UWS Academic Portal An empirical study of FDI determinants Beloucif, Ahmed; Islam, Mohammad Shaiful; Boukhobza, Tahar Published in: Proceedings of the 2020 British Academy of Management Annual Conference Accepted/In press: 08/06/2020 Document Version Peer reviewed version Link to publication on the UWS Academic Portal Citation for published version (APA): Beloucif, A., Islam, M. S., & Boukhobza, T. (Accepted/In press). An empirical study of FDI determinants: a panel data analysis of South and South-east Asia. In Proceedings of the 2020 British Academy of Management Annual Conference British Academy of Management. General rights Copyright and moral rights for the publications made accessible in the UWS Academic Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact pure@uws.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Download date: 29 May 2022 An Empirical Study of FDI Determinants: A Panel Data Analysis of South and South-east Asia Dr Ahmed Beloucif School of Business & Creative Industries, University of the West of Scotland, Paisley, UK Dr Mohammad Shaiful Islam School of Business & Creative Industries, University of the West of Scotland, Paisley, UK Dr Tahar Boukhobza University of Hertfordshire, Hatfield, UK Abstract This paper explores and compares the determinants of Foreign Direct Investment (FDI) in South and South-east Asia over 21 periods ranging from 1996 to 2016. Using panel data regression with random effects and fixed effects models, the study finds that market size, labour productivity and infrastructure of the host country exercise a significant influence upon FDI inflows of both regions. Trade openness, inflation rate, natural resource endowment and the unemployment rate are more important in South Asia, while the exchange rate, control of corruption and political stability are more important in South-east Asia for foreign investors. It appears that foreign investors hold different perceptions of investment attributes while deciding investment location into these two regions. The results imply that in seeking to become a potential destination of FDI, policies for both regions should be devised towards market growth, political stability, and the development of the quality of infrastructure, human capital and labour productivity.Sound macroeconomic stability with a flexible and stable exchange rate system is also needed to attract FDI. Keywords – FDI, Determinants, FDI inflows, Panel Data, South and South-east Asia. 1.INTRODUCTION & CONTEXT The inflows of foreign direct investment (FDI) are often considered as an essential ingredient that spurs economic growth by bringing technology, knowledge, capital and jobs, which is likely to generate a positive impact on the host economy (Cambazoglu and SimayKaraalp, 2014). Therefore, governments of many developing and least developed countries (LDC) are unequivocally entrusted the private sector and foreign investors to transform their economy and accelerate economic growth. Consequently, many countries around the world are opening their economy to foreign investors, restructuring and liberalising their FDI regimes, and offering several fiscal and non-fiscal incentives to attract the optimal level of FDI. Alike other countries around the world, South and South-east Asian countries have recognised that FDI is one of the most significant sources of investment that has the potential to generate a significant impact on poverty alleviation, foster economic growth, and increase employment opportunity.Both South Asian and South-east Asian economies have the absolute advantage to attract global investors over other regions. All countries of these regions have opened up and formulated policies favourable to foreign investment. Overall, there has been a growing number of policies restructured and implemented all over South and South-east Asia. The persistent nature of the reforms pursued by the governments suggest that these reforms are the part of the long-term strategy of South and South-east Asian economies to sustain their future as a potential destination of FDI. Despite the economic reforms pursued by the governments of both regions, South Asia still lags far behind South-east Asia in terms of their FDI performance (UNCTAD, 2018). Between 1996 and 2016 (Figure.1), South Asia’s annual average share of world FDI was 1.7%, compared with South-east Asia’s 5.4%. Within this timeframe, South-east Asian countries hosted nearly three times more FDI, which amounted to US$1337.8 billion compared to that of South Asia (US$481.5 billion).These differences raise questions for both policymakers and academia as to what are the significant determinants of FDI inflow in South Asia and South-east Asia. It is also imperative to know whether there are any similarities or differences in their FDI determinants. All of these need to be addressed from both theoretical and empirical perspectives to gain an understanding of the FDI movement into these regions. In light of these observations, the purpose of this paper is to explore and compare the determinants of FDI in South Asia and South-east Asia. US$ Billion Figure 1: Net FDI in South and South-east Asia Source: Created from UNCTAD Database The rest of the paper is organised as follows: the next section reviews the relevant theoretical literature and hypotheses in terms of the relationship between macroeconomic factors and FDI inflows. Section 3 outlines the methodology of the study. Section 4 presents and discusses the results, and Section 5 provides a final summary and conclusion. 2 2.DETERMINANTS AND THEIR IMPACT ON FDI INFLOWS The importance of and growing interest in FDI has led to develop a number of paradigms. The formulation of proper theories to explain foreign direct investment was initiated in the 1960s (Rayome and Baker, 1995). Since then, Industrial organisation approach of Hymer (1960), the Product Life Cycle Theory of Vernon (1966), the Monopolistic power theory of Kindleberger (1969), Knickerbocker's (1973) theory of oligopolistic competition, the Internalization theory of Buckley and Casson (1976), and Dunning’s (1977) Eclectic paradigmhave attempted to explain the existence and the expansion of the international business activities of multinationals via FDI. Within the mainstream theories, Dunning’s eclectic paradigm is regarded as one of the most robust and comprehensive theories for analysing the determinants of FDI. In his eclectic/OLI paradigm, Dunning (1977) claims that a firm will undertake FDI when all three conditions are satisfied. i.e.: (i) it must possess ownership advantages (“O”) in order to have a competitive advantage over other firms; (ii) it must have location-specific advantages (“L”) in host countries for instance, lower labour costs or transportation costs; (iii) it must be beneficial for the firm to exploit ownership advantages by internalizing (“I”) its production process abroad rather than licensing or exporting.FDI theories find out several determinants that could explain FDI flows into a specific location, linking the macro aspects such as trade openness, market size, infrastructure quality, natural resource availability, political and macroeconomic stability. We discuss the macroeconomic influences on FDI in the hypotheses below. 2.1 Market Size Market size represents the size of the host market (Ibrahim and Hassan, 2013). Tan, Wong and Goh (2018) argue that the size of the host market is a significant pull factor for attracting FDI. Similarly, Khamphengvong, Xia and Srithilat (2018) argued that foreign investors would prefer a large market when investing abroad. Besides, Zheng (2009) claimed that “Market size directly affects investment return and profits, and a higher market growth indicates a potential larger market and more promising prospects” (p.268).Therefore, foreign investors seek to invest in a country with a large market size as it is important for the exploitation of economies of scale and utilisation of resources (Chakrabarti, 2001).Market size measured by Gross Domestic Product (GDP) or per-capita GDP has been identified by vast empirical literature as an important determinant of FDI. Chakrabarti (2001) argues that “Market size has, by far, been the single most widely accepted as a significant determinant of FDI flows” (p.96). Tan, Wong and Goh (2018) for ASEAN; Nkoa (2018) for African countries; Adhikary (2017) for SAARC countries; Aziz and Mishra (2016) for Arab countries; and Al-Khouri (2015) for MENA countriesfound market size as the significant determinant of inward FDI.According to Billington (1999), the larger the host market, total income, and its growth potential, the higher the amount of FDI investment. In light of the above discussion, we present the following hypothesis: H1: Market size has a positive impact on FDI inflows. 2.2 Trade Openness The degree of trade openness reflects the willingness of a country to accept foreign investment (Aziz and Mishra, 2016). Therefore, the more open economy will attract more FDI. However, there is mixed evidence regarding the significance of trade openness in determining FDI (Chakrabarti, 2001). For instance, Tan, Wong and Goh (2018), Aziz and Mishra (2016) and Gupta and Singh (2016) found a strong positive effect of trade openness on inward FDI. In contrast, Kumari and Sharma (2017), conclude that trade openness is statistically insignificant to attract FDI. Contrary to conventional wisdom, Ibrahim and 3 Abdel-Gadir (2015) for Oman, and Bhatt (2008) for ASEAN countries founda negative impact of trade openness on the inflows of FDI. Therefore, after reviewing the above empirical studies, it is reasonable to draw the following hypothesis: H2: Trade openness has a positive impact on FDI inflows 2.3 Labour Productivity Labour productivity generally reflects the efficiency of labour in an economy. Hoang and Bui (2015), found that foreign investors are particularly interested in labour productivity rather than labour cost while investing in the ASEAN region. Studies such as Kalyoncu, Tuluce and Yaprak (2015) for Turkey; and Villaverde and Maza (2012) for Spain also found a positive association between inward FDI and labour productivity of the host country. Thus, it is reasonable to draw the following hypothesis: H3: Labour productivity has a positive impact on FDI inflows 2.4 Infrastructure The infrastructure of the host country is often considered as a significant factor to FDI. Prior empirical studies such as Xaypanya, Rangkakulnuwat and Paweenawat (2015), Bhatt (2008) and Hoang and Bui (2015) for ASEAN region; Ravinthirakumaran et al. (2015) for Sri Lanka; and Asiedu (2006) for African countries identified a significantly positive relationship between inward FDI and infrastructure of the host country.Conversely, Onyeiwu and Shrestha (2004), andTampakoudis et al. (2017) confirmed that infrastructure is insignificant for inward FDI.Alam and Shah (2013), concluded that basic infrastructure is sufficient to attract FDI. However, Kumari and Sharma (2017) revealed a negative impact of infrastructure on FDI inflows for the panel of 20 developing Asian countries. They conclude that the impact of infrastructure on FDI can vary from negligible to negative. In light of the above discussion, we present the following hypothesis: H4: The infrastructure of the host country has a positive impact on FDI inflows 2.5 Human Capital Human capital generally reflects the quality of labour of an economy. It is often considered that countries with high-quality labour can handle contemporary technologies more efficiently, and in general have higher labour productivity. A well-educated labour force worked as a positive externality in attracting FDI (Aziz and Mishra, 2016). Seetanah and Rojid (2011), while investigating the determinants of FDI in Mauritius, found a positive relationship between FDI and human capital. Further, Hakro and Ghumro (2011), while investigating the determinants of FDI on Pakistan, exhibited a long-run relationship between human capital and FDI. Rjoub et al. (2017), Kumari and Sharma (2017), Hoang and Bui (2015), Yohanna (2013) and Sun, Tong and Yu (2002) also found a significantly positive association between human capital and inward FDI. Thus, from the above literature, it is reasonable to draw the following hypothesis: H5: The stock of human capita has a positive impact on FDI inflows 2.6 Political Stability Political stability indicates the degree of political risk, institutional quality and the investment climate of an economy. It decreases the uncertainty about the profitability and cost of the investment, which adds an important dimension to attract foreign investment (Sun, Tong and Yu, 2002). Tan, Wong and Goh (2018), while studying the determinants of FDI for ASEAN member states, explored that political instability possesses a positive long-run relationship with FDI. Similarly, Leonardo et al. (2018), Rjoub et al. (2017), Zheng (2009), Asiedu (2006), and Sun, Tong and Yu (2002) found a significant positive relationship between political stability of the host country and inward FDI.On the other hand, Alam and Shah 4 (2013)found no significant impact of political stability on FDI inflowsfor a panel of ten OECD countries. Further, political stability has found to be insignificant to inward FDI of India in the study of Sury (2008). It implies that political stability has minimal impacts on the decisions of foreign investors. Interestingly, Lucke and Eichler (2016) proved that that foreign investors prefer to invest in developed countries that are politically unstable compared to their home country. In light of the above discussion, we present the following hypothesis: H6: The relationship between political stability and inward FDI will be positive 2.7 Corruption Corruption generally creates pressure on and difficulties for MNCs to do business in countries with weak governance (Desai, Foley, and Hines, 2004). Leonardo et al. (2018) while examined the perception and policy determinants of FDI to European developing countries revealed that corruption has a deleterious effect on FDI inflows. Su et al. (2018), Dauti (2015) and Asiedu (2006) also demonstrated a negative impact of corruption on FDI. In contrast, Wood et al. (2014) while investigating the determinants of FDI in Africa found no statistical significance of corruption on inbound FDI. Contrary to conventional wisdom, Helmy (2013) demonstrated that FDI varies positively with corruption and concludes that corruption does not hinder inward FDI in the MENA region. Similarly, Lucke and Eichler (2016) found that foreign investors prefer to invest in developed countries that are more corrupt compared to the investor’s home country. In light of the above discussion, we present the following hypothesis: H7: The relationship between the control of corruption and inward FDI will be positive 2.8 Inflation Rate Inflation reflects macroeconomic instability. As the unstable macro-economy may create uncertainty in the investment climate, many empirical studies have found a negative impact of inflation rate for inward FDI. For instance, Rachdi, Brahim and Guesmi (2016) found that the inflation rate played a negative role in the inward FDI in emerging countries. Studies such as Ibrahim and Hassan (2013) for Sudan, Kok and Ersoy (2009) for 24 developing countries, Asiedu (2002) for 71 developing countries, Demirhan and Masca (2008) for 38 developing countries, Cevis and Camurdan (2007) for 17 developing and transition economies also observed a negative impact of inflation rate for inward FDI. On the contrary, Aziz and Mishra (2016)found that the inflation rate is significant and positively attracting FDI in Arab countries. Yohanna (2013) for Nigeria and Jadhav (2012) for BRICS countries also found a positive impact of inflation rate on FDI inflow. Reflecting the above literature, we draw the following hypothesis: H8: The relationship between the inflation rate and inward FDI will be negative 2.9 Exchange Rate A stable exchange rate improves certainty in the local economy, and accordingly increases investment profitability (Mohammadvandnahidi, Jaberikhosroshahi and Norouzi, 2012). A depreciation of the host country’s currency tends to reduce the cost of all production inputs including land, labour and machinery, which eventually raises the profit margin of the foreign investors and therefore stimulates foreign investment (Ismail, 2009; Boateng et al., 2015). Regarding the relative impacts of exchange rate on the level of FDI inflows in mixed. Saleem et al. (2018), Gupta and Singh (2016), Rachdi, Brahim and Guesmi (2016), Ang (2008) and Ramirez (2006) confirmed that appreciation of the exchange rate decreased the inflows of FDI. In contrast, Aziz and Mishra (2016), Boateng et al. (2015), and Malefane (2007) found that an appreciation of host country’s exchange rate surged the inflows of FDI. 5 However, Wood et al. (2014) and Zheng (2009) found no significant effect of exchange rate on inbound FDI. In light of the above discussion, this study draws the following hypothesis: H9: The appreciation of the host currency leads to a decrease in FDI inflows 2.10 Unemployment rate The unemployment rate reflects the labour availability in the host economy. The high rate of available labour in the host economy will attract more foreign investors. The argument is that in the case of a high unemployment rate people are valuing their job and work hard for a lower wage (Boateng et al., 2015). Thus, the higher unemployment rate acts as an incentive for inward FDI. The positive effect of high unemployment rates on FDI inflow has also been supported by empirical work of Chidlow et al. (2009) and Nunnenkamp et al. (2007). On the other hand, Boateng et al. (2015) and Gupta and Singh (2016) have found a negative sign for unemployment rates symbolising an adverse effect on FDI inflows. In light of the above discussion, we draw the following hypothesis: H10: The unemployment rate has a positive impact on FDI inflows 2.11 Natural Resources Natural resources play a vital role in overall FDI attraction or decisions (Rjoub et al., 2017). Ibrahim and Abdel-Gadir (2015) found that FDI flows are positively influenced by natural resources. Similarly, Onyeiwu and Shrestha (2004) concluded that countries with wellendowed natural resources tend to attract more FDI. Asiedu (2006) also confirms that natural resource endowment is one of the critical drivers of FDI to the African region. In contrast, Lucke and Eichler (2016) found that the relationship between FDI and natural resources is negative and significant. From the above discussion, this study draws the following hypothesis: H11: Natural resource endowment has a positive impact on FDI inflows 2.12 Tax Rate High taxation is one of the major constraints to foreign investment (Hess, 2000). Further poorly implemented and unreliable tax laws indicate an unfriendly business climate, which hampers the flows of FDI (Onyeiwu and Shrestha, 2004). Wood et al. (2014) confirmed negative effects of high tax rates in FDI inflows for African countries, and concluded that this negative correlation indicates that foreign MNCs, at least partially, invest in Africa to save costs. Koojaroenprasit (2013) and Sury (2008) found that a higher amount of corporate tax had a decremental effect on the inward FDI. However, Alam and Shah (2013), and Onyeiwu and Shrestha (2004) found no statistically significant effect of corporate taxes on inward FDI of the host country. This study draws the following hypothesis to evaluate the impact of the corporate tax rate in FDI H12: The relationship between corporate tax rate and FDI inflows will be negative 3. DATA, METHODOLOGICAL FRAMEWORK &MODELLING 3.1 Sources of data and definitions of variables The analysis in this study covers eight South-east Asian countries and six South Asian countries over 21 periods ranging from 1996 to 2016. The variables used in this study and source of data is presented in Table 1. FDI as a percentage of GDP, was applied as the dependent variable to model the determinants of FDI. Independent variables consist of GDP per capita as a proxy for market size, trade openness (sum of import and export as % of GDP), inflation rate and exchange rate as a proxy for the macroeconomic stability, secondary school enrolment as a proxy for human capital, labour productivity proxies by GDP per 6 person employed; mobile-cellular telephone subscriptions as a proxy of infrastructure, unemployment rate, corporate tax rate, natural resource endowment, political stability, and control of corruption. Data for all the variables are obtained from sources such as the World Bank’s World Development Indicators, the World Bank’s World Governance Indicators, the United Nations Conference on Trade and Development (UNCTAD), and KPMG databases. All the tests are performed, and the findingsof this study are based on the data that has been run by using data analysis and statistical software called STATA. Table 1: Variables and Data Specifications The summary statistics and correlation of the variables can be found in Tables 2 and 3 respectively. The table 2 indicates that, over the sample period,South-east Asian countries displaying on average higher FDI inflows, also having larger market sizes, possess more open economies, as well as higher exchange rates, human capital, control of corruption, political stability, labour productivity, natural resources and better infrastructure. South-east Asian countries also display on average slightly higher unemployment rates (4.39% compared with 4.11% for South Asia). On the other hand, South Asian countries possess a higher inflation rate as well as higher corporate tax rates. 7 Table 2: Descriptive Statistics Notes: The 6 South Asian countries in panel A are: Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka. The 8 South-east Asian countries in panel B are: Brunei, Indonesia, Myanmar, Malaysia, Philippines, Singapore, Thailand and Vietnam. FDIGDP is the FDI as a percentage of GDP, GDPCAP is GDP per capita, OPENNESS counts for exports plus imports as a percentage of GDP, INFLATION denotes inflation rate, EXCHANGE signifies exchange rate, HUCAPITAL is school enrolment (secondary, % gross),, PRODUCTIVITY is GDP per person employed, INFRASTRUCTURE is mobile cellular subscriptions (per 100 people), UNEMPLOYMENT counts for unemployment rate, TAX is corporate tax rate, NRESOURCE is total natural resources rents (% of GDP), POLSTA signifies political stability, and CORRUPTION counts for control of corruption. Table 3 suggest that the highest correlation regarding South Asia exists between CORRUPTION and POLSTA (0.82), followed by EXCHANGE and NRESOURCE (0.69). Concerning South-east Asia, the highest correlation exists between POLSTA and EXCHANGE (0.83), followed byPRODUCTIVITY and GDPCAP (0.64). From the correlation matrix in table 3, it can be seen that there is the existence of high correlation among the few independent variables, which may causemulticollinearity problem (Baltagi, 2001). Table 3: Correlation between Variables 8 The basic problem of multicollinearity makes it very difficult to assess the effect of independent variables on dependent variables. According to Saunders, Lewis and Thornhill (2015), if variables are highly correlated then, one of the highly correlated variables must be removed from the model. This study applied Variance Inflation Factors (VIF) to detect multicollinearity. Much divergence exists in the literature regarding the acceptable value of the VIF as the threshold for collinearity. Commonly recommended maximum acceptable level of VIF in the literature is a value of 10 (Hair et al., 2009); meaning that a VIF equal to or greater than 10 would suggest the existence of multicollinearity among the variables. The result of the multicollinearity test in Table 4 indicates that for panel A, POLSTA has the highest VIF value of 6.62, while for panel B, EXCHANGE has the highest VIF value of 4.56. VIF values of each variable are lower than recommended value of 10 for both panels. Mean VIF is 2.80 for the panel of South Asian countries while it is 1.87 for the panel of South-east Asian countries do not exceed the maximum acceptable value of 10, implying that multicollinearity problem is not found among the variables. Therefore, none of the variables needs to be removed from the models. Table 4: Multicollinearity Test The unit root is a stochastic trend in the time series that cause unpredictable behaviour and misleading outcomes (Kennedy, 2003). Therefore, prior to any econometric estimation, panel-based stationarity tests have been applied in this study to investigate the stationarity of each variable. Using the Im–Pesaran–Shin (IPS), Levin–Lin–Chu (LLC) and Fisher-type tests, stationarity test for all variables is reported in Table 5. Concerning panel unit root tests of South Asia, this study finds that FDIGDP, INFLATION, EXCHANGE, NRESOURCE, CORRUPTION, POLSTA, UNEMPLOYMENT and TAX are stationary at level, while GDPCAP, OPENNESS, INFRASTRUCTURE, PRODUCTIVITY and HUCAPITAL are confirmed to be stationary at I(1). Concerning panel unit root tests of South-east Asia, this study finds that FDIGDP, INFLATION, EXCHANGE, POLSTA and UNEMPLOYMENT are stationary at level, whereas NRESOURCE, CORRUPTION, TAX, INFRASTRUCTURE, PRODUCTIVITY, HUCAPITAL, OPENNESS, GDPCAP are confirmed to be stationary at I(1). 9 Table5: Panel Unit Root Tests Significant at *5%, and **10% Level; I(1) indicates integrated at order one 3.2 Model Estimation In order to evaluate the significant determinants of FDI inflows for the South and South-east Asia, panel data analysis has been employed. A panel dataset offers several econometric benefits over traditional time series or cross-section datasets (Hsiao, 1985; Baltagi, 2005). As described by Ranjan and Agrawal (2011), panel data reduces the risk of obtaining biased results, improves the problem of multicollinearity and provides a large number of observations which eventually produce more reliable parameter estimates. The relationship between the dependent and independent variables in the panel data model is as follows: FDIGDP = f (market size, trade openness, labour productivity, human capital, level of infrastructure, inflation rate, exchange rate, political stability, control of corruption, tax rate, unemployment rate, natural resource endowment)....… (1) Equation (1) can be changed into a simple linear panel data model form as follows: FDIGDPi,t = β0+ β1*GDPCAPi,t +β2*OPENNESSi,t +β3*PRODUCTIVITYi,t + β4*HUCAPITALi,t +β5*INFRASTRUCTUREi,t+β6*EXCHANGEi,t+ β7*INFLATIONi,t+β8*POLSTAi,tβ9*CORRUPTIONi,t+ β10*TAXi,t+ β11*UNEMPLOYMENTi,t +β12*NRESOURCEi,t +εi,t….. (2) Where irefers to the examined countries, t is the time period from 1996 to 2016, βare the coefficient, and εis the error term. Because the examining countries is smaller than the number explanatory variables, therefore, to uphold the explanatory power of the model, this study introduces different variables alternatively in different models as applied by Economou et al. (2017), Lucke and Eichler (2016) and Hong and Bui (2015). According to Helmy (2013), this approach also increases the explanatory power of the models and the reliability of the results. 10 First of all, this study tests the significance of standard FDI determinants, including market size, trade openness, exchange rate and inflation rate. After that, the rest of the variables have been introduced subsequently. The basic model to explore the significant determinants of FDI for both panels of South Asia and South-east Asia are structured as follows: FDIGDPit= αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5Zit+ eit……. (Basic Model) Here, irefers to the examined countries, t is the time period 1996 to 2016, βare the coefficient, and eitisthe error term. This study also tests the impact of other Zit variables including PRODUCTIVITY, NRESOURCE, TAX, HUCAPITAL, INFRASTRUCTURE, CORRUPTION, POLSTA and UNEMPLOYMENT alternatively in different models presented in equation i to viii. Each one of the equations (i - viii) presents an alternative augmented version of the basic model. FDIGDPit= FDIGDPit= FDIGDPit= FDIGDPit= FDIGDPit= FDIGDPit= FDIGDPit= FDIGDPit= αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5PRODUCTIVITYit+ eit…….(i) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5NRESOURCEit+ eit…….(ii) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5TAXit+ eit…….(iii) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5HUCAPITALit+ eit…….(iv) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5INFRASTRUCTUREit+ eit…….(v) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5CORRUPTIONit+ eit…….(vi) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5POLSTAit+ eit…….(vii) αi + β1GDPCAPit+ β2OPENNESSit + β3EXCHANGEit+ β4INFLATIONit+ β5UNEMPLOYMENTit+ eit….(viii) 4 FINDINGS &DISCUSSION In order to explore the determinants of FDI in the South and South-east Asia, estimates generated with the random-effects (RE) and the fixed-effects (FE) model, whereas Hausman’s specification test helped to choose the appropriate panel data model. The following sections discuss and compare the empirical findings of this study to the existing literature. 4.1 South Asia The empirical results for South Asia present in Table 6 show the results of both RE and FE for every model. In general, the results of the RE and FE are quite similar. However, the significant difference is that the value of the estimated coefficient is reduced in FE compared to that of RE. Apart from the basic model, FE is preferred over RE for other models as the Hausman specification test rejects the null hypothesis. Therefore, for the basic model, results of the RE is discussed, while for other models results of the FE is discussed. The coefficient of GDPCAP indicates that the market size of the host country proxied by GDP per capita is statistically significant in attracting FDI in South Asia. Hence, it can be argued that market-seeking is an important motive of FDI in South Asian countries. This finding is in line with many empirical studies such as Aziz and Mishra (2016), Hoang and Bui (2015), Alam and Shah (2013), Ang (2008), Bhatt (2008), Asiedu (2006) who also found a positive and significant impact of market size in the FDI inflow of the host country. This finding supports Dunning's (1993) eclectic paradigm, which states that getting better access to the host market is one of the primary motives of foreign investors for investing abroad. The coefficient of OPENNESS indicates that there is a significantand negative relationship between FDI inflows and trade openness in South Asia. This finding is not consistent with most of the empirical literature which reported a positive association between FDI inflows and trade openness (e.g.: Rachdi, Brahim and Guesmi, 2016; Gupta and Singh, 2016; and Asiedu, 2002) but this finding is harmonised with that of Ibrahim and Abdel-Gadir 11 (2015), Koojaroenprasit (2013) and Bhatt (2008) who also found that FDI is negatively related to the degree of openness. The coefficient of INFLATION signifies that the relationship between FDI inflows and the inflation rate is significantand positivein South Asia. The effect of inflation on inward FDI is also found to be significant by Aziz and Mishra (2016), Gupta and Singh (2016) and Rachdi, Brahim and Guesmi (2016). The coefficient of EXCHANGE specifies that there is no significant relationship between exchange rate and FDI inflows in South Asia. Xaypanya, Rangkakulnuwat and Paweenawat (2015), Wood et al. (2014) and Zheng (2009) also reported a statistically insignificant relationship between inward FDI and the exchange rate of the host country. The coefficient of PRODUCTIVITY in the model (1) indicates that there is a significantand positive relationship between labour productivity and FDI inflows in South Asia. Thus, it can be argued that in determining the investment location in South Asia, foreign investors are highly interested in the labour productivity of the host country. This finding is consistent with the findings of Hoang and Bui (2015), Kalyoncu, Tuluce and Yaprak (2015) and Villaverde and Maza (2012) who conclude that foreign investors are particularly interested in labour productivity. The coefficient of NRESOURCE in the model (2) indicates that natural resource endowment is significant and positive to the FDI inflows in South Asia. This relationship indicates that resource-seeking is an important motive of FDI in South Asian countries. This finding is consistent with Rjoub et al. (2017), Ibrahim and Abdel-Gadir (2015), Asiedu (2006) and Onyeiwu and Shrestha (2004). The coefficient of TAX in the model (3) indicates that the corporatetax rate is negative and statistically insignificant in attracting FDI inflows in South Asia. This finding is supported by Onyeiwu and Shrestha (2004), Alam and Shah (2013) and Hunady and Orviska (2014) who also found no statistically significant effect of corporate taxes on FDI inflow. The coefficient of HUCAPITAL in the model (4) indicates that human capital is insignificant in attracting FDI inflows in South Asia, while a positive coefficient signifies the constructive relationship between human capital and inward FDI. This finding indicates that human capital in this region is not an essential consideration for the foreign investors. The coefficient of INFRASTRUCTURE in the model (5) indicates that infrastructure is positive and significant to the FDI inflow in the context of South Asian countries. Similarly, Hoang and Bui (2015) and Xaypanya, Rangkakulnuwat and Paweenawat (2015) for ASEAN countries, Ravinthirakumaran et al. (2015) for Sri Lanka, and Huyen (2015) for Vietnam observed a significant relationship between FDI inflows and infrastructure quality.As argued by Hoang and Bui (2015) infrastructure facilities including communication technology offered by the host country are directly linked to the return on investment; thus, foreign investors are particularly interested in the infrastructure of the host country. 12 Table 6: Statistical Output- South Asia The coefficient of CORRUPTION in the model (6) indicates that the control of corruption is not significant to the FDI inflows in South Asia. Similar to this finding, Wood et al. (2014), while investigating the determinants of FDI in Africa found no statistical significance of corruption in FDI inflows, and concludes that corruption does not determine the decisions of foreign investors. The coefficient of POLSTA in the model (7) indicates that the political stability of the host country is insignificant in attracting FDI inflows in South Asia. However, the positive coefficient of political stability indicates that it will lead to more FDI. This finding is consistent with the findings of Alam and Shah (2013), Sury (2008) and Bevan and Estrin (2004) who also found political stability to be an insignificant determinant of FDI inflow in their respective studies. The coefficient of UNEMPLOYMENT in the model (8) indicates that the unemployment rate is positive and significant in attracting FDI inflows in South Asia. This implies that availability of the labour force is an important consideration for the foreign investor while selecting FDI destinations in South Asia. This finding is in line with the empirical findings of Gupta and Singh (2016), Boateng et al. (2015) and Nunnenkamp et al. (2007) who found unemployment rate to be a significant determinant of FDI inflow. 4.2 South-east Asia The empirical results for South-east Asia presented in Table 7 shows the results of both RE and FE for every model. FE is preferred over RE for all models as the Hausman specification test rejects the null hypothesis. Therefore, the discussion presented in the following sections reflects the result of the FE for all models. The coefficient of GDPCAP indicates that the market size of South-east Asia is statistically significant in attracting FDI. Hence, it can be argued that market-seeking is an important motive of FDI in South-east Asia. This result is in accordance with the findings of Tan, Wong and Goh (2018), Hoang and Bui (2015), Xaypanya, Rangkakulnuwat and 13 Paweenawat (2015) and Ismail (2009), who also state that the size of the local market is an important factor in attracting FDI to South-east Asian countries. Thecoefficient of OPENNESS is negative and insignificant. This result suggests that trade openness is not a significant factor in attracting FDI in South-east Asia. This finding is consistent with the findings of Kumari and Sharma (2017), who for 20 developing countries from the whole of South, East and South-east Asia found that trade openness, is insignificant to FDI inflows. Besides, Bhatt (2008) also reports an inverse relationship between FDI and trade openness of South-east Asia. The coefficient of INFLATION indicates that the relationship between FDI inflows and inflation is negative and insignificant in South-east Asia. This finding is also consistent with the conclusion of Hoang and Bui (2015), who reported that inflation is negative and statistically insignificant in South-east Asia. The coefficient of EXCHANGE indicates that there is a significant relationship between exchange rate and FDI inflows in South-east Asia. This shows that better economic condition or strong local currency causes inward FDI to surge into South-east Asia. Hadi et al. (2018), Hoang and Bui (2015), Ismail (2009) and Bhatt (2008) also found a significant relationship between FDI inflows and exchange rate in South-east Asia. The coefficient of PRODUCTIVITY in the model (1) indicates that there is a positive and significant relationship between labour productivity and FDI inflows in South-east Asia. This finding is consistent with the findings of Hoang and Bui (2015) who state that in determining the location of investments in the South-east Asian region, foreign investors are highly interested in the labour productivity of the host country. The coefficient of NRESOURCE in the model (2) indicates that natural resource endowment is negative and insignificant to the FDI inflows in South-east Asia. This result indicates that resource seeking is not an important motive of FDI in South-east Asia. This finding is in line with the findings of Asiedu (2004), who argued that natural resource availability does not have a significant impact on FDI. The coefficient of TAX in the model (3) indicates that the corporatetax rate is statistically insignificant to attract FDI inflows in South-east Asia. Alam and Shah (2013) and Onyeiwu and Shrestha (2004) also found no statistically significant relationship between corporate taxes and FDI inflow. Contrary to this finding, Wood et al. (2014), Koojaroenprasit (2013) and Ang (2008) reported that corporate taxes have a negative impact on FDI inflow. The coefficient of HUCAPITAL in the model (4) indicates that human capital is insignificant in attracting FDI inflows in South-east Asia, while a positive coefficient indicates a constructive relationship between human capital and inward FDI. This finding indicates that in determining the investment location in South-east Asia, human capital is not an important consideration for foreign investors; however, skilled labour of the host country works as a positive externality to attract FDI inflows. Hoang and Bui (2015) also found a positive impact of human capital on the inflows of FDI in South-east Asia. The coefficient of INFRASTRUCTURE in the model (5) indicates that infrastructure is highly significant to the FDI inflow in the context of South-east Asia. Similarly, Hoang and Bui (2015) and Xaypanya, Rangkakulnuwat and Paweenawat (2015) observed a significantly positive relationship between FDI inflows and infrastructure quality for South-east Asia. This finding indicates that, in determining the location of investments in South-east Asia, foreign investors are particularly interested in the infrastructure of the host country. Therefore, it can 14 be argued that the policy regarding an improvement in infrastructure facilities can build up the confidence of foreign investors and then increase FDI inflow into the South-east Asian region. The coefficient of CORRUPTION in the model (6) indicates that the control of corruption is positive and highly significant to the FDI inflows in South-east Asia. The coefficient value of 1.642 indicates that 1% increase in the control of corruption leads to a 1.642% increase in FDI inflows. Similar to this finding, Leonardo et al. (2018), Su et al. (2018) and Dauti (2015) also highlighted the positive impact of the control of corruption on the FDI inflows. Table.7: Statistical Output- South-east Asia The coefficient of POLSTA in the model (7) indicates that the political stability of the host country is significant in attracting FDI inflows in South-east Asia. The coefficient value of 1.487 indicates a highly significant positive relationship as a 1% increase in the political stability leads to a 1.487% increase in FDI inflows. Similar to this finding, Tan, Wong and Goh (2018) also revealed that political stability is an important factor for foreign investors to decide where to pursue their operations when making an investment decision in the Southeast Asian region. A host country with a high degree of political stability may encourage foreign investors to invest in the host market because the political stability could maintain the orderly economic process, which in turn would increase the profitability of the investors. The coefficient of UNEMPLOYMENT in the model (8) indicates that the unemployment rate is insignificant in attracting FDI inflows in South-east Asia. The statistically insignificant relationship between inward FDI and unemployment rate implies that the availability of the labour is not an important consideration for the foreign investor while selecting an FDI destination to the South-east Asian countries. Therefore, it can be argued that foreign investors are particularly interested in labour productivity rather than labour availability while deciding investment locations in this region. Similar to this, Gupta and Singh (2016) and Boateng et al. (2015) also found similar result in their respective studies. 15 5. CONCLUSIONS & POLICY IMPLICATIONS A comparison of the empirical results for South Asia and South-east Asia in Figure 2 indicates that the market size variable is significant and positive for both regions. This indicates that market seeking is an important motive of the foreign investor investing in these regions. South Asia and South-east Asia are the two most populous regions in the world. Although per-capita GDP is still very low in most countries of these regions; however, rapid economic growth, increasing purchasing power, and long-term market potential have made both regions attractive to market-seeking FDI. According to the World Investment Report2017, countries with business-friendly environments, a dynamic market size, market position, and high economic growth continue to attract foreign investors seeking to tap into a major economy. Therefore, to attract foreign investment, more preferential foreign investment policies should be offered. More sectors, such as telecommunications, financial and services, should be opened to FDI. Restriction to various sectors should be lightened to attract further foreign investment. Figure 2: Comparison of FDI determinants between South and South-east Asia The trade openness variable is significant for South Asia but statistically insignificant for South-east Asia. This may reflect the different strategies pursued by the two regions. Empirical findings also indicate that human capital is not an important consideration for foreign investors while investing in either South or South-east Asian countries. This might be since the majority of human capital in these regions has not reached the minimum threshold to attract FDI. It is well documented in the literature that a host country needs to have a certain accumulating level of human capital to enjoy the benefits from FDI spillovers. Therefore, policymakers of these regions should put the focus on transformational investment in their people to develop human capital. This will not only upgrade human capital but promote the country’s competitiveness in the global markets. Besides, the labour productivity of the host country is a major factor for foreign investors while choosing their FDI location. Hence policymakers of the respective countries of these regions can increase labour productivity by improving the quality of education and training, increasing R&D and invest in new technologies. Such policies not only increase productivity but can directly or indirectly fuel human capital. The significance of inflation rate and exchange rate in explaining the distribution of FDI in South Asia and South-east Asia, indicates that FDI in these regions is much more sensitive to the macroeconomic stability of the host country. Therefore, it is essential for the host countries of these regions to maintain macroeconomic stability through monetary policies while strengthening exchange rate management. Natural resource variable is significant to South Asia but insignificant to South-east Asia. This might be due to the fact that South Asian countries are much more enriched with natural resources compared to South-east Asia. Therefore, unlike South-east Asia, resource seeking is an important motive of the foreign 16 investor investing in South Asia.Foreign investors are not particularly interested in corruption and the political stability of South Asia, while these factors are highly important to the investors when making an investment decision in the South-east Asian region. However, it is necessary for the countries of both regions, to have a low level of corruption and a high degree of political stability to increase their potential as an investment location. The infrastructure of the host country is significantly important to foreign investors. This finding indicates that, in determining the location of investments in both South and Southeast Asia, the foreign investors are concerned about the infrastructure of the host country. Several countries of these regions are still having difficulties with fragile road conditions, frequent electricity blackouts, congested roads, and insufficient access to drinking water. A flimsy infrastructural framework surges the cost of doing business and confines the attractiveness of the country as an investment location. Therefore, governments of these regions need to take measures to upgrade infrastructure by increasing government expenditure on sectors like energy, telecommunication, transport and water. This study has explored FDI determinants in South and South-east Asia and filled a gap in the existing literature by providing a comprehensive empirical comparison analysis. The two panel datasets and appropriate methodology were employed to identify significant determinants of FDI inflows of these two Asian regions. Although some important findings are derived from this study, which contribute to the existing knowledge, there are, however, some limitations to this research. 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