 per cent higher). In all these countries,
the poor gains a disproportionately higher proportion of primary schools subsidies
while subsidies to higher education accrue mainly to the wealthy.
More specific evidence can be found in Glick and Razakamanantsoa (2006)
who find in Madagascar that primary and secondary education are more equally
distributed than consumption expenditures. University enrolments are, however,
concentrated among the wealthy than consumption. Primary schooling was found
to be per capita progressive while secondary and university schooling were found to
be per capita regressive – they accrue disproportionately to the well off. Younger
(1999) also uses a combination of benefit incidence and behavioural approaches to
assess the relative progressivity of public services in Ecuador in 1994 and finds that
primary education is the most progressive, followed by secondary education, public
universities, and then private universities. He actually compared three different
versions of the benefit incidence method: standard (with unit cost which varies by
region), uniform (binary indicator: one if a service is used and zero otherwise), and
CV based and concludes that the three methods yield similar results in terms of the
ranking of public services with consistent ordering.
In a related study in Peru in 1998, Younger (2000) finds that, spending on primary
education is the most progressive, followed by secondary schooling, then post-secondary.
He also observed that though social spending is likely to have an equalizing
re-distributional effect, its overall impact on poverty was only marginal. Glick and
Sahn (2006) find in Madagascar that fee increases reduce public and total (public
plus private) primary enrolment proportionately much more for the poor than the
well-off, making the distribution of schooling less equitable. They also find that
improvement in public school quality tends to benefit the poor disproportionately.
4. Method of analysis
In this section, we derive a school choice model that enables us to understand
behavioural responses to public spending. Following previous authors (Gertler and
Glewwe, 1990; Younger, 1999; Glick and Sahn, 2001), we assume that households
derive utility from the human capital of children, which depends on schooling and
on the consumption of all other goods (net income). Confronted with the decision to
enrol in public school, private school, and non-enrolment, parents choose the option
that yields the highest utility. Schooling raises the human capital, a kind of asset to
parents which is achieved at the cost of school fees and reduced consumption of
other goods. An individual will only choose the non-enrolment/no-school option only
if it yields utility higher than all other alternatives. For each option (say option j), the
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indirect utility associated with choosing that option depends on the following simple
linear specification:
Vij ¼ c Y iPij

 
þb1Sijþeij (1)
where c(Y−Pj) is net household income (proxied by household expenditure, Y ) less
school cost at option j (Pij), which includes both the direct and the indirect (opportunity)
costs. Finally, ej is a noise term specific to the household and unobserved by the
researcher, which can be correlated across options within a branch. The function Sij,
which represents the increase in human capital, is expected to vary across options since
the quality of the alternatives may differ. For the non-enrolment option, Sij is normalized
to zero based on the assumption that the individual gains no utility from not attending
school. Since the change cannot be directly observed, β1Sij is replaced by a reduced form
equation for the utility from human capital as follows:
b1Sij ¼ YQjþdjX iþnij (2)
where Qj is a vector of school quality variables and Xi is a vector of observed household
and individual characteristics. This is what Glick and Sahn (2001) referred to as
representing a production function of human capital in which both school and household
variables are inputs. Substituting (3.4) into (3.3) yields:
Vij ¼ c Y iPij

 
þYQjþdjX iþeij (3)
where εij¼ eij + nij and δj, the coefficient on household and individual characteristics are
allowed to be constant across alternatives. We assume the function, Sij to be linear while
net income is assumed to be logarithmic – i.e. c Y iPij

 
¼ alog YiPij

 
[6].
The specification used in this study is a NMNL model with three options for school
choice considered – no-school/non-enrolment, public, or private. The NMNL model
allows us to relax the homoscedasticity (independence of irrelevant alternatives (IIA))
assumption of a potential conditional logit model[7]. In this framework, since the
decision to choose a particular provider is a discrete choice problem, the determination
of demand involves estimating the probability that a particular service provider –
public or private – will be chosen.
In this model, two of the options are in one nest while the other option (with utility
normalized to zero) is in the second nest. Thus, the probability that a person chooses
option j is given as:
pj ¼
exp Vj=s

 

P
kexp Vk=s

 
s1
1þ

P
kexp Vk=s

 
s ; k ¼ j; i (4)
For the options not chosen, because we do not have figures for direct costs and travel
time, we estimate them using the median observed cost for the child’s region, area
(rural/urban), and type of school (public/private). The use of median scores is to avoid
the extreme values bias often associated with mean scores. For the no-school option,
the net income is just the gross income. The national survey does not contain quality
information; hence the Sij is simply a function of household and individual characteristics
plus an option-specific dummy. By leaving out important quality variables that probably
correlate with net income and with the probability of choosing a particular provider could
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mean that we are overestimating elasticity estimates which could tend to underestimate
the incidence of the education services (Younger, 1999). However, in Younger (1999) this
omitted variable does not affect the progressivity of services.
4.1 Data and variables
The data for this study are drawn from the latest round of the Ghana Living Standards
Survey (GLSS5 2005/06). GLSS5 includes a sample of 8,687 households containing 37,128
household members. The survey collected information on individual and households, as
well as information on current education level and type of school, employment, income,
and consumption. Our sample includes all children who are attending school or who are
“eligible” to attend school. The latter group includes all children of the appropriate age
who have not yet graduated from the level of school under consideration. Public education
expenditures as well as unit costs incurred at each education level were obtained from the
Ministry of Education Science and Sports (MOESS).
We considered three samples for our demand function estimations: pre-school,
primary, and secondary. We did not include tertiary education in the demand
function estimates for two reasons: rationing of tertiary education and the share of the
private sector was too small[8]. Our sample includes all children who are attending
school or who are “eligible” to attend school. The latter group includes all children
of the appropriate age who have not yet graduated from the level of school under
consideration. We follow the standard practice and include all children of an
appropriate age who have already graduated from the previous level (at least three for
pre-school, at least six for primary, at least 15 for secondary, at least 18 for tertiary).
In order to account for late entry and overage attendance – a typical phenomenon in
Ghana – we truncate the sample at a maximum age (eight for pre-school, 14 for
primary, and 21 for secondary)[9]. Given that those who have already graduated from
the current level do not have demand for that level, we omit children of an appropriate
age who have already graduated this level. Each model includes similar regressors of
age, gender, relationship with head of household, net income, years completed at the
current level, head’s education and age, a dummy of household composition (number of
men, number of women, number of children) while controlling for religion, area of
residence, and sector of employment.
As we noted, the cost of schooling (Pj) includes both the direct and the indirect
(opportunity) cost. The GLSS5 survey defines the direct cost as the sum of registration
and tuition fee, cost of uniforms, cost of books, transport cost, parent teacher association
dues, feeding and boarding, and expenses on extra classes. Opportunity cost measures
the cost of the time needed to stay in school (Younger, 1999), and it is calculated for
children aged ten or older as the time spent at school (six hours) plus travel time
multiplied by a predicted wage estimated from a simple OLS wage function. For children
below this age, we assume a zero opportunity cost. Net household expenditure differs
from gross expenditure by the total school cost, as shown in Table I. The indirect cost
(opportunity cost) constitutes the lion’s share of total cost, representing 55 per cent of
total school cost in both primary and secondary samples. These costs are slightly higher
for private providers than the public provider.
The means of net income and their standard deviations were ¢17.9 and ¢15.9 million,
respectively, for the pre-school sample, 20.1 and 19.7 for the primary sample, 23.4 and
26.4, respectively, for secondary sample (Table I). These figures also vary across options.
For instance, within the pre-school sample, mean net income is 14.7 for the no-school
option, 18.1 for the public school option, and 28.1 for the private school option. The mean
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ages for the various samples are 4.9 for pre-school, 9.5 for primary, and 18.1 for secondary.
Females constitute more than 48.0 per cent of each sample. Majority of households (over
70.0 per cent) dwell in rural areas with over 20 per cent headed by women (Table I).
5. Results and discussion
5.1 Demand for schooling in Ghana
Table II presents estimates of the NMNL model of school choice for pre-school, primary,
and secondary schooling. For each model, the Wald tests reject the null of all coefficients
being zero and the null of equality of coefficients across the public and private options.
Due to the nature of our nested structure, we have to constrain the no-school option to
unity; hence its dissimilarity parameter is one. From Table II, the dissimilarity parameters,
σ (0.88 for pre-primary, 0.26 for primary, and 0.24 for secondary) are between zero and one,
indicating that our model is consistent with the additive random utility maximization.
The LR tests, τ rejects the IIA assumption and give strong support for the NMNL instead
of a MNL model. The coefficient for net income, the variable of interest is positive, and
significant in all equations. As for the child’s characteristics, we find that age increases the
probability of enroling in both pre-school and primary school (both public and private) but
turns negative for secondary.
Being the child of the household’s head positively and significant increases the
probability of enrolments, both public and private. Despites the slight gender difference
in school attendance reported earlier, the gender indicator (¼ 1 if female) is not
statistically significant at any conventional significant levels, except in the secondary
equation where females have a lower probability of enrolment. The number of years
Pre-school Primary Secondary
Variable Mean SD Mean SD Mean SD
Age of child 4.88 1.53 9.51 2.50 18.11 1.86
Gender (female¼ 1) 0.49 0.50 0.48 0.50 0.52 0.50
Child of household head 0.81 0.39 0.77 0.42 0.67 0.47
Years completed 0.00 0.00 3.73 1.89 1.97 0.74
Household head’s years 6.44 7.65 7.23 8.28 8.42 9.07
Household head’s age 44.16 13.06 47.55 12.74 48.26 14.20
Gender of head (female¼ 1) 0.19 0.39 0.22 0.42 0.25 0.43
Number of men 1.23 0.98 1.30 1.05 1.80 1.26
Number of women 1.56 0.99 1.63 1.08 2.03 1.26
Number of children 3.96 2.26 3.95 2.26 2.66 2.33
Head works in formal sector 0.08 0.27 0.10 0.29 0.15 0.36
Head works in informal sector 0.84 0.36 0.82 0.38 0.78 0.41
Catholic 0.15 0.36 0.16 0.37 0.17 0.38
Moslem 0.21 0.41 0.20 0.40 0.20 0.40
Traditional religion 0.11 0.32 0.10 0.30 0.09 0.28
Resides in Accra (GAMA) 0.05 0.22 0.06 0.24 0.11 0.32
Resides in other urban 0.20 0.40 0.23 0.42 0.29 0.45
Resides in rural coastal 0.08 0.27 0.09 0.29 0.06 0.24
Resides in rural forest 0.27 0.44 0.26 0.44 0.20 0.40
Resides in rural Savannah 0.41 0.49 0.35 0.48 0.34 0.47
Net income (GH¢ “000”) 1.79 1.59 2.01 1.20 2.34 2.64
Travel time (hrs) 4.35 3.12 4.37 3.15 4.30 3.05
Source: Author’s own estimation based on GLSS5 surveys
Table I.
Descriptive statistics
of regressors
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previously completed at the current level has a positive and significant effect on the
probability of enrolment at all levels[10]. Years of schooling of the household’s head
positively and significantly increases the probability of a child’s school attendance
both across samples and equations, with the exception of the secondary model, as
shown in Table II.
Pre-school Primary Secondary
Variables Private Public Private Public Private Public
Constant 2.61** 1.30 19.25*** 19.15*** −7.73** −7.87***
(1.28) (1.22) (2.59) (2.55) (3.01) (3.01)
Age 0.42*** 0.48*** 0.95*** 0.91*** −0.76*** −0.76***
(0.04) (0.03) (0.09) (0.09) (0.12) (0.12)
Child of head 0.08 0.14 0.58*** 0.51*** 0.36*** 0.35***
(0.17) (0.12) (0.11) (0.11) (0.12) (0.12)
Gender (female¼ 1) 0.10 0.12 0.03 0.01 −0.42*** −0.43***
(0.11) (0.08) (0.09) (0.08) (0.12) (0.12)
Years completed 0.27*** 0.32*** 1.75*** 1.77***
(0.09) (0.09) (0.14) (0.14)
Head years of school 0.07*** 0.08*** 0.06*** 0.06*** −0.01 −0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Head’s age −0.01 0.01* −0.00 0.00 0.01 0.01
(0.01) (0.00) (0.00) (0.00) (0.00) (0.00)
Head (female¼ 1) 0.60*** 0.81*** 0.59*** 0.59*** −0.01 −0.00
(0.16) (0.13) (0.13) (0.13) (0.12) (0.12)
Number of men −0.22*** −0.06 −0.07 −0.06 0.02 0.02
(0.07) (0.05) (0.05) (0.05) (0.05) (0.05)
Number of women −0.18** −0.04 0.05 0.05 0.06 0.06
(0.07) (0.05) (0.05) (0.05) (0.05) (0.05)
Number of children −0.26*** −0.02 −0.01 0.02 0.04 0.03
(0.04) (0.02) (0.02) (0.02) (0.03) (0.03)
Head, informal (¼ 1) −0.19 −0.18 −0.22 −0.20 −0.47*** −0.47***
(0.06) (0.04) (0.19) (0.19) (0.17) (0.17)
Traditional religion −3.70** −0.98*** −0.70*** −0.59*** −12.20*** 0.49
(1.67) (0.18) (0.16) (0.14) (3.40) (0.46)
Accra 3.82*** 1.23*** 0.93*** 0.55** −0.87*** −0.92***
(0.30) (0.31) (0.35) (0.28) (0.22) (0.22)
Rural coastal area 1.55*** 0.59*** 0.28 0.29* −0.67*** −0.67***
(0.26) (0.16) (0.18) (0.17) (0.23) (0.23)
Rural forest area 1.58*** 0.91*** 1.07*** 1.06*** −0.69*** −0.72***
(0.22) (0.12) (0.14) (0.14) (0.19) (0.19)
Rural Savannah area 1.71*** 0.83*** 1.01*** 1.04*** −0.58*** −0.77***
(0.31) (0.17) (0.17) (0.18) (0.21) (0.21)
Log (net expenditure) 5.50*** 5.50*** 3.94*** 3.94*** 3.24*** 3.24***
(1.56) (1.56) (1.35) (1.35) (1.26) (1.26)
σ 0.88*** 0.88*** 0.26*** 0.26*** 0.24*** 0.24***
(0.24) (0.24) (0.09) (0.09) (0.02) (0.02)
τ 15.28*** 15.28*** 52.48*** 52.48*** 7.06*** 7.06***
Loglikelihood −3,062.69 −3,062.69 −4,628.60 −4,628.60 −1,666.99 −1,666.99
% correctly predicted 75.2 75.2 86.7 86.7 79.3 79.3
Observations 4,047 4,047 8,234 8,234 2,632 2,632
Notes: Base choice is non-enrollment. For area of residence, the base category is other urban. The base for religion is
other religions. SEs in parentheses. ***po0.01; **po0.05; *po0.1
Source: Own calculations based on GLSS 2005/2006
Table II.
Nested multinomial
logit estimates for
choice of schooling
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The age of the household head is not significant at all conventional significant
levels, with the exception of the public pre-school equation. A female headed household
has a higher probability of enrolment in all models, exception secondary. Number of
men, number of women, and number of children negatively and significantly decrease
the probability of private pre-school enrolments at 1.0 per cent significant level. They
are insignificant in both primary and secondary equations. We also controlled for
regional and religious differences, as well as the sector of employment of the head of the
household; only significant variables are reported (Table II).
5.2 The elasticity of demand
The coefficients of the NMNL estimates can be difficult to interpret, thus we explore the
influence of price and income variables by the analysis of elasticities. If we let Pj to
represent the price for provider j, which by assumption only enters utility of option
j (Vj), then it follows that:
@pj
@pj
¼ pj
@Vj
@pj
1
s 1þ
s1ð Þexp Vj=s

 
P
iexp Vi=s

 

sexp Vi=s

 
P
kexp Vk=s

 
 s1
1þ
P
kexp Vk=s

 
 s
" #
; k ¼ j; i (5)
Hence, the own elasticity:
ejj ¼
@Vj
@pj
pj
s 1spjþ
s1ð Þexp Vj=s

 
P
kexp Vk=s

 
" #
(6)
Note that this equals the standard formula for multinomial logit when sigma is 1. Note
also that @Vj
@pj
 
¼ 
a
ypj
¼ 
a
Net expenditurej
where α is the coefficient on net household
expenditure.
Table III reports price and income elasticities of demand calculated at mean
levels for each schooling option by expenditure quintile. As would be expected, the
price elasticities are consistently negative in all equations and options. For all
income quintiles, the elasticities are significantly higher (in absolute terms) for
private providers than the public provider. Demand for schooling becomes more elastic
Own price elasticity Income elasticity
Pre-school Primary Secondary Pre-school Primary Secondary
Private
Poorest quintile −0.043 −1.551 −4.149 0.048 0.124 0.125
2 −0.022 −0.542 −1.345 0.035 0.089 0.101
3 −0.016 −0.378 −0.917 0.031 0.076 0.093
4 −0.012 −0.301 −0.646 0.028 0.065 0.086
Richest quintile −0.009 −0.192 −0.393 0.024 0.048 0.082
Public
Poorest quintile 0.033 −0.147 −0.850 0.012 0.521 0.005
2 −0.008 −0.096 −0.369 0.010 0.041 0.048
3 −0.011 −0.123 −0.491 0.011 0.043 0.050
4 −0.008 −0.131 −0.443 0.013 0.048 0.048
Richest quintile −0.007 −0.124 −0.433 0.015 0.057 0.051
Source: Own estimations
Table III.
Price and income
elasticity of
education demand
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as one move from primary to secondary. For instance, a 1.0 per cent increase in direct
cost of schooling (including tuition, textbooks and supplies) would result in a reduction in
demand for public pre-schools by 0.033 per cent among the poorest quintile as compared
to 0.043 per cent in private pre-school, other factors being constant. A similar pattern is
observed for primary and secondary schooling, though with higher elasticities, as shown
in Table III.
All income elasticities also have the expected positive sign, with the lower income
groups exhibiting higher price and income elasticities. That is, demand for schooling
among the lowest income individuals is substantially more price and income elastic
than among the richest group. This suggests that income, proxied by household
expenditure is a crucial determinant of school enrolment and school choice. A one
per cent increase in income would lead to about 0.125 percentage point increase in the
demand for private secondary schools as compared with 0.05 percentage point increase
in public secondary schools among the poorest income group (Table III).
5.3 Policy simulation
We complement the above elasticities of demand by carrying out a number of policy
simulations. Making education, especially public education more accessible to the people
may involve one or more of the following: making public schools free, offering subsidies
that make private schools free, and increasing the income of poorer households. We do
not have information to calculate the relative cost of these policies but at least we can
simulate the impact of each in a simple way. The procedure followed here is, first, we set
public provider’s price to zero and simulate the change in predicted probability on both
the three options (no-school, private and public). Second, we set private providers’ price to
zero. Third, we increase the income of the poorest households (details below) and
simulate its effect on predicted probability. These predicted probabilities are then
compared with the predicted probabilities for our baseline model.
Table IV reports the results of the simulations for the three samples (pre-school,
primary, and secondary, respectively). From the baseline model (when all variables are at
their actual values), we find that, 32.2 per cent of the pre-school sample, 62.5 per cent of
the primary sample, and 21.4 per cent of the secondary sample will choose public school.
A larger proportion, however, (48.2, 19.8, and 74.6 per cent, respectively) will stay out of
school as shown in Table IV. When we set the public provider’s price to zero, we find that,
the probability of attending public schools rose to 51.5, 73.1, and 26.5 per cent for
pre-school, primary, and secondary schools, respectively, while the probability of
choosing the private option declines significantly, indicating a substitution into public
option. Making private provider’s price equal to zero also resulted in a massive
substitution into private schooling but compared to the first simulation has a smaller
impact on the probability of not attending a school. The fact that non-enrolment is still
high even after setting prices to zero is a reflection of the negative effect of high
opportunity costs on schooling demand. Akyeampong (2009) highlighted the inability of
the FCUBE to reduce the indirect cost of schooling as a major issue with the programme.
In our next simulation, we move all households in the poorest quintile into the second
quintile, and then move all poor households to the upper poverty line set by the Ghana
Statistical Service (GSS, 2007). We find no drastic change in probabilities. The marginal
changes in the probabilities due to changes in income of the poorest households are,
however, consistent with the low income elasticities (not reported). This could be
suggesting that merely moving households to the poverty line is not particularly effective
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in increasing attendance; income must be raised well above the poverty line in order to
make significant impacts on school attendance[11].
5.4 Benefit incidence and welfare dominance
Benefit incidence has become a fairly standard first line method of assessing the impact
of public expenditures. BIA involves three steps: valuing public services (estimating the
unit cost or unit subsidies implied by the provision of public services), imputing the unit
subsidy to households or individuals who use the service, and aggregating individuals or
households into subgroups of the population to compare the distribution of the subsidy
among different groups. One key issue is the degree of progressivity in benefits, usually
depicted via concentration curves. The concentration curve is a normative tool similar to
the Lorenz curve, and plots the cumulative shares of individuals in the population,
ranked by household expenditure per capita/per equivalent adults on the x-axis and the
cumulative shares of benefits on the y-axis. However, unlike the Lorenz curve, which
represents the cumulative percentage of total income held by a cumulative proportion of
the population (after ordering income in increasing magnitude), a concentration curve can
lie above the diagonal – the poorest 40 per cent of the population cannot earn more than
40 per cent of total income, but they can receive more than 40 per cent of total benefits
from public spending (Hakro and Akram, 2007).
The concentration curves are compared against two benchmarks – Lorenz curve of
household expenditures and the 45-degree line as shown in Figure 1 – which yields two
measures of progressivity: “Expenditure progressivity” or simply progressivity and
“per capita progressivity” (Younger, 1999; Sahn and Younger, 2000). If a concentration
curve is at all points above (dominates) the Lorenz curve then the benefit is said to be
progressive – it is more equitably distributed than household expenditures.
Predicted probabilities Change in probabilities
Pre-school Primary Senior Pre-school Primary Senior
Baseline probabilities
None 0.4816 0.1983 0.7459 0.0 0.0 0.0
Private 0.1801 0.1773 0.043 0.0 0.0 0.0
Public 0.3383 0.6245 0.2135 0.0 0.0 0.0
Price of public option is zero
None 0.3663 0.1694 0.7065 −0.1153 −0.0289 −0.0394
Private 0.1184 0.0794 0.0088 −0.0617 −0.0979 −0.0342
Public 0.5153 0.7511 0.2848 0.1770 0.1266 0.0713
Price of private option is zero
None 0.3864 0.1721 0.7142 −0.0952 −0.0262 −0.0317
Private 0.4345 0.49 0.1352 0.2544 0.3127 0.0922
Public 0.1791 0.3378 0.1505 −0.1592 −0.2867 −0.063
Moving everybody out of poorest quintile
None 0.481 0.1975 0.741 −0.0006 −0.0008 −0.0049
Private 0.1964 0.1778 0.0452 0.0163 0.0005 0.0022
Public 0.3226 0.6247 0.2137 −0.0157 0.0002 0.0002
Moving poor households to the poverty line
None 0.4814 0.1977 0.7390 −0.0002 −0.0006 −0.0069
Private 0.1956 0.1777 0.0470 0.0155 0.0004 0.0040
Public 0.323 0.6246 0.2140 −0.0153 0.0001 0.0005
Source: Own estimation based on Table II
Table IV.
Simulated
probabilities by
expenditure
quintiles, and type
of provider
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A concentration curve that lies above the diagonal (45-degree line) is said to per capita
progressivity (pro-poor) – poorer households receive disproportionately larger shares
of the benefit. A concentration curve that lies below the Lorenz curve is classified as
regressive – the benefit accrues disproportionately to the wealthy. It is also possible
to rank different services according to their progressivity. For example, a given
subsidy is said to dominate another if its concentration curve is everywhere above the
concentration curve for the other. The results of this analysis are presented in
Figure 2 and Table V.
We begin by estimating the incidence of public education expenditures for all levels
of education (primary, secondary, and post-secondary). Note that benefit incidence and
progressivity analyses refer to publicservices only. Subsidies refer to recurrent
expenditures on each level of education. Figure 2 presents concentrations curves the for
the various education services considered. We first evaluate these curves against our
benchmarks of Lorenz curve (household consumption expenditure) and the perfect
equality line (45-degree line). One can easily tell which of these services is progressive.
The fact that the concentration curves for children dominate the 45-degree line
indicates that there are more children in the poorest quintile than in the richest quintile.
For instance, 20.8 per cent of school-age children (3-23 years) are found in the poorest
quintile (25.5 per cent for poorer households) compared to about 17.9 per cent for the
richest quintile (13.0 per cent for richer households), thus showing that poorer
households tend to have more children.
Primary (and pre-schooling) schooling benefits are more concentrated among the
poorest quintile with the bottom two quintiles receiving a cumulative share of about
41 per cent, indicating that primary education subsidies are definitely progressive.
The poorest quintile’s rate of participation in public primary is higher than that of the
richest quintile (79 per cent against 70.0 per cent) mainly because a large proportion of
children in the richest quintile are enroled in private primary. Secondary and tertiary
schooling are all dominated by the 451 line, hence are said to be per capita regressive.
Benefits from these services accrue more disproportionately to the well-off households;
hence are poorly targeted. Senior high (including TVET) is progressive in terms of
household expenditures with the poorest quintile receiving 15.5 per cent of the benefits
Source: Hakro and Akram (2007)
100
0 100Cumulative percent of population
Cu
m
u
la
tiv
e
 p
er
ce
nt
 o
f b
en
ef
its
,
 
in
co
m
e,
 
o
r
co
n
su
m
pt
io
n
propoor spending
45 degree line
progressive
Lorenz curve
of income or
consumptionregressive
(poorly targeted)
Figure 1.
Lorenz and
concentration curves
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of this level compared to 26.3 per cent for the richest quintile[12]. Post-secondary
(universities, polytechnics, and teacher education) education is regressive in absolute
terms (both in terms of household expenditures and the 450 line) with the richest
quintile appropriating 50.3 per cent of the benefits, as shown in Table V. School
enrolment statistics show that people are terminating schooling after primary, and
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
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f b
en
ef
its
0 0.2 0.4 0.6 0.8 1
Household expenditure Household expenditure45 degree line
Pre-school Children 3-5
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
 o
f b
en
ef
it
0 0.2 0.4 0.6 0.8 1
Cumulative share of sample, poorest to richest
45 degree line
Children 6-11 Primary schooling
Cumulative share of sample, poorest to richest
(a) (b)
45 degree line
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
 o
f b
en
ef
it
0 0.2 0.4 0.6 0.8 1
Cumulative share of sample, poorest to richest
45 degree line
Children 12-14
Household expenditure
Junior High
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
 o
f b
en
ef
it
0 0.2 0.4 0.6 0.8 1
Cumulative share of sample, poorest to richest
Senior high schooling
Household expenditure
Children 15-17
(c) (d)
45 degree line Household expenditure
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
 o
f b
en
ef
it
0 0.2 0.4 0.6 0.8 1
Cumulative share of sample, poorest to richest
Adults 18-30 Tertiary schooling
0
0.2
0.4
0.6
0.8
1
Cu
m
ul
at
ive
 s
ha
re
 o
f b
en
ef
it
0 0.2 0.4 0.6 0.8 1
Cumulative share of sample, poorest to richest
45 degree line Household expenditure
Pre-school Primary schooling
Senior high schoolingJunior high schooling
Tertiary schooling
(e) (f)
Source: Authors estimation based on GLSS5 data
Notes: “Others” include teacher and vocational training and tertiary includes universities
and polytechnics. (a) Pre-school, 2005/2006; (b) primary schooling, 2005/2006; (c) junior
high schooling, 2005/2006; (d) senior high schooling, 2005/2006; (e) tertiary schooling,
2005/2006; and (f) all public schools, 2005/2006
Figure 2.
Concentration curves
for public schooling,
2005/2006
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largely after junior high. This partly explains the less progressivity of post-basic
benefits.
Though subsidies to teacher education are more equally distributed than those to
universities and polytechnics, its contribution seems too small to affect the overall
progressivity of post-secondary subsidies. With regards to gender, males received
slightly higher education benefits (51.7 per cent) than women (48.3 per cent) with much
disparity coming from secondary (Table V). There was almost equal distribution of
% of benefits received Change in benefits
Quintile 1989 1992 2005 1989-1992 1992-2005 1989-2005
Primary
1 21.2 21.8 18.4 0.6 −3.4 −2.8
2 22.1 23.6 22.2 1.5 −1.4 0.1
3 22.2 21.7 21.3 −0.5 −0.4 −0.9
4 20.3 18.8 21.5 −1.5 2.7 1.2
5 14.3 14.0 16.3 −0.3 2.3 2.0
Total of which 100 100 100 0.0 0.0 0.0
Accra 6.3 5.3 2.9 −1.0 −2.4 −3.4
Other-urban 23.1 24.6 20.3 1.5 −4.3 −2.8
Rural 70.6 70.1 76.8 −0.5 6.7 6.2
Secondary
1 16.8 14.9 16.0 −1.9 1.1 −0.8
2 18.0 21.8 18.3 3.8 −3.5 0.3
3 21.8 21.1 18.3 −0.7 −2.8 −3.5
4 23.4 23.5 22.5 0.1 −1.0 −0.9
5 19.9 18.6 24.9 −1.3 6.3 5.0
Total of which 100 100 100 0.0 0.0 0.0
Accra 11.1 12.0 8.9 0.9 −3.1 −2.2
Other-urban 23.3 30.1 30.9 6.8 0.8 7.6
Rural 65.6 57.8 60.2 −7.8 2.4 −5.4
Tertiary
1 7.7 6.0 4.0 −1.7 −2.0 −3.7
2 3.8 9.5 5.5 5.7 −4.0 1.7
3 19.2 19.0 17.6 −0.2 −1.4 −1.6
4 19.2 20.2 22.2 1.0 2.0 3.0
5 50.0 45.2 50.3 −4.8 5.1 0.3
Total of which 100 100 100.0 0.0 0.0 0.0
Accra 42.3 27.4 27.1 −14.9 −0.3 −15.2
Other-urban 34.6 47.6 47.2 13.0 −0.4 12.6
Rural 23.1 25.0 25.6 1.9 0.6 2.5
All education
1 17.1 16.4 14.8 −0.7 −1.6 −2.3
2 17.0 20.7 17.5 3.7 −3.2 0.5
3 21.4 21.0 19.4 −0.4 −1.6 −2.0
4 20.8 21.1 22.0 0.3 0.9 1.2
5 23.7 20.8 26.3 −2.9 5.5 2.6
Total of which 100.0 100.0 100 0.0 0.0 0.0
Accra 15.6 11.6 10.0 −4.0 −1.6 −5.6
Other-urban 25.7 30.5 29.6 4.8 −0.9 3.9
Rural 58.7 57.9 59.2 −0.8 1.3 0.5
Notes: 1989, 1992 (estimated by Demery et al., 1995), 2005 (author’s own estimations). 1989 is ranked
by household expenditure per capita, 1992 and 2005 are ranked by household expenditure per adults
equivalent
Table V.
Distribution of
public education
subsidies: 1989-2005
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benefits between males and females for post-secondary, mainly because of teacher
education which tend to enrol more female students.
Our next task is to evaluate these services against each other - relative progressivity
(Younger, 1999). A casual observation of these curves can tell us which services are more
progressive. Primary (and pre-school) services are the most progressive since their
concentration curves dominate those of all other services, followed by secondary, which
in turn is more progressive than post-secondary; a pattern that has become standard for
developing countries (Glick and Razakamanantsoa, 2006)[13]. The analysis of school
enrolment rates shows that a number of children in poorest households tend to terminate
their schooling at primary level, and some at JHS. Thus, the widening gap between
primary and secondary education is a reflection of this trend.
5.5 Changes in the incidence of education subsidies: 1989-2005
Here we consider the change in the benefit incidence of public education spending by
comparing the results to Demery et al. (1995). The study of Demery et al. (1995) is based
on the GLSS 2 (1989) and 3 (1992) while the present study is based on GLSS 5 (2005/
2006), all of which are nationally representative household surveys conducted by the
GSS. For the sake of this comparison, we group pre-school and primary into primary
education; JHS, SHS, and TVET are grouped into secondary, while universities,
polytechnics, and teacher education are grouped into post-secondary. After basic
school ( JHS), children can either enrol in SHS or TVET. However, SHS is required for
tertiary (university and polytechnic) and teacher training. For 2005, the benefit to
secondary is a weighted average of JHS and SHS (including TVET). The poorest
quintile remains the smallest beneficiary of total education benefits, showing a
declining share of total benefits between 1989 and 2005. For instance, the share of total
benefits accruing to the poorest quintile has declined by 2.3 percentage points; falling
from 17.1 per cent in 1989 to 14.8 per cent in 2005. It declined by 0.7 percentage point
between 1989 and 1992, and further by 2.6 percentage points between 1992 and 2005, as
shown in Table V. The bottom two quintiles accounted for an accumulated share of
32.3 per cent of total benefits in 2005 compared with their cumulative income share of
about 16 per cent. The richest quintile, however, appropriated 26.3 per cent of total
education benefits in 2005, gaining by 5.5 percentage points between 1992 and 2005
and 2.6 percentage points over the period 1989-2005 (Table V). The bottom two
quintiles witnessed a decrease in primary education benefits over the period 1992 and
2005, with benefits decreasing by 3.4 and 1.4 per cent, respectively, over this period.
After primary education, benefits accrue disproportionately to the richest quintile. For
instance, the poorest quintile received 14.9 per cent of secondary[14] education benefits in
1992 and 16.0 per cent in 2005, indicating an increase of 1.1 percentage points. The richest
quintile on the other hand has gained by 6.3 percentage points over this period (rising
from 18.6 per cent in 1992 to 24.9 per cent in 2005). The poorest quintile continues to
make a generally poor showing in tertiary education compared with the richest quintile
(4.0 per cent against 50.3 per cent). Between 1992 and 2005, the poorest quintile’s share of
tertiary education benefits has declined by 2.0 percentage points while the richest
quintile’s share has increased by 5.1 percentage points (Table V)[15].
The share of rural areas in total education subsidies has increased by 1.3 percentage
points between 1992 and 2005, with its share of the total benefits remaining consistently
high (58.7, 57.9, and 59.2 per cent in 1989, 1992, and 2005, respectively). In terms of
education levels, rural areas received 77.1 per cent of primary education subsidies in
2005 (rising by 7.0 percentage points from 70.1 per cent in 1992). It also received
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60.2 and 25.6 per cent of secondary and post-secondary subsidies, respectively, in
2005. Accra’s share of primary education benefits have decreased consistently
between 1989 and 2005, with its share in total education subsidies dropping by 5.6
percentage points over this period (Table V).
5.6 Valuation of public education services based on the CV
The above analysis is based on the standard benefit incidence approach which is often
criticized for its arbitrary valuation of public services[16]. The alternative is to use
benefits estimated from the school choice model. CV is that amount of money that when
subtracted from the individual’s income in the new state (1) makes utility in the new
state, with the subtraction, equal to utility in the original state (0). That is:
V ¼ c Y iPj
1
 
þþYQjþdjX iþe1j ¼ c Y iPj
0
CV
 
þYQjþdjX iþe0j (7)
where P 0 is price in the original state, P1 is price in the new state, etc. In the method
developed by Morey and Rossman (2007), it is supposed that the household-specific
epsilon terms are the same in both states and therefore cancel by assumption. We use
the NMNL estimates presented above to calculate CV for public schooling, which we
then used to assess the benefits of public schooling to households.
Specifically, for a household where public schooling is the best option and private
schooling provides the second highest level of utility, CV is defined implicitly by the
following equation:
CV ¼ YP0
 
e
VprivateVpublicð Þ
a
 
YP1
 
(8)
where Vpublic and Vprivate are the estimated utilities associated with public and private
options, respectively, α is the coefficient of net income. In the case where the second
best option for a household is no schooling then we replace Vprivate by 0. Where the best
option is either “no schooling” or “private schooling”, then CV¼ 0.
Our finding agrees largely with Younger (1999) that the method of valuation does
not affect the ranking of social services. Services that are (per capita or expenditure)
progressive with one method is in most cases so with other methods. What is, however,
unclear, is the order of progressivity. For both pre-school and primary schooling, the
standard method is obviously the most progressive, followed by the uniform and then
the CV. For secondary schooling (only senior high), however, the test could not confirm
dominance of any one method over another.
6. Conclusion
In this paper, we have examined distribution, coverage, utilization as well as the benefit
incidence of public education spending in Ghana, considering all levels of education -
pre-schooling, primary, secondary, and tertiary. The demand estimates show that price
and income are important determinants of school enrolments. The fact that the demand
for schooling by the poor is more price elastic than the rich suggests that price
increases for public schooling will have negative implications for equity. Increases in
cost will result in a larger than proportionate reduction in demand among the poor
compared with the wealthy, making the distribution of public primary school benefits
less progressive. From our policy simulations, though setting prices to zero led to dramatic
increases in school enrolment, the high probability of non-attendance reflects the high
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opportunity cost to schooling. A high proportion of school-age children are found to be
terminating their schooling at primary and many more at the junior high school level.
Standard fiscal incidence analysis suggests that overall public spending can be
made more progressive by increasing subsidies (i.e. lowering cost) on services used
relatively more by the poor while decreasing subsidies (raising cost) on services used
by the rich. This implies that since secondary, and most especially, tertiary schooling
are dominated by the top income group, welfare can be improved by reallocating public
expenditures to primary (or basic) schooling. In Ghana, the highest rate of return (social
and private) occurs at the senior secondary level (followed closely by primary). At the
same time, the poor tend to terminate education at primary (or basic level). It is,
therefore, important to make post basic education more accessible to the poor in order
to provide themwith higher earnings potential, thereby helping to lift them out of poverty.
Notes
1. Public social spending has increased consistently during the last decades, reaching over
23 per cent of total government expenditures and over 57 per cent of discretionary
expenditures in 2005.
2. Two general approaches have been widely used to assess the welfare impact of public spending:
first, benefit incidence studies (BIA), and second, behavioural approaches. BIA has been criticized
because: it is static, it assigns the same unit costs to all users of public services, it assumes all
users benefit equally from public services, and it does not have behavioural foundations and
therefore cannot be used for policy simulations. The behavioural approach (also called the
willingness-to-pay), on the other hand tends to gloss over the distributional implications of the
demand estimates (Younger, 1999) and the expenditures financing those public services.
3. The reforms have replaced the four-year middle schools with a three-year junior secondary,
and reduces senior secondary from seven years – five years ordinary level and two years
advanced level - to three years. Primary and junior secondary have become basic education.
4. The central government remains the main financier of education, contributing about
82.0 per cent (including the GETFUND) of total education spending between 2003 and 2008.
5. The programme was first piloted in 40 most deprived districts in 2004. Gross enrolment
shot up by 14.5 per cent while that of pre-school was 38 per cent. Given its success, the
programme was adopted nationally in 2005, which offers GH¢2.5 per boy child and GH¢3.5
per girl child to cover school fees and levies such as cultural dues, sports dues, and
development levies.
6. An issue is the functional form for net income, c (Yi−Pj), for which there is no consensus in
the literature. Previous authors have used various specifications: linear (Akin, 1985; Dor and
van der Gaag, 1993), quadratic (Gertler and van der Gaag, 1990; Gertler and Glewwe, 1990),
and logarithmic, Younger (1999, 2000).
7. The nested logit reduces to the conditional logit if the two dissimilarity parameters are both
equal to 1 (Cameron and Trivedi, 2009).
8. Tertiary is, however, included in the BIA.
9. For secondary school, some authors include even children of secondary age who have not
yet graduated from primary school (Younger, 1999). Secondary includes only senior high.
10. This is what Younger (1999) called the “sheepskin effect”, which shows the fact that: returns
to schooling is not simply the accumulation of human capital (which suffers from
diminishing marginal return); rather it signals achievement by the completion of a level
and the reward of a particular degree; one is more likely to attend a good school for several
years than a bad one, reflecting the unobserved quality differences in schools.
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11. A similar trend was observed by moving everybody out of the poorest quintile, which involves
awarding the minimum income in the 2nd quintile to all households in the poorest quintile.
12. Junior high and senior high (including TVET) all dominate the Lorenz curve of household
expenditures. Junior high crosses the 45-degree line – the poorest quintile captured
16.2 per cent of subsidies to this level compared to 18.9 per cent for the richest quintile.
For this service, all quintiles received higher benefits than the richest quintile, with the
exception of the bottom quintile.
13. More pointedly, primary is the most progressive, followed by JHS, SHS/TVET, and then
post-secondary. Subsidies to TVET/TTC dominate those of SHS. The poorest quintile’s
share of post-secondary benefits was only about 4.0 per cent.
14. For 2005, the benefit to secondary is a weighted average of JHS and SHS (including TVET).
15. Ranking in terms of expenditure per capita, the change in tertiary benefits for the richest quintile
was 21 percentage points as against a decline of 5.5 percentage points for the poorest quintile.
16. For instance, by assigning the same unit cost to all observed users, the standard approach is
assuming that all households benefits equally from public services. In practice, however,
children from poorer households are more likely to attend poorer quality schools compared
to children from wealthier homes.
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Further reading
Duclos, J.-Y., Abdelkrim, A. and Carl, F. (2006), “DAD: A software for distributive analysis/
analyse distributive, MIMAP programme”, International Development Research Centre,
Government of Canada, and CIRPÉE, Université Laval.
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(GLSS 5)”.
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Basic Education (FCUBE), Ministry of Education, Accra.
Corresponding author
Associate Professor Peter Quartey can be contacted at: peter.quartey@btinternet.com
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