EXPORTS AND ECONOMIC GROWTH: AN ERROR CORRECTION MODEL
Emmanuel Anoruo Department of Management Science and Economics Coppin State College 2500 W. North Avenue Baltimore, MD 21216 U.S.A. Ph: (410) 383-5582 Email:
[email protected]
Sanjay Ramchander* Department of Finance and Real Estate College of Business Colorado State University Fort Collins, CO 80523 Ph: (970) 491-6681 Email:
[email protected]
________________ * Corresponding author
EXPORTS AND ECONOMIC GROWTH: AN ERROR CORRECTION MODEL Abstract The relationship between exports and economic growth has been a popular subject of debate among development economists. This paper uses a theoretically consistent method to examine the export-led growth (ELG) hypothesis for five emerging economies of Asia namely — India, Indonesia, Korea, Malaysia, and the Philippines. Specifically, the paper employs a cointegration estimation procedure to examine the export-economic growth nexus, and employs a vector error correction model to abstract simultaneously the short- and long-run information in the modeling process. Results from the study provide evidence in support of the ELG hypothesis in that export growth has a causal influence on economic growth for all countries with the exception of Indonesia. From a policy perspective, the acceptance of the ELG hypothesis lends credence to the view of ‘outward orientation’ as an effective policy for economic growth, especially for countries with nascent economies.
EXPORTS AND ECONOMIC GROWTH: AN ERROR CORRECTION MODEL
I. Introduction The purpose of this paper is to test the probity of the export-led growth (ELG) hypothesis for five emerging economies of Asia — namely India, Indonesia, Korea, Malaysia, and the Philippines. The issue of the links between export performance and economic growth in a trading world economy are a perennial source of concern and controversy, more so with the emergence of a significant body of empirical work in the development economics literature since the late 1960s. While classical trade theory provides important insights into the static gains of trade (i.e., the impact of trade on national economic well-being), it fails to fully account for the dynamic relationship between trade policies and economic growth. The rapid economic growth witnessed by the so-called newly industrialized countries has revived the debate on optimal growth strategies for emerging market economies. The current debate centers on whether a developing country would be better served by trade policies oriented toward import substitution or export promotion. Import substitution strategies seek to promote rapid industrialization and therefore development by erecting high barriers to foreign goods such as tariffs and quotas to encourage local production. This approach to development thus applies the ‘infant industry’ argument for protection to one or more targeted industries in the developing country. As the industrialization process takes hold, the government lowers the trade barriers. On the other hand, outward-looking development (or ELG) strategies involve government support for manufacturing sectors in which a country has a potential comparative advantage. This framework argues that international trade promotes specialization in production of export products, which in turn boosts the productivity level and causes the general level of skills to rise in the export sector. This then leads to a re-allocation of resources from the inefficient non-trade sector to the trade sector. Thus, the entire economy would benefit due to the dynamic spillover benefit from the export sector’s growth. Empirical and anecdotal evidence tends to support the notion that those economies which actively pursue exportpromotion policy have been more successful than those that have pursued import substitution
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policies (see, for example, Feder, 1982 and Krueger, 1990)1. This paper incorporates the recent advances made in time series analysis, and proposes a theoretically consistent method to examine the ELG hypothesis for several emerging economies. Specifically, unit root tests, cointegration analysis and error-correction techniques are employed in a multi-variate framework that directly addresses the problem of omitted variables (an issue that is often overlooked in past studies).2 The estimation technique places minimal restrictions on the explicit structure of the relationship between exports and economic growth, and abstracts simultaneously the short- and long-run information in the modeling process. Additionally, this study by using an extensive sample period and large information set proposes to obtain more robust results than those of the earlier studies. Apart from its important policy implications, the present discussion is topical considering that many economists attribute the recent Asian economic crisis to the unsustainable level of current account deficits that were maintained by these countries. Furthermore, emerging economies may be characterized by potentially unique monetary policy and macroeconomic transmission mechanisms that are arguably very different from those of industrialized nations. Developing economies also experience numerous other drawbacks, such as an inefficient public enterprise, deficient infrastructure, tight trade controls, restrictive regulations in the financial sector, pro-cyclical macroeconomic policy responses to large capital inflows, poor corporate governance, and political uncertainty. Under such conditions, there may be wide disparities in the macroeconomic dynamics governing policy transmission between developing and developed economies. The outline of the remainder of this study is as follows. The next section conducts a brief 1
Import-substituting industrialization has come under increasingly harsh criticism, since many countries that pursued such strategies have not shown any signs of catching up with the advanced countries. India is an excellent example. After 40 years of ambitious economic plans between the 1950s and late 1980s, India found itself with per capita income only a few percent higher than before. But after adopting market friendly reforms beginning in the early 1990s, India has shown tremendous strides in both export revenue and economic growth.
2
The deployment of a multi-variate estimation procedure is especially important since causality findings from bivariate VARS can easily be overturned by the addition of a third (or more) variable (see Lutkepohl, 1989). We thank the anonymous referee for this suggestion.
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review of the existing literature, their methodological drawbacks and our approach to redress this issue. Section III provides a discussion on the methodological issues. The data employed and results of the study are presented in Section IV. The final section summarizes the findings of the study and makes several policy implications.
II. Literature Review The empirical investigation into the relationship between export growth and economic expansion has primarily taken three different, but related, forms. The context of these studies has ranged from individual-country analyses to multi-country investigations. Early studies have undertaken correlation-type analysis between an economic growth variable and some variant of export growth (example, Michaely, 1977, Balassa, 1978, Heller and Porter, 1978, Tyler, 1981 and Kavoussi, 1984). The evidence of a highly significant positive correlation between the two variables was interpreted as support of the hypothesis that export-promoting measures have fueled economic growth. The second type of investigation, which derives its basis from neoclassical growth accounting technique of production function, specifies and estimates a production function of labor, capital and export levels regressed on real gross domestic product (example, Michalopoulos and Jay, 1973, Feder, 1982, Balassa, 1985, Rana, 1988 and Ram, 1987). A highly significant positive value of the coefficient of the export growth variable in the growth accounting equation was treated as evidence supporting the export-oriented growth hypothesis. Recent studies examine the issue by employing Granger causality tests based on vector autoregressive (VAR) models to determine the direction of the causality in this relationship3. The evidence from the causality investigations has been conflicting. Marin’s (1992) and Serletis’ (1992) test results, for instance, support the ELG hypothesis. Giles et al. (1992), on the other hand, using New Zealand data, finds support in only specific commodity groups. Moreover, others such as Jung and Marshall (1985), Chow (1987), Ahmad and Kwan 3
The ‘technology theory of trade’ posits that causality runs from output growth to exports. For instance, if a certain sector of the economy achieves technological innovation, it is possible that the output from this sector will far exceed the increase in domestic demand. Thus, the producers are likely to sell this surplus in the foreign market.
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(1991) and Sharma and Dhakal (1994) find only marginal support for uni-directional causality from exports to economic growth. Although the existing literature has helped provide numerous insights and raised the general awareness of policy makers toward this issue, the conceptual and methodological approach undertaken in these studies raises a number of serious concerns. First, the singleequation studies using OLS regression may suffer from a simultaneous-equation bias which can lead to invalid inferences. Second, most early studies make the a priori assumption that export growth causes output growth, thus ignoring the potential of a feed back effect (see Michaely, 1977, Kavoussi, 1984 and Kunst and Marin, 1989). Third, the few studies that do accommodate the concepts of causality and exogeneity suffer from an additional methodological constraint, in that the ELG nexus, inherently, is a long run behavioral relationship whose analysis requires methodologies for estimating a long run equilibria (see Ahmad and Harnihurun, 1995). Furthermore, VAR/Granger type analyses (which are essentially autoregressive distributed lag models) are strictly appropriate only when all the variables in the model are stationary (see Charemza and Deadman, 1992, pg. 194). If stochastic trends exist, detrended values of the timeseries with appropriate differencing should be used in order to make the regression analysis meaningful.4 Finally, the mixed and conflicting evidence amassed by previous studies is possibly a result of omitted variables that serve to mediate the linkages between export growth and economic development. Modeling the ELG hypothesis in a bi-variate framework entails the risk of inaccurate inferences being drawn, since it is clear that economic growth depends on many other factors besides exports (see for example, Glasure and Lee, 1999). By not accounting for these variables in the model, the results may mask or overstate the causal relationship between exports and economic growth. This study attempts to overcome these methodological deficiencies by examining the export led growth hypothesis in a multi-variate framework that is consistent with the theoretical inferences posited by the ELG hypothesis. 4
In fact, Toda and Phillips (1993) argue that in the presence of stochastic trends, the empirical use of the asymptotic Granger causality tests in first difference vector error correction models is superior to Granger tests in level VAR models.
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III. Methodological Issues This paper employs a methodology that attempts to address the shortcomings in the earlier literature. The empirical process comprises three parts: (1) testing for a unit root, I(1), in each series; (2) testing for the number of cointegrating vectors in the system, given that we cannot reject the null hypothesis of a unit root in the variables; and (3) estimating and testing for causality in the framework of a multi-variate vector error-correction model (VECM). If the variables for a particular country are found to be stationary in their level representation, then the standard vector auto regression (VAR) model is appropriate in detecting the direction of causality (in the Granger sense) between exports and economic growth.
Unit Root Test To test for a unit root in each series, we employ the augmented Dickey-Fuller (ADF) methodology (see Dickey-Fuller, 1981). The ADF test is estimated by the following regression: p
∆Yt = a 0 + zt + a1Yt − 1 + + ∑ ai∆Yt − 1 + εt
(1)
i =1
where a0 is a constant, t is a deterministic trend, and enough lagged differences are included to ensure that the error term becomes white noise. If the autoregressive representation of Yt contains a unit root, the t-ratio for a1 should be consistent with the hypothesis a1=0.
Cointegration Test Engle and Granger (1987) observe that even though economic time series may wander through time, that is, may have the characteristic of nonstationarity in their level, there may exist some linear combination of these variables that converges to a long run relationship over time. If the series individually are stationary only after differencing but one finds that a linear combination of their levels is stationary, then the series are said to be cointegrated. In the context of the present analysis, the existence of a common trend between the export and economic development variables means that in the long run the behavior of the common trend will drive the behavior of the two variables, and that there exists some convergence of policies.
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In other words, a finding of cointegration would simply mean that the transmission mechanism underlying the export led growth hypothesis is stable, and thus more predictable over long periods. Furthermore, shocks that are unique to one time series will quicky dissipate as the variables adjust back to their common trend. To investigate the existence of a long run equilibrium relationship between exports and economic growth, we employ the maximum-likelihood test procedure established by Johansen and Juselius (1990) and Johansen (1991).5 Specifically, Yt is a vector of n stochastic variables, then there exists a k-lag vector autoregression with Gaussian errors of the following form:
∆ Y t = a + Γ 1∆ Y t
− 1
+ ... + Γ k
− 1
∆Yt
− k − 1
+ ΠYt
− 1
+ zt
(2)
where '1,......, 'k-1 and A are coefficient matrices, zt is a vector of white noise process and " contains all deterministic elements. The focal point of conducting Johansen’s cointegration test is to determine the rank (r) of the p x p A matrix. In the present application, there are three possible ranks. First, it can be of full rank , which would imply that the variables are given by a stationary process, which would contradict the earlier finding that the two variables are nonstationary. Second, the rank of A can be zero, in which case it indicates that there is no long run relationship between export growth and economic development. In instances when A is of either full rank or zero rank, it will be appropriate to estimate the model in either levels or first differences, respectively. Finally, in the intermediate case when 0 < r < p (reduced rank), there are r cointegrating relations among the elements of Yt and p-r common stochastic trends. The number of lags used in the vector autoregression is chosen based on the evidence provided by Akaike’s Information Criterion
5
This approach is especially appealing since it provides a unified framework for estimating and testing cointegrating relations in the context of a VECM model. Thus, by treating all the variables as endogenous, this approach avoids the arbitrary choice of the dependent variable in the cointegrating equations, as in the EngleGranger methodology. They have also been shown to have good large- and finite-sample properties (see Phillips, 1991, Cheung and Lai, 1993, and Gonzala, 1994).
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(AIC) (see Akaike, 1973).6 The cointegration procedure yields two likelihood ratio test statistics, referred to as the trace test and the maximum eigenvalue (8-max) test, which will help determine which of the three possibilities is supported by the data. 7 The study employs both tests to examine the sensitivity of the results to different tests. In the trace test, the null hypothesis that there are at most r cointegrating vectors is tested against the general alternative, whereas in the maximum eigenvalue test the null hypothesis of r cointegrating vectors is tested against the alternative of at least (r+1) cointegrating vectors.8
Causality Test Under the Multi-variate VECM Framework Causality inferences in the multi-variate framework are made by estimating the parameters of the following VECM equations. m
∆GGrow = α +
∑
∑
i =1
∑ i =1
γ j∆EGrow t − j +
j =1
m
∆EGrow = a +
∑
∑
δ∆ M s +
k =1
c j∆EGrow t − j +
j =1
∑
∑ ζ∆RER +θZ
t − 1 + εt
(3)
l =1
p
0
n
bi∆GGrow +
p
0
n
βi∆GGrowt − i +
d∆ M s +
k =1
∑ e∆RER + fZ
t − 1 + ξt
(4)
l =1
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The optimal lag length chosen is the one that minimizes AIC, where AIC = ln det Skn + (2d2k)/T and k = 1, 2,...., n, d is the number of variables in the system, n is the maximum lag length considered, det denotes the determinant, and Sk is the estimated residual variance-covariance matrix for lag k.
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The trace test statistic is given by: N
TR = T ∑ ln(1 − λi ) i = r +1
where 8r+1, ...., 8N are the N-r smallest squared canonical correlations between Xt-k and ) Xt series, corrected for the effect of the lagged differences of the Xt. The maximum eigenvalue statistic is given by 8max = T ln(1-8r+1) Since the asymptotic distributions of the trace and maximum eigenvalue test statistics follow P2 distributions, a simulation procedure is needed to identify proper critical values for each test (see Osterwald-Lenum, 1992). 8
In order to mitigate the bias arising from small sample size, this study utilizes both the Reinsel and Ahn (1988) and Cheung and Lai (1993) test procedures to check for the significance of the results. Under the Reinsel and Ahn (1988) procedure, the trace test statistic is multiplied by a factor of (T-nK)/T, where T represents the size of the sample, n stands for the lag length, and K represents the number of series in the system. Under the Cheung and Lai procedure, the Osterwald-Lenum (1992) critical values are multiplied by a factor equal to 0.1+0.9T/(T-nk).
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where GGrow and EGrow denote GDP and export growth rates respectively, Ms is the M2 real money supply, RER is the real exchange rate (with respect to the U.S. dollar) and zt-1 is the errorcorrection term which is the lagged residual series of the cointegrating vector. The errorcorrection term measures the deviations of the series from the long run equilibrium relation. For example, from equation (3), the null hypothesis that EGrow does not Granger-cause GGrow is rejected (in other words, the ELG hypothesis is supported) if the set of estimated coefficients on the lagged values of EGrow is jointly significant. Furthermore, in those instances where EGrow appears in the cointegrating relationship, the ELG hypothesis is also supported if the coefficient of the lagged error-correction term is significant. Changes in an independent variable may be interpreted as representing the short run causal impact while the error-correction term provides the adjustment of GGrow and EGrow toward their respective long run equilibrium. Thus, the VECM representation allows us to differentiate between the short- and long-run dynamic relationships.
IV. Data and Empirical Findings The empirical analysis is conducted using annual observations of GDP, exports, broad real money supply (under the M2 definition) and real exchange rate covering the periods, 1950 to 1998 for India; 1969 to 1998 for Indonesia, 1953 to 1998 for Korea; 1955 to 1998 for Malaysia; and 1949 to 1998 for the Philippines. All data were obtained from the International Financial Statistics published by the International Monetary Fund (IMF). Growth rates are calculated by the transformation, (Yit-Yit-1)/Yit*100, where Y represents GDP, exports, and broad money supply. This study employs data on broad money supply (M2) and real exchange rate to act as variables mediating the relationship between economic growth and exports. The choice of the control variables is motivated by existing theoretical and empirical work in the growth literature. For instance, Glasure and Lee (1999), Cheng and Lai (1997), Piazola (1995), Ahsan, Kwan and Balbir (1992) and Grier and Tullock (1989) supply evidence that changes in the real money
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supply are important determinants of GDP growth rate. Other studies such as Glasure (1998), Lee and Glasure (1998) and Marin (1992) document the importance of real exchange rates in transmitting the effects of external shocks (such as the oil price shock in the 1970's and 1980's) on trade balance. The time series properties of GDP growth rate (GGrow), export growth rate (EGrow), real money supply (M2) and real exchange rate (RER) are first investigated. Table 1 reports ADF test results for stationarity of all the time series over the various sample periods. For the levels of the series, with the exception of the M2 variable for India and Indonesia, none rejects the null hypothesis of nonstationarity at the 5 percent level. In general, the evidence suggests the presence of I(1) for most of the variables. Tests for cointegration are performed for those countries whose variables were found to be nonstationary in the levels (i.e., Korea, Malaysia and the Philippines). Table 2 reports the Johansen test results for cointegration. For the trace test, we start with r#0 and move upwards. We stop the first time we are unable to reject the null hypothesis. For instance, in the case of Korea, the hypothesis of r=0 is rejected as the computed value of the test statistic (153.85) is greater than the critical value (58.93). Similarly, the null hypothesis of r#1 and r#2 is also rejected. However, in the next step, the null hypothesis of at most three cointegrating vectors (r#3) cannot be rejected at the 5 percent level of significance. Thus, there is evidence of three or fewer CV’s in the system. The maximum eigenvalue test provides a more conclusive evidence regarding the exact number of CV’s in the system. The results again confirm that there are three cointegrating vectors (r=3). Based on these results it can be said that there are three common factors (permanent components) driving the entire system in Korea. The results for Malaysia and the Philippines suggest that there are two and three cointegrating equations, respectively. The existence of more than one cointegrating vector indicates that the system under examination is stationary in more than one direction and, hence, more stable. In sum, the Johansen test results suggest that there is a long run, steady state relationship among exports, economic growth,
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money supply and real exchange rates for Korea, Malaysia and the Philippines.9 We applied both the Reinsel and Ahn (1988) nor the Cheung and Lai (1993) procedures to check for small sample bias. Neither test provided evidence against our cointegration results. Given the cointegration results, the next stage in our model building process requires the construction of a multi-variate VECM for Korea, Malaysia and the Philippines where the time series are found to be cointegrated. Table 3 provides causality results that are ascertained from estimating the parameters in the GDP and export growth equations given in Equations (3) and (4), and the VAR system of equations. Several important observations pertaining to the ELG hypothesis can be made by first examining the results of the GDP growth equation that is exhibited in Panel A. First, the error-correction term, which measures the speed of adjustment to past shocks in equilibrium, emerges as an important channel of influence for Korea. This implies that the variables in the Korean system have a strong tendency to adjust to their past disequilibrium by moving toward the trend values of their counterparts. Second, and perhaps most important, in terms of the short run dynamics between exports and GDP growth, it can be seen that changes in exports have a significant causal influence (in the Granger-sense) on GDP growth rates for all the three countries - Korea, Malaysia and the Philippines. Third, while on the one hand exchange rate movements play an influential role in the GDP growth equation for Korea and Malaysia, on the other hand, money supply changes are an important channel of influence on the Philippine economic performance. Panel B reports the results from the export growth (EGrow) equation. It is theoretically plausible for economic growth to cause export growth especially if innovation and technical progress in a growing economy help improve export performance. Such evidence have in fact been found for the United States (see Ghartey, 1993). Our results indicate that the errorcorrection terms are statistically significant for all countries examined. This corroborates the previous finding of a cointegrating relationship. With the exception of the Philippines, the
9
India and Indonesia did not enter the cointegration system since their money supply variables were found to be stationary in the levels.
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hypothesis that output growth does not prima facie causes export growth in the short run is rejected for all countries in the system (at the 10 percent level of significance). Furthermore, money supply changes in Korea and the Philippines are found to have an important influence on their exports. Table 4 presents the short-run dynamic relationships that are based on a VAR system, for India and Indonesia. The paper employs de-trended values of the time-series with appropriate differencing in order to make the VAR analysis meaningful. Specifically, the following VAR is estimated:
India m
∆GGrow = α +
∑ i =1
∑
γ j∆EGrow t − j +
j =1
m
∆EGrow = a +
∑
i =1
∑
δ Ms+
k =1
∑
c j∆EGrow t − j +
j =1
∑ζ ∆
2
RER +εt
(5)
l =1
p
0
n
bi∆GGrow +
p
0
n
βi∆GGrowt − i +
∑
d Ms+
k =1
∑∆
2
(6)
eRER +ξt
l =1
Indonesia m
∆GGrow = α +
t −i
+
i =1
∑ i =1
∑ γ ∆EGrow j
t− j
+
j =1
m
∆EGrow = a +
∑ j =1
∑ δ M + ∑ ζ∆RER +ε s
k =1
c j∆EGrow t − j +
∑
t
(7)
l =1
p
0
n
bi∆GGrow +
p
0
n
∑ βi∆GGrow
d Ms+
k =1
∑ e∆RER +ξ
t
(8)
l =1
In the above equations, ) represents the first difference operator and )2 is the second difference operator. It is observed from Table 4, that while exports lead economic growth for India, the converse situation where economic growth stimulates export performance is documented for Indonesia. The control variables, exchange rates and money supply, do not
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carry statistically significant coefficients. In sum, the results from Tables 3 and 4 taken together suggest that (a) the export-led growth hypothesis is clearly supported by our results for India, Korea, Malaysia and the Philippines, (b) a weak feedback relationship (i.e., bi-directional causality) emanating from economic growth to exports is observed for Indonesia, Korea and Malaysia; (c) exchange rate movements have a significant influence on Korean and Malaysian economic growth; and (d) change in money supply have a pronounced impact on Korean and Philippine export growth. The above results are largely consistent with the development economics literature in that export promotion policies engender economic growth by encouraging and making it feasible for firms in the trade sector to efficiently and fully utilize their economic resources. A re-allocation of resources takes place within the economy from the inefficient non-trade sector to the efficient trade sector. The ensuing re-allocation of resources leads to a more efficient allocation of a nation’s resources and a higher level of material well-being in the domestic economy. The simultaneous short run feedback influence of Indonesian, Korean and Malaysian economic growth on their exports may be attributed to the favorable shift in their country’s production possibilities frontier (which are primarily driven by expanding resource supplies and/or technological progress) that enables its producers to sell their surplus units to foreign markets. To obtain additional insights into the short-run transmission mechanisms between exports and economic growth, impulse response functions (IRFs) are computed. The study employs Choleski decomposition to produce the orthogonal residuals necessary to compute IRFs.10 The Choleski decomposition requires that variables in the VAR be ordered in a particular fashion. Specifically, in the presence of cross-equation residual correlation, a change in the higherordered variable will result in a corresponding change in all lower-ordered variables. The extent of the response among the lower-ordered variables depends on the degree of the residual correlation. The present study employs two different ordering schemes: (i) GGrow, EGrow, M2,
10
It must be noted that the Choleski decomposition is not without any shortcomings (see Wheeler, 1999). A major criticism of the Choleski decomposition is that it places a recursive structure on contemporaneous relationships.
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RER; and (ii) EGrow, GGrow, M2, RER. In the former ordering system, GGrow is the higherordered variable, and the corresponding response of EGrow to changes in GGrow is presented in Figures 1A-5A. In the second ordering system, EGrow takes precedence over GGrow as the higher-ordered variable, and its impact on GGrow is shown in Figures 1B-5B. Of course, other such ordering systems could be constructed, but our ordering systems seem reasonable in light of the information lags present and the deployment of annual data. It is also consistent with the principal purpose of our investigation, i.e., testing the dynamic relationship between exports and economic growth. The IRFs (10 periods) from shocks of each variable are traced by using the simulated response of the estimated autoregressive system. An inspection of the graphs reveals that the IR analysis are in conformity with the causality tests. Looking at the individual country impulse response graphs, it can be observed that both GDP and exports, on average, fully accommodate shocks to the other variable within four to five periods. India, however, stands out as an exception to this observation. The country’s economic growth is seen to take an extended period of time to fully digest innovations in its export sector. Furthermore, in the cases of Korea and the Philippines, it is surprising to observe that the immediate impact of a one-unit shock in exports on economic performance is negative. However, the sign is quickly reversed in the subsequent periods as their economies respond positively to the stimulus in exports. In summary, the results from the impulse response functions support the presence of significant dynamic relationship between exports and economic growth.
V. Summary and Conclusions During the past few decades, the export-led growth hypothesis has been a topic of sustained interest and controversy in the economic development literature. This study improves upon past studies by proposing a theoretically reasonable approach to reexamine the GDP-export relationship for five emerging economies of Asia namely — India, Indonesia, Korea, Malaysia, and the Philippines. The emerging countries of Asia provide an excellent avenue to examine the
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issues relevant to our study. Specifically, we utilize the Johansen’s cointegration process for testing the rank of the cointegration space spanned by the stochastic process of exports, GDP growth, real money supply, and real exchange rate. We then employ the long run equilibrium restriction from the cointegration model to examine the temporal interrelationships between these variables. The study makes several important findings. First, we confirm that export-led growth nexus is inherently a steady state, long run phenomenon, in that they are found to be cointegrated in the cases of Korea, Malaysia and the Philippines. Second, based on the VECM results, we surmise that both exports and economic growth are related to past deviations (error-correction terms) from the empirical long run relationship. This implies that all variables in the system have a tendency to quickly revert back to their equilibrium relationship. Finally, we find support in favor of ELG hypothesis in that export growth has a causal influence on economic growth for all countries with the notable exception of Indonesia. This implies that any rise in export growth would have a positive influence on economic development in both the long- and short-runs. Evidence from the impulse response function corroborates this finding while providing additional insights into the transmission mechanism. From a policy perspective, the results from our study imply that countries having nascent economies should adopt export-oriented measures in conjunction with sound fiscal and monetary policies in order to stimulate economic growth.
ACKNOWLEDGMENTS The authors would like to thank an anonymous referee whose helpful comments and suggestions have been instrumental in improving the paper. The authors are responsible for any remaining errors.
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References Ahmad, J. and S. Harnihurun, 1995. Unit Roots and Cointegration in Estimating Causality Between Exports and Economic Growth: Empirical Evidence from the ASEAN Countries. Economics Letters 49: 329-334 Ahmad, J. and A.C. Kwan, 1991. Causality Between Exports and Economic Growth: Empirical Evidence from Africa. Economics Letters 37: 243-248 Ahsan, S.M., A.C. Kwan, and B.S. Sahni, 1992. Public Expenditures and National Income Causality: Further Evidence on the Role of Omitted Variables. Southern Economic Journal 59: 623-634 Akaike, H, 1973. Information Theory and an Extension of the Maximum Likelihood Principle. In 2nd International Symposium on Information Theory, edited by B.N. Petrov and F. Craki, Budapest: Akademiai Kiado Balassa, B., 1978. Exports and Economic Growth: Further Evidence. Journal of Development Economics 5: 181-189 Balassa, B. 1985. Exports, Policy Choices and Economic Growth in Developing Countries After the 1973 Oil Shock. Journal of Development Economics 18: 25-35 Charemza, W.W. and Deadman, D.F. 1992. New Directions in Economic Practice, (Edward Elgar Publishing Limited, England) Cheng, B.S. and T.W. Lai, 1997. Government Expenditures and Economic Growth in South Korea: A VAR Approach. Journal of Economic Development. 22: 11-24 Cheung, Y.W. and K.S. Lai, 1993. Finite-sample Sizes of Johansen’s Likelihood Ratio Tests for Cointegration. Oxford Bulletin of Economics and Statistics 55: 313-328 Chow, P.C.Y. 1987. Causality Between Export Growth and Industrial Performance: Empirical Evidence from the NIC’s. Journal of Development Economics 26: 53-63 Dickey, D.A. and W.A. Fuller, 1981. Likelihood Ratio Statistics for Autoregressive Time Series. Econometrica 49: 1057-1072 Engle, R.F. and C.W.J. Granger, 1987. Cointegration and Error Correction: Representation, Estimation, and Testing. Econometrica 55: 251-276 Feder, G., 1982. On Exports and Economic Growth. Journal of Development Economics 12: 59-73 Ghartey, E.E., 1993. Causal Relationship Between Exports and Economic Growth: Some Empirical Evidence in Taiwan, Japan and the U.S. Applied Economics 25: 1145-1153
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Giles, D.E.A., J.A. Giles and E. McCann, 1992. Causality, Unit Roots and Export-Led Growth: The New Zealand Experience. Journal of International Trade and Economic Development 2: 195-218 Glasure, Y., 1998. Trade Conflict Resolutions and Economic and Export Performances: The Korean Experience Between 1973-1994. presented at the American Economic Association Conference, Chicago, IL Glasure, Y. U. and A.R. Lee, 1999. The Export-Led Growth Hypothesis: The Role of the Exchange Rate, Money and Government Expenditure from Korea. Atlantic Economic Journal, 27: 260-272 Grier, K.B. and G. Tullock (1989), “An Empirical Analysis of Cross-National Economic Growth, 1951-80,” Journal of Monetary Economics, 259-276 Gonzala, J.,1994. Comparison of Five Alternative Methods of Estimating Long Run Equilibrium Relationships. Journal of Econometrics, 60: 203-233 Heller, P.S. and R.C. Porter, 1978. Exports and Growth: An Empirical Investigation. Journal of Development Economics 3: 191-193 Johansen, S., 1991. Estimation and Hypothesis Testing for cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica 59: 1551-1580 Johansen, S. and K. Juselius, 1990. Maximum Likelihood Estimation and Inference on Cointegration - With Applications to the Demand for Money. Oxford Bulletin of Economics and Statistics 52: 169-210 Jung, W.S. and P.J. Marshall, 1985. Exports, Growth and Causality in Developing Countries. Journal of Development Economics 18: 1-12 Kavoussi, R.M., 1984. Export Expansion and Economic Growth: Further Empirical Evidence. Journal of Development Economics 14: 241-250 Krueger, A., 1990. Perspectives on Trade and Development. Chicago: University of Chicago Press. Kunst, R.M. and D. Marin, 1989. On Exports and Productivity: A Causal Analysis. Review of Economics and Statistics 71: 699-703 Lee, A.R. and Y.U. Glasure, 1998. The Political Dynamics of Trade Negotiation: The KoreanUS Experience Between 1960 and 1990,” in Walter Jung, Xiaobing Li eds, Korea and Regional Geographics, New York, NY: University Press of America Lutkepohl, H., 1989. Asymptotic Distributions of Impulse Response Functions of Estimated VAR Models with Orthogonal Residuals. Journal of Econometrics. 72: 116-125
18
Marin, D., 1992. Is the Export-Led Growth Hypothesis Valid for Industrialized Countries?. The Review of Economics and Statistics 74: 678-688 Michaely, M., 1977. Exports and Growth: An Empirical Investigation. Journal of Development Economics 4: 49-53 Michalopoulos, D. And K. Jay, 1973. Growth of Exports and Income in the Developing World: A Neo-classical View. Discussion Paper No. 28, Agency for International Development, Washington, D.C. Osterwald-Lenum, M., 1992. A Note with Quantiles of the Asymptotic Distribution of the Likelihood Cointegration Rank Test Statistics: Four Cases Oxford Bulletin of Economics and Statistics 54: 461-472 Phillips, P.C.B., 1991. Optimal Inference in Cointegrated Systems. Econometrica 59: 283306 Piazola, M., 1995. Determinants of South Korean Economic Growth, 1955-1990. International Economic Journal 9: 109-133 Ram, R., 1987. Exports and Economic Growth in Developing Countries: Evidence from TimeSeries and Cross-section Data. Economic Development and Cultural Change 36: 51-72 Rana, P.B., 1988. Exports, Policy Changes and Economic Growth in Developing Countries After the 1973 Oil Shock: Comment. Journal of Development Economics 18: 261-264 Reinsel, G.C. and S. K. Ahn, 1988. Asymptotic Distribution of the Likelihood Ratio Test for Cointegration in the Nonstationary Vector AR Model. Technical Report, University of Wisconsin, Madison. Serletis, A., 1992. Export Growth and Canadian Economic Development. Journal of Development Economics 38: 133-145 Sharma, C.S. and D. Dhakal, 1994. Causal Analyses Between Exports and Economic Growth in Developing Countries. Applied Economics 26: 1145-1157 Toda, H.Y. and P.C.B. Phillips, 1993. Vector Autoregressions and Causality. Econometrica 61: 1367-1393 Tyler, G.W., 1981. Growth and Export Expansion in Developing Countries: Some Empirical Evidence. Journal of Development Economics 9: 121-130 Wheeler, M., 1999. The Macroeconomic Impacts of Government Debt: An Empirical Analysis of the 1980s and 1990s. Atlantic Economic Journal 27: 273-284
19
Table 1. ADF Unit Root Test
Country/Period
SeriesR
Level
India 1950-1998
GGrow
Tµ = -2.41 TJ = -5.25
Tµ = -6.70** TJ = -6.66**
— —
Tµ = -4.00** TJ = -3.07
TJ = -4.06**
—
RER
Tµ = -2.45 TJ = -2.05
Tµ = -1.15 TJ = -2.54
Tµ = -4.84** TJ = -4.90**
M2
Tµ = -3.82** TJ = -3.94**
— —
— —
GGrow
Tµ = -1.69 TJ = -3.19
Tµ = -8.07** TJ = -8.74**
— —
EGrow
Tµ = -2.31 TJ = -2.99
Tµ = -4.40** TJ = -4.42**
— —
RER
Tµ = -0.94 TJ = -1.98
Tµ = -3.67** TJ = -4.20**
— —
M2
Tµ = -16.20** TJ = -15.98**
— —
— —
GGrow
Tµ = -1.58 TJ = -2.59
Tµ = -4.94** TJ = -5.22**
— —
EGrow
Tµ = -0.33 TJ = -2.60
Tµ = -3.72** TJ = -3.79**
— —
RER
Tµ = -1.07 TJ = -2.41
Tµ = -3.56** TJ = -3.63**
— —
M2
Tµ = -1.75 TJ = -2.81
Tµ = -4.37** TJ = -4.50**
— —
EGrow
Indonesia 1969-1998
Korea 1953-1998
Tµ = -2.65
First Difference
Second Difference
* indicates statistical significance at the 5% level. Tµ = without trend; TJ = with trend. The critical values at the 5% significance level are –2.97 and –3.58, respectively, for without trend and with trend. The critical values at the 10% significance level are –2.60 and –3.18, respectively, for without trend and with trend. R GGrow = GDP growth rate; EGrow = export growth rate, RER = real exchange rate and M2=broad money supply.
20
Table 1. ADF Unit Root Test (Continued)
Country/Period
SeriesR
Level
First Difference
Malaysia 1955-1998
GGrow
Tµ = -2.38 TJ = -2.40
Tµ = -5.53** TJ = -5.48**
— —
EGrow
Tµ = -2.01 TJ = -2.40
T µ = -4.80** T J = -4.80**
— —
RER
Tµ = -1.20 TJ = -0.06
Tµ = -2.74* TJ = -3.20*
— —
M2
Tµ = -1.94 TJ = -1.94
Tµ = -3.90** TJ = -3.88**
— —
GGrow
Tµ = -2.77 TJ = -2.83
Tµ = -6.53** TJ = -6.61**
— —
EGrow
Tµ = -2.62 TJ = -2.85
Tµ = -5.40** TJ = -5.41**
— —
RER
Tµ = -1.82 TJ = -0.66
Tµ = -3.39** TJ = -4.12**
— —
M2
Tµ = -2.18 TJ = -2.47
Tµ = -4.28** TJ = -4.28**
— —
The Philippines 1949-1998
**
Second Difference
indicates statistical significance at the 5% level. Tµ = without trend; TJ = with trend. The critical values at the 5% significance level are –2.97 and –3.58, respectively, for without trend and with trend. The critical values at the 10% significance level are –2.60 and –3.18, respectively, for without trend and with trend. R GGrow = GDP growth rate; EGrow = export growth rate, RER= real exchange rate and M2=broad money supply.
21
Table 2. Multi-variate Cointegration Tests
Trace Test
Maximum Eigenvalue Test
Country (Null hypothesis)
Test Statistic
Critical Value
Null hypothesis
Test Statistic
Critical Value
153.85** 77.30** 30.48** 10.09
58.93 39.33 23.83 11.54
r=0 r#1 r#2 r#3
76.55** 46.82** 20.40** 10.09
31.00 24.35 18.33 11.54
94.08** 52.80** 16.38 0.25
58.93 39.33 23.83 11.54
r=0 r#1 r#2 r#3
41.28** 36.42** 16.13 0.25
31.00 24.35 18.33 11.54
123.07** 63.28** 27.56** 1.14
58.93 39.33 23.83 11.54
r=0 r#1 r#2 r#3
59.72** 35.72** 26.42** 1.14
31.00 24.35 18.33 11.54
Korea r=0 r#1 r#2 r#3 Malaysia r=0 r#1 r#2 r#3 Philippines r=0 r#1 r#2 r#3 **
indicates statistical significance at the 5% level. The critical values are obtained from the Microfit 4.0 program.
22
Table 3. Multi-variate Granger-Causality Tests Based on VECM (F-Statistics) Panel A: GDP Growth Equation (Dependent Variable: GGrow)S
INDEPENDENT VARIABLES Country
zt-1
Korea
22.41***
Malaysia
0.71
Philippines
1.06
'RER
'M2
LagsR
1.25
4.44**
0.64
1, 1, 1, 1
6.56***
0.24
8.44***
1.48
2, 1, 1, 1
3.59**
2.34
0.12
3.57**
3, 1, 1, 1
'EGrow
22.56***
'GGrow
Panel B: Export Growth Equation (Dependent Variable: EGrow)S INDEPENDENT VARIABLES
Country
zt-1
'EGrow
'GGrow
'RER
'M2
LagsR
4.51**
0.92
3.11*
1.09
24.86***
1, 1, 1, 1
Malaysia
14.56***
2.84*
2.65*
0.05
0.90
1, 1, 1, 1
Philippines
37.27***
1.55
0.97
0.04
21.71***
1, 1, 1, 1
Korea
* ** ***
, , associated with the F-statistics represent statistical significance at the 10%, 5% and 1% level respectively. The standard t-test is used to determine the level of marginal significance for the error correction term (zt-1). S Results for Panels A and B are obtained from the estimation of Equations (3) and (4) respectively. R Lags represent the optimal lag length employed for GGrow and EGrow as determined by the AIC.
23
Table 4. Causality Tests based on VAR (F-Statistics) Panel A: GDP Growth Equation (Dependent Variable: GGrow)S
INDEPENDENT VARIABLES Country
'EGrow
'GGrow
'RER
'M2
LagsR
India
5.95***
16.22***
0.41
1.08
2, 2, 2, 2
Indonesia
0.46
1.21
0.09
1.29
2, 2, 2, 2
Panel B: Export Growth Equation (Dependent Variable: EGrow)S INDEPENDENT VARIABLES Country
'EGrow
'GGrow
'RER
'M2
LagsR
India
15.38***
0.10
0.07
0.28
2, 2, 2, 2
Indonesia
2.89*
2.79*
0.55
0.29
2, 2, 2, 2
* ** ***
, , associated with the F-statistics represent statistical significance at the 10%, 5% and 1% level respectively. Results for Panels A and B are obtained from the estimation of Equations (5), (6), (7) and (8) respectively. R Lags represent the optimal lag length employed for GGrow and EGrow as determined by the AIC. S
24
Figure 1A India: Response of GDP Growth to Exports
Figure 2A Indonesia: Response of GDP Growth to Exports
8 100
6
80 60 Standard Deviation
Standard Deviation
4 2 0 -2
40 20 0
-4
-20
-6
-40
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
7
8
9
10
8
9
10
8
9
10
8
9
10
Figure 4A Malaysia: Response of GDP Growth to Exports
Figure 3A Korea: Response of GDP Growth to Exports
10
10
8
8
6 Standard Deviation
6 Standard Deviation
6 Periods
Periods
4 2
4 2
0
0
-2
-2 -4
-4 1
2
3
4
5
6 Periods
7
8
9
1
10
2
3
4
5
6
7
Periods
Figure 5A Philippines: Response of GDP Growth to Exports
Figure 1B India: Response of Exports to GDP Growth
8
15
6
Standard Deviation
Standard Deviation
10
4
2
0
5
0
-5
-2
-4
-10
1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
Periods
Periods
Figure 3B Korea: Response of Exports to GDP Growth
20
Figure 2B Indonesia: Response of Exports to GDP Growth
200
15
Standard Deviation
Standard Deviation
150
100
50
10
5
0
-5
0
-10
-50 1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6 Periods
Periods
25
7
Figure 4B Malaysia: Response of Exports to GDP Growth
Figure 5B Philippines: Response of Exports toDGDP Growth
20
40
30
10 Standard Deviation
Standard Deviation
15
5
0
-5
20
10
0
-10
-10
-20
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6 Periods
Periods
26
7
8
9
10
