Inward foreign direct investment and trade openness in Vietnam: A nonlinear autoregressive distributed lag approach

Lee, Jen Yao; Hsiao, Ya-Chuan; Bui, Ngochien; Nguyen, Tien-Thinh Article Inward foreign direct investment and trade openness in Vietnam: A nonlinear autoregressive distributed lag approach Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Lee, Jen Yao; Hsiao, Ya-Chuan; Bui, Ngochien; Nguyen, Tien-Thinh (2021) : Inward foreign direct investment and trade openness in Vietnam: A nonlinear autoregressive distributed lag approach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 9, Iss. 3, pp. 1-18, https://doi.org/10.3390/economies9030120 This Version is available at: https://hdl.handle.net/10419/257278 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ economies Article Inward Foreign Direct Investment and Trade Openness in Vietnam: A Nonlinear Autoregressive Distributed Lag Approach Jen-Yao Lee , Ya-Chuan Hsiao, Ngochien Bui * and Tien-Thinh Nguyen   Citation: Lee, Jen-Yao, Ya-Chuan Hsiao, Ngochien Bui, and Tien-Thinh Nguyen. 2021. Inward Foreign Direct Investment and Trade Openness in Vietnam: A Nonlinear Autoregressive Distributed Lag Approach. Economies 9: 120. https://doi.org/10.3390/ economies9030120 Academic Editor: Sajid Anwar Received: 3 July 2021 Accepted: 18 August 2021 Published: 24 August 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan; [email protected] (J.-Y.L.); [email protected] (Y.-C.H.); [email protected] (T.-T.N.) *Correspondence: [email protected] Abstract: This study aims to examine the asymmetric relationship between trade openness and FDI (foreign direct investment) inflows to Vietnam by using NARDL (nonlinear autoregressive distributed lag) during the period from 1997 to 2019. Our findings show that the influence of FDI on trade openness is asymmetric in the short-run and long-run. But the influence of trade openness on FDI is symmetric in the short-run and asymmetric in the long run. Keywords: ARDL model; NARDL model; foreign direct investment; trade openness; tax rate; political stability 1. Introduction Since the Government of Vietnam implemented the “Doi Moi” policy in 1986 and the Foreign Direct Investment Law in 1987, Vietnam’s economic growth has achieved remarkable development, so FDI has always played an essential role in Vietnam’s economy. Although some remarkable events are influencing the world economy such as the US- China trade war and the COVID-19 pandemic, the growth of FDI inflows to Vietnam has always remained relatively stable compared with others globally. According to the World Bank, Vietnam is one of the most attractive countries for FDI in Asia. On 24 January 2021, UNCTAD’s latest Global Investment Trends Monitor announced that global foreign direct investment (FDI) flows fell by 42% worldwide by 2020 compared to the data in 2019, but that figure significantly increased by 12% in East Asia. Furthermore, Vietnam has become the spotlight for foreign investors (UNCTAD 2020). What makes Vietnam a destination for attracting foreign investors? The Vietnamese government has argued that tax rate reform and political stability are typical factors for attracting foreign investment. Some studies have realized that tax incentives preferences have completely positive effects on foreign investment in Vietnam and contributed to improving Vietnam’s comparative advantage in attracting FDI (Le 2004;Mai 2002;Yui 2006; Van 2019). In addition, political stability is important for foreign investors’ decision-making. Some studies stated that political stability is one of the dominant and necessary factors to create gravitation for foreign investors in Vietnam (Leung 2009;Ratnasingam and Ioras 2009;Delaunay and Torrisi 2012). Numerous studies have pointed out that FDI is crucial to the impact and development of Vietnam’s economy. As a channel to increase capital, foreign direct investment has mainly had a major and positive impact on Vietnam’s economic growth (Anwar and Nguyen 2011;Thu et al. 2010). Jenkins (2006) argued that the influence of foreign direct investment on direct employment in Viet Nam has been remarkably restrained due to the high labor efficiency and the low ratio of value-added tax to the output of much of this investment. The impact of FDI also influences the labor form, workers’ living standards in Vietnam Economies 2021,9, 120. https://doi.org/10.3390/economies9030120 https://www.mdpi.com/journal/economies Economies 2021,9, 120 2 of 18 (McLaren and Yoo 2017), and the influence of FDI on Infrastructure Bottlenecks in Vietnam (Tran 2009). In order to boost FDI inflows, Vietnam needs to strengthen coordination and improve more policies, expand markets, and find new partners (Freeman 2002). Most empirical studies on FDI in Vietnam rarely mention the relationship of the open door policy and foreign direct investment inflows into Vietnam. Theoretically, the effect of trade openness on the inflow of FDI varies according to the motivation for engaging in FDI activities (Dunning 1993;Markusen and Maskus 2002). Thus, this paper analyzes the role of trade openness in attracting FDI inflows to Vietnam in the endogenous growth theoretical framework. The research also uses annual data for the period 1997–2019. This current study further moved toward investigating regional macroeconomic fundamentals, which comprises trade openness, political stability, and tax rate impacts on Vietnam FDI inflow. The political stability and tax rate can both be considered as control variables to observe in this model. Additionally, there has been empirical evidence of an asymmetric response of trade openness to foreign direct investment (Babatunde 2011). FDI inflows can help an economy by giving advantages for improving the level of the service sectors, consisting of telecommunications, banking and finance, transport, business and legal services, wholesale and retail trade. Therefore, in this study, we strive to assess the asymmetric influence of trade openness uncertainty on Vietnam’s FDI flows. It tests the concept that the more open developing markets are, the more attractive country’s FDI inflows will be. Since few previous studies in Vietnam have particularly considered the asymmetric co-integration possibility and the long-term relationship between macroeconomics factors and FDI, this study will apply ARDL and the Nonlinear ARDL approaches as developed by Pesaran et al. (2001) and Shin et al. (2014) respectively, to examine short-term and long-term relationships and analyze the asymmetric effects between the variables. In summary, this paper highlights the influence of macroeconomic factors on attracting foreign investment capital and the economy expansion in Vietnam. Regarding the ARDL and non-ARDL research methods, the authors will consider the correlation between FDI and the openness of the economy and examine the asymmetric influence of the two mentioned factors in the models with the rest of the macroeconomic factors. These methods also aim to identify a positive relationship between FDI and TO in the research model. This paper will help the Vietnam government and other developing countries create a foundation for balancing tax policies and political stability, thereby boosting economic openness and attracting more foreign direct investment. 2. Literature Review 2.1. Foreign Direct Investment On a global scale, research has evinced that FDI plays a critical deterministic role in developing countries’ economies. Caves (1971) explained the direction of FDI investment in two ways: vertical and horizontal motivations. Horizontal FDI is a type of investment aimed at finding markets. The main goal of this type of investment is that foreign firms use some of the host country’s advantages to distribute products, sell products, and extend the life cycle of the business cycle. Meanwhile, vertical FDI is the type of investment aimed at finding resources. The main goal of this type of investment is to exploit raw materials, take advantage of the host country’s technologies, resources, and cheap labor costs to optimize costs as well as the production process of the product. Through the OLI (Ownership- Location-Internalization) framework, Dunning (1988) proved the determinant factors to FDI, which is related to three groups of advantages: advantage of ownership (O), advantage of Location (L), and advantage of Internalization (I). This article found that the aim of FDI into host countries is to minimize their cost of market research, tariff, and non-tariff barriers. Nunnenkamp (2002) studies the determinants of FDI in developing countries in the context of globalization. The results show that globalization has a significant effect on FDI. Therefore, non-traditional factors gradually become more critical to FDI attraction, such as costs, additional production factors, as well as economy openness. Meanwhile, Economies 2021,9, 120 3 of 18 traditional factors such as market size and growth rate decrease slightly in the impact of FDI inflow. Many empirical studies related to factors affecting FDI by different methods. Demirhan and Masca (2008) identified the factors affecting FDI in 38 developing countries 2000–2004, using a cross-data analysis model, including size market; inflation rate; the infrastructure; labor costs; economy openness; political risks, and tax rate. The result also showed that the above factors all positively affect FDI attraction, except for labor costs and political risks. Jayasekara (2014) accomplished the determinants of FDI in Sri Lanka, India, Bangladesh, and Pakistan from 1975 to 2012, applying the modified smallest regression model (FM-OLS). The factors included in the analysis model include, GDP growth rate representing market size, the inflation rate, government spending, exchange rates represent macroeconomic stability, the loan interest rate for financial development, the total value of imports and exports representing the openness of the economy, workforce, and the number of telephone lines per 100 people in the country representing infrastructure. The results show that there are positive effects on GDP growth, government spending, total exports and imports, workforce, and infrastructure. However, inflation, exchange rate, and interest rates negatively affect the FDI attraction and competition among countries. In addition, the study has shown that by adding tariffs on international trade, the country’s socio-economic conditions also affect FDI inflows. McLean and Shrestha (2002) determine that FDI plays a more essential role in economic growth of developing countries than developed nations. 2.2. Trade Openness Goldberg and Klein (1998) indicate that FDI promotes more significant trade in exports, import substitution, or intermediate inputs. Trade openness prompts export-oriented FDI, while trade restrictions appeal for “tariff jump” FDI, the primary goal of taking advantage of the domestic market (Liargovas and Skandalis 2012). At the same time, the literature on trade liberalization shows that liberalization promotes domestic investment by accepting domestic agents to import relatively cheap and more efficient capital products, thereby reducing structural constraints on investment and increasing the efficiency of capital accumulation (Kosteletou and Liargovas 2000). Similarly, some transnational studies have concluded that foreign direct investment can only promote economic growth if the host country’s trade openness is sufficiently high (Lee 1995). In other terms, Markusen and Maskus (2002) point out that the relationship between trade openness and FDI inflows is very complicated and needs to be carefully explained, possibly depending on the characteristics of each case. In theory, the impact of trade openness on FDI inflows varies with the motivation for engaging in FDI activities. Makoni (2018) and Zaman et al. (2018) found a positive relationship between trade openness and FDI inflows; Khan and Hye (2014); Adow and Tahmad (2018); Cantah et al. (2018) and Rathnayaka Rathnayaka Mudiyanselage et al. (2021) found a negative relationship; while Ho et al. (2013) and Wickramarachchi (2019) found that trade openness had no significant impact on FDI inflows. 2.3. Political Stability On the other hand, economic growth and political stability are closely related; thus, political stability is also the decisive factor for a multinational company to make new investment decisions. According to empirical studies, FDI inflows are influenced by the political stability index of the host country (La Porta et al. 1999;Kim 2010;Shahzad and Al-Swidi 2013), with such research as Akin (2019) also further explains that foreign companies usually consider low labor costs or commodity resources, and low taxes, political stability, economic freedom, and current free trade of the host country in order to make their final investment decisions. Contrary to opinions about the support of political stability factor positively affecting FDI, the empirical evidence of Kurecic and Kokotovic (2017) suggested that political stability did not produce a statistically significant impact on foreign investors, being only an initial condition for beginning investment in smaller economies as developing countries. Economies 2021,9, 120 4 of 18 2.4. Tax Rate Furthermore, a very key contribution by Scholes and Wolfson (1990) and Cassou (1997) using a panel methodology, determined a significant negative relationship between FDI inflow and corporate tax rate, pointing out that host country corporate income tax rates have a significant effect on the inflow of investment. Scholes and Wolfson (1990) argued that it is definitely possible for overseas investors to improve their own investment in response to higher US corporate taxes. Some empirical analysis also pointed out that tax incentives have a significant impact on FDI decision-making (Tung and Cho 2000;Hsu et al. 2019;Etim et al. 2019;Siregar and Patunru 2021). The rates of tax can either positively or negatively affect the inflow of foreign direct investments (FDIs) in a country, due to the taxation system of the host country (Ojeka et al. 2021). 3. Data Sources and Description of Variable 3.1. Data Time series data per annum on FDI, trade openness, tax, and political stability covering the 1997–2019 period has been used in this study. Data were collected and aggregated from various sources, namely World Bank data, and annual reports by the general statistics office of Vietnam. The data of foreign direct investment inflow (FDI) and political stability (PS) variables are completely gathered from World Bank source. Trade openness (TO) is measured by the total sum of exports and imports divided by GDP. Because of drawing attention to foreign investment capital, the Vietnamese government has consecutively changed corporate income tax rate. Before 1999, the government did apply a corporate tax rate of 25% to FDI enterprises. In 2003, the Law on Corporate Income Tax underwent major reform when it unified tax obligations, and tax incentives between domestic enterprises and FDI enterprises at the same rate became 28%. In the period 2009–2015, the corporate tax for FDI companies was 25%; after 2016, this rate declined to 20%. In addition, the government also applied a tax rate to transferring profits overseas with rates of 5% to 10% for the period before 2000, and after 2000 at 3% to 7%. Therefore, the actual corporate income tax for FDI companies will be calculated as corporate income tax plus tax on repatriation of profits abroad. For experimental design, descriptive statistics (mean, median, standard deviation, skewness, and kurtosis) were used in the calculation to check the nature of the data distribution. The Jarque Bera test determines the normal distribution of the data. Based on the description statistic in Table 1, most of the variables are left deviations (positively skewed) except for the tax. For Kurtosis method measuring the peakness or flatness of the distribution of the analyzed series, all variables are platykurtic. After analyzing the goodness-of-fit test, the probability of TO, TAX, FDI, PS has statistical meaning. Thus, according to the Jarque-Bera statistic, the time series data matches a normal distribution. Table 1. Result of description. TO TAX FDI PS Mean 147.525 0.302 6.64 ×1090.255 Median 152.217 0.303 7.43 ×1090.252 Maximum 210.400 0.330 1.61 ×1010 0.526 Minimum 94.344 0.256 1.30 ×109−0.022 Std. Dev. 34.514 0.027 5.05 ×1090.137 Skewness 0.238 −0.623 0.410 0.114 Kurtosis 2.126 2.070 1.883 2.455 Economies 2021,9, 120 5 of 18 Table 1. Cont. TO TAX FDI PS Jarque-Bera 0.948 2.315 1.839 0.335 Probability 0.622 0.314 0.399 0.846 Sum 3393.065 6.945 1.53 ×1022 5.858 Sum Sq. Dev. 26,210.57 0.016 5.61 ×1020 0.412 3.2. Unit Root Test The purpose of the unit root test is to examine whether the data is stationary or not. Thus, this paper used ADF and Phillips-Perron tests. According to Dickey and Fuller (1981) the time series attributes of each research variable that are studied for unit roots are studied through the enhanced Augmented Dickey-Fuller test (ADF). The Phillips-Perron (PP) test is also used to confirm the ADF test (Phillips and Perron 1988) Estimate the common equality of ADF and PP tests according to the following formula: ∆Yt=C0+α1Yt−1+α2t+ n ∑ j=1 rj∆Yt−j+εt ∆Yt=C0+α1Yt−1+α2t+εt Among them: Yis a time series, tis a linear time trend, ∆ is the first difference operator, which is a constant, nis the optimal number of lags in the dependent variable, and εt is a random error term. The null hypothesis (H1). that the time series is not stationary (with unit root). The alternative hypothesis (H2). that the time series is stationary (no unit root). If the calculated test statistic is less than the critical value of the test statistic, then the null Hypothesis (H1) will be rejected. Unit root test result reports in Table 2. According to the ADF and Phillips-Perron tests, results indicate that only LNPS is stationary at I(0), and all variables are stationary after the first difference, with at least 2 out of 3 conditions being met (none, intercept, trend and intercept). The ARDL and NARDL models, developed by Pesaran et al. (2001) and Shin et al. (2014) allow for simultaneous analysis for both short-term and long-term asymmetric effects between variables, regardless of the static variables at I(0) or I(1) (Ding et al. 2017); therefore, a unit root test is performed to ensure that the variable is not stationary at I(2). Table 2. Unit root test result. Variable ADF PP At Level None Intercept Trend and Intercept I None Intercept Trend and Intercept I LNFDI 1.637 (0.971) −0.114 (0.936) −2.559 (0.299) -1.418 (0.9562) −0.281 (0.913) −2.528 (0.3129) - LNTO 4.674 (1.000) −0.694 (0.827) −1.983 (0.5746) -6.604 (1.000) −0.838 (0.788) −3.145 (0.121) - LNTAX 1.263 (0.942) −0.358 (0.900) −1.675 (0.727) -1.331 (0.949) −0.312 (0.908) −1.677 (0.727) - LNPS −1.033 (0.261) −2.942 ** (0.060) −5.305 *** (0.003) I(0) −2.695 *** (0.009) −5.423 *** (0.001) −5.289 *** (0.002) I(0) Economies 2021,9, 120 6 of 18 Table 2. Cont. Variable ADF PP At Level None Intercept Trend and Intercept I None Intercept Trend and Intercept I At First Difference LNFDI −2.955 *** (0.005) −3.321 *** (0.007) −3.160 (0.119) I(1) −2.925 *** (0.005) −3.260 ** (0.030) −3.070 * (0.102) I(1) LNTO −0.745 (0.379) −6.669 *** (0.000) −6.540 *** (0.000) I(1) −3.882 *** (0.000) −7.461 *** (0.000) −7.594 *** (0.000) I(1) LNTAX −4.472 *** (0.000) −4.741 *** (0.000) −4.771 *** (0.001) I(1) −4.487 *** (0.000) −4.741 *** (0.001) −4.771 ** (0.005) I(1) LNPS −6.089 *** (0.000) −3.612 ** (0.020) −3.694 * (0.058) I(1) −21.050 *** (0.000) −22.560 *** (0.000) −22.857 *** (0.000) I(1) Note: 1. Table 2shows values of t-statistics and p-values in parentheses. 2. ***, ** and * indicate significance at 1%, 5%, and 10% levels. 3. None, Intercept, Trend, and Intercept are conditions of unit root test. 4. Symbol “I” indicates an order of integration. 3.3. Methodology After examining the unit root test, the authors continued to conduct the data analysis process based on the primary method, the autoregression distribution lag model (ARDL), to determine the relationship between endogenous variables (FDI, TO) and exogenous variables (PS, TAX) in the short and long term. Following that, to observe the nexus between endogenous variables (FDI and TO) more clearly, we continued to use the non-linear ARDL method to analyze the asymmetric effect between them in the analytical model. Consequently, the authors used regression diagnostics test to evaluate the variables in the selected model as to whether or not there was a large undue influence on the analysis. Breusch–Godfrey test, Harvey test, and Jarque-Bera test were used for assessing assumptions; cumulative sum (CUSUM) and CUSUM of squares (CUSUMSQ) tests were used for assessing the structure stability. 3.4. The Model This study indicated the impact of macroeconomic factors on foreign direct investment and trade openness, based on endogenous growth theory, and followed Ding et al. (2017) a study based on the following equation: FDI =f(TO;TAX,PS)(1) TO =f(FDI ;TAX,PS)(2) After changing it into a linear form, Equations (1) and (2) can be considered as the following step: LNFDI =α+δ1LNTO +δ2LNTAX +δ3LNPS +εt(3) LNTO =β+λ1LNFDI +λ2LNTAX +λ3LNPS +ρt(4) where: LN: represents the logarithm. LNFDI: the logarithm of the foreign direct investment. LNTO: the logarithm of the trade openness. LNTAX: the logarithm of the tax rate. LNPS: the logarithm of the political stability. Moreover, Equations (3) and (4) showed that δ1 to δ3 , λ1 to λ3 coefficients correspond to long-term elasticities, εt and ρt stands for the random remainder of the estimated regression. Economies 2021,9, 120 7 of 18 3.4.1. ARDL Model This study has used autoregressive distributed lag (ARDL), proposed by Pesaran et al. (2001), to define the impact of long-run and short-run associations between the variables of interest (FDI, trade openness, tax, political stability) due to the following benefits. The ARDL model is carried out in the following sequence: First, the co-integration between the variables are analyzed by the Bound test, which helped to determine the long-run relationship between the variables; second, determining the lags of the variables, which used the SBC or AIC criteria; third, running the ARDL model with the defined lags to test the long-run relationship between the variables in the model; and subsequently calculating the short-term effects of variables by error correction model (ECM), based on the ARDL approach to defining the co-integration relationship between the observed variables. According to Pesaran et al. (2001), the ARDL method has several dominances over other co-integration methods: First, in the case of small sample sizes, the ARDL model is the more statistically significant approach, aiming to test for co-integration, while that of the Johansen’s co-integration technique requires a larger number of samples to achieve reliability; secondly, in contrast to conventional methods for finding long-run relationships, the ARDL method does not estimate a system of equations; thirdly, other co-integration techniques require that the regressors are included in the association with the same delay whereas in the ARDL approach, the regressors can tolerate different optimal lags; and subsequently, if the author does not guarantee the properties of the unit root or the stationarity of the data system, the association level I(1) or I(0), the application of ARDL is the most appropriate for the study experiment. The ARDL model is defined as follows: ∆LNFDIt,i=α1+∑m i=1µ1i∆LNFDIt−i+∑n i=0µ2i∆LNTOt−i +∑k i=0µ3i∆LNTAXt−i+∑r i=0µ4i∆LNPSt−i +γ1LNFDIt−1+γ2LNTOt−1+Y3LNTAXt−1+Y4LNPSt−1+ωt (5) ∆LNTOt,i=α2+∑m i=0θ1i∆LNFDIt−i+∑n i=1θ2i∆LNTOt−i +∑k i=0θ3i∆LNTAXt−i+∑r i=0θ4i∆LNPSt−i +π1LNFDIt−1+π2LNTOt−1+π3LNTAXt−1+π4LNPSt−1+θt (6) where µ and θ are short-run coefficients. γ and π are the long-run coefficients. The symbol ∆ denotes the first differences of the variables, while m,n,k,rrepresents the lags of the variables. Bound test, mainly based on the F statistic to test the co-integration between observed variables. Accordingly, Pesaran et al. (2001) and Qamruzzaman et al. (2019) have provided more concrete evidence to demonstrate the co-integration relationship in the long-run model. Thus, these tests aim to define the long-run relationship that exists among these elements by handling an F-test with the hypotheses: - Hypothesis H1: γ1=γ2=γ3=γ4 π1=π2=π3=π4= 0 there is no co-integration relationship between variables; - Hypothesis H2: γ16=γ26=γ36=γ4 π16=π26=π36=π46= 0 a co-integration relationship exists between variables. The null hypothesis is rejected when the value of the F-statistic is larger than the upper critical bounds value, and it is not rejected if this value is lower than the lower bounds value. Contrariwise, when the nexus of co-integration between these variables is indeterminate, the error correction model (ECM) is implemented to identify co-integration relationship. If the estimated coefficient is significant, it has sufficient evidence to conclude that the co-integration nexus between variables is available (Bahmani-Oskooee and Fariditavana 2015). Economies 2021,9, 120 8 of 18 Once the result indicates that co-integration relationship between these variables is present, it means that a long-run relationship between them exists in the model. The long-run ARDL model is expressed as follows: ∆LNFDIt,i=α1+∑m i=1µ1i∆LNFDIt−i+∑n i=0µ2i∆LNTOt−i +∑k i=0µ3i∆LNTAXt−i+∑r i=0µ4i∆LNPSt−i+ψECTt−i+ω1t (7) ∆LNTOt,i=α2+∑m i=0θ1i∆LNFDIt−i+∑n i=1θ2i∆LNTOt−i+∑k i=0θ3i∆LNTAXt−i +∑r i=0θ4i∆LNPSt−i+φECTt−i+ω2t(8) Estimating the short-term coefficients of the ARDL model followed by the error correction model (ECM) with selected lag length. The error correction model is presented as follows: ECT1t=LNFDIt−1−γ2 γ1 LNTOt−1−γ3 γ1 LNTAXt−1−γ4 γ1 LNPSt−1(9) ECT2t=LNTOt−1−π1 π2 LNFDIt−1−π3 π2 LNTAXt−1−π4 π2 LNPSt−1(10) 3.4.2. Non-Linear Autoregressive Distributed Lagged (NARDL) The notion of nonlinearity among dependent and explanatory variables has recently become one of the significant aspects when evaluating relationships in empirical investigations. When it comes to nonlinearity, Shin et al. (2014) proposed a new non-linear co-integration equation, which has become generally known as a NARDL by combining two sets of additional explanatory variables in the equation: positive and negative shocks. More importantly, we can use Equations (8) and (9) below to estimate the level of positive and negative shocks in explanatory variables. FDI+ t=∑t i=1LNFDI+ i=∑t i=1MAX(∆LNFDIk, 0) FDI− t=∑t i=1LNFDI− i=∑t i=1MIN(∆LNFDIk, 0)(11) TO+ t=∑t i=1LNTO+ i=∑t i=1MAX(∆LNTOk, 0) TO− t=∑t i=1LNTO− i=∑t i=1MIN(∆LNTOk, 0)(12) Thus, the asymmetric relationship between FDI and TO is estimated by the following equation: ∆LNFDIt,i=α0+∑m i=1µ1i∆LNFDIt−i+∑n i=0µ+ 2i∆LNTO+ t−i+∑n i=0µ− 2i∆LNTO− t−i +∑k i=1µ3i∆LNTAXt−i+∑r i=0µ4iLNPSt−i+Y1LNFDIt−1 +Y+ 2LNTO+ t−1+Y− 2LNTO− t−1+Y3LNTAXt−1+Y4LNPSt−1+ω1t (13) ∆LNTOt,i=α0+∑m i=0θ+ 1i∆LNFDI+ t−i+∑m i=0θ− 1i∆LNFDI− t−i+∑n i=1θ2i∆LNTOt−i +∑k i=1θ3i∆LNTAXt−i+∑r i=0θ4iLNPSt−i+π+ 1LNFDI+ t−1 +π− 1LNFDI− t−1+π2LNTOt−1+π3LNTAXt−1+π4LNPSt−1+ω2t (14) Empirical analysis proceeds in the following three steps: First, Equations (12) and (13) is estimated by the method of least squares (OLS). Step two, null hypothesis H1: There is no long-run relationship between variables (H1: γ1=γ− 2=γ+ 2=γ3=γ4 π+ 1=π− 1=π2=π3=π4 = 0) is tested based on the F-statistics (Pesaran et al. 2001;Shin et al. 2014). Finally, short-run and long-run asymmetry tests are performed based on the Wald test: HLR: γ+ 2/γ1=γ− 2/γ1 π+ 1/π2=π− 1/π2 , or HSR : ∑n i=0µ+ 2i=∑n i=0µ− 2i ∑m i=0θ+ 1i=∑m i=0θ− 1i If only the HSR hypothesis:  ∑n i=0µ+ 2i=∑n i=0µ− 2i ∑m i=0θ+ 1i=∑m i=0θ− 1i is rejected, then this model is asymmetric in the short-run. Economies 2021,9, 120 15 of 18 Economies 2021, 9, x FOR PEER REVIEW 14 of 18 4.3.3. Diagnostics Test With the similar diagnostics test in Table 4c, when the authors implemented the Nonlinear ARDL method to analyze the positive and negative shocks of FDI and trade openness, the results in Table 5 revealed some differences in the residuals. For R-square value close to 1 in both models, this strongly suggests that the research model has high reliability to explain the relationship between observed variables. Although the values of the F-statistic are smaller than those in Table 5c, they are all significant at 1%. This proves that the overall model fits. According to Figure 2, for the residual diagnostic test, the results of Breusch-Godfrey serial correlation levels are both significant at 5% but insignificant at 1%. As mentioned in the ARDL model, we conclude that the problem of serial correlation in this model is not serious. Unlike the Harvey test in Table 4c, all these results in Table 5c are insignificant, so the residual variables in these models are all homoscedastic; additionally, the values of the observations R-square of the Jarque-Bera test are insignificant, which signifies that these are normal residual distributions. Furthermore, the graph of CUSUM and CUSUM of squares are significant at 5% critical bound, which means these have parameter constancy and model stability when the negative and positive shock of FDI and trade openness are added in these experimental models applying the non-linear ARDL method. Figure 2. The cumulative sum of recurrent residuals (CUSUM) and cumulative sum of recurrent residuals squares (CUSUM of Squares). Source: Conducted by researcher based on Eviews 10. Figure 2. The cumulative sum of recurrent residuals (CUSUM) and cumulative sum of recurrent residuals squares (CUSUM of Squares). Source: Conducted by researcher based on Eviews 10. Economies 2021, 9, x FOR PEER REVIEW 15 of 18 4.3.4. Asymmetric Estimation According to the NARDL model in Table 5d, when FDI is the main variable, the values of Wald Test results in the long run (−4.494, p = 0.0002) and the short run (0.197, p = 0.8888) imply that the model is asymmetric in the long run but symmetric in the short run, but the model is asymmetric in the short and long run (W SR = 4.485, W LR = 5.161) when trade openness is the essential variable. As the graph’s tail extends further (Figure 3), the asymmetric disparity of Model 3 among positive and negative volatilities of trade openness is more obvious. However, only the long-run coefficient of trade openness in the positive change (LNTO + = 9.505) is statistically significant; the magnitude of the long-run coefficient of trade openness with the positive change (reflecting the widening trade openness) is much larger than the negative change (reflecting the narrowing trade openness). For that reason, in the long term, the widening trade openness will have a stronger impact on FDI inflows in Vietnam than the trade openness shrinking in Model 3. Conversely, there is an asymmetry in the short run and the long run in Model 4; nevertheless, only the positive coefficient of FDI (LNFDI + = 0.134) is statistically significant in the long run, and then it can be concluded that the positive growth of foreign direct investment will strongly affect trade openness, at a rate of 0.134% long term. Meanwhile, the positive and negative coefficients of FDI are both statistically significant, but the negative coefficient (2.033) is much larger than the positive coefficient (0.098), so we can conclude that the decrease of FDI means the amount of foreign investment capital has a profound impact on trade openness short term. Thus, if the amount of foreign investment increases or decreases by 1%, this leads to an increase or decrease in trade openness at the rate of 0.098% or 2.033% respectively. Figure 3. NARDL multiplier graph. 5. Conclusions ARDL and NARDL methods are used to examine the factors affecting foreign investors’ investment decisions in Vietnam and the asymmetric impact between the FDI and Trade Openness. In general, the results show that the political stability does not affect the decisive impact of foreign investors, and the expansion of the economy in the long run. Compared with previous studies, this result is the opposite of the evidence proposed by Kim (2010) and Akin (2019)where the most important determinant of FDI is political stability, and there is a causal relationship from political stability to other economic factors. It is mooted that the collected data from the World Bank has not sufficiently reflected the character of political stability affecting the economy mainly because this issuance includes many Figure 3. NARDL multiplier graph. Conversely, there is an asymmetry in the short run and the long run in Model 4; nevertheless, only the positive coefficient of FDI (LNFDI + = 0.134) is statistically significant in the long run, and then it can be concluded that the positive growth of foreign direct Economies 2021,9, 120 16 of 18 investment will strongly affect trade openness, at a rate of 0.134% long term. Meanwhile, the positive and negative coefficients of FDI are both statistically significant, but the negative coefficient (2.033) is much larger than the positive coefficient (0.098), so we can conclude that the decrease of FDI means the amount of foreign investment capital has a profound impact on trade openness short term. Thus, if the amount of foreign investment increases or decreases by 1%, this leads to an increase or decrease in trade openness at the rate of 0.098% or 2.033% respectively. 5. Conclusions ARDL and NARDL methods are used to examine the factors affecting foreign investors’ investment decisions in Vietnam and the asymmetric impact between the FDI and Trade Openness. In general, the results show that the political stability does not affect the decisive impact of foreign investors, and the expansion of the economy in the long run. Compared with previous studies, this result is the opposite of the evidence proposed by Kim (2010) and Akin (2019)where the most important determinant of FDI is political stability, and there is a causal relationship from political stability to other economic factors. It is mooted that the collected data from the World Bank has not sufficiently reflected the character of political stability affecting the economy mainly because this issuance includes many factors such as fiscal policy uncertainty, monetary policy uncertainty, and trade policy uncertainty (Qamruzzaman et al. 2019). Based on the empirical study, covering industrial upgrades and assisting domestic enterprises to integrate into the global production network are the most efficient ways to attract FDI. Furthermore, because the openness of the Vietnamese economy is quite high and many enterprises participate in many free trade agreements, it is essential to think of Vietnam becoming a new “special economic zone” to attract more FDI in Asia. There will be consistent, innovative, and effective policies that can encourage science and technology development from the reasons mentioned above; hence, the Vietnamese government should strengthen regional cooperation and integration to attract FDI and expand the market. For instance, when trade between other nations is open, the body authority should pay attention to improving the quality of export goods, inaugurating appropriate technology, and strengthening market knowledge so that it can compete with other countries in the region and globally. More specifically, effective markets in terms of institutions, trade openings, tax policies, and better infrastructure are important determinants to attract foreign direct investment; while additionally, governments in developing countries can significantly promote foreign direct investment by introducing appropriate macroeconomic policies. Finally, this study has several limitations. An annual time-series database of 23 years might be insufficient to capture the whole picture, and the ARDL and NARDL models could be limited to four variables, thus overlooking other influencing elements. To address these limitations, the future research directions could be pursued; for example, more variables could be added to raise the extensiveness of the analysis. Author Contributions: Conceptualization, J.-Y.L., Y.-C.H., N.B., T.-T.N.; methodology, J.-Y.L.; software, N.B., T.-T.N.; validation, J.-Y.L., Y.-C.H.; formal analysis, J.-Y.L., N.B.; investigation, N.B., T.-T.N.; resources, J.-Y.L., Y.-C.H.; data curation, J.-Y.L., N.B.; writing—original draft preparation, N.B., T.-T.N.; writing—review and editing, J.-Y.L., Y.-C.H.; visualization, J.-Y.L.; supervision, J.-Y.L.; project administration, N.B., J.-Y.L.; funding acquisition, J.-Y.L., Y.-C.H. All authors have read and agreed to the published version of the manuscript. Funding: The APC was funded by National Kaohsiung University of Science and Technology, Taiwan. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Worldbank, General Statistic Office of Vietnam. Economies 2021,9, 120 17 of 18 Acknowledgments: We would like to thank three anonymous referees for their useful comments and constructive suggestions. Conflicts of Interest: The authors declare no conflict of interest. References Abdlaziz, Rizgar Abdlkarim, Khalid Abdul Rahim, and Peter Adamu. 2016. Oil and Food Prices Co-Integration Nexus for Indonesia: A Non-Linear Autoregressive Distributed Lag Analysis. International Journal of Energy Economics and Policy 6: 82–87. Adow, Anass Hamedelneel, and Abdel Mahmoud Ibrahim Tahmad. 2018. The impact of trade openness on foreign direct investment in Sudan by sector in the 1990–2017 period: An empirical analysis. Economic Annals-XXI 172: 14–21. Akin, Tugba. 2019. 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