Does foreign direct investment matter for economic growth in asean countries?

Nội dung text: Does foreign direct investment matter for economic growth in asean countries?

1. DOES FOREIGN DIRECT INVESTMENT MATTER FOR ECONOMIC GROWTH IN ASEAN COUNTRIES? Hoang Chi Cuong1, Cao Thi Thu, Nguyen Thi Tinh2 Abstract: Does Foreign Direct Investment (FDI) stimulate economic growth of the host country? Empirical evidences have been mixed in the cases of developed, developing and transitional economies. There remain gaps in the literature. We strive to fill these gaps with an improved empirical study on the case of ASEAN countries. To do so, we employ a panel data set from 2001 to 2017. Then, we control for heterogeneity and rule out potential biases by constructing different models, the Fixed Effects Model, Random Effects Model, and Dynamic Panel Model, to obtain consistent estimations. The empirical results show that Foreign Direct Investment has had a positive impact on economic growth of ASEAN countries. It is a real relationship not a wishful thinking. The other determinant of economic growth is the Trade Openness. Some policy implications are also proposed in this research. Keywords: ASEAN countries, Economic growth, Foreign Direct Investment 1. INTRODUCTION The Association of Southeast Asian Nations, or ASEAN, was established on 8 August 1967 in Bangkok, Thailand, with the signing of the ASEAN Declaration (Bangkok Declaration) by the Founding Fathers of ASEAN, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Brunei Darussalam then joined on 7 January 1984, Viet Nam on 28 July 1995, Lao PDR and Myanmar on 23 July 1997, and Cambodia on 30 April 1999, making up what is today the ten Member States of ASEAN. The ASEAN Community is comprised of three pillars, namely the ASEAN Political-Security Community, ASEAN Economic and ASEAN Socio- Cultural Community. Investment activities in the ASEAN region are regulated by the ASEAN Comprehensive Investment Agreement (ACIA), which entered into force on 29 March 2012. ACIA is the successor and adjustment of the ASEAN Investment Guarantee Agreement (AIGA) in 1987, and the Framework Agreement on the ASEAN Investment Area (AIA Framework Agreement) in 1998. This is to accommodate the new context of regional integration under the ASEAN Vision 2020. The goal of the ACIA is to create a free and open investment regime in ASEAN through progressive liberalization of investment. Recently, ASEAN is an increasingly attractive destination for foreign investors. In the Asia-Pacific region, ASEAN is one of the largest receivers of FDI in relation to its economic size (GDP: Gross Domestic Product). FDI inflows into ASEAN followed an upward trend, from 1 Vietnam Maritime University; Email: cuonghc.qtc@vimaru.edu.vn 2 Hai Phong University of Management and Technology 220
2. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 221 about 22,150 million USD in 2001 to 84,144 million USD in 2007. The value of net FDI inflows declined in the duration of 2007-2009, due to the 2008 global financial and economic crisis. Since 2010 net FDI inflows into ASEAN have rebounded strongly and reached at 167,052 million USD in 2017 (See Figure 1). Figure 1: Foreign Direct Investment (FDI) in ASEAN during 2001-2017, net inflows (BoP, current million USD). Source: The World Bank, 2021. The research question is “Does FDI inflows accelerate economic growth of ASEAN?” It is well-known from FDI literature that one of the fundamental motives driving most developing countries to embrace FDI is the promise that multinational enterprises would come along with assets which were hitherto absent in the host countries and empower the domestic economies with new potentials for economic growth and development (Blomstrửm and Kokko, 1998). This fascination reached its peak in the last two decades during which developing countries have tried to out-compete each other through various incentives to foreign investors. Wan (2009) observed for example that between 1991 and 2000, inward FDI to the developing countries increased by seven folds, while their combined FDI stock expanded rapidly to five times greater than the earlier decade. In this research, I will set out to investigate whether FDI has had a positive effect on economic growth of ASEAN. The research is divided into six sections. Section 2 presents a brief review of related literature, section 3 explicates the econometric models and data sources, section 4 demonstrates the estimated results, section 5 gives an analysis of the estimated results, while section 6 concludes and proposes some recommendations. 2. LITERATURE REVIEW Following the endogenous growth theory developed by Romer (1986), there has been a large body of empirical work in recent years indicating that FDI does play an important role in economic growth. The literature approaching this issue can be categorized into micro studies using firm-level data or macro studies utilizing country-level data. Micro studies usually do not provide evidence that economic growth can be positively attributed to FDI in a specific recipient economy. These studies focus on technology spillovers, wage, productivity spillovers, export, or import etc. The empirical results of micro studies are
3. 222 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI diverse. Blomstrửm and Wolff (1989, 1994), for example, find positive impact of FDI in the host country. On the contrary, Aitken and Harrison (1999), Harrison and McMillan (2003) find no significant effect of FDI making the issue more controversial. This supports for the claim of Lipsey (2002). Macroeconomic analyses that account for group of developed countries often report either a negative impact on growth (Mencinger, 2003; Johnson, 2006; Tỹrkcan et al., 2008; Nath, 2009; Kondyan, 2012; Imtiaz, 2017) or an inconclusive effect (De Mello, 1999). By contrast, several macro-based articles on both developed and developing countries or developing countries themselves indicate a positive effect of FDI inflows in recipient economies (Sasi and Mehmet, 2015; Mahmoodi and Mahmoodi, 2016; Sakyi and Egyir, 2017; Ahmad et al., 2018), albeit differing by country, and indicating the importance of host economy characteristics. Notably, some studies find a negative impact of FDI on economic growth in developing world (Adams, 2009). Recently, some macroeconomic studies focus on single-country study due to the heterogeneous relationship between FDI and growth. Researchers utilized country-level data or provincial level data. The results of these researches are also mixed and there exist debates and controversies on the issue. For example, Antwi et al. (2013), Zhao (2013), Agbola (2014) provide the link between FDI and economic growth, while Ye (2010), Nerija et al. (2015), Jorge and Werner (2018) find no evidence for FDI to stimulate economic growth. One way to explain the diversity of empirical findings is the variety of applied methods (ARDL, ECM, GMM, MSMs, FE, RE, OLS, 2 SLS, 3 SLS, PVAR, VECM, Granger causality test etc), country samples (single country: China, Ghana, Indonesia, Malaysia, Mexico, Nigeria, Singapore, South Africa, Spain, the Philippines, Venezuela, Vietnam, etc; a group of countries: Africa, ASEAN, Central and Eastern Europe, developing countries, developed countries, both developed and developing countries, EAGLE countries, Emerging ASIAN Economies, Latin America, OECD, Transitional Economies etc), variables employed (GDP per capita growth, GDP growth, Net FDI inflows, domestic investment, government expenditure, trade openness, institutional quality, tax, human capital, inflation, wage, productivity, export, import), and observation time. These mixed evidences of empirical works in the literature imply that the question of the FDI-economic growth nexus is still an open one. Notably, the study on the impact of FDI on economic growth of ASEAN is rarely conducted, hence the imperative for additional studies. In this research, I will conduct an improved empirical study to check whether FDI has boosted economic growth of ASEAN. To do so, I employ a panel data set of 8 ASEAN countries during 2001-2017. Then, I control for unobserved heterogeneity, that is the country-specific effects, and rule out potential biases by constructing different models (fixed effects model, random effects model and dynamic panel model with generalized method of moments (GMM) estimator for the parameters) to obtain more consistent and efficient estimation. Moreover, I will examine the effects of other determinants on economic growth of ASEAN. The next section will explicate the econometric models and data sources.
4. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 223 3. THE ECONOMETRIC MODEL SPECIFICATION AND DATA SOURCES Fixed Effects Model As discussed in the previous section, one of the possible options to handle the unobserved heterogeneity is to use fixed effects to control for the unobserved effects. The panel data set used in this research includes 8 ASEAN countries over the period 2001-2017. The advantages of using panel data can be summarised as more informative data, more variability, less multicollinearity among variables, more degrees of freedom, and more efficiency. By incorporating the fixed effects to control for the unobserved heterogeneity in model, I intend to control for the fixed effects, that is, the unobserved effects that differ among ASEAN countries, but are constant within countries over time, like culture, climate, people, and many other social factors. The geographical location and everything associated with it are also constant over time. There are primarily three ways to control for the fixed effects: within transformation, between transformation, and using dummy variable least square regression. In most research, within estimation equations are employed to obtain the estimators. So, I will discuss the within transformation and within estimation equations are used in this research. Suppose we have a fixed effects model taking the following form: Yi,t = β0 + β[FDI]i,t + γ[CONTROLS]i,t + vi + εi,t (1) In this model, vi is a vector of fixed effects estimators the author includes in model to represent the unobserved factors that are different among ASEAN countries but fixed within countries over time. First, we could conduct the fixed effects transformation in terms of deviation from the group means. If we average the equation over time for each country, we get the equation of the group means as below. Yi = β0 + β[FDI]i + γ[CONTROLS]i + vi + i (2) If we subtract (3) from the equation (2) for each time period, we could get the within transformation equation as following. Yi,t - Yi = β([FDI]i,t - [FDI]i) + ([CONTROLS]i,t - [CONTROLS]i) + (εi,t - i) (3) If we use lower case letters to represent the differenced variables, we could rewrite (3) to get (4). yi,t = β[fdi]i,t + [controls]i,t + (εi,t - i) (4) We can observe that after our within transformation, the unobserved effects vi have been eliminated from model via the subtraction. Therefore, we could estimate (4) by a fixed effects panel data model using the general least-square (GLS) regression. The resulting estimators are called fixed-effects estimators or the within estimators. Random Effects Model Fixed effects model is the best fit if we assume that the unobserved heterogeneity among countries only results in parametric shifts of the regression function and that it is correlated with one or more of the explanatory variables. We may also use a random effects model to control for the unobservable heterogeneity through a general least-square (GLS) estimation process if it is assumed that the error terms of each individual country are randomly distributed across countries and hence the unobserved effects are uncorrelated with any explanatory variables.
5. 224 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI Random effects model will be the best fit if our data are samples drawn from a large dataset with substantial cross-section and time-series variation. If we have the following model: Yi,t = β0 + β[FDI]i,t + γ[CONTROLS]i,t + ai + εi,t (5) Where: ai represents the unobserved heterogeneity for each country that independently distributes across different countries and is constant over time. Apparently, under these random effects assumption, we may possibly approach the same estimation by running a random effects panel data model with a set of time dummy variables. Dynamic Panel Model The analysis in the previous sections shows that fixed effects model could improve the model estimation by within transformation and subtracting the fixed effects from the error term to control for the unobservable heterogeneity. However, if we want to examine the dynamic effects of the panel data using a first-order model by adding in a lagged dependent variable as instrumental variable, even though we assume that ei,t itself is not serially correlated, the lagged dependent variable, the initial real GDP per capita growth in this research, will be correlated with the disturbance term. A possible way to solve this problem is to use a first-differenced model. The dynamic panel model advances the previous models by using first differenced model and including a lagged dependent variable. The result will be more powerful model which controls for the unobserved individual country-specific effects. Therefore, the author will use the dynamic panel first-differenced estimator developed by Arellano and Bond (1991). In this section, the author uses the dynamic panel procedure with the Arellano-Bond first differenced estimator both to control for the unobserved heterogeneity and eliminate the biases induced by adding in the lagged dependent variable, that is, the lagged real GDP per capita growth. The author starts the dynamic panel procedure with a first-order model by adding a lagged dependent variable as an instrumental variable in the model: yi,t = β0 + yi,t-1 + βXi,t + i + εi,t (6) Where y is the real GDP per capita growth and X that includes FDI as well as the set of exogenous explanatory variables of all the other determinants of economic growth. The error term is composed of two parts: i is the unobserved country-specific effect and ei,t is the random error term. At this stage, this model assumes that the random error term ei,t is not serially correlated and the lagged dependent variable yi,t-1 is correlated with the unobserved country-specific effect i. If there is correlation between yi,t-1 and i the use of standard panel data estimator is not appropriate. Therefore, Arellano-Bond first-differenced estimator is utilized here to eliminate the country-specific effect i. yi,t - yi,t -1 = β0 + (yi,t-1 - yi,t-2) + β(Xi,t − Xi,t-1) + (εi,t - εi,t-1) (7) In this equation, the country-specific effect i correlated with yi,t-1 is swept from the model through first differencing. However, after controlling for the unobserved country-specific by first differencing, there still exist complications in the model: the correlation between the lagged dependent variable and the disturbance term. Arellano and Bond (1991) suggested that we could use the lagged levels, yi,t-2 and yi,t-3 as instrumental variables. Then we could apply the standard instrumental techniques to estimate the model. Adding the instrumental variables assumes that the differenced disturbance terms (ei,t - ei,t-1) are not second-order serially correlated.
6. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 225 Data Sources This research employs a panel data set of eight ASEAN countries including Brunei Darussalam, Indonesia, Cambodia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam from 2001 to 2017. Due to lack of data in this duration, Lao PDR and Myanmar are dropped out of the country samples. The data is collected from trustworthy source, the World Bank (see the Appendix 1). 4. The Econometric Estimation Results Table 1: Fixed Effects Models. Dependent Variable: Economic Growth (Real GDP Per Capita Annual Growth) (1) (2) (3) (4) (5) (6) (7) -0.000153* -0.000152* -0.000114 -0.0000653 -0.0000536 0.0000115 0.00000642 LGDPPC (0.0000766) (0.0000769) (0.0000767) (0.0000878) (0.0000904) (0.0000884) (0.0000869) 0.2439821 0.244 0.283 0.294 0.295 0.246 0.242 NETFDI (0.0731135) (0.0737) (0.0737) (0.0742) (0.0745) (0.0725) (0.0713) 0.00195 -0.00498 -0.00224 -0.00327 -0.00526 -0.00248 GOVSPG (0.0255) (0.0251) (0.0252) (0.0253) (0.0242) (0.0238) -0.0300* -0.0330 -0.0338 -0.0424 -0.0434 CREDIT (0.0117) (0.0120) (0.0121) (0.0118) (0.0116) -0.195 -0.179 -0.125 -0.0894 LABOR (0.172) (0.175) (0.168) (0.166) 0.00587 0.0269* 0.0260* TRADEO (0.0103) (0.0115) (0.0113) -1.387 -1.320 POPGRW (0.392) (0.387) -0.972* CRISIS (0.428) -1.65e-09 -1.59e-09 -2.30e-09 -2.80e-09 -2.96e-09 -8.51e-10 0.229 _cons (0.1934) (0.1942) (0.190) (0.190) (0.190) (0.182) (0.205) N 136 136 136 136 136 136 136 R-square 0.087 0.087 0.133 0.142 0.144 0.224 0.256 adj. R-square 0.022 0.014 0.056 0.058 0.053 0.135 0.163 rmse 2.256 2.265 2.216 2.214 2.220 2.122 2.086 Notes: Standard errors in parentheses and * p<0.05, p<0.01, p<0.001 Table 2: Random Effects Models Dependent Variable: Economic Growth (Real GDP Per Capita Annual Growth) (1) (2) (3) (4) (5) (6) (7) -0.000137 -0.000137 -0.000137 -0.000138 -0.000147 -0.000154 -0.000153 LGDPPC (0.0000140) (0.0000144) (0.0000143) (0.0000165) (0.0000192) (0.0000168) (0.0000165) 0.286 0.285 0.300 0.302 0.250 0.218 0.214 NETFDI (0.0364) (0.0371) (0.0384) (0.0437) (0.0585) (0.0555) (0.0546)
7. 226 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI 0.00245 -0.000599 -0.000367 -0.00193 0.00152 0.00470 GOVSPG (0.0247) (0.0247) (0.0249) (0.0248) (0.0237) (0.0234) -0.00686 -0.00697 -0.0126* -0.0236 -0.0235 CREDIT (0.00494) (0.00504) (0.00632) (0.00659) (0.00648) -0.00410 0.000335 -0.0255 -0.0224 LABOR (0.0331) (0.0368) (0.0328) (0.0323) 0.00652 0.0142 0.0141 TRADEO (0.00440) (0.00466) (0.00458) -1.146 -1.099 POPGRW (0.325) (0.321) -0.962* CRISIS (0.424) 3.571 3.556 3.974 4.263 3.812 7.112 7.048 _cons (0.263) (0.305) (0.428) (2.368) (2.651) (2.500) (2.461) N 136 136 136 136 136 136 136 within 0.0852 0.0852 0.1025 0.1029 0.1189 0.1915 0.2232 R-sq between 0.9838 0.9838 0.9758 0.9756 0.9757 0.9847 0.9850 overall 0.4412 0.4413 0.4494 0.4494 0.4588 0.5068 0.5260 rmse 2.214 2.222 2.214 2.222 2.208 2.120 2.086 Notes: Standard errors in parentheses and * p<0.05, p<0.01, p<0.001 Table 3: Dynamic Panel Models Dependent Variable: Economic Growth (Real GDP Per Capita Annual Growth) (1) (2) (3) (4) (5) (6) (7) GDPPCGR -0.194 -0.193 -0.201 -0.194 -0.271 -0.254 -0.255 (L1) (0.113) (0.113) (0.112) (0.111) (0.0984) (0.0982) (0.0987) GDPPCGR -0.137 -0.135 -0.148 -0.137 -0.158 -0.180* -0.182* (L2) (0.0946) (0.0953) (0.0943) (0.0942) (0.0822) (0.0830) (0.0851) -0.00113 -0.00114 -0.00113* -0.00114* -0.00145 -0.00173 -0.00173 LGDPPC (0.000581) (0.000583) (0.000575) (0.000572) (0.000503) (0.000533) (0.000536) 0.411 0.412 0.377 0.371 0.380 0.417 0.415 NETFDI (0.0885) (0.0897) (0.0912) (0.0908) (0.0792) (0.0824) (0.0845) -0.00155 0.0000873 0.00131 0.00239 0.00121 0.00125 GOVSPG (0.0207) (0.0204) (0.0203) (0.0178) (0.0176) (0.0178) -0.0693 -0.0670 -0.0686 -0.0551 -0.0557 CREDIT (0.0459) (0.0457) (0.0398) (0.0406) (0.0408) -0.574 -0.549 -0.563 -0.550 LABOR (0.413) (0.360) (0.357) (0.367) 0.0780 0.0739 0.0738 TRADEO (0.0182) (0.0182) (0.0184) 0.882 0.891 POPGRW (0.604) (0.607) 0.00254 CRISIS (0.523)
8. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 227 0.193 0.195 0.348 0.342 0.471 0.521* 0.523 _cons (0.284) (0.285) (0.299) (0.297) (0.261) (0.261) (0.304) N 112 112 112 112 112 112 112 Sargan Test 0.7165 0.7307 0.7250 0.7560 0.5710 0.5898 0.6058 (P value) Arellano-Bond 0.0402 0.0410 0.0430 0.0498 0.0185 0.0189 0.0199 test (P value) Notes: Standard errors in parentheses and * p<0.05, p<0.01, p<0.001 In which: NETFDI is the net FDI capital (inflows – outflows) of country i at year t (USD). LGDPPC is the lagged value of GDP per capita of country i at year t (USD). GOVSPG is the Government spending of country i at year t (USD) CREDIT is the credit to private sector of country i at year t (USD) LABOR is the number of labor from 16 year old of country i at year t. TRADEO is the trade openness of country i at year t [= (exports + imports)/GDP] POPGRW is the population growth rate of country i at year t CRISIS is the dummy variable that takes the value of 1 in between 2008-2011 and vice versa 0 in other years Those variables have been used in Sasi and Mehmet (2015), Mahmoodi and Mahmoodi (2016), Sakyi and Egyir (2017) and Ahmad et al. (2018) for the case of other countries in the world. 5. AN ANALYSIS OF THE ESTIMATION RESULTS The estimation results are summarised from Table 1 to Table 3 above with different sets of control variables. Since FDI is the key explanatory variable and primary focus, it is included in every model in my research to examine the possible impact of FDI on economic growth of ASEAN when other variables are controlled for. Lagged GDP per capita variable is the first control variable in the model to capture the “convergence rate”, ceteris paribus. Neo-classical growth theory argues that poor countries tend to catch up with the rich countries. Therefore, developing countries will grow faster and eventually all countries will converge in terms of per capita income. The lagged GDP per capita is supposed to be negatively related to economic growth and the coefficient of the lagged GDP per capita is the rate that the poor country could catch up with the rich country when all the other explanatory variables except for FDI are held constant. Government spending and credit to private sector are included next to examine the “crowding-out” or “crowding-in” effect of FDI in ASEAN countries. After I examine the variables that have interactive relationship with FDI, labor force participation rate entered the model to test the impact of the human capital on economic growth of the region. Finally, three additional control variables are added including trade openness, population growth and a 2008 crisis dummy. The coefficients of lagged GDP per capita (LGDPPC marked here) are negative and statistically significant at Model 7 of Random Effects and Dynamic Panel Model estimations
9. 228 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI but not significant at Model 7 of Fixed Effects model estimations. So, the authors have no conclusion for this variable. The coefficient of credit to private sector variable-a proxy for private investment (CREDIT marked here) is negative and statistically significant at the FE and RE Model 7 but not significant at Model 7 of Dynamic Panel Model estimations. So, the authors also have no conclusion for this variable. Population growth (POPGRW marked here) is negative and statistically significant at Model 7 of the Fixed Effects and Random Effects estimated results but not significant at Model 7 of Dynamic Panel Model estimations. So, the authors also have no conclusion for this variable. Trade openness variable (TRADEO marked here) shows a significant and positive effect at Model 7 of the Fixed Effects, and Random Effects estimated results after the author controls for population growth and the 2008 global financial and economic crisis. It also presents a positive and significant impact at Model 7 of Dynamic Panel Model estimations. This means open to trade has promoted economic growth of ASEAN. Trade openness can boost economic growth in the following ways. First, international trade can allow countries to specialize in sectors where they have a relatively lower opportunity cost to exploit the comparative advantage over other countries. Over time, higher degree of openness helps economies to employ more of their human, physical and capital resources in areas where they have highest productivity and returns in open international markets. Second, trade openness can help the diffusion of advanced technology and new ideas to developing countries to help them improve productivity and economic efficiency. Third, free trade will give consumers access to cheaper products and increase the purchasing power and living standard for both developed and developing countries. Fourth, it will also allow producers to attain cheaper inputs and hence reduce their cost and improve their competitiveness. The coefficients of government spending (GOVSPG marked here) and human capital (LABOR marked here) are statistically insignificant at all the Fixed effects, Random effects, and Dynamic panel models. This suggests that local investment from government is less effective in terms of promoting economic growth of ASEAN. Regarding human capital, previous literature indicates that a certain level of economic development and education made it possible for host countries to imitate and learn advanced technologies and experiences brought by FDI. However, when host countries could not reach this level, not only could domestic firms hardly effectively learn technologies from foreign firms, their market share also would be eroded by foreign firms. That is, the inflows of FDI could not help host countries improve their technologies, but help foreign firms make use of the domestic low- cost raw materials and labours and transfer all the profit out of host countries. As suggested by Romer (1993b), if ASEAN developing countries would like to gain more from FDI inward they should narrow down the gap in knowledge, or ideas, rather than in physical capital. Much of that capital was the human or organizational capital of multinational firms. For more rapid growth “ one of the most important and easily implemented policies is to give foreign firms an incentive to close the idea gap, to let them make a profit by doing so by creating an economic environment that offers an adequate reward to multinational corporations when they bring ideas from the rest of the world and put them to use with domestic resources ”
10. INTERNATIONAL CONFERENCE PROCEEDINGS: GLOBAL FDI AND RESPONSES OF FDI ENTERPRISES IN VIETNAM IN THE NEW CONTEXT 229 The coefficient of the CRISIS dummy is negative and significant at Fixed Effects and Random Effects Model 7 but not significant at Model 7 of Dynamic Panel Model estimations. So, the authors also have no conclusion for this variable. Now we turn into discussion on the most important variable in the research, NETFDI. The coefficients of the NETFDI variable are positive and stably significant at 1% level from Model 1 to Model 7 in all estimated approaches: the Fixed Effects, Random Effects and Dynamic Panel Model with generalized method of moments (GMM). This means FDI has accelerated economic growth of ASEAN during the observation period 2001-2017. This can be explained that FDI, usually through direct or indirect participation of multinational enterprises (MNEs), are firms that carry the most advanced technology and research and development (R&D). Therefore, FDI by MNEs is assumed to be a primary channel through which ASEAN can access advanced technology. Other than that, FDI is also a channel through which many other highly productive resources could be introduced into the recipient economies via the cooperation between the MNCs and local firms. Those resources include capital stock, managerial expertise, accounting and auditing standards and knowledge of international markets. They are assumed to help the recipient economies improve the productivity and economic efficiency. Meanwhile, FDI might help to stimulate domestic investment in related industries. Interestingly, when I control for unobserved heterogeneity and rule out the potential biases by using dynamic panel first- differenced estimator with a lag dependent variable as an instrumental variable, the impact of FDI on economic growth of ASEAN is clearer due to its larger coefficients, around 0.4 in comparison with around 0.3 in the FE and RE Models. 6. CONCLUDING REMARKS AND RECOMMENDATIONS This research has extended the past literature by elucidating the linkage between FDI, trade openness, the 2008 global crisis, and other determinants and economic growth of ASEAN countries. The empirical results show that FDI has boosted economic growth of ASEAN after the author controls for country-specific effects to get persuasive and consistent estimation results. FDI has directly stimulated economic growth of ASEAN. It is recommended that ASEAN countries should introduce a preferential treatment policy with respect to attracting foreign investors to the country to promote economic growth. For the sectors or industries where FDI is associated with a crowding-out effect, there should be safety measures to protect domestic investors from falling out of business. Other important determinant of economic growth of ASEAN is trade openness. Overall, FDI is considered as one of the most important driving forces of economic growth for many countries. Nevertheless, the recipient economy’s benefits from FDI vary greatly, both across and within countries. This suggests that the country’s characteristics are important factors in distribution of benefits of FDI. To gain more from inward FDI, the host country must develop to a certain threshold, like a sufficient level of human capital, good infrastructure, developed financial markets and incentive policy to have enough absorptive capacity. Thus, econometric model should be constructed to evaluate the real effect of FDI on the host country. However, the effects were robust to changes in methods of estimation, economic models, observation periods, country samples, and variables employed. Hence, the results and analyses will be more reliable and persuasive if correctly specified models and consistent estimation techniques are rigorously employed.
11. 230 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC TẾ FDI TOÀN CẦU VÀ ỨNG BIẾN CỦA DOANH NGHIỆP FDI TẠI VIỆT NAM TRONG BỐI CẢNH MỚI APPENDIX APPENDIX 1: VARIABLES AND DATA RESOURCES Variables Data Resources The World Bank, Retrieved on 5 July 2019 from website LGDPPC The World Bank, Retrieved on 5 July 2019 from website NETFDI The World Bank, Retrieved on 5 July 2019 from website GOVSPG The World Bank, Retrieved on 5 July 2019 from website CREDIT The World Bank, Retrieved on 5 July 2019 from website LABOR The World Bank, Retrieved on 5 July 2019 from website TRADEO The World Bank, Retrieved on 5 July 2019 from website POPGRW APPENDIX 2: SUMMARY OF THE STATISTICS (PERIOD: 2001-2017, COUNTRIES: 8, OBSERVATIONS: 136) Variable Mean Std. Dev. Min Max LGDPPC 12494.08 16336.35 431.2023 54764.86 NETFDI 5.602637 6.264438 - 1.855686 28.01695 GOVSPG 6.032437 7.966543 -6.66494 82.09405 CREDIT 70.13087 40.90965 5.987943 149.3734 LABOR 69.2685 6.978584 59.739 85.391 TRADEO 147.1752 94.30521 37.42134 437.3267 POPGRW 1.418667 0.7069448 -1.474533 5.321517 CRISIS 0.2352941 0.4257507 0 1 APPENDIX 3: THE CORRELATION MATRIX LGDPPC NETFDI GOVSPG CREDIT LABOR TRADEO POPGRW CRISIS LGDPPC 1.0000 NETFDI 0.5490 1.0000 GOVSPG -0.1590 0.0541 1.0000 CREDIT 0.1927 0.3203 -0.0691 1.0000 LABOR -0.3022 0.2002 0.1968 -0.0978 1.0000 TRADEO 0.6293 0.7911 -0.0237 0.5493 -0.0567 1.0000 POPGRW 0.2400 0.1678 0.0188 -0.1876 -0.2218 0.3090 1.0000 CRISIS 0.0027 -0.0124 0.0630 -0.0350 0.0212 -0.0047 0.0669 1.0000
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