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: eanoruo@coppin.edu

Sanjay Ramchander*

Department of Finance and Real Estate

College of Business

Colorado State University Fort Collins, CO 80523

Ph: (970) 491-6681

Email: sanjay.ramchander@colostate.edu

________________ * 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.

3

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 export-

promotion policy have been more successful than those that have pursued import substitution

4

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 bi-

variate 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.

5

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.

6

(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 single-

equation 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 time-

series 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.

7

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:

t

p

1i

1ti1t10t YaYaztaY ε+∑ ∆++++=∆=

−− (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.

8

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:

tt1t zYY...YaY 1t1k1k1t +Π+∆Γ++∆Γ+=∆ −−−−− (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 Engle-

Granger methodology. They have also been shown to have good large- and finite-sample properties (see Phillips,

1991, Cheung and Lai, 1993, and Gonzala, 1994).

9

(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.

tt

p

l

s

k

jtj

n

j

it

m

i

ZRERMEGrowGGrowiGGrow εθζδγβα ++∆+∆+∆+∆+=∆ −==

−

=

−

=∑∑∑∑ 1

1

0

111

(3)

tt

p

l

s

k

jtj

n

j

m

i

i fZREReMdEGrowcGGrowbaEGrow ξ++∆+∆+∆+∆+=∆ −==

−

==∑∑∑∑ 1

1

0

111

(4)

6 The optimal lag length chosen is the one that minimizes AIC, where

AIC = ln det Skn + (2d

2k)/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.

7 The trace test statistic is given by:

)1ln(1

∑ −=+=

N

ri

iTTR λ

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).

10

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 error-

correction term which is the lagged residual series of the cointegrating vector. The error-

correction 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

11

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,

12

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 error-

correction 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.

13

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

t

p

l

s

k

jtj

n

j

it

m

i

RERMEGrowGGrowiGGrow εζδγβα ∑∑∑∑==

−

=

−

=

+∆++∆+∆+=∆1

2

0

111

(5)

t

p

l

s

k

jtj

n

j

m

i

i eRERMdEGrowcGGrowbaEGrow ξ∑∑∑∑==

−

==

+∆++∆+∆+=∆1

2

0

111

(6)

Indonesia

t

p

l

s

k

jtj

n

j

it

m

i

RERMEGrowGGrowiGGrow εζδγβα ∑∑∑∑==

−

=

−

=

+∆++∆+∆+=∆1

0

111

(7)

t

p

l

s

k

jtj

n

j

m

i

i REReMdEGrowcGGrowbaEGrow ξ∑∑∑∑==

−

==

+∆++∆+∆+=∆1

0

111

(8)

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

14

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 higher-

ordered 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.

15

RER; and (ii) EGrow, GGrow, M2, RER. In the former ordering system, GGrow is the higher-

ordered 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

16

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.

17

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

18

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 Korean-

US 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

19

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: 283-

306

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 Time-

Series 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

20

Table 1. ADF Unit Root Test

Country/Period SeriesR Level First Difference Second Difference

India 1950-1998 GGrow Tµ = -2.41 Tµ = -6.70** —

TJ = -5.25 TJ = -6.66** —

EGrow Tµ = -2.65 Tµ = -4.00**

TJ = -3.07 TJ = -4.06** —

RER Tµ = -2.45 Tµ = -1.15 Tµ = -4.84**

TJ = -2.05 TJ = -2.54 TJ = -4.90**

M2 Tµ = -3.82** — —

TJ = -3.94** — — Indonesia 1969-1998 GGrow Tµ = -1.69 Tµ = -8.07** —

TJ = -3.19 TJ = -8.74** —

EGrow Tµ = -2.31 Tµ = -4.40** —

TJ = -2.99 TJ = -4.42** —

RER Tµ = -0.94 Tµ = -3.67** —

TJ = -1.98 TJ = -4.20** —

M2 Tµ = -16.20** — —

TJ = -15.98** — — Korea 1953-1998 GGrow Tµ = -1.58 Tµ = -4.94** —

TJ = -2.59 TJ = -5.22** —

EGrow Tµ = -0.33 Tµ = -3.72** —

TJ = -2.60 TJ = -3.79** —

RER Tµ = -1.07 Tµ = -3.56** —

TJ = -2.41 TJ = -3.63** —

M2 Tµ = -1.75 Tµ = -4.37** —

TJ = -2.81 TJ = -4.50** —

* 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 1. ADF Unit Root Test (Continued)

Country/Period SeriesR Level First Difference Second Difference

Malaysia 1955-1998 GGrow Tµ = -2.38 Tµ = -5.53

** —

TJ = -2.40 TJ = -5.48**

—

EGrow Tµ = -2.01 T µ = -4.80** —

TJ = -2.40 T J = -4.80**

—

RER Tµ = -1.20 Tµ = -2.74* —

TJ = -0.06 TJ = -3.20*

—

M2 Tµ = -1.94 Tµ = -3.90**

—

TJ = -1.94 TJ = -3.88** —

The Philippines 1949-1998 GGrow Tµ = -2.77 Tµ = -6.53

** —

TJ = -2.83 TJ = -6.61**

—

EGrow Tµ = -2.62 Tµ = -5.40** —

TJ = -2.85 TJ = -5.41** —

RER Tµ = -1.82 Tµ = -3.39**

—

TJ = -0.66 TJ = -4.12**

—

M2 Tµ = -2.18 Tµ = -4.28**

—

TJ = -2.47 TJ = -4.28**

—

**

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.

22

Table 2. Multi-variate Cointegration Tests

Trace Test Maximum Eigenvalue Test Country

Test Critical Null Test Critical

(Null hypothesis) Statistic Value hypothesis Statistic Value

Korea

r=0 153.85** 58.93 r=0 76.55**

31.00

r#1 77.30**

39.33 r#1 46.82**

24.35

r#2 30.48**

23.83 r#2 20.40**

18.33

r#3 10.09 11.54 r#3 10.09 11.54

Malaysia

r=0 94.08** 58.93 r=0 41.28**

31.00

r#1 52.80**

39.33 r#1 36.42**

24.35

r#2 16.38 23.83 r#2 16.13

18.33

r#3 0.25 11.54 r#3 0.25 11.54

Philippines

r=0 123.07** 58.93 r=0 59.72**

31.00

r#1 63.28**

39.33 r#1 35.72**

24.35

r#2 27.56** 23.83 r#2 26.42** 18.33

r#3 1.14 11.54 r#3 1.14 11.54

**

indicates statistical significance at the 5% level. The critical values are obtained from the Microfit 4.0 program.

23

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 'EGrow 'GGrow 'RER 'M2 LagsR

Korea 22.41***

22.56***

1.25 4.44**

0.64 1, 1, 1, 1

Malaysia 0.71 6.56***

0.24 8.44***

1.48 2, 1, 1, 1

Philippines 1.06

3.59**

2.34 0.12 3.57**

3, 1, 1, 1

Panel B: Export Growth Equation (Dependent Variable: EGrow)S

INDEPENDENT VARIABLES

Country zt-1 'EGrow 'GGrow 'RER 'M2 LagsR

Korea 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

*,

**,

*** 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.

24

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.

S 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.

25

-6

-4

-2

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10

Figure 1A

India: Response of GDP Growth to Exports

Sta

nd

ard

Dev

iati

on

Periods

-40

-20

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Figure 2A

Indonesia: Response of GDP Growth to Exports

Periods

Sta

ndar

d D

evia

tion

-4

-2

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10

Figure 3A

Korea: Response of GDP Growth to Exports

Periods

Sta

ndar

d D

evia

tion

-4

-2

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10

Figure 4A

Malaysia: Response of GDP Growth to Exports

Periods

Sta

nd

ard

Dev

iati

on

-4

-2

0

2

4

6

8

1 2 3 4 5 6 7 8 9 10

Figure 5A

Philippines: Response of GDP Growth to Exports

Periods

Sta

nd

ard

Dev

iati

on

-10

-5

0

5

10

15

1 2 3 4 5 6 7 8 9 10

Figure 1B

India: Response of Exports to GDP Growth

Periods

Sta

ndar

d D

evia

tion

-50

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10

Figure 2B

Indonesia: Response of Exports to GDP Growth

Periods

Sta

nd

ard

Dev

iati

on

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Figure 3B

Korea: Response of Exports to GDP Growth

Periods

Sta

nd

ard

Dev

iati

on

26

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10

Figure 4B

Malaysia: Response of Exports to GDP Growth

Periods

Sta

ndar

d D

evia

tion

-20

-10

0

10

20

30

40

1 2 3 4 5 6 7 8 9 10

Figure 5B

Philippines: Response of Exports toDGDP Growth

Periods

Sta

ndar

d D

evia

tion