Ảnh hưởng của FDI đến phát triển bền vững tại Việt Nam

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Bài viết cung cấp các khái niệm cơ bản về phát triển bền vững, giải thích mối quan hệ nhân quả giữa dòng vốn FDI đến các yếu tố kinh tế - xã hội – môi trường ở Việt Nam cũng như đưa ra đánh giá tổng quan về hiệu quả phát triển bền vững hiện nay. Mời các bạn cùng tham khảo!

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INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 IMPACT OF FDI ON SUSTAINABLE DEVELOPMENT IN VIETNAM ẢNH HƯỞNG CỦA FDI ĐẾN PHÁT TRIỂN BỀN VỮNG TẠI VIỆT NAM MA, Tran Thuy Nhung University of Economics and Law – Vietnam National University - HCM MA, Nguyen Thi Truc Phuong - Generali Vietnam thuynhung.tr@gmail.com Abstract The research aims to bring into discussion the relevance of Foreign Direct Investment (FDI) inflows on the sustainable development efficiency in Vietnam through employing the ex- perimental model of Auto regression Vector (VAR). The article also provides the basic concepts of the sustainable development and exacerbates the correlation between FDI inflows and the socio-economic and environmental factors in Vietnam. With the variables which represented for the efficiency of sustainable development in Vietnam for the period from 1990 to 2018, the study results show that the growth of FDI intakes has a certain impact on the environmental health index, the social life quality and the economic background. On the other hand, the sustainable development of these factors also shares a correlation with the FDI inflows growth. This presen- tation is a supplement to the previous research results, presented in the seminar “Current situation of the development of the FDI sector in implementing the sustainable development strategy in Vietnam” in 2018 exploring other angles in which FDI is connected to the national sustainable development. Finally, within the scope of the paper, several recommendations and solutions helped to optimally exploit FDI intakes and to increase the efficiency of sustainable development in Vietnam in a more comprehensive manner in the future are introduced and presented. Keywords: FDI, GDP, sustainable development, VAR, CO2. Tóm tắt Tham luận kiểm tra mối quan hệ nhân quả giữa dòng vốn FDI và hiệu quả phát triển bền vững ở Việt Nam thông qua mô hình thực nghiệm Vector tự hồi quy (VAR). Bài viết cung cấp các khái niệm cơ bản về phát triển bền vững, giải thích mối quan hệ nhân quả giữa dòng vốn FDI đến các yếu tố kinh tế - xã hội – môi trường ở Việt Nam cũng như đưa ra đánh giá tổng quan về hiệu quả phát triển bền vững hiện nay. Với các biến số đại diện cho hiệu quả phát triển bền vững trong giai đoạn 1990 – 2018, kết quả nghiên cứu cho thấy sự tăng trưởng của dòng vốn FDI có ảnh hưởng đến chỉ số sức khỏe môi trường, đời sống xã hội và nền tảng kinh tế. Mặt khác, việc phát triển bền vững các yếu tố này cũng có mối quan hệ tương quan đến lưu lượng dòng vốn FDI. Tham luận này là tham luận bổ sung cho kết quả nghiên cứu trước đó, đã được trình bày trong hội thảo “Thực trạng phát triển khu vực FDI trong thực hiện chiến lược phát triển bền vững ở Việt Nam”năm 2018, vì vậy, tham luận cũng trình bày một số kiến nghị và giải pháp để khai thác tối ưu dòng vốn FDI, làm gia tăng hiệu quả phát triển bền vững ở Việt Nam một cách toàn diện hơn trong tương lai. Từ khóa: FDI, GDP, phát triển bền vững, VAR, CO2... 493
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 1. Introduction Foreign direct investment (FDI) has been known as the appealed capital resources to both developed and developing countries during their route to promote those national economic growth on one side and on the other side indirectly involving in the socio-economic development per- formance such as human capital growth or the technological transfers. As such, FDI has presently been considered as a significant investment source for a country’s development and even for its sustainable development, introduced (WCED, 1987) as constant development that satisfies the present demand without compromising the capability of future generations to meet their own needs and recently is one of the sought-after goals over the world. In which, the three main di- mensions of the sustainable development including economic, environmental and social factors are crucially contribution to the promotion of sustainable development both at national and in- ternational level. Although the prevailing point that FDI has been stood at all times as a beneficial investment instrument for the sustainable development in developing countries, the conventional reliance on nonrenewable natural-resource consumption and extraction of FDI flows, especially in mineral, fuel production or manufacturing industries resulted the intense debate on its envi- ronmental consequences which is considered as negative impact on one of three main require- ments of the sustainability of growth. As the promoting factor for economic growth, FDI may have an influence on the level of greenhouse gas emissions, one of the criteria of environmental requirement to achieve the sus- tainable development. Consequently, developing nations and their companies tend to lower their environmental standard to attract FDI flows as well set the priority to promote their economy and business to the detriment of energy rationalization (Mabey & McNally, 1999) – as the latter reduces costs rather than generating turnover. Despite of various results of studies conducted by other researchers showed mixed results, yet taking into account the fact that in some developing countries significant portion of FDI flows into the industries, which have vital impact on the level of greenhouse gas emissions, Vietnam as particular. Besides the gross positive impact of the flow of external investment on the economic performance, FDI appears to significantly contribute to the depletion of natural resources (i.e. water pollution, environmental degradation and biodiversity reduction), which are seriously increasing over the time. In fact, according to World Bank’s the forecast, due to the impacts of environmental pollution, Vietnam’s GDP will be fell 2.5% each year. As a result, the social welfare and social costs for recovery process from the negative envi- ronmental consequences apparently incurred. Although there are a various of studies in investi- gating the connection between FDI and sustainable factors in academic world, the differentiation in identifying the sustainable development as well as selection of appropriate elements and re- searched perspective (i.e. Corporation level, National extent or International magnitude). Ac- cordingly, at each different perspective, the correlation relationship between the FDI and country’s sustainable development has a certain range of differences. Therefore, it is necessity to call for comprehensive studies regarding the impact of FDI on the economy, society and environment, especially in Vietnam to examine the all-inclusive effects of both positive and negative genre. Based on that, to propose the necessary solutions to promote positive effects and limit the negative from this investment flow to the socio-economic environment and ensure the process of further integration of the economy into attraction of foreign investment. With limited resources in the 494
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 ability to exploit the detail and specificity of the concept of sustainable development, and its con- tent cannot be encapsulated in the scope of this essay, this study is a first step in the comprehensive analysis of the role of FDI for the Vietnamese sustainable development strategy by employing the VAR model and wide range of causality test. 2. Literature Reviews 2.1. The concepts of Sustainable Development The main theoretical foundation of the paper is based on the notion of a balance between the three fundamental elements of development that were formulated and published in the 2002 Earth Summit on Environment and Development in Johannesburg (South Africa) with the concept of “Sustainable development is a development process that combines closely, rationally and har- moniously between three aspects of development, including: economic development, social de- velopment, and environmental protection”. Specifically: iEconomic development, in this context, is the concept of fast, safe, constant, accom- plished and prolonged economic growth through the efficient allocation of resources and building a complete and balanced economic structure in accordance with market rules, ensuring prosperity for all participants and contributing to the implementation of community responsibility while eliminating the negative impact on the ecosystem,. A sustainable economy must satisfy 3 factors: high and constant GDP growth rate, income per capital; appropriate GDP structure and effective economic growth. iSocial development is a factor to ensure social justice, eliminate the inequalities in in- come distribution, gender, geography, and achieve efficiency in resource allocation for health, education and cultural activities. iEnvironmentally sustainable development is to minimize the negative impacts of eco- nomic activities on the environment and natural conditions, ensure the rational exploitation and use of natural resources does not exceed the threshold of the ecosystem, protect biodiversity and improve living quality. The concept of the sustainable development involved many levels that can be mentioned such as the organization and private enterprise level (Veselovská, 2017), municipal level and local community (Smiciklas et al., 2017), level of region - multinational coalition (Trica et al., 2019), global strategic level (Olawumi et al., 2018), etc. At each extent, the harmonious goals and orientations will differ from one to another. Therefore, this article only focuses on analyzing the link between FDI inflows and a number of factors representing the main criteria of sustainable development at the national level. Accordingly, in order to achieve social sustainability, countries are required to carry out the reserved and stabilized allocation of four types of capital including natural capital, productive capital, human capital and social capital in most of the stages. (Shi et al., 2019). Strong sustainability growth depends on the irreplaceable role of natural capital in the development process as well as the reasonableness of capital structure so that the ecological threshold was not exceeded (Liobikiene, 2019) and economic development was not outweighed the natural restrain (Wu, 2013). According to this directive, the paper selected the percentage of renewable energy consumption over the total consumed energy to illustrate the natural capital and the finitude ecological threshold. 495
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 2.2. Literature Reviews Although the connection between Foreign Direct Investment (FDI) and the sustainable de- velopment, according to several studies conducted by theorical researchers showed mixed results, it can be concluded through various of empirical studies to some extent. Accordingly, most studies focus on studying the instinct response of FDI inflows growth to the economic development (Behname, 2012), human capital efficiency (Majeed & Admad, 2008) or the environmental sus- tainability (Klarin, 2018). Some aspects of employees’ wages and working conditions in some developing countries have also been assessed to figure out that the effectiveness of social respon- sibility shared immediate relation with the influence of FDI inflows (Brown, 2004). In addition, based on Nyankweli (2012), there was an indirect correlation between FDI growth and poverty reduction efficiency in African countries, and most of other researches showed positive comments on the ramifications of FDI on the sustainable development. In fact, the assessment performed in the articles on environmental pollution (Perez et al., 2011), displayed that the escalation of carbon dioxin (CO2) emissions was related to the growth of FDI inflows at the first hand. Obviously, the fluctuation in the flow of Foreign Direct Investment has put a certain impact on the effective- ness of sustainable development, yet depended on the perception in defining the standard meas- urement for the sustainability and the development direction of the country (Shi et al., 2019) as well as the legitimate system and international cooperation mechanism (Franc, 2015) at single point of time when this cause-effect relationship was considered as positive or negative trend. Consequently, some countries that aim for outstanding economic growth, and accept trade-offs between the natural environment and man-made, tend to preponderate the economic category such as increasing average income over the social responsibility through optimally exploiting re- sources for the profit’s sake rather than protecting the environment, and realizing social equality (Fang-Mei Tai, Shu-Hao Chuang, 2014). Therefore, this presentation employed the below six factors representing three economic - social - environmental aspects as the fundamental factor during the investigation of the correlation between these specific sustainable indicators and the flow of FDI: iSocial factors involve national prevalence of educational attainment, based on adult data (Uddin et al., 2015) and per capita income (Gupta, 2002); iEconomic group consists with national annual inflation rate (Gupta, 2002) and GDP of country at current prices (Ferrer Zermeño, 2015); and iThe environmental factors illustrated in the model is the percentage of the renewable energy consumption over the total consumed energy (Khandker, 2018) and the level of carbon dioxin (CO2) generated and released into the environment by human being (Perez et al., 2011). 3. Methodologies and Data The study focuses on assessing the impact of FDI intakes on the effectiveness of sustainable development at the national level, therefore, the researched data including the secondary data on GDP, the proportion of renewable energy over the total energy consumption, inflation rate, and average income collected from the annual socio-economic reports of the General Statistics Office for the period from 1990 to 2018. Due to the lack of national data, some related figures such as Vietnamese literature rate (for over 15 years old), carbon dioxin (CO2) emissions, etc. were ex- 496
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 tracted from the CIA Source Book, CEIC database, Statista and World Bank. In order to explore the association between Vietnamese economic growth and FDI, a simple regression analysis is also utilized. Databases are all represented in the appropriate units. To assess the FDI inflows impact on the GDP and its growth, Ordinary Least Squares (OLS) minimum squares method is employed to estimate the coefficient of explanatory variable by men- tioned endogenous growth production function. Accordingly, the value added of GDP is deter- mined to consider the effect of FDI inflows on national growth. The research is also based on Cobb - Douglas production function (Cobb and Douglas, 1928) and the theory of Musgrave and Rostow (Aladejare, 2013) that allows the model to describe as follows: Y = f(A, K, L, H…) with Y is the change of capital accumulation, A, K, L, etc. are indicators of labor, capital, technology, etc. which the necessities for manufacturing. According to the aggregate production function of Robert Solow, the value of GDP can be used as output of the economy (Whelan, 2005). Therefore, in this paper, the value of GDP will represent the change of capital accumula- tion, the percentage change in the FDI flow the explanatory variable involved in the model. At the same time, to smooth out the data set in time series and address the objective to call for a dis- cussion regarding potential cause-effect between the two basic elements; the production function will be transformed as follows: GDP = f (FDI) a logGDP = β0 + β1 FDI+ ε While GDP is the national gross domestic product by year conversed at the current prices, FDI is the flow of foreign capital invested in Vietnam in the period 1990 - 2018, ε is the error value. On the other hand, the effectiveness of FDI inflows may include literacy rate (over 15 years old) and specific socio-economic - environmental variables. Accordingly, the model to determine the efficiency of FDI inflows is indirectly illustrated through the evaluation of the effectiveness of environmental - economic - social factors as below formula: FDI inflow = (t, INCt, INFt, GDPt, LRt, REt) (2) with LR (literacy rate) is the national literacy rate, which measures literacy among persons aged 15 to 24 years (UNESCO), based on adult data; INC is the income per capita, INF is the in- flation rate, RE is the portion of renewable energy consumption out of total consumed energy and CO represents the amount of CO2 generated and released into the environment by humans. Assuming a linear relationship between FDI and independent variables, equation (2) can be written as follows: FDI inflow = α0 + α1t + α2INCt + α3INFt + α4GDPt + α5LRt + α6REt + μ (3) From the theoretical framework presented, the expected hypothesis is: α1> 0, α2> 0, and α3
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 stationary, a formal method to test for stationarity of a series, known as Augmented Dickey Fuller Unit Root test is then utilized to verify whether all variables are found to be stationary and the presence of unit roots for each variable before undertaking the model. Next, the mentioned model is formulated to test for a causal relation with Granger Causality Test. According to Komolafe (1996), two variables are considered to be consolidated if there is a long-term relationship between dependent and independent variables. This unity requires eco- nomic fluctuations to occur only in the short term and equilibrium is long-term. This ensures the correctness of the basis of the effect of capita on growth (Mallick, Das and Pradhan, 2016). If there is a difference, it is indispensable to show the error Auto regression Vector model estimation (VAR) to evaluate the causal impact of the explanatory variables in a unified system. Due to the requirement from VAR model, the data would be first order differ to ensure the stationarity. 4. Results Table 1 presents the regression results of the variable of FDI inflows to Vietnam with the GDP growth value in billion USD during the period 1990 – 2018. Table 1. Growth model regression results Variables Coefficient Standard Error t-Statistic Prob βo 1.6616 0.0312 53.1893 0.0000 FDI 0.0458 0.0046 9.9299 0.0000 R-Sq 0.7850 R-Sq (Adj) 0.7771 VIF 1.0000 F-Stat 98.6043 Prob (F-Stat) 0.0000 DW d = 0.2292 Breusch – Godfrey Serial Correlation LM Test ρvalue = 0.0000 Heteroskedasticity Test : Breusch – Pagan- Godfrey ρvalue = 0.0041 With the significance level of 5%, from the test values in Table 1, although there is no mul- ticollinearity (VIF
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 2. Result of Unit Root Tests Using Augmented Dickey Fuller (ADF) Variables ADF Test Statistic ADF Difference Remarks t-stat 5% level t-stat 5% level LR -1.2367 -3.6892 -5.3959 -2.9762 I(1) GDP 2.3793 -2.9762 -4.5339 -2.9810 I(2) INC 4.7308 -2.9718 - - - INF -2.8243 -2.9718 -6.8549 -2.9810 I(1) FDI 0.9942 -2.9718 -4.1326 -2.9763 I(1) RE -2.5641 -2.9718 -6.8363 -2.9810 I(2) CO 3.6436 -2.9718 - - - * MacKinnon (1996) one-sided p-values. Cointegration test by Johansen Cointergation Test is based on Trace Test Standard gave re- sults in Table 3 with 5 existing merging vectors. In general, the cointegration test shows that the amount of FDI capital and its representative factors for sustainable development such as economic growth through GDP, inflation or environmental and social factors (through literacy rate, average income, clean energy consumed and emissions) are co-linked, meaning that equilibrium condi- tions can keep them proportional over the long term at the significance level 0.05. Table 3. Johansen Cointegration Tests Results Hypothesized Eigenvalue Trace Statistic 0.05 Prob.** No. of CE(s) Critical Value None * 0.969815 255.6840 125.6154 0.0000 At most 1 * 0.907118 161.1725 95.75366 0.0000 At most 2 * 0.747788 97.00889 69.81889 0.0001 At most 3 * 0.666872 59.81676 47.85613 0.0026 At most 4 * 0.537537 30.13757 29.79707 0.0457 Trace test indicates 5 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values On a visual basis, the equation between FDI flows and the variables representing sustain- able development can be estimated through the Vector autoregression (VAR) technique to identify the causal effects of each independent variable and investigate the ability to increase capital and vice versa. The VAR model is needed for mechanism that predicts random disturbances in a con- tinuous time series with the exogenous assumption provided by the delay of the endogenous vari- ables. To implement the VAR technique, the data series of the independent variables must be a stop string, therefore the data of the variables will be differentially conducted later on. The optimal 499
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 delay according to the compatible VAR model is calculated as p = 2. Accordingly, the estimation results of the VAR model with the optimal delay of 2 are illustrated in Table 4. The VAR model takes the form: D(FDI) = C(46)*GDP2(-1) + C(47)*GDP2(-2) + C(48)*RE2(-1) + C(49)*RE2(-2) + C(50)*D(LR(-1)) + C(51)*D(LR(-2)) + C(52)*D(FDI(-1)) + C(53)*D(FDI(-2)) + C(54)*INC(- 1) + C(55)*INC(-2) + C(56)*D(INF(-1)) + C(57)*D(INF(-2)) + C(58)*CO(-1) + C(59)*CO(-2) + C(60) Table 4. Vector Autoregression Estimates GDP2 RE2 D(LR) D(FDI) INC D(INF) CO GDP2(-1) -0.002239 -0.150513 0.012174 0.787970 35.38805 2.072893 0.337254 (0.39949) (0.83583) (0.07009) (0.67145) (16.4198) (2.54432) (5.26266) [-0.18008] [ 0.17370] [ 1.17353] [ 2.15521] [ 0.81471] [ 0.06408] GDP2(-2) -0.283559 0.708098 0.012112 0.182934 -9.069726 -2.189656 -0.260755 (0.29452) (0.61620) (0.05167) (0.49502) (12.1052) (1.87576) (3.87981) [-0.96279] [ 1.14914] [ 0.23442] [ 0.36955] [-0.74924] [-1.16734] [-0.06721] RE2(-1) -0.013068 -0.220826 -0.008085 0.174534 6.568025 0.425530 1.010280 (0.13053) (0.27309) (0.02290) (0.21938) (5.36481) (0.83130) (1.71946) [-0.10012] [-0.80862] [-0.35307] [ 0.79557] [ 1.22428] [ 0.51188] [ 0.58756] RE2(-2) -0.058209 -0.164294 -0.029195 0.330595 8.962258 -0.362729 -0.550858 (0.11805) (0.24698) (0.02071) (0.19841) (4.85198) (0.75184) (1.55510) [-0.49309] [-0.66520] [-1.40973] [ 1.66621] [ 1.84713] [-0.48246] [-0.35423] D(LR(-1)) 0.124014 -3.103619 -0.410815 1.035277 114.9644 9.919123 -22.37115 (1.50216) (3.14284) (0.26353) (2.52476) (61.7410) (9.56706) (19.7884) [ 0.08256] [-0.98752] [-1.55888] [ 0.41005] [ 1.86204] [ 1.03680] [-1.13052] D(LR(-2)) -0.139812 7.712240 0.213164 -2.072442 103.6451 6.040560 7.247086 (1.67435) (3.50310) (0.29374) (2.81417) (68.8183) (10.6637) (22.0568) [-0.08350] [ 2.20155] [ 0.72569] [-0.73643] [ 1.50607] [ 0.56646] [ 0.32857] D(FDI(-1)) -0.185819 -0.345501 -0.005028 0.227991 10.45719 2.403648 0.699897 (0.18182) (0.38040) (0.03190) (0.30559) (7.47300) (1.15798) (2.39515) [-1.02201] [-0.90825] [-0.15762] [ 0.74606] [ 1.39933] [ 2.07573] [ 0.29221] D(FDI(-2)) 0.100856 0.268305 0.018924 0.150809 4.428821 -0.812471 4.423993 (0.20862) (0.43647) (0.03660) (0.35063) (8.57440) (1.32864) (2.74816) [ 0.48346] [ 0.61472] [ 0.51708] [ 0.43011] [ 0.51652] [-0.61150] [ 1.60980] INC(-1) -0.002299 -0.012382 0.000134 -0.002577 1.136048 -0.068610 -0.094069 (0.00427) (0.00894) (0.00075) (0.00718) (0.17566) (0.02722) (0.05630) [-0.53798] [-1.38472] [ 0.17822] [-0.35883] [ 6.46748] [-2.52071] [-1.67088] INC(-2) 0.002571 0.012574 -0.000715 0.001264 -0.202135 0.058316 0.093271 (0.00437) (0.00915) (0.00077) (0.00735) (0.17973) (0.02785) (0.05760) [ 0.58800] [ 1.37435] [-0.93205] [ 0.17197] [-1.12468] [ 2.09398] [ 1.61918] 500
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 D(INF(-1)) -0.016130 0.069266 0.001498 -0.037260 0.269152 -0.223364 -0.051506 (0.02373) (0.04965) (0.00416) (0.03989) (0.97542) (0.15115) (0.31263) [-0.67967] [ 1.39502] [ 0.35984] [-0.93412] [ 0.27594] [-1.47781] [-0.16475] D(INF(-2)) -0.002939 -0.059846 -0.002150 -0.004755 -2.068337 -0.184777 -0.030254 (0.01957) (0.04095) (0.00343) (0.03290) (0.80446) (0.12465) (0.25784) [-0.15016] [-1.46146] [-0.62611] [-0.14453] [-2.57109] [-1.48231] [-0.11734] CO(-1) -0.001767 0.061796 0.007761 -0.043224 -1.386047 -0.059026 1.034455 (0.02547) (0.05330) (0.00447) (0.04281) (1.04700) (0.16224) (0.33557) [-0.06937] [ 1.15949] [ 1.73660] [-1.00955] [-1.32383] [-0.36383] [ 3.08268] CO(-2) 0.007571 -0.049954 -0.002544 0.065278 3.155579 0.258654 0.119143 (0.03182) (0.06657) (0.00558) (0.05348) (1.30771) (0.20264) (0.41913) [ 0.23796] [-0.75043] [-0.45570] [ 1.22069] [ 2.41306] [ 1.27645] [ 0.28426] C -0.052496 -1.799079 0.231692 -0.070970 -90.28289 -8.970082 5.287529 (0.77012) (1.61126) (0.13511) (1.29438) (31.6531) (4.90480) (10.1451) [-0.06817] [-1.11657] [ 1.71488] [-0.05483] [-2.85226] [-1.82884] [ 0.52119] R-squared 0.460012 0.644300 0.676144 0.468672 0.999138 0.716083 0.988309 Adj. R2 -0.295971 0.146320 0.222745 -0.275188 0.997932 0.318599 0.971941 Sum sq. resids 7.102784 31.09155 0.218608 20.06492 11998.99 288.1074 1232.597 S.E. equation 0.842780 1.763280 0.147854 1.416507 34.63955 5.367564 11.10224 F-statistic 0.608495 1.293826 1.491278 0.630054 828.0534 1.801540 60.38074 Log likelihood -19.74360 -38.19922 23.76842 -32.72468 -112.6447 -66.02918 -84.19850 Akaike AIC 2.779488 4.255937 -0.701473 3.817974 10.21158 6.482335 7.935880 Schwarz SC 3.510814 4.987263 0.029852 4.549300 10.94290 7.213660 8.667205 Mean dependent 0.384000 0.263571 0.255600 0.582800 1056.302 -0.193600 111.3975 S.D. dependent 0.740315 1.908418 0.167707 1.254387 761.6354 6.502433 66.27837 Although the relative square R (R2) relevance shows that the model is relevant, the coeffi- cients of the independent variables are independent (Prob> 0.05). Therefore, the true results of the VAR model are not statistically significant. This is also shown through the results of stability test (Figure 1) when there is at least one stems outside the unit circle leading to the VAR model unsatisfied the stable conditions. In spite of the instability of the VAR model, the aim of the study is still fulfilled to inves- tigate the interrelationship between FDI inflows and the variables representing sustainable de- velopment. Therefore, the tests for decomposition of variance and Granger causality tests is still performed. Decomposition of variance together with stability test are two methods of analyzing the structure of the VAR model. Accordingly, the variance decomposition test scrutinizes the fluctuation of a variable affected by the shock of that variable and the shock of other endogenous variables (Campbell, 1991). This method provides insight view on the importance of random 501
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 errors to variables in the VAR model, thereby showing the tendency of interac- tion between variables (Pedro, 2001). This study uses the variance decomposition method as an approach of the VAR model to evaluate the impact on the variation of D(FDI) (1st difference of FDI) by shock of itself and other macro variables, including: GDP2 (GDP’s 2nd difference), D(LR) (1st difference of LR), D(INF) (1st difference of INF), INC, RE2 (2nd difference of RE) and CO. Figure 1. AR Root Graph Table 5. Variance Decomposition of D(FDI) Period S.E. GDP2 RE2 D(LR) D(FDI) INC D(INF) CO 1 0.842780 7.306767 5.854577 1.174165 85.66449 0.000000 0.000000 0.000000 2 0.898224 9.927857 8.106474 1.086534 69.55347 7.320304 2.115846 1.889515 3 0.977105 9.620329 12.68852 3.911337 61.18657 8.074283 1.899218 2.619744 4 1.009994 9.856032 13.02918 3.860839 60.20356 7.930043 2.121529 2.998821 5 1.030109 9.692118 13.82255 4.254425 59.09954 7.716627 2.067376 3.347364 6 1.044755 9.973519 13.76877 5.978777 57.35129 7.576512 2.111783 3.239348 7 1.053204 9.748724 13.54555 5.815412 57.37711 7.952653 2.111470 3.449081 8 1.060925 9.861604 14.09260 5.922648 56.77886 7.849802 2.082776 3.411718 9 1.068722 9.849027 14.10340 5.937550 56.72564 7.893217 2.084121 3.407042 10 1.071936 9.827984 14.06897 6.103525 56.58726 7.927090 2.080369 3.404798 Cholesky Ordering: GDP2 RE2 D(LR) D(FDI) INC D(INF) CO 502
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 6. Variance Decomposition of variables to FDI Period S.E. GDP2 RE2 D(LR) D(INF) INC D(FDI) CO 1 0.842780 0.000000 0.000000 0.000000 1.257447 2.166081 85.66449 0.052914 2 0.898224 8.677598 5.776977 0.134501 14.13502 15.61709 69.55347 0.035929 3 0.977105 7.338577 7.043256 2.134581 25.54019 11.47031 61.18657 4.276880 4 1.009994 11.68388 6.877045 2.121975 21.96318 5.575796 60.20356 8.918997 5 1.030109 11.34347 12.28104 3.031615 24.01007 5.233619 59.09954 12.11727 6 1.044755 12.97738 11.93464 4.258334 23.63543 6.921864 57.35129 13.29907 7 1.053204 13.01102 12.65697 4.755726 23.73548 9.194279 57.37711 13.57990 8 1.060925 12.87798 12.52830 4.876448 23.33785 10.96904 56.77886 13.92204 9 1.068722 13.41690 12.52840 4.838804 23.46728 12.02592 56.72564 14.33653 10 1.071936 13.46452 12.65962 4.913948 23.36635 12.87156 56.58726 14.43476 Cholesky Ordering: GDP2 ENC2 D(LR) D(FDI) INC D(INF) CO The flow of FDI in year 1 approximately accounts for 85.664% of its current volatility, but the volatility of inflation and CO2 emissions at this time has no causal relationship to FDI growth. However, the growth of FDI inflows in the past two years actually increased 15.617% per capita income and 0.0359% of CO2 emissions at present. In general, the increase in FDI has a certain impact on all three aspects of sustainable development, of which economic and environmental factors are the most crystal clear, while the social aspect has not really in the influential scope of foreign investment attraction activities. Especially, effectiveness in the usage of FDI capital re- veals both positive and negative effects on the local environment by increasing CO2 consumption but also promoting the volume of research and replacement with other renewable energy. At the same time, according to Table 3, the cointegration between the variables and the cointegration relationship has eliminated the ineffective regression phenomenon, confirming there is a Granger causality between them. Table 7. Results of the Granger causality test Hypothesis Ho df Chi - sd Prob. CO has a causal relationship with FDI 1 5.0221 0.0250 FDI has a causal relationship with CO 1.6671 0.1966 RE has a causal relationship with FDI 7 30.419 0.0001 503
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 FDI has a causal relationship with RE 7.6421 0.3652 GDP has a causal relationship with FDI 1 6.3344 0.0118 FDI has a causal relationship with GDP 0.2909 0.5986 INC has a causal relationship with FDI 8 208.73 0.0000 FDI has a causal relationship with INC 17.715 0.0235 INF has a causal relationship with FDI 1 0.2544 0.6140 FDI has a causal relationship with INF 0.3694 0.5433 LR has a causal relationship with FDI 1 2.5096 0.1132 FDI has a causal relationship with LR 0.0443 0.8332 The results of the Granger causality test are as follows: There is a one-way causal relation- ship from CO2 emissions, GDP value to FDI inflows at the significant level of 5% but there is no opposite side-effect in the short term. In the long run, the percentage of renewable energy consumption over total consumed energy illustrated a one-way cause-effect relationship with FDI. However, the income per capita and FDI are the only pair which shares a two-way cause- effect connection. The Granger causality test, unfortunately, is unable to comprehensively clarify the impacts of the variables on FDI inflows and vice versa because this test only determines the correlation between the variables yet has assessed the degree of the impact. As a result, the study continues to examine the repulsion function of FDI inflows to the variables representing sustainable devel- opment and vice versa to shed a light on the impact level. Propulsion analysis showed that the response of one variable for another increased by one standard distribution unit (Han et al., 2000). Table 8. Impulse Response of variables Period GDP2 RE2 D(LR) D(FDI) INC D(INF) CO 1 0.000000 0.000000 0.000000 1.311050 5.098115 0.601897 -0.255386 2 -0.264597 -0.490182 -0.006991 0.274380 20.01757 2.682151 0.142841 3 0.007240 0.414329 0.030557 -0.211805 15.47238 -3.092994 4.117505 4 0.221635 -0.268692 0.009005 -0.173960 4.667654 0.572296 5.857544 5 0.034385 0.691803 0.024596 0.156940 24.77207 1.951783 6.152475 6 -0.145882 -0.124141 0.027603 0.098943 42.47073 -0.022450 4.705578 7 -0.051705 -0.277294 -0.019576 0.233836 59.45377 -0.660088 3.617805 8 0.025025 -0.052484 0.010837 -0.090170 68.71407 0.146285 3.754521 9 0.091070 -0.076254 -0.007303 -0.054475 71.49600 -0.618058 4.117553 10 0.038343 0.124424 -0.010975 -0.018344 74.34425 -0.003182 4.198572 Cholesky Ordering: GDP2 ENC2 D(LR) D(FDI) INC D(INF) CO 504
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Figure 2. Impulse Response of FDI While Figure 2 visualizes the response of FDI to a change in a standard distribution unit of the respective variables, Table 8 depicts the responses of the variables representing sustainable development to a change in one distribution unit of the FDI inflow standard distribution. Similar to the results of the Granger causality test, the factors of CO2 emissions and the GDP value have a quite lucid impact on FDI inflows in the first periods. However, these retorts would gradually decrease over the time. In contrast, FDI inflows hardly increase GDP growth in the first year yet tend to drive per capita income to rise in the long run. In terms of the environment, whenever the foreign investment is grown, the amount of CO2 generated decreases in the early year. However, this trend would turn to steadily surge over the next following years, while the renewable energy consumption decreases in the long term. This proves that the escalate of FDI, unfortunately, has a negative effect on the local environment. It is also in line with the reality in Vietnam. Since 2006, Vietnam has implemented a comprehensive decentralization, promoting localities to ac- tively attract FDI for economic development, leading to the less stringent environmental regula- tions and incentives beyond the framework. The lack of mechanism of control and the 505
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 management weakness contributed to boost quantity of business rather than qualified ones. As such, most of them were with outdated technology (only 6% of enterprises with advanced tech- nology), mainly focused on the assembly line, did not comply with the regulations on environ- mental protection or indiscriminate exploitation and over-consuming of resources, causing serious harm to the ecosystem and biodiversity in Vietnam. In particular, the environmental pollution of the FDI sector had become alarming as FDI tends to move into industries at high risk of pollution (according to a 2015 survey by the Center for People and Nature. (Vietnam Union of Science and Technology Associations), there are 15 large-scale projects in Nam Dinh Province focusing on the textile and dyeing sector), or detecting illegal discharges by a series of FDI enterprises such as Vedan Company in Dong Nai Province, Miwon Company in Phu Tho Province, Tung Kuang in Hai Duong Province, Taiwan Fomosa in Ha Tinh Province, etc. The root cause of this situation is due to the lack of Vietnamese environmental regulations and the limitations of tech- nology provisions. Although Vietnamese legal framework based on the international standards, the appraisal and post-implementation review appeared to be sketchy and bureaucratic, allowing enterprises to deceive, employing outdated production lines, exploiting devastating resources, destroying ecosystems to maximize profits and being ready to violate again and again, causing serious social and environmental consequences. 5. Discussions and Recommendations The article employs the VAR tool together with the persistent data in the period 1990 - 2018 to explore whether the two-way cause-effect association between FDI inflows and the cri- teria representing the sustainable development at the national level in Vietnam. Granger test, Cointegration test, and causality tests are fully conducted to scrutinize the data and standardize the model. Main findings include: Unit root test by Augmented Dickey-Fuller method with 5% significance shows that most of the variables of the sustainable development and FDI inflows (except CO2 emissions and per capita income) are not stable at the 1st level, even GDP and the proportion of renewable energy consumption over the total consumed energy RE is not fixed at the 2nd difference. This result matches with most of the prior empirical studies in a range of developing countries such as Chile, Malaysia and Thailand (Rahaman, 2015). Cointegration test by Johansen Cointergation Test shows the amount of FDI capital and the criteria represented for the sustainable development such as economic growth (through GDP, inflation), environmental element and social factors (through ratio literacy, average income, clean energy consumption and emissions) are co-linked, meaning that conditions in equilibrium can keep them proportional over the long run at a significance level of 0.05. This relationship is dis- tintly analyzed in the results of decomposition of variance, Granger causality and the repulsion function. Accordingly, there is a one-way causal relationship from CO2 emissions, the value of GDP to the flow of foreign investment is significant at 5%, but there is no opposite in the short term. In the long run, the percent of renewable energy consumption over total consumed energy also shares a one-way cause-effect connection with FDI while the income per capita and FDI are the only pair which are confirmed to maintain a two-way causal relationship. From the above findings, although there is rare chance to build a strong VAR model for 506
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 the researched correlation, it can be concluded that FDI inflows has a certain impact on the effi- ciency of sustainable development in Vietnam. These effects are positive in the long term but the degree of them is relatively low. Therefore, it calls for the numerous econometric studies and the extensive implementation of proposed solutions in Vietnam policies such as: iMaintaining the appropriate filter for proposed foreign investment plans and sources, weighing them in the context of both economic benefits and environmental sustainability, as well as in terms of social security and national security; iStringently addressing the current environmental degradation, promoting the compre- hensive inspection and supervision activities for foreign-invested enterprises to actively prevent the relevant risks; iIndustriously orienting the businesses, especially the manufacturing and mineral enter- prises to transit to renewable energy sources from fossil fuels; and iImproving the efficiency of social responsibility implementation by a Governmental transparent plan on each inevitable level of FDI growth as well as steady per capita income, eras- ing the income gap among business areas. 6. Conclusions According to the research results, in general, even though it is rare chance to generalize the causal relationship between FDI inflows and the sustainable development, there is always a certain influence of FDI intake on economic growth and social security (average income, educa- tional attainment) as well as some environmental aspects and vice versa. These two dimensions of impact are both in positive and negative genre, especially in the long term. Therefore, it is a necessity to pay a level of consideration albeit implementing FDI attraction policies to ensure efficiency in investment management and resource allocation. In scope of this research, the VAR model is not stable enough with the data set in the period 1990 - 2018 and the selected variables are unable generalize all the criteria of sustainable devel- opment. Therefore, it is called for various of comprehensive and thorough studies to corroborate the ability to predict the impact of changes in FDI flows on economic - environmental and social factors in the future. REFERENCE 1. Arafatur Rahaman (2015). Effects of Foreign Direct Investment on GDP: Empirical Evidence from Developing Country. Advances in Economics and Business, Volume 3, No.12, pp. 587 - 592. 2. Ashley Wagner (1995). The failure of corporate social responsibility provisions within international trade agreements and export credit agencies as a solution. Boston University International Law Journal, Vol. 35 3. Carlos Encinas-Ferrer & Eddie Villegas-Zermeño (2015). Foreign direct investment and gross domestic product growth. Procedia Economics and Finance 24 (pp. 198 – 207). Amsterdam, Netherland: Elsevier 507
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