Chinese Foreign Direct Investment Outflows and Host Country Economic Growth

Chinese Foreign Direct Investment Outflows and Host Country Economic Growth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chinese Foreign Direct Investment Outflows and Host Country Economic Growth Massimiliano Caporin, Arusha Cooray, Bekhzod Kuziboev, Jie Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3892998/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We examine how outward Chinese Foreign Direct Investment (FDI) flows affect the economic growth of 41 host nations over the 2005 to 2021 period. We also investigate the indirect effects of outward Chinese FDI flows on the economic growth of these countries through the government effectiveness and human capital channels. The empirical results reveal that Chinese FDI has significant positive impacts on host country economic growth. Significant threshold effects, however, are detected on the indirect impacts of FDI flows on host countries through the government effectiveness and human capital channels. The results suggest that when government effectiveness and human capital in host countries exceed a certain threshold, that Chinese FDI does not necessarily lead to economic growth in the group of countries under study. Foreign Direct Investment China host countries human capital government effectiveness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The emergence of China as an important source of foreign direct investment (FDI) for countries (e.g., Buckley et al., 2007 ; Morgan, 2021 ), has given rise to the question of whether Chinese FDI has led to economic growth in host nations. The ambitious Belt and Road Initiative (BRI) launched in 2013 to connect China with Asia, Africa, and Europe to promote economic development through improvements in infrastructure and connectivity, has seen a large volume of FDI flow from China into countries across the globe. More than 90 countries have joined China’s BRI, accounting for more than two-thirds of the world’s population. Over the 2005 to 2019 period, the low and middle-income nations have received 83.4 percent of the $ 815.3 billion worth of Chinese construction projects around the world, while high-income economies, received 62.1 percent of Chinese FDI outflows, totaling $ 1.23 trillion (ChinaPower 2023), making China the fourth largest source economy for FDI outflows (UNCTAD 2022 ). Neo classical theory postulates that FDI can lead to economic growth through capital accumulation, while New Growth Theory states that FDI can lead to growth through human capital accumulation, R&D, and externalities (Balasubramanyam et al. (1999). FDI can therefore, serve not only as a means of direct capital financing, but also act as a channel for generating positive externalities, contributing to long-run growth in host countries through the transfer of technological, intellectual, managerial skills and capital (Markusen and Nesse 2006 , Caves 1996 ), generating employment opportunities (Markusen and Nesse 2006 ), augmenting domestic savings, (Bosworth and Collins 1999 ), providing access to world markets (Iamsiraroj and Ulubasoglu 2015) and increasing the tax base of host nations (Markusen and Nesse 2006 ) . Some studies however, show that FDI may not lead to growth in host countries because the transmission of technology may not be suited to the receiving country, or that host nations may not possess the conditions conducive for FDI to have maximal benefits (Borensztein et al. ( 1998 ). Similarly, local firms could lose markets if faced by competition from multinational corporations. The prior literature has largely been based on outward FDI flows from developed countries. Assuming that FDI bring in more advanced technologies and skills to host countries and these MNCs are subject to more stringent regulations in home countries, inward FDIs could lead to growth in host countries. Alternatively, FDI may not contribute to an increase in growth if accompanied by to competition and a race to the bottom with falling labour, environmental and other standards in host nations. However, such an assumption which holds for FDIs from developed countries may not necessarily hold for FDIs from emerging markets such as China. Studies which investigate outward FDI from China show that Chinese FDI is attracted to nations with weak institutions (Buckely et al. 2007, Fu et al. 2020), natural resources (Cheung and Quan 2009, Fu et al. 2020), GDP, cultural proximity, and common borders (Cheng and Ma 2010 ). A feature of Chinese investment is that it is mainly concentrated in developing nations with significant implications for growth and development in these countries (Cheung and Quan 2009). Emerging country FDI may be used as a means towards entering host countries with similar institutional risk (Arite 2013), or countries operating under difficult institutional climates. According to Cuervo-Cazurra and Genc ( 2008 ), these weak institutional climates can be used to developing-country MNEs advantage, as they are used to operating under such circumstances. Against this backdrop, we investigate how Chinese FDI has influenced home country economic growth. The last decade has witnessed a significant rise in FDI outflows from China. FDI is a significant contributor to host country economic growth and therefore it is of critical importance to understand the effects of FDI on host nations. While media coverage has highlighted the adverse effects of Chinese investments on host countries including debt traps, the environment (Radwin 2022), there is an absence of empirical evidence to suggest this. Thus, our contribution to the literature is twofold. The present study extends upon the literature by, one, investigating the direct impact of Chinese FDI outflows on host country economic growth. To this end, we use data for 41 host countries over the 2005–2021 period. As stated above, the effects of FDI on economic growth rather than being a direct process, maybe an indirect one which take place through other channels. Therefore, a second contribution of the study is that we also investigate the indirect effects of Chinese FDI on economic growth through the additional channels of human capital and government effectiveness in host nations. Studies show that the capacity of host economies to benefit from FDI depends on a host country’s education levels. Studies suggest that countries require a minimum level of human capital to benefit from FDI and positive externalities of FDI inflows (Borensztein et al. 1998 , Li and Liu 2005 , Kokko 1994 ). According to these studies, if countries have the minimum threshold of human capital, FDI will lead to the further enhancement of human capital through education, skills transfer and on the job training in host countries (Cooray 2016 , Nunnenkamp 2002 , Bloomstrom and Kokko 2003, Li and Liu 2005 , Egger et al. 2010) leading to economic growth. If on the other hand, there are significant gaps in knowledge between source and host nations, FDI may not lead to a transfer of skills or generate positive externalities in host nations. Does Chinese FDI require these minimum levels of human capital? Or is Chinese FDI attracted to low level skills in which case, FDI may not increase growth in the host country through education. Evidence shows that China has invested in several countries which include both, those with high levels of education (Western and Eastern Europe) and low levels of education (Asia and Africa). We account for the indirect effects of FDI on economic growth through human capital by incorporating an interaction term between FDI and human capital. There has also been consensus among economists on the importance of institutions for economic performance. For example, Acemoglu et al. ( 2001 ) highlight the importance of institutions for economic growth, Wernick et al. ( 2009 ) the significance of institutions for FDI, Globerman and Shapiro ( 2002 ) governance infrastructure, Altomante (2000) a host country’s institutional framework. Countries with more effective governments, will channel funds into productive investments experiencing higher growth rates while those with weak and ineffective governments can influence the direction of funds into projects that are of political interest thus reducing economic growth (La Porta et al. 2002 ; Shleifer and Vishny 1994 ). Studies on China suggest that China is attracted to nations with similar institutional risk (Arita 2013), and those operating under weak institutional climates (Cuervo-Cazurra and Genc 2008 ). Hence, to account for this, we estimate the effect of FDI on economic growth through government effectiveness by incorporating an interaction term for FDI with government effectiveness. We perform several robustness checks to test the validity of our results including fixed effects estimation, marginal plots, partially non-linear models, instrumental variable (IV) estimation, sub-sample tests, and threshold estimation. Our results suggest that Chinese FDI has a positive and significant impact on host country economic growth. The indirect impacts of FDI on economic growth through the government effectiveness and human capital channels are found to exhibit threshold effects. The rest of this paper is structured as follows. Section 2 discusses the literature. Section 3 presents the data and model. Section 4 evaluates the empirical results and Section 5 concludes. 2. Theoretical Foundation Studies on the effects of FDI on economic growth have shown mixed results. The literature shows that FDI can facilitate economic development in host countries not only through direct capital financing but through positive spillovers including technological diffusion, innovation, the transfer of managerial skills, markets, demand for raw materials, promotion of exports, and productivity growth (Blomström & Kokko, 1996; Buckley et al., 2007 ; Lall & Albaladejo, 2004), human capital (Kokko 1994 , Ahmed and Kialashaki 2019 ), greater openness (Balasubramanyam et al. 1999, Ekanayake et al. 2023 ), financial sector development (Hermes and Lensink 2003 ), stronger institutions (Wernick et al. 2009 ). A number of studies show that the beneficial effects of FDI on host countries depend on host countries characteristics. Borensztein et al. ( 1998 ) show that countries with higher absorption capacity, measured by a minimum threshold level of human capital can gain from FDI inflows. Li and Liu ( 2005 ) similarly argue that FDI increases economic growth in developing countries both directly and indirectly through its interaction with human capital. This is supported by Kokko ( 1994 ) who states that the positive externalities of FDI are higher the more educated a nations stock of human capital, which in turn leads to a higher level of competition and lower conditions for the entry of source firms. Similar findings are put forward by Ahmed and Kialashaki ( 2019 ) who find that human capital provides the strongest support for affecting GDP and catching up for a group of Asia-Pacific countries. The literature also highlights the importance of institutions and government effectiveness for FDI inflows. Altomonte ( 2000 ) in a study of FDI inflows into Central and Eastern Europe, finds that MNCs not only consider conventional determinants but also factors linked to the institutional environment of host economies when considering investing in a country. Examining the relation between governing institutions and FDI inflows to emerging economies, Wernick et al. ( 2009 ) find that governing institutions influence FDI inflows into host nations. Dawson similarly in an investigation of the relationship between institutions, investment, and growth, finds that market institutions have a positive impact on economic growth and that economic freedom affects growth through investment. Brewer ( 1993 ) argues that government policies influence FDI decisions through their effects on market imperfections. Buckley et al. ( 2007 ) on the contrary, find that Chinese FDI is drawn to countries with weak institutions and Fu et al. (2020) observe that Chinese investment is drawn to nations with natural resources, the weaker the institutional climate of the country. Choi and Samy ( 2008 ) similarly, find that democratic institutions have a weak association with an increase in FDI inflows. Henisz ( 2000 ) argues that as political hazards rise, MNCs face a growing risk of opportunistic exploitation by host country governments which can be minimized by collaborating with host-country firms. As hazards rise however, the local collaborative partner can use the political system for its own gain at the cost of the MNC according to Henisz ( 2000 ). Another strand of the literature has focused on the importance of trade openness and the financial sector. Nair-Reichert and Weinhold ( 2001 ) observe a positive relationship between FDI and economic growth in countries with more open economies. They argue that more open countries gain greater benefits from FDI. These findings are supported by Balasubramanyam eta al. (1999) who show that trade openness is vital for FDI to have a positive effect on economic growth. Doğan et al. ( 2020 ) similarly observe that FDI enhances economic growth in a group of 32 European countries over the period 1995–2014 in a study of energy consumption, FDI and trade on economic growth. Beata ( 2004 ) in a study of spillovers from FDI on firm-level data from Lithuania, find that positive productivity spillovers from FDI take place through contacts between foreign affiliates and their local suppliers in upstream sectors. More recently Ekanayake et al. ( 2023 ) find that a large part of the effect of trade in income takes place through education and fertility. Others argue that FDI leads to growth in nations with well-developed financial markets (Alfaro et al. 2004 ). Hermes and Lensink ( 2003 ) argue that strong financial systems enable technological spillovers in host nations. A further group of works shows that FDI may not lead to positive outcomes in host economies under certain circumstances. The diffusion of technology for example, may not be suited to firms in host countries. Carkovic and Levie (2002) find that FDI does not have a robust, independent effect on economic growth. Similarly, foreign companies could operate in isolation of local companies, in which case there would be no spillover effects to local companies. These companies could, on the other hand, compete with host companies leading to the closure of local firms (Markusen & Venables, 1999 ). Other problems associated with FDI include a deteriorating balance of payments as dividends are sent back to home countries. Some studies show that MNCs have damaging environmental impacts on host nations. Hence, the effect of FDI on host economies is not clear-cut. Studies also show that FDI has positive and significant effects on developed countries, but insignificant effects on developing countries (Dimelis and Papaioannou 2010 ). Kottaridi and Stengos). Gunby et al. ( 2017 ) in a meta investigation of FDI into China, note that the effect of FDI on Chinese economic growth is much smaller than one would expect. They argue that publication bias and a profusion of estimates inflate observed values. Once these effects are taken into account, the estimated effect of FDI on economic growth in China is reduced to statistical insignificance. Belloumi ( 2014 ) similarly, in a test of the relationship between FDI, trade openness and economic growth in Tunisia which has been facing unemployment problems and lack of technological progress, finds no significant Granger causality from FDI to economic growth or from economic growth to FDI. Thus, while our study is related to the existing literature, the point of departure is that we consider how FDI outflows from a single country, China, affects host country growth, considering the fact that the outflows can also affect host nations indirectly through the channels of human capital and government effectiveness. 3. The Model and Data The sample comprises data for 41 host countries, selected on the basis of data availability over the 2005 to 2021 period. 1 Our empirical model takes the following form: $$\begin{gathered} PGDPG={\beta _0}+{\beta _1}\ln OFD{I_{it}}+{\beta _2}\ln PGDPL+{\beta _3}\ln P{D_{it}}+{\beta _4}\ln T{O_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\beta _5}\ln D{C_{it}}+{\beta _6}\ln S{E_{it}}+{\beta _7}GE{I_{it}}+{\mu _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 1 where PGDPG is the growth rate of GDP per capita; our main independent variable of interest is outward foreign direct investment from China which is denoted by OFDI. The control variables include GDP per capita lagged by one year ( PGDPL ) which is a standard measure of convergence, and population density ( PD ) as used in growth models. Additional control variables include, trade openness to GDP ( TO ) used as a proxy for trade policy as some studies show that trade contributes to economic growth (Burnsideand and Dollar 2000). Domestic credit to the private sector ( DC ) is used to measure the role of the financial sector in economic growth as Rousseau and Wachtel ( 2005 ), Levine and Zervos (1998), Beck et al. ( 1999 ) among others, highlight the importance of the role of finance in economic growth. Studies suggest that countries with higher levels of human capital receive more FDI and thus experience higher growth rates (Balasubramanyam et al. 1999 among others). The school secondary enrollment rate ( SE ) is employed to capture human capital as Mankiw et al. ( 1992 ) among others. Institutional quality has been linked to FDI and economic growth. Therefore, the government effectiveness index ( GEI ) from the World Bank is used to capture institutions. \({\mu _i}\) is a country fixed effect; and \({\varepsilon _{it}}\) indicates a random error term. The data definitions and sources are given in Table 1 . Table 1 Definition of variables Type Notation Name Definition Source Dependent variable PGDPG Economic growth PGDP growth rate WDI database Main independent variable OFDI Outward foreign direct investment Outward foreign direct investment from China CGIT database Control variables PD Population density Population per unit area WDI database TO Trade openness Share of trade in GDP WDI database DC Domestic credit Share of domestic credit to the private sector in GDP WDI database SE School enrollment rate Secondary school enrollment rate WDI database GEI Government effectiveness Government effectiveness index WDI database The data for PGDPG , PD , TO , DC , SE , and GEI are collected from the World Development Indicator (WDI) database. Data for OFDI come from the CGIC (China Global Investment Tracker) database. Table 2 provides the descriptive statistics of the variables included in this study. Table 2 Statistical description of variables Variables Obs Unit Mean S.D. Min. Median Max. PGDPG 697 % 2.515 4.003 -18.485 2.926 15.520 OFDI 697 Million USD 1396.389 2014.254 1.000 640.000 22140.000 PGDPL 656 constant 2015 USD 5878.843 8307.025 305.979 3205.968 58672.484 PD 697 people per sq. km of land area 125.488 194.457 1.646 73.045 1286.172 TO 697 % 66.636 35.485 11.670 54.440 203.855 DC 697 % 41.532 34.897 2.010 33.072 303.380 SE 697 % 74.288 28.089 9.631 82.682 158.630 GEI 697 - -0.376 0.558 -1.623 -0.432 1.505 We further estimate the interaction effects of enrollment rate and GEI on the relationship between OFDI and PGDP, respectively. $$\begin{gathered} PGDPG={\beta _0}+{\beta _1}\ln OFD{I_{it}}+{\beta _2}PGDPL+{\beta _3}\ln P{D_{it}}+{\beta _4}\ln T{O_{it}}+{\beta _5}\ln D{C_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\beta _6}\ln S{E_{it}}+{\beta _7}GE{I_{it}}+{\beta _8}\ln OFD{I_{it}}*GE{I_{it}}+{\mu _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 2 $$\begin{gathered} PGDPG={\beta _0}+{\beta _1}\ln OFD{I_{it}}+{\beta _2}PGDPL+{\beta _3}\ln P{D_{it}}+{\beta _4}\ln T{O_{it}}+{\beta _5}\ln D{C_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\beta _6}\ln S{E_{it}}+{\beta _7}GE{I_{it}}+{\beta _8}\ln OFD{I_{it}}*\ln S{E_{it}}+{\mu _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 3 $$\begin{gathered} PGDPG={\beta _0}+{\beta _1}\ln OFD{I_{it}}+{\beta _2}PGDPL+{\beta _3}\ln P{D_{it}}+{\beta _4}\ln T{O_{it}}+{\beta _5}\ln D{C_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\beta _6}\ln S{E_{it}}+{\beta _7}GE{I_{it}}+{\beta _8}\ln OFD{I_{it}}*GE{I_{it}}+{\beta _9}\ln OFD{I_{it}}*\ln S{E_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\beta _{10}}\ln OFD{I_{it}}*\ln S{E_{it}}*GE{I_{it}}+{\mu _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 4 The equations ( 2 ) to ( 4 ) still follow the strict linear assumptions, which may lead to misspecification due to the possible presence of non-linearities. The linear regression model can only be considered an approximation of nonlinear relationship between variables. The relation among variables might change according to factors related to the economy, technology, population, government governance, trade, and education. In addition, the impact of OFDI on PGDPG is subject to heterogeneous changes in different periods and countries. As an alternative to the linear model we might consider the partially linear functional-coefficient models (PLFC), a semi-parametric method that not only captures the nonlinear structure of functional coefficients and heterogeneity over countries and time, but also controls the linear influence of other variables (Du et al., 2020 ). Therefore, we apply the PLFC model with fixed effects estimation to address potential misspecification issues. The model is represented as: $$\begin{gathered} PGDPG={\phi _0}+G(GE{I_{it}})\ln OFD{I_{it}}+{\phi _1}PGDPL+{\phi _2}\ln P{D_{it}}+{\phi _3}\ln T{O_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\phi _4}\ln D{C_{it}}+{\phi _5}\ln S{E_{it}}+{\phi _6}GE{I_{it}}+{\upsilon _i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 5 $$\begin{gathered} PGDPG={\alpha _0}+G(\ln S{E_{it}})\ln OFD{I_{it}}+{\alpha _1}PGDPL+{\alpha _2}\ln P{D_{it}}+{\alpha _3}\ln T{O_{it}} \hfill \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+{\alpha _4}\ln D{C_{it}}+{\alpha _5}\ln S{E_{it}}+{\alpha _6}GE{I_{it}}+{w_i}+{\varepsilon _{it}} \hfill \\ \end{gathered}$$ 6 where G ( GEI it ) and G ( lnSE it ) are an unknown function of GEI it and lnSE it , respectively. The model captures the nonparametric impact of OFDI on the economic growth and at the same part allows for a linear impact of other control variables. Growth and OFDI could both be related to further country-specific variables omitted from the model. If this is the case, the estimates could be biased and inconsistent and the relation between the variables may not be a causal relation. Therefore, as a further test for detecting causality relationships, we use the instrumental variable (IV) method for parameters' estimation. Considering the available data, we instrument OFDI in the host country with the average value of OFDI in the rest of the countries. By excluding the target country in the construction of the instruments, we control for the correlation with the country-specific omitted variables. Differently, for OFDI, the average value represents a proxy for China policy in foreign direct investments and would be thus correlated with the OFDI in the target country. 4. Results and discussion 4.1 Results of the baseline model We check the stationarity of the panel datasets using the Levin, Lin, and Chu (LLC) unit root test (Levin et al., 2002 ) before estimating the models. As can be seen from Table 3 , the LLC test shows that all variables are stationary in the levels. 2 Table 3 Panel unit-root test Variables Statistic p-value PGDPG -3.8664 0.0001 lnOFDI -7.7315 0.0000 lnPGDPL -6.2218 0.0000 lnPD -8.3635 0.0000 lnTO -2.9226 0.0017 lnDC -6.4617 0.0000 lnSE -3.8671 0.0001 GEI -4.8775 0.0000 Note: ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively; the null hypothesis is that the data series contains a unit root. We first examine the linear impact of OFDI on PGDPG using an ordinary panel data model. Based on the results of the Hausman test, we adopt a fixed effect model. In order to avoid heteroscedasticity, autocorrelation and contemporaneous correlation problems, Feasible Generalized Least Squares (FGLS) is used to estimate the panel data model. In Table 4 , M1 shows the estimated coefficient of OFDI on PGDPG without considering the control variables. The coefficient is positive and significant at the 1% level, suggesting that an increase in Chinese OFDI leads to an increase in economic growth in host nations. The coefficient on OFDI continues to be significant and positive after adding control variables in models M2-M4. According to M2, an increase in OFDI by 1% will result in an increase in PGDPG by 0.08%. Table 4 Results of baseline model M1 M2 M3 M4 M5 lnOFDI 0.072 *** 0.078 *** 0.042 * 0.561 *** -1.107 ** (0.012) (0.016) (0.023) (0.189) (0.551) lnPGDPL -9.965 *** -9.967 *** -15.833 *** -9.540 *** (1.294) (1.611) (1.878) (1.345) lnPD -2.365 ** -2.141 * -11.948 *** -1.565 (1.105) (1.300) (2.778) (2.472) lnTO 3.828 *** 3.897 *** 2.178 *** 3.356 *** (0.320) (0.326) (0.664) (0.354) lnDC -0.256 0.044 0.747 0.206 (0.309) (0.315) (0.469) (0.505) lnSE 1.639 ** 1.968 ** 0.953 0.955 (0.823) (0.811) (1.078) (1.041) GEI 1.275 *** 1.861 *** 0.351 1.406 *** (0.382) (0.361) (0.710) (0.545) GEI*lnOFDI -0.065 ** -1.496 *** (0.030) (0.495) lnSE*lnOFDI -0.124 *** 0.263 ** (0.045) (0.123) GEI*lnSE*lnOFDI 0.335 *** (0.107) Constant 502.991 *** -20.471 0.101 -611.754 *** 92.363 (23.592) (141.780) (145.431) (203.875) (169.089) Hausman test 34.85 *** 84.33 *** 85.40 *** 83.14 *** 91.11 *** Individual fixed effects Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Wald chi2 712.76 *** 1347.68 *** 704.87 *** 707.63 *** 1290.83 *** N 697 697 697 697 697 Note: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively. In column M2, the lagged value of GDP per capita (GDPL) is negative and significant, suggesting convergence among the economies. Population density has a negative and significant effect on economic growth while trade has a positive and significant impact on economic growth. Domestic credit is not statistically significant. The stock of human capital as measured by the secondary enrolment ratio is positive and significant at the 5% level, while government effectiveness is positive and significant at the 1% level. We next add the interaction term between government effectiveness and Chinese FDI, GEI * lnOFDI in M3 and SE * lnOFDI in M4. OFDI continues to have a positive and significant effect on economic growth and the coefficients on the control variables are consistent with the results obtained in M2 and government effectiveness on its own is statistically significant and positive. The coefficient on the interaction term is surprisingly negative GEI * lnOFDI in M3. In M4, secondary enrolment (SE) is surprisingly not statistically significant, and the interaction term is negative and significant contrary to expectations. To gain a better idea of the impact of these variables on economic growth, we look at the marginal effects of OFDI on per capita growth depending on government effectiveness (GEI) in Fig. 1 . The solid line represents the marginal effect of Chinese FDI, and the dashed line shows the 90% confidence interval around the marginal effect. As shown by Fig. 1 , the marginal effect of OFDI on PGDPG is 0.042–0.065* GEI . This implies that when GEI is less than 0.646, the coefficient on OFDI is significant and positive. The graph shows that GEI causes Chinese FDI to promote growth in low-income countries. However, the marginal effect of Chinese FDI on economic growth falls as GEI increases, in the sample of host countries under study. The results suggest that countries with well-functioning governments, do not require Chinese FDI to promote economic growth. Figure 2 shows the estimated marginal effect of lnOFDI on economic growth depending on human capital ( lnSE) . The results show that with the growth of lnSE , the marginal effect of lnOFDI on PGDPG is 0.561 − 0.124* lnSE . That is, when lnSE is less than 4.524, the marginal effect of lnOFDI on PGDPG is significant and positive, leading to growth in host countries. When however, lnSE exceeds the above-mentioned threshold, lnOFDI has a negative and insignificant effect on per capita income growth. These results also suggest that when the level of human capital in a host country tends to increase, the Chinese FDI inflows do not promote economic growth in the host country. Based on M2, M3 and M4, we add interaction terms for both GEI * lnOFDI and lnSE * lnOFDI , and all three terms in M5. Figure 3 (A) reports the estimated marginal effect of lnOFDI , lnSE , and GEI , which equals − 1.107–1.496* GEI + 0.263* lnSE + 0.335* lnSE * GEI . The trend of the marginal effects of OFDI with the growth of GEI and lnSE is shown in Fig. 3 (B-C). With the growth of lnSE and GEI , the impact of OFDI shows a U-shaped trend. When GEI <-0.616, the marginal effect curve of OFDI is above 0, indicating that OFDI promotes economic growth. However, the marginal effect curve of OFDI is below 0, OFDI does not promote economic growth, that is, when − 0.616 ≤ GEI ≤ 0.407. It may be due to the negative effect of OFDI caused by the influence of the COVID-19 epidemic on global economic activities. When growth in GEI exceeds the threshold, OFDI plays a positive role in promoting economic growth. The 90% confidence interval (CI) indicates a significant marginal effect of OFDI when GEI < -0.616; otherwise, it is insignificant. Similarly, when lnSE < 3.274, the coefficient on lnOFDI is significantly positive, indicating that OFDI promotes economic growth. The coefficient on lnOFDI is negative and not significant when 3.274 ≤ lnSE ≤ 4.297, suggesting that OFDI does not affect economic growth. However, the coefficient on lnOFDI is positive and insignificant when lnSE surpasses the threshold. As can be seen from M5 in Table 4 , lnPGDPL has a significant negative impact on PGDPG , suggesting convergence among the nations. Trade ( lnTO) leads to an increase in PGDPG . The increase in international trade helps to promote economic growth. Population growth ( InPD ) and domestic credit ( lnDC ) have no significant impact on PGDPG . The above analysis is based on simple linear function models which may not be capturing the real effects of OFDI on economic growth. As a result, it is necessary to loosen the linear assumptions. 4.2 Partially linear functional-coefficient panel model Next, we use a partially linear functional-coefficient model to investigate the non-linear impacts of lnOFDI on PGDPG . The marginal effect of lnOFDI on PGDPG increases as GEI increases, as shown by Fig. 4 . When GEI <-1.382, the marginal effect of lnOFDI on PGDPG is negative and significant, suggesting that OFDI leads to the decline of PGDPG . It is possible that at the initial stages, OFDI favours downstream processing industries with lower value-added investment and low-level skills in low income countries. When − 1.382 ≤ GEI <-0.956, the marginal effect of lnOFDI is insignificant. The confidence interval contains 0, indicating that the null hypothesis with a coefficient of 0 cannot be rejected. The stage could be a transition period. OFDI then begins to have a positive impact on economic growth. When − 0.956 ≤ GEI < 0.705, the marginal effect of lnOFDI is significant and positive, suggesting that OFDI promotes an increase in PGDPG . However, when GEI exceeds the threshold, lnOFDI is negative and insignificant. While, here, the marginal effects can be divided into four periods the conclusion that when GEI exceeds a threshold of 0.705, that the marginal effect of OFDI on economic growth is negative is consistent with our previous result that in nations with effective governments, Chinese FDI does not lead to economic growth. Table 5 Linear part of the partially linear functional-coefficient panel data model Dependent variable: PGDPG M6 M7 lnPGDPL -40.280 *** -38.941 *** (3.514) (2.888) lnPD -7.865 -9.371 (5.042) (5.806) lnTO 7.598 *** 8.631 *** (1.225) (1.437) lnDC -1.709 -1.623 (1.365) (1.306) lnSE 4.767 ** 2.778 ** (2.328) (1.217) GEI 2.410 * 5.328 * (1.418) (2.879) R-squared 0.236 0.289 N 697 697 Note: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively. M6 in Table 5 reports the linear impacts of the control variables. As mentioned above, the impact of lnPGDPL on PGDPG is significant and negative. lnTO and GEI have a significant positive impact on PGDPG . lnPD does not have a significant effect on PGDPG. In contrast, the secondary enrolment ratio lnSE has a significant and positive impact on PGDPG . The semiparametric model is better at revealing variable impact compared to the linear model. Figure 5 illustrates the heterogeneous impact of lnSE on the relationship between lnOFDI and PGDPG . The coefficient on lnOFDI changes from negative and insignificant to positive and significant when lnSE increases to 4.026. It suggests that the growth in lnSE strengthens the positive impact of China's OFDI on PGDPG . As suggested by the literature, education can augment technological progress and accelerate the diffusion of advanced technology, allowing for the accumulation of human and physical capital to contribute to economic growth. When the value of lnSE is greater than 4.776, the confidence interval widens abruptly which suggests that the estimate results may not be reliable here. Here too, threshold effects are detected as before. 4.3 Robustness tests We undertake several additional robustness checks to test for the robustness of our results. Testing for Endogeneity Next, we employ instrumental variable estimation to examine the potential endogeneity of the relationship between OFDI and PGDP . OFDI is regarded as an endogenous variable, and we instrument it with the average value of OFDI of all other countries in the same year, excluding the local country. Our instrumental variable is correlated with OFDI, and uncorrelated with the random error term, and passes the Anderson canon. Corr. LM statistic, the Cragg-Donald Wald F statistic, and Sargan statistic tests. The results are for the IV estimation are shown in R1 of Table 6 . The coefficient on lnOFDI is positive and significant at the 1% level, indicating that OFDI promotes economic growth in the sample of countries being investigated. The results confirm the robustness of the empirical findings. Table 6 Results of robustness test Dependent variable: PGDPG R1 R2 R3 R4 R5 \(\hat {\gamma }\) 1.069 4.533 CI[0.956, 1.317] CI[4.528, 4.540] lnOFDI 0.955 *** Figure 6 Figure 7 (0.245) Low regime 0.092 * 0.078 (0.049) (0.055) High regime 0.775 *** 0.157 ** (0.183) (0.069) lnPGDPL -11.312 *** -33.206 *** -34.713 *** -7.915 *** -8.124 *** (1.564) (3.718) (3.088) (1.069) (1.084) lnPD -4.338 * -1.126 -1.279 -2.392 -1.266 (2.267) (5.554) (6.085) (1.728) (1.719) lnTO 1.247 5.279 *** 6.417 *** 1.769 ** 2.049 *** (0.915) (1.116) (1.339) (0.726) (0.732) lnDC -0.101 -2.069 -1.863 0.236 0.093 (0.561) (1.488) (1.511) (0.454) (0.459) lnSE 0.378 0.535 1.358 -0.101 -0.446 (1.054) (1.466) (1.197) (1.081) (1.101) GEI -0.236 4.306 * 4.629 0.352 0.786 (1.328) (2.563) (3.224) (0.862) (0.866) Constant 68.150 *** 66.156 *** (10.512) (10.612) Anderson canon. corr. LM statistic 37.753 *** Cragg-Donald Wald F statistic 39.764 Sargan statistic 0.000 R-squared 0.187 0.252 0.265 0.299 N 697 697 697 697 697 Note: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively. Subsample tests Further, we leave out the years 2005–2007 and run the estimation on the sub-sample from 2008 to 2021 and re-estimate the nonlinear impact of OFDI on PGDPG . The interactive influence of GEI on the nonlinear relationship between OFDI and PGDPG is shown in Fig. 6 . The estimation for the linear part is shown in R2 of Table 6 . Figure 6 shows that when GEI≥-0.447, the marginal effect of lnOFDI on PGDPG is significantly positive and increases with GEI. The threshold value of GEI was close to -0.956 in Fig. 4 . The removal of the years, 2005 to 2007 causes a change in the threshold value of GEI <-0.447. As can be seen from the Figure, OFDI has a positive impact on PGDPG. Figure 7 and R3 in Table 6 indicate that the interaction influence of lnSE on OFDI and PGDPG is consistent with that of Section 4.4. The evidence supports the empirical findings. Alternative regression method Finally, we use the panel threshold regression model proposed by Hansen ( 1999 ) to verify the robustness of the empirical results. GEI and lnSE are selected as threshold variables. The results are shown in R4-5 of Table 6 . The threshold values ( \(\hat {\gamma }\) ) of GEI and lnSE are 1.699 and 4.533, respectively. lnOFDI has a significant positive impact on PGDPG when GEI and lnSE exceed the threshold values, respectively. The result is in line with our previous empirical findings. 5. Conclusions and policy implications This study examines the effects of outward Chinese FDI flows on the economic growth of 41 host nations over the 2005 to 2021 period. We also examine the effects of outward Chinese FDI flows on the economic growth in these host nations via the government effectiveness and human capital channels. Overall, the empirical results reveal that Chinese FDI has positive impacts on host country economic growth. Significant threshold effects are detected on the indirect impacts of FDI flows on host countries through the government effectiveness and human capital channels. While at lower levels of GEI and human capital, Chinese FDI is found to promote economic growth in host nations, Chinese FDI is not found to necessarily lead to economic growth when host nations government effectiveness and human capital levels are above a certain threshold. The results suggest that when the quality of public services and civil services, are strong and political intervention is low, and the education levels of the population are high, that countries do not necessarily have to depend on Chinese FDI for economic growth. Trade is found to have a positive significant effect on economic growth, while population density and the financial sector have no significant effects on economic growth. Host countries are recommended to strengthen their institutions and develop their stocks of capital through education and training. In addition, countries should promote trade openness to promote economic growth. Having an effective government, a skilled and educated workforce and open economies will encourage more FDI without fostering dependence. Declarations Author Contribution Author Contributions:MC: empirical analysis and empirical methods.AC: literature and writing up of the manuscript. BK: data collection and preparation of data. JL: Empirical estimation and analysis of results.All authors reviewed the manuscript References Acemoglu, D., Johnson, S., & Robinson, J. A. (2001) The colonial origins of comparative development: An empirical investigation, American Economic review, 91 (5), 1369-1401. 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(2018). A retrospective and agenda for future research on Chinese outward foreign direct investment, Journal of International Business Studies, 49 , 4-23. Buckley P, Clegg I, Cross, A, Liu X, Voss H, Zheng P (2007) The determinants of Chinese outward foreign direct investment Journal of International Business Studies, 38. 499-518. Carkovic, M., & Levine, R. (2005). Does foreign direct investment accelerate economic growth. Does Foreign Direct Investment Promote Development, 195 , 220. Caves, R. E. (1996) Multinational Enterprise and Economics Analysis. 2nd ed. Cambridge: Cambridge University Press, 1996. Chakraborty, C., & Nunnenkamp, P. (2008). Economic reforms, FDI, and economic growth in India: a sector level analysis. World development, 36 (7), 1192-1212. Cheng, L and Ma Z (2010) China's outward foreign direct investment. In China's growing role in world trade , pp. 545-578. University of Chicago press. Cheung, Y. W., & Qian, X. (2009). 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Journal of Law, Economics, and Organization, 16 (2), 334-364. Hansen, B.E., (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics 93, 345-368. Hermes, N., & Lensink, R. (2003). Foreign direct investment, financial development and economic growth. Journal of Development Studies, 40(1), 142–163. Iamsiraroj, S., & Ulubaşoğlu, M. A. (2015). Foreign direct investment and economic growth: A real relationship or wishful thinking? Economic modelling, 51 , 200-213. Kao, C., (1999) Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90, 1-44. Kokko A (1994) Technology, market characteristics, and spillovers, Journal of Development Economics, 43 (1994), pp. 279-293. La Porta, R., Lopez‐de‐Silanes, F., & Shleifer, A. (2002). Government ownership of banks. The Journal of Finance, 57 (1), 265-301. Levin, A.T., Lin, C.F.J., Chu, C.-S.J., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1-24. Lin, J.Y., 2003. Development strategy, viability, and economic convergence. Economic Development and Cultural Change 51, 277-308. Li, X., & Liu, X. (2005). Foreign direct investment and economic growth: An increasingly endogenous relationship. World Development, 33(3), 393–407. Mankiw, G, Romer, D and Weil, D. (1992) A Contribution to the Empirics of Economic Growth, Quarterly Journal of Economics, 107, 407-437. Markusen, A., and K. Nesse. (2006) Institutional and Political Determinants of Incentive Competition: Reassessing Causes, Outcomes, Remedies, Project on Regional and Industrial Economics. Markusen, J. R., & Venables, A. J. (1999). Foreign direct investment as a catalyst for industrial development. European Economic Review, 43 (2), 335-356. Morgan, P. (2021). ‘Many Chinas? ’Provincial internationalization and Chinese foreign direct investment in Africa, Oxford Development Studies, 49 (4), 351-367. Nair-Reichert U and Weinhold D (2001) Causality tests for cross-country panels: A new look at FDI and economic growth in developing countries, Oxford Bulletin of Economics and Statistics, 63 (2001), 153-171. Nunnenkamp, P. (2002). Determinants of FDI in developing countries: has globalization changed the rules of the game? (No. 1122). Kiel working paper. Du, K., Zhang, Y., Zhou, Q., 2020. Fitting partially linear functional-coefficient panel-data models with Stata. The Stata Journal 20, 976 - 998. Hansen, B.E., 1999. Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics 93, 345-368. Levin, A.T., Lin, C.F.J., Chu, C.-S.J., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1-24. Rousseau P and Wachtel P. (2005) Equity Markets and Growth: Cross Country Evidence on Timing Outcomes, 1980-1995, Journal of Banking and Finance, 24, 1933-1957. Shleifer, A., & Vishny, R. W. (1994). Politicians and firms. The Quarterly Journal of Economics, 109(4), 995-1025. UNCTAD (2022) Handbook of Statistics 2022, Geneva: https://hbs.unctad.org/foreign-direct-investment/ Wernick, D. A., Haar, J., and Singh, S. (2009). Do governing institutions affect foreign direct investment inflows? New evidence from emerging economies. International Journal of Economics and Business Research, 1 (3), 317-332. Footnotes The countries included in out analyses are: Algeria, Angola, Argentina, Bangladesh, Belarus, Brazil, Cameroon, Congo, Ecuador, Egypt, Ethiopia, Ghana, Guinea, India, Indonesia, Iran, Jordan, Kazakhstan, Kenya, Kuwait, Laos, Malaysia, Mongolia, Myanmar, Nepal, Niger, Nigeria, Pakistan, Peru, Philippines, Russian Federation, Saudi Arabia, Serbia, South Africa, Sri Lanka, Tanzania, Thailand, Turkiye, UAE, Uganda, Uzbekistan. For all variables we reject the null hypothesis of the existence of unit roots at the 1% significance level. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3892998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270127572,"identity":"d6d53f29-9070-4d8b-8b34-d521a3e9ab64","order_by":0,"name":"Massimiliano Caporin","email":"","orcid":"","institution":"University of Padova","correspondingAuthor":false,"prefix":"","firstName":"Massimiliano","middleName":"","lastName":"Caporin","suffix":""},{"id":270127573,"identity":"a8ce6e62-cab9-4140-91ab-220ac7ceb395","order_by":1,"name":"Arusha 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ln\u003cem\u003eOFDI\u003c/em\u003e and \u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/f3edb228f4d71b0b83b64fc4.png"},{"id":50430243,"identity":"2ffd8479-da61-44b0-92e2-efbfc0d707c0","added_by":"auto","created_at":"2024-01-31 11:53:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38951,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal effect of ln\u003cem\u003eOFDI\u003c/em\u003e and ln\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/d415088836246823732b99a5.png"},{"id":50430244,"identity":"62826cb3-9618-44fb-9987-8cf0a3447ae0","added_by":"auto","created_at":"2024-01-31 11:53:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223669,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal effect of ln\u003cem\u003eOFDI\u003c/em\u003e, ln\u003cem\u003eSE\u003c/em\u003e and \u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/ad892b6797c76b3196e4824d.jpg"},{"id":50430822,"identity":"5eea93c6-a5ae-446b-b2e1-fe1f859ddaa3","added_by":"auto","created_at":"2024-01-31 12:01:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45591,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional coefficients of \u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/2356c41162b4548593bcb9aa.png"},{"id":50430242,"identity":"281c8c06-05d9-4852-b868-e54d2e552757","added_by":"auto","created_at":"2024-01-31 11:53:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45161,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional coefficients of \u003cem\u003elnSE\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/2fda1df96c38a3526a64d053.png"},{"id":50430823,"identity":"75bbd792-29e6-4ea7-859a-cffb0f21c521","added_by":"auto","created_at":"2024-01-31 12:01:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41649,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional coefficients of \u003cem\u003eGEI \u003c/em\u003efrom 2008 to 2020\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/b7bab1541161878c5881648a.png"},{"id":50430240,"identity":"ac31c6a0-a47c-4b65-a860-a6cac7fe2c59","added_by":"auto","created_at":"2024-01-31 11:53:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":44782,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional coefficients of \u003cem\u003elnSE \u003c/em\u003efrom 2008 to 2020\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/e655003572d3b92f6b43f430.png"},{"id":52720204,"identity":"18dabb85-a700-416d-9d41-370d63d6cfec","added_by":"auto","created_at":"2024-03-15 01:07:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":962192,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3892998/v1/735720f4-4199-43c7-b3be-05111fc118d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chinese Foreign Direct Investment Outflows and Host Country Economic Growth","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe emergence of China as an important source of foreign direct investment (FDI) for countries (e.g., Buckley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Morgan, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), has given rise to the question of whether Chinese FDI has led to economic growth in host nations. The ambitious Belt and Road Initiative (BRI) launched in 2013 to connect China with Asia, Africa, and Europe to promote economic development through improvements in infrastructure and connectivity, has seen a large volume of FDI flow from China into countries across the globe. More than 90 countries have joined China\u0026rsquo;s BRI, accounting for more than two-thirds of the world\u0026rsquo;s population. Over the 2005 to 2019 period, the low and middle-income nations have received 83.4 percent of the \u003cspan\u003e$\u003c/span\u003e815.3\u0026nbsp;billion worth of Chinese construction projects around the world, while high-income economies, received 62.1 percent of Chinese FDI outflows, totaling \u003cspan\u003e$\u003c/span\u003e1.23 trillion (ChinaPower 2023), making China the fourth largest source economy for FDI outflows (UNCTAD \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeo classical theory postulates that FDI can lead to economic growth through capital accumulation, while New Growth Theory states that FDI can lead to growth through human capital accumulation, R\u0026amp;D, and externalities (Balasubramanyam et al. (1999). FDI can therefore, serve not only as a means of direct capital financing, but also act as a channel for generating positive externalities, contributing to long-run growth in host countries through the transfer of technological, intellectual, managerial skills and capital (Markusen and Nesse \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Caves \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), generating employment opportunities (Markusen and Nesse \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), augmenting domestic savings, (Bosworth and Collins \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), providing access to world markets (Iamsiraroj and Ulubasoglu 2015) and increasing the tax base of host nations (Markusen and Nesse \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eSome studies however, show that FDI may not lead to growth in host countries because the transmission of technology may not be suited to the receiving country, or that host nations may not possess the conditions conducive for FDI to have maximal benefits (Borensztein et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Similarly, local firms could lose markets if faced by competition from multinational corporations.\u003c/p\u003e \u003cp\u003eThe prior literature has largely been based on outward FDI flows from developed countries. Assuming that FDI bring in more advanced technologies and skills to host countries and these MNCs are subject to more stringent regulations in home countries, inward FDIs could lead to growth in host countries. Alternatively, FDI may not contribute to an increase in growth if accompanied by to competition and a race to the bottom with falling labour, environmental and other standards in host nations. However, such an assumption which holds for FDIs from developed countries may not necessarily hold for FDIs from emerging markets such as China. Studies which investigate outward FDI from China show that Chinese FDI is attracted to nations with weak institutions (Buckely et al. 2007, Fu et al. 2020), natural resources (Cheung and Quan 2009, Fu et al. 2020), GDP, cultural proximity, and common borders (Cheng and Ma \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A feature of Chinese investment is that it is mainly concentrated in developing nations with significant implications for growth and development in these countries (Cheung and Quan 2009). Emerging country FDI may be used as a means towards entering host countries with similar institutional risk (Arite 2013), or countries operating under difficult institutional climates. According to Cuervo-Cazurra and Genc (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), these weak institutional climates can be used to developing-country MNEs advantage, as they are used to operating under such circumstances.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, we investigate how Chinese FDI has influenced home country economic growth. The last decade has witnessed a significant rise in FDI outflows from China. FDI is a significant contributor to host country economic growth and therefore it is of critical importance to understand the effects of FDI on host nations. While media coverage has highlighted the adverse effects of Chinese investments on host countries including debt traps, the environment (Radwin 2022), there is an absence of empirical evidence to suggest this. Thus, our contribution to the literature is twofold. The present study extends upon the literature by, one, investigating the direct impact of Chinese FDI outflows on host country economic growth. To this end, we use data for 41 host countries over the 2005\u0026ndash;2021 period. As stated above, the effects of FDI on economic growth rather than being a direct process, maybe an indirect one which take place through other channels. Therefore, a second contribution of the study is that we also investigate the indirect effects of Chinese FDI on economic growth through the additional channels of human capital and government effectiveness in host nations. Studies show that the capacity of host economies to benefit from FDI depends on a host country\u0026rsquo;s education levels. Studies suggest that countries require a minimum level of human capital to benefit from FDI and positive externalities of FDI inflows (Borensztein et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Li and Liu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Kokko \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). According to these studies, if countries have the minimum threshold of human capital, FDI will lead to the further enhancement of human capital through education, skills transfer and on the job training in host countries (Cooray \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Nunnenkamp \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Bloomstrom and Kokko 2003, Li and Liu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Egger et al. 2010) leading to economic growth. If on the other hand, there are significant gaps in knowledge between source and host nations, FDI may not lead to a transfer of skills or generate positive externalities in host nations. Does Chinese FDI require these minimum levels of human capital? Or is Chinese FDI attracted to low level skills in which case, FDI may not increase growth in the host country through education. Evidence shows that China has invested in several countries which include both, those with high levels of education (Western and Eastern Europe) and low levels of education (Asia and Africa). We account for the indirect effects of FDI on economic growth through human capital by incorporating an interaction term between FDI and human capital. There has also been consensus among economists on the importance of institutions for economic performance. For example, Acemoglu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) highlight the importance of institutions for economic growth, Wernick et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) the significance of institutions for FDI, Globerman and Shapiro (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) governance infrastructure, Altomante (2000) a host country\u0026rsquo;s institutional framework. Countries with more effective governments, will channel funds into productive investments experiencing higher growth rates while those with weak and ineffective governments can influence the direction of funds into projects that are of political interest thus reducing economic growth (La Porta et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Shleifer and Vishny \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Studies on China suggest that China is attracted to nations with similar institutional risk (Arita 2013), and those operating under weak institutional climates (Cuervo-Cazurra and Genc \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Hence, to account for this, we estimate the effect of FDI on economic growth through government effectiveness by incorporating an interaction term for FDI with government effectiveness.\u003c/p\u003e \u003cp\u003eWe perform several robustness checks to test the validity of our results including fixed effects estimation, marginal plots, partially non-linear models, instrumental variable (IV) estimation, sub-sample tests, and threshold estimation. Our results suggest that Chinese FDI has a positive and significant impact on host country economic growth. The indirect impacts of FDI on economic growth through the government effectiveness and human capital channels are found to exhibit threshold effects.\u003c/p\u003e \u003cp\u003eThe rest of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e discusses the literature. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the data and model. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e evaluates the empirical results and Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes.\u003c/p\u003e"},{"header":"2. Theoretical Foundation","content":"\u003cp\u003eStudies on the effects of FDI on economic growth have shown mixed results. The literature shows that FDI can facilitate economic development in host countries not only through direct capital financing but through positive spillovers including technological diffusion, innovation, the transfer of managerial skills, markets, demand for raw materials, promotion of exports, and productivity growth (Blomstr\u0026ouml;m \u0026amp; Kokko, 1996; Buckley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lall \u0026amp; Albaladejo, 2004), human capital (Kokko \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, Ahmed and Kialashaki \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), greater openness (Balasubramanyam et al. 1999, Ekanayake et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), financial sector development (Hermes and Lensink \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), stronger institutions (Wernick et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA number of studies show that the beneficial effects of FDI on host countries depend on host countries characteristics. Borensztein et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) show that countries with higher absorption capacity, measured by a minimum threshold level of human capital can gain from FDI inflows. Li and Liu (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) similarly argue that FDI increases economic growth in developing countries both directly and indirectly through its interaction with human capital. This is supported by Kokko (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) who states that the positive externalities of FDI are higher the more educated a nations stock of human capital, which in turn leads to a higher level of competition and lower conditions for the entry of source firms. Similar findings are put forward by Ahmed and Kialashaki (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who find that human capital provides the strongest support for affecting GDP and catching up for a group of Asia-Pacific countries.\u003c/p\u003e \u003cp\u003eThe literature also highlights the importance of institutions and government effectiveness for FDI inflows. Altomonte (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) in a study of FDI inflows into Central and Eastern Europe, finds that MNCs not only consider conventional determinants but also factors linked to the institutional environment of host economies when considering investing in a country. Examining the relation between governing institutions and FDI inflows to emerging economies, Wernick et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) find that governing institutions influence FDI inflows into host nations. Dawson similarly in an investigation of the relationship between institutions, investment, and growth, finds that market institutions have a positive impact on economic growth and that economic freedom affects growth through investment. Brewer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) argues that government policies influence FDI decisions through their effects on market imperfections. Buckley et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) on the contrary, find that Chinese FDI is drawn to countries with weak institutions and Fu et al. (2020) observe that Chinese investment is drawn to nations with natural resources, the weaker the institutional climate of the country. Choi and Samy (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) similarly, find that democratic institutions have a weak association with an increase in FDI inflows. Henisz (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) argues that as political hazards rise, MNCs face a growing risk of opportunistic exploitation by host country governments which can be minimized by collaborating with host-country firms. As hazards rise however, the local collaborative partner can use the political system for its own gain at the cost of the MNC according to Henisz (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother strand of the literature has focused on the importance of trade openness and the financial sector. Nair-Reichert and Weinhold (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) observe a positive relationship between FDI and economic growth in countries with more open economies. They argue that more open countries gain greater benefits from FDI. These findings are supported by Balasubramanyam eta al. (1999) who show that trade openness is vital for FDI to have a positive effect on economic growth. Doğan et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) similarly observe that FDI enhances economic growth in a group of 32 European countries over the period 1995\u0026ndash;2014 in a study of energy consumption, FDI and trade on economic growth. Beata (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) in a study of spillovers from FDI on firm-level data from Lithuania, find that positive productivity spillovers from FDI take place through contacts between foreign affiliates and their local suppliers in upstream sectors. More recently Ekanayake et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) find that a large part of the effect of trade in income takes place through education and fertility. Others argue that FDI leads to growth in nations with well-developed financial markets (Alfaro et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Hermes and Lensink (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) argue that strong financial systems enable technological spillovers in host nations.\u003c/p\u003e \u003cp\u003eA further group of works shows that FDI may not lead to positive outcomes in host economies under certain circumstances. The diffusion of technology for example, may not be suited to firms in host countries. Carkovic and Levie (2002) find that FDI does not have a robust, independent effect on economic growth. Similarly, foreign companies could operate in isolation of local companies, in which case there would be no spillover effects to local companies. These companies could, on the other hand, compete with host companies leading to the closure of local firms (Markusen \u0026amp; Venables, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Other problems associated with FDI include a deteriorating balance of payments as dividends are sent back to home countries. Some studies show that MNCs have damaging environmental impacts on host nations. Hence, the effect of FDI on host economies is not clear-cut. Studies also show that FDI has positive and significant effects on developed countries, but insignificant effects on developing countries (Dimelis and Papaioannou \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Kottaridi and Stengos). Gunby et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in a meta investigation of FDI into China, note that the effect of FDI on Chinese economic growth is much smaller than one would expect. They argue that publication bias and a profusion of estimates inflate observed values. Once these effects are taken into account, the estimated effect of FDI on economic growth in China is reduced to statistical insignificance. Belloumi (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) similarly, in a test of the relationship between FDI, trade openness and economic growth in Tunisia which has been facing unemployment problems and lack of technological progress, finds no significant Granger causality from FDI to economic growth or from economic growth to FDI.\u003c/p\u003e \u003cp\u003eThus, while our study is related to the existing literature, the point of departure is that we consider how FDI outflows from a single country, China, affects host country growth, considering the fact that the outflows can also affect host nations indirectly through the channels of human capital and government effectiveness.\u003c/p\u003e"},{"header":"3. The Model and Data","content":"\u003cp\u003eThe sample comprises data for 41 host countries, selected on the basis of data availability over the 2005 to 2021 period.\u003csup\u003e1\u003c/sup\u003e\u003ca id=\"#FNLinkFn1\" class=\"FNLink\" href=\"#Fn1\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOur empirical model takes the following form:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\beta _0}+{\\beta _1}\\ln OFD{I_{it}}+{\\beta _2}\\ln PGDPL+{\\beta _3}\\ln P{D_{it}}+{\\beta _4}\\ln T{O_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\beta _5}\\ln D{C_{it}}+{\\beta _6}\\ln S{E_{it}}+{\\beta _7}GE{I_{it}}+{\\mu _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003ePGDPG\u003c/em\u003e is the growth rate of GDP per capita; our main independent variable of interest is outward foreign direct investment from China which is denoted by OFDI. The control variables include GDP per capita lagged by one year (\u003cem\u003ePGDPL\u003c/em\u003e) which is a standard measure of convergence, and population density (\u003cem\u003ePD\u003c/em\u003e) as used in growth models. Additional control variables include, trade openness to GDP (\u003cem\u003eTO\u003c/em\u003e) used as a proxy for trade policy as some studies show that trade contributes to economic growth (Burnsideand and Dollar 2000). Domestic credit to the private sector (\u003cem\u003eDC\u003c/em\u003e) is used to measure the role of the financial sector in economic growth as Rousseau and Wachtel (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), Levine and Zervos (1998), Beck et al. (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) among others, highlight the importance of the role of finance in economic growth. Studies suggest that countries with higher levels of human capital receive more FDI and thus experience higher growth rates (Balasubramanyam et al. 1999 among others). The school secondary enrollment rate (\u003cem\u003eSE\u003c/em\u003e) is employed to capture human capital as Mankiw et al. (\u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e) among others. Institutional quality has been linked to FDI and economic growth. Therefore, the government effectiveness index (\u003cem\u003eGEI\u003c/em\u003e) from the World Bank is used to capture institutions. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu _i}\\)\u003c/span\u003e\u003c/span\u003e is a country fixed effect; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003e indicates a random error term.\u003c/p\u003e\n\u003cp\u003eThe data definitions and sources are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDefinition of variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNotation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eName\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDefinition\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDependent variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePGDPG\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEconomic growth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePGDP growth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMain independent variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOutward foreign direct investment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOutward foreign direct investment from China\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCGIT database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl variables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation density\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation per unit area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrade openness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShare of trade in GDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDomestic credit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShare of domestic credit to the private sector in GDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSchool enrollment rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary school enrollment rate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment effectiveness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment effectiveness index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWDI database\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe data for \u003cem\u003ePGDPG\u003c/em\u003e, \u003cem\u003ePD\u003c/em\u003e, \u003cem\u003eTO\u003c/em\u003e, \u003cem\u003eDC\u003c/em\u003e, \u003cem\u003eSE\u003c/em\u003e, and \u003cem\u003eGEI\u003c/em\u003e are collected from the World Development Indicator (WDI) database. Data for OFDI come from the CGIC (China Global Investment Tracker) database.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides the descriptive statistics of the variables included in this study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistical description of variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnit\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS.D.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMin.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMax.\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePGDPG\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-18.485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.520\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMillion USD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1396.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2014.254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e640.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22140.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePGDPL\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003econstant 2015 USD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5878.843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8307.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e305.979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3205.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58672.484\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epeople per sq. km\u003c/p\u003e\n\u003cp\u003eof land area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e125.488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e194.457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1286.172\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.670\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.440\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e203.855\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.072\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e303.380\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e74.288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e82.682\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e158.630\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.505\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe further estimate the interaction effects of enrollment rate and GEI on the relationship between OFDI and PGDP, respectively.\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\beta _0}+{\\beta _1}\\ln OFD{I_{it}}+{\\beta _2}PGDPL+{\\beta _3}\\ln P{D_{it}}+{\\beta _4}\\ln T{O_{it}}+{\\beta _5}\\ln D{C_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\beta _6}\\ln S{E_{it}}+{\\beta _7}GE{I_{it}}+{\\beta _8}\\ln OFD{I_{it}}*GE{I_{it}}+{\\mu _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\beta _0}+{\\beta _1}\\ln OFD{I_{it}}+{\\beta _2}PGDPL+{\\beta _3}\\ln P{D_{it}}+{\\beta _4}\\ln T{O_{it}}+{\\beta _5}\\ln D{C_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\beta _6}\\ln S{E_{it}}+{\\beta _7}GE{I_{it}}+{\\beta _8}\\ln OFD{I_{it}}*\\ln S{E_{it}}+{\\mu _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\beta _0}+{\\beta _1}\\ln OFD{I_{it}}+{\\beta _2}PGDPL+{\\beta _3}\\ln P{D_{it}}+{\\beta _4}\\ln T{O_{it}}+{\\beta _5}\\ln D{C_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\beta _6}\\ln S{E_{it}}+{\\beta _7}GE{I_{it}}+{\\beta _8}\\ln OFD{I_{it}}*GE{I_{it}}+{\\beta _9}\\ln OFD{I_{it}}*\\ln S{E_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\beta _{10}}\\ln OFD{I_{it}}*\\ln S{E_{it}}*GE{I_{it}}+{\\mu _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe equations (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) to (\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) still follow the strict linear assumptions, which may lead to misspecification due to the possible presence of non-linearities. The linear regression model can only be considered an approximation of nonlinear relationship between variables. The relation among variables might change according to factors related to the economy, technology, population, government governance, trade, and education. In addition, the impact of \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is subject to heterogeneous changes in different periods and countries. As an alternative to the linear model we might consider the partially linear functional-coefficient models (PLFC), a semi-parametric method that not only captures the nonlinear structure of functional coefficients and heterogeneity over countries and time, but also controls the linear influence of other variables (Du et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, we apply the PLFC model with fixed effects estimation to address potential misspecification issues. The model is represented as:\u003c/p\u003e\n\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ5\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\phi _0}+G(GE{I_{it}})\\ln OFD{I_{it}}+{\\phi _1}PGDPL+{\\phi _2}\\ln P{D_{it}}+{\\phi _3}\\ln T{O_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\phi _4}\\ln D{C_{it}}+{\\phi _5}\\ln S{E_{it}}+{\\phi _6}GE{I_{it}}+{\\upsilon _i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ6\" class=\"mathdisplay\"\u003e$$\\begin{gathered} PGDPG={\\alpha _0}+G(\\ln S{E_{it}})\\ln OFD{I_{it}}+{\\alpha _1}PGDPL+{\\alpha _2}\\ln P{D_{it}}+{\\alpha _3}\\ln T{O_{it}} \\hfill \\\\ \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;+{\\alpha _4}\\ln D{C_{it}}+{\\alpha _5}\\ln S{E_{it}}+{\\alpha _6}GE{I_{it}}+{w_i}+{\\varepsilon _{it}} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cem\u003eG\u003c/em\u003e(\u003cem\u003eGEI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e) and \u003cem\u003eG\u003c/em\u003e(\u003cem\u003elnSE\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e) are an unknown function of \u003cem\u003eGEI\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003elnSE\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e, respectively. The model captures the nonparametric impact of \u003cem\u003eOFDI\u003c/em\u003e on the economic growth and at the same part allows for a linear impact of other control variables.\u003c/p\u003e\n\u003cp\u003eGrowth and OFDI could both be related to further country-specific variables omitted from the model. If this is the case, the estimates could be biased and inconsistent and the relation between the variables may not be a causal relation. Therefore, as a further test for detecting causality relationships, we use the instrumental variable (IV) method for parameters' estimation. Considering the available data, we instrument OFDI in the host country with the average value of OFDI in the rest of the countries. By excluding the target country in the construction of the instruments, we control for the correlation with the country-specific omitted variables. Differently, for OFDI, the average value represents a proxy for China policy in foreign direct investments and would be thus correlated with the OFDI in the target country.\u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Results of the baseline model\u003c/h2\u003e\n\u003cp\u003eWe check the stationarity of the panel datasets using the Levin, Lin, and Chu (LLC) unit root test (Levin et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) before estimating the models. As can be seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the LLC test shows that all variables are stationary in the levels.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePanel unit-root test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePGDPG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.8664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.7315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPGDPL\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-6.2218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-8.3635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.9226\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-6.4617\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-3.8671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.8775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively; the null hypothesis is that the data series contains a unit root.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eWe first examine the linear impact of \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e using an ordinary panel data model. Based on the results of the Hausman test, we adopt a fixed effect model. In order to avoid heteroscedasticity, autocorrelation and contemporaneous correlation problems, Feasible Generalized Least Squares (FGLS) is used to estimate the panel data model. In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, M1 shows the estimated coefficient of \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e without considering the control variables. The coefficient is positive and significant at the 1% level, suggesting that an increase in Chinese \u003cem\u003eOFDI\u003c/em\u003e leads to an increase in economic growth in host nations. The coefficient on \u003cem\u003eOFDI\u003c/em\u003e continues to be significant and positive after adding control variables in models M2-M4. According to M2, an increase in \u003cem\u003eOFDI\u003c/em\u003e by 1% will result in an increase in \u003cem\u003ePGDPG\u003c/em\u003e by 0.08%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of baseline model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM5\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.072\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.561\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.107\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.016)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.023)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.189)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.551)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPGDPL\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.965\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.967\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-15.833\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.540\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.294)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.611)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.878)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.345)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.365\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-11.948\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.565\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.105)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.300)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.778)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.472)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.828\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.897\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.178\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.356\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.320)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.326)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.664)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.354)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.206\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.309)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.315)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.469)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.505)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.639\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.968\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.823)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.811)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.078)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.041)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.861\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.406\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.382)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.361)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.710)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.545)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI*lnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.065\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.496\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.030)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.495)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnSE*lnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.263\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.045)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.123)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI*lnSE*lnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.335\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.107)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e502.991\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-20.471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-611.754\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.363\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.592)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(141.780)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(145.431)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(203.875)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(169.089)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHausman test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndividual fixed effects\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime fixed effects\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWald chi2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e712.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1347.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e704.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e707.63\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1290.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn column M2, the lagged value of GDP per capita (GDPL) is negative and significant, suggesting convergence among the economies. Population density has a negative and significant effect on economic growth while trade has a positive and significant impact on economic growth. Domestic credit is not statistically significant. The stock of human capital as measured by the secondary enrolment ratio is positive and significant at the 5% level, while government effectiveness is positive and significant at the 1% level.\u003c/p\u003e\n\u003cp\u003eWe next add the interaction term between government effectiveness and Chinese FDI, \u003cem\u003eGEI\u003c/em\u003e*\u003cem\u003elnOFDI\u003c/em\u003e in M3 and \u003cem\u003eSE\u003c/em\u003e*\u003cem\u003elnOFDI\u003c/em\u003e in M4. OFDI continues to have a positive and significant effect on economic growth and the coefficients on the control variables are consistent with the results obtained in M2 and government effectiveness on its own is statistically significant and positive. The coefficient on the interaction term is surprisingly negative \u003cem\u003eGEI\u003c/em\u003e*\u003cem\u003elnOFDI\u003c/em\u003e in M3. In M4, secondary enrolment (SE) is surprisingly not statistically significant, and the interaction term is negative and significant contrary to expectations.\u003c/p\u003e\n\u003cp\u003eTo gain a better idea of the impact of these variables on economic growth, we look at the marginal effects of OFDI on per capita growth depending on government effectiveness (GEI) in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The solid line represents the marginal effect of Chinese FDI, and the dashed line shows the 90% confidence interval around the marginal effect. As shown by Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the marginal effect of \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is 0.042\u0026ndash;0.065*\u003cem\u003eGEI\u003c/em\u003e. This implies that when \u003cem\u003eGEI\u003c/em\u003e is less than 0.646, the coefficient on \u003cem\u003eOFDI\u003c/em\u003e is significant and positive. The graph shows that GEI causes Chinese FDI to promote growth in low-income countries. However, the marginal effect of Chinese FDI on economic growth falls as \u003cem\u003eGEI\u003c/em\u003e increases, in the sample of host countries under study. The results suggest that countries with well-functioning governments, do not require Chinese FDI to promote economic growth.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the estimated marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e on economic growth depending on human capital (\u003cem\u003elnSE)\u003c/em\u003e. The results show that with the growth of \u003cem\u003elnSE\u003c/em\u003e, the marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is 0.561\u0026thinsp;\u0026minus;\u0026thinsp;0.124*\u003cem\u003elnSE\u003c/em\u003e. That is, when \u003cem\u003elnSE\u003c/em\u003e is less than 4.524, the marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is significant and positive, leading to growth in host countries. When however, \u003cem\u003elnSE\u003c/em\u003e exceeds the above-mentioned threshold, \u003cem\u003elnOFDI\u003c/em\u003e has a negative and insignificant effect on per capita income growth. These results also suggest that when the level of human capital in a host country tends to increase, the Chinese FDI inflows do not promote economic growth in the host country.\u003c/p\u003e\n\u003cp\u003eBased on M2, M3 and M4, we add interaction terms for both \u003cem\u003eGEI\u003c/em\u003e*\u003cem\u003elnOFDI\u003c/em\u003e and \u003cem\u003elnSE\u003c/em\u003e*\u003cem\u003elnOFDI\u003c/em\u003e, and all three terms in M5. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e(A) reports the estimated marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e, \u003cem\u003elnSE\u003c/em\u003e, and \u003cem\u003eGEI\u003c/em\u003e, which equals \u0026minus;\u0026thinsp;1.107\u0026ndash;1.496*\u003cem\u003eGEI\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.263*\u003cem\u003elnSE\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.335*\u003cem\u003elnSE\u003c/em\u003e*\u003cem\u003eGEI\u003c/em\u003e. The trend of the marginal effects of \u003cem\u003eOFDI\u003c/em\u003e with the growth of \u003cem\u003eGEI\u003c/em\u003e and \u003cem\u003elnSE\u003c/em\u003e is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (B-C). With the growth of \u003cem\u003elnSE\u003c/em\u003e and \u003cem\u003eGEI\u003c/em\u003e, the impact of \u003cem\u003eOFDI\u003c/em\u003e shows a U-shaped trend. When \u003cem\u003eGEI\u003c/em\u003e\u0026lt;-0.616, the marginal effect curve of \u003cem\u003eOFDI\u003c/em\u003e is above 0, indicating that \u003cem\u003eOFDI\u003c/em\u003e promotes economic growth. However, the marginal effect curve of \u003cem\u003eOFDI\u003c/em\u003e is below 0, \u003cem\u003eOFDI\u003c/em\u003e does not promote economic growth, that is, when \u0026minus;\u0026thinsp;0.616\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eGEI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.407. It may be due to the negative effect of \u003cem\u003eOFDI\u003c/em\u003e caused by the influence of the COVID-19 epidemic on global economic activities.\u003c/p\u003e\n\u003cp\u003eWhen growth in \u003cem\u003eGEI\u003c/em\u003e exceeds the threshold, \u003cem\u003eOFDI\u003c/em\u003e plays a positive role in promoting economic growth. The 90% confidence interval (CI) indicates a significant marginal effect of \u003cem\u003eOFDI\u003c/em\u003e when \u003cem\u003eGEI\u003c/em\u003e \u0026lt; -0.616; otherwise, it is insignificant. Similarly, when \u003cem\u003elnSE\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3.274, the coefficient on \u003cem\u003elnOFDI\u003c/em\u003e is significantly positive, indicating that \u003cem\u003eOFDI\u003c/em\u003e promotes economic growth. The coefficient on \u003cem\u003elnOFDI\u003c/em\u003e is negative and not significant when 3.274\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003elnSE\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;4.297, suggesting that \u003cem\u003eOFDI\u003c/em\u003e does not affect economic growth. However, the coefficient on \u003cem\u003elnOFDI\u003c/em\u003e is positive and insignificant when lnSE surpasses the threshold.\u003c/p\u003e\n\u003cp\u003eAs can be seen from M5 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cem\u003elnPGDPL\u003c/em\u003e has a significant negative impact on \u003cem\u003ePGDPG\u003c/em\u003e, suggesting convergence among the nations. Trade (\u003cem\u003elnTO)\u003c/em\u003e leads to an increase in \u003cem\u003ePGDPG\u003c/em\u003e. The increase in international trade helps to promote economic growth. Population growth (\u003cem\u003eInPD\u003c/em\u003e) and domestic credit (\u003cem\u003elnDC\u003c/em\u003e) have no significant impact on \u003cem\u003ePGDPG\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe above analysis is based on simple linear function models which may not be capturing the real effects of OFDI on economic growth. As a result, it is necessary to loosen the linear assumptions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Partially linear functional-coefficient panel model\u003c/h2\u003e\n\u003cp\u003eNext, we use a partially linear functional-coefficient model to investigate the non-linear impacts of \u003cem\u003elnOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e. The marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e increases as GEI increases, as shown by Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. When \u003cem\u003eGEI\u003c/em\u003e\u0026lt;-1.382, the marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is negative and significant, suggesting that \u003cem\u003eOFDI\u003c/em\u003e leads to the decline of \u003cem\u003ePGDPG\u003c/em\u003e. It is possible that at the initial stages, \u003cem\u003eOFDI\u003c/em\u003e favours downstream processing industries with lower value-added investment and low-level skills in low income countries. When \u0026minus;\u0026thinsp;1.382\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eGEI\u003c/em\u003e\u0026lt;-0.956, the marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e is insignificant. The confidence interval contains 0, indicating that the null hypothesis with a coefficient of 0 cannot be rejected. The stage could be a transition period. \u003cem\u003eOFDI\u003c/em\u003e then begins to have a positive impact on economic growth. When \u0026minus;\u0026thinsp;0.956\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eGEI\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.705, the marginal effect of \u003cem\u003elnOFDI\u003c/em\u003e is significant and positive, suggesting that \u003cem\u003eOFDI\u003c/em\u003e promotes an increase in \u003cem\u003ePGDPG\u003c/em\u003e. However, when \u003cem\u003eGEI\u003c/em\u003e exceeds the threshold, \u003cem\u003elnOFDI\u003c/em\u003e is negative and insignificant. While, here, the marginal effects can be divided into four periods the conclusion that when GEI exceeds a threshold of 0.705, that the marginal effect of OFDI on economic growth is negative is consistent with our previous result that in nations with effective governments, Chinese FDI does not lead to economic growth.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLinear part of the partially linear functional-coefficient panel data model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDependent variable: \u003cem\u003ePGDPG\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM6\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eM7\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPGDPL\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-40.280\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-38.941\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.514)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.888)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-9.371\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.042)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.598\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.631\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.437)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.623\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.365)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.306)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.767\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.778\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.328)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.217)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.410\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.328\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.418)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.879)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003eNote: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eM6 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e reports the linear impacts of the control variables. As mentioned above, the impact of \u003cem\u003elnPGDPL\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e is significant and negative. \u003cem\u003elnTO\u003c/em\u003e and \u003cem\u003eGEI\u003c/em\u003e have a significant positive impact on \u003cem\u003ePGDPG\u003c/em\u003e. lnPD does not have a significant effect on PGDPG. In contrast, the secondary enrolment ratio \u003cem\u003elnSE\u003c/em\u003e has a significant and positive impact on \u003cem\u003ePGDPG\u003c/em\u003e. The semiparametric model is better at revealing variable impact compared to the linear model.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the heterogeneous impact of \u003cem\u003elnSE\u003c/em\u003e on the relationship between \u003cem\u003elnOFDI\u003c/em\u003e and \u003cem\u003ePGDPG\u003c/em\u003e. The coefficient on \u003cem\u003elnOFDI\u003c/em\u003e changes from negative and insignificant to positive and significant when \u003cem\u003elnSE\u003c/em\u003e increases to 4.026. It suggests that the growth in \u003cem\u003elnSE\u003c/em\u003e strengthens the positive impact of China's \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e. As suggested by the literature, education can augment technological progress and accelerate the diffusion of advanced technology, allowing for the accumulation of human and physical capital to contribute to economic growth. When the value of lnSE is greater than 4.776, the confidence interval widens abruptly which suggests that the estimate results may not be reliable here. Here too, threshold effects are detected as before.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Robustness tests\u003c/h2\u003e\n\u003cp\u003eWe undertake several additional robustness checks to test for the robustness of our results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting for Endogeneity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we employ instrumental variable estimation to examine the potential endogeneity of the relationship between \u003cem\u003eOFDI\u003c/em\u003e and \u003cem\u003ePGDP\u003c/em\u003e. \u003cem\u003eOFDI\u003c/em\u003e is regarded as an endogenous variable, and we instrument it with the average value of \u003cem\u003eOFDI\u003c/em\u003e of all other countries in the same year, excluding the local country. Our instrumental variable is correlated with OFDI, and uncorrelated with the random error term, and passes the Anderson canon. Corr. LM statistic, the Cragg-Donald Wald F statistic, and Sargan statistic tests. The results are for the IV estimation are shown in R1 of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The coefficient on lnOFDI is positive and significant at the 1% level, indicating that OFDI promotes economic growth in the sample of countries being investigated. The results confirm the robustness of the empirical findings.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of robustness test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDependent variable: \u003cem\u003ePGDPG\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR3\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR4\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR5\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\hat {\\gamma }\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.533\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCI[0.956, 1.317]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCI[4.528, 4.540]\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnOFDI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.245)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow regime\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.092\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.049)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.055)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh regime\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.775\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.157\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.183)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPGDPL\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-11.312\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-33.206\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-34.713\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.915\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-8.124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.564)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.718)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.088)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.084)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnPD\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4.338\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.266\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.267)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(5.554)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(6.085)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.728)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.719)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnTO\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.247\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.279\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.417\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.769\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.915)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.116)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.339)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.726)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.732)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnDC\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.561)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.488)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.511)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.454)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.459)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elnSE\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.378\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.446\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.054)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.466)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.197)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.081)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.101)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eGEI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.306\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.352\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.786\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(1.328)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(2.563)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(3.224)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.862)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0.866)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.156\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.512)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10.612)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnderson canon. corr.\u003c/p\u003e\n\u003cp\u003eLM statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.753\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCragg-Donald Wald\u003c/p\u003e\n\u003cp\u003eF statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.764\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSargan statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.299\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e697\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: standard errors in parentheses; ***, ** and * represent the significance at the 1%, 5%, and 10% level respectively.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSubsample tests\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eFurther, we leave out the years 2005\u0026ndash;2007 and run the estimation on the sub-sample from 2008 to 2021 and re-estimate the nonlinear impact of \u003cem\u003eOFDI\u003c/em\u003e on \u003cem\u003ePGDPG\u003c/em\u003e. The interactive influence of \u003cem\u003eGEI\u003c/em\u003e on the nonlinear relationship between \u003cem\u003eOFDI\u003c/em\u003e and \u003cem\u003ePGDPG\u003c/em\u003e is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The estimation for the linear part is shown in R2 of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows that when GEI\u0026ge;-0.447, the marginal effect of lnOFDI on PGDPG is significantly positive and increases with GEI. The threshold value of GEI was close to -0.956 in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The removal of the years, 2005 to 2007 causes a change in the threshold value of GEI \u0026lt;-0.447. As can be seen from the Figure, OFDI has a positive impact on PGDPG. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and R3 in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that the interaction influence of lnSE on \u003cem\u003eOFDI\u003c/em\u003e and \u003cem\u003ePGDPG\u003c/em\u003e is consistent with that of Section 4.4. The evidence supports the empirical findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlternative regression method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we use the panel threshold regression model proposed by Hansen (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e) to verify the robustness of the empirical results. \u003cem\u003eGEI\u003c/em\u003e and \u003cem\u003elnSE\u003c/em\u003e are selected as threshold variables. The results are shown in R4-5 of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The threshold values (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\hat {\\gamma }\\)\u003c/span\u003e\u003c/span\u003e) of \u003cem\u003eGEI\u003c/em\u003e and \u003cem\u003elnSE\u003c/em\u003e are 1.699 and 4.533, respectively. \u003cem\u003elnOFDI\u003c/em\u003e has a significant positive impact on \u003cem\u003ePGDPG\u003c/em\u003e when \u003cem\u003eGEI\u003c/em\u003e and \u003cem\u003elnSE\u003c/em\u003e exceed the threshold values, respectively. The result is in line with our previous empirical findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions and policy implications","content":"\u003cp\u003eThis study examines the effects of outward Chinese FDI flows on the economic growth of 41 host nations over the 2005 to 2021 period. We also examine the effects of outward Chinese FDI flows on the economic growth in these host nations via the government effectiveness and human capital channels. Overall, the empirical results reveal that Chinese FDI has positive impacts on host country economic growth. Significant threshold effects are detected on the indirect impacts of FDI flows on host countries through the government effectiveness and human capital channels. While at lower levels of GEI and human capital, Chinese FDI is found to promote economic growth in host nations, Chinese FDI is not found to necessarily lead to economic growth when host nations government effectiveness and human capital levels are above a certain threshold. The results suggest that when the quality of public services and civil services, are strong and political intervention is low, and the education levels of the population are high, that countries do not necessarily have to depend on Chinese FDI for economic growth. Trade is found to have a positive significant effect on economic growth, while population density and the financial sector have no significant effects on economic growth. Host countries are recommended to strengthen their institutions and develop their stocks of capital through education and training. In addition, countries should promote trade openness to promote economic growth. Having an effective government, a skilled and educated workforce and open economies will encourage more FDI without fostering dependence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions:MC: empirical analysis and empirical methods.AC: literature and writing up of the manuscript. BK: data collection and preparation of data. JL: Empirical estimation and analysis of results.All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., Johnson, S., \u0026amp; Robinson, J. A. 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Journal of Econometrics 93, 345-368.\u003c/li\u003e\n\u003cli\u003eLevin, A.T., Lin, C.F.J., Chu, C.-S.J., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics 108, 1-24.\u003c/li\u003e\n\u003cli\u003eRousseau P and Wachtel P. (2005) Equity Markets and Growth: Cross Country Evidence on Timing Outcomes, 1980-1995, Journal of Banking and Finance, 24, 1933-1957.\u003c/li\u003e\n\u003cli\u003eShleifer, A., \u0026amp; Vishny, R. W. (1994). Politicians and firms. The Quarterly Journal of Economics, 109(4), 995-1025.\u003c/li\u003e\n\u003cli\u003eUNCTAD (2022) Handbook of Statistics 2022, Geneva: https://hbs.unctad.org/foreign-direct-investment/\u003c/li\u003e\n\u003cli\u003eWernick, D. A., Haar, J., and Singh, S. (2009). Do governing institutions affect foreign direct investment inflows? New evidence from emerging economies. International Journal of Economics and Business Research, \u003cem\u003e1\u003c/em\u003e(3), 317-332.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The countries included in out analyses are: Algeria, Angola, Argentina, Bangladesh, Belarus, Brazil, Cameroon, Congo, Ecuador, Egypt, Ethiopia, Ghana, Guinea, India, Indonesia, Iran, Jordan, Kazakhstan, Kenya, Kuwait, Laos, Malaysia, Mongolia, Myanmar, Nepal, Niger, Nigeria, Pakistan, Peru, Philippines, Russian Federation, Saudi Arabia, Serbia, South Africa, Sri Lanka, Tanzania, Thailand, Turkiye, UAE, Uganda, Uzbekistan.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For all variables we reject the null hypothesis of the existence of unit roots at the 1% significance level.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Foreign Direct Investment, China, host countries, human capital, government effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-3892998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3892998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe examine how outward Chinese Foreign Direct Investment (FDI) flows affect the economic growth of 41 host nations over the 2005 to 2021 period. We also investigate the indirect effects of outward Chinese FDI flows on the economic growth of these countries through the government effectiveness and human capital channels. The empirical results reveal that Chinese FDI has significant positive impacts on host country economic growth. Significant threshold effects, however, are detected on the indirect impacts of FDI flows on host countries through the government effectiveness and human capital channels. The results suggest that when government effectiveness and human capital in host countries exceed a certain threshold, that Chinese FDI does not necessarily lead to economic growth in the group of countries under study.\u003c/p\u003e","manuscriptTitle":"Chinese Foreign Direct Investment Outflows and Host Country Economic Growth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 11:53:27","doi":"10.21203/rs.3.rs-3892998/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfff2c06-2c4d-452a-a3d2-8490b6da96a1","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T00:59:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 11:53:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3892998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3892998","identity":"rs-3892998","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}