Download A Time Series Analysis of the Impact of Educational

A Time Series Analysis of the Impact of Educational Expenditure on Economic
Growth in Nigeria: An Autoregressive Model
by
Adebiyi, Michael Adebayo, PhD
Department of Economics
University of Lagos
Lagos, Nigeria
E-mail: [email protected]
Tel: 234-8023056529
Abstract
This paper aims at investigating the impact of educational expenditure on economic
growth in Nigeria. Using a vector autoregressive (VAR) model, the findings reveal that
the impact of real capital educational expenditure on economic growth is consistently
negative in Nigeria, which is a paradox.
Key Words: Education Expenditure, Economic Growth, Error Corrections and
Autoregressive Models.
1
Introduction
In any country, there can be no meaningful economic growth without adequate human
and natural resources. Human capital is so important that in the Khartoum Declaration of
1988, it was asserted that:
…the human dimension is the sine qua non of economic recovery …no
SAP or economic recovery programme should be formulated or can be
implemented without having at its heart detailed social and human
priorities. There can be no real structural adjustment or economic recovery
in the absence of the human imperative (Adedeji et.al. 1990: 390)
The concept of human capital refers to the abilities and skills of human resources of a
country, while human capital formation refers to the process of acquiring and increasing
the number of persons who have the skills, education and experience that are critical for
economic growth and development of a country (Okojie 1995:44). Human resources are
all embracing, that is, it is inclusive of persons who works now, or are likely to be
productively employed sooner or later. It is a continuous, a continuing process from
childhood to old age, and a must for any society or enterprise that wishes to survive under
the complex challenges of a dynamic world (Adebiyi, 2003).
Yesufu (2000: 321), in agreement with this view, opines that “the essence of human
resources development becomes one of ensuring that the workforce is continuously
adapted for, and upgraded to meet, the new challenges of its total environment”. This
implies that those already on the job require retraining, reorientation or adaptation to
meet the new challenges. This special human capacity can be acquired and developed
through education, training, health promotion, as well as investment in all social services
that influence man’s productive capacities (Adamu, 2003)
In human capital development, education is essential. Education is concerned with the
cultivation of “the whole person” including intellectual, character and psychomotor
development. It is the human resources of any nation, rather than its physical capital and
material resources, which ultimately determine the character and pace of its economic
and social development. According to Harbison:
“Human resources constitute the ultimate basis for the wealth of nations.
Capital and natural resources are passive factors of production; human
beings are the active agents who accumulate capital, exploit natural
resources, build social, economic and political organization, and carry
forward national development. Clearly, a country which is unable to
develop the skills and knowledge of its people and utilize them effectively
in the national economy will be unable to develop anything else”
(Harbison, 1973, p.3).
Education occupies an important place in most plans for economic and social
development. Whichever way one looks at it, the education sector is important in human
development as a supplier of the trained manpower. It is a prerequisite for the
accomplishment of other development goals. Also, it is the main sector through which
national identity goals and aspirations are evaluated and realised (Adebiyi, 2003).
Therefore, positive social change is likely to be associated with the production of
qualitative citizenry. It would seem to follow naturally that if more individuals were
educated, the wealth of the nation would rise, since higher education attracts higher
wages and in the aggregate, higher national income (Ayara, 2002). This increasing faith
in education as an agent of change in many developing countries, including Nigeria, has
led to a heavy investment in the sector and, thus, the delegation of responsibility for
manpower development to the schools. The pressure for higher or school education in
many developing countries has undoubtedly been helped by public perception of
financial reward from pursuing such education. Generally, this goes with the belief that
expanding education promoted economic growth (Ayara, 2002).
However, the paradox accompanying this belief is that, despite the huge investment on
education, there is no strong evidence of growth-promoting externalities of education in
Nigeria. Rather, educational expansion, according to Ayara (2002: 372), “further deepens
social inequality and inculcates negative social changes, such as cultism, rent seeking,
sexual harassment, result racketeering, industrial disputes, brain drain, among other social
vices in the Nigerian school system and the society at large”.
The puzzle is: why is it that Nigeria which invested substantially in education over the
years had been facing declining real income with sluggish economic growth rate? It is
also pertinent to ask: when education does not lead to an expansion of productive
capacity, can a poor developing country, like Nigeria, afford the luxury of an educational
wastage? In any case, the resources devoted to education represent a cost to the society
not only because they are economic resources but also because they have alternative uses.
Also, there is considerable evidence that political and social pressures based on
anticipated gains from additional education have frequently led to educational expansion
far ahead of the economy’s need for educated manpower. This may lead to a lot of
frustration since an “educated labour force” feels entitled to jobs commensurate with the
educational qualification received, which is not always possible to guarantee. Therefore,
2
if economic growth lags behind because educational budget increases, the need to revisit
the paradox becomes necessary.
The efforts to resolve this puzzle are organized as follows. Section 2 provides a
theoretical excursion into the study. Methodology, which includes the sources of data,
model specification and econometric framework, is discussed in Section 3, while the
empirical results and sensitivity analyses are explained in Section 4. Section 5 provides
some explanations for the existence of the paradox and, finally, Section 6 closes the study
with summary and policy recommendations.
2.
Theoretical Underpinnings and Literature Review
2.1
Relationship between Educational Capital and Growth
To date, researchers have mostly found a positive relationship between enrollment rates
and/or years of schooling and gross domestic product (GDP) growth in developing
countries (Baldacci, Clement, Qui and Gupta, 2005). Moreover, a recent study
(Coulombe, Tremblay, and Marchand, 2004), using a more refined measure of individual
skills, found that a country with literacy scores above the sample’s average also
experienced an above- average increase in annual per capita GDP growth. However,
while results at the microeconomic level suggest that investing in education is an
effective way to spur economic growth, macroeconomic evidence points to a weak
relationship, at best, between education and growth.
Scholars have not been able to resolve whether higher government outlays on education
will always boost growth or not. They have not resolve the question: why might higher
spending be ineffective? However, one reason, according to Baldacci, Clement, Qui and
Gupta, 2005 is the macroeconomic effects of excessive public outlays. Empirical studies
find a negative association between large fiscal deficits and growth in developing
countries. If higher spending on education leads to expanding fiscal deficits, the negative
impact on macroeconomic stability and growth could more than offset the beneficial
effects of such spending on social indicators (Baldacci, Clement, Qui and Gupta, 2005).
They further identify poor governance and poorly targeted outlays as additional reasons.
They cited an example of spending on tertiary education, which might yield few benefits
for children from low-income families who could not even afford to complete secondary
school.
Another reason while rising educational expenditure might not increase economic
growth, according to Baldacci, Clement, Qui and Gupta (2005), is poor institutions. Poor
institutions may reduce the quality of spending (for example, corruption may divert funds
allocated for teaching supplies to "ghost" teachers). When this is the case, returns on
education tend to be lower than envisaged. More importantly, education spending is
likely to be ineffective if students are in poor health. Thus, the interaction between
education and health needs to be captured for a better understanding of economic growth
in developing countries.
3
Further, the authors undertake a study—using a panel data set for 120 developing
countries. They attempt to capture the potential feedback between social spending, social
indicators, and growth. With a simple economic model, they arrive at the following
findings: one, both education capital and health capital contribute positively to output
growth, but through slightly different channels; two, education spending has both an
immediate and a lagged effect on education capital; three health spending has a positive
and significant immediate impact on health capital; four, education and health capital
have strong links; five, improvements in gender equality improve health and education
capital through higher access to basic services; six, higher income levels and greater
human capital reinforce each other and contribute to a virtuous circle of growth and
higher human capital; seven, governance has a significant direct impact on the links
between social spending and social indicators, with health spending being particularly
sensitive to governance; and eight, the impact of education and health capital on growth
varies in different country groups (Baldacci, Clement, Qui and Gupta (2005).
2.2
Education Expenditure Pyramid in Developing Countries
In many developing countries, budgetary allocations for the formal education system
have the shape of an inverted pyramid in which secondary and tertiary education receive
more than four times of public resources as much as primary education1. In many cases
primary schools are starved of financing while universities receive heavy subsidies. The
majority of the population, particularly the poor, may lack adequate educational facilities,
or may find that the opportunity cost of attending school exceeds short run private
benefits, while the children from middle and upper class backgrounds benefit from
comparatively generously financed university education2.
Not only is this inversion of the financial pyramid not equitable, it is also not efficient.
Particularly in the poorest developing countries, where primary education has been most
neglected, the social rate of return on investing in basic education is high 3. In addition to
high returns, investing in primary education has the advantage of bringing government
1
The inverted pyramid applies not only to public expenditure on formal education but also to health,
pensions, public food distribution, transportation (compare air travel with farm-to-market roads), irrigation
(compare expenditures on large scale water management projects with small scale irrigation facilities),
industrial support, etc. In each case, expenditure per beneficiary increases as one climbs the pyramid while
net social returns tend to fall. Thus the point made in the study about the composition of educational
expenditure has wide applicability to other sectors.
2
For example in Indonesia in 1978 it is estimated that 83 per cent of state subsidies to higher education
accrued to the upper income group, 10 per cent to the middle income group and only 7 per cent to the lower
income group. Indonesia is perhaps an extreme case, but a similar pattern is evident in Nigeria, Chile,
Colombia and Malaysia.
3
In Africa, for example, the rates of return on investments in education are estimated to be 26 per cent for
primary education, 17 per cent for secondary education, and 13 per cent for higher education. These rates
of return include public subsidies in total costs but do not attempt to include positive externalities in the
benefits. Thus they understate the true social rates of return.
4
closer to the people it serves while simultaneously giving people greater control over
their own lives and is a basic institution of the communities in which they live. Primary
schools are easier for local communities (villages, small towns, urban neighbourhoods) to
control than secondary schools, colleges and universities. There is more opportunity for
participatory development, for the active involvement of people in education and hence
there is a greater likelihood that educational programmes will enjoy sustained support
from the community.
Contrary to common belief, public expenditure on education, in developing countries, has
not, in general, been an equalizing factor, providing equal opportunities to all social
classes and groups. There are important exceptions, but more often than not, the
educational system is no more egalitarian than the society of which it is a part. The
inverted educational expenditure pyramid mirrors the stratification, privilege and
discrimination against women and other groups, which is characteristic of a larger
society. This is hardly surprising, but it suggests that while it might be easy technically to
design policies for reallocating resources from tertiary to primary and secondary
education, it might be difficult politically to implement such policies. The commitment of
the government evidently is crucial, but this in turn depends on securing popular support
for human development (Griffin and McKinley (1992).
In some circumstances, it may be advisable to reallocate expenditure in stages, thereby
minimizing opposition until a political coalition can be formed to support more wideranging measures. In the initial phases of a human development strategy, the demand for
education by the poor may be low while the middle and upper classes may fully
appreciate the advantages of secondary and higher education and press for additional
funding. In such circumstances, a massive reallocation of resources in favour of basic
education should be delayed until the urban and rural poor can be organized to demand a
change in priorities (Griffin and McKinley (1992).
Even if primary education is free, the opportunity cost to poor families of sending their
children to school can be high. In addition to the cost of books and supplies, transport and
school uniforms, there is the loss of child labour and resulting decline in household
income. At very low levels of income, discount rates may be high and households may be
reluctant to give up current income for the sake of a higher income in future.
The demand for education by the poor may be depressed for a second reason, namely, a
perception that for low income households the returns to education are low. There is, in
fact, evidence that in both urban and rural areas there are positive connections among
additional education, an increase in the productivity of labour and higher incomes
(Griffin and McKinley, 1992). These connections however may not be widely understood
and it may be necessary as part of a human development strategy to supply the relevant
information to the public.
One aspect of social stratification in developing countries is that not all groups have equal
access to formal education, and even in cases where apparently there are equal access;
there are enormous variations in the quality of education offered. The rich have greater
access than the poor. The urban population has greater access than the rural. There is thus
a strong prima facie case for reducing the concentration of educational expenditure on
high cost urban colleges and universities and instead distributing resources broadly
among the entire population. This implies in particular a reallocation of expenditure
5
toward primary and secondary schools, and particularly to schools located in rural areas,
where most of the poor are located and where historically education has been severely
neglected (Griffin and McKinley, 1992).
Discriminatory access to education by gender is highly evident throughout the developing
world. Women are systematically under-represented at all levels of education. On
average in the developing countries the literacy rate of women is only 69 per cent as high
as the rate for men; women receive only 54 per cent as many years of schooling as men.
Their enrollment rate in primary school has risen sharply and is now 93 per cent of the
men's, but in secondary and tertiary education, women lag behind, their enrollment rates
being 73 and 53 per cent, respectively, of the men's enrollment rates (UNDP, 1992a). In
Pakistan the situation is much worse than the average: three-quarters of the girls have
dropped out of primary school before the final year. In the rural areas, among the poorest
20 per cent of the population, over 97 per cent of the women are illiterate, and even
among the richest 20 per cent of the rural population, 87 per cent of the women are
unable to read and write. The virtual absence of educational opportunities for women in
Pakistan is responsible for the poor educational performance of the country as a whole.
Although Pakistan is an extreme case, the situation of women in other developing
countries such as Bangladesh and Ghana is broadly similar (UNDP, 1992b).
Quite apart from educational biases against women, the poor and rural areas, there is a
bias in favour of spending on physical capital as against human capital -- teachers,
lecturers, instructors, and professors. The reason for this is an artificial division of public
expenditure between capital and recurrent items combined with a belief that only capital
expenditures contribute to development. The resulting bricks-and-mortar approach to
education leads to a misallocation of resources, placing excessive emphasis on school
construction (public investment) and insufficient emphasis on teachers and school
supplies (Griffin and McKinley, 1992).
There has been under-investment in education. In 1985, for instance, public expenditure
on education was only $27 per inhabitant in the developing countries as compared to
$515 in the developed countries, a ratio of 1:19. That is, relative to income, the
developing countries spend far less on education than the developed countries. Within the
education budget, the developing countries spend proportionately much less on teaching
materials. In Japan, for example, 6.5 per cent of the education budget is allocated to
teaching materials; in India, only 1.3 per cent. Within the education budget, and relative
to the distribution of students across primary, secondary and tertiary education, the
developing countries spend proportionately much less than the developed countries on
the first two tiers of the pyramid. In Japan, for example, 38.2 per cent of the education
budget is allocated to primary education, while 50 per cent of the students are enrolled.
Thus the expenditure/enrollment ratio is 0.8. The ratios in secondary and tertiary
education in Japan are 0.9 and 1.1, respectively. In Nigeria, in contrast, the
expenditure/enrollment ratio is 0.2 at the primary level, 2.0 at the secondary level and
21.7 at the tertiary level (Griffin and McKinley, 1992).
The level of expenditure that can be justified for each of the three tiers of the education
pyramid depends in part on considerations of equity and in part on the social rates of
return on expenditure at each tier. The latter in turn depend on the balance of supply and
demand for different types of human capital, a balance that will vary with the level of
development. Every developing country obviously needs trained scientists, engineers,
6
managers and teachers- and other people with professional and technical skills. The issue,
thus, is not whether resources should be devoted to tertiary education, but how large an
allocation of limited public revenues should tertiary education receive.
Many highly educated people in developing countries lack opportunities to apply their
skills and talents. This suggests a relative excess supply and an inefficient use of the
existing stock of human capital. Poor utilization of highly educated people lowers both
the private and social rates of return on expenditure in tertiary education, compared to the
return to primary and secondary education. Many educated people, who migrated,
become unemployed in developed countries. This "human capital flight" or "brain drain"
constitutes a loss of resources to developing countries and a poor investment of public
funds (Griffin and McKinley, 1992).
2.3 The Economics of Education in Nigeria
Much importance is attached to education. It is viewed as a means of understanding,
controlling, altering and redesigning human environment with a view to achieving and
sustaining a better quality of life (CBN 2000:98). The huge capital outlay and heavy
recurrent expenditure requirement on education is often justified as an investment for the
future. It was widely accepted that the key to socio-economic and political
transformation, which the Nigerian public desires, lies in education. It is the greatest
instrument for the achievement of freedom for all and life more abundant (Taiwo 1986;
Ayara, 2002). This implies freedom from diseases, freedom from poverty and freedom
from oppression. Thus, Nigeria looks forward to an educated electorate and citizenry to
realize her objectives of freedom and prosperity.
The pre-independent constitutional changes gradually brought Nigerians into a position
of greater authority to manage their own affairs, dictated by new policy and the need to
move at a faster pace. Constitutional development made such progress possible so much
that the Eastern and the Western regions achieved regional self-government in 1957
while the Northern achieved hers in1959, and independence was fixed for 1 October 1960
(Ayara, 2002).
In anticipation of the manpower needs of independent Nigeria, the Federal Ministry of
Education appointed in April 1959 a commission to conduct an investigation into
Nigerian’s higher education needs over the next twenty years. The commission, in its
report submitted, recommended that education had become an investment for which
financing must be sought from within Nigeria as well as external sources. It was no
longer a matter of national budget allocation but an issue on the future needs of the
country, which was so massive that the Nigeria resources alone would not be adequate to
carry.
In Nigeria, education attracts considerable portion of public expenditure because of its
position as a social service with direct economic significance with generally acclaimed
positive spillover effects. In virtually all countries, public expenditure on education has
been rising not only in absolute term but also in relative to gross domestic product (GDP)
and total public revenue (Ayara, 2002; Ogbodo 1988:93).
The financing of education in most developing countries has been an intractable problem
because of the uncontrolled increase in school age population, which, in turn, has
constantly pushed the costs of education upward. According to Ogbodo (1988), other
7
factors that have contributed to the problem of financing education in Nigeria include the
widening perception of education as the key to economic growth and social mobility, the
widespread adoption of public policies aimed at democratizing educational opportunities,
and the rapid expansion, upgrading and diversification of manpower requirements owing
to technological advances in the economy and a new emphasis on economic development
(Ayara, 2002).
To contribute significantly to economic growth, education must be of high quality and
meet the skill demand needs of the economy. The world Conference on Education for all
states has declared that “the focus of basic education must be on actual learning
acquisition and outcome” (Chinapah 1997). However, the result of the Monitory of
Learning Achievement (MLA) project in Nigeria is provided by Ayara (2002) in Table 1.
This Table provides a good insight into the quality and effectiveness of basic education in
Nigeria. The MLA has a special and deliberate focus on minimum basic learning
competencies in the domains of literacy, numeracy and life skills. The national mean
scores on the literacy, numeracy and life skills tests were 25.1 per cent, 32.2 per cent and
32.6 per cent, repetitively, and the performance was poor in virtually all states (CBN
2000). While there was no difference in their performance of either gender, pupils in
private schools performed much better than those in public schools, and those in urban
areas better than their rural counterparts (Ayara, 2002; CBN 2000).
Table1. Performance of Nigerian Primary School Pupils in Literacy, Numeracy and Life
Skills
MLA domain
Mean Score (%)
Sex
National
Literacy (Total)]
Numeracy
(Total)
Life Skills
(Total)
Type of Residence
Type of School
Male
Female
Urban Rural
Private
Public
25.1
24.8
25.8
28.9
22.6
40.8
22.2
32.2
32.4
31.9
35
32.3
43.1
30.1
32.6
32.6
32.8
35
31
43.1
30.6
Source: Ayara, 2002).
Reasons for the low quality of education in Nigeria primary schools and indeed at all
other levels, has very little to do with the curriculum, which is widely regarded as being
of high quality. The delivery of the curriculum is the main area of challenge.
Consequently, wide gaps exist between the contents of the outlined curricula and
classroom teaching, with the result that students are not attaining the desired level of
8
knowledge and skills. Consequently, the development affects productivity in the labour
market (Ayara, 2002).
3.
Methodology and Econometric Framework
3.1
The model
In an attempt to promote economic development, factors of production, such as capital
and labour, are used. But the efficient use of labour and capital resources for greater
productivity required that the workers are well trained and skillful. The training and skills
acquisition for this study assumes a stable production function in which changes in output
are due to changes in the quantity and quality of inputs, economies of scale and advances
in knowledge. Considering such aggregate production function in which technical
changes resulting from the inputs qualities are explicitly augmenting, Solow argues that
disembodied technical change is input augmenting, in which existing capital and labour
are by one means or another, made more productive (Ayara, 2002; Jhingan 2000:591).
Solow expressed the aggregate production function for such technical change as:
Z = f (X, Y, t)…………………………………………..(1)
Where
Z = output
X = capital input
Y = labour input
t = technical change
Taking Hicks-neutral technical change as the basis, Solow postulates the production
function in a special form as:
Z = I(t) F(X, Y)……………………………………………………………..(2)
Where I(t) is an index of technical change representing total factor productivity (TFP). A
growing body of research suggests that even after physical and human capital
accumulations are accounted for, something else also accounts for the growth rate of
gross domestic product (GDP) per capita. Economists typically refer to the “something
else” as total factor productivity (Easterly and Levine 2001). According to Klenow
(2001:221), the total factor productivity (TFP) could reflect disembodied technology,
human capital externalities, access to specialized or high-quality capital or intermediate
goods, the degree of competition, or measurement error (Ayara, 2002).
Differentiating equation 2 totally and dividing through by Z we have:
∆Z
Z
= ∆I + IdX. X + IdY . Y
I
dX Z
dY
Z
……………………………………(3)
9
Following Ayara (2002), Mankin, Rommer and Weil (1992) and Pritchett (2001), the
Solow aggregate production framework is extended to introduce educational capital as
the technical change factor. Because the weights in the aggregate production function
represent the factor share of national output, the efficiency of educational capital in a
growth model ought to be equal to the share of educational capital in gross domestic
product (GDP), which can be estimated. Under the assumption of constant returns to
scale, the share of physical capital, labour and the technical change add up to one. That is,
if  (t) is the capital share and (t) is the labour share, then the share of the educational
capital is given as:
 (t)
=1 -  (t) -  (t) …………………………………………………….(4)
Thus the “residual” (∆I/I) can be measured by subtracting from the rate of change of
output that part of the growth rate which is accounted for by a weighted sum of the rates
of change of physical capital and labour factor inputs. Therefore, the residual is attributed
to technical progress, which, on the other hand, also serves as a measure of our ignorance
(Ayara, 2002).
By substituting equation (4) into equation (3) and manipulating further, we have an
augmented Solow’s fundamental equation.
∆Z
= (t) ∆X
+ (t) ∆Y + (t) ∆I …………………………….(5)
Z
X
Y
I
This equation says that the growth rate of output (∆Z/Z) is equal to the rate of growth of
physical capital (∆X/X) and the growth rate of labour (∆Y/Y) plus the growth rate of total
factor productivity (∆I/I), which is attributed, in this study, to changes in capital
formation and improvement in human capital resulting from education. It is expected
from the model that the more the number of labourers, physical capital and educational
capital that is employed, the higher the level of national productivity (i.e. , . and ).
Taking natural logs to produce a linear equation in levels, we can normalize equation (5)
as follows:
Ln Z = Ln A +  LnX +  LnY + LnI……..………………………………..(6)
3.2
Econometric Framework
This paper uses the forecast error variance decomposition and the impulse responses from
estimated vector autoregressive models (VAR) to examine the effects of shocks to public
education expenditure. VAR models are the best method for investigating shock
transmission among variables because they provide information on impulse responses
(Adrangi and Allender (1998). Zellner and Palm (1974), Zellner (1979), and Palm (1983)
show that any linear structural model can be written as a VAR model. Therefore, a VAR
model serves as a flexible approximation to the reduced form of any wide variety of
simultaneous structural models.
10
Let consider a bivariate AR (1) model. Let yt be a measure of real capital educational
expenditure and zt be the growth rate in real gross domestic product. A VAR system can
be written as follows
 yt 
 yt 1  u yt 
 z   A0  AL z    
 t
 t 1  uzt 
A0 is s vector of constants, A (L) a 2X2 matrix polynomial in the lag operator L, and uit
serially independent errors for i. Suppose the structural equations can be represented as
follows
yt  b10  b12 zt  b11 yt 1  b13 zt 1  u yt ...........................................(1)
zt  b20  b21 yt  b22 yt 1  b23 zt 1  u zt ..............................................(2)
which can be rewritten as
yt  b12 zt  b10  b11 yt 1  b13 zt 1  u yt ...........................................(3)
zt  b21 yt  b20  b22 yt 1  b23 zt 1  u zt ..............................................(4)
and in matrix form
1 b12   yt  b10  b11 b13   yt 1  u yt 
b 1   z   b   b b   z    
 21   t   20   22 23   t 1  uzt 
let
1 b 
B =  12 
b211 
 yt 
Z=  
 zt 
b10 
V0=  
b20 
b b13 
V1=  11 
b22 b23 
which allows us to write a more compact form of the structural equation as
BZt =V0+V1Zt-1+uit
Assuming that B is invertible, we pre-multiply the equation by B-1 to obtain
Zt =A0 +A1Z t-1+  it ………………………………… (5)
Where
A0 = B-1V0
A1=B-1V1
and  t =B-1uit
11
Given the aij is the element of the ith row and jth column, we can now write our VAR in
standard form.
yt  a10  a11 yt 1  a12 zt 1   yt ...........................................(6)
zt  a20  a21 yt 1  a22 zt 1   zt ..............................................(7)
and the matrix form,
 yt  a10  a11 a12   yt 
 z   a   a a    .......................................(8)
 t   20   21 22   zt 
Note that the errors are a composite of two errors uyt and uzt since  t=B-1uit i.e.
1
 yt  1 b12  u yt 
 
  
 zt  b211  u zt 
so that
u b u
 yt  yt 12 zt .................................................................(9)
1  b12b21
 zt 
u zt  b21u yt
1  b12b21
.................................................................(10)
Since the uits are white noise, so are the  t s.
From Equations 9 and 10, we can see that policy errors can be caused by exogenous y
and policy disturbances. Let  u be the 2X2 variance-covariance matrix of uit and  
that of  it . Then   = B  u B1. To determine the impact of policy on output, we need to
look at the effect of uzt but unless b21 =0,  zt is not equal to uzt and therefore does not
provide a measure of the policy shock. If we estimate our VAR in Equations 6 and 7 as it
is, B and  u will not be identified without further restrictions since estimation of the
reduced form in Equations 6 and 7 will yield less parameters than the structural form in
Equations 1and 2. One of the most common restrictions is to assume that the structural
shocks are uncorrelated so that the off diagonal elements in the covariance matrix are
zero (Simatele, 2003; Bernanke and Blinder, 1992).
Two results obtained from VARs that are useful for analyzing transmission mechanisms
are impulse response functions and forecast error variance decompositions. The impulse
responses tell us how growth rate of gross domestic product responds to shocks in real
educational expenditure and other policy variables, while the variance decompositions
show the magnitude of the variations in growth rate in real GDP due to real capital
educational expenditure and other policy variables.
If we assume a stable system (like Simatele, 2003), we can iterate Equation 5 backwards
and let n approach infinity and solve to obtain

Zt =    A1i t  i
i 0
12
Where the s are the means of yt and zt and use Equation 8 to get
 a a
 yt   y 
 11 12  1  b12  u yt 
1



z   


  ..................(11)
 t   z  1  b12b21 i  0 a21 a22   b211  uzt 
We define the 2X2 matrix as F (i) with elements Fjk (i) such that
A1i 1 b12 


1  b12b21 b211 
and we write in moving average form as
F(i)=
 yt  u y    F11(i) F12 (i)  u yt 1 

 z        F (i) F (i) 
22
 uzt 1 
 t  uz  i  0  21
or in a more compact form

Zt =    F (i )ut  i …………………………………(12)
i 0
Fjk(i) are the impulse response functions. As we vary (i), we get a function describing the
response of variable j to an impulse in variable k (Simatele, 2003).
To derive the forecast error variance decompositions, we use Equation 12 to make a
forecast of zt+1. The one-step-ahead forecast error is Fut+1and in general the n-period
forecast error Z t  n  Et Z t  n is

Z t  n  Et Z t  n =  F (i )u t  n i .................................................(13)
i 0
and the mean square error (MSE)

( Z t  n  Et Z t  n ) 2=  z2  F (i ) ………………………………..(14)
10
where  z2 is the variance of zt+n.
To show that the decomposition more explicitly, let us narrow down on yt
yt n  Eyt n ) 2   y2  F (i) 2 .............................................................(15)
The share of  z2 due to uyt and uzt are
 y2 [ F11 (0) 2  F11 (1) 2  ...  F11 ( N  1) 2 ]
.......................................(16)
 Y2 (n) 2
 z2 [ F11 (0) 2  F11 (1) 2  ...  F11 ( N  1) 2 ]
.......................................(17)
 Y2 (n) 2
13
Since the variance decomposition tells us the share of the total variance attributed to a
given structural shocks, for an exogenous sequence y, uzt will not explain any of the
forecast error variance of yt.
In using VAR model, the selection of lag order is very essential. Without a formal
method, the selection of lag order in a VAR model will be arbitrary and could lead to
specification error (Fair and Schiller, 1990; Funke, 1990). Several criteria, similar to
those used in the distributed lag models, are suggested to determine the model dimension
(Lutkepohl, 1985).
3.3
Sources of Data and Definitions of Variables
The empirical data for the analysis are derived from the Central Bank of Nigeria’s
Statistical Bulletin, and is complemented with some data from the International
Monetary Fund (IMF), and the International Financial Statistic yearbook. In terms of
measurement of the variables, the dependent variable is the growth rate of real gross
domestic product (RGDPG), which is conceptually more appropriate in growthaccounting regressions than per capita GDP (Pritchet 2001). The explanatory variables in
the model are defined as follows: real gross capital formation (RGCF) is the proxy for
physical capital and is derived by deflating the gross fixed capital formation by consumer
price index; unemployment rate (UR), which shows the rate of utilization of human
resources, is the proxy for labour input; while real capital expenditure on education
(REDUC) and enrolments in primary and secondary schools (ENROL) are used to
represent educational capital.
4
Empirical Results and Sensitivity Analyses
4.1 Unit Root Tests
In the literature, most time series variables are non-stationary and using non-stationary
variables in the model might lead to spurious regressions. The first or second differenced
terms of most variables will usually be stationary (Ramanathan 1992). All the variables
are tested at levels for stationarity using the Augmented Dickey-Fuller (ADF) and
Phillip-Perron tests. The tests in Table 2 reveal that all the variables are not stationary at
levels except growth rate of real GDP (RGDPG) and real educational expenditure
(REDUC).
Table 2: Unit Root Test Using Augmented Dickey Fuller (ADF) and Phillips-Perron
(PP) Tests: 1970- 2003.
Variables ADF
Test
at
Level
RGDPG -3.87*
REDUC -4.68*
ENROL -2.41
95%
ADF
Critical
Level
-3.55
-3.55
-3.55
Order of
Integration
PP Test 95% PP Order of
at Level Critical
integration
Level
I (0)
I (0)
I (2)
-4.66*
-3.00
-1.89
14
-3.55
-3.55
-3.55
I (0)
I (2)
I (2)
RGCF
UR
-1.29
-2.19
-3.55
-3.55
I (2)
I(1)
-2.09
-2.00
-3.55
-3.55
I (2)
I(1)
Notes: the dependent variable is the real growth rate of gross domestic product (RGDPg), The explanatory variables
in the model are defined as follows: the proxy for physical capital is the real gross capital formation (RGCF), which is
derived by deflating the gross fixed capital formation by consumer price index (CPI); Unemployment rate (UR), which
reflect the rate of utilization of human resources, shows the proxy for labour input; while real capital expenditure on
education (REDUC) and enrolments in primary and secondary schools (ENROL) are used to represent educational
capital.
*Significant at 5 per cent level
Source: Own Computations
4.2
Co-integration Test and Results
Co-integration tests are conducted by using the reduced rank procedure developed by
Johansen (1988) and Johansen and Juselius (1990). This method should produce
asymptotically optimal estimates since it incorporates a parametric correction for serial
correlation. The nature of the estimator means that the estimates are robust to
simultaneity bias, and it is robust to departure from normality (Johansen, 1995). Johansen
method detects a number of cointegrating vectors in non-stationary time series. It allows
for hypothesis testing regarding the elements of co-integrating vectors and loading
matrix. Johansen procedure is used to determine the rank r and to identify a long-run
relationship. The number of lags used in the VAR is based on the evidence provided by
the Akaike Information Criteria. However, in the case of serial correlation, sufficient
numbers of lags are introduced to eliminate the serial correlation of the residuals. The cointegration tests include the growth rate of real gross domestic product (RGDPg), the real
gross capital formation (RGCF), unemployment rate (UR), real capital expenditure on
education (REDUC) and enrolments in primary and secondary schools (ENROL).
Table 3 reports the estimates of Johansen procedure and standard statistics.
Table 3: Johansen Co-Integration Test
Eigenvalue
Likelihood
Ratio (LR)
0.844953
0.748535
0.474186
0.239428
1.61E-05
133.1517
73.50277
29.32828
8.758434
0.000514
5 Percent
Critical
Value
68.52
47.21
29.68
15.41
3.76
Hypothesized
No. of CE(s)
None *
At most 1 *
At most 2
At most 3
At most 4
* denotes rejection of the hypothesis at 5% significance level.
L.R. test indicates 2 co-integrating equation(s) at 5% significance level
Source: Own Computations
Notes: VAR includes seven lags on each variable and a constant term. The estimation period is 1986:1-2002:4. None of
the deterministic variable is restricted to the co-integration space; Likelihood ratio is trace test statistics, adjusted for
degrees of freedom. The critical values are taken from Osterwald-Lenum (1992). The * indicates rejection of likelihood
ratio tests at 5% significance level. L.R. test indicates 2 co-integrating equation at 5% significance level.
15
In determining the number of co-integrating vectors, we used the degrees of freedom,
adjusted version of trace statistics, given the existence of small samples with too many
variables or lags. Johansen procedure tends to over estimates the number of cointegrating vectors. The test statistics strongly reject the null hypothesis of no cointegration in favour of two co-integration relationships.
4.3
Correlation Matrix
Table 4 provides the correlation matrix. According to the Table, negative correlation
exists between the growth rate of real gross domestic product and real capital expenditure
on education (REDU) (-0.089), real gross capital formation (RGCF) (-0.589),
unemployment rate (UR) (-0.293) and enrolments (ENROL) (-0.369). The negative
correlation between growth rate of real gross domestic product and real capital
expenditure on education, including primary and secondary enrolments, reveal the
paradox of education expenditure in Nigeria. Positive correlation exists between capital
expenditure on education and real capital formation (0.171). Also, positive correlation
exists between real gross capital formation (RGCF) and enrolment (0.614). The positive
correlation may be attributable to the spillover effect of capital formation on education.
This implies that an increase in real capital formation will increase capital expenditure on
education and enrolment in primary and secondary schools.
Table 4: Correlation Matrix, 1970- 2003
RGDPG
REDUC
RGCF
RGDPG
1.000000 -0.089906 -0.589101
REDUC
-0.089906
1.000000
0.170883
RGCF
-0.589101
0.170883
1.000000
UR
-0.293713 -0.014519
0.131467
ENROL
-0.369677 -0.469333
0.613669
UR
-0.293713
-0.014519
0.131467
1.000000
0.100669
ENROL
-0.369677
-0.469333
0.613669
0.100669
1.000000
Source: Own Computations
Note: Variables are as defined in Table 2
However, correlation should not be seen as causality. The correlation between two totally
unrelated series could be strong while causality between the same variables may be nonexistent. Therefore, in Table 5, we perform formal tests of causality in addition to
reporting simple correlation coefficients between two variables.
4.4
Pairwise Granger Causality Test
Pairwise Granger causality test on real growth rate of gross domestic product (RGDPg),
real gross capital formation (RGCF), unemployment rate (UR), real capital expenditure
on education (REDUC) and enrolment in primary and secondary schools (ENROL) are
presented in Table 5.
Table 5: Pairwise Granger Causality Tests
Sample: 1970 2003
Lags: 2
Null Hypothesis:
Obs
16
F-Statistic Probability
REDUC does not Granger Cause 32
RGDPG
RGDPG does not Granger Cause REDUC
RGCF does not Granger Cause RGDPG 32
RGDPG does not Granger Cause RGCF
0.61931
0.54579
6.01184
5.10157
0.81287
0.00693
0.01320
0.45415
UR does not Granger Cause RGDPG
32
RGDPG does not Granger Cause UR
ENROL does not Granger Cause 32
RGDPG
RGDPG does not Granger Cause ENROL
0.08074
0.92022
5.68606
0.92266
0.41057
0.00869
1.60265
0.21993
RGCF does not Granger Cause REDUC 32
REDUC does not Granger Cause RGCF
UR does not Granger Cause REDUC
32
REDUC does not Granger Cause UR
ENROL does not Granger Cause 32
REDUC
REDUC does not Granger Cause ENROL
3.16796
0.98352
1.10613
0.69200
9.31172
0.05809
0.38699
0.34537
0.50923
0.00084
3.40141
0.04814
UR does not Granger Cause RGCF
32
RGCF does not Granger Cause UR
ENROL does not Granger Cause LRCF 32
LRCF does not Granger Cause ENROL
0.34462
0.54572
0.28302
3.18904
0.71156
0.58568
0.75571
0.05711
ENROL does not Granger Cause UR
UR does not Granger Cause ENROL
1.37465
2.49402
0.27007
0.10141
32
Source: Own Computations
Note: Variables are as defined in Table 2
The Pairwise Granger causality tests were inconclusive at 5 per cent level of significance.
The results alternated between bi-directional, no causality and uni-directional, depending
on the lag length allowed. The outcome in respect of two-lag length is presented in Table
5. The Table reveals the following: one, real capital expenditure on education Grangercauses the growth rate of real gross domestic products and real gross capital formation;
two, growth rate of real GDP Granger-causes real gross capital formation; three, growth
rate of real GDP Granger-causes primary and secondary schools enrolment; four, there is
bi-directional causality between real capita expenditure on education and enrolments in
primary and secondary schools; and five, enrolments Granger-cause both real gross
capital formation and unemployment rate.
4.5
Is Education a Growth Promoter in Nigeria?
The empirical results of estimating the growth-accounting in Equation 6 are presented in
Tables 6 and 7. The Table 6 presents the results from estimation of the Equation in static
form. The adjusted coefficient of determinations (R2), t-statistic and the Durbin-Watson
statistics are shown in the Table. From the Table, we observe that the growth of real gross
17
domestic product (RGDPG) is negatively affected by the amount of physical capital; and
labour inputs (using unemployment rate). Contrary to our a priori expectation, the
estimate of the impact of real capital educational expenditure (REDUC) and primary and
secondary schools enrolments on the growth of real GDP is negative. Thus, the growth of
educational capital and enrolments show a negative but insignificant effect on economic
growth in Nigeria. The observation of the negative impact of REDUC on the growth of
real GDP is puzzling and very crucial in this study.
31 per cent of the variation in the RGDPG is explained by the variables in the model. The
remaining influence must reflect some combination of measurement errors, random
fluctuations, temporary disequilibria, and / or the net influence of other factors that also
systematically affect the growth of economic productivity in Nigeria.
Table 6: Long Run Static Regression of Growth Rate of Real Gross Domestic
Product, 1970- 2003
Dependent Variable: RGDPG
Method: Least Squares
Sample: 1970 2003
Included observations: 34
Variable
Coefficien Std. Error
t
REDUC
RGCF
UR
ENROL
C
Adjusted R-squared
Durbin-Watson stat
-0.001404
-6.826772
-1.572802
-0.071726
69.49788
0.311162
2.177434
0.159972
3.000312
1.042766
2.494251
15.58591
t-Statistic
Prob.
-0.008778
-2.275354
-1.508298
-0.028756
4.459021
0.9931
0.0305
0.1423
0.9773
0.0001
Source: Own Computations
Note: Variables are as defined in Table 2
To further verify the impact of real capital educational expenditure on growth rate of real
GDP, we estimate the error correction and autoregressive models
4.6 Error-Correction Model (ECM) of Growth Rate of Real Gross Domestic Product
In order to capture the short-run deviations that might have occurred in estimating the
long-run co-integrating equation, a dynamic error-correction model is formulated. The
ECM is estimated with respect to the dependent variable, growth rate of real GDP, using
ordinary least squares. The coefficient of error correction term depicts the speed of
convergence to equilibrium once the equation is shocked. The dynamic error correction
formulation is presented as follow:
18
1
1
1
1
1
i 0
i 0
i 0
i 0
i o
RGDPGt  ho  h1i  RGDPGt 1  h2i  EDUCt 1  h3i  RGCFt 1  h4i  URt 1  h5i  ENROLt 1  h6 ECM t 1  ui....(18)
where ECM is the error correction term (lagged residual of static regression) and ‘’
stands for first difference. All the variables (first order differenced) in the equation are
stationary and therefore OLS method gives consistent estimates (Enders, 1995). The
model is estimated by the OLS method.
An important feature to notice is the coefficients of error correction model and real
capital expenditure on education. The coefficient of the error-correction terms carries the
correct sign and it is statistically significant at 1 percent, with the speed of convergence to
equilibrium of 58 per cent (see Table 7). In the short run, growth rate of real GDP is
adjusted by 58 percent of the past year’s deviation from equilibrium. 67 per cent of the
variation in the RGDPG is explained by the variables in the model.
Like the long-run static regression results in Table 6, the growth of capital expenditure
on education (at second difference) shows a negative but significant effect on economic
growth in Nigeria. Also, the enrolments in primary and secondary school have negative
impact on economic growth.
Table 7: Parsimonious Error Correction Model
Dependent Variable: D(RGDPG,1)
Method: Least Squares
Sample(adjusted): 1973 2003
Included observations: 31 after adjusting endpoints
Variable
Coefficien Std. Error t-Statistic
t
D(RGDPG(-1),1)
D(REDUC(-2),1)
D(RGCF,1)
D(RGCF(-1),1)
D(ENROL,1)
ECM(-1)
C
Adjusted R-squared
Durbin-Watson stat
0.299364
-0.492249
8.255548
15.51167
-39.35989
-0.576027
1.498234
0.670761
2.402412
0.176624
0.191344
4.315940
6.335894
13.43585
0.275243
1.496015
1.694924
-2.572587
1.912804
2.448221
-2.929469
-5.725950
1.001483
Prob.
0.1030
0.0167
0.0678
0.0220
0.0073
0.0000
0.3266
Source: Own computations
Note: Variables are as defined in Table 2
Before advancing explanation for this apparent paradox, it is necessary to show that this
information on educational capital is not the result of pure measurement error or failure to
account for the quality of leaning output. The estimated coefficient is not the result of a
peculiar data because different specifications used produced consistently negative
19
coefficient estimates of real capital expenditure on education. Changing the data on
growth to per capita GDP yields even larger negative estimate for education, and hence,
only deepens the puzzle. Relaxing the assumption of constant returns to scale does not
alter the negative estimate on educational capital. However, while differences in
educational quality can account for heterogeneity in the impact of learning, it should not
explain a low average impact. In fact, owing to the general underlying positive
covariance between quantity and quality of schooling, one would expect that excluding
quality would bias the estimated return upward as more schooling is accumulated where
quality is high (Ayara, 2002; Pritchett, 2001; Schultz, 1988; Behrman and Birdsall,
1983). Therefore, the impact of an additional unit of educational capital is assumed
higher when the quality of schooling is high. This suggests that the lack of quality of
adjustment causes an upward bias, so the negative estimate in this analysis, which is not
adjusted for quality, is underestimated (Ayara, 2002).
4.7
Forecast Error Variance Decomposition
An examination of the short-run dynamic properties of the growth rate of real GDP is
further investigated by estimating forecast error variance decomposition and generalized
impulse response analysis. Forecast error variance decomposition (FEVD), provides
complementary information on the dynamic behaviour of the variables in the system. It is
possible to decompose the forecast variance into the contributions by each of the different
shocks. When calculated by the structural shocks, as in the present case, the FEVD
provides information on the importance of various structural shocks explaining the
forecast error variability of growth rate of real GDP and its determinants.
Table 8 presents the FEVD of the four endogenous variables. By definition, the variance
decomposition shows the proportion of forecast error variance for each variable that is
attributable to its own innovation and to innovation in the other endogenous variables.
“Own shocks” variation ranged from 44.7 per cent to 100 percent over the ten-year
horizon (Table 8). The innovations of real capital expenditure on education, which
accounts for the forecast error variance of growth rate of real GDP, ranged from 0 to 16
percent. The persistence of past growth rate of real GDP shocks after ten quarter of the
shocks explains 44.7 percent of the variation in current growth rate of real GDP, while
educational expenditure (REDUC), real gross capital formation (RGCF) and
unemployment rate (UR) account for about 16 %, 20% and 3% respectively.
Table 8: Variance Decomposition of Growth Rate of Real Gross Domestic Product
(RGDPG)
Perio
d
1
2
3
4
5
S.E.
RGDPG
REDUC
RGCF
UR
4.127238
5.102594
6.378424
6.658837
7.579153
100.0000
69.91362
52.77587
48.83673
48.88047
0.000000
2.531565
4.304948
6.584469
11.15188
0.000000
6.214427
20.54543
21.41548
21.96741
0.000000
0.113079
1.484970
3.878748
3.064302
20
6
7
8
9
10
8.004721
8.150808
8.279557
8.296502
8.308540
45.67163
44.81930
44.93060
44.74823
44.68050
14.10469
15.82549
16.29233
16.24535
16.24767
20.94911
20.22282
19.87129
19.80496
19.76455
2.747638
3.183471
3.092186
3.203353
3.313785
Note: Variables are as defined in Table 2
Source: Own Computations
4.8
Impulse Response Functions
The impulse response functions are reported in Table 9 and Figure 1. Impulse response
functions are devices to display the dynamics of the variables tracing out the reaction of
each variable to a particular shock at time t. Tables 9 and Figure show the results of the
impulse response analyses derived from the estimated VAR models.
Table 9: Response of RGDP to One S.D. Innovations
RGDPG
REDUC
LRCF
UR
ENROL
Perio
d
1
2
3
4
5
6
7
8
9
10
4.127238
(0.51590)
-1.081174
(0.76985)
-1.807888
(0.94538)
-0.427514
(0.88144)
-2.534645
(0.99683)
-1.088882
(1.05147)
-0.715301
(1.05916)
-1.012126
(1.03728)
-0.025906
(0.96539)
-0.206619
(0.84477)
0.000000
(0.00000)
-0.811868
(0.73451)
-1.045135
(0.75176)
-1.080797
(0.70862)
-1.867211
(0.72569)
-1.622227
(0.88155)
-1.214953
(0.90537)
-0.809197
(0.91321)
-0.115857
(0.91369)
0.184589
(0.84788)
0.000000 0.000000 0.000000
(0.00000) (0.00000) (0.00000)
-1.272013 -0.171586 -2.350923
(1.20214) (0.81550) (0.63783)
-2.596295 -0.758095 1.723836
(1.06255) (0.82106) (0.61471)
-1.066247 1.056263 0.228795
(1.16897) (0.87310) (0.66275)
-1.767263 0.201005 0.170097
(0.83754) (0.68906) (0.52572)
-0.896881 0.017894 1.417742
(1.24289) (0.73290) (0.72357)
-0.109122 0.595311 0.077753
(1.13225) (0.72213) (0.59621)
-0.432222 0.069040 0.494588
(1.01565) (0.66148) (0.54772)
0.100788 0.291896 0.414023
(0.96275) (0.57177) (0.52332)
0.108124 0.287465 -0.169690
(0.73094) (0.51267) (0.46165)
Note: Variables are as defined in Table 2
Source: Own Computations
From to Table 9 and Figure 1, past growth rate of real GDP shocks has a positive
relationship with current growth rate of real GDP in the short- run. Also, educational
expenditure and enrolment shocks reduce growth rate of real GDP in the short run. This
provides additional justification for paradox of educational expenditure and economic
21
growth relationship in Nigeria. In the long-run, a shock in real capital expenditure on
education or enrolments in primary or secondary schools have no effect on growth rate of
real GDP in Nigeria.
Response of RGDPG to One S.D. Innovations
6
4
2
0
-2
-4
1
2
3
4
5
R GD PG
R ED U C
LR C F
6
7
8
9
10
UR
EN R OL
Source: Own Computations.
Figure 2: Generalized Impulse Response Growth Rate of Real Gross Domestic Product
to one S.E Shock in its Explanatory Variables. The Size is 5%
5
Explanations for the Paradox
The aggregate data used on the long run static model and dynamic models of error
correction and autoregressive models suggest that education has not had the expected
positive growth impact on economic growth in Nigeria, which is widely acknowledge by
other scholars (Ayara, 2002; Pritchett, 2001). The resolution in this puzzle begins with a
proper understanding of the causes of such unanticipated relationship. Ayara (2002) and
Pritchett (2001), in similar studies, observe that a single answer to this puzzle is grossly
insufficient; hence they propose three possibilities that could account for such results.
These possibilities, Pritchett argues, may be arise because: one, the newly created
educational capital has gone into piracy (that is, private remunerative but socially
unproductive activities); or two, there has been slow growth in the demand for
educational labour, so that the supply of educational capital has outstripped demand and
returns to schooling has declined; or three, the education system had failed, such that a
year of schooling provide few skills (Ayara, 2002).
However, several other empirical studies on schooling such as Kyriacou (1990); Lan,
Jamison and Louat (1991); Dasgupta and weale (1992), Islam (1995), Hoeffler (1997)
and Ayara (2002) seem to support these arguments. The justification for this particular
study emanates from the fact that other works in this area are based on cross-country data
in which Nigeria is either not considered at all or is treated just as a data point. Few other
country-specific studies (not in Nigeria) that exist are based on panel data. The above
points, however, may only explain the Nigeria case partially, which may not be
22
exhaustible because they lack the proper focus of the local characteristics of the Nigerian
situation. In addition, therefore, the Nigerian experience would include the following
explanations.
First, since emphasis is on paper qualification today, the Nigerian labour market is
flooded with misfits and incompetent workers as people struggled to obtain certificates
(and degrees) by all means, at times with the aid of their parents and / or examination
officials (Ayara, 2002). Sometimes, employment is based on whom you know rather than
what you know- i.e. training, competence and experience are jettisoned. This is one of the
reasons for inadequate manpower in the midst of many certificate holders in Nigeria.
Another reason is the outcome of the introduction of new technology such as the
computer, which renders the old crops of worker redundant. Next is the issue of job
mismatched, in which a qualified medical doctor may be employed as a schoolteacher in
the absence of something to do in order to survive (Ayara, 2002).
Second, it is not a theoretical possibility that there can be a wasteful oversupply of
education in Nigeria today. It happens and it is called the “external brain drain” (Ayara,
2002). In Nigeria, brain drain is encouraged since the society is porous. As a result,
school products are forced to travel abroad for greener pastures. Clearly, the brain drain is
not the result of a simple quantitative oversupply of trained personnel; it comes about
because too many are with the kind of skills for which there is an insufficient effective
demand at home (Gordon 1973:3-4). The case of emigrant medical doctors and nurses
from Nigeria to Canada, for instance, illustrates this problem. Even though we have
enough people in Nigeria with sufficient income to bid for these trained medical
practitioners away from the income they can command abroad, unwillingness to invest at
home due to lack of enabling environment and inconsistent government policies have
hindered them. However, excess qualified manpower also tends to draw wasteful
investment from the domestic economy in the form of costs that may bear little or no
return. Again, it leads to arbitrary substitution of qualified people by people who are
overqualified, which indeed is one reason why additional education tends to be socially
wasteful although personally profitable. These problems are certainly counterproductive
and such imbalance can decrease the prevailing level of output (Ayara, 2002).
Third, Nigeria has experienced increasing number of strikes since independence, which
are caused by workers’ agitation for salary increases and improved conditions of service.
Existence of industrial disputes and job discontinuities create a non-integrated
educational system in Nigeria. For instance, because of strikes, some universities in
Nigeria have to jettison three school years (1994/95), 2001/2002 and 2004/2005
academic sessions) in less than one decade. These industrial actions usually warrant
stoppages and loss of man-days, which inversely affect the real growth in gross domestic
product, especially when a large number of workers or many sectors of the economy are
involved in the dispute (Ayara, 2002; Nyong (1998). For instance, in August 2005,
Federal government raised the fuel price from N52 to N65 per litre and the Nigerian
Labour Congress, which is the umbrella of trade unions in Nigeria, determines to embark
on a nationwide strike if government fails to reverse the fuel price back to N52 per litre.
However, this should not be taken as an empty treat since the Union had embarked on
23
similar strikes as from 2000, making it the 6th times such occurrence had taking place.
This usually had negative consequences on the economy, including educational sector.
Four, another important explanation for this paradox is the “benefit capturing syndrome”
in Nigeria (Ayara, 2002). Benefit capture depicts a scenario where benefits that should
have accrued to the beneficiary of a designed programme are captured away at every
stage of the programme’s development (Ekong 1997:560). This implies the illegal
diversion or legal misappropriation of benefits (financial and otherwise) meant for an
educational programme such that the programme collapses or suffers some drastic
setbacks and frustrations. This has a severe negative impact on our educational system
and the economy generally (Ayara, 2002).
6
Summary and Concluding Remarks
This study sets out to empirically investigate the paradox of educational expenditure and
economic growth relationship in Nigeria, using annual time series data from 1970 to
2003. Some statistical tools are employed to explore the relationship between these
variables. The study examines stochastic characteristics of each time series by testing
their stationarity using Augmented Dickey Fuller (ADF) and Phillip Perron (PP) tests.
Then, the relationship between growth rate of real GDP and real capital expenditure on
education is dynamically examined using error correction mechanism and the effects of
stochastic shocks of each of the endogenous variables are explored, using Vector
Autoregressive (VAR) model.
The findings reveal that an increase in real capital expenditure on education reduces
growth rate of real gross domestic products, which is a paradox. To reverse this trend,
therefore, the following recommendations are necessary.
First, there is need for effective demand management by improving the educational sector
through better incentives for teachers, and non-interference with decisions such as
curriculum or teachers’ responsibilities.
Second, parents should not wish to fulfill their life expectations in their children by
selecting careers for them or by suggesting subjects that they should study. They should
not also encourage or assist their children to purchase certificates or degrees.
Government, in its employment policies, should lay more emphasis on specialization and
competence rather than paper qualification and ill-gotten certificates. Although strikes
can be regarded as part of industrial growth, the frequency of strikes should be
discouraged in Nigeria because of the huge cost to the economy in terms of output loss.
Third, policymakers should focus on improving the overall quality of the teaching force.
If one were simply to redistribute existing teachers, the overall policy goals would be
achieved. But the research evidence suggests that many of the policies that have been
pursued worldwide have not been very productive (Hanushek, 2005). Especially, policies
that have led to changes in measured aspects of teacher quality- such as degrees or other
teacher qualifications-do not seem to have improved the quality of teachers, at least when
that quality is measured by looking at student performance.
Moreover, the ability to improve the teaching force will depend on the people who can be
attracted to teaching. If the teaching force is to be improved, either the hiring must select
better teachers or retention policies must be skewed toward the best teachers. These
24
considerations make the case for building a plan of improvement over time. One-time
adjustments or changes in programmes are unlikely to be effective in Nigeria. The most
feasible approach, given currently available information, is to experiment with alternative
incentive schemes (Hanushek, 2005). These might involve new contracts and approaches
to teacher compensation, introduction of parental choice across schools, merits awards
for schools, and the like. The unifying theme is that each new policy should be designed
to improve student achievement directly. For example, merit awards to teachers could be
directly linked to objectives information about student performance (Hanushek, 2005).
Finally, there is the need to get a better handle on what works and what does not in
Nigeria. Too often, there is no regular evaluation of policies and programmes. And when
evaluations are conducted, they frequently focus on inputs to the system rather than on
student achievement and outcomes. This underscores the need to assess student’s
outcomes that are related to both new and existing programmes. The key element is
measuring student performance directly.
References
Adamu, P.A. 2003. The Impact of Human Capital formation on Economic Development
in Nigeria: An Error correction Approach. In: Human Resource
Development in Africa: Selected Papers for the Year 2002 Annual
Conference, The Nigerian Economic Society (NES), Part Two, pp.53-77.
Adebiyi, M.A., 2003. Debt Service-Education Expenditure Nexus: The Nigerian
Experience. In: Human Resource Development in Africa: Selected Papers
for the Year 2002 Annual Conference, The Nigerian Economic Society
(NES), Part Three, Pp. 243-267.
Adedeji, A.S. Rasheed and M. Morrison, Eds. 1998. The Human Dimension Africa’s
Persist Economic Crisis. United Nations Economic Commission for
Africa. Hans Zell Publisher, Pp. 377-391.
Adrangi, B. and M. Allender, 1998. Budget Deficits and Stock Prices: International
Evidence” Journal of Economics, Vol. 22(2-3): 57- 66.
Ayara, N.A. (2002). The Paradox of Education and Economic growth in Nigeia: An
Empirical Evidence. In: Human Resource Development in Africa: Selected
Papers for the Year 2002 Annual Conference, The Nigerian Economic
Society (NES), Part Five, Pp. 371-393.
Baldacci, E Clements B, Cui Q & S. Gupta (2005). What does it Take to Help the Poor?
Finance and Development: A Quarterly Magazine of the IMF, June, Vol.
42, No 2.
Behrman.J. & N.Birdsall (1983). The Quality of Schooling: Quantity Alone is Misleading
World Bank Reprint series 311:938-46.
Bernanke, B. S. and A.S. Blinder (1992). The Federal Funds Rate and the
Channels of
25
Monetary
Transmission.
(September): 901 - 921.
American
Economic
Review,
82/4
Central Bank of Nigeria (2000). The Economy and
Implications for Development. Lagos:
Realm Communications
Chinpah, G. (1997). Handbook in Monetary Learning Achievements Towards Capacity
Building Conrad, J. and 1999. Resource Economics. New York:
Cambridge University Press.
Coulombe, Serge, Jean-François Tremblay, and Sylvie Marchand (2004). Literacy
Scores, Human Capital and Growth Across Fourteen OECD Countries.
International Adult Literacy Survey Monograph Series (Ottawa: Statistics
Canada)
Dasgupta, P. and M. Weale (1992). On Measuring the Quality of Life. World
Development 20(1)
Easterly, William and Rose Levine (2001). Comment on: “It’s not Factor Accumulation:
Stylized Facts and Growth Models”. The World Bank Economic Review 15(2): 221-24.
Ekong C.N. (1997). Framework for Building Sustainable Poverty Alleviation Strategy in
Nigeria Ibadan: NES.
Enders, W.(1995) Applied Econometric Time Series, New York, John Wiley, 1995.
Fair Ray C. and Robert J. Schiller, (1990). Comparing Information in Forecasts from
Econometric Models. The American Economic Review, 80 (3)
(June): 375-389.
Funke, Michael, (1990). Assessing the Forecasting Accuracy of Monthly Vector
Autoregressive Models: The Case of five OECD countries. International
Journal of Forecasting, 6: 363-378.
Griffin, K and T. McKinley (1992). Towards a Human Development Strategy,
Occasional Paper 6, December
Hanushek E.A (2005). Why Quality Matters in Education. In: Cultivating Minds:
How Investing in Education Boosts Development. Finance and
Development, June pp. 15-19
Harbison, F.H. (1973). Human Resources as the Wealth of Nations. New York, Oxford
University Press.
Hoeffler, .E. (1997). The Augmented Solow Model and the Africa Growth Debate:
Center for the Study of African Economies, Oxford University, processed.
26
Islam, N. (1995). Growth Empirics: A Panel Data Approach. Quarterly Journal of
Economics, 110(4).
Jhingan, M.L. (2000). Macroeconomic Theory. Delhi:Vrinda Publications (P) Ltd.
Johansen, S. (1988), "Statistical Analysis of Co integrating Vectors". Journal of
Economic Dynamics and Control, 12, pp.231-54.
Johansen, S., Juselius, K. (1990), “Maximum Likelihood Estimation and Inference on
Co-integration - With Applications to the Demand for Money”. Oxford Bulletin of
Economics and Statistics, 52, pp.169-210.
Johansen, S. (1995), Likelihood-based Inference in Co-integrated Vector Autoregressive
Models, Oxford, Oxford University Press.
Klenow P. (2001). It’s Not Factor Accumulating: Stylized Facts and Growth Models. The
World Bank Review,15 (2).
Kyriacou, G. (1990). Level and Growth Effects of Human Capital: A Cross-Country
Study of the Convergence Hypothesis. New York University Economic
Research Report 91.
Lan, L., Jamison D and L.Louat (1991). Impact of Education by Religion. World Bank.
Washington, D.C. Processed.
Lutkepohl, Helmut, (1985). Comparison of Criteria for Estimating the Order of A Vector
Autoregressive Process. Journal of Time Series Analysis, 6(1): 35-52.
Mankin, G., D. Rommer, and D. Well. (1992). A Contribution to the Empirics of
Economic Growth. Quarterly Journal of Economics 107: 407-37.
Nyong, M. O. (1998). June 12 Strike Activity and Economic Development in Nigeria.
The Nigerian Journal of Economic and Social Studies 40 (3): 53-58.
Ogbodo, C.M. (1988). Financing Education in Nigeria. Administration Planning and
Supervision, Lagos: Joja Educational Research and Publishers Limited.
Okojie, C.E.E (1995). Human Capital Formation for Productivity Growth in Nigeria.
Nigerian Economic and Financial Review: June pp. 44-5.
Osterwald-Lenum, M., (1992), “A Note with Quantiles of the Asymptotic Distribution of
the Maximum Likelihood Cointegration Rank Test Statistics,” Oxford
Bulletin of Economics and Statistics, 54, pp.461-472.
Palm, Franz, (1983). Structural Econometric Modelling and Time Series Analysis. In:
Arnold Zellner, Applied Time Series Analysis of Economic Data,
Economic Research Report ER-5 (US Department of Commerce,
Washington, DC), 199-233.
Pritchett, L. (2001). Where Has all the Education Gone? The World Bank Economic
Review 15 (3)
27
Ramanathan, R. (1992). Introductory Econometrics with Applications, Second Edition,
Harcourt New York, Brace Jovanovich.
Schultz. T.P. (1998). Education Investments and Returns. In: Handbook of Development
Economics, 1.H. Cheney and T.N. Srinivasan, eds.
Simatele, M. C. H. (2003). Financial Sector Reforms and Monetary Policy in Zambia.
PhD Dissertation, Economic Studies, Department Of Economics
School of Economics and Commercial Law, Göteborg University
Taiwo,C.O. (1986).The Nigeria Education System: Past, Present and Future, Lagos:
Nelson Pitman Limited.
UNDP (1992a). Human Development Report 1992, New York: Oxford University Press,
UNDP (1992b). Making People Matter: Introductory Comment on a Human
Development Strategy for Ghana, Accra, Ghana, draft June 1992.
Yesufu, T.M. (2000). The Human Factor in National Development: Nigeria. Ibadan:
Spectrum Books.
Zellner Arnold, (1979). Statistical Analysis of Econometric Models. Journal of the
American Statistical Association, 74 (367) (September): 628-643.
__________ and F. Palm, (1974). Time Series Analysis and Simultaneous Equation
Econometric Models. Journal of Econometrics, 2(1) (May): 17-54.
28