The causality between education and GDP Growth in Asian Countries

This paper investigates the causal relationship between education and GDP in 40 Asian countries by using panel unit root tests and panel cointegration analysis for the period 1970-2010. A three-variable model is formulated with capital formation as the third variable. The results show a strong causality from investment and economic growth to education in these countries. Yet, education does not have any significant effects on GDP and investment in shortand long-run. It means that it is the capital formation and GDP that drives education in mentioned countries, not vice versa. So the findings of this paper support the point of view that it is higher economic growth that leads to higher education proxy. It seems that as the number of enrollments raise, the quality of the education declines. Moreover, the formal education systems are not market oriented in these countries. This may be the reason why huge educational investments in these developing countries fail to generate higher growth. By promoting practice-oriented training for students particularly in technical disciplines and matching education system to the needs of the labor market, it will help create long-term jobs and improve the country’s future prospects.


INTRODUCTION
Generally, it is argued that higher formal education cause more economic growth. Lucas (1988) argues the accumulation of human capital is responsible for sustained growth, and education is the main channel through which the human capital accumulates. Romer (1986,1990) show that human capital, which generates innovations, stimulate growth. As it is well documented in the literature, education also constructs spillover affects, improves the adaptation speed of entrepreneurs to disequilibrium, and boosts research productivity.
Furthermore, there is the possibly feedback effects from economic growth to human capital. It is argued that economic growth could lead to human capital accumulation (Mincer, 1996). So, the causal chain between economic growth and education implied by the existing macroeconomic paradigms seems relatively ambiguous. The subject, therefore, as to the dynamic causal relationships in the Granger sense remains uncertain and is a practical one.
There is mixed evidence in the empirical literature regarding the relation between education and economic growth. Benhabib and Pritchett (1997) report fragile correlation between growth and education. Levine and Renelt (1992) show that education does not have significant impact in many of the growth regressions they have estimated. Bils and Klenow (2000) finds the weak causality from education to growth; so that the statistical significance of education in growth regressions may arise from just omitted variables. therefore, the crosssectional studies seem to yield mixed results. Dessus (1999) argues that the findings of Pritchett (1997) may be due to specification bias. Dessus' (1999) panel data results suggest that as the education quantity increase, the quality of the education decrease. This may be the reason why enormous educational investments in developing countries fail to generate higher growth.
The focus of the paper is, therefore, to examine the relationship between education and economic growth in 40 Asian countries for the period 1970-2010 The direction of causality between these two variables is examined by utilizing a cointegration and error correction modeling framework. The paper is organized in four sections. Section 2 discusses the methodology, data and empirical results of the study. Section 3 concludes.

DATA AND EMPIRICAL RESULTS
We apply a three variable model to examine the causal relationship between human capital GDP with gross investment included in model as conditioning variable along with these two variables. Human capital is proxied by enrolment ratio in all levels of education (including tertiary, secondary and primary education measured as the percentage of the working age population) as well as public expenditures on education relative to total public expenditures. We apply the principle component approach to merge the proxies into one measurement (HUM). The data were obtained from the Barro  To test the nature of association between the variables while avoiding any spurious correlation, the empirical investigation in this paper follows the three steps: We begin by testing for non-stationarity in the three variables of HUM, GDP and INV. Prompted by the existence of unit roots in the time series, we test for long run cointegrating relation between three variables at the second step of estimation using the panel cointegration technique developed by Pedroni (1995Pedroni ( , 1999. Granted the long run relationship, we explore the causal link between the variables by testing for granger causality at the final step.

Panel Unit Roots Results
The panel data technique referred above has appealed to the researchers because of its weak restrictions. It captures country specific effects and allows for heterogeneity in the

International Letters of Social and Humanistic Sciences Vol. 50
direction and magnitude of the parameters across the panel. In addition, it provides a great degree of flexibility in model selection. Following the methodology used in earlier works in the literature we test for trend stationarity of the three variables of HUM, GDP and INV. With a null of non-stationary, the test is a residual based test that explores the performance of four different statistics. Together, these four statistics reflect a combination of the tests used by Levin-Lin (1993) and Im, Pesaran and Shin (1997). While the first two statistics are nonparametric rho-statistics, the last two are parametric ADF t-statistics. Sets of these four statistics have been reported in Table 1.
The first three rows report the panel unit root statistics for HUM, GDP and INV at the levels. As we can see in the table, we cannot reject the unit-root hypothesis when the variables are taken in levels and thus any causal inferences from the three series in levels are invalid. The last three rows report the panel unit root statistics for first differences of HUM, GDP and INV. The large negative values for the statistics indicate rejection of the null of non-stationary at 1% level for all variables. It may, therefore be concluded that the three variables of HUM, GDP and INV are unit root variables of order one, or, I (1) for short.

Panel Cointegration Results
At the second step of our estimation, we look for a long run relationship among HUM, GDP and INV using the panel cointegration technique developed by Pedroni (1995Pedroni ( , 1999. This technique is a significant improvement over conventional cointegration tests applied on a single country series. While pooling data to determine the common long run relationship, it allows the cointegrating vectors to vary across the members of the panel. After including INV as an additional variable, the cointegration relationship we estimate is specified as follows: Volume 50 values except for the panel-v test which reject the null of cointegration when it has a large positive value. All of these seven statistics under different model specifications are reported in Table 2. The statistics for all different model specifications suggest rejection of the null of no cointegration for all tests except the panel and group   tests. However, according to Perdroni (2004),  and PP tests tend to under-reject the null in the case of small samples. We, therefore, conclude that the three unit root variables HUM, GDP and INV are cointegrated in the long run.

Panel Causality Results
Cointegration implies that causality exists between the series but it does not indicate the direction of the causal relationship. With an affirmation of a long run relationship among HUM, GDP and INV, we test for Granger causality in the long run relationship at the third and final step of estimation. Granger causality itself is a two-step procedure. The first step relates to the estimation of the residual from the long run relationship. Incorporating the residual as a right hand side variable, the short run error correction model is estimated at the second step. Defining the error term from equation (1) to be it ECT , the dynamic error correction model of our interest by focusing on Human capital (HUM) and GDP is specified as follows: Where  is a difference operator; ECT is the lagged error-correction term derived from the long-run cointegrating relationship; the y  and h  are adjustment coefficients and the yit  and hit  are disturbance terms assumed to be uncorrelated with mean zero.
Sources of causation can be identified by testing for significance of the coefficients on the lagged variables in Eqs (2)  causality in the sense that the dependent variable responds only to short-term shocks to the stochastic environment. Another possible source of causation is the ECT in Eqs. (2) and (3). In other words, through the ECT, an error correction model offers an alternative test of causality (or weak exogeneity of the dependent variable). The coefficients on the ECTs represent how fast deviations from the long run equilibrium are eliminated following changes in each variable. If, for example, yi  is zero, then GDP does not respond to a deviation from the long run equilibrium in the previous period.
for all i is equivalent to both the Granger non-causality in the long run and the weak exogeneity (Hatanaka, 1996).
It is also desirable to check whether the two sources of causation are jointly significant, in order to test Granger causality. This can be done by testing the joint hypotheses 0 : . This is referred to as a strong Granger causality test. The joint test indicates which variable(s) bear the burden of short run adjustment to re-establish long run equilibrium, following a shock to the system (Asafu-Adjaye, 2000).
The results of the F test for both long run and short run causality are reported in Table  3. As is apparent from the Table, the coefficients of the ECT, GDP and INV are significant in the HUM equation which indicates that long-run and short-run causality run from GDP and INV to human capital. So, GDP and INV strongly Granger-causes human capital. INV does Granger cause GDP at short run at 5% level, without any significant effect on output in long run. Weak exogeneity of GDP indicate that this variable does not adjust towards long-run equilibrium.
Moreover, the interaction terms in the HUM equation are significant at 1% level. These results imply that, there is Granger causality running from GDP and INV to human capital in the long-run and short run, while human capital have a neutral effect on GDP in both the short-and long-run. In other words, GDP is weakly exogenous and whenever a shock occurs in the system, human capital would make short-run adjustments to restore long-run equilibrium.

CONCLUSION
The objective of this study is to examine Granger causality between human capital and income 40 Asian countries over the period 1970-2010. Real capital formation is also included in the model along with these two variables. The panel integration and cointegration Volume 50 techniques are employed to investigate the relationship between the three variables: human capital proxy, GDP, and investment. The empirical results indicate that we cannot find enough evidence against the null hypothesis of unit root. However, for the first difference of the variables, we rejected the null hypothesis of unit root. It means that the variables are I(1). The results show that there is a long-run relationship between human capital and GDP. Utilizing Granger Causality within the framework of a panel cointegration model, the results suggest that there is strong causality running from GDP and investment to human capital with no feedback effects from human capital to GDP for Asian countries. It means that it is the investment and GDP that drives the human capital in mentioned countries, not vice versa. So the findings of this paper support the point of view that it is higher economic growth that leads to higher human capital. According to the results, it seems that, to some extent, investments have contributed to human capital and economic growth during the sample period. It seems that as the number of enrollments increase, the quality of the education declines. This may be the reason why huge educational investments in these developing countries fail to generate higher growth. In order to match education opportunities with the demands of the labour market, support should be provided for integrating labour market data into educational planning and establishing technical and start-up centres at universities