Exports and Economic Growth in ASEAN Countries

Exports and Economic Growth in ASEAN Countries: Evidence from Panel Data Analysis Fumitaka

Furuoka *

This study employs panel data analysis to examine the relationship between exports and economic growth in five ASEAN (Association of South East Asian Nations) countries (i.e., Malaysia, Indonesia, the Philippines, Singapore and Thailand). Three separate methods have been used: (1) Pooled Ordinary Least Squares (OLS), (2) One-way fixed/ random effects, and (3) Two-way fixed/random effects. Empirical results show that the one-way fixed effects analysis is the best model among all the models. As the one-way fixed effects model shows, there exists a significant positive relationship between exports and economic growth in the five ASEAN nations. Panel cointegration test, however, implies that there is no cointegrating relationship between these two variables.

Introduction With conspicuous imbalance in the distribution of wealth between the ‘have’ and ‘have-not’ nations, an important question for development economists remains: How can poor nations break free from the ‘vicious circle’ of poverty and boost the much-needed economic growth? Or, in other words, what could be their ‘engine’ of growth? One of the most viable development strategies for a country’s economic success is to find its own niche in the global marketplace, which means to be able to tap the demands of the world economy. Many developing countries have been trying to overcome a dismal economic situation by promoting international trade. In these efforts, exports have been viewed as ‘engine’ of economic growth. In recent decades, the validity of export-led growth strategy has been supported by impressive success stories from Asian countries. Japan’s remarkable performance in the global export market in the 1960s was repeated in the 1970s by Asian Newly Industrializing Economies (NIEs) and, in the 1980s, by a few ASEAN (Association of South East Asian Nations) countries. China’s recent economic success has highlighted the importance of exports in boosting a nation’s economic development. Till the end of the 1970s, China’s doors were closed to foreigners, and the country was in the grip of economic stagnation and pervading poverty. Since the introduction of the ‘open-door policy’ in the end of the 1970s, China has been experiencing a very rapid economic growth. * Lect urer, Sc hool of Business and Economics, Universiti Malaysia Sab ah, Sabah, Malaysia. E-mail: [email protected] Exports GrowthPress. in ASEAN Countries: Evidence from Panel Data Analysis © 2009and TheEconomic Icfai University All Rights Reserved.

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Malaysia, Indonesia, the Philippines, Singapore and Thailand—the five ASEAN countries chosen as case studies in the current inquiry—also provide a good example of economic success because they were able to find their own niche in the global economy. Though all of these economies have been export-driven, there are differences in terms of each country’s main export commodities. Among these five ASEAN nations, Singapore could be placed at one end of the scale as its exports consist mostly of manufactured goods, while Indonesia could be placed at the other end since it exports mainly primary commodities (e.g., petroleum, plywood and rubber). On the other hand, Malaysia and Thailand occupy a position somewhere in the middle as they export both primary commodities and manufactured goods. Thailand’s exports include automobiles, electronic parts and rice, and the main bulk of Malaysia’s exports are electronic components, petroleum, Liquefied Natural Gas (LNG) and palm oil. The Philippines found its niche in the export of labor. The country relies heavily on the remittances that the Filipino workers send home. In 2005, immigrants from the Philippines sent more than $10 bn back home, which accounted for 13.5% of the country’s Gross Domestic Product (GDP).1 Although quite a number of developing countries have adopted export-driven development strategy, systematic empirical research on the relationship between a country’s exports and its economic growth is still lacking. To address this issue, this study chooses five ASEAN countries (i.e., Indonesia, Malaysia, the Philippines, Singapore and Thailand) to examine the relationship between exports and economic growth. The main objective of this study is to examine the ‘export-led growth’ hypothesis by employing panel data analysis, for the above-mentioned five ASEAN countries.

Literature Review The starting point of the debate over the relationship between a country’s economic performance and its exports can be traced back to the founding fathers of modern economic thought. Classical economists Adam Smith and David Ricardo emphasized the importance of international trade in a country’s economic growth. They argued that a country could benefit considerably if it specialized in a certain commodity or product and then exported it to the foreign countries that lacked this commodity (Smith, 1776; and Ricardo, 1817). With the evolution of economic thought, several shortcomings of the classical theory of international trade became evident. First of all, the theory does not incorporate a perspective on the consequences of the deteriorating terms of trade, which became a central trade issue between the developed and developing nations. As Cypher and Dietz (1998, p. 305) critically observed, “Especially for poor, less-developed nations, we show that the generalized argument in favor of free trade policy derived from (classical) trade theory cannot be sustained once one takes the long-term historical trend of the terms of trade into consideration”. Secondly, it is not always possible to spot in advance a country’s comparative advantage. As a result, many developing countries are experiencing serious difficulties in finding their own 1

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“Emerging-Market Indicators”, The Economist, November 26, 2005, p. 108. The Icfai University Journal of Applied Economics, Vol. VIII, No. 2, 2009

niche in the global marketplace. This fact was noted by Hausmann and Rodrik (2002), who maintained that for developing nations economic development could become a trial and error process of discovering their own strengths in the global competition. A number of empirical studies have been conducted to examine the relationship between exports and economic development. Early studies lend empirical support to the ‘export-led growth’ hypothesis (Michaely, 1977; Balassa, 1978; Feder, 1983; and Ram, 1985). However, these studies are criticized because they employed cross-section data which are, methodologically, unable to establish causal relationship between the variables (Love and Chandra, 2005). Some research studies employed time series data and analyzed the Granger causal relationship between exports and economic development (Jung et al., 1985; and Dodaro, 1993). The results provided weak empirical evidence to support the ‘export-led growth’ hypothesis. In Jung et al.’s (1985) research, out of 37 countries, causal relationship between exports and economic development was detected for only four countries. In the context of ASEAN countries, time series analysis that tested the Export-Led Growth (ELG) hypothesis showed mixed results. For example, a study by Ahmad and Harnhirun (1996) that tested the hypothesis for five ASEAN countries (i.e., Malaysia, Indonesia, Singapore, Thailand, and the Philippines) over the period 1966-1986, did not find a cointegrating relationship between exports and economic development. At the same time, Ahmad and Harnhirun’s empirical findings indicated that economic growth had been causing the expansion of exports, and not vice versa. For the Philippines, Amrinto (2006) used parametric and semi-parametric Error Correction Model (ECM) to test the ELG hypothesis over the period 1981-2004. Results from the parametric ECM indicated that there was an unidirectional causality between the Philippines’ exports and output in the short run while findings from the semi-parametric ECM established a bilateral causality between the two variables. In the Indonesian context, an empirical analysis identifying the determinants of economic growth during the period 1965-1992 was done by Piazolo (1996). The study included six variables—exports, government expenditure, population, capital formation, inflation and foreign investment—into the econometric model, and its results supported the existence of the ELG hypothesis in Indonesia. To test the ELG hypothesis in the Malaysian context for the period 1960-2001, Keong et al. (2005) used the bounds test method to examine the unidirectional causality from exports to growth, but they did not test for unidirectional causality from growth to exports. The study detected a cointegrating relationship between the country’s exports and economic growth as well as a short-run causality from exports to economic growth. On the other hand, Furuoka (2007) detected the unidirectional causality from GDP to exports, but not vice versa.

Research Methodology A panel data analysis is used in the current study to examine the relationship between the amount of national income and the volume of exports in five ASEAN countries, i.e., Malaysia, Indonesia, Thailand, the Philippines and Singapore for the period 1985-2002.2 It is hypothesized that the size of GDP is influenced by the amount of exports (EX). Three separate methods are used to 2

The data is obtained from the Asian Development Bank (2003).

Exports and Economic Growth in ASEAN Countries: Evidence from Panel Data Analysis

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analyze the model: (1) Pooled Ordinary Least Squares (OLS); (2) One-way fixed/random effects; and (3) Two-way fixed/random effects. Fixed effects approach is better suited for the cases where there exist unobservable country effects and time effects. Firstly, to examine the determinants of the size of national income without taking into account the country effects and time effects, a pooled OLS regression model could take a form:

GDPit =  + 1 EXit + it

...(1)

where GDPit is the size of GDP in country i in year t, EXit is the amount of exports in country i in year t ,  is the intercept,  1 is the slope parameter, and  it is the er ror term. Incorporating the country effects, the one-way fixed effects model could be:

GDPit = i + 1 EXit + it

...(2)

where i is recipient effects. Finally, incorporating both country effects and time effects, the two-way fixed effects model could take a form:

GDPit = 0 + i + t + 1 EXit + it

...(3)

where 0 is the intercept, i is country effects, and t is time effects. If there exist country effects in the regression model, the pooled OLS, or Equation (1), does not effectively estimate the linkage between the independent and dependent variables. Similarly, if there exist time effects, the one-way fixed effects model, or Equation (2), does not effectively estimate the regression model. Thus, there is need to analyze the significance of country effects as well as time effects. The F-test is used for this purpose (Greene, 2003). On the other hand, the Hausman specification test is employed to determine whether fixed effects approach is better suited for the analysis than the random effects approach. The random effects model could be written as:

GDPit =  + ui + 1 EXit + it

...(4)

where ui is group-specific random element. In the second stage, this paper uses two panel unit root tests (IPS test and MW test) to determine whether both the variables are integrated of order one or not. The IPS test is based on the mean value of individual ADF statistics, or t-bar (Im et al., 2003). There are two steps to estimate the IPS test statistic. In the first step, obtain the individual ADF statistics. In the second step, obtain t-bar or mean values of individual ADF statistics.

t-barNT =

1 N

N

t iT  i 1

...(5)

i

The corresponding standardized t-bar statistic is given by:



Ztbar =

N tbar  N 1 E (tTi ) N 1

N

Var (tT )  i 1

10

i

 ...(6)

The Icfai University Journal of Applied Economics, Vol. VIII, No. 2, 2009

where E (tT ) is the mean of tT and Var (tT ) is the variance of tT . Im et al. (2003) provide Monte Carlo estimate of E (tT ) and Var (tT ). The present paper also employs the MW test which is based on combined significance levels (p -values) from individual unit root tests. According to Maddala and Wu (1999), if the test statistics are continuous, the significance level i (i = 1, 2, …, N ) are independent and uniform (0, 1) variables, the (–2S log pi ) has a chi-square distribution with two degrees of freedom. They use combined p -values, or  which is expressed as:

  2

N

log  i  i

...(7)

1

where  has a chi-square distribution with 2N degrees of freedom. On the other hand, if the independent and dependent variables are cointegrated, the residual eit will be integrated of order zero, denoted by I(0). This paper uses Pedroni’s method to test whether the residual is integrated of order zero. Pedroni (1999 and 2004) used two types of panel cointegration tests. The first is the ‘panel statistic’ that is equivalent to unit root statistic against homogenous alternative, while the second is the ‘group mean statistic’ that is analogous to the panel unit root tests against heterogeneous alternative. Pedroni (2004) argues that the ‘panel statistic’ can be constructed by taking the ratio of the sum of the numerators and the sum of the denominators of the analogous conventional time series statistics. The ‘group mean statistic’ can be constructed by first computing the ratio corresponding to the conventional time series statistics and then computing the standardized sum of the entire ratio over the N dimension of the panel. This paper uses two panel cointegration tests as suggested by Pedroni (1999 and 2004), the ‘panel ADF statistic’ and ‘group mean ADF statistic’. The two versions of the ADF statistics could be defined as:  Z t   ~ s 2NT  

Panel:

Group Mean: N

1/ 2

N

T

 i t

Zt N

1 1

1 / 2



eˆ2i , t 1   

1 / 2

N

T

eˆi t  i t

 T   ˆi eˆ2i ,t 1  s   i 1  t 1  N



, 1  eˆi , t

1 1

1 / 2

T

eˆi t  t

, 1  eˆi , t

...(8)

...(9)

1

sNT is the contemporaneous panel where eˆi ,t represents the residuals from the ADF estimation, ~ variance estimator, and sˆi is the standard contemporaneous variance of the residuals from the ADF regression.3 The asymptotic distribution of panel and group mean statistics can be expressed as:

N ,T   N  3

 N (0,1)

...(10)

This paper uses the unweighted versions of statistics. Pedroni (2004) maintained that in Monte Carlo simulation unweighted statistics tended to outperform the weighted statistics.

Exports and Economic Growth in ASEAN Countries: Evidence from Panel Data Analysis

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where N,T is the appropriately standardized form of each statistic,  is the mean adjustment term and  is the variance adjustment term. Pedroni provided Monte Carlo estimate of  and (Pedroni, 1999).

Empirical Results The results of regression analyses are presented in Tables 1 and 2. The multiple coefficient of determination (R2) is 0.870. Controlling for country effects causes R2 to increase considerably to 0.952. Conditioning on both country effects and time effects leads to a further slight improvement of R2 to 0.959. Comparing the one-way fixed effects model with the random effects model, the Lagrange Multiplier (LM) test and Hausman’s specification test indicate that fixed effects model is a better choice for the analysis. Furthermore, to compare the pooled OLS model with the one-way fixed effects model, the null hypothesis, i (recipient effects) equals zero is rejected at 0.01 level of significance. This implies the presence of country effects in the model. Table 1: Panel Data Analysis (Pooled OLS, One-Way Fixed Effects and One-Way Random Effects) Dependent Variable: GD P Poole d OLS

One -Way Fixed E ffec ts

One-Wa y Random E ffects

EX

2.071 (24.35)**

1.497 (18.35)**

1.561 (19.85)**

R2

0.870

0.952

0.959

Lagrange Multiplier Test (One-Way) (Random Effects/Fixed Effects vs. Classical Regression Model)

108.49**

Hausman’s Specification Test (One-Way) (Fixed Effects vs. Random Effects)

10.10**

F -Test for Model Specification (One-Way Fixed Effects vs. Pooled OLS)

36.04**

N o t e : Numbers in parentheses in pooled and fixed effect model are t-statistics. Number in parenthesis in random effects is coefficient divided by standard error. ** indicates significance at the 0.01 level.

The same method could be applied to examine the significance of time effects. Table 2 shows the results of the two-way fixed effects model. LM test and Hausman’s specification test show that the two-way fixed effects regression is better than the random effects model. To compare the one-way fixed effects model with the two-way fixed effects model, the null

hypothesis,t (time effects) equals zero could not be rejected. The result implies that one-way fixed effects analysis is the best among the three different models. As the one-way fixed effects model shows, independent and dependent variables have significant relationship in the five 12

The Icfai University Journal of Applied Economics, Vol. VIII, No. 2, 2009

Table 2: Panel Data Analysis (Two-Way Fixed Effect and Two-Way Random Effects) Dependent Variable: GD P Two -Way Fixed Effe cts

Two -Way Ran dom Effects

EX

1.376 (13.74)**

1.480 (16.65)**

R2

0.959

0.870

Lagrange Multiplier Test (Two-Way) (Random Effects/Fixed Effects vs. Classical Regression Model)

115.19**

Hausman’s Specification Test (Two-Way)(Two-Way Fixed Effects vs. Random Effects)

5.11*

F -Test for Model Specification (Two-Way Fixed Effects vs. Pooled OLS)

6.59**

F -Test for Model Specification (Two-Way Fixed Effects vs. One-Way Fixed Effects)

0.83

N o t e : Number in parenthesis in fixed effect model is t-statistic. Number in parenthesis in random effects is coefficient divided by standard error. * indicates significance at 0.05 level, and ** indicates significance at 0.01 level.

ASEAN countries chosen for this study. Volume of exports are positively correlated to the size of national income. This implies that GDP tends to increase as EX increases in these countries. Results of the panel unit root tests and panel cointegration test are presented in Table 3. Before conducting the test for panel cointegration, there is a need to ensure that both the variables are integrated of order one, or I(1). The IPS and MW tests could not reject the null hypothesis of unit root at level, with or without linear trends. However, both the panel unit root tests could reject the null hypothesis of unit root at first difference, with or without trend. The results indicate that there is a strong evidence of stationarity for both GDP and EX at first difference. This implies that both the variables can be considered to be integrated of order one, I(1). On the other hand, both panel cointegration tests, i.e., the ‘panel statistic’ and the ‘group mean statistic’ failed to reject the null hypothesis of no cointegration. These statistics indicate that there is no cointegrating relationship between GDP and EX. This paper could not proceed to estimate the ECM because there is insufficient empirical evidence to establish cointegrating relationship between the two variables. In short, the one-way fixed effects model shows that GDP and EX have significant relationship in the five ASEAN countries, i.e., Malaysia, Indonesia, the Philippines, Singapore and Thailand. This implies that the size of a country’s national income tends to expand as the country’s Exports and Economic Growth in ASEAN Countries: Evidence from Panel Data Analysis

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Table 3: Panel Unit Root Test and Panel Cointegration Test Panel Unit Root Tests Le v e l In dividu al E f fe ct s

Fir st Di ffere nce

I nd iv id ua l Ef fe ct s and Linear Trends

In dividu al E f fe c t s

I nd iv id ua l Ef fe ct s and Linea r Tre nds

IPS Test

GDP

4.826

1.892

–1.667*

–1.858*

EX

3.404

0.030

–3.558**

–3.004**

MW Test

GDP

1.362

6.785

19.477*

18.063*

EX

1.168

8.284

31.387**

28.263**

Pa ne l Coi nt eg ra tio n Te st Ped ro ni Tes t

Panel ADF Statistic

Group Mean ADF Statistic

–1.539

–0.415

N o t e : * indicates significance at 0.05 level, and ** indicates significance at 0.01 level.

exports increase. On the other hand, the panel unit root tests show that GDP and EX are integrated of order one, or I(1). Panel cointegration test, however, indicates that there is no cointegrating relationship between the two variables.

Conclusion ASEAN countries are dynamic developing nations that enjoy rapid economic growth. International trade is playing an important role in propelling these countries towards the status of fully developed economies. This study empirically analyzes the relationship between exports and economic growth in five selected ASEAN countries, namely Malaysia, Indonesia, Thailand, the Philippines and Singapore. The one-way fixed effects model leads to the conclusion that, in the case of these five nations, there exists a significant positive relationship between the size of the national income and the volume of exports. This implies that with an increase in export earnings, the size of GDP in these five ASEAN countries have also been expanding. On the other hand, panel unit root tests show that GDP and exports are integrated of order one. Panel cointegration test, however, indicates that there is no cointegrating relationship between exports and economic growth of the five ASEAN countries considered in this study. Findings of the current research encourage a closer look at other factors that may influence the pace of economic growth in ASEAN countries (e.g., domestic consumption, government expenditure, etc.). Future studies on this topic may incorporate variables other than the present study’s variables in order to capture the complexities of economic growth. 14

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References 1. Ahmad J and Harnhirun A (1996), “Cointegration and Causality Between Exports and Economic Growth: Evidence from ASEAN Countries”, Canadian Journal of Economics, Vol. 29, pp. 413-416. 2. Amrinto L (2006), “A Semiparametric Assessment of Export-Led Growth in the Philippines”, MS Thesis, Agricultural Economics and Agribusiness, Louisiana State University. 3. Asian Development Bank (2003), Key Indicator 2003: Education for Global Participation, Accessed on December 8, 2005, available at http://www.adb.org/Documents/Books/ Key_Indicators/2003/default.asp 4. Balassa B (1978), “Exports and Economic Growth: Further Evidence”, Journal of

Development Economics, Vol. 5, No. 2, pp. 181-189. 5. Cypher J M and Dietz J L (1998), “Static and Dynamic Comparative Advantage: A MultiPeriod Analysis with Declining Terms of Trade?”, Journal of Economic Issues, Vol. 32, No. 2, pp. 305-314. 6. Dodaro S (1993), “Exports and Growth: A Reconsideration of Causality”, Journal of Developing Areas, Vol. 27, pp. 227-244. 7. Feder G (1983), “On Exports and Economic Growth”, Journal of Development Economics, Vol. 12, Nos. 1-2, pp. 59-73. 8. Furuoka F (2007), “Do Exports Act as ‘Engine’ of Growth? Evidence from Malaysia”, Economics Bulletin, Vol. 6, No. 37, pp. 1-14. 9. Greene W H (2003), Econometric Analysis, Prentice-Hall Inc., New Jersey. 10. Hausmann R and Rodrik D (2002), “Economic Development as Self-Discovery”, NBER Working Paper 8952, National Bureau of Economic Research, Cambridge, MA. 11. Im K S, Pesaran M H and Shin Y (2003), “Testing for Unit Roots in Heterogeneous Panels”,

Journal of Econometrics, Vol. 115, pp. 53-74. 12. Jung W S, Peyton J and Marshall P J (1985), “Exports, Growth and Causality in Developing Countries”, Journal of Development Economics, Vol. 18, pp. 1-13. 13. Keong C C, Yusop M and Liew K S V (2005), “Export-Led Hypothesis in Malaysia: An Investigation Using Bounds Test”, Sunway Academic Journal, Vol. 2, pp. 13-22. 14. Love J and Chandra R (2005), “Testing Export-Led Growth in South Asia”, Journal of Economic Studies, Vol. 32, pp. 132-145. 15. Maddala G S and Wu S (1999), “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test”, Oxford Bulletin of Economics and Statistics, Special Issue, Vol. 61, November, pp. 631-652. Exports and Economic Growth in ASEAN Countries: Evidence from Panel Data Analysis

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16. Michaely M (1977), “Exports and Growth: An Empirical Investigation”, Journal of Development Economics, Vol. 4, pp. 49-53. 17. Pedroni P (1999), “Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors”, Oxford Bulletin of Economics and Statistics, Special Issue, Vol. 61, November, pp. 653-670. 18. Pedroni P (2004), “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with An Application to the PPP Hypothesis”, Econometric Theory, Vol. 20, pp. 597-625. 19. Piazolo M (1996), “Determinants of Indonesian Economic Growth, 1965-1992”, Seoul Journal of Economics, Vol. 9, No. 4, pp. 269-298. 20. Ram R (1985), “Exports and Economic Growth: Some Additional Evidence”, Economic Development and Cultural Change, Vol. 33, No. 2, pp. 415-425. 21. Ricardo D (1817), The Principles of Political Economy and Taxation, Reprint (1948), J M Dent and Sons, London. 22. Smith A (1776), An Inquiry into the Nature and Causes of the Wealth of Nations, Reprint (1977), J M Dent and Sons, London.

Reference # 05J-2009-03-01-01

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