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International Research Journal of Finance and EconomicsISSN 1450-2887 Issue 29 (2009)

EuroJournals Publishing, Inc. 2009

http://www.eurojournals.com/finance.htm

Foreign Direct Investment and Economic Growth: The Case of

the GCC Countries

Reyadh Y. Faras

Department of Economics, College of Business Administration

Kuwait University P.O.Box 5486, Safat 13055, Kuwait

Khalifa H. Ghali

Department of Economics, College of Business Administration

Kuwait University P.O.Box 5486, Safat 13055, Kuwait

E-mail: [email protected]

Abstract

The objective of this paper is to contribute to the empirical literature on the

relationship between the inflow of foreign direct investment (FDI) and economic growth inthe host country and that by investigating this relationship in the particular case of the GCC

countries, whose specific features as oil producing countries makes this investigationappealing and insightful. The research methodology adopted in this paper extends the

existing literature in two aspects. First, the paper offers a country-specific analysis of the

issue. Hence, compared to the level of generalization in the literature, results in this paperare more precise and address the specific countries concerns with regard to FDI. Second,

the paper uses a cointegration technique based on the autoregressive distributed lag

approach (ARDL) developed by Pesaran and Shin (1995, 1998) which is proven to performbetter than other conventional cointegration techniques, in particular in small samples as isthe case for GCC countries. The main findings of the paper show the existence of

significant dissimilarities among the 6 countries as to the importance and contribution of

FDI inflows to economic growth.

Keywords: FDI, ARDL, Economic Growth, GCC countries

JEL Classification Codes: C32, F21, O1

1. IntroductionThe relationship between the inflow of foreign direct investment (FDI) and economic growth in thehost country has become one of the most debated issues in the empirical literature. The question bears

upon whether FDI promotes economic growth or it is only being attracted by favorable economic

conditions in the host country and by profits.

Proponents of a positive impact of FDI on economic growth have advanced a number oftheoretical justifications to believe that FDI inflows would be beneficial to economic growth in the

host country. According to these justifications, there are a number of channels through which FDI

contributes to economic growth. The first channel is through the spillover effect; FDI with moderntechnology and new production and management techniques would benefit domestic industries through

on the job training of domestic labor force and joint ventures with local producers. The second channel

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International Research Journal of Finance and Economics - Issue 29 (2009) 135

is through investing in economic sectors that lack sufficient domestic investments due to high costs, the

need for advanced technology, and the need for high-skilled labor. The third channel is when FDIinflows can be used to promote a balanced-sector economic growth and that by directing these

investments to deficient sectors of the economy. The fourth channel works through attracting more and

more FDI inflows and, hence, the accumulation of productive capital in the future. Finally, the

presence of FDI from politically powerful countries helps in increasing political stability in the hostcountry thereby improving the quality of the business environment, which in turn promotes economic

growth.On the other hand, there are points of view for which FDI inflows do not contribute to

economic growth of the host country. Rather, FDI is being attracted by economic growth and the

favorable economic conditions in the host countries. In addition, these points of view generally

attribute the inflows of FDI to a specific country to two major reasons. One major reason is the size ofthe economy. Many research findings show that the size of the economy is a major determinant of FDI

inflows. A second reason is that rapidly growing economies attract more FDI as they need to fill gaps

in sectors where they lack the necessary resources to prosper.

On the empirical side, the relationship between FDI inflows and economic growth has beenextensively investigated in the empirical literature. However, the results are highly diverse and no

consensus judgment has been reached as to whether and how FDI inflows can promote economic

growth in the host country. The reasons behind this divergence can be explained by a number of issuesthat are related to the estimation process; including sample selection (e.g. developed versus less

developed countries), the choice of the time period, the estimation methodology (i.e. time series versus

cross- section), the choice of the estimation techniques (e.g. OLS, Granger Causality, Cointegration,Error correction models). Early studies of the FDI-growth nexus used the technique of Ordinary Least

Squares (OLS) where GDP growth was regressed on a number of explanatory variables including trade

(exports or openness), plus FDI using either time series or cross section data (Balasubramanyam,Salisu and Sapford, 1996; Olofsdotter, 1998). A later generation of studies used the technique of

Granger Causality (GC) testing, which allows for the possibility of testing causality in both directions

(Zhang, 2001; Choe, 2003; Chowdhury and Mavrotas, 2006). A conventional GC model is specified in

a bivariate equation. A commonly cited problem with these models is restricting the number of

variables to two, which may create the problem of model misspecification. To overcome the problemwith GC models, researchers used the technique of multivariate cointegration, which allows for the use

of more than two variables (Basu, Chakraborty and Reagle, 2003; Hansen and Rand, 2006; Zhang,2000; Cuadros, Orts and Alguacil, 2004; Ramirez, 2000).

In addition to this diversity, the findings of the empirical literature aiming at identifying the

impact of FDI on growth mainly show that there is no universal answer to the question of how FDIimpacts growth in its host country. The impact of FDI is found to depend on a multitude of factors,

such as the level of technology used in domestic production in the host country, the level of education

of the host country workforce, the level of financial sector and institutional development, etc. All these

factors and more contribute to the question of whether the host country in question can attract and,therefore, benefit from FDI.

The objective of this paper is to investigate the impact of FDI inflows on economic growth inthe particular case of the Gulf Cooperation Council (GCC) which includes 6 countries (Bahrain,Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates). There are several motivations for

this study. To start with, GCC countries have a number of features that render this investigationappealing, and indeed insightful. It is common knowledge that these countries are wealthy oil-

producing countries with the highest levels of income per capita in the world. In addition the GCC

economies are highly dominated by the oil sector with some disparities as to the importance given todeveloping the non-oil sector of the economy. The pace of economic reforms adopted to develop the

non-oil sector on one hand and the attitudes towards attracting FDI on another hand significantly differ

from one GCC country to another. In light of these circumstances, we believe that an empiricalanalysis in this unique context would certainly make it possible to interject valuable insights into the

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136 International Research Journal of Finance and Economics - Issue 29 (2009)

literature on the issue of FDI-growth relationship. This would be justifiable, in particular in light of the

fact that there is absolutely no literature involving a time series analysis in the case of the GCCcountries.

The research methodology adopted in this paper extends the existing literature in two aspects.

First, the paper offers a country-specific analysis of the issue. Hence, compared to the level of

generalization in the literature, results in this paper are more precise and address the specific countriesconcerns with regard to FDI. Second, the paper uses a cointegration technique based on the

autoregressive distributed lag approach (ARDL) developed by Pesaran and Shin (1995, 1998) which isproven to perform better than other conventional cointegration techniques, in particular in smallsamples as is the case for GCC countries. The main finding of the paper is the existence of significant

dissimilarities among the 6 countries as to the importance and contribution of FDI inflows to economic

growth.The remaining of the paper is organized as follows. Section 2 presents a descriptive background

on FDI inflows to GCC countries. Section 3 presents the analytical model and the econometric

methodology. Section 4 contains the empirical results, and section 5 presents some policy

recommendations.

2. FDI in the GCC Countries: A Descriptive BackgroundFor a long time the concept of FDI has not been popular in the GCC countries because they are

traditionally considered as capital (and investment) exporting countries. The main logic behind this is

that they do not need to import foreign investments, and instead rely on their domestic (mainly public)investments.

Table 1: The FDI/GDP Ratio in the GCC Countries (Average 1970-2006)

Kuwait UAE Saudi Arabia Qatar Oman Bahrain

FDI/GDP (%) 0.08 0.32 0.64 1.32 1.52 3.93

Table 1 shows the shares of FDI/GDP ratios in the GCC countries for the 1970-2006 period.

Clearly there are significant differences between oil rich countries (i.e. Kuwait, Saudi Arabia, and

UAE) and relatively oil poor countries (i.e. Oman and Bahrain). It is clear that there are wide variationsin these shares (ranging from 0.08% in Kuwait to 3.93% in Bahrain) which reflect different attitudes

towards hosting FDI in the GCC countries.

Table 2: FDI Inflows as a percentage of Fixed Capital Formation (FCF) in the GCC Countries (Average

1970-2006)

Kuwait UAE Saudi Arabia Qatar Oman Bahrain

FDI/FCF (%) 0.53 4.8 0.66 4.00 6.86 25.2

In addition, the GCC countries also differ with respect to the size of FDI relative to fixedcapital formation (FCF). Table 2 presents the averages of FDI inflows as a percentage of FCF for the

period 1970-2006. Again, clearly there are significant differences between oil rich countries and

relatively oil poor countries. The average is very low in Kuwait 0.53% and Saudi Arabia 0.66%, but

relatively high in Oman 6.86% and even higher in Bahrain 25.2%. These relatively low ratios reflectthe attitude of the GCC countries in the three decades (70s to 90s) where they conducted national

development plans that relied heavily on public funds (i.e. oil revenues) and marginalized both private

and foreign investments.

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It should be noted that these averages are for the whole sample period. These averages have

risen dramatically in the last few years. For example, in 2007, the share of FDI in fixed capitalformation in Bahrain was as high as 44.7% and in Oman 39.7%. These changes have come as a result

of the revised role of the public sector in the development process in these countries to rely more on

FDI inflows.

Table 3 shows average growth rates of GDP and FDI inflows in the GCC countries for the1992-2006 period. A number of general facts arise from the table: (i) FDI inflow rates are many times

as large as those of GDP growth, (ii) in recent years (2002-2006) the growth of FDI has been thehighest in absolute terms and relative to GDP, (iii) for the whole period (1992-2006), FDI growth isapproximately ten times that of GDP, and (vi) FDI growth rates in the UAE and Oman have reached a

record high in recent years (2002-2006) with 875% and 416%, respectively.

Table 3: Growth rates of GDP and FDI Inflows in the GCC Countries (Averages for the periods: 1992-1996,

1997-2001, 2002-2006)

1992-1996 1997-2001 2002-2006 1992-2006Country

GDP FDI GDP FDI GDP FDI GDP FDI

Kuwait 27.68 80.52 3.6 4.11 23.00 64.78 18.10 49.80

UAE 5.71 208.07 10.00 -174 14.53 874.6 9.39 214.79

Saudi Arabia 6.17 40.34 3.53 61.80 14.00 156.95 7.90 86.36Qatar 5.88 78.25 15.67 15.74 24.95 50.85 15.50 48.28

Oman 6.21 4.21 6.20 97.06 12.63 415.92 8.35 172.39

Bahrain 5.76 43.09 5.68 -14.9 14.07 99.19 8.11 38.41

Average 9.56 75.74 7.45 -1.69 17.19 277.05 11.22 101.67

3. The Model and the Econometric MethodologyIt is common practice in the literature to use the growth rate of real GDP as a measure of economicgrowth. As explanatory variables, there are several variables. In the current study we will use two

explanatory variables in addition to FDI. One variable is openness which measures the exposure of the

host country to international trade (Balasubramanyam, Salisu and Sapsford, 1996, Carkovic andLevine, 2005). This is measured by the share of exports plus imports to GDP, which is also the share of

foreign trade in GDP. This measure of foreign trade has the advantage of combining the effects of

exports and imports together as being influential factors that affect economic growth. It is expected that

the more open is the economy the higher it grows. The second variable is fixed capital formation(FCF), which measures national investment, both public and private (Johnson, 2006,

Balasubramanyam, Salisu and Sapsford, 1996, Hansen and Rand, 2006). This variable is expressed as a

percentage of GDP. This paper specifies and uses the following model:

GDP =f(FDI, OPEN, FCF)

where:

GDP = real GDP

FDI = real FDI inflow as a percentage of GDP

OPEN = foreign trade (exports plus imports) as a percentage of GDP

FCF = Gross Fixed Capital Formation as a percentage of GDP

The econometric methodology that we use in this paper is based on the autoregressive

distributed lag (ARDL) approach advanced by Pesaran and Shin (1995, 1998), Pesaran et. al. (1996),

and Pesaran (2001). Recent investigations on the performance of this approach compared to othercointegration methodologies have shown that the ARDL approach is preferable to other conventional

cointegration techniques such as the Engle-Granger (1987), Johansen (1988), Johansen and Juselius

(1990), and Gregory and Hansen (1996). The attractiveness of the ARDL approach is that it is usefulirrespective of the order of integration of the included variables. The test statistic used in this procedure

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is the conventional Wald or F-statistic in a generalized Dickey-Fuller type regression. This statistic is

used to test the statistical significance of the lagged levels of the regressors in a conditional unrestrictedequilibrium error-correction model (see Pesaran, et. Al., 2001). From an econometric point of view, the

robustness of the ARDL approach compared to other cointegration techniques is that it performs better

in small sample sizes, which is the case in this investigation.

The ARDL approach involves estimating the conditional error correction version of the ARDL

model for variables under estimation. The augmented ARDL ( ),...,,,( 21 kqqqp is given by the

following equation (See Pesaran and Pesaran, 1997; Pesaran and Shin, 2001);

ttiti

k

i

it wxqLaypL +++= =

),(),(1

0 nt ,...,1= (1)

where:p

pLLLpL = ...1),(2

21

kiLLLqLq

iqiiiii ,...,2,1...),(1

1

2

210 =++++=

ty is the dependent variable, 0 is the constant term, L is the lag operator such that

1is,1 = swyLy ttt vector of deterministic variables such as intercept term, time trends, or

exogenous variables with fixed lags. The long-run elasticities are estimated by:

( )( )

kip

q

p

qiiiii

i ,...,2,1...1

...

,1

,1

21

10=

+++==

(2)

Where kiqp i ,...2,1,and = are the estimated values of kiqandp i ,...2,1, = .

The long-run coefficients are estimated by:

p

kqqqp

21

21

...1

),...,,,(

= (3)

where ),...,,,( 21 kqqqp denotes the OLS estimates of in equation (1) for the selected ARDL

model.The error-correction model (ECM) related to the ARDL ( ),...,,, 21 kqqqp can be obtained by

writing equation (1) in terms of lagged levels and the first difference of :,...,,, 21 tktttt wandxxxy

tjti

q

j

ij

k

i

t

p

j

jtit

k

i

itt xywxECpayi

+++=

==

==

,

11

1

1

1

*

1

010

1

),1( (4)

where ECM is the error-correction model and it is defined as follows:

tititt wxayECM = (5)

tx is the k- dimensional forcing variables which are not cointegrated among themselves. t is a vector

of stochastic error terms, with zero means and constant variance-covariance.The existence of an error-correction term among a number of cointegrated variables implies

that changes in the dependent variable are a function of both the level of disequilibrium in thecointegration relationship (represented by the ECM) and the changes in other explanatory variables.

This tells us that any deviation from the long-run equilibrium will feed back on the changes in the

dependent variable in order to force the movement towards the long-run equilibrium (see Masih andMasih, 2002, p. 69).

The ARDL approach involves two steps for estimating the long-run relationship (Pesaran et al.,

2001). The first step is to examine the existence of long-run relationship among all variables in the

equations under estimation. The second step is to estimate the long-run coefficients of the same

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equation. We run the second step only if we find a long-run relationship in the first step (Narayan, et al.

2004).This paper uses a more general form of ECM with unrestricted intercept and unrestricted trends

(Pesaran et al., 2001, p. 296):

ttt

P

i

itxyxtyyt uXwzxytccy ++++++=

=

11

1

'

1.110 (6)

Where .0and0 10 cc The Wald test (F-statistics) tests for the null hypotheses ,0:,0: .00

. == xyxyyxyxyy HH

against

the alternative hypotheses ,0:1 yyyyH

.0: .0

. xyxxyxH

Hence, the joint null hypothesis of interest

in the above equation is given by: ,.000xyxyy HHH

I= and the alternative hypothesis is

correspondingly stated as: ..110xyxyy HHH

I=

The asymptotic distributions of the F-statistics are non-standard under the null hypothesis of nocointegration relationship between the examined variables, irrespective of whether the variables are

purely I(0) or I(1) or mutually cointegrated. Two sets of asymptotic critical values are provided by

Pesaran and Pesaran (1997). The first set assumes that all variables are I(0) while the second set

assumes that all variables are I(1). If the computed F-statistics is greater than the upper bound criticalvalue, then we reject the null hypothesis of no cointegration and conclude that there exists steady state

equilibrium between the variables. If the computed F-statistics is less than the lower bound critical

value, then we can not reject the null of no cointegration. If the computed F-statistics falls within thelower and upper bound critical values, then the result is inconclusive; in this case, following Kremers,

et al. (1992) and Bannerjee et al. (1998), the error correction term will be a useful way of establishingcointegration. The second step involves estimating the long-run coefficient of the same equation and

the associated ARDL error coercion models.

4. Empirical Results

We herein apply our methodology to data on each one of the 6 GCC countries. Data used are obtainedfrom the World Investment Report published by UNCTAD. Data are annual and cover the period1970-2006. Except when the variable contains negative values, all variables were transformed into

their natural logarithm for the usual statistical reasons.

4.1. Test Results for Unit RootsBefore we proceed with the ARDL bounds test, we test for the stationarity status of all variables to

determine their order of integration. This is necessary to ensure that the variables are not I(2) stationaryso as to avoid spurious results. According to Ouattara, (2004) in the presence of I(2) variables the

computed F-statistics provided by Pesaran et. al. (2001) are not valid because the bounds test is based

on the assumption that the variables are I(0) or I(1). Therefore, the implementation of unit root tests in

the ARDL procedure might still be necessary in order to ensure that none of the variables is integratedof order 2 or beyond.

Table 4 below provides the results of testing the stationarity status of the variables using both

the augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) tests. The results in this table show

that all variables included in the analysis are integrated of order one I(1).

4.2 Results of the ARDL Approach

The ARDL model requires a priori knowledge or estimation of the orders of the extended ARDL. This

appropriate modification of the orders of the ARDL model is sufficient to simultaneously correct forresidual serial correlation and the problem of endogenous regressors (Pesaran and Shin, 1998, p. 386).

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The order of the distributed lag on the dependent variable and the regressors can be selected using

either the Akaike Information Criterion (AIC) or the Schwartz Bayesian Criterion (SBC). However,depending on Monte Carlo evidence, Pesaran and Smith (1998) found that SBC is preferable to AIC, as

it is a parsimonious model that selects the smallest possible lag length, while AIC selects the maximum

relevant lag length. In this study we use the SBC as a lag selection criterion.According to the ARDL approach discussed above, a significant F-statistic for testing the joint

significance of the lagged level indicates the existence of long-run relationship. The results of the

bounds tests for cointegration for all GCC countries are reported in Table 5. They indicate that the nullhypothesis of no cointegration is rejected for all 6 countries as the respective F-statistic for each ofthese countries exceeds the upper bound critical values.

Table 4: Test Results for Unit Root Tests

ADF PPCountry/variable

Level First Difference Level First Difference

Bahrain

RGDP -1.652 -5.335 -1.701 -10.169

FDI -1.154 -8.514 -1.751 -10.785

FCF -2.123 -3.595 -2.412 -3.435

OPEN -2.606 -4.123 -2.128 -5.922Saudi Arabia

RGDP -1.718 -6.177 -2.189 -5.996

FDI -1.860 -4.095 -2.012 -6.709

FCF -2.565 -3.069 -2.810 -3.133

OPEN -2.698 -4.156 -2.577 -4.562

Kuwait

RGDP -1.427 -4.554 -1.348 -4.688

FDI -1.771 -3.820 -2.060 -7.847

FCF -1.253 -4.186 -2.685 -4.151

OPEN -1.345 -4.264 -2.390 -4.194

Oman

RGDP -2.486 -3.015 -2.273 -3.542

FDI -2.524 -4.292 -2.571 -4.892FCF -0.992 -3.693 -1.239 -4.099

OPEN -1.503 -3.519 -2.612 -4.664

Qatar

RGDP -0.139 -3.810 -1.227 -3.744

FDI -2.720 -4.614 -1.369 -4.201

FCF -0.866 -4.001 -2.255 -4.726

OPEN -1.927 -3.300 -1.025 -4.261

United Arab Emirates

RGDP -0.447 -4.752 -1.322 -4.622

FDI -0.731 -3.621 -2.023 -4.853

FCF -1.753 -4.285 -2.634 -4.196

OPEN -1.244 -4.169 -2.327 -4.162

Table 5: F-statistics for testing the existence of a long-run relationship among variables

Country Equation The computed F-statistics Outcome

Bahrain F(RGDP, FDI, OPEN, FCF) 5.372* Cointegration

Saudi Arabia F(RGDP, FDI, OPEN, FCF) 6.001* Cointegration

Kuwait F(RGDP, FDI, OPEN, FCF) 4.944* Cointegration

Oman F(RGDP, FDI, OPEN, FCF) 4.093* Cointegration

Qatar F(RGDP, FDI, OPEN, FCF) 5.115* Cointegration

United Arab Emirates F(RGDP, FDI, OPEN, FCF) 5.706* Cointegration

* indicates rejection of the null of no cointegration at the 1% level. The relevant critical values for the test are taken from

Pesaran and Shin (2001)

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Since the first stage indicates the existence of cointegration between the variables, we can now

move to the second stage where we retain the lagged level of the variables and estimate the growthequation in (4) based on the ARDL model that is selected by the SBC. Table 6 below reports the long-

run and short-run relationships estimated for each country in the sample.

Table 6: Estimated long-run and short-run coefficients using the augmented ARDL model based on SBC

Country: Bahrain, Dependent Variable: RGDP, ARDL(1,0,0,0)Long-run coefficients Short-run coefficients

Regressor Coefficient T-Ratio Regressors Coefficient T-Ratio

0 -1.703 -0.376 0 -0.391 -0.108*

LFDI 0.162 2.520* LFDI 0.041 1.980*

LOPEN 0.956 4.291* LOPEN 0.274 3.607*

LFCF 0.428 2.533* LFCF 0.132 2.002*

Trend 0.007 6.832* Trend 0.001 4.357*

Dummy 0.082 1.201 Dummy 0.007 0.926

Ecmt-1 -0.021 -3.294*

* Significant at the 5% level

Country: Saudi Arabia, Dependent Variable: RGDP, ARDL(1,0,0,0)

Long-run coefficients Short-run coefficientsRegressor Coefficient T-Ratio Regressors Coefficient T-Ratio

0 0.739 1.301 0 0.227 0.381

LFDI 0.241 4.266* LFDI 0.105 3.675*

LOPEN 1.312 3.001* LOPEN 0.845 0.441

LFCF 1.076 4.283* LFCF 0.363 2.827*

Trend 0.036 1.042 Trend 0.012 0.206

Dummy 0.109 2.101* Dummy 0.027 1.205

Ecmt-1 -0.072 -5.109*


Country: Kuwait, Dependent Variable: RGDP, ARDL(1,0,0,0)

Long-run coefficients Short-run coefficientsRegressor Coefficient T-Ratio Regressors Coefficient T-Ratio

0 -0.901 -0.061 0 -0.014 -1.023

LFDI -0.012 -1.240 LFDI -0.006 -0.892

LOPEN 0.937 2.368* LOPEN 0.326 2.974*

LFCF 0.741 3.016* LFCF 0.294 2.107*

Trend -0.078 -1.200 Trend 0.016 0.258

Dummy -0.455 -3.406* Dummy -0.150 -2.320*

Ecmt-1 -0.016 -2.007*


Country: Oman, Dependent Variable: RGDP, ARDL(1,0,0,0)

Long-run coefficients Short-run coefficients


0 -1.002 -1.051 0 -0.406 -0.193

LFDI 0.302 2.156* LFDI 0.108 1.302

LOPEN 0.667 3.298* LOPEN 0.231 2.006*

LFCF 0.910 2.055* LFCF 0.275 2.463*

Trend -0.007 -0.264 Trend -0.001 -0.076

Dummy 0.012 1.023 Dummy 0.001 0.251

Ecmt-1 -0.021 -3.019*

*Significant at the 5% level

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Country: Qatar, Dependent Variable: RGDP, ARDL(1,0,0,0)



0 0.782 0.972 0 -0.014 -1.482

LFDI 0.103 2.012* LFDI 0.097 1.992*

LOPEN 1.016 3.016* LOPEN 0.671 2.786*

LFCF 0.615 5.816* LFCF 0.185 4.091*

Trend 0.041 1.231 Trend 0.009 1.032Dummy -0.769 -2.102* Dummy -0.254 -1.965*

Ecmt-1 -0.036 -4.118*


Country: United Arab Emirates, Dependent Variable: RGDP, ARDL(1,0,0,0)



0 1.805 2.373 * 0 0.722 1.401

LFDI 1.023 3.462* LFDI 0.364 2.730*

LOPEN 1.406 4.267* LOPEN 0.381 3.652*

LFCF 0.923 3.004* LFCF 0.275 2.405*

Trend 0.190 1.209 Trend 0.032 0.247Dummy 0.516 2.182* Dummy 0.120 1.208

Ecmt-1 -0.071 -5.901*


Before going through the results of countries individually, there are few general comments that

are noteworthy. First, the coefficients of the variables of interest to the study (i.e. FDI, Openness, and

FCF) are generally statistically significant, with few exceptions. Second, the variables that have thehighest effects on economic growth for the 6 countries are openness and fixed capital formation. FDI

capital has a relatively lower effect both in the long-run as well as in the short-run. Third, the

coefficient of the error component is relatively small for the 6 countries, which means that economicgrowth is not rapidly adjusting to changes in the long-run equilibrium component (Ecm

t-1).

Now we turn to the empirical results for each of the six countries. Starting with Bahrain, the

empirical results show that FDI has a long-run as well as a short-run effect on economic growth. Thelong-run elasticity of RGDP with respect to FDI is 0.162, implying that an increase in FDI by 10%

leads to a long-run increase in RGDP in the long-run by 1.6%. In the short-run, there is a causal flow

going from FDI to RGDP. The short run coefficient is smaller with a value of 0.041, which means that

an increase in the growth rate of FDI in the short run by 10% causes RGDP growth rate to increase byabout 0.4%. In addition, the error-correction term is significant with the correct sign but is very low.

The coefficient of the ECM is -0.021 meaning that a deviation of RGDP from the long-run equilibrium

following a short-run shock is corrected by 2.1% in each year.For Saudi Arabia, the empirical results show the existence of short-run and long-run effects of

FDI inflows on economic growth. The long-run elasticity of RGDP with respect to FDI is 0.241,

implying that an increase in FDI inflows by 10% leads to an increase in RGDP in the long-run by2.4%. In the short-run, there is a causal flow going from FDI to RGDP. The short run coefficient is

smaller with a value of 0.105, which means that an increase in the growth rate of FDI in the short run

by 10% causes RGDP growth rate to increase by about 1%. In addition, the error-correction term issignificant with the correct sign but is very low. The coefficient of the Ecmt-1 is -0.072 meaning that a

deviation of RGDP from the long-run equilibrium is corrected by 7.2% in each year.

For Kuwait, the empirical results show the existence of a long-run equilibrium relationship

between the variables. However, neither the long-run nor the short-run effects of FDI inflows onRGDP are statistically significant. The error-correction term is significant with the correct sign but is

very low. The coefficient of the ECM is -0.016 meaning that a deviation of RGDP growth from the

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long-run equilibrium following a short-run shock is corrected by 1.6% each year. In this case, FDI does

not have a causal effect on FDI inflows either in the short-run or in the long-run. In the long-run,RGDP cointegrates only with openness and FCF. This could, in fact be attributed to the fact that for a

long period of time Kuwait has been a net exporter of capital.

For Oman, the empirical results show the existence of a long-run equilibrium relationshipbetween the variables. The long-run elasticity of RGDP with respect to FDI is 0.302, implying that an

increase in FDI inflows by 10% leads to an increase in RGDP in the long-run by 3%. The short run

coefficient is smaller with a value of 0.108, which means that an increase in the growth rate of FDI inthe short run by 10% causes RGDP growth rate to increase by about 1%. In addition, the error-correction term is significant with the correct sign but is very low. The coefficient of the Ecm t-1 is -

0.021 meaning that a deviation of RGDP growth from the long-run equilibrium following a short-run

shock is corrected by 2.1% in each year.For Qatar, the empirical results show the existence of a long-run equilibrium relationship

between the variables. The long-run elasticity of RGDP with respect to FDI is 0.103, implying that an

increase in FDI inflows by 10% leads to an increase in RGDP in the long-run by 1%. The short runcoefficient is smaller with a value of 0.097, which means that an increase in the growth rate of FDI in

the short run by 10% causes RGDP growth rate to increase by less than 1%. In addition, the error-

correction term is significant with the correct sign but is very low. The coefficient of the Ecm t-1 is -

0.036 meaning that a deviation of RGDP growth from the long-run equilibrium following a short-runshock is corrected by 3.6% in each year.

For UAE, the empirical results show also the existence of a long-run equilibrium relationship

between the variables. The long-run elasticity of RGDP with respect to FDI is 1.02, implying that anincrease in FDI inflows by 10% leads to an increase in RGDP in the long-run by 10.2%. It should be

noted that this is the highest elasticity and the only one exceeding one. The short run coefficient is

smaller with a value of 0.364, which means that an increase in the FDI growth rate in the short run by10% causes RGDP growth rate to increase by about 3.6%. In addition, the error-correction term is

significant with the correct sign but is very low. The coefficient of the Ecmt-1 is -0.071 meaning that a

deviation of RGDP growth from the long-run equilibrium following a short-run shock is corrected by7.1% in each year.

Overall, the results show that, for most of the GCC countries, there is a weak but statisticallysignificant causal impact of FDI inflows on economic growth. This may be attributed to two facts.

First, RGDP in the GCC countries is highly volatile as a result of the dependence on oil exports whichin turn is subject to sharp swings in the price of oil in the world market. This volatility weakens the

relation between FDI inflows and RGDP, especially during periods of instability in the oil market.

Second, the low effect of the FDI variable could be the result of controlling for the effects of fixedcapital formation and international trade in our model.

5. Policy RecommendationsThe paper has shown interesting facts about the GCC countries with respect to their attitude towards

attracting FDI to their countries. Hence, a number of policy recommendations can be drawn from theanalysis:

1. The GCC countries policies towards attracting FDI vary widely from one country toanother. Therefore, it is better for these countries to coordinate their efforts in order toprovide a more favorable environment that attracts FDI inflows.

2. As other studies in the literature have shown, FDI needs more than financial incentives. FDIinflows need a more stable macroeconomic environment, namely; low inflation, stableinterest and exchange rates, robust public finance, and mature financial and legal

institutions.

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3. Even though the GCC countries are capital exporting countries, they need to pay moreattention to FDI inflows as an important source of importing technology, business practices,and upgrade local human capital.

4. Among the three determinants of economic growth examined in the paper, FDI appears tohave the smallest effect on economic growth. This implies that FDI is underutilized.

5. There must be a distinction between real and financial FDI. As mentioned earlier, the GCCcountries have a surplus in capital, but they need advanced technologies and know-how.

These can be attracted through the realization of long term productive investment projects.

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