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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 AL 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) 10 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. 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