The paper is concerned with the analysis of the main determinants of foreign direct investment in twelve MENA countries for the period 1975-2006. By employing a panel data methodology, the study reveals that the key determinants of FDI inflows in MENA countries are the size of the host economy, the government size, natural resources and the institutional variables. The paper concludes that, countries that are receiving fewer foreign investments could make themselves more attractive to potential foreign investors. So, the policy makers in the MENA region should remove all barriers to trade, develop their financial system and build appropriate institutions.Submitted: November 22, 2009.

1. Introduction

Increased globalisation over the last two decades has led to strong growth of international business activity and FDI. The continuous international capital inflows to developing countries, and especially Foreign Direct Investment (FDI) is expected to contribute to increasing efficiency and productivity, and to further growth opportunities in recipient countries such as technology transfer, export development, job and skill creation, and the upgrading of management knowledge and skills ( Bwalya, 2006). However, from the perspective of the MENA countries, given their low savings rates and access to international capital markets, their capacity to invest is limited unless it is supplemented by other external finance such as FDI. Attracting FDI has been a widely recommended policy to developing countries because of the believe that FDI brings with it several positive externalities as mentioned earlier such as productivity gains and technology transfers (Chan and Gemayel, 2004). However, comparing the distribution of FDI inflows across developing regions, the MENA region has attracted only small proportion of the global stock of FDI (UNCTAD, 2003). Moreover, the existing literature on FDI inflows seems to match the pattern of distributions of these flows. This could partially explain the scarcity of studies dealing with FDI inflows directed to MENA region, as compared with Asia and Latin America (see for example studies by Choi, 1995, Poon, and Thompson, 1998, and Zhang, 2001). The poor performance of the MENA countries in attracting FDI raises the following questions: what factors are responsible for this and what can policy makers in these countries do improve the flow of FDI to their countries? To examine these questions panel-data technique was used to explore the main drivers of FDI inflows in MENA countries. The time period ranges from 1975 to 2006, with balance coverage for the individual samples. This study contributes to current research in various ways. It is the first comprehensive study of FDI in MENA region, combining together the analysis of wide range of factors that seem to be the main determinants of FDI. In addition, the paper examines the role of institutions and financial development factors that have not been adequately explored in the current literature. The rest of the paper is structured as follows. Section 2 includes FDI flows trends and performance in MENA countries. Section 3 outlines a model specification and econometric methodology. Section 4, contains the main findings of the study, their analyses and assessments, and the final section offers some concluding remarks in the light of the previous analysis.

2. Review of Literature

The MENA region is under-researched in issues relating to international capital mobility. However, there are a few studies on the volume, determinants and growth effects of FDI in the MENA region, some of which were done by international agencies. Almost all of these studies are empirical in nature and have highlighted that the countries in MENA region receive less FDI than other developing in general. However, they have approached the problem in different ways.Chan and Gemayel (2004) run a random effects model on the determinants of FDI to the MENA region to investigate the relationship between different types of risk instability and the pattern of FDI to the MENA region compared to other developed countries. They argue that the instability measure of each risk index provides a better fit than the index itself when comparing FDI inflows to GDP for MENA countries. Their findings suggest that the instability associated with investment risk is a much more critical determinant of foreign direct investment in the MENA region than it is for developing countries, which have lower investment risk. This, according to them, explains the low flows of FDI in the MENA region compared to other countries. Krogstrup and Mattar (2005) analyze data on four different aspects of absorptive capacity-technology gaps, educational levels, financial sector development and institutional development-for Arab countries in relation to FDI. They argue that the conclusion is very sensitive to the measure used. However, their analysis has made one point clear: There is no a prior reason for Arab countries to expect the host of positive externalities that are usually argued to follow an increase in FDI inflows. They conclude that there is no general economic rationale for implementing costly incentive schemes such as tax holidays, investment subsidies, export credits and other measures, favoring FDI over domestically financed investment. Finally, Arab countries would benefit by improving their capacity to absorb FDI through upgrading human capital stock and better education, and improvement in the functioning of the financial sector. Onyeiwu (2004) analyzes FDI determinants to developing countries with special focus on the MENA region. He estimates fixed effects panel regressions to investigate whether the determinants of FDI affect MENA countries differently. His results indicate that some of the variables that influence FDI flows to developing countries are not important for flows to the MENA region. These include the rate of return on investment, infrastructure,

economic growth, and inflation. He argues that trade openness increases FDI flows to the MENA region while corruption and bureaucratic red tape were found to reduce flows to the region. He concludes that trade liberalization and privatization are important preconditions for FDI flows to the region.Omran and Bolbol (2003) estimate a growth equation to assess the impact of FDI in the countries of the Arab world. Their study shows that FDI has yet to have an independent effect on growth. They also imply that countries should reform their domestic financial systems before working on attracting FDI. Kamaley (2003) evaluate the FDI flow to the MENA region and analyze its determinants during the 1990s using fixed effects and dynamic GMM models. He finds that openness and the degree of democracy are nsignificant factors for FDI to the MENA region. In addition, the paper argues that FDI follows countries fundamentals, namely lagged GDP growth and openness proxied for by trade relative to GDP, more than cyclical variables such as international interest rates. Accordingly, the study concludes, the MENA countries should focus on improving their fundamentals to attract FDI.

3. Data and Methodology

The panel data set used for this analysis covers 12 MENA1 and runs from 1975-2006. The database has been built using a number of different sources. The main sources were the World Development Indicators (WDIs) database, compiled by the World Bank (2007), and the International Country Risk Guide (ICRG) published by the Political Risk Services (PRS) group.2 All values used in the analysis are expressed in US dollars in real terms. However, our econometric investigation with panel data described in the next sub-section use a regression specification given by the following expression: FDIit, /GDP it = 0i+ 1LnGDPit+ 2FDevit+3 Inst it + 4 Policy it + + 5Zit +eit...... (3.1) 1 These countries includes: Algeria, Egypt, Jordan, Morocco, Syria, Tunisia, Bahrain, Kuwait, Oman, Qatar, Saudi Arab and , UAE 2 On a monthly basis since 1980, ICRG has produced political, economic and financial risk ratings for countries important to international business. ICRG now monitors 140 countries. Data on institutions quality variables come from this source. http://www.prsgroup.com/ICRG.aspx

Where FDIit, /GDP it refers to foreign direct investment as a share of GDP; Ln GDP measure of market size; FDevit is a measure of financial development; Inst it is a measure of institutional development; Policyit represent measures of macroeconomic policies; Zit is a set of other exogenous control variables. However, the Appendix describes in details the data used in the empirical analysis. 3.2 Econometric Methodology To analyze the determinants FDI in our sample countries we employ both fixed and random panel data techniques. In fact the use of panel data allows not only to control for unobserved (cross-sectionally) heterogeneity but also to investigate dynamic relations3.Moreover, equation (3.1) above, represent a simple panel regression model that facilitate the discussion of unobserved heterogeneity issues and they are derived from the general framework as follows: Yit=0+1Xit+eit (3.2) and eit =ai+it (3.3) Or:Yit=i+1Xit+it (3.4) Where, i = 0 + ai. That is, this simple model allows the panel error term (eit) to have two components: an individual-specific, time-invariant component, ai the source on unobserved heterogeneity; and a time-varying idiosyncratic component, it. In discussion of this model, we maintain the assumption that the time-varying error term (it.) satisfies all the desirable statistical properties (in particular, it will be assumed to be uncorrelated with Xit), and concentrate attention on the relationship between ai and Xit. Model (3.4) is a heterogeneous intercepts model: generally, 1 is the parameter of interest and ai (or i) are nuisance parameters. However, the OLS estimator may fail the Gauss-Markov theorem in two ways: First, if the ais are not all zero (i.e. there is heterogeneity), but ai is uncorrelated with Xit: in this case, OLS will provide consistent estimation of 1, but the standard error will be biased leading to invalid inference. Second, as well as heterogeneity existing, is correlated with Xit: in this case, OLS estimation will be biased. These two cases 3 Obviously cross-sectional data provides only a snapshot of the point-in-time distribution of outcome across the sample, and will not inform on the dynamic / adjustments; in contrast, repeated observations on the same individuals will help inform the dynamics.

essentially distinguish what are called random effects and fixed effects approaches respectively. However, Panel data analysis requires choosing the appropriate specification between fixed and random effects models. The fixed effects model assumes that ui are fixed, time-invariant parameters, and the Xit are independent of the vit for all i and t. When N is large, the fixed effects model involves too many individual dummies, which may aggravate the problem of multicollinearity among the regressors. The fixed effects (FE) least square, also known as least squares dummy variables (LSDV), suffers therefore from a large loss of degrees of freedom. Moreover, the FE cannot estimate the effect of any unobservable variable like entrepreneurial or managerial skills, religion, culture or government authorities' ability to manage a country and attract FDI. Nevertheless, the issue of too many parameters in the FE model and its corollary problem of loss of degrees of freedom can be avoided if the ui is assumed to be random (Balgati, 2003). That is the random effects (RE) model where Xit are assumed independent of the ui and vit , for all i and t. The RE however, is appropriate only when the random process is conducted from a large population. Moreover, Greene (2000) suggests that the RE approach may suffer from the inconsistency due to omitted variables because of the treatment of the individual effects as uncorrelated with the other regressors. Finally to determine which of the two alternative models (fixed versus random effects) should be chosen, we use Hausmans (1978) specification test.

4. Empirical Results

Table 1 in the appendix below shows the results of the panel regression. The regression results for the four models can be summarized as follows: In Model 1-4, which examine the main determinants of FDI in MENA countries the market size variable exhibit positive and significant, sign in most of the specifications. The coefficient of the variable, LnGDP (i.e. the size of the market) accurately reflects theoretical expectations. The significance of the variable even in log form confirms that the relationship between FDI and Market size is not a simple linear relationship, but one in which the benefit from expanding the market size is increasing but at a decreasing rate. With regards to the other factors, the financial development variable shows no significant effect on all specifications. This result clearly indicate that, the underdeveloped and shallow financial systems and the

concentration of credit to the public sector, all lead to the negative contribution of finance to growth in MENA countries. In the third and the fourth columns of the Table, the ICRG, investment profile and corruption variables are included into a regression equation along with the other variables. Interestingly the coefficients of the two institution quality indicators gives their expected sign and they are all statistically significant. The coefficients of the investment profile variable is positive and this result indicate that the economic reforms policies implemented in some MENA countries have led to improve the business climate in these countries which in turn lead to stimulate FDI. For the other control variables, as expected, FDI increase as economic growth one of the control variables- strengths. As shown in column 4, the GDP growth, which is the indicator of the market prospects, is positive and significant. In contrast the market potential as measured by population growth exerts no influence in stimulating FDI to MENA countries. The infrastructure index, which is one of the major determinants of FDI in developing countries, is statistically insignificant. This reveals that infrastructure in MENA countries is not well developed to attract FDI inflows to the region. Another major factor that determines FDI inflows into MENA countries is natural resources4. This result may reflect the fact that much of the FDI flows to MENA countries goes to naturalresource economies5.

5. Conclusion and Policy Implications

The paper is concerned with the analysis of the main determinants of foreign direct investment in MENA countries. The estimation is run for twelve MENA countries on the determinants of FDI over the period 1975-2006. After conducting both random and fixed test, we choose fixed test methodology and that according to Hausman test. The study reveals that the key determinants of FDI inflows in MENA countries are the size of the host economy, the government size, natural resources and the institutional variables. The financial development factors represented by global financial development index show any significant effect on the determinants of FDI in MENA countries. The paper concludes that, countries that are receiving fewer foreign investments could make themselves more attractive to potential foreign investors.4 According to literature survey, about 30% of FDI to developing countries are directed to countries that are oil and gas exporters and another 12% of FDI to countries that are rich in mineral resources.5 Over 80 percent of FDI in the region is concentrated in the following resource-rich countries: Saudi Arabia, Egypt, Tunisia, Bahrain and Morocco ( Eid and Papua 2002)

The results have several policy implications. First, it suggest that, to attract FDI flows the policy makers in the MENA region should remove all barriers to trade, develop their financial systems, reduce the level of corruption, improve policy environment, and build appropriate institutions. Secondly, policies aimed at reducing the size of the government through privatization and reducing macroeconomic instability are important and should not be overlooked.

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