Linkage between international trade and economic growth in GCC countries: Empirical evidence from...

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Linkage between internationaltrade and economic growthin GCC countries: Empiricalevidence from PMG estimationapproachJamel Jouiniaa Department of Economics, College of BusinessAdministration, King Saud University, Riyadh 11587,Saudi ArabiaPublished online: 14 Apr 2014.

To cite this article: Jamel Jouini (2014): Linkage between international trade andeconomic growth in GCC countries: Empirical evidence from PMG estimation approach,The Journal of International Trade & Economic Development: An International andComparative Review, DOI: 10.1080/09638199.2014.904394

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The Journal of International Trade & Economic Development, 2014

http://dx.doi.org/10.1080/09638199.2014.904394

Linkage between international trade and economic growth inGCC countries: Empirical evidence from PMG estimation

approach

Jamel Jouini∗

Department of Economics, College of Business Administration, King Saud University,Riyadh 11587, Saudi Arabia

(Received 5 February 2013; accepted 11 March 2014)

This paper explores the empirical evidence of the links between economicgrowth and openness to international trade by controlling for auxiliary vari-ables in the model for the six Gulf Cooperation Council (GCC) countries overthe annual sample period 1980–2010. After testing for cointegration basedon a recent bootstrap panel test, we employ the Pooled Mean Group (PMG)estimation technique of M.H. Pesaran, Y. Shin, and R. Smith (1999. “PooledMean Group Estimation of Dynamic Heterogeneous Panels.” Journal of theAmerican Statistical Association 94: 621–634) that is appropriate for drawingsharper conclusions in dynamic heterogeneous panels by considering long-runequilibrium relations. The results show evidence of cointegration relationshipbetween the variables of interest, and reveal that economic growth respondspositively to trade openness over both the short run and long run. The evi-dence is robust to using various trade openness measures and to alternativemodel specifications, suggesting thus the non-fragility of the linkage betweeneconomic growth and openness to international trade for the GCC region. Ourfindings are then promising and support the view that economic growth isdirectly and robustly linked to trade openness for the GCC countries.

Keywords: economic growth; trade openness; dynamic heterogeneous panels;PMG approach; GCC region

JEL Classifications: C23, F10, F14, F43, O53

1. Introduction

Several empirical works in the current literature focus on the linkage betweeneconomic growth and trade openness.1 The exports have direct effect on GrossDomestic Product (GDP) since, as foreign demand, they are considered as part ofGDP according to the national income accounts definition. They also have a moreimportant indirect impact through the main role played in improving investment,

∗Email: [email protected]

C© 2014 Taylor & Francis

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http://dx.doi.org/10.1080/09638199.2014.904394

mailto:[email protected]

2 J. Jouini

increasing thus employment productivity. The growth of exports can also helpincreasing imports since it allows alleviating the binding foreign exchange con-straints by providing foreign currency, which enhances growth in the economiescharacterized by high exports. Therefore, improving exports allows protectingagainst imports especially in developing countries. This implies that the exports–GDP nexus is also influenced by imports that are considered as important factorin determining economic growth since they contribute to all GDP components, inparticular consumption, investment and current domestic production.

The contribution of exports and imports to economic growth depends on thedegree of international trade that varies from one country to another. Variousmeasures can be used to capture the openness to trade2 of a country and, thus,to tell us about the extent to which a country is tied to other countries. In thiscontext, Dollar (1992) investigates empirically the long-run trade orientation ofa set of countries over the period 1976–1985 based on a real exchange rate dis-tortion index3 estimated by using the international comparison of prices preparedby Robert Summers and Alan Heston. This index is based on data for GDP percapita, average price level in US dollars and GDP growth rate per capita. Theauthor shows evidence of overvaluation by 33% for Latin America relative to Asiaand by 86% for Africa, and finds that Asian countries are more outward oriented.In his study, Rodrik (1998) employs two measures, namely the terms-of-trade riskand the Gini-Hirschman index of concentration defined over 239 three-digit Stan-dard International Trade Classification (SITC) categories of exports, as computedby UNCTAD (United Nations Conference on Trade and Development). Otherindicators are considered in the related literature to measure the trade opennessof countries such as the trade (exports plus imports) share in GDP as done inseveral empirical works (see, for example, Bajwa and Siddiqi 2011; Sakyi 2011;Ulasan 2012; Zeren and Ari 2013). Other proxies, such as tariff and non-tariffbarriers considered as direct trade policy measures, can also be employed (seeUlasan 2012). The disadvantage behind the use of these measures is that they donot account for heterogeneity across countries in terms of trade policy barriers,and do not apprehend the most important openness dimension of countries.

Trade openness does not necessarily lead to economic growth, and if it is thecase, its impact depends on country-specific conditions. Most of empirical worksoutline that there is strong significant positive linkage between economic growthand trade openness (see Edwards 1998; Lee, Ricci, and Rigobon 2004; Foster2008; Chang, Kaltani, and Loayza 2009; Kim 2011). However, these studies arequestionable in terms of robustness and do not lead to decisive answers. In thiscontext, Edwards (1993), Pritchett (1996), and Rodriguez and Rodrik (2001) arguethat such finding may be due to misspecification or openness measures that canbe tied to macroeconomic policies and other main factors such as institutions andgeography. All these insights make the evidence on significant trade openness–economic growth nexus not robust and convincing. Therefore, the investigation ofsuch nexus will be an attractive subject in order to circumvent some shortcomings

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The Journal of International Trade & Economic Development 3

raised in the literature and to check whether the linkage is robust based on variousopenness indicators.

This paper investigates empirically the trade openness–economic growth con-nection for the six Gulf Cooperation Council (GCC) countries during the annualsample period 1980–2010.4 The objective is to see whether trade openness spurseconomic growth from the early 1980s,5 since GCC countries know rapid expan-sion of trade transactions thanks to their highly open economies to foreign trade.Our study contributes to the debate over such connection in three main ways. First,we outline a lack of related empirical works for the GCC region despite the factthat GCC countries are major exporting countries given their hydrocarbons (oiland gas) sector that accounts for nearly 75% of exports and 43% of GDP in the re-gion. In addition, the rise of foreign exchange accumulation in the region throughexport earnings6 would stimulate imports7 as main channel for foreign advancedtechnologies to flow into GCC economies. In that regard, international trade woulddirectly influence economic growth in GCC countries. Relevant results from thestudy aid the GCC authorities to make deep policies that help to boost economicgrowth and trade.

Second, most of empirical studies have extensively focused on the linkagebetween economic growth and trade openness based on only one measure. Yet,relative to these works, the conclusions drawn from such linkage cannot be con-sidered as satisfactory and robust since the significance results may vary throughopenness indicators as argued by Edwards (1993), Pritchett (1996), and Rodriguezand Rodrik (2001). Moreover, the reliability of the conclusions about the tradeopenness–economic growth nexus may differ according to the manner of mea-suring the trade variables (nominal or real terms). Therefore, we employ varioustrade openness variables (export share in GDP, import share in GDP, trade sharein GDP and import coverage of exports) measured in current and constant pricesto avoid misleading conclusions about such nexus. In addition, unlike most em-pirical studies that are limited to the simple use of openness variables, we includereasonably comprehensive auxiliary variables as specific channels through whichinternational trade may affect economic growth. The inclusion of these variablesin the model is particularly important, as trade openness alone would not be strongenough to arouse economic growth. As doing this, we investigate the influence oftrade openness on economic growth based on a more generalized model, whichallows avoiding misspecification that may bias the results as pointed out by Ro-driguez and Rodrik (2001).

Third, our paper differs from the previous literature on the relationship betweeneconomic growth and international trade in the econometric method. We continuein the same momentum of works related to the trade openness–economic growthnexus using panel methods. Indeed, we opt for the Pooled Mean Group (PMG) ap-proach of Pesaran, Shin, and Smith (1999) for estimating dynamic heterogeneouspanel models that, to the best of our knowledge, has not been previously usedto examine such nexus. As argued by some papers (see Felbermayr 2005), em-ploying dynamic econometric specification is advantageous since it better fits the

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4 J. Jouini

theoretical works and brings solutions for some shortcomings raised by other mod-els. Accordingly, we can obtain accurate results and explore appropriate economicand policy implications.

The rest of the paper is as follows. Section 2 reviews works related to theconnection between economic growth and international trade. Section 3 presentsthe econometric approaches used in the empirical issue. Section 4 describes thedata and their statistical properties, and discusses the results. The findings showevidence of long-run relationship among variables, and reveal that trade opennesspositively affects economic growth in GCC countries over both the short runand long run. The robustness check conducted in Section 5 indicates how robustthe results are to employing an alternative model specification and to includingadditional determinants in the economic growth model. The trade openness–economic growth nexus is then not fragile and, consequently, GCC authorities canmake deep policies based on such nexus. Section 6 presents economic and policyimplications of the results. Section 7 concludes the paper.

2. Economic growth and trade openness

The relationship between international trade and economic growth has been anissue of global debate in the development literature. In the nineteenth century,David Ricardo showed, by his theory of comparative advantage, that opennessabroad allows a country to reorient its scarce resources to more efficient sectors.Theories that followed have confirmed these gains and added those related to theremuneration of production factors. However, even in the new trade theories thataccount for returns to scale and imperfect competition, gains remain static. It isthen in the growth theory, we can pick up dynamic gains. Within this context,neoclassical growth models drawn from the Solow’s (1957) model consider tech-nological change as exogenous and, consequently, trade policies do not impacteconomic growth of a country. However, new economic growth theories assumethat technological change is an endogenous variable. These theories can then becombined with those of international trade.

In their theoretical models related to endogenous growth, Grossman and Help-man (1991) study the linkage between innovation and growth in the global econ-omy. They show that comparative advantage is created endogenously in industrialresearch and examine the interactions between international trade and economicgrowth. Within this context, they outline that trade openness improves the trans-fer of new technologies, which enables the country to experience technologicalprogress and productivity improvement. These benefits depend on the opennessdegree of the economies. Grossman and Helpman (1992) stress that a country canboost economic growth by improving domestic investment given its comparativeadvantages. Rivera-Batiz and Romer (1991) rely on an analogy with the theory orconsumer behavior to examine the implications of trade restrictions on economicgrowth based on two similar regions, namely Europe and North America. Theyshow that trade restrictions reduce worldwide economic growth. Romer (1990)

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develops a model in which he shows that investment decisions induce technologicalchange, which drives economic growth.

At empirical stage, various research works related to the trade openness–economic growth nexus have been proposed in the literature. In this context, Fosu(1990) argues that export increases improve economic growth in African countriesbased on an augmented production function. Riezman, Whiteman, and Summers(1996) employ the error variance decomposition to investigate the export-ledgrowth hypothesis for a set of countries by including imports in the model. Theempirical evidence outlines that such hypothesis would be verified directly andindirectly through imports. Asafu-Adjaye and Chakraborty (1999) show evidenceof cointegrating relationship between real output, exports and imports in inward-oriented countries, and find indirect causal links running from exports to importsand then real economic growth. Baharumshah and Rashid (1999) examine thelong-run relationship between exports, imports and GDP in Malaysia, and stressthat foreign technology imports can predict economic growth. In the same context,Ramos (2001) opts for the Granger non-causality methodology to investigate thelinks between exports, imports and Portuguese economic growth during the annualperiod 1965–1998. The findings show evidence of bidirectional causality betweeninternational trade and economic growth. Mamun and Nath (2005) conclude infavor of a unidirectional long-run causality from exports to economic growth inBangladesh over the period 1976–2003 based on the Vector Autoregressive andVector Error Correction Model (VAR–VECM) methodology. Shirazi and Manap(2005) test the export-led growth hypothesis in South Asia using the Grangernon-causality and cointegration techniques. They show evidence of bidirectionalcausal links between international trade and GDP in Bangladesh.

Ugur (2008) investigates the import–GDP causal nexus in Turkey by decom-posing imports to their categories and based on a VAR process. The results outlinethat causality between economic growth and imports is bidirectional for some cat-egories and unidirectional for others. Gurgul and Lach (2010) investigate the linearand non-linear causal links between international trade and economic growth overthe period 1996Q1–2009Q3 and pre-crisis period 1996Q1–2008Q3 to infer theinfluence of the recent global financial crisis on the Polish economy. The findingsindicate linear bidirectional causality between exports and economic growth overthe two periods, and no causal dependence between imports and GDP. They alsoshow bidirectional causality between exports and imports over the pre-crisis pe-riod, and unidirectional causal link from imports to exports over the full period,which implies that over bullish periods, imports growth precedes exports growth.The non-linear analysis reveals unidirectional causality from economic growth tointernational trade and from imports to exports.

Frankel and Romer (1999) estimate a model on a cross-section of 63 countriesfor 1985 to investigate the trade–income nexus. To that effect, they first construct aninstrument for trade based on geographic variables, and then employ it to examinethe response of income to trade. In addition to trade as explanatory variable,the model also includes the country size. The findings reveal substantial positive

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6 J. Jouini

impacts of trade and size on income. The authors show that these effects are robustto alternative specifications, sample and instrument. More recently, Ulasan (2012)explores the long-run trade openness8–economic growth nexus in Organizationfor Economic Cooperation and Development (OECD) and non-OECD countriesover the period 1960–2000 based on cross-country regressions. Positive significantimpact of many openness variables on economic growth is found. The evidencedisappears when adding other determinants in the model, confirming thus thefragility of the links between trade openness and economic growth for the selectedset of countries.

The above empirical works make use of different econometric methodologiesdeveloped in the time series framework where the short span of the variables maylead to conflicting results because of the low statistical power of the country-by-country techniques. Other studies are also conducted in the cross-countryregressions framework where the common practice for dealing heteroscedasticitywhen testing for significance of the variables is to use the heteroscedasticityconsistent (HC) standard errors. However, these standard errors are consistent butbiased and work well only asymptotically. For small samples, the HC standarderrors are generally larger than the usual standard errors (see Wooldridge 2003),which can lead to misleading conclusions in terms of significance of the variables.Thus, the use of HC standard errors is not recommended as long as the errors arehomoscedastic and normally distributed. To overcome these shortcomings relatedto the choice of the econometric method especially for small samples, severalempirical works have used panel approaches.9 Such techniques are the appropriateway to extract more information and to produce more reliable estimates than thetime series and cross-section regressions do. Moreover, in the panel context, we canembody country and time specific effects to guard against unobserved variablesthat correlate with the explanatory variables.

Within this context, Felbermayr (2005) revisits the linkage between incomeper capita and trade openness measured by exports plus imports over GDPbased on a dynamic econometric model estimated by the Blundell and Bond’ssystem-Generalized Method of Moments (GMM) method. The results indicatethat openness strongly influences income for the considered set of countries.Reppas and Christopoulos (2005) analyze the relationship between exports andeconomic growth for a set of Asian and African countries over the annual period1969–1999 based on the panel FMOLS technique. The findings show evidenceof significant unidirectional causality from economic growth to exports. Konya(2006) develops a new panel data method based on SUR systems to investi-gate the causal links between real exports and real GDP (RGDP) for OECDcountries over the period 1960–1997. The empirical causality results depend oncountries. Hsiao and Hsiao (2006) investigate empirically the relationship be-tween GDP, exports and foreign direct investment (FDI) in Asian countries overthe period 1986–2004 based on panel VAR process and Granger non-causalitytests. The FDI affects GDP directly or indirectly through the channel of exports,and there is evidence of a two-way causal link between exports and GDP. The

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findings have important economic and policy implications. Bajwa and Siddiqi(2011) investigate the effect of the implementation of South Asian Associationfor Regional Cooperation (SAARC) in late 1985 on the relationship between theratio of exports plus imports to GDP and economic growth for Asian countriesbased on panel cointegration methods. Globally, the results indicate that the sit-uation of the selected countries has been improved after the implementation ofSAARC.

Aljebrin and Ibrahim (2012) provide an empirical test about the import de-mand determinants (real income, private consumption, international reserves andgross capital formation) for GCC countries over the annual period 1994–2008by opting for panel SUR model. The results reveal significant positive relation-ship between imports and their determinants over both the short run and longrun. There are also significant negative links between the demand for importsand the relative price of imports to domestic price and government expendituresover the long run, but insignificant negative connection over the short run. Tekin(2012) examines the causal links between real GDP, real exports and FDI inleast developed countries over the period 1970–2009 based on the panel causalityapproach suggested by Konya (2006). The results show evidence of unidirec-tional causality for the exports–GDP and FDI–GDP nexuses, and bidirectionalcausal relationship between FDI and exports for Haiti and Mauritania. Gries andRedlin (2012) study the dynamic patterns between GDP per capita and tradeopenness measured by the sum of exports plus imports divided by GDP for aset of countries from 1970 to 2009 based on panel cointegration methods. Theresults indicate positive bidirectional causality between openness and economicgrowth over the long run. However, international trade negatively affects GDPover the short run. By considering subpanels, the authors stress that the impact ofopenness on economic growth is not the same for low-income and high-incomecountries.

Sakyi et al. (2012) use heterogeneous panel cointegration methods to studythe empirical relationship between trade openness (trade share in GDP based onconstant 2005 PPP, purchasing power parity, dollars) and economic growth for 85middle-income countries over the period from 1970 to 2009. The results showevidence of bidirectional causal links between trade and development over thelong run. However, the evidence of such causal relationship is not supported overthe short run. Herzer (2013) opts also for heterogeneous panel cointegration tech-niques to investigate the openness–income nexus for 81 developed and developingcountries over the period 1960–2003. The author uses the trade relative to GDPat PPP as suggested by Alcala and Ciccone (2004). The findings indicate that thetrade openness positively affects the income based on the whole sample. How-ever, cross-country differences are observed in such effect between the two sets ofcountries. Indeed, the impact is positive for developed countries and negative fordeveloping countries. The author provides some explanations for the cross-countryheterogeneity in the response of income to trade. Zeren and Ari (2013) make use ofGranger non-causality test in heterogeneous panel model to examine the linkagesbetween the ratio of exports plus imports to GDP and economic growth for the G7

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8 J. Jouini

countries over the period 1970–2011. The findings reveal positive bidirectionalcausal links between openness and economic growth.

3. Econometric methodology

We present the panel cointegration and stationarity tests, and the PMG approachused to estimate dynamic heterogeneous panels to assess the influence of tradeopenness on economic growth in the GCC region.

3.1. Cointegration and stationarity tests

Unlike tests developed in the time series framework whose power depends onthe sample size of the variables, the panel unit root and cointegration tests aremore powerful since they combine information from both cross-section and timedimensions, allowing thus to increase the number of observations.10 In this paper,we employ the Lagrange multiplier (LM) tests developed by Westerlund (2005)for cointegration and Carrion-i-Silvestre, Castro, and Bazo (2005) for stationarity.These tests account for structural changes, which is motivated by the fact that theconsidered period (1980–2010) contains significant events and facts such as theIraqi invasion of Kuwait in 1990–1991 and the attacks of September 2001. Theseevents cause profound changes in oil prices, and consequently, changes in exportearnings since GCC countries are major oil-exporting countries.

The long-run relationship among variables accounts for breaks in the leveland/or trend, and heterogeneity between groups. It is expressed as follows:

yit = αij + βij t + θ ′iXit + εit , i = 1, 2, . . . , N,

t = 1, 2, . . . , T , j = 1, 2, . . . , Mi + 1 (1)

where N is the number of groups, T is the time series dimension, Mi is the numberof breaks11 for country i, yit is the dependent variable (real GDP), Xit is the vectorof explanatory variables (openness measures and other determinants), εit is thedisturbance term, αi and βi are country specific coefficients, and θi is the vectorof slope coefficients.

To test the null hypothesis of cointegration for the case of cross-countryindependence, Westerlund (2005) develops the following LM test statistic:12

LM = N−1N∑

i=1

Mi+1∑

j=1

Tij∑

t=Ti,j−1+1

[(Tij − Ti,j−1

)σi

]−2S2

it (2)

where Tij is the jth break date of country i, Sit = ∑ts=Ti,j−1+1 εst with εit the

residuals of the above model,13 and σi is the Newey and West (1994) estimator ofthe long-run standard deviation based on εit . We conclude in favor of cointegration

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among variables when the LM observed value is less than any critical value sinceunder the alternative hypothesis the LM statistic diverges to positive infinity. Thestationarity test of Carrion-i-Silvestre, Castro, and Bazo (2005) is derived in thesame way as the Westerlund’s (2005) cointegration test. Indeed, we replace εit inthe above LM statistic with the demeaned and detrended counterpart of Sit , whichcan be obtained by setting θi = 0 in the above long-run relationship. Under thenull hypothesis of stationarity, the test is normally distributed.

The assumption of cross-country independence is too restrictive and can berelaxed to account for dependence between countries. To that effect, we make useof the bootstrapped version of the LM statistic by extending the bootstrap test ofWesterlund and Edgerton (2007) to take into account breaks. Within this context,the bootstrap p-value is computed as the proportion of bootstrap samples yieldinga statistic greater than the asymptotic statistic. Therefore, the null hypothesis ofstationarity or cointegration is rejected when the bootstrap p-value is less than afixed significance level.

3.2. PMG approach

The PMG approach proposed by Pesaran, Shin, and Smith (1999) allows estimatingthe following error correction model:

�yit = φiξi,t−1 +p−1∑

l=1

λ′il�yi,t−l +

q−1∑

j=0

δ′ij�Xi,t−j + γ ′

i X′it + αi + βit + uit

(3)

where ξi,t−1 = yi,t−1 − θ ′Xit is the deviation from the long-run equilibrium, yit isthe dependent variable (real GDP), Xit is the vector of explanatory variables (tradeopenness indicators and other auxiliary determinants) that are expected to impactyit over both the short run and long run, and uit is the error term whose variancediffers across groups. The coefficient φi is the short-run error correction term thatmeasures the adjustment speed towards the long-run state, θ is the vector of long-run coefficients, δ and γ are the vectors of short-run coefficients, and αi and βi

are country specific effects. Following Pesaran, Shin, and Smith (1999), we applythe maximum-likelihood method to estimate the model by assuming initially thatthe error terms are normally distributed, and based on the optimization algorithmof Newton–Raphson.14

The PMG approach allows then estimating dynamic heterogeneous panels byconsidering long-run equilibrium relations contrarily to other techniques, suchas the dynamic panel GMM method, that purge any potential long-run linkageamong variables. This is of great interest for our empirical study since economicgrowth and openness measures may be linked by a common trend that pilots theircomovements over the long run. As can be seen in the model, the PMG methodallows the intercepts, time trends and short-run parameters to differ across groups,but the long-run coefficients are constrained to be identical.15 Pesaran, Shin, and

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Smith (1999) outline that the homogeneity assumption of the long-run equilibriumrelationships among variables across groups may be due to budget or solvencyconstraints, arbitrage conditions or common technologies that influence all groupsin the same way. They also stress that assuming identical short-run coefficients anderror variances across groups is less compelling, and that heterogeneity in thesecoefficients allows the dynamic specification to differ across groups. Therefore, thePMG estimation approach allows identical long-run coefficients without assuminghomogeneous short-run parameters.

By doing this, the PMG estimation approach differs from some usual tech-niques. Within this context, we can refer to the mean group (MG) estimationmethod that estimates a regression for each group and then computes the coeffi-cient means. The MG long-run estimators are consistent, but they are inefficientif coefficient homogeneity holds. Under these conditions, the PMG estimation ap-proach is useful since it provides consistent and efficient long-run estimators whenparameter homogeneity holds. In addition, the PMG approach is preferable to theMG method since it provides estimates that are less sensitive to outlier estimates.Another method consists in pooling the data and assuming identical regressioncoefficients and error variances such as the dynamic fixed effect (DFE) method.16

The advantage of the PMG estimation approach over such method is that it consid-ers different short-run dynamics across groups. We also consider the panel GMMdifference technique that allows only the constant terms to vary across groups andthe slope coefficients to be identical, which may be inappropriate for long paneltime dimension as argued by Im, Pesaran, and Shin (2003).Therefore, the esti-mates may be inconsistent and misleading. Accordingly, an estimation approachthat imposes weaker homogeneity would be of great interest in empirical applica-tions. For all these reasons, we believe that the PMG estimation approach is veryuseful for investigating the linkage among variables in dynamic heterogeneouspanel models.

4. Empirical illustration

This paper investigates a growth model linking real GDP to nominal and realtrade openness indicators17 for the GCC region (Bahrain, Kuwait, Oman, Qatar,Saudi Arabia and the United Arab Emirates (UAE)) over the annual period 1980–2010. We cannot consider a longer and more recent period because there are noavailable data for some variables. However, this period is long enough to enableus to better investigate the long-run economic growth dynamics in the panelframework considered here. As trade openness variables, we use the nominal ratioof exports to GDP (CEXP), the nominal ratio of imports to GDP (CIMP), thenominal openness degree measured as the nominal ratio of the sum of exportsand imports to GDP (COPEN), the real ratio of exports to GDP (REXP), the realratio of imports to GDP (RIMP), and the real openness degree measured as thereal ratio of the sum of exports and imports to GDP (ROPEN).18 We also includethree auxiliary variables such as the labor force (LAB), the investment defined

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as the ratio of gross fixed capital formation to GDP at current prices (CINVT)and the ratio of government expenditures to GDP at current prices (CGOV). Theinclusion of these auxiliary variables in the model is motivated by the fact thatthey are key drivers of economic activity in the GCC region. In addition, theycan be considered as transmission channels through which the linkage betweeneconomic growth and trade openness is examined (see Section 4.1). All data aregathered from the database of the UNCTAD, except the real trade indicators thatare drawn from the database of the Penn World Tables. All variables are takenin logarithm, implying thus coefficients that measure the real GDP’s elasticity toeach explanatory variable.19

4.1. GCC economic insights

The GDP of the GCC region grows at a rate of 14.2%, achieving thus 2% ofthe global GDP in 2011 in nominal terms,20 making thus this region among thefastest growing regions on a global scale. This fast economic growth is mainlyexplained by the rise in hydrocarbons export21 earnings through the increaseof oil price especially since 2001 by a rate of about 16.3% per year.22 Theseexport earnings improve the surplus inflows of foreign currency. In this con-text, the current account surplus in the GCC region reached about US$322bn(23% of GDP) in 2011 of which 49% is for Saudi Arabia that is the majorGCC exporter of hydrocarbons.23 These inflows of foreign exchange are, inturn, flowed into investments abroad and imports24 of capital goods to stimu-late development, leading thus to economic growth. These insights imply thattrade openness variables are expected to positively influence GDP in the GCCregion.

The employment variable is a good country size indicator.25 Within this con-text, Frankel and Romer (1999) indicate that country size can be considered asimportant trade determinant since, in larger countries, there are more opportuni-ties for within country trade. Jadresic (2002) indicates that in the GCC region, thelabor markets are highly segmented between public and private sectors, and na-tionals and non-nationals. In addition, the majority of the labor force comes fromabroad. In this context, the foreign labor force represents more than 80% of totallabor force in Kuwait, Qatar, and the UAE. Through the hydrocarbons revenue,the GCC governments make heavy investments in many sectors to diversify theireconomies, driving thus GDP growth. In this context, Sturm et al. (2008) outlinethat Bahrain and the UAE are the most advanced countries in the diversificationprocess and have turned into tourism and finance, Kuwait into commodities, Omaninto tourism and manufacturing, and Saudi Arabia into manufacturing. The gov-ernment expenditures continue to grow given the commitment of GCC countriesin development projects. Within this context, total GCC government expendituresare evaluated at US$468bn in 2011 representing 34% of GDP,26 which is in linewith the global average, 33%. It is expected that GCC government expenditureswill reach 36% of GDP in 2013 through the high oil price in 2012–2013, allow-

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12 J. Jouini

ing thus GCC countries to easily finance their development projects. For logicalreasons, it is expected that labor force, investment and government expendituresaffect economic growth in the GCC region.

4.2. Preliminary analysis of data

The summary statistics reported in Table 1 indicate that the highest level aver-age of the real GDP is observed for Saudi Arabia, which confirms the fact thatthis country is the largest one in the region in terms of GDP. This feature mayalso be explained by the rise in hydrocarbons exports for this country. Bahrainhas the lowest average in terms of real GDP. Regarding trade openness, the bestperformance is recorded in Bahrain whatever the measure used, indicating thusthat the Bahraini economy is the most open abroad. Saudi Arabia is character-ized by smallest averages in terms of all trade openness indicators, which is inline with Gylfason (1999) who stresses that the average ratio of exports to GDPtends to be smaller in larger countries. Within this context, Nusair (2012) pointsout that, in general, the nominal exchange rate as a policy instrument for ad-justment is less effective for high openness degree since the internal price levelwould be destabilized by exchange rate changes in a highly open economy. Be-sides, these changes induce beneficial effects on trade terms and real wages.Another striking feature is that the average values are similar for each trade open-ness variable measured at current or constant prices. The trade variables are lessvolatile than the real GDP since they have lower risk as measured by the standarddeviation.

We now turn to the auxiliary variables. Saudi Arabia, as the largest countryin the GCC region in terms of area and population, records the highest averagevalues for labor and government expenditures. Bahrain is characterized by thelowest average for labor force, which may be explained by the fact that Bahrainis the smallest country in terms of population (about 1.5 millions in 2012). TheUAE spend less than the other countries in the region. The UAE devote moreresources to investment since its average value is the highest. By cons, the lowestaverage value is observed in Kuwait. In terms of volatility, the auxiliary variablesare comparable to real GDP.

The empirical unconditional correlations between real GDP and all variablespresented in Table 2 are computed for each country and over the whole samplecomposed of all observations. The values by country show mixed (positive ornegative) association of the trade openness variables and investment with realGDP. On the other hand, the labor force (ratio of government expenditures toGDP) is positively (negatively) correlated to real GDP. Over the whole sample,the calculations show evidence of negative and moderate dependence between realGDP and all explanatory variables, except of the labor force that is positively andhighly correlated with real GDP.

The empirical correlations among trade openness variables are reported inTable 3 for nominal measure and in Table 4 for real measure. Regarding the nominal

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Tabl

e1.

Sum

mar

yst

atis

tics

ofth

ese

ries

.

Cou

ntry

RG

DP

CE

XP

CIM

PC

OP

EN

RE

XP

RIM

PR

OP

EN

LA

BC

INV

TC

GO

V

Bah

rain

Mea

n9.

072

4.47

54.

206

5.04

34.

474

4.20

55.

042

5.57

23.

049

2.92

7M

axim

um9.

788

4.60

14.

336

5.17

04.

601

4.29

75.

154

6.56

73.

488

3.31

5M

inim

um8.

585

4.36

24.

097

4.94

84.

350

4.12

34.

943

4.91

12.

514

2.50

2S

td.d

ev.

0.37

70.

091

0.08

70.

086

0.08

00.

067

0.07

10.

412

0.25

50.

226

Kuw

ait

Mea

n10

.874

3.98

03.

495

4.46

63.

997

3.51

14.

483

6.74

22.

794

3.19

1M

axim

um11

.442

4.15

73.

675

4.51

54.

094

3.74

94.

523

7.21

33.

604

5.30

3M

inim

um10

.051

3.79

83.

276

4.39

73.

844

3.19

24.

420

6.13

42.

328

2.41

2S

td.d

ev.

0.32

50.

129

0.13

10.

037

0.09

00.

179

0.03

80.

282

0.24

90.

525

Om

anM

ean

9.94

74.

068

3.63

64.

570

3.92

73.

494

4.42

96.

502

3.16

83.

132

Max

imum

10.6

314.

151

3.80

04.

624

4.01

13.

624

4.46

17.

103

3.52

03.

417

Min

imum

8.95

03.

897

3.48

64.

477

3.80

23.

390

4.38

25.

819

2.74

42.

656

Std

.dev

.0.

443

0.07

80.

090

0.04

60.

072

0.07

50.

027

0.35

80.

238

0.20

9

Qat

arM

ean

10.0

794.

149

3.37

24.

529

4.17

33.

396

4.55

35.

777

3.16

63.

209

Max

imum

11.5

554.

210

3.57

74.

591

4.27

63.

712

4.72

67.

181

3.69

73.

820

Min

imum

9.42

74.

097

3.24

84.

483

4.08

83.

257

4.47

14.

679

2.66

02.

394

Std

.dev

.0.

647

0.04

10.

104

0.03

50.

069

0.14

80.

084

0.60

10.

328

0.44

6

Sau

diA

rabi

aM

ean

12.3

863.

848

3.31

24.

312

3.86

23.

326

4.32

68.

600

2.99

93.

263

Max

imum

12.7

934.

147

3.46

04.

554

4.08

73.

427

4.49

59.

165

3.29

43.

562

Min

imum

12.0

403.

5507

3.23

94.

135

3.65

83.

249

4.23

47.

816

2.80

62.

766

Std

.dev

.0.

226

0.21

00.

067

0.14

70.

152

0.06

80.

093

0.36

20.

125

0.18

2

UA

EM

ean

11.5

984.

018

3.81

94.

617

3.98

23.

782

4.58

07.

214

3.17

62.

592

Max

imum

12.2

634.

229

3.97

24.

784

4.27

33.

973

4.82

78.

503

3.42

73.

095

Min

imum

10.9

873.

821

3.63

14.

473

3.80

23.

600

4.40

66.

306

2.85

61.

750

Std

.dev

.0.

416

0.16

00.

124

0.14

00.

191

0.15

00.

169

0.62

50.

157

0.43

3

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Table 2. Empirical correlations between RGDP and all variables.

Country CEXP CIMP COPEN REXP RIMP ROPEN LAB CINVT CGOV

Bahrain –0.315 –0.722 –0.578 –0.044 –0.650 –0.442 0.942 0.144 –0.611Kuwait 0.874 –0.918 –0.419 0.848 –0.842 0.166 0.763 –0.339 –0.682Oman 0.363 0.420 0.469 0.533 0.600 0.752 0.987 –0.111 –0.051Qatar 0.086 0.511 0.565 0.728 0.676 0.799 0.942 0.760 –0.851Saudi

Arabia0.898 –0.325 0.611 0.949 –0.513 0.807 0.834 –0.409 –0.611

UAE 0.989 0.921 0.972 0.973 0.924 0.960 0.949 –0.530 –0.887Whole

sample–0.552 –0.575 –0.637 –0.471 –0.517 –0.552 0.934 –0.046 –0.190

Table 3. Empirical correlations among nominal trade openness measures.

Country CEXP CIMP COPEN

BahrainCEXP 1.000 0.715 0.908CIMP 1.000 0.942COPEN 1.000

KuwaitCEXP 1.000 –0.747 –0.001CIMP 1.000 0.550COPEN 1.000

OmanCEXP 1.000 0.307 0.904CIMP 1.000 0.682COPEN 1.000

QatarCEXP 1.000 –0.382 0.463CIMP 1.000 0.642COPEN 1.000

Saudi ArabiaCEXP 1.000 –0.178 0.785CIMP 1.000 0.463COPEN 1.000

UAECEXP 1.000 0.935 0.985CIMP 1.000 0.982COPEN 1.000

Whole sampleCEXP 1.000 0.542 0.870CIMP 1.000 0.877COPEN 1.000

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Table 4. Empirical correlations among real trade openness measures.

Country REXP RIMP ROPEN

BahrainREXP 1.000 0.510 0.823RIMP 1.000 0.908ROPEN 1.000

KuwaitREXP 1.000 –0.660 0.407RIMP 1.000 0.304ROPEN 1.000

OmanREXP 1.000 0.057 0.858RIMP 1.000 0.558ROPEN 1.000

QatarREXP 1.000 0.531 0.880RIMP 1.000 0.870ROPEN 1.000

Saudi ArabiaREXP 1.000 –0.624 0.801RIMP 1.000 –0.046ROPEN 1.000

UAEREXP 1.000 0.958 0.991RIMP 1.000 0.988ROPEN 1.000

Whole sampleREXP 1.000 0.622 0.893RIMP 1.000 0.899ROPEN 1.000

variables (Table 3), the correlations are quite high and positive for Bahrain, Omanand the UAE, and over the whole sample. By cons, they are mixed for the otherGCC countries. For the real variables (Table 4), the results are quite similar tothose of their nominal counterparts. The only difference is that the values relatedto Qatar become all positive. The correlations differ slightly between nominaland real values for countries having positive coefficients. This correlation analysistells us about the linkage between real GDP and trade openness indicators, butcannot be determinative of the impact of such indicators on economic growth. Anempirical work in terms of causal linkage from trade openness to real GDP in theGCC region will, thus, be an attractive subject.

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16 J. Jouini

4.3. Stationarity and cointegration analysis

We first make use of the test proposed by Breusch and Pagan (1980) to test forcross-country dependence in order to choose the appropriate version of the aboveLM stationarity and cointegration tests. Indeed, we employ the asymptotic versionfor the case of independence or the bootstrap version for the case of cross-countrydependence. The results (not reported here) conclude in favor of dependencebetween countries since the test rejects the null hypothesis of no cross-countrydependence. Therefore, we opt for the bootstrap version of the above LM statisticto test for stationarity and cointegration between variables.

Given the small sample size (T = 31), considering one break date is sufficientto examine the trending behavior of the variables and to capture regime-shiftsin the data. To identify the break date, we use a trimming of 30%, which im-plies that the minimal number of observations in each regime is fixed at [0.3T ]where [.] is the integer part of argument. The number of bootstrap replications isequal to B = 999 and satisfies the condition that α (B + 1) is an integer withα any significance level of the test. According to Davidson and MacKinnon(2000), this choice allows deleting all eventual bias when estimating the bootstrapp-value.

The results depicted in Table 5 point out that for all specifications, the LMstationarity test concludes in favor of unit root for all level variables and stationarityfor all variables in first differences, which implies that all series are integrated oforder one.27 We then proceed to test for cointegration to check the emergenceof the long-run link between the variables we consider. The results presented inTable 6 outline that the LM cointegration test strongly supports the existenceof long-run relationship between real GDP and trade openness in the presenceof auxiliary variables. Therefore, there is great evidence in favor of the tradeopenness–economic growth nexus for the GCC region. The evidence is robustto using various openness indicators since the cointegration test is insignificantfor all models.28 These insights suggest that the variables are related through acommon trend piloting their comovements in the long run.

4.4. Detection of breaks

We only report the break date in trend, which implies that the break models fita step function through this trend. The results depicted in Table 7 indicate theselection of a break date for all GCC countries with the exception of Bahrainwhatever the long-run relationship. This insight outlines that the period underinvestigation was marked by the occurrence of some economic facts altering thevariables. A noticeable feature of the results is that for Qatar and the UAE, wedetect a break date through all models.29 However, the detection of a break datevaries across models for Kuwait, Oman and Saudi Arabia.

The break detection results may corroborate the cross-country dependenceassumption since the depicted break dates are similar for the GCC countries,

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Tabl

e5.

Res

ults

ofth

epa

nelL

Mst

atio

nari

tyte

st.

Var

iabl

eIn

terc

ept

Tre

ndB

reak

sin

inte

rcep

tB

reak

sin

tren

d

RG

DP

Lev

el10

.402

(0.0

00)∗∗

∗6.

496

(0.0

00)∗∗

∗6.

129

(0.0

00)∗∗

∗3.

371

(0.0

00)∗∗

∗

Firs

t-di

ff.

3.77

2(0

.130

)2.

189

(0.2

60)

0.81

3(0

.329

)2.

210

(0.2

51)

CE

XP

Lev

el4.

569

(0.0

00)∗∗

∗5.

799

(0.0

00)∗∗

∗1.

889

(0.0

09)∗∗

∗4.

565

(0.0

00)∗∗

∗

Firs

t-di

ff.

0.42

9(0

.203

)1.

979

(0.1

13)

0.42

9(0

.142

)1.

979

(0.1

07)

CIM

PL

evel

5.51

0(0

.002

)∗∗∗

3.80

3(0

.001

)∗∗∗

0.93

4(0

.006

)∗∗∗

2.20

3(0

.003

)∗∗∗

Firs

t-di

ff.

–0.1

02(0

.331

)0.

544

(0.6

28)

–0.1

02(0

.311

)0.

544

(0.6

25)

CO

PE

NL

evel

6.93

3(0

.000

)∗∗∗

2.80

8(0

.002

)∗∗∗

0.75

5(0

.005

)∗∗∗

4.52

8(0

.000

)∗∗∗

Firs

t-di

ff.

–0.7

05(0

.831

)1.

467

(0.1

54)

–0.7

05(0

.807

)1.

467

(0.1

51)

RE

XP

Lev

el3.

379

(0.0

00)∗∗

∗3.

817

(0.0

02)∗∗

∗0.

449

(0.0

02)∗∗

∗2.

345

(0.0

05)∗∗

∗

Firs

t-di

ff.

0.13

9(0

.196

)1.

244

(0.2

28)

0.13

9(0

.157

)1.

401

(0.2

83)

RIM

PL

evel

3.45

3(0

.000

)∗∗∗

3.80

7(0

.000

)∗∗∗

2.50

8(0

.006

)∗∗∗

2.71

3(0

.003

)∗∗∗

Firs

t-di

ff.

0.80

6(0

.210

)0.

805

(0.4

80)

0.80

6(0

.210

)0.

805

(0.4

82)

RO

PE

NL

evel

4.32

9(0

.000

)∗∗∗

4.93

5(0

.000

)∗∗∗

1.77

7(0

.001

)∗∗∗

4.44

1(0

.000

)∗∗∗

Firs

t-di

ff.

1.77

5(0

.114

)0.

971

(0.3

61)

1.77

5(0

.119

)1.

390

(0.2

05)

LA

BL

evel

11.0

83(0

.000

)∗∗∗

4.28

2(0

.000

)∗∗∗

8.32

4(0

.000

)∗∗∗

6.32

0(0

.000

)∗∗∗

Firs

t-di

ff.

1.81

7(0

.199

)4.

647

(0.1

80)

2.62

3(0

.155

)3.

552

(0.4

80)

CIN

VT

Lev

el2.

545

(0.0

05)∗∗

∗3.

061

(0.0

01)∗∗

∗0.

048

(0.0

03)∗∗

∗2.

488

(0.0

06)∗∗

∗

Firs

t-di

ff.

–1.1

41(0

.947

)0.

312

(0.7

53)

–1.1

41(0

.921

)0.

312

(0.7

84)

CG

OV

Lev

el3.

602

(0.0

00)∗∗

∗6.

436

(0.0

00)∗∗

∗1.

211

(0.0

03)∗∗

∗3.

787

(0.0

00)∗∗

∗

Firs

t-di

ff.

3.33

4(0

.114

)3.

262

(0.1

15)

2.49

6(0

.119

)4.

005

(0.1

12)

Not

es:T

heva

lues

inpa

rent

hese

sar

eth

ebo

otst

rap

p-va

lues

.∗∗∗

indi

cate

sno

n-st

atio

nari

tyat

1%le

vel.

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Table 6. Results of the panel LM cointegration test.

Model Intercept Trend Breaks in intercept Breaks in trend

Model 1 0.683 0.617 9.979 21.4100.997 0.958 0.898 0.781

Model 2 0.775 1.387 –46.581 41.5910.997 0.937 0.968 0.471

Model 3 0.673 1.717 24.624 57.0310.998 0.779 0.312 0.280

Model 4 2.399 0.372 13.549 33.0760.985 0.998 0.819 0.679

Model 5 1.427 0.620 10.848 68.6850.982 0.977 0.786 0.186

Model 6 1.927 0.327 11.284 35.8290.953 0.987 0.802 0.649

Notes: For all models, the vector of explanatory variables is composed of one trade measure, labor,investment and ratio of government expenditures to GDP. For model 1, the trade measure is CEXP;for model 2, the trade measure is CIMP; for model 3, the trade measure is COPEN; for model 4, thetrade measure is REXP; for model 5, the trade measure is RIMP; and for model 6, the trade measureis ROPEN. RGDP is the dependent variable for all models. For each model, the values refer to theobserved value of the LM statistic and the bootstrap p-value, respectively.

implying thus that a shock in one economy may affect simultaneously the othereconomies. The most detected break date is 1988, which supports the strongevidence of this shift point. Other breaks are also occurred in 1993, 1995, 1998and 2001. Some detected break dates could be linked to various internationalevents such as the attacks of 11 September 2001. The other break dates may belinked to domestic economic events.

4.5. Discussion of the estimate results

Given that a long-run equilibrium relationship between the variables exists, weestimate the above error correction model to investigate the effect of openness

Table 7. Estimated break date.

Model Bahrain Kuwait Oman Qatar Saudi Arabia UAE

Model 1 – – – 1988 1988 1988Model 2 – 1995 1988 1988 – 1988Model 3 – – – 1988 1993 1988Model 4 – 1995 1988 2001 1988 1988Model 5 – 1998 1988 1988 1993 1988Model 6 – 1995 1988 1988 1988 1988

Notes: For all models, the vector of explanatory variables is composed of one trade measure, labor,investment and ratio of government expenditures to GDP. For model 1, the trade measure is CEXP;for model 2, the trade measure is CIMP; for model 3, the trade measure is COPEN; for model 4, thetrade measure is REXP; for model 5, the trade measure is RIMP; and for model 6, the trade measureis ROPEN. RGDP is the dependent variable for all models.

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Table 8. PMG long-run estimates.

Dependent variable: RGDP

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

TRADE 0.218∗∗∗ 0.084∗ 0.176∗∗ 0.036∗∗∗ 0.107∗∗∗ 0.083∗∗

(0.067) (0.048) (0.085) (0.011) (0.030) (0.035)0.160 0.450 0.310 0.350 0.630 0.980

LAB 0.765∗∗∗ 0.064 0.781∗∗∗ 0.061∗∗∗ 0.062 0.063∗∗∗

(0.101) (0.047) (0.104) (0.022) (0.057) (0.020)0.470 0.980 0.430 0.160 0.110 0.880

CINVT 0.080∗ 0.084∗∗∗ –0.070∗ 0.096∗∗∗ 0.079∗∗∗ 0.088∗∗∗

(0.044) (0.027) (0.036) (0.015) (0.018) (0.014)0.280 0.540 0.150 0.170 0.500 0.610

Exp

lana

tory

vari

able

s

CGOV –0.193∗∗∗ –0.414∗∗∗ –0.467∗∗∗ –0.479∗∗∗ –0.457∗∗∗ –0.499∗∗∗

(0.039) (0.026) (0.033) (0.020) (0.022) (0.021)0.500 0.810 0.430 0.360 0.360 0.250

Notes: For model 1, TRADE is CEXP; for model 2, TRADE is CIMP; for model 3, TRADE is COPEN;for model 4, TRADE is REXP; for model 5, TRADE is RIMP; and for model 6, TRADE is ROPEN.RGDP is the dependent variable for all models. The values in parentheses are the asymptotic standarderrors (see Pesaran, Shin, and Smith 1999). For each variable, the bottom value is the p-value associatedto the Hausman test statistic for equal long-run parameters. ∗∗∗, ∗∗ and ∗ indicate statistical significanceat 1%, 5% and 10% levels, respectively.

indicators on economic growth over both the short run and long run. Before as-sessing this effect, we want to ensure that the long-run responses of real GDP toexplanatory variables are identical across GCC countries based on the Hausman-type test discussed in Pesaran, Smith, and Im (1996). The results displayed inTable 8 show evidence of common long-run coefficients across countries whateverthe used trade openness measure since the test does not reject the null hypothesisof equal long-run parameters. This finding may be explained by the fact that GCCcountries are strongly linked economically and share common features in terms ofdevelopment strategies and outward-oriented policies such as the stimulation ofinvestment and attraction of high-tech international companies. We take into ac-count the assumption of heterogeneous short-run dynamics across GCC countries,due to some distinguishing characteristics between them in terms of economyliberalization efforts, world demand, economy and country size. Within this con-text, Saudi Arabia is considered as the largest country in the region in terms ofGDP, population, land area and oil reserves (19.1% of total global reserves). Thequarter of global exports of liquefied natural gas is monopolized by Qatar, makingit the largest exporter in the world. Oman opts for enhanced recovery techniquesto stimulate its hydrocarbons production, and is involved in the re-export trade.

We now discuss the empirical findings under the considered trade opennessmeasures. The estimate results depicted in Table 8 indicate that the opennessvariables are found to be relevant drivers of economic growth for the GCC regionover the long run since the associated coefficients of such variables are positively

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Table 9. PMG short-run estimates.



TRADE 0.078∗∗∗ 0.037∗∗∗ 0.058∗∗ 0.032∗∗∗ 0.065∗∗∗ 0.070∗∗∗

(0.022) (0.013) (0.025) (0.008) (0.022) (0.018)LAB 0.274∗∗∗ 0.029∗∗∗ 0.256∗∗ 0.054∗∗∗ 0.038∗∗∗ 0.053∗∗∗

(0.077) (0.010) (0.113) (0.013) (0.013) (0.014)CINVT 0.029∗∗∗ 0.037∗∗∗ –0.023∗∗ 0.086∗∗∗ 0.048∗∗∗ 0.073∗∗∗

(0.008) (0.013) (0.010) (0.021) (0.016) (0.019)CGOV –0.069∗∗∗ –0.184∗∗∗ –0.153∗∗ –0.427∗∗∗ –0.279∗∗∗ –0.418∗∗∗

(0.019) (0.066) (0.067) (0.104) (0.095) (0.108)D(TRADE) –0.008 –0.032 –0.070∗ 0.046 0.016 0.045

(0.018) (0.022) (0.041) (0.038) (0.071) (0.035)D(LAB) 0.006∗∗∗ –0.115 0.095 –0.683∗ –0.120∗∗∗ –0.191

(0.001) (0.123) (0.090) (0.361) (0.040) (0.136)D(CINVT) –0.064 –0.057∗ –0.061 –0.118∗∗ –0.056 –0.052

(0.060) (0.051) (0.065) (0.056) (0.047) (0.048)

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D(CGOV) –0.022 0.023 –0.059 0.131 0.067 0.199∗∗

(0.022) (0.023) (0.045) (0.089) (0.045) (0.092)Adj. speed –0.359∗∗∗ –0.443∗∗∗ –0.328∗∗ –0.892∗∗∗ –0.609∗∗∗ –0.837∗∗∗

(0.101) (0.160) (0.144) (0.217) (0.208) (0.217)Trend 0.002 0.014∗∗∗ 0.001 0.024∗∗∗ 0.018∗∗∗ 0.024∗∗∗

(0.002) (0.005) (0.001) (0.007) (0.006) (0.008)Intercept 1.806∗∗∗ 4.533∗∗∗ 2.132∗∗ 9.635∗∗∗ 6.415∗∗∗ 8.848∗∗∗

(0.536) (1.521) (0.951) (2.379) (2.138) (2.225)

Notes: For model 1, TRADE is CEXP; for model 2, TRADE is CIMP; for model 3, TRADE is COPEN;for model 4, TRADE is REXP; for model 5, TRADE is RIMP; and for model 6, TRADE is ROPEN.RGDP is the dependent variable for all models. The values in parentheses are the asymptotic standarderrors (see Pesaran, Shin, and Smith 1999). ∗∗∗, ∗∗ and ∗ indicate statistical significance at 1%, 5% and10% levels, respectively.

significant at conventional levels. These results indicate that over the period 1980–2010, a 10% rise in nominal exports to GDP ratio, nominal imports to GDP ratio,nominal openness, real exports to GDP ratio, real imports to GDP ratio and realopenness allows increasing real GDP of 2.18%, 0.84%, 1.76%, 0.36%, 1.07% and0.83%, respectively. The PMG short-run estimates30 reported in Table 9 point outthat the adjustment of economic growth to the long-run equilibrium is positivelydriven by short-run adjustments in all openness indicators. All these findingsindicate that trading is beneficial for economic growth in the GCC region.31 Thestatistical significant linkage between economic growth and trade measures mayconfirm the quite high correlation between these measures over the whole sampleas can be easily seen from Tables 3 and 4. The positive significant connectionof economic growth to international trade is in line with other empirical workssuch as Dollar and Kraay (2003), Alcala and Ciccone (2004), Ismail et al. (2010),and Ercakar (2011). Another striking feature is that the impact of these variables

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on economic growth over the short-run is smaller than that found over the longrun. Also, the contributions of all openness variables to economic growth areapproximately identical over the short run.

Regarding the auxiliary variables, the labor force positively affects economicgrowth, except for the case where the nominal and real ratios of imports to GDPare used as trade measures over the long run. The investment ratio has the powerto predict real GDP over both the short run and long run. The impact is positive32

and marginal whatever the trade variable especially over the short run, exceptwhen involving the nominal openness degree in the analysis where the effect isnegative. The ratio of government expenditures to GDP has a negative impact oneconomic growth over both the short run and long run through all models. Thefindings are in line with those of Ulasan (2012) who shows evidence of negative(positive) impact of government expenditures (investment) on economic growthin the context of openness–economic growth linkage for OECD and non-OECDcountries.

Overall, the estimate results outline that as for trade openness measures, theresponses of real GDP to the fluctuations of auxiliary variables are more importantover the long run rather than the short run. Over both the short run and long run,the significance of the investment and government expenditures persists oncetrade openness measure changes in the model. Nevertheless, the labor force issignificant whatever the variable of trade openness we use only over the short run.All variables included in the analysis vary faster than economic growth since theassociated elasticities are less than 1 in absolute value.

The short-run error correction term (speed of adjustment, Table 9) is signifi-cantly negative for all models. This result confirms the cointegrating relationshipbetween the variables of interest, and implies that the linkage between real GDPand explanatory variables is characterized by high predictability and that the spreadmovement is mean reverting. The adjustment speed from short-run disequilibriumtowards the long-run state depends on the used trade openness variable since theerror correction term varies through models. The correction mechanism takes twoto three years to restore the long-run equilibrium for the current openness mea-sures, as the coefficient varies from –0.443 to –0.328. However, it takes one totwo years for the real openness indicators, as the coefficient varies from –0.892to –0.609. These insights imply that the correction mechanism is faster wheninvolving real measures in the analysis rather than nominal indicators.

We have also applied the PMG approach to estimate the above error correctionmodel where the nominal import coverage of exports is included as opennessmeasure. The empirical results (not reported here) indicate that such measure isintegrated of order 1, and that there is cointegrating linkage between real GDPand all explanatory variables. Regarding the coefficient estimates, the new trademeasure positively affects economic growth over both the short run and long run.A noticeable feature is that the impact of such measure on real GDP is higherthan that of all the other considered openness variables, as it is equal to 0.279over the long run and 0.122 over the short run. These findings contrast with that

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Table 10. Estimates of the SUR model.



TRADE 0.524∗∗∗ 0.379∗∗∗ 0.521∗∗∗ 0.452∗∗∗ 0.395∗∗∗ 0.464∗∗∗

(0.028) (0.034) (0.029) (0.032) (0.037) (0.031)LAB 1.179∗∗∗ 1.211∗∗∗ 1.190∗∗∗ 1.180∗∗∗ 1.222∗∗∗ 1.191∗∗∗

(0.012) (0.013) (0.012) (0.012) (0.013) (0.011)CINVT 0.078∗ 0.267∗∗∗ 0.069 0.067 0.228∗∗∗ 0.058

(0.045) (0.060) (0.050) (0.050) (0.061) (0.050)CGOV 0.067∗∗ 0.019 –0.050 0.154∗∗∗ –0.003 0.030

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(0.031) (0.049) (0.037) (0.041) (0.051) (0.042)

Notes: For model 1, TRADE is CEXP; for model 2, TRADE is CIMP; for model 3, TRADE is COPEN;for model 4, TRADE is REXP; for model 5, TRADE is RIMP; and for model 6, TRADE is ROPEN.RGDP is the dependent variable for all models. The values in parentheses are the standard errors. ∗∗∗,∗∗ and ∗ indicate statistical significance at 1%, 5% and 10% levels, respectively.

of Ercakar (2011) who shows that for Turkey, the import coverage of exportsvariable negatively influences economic growth. The auxiliary variables showsimilar behavior in terms of significance and sign as that of the above results.The correction mechanism to restore the long-run equilibrium state is comparableto that of the above cases based on nominal measures since the error correctionterm is equal to –0.439. The obtained findings then indicate that the evidence isrobust to using various openness indicators over both the short-run and long run,implying thus the non-fragility of the international trade–economic growth nexusin the GCC region.

5. Check of robustness

To better evaluate the influence of trade openness on economic growth in theGCC region and to check the robustness of the above basic empirical results, weconduct a further sensitivity analysis in which we consider an alternative modelspecification and include additional auxiliary variables in the above PMG model.

5.1. Alternative model specification

We employ the Zellner’s (1962) SUR procedure that is the appropriate methodhere to handle dependence between GCC countries as shown above by the test ofBreusch and Pagan (1980). Another attraction of this method is that it is suitablewhen the cross-section dimension N is reasonably small relative to the time seriesdimension T , which is the case for our study (N = 6 and T = 31). The model isestimated by the feasible generalized least-squares method. The estimate resultsreported in Table 10 reveal that the openness measures positively affect economicgrowth in the GCC region, which confirms the insights drawn from the PMG

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model. Regarding the magnitude, the SUR estimated coefficients are higher thanthe above PMG estimates. The labor force exerts a positive impact on real GDPfor all models. A striking feature is that the corresponding elasticities exceedone, implying that the induced economic growth increase is more important thanthe rise of labor force in terms of percentage. This does not happen in the PMGframework, as indicated by the labor coefficients reported in Table 8. The twoother auxiliary determinants (investment and ratio of government expenditures toGDP) have the power to predict real GDP for some cases.

5.2. Additional auxiliary determinants

We add other economic growth determinants in the above PMG model such asFDI as percentage of GDP,33 consumer price index (CPI) as indicator of inflation,and money supply M3.34 These variables are then considered as additionaltransmission channels through which the responses of economic growth to thefluctuations of openness measures are examined. The application of the LMstationarity test indicates that the additional variables are integrated of orderone. Based on some tests,35 the inclusion of these determinants in the modeldoes not alter the cointegration results since we conclude in favor of long-runrelationship between economic growth, openness variables and all auxiliaryvariables.36 The estimate results depicted in Tables 11 and 12 outline that overboth the long run and short run37 the positive impact of trade indicators oneconomic growth persists once other determinants are accounted for. Regardingthe extent, the results are globally satisfactory and indicate that including threeadditional auxiliary variables in the specification allows getting estimate valuesquite close to the above basic values. The findings then point to the robustness ofthe trade openness–economic growth linkage in the GCC region. This conclusioncontradicts that of Ulasan (2012) who argues that for OECD and non-OECDcountries, the positive significant influence of many trade measures on economicgrowth disappears when adding other variables in the model, confirming thusthe fragility of the linkage between economic growth and openness in thesecountries.

The initial auxiliary variables (labor force, investment and government expen-ditures as percentage of GDP) behave, in general, similarly as the above basiccase in terms of significance and sign of their coefficients. The exception is theinvestment that becomes positive when using the current openness degree as trademeasure over both the long run and short run. Over the long run, the additionalauxiliary variables (especially the CPI and money supply) exert an effect on eco-nomic growth for some models (see Table 11). However, all added variables aresignificant over the short run (see Table 12). The impact of FDI is marginal, whichis in line with the results of Hussein (2009) who finds weak association betweenFDI and economic growth in the GCC region during the period 1999–2007 basedon panel analysis. The CPI and money supply coefficients keep the same sign(negative and positive, respectively) whatever the used trade variable. By cons,

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Table 11. PMG long-run estimates.



TRADE 0.176∗∗∗ 0.095∗ 0.167∗ 0.062∗∗ 0.079∗∗∗ 0.184∗∗∗

(0.048) (0.051) (0.093) (0.027) (0.030) (0.045)0.130 0.330 0.110 0.970 0.330 0.160

LAB 0.196∗∗∗ 0.063 0.257∗∗∗ 0.188∗∗∗ 0.090∗∗∗ 0.173∗∗∗

(0.042) (0.056) (0.085) (0.049) (0.030) (0.053)0.190 0.290 0.170 0.720 0.450 0.420

CINVT 0.080∗∗∗ 0.073∗∗ 0.001 0.076∗∗∗ 0.099∗∗∗ 0.074∗∗∗

(0.026) (0.030) (0.033) (0.019) (0.016) (0.019)0.150 0.700 0.170 0.630 0.900 0.830

CGOV –0.305∗∗∗ –0.398∗∗∗ –0.436∗∗∗ –0.337∗∗∗ –0.368∗∗∗ –0.368∗∗∗

(0.035) (0.026) (0.027) (0.024) (0.028) (0.025)0.190 0.320 0.170 0.790 0.320 0.640

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FDI 0.002∗∗ 0.001 0.001 0.001 –0.001 –0.001(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)0.590 0.480 0.870 0.100 0.210 0.140

CPI –0.709∗∗∗ –0.001 –0.199 –0.531∗∗∗ –0.603∗∗∗ –0.467∗∗∗

(0.164) (0.158) (0.168) (0.112) (0.125) (0.113)0.190 0.420 0.680 0.880 0.410 0.540

M3 0.053∗∗∗ 0.038∗∗ 0.041∗∗∗ 0.022∗∗∗ 0.014 0.014∗∗

(0.011) (0.015) (0.012) (0.007) (0.009) (0.007)0.540 0.390 0.840 0.350 0.690 0.730

Notes: For model 1, TRADE is CEXP; for model 2, TRADE is CIMP; for model 3, TRADE is COPEN;for model 4, TRADE is REXP; for model 5, TRADE is RIMP; and for model 6, TRADE is ROPEN.RGDP is the dependent variable for all models. The values in parentheses are the asymptotic standarderrors (see Pesaran, Shin, and Smith 1999). For each variable, the bottom value is the p-value associatedto the Hausman test statistic for equal long-run parameters. ∗∗∗, ∗∗ and ∗ indicate statistical significanceat 1%, 5% and 10% levels, respectively.

the FDI estimate coefficient changes sign through models. A noticeable featureis that the impact of CPI on economic growth is more important than that of thetwo other variables. The correction mechanism almost takes the same time as theabove basic cases to restore the long-run equilibrium since the error correctionterm varies from –0.711 to –0.421. As for the models without additional variables,the adjustment speed estimates are sensitive to trade measures since they differaccording to such measures are in nominal or real terms.

The findings are in line with some empirical works in the literature. Indeed,Rajagopal (2007) discusses the influence of trade on tariff structure, export com-petitiveness, inflation and economic growth in Latin American countries. He findsthat trade openness, inflation and economic growth are interrelated. Ismail et al.(2010) investigate the linkage between exports, inflation, investment and economicgrowth for Pakistan based on cointegration approaches. The findings reveal thatover the short-run exports and investment positively affect economic growth, and

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Table 12. PMG short-run estimates.



TRADE 0.081∗∗∗ 0.040∗∗∗ 0.075∗∗∗ 0.044∗∗∗ 0.043∗∗ 0.107∗∗∗

(0.030) (0.015) (0.021) (0.012) (0.017) (0.040)LAB 0.091∗∗∗ 0.027∗∗∗ 0.116∗∗∗ 0.133∗∗∗ 0.048∗∗ 0.100∗∗∗

(0.034) (0.010) (0.003) (0.035) (0.019) (0.037)CINVT 0.037∗∗∗ 0.031∗∗∗ 0.003∗∗∗ 0.054∗∗∗ 0.053∗∗ 0.043∗∗∗

(0.014) (0.012) (0.001) (0.014) (0.021) (0.016)CGOV –0.141∗∗∗ –0.167∗∗∗ –0.197∗∗∗ –0.240∗∗∗ –0.198∗∗ –0.213∗∗∗

(0.052) (0.064) (0.056) (0.063) (0.079) (0.079)FDI 0.003∗∗∗ 0.002∗∗∗ 0.003∗∗∗ 0.003∗∗∗ –0.002∗∗ –0.003∗∗∗

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)CPI –0.328∗∗∗ –0.003∗∗∗ –0.090∗∗∗ –0.377∗∗∗ –0.324∗∗ –0.270∗∗∗

(0.122) (0.001) (0.025) (0.100) (0.130) (0.100)M3 0.024∗∗∗ 0.016∗∗∗ 0.019∗∗∗ 0.015∗∗∗ 0.008∗∗∗ 0.008∗∗∗

(0.009) (0.006) (0.005) (0.004) (0.003) (0.003)D(TRADE) –0.011 –0.054∗∗ –0.080∗∗∗ 0.062 –0.017 –0.051

(0.025) (0.026) (0.023) (0.048) (0.048) (0.060)D(LAB) –0.220 –0.234 –0.166 –0.324 –0.202 –0.064

(0.230) (0.181) (0.139) (0.263) (0.146) (0.064)

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D(CINVT) –0.058 –0.051 –0.041 –0.045 –0.053 –0.069(0.046) (0.050) (0.055) (0.054) (0.049) (0.049)

D(CGOV) –0.025 0.001 –0.032 0.009 –0.010 0.038(0.026) (0.001) (0.030) (0.035) (0.063) (0.047)

D(FDI) –0.001 0.001 0.002 –0.001 0.002 0.001(0.004) (0.002) (0.003) (0.003) (0.003) (0.002)

D(CPI) 0.049 0.068 –0.005 0.222 0.228 0.341∗

(0.109) (0.068) (0.042) (0.296) (0.182) (0.196)D(M3) 0.040 0.011 0.024 0.051 0.043 0.046

(0.047) (0.007) (0.025) (0.040) (0.033) (0.040)Adj. speed –0.462∗∗∗ –0.421∗∗∗ –0.451∗∗∗ –0.711∗∗∗ –0.538∗∗ –0.579∗∗∗

(0.172) (0.160) (0.128) (0.188) (0.215) (0.215)Trend 0.019∗∗∗ 0.014∗∗∗ 0.013∗∗∗ 0.028∗∗∗ 0.022∗∗∗ 0.021∗∗∗

(0.006) (0.005) (0.004) (0.009) (0.008) (0.007)Intercept 5.133∗∗∗ 4.096∗∗∗ 4.290∗∗∗ 8.039∗∗∗ 6.700∗∗∗ 6.297∗∗∗

(1.852) (1.445) (1.240) (2.107) (2.679) (2.320)

Notes: For model 1, TRADE is CEXP; for model 2, TRADE is CIMP; for model 3, TRADE is COPEN;for model 4, TRADE is REXP; for model 5, TRADE is RIMP; and for model 6, TRADE is ROPEN.RGDP is the dependent variable for all models. The values in parentheses are the asymptotic standarderrors (see Pesaran, Shin, and Smith 1999). ∗∗∗, ∗∗ and ∗ indicate statistical significance at 1%, 5% and10% levels, respectively.

that inflation has a significant negative impact on economic growth. Over the longrun, the same conclusions are obtained for investment and inflation in terms of sig-nificance, but exports do not affect economic growth. Ercakar (2011) analyzes thelong-run links between economic growth, FDI, trade and inflation in Turkey over

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the period 1970–2008. The findings show evidence of one cointegrating relation-ship among variables and outline that FDI, inflation and trade surplus positivelyaffect economic growth. On the other hand, import coverage of exports negativelyinfluence economic growth.

6. Policy implications

In light of the above empirical findings, the conclusion that economic growth inthe GCC region is positively sensible to trade openness over both the short runand long run should be supported. The trade openness indicators can then beconsidered as channels through which economic growth can be enhanced in theGCC region. The empirical analysis conducted in this paper then helps makers todevelop policies and strategies in order to enhance economic growth and trade.

First, the GCC countries should devote more government expenditures todevelop effective trade policies and strategies that allow us to encourage and con-solidate diversification from hydrocarbon dependence by promoting exports innon-oil sectors such as services and investment into petrochemicals, metals andconstruction. Within this context, Nusair (2012) argues that the diversification ofthe production and exports process allows reducing the need for exchange rateadjustments to confront shocks, leading thus countries to form and maintain a cur-rency union. The governments should also strengthen imports of high-tech equip-ment that help domestic production sectors adopt new technologies to make heavyinvestments and development projects, accelerating thus economic activity growth.

Second, through their high oil exports, the GCC countries are considered asvery open abroad, which could enable them to specialize in new sectors in whichthey create a comparative advantage. The exports in these sectors can help GCCcountries to take part in the global specialization and to improve the foreigncurrency that boosts imports as main channel to transfer more high technologiesto flow into their economies in order to enhance domestic sectors and increasetheir competitiveness on a global scale. In that regard, the creation of sectorscharacterized by comparative advantage would directly promote economic growthin the GCC region.

Third, the GCC governments should improve the accessibility to FDI that canbe considered as source of high-tech trade and technological know-how that al-lows us to consolidate the high-tech intensive industries and innovative capacitiesof competitive domestic firms in order to improve the international trade per-formance and, thus, economic growth. The GCC governments should also takeinto consideration the strengthening of human capital that contributes to betterexploit productive and sophisticated resources, and deals with high technologiestransferred from FDI.

7. Conclusion

Despite the fact that there exist many empirical works in the literature on therelationship between economic growth and trade openness, the studies related

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to the GCC region are very rare. To that effect, this paper investigates empiri-cally the linkage between economic growth and trade openness in GCC countriesover the annual period 1980–2010 by opting for the PMG approach of Pesaran,Shin, and Smith (1999) used to estimate dynamic heterogeneous panels. The es-timate results reveal long-run linkage among variables, and outline that GCCeconomic growth is positively sensible to the movements of openness variablesover both the short run and long run. The sensitivity analysis shows how robustthe findings are to employing an alternative model specification and to control-ling for additional determinants in the model, pointing thus to the robustnessof the GCC trade openness–economic growth nexus. Therefore, the PMG ap-proach is suitable for analyzing the linkage between economic growth and inter-national trade in GCC countries. The results have important economic and policyimplications.

Many future research projects can be raised within the same context. Indeed,one could investigate the bidirectional causal links between economic growth andtrade openness in the GCC region to see whether there is a clear consensus on theselinks between countries. This empirical analysis is the scope of a current paper.Another research avenue consists in providing an empirical test of the bidirectionalcausality between economic growth, and exports and imports categories, compar-ing with the results obtained for total trade in GCC countries. This empirical workaims at determining which category allows a significant causal connection witheconomic growth and in what extent.

AcknowledgementsThe author is grateful to the editor and the anonymous referees for their helpful commentsand suggestions, which have significantly improved the paper. He would like to thank theDeanship of Scientific Research at King Saud University represented by the research centerat CBA for supporting this research financially.

Notes1. See, for example, Mamun and Nath (2005), Reppas and Christopoulos (2005), Shirazi

and Manap (2005), Ugur (2008), Gurgul and Lach (2010), Sakyi (2011) and Ulasan(2012).

2. Trade openness may be considered as source of imported inflation especially forcountries that are highly dependent from abroad.

3. This index measures the extent to which the real exchange rate is distorted away fromits free trade level by the trade regime.

4. The sample period is considered as sufficiently recent to account for the latest eco-nomic events.

5. During these years, the GCC region has been considered as a free trade area.6. Note that more than 80% of the GCC’s total revenue comes from the oil

sector.7. Rivera-Batiz (1985) outlines that imports could be risen due to the increase of eco-

nomic activity, as high real income improves consumption.

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28 J. Jouini

8. Various openness variables are used, namely direct trade policy indicators (tariff ratesand non-tariff barriers), the ratio of exports to GDP, the ratio of imports to GDPand the ratio of trade volume (exports plus imports) to GDP. For the last variable,measures from the World Bank and the Penn World Tables are employed.

9. These approaches embody the dynamic econometric panel model, the panel FullyModified OLS (FMOLS) method, the panel Seemingly Unrelated Regression (SUR)model, the panel cointegration techniques, the panel VAR process and Granger non-causality tests, and others.

10. The readers are referred to Breitung and Pesaran (2008) for a literatureoverview.

11. The number of breaks is determined based on the Bayesian information criterion, andthe break locations are estimated by the OLS method as suggested by Bai and Perron(1998, 2003).

12. The LM statistic is normally distributed under the null hypothesis.13. The residuals can be obtained by estimating the model by the Fully Modified OLS

method proposed by Phillips and Hansen (1990).14. Pesaran, Shin, and Smith (1999) argue that the obtained PMG estimators are consistent

and normally distributed asymptotically.15. According to Pesaran, Shin, and Smith (1999), the PMG approach can be adapted to

allow only a subset of the long-run parameters to be identical while the others differacross groups.

16. The readers are referred to Pesaran, Shin, and Smith (1999) for more details on theMG and DFE methods.

17. Real measures are preferable on theoretical grounds to nominal measures largely andconventionally advocated in the related literature.

18. The nominal variables are taken in current values, while the real variables are mea-sured at constant prices (2005) and constant exchange rates (2005).

19. Throughout the paper, some empirical results are not reported to preserve space, butthey are available upon request from the author.

20. The nominal GDP of GCC countries is evaluated at US$1.4 trillion in 2011.21. Unlike the other GCC countries, the UAE are less dependent on exports and rely on

services to boost the GDP.22. Oil price registers a contraction in 2009 since it decreased by 33% because of the

global financial crisis and economic slowdown observed at the end of 2008, causingthus a fall by about 20% in the nominal GDP in the GCC region.

23. These insights show the crucial role of the GCC region in the global economy.24. A large portion of imports is destined for re-export.25. Saudi Arabia monopolizes 45% of the labor force in the GCC region.26. Within the GCC region, the ratio of government expenditures to GDP ranges from

27% in Qatar to 43% in Kuwait.27. We have applied tests developed in the time series framework to test for unit root in

all level and first-differences series for each country. The results (not reported here)outline that we generally conclude in favor of variables integrated of order one.

28. We have applied other panel cointegration tests and found long-run linkage betweenthe variables.

29. For the UAE, the break date detected for all models is located in 1988.30. The PMG short-run estimates are the means of the coefficients obtained by averaging

across countries.31. We have also estimated the models with only the openness measures as explana-

tory variables as they are the main variables of interest. The results (not reportedhere) confirm the significant positive linkage between economic growth and tradeopenness.

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32. This finding is in line with several empirical works in the literature mentioningthat investment is among traditional determinants that positively affect economicgrowth.

33. The growth models consider FDI as factor of economic growth through the role playedin technological diffusion. Faras and Ghali (2009) opt for the ARDL approach, andfind that the contribution of FDI inflows to economic growth differs across GCCcountries.

34. The variables are taken in logarithm except of FDI that contains negativevalues.

35. The LM cointegration test presented in this paper is not applied when adding the newdeterminants because the number of explanatory variables exceeds the number ofgroups in this case. Therefore, we have employed other tests developed in the panelframework to test for cointegration between variables.

36. The unit root and cointegration test results are not reported, but they are availableupon request.

37. The Hausman test concludes in favor of common long-run coefficients as shown inTable 11.

ReferencesAlcala, F., and A. Ciccone. 2004. “Trade and Productivity.” Quarterly Journal of Economics

119: 613–646.Aljebrin, M.A., and M.A. Ibrahim. 2012. “The Determinants of the Demand for Imports in

GCC Countries.” International Journal of Economics and Finance 4: 126–138.Asafu-Adjaye, J., and D. Chakraborty. 1999. “Export Growth and Import Compression:

Further Time Series Evidence from LDCs.” Australian Economic Papers 38: 164–175.Baharumshah, A.Z., and S. Rashid. 1999. “Exports, Imports and Economic Growth in

Malaysia: Empirical Evidence Based on Multivariate Time Series.” Asian EconomicJournal 13 (4): 389–406.

Bai, J., and P. Perron. 1998. “Estimating and Testing Linear Models with Multiple StructuralChanges.” Econometrica 66: 47–78.

Bai, J., and P. Perron. 2003. “Computation and Analysis of Multiple Structural ChangeModels.” Journal of Applied Econometrics 18: 1–22.

Bajwa, S., and M.W. Siddiqi. 2011.“Trade Openness and its Effects on Economic Growthin Selected South Asian Countries: A Panel Data Study.” World Academy of Science,Engineering and Technology 50: 1073–1078.

Breitung, J., and M.H. Pesaran. 2008. “Unit Roots and Cointegration in Panels.” In TheEconometrics of Panel Data, edited by L. Matyas and P. Sevestre. 3rd ed. Berlin,Hedeilberg: Springer-Verlag.

Breusch, T., and A.R. Pagan. 1980. “The Lagrange Multiplier Test and its Applicationto Model Specifications in Econometrics.” Reviews of Economics Studies 47: 239–253.

Carrion-i-Silvestre, J.L., T.D.B. Castro, and E.L. Bazo. 2005. “Breaking the Panels: AnApplication to the GDP per Capita.” Econometrics Journal 8: 159–175.

Chang, R., L. Kaltani, and N.V. Loayza. 2009. “Openness Can Be Good for Growth: TheRole of Policy Complementarities.” Journal of Development Economics 90 (1): 33–49.

Davidson, R., and J. MacKinnon. 2000. “Bootstrap Tests: How Many Bootstraps?” Econo-metric Reviews 19: 55–68.

Dollar, D. 1992.“Outward-Oriented Developing Economies Really Do Grow More Rapidly:Evidence From 95 LDCs, 1976–1985.” Economic Development and Cultural Change40: 523–544.

Dow

nloa

ded

by [

New

cast

le U

nive

rsity

] at

03:

14 0

4 M

ay 2

014

30 J. Jouini

Dollar, D., and A. Kraay. 2003. “Institutions, Trade, and Growth.” Journal of MonetaryEconomics 50 (1): 133–162.

Edwards, S. 1993. “Openness, Trade Liberalization, and Growth in Developing Countries.”Journal of Economic Literature 31 (3): 1358–1393.

Edwards, S. 1998. “Openness, Productivity and Growth: What Do We Really Know?”Economic Journal 108 (447): 383–398.

Ercakar, M.E. 2011. “Growth, Foreign Direct Investment, Trade and Inflation: An Em-pirical Application on Turkey.” Middle Eastern Finance and Economics 9: 137–147.

Faras, R.Y., and K.H. Ghali. 2009. “Foreign Direct Investment and Economic Growth: TheCase of the GCC Countries.” International Research Journal of Finance and Economics29: 134–145.

Felbermayr, G.J. 2005. “Dynamic Panel Data Evidence on the Trade-Income Relation.”Review of World Economics 141: 583–611.

Foster, N. 2008. “The Impact of Trade Liberalization on Economic Growth: Evidence froma Quantile Regression Analysis.” Kyklos 61: 543–567.

Fosu, A.K. 1990. “Exports and Economic Growth: The African Case.” World Development18: 831–835.

Frankel, J.A., and D. Romer. 1999. “Does Trade Cause Growth?” American EconomicReview 89: 379–399.

Gries, T., and M. Redlin. 2012. “Trade Openness and Economic Growth: A Panel CausalityAnalysis.” Working Paper 2011–06. Germany: Center for International Economics,University of Paderborn, Paderborn.

Grossman, G.M., and E. Helpman. 1991. Innovation and Growth in the Global Economy.Cambridge, MA: MIT Press.

Grossman, G.M., and E. Helpman. 1992. Innovation and Growth: Technological Competi-tion in the Global Economy. Cambridge, MA: MIT Press.

Gurgul, H., and L. Lach. 2010. “International Trade and Economic Growth in the PolishEconomy.” Operations Research and Decisions 3–4: 5–29.

Gylfason, T. 1999. “Exports, Inflation and Growth.” World Development 27: 1031–1057.

Herzer, D. 2013. “Cross-country Heterogeneity and the Trade-Income Relationship.” WorldDevelopment 44: 194–211.

Hsiao, F.S.T., and M-C.W. Hsiao. 2006. “FDI, Exports, and GDP in East and Southeast Asia– Panel Data Versus Time-Series Causality Analyses.” Journal of Asian Economics 17:1082–1106.

Hussein, M.A. 2009. “Impacts of Foreign Direct Investment on Economic Growth inthe Gulf Cooperation Council (GCC) Countries.” International Review of BusinessResearch Papers 5: 362–376.

Im, K., M. Pesaran, and Y. Shin. 2003. “Testing for Unit Roots in Heterogeneous Panels.”Journal of Econometrics 115: 53–74.

Ismail, A., K. Zaman, R.M. Atif, A. Jadoon, and R.Seemab. 2010. “The Role of Exports,Inflation and Investment on Economic Growth in Pakistan (1980–2009).” InternationalJournal of Economics and Research 1: 1–9.

Jadresic, E. 2002.“On a Common Currency for the GCC Countries.” IMF Policy DiscussionPaper PDP/02/12, International Monetary Fund.

Kim, D.H. 2011. “Trade, Growth and Income.” Journal of International Trade and EconomicDevelopment 20 (5): 677–709.

Konya, L. 2006. “Exports and Growth: Granger Causality Analysis on OECD Countrieswith a Panel Data Approach.” Economic Modelling 23: 978–992.

Lee, H.Y., L.A. Ricci, and R. Rigobon. 2004. “Once Again, is Openness Good for Growth?”Journal of Development Economics 75 (2): 451–472.

Dow

nloa

ded

by [

New

cast

le U

nive

rsity

] at

03:

14 0

4 M

ay 2

014


Mamun, K.A.Al., and H.K. Nath. 2005. “Export-Led Growth in Bangladesh: A Time SeriesAnalysis.” Applied Economics Letters 12: 361–364.

Newey, W.K., and K.D. West. 1994. “Automatic Lag Selection in Covariance Matrix Esti-mation.” Review of Economic Studies 61: 631–53.

Nusair, S.A. 2012. “Is the Gulf Cooperation Council an Optimum Currency Area?” TheDeveloping Economies 50: 351–377.

Pesaran, M.H., Y. Shin, and R. Smith. 1999. “Pooled Mean Group Estimation of Dy-namic Heterogeneous Panels.” Journal of the American Statistical Association 94: 621–634.

Pesaran, M.H., R.P. Smith, and K.S. Im. 1996. “Dynamic Linear Models for HeterogeneousPanels.” In The Econometrics of Panel Data: A Handbook of Theory with Applications,edited by L. Matyas and P. Sevestre. 2nd ed. Dordrecht: Kluwer Academic Publishers.

Phillips, P.C.B., and B.E. Hansen. 1990. “Statistical Inference in Instrumental VariablesRegression with I (1) Processes.” Review of Economic Studies 57: 99–125.

Pritchett, L. 1996. “Measuring Outward Orientation in LDCs: Can It Be Done?” Journalof Development Economics 49 (2): 307–335.

Rajagopal, 2007. “Trade Openness and Inflation in Latin American Countries.” WorkingPaper MKT-05, Tecnologico de Monterrey, Campus Ciudad de Mexico.

Ramos, F.F.R. 2001. “Exports, Imports and Economic Growth in Portugal: Evidence fromCausality and Cointegration Analysis.” Economic Modelling 18: 613–623.

Reppas, P.A., and D.K. Christopoulos. 2005. “The Export-Output Growth Nexus: Evidencefrom African and Asian Countries.” Journal of Policy Modeling 27: 929–940.

Riezman, G.R., C.H. Whiteman, and P.M. Summers. 1996. “The Engine of Growth orits Handmaiden? A Time Series Assessment of Export-Led Growth.” Empirical Eco-nomics 12: 77–110.

Rivera-Batiz, F.L. 1985. International Finance and Open Economy Macroeconomics. NewYork: Macmillan.

Rivera-Batiz, L.A., and P.M. Romer. 1991. “International Trade with Endogenous Techno-logical Change.” European Economic Review 35: 971–1001.

Rodriguez, F., and Rodrik, D. 2000. “Trade Policy and Economic Growth: A Skeptic’sGuide to the Cross-National Evidence.” In NBER Macroeconomics Annual, edited byB. Bernanke and K. Rogoff, 261–325. Cambridge, MA: MIT Press.

Rodrik, D. 1998. “Why Do More Open Economies Have Bigger Governments?” Journalof Political Economy 106 (5): 997–1032.

Romer, P.M. 1990. “Endogenous Technological Change.” Journal of Political Economy 98:71–102.

Sakyi, D. 2011. “Trade Openness, Foreign Aid and Economic Growth in Post-LiberalizationGhana: An Application of ARDL Bounds Test.” Journal of Economics and Interna-tional Finance 3: 146–156.

Sakyi, D., J. Villaverde, A. Maza, and K.R. Chittedi. 2012. “Trade Openness, Growthand Development: Evidence from Heterogeneous Panel Cointegration Analysis forMiddle-Income Countries.” Cuadernos de Economıa 31: 21–40.

Shirazi, N.S., and T.A.A. Manap. 2005. “Export-Led Growth Hypothesis: Further Econo-metric Evidence from South Asia.” The Developing Economies 4: 472–488.

Solow, R. 1957. “Technical Change and the Aggregate Production Function.” Review ofEconomics and Statistics 39: 312–320.

Sturm, M., J. Strasky, P. Adolf, and D. Peschel. 2008. “The Gulf Cooperation Council coun-tries: Economic Structures, Recent Developments and Role in the Global Economy.”Occasional Paper Series 92. Frankfurt: European Central Bank.

Tekin, R.B. 2012. “Economic Growth, Exports and Foreign Direct Investment in LeastDeveloped Countries: A Panel Granger Causality Analysis.” Economic Modelling 29:868–878.

Dow

nloa

ded

by [

New

cast

le U

nive

rsity

] at

03:

14 0

4 M

ay 2

014

32 J. Jouini

Ugur, A. 2008. “Import and Economic Growth in Turkey: Evidence from Multivariate VARAnalysis.” Journal of Economics and Business 11: 54–75.

Ulasan, B. 2012. “Openness to International Trade and Economic Growth: ACross-country Empirical Investigation.” Economics Discussion Paper 2012–25, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2012-25.

Westerlund, J. 2005. “Testing for Panel Cointegration with Multiple Structural Breaks.”Oxford Bulletin of Economics and Statistics 68: 101–132.

Westerlund, J., and D. Edgerton. 2007. “A Panel Bootstrap Cointegration Test.” EconomicsLetters 97: 185–190.

Wooldridge, J.M. 2003. Introductory Econometrics: A Modern Approach. Mason, OH:South-Western.

Zellner, A. 1962. “An Efficient Method of Estimating Seemingly Unrelated RegressionEquations and Tests of Aggregation Bias.” Journal of the American Statistical Associ-ation 57: 500–509.

Zeren, F., and A. Ari. 2013. “Trade Openness and Economic Growth: A Panel CausalityTest.” International Journal of Business and Social Science 4: 317–324.

Dow

nloa

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