The role of investment to economic growth in Vietnam: A nexus between domestic investment and foreign direct investment

Nội dung text: The role of investment to economic growth in Vietnam: A nexus between domestic investment and foreign direct investment

1. THE ROLE OF INVESTMENT TO ECONOMIC GROWTH IN VIETNAM: A NEXUS BETWEEN DOMESTIC INVESTMENT AND FOREIGN DIRECT INVESTMENT Nguyen Tran Thai Ha1 – Ho Ngoc Thuy2 Abstract: This study examines the role of investment in Vietnam's short-term and long-term economic growth. The research method is based on the Granger test, Johansen cointegration, and Vector Error Correction Model (VECM), using the World Bank and General Statistics Office's economic data during the period 2000–2019. Empirical results show that domestic investment and foreign direct investment positively impact economic growth in the long-run. However, foreign direct investment has short-term crowding-out effects before exposing spillover effects on the economy. Meanwhile, domestic investment plays a key role in promoting economic growth in both the short-and long-term. Our research implies that investment activities should be focused, but foreign direct investment should only play a supporting role for capital and technology; meanwhile, the government is encouraged to provide policies to enhance domestic investment development. Keywords: Economic growth, domestic investment, foreign direct investment, VECM, Vietnam. 1. INTRODUCTION The role of investment is undeniable for economic growth. Indeed, developing countries are characterized by low characteristics and underdeveloped infrastructure, so an ongoing investment is always needed, but they do not have many choices of resources for investment. Moreover, the economic crises have pushed the nations' economies to decline, leading to the nation's economic restructuring. In this context, the accumulated investment, such as domestic investment (DI), foreign direct investment (FDI), plays a vital role in economic development (Solow, 1956). On the one hand, FDI not only promotes and creates growth but also acts as a vehicle for technology transfer and makes spillover effects from developed countries to host countries (Makki and Somwaru, 2004; Nguyen et al., 2020). Previouslly, Easterly et al. (1994) argued that technology transfer occured through four models: transfer of technology and new ideas; import of high technology; apply foreign technology and the qualifications of human resources. On the other hand, DI is also getting more and more attention due to its contribution to the structure of the economy in the countries. Firebaugh (1992) mentioned that DI held excellent potential for developing relationships within domestic industries. There is irrefutable evidence that domestic investment is one of the tools that help the economy grow faster and easier to maintain through productivity, capital generation, development progress, and exports (Adams, 2009; Omri and Kahouli, 2014). Thus, DI and FDI are believed to critical resources for growth of any economy (Szkorupová, 2015; Marcin, 2008). However, previous studies on FDI and its effects on economic growth were positively concluded. For example, Kim and Hwang (2000, cited in Ito and Krueger, 2000) studied the role of FDI in Korea's economic growth. They argued that steady inflows of foreign investment could help Korea weather the severe financial crisis of the 1990s, but they were concerned that increased FDI would lead to a control 1 Department of Business Administration, Asia University, Taiwan; Faculty of Finance and Accounting, Saigon University, Vietnam. Email: nguyen.tranthaiha@sgu.edu.vn. 2 Faculty of Finance and Accounting, Saigon University, Vietnam. Email: hnthuy@sgu.edu.vn. 222
2. of foreign entities to the domestic economy. Chan (2000, cited in Ito and Krueger, 2000) studied the relationship between FDI and economic growth in Taiwan. He focused on whether fluctuations in FDI could be used to predict fluctuations in economic growth. His conclusion was that on the one hand, foreign investors were attracted by GDP growth, and on the other hand, FDI contributed to that growth, not by increasing capital accumulation, but through the spillover of technology. By contrast, previous empirical studies showed that the effects that the impact of FDI on economies were different, depending on the host country's absorption to reap the benefits of FDI. On the one hand, FDI inflows pressures domestic firms to renew their technology and improve production efficiency. FDI projects also has a positive impact on improving the management capacity and qualifications of domestic workers, creating an effective channel of positive spillover effects (Marcin, 2008; Wang, 2010). On the other hand, the competition of FDI also limits the growth of domestic firms. Markusen and Venables (1999) argued that the entry of foreign firms reduced the profits of domestic firms, leading to a decline in their operations. Similarly, Aitken and Harrison (1999) argued that the increase in the average cost of domestic firms as a factor caused a negative spillover effect due to the presence of foreign firms. As a result, it could cause their market share to be significantly lost, forcing them to operate at a less efficient scale, thus increasing their average cost. Studies by Szkorupová (2015), Epstein and Braunstein (2002) showed that FDI could bring crowding–out effect on DI in the long–term and lead to the collapse of domestic firms. Because of the controversy surrounding the roles of FDI and DI in economic growth, this article reveals them in the economic context of Vietnam. To the best of our knowledge, this paper use the vector error correction model (VECM) to analyze their relationships and roles to Vietnamese economic growth. This is imperative because the integration and openness mechanism leading to the increase of foreign investments can influence on domestic investment and economic growth. Therefore, the importance and contemporary nature of this issue need to be explored. The study will contribute to the previous literature on two aspects: First, the study contributes empirical evidence on the effects of investment on economic growth. In addition, although many studies admit that FDI promotes capital flows and technology, which in turn can improve economic growth, the its impacts on domestic investment are still controversial. These clues lead to the second contribution of the study, in which the authors consider the effects of FDI on DI and their effects in the long run. 2. VIETNAMESE BACKGROUND Vietnam's economy has a significant development after the critical reforms in 1986 and the accession to the World Trade Organization (WTO) in 2007. During the 2000s, the Vietnamese economy grew significantly, achieving an average of 6.8% annually and 7.02% in 2019. Although the negative impact of the Asian financial crisis stalled the fast–growth of the Vietnam economy in 1998– 1999 and the global recession of 2018–2019, this country recovered in 2000 and has continued its next growth until now. It is also in the group of high–growth countries in the world and a popular destination for FDI inflows3. According to the General Statistics Office (GSO), Vietnam had more than 33,900 FDI projects with a total registered capital of 454 billion USD until 2019. Notably, the FDI growth rate often exceeds the GDP growth rate of Vietnam, while the contribution of FDI to GDP increased from 2% in 1992 to 12% in 2000, and got 25.8% in 2010. Indeed, FDI always fluctuated around 23% in the period 2011–2019. The total investment capital of DI and FDI and economic growth in Vietnam are shown in Figure 1. 3 See more at 223
3. The fluctuation in FDI inflow can be seen as closely related to the economic cycle in Vietnam. In the period 2001–2005, GDP increased by 7.5% per year, and the contribution of FDI capital in GDP was 14.6% per year. From 2008 to 2009, GDP was increased by 5.78% per year, and the contribution of FDI capital in GDP was 18.14% per year. The economic cycle with an upward trend in 2000–2007 coincided with high FDI inflows and low FDI inflows in economic downturned of 1997–1998 and 2008–2009. Observation, there might exist a correlation between FDI and economic growth. Meanwhile, DI from 2000–2014, its growth rate was higher than FDI, reaching 59.9% in 2001 and decreasing over the years, the lowest in 2008, about 33.9%. The proportion of DI per GDP achieved 35.7% and 33.3% in 2017 and 2018, while the proportion of FDI per GDP got an average annual of 23% from 2016 to 2018. In Figure 1, we can observe that FDI and DI might run two opposite trends. This put the question of whether such correlation may be the result of direct mutual or negative causality from FDI to DI in the long–term. Figure 1. Domestic investment, Foreign direct investment, and Economic growth 60 4000000 3500000 3000000 40 2500000 2000000 1500000 20 1000000 500000 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 GDP DI FDI Source: www.gso.go n Overall, Vietnam has seen remarkable economic development, along with even more incredible progress in foreign investment. For a small country that just suffered from a long civil war like Vietnam, FDI and DI are extremely important in its course from poverty reduction to modernization as well as global integration. Economic development is the key attraction of international investors who seek opportunities. Although there are still issues regarding the effectiveness of FDI implementation, the pivotal role of this source of funds in the economic development of Vietnam is undeniable. 3. LITERATURE REVIEW From a theoretical review, the neoclassical theory holds that the total output of an economy is closely related to the total number of labor, human capital, physical capital, and technological level. Therefore, FDI and DI contribute positively to economic growth in countries because it meets the needs of capital formation (Firebaugh, 1992; De Mello, 1997). Romer (1993) states that FDI and DI are very useful for the construction of physical infrastructure, thus, increases the absorption capacity of the FDI inflows. However, under the assumption of diminishing margins, economic growth not only slows 224
4. down when absorbed FDI increases but also stops when the economy reaches a steady state. Since capital flows are scarce in poor countries, neoclassical growth theory implies that the returns on capital flows in poor countries should be higher than in rich countries. As a result, FDI inflows from rich countries move to poor countries and stop when countries are equally rich. This neoclassical prediction implies that the effect of FDI is limited to its output growth performance in the short run, with no change in the long–run growth rate. They also suggest that there is a symbiotic relationship between FDI and DI (Szkorupová, 2015). Meanwhile, the endogenous growth theory shows that FDI's spillover effects (i.e., technology transfers and knowledge spillovers) can be transformed into productivity gains and, thus, economic growth goes up in the long run (Grossman and Helpman, 1991; Barro and Sala–I–Martin, 1997). Endogenous growth models, characterized by constant returns to a set of endogenous factors of production, regarding technological change as endogenous rather than exogenous. Technological change is, therefore, considered an important determinant of long–term economic growth (Romer, 1993). In this respect, FDI acts as a channel to diffuse new ideas and new technologies and apply high– tech products from advanced economies (Kumar and Pradhan, 2005). FDI not only diversifies the capital structure of recipients but also provides positive external factors such as technology and knowledge dissemination (Blomstrom and Kokko, 1998), thereby increasing total factor productivity (Nath, 2009). In addition, Markusen and Venables (1999) argue that there are two channels through which FDI affects the host economy: creating market competition through which multinational companies can replace local firms and create a cohesive effect through which multinationals can complement local firms. From the literature review, the effect of FDI on economic growth is discussed by Moosa (2002), which focuses on the host country's ability to absorb and reallocate in order to reap the benefits of foreign investments. The author finds that FDI can have a positive effect on growth if surplus resources can be absorbed or increased efficiency through reallocation. That means that if FDI succeeds in improving domestically reproducible sources' efficiency by reallocating them from low–productivity sectors to highly efficient ones. To an extent, FDI can also improve the efficiency of domestic firms through knowledge diffusion. These spillovers of knowledge originate from human capital being transferred through FDI. Coe et al. (1997) emphasizes the importance of R&D spillovers in growth models, and they see FDI as a channel of technology transfer. Therefore, the spillover of international R&D, caused by investment, is a key factor in the growth process. Nevertheless, even when FDI is essential for developing economies in particular, many economists argue that the lack of inadequate infrastructure and institutional structure still creates problems for countries, declining significantly the benefiting from FDI (Phung et al., 2019). Erhieyovwe and Onokero (2013) claim that FDI increases investment, reducing the gap between savings and investment. FDI is also the key to global economic integration, bringing financial stability, promoting economic growth, and improving social welfare (Borensztein et al., 1998; Nguyen et al., 2020). However, in the context of the asserted role of FDI, its effects on other economic entities are controversial. These FDI influences may concern the level of the human capital, the financial system, and the qualities of the domestic firms (Fu et al., 2011; Phung et al., 2019). On the one hand, Ang (2007) investigates the long–run relationship between private, public investment, and FDI in Malaysia during the period of 1965 – 2003. The results reveal that both public investment and FDI are complementary rather than competing with private domestic investment by implementing the multivariate cointegration method. Also Malaysia's case, Lean and Tan (2011) apply the VAR system and VECM to reveal the unclear effect of FDI on DI in Malaysia. As a result, they find that FDI is 225
5. complementary (crowd–in) rather than a substitute (crowd–out) DI in the host country of Malaysia. Later, concerning the crowding–in of FDI inflows on DI, Al–sadiq (2013) finds that a 1% point increase in FDI inflow increases by 9% point on DI in 91 developing countries period of 1970–2000. However, FDI activities' crowding–out effects on domestic investment are found, and it is likely to inhibit growth, increase unemployment, as well as further poor. Adams (2009) also finds that FDI has an initial negative effect on DI, although they are positive and significantly correlated with economic growth in Sub–Saharan Africa for the period 1990–2003. On the one hand, Multinational Firms (MFs) with their superior managerial, financial and technological advantages allow them to build monopoly or monopoly positions and crush the domestic competition. Thus, FDI may increase business difficulties with local firms (Markusen and Venables, 1999; Helpman et al., 2004). On the other hand, when MFs focus on exploiting natural resources, this activity creates an increase in the real exchange rate, leading to the loss of economic competitiveness, known as "Dutch disease" (Bresser–Pereira, 2008). From an institutional perspective, the documents show that MNFs cause corruption in the licensing and management of local markets in developing countries. Thus, FDI strengthens poor governance to narrow domestic investment (Kurul, 2017). Similarly, Pham (2016) claims that FDI has a tendency to crowd–out DI and make domestically–owned Vietnamese lose the market share. However, they can gain a positive effect when the FDI inflow into their industry is high. Therefore, inconsistencies in the role of FDI in economic growth and other economic entities require further studies, especially in the context of individual countries with economic characteristics. Therefore, this study continues to seek evidence for the role of FDI in Vietnam's economic context as a scientific answer. 4. METHODOLOGY AND DATA In this study, variables with a potential relationship to FDI inflows include economic growth and DI. The variables in this study include GDP growth rate (GDPCG), the proportion of FDI to GDP (FDIR), and the proportion of DI to GDP (DIR). These data of Vietnam is obtained from the World Bank database and General Statistics Office during the period 2000–2019. The econometric method is the vector error correction model (VECM). This model allows an error correction form which reflects in two relationships: long–term relationship and the short–run dynamics between the variables. This econometric approach tries to explain the impact of FDI inflows on other variables in cointegrated relations. This model is developed from model of Szkorupová (2015), and is presented by the following: An important parameter in the estimation of the VECM dynamic model is the coefficient of the error correction term ( ), which measures the speed of adjustment of economic growth to its equilibrium level. In VECM, GDPCG, FDI and DI are assumed as endogenous in order to establish the long and short–run relations (Adams, 2009; Andrei and Andrei, 2015; Szkorupová, 2015). is an unrestricted intercept in this model, is a matrix of coefficients measuring short–run effects, and are the matrix of coefficients measuring short–run effects of exogenous 226
6. variables,  presents the matrix of long–run coefficients,  is the restricted intercept in the cointegrating vector, is a matrix of coefficients measuring the speed of adjustment to equilibrium and is the error term. 5. EMPIRICAL RESULTS In order to examine the long–run relationship among variables, economic variables are required as stationary series. Granger and Newbold (1974) noted that the regression results from the VECM models using non–stationary variables would be spurious, following by ineffective T–test or F–test. Augmented Dickey–Fuller tests will exam the unit root testing procedure (Dickey and Fuller, 1979). If these variables are non–stationary at the root level, then they are tested at order first difference I (1), and at the second difference I (2). Enders (1995) claims that testing unit roots should include trend and intercept. The testing results are shown in Table 1. Table 1 presents that the critical values at 5% of series are lower than t–statistic values at the root level, but they are higher than the t–statistic value at the first difference. It means that the H0 hypothesis of the unit root is rejected by ADF tests at first difference. Thus, these series are non– stationary at I(0), but GDPCR and FDIR are stationary at I(1) with intercept and trend, meanwhile, DIR is stationary at I(1) with intercept. Table 2 will show the selected lag order. If the lag order is too long or too short, the estimates are ineffective, and the error terms will not gain white noise. The AIC (Akaike information criterion), SC (Schwart Bayesian criterion), and HQ (Hannan–Quinn Information Criterion) are considered to choose the lag order. The results indicate that the selected lag order is 2. Table 1. The results of the unit root test ADF statistic at the root level ADF statistic at 1st difference Variable Intercept Trend and Intercept Intercept Trend and Intercept –1.928027 –1.794971 –4.005840 –3.773159 GDPCG [–3.029970] [–3.673616] [–3.040391] [–3.710482] –1.703210 –2.568206 –3.474665 –3.359071* FDIR [–3.029970] [–3.690814] [–3.040391] [–3.690814] –1.872611 –2.116197 –4.753686 –3.140814 DIR [–3.065585] [–3.733200] [–3.040391] [–3.710482] Note: [ ] is critical values at 5% significant statistic. Source: Eviews Table 2. The results of the unit root test Lag LogL LR FPE AIC SC HQ 0 –55.34699 – 5.727325 7.418374 7.611521 7.428265 1 –43.34415 18.00425 2.146667 6.418019 6.804314 6.437801 2 –32.35022 13.74241* 0.946427* 5.543778 6.123219* 5.573450 3 –30.99714 1.353084 1.486037 5.874642 6.647231 5.914205 4 –23.78411 5.409769 1.258517 5.473014* 6.438750 5.522468* Note: * indicates lag order selected by the criterion. Source: Eview 227
7. Granger test in Table 3 is performed on stationary series, and selected lag order is based on the AIC, SC, and HQ standards. Table 3 shows that FDIR affects GDPCG and DIR, but there is not enough statistical evidence to confirm opposite relationships. Table 3. Pairwise Granger causality test Null Hypothesis: Obs F–Statistic Prob. DFDIR does not Granger Cause GDPCG 5.29455 0.0225 17 DGDPCG does not Granger Cause FDIR 0.35349 0.7093 DDIR does not Granger Cause GDPCG 1.96796 0.1823 17 DGDPCG does not Granger Cause DIR 0.43880 0.6548 DDIR does not Granger Cause FDIR 1.50109 0.2619 17 DFDIR does not Granger Cause DIR 4.96031 0.0269 Source: Eviews Next, if the cointegrated relations are found, there is a linear, stable, and long–run relationship among variables, such that the disequilibrium errors would tend to fluctuate around zero mean. Johansen's procedure is employed to test the cointegration. Table 4 shows the results of the cointegration test. Table 4. The results of the cointegration test Unrestricted Cointegration Rank Test (Trace) Hypothesized No. of CE(s) Eigenvalue Trace Statistic 1% Critical Value Prob. None* 0.856135 67.67073 49.36275 0.0000 At most 1 0.727452 34.70983 31.15385 0.0031 At most 2 0.523751 12.61083 16.55386 0.0482 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Max–Eigen Hypothesized No. of CE(s) Eigenvalue 1% Critical Value Prob. Statistic None* 0.856135 32.96091 30.83396 0.0048 At most 1 0.727452 22.09900 23.97534 0.0197 At most 2 0.523751 12.61083 16.55386 0.0482 Source: Eviews In Table 4, the results of the Trace test and Max–eigenvalue test indicate one cointegrating at the 1% level. Thus, there is a long–run equilibrium between the variables (GDPCG, FDIR, and DIR), matching with previous studies (Asghar et al., 2011; Nosheen, 2013; Amit et al., 2016; Khan et al., 2018). Table 5 shows the results of long–term and short–term relationships among variables. The diagnostic results show that the residual value is not correlation and heteroskedasticity. In cointegration equations, GDPCG, FDIR, and DIR have long–term corrections. Specifically, an increased domestic investment rate will improve economic growth by 0.136008 (2.53862) units in the long run. However, the FDIR shows no long–term statistical evidence of possible economic improvement. In the Error Correction equation, the adjustment speed to the equilibrium status of GDPCG is –0.275173 (– 2.93303). This shows that if the impacts of independent variables push the economic growth increase (decrease) in this year, it will decrease (increase) towards the equilibrium status of about 0.275173 in the next year. 228
8. Table 5. Vector Error Correction Estimates Cointegrating Equation Coint. Equation 1 GDPCG(–1) 1.000000 0.161831 FDIR(–1) [1.15126] 0.136008 DIR(–1) [2.53862] –10.46494 C [–5.32488] Error Correction D(GDPCG) D(FDIR) D(DIR) –0.275173 0.392940 –0.642428 CointEq1 [–2.93303] [ 0.99275] [–1.24269] –0.307280 0.447341 –0.994972 D(GDPCG(–1)) [–1.30316] [ 0.44968] [–0.76578] –0.211200 1.148702* 0.272299 D(GDPCG(–2)) [–1.35912] [ 1.75216] [ 0.31801] –0.217853 0.302688 –0.674094* D(FDIR(–1)) [–3.51466] [ 1.15749] [–1.97363] 0.137424* –0.236602 0.674524 D(FDIR(–2)) [ 1.92550] [–0.78578] [ 1.71516] 0.249546 0.055772 0.296628 D(DIR(–1)) [ 4.22894] [ 0.22403] [ 0.91226] 0.145673 –0.626026 0.432156 D(DIR(–2)) [ 2.21963] [–2.26098] [ 1.19500] 0.158311* 0.144740 –0.159537 C [ 1.77943] [ 0.38406] [–0.32491] R–squared 0.845155 0.496807 0.555768 Observations 17 17 17 F–statistic 9.096772 1.645515 2.085123 Diagnostic test Lag 1 Lag 2 Lag 3 F–test – Serial correlation test 0.35883 0.6183 0.7138 2 – Heteroskedasticity test 88.34826 Note: t–statistics in []; *,* *, are significant levels. Source: Eviews Source: Eviews However, in short–term equations, DIR is reported to have a positive influence on GDPCR at 0.249546 unit and 0.158311 unit with 1% and 5% significance in both first and second lag, respectively. Meanwhile, FDIR has a negative impact on GDPCG at the first lag and bring back a positive impact on GDPCG at the second lag. Moreover, FDIR brings a negative impact with – 0.674094 unit on DIR at the first lag. It means that a rise of the FDIR probably creates a crowding–out 229
9. effect before having a spillover effect in the economy. The significant increase of FDIR may limit the role of DIR in stimulating economic growth. It can be seen that DIR may exert a positive effect on GDPCR immediately after a period; FDIR only exhibited later lags of GDPCR. It illustrates that the spillover effect of FDIR probably occurred in later periods when domestic firms connect with MFs and receive foreign capital and technology. Notably, the empirical results confirm that DIR is the main contributor to economic growth with positive effects in all periods. By constrast, the economic growth mainly makes an attraction on FDIR, while there is no evidence it pushs DI significantly in short–tem. All reaction functions of entities in the economy also illustrate these phenomena, presented in Figure 2. In this Figure 2, there is a shock to the FDIR; the GDPCR has a decreasing response for 1.5 periods, then recovers and peaks after 4 periods, then adjust to equilibrium at the 5th period. For shocks in DIR, the response of GDPCR is a strong increase in the first two periods before returning to equilibrium in the following periods. Meanwhile, DIR's decreased response is noted for the shock in FDIR during the two first periods, then recovering in the next three periods before adjusting to equilibrium. This implied that GDPCR is positively sensitive to the change in DIR. Meanwhile, there is crowding and spillover effects of FDIR on DIR in the first and subsequent periods, respectively. Figure 2. Domestic investment, Foreign direct investment and Economic growth Response to Cholesky One S.D. (d.f. adjusted) Innovations Response of GDPCG to GDPCG Response of GDPCG to FDIR Response of GDPCG to DIR .3 .3 .3 .2 .2 .2 .1 .1 .1 .0 .0 .0 -.1 -.1 -.1 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Response of FDIR to GDPCG Response of FDIR to FDIR Response of FDIR to DIR 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Response of DIR to GDPCG Response of DIR to FDIR Response of DIR to DIR 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Source: Eviews In Table 6, 30.89102% of volatility in the current GDPCR is explained by itself in the 5th previous periods, and it tends to increase steadily in recent periods. In contrast, the FDIR and DIR of the 5th 230
10. previous periods explained 14.50379% and 54.60519% of the current volatility of GDPCR, respectively. This shows that economic growth is the result of investment activities in the past, especially domestic investment. In addition, the current volatility of DIR is also explained by the FDIR in past periods. It means that FDIR and DIR are closely related, in the direction of both crowding–out impacts as well as a spillover effect. Table 6 also shows that previous GDPCR mainly explains the volatility in the current FDIR than the volatility in the current DIR with 66.49860% and 12.80244%, respectively. Table 6. Variance Decomposition Variance Decomposition of GDPCG Period SE. GDPCG FDIR DIR 1 0.331795 100.0000 0.000000 0.000000 2 0.503030 45.13424 11.33268 43.53308 3 0.624256 34.43970 15.19129 50.36901 4 0.669647 31.07722 14.33601 54.58677 5 0.673300 30.89102 14.50379 54.60519 Variance Decomposition of FDIR 1 1.405481 53.54379 46.45621 0.000000 2 2.606875 59.56048 39.64794 0.791576 3 3.092921 63.54292 35.52364 0.933435 4 3.561871 66.86009 32.38564 0.754264 5 3.999924 66.49860 30.61967 2.881725 Variance Decomposition of DIR 1 1.831199 29.66669 5.004351 65.32896 2 2.505526 15.88861 3.457845 80.65354 3 2.954858 13.32861 3.856915 82.81447 4 3.574024 13.48538 7.269720 79.24489 5 3.691049 12.80244 7.532221 79.66534 Source: Eviews 5. CONCLUSION As the focus on foreign direct investment and domestic investment, this study looks at the foreign direct investment over periods to capture cumulative responses to the economic growth and its entities. Interestingly, using Vietnam's economic data from 2000 to 2019, the study finds that foreign direct investment overwhelms domestic investment in the early periods before revealing a spillover effect later. Over time, the increase in foreign direct investment inflows does not increase (decrease) domestic investment, and domestic investment tends to return to equilibrium. This study supports the view of Omri and Kahouli (2014) who argue that there is a relationship between foreign investment and economic growth, as well as domestic investment and economic growth, and there is a one–way causality relationship from foreign direct investment to domestic investment. Some of the recommendations are drawn from the findings of this study: (i) government should direct economic policy to continue to attract foreign direct investment; (ii) the government should enact 231
11. regulations to control foreign investment activities in order to minimize its negative effects and overwhelming effects on domestic investment; (iii) the government should provide sufficient resources and mechanisms to encourage domestic investment because of its essential role in the economy; (iv) policies should be focused on removing barriers that prevent local firms from establishing appropriate linkages, and entering the supply chains of multinational firms; (iv) it is recommended that the government continue to improve the nation's human resources, competitiveness, and technology access capacity to take advantage of the knowledge and technology spillover effects that come from foreign investments. This has a dual effect in attracting foreign investment and improving the quality of domestic investment (Alfaro et al., 2006) and enable host economies to maximize the benefits of received investment from foreigners. This study still has limitations, and due to the limitations of the data and the research methodology, this study may not be able to expose tripler–dimensional relationships significantly. Furthermore, human capital and technology's role remains beyond the scope of this study and maybe an interesting issue to focus on more in future studies intensely. REFERENCES 1. Adams, S. 2009. Foreign Direct investment, domestic investment, and economic growth in Sub– Saharan Africa. Journal of Policy Modeling, 31, 939–949. 2. Aitken, B. J. & Harrison, A. E. 1999. Do Domestic Firms Benefit from Direct Foreign Investment? Evidence from Venezuela. American Economic Review, 89, 605–618. 3. Al–sadiq, A. J. 2013. Outward Foreign Direct Investment and Domestic Investment: The Case of Developing Countries. IMF Working Papers, 13, 1. 4. Andrei, D. M. & Andrei, L. C. 2015. Vector Error Correction Model in Explaining the Association of Some Macroeconomic Variables in Romania. Procedia Economics and Finance, 22, 568–576. 5. Ang, J. B. 2007. Are saving and investment cointegrated? The case of Malaysia (1965–2003). Applied Economics, 39, 2167–2174. 6. Barro, R. & Sala–I–Martin, X. 1997. Technological Diffusion, Convergence, and Growth. Journal of Economic Growth, 2, 1–26. 7. Blomstrom, M. & Kokko, A. 1998. Multinational Corporations and Spillovers. Journal of Economic Surveys, 12, 247–277. 8. Borensztein, E., De Gregorio, J. & Lee, J. W. 1998. How Does Foreign Direct Investment Affect Economic Growth? Journal of International Economics, 45, 115–135. 9. Bresser–Pereira, L. C. 2008. The Dutch disease and its neutralization: a Ricardian approach. Revista de Economia Política, 28, 47–71. 10. Coe, D. T., Helpman, E. & Hoffmaister, A. W. 1997. North–South R&D Spillovers. The Economic Journal, 107, 134–149. 11. De Mello, L. R. 1997. Foreign Direct Investment in Developing Countries and Growth: A Selective Survey. Journal of Development Studies, 34, 1–34. 12. Dickey, D. A. & Fuller, W. A. 1979. Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74, 427–431. 13. Easterly, W., King, R., Levine, R. & Rebelo, S. 1994. Policy, Technology Adoption, and Growth. National Bureau of Economic Research. 14. Enders, W. 1995. Applied Econometric Time Series. Journal of the American Statistical Association, 90, 1135. 232
12. 15. Epstein, G. & Braunstein, E. 2002. Bargaining Power and Foreign Direct Investment in China: Can 1.3 Billion Consumers Tame the Multinationals?: Political Economy Research Institute, University of Massachusetts at Amherst. 16. Erhieyovwe, E. K. & Onokero, I. I. 2013. Corruption, Foreign Direct Investment and Its Impact on Exchange Rate of the Nigerian Economy. Mediterranean Journal of Social Sciences. 17. Firebaugh, G. 1992. Growth Effects of Foreign and Domestic Investment. American Journal of Sociology, 98, 105–130. 18. Fu, X., Pietrobelli, C. & Soete, L. 2011. The Role of Foreign Technology and Indigenous Innovation in the Emerging Economies: Technological Change and Catching–up. World Development, 39, 1204– 1212. 19. Granger, C. W. J. & Newbold, P. 1974. Spurious regressions in econometrics. Journal of Econometrics, 2, 111–120. 20. Grossman, G. M. & Helpman, E. 1991. Quality Ladders in the Theory of Growth. The Review of Economic Studies, 58, 43. 21. Helpman, E., Melitz, M. J. & Yeaple, S. R. 2004. Export Versus FDI with Heterogeneous Firms. American Economic Review, 94, 300–316. 22. Ito, T. & Krueger, A. O. 2000. The Role of Foreign Direct Investment in East Asian Economic Development, University of Chicago Press. 23. Kumar, N. & Pradhan, J. P. 2005. Foreign Direct Investment, Externalities and Economic Growth in Developing Countries: Some Empirical Explorations. Multinationals and Foreign Investment in Economic Development. Palgrave Macmillan UK. 24. Kurul, Z. 2017. Nonlinear relationship between institutional factors and FDI flows: Dynamic panel threshold analysis. International Review of Economics & Finance, 48, 148–160. 25. Lean, H. H. & Tan, B. W. 2011. Linkages between Foreign Direct Investment, Domestic Investment and Economic Growth in Malaysia. Journal of Economic Cooperation and Development, 32, 75–96. 26. Makki, S. S. & Somwaru, A. 2004. Impact of Foreign Direct Investment and Trade on Economic Growth: Evidence from Developing Countries. American Journal of Agricultural Economics, 86, 795–801. 27. Marcin, K. 2008. How does FDI inflow affect productivity of domestic firms? The role of horizontal and vertical spillovers, absorptive capacity and competition. The Journal of International Trade & Economic Development, 17, 155–173. 28. Markusen, J. R. & Venables, A. J. 1999. Foreign Direct Investment as a Catalyst for Industrial Development. European Economic Review, 43, 335–356. 29. Moosa, I. A. 2002. Theories of Foreign Direct Investment. Foreign Direct Investment. Palgrave Macmillan UK. 30. Nath, H. K. 2009. Trade, Foreign Direct Investment, and Growth: Evidence from Transition Economies. Comparative Economic Studies, 51, 20–50. 31. Nguyen, T. T. H., M. Johari, S., Thi Huyen Thuong, T., Thi Minh Phuong, N. & Hong Anh, L. T. 2020. The Impact of Innovation on Economic Growth: The Spillover Effect of Foreign Direct Investment. Humanities & Social Sciences Reviews, 8, 708–714. 32. Omri, A. & Kahouli, B. 2014. The nexus among foreign investment, domestic capital and economic growth: Empirical evidence from the MENA region. Research in Economics, 68, 257–263. 33. Pham, H. 2016. Foreign Direct Investment, Productivity and Crowding–Out: Dynamic Panel Evidence on Vietnamese Firms. Proceedings of the 5th Economic & Finance Conference, Miami. International Institute of Social and Economic Sciences. 233
13. 34. Phung, T. D., Van, V. T. T., Thuong, T. T. H. & Ha, N. T. T. 2019. Innovation and Economic Growth: The Contribution of Institutional Quality and Foreign Direct Investment. Asian Economic and Financial Review, 9, 1266–1278. 35. Romer, P. 1993. Idea Gaps and Object Gaps in Economic Development. Journal of Monetary Economics, 32, 543–573. 36. Solow, R. M. 1956. A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70, 65. 37. Szkorupová, Z. 2015. Relationship between Foreign Direct Investment and Domestic Investment in Selected Countries of Central and Eastern Europe. Procedia Economics and Finance, 23, 1017–1022. 38. Wang, M. 2010. Foreign direct investment and domestic investment in the host country: evidence from panel study. Applied Economics, 42, 3711–3721. 234