Innovation and foreign direct investment attraction in developing countries

Tu, Nguyen Anh Article Innovation and foreign direct investment attraction in developing countries Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Tu, Nguyen Anh (2024) : Innovation and foreign direct investment attraction in developing countries, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-13, https://doi.org/10.1080/23322039.2024.2312386 This Version is available at: https://hdl.handle.net/10419/321428 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Innovation and foreign direct investment attraction in developing countries Nguyen Anh Tu To cite this article: Nguyen Anh Tu (2024) Innovation and foreign direct investment attraction in developing countries, Cogent Economics & Finance, 12:1, 2312386, DOI: 10.1080/23322039.2024.2312386 To link to this article: https://doi.org/10.1080/23322039.2024.2312386 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 12 Feb 2024. Submit your article to this journal Article views: 5173 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Innovation and foreign direct investment attraction in developing countries Nguyen Anh Tu Faculty of Banking, Ho Chi Minh City University of Banking, Ho Chi Minh city, Vietnam ABSTRACT The present study investigates the relationship between innovation (INN) and foreign direct investment (FDI) attraction across 66 developing countries from 2013 to 2021. Adopting the Difference Generalized Method of Moments estimation, the study reveals a statistically positive INN-FDI nexus. Panel Granger causality analysis further indicates a bidirectional between the two variables. Additionally, via feature importance analysis, it is evident that market size, labor resources, and financial development play a critical role in strongly influencing FDI inflows, while innovation shows smaller magnitude. Furthermore, trade openness demonstrates a significantly positive impact on FDI with low impact, while inflation has an insignificantly negative effect on FDI. Policy implications are also discussed. IMPACT STATEMENT Developing countries, particularly those seeking to attract foreign direct investment (FDI), consider paying attention to the innovation (INN) factor. Alongside traditional factors such as market size and abundant labor force are strengths of FDI attraction for developing countries, new models should be continually developed. In this context, the researcher hypothesizes that multinational enterprises are interested in new resources related to INN to meet their production requirements. Indeed, INN and its efficacy in attracting FDI in developing countries, which pay less resources to allocate innovation, remains unproven. Drawing upon data from the global innovation index, the experimental findings of this research show the bidirectional FDI-INN nexus and suggest that policies aimed at attracting FDI should prioritize those based on advanced technologies. Additionally, via feature importance analysis, market size and labor force cannot be ignored in the analyzed context. ARTICLE HISTORY Received 26 October 2023 Revised 22 December 2023 Accepted 25 January 2024 KEYWORDS Foreign direct investment; innovation; feature importance; developing countries; D-GMM REVIEWING EDITOR David McMillan; University of Stirling; UK SUBJECTS Economics; Finance; Economics and Development; Regional Development; Development Studies 1. Introduction Over the past few decades, foreign direct investment (FDI) has played a significant role in developing countries (DGCs). FDI followed as multinational companies (MNCs) established subsidiaries or outsourced manufacturing operations to host countries to take benefits offered. In addition to providing capital, MNCs are expected to generate spillover effects in host countries by introducing new technologies, transferring skills, enhancing competition, and opening up markets (Khachoo and Sharma, 2016). Previous research has consistently shown that FDI has a positive impact on various aspects, including economic growth (A. T. Nguyen et al., 2022), international trade (Asiedu & Lien, 2004), labor capacity (Rodr ıguez & Pallas, 2008), technological advancement, research and development (Erdal & G€ oc¸er, 2015), as well as reducing carbon emissions (Ben Jebli et al., 2019). The global flow of FDI into DGCs has exhibited a consistent upward trend. As depicted in Figure 1,FDI inflows into DGCs remained relatively low prior to 2002, accounting for less than $200 billion and constituting less than 20% of the global share. The figures can be explained by the dominance of key manufacturing CONTACT Nguyen Anh Tu [email protected] Faculty of Banking, Ho Chi Minh City University of Banking, No. 56, Hoang Dieu 2 Road, Ho Chi Minh city, Vietnam Supplemental data for this article can be accessed online at https://doi.org/10.1080/23322039.2024.2312386 ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2312386 https://doi.org/10.1080/23322039.2024.2312386 industries and the technological advantages held by developed countries, which limited the influx of FDI into DGCs. However, since 2002, the value and global proportion of FDI inflows into DGCs have risen significantly, reaching 50% of the global share in 2014. The year 2020 marked a peak in FDI flows into DGCs, comprising approximately 67% of the global share. The figure underscores the attractiveness of DGCs as prime destinations for FDI worldwide. The growth of FDI in DGCs signifies the expanding business opportunities in these countries, motivating MNCs to seek profitable ventures in the region. The remarkable increase in FDI can be attributed to their advantages in terms of production efficiency (Coughlin & Segev, 1999), infrastructure, such as transportation and electricity (Head & Ries, 1996), low labor costs (Cheng & Kwan, 2000), or sizeable markets (Narula & Dunning, 2000). However, the issues of population aging and high inflation in host countries may cause these advantage factors to diminish (Ezeoha & Cattaneo, 2012; Pavl ınek, 2018). Plus, stringent regulations, especially environmental ones, to select higher quality new generation FDI inflows have been put on the agenda in DGCs (Y. Kim & Rhee, 2019). As a result, countries followed the neoclassical Solow (Solow, 1956), which highlighted the crucial role of capital and labor (Olorogun et al., 2022), ought to adjust paths to attract FDI in the new competitive context. Within this background, innovation 1 (INN) is assumed a pivotal role in governmental policy agendas to foster a technology-driven development model (Looy et al., 2003). This is a very recent and encouraging trend that makes room for INN so that it is not only in a few elite economies and clusters but also DGCs (Cornell University, INSEAD, & WIPO, 2020). Mueller (2006) further contends that INN, acting as the foundation for research and development, catalyzes regional economic growth, complementing the roles played by capital and labor. This fact challenges Therefore, this study specifically focuses on examining the role of INN in attracting FDI to DGCs. The INN index and FDI inflows into DGCs are globally correlated as MNCs prioritize establishing long-term production and consumption markets in countries where new elements like technology foster competitive environments. In today’s dynamic landscape of global competition, where the inevitable development of new technologies and products occurs to further competitive advantages, traditional determinants such as abundant natural resources or existing factors no longer hold primary importance in driving competitiveness (Erdal & G€ oc¸er, 2015). Instead, DGCs have recently incorporated innovation policies into their national policy scheme, and MNCs have placed innovation programs at the center of policymaking (Lewis et al., 2021). By exploring the relationship between INN and FDI, our findings show that innovation is not only a phenomenon found in advanced economies but also a required condition in DGCs, which seek a new pattern to receive FDI inflows. The findings add to the literature an argument that DGCs consider adapting INN as a new factor of FDI attraction. This article also examines other potential drivers of FDI, which currently Figure 1. Evolution of FDI inflows in developing countries, 1970–2021. Source: UNCTAD (2023). 2 N. A. TU play significant roles in attracting FDI. Based on the empirical findings, the study discusses policyenhancing FDI inflows to this region. 2. Review of related literature FDI has been observed to positively impact the performance of local companies (LCCs). The positive effect is primarily because multinational corporations (MNCs) have access to advanced technologies and specialized know-how, which surpasses that available to local companies (Cantwell & Iammarino, 2000). FDI generates spillover effects within and across industries via competitiveness and labor mobility, helping LCCs increase their productivity (Khachoo & Sharma, 2016). Regarding the intra-industry channel, FDI influences LCCs through learning by doing (G€ unther, 2002) or transferring human capital from MNCs. As a result, LCCs perform better due to experience and knowledge from trained human resources (Cheung & Ping, 2004). MNCs support the effectiveness of LCCs primarily through the transfer of new products, knowledge, and technology. This phenomenon is known as the inter-industry channel (Iammarino & McCann, 2013). When MNCs enter local markets with high levels of productivity and advanced technology, it pressures LCCs to pursue innovation by increasing their investment in research and development (R&D) (Keller, 2010). R&D, in turn, enables LCCs to enhance their competitive capabilities by adopting new technologies and improving management techniques, all facilitated by FDI. Cheung and Ping (2004) identify three strategies through which MNCs support the potential for innovation among LCCs. Firstly, MNCs introduce advanced products and technologies, allowing LCCs to learn from these technologies. Secondly, since these products have already been tested in developed markets, LCCs are incentivized to engage in innovation activities. Lastly, MNCs and LCCs can share expertise and transfer technology, leading to direct vertical integration between the two entities. Empirical research has supported the positive effects of FDI on INN. Ascani et al. (2020) state that superior knowledge possessed by MNCs can be diffused to LCCs, thereby increasing LCCs’capacity for innovation, particularly in science-based industries. Erdal and G€ oc¸er (2015) find that FDI stimulates R&D activities and patent applications in ten developing countries in Asia. Crescenzi et al. (2015) examine the impact of MNCs on LCCs’innovative performance in the UK. The findings reveal that LCCs operating in regions with higher levels of MNCs investment demonstrated greater innovative performance than others. However, the level of competitive positioning between MNCs and LCCs, particularly in terms of vertical connectedness, determines the speed of innovation. Particularly, LCCs primarily operating in the local market have stronger connections with MNCs, allowing them to benefit from spillover effects. Conversely, LCCs with access to the global market are less driven to rely on vertical FDI for their innovation efforts. Furthermore, factors conducive to innovation are crucial for attracting FDI flows. It indicates a nation’stransition to advanced manufacturing processes. As a result, MNCs could optimize inputs and outputs to reduce production costs. Additionally, FDI and innovation exhibit a bidirectional relationship, supporting positive spillover effects on a nation’s innovation ability. Governments recognize the role of FDI in knowledge transfer, international technology diffusion, and fostering innovation development (Li et al., 2021). Governments also strengthen intellectual property protection to attract FDI (Hsu & Tiao, 2015; Khoury & Peng, 2011). The existing studies have identified research gaps in the relationship between FDI and INN. In other words, it is still unclear if these increases in innovation have increased the appeal of FDI. Furthermore, previous research has primarily focused on developed economies with high levels of innovation (Crescenzi et al., 2015; Li et al., 2021), while DGCs often face challenges such as insufficient technological infrastructure and limited investment in R&D (Luthra & Mangla, 2018; S. Wang et al., 2021). Therefore, there is a need to address this gap and explore the FDI-INN nexus in DGCs. Secondly, prior studies often used patent applications, R&D expenditure, and scientific publications as proxies for measuring INN (Ascani et al., 2020; Erdal & G€ oc¸er, 2015; Vernon et al., 2021), while this article utilizes the INN index from the Global Invention Index, which incorporates seven pillars to assess inventive innovation (Soumitra Dutta, 2011). INN encompasses social, business model, and technological innovation, and it may also be more broadly defined and horizontal in character (Cornell University, INSEAD, & WIPO, 2020). Using a broader measure of INN, this study offers new insights into the FDI-INN nexus in DGCs and provides pragmatic managerial implications. COGENT ECONOMICS & FINANCE 3 3. Model and data 3.1. Proposed model Based on previous literature models (Economou et al., 2017; Saini & Singhania, 2018; Xaypanya et al., 2015), we propose an empirical model to examine the FDI-INN nexus as the following equation: FDIit ¼b0þb1INNit þb2TRAit þb3MARit þb4LABit þb5INFit þb6FIDit þeit (1) where FDI represents FDI inflows, INN is the innovation variable, and other control variables include trade openness (TRA), market size (MAR), labor force (LAB), inflation rate (INF), and financial development (FID). In Equation (1),irepresents countries (i¼1, 2 …,N), and trepresents test periods (t¼1, 2, …,T). The model also includes error term eit to account for unobserved factors. The following steps will be taken to investigate the relationship between those variables in Equation (1): (i) testing the stationarity of variables; (ii) adopting Generalized Method of Moments (GMM) for coefficients; and (iii) examining the contribution of explanatory variables by Feature Importance Analysis (FIA). In step (i), since the panel data includes time series characteristics, it is crucial to test for stationarity. Stationarity is a prerequisite to avoid spurious regression, where statistical significance between variables can be falsely observed due to non-stationary phenomena (Gujarati & Porter, 2009; Y. Wang et al., 2017). In this study, we applied three approaches to test for unit root, i.e., Im-Pesara-Shin proposed by Im et al. (2003), ADF-Fisher, and PP-Fisher proposed by Maddala and Wu (1999). In step (ii), the GMM estimator is employed to address the potential endogeneity issues that may arise within the macro-variables in Equation (1) (Antonakis et al., 2010; Hill et al., 2021). The GMM estimator, proposed by Hansen (1982), is considered more suitable than other instrumental variable methods like 2SLS, particularly when dealing with heteroskedasticity (Baum et al., 2003). Furthermore, considering the short panel characteristics of 66 countries over nine years (panels with large N and small T), the Ordinary Least Squares (OLS) estimator, such as the Fixed Effects Model (FEM), cannot yield stable covariance estimates (Allison & Waterman, 2002). Hence, we exclude OLS and 2SLS methods and opt for the GMM estimator due to its suit for the short panel (G oes, 2016). The GMM method comprises two types, i.e., Difference GMM (D-GMM) and System GMM (S-GMM). In this study, we employ D-GMM, which Arellano and Bond (1991) introduced. The estimation form for D-GMM is as follows: DYit ¼X t−1 k dkDYik þa1DXit þDeit (2) where dkrepresents the coefficient of the lagged dependent variable, k, and eit denotes the error term varying across countries (i) and time periods (t). It is important to note that when the regressive process occurs continuously, DYit may still be correlated with Deit, leading to a potential downward bias in the D-GMM estimation, especially when Tis small in the sample. The S-GMM estimator addresses this concern, which involves estimating two simultaneous equations (one at the level with first-lagged instruments and another in difference with k-lagged instruments) (Blundell &Bond,1998). Let dPOLS,dDGMM,anddFEM represent the coefficients of the lagged variable Yit−1obtained through pooled OLS estimation, D-GMM, and fixed effects, respectively. If dPOLS >dDGMM >dFEM, choosing D-GMM is appropriate. Conversely, if dDGMM <dFEM, indicating a tendency towards downward bias, S-GMM estimation is preferred (Bond, 2002). Additionally, we conducted robustness tests to ensure the effectiveness of the GMM estimator. Two commonly used tests are the J Sargan-Hansen and the Arellano-Bond tests for over-identification restrictions in linear models with instruments (Anatolyev & Gospodinov, 2011). The J Sargan–Hansen test (Hansen, 1982;Sargan,1958) assesses the appropriateness of instruments by testing the null hypothesis that the instruments are strictly exogenous. The Arellano and Bond (1991) test examines the presence of first- and second-order autocorrelation of residuals, with the null hypothesis H 0 stating that there is no such autocorrelation. Furthermore, we adopted the Granger causality test (Granger, 1969) to investigate the directions of causality between variables. The approach assumes that all of the coefficients in the panel are the same and that there are no cross-sectional variations. The Granger causality test can show the overall direction of causality among variables. The null hypothesis, which states that there is no pairwise Granger 4 N. A. TU causality among the variables, would be rejected at certain significance levels based on an analysis of the F-statistics (D.-W. Kim et al., 2018; Onifade et al., 2020). In step (iii), we conducted FIA by using a game-theoretic approach –SHapley Additive exPlanations (SHAP) –to assess the contribution of explanatory variables to the dependent variable and identify the dominant factor in the relationship (Colak Oz et al., 2023). This analysis enables us to provide hierarchical drivers of FDI in the interests of DGCs. 3.2. Innovation index variable The INN index is a composite index derived from seven pillars, each divided into three sub-pillars, that assess different aspects of innovativeness in a country. The seven pillars include institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. Each pillar has three sub-pillars, and each subpillar has two to five individual indicators. The indicators’scores aggregate the sub-pillar scores, and the sub-pillar scores comprise the pillar scores. The average scores of the first five and the last two pillars complement the INN (Cornell University, INSEAD, & WIPO, 2020). A higher value of the INN indicates a higher level of FDI (Ascani et al., 2020). In our sample, the data includes 66 DGCs (classified as low-income, lower-middle-income, and uppermiddle-income countries, according to the World Bank) between 2013 and 2021. DGCs have paid attention to enhancing their performance of seven pillars for consecutive years. In particular, several developing nations are outperforming expectations given their stage of economic growth (WIPO, 2021). For the upper-middle-income group, Bulgaria has output sub-indices comparable to the high-income group, such as Norway and Italy. At the same time, India’s innovation performance is above the average for the upper middle-income group in five of the seven innovation pillars. India is a successful case in developing sophisticated services that are technologically dynamic and traded internationally. Lowincome sub-Saharan African economies such as Malawi, Madagascar, Ethiopia, and Guinea are efficient cases of transforming their limited innovation inputs and resources into innovation outputs. Readers could refer to the sheet “Appendix 1”(supplementary material) in the data file for detailed scores of seven pillars in our sample. 3.3. Data sources and expected signs The data for the INN variable were extracted from the Global Innovation Index. FDI, TRA, MAR, INF, and FID variables were extracted from the World Development Indicator. Variables measurement and expected signs between the independent variables and the dependent variable (FDI) are presented in Table 1. The tests were conducted using Eviews 10 software, while the FIA was performed using Python programming language code. In total, there were 594 observations available for the analysis. In order to Table 1. Variables measurement and expected signs. Variable Acronym Description Reference Expectation Foreign direct investment FDI FDI/GDP (%) A. T. Nguyen et al. (2022); Economou et al. (2017) Innovation INN Innovation index (score) S Dutta et al. (2017) Positive Market size MAR Current GDP (natural logarithm) Asiedu and Lien (2004) Positive Human resources LAB Total labor force from 15 years old (natural logarithm) Rodr ıguez and Pallas (2008) Positive Trade openness TRA Ratio of total imports and exports to GDP (%) Asiedu and Lien (2004); Minh and Trinh (2023) Positive Inflation INF Consumer price index (%) Buckley et al. (2010) Negative Financial Development FID Overall Financial Development Index C. P. Nguyen and Lee (2021) Positive Source: Authors’work. COGENT ECONOMICS & FINANCE 5 access and replicate the results, the data has been deposited in the Mendeley data repository at the following link: https://data.mendeley.com/datasets/s7brrc9b63/3 4. Findings and discussion 4.1. Descriptive statistics The variables’description is presented in Table 2. From 2013 to 2021, the average FDI/GDP ratio was 3.64%. The number indicates that FDI substantially influences the region’s economy relative to GDP size. Moreover, the standard deviation of FDI stood at 4.09%, surpassing the average value. It suggests a considerable disparity in the countries’ability to attract FDI. Regarding the INN index, the mean value amounts to 29.67. China had the first position between 2013 and 2021 with the highest score at 54.8. These numbers demonstrate China’s supremacy in terms of FDI volume and the INN category among DGCs. Notably, the standard deviation averagely is 6.46 and is lower than the minimum score, meaning the scores between countries are small. In other words, the 66 developing countries in the sample have the same condition in term of INN. Other variables like trade openness, market size, labor force, and financial development indicate significantly high average levels for DGCs, further reinforcing their capacity to attract FDI. 4.2. Correlation The correlation test results for the analyzed variables are presented in Table 3. Most variables exhibit a significant correlation with each other at a 1% level of statistical significance. However, it is crucial to highlight that one pair of independent variables shows a high correlation coefficient of r(MAR, LAB) ¼ 0.837, suggesting potential multicollinearity issues in the model (Gujarati & Porter, 2009). To investigate this problem, we conducted the Wald test to verify the necessity of including both variables in the proposed model. The test results indicate an F-value (MAR, LAB) ¼18.16 at a significance level of 1%, which allows us to reject the null hypothesis H0 (a ¼b¼0). Therefore, the selected variables meet the inclusion criteria for the proposed model. 4.3. Panel unit root tests Table 4 presents the stationarity analysis for the variables included in the model, demonstrating that they are predominantly stationary at the level. Both the ADF-Fisher and PP-Fisher methods confirm this finding. Stationarity at the level implies that the variables follow a random walk, with their estimated values (mean, variance) remaining stable over time. Thus, the variables included in the model fulfill the necessary conditions for subsequent testing. 4.4. Regression estimation results The estimated coefficients of FDI(-1) using different estimators are presented in the first three rows of Table 5. The POLS, D-GMM, and FEM estimators yield coefficients of 0.75, 0.36, and 0.23, respectively. Notably, the estimated coefficient obtained via D-GMM surpasses the lower bound. It suggests that the D-GMM method is suitable for the sample under examination. Table 2. Variables description. FDI INF INN LAB MAR TRA FID (% GDP) (%) (Score) (logarithm) (Logarithm) (% GDP) (Score) Mean 3.64 6.4 29.67 16.01 24.86 70.58 0.27 Median 2.59 3.6 29.35 15.86 24.58 63.67 0.23 Maximum 39.46 557.2 54.8 20.49 30.51 186.47 0.74 Minimum 0.0022 −3.75 16.7 12.44 19.77 16.35 0.06 Std. Dev. 4.09 26.48 6.46 1.51 1.69 30.73 0.15 Observations 594 594 594 594 594 594 594 Source: Authors’work. 6 N. A. TU The findings in Table 5 reveal a significant positive impact of INN on FDI at a 1% significance level. It aligns with previous research conducted by Li et al. (2021) on innovative activities in OECD countries. These results provide evidence of a positive correlation between innovation and FDI attraction in DGCs. One possible explanation for this relationship is that increased innovation enhances the attractiveness of DGCs to MNCs, leading to higher inflows of FDI into these countries. This finding aligns with the practical deployment of innovative initiatives in DGCs globally to attract FDI, e.g., Panama and Tunisia (Lewis Table 3. Correlation matrix. FDI INF INN LAB MAR TRA FID FDI 1 INF −0.039 (–0.955) 1 INN 0.057 (1.386) −0.116 a (–2.829) 1 LAB −0.222 a (-5.537) 0.012 (0.293) 0.164 a (4.053) 1 MAR −0.272 a (-6.871) −0.029 (–0.721) 0.444 a (12.07) 0.837 a (37.15) 1 TRA 0.366 a (9.556) −0.036 (–0.881) 0.303 a (7.748) −0.428 a (–11.52) −0.294 a (–7.479) 1 FID −0.091 b (-2.233) −0.048 (–1.162) 0.689 a (23.13) 0.358 a (9.322) 0.635 a (20.023) 0.139 a (3.436) 1 Coefficient (t-statistic). a p<0.01. b p<0.05. Source: Authors’work. Table 4. Panel unit root tests. Im-Pesaran-Shin W-stat ADF-Fisher Chi square PP-Fisher Chi square Intercept Intercept & trend Intercept Intercept & trend Intercept Intercept & trend FDI −1.12 −0.62 163 b 165 b 305 a 370 a INF −5.57 a −0.96 270 a 192 a 297 a 277 a INN 3.94 1.04 179 b 120 173 a 364 a LAB 0.92 3.14 124 c 92 214 a 201 a MAR 0.13 −1.94 b 153 c 231 a 137 155 c TRA −2.75 a −0.75 190 a 181 a 258 a 189 a FID 1.02 0.56 127 c 133 193 a 331 a a p<0.01. b p<0.05. c p<0.1. Source: Authors’work. Table 5. Regression estimation results. Testing for chosen D-GMM dPOLS dD−GMM dFEM 0.7501 0.3683 0.2339 Coefficients estimated for Equation (1) following D-GMM Variable Coefficient Granger causality with FDI FDI(-1) 0.3683 (21.66) a N/A INF −0.0014 (−0.20) No INN 0.1768 (4.96) a INN () FDI b LAB 7.9408 (4.02) a LAB )FDI c MAR 0.5030 (1.69) c MAR )FDI c TRA 0.0094 (1.93) c TRADE )FDI a FID 5.0445 (2.21) b FID )FDI c Robustness tests J-statistic 34.25 Prob(J-statistic) 0.1589 Prob [AR(1)] 0.0035 Prob [AR(2)] 0.4616 Coefficient (t-statistic). a p<0.01. b p<0.05. c p<0.1. Source: Authors’work. COGENT ECONOMICS & FINANCE 7