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Can Foreign Direct Investments Influence Sri Lankan Economic Growth? An Econometric Analysis Deyshappriya N.P.R. Faculty of Management Uva Wellassa University Sri Lanka [email protected] Abstract According to the early economics literature, factor accumulation was the key component of economic growth, but in this respect he recent economic history had highlighted additional factors such as total factor productivity, international trade and foreign direct investment. As a whole, the economic integration of developing countries has increased dramatically in 1990s, consequently most of the developing countries concern about the foreign direct investment (FDI) as a tool of economic growth, since it provides more job opportunities, technological transfers and foreign reserves in order to achieve a higher level of economic growth. In the context of Sri Lanka, especially prior to 1970’s FDI was not seen as an instrument of economic growth. The period of 1970-1977 was characterized by a highly regulated economy although FDI was encouraged by the White paper in 1972, the climate for such foreign investment or any private investments were not congenial. Since 1977, the country has practiced the open economy policy, therefore has vigorously promoted foreign capital inflows where FDI particulars are viewed as a necessary condition to accelerate the growth. In this setting therefore, it has a timely importance to examine the effects of FDI on economic growth. In this study, I attempt to identify the relationship between FDI and Sri Lankan economic growth. The current study is basically based on time series data during the period of 1990 – 2009 which have been collected from the various issues of Central Bank annual reports. In accordance with the theory macroeconomics time series data follow the unit root process, Augmented Dicky Fuler test was employed to check the stationary of the variables. After checking the stationary of the variables by employing ADF test, the Vector Autoregressive (VAR) model has been employed to identify the short run dynamics, followed by the Granger Causality Test. According to the results, though FDI positively related to economic growth of Sri Lanka, the magnitude of contribution is quite low compared to the other determinants of economic growth. Hence, it is very crucial to provide and maintain the encouraging vicinity for FDI, in order to enhance the contribution of FDI by getting the optimum benefit of the FDI inflows. Keywords: Foreign Direct Investment, Economic Growth, ADF Test, VAR Model, Granger Causality Test 01. Introduction 01.1 Background of the study Many policy makers and academics contend that foreign direct investment (FDI) can have important positive effects on a host country‟s development effort. In addition to the direct capital financing it supplies, FDI can be a source of valuable technology and knowledge while fostering linkages with local firms, which can help jump-start the economic growth . Based on these arguments, developed and developing countries have offered incentives to encourage foreign direct investments in their economies. Especially, FDI provides much needed resources to developing countries such as capital, technology, managerial skills, entrepreneurial ability, brands, and access to markets. These are essential for developing countries to industrialize, develop, and create jobs attacking the poverty situation in their countries. As a result, most developing countries recognize the potential value of FDI and have liberalized their investment regimes and engaged in investment promotion activities to attract various countries. Globalization and regional integration arrangements can change the level and pattern of FDI and also it reduces the trade costs. However, FDI inflows to developing countries started to pick up in the mid-1990s largely as a result of progressive liberalization of FDI policies in most of these countries and the adoption of generally more outward- oriented policies. In the context of Sri Lanka, before 1977, since we had practiced a closed economic situation, there were plenty of limitations for international trade and FDI. However, “White Paper” which is presented in 1966 and foreign advisory committee that was set up in 1968 have looked to possibility of improving contribution of FDI on economic growth of the county. In fact after realizing the significance of market economic policies, both economists and politicians had discovered the possibility of capturing FDI more and more. As a result of that, Foreign Investment Act was established in 1978, in order to pave the way to attract many All these efforts have leaded to attract enormous FDI up to now. It specially includes free trade zones such as Katunayake (1978), Biyagama (1986) Koggala, (1991) Pallekelle (1996) Mirigama (1997) and Malwatte (1997) which has created thousands of employment opportunities and contributed to national economy by providing export income. Therefore this study mainly focused on analyzing the effect of FDI on Sri Lankan economic growth. According to the structure of the study, hereafter the reader can go through problem statement, research objective, literature review, methodology, data analysis, results and discussion and conclusions and recommendations. 01.2 Problem statement However, we had attracted significance level of FDI opportunities; the economy of Sri Lanka is still struggling to overcome from the developing status, since our economic growth is not sufficient to pull our economy to a developed category from the current situation of under development. Consequently, it is doubtable whether the contribution of FDI on Sri Lankan economic growth is significant. Therefore, it is worthwhile to create a clear picture about the effect of FDI on Sri Lankan economic growth and hence more specifically, the research question can be interpreted as follows; Is FDI an important factor in explaining Sri Lankan economic growth? For this purpose the current research paper, I have employed the number econometric tools in order to quantify the question based relationship. 01.3 Objective of the study In accordance with the research problem, the key objective of this research is, identifying the relationship and degree of significance of FDI on economic growth in Sri Lanka. The several steps have been included along with the different theoretical supports to achieve this unique objective. 02. Literature review International trade has grown radically in the past fifty years. However, in the past twenty years, FDI has increased enormously, with a faster growth than international trade. Kreinin, Plummer and ABE (1998) found that, in recent decades, international trade has increased at a percentage of GDP in most major economies, but FDI and other financial flows have been growing exponentially. The total value of inward FDI in the world has increased from about US$ 200 billion in 1993 to US$ 1.3 trillion in 2000 (UNCTAD,2000). FDI with a rapid growth has made researchers and government policy makers interested. Foreign Direct Investment (FDI) is one form of capital flows which has a particular impact on economic growth in developing countries, and multinational enterprises (MNEs) are the main drivers of FDI (Fortanier, F. and Maher, M., 2002). OECD (1978) defined the main forms of FDI as follows: Outlays for the establishment of a new enterprise or for the expansion of an existing enterprise whose operation is controlled by the foreign investor. Financial outlays for the acquisition of an existing enterprise (or part of it) either through direct purchase or through purchases of equity, with a controlling interest by the foreign investor. The notion of control is not defined, but control is assumed when the foreign investor owns at least between 10 and 51 percent of the enterprise‟s value according to different definitions used by different governments. Intra-corporate long-term loans. The linkage between FDI and economic growth has been studied in past twenty years. Most of the studies focus on the impact of inward FDI on economic growth through either direct or indirect effect. Generally speaking, inward foreign direct investment (FDI) can lead to job creation, increasing of tax revenue, introducing of advanced management skills and technologies, benefiting the insufficient domestic capital formation, and increase foreign exchange reserves. It provides a unique combination of long-term finance, technology, training, know-how, managerial expertise and marketing experience (Bende-Nabende, 1999). One of the most direct effects of inward FDI on economic development is that inward FDI is an important financing source of domestic capital. It can increase the production of the host country by adding to the country‟s savings and investments, and it is more stable than other forms of private capital inflows, e.g. portfolio equity and debt flows (Fortanier, F. and Maher, M., 2002). However, inward FDI is more than a form of capital flow. Todaro (1982), Dunning (1970) and Krueger (1987) argued that through the capital accumulation in the host country, inward FDI was expected to generate non-convex growth by encouraging the incorporation of new inputs and foreign technologies in the production function of the host country. The more important effect of FDI is to increase the productivity of the host country through technology transfer. Although technology can also be transferred through foreign trade, as argued earlier, inward FDI has a unique impact on the transfer. Fortanier, F. and Maher, M. (2002) summarized four channels through which inward FDI may lead to technology transfer, namely, vertical linkages, horizontal linkages, labour migration and the internationalization of R&D activities. Vertical linkage indicates backward linkages with suppliers and forward linkages with buyers (either individual consumers or other firms). These business partners of the host country may be able to partly or entirely absorb some explicit and implicit technology. Horizontal linkages refer to relations with the competitors of the MNEs‟ subsidiaries. The diffusion of technology takes place through the competitors in two ways: demonstration and competition. The MNEs expose the superior technology to the local firms and lead them to update their technology. The entrance of foreign firms also strengthens the competition in the host countries and forces the local firms to improve the production technology. These two effects are difficult to disentangle and may reinforce each other. Labour migration is another way through which technology may be transferred and disseminated. Employers by the MNEs acquire superior technology and management skills. When they switch to work for local firms or start their own business, their acquired advanced technology and management skills spread. The MNEs will also bring some R&D activities to the host country, which may also lead to the improvement of technology. However, economic growth can also benefit inward FDI. Economic growth induces the increase in domestic market size which is a determinant of inward FDI. Meyer (1999) argued that output growth was an important reflection of market size in one host country, and „penetration of foreign market is a major motive for FDI‟. Rapid economic growth, accompanied by an increasing per capita income, will create huge opportunities by expanding the domestic consumption demand (for both industrial and consumer goods) in the host country. Output growth is considered as one important determinant for FDI inflows to a host country and this argument is often called a “market size hypothesis” (OECD, 1983; Moore, 1993; Shan, 2002). More importantly, rapid economic growth in the host country will build the confidence of overseas investors for investing in the host country (Shan, 2002). According to the static investment theory, a risk is always associated with an investment and investors always try to reduce the risk in pursuing a high return. A high-speed growth which indicates a low risk in the investment is undoubtedly attractive for the investors. Thirdly, economic growth is associated with an increase in capital demand. The increase in capital demand pushes the governments to embark on incentive policies towards attracting FDI inflow in the case of shortage of domestic capital. The increasing capital demand also raises the price of capital, indicating an increase in the return of capital, and consequently induces inward FDI. Finally, economic growth is also accompanied by an improvement in investment environment, such as the infrastructure, energy supply, legal system, human capital, education, and R&D level. A good investment environment can induce foreign investment. Hence, in empirical studies, it is shown that the causality between inward FDI and economic growth can run in either direction, that is, not only can inward FDI „Granger cause‟ economic growth but also economic growth can cause FDI. Toda and Yamamoto (1995) found that there was indeed a two-way causality between FDI and output in China. Shan (2002) also found the evidence of bi-directional causalities between inward FDI and output growth in the case of China. However, the studies on the causality between inward FDI and economic growth are rare as compared to the studies on exports and economic growth. 03. Methodology 03.1 Data Since this study mainly based on time series data during the period of 1990- 2009, the data set was collected by the various issues of Central Bank annual reports. In addition to that, several issues of Socio Economic Statistics published by the Central Bank of Sri Lanka were considered. 03.2 Theoretical Model In economic literature, Cobb-Douglas production function which has been established by Charles Cobb and Paul Douglas in 1900–1928 provides extensive applications for growth accounting. Since it is a more realistic production function, current study is engaged in this production function in order to launch a solid theoretical background. Cobb- Douglas production function can be interpreted as follows. Y AK L1 In above function; Y- Output level A- Total Factor Productivity K- Capital L – Labour α and (1- α) – Labour and Capital elasticity of output Based on the Cobb Douglas production function, I developed another model using the variables which are appropriate for this study as follows. Especially, I established the following model by incorporating FDI in to initial Cobb-Douglas production function. In addition to FDI, I have included several explanatory variables such as total trade and domestic investment and labour which can be used to explain the growth rate of real GDP. Further, domestic investment has been considered as a proxy for capital stock. GRRGDP 0GRDINV 1 GRL2 GRFDI 3 GRTOT 4 Where; GRRGDP - Growth Rate of Real GDP GRDINV - Growth Rate of Domestic Investment GRL - Growth Rate of Labour GRFDI - Growth Rate of Foreign Direct Investment GRTOT - Growth Rate of Total Trade 03.3 Estimation techniques 03.3.1 Unit Root Test Before moving down to empirically estimated above model, it is wise to check data for stationarity in order to avoid the spurious regression in time series data. Therefore, unit root test was done since; the unit root test that captures the order of integration of the time series can be utilized to examine the stationarity. The unit root tests are carried out for all the variables in the model by using the Augmented Dickey-Fuller (ADF) test. The ADF test for one unit root is based on the following regression X t X t 1 t i 1 i X t i t n Where Xt can be real inward FDI, real exports and real GDP, t represents time, ξ t is random error term, and n is the number of lag, selected in terms of Schwarz Criterion (SC). The null hypothesis is δ = 0. If this null hypothesis is not rejected, the corresponding time series will be non-stationary; otherwise, the time series will be regarded as stationary and said to be integrated of order zero, denoted as I(0). Unless the null hypothesis is rejected one should correct the variables by taking their appropriate log transformation or differences. 03.3.2 Vector Auto Regression (VAR) Model In this effort to identify the relationship between economic growth and FDI, basically I had intended to use Vector Auto Regression (VAR) model to identify the short run dynamics of the mentioned realtionship based on the integratedness of the variables. Based on the following theoretical VAR model, the model has been estimated by considering the all explanatory variables. Here it considers only bi-variants model to explain the theoretical based of VAR. y1t b11 y1t-1 b1q y1t-q b 21 y 2t-1 b 2q y 2t-q e y1t y 2t c11 y1t-1 c1q y1t-q c 21 y 2t-1 c 2q y 2t-q e y2t Since the interpretation of coefficients of the VAR model is quite complex and meaningless things, in accordance with the econometrics theories I used Impulse Response Function, Variance Decomposition and Granger Casualty Test to evaluate the outcome of the VAR model. 04. Results and Discussion As mentioned above, my focus is to put more weightage on econometric analysis rather than descriptive analysis. However, several graphs have been included to illustrate and identify the relationship between various explanatory variables and the real GDP. 04.1 Descriptive Analysis In accordance with the title of the paper, my first effort is to illustrate the relationship between real GDP and Foreign Direct Investment (FDI) during the period of last two decades starting from 1990. Figure – 01: Relationship between Real GDP and FDI Source: Central Bank Annual Reports It is apparent that both real GDP and FDI are illustrating an increasing trend over the time even though FDI has shown a little bit of fluctuating manner. Especially, FDI has been increasing dramatically after 2005, compared to the other periods while the real GDP has been showing only a gradual increment. Mainly, the outward economic policy which has been promoted during the period of 2000s has significantly influenced the inward of FDI after 2005. Apart from the behavior of two series, the most important fact is that the positive relationship between real GDP and FDI by showing the impact of FDI on real GDP. In fact the contribution of international trade is vital in economic performance in the country with the globalization. Even though, it is very difficult to build a clear picture on the underlying relationship in the context of Sri Lanka, the total trade is indicating much more fluctuating manner. Figure – 02: Relationship between Total Trade and Economic Growth Rate Source: Central Bank Annual Reports According to the above graph, it is obvious that there is no specific pattern between two series of data as the previous graph on real GDP and FDI. It implies that total trade is not a significant factor in explaining economic growth in Sri Lanka during the sample period. However, not only FDI, but domestic investment also plays a massive role. Therefore, it is wise to identify the actual performance of both domestic investment and FDI. Figure – 03: Domestic Investment Ratio and Growth Rate of FDI Source: Central Bank Annual Reports It is obvious that domestic investment shows a smoothing pattern over the time; while growth rate of FDI indicates fluctuate pattern as usually. Basically FDI inflows depend on various factors including both domestic and international economic conditions. Consequently, FDI can be identified as a more general and more capricious measurement compared to domestic investments. Therefore, it is very essential to maintain a stable domestic economic and political culture in order to maximize the FDI inflows since we are unable to influence the global scenarios. 04.2 Econometric Analysis 04.2.1 Results of the Unit Root Test Before moving to estimate the VAR model, I checked the stationary of the variables as mentioned in the methodology. I used Augmented Dickey Fuller (ADF) test as a unit root test along with the Akaike Info Criteria (AIC) and according to the ADF results, all the variables are stationary in their level forms. The following table indicates the stationarity of all other variables at their level forms since the probability value of each series is less than 0.05. Table- 01: ADF test results for level form of the variables Series GRFDI GRL GRRGDP GRDINVEST GRTOT Prob. 0.0065*** 0.0001*** 0.0158** 0.0295** 0.0096*** Lag 1 0 0 1 0 Max Lag 3 3 3 3 3 Obs 16 18 18 17 18 *** - 1% Significance level ** - 5%Significancelevel Since all the variables are stationary at the level forms, they can be interpreted as I(0) variables where both OLS and VAR models can be applied to analyze the effect of FDI on economic growth of Sri Lanka. However, it is quite better to apply VAR model rather than OLS method, since VAR model facilitates a path way to identify the short run dynamics of concerned relationship. Once the initial VAR model is estimated, I re-estimated the VAR model by applying the appropriate lag length. In the lag selection criteria, the Schwarz Information Criteria was employed since the research is dealing with a small time period. According to the Schwarz Information Criteria, one lag was included and this lag length was justified by the other criteria as well. Furthermore, the stability of the VAR model is quite crucial to provide a solid basis for policy analysis. Hence, the Auto Regressive Root Graph was considered for that task and the graph can be illustrated as follows. 04.2.2 VAR Model and Auto Regressive Root Graph The coefficients of the VAR model are usually not going to be interpreted since it is a more complex and meaningless task. However, I used three analytical tools to explain the VAR output namely, Impulse Response Function (IRF), Variance Decomposition (VD) and Granger Casualty Test (GCT). Apart from that, the stability of VAR estimation is quite important. In that sense, the auto regressive root graph was utilized as follows. Figure – 04: Auto Regressive Root Graph Inverse Roots of AR Characteristic Polynomial 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 In accordance with the above graph, since all the variables are inside the circle, the estimated VAR model has a higher level of stability to explain the short run dynamic of FDI on economic growth. 04.2.3 Variance Decomposition Analysis Variance decomposition decomposes the variance in an endogenous variable in to the component when shocks are given to any endogenous variables in the VAR. The variance decomposition gives the information about the relative importance of each random innovation in the VAR. The column S.E. in the below Variance Decomposition table is the forecast error of the variable for each forecast horizon. The source of this forecast error is the variation in current and future values of the innovations to each endogenous variable in the VAR. Table- 02: Variance Decomposition Analysis Period S.E. GRL 1 2 3 4 5 6 7 8 9 10 2.203661 2.725159 2.800075 2.822757 2.827174 2.827545 2.827584 2.827586 2.827586 2.827586 1.252729 1.206248 1.167074 1.165754 1.165268 1.165219 1.165219 1.165219 1.165219 1.165219 '_________ GRRGDP __________ __________ 98.74727 __________ 93.07922 __________ 92.49826 __________ 92.40363 __________ 92.39060 ______ 92.38993 92.38991 92.38990 92.38990 92.38990 GRFDI TOT DINVEST 0.000000 3.091372 3.022618 3.015655 3.014418 3.014289 3.014292 3.014294 3.014294 3.014294 0.000000 0.844502 1.185792 1.256422 1.268709 1.269493 1.269514 1.269516 1.269517 1.269518 0.000000 1.778662 2.126255 2.158538 2.161003 2.161068 2.161065 2.161071 2.161073 2.161074 Moreover, it can be seen according to the above graph, GRRGDP accounts for its variance in a magnificent proportion followed by the GRFDI. Even though GRFDI maintained the second best relative importance among the other endogenous variables, it accounts only for quite low proportion. Consequently, the effect of the GRFDI on GRRGDP is considerably low in the Sri Lankan economy. 04.2.4 Impulse Response Function IRFs trace out the expected responses of current and future values of each of the variables to a shock in one of the VAR equations. In this regards, shocks can be defined or measured in different ways. The shock may be equal to the one standard deviation or one unit of the residual; otherwise one can follow the generalized impulse method depending on the statistical package which they are using. In this study, I gave shock to the residual of each endogenous variable which is equal to the one standard deviation and the following graphs illustrate the possible outcomes of these shocks. According to the results of the Impulse Response Function, it can be seen that at 5 percent significance level the response of GRRGDP is not statistically significant with respect to the shocks of each endogenous variables. Even though the shock of GRFDI was unable to create a statistically significant response, the trend is much crucial. Specifically, the graph of response of GRRGDP to GRFDI is showing that a positive shock of GRFDI will create a positive and increasing effect on GRRGDP up to 2 years and then this effect will gradually decrease and after 3 years of time the effect will die out. Furthermore, all other variables such as growth rate of total trade, growth rate of domestic investment and growth rate of labour have not been able to maintain a considerable effect on growth rate of real GDP. As a whole, FDI can influence GRRGDP compared to the other endogenous variables, even though the relationship is not statistically significant. Figure – 05: Impulse Response Functions Response to Nonfactorized One S.D. Innovations ± 2 S.E. Response of GRRGDP to GRL Response of GRRGDP to GRRGDP 6 6 4 4 2 2 0 0 -2 -2 -4 -4 1 2 3 4 5 6 7 8 9 1 10 2 Response of GRRGDP to GRFDI 3 4 5 6 7 8 9 10 9 10 Response of GRRGDP to TOT 6 6 4 4 2 2 0 0 -2 -2 -4 -4 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 Response of GRRGDP to DINVEST 6 4 2 0 -2 -4 1 2 3 4 5 6 7 8 9 10 04.2.5 Granger Causality Test Basically, Granger Causality Test can be employed in order to examine the direction of the causality among the variables. Granger-causality requires the lagged values of particular variable that is related to subsequent values in another variable. Further, we need to keep constant the lagged values of secondly mentioned variable and any other explanatory variables. The results of the Granger Causality test can be summarized as follows.1 1 Refer the appendices for the full output GRFDI does not Granger Cause GRRGDP GRRGDP does not Granger Cause GRFDI 16 0.09430* 0.60541 0.9107 0.5631 * - Significance at 10 percent level The results of Granger Causality Test imply that GRFDI Granger causes GRRGDP, however this causality is significant only at 10% significance level and there is no reverse relationship in between these two variables. This direction of causality stresses that even though GRFDI causes to influence the GRRGDP, in fact the magnitude of this relationship is quite low since it is only significant at 10% level. The same results can be found by reviewing the literature also, for an example Athukorala(2003). According to Athukorala (2003), “It is evident in the results that the regression analysis does not provide much support for the view of a robust link between FDI and growth in Sri Lanka”. In fact, the inflows of FDI maintain a considerable level; the economic and political back ground of the country is still unfavorable and insufficient to get the maximum benefits from the inward FDI. Therefore, the contribution of FDI on economic growth is still maintaining a lower level. Moving to the other pair wise causalities, there is bi direction causality in between growth rate of total trade and growth rate of FDI and also it is significant at 5% level. The rationale behind this is when the trade agreements are expanded and when the country is more open to the world, total trade shows an increasing pattern and since the country is more open to the world, there is a higher potential to attract the FDI. Moreover, GRFDI also Granger causes to GRDINVEST and this causality is significant at 10% level. It is obvious that when the foreign companies setup their business domestically, there should be a promotable infrastructure facilities and stable financial system. Thus, in order to ensure the attraction of business vicinity, domestic investment should be increased. 05. Conclusions and recommendations This study attempted to quantify the relationship between FDI and economic growth of Sri Lanka using VAR analysis. As a whole, even though GRFDI shows a positive effect on GRRGDP, the magnitude of this effect is quite low. According to the Impulse Response Function, a shock in GRFDI may cause to increase the economic growth for two years and then it leads to pull down the economic growth. However, after three years of time the effect will die out. Variance decomposition proposed that the variation which is explained by GRFDI is quite low. Moreover, Granger causality test discovered that one way causality which is going from GRFDI to GRRGDP and however there is only 90% confidence about this direction of causality. In fact this also justified that even though GRFDI can influence the GRRGDP in a positive manner, this is considerably low. Furthermore, the results indicated that there is a bi-directional causality in between GRFDI and GRTOT. In the current context of Sri Lanka, the significance of FDI is at a lower level even though there is a potential to utilize FDI to enhance the growth rate in Sri Lanka. However, the factors which can enhance the contribution of FDI such as infrastructure facilities, stable economic and political situations are not currently working smoothly. In fact after finishing the civil war situation, FDI inward has grown rapidly even if the promotable vicinity is not present yet. Therefore, this study strongly recommends that to build and maintain supportable infrastructure facilities along with a stability of economic condition in the country by attracting FDI in order to achieve a higher economic growt h. References Athukorala, P.P.A.W. (2003). “The Impact of Foregin Direct Investment for Economic Growth; a Case Study in Sri Lanka”. 9th International conference on Sri Lankan Studies. Bende-Nabende A., and Ford, J.L., (1998). “FDI, Policy Adjustments and Endogenous Growth: Multiplier Effects from a Small Dynamic Model for Taiwan, 1959- 1995”, World Development, Vol. 26 No. 7, pp. 1315-1330 Bende-Nabende, A., J. Ford, B. Santoso, and S. Sen (2003). “The Interaction between FDI, Output and the Spillover Variables: Cointegration and VAR Analyses for APEC, 1965.99”. Applied Economics Letters, 10 (3): 165.72. Dunning, J. (1993). “The Globalization of Business”, London: Routledge. Fortanier, F. (2004). “The impact of foreign direct investment on development: bridging international business with development economics & industrial economics”, Paper presented at the AIB 2004 Meeting, Stockholm. Kreinin, M.E. and M. G. Plummer (2000), “Economic Integration and Development: Has Regionalism Delivered for Developing Countries?” (Cheltenham, Edward Elgar). Kreinin, M.E. and M. G. Plummer (2000), “Economic Integration and Asia: The Dynamics of Regionalism in Europe”, North Ameica, and the Asia-Pacific (Cheltenham, Edward Elgar). Kreinin, Mordchai, Michael G. Plummer, and Shigeyuki ABE. (1998) “Export and Direct Foreign Investment Links: A Three Country Comparison”. International Economic Links and Policy Formulation. Meyer, K. (2004). “Perspectives on multinational enterprises in emerging economies”, Journal of International Business Studies, 35(4): 259-276. Moore, M. O. (1993), “Determinants of German Manufacturing Direct Investment 1980-1988”, Weltwirtschaftsliches Archiv, 129, pp. 120-137. OECD (1998). “Open Markets Matter: The benefits of trade and investment liberalization”, Paris: OECD UNCTAD, (1999) “Trends in international investment agreements: An Overview” UNCTAD Series on Issues in International Investment Agreements Appendices 01. Lag Selection Criteria of VAR VAR Lag Order Selection Criteria Endogenous variables: GRL GRRGDP GRFDI TOT DINVEST Exogenous variables: C @TREND Date: 04/29/11 Time: 22:15 Sample: 1990 2008 Included observations: 17 Lag LogL LR FPE AIC SC HQ 0 1 -306.4447 -288.5269 NA* 21.07973 1.02e+10* 3.02e+10 37.22879* 38.06199 37.71892* 39.77743 37.27751* 38.23251 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion 02. Re-estimation of VAR with selected lag length Vector Autoregression Estimates Date: 04/25/11 Time: 09:56 Sample (adjusted): 1991 2007 Included observations: 17 after adjustments Standard errors in ( ) & t-statistics in [ ] GRL(-1) GRL GRRGDP GRFDI TOT DINVEST -0.188427 -0.004981 -3.075031 -6.958239 -0.052814 (0.25790) [-0.73061] (0.35023) [-0.01422] (11.3149) [-0.27177] (12.6476) [-0.55016] (0.19659) [-0.26865] GRRGDP(-1) -0.157946 (0.25901) [-0.60979] -0.176813 (0.35174) [-0.50269] -8.887991 (11.3636) [-0.78215] 2.792415 (12.7021) [ 0.21984] 0.497850 (0.19744) [ 2.52152] GRFDI(-1) 0.010269 (0.00740) [ 1.38723] 0.004898 (0.01005) [ 0.48728] 0.024353 (0.32476) [ 0.07499] 0.007831 (0.36301) [ 0.02157] -0.003589 (0.00564) [-0.63604] TOT(-1) -0.009409 (0.00754) [-1.24779] -5.61E-05 (0.01024) [-0.00548] 0.080391 (0.33080) [ 0.24302] -0.070772 (0.36977) [-0.19140] 0.003328 (0.00575) [ 0.57906] DINVEST(-1) 0.134220 (0.29323) [ 0.45773] -0.316694 (0.39820) [-0.79532] 1.345447 (12.8646) [ 0.10459] -3.231800 (14.3799) [-0.22474] 0.682953 (0.22352) [ 3.05546] C -1.702748 (7.88097) [-0.21606] 12.29321 (10.7022) [ 1.14866] 71.72536 (345.757) [ 0.20744] 48.74796 (386.483) [ 0.12613] 6.103630 (6.00745) [ 1.01601] @TREND 0.100077 (0.12446) [ 0.80409] 0.226718 (0.16901) [ 1.34142] -1.891894 (5.46033) [-0.34648] 7.220164 (6.10349) [ 1.18296] -0.054734 (0.09487) [-0.57692] R-squared Adj. R-squared Sum sq. resids S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent 0.390179 0.024286 48.56123 2.203661 1.066374 -33.04366 4.711019 5.054107 1.522056 2.230918 0.191375 -0.293800 89.55192 2.992523 0.394446 -38.24560 5.323012 5.666100 5.735961 2.630898 0.110276 -0.423559 93469.89 96.67983 0.206573 -97.32550 12.27359 12.61668 39.81887 81.03043 0.161284 -0.341945 116786.1 108.0676 0.320498 -99.21848 12.49629 12.83938 37.17824 93.28849 0.642348 0.427757 28.21704 1.679793 2.993360 -28.42901 4.168119 4.511207 25.12941 2.220576 Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion 5.39E+09 3.80E+08 -288.5269 38.06199 39.77743 03. Variance Decomposition (Multiple Graph) Variance Decomposition Percent GRRGDP variance due to GRL Percent GRRGDP variance due to GRRGDP 100 100 80 80 60 60 40 40 20 20 0 0 1 2 3 4 5 6 7 8 9 1 10 Percent GRRGDP variance due to GRFDI 3 4 5 6 7 8 9 10 Percent GRRGDP variance due to TOT 100 100 80 80 60 60 40 40 20 20 0 2 0 1 2 3 4 5 6 7 8 9 10 Percent GRRGDP variance due to DINVEST 100 80 60 40 20 0 1 2 3 4 5 6 04. Granger Causality Test Pair wise Granger Causality Tests Date: 05/02/11 Time: 22:47 Sample: 1990 2008 Lags: 2 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Null Hypothesis: Obs F-Statistic Prob. GRRGDP does not Granger Cause GRL GRL does not Granger Cause GRRGDP 17 3.81695 0.06351 0.0521 0.9388 GRFDI does not Granger Cause GRL GRL does not Granger Cause GRFDI 16 1.36654 1.00847 0.2951 0.3961 TOT does not Granger Cause GRL GRL does not Granger Cause TOT 17 1.79098 0.37585 0.2086 0.6945 DINVEST does not Granger Cause GRL GRL does not Granger Cause DINVEST 17 1.07134 0.88814 0.3732 0.4368 GRFDI does not Granger Cause GRRGDP GRRGDP does not Granger Cause GRFDI 16 0.09430 0.60541 0.9107 0.5631 TOT does not Granger Cause GRRGDP GRRGDP does not Granger Cause TOT 17 1.51781 0.04270 0.2584 0.9583 DINVEST does not Granger Cause GRRGDP 17 GRRGDP does not Granger Cause DINVEST 0.71308 5.94303 0.5098 0.0161 TOT does not Granger Cause GRFDI GRFDI does not Granger Cause TOT 16 0.04848 0.02182 0.9529 0.9785 DINVEST does not Granger Cause GRFDI GRFDI does not Granger Cause DINVEST 16 0.12103 0.07149 0.8872 0.9314 DINVEST does not Granger Cause TOT TOT does not Granger Cause DINVEST 17 1.26513 1.00747 0.3173 0.3940