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Factors affecting foreign direct investment inflows in ten asean countries from 2010 to 2021

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This report aims to investigate the factors that affect the inflow of Foreign Direct Investment (FDI) in 10 ASEAN countries from 2010 to 2021. The authors make use of an extensive dataset gathered from different sources, such as national statistical agencies, international organizations, and scholarly publications and use multiple regression analysis with Fixed effects model to determine the importance and extent of the factors that affect the inflow of Foreign Direct Investment (FDI) in this region. Several key variables are taken into account, including market size, trade openness, infrastructure and inflation. The findings emphasize the significance of market size, trade openness, and favorable business infrastructure in attracting foreign investment. It helps policymakers and businesses formulate strategies to attract and retain FDI, thereby fostering economic growth and development in the ASEAN countries.

FOREIGN TRADE UNIVERSITY FACULTY OF BANKING AND FINANCE *** MIDTERM REPORT FACTORS AFFECTING FOREIGN DIRECT INVESTMENT INFLOWS IN TEN ASEAN COUNTRIES FROM 2010 TO 2021 Class: TCHE414 Instructor: Assoc Prof., PhD Mai Thu Hien Group: Group Hanoi, June 2023 TABLE OF CONTENTS ABSTRACT .2 INTRODUCTION 1.1 Rationale and objectives of the study .3 1.1.1 Rationale 1.1.2 Objectives 1.2 Objects and scope 1.3 Structure of the study CHAPTER 2: THEORETICAL BASIS AND LITERATURE REVIEW 2.1 Theoretical Review 2.1.1 Foreign Direct Investment 2.1.1.1 Definition 2.1.1.2 Classification 2.1.2 Theoretical Framework 2.2 Factors affecting FDI inflows 2.2.1 GDP per capita 2.2.2 Trade openness 2.2.3 Infrastructure 2.2.4 Inflation CHAPTER 3: RESEARCH METHODOLOGY .10 3.1 Model specification 10 3.2 Estimated results 10 3.3 Data collection 11 CHAPTER 4: RESULT AND DISCUSSION .13 4.1 Descriptive analysis 14 4.2 Multicollinearity test .16 4.3 Research results with OLS, FEM, REM tools .17 CHAPTER 5: RECOMMENDATIONS FOR VIETNAM .23 CONCLUSION .25 REFERENCES 26 FACTORS AFFECTING FOREIGN DIRECT INVESTMENT INFLOWS IN TEN ASEAN COUNTRIES FROM 2010 TO 2021 School of Economics and International Business, Foreign Trade University Phạm Duy Sơn Faculty of International Economics, Foreign Trade University Abstract This report aims to investigate the factors that affect the inflow of Foreign Direct Investment (FDI) in 10 ASEAN countries from 2010 to 2021 The authors make use of an extensive dataset gathered from different sources, such as national statistical agencies, international organizations, and scholarly publications and use multiple regression analysis with Fixed effects model to determine the importance and extent of the factors that affect the inflow of Foreign Direct Investment (FDI) in this region Several key variables are taken into account, including market size, trade openness, infrastructure and inflation The findings emphasize the significance of market size, trade openness, and favorable business infrastructure in attracting foreign investment It helps policymakers and businesses formulate strategies to attract and retain FDI, thereby fostering economic growth and development in the ASEAN countries Keywords: FDI, ASEAN, FEM Introduction 1.1 Rationale and objectives of the study 1.1.1 Rationale ASEAN countries have witnessed a steady increase in FDI inflows in recent years The region's attractive investment climate, growing consumer markets, and strategic geographic location have made it an appealing destination for foreign investors It is mainly driven by factors such as regional integration, growing consumer markets, favorable investment climates, and government support The rationale stems from the growing importance of FDI in driving economic growth and development in the region While ASEAN has attracted significant FDI inflows, there is a need to understand the specific factors influencing FDI, given its increasing significance in the ASEAN economies This study aims to bridge the research gap by examining the key determinants that impact FDI inflows in ASEAN countries during the specified timeframe 1.1.2 Objectives - To identify and analyze the economic factors influencing FDI inflows in ASEAN countries, considering variables such as market size, trade openness, infrastructure, and inflation - To compare the variations in FDI patterns among individual ASEAN countries and identify country-specific factors that influence FDI inflows - To provide recommendations and insights for policymakers, businesses to enhance the attractiveness of ASEAN countries for FDI, foster economic growth, and contribute to the sustainable development of the region 1.2 Objects and scope Our research pays attention to the factors that have an impact on FDI inflows in 10 ASEAN countries including Market size, Trade openness, Infrastructure and Inflation The analysis will cover the period from 2010 to 2021, allowing for a comprehensive examination of the trends and changes in FDI inflows over a significant timeframe in which ASEAN members gradually receive significant FDI from other countries and intra-ASEAN countries The study will focus on quantitative analysis, by applying relevant techniques to conduct a result based on the collected data 1.3 Structure of the study Introduction: Provide an overview of the importance of FDI and the significance of analyzing factors affecting FDI inflows in ASEAN countries Literature review: - Review of literature on theoretical framework and factors affecting FDI inflows Research methodology Provide a clear and detailed description of the methods and procedures that will be used to conduct the research Result and discussion Present and interpret the findings of your research This section provides a comprehensive and thoughtful analysis of our findings, demonstrating the value and significance of our proposed research project Recommendations for Vietnam’s service industry Based on the result, we provide recommendations for the specific needs and challenges of Vietnam, considering the unique characteristics and aspirations of the country Conclusion Summarize the key points of this proposal and reinforce the recommendations 2.1 Theoretical Basis and Literature Review 2.1.1 Foreign Direct Investment 2.1.1.1 Definition Foreign Direct Investment (FDI) is defined by the International Monetary Fund (IMF) as the “cross border investment”, which means an investor that is “resident in one country has control or a significant degree of influence on the management of an enterprise that is resident in another economy” FDI is “a form of international capital flows” FDI is also considered a component of a nation’s financial accounts According to Eurostat, FDI is “the category of international investment that reflects the objective of obtaining a lasting interest by an investor in one economy in an enterprise resident in another economy” 2.1.1.2 Classification (1) Horizontal FDI Horizontal FDI is the most common form of FDI In this type of FDI, enterprises invest in foreign companies which manufacture products and provide services similar to those the investor firms produce in their domestic markets (2) Vertical FDI A vertical FDI refers to an investment which is made in a firm operating in a supply chain This firm may or may not be in the same sector with the investor company Companies that take part in vertical FDI often have the objectives to minimize raw material costs or gain better control over their supply chain There are two sub-categories of vertical FDI: ● Forward vertical FDI: A multinational decides to acquire or operate in the role of a distributor ● Backward vertical FDI: A multinational decides to acquire or operate in the role of a supplier (3) Conglomerate FDI Conglomerate FDI occurs when investments are made in two wholly separate enterprises in two completely different industries As a result, FDI is not directly related to the investor's company (4) Platform FDI Platform FDI refers to the type of investment when an enterprise has an expansion plan into a foreign country, but the products manufactured are exported to another third country 2.1.2 Theoretical Framework Yasmin et al (2003) conducted a research on factors affecting FDI in developing countries, which gave the results that domestic investment, labor force, external debt and trade openness are the significant determinants of the FDI inflows in the upper and lower middle-income countries Meanwhile, urbanization, market size, living standard, inflation, current account balance and wages are significant factors in the lower income countries Moreover, the authors showed that the upper middle income group with better internal and external balances, high level of real GDP per capita, trade openness and large market size has received higher flow of FDI Demirhan and Masca (2008) conducted research about determinants of foreign direct investment (FDI) inflows in developing countries over the period of 2000-2004 based on a sample of cross-sectional data on 38 developing countries Among the significant factors, there are determinants that have positive correlation with FDI inflows, which are growth rate per capita, telephone main lines and level of openness In contrast, inflation rate and tax rate have a negative impact on the FDI inflows Nunnenkamp (2002) studied the determinants of FDI inflows in developing countries in the context of globalization The research classifies the factors into categories: traditional factors, such as market size and growth; and non-traditional factors, such as cost, additional factors of production and level of openness However, traditional market-related determinants are still dominant factors determining the allocation of FDI Bakar et al (2012) focused on the effects of infrastructure on the FDI inflows in Malaysia However, the study also analyzed the effect of other determinants of FDI, namely market size, trade openness, and human capital along with infrastructure The result showed that the higher level of infrastructure would more likely to attract FDI since it would allow MNC to operate with higher efficiency Besides, real GDP per capita as a proxy of market size has shown a positive impact on FDI inflows Also, the proportion of trade share per real GDP representing the level of trade openness has a positive correlation with FDI inflows By contrast, human capital has a negative relationship with FDI inflows 2.2 Factors affecting FDI inflows and hypothesis: 2.2.1 Market size Numerous studies have explored the correlation between foreign direct investment (FDI) and gross domestic product (GDP) To be more specific, GDP serves as an indicator of a country's market size or overall economic magnitude, which is expected to have a positive influence on FDI inflows (Asiedu, 2001; Polyxeni & Theodore, 2019; Hoa et al., 2021) However, when examining developing countries, researchers often opt for the dependent variable of GDP per capita This choice facilitates easier comparisons of economic conditions among countries, allowing for a more meaningful analysis of FDI impact The research findings by Hakizimana (2015) provide evidence supporting a strong and positive relationship between foreign direct investment (FDI) and GDP per capita in Rwanda, an economically disadvantaged country This positive relationship further contributes to overall economic growth Similarly, Kok and Ersoy (2009) found that an increase in GDP per capita has a favorable impact on FDI inflows In the literature examining the determinants of FDI, Chakrabarti (2001) emphasizes the significance of the host country's market size, as measured by GDP per capita, which consistently proves to be a robust variable Likewise, Al-Sadig (2009) utilizes GDP per capita (GDPPC) as a dependent variable to account for the market size and potential of the host country in analyzing FDI inflows Furthermore, in the context of researching FDI inflows in emerging markets, Arbatli (2011) adopts the natural logarithm and considers GDP per capita as a determining factor A recent paper conducted in India, Iran and Pakistan in the period from 1982 to 2012 indicated the same results as GDP per capita is one of the conventional determinants to pursue a more and sustained FDI inflows In light of these findings, our team would like to propose the first hypothesis: H1: Market size positively affects FDI inflows 2.2.2 Trade openness According to Alotaibi & Mishra in 2014, trade openness is defined as the sum of imports and exports normalized by GDP; it illustrates the global liberalization of capital and services and is connected to a nation's expansion into international markets (Seyoum, Wu, & Lin, 2014) This factor of economics has been proved to have significant impacts on the inflow of FDI, as various prior researchers have demonstrated the positive influence of trade openness on FDI inflow (e.g., Busse & Hefeker, 2007; Efobi et al., 2015; Ezeoha & Ugwu, 2015; Rauf et al., 2016; Ullah & Rahman, 2014) Likewise, the works of Biglaiser and deRouen (2006) and Chakrabarti (2001), have also discovered a positive relationship between trade openness and the flow of foreign direct investment (FDI) Another paper conducted in 2011, using a sample of 36 developing countries during the 1990-2008 period also showed that trade openness contributes positively to the inflow of FDI in developing economies (Liargovas & Skandalis, 2011) The same finding has been found in the research into a specific set of Asian countries including India, Iran, and Pakistan from 1982 to 2012, Zaman et al (2018) and Patsupathi and Sakthi (2019) employed fixed-effect and Pooled OLS techniques on panel data to indicate the significant effects of trade openness to FDI inflows Furthermore, Mudiyanselage et al (2021) discovered that high levels of trade openness are associated with increased FDI inflows on both global and national scales They suggested that promoting trade openness represents a viable strategy for fostering sustained foreign direct investment inflows in the long term However, other studies, including the one conducted by Seim (2009), have found a negative relationship between FDI inflows and the level of openness in countries undergoing transition As a result, it is implied that the relationship between trade openness and FDI inflows is quite controversial and needs careful investigation Moreover, the nature of this relationship may vary depending on the specific characteristics of each case The theoretical perspective suggests that the impact of trade openness on FDI inflows is contingent upon the underlying motivations for engaging in FDI activities, as highlighted by Markusen and Maskus (2002) and Dunning (1993) Therefore, our team would like to propose the hypothesis: H2: Trade openness positively affects the FDI inflows 2.2.3 Infrastructure A well-developed and accessible infrastructure is of great importance when it comes to facilitating the operation of multinational companies' affiliate production and trade endeavors By improving infrastructure, significant cost reductions can be achieved (Asiedu, 2004), thereby influencing investors' decisions on where to establish their operations positively (Shah and Ahmed, 2003) While infrastructure alone may not be the sole determinant of multinational corporations' production activities, it acts as a crucial factor of economic activity in developing nations (Khan and Kim, 1999) Given all other factors being equal, multinational corporations are likely to favor countries with wellestablished and developed infrastructure This preference stems from the fact that in such economies, multinational corporations can effectively utilize imported machinery and equipment to optimize their operations (Shah, 2014) Dunning's eclectic paradigm (1977, 1979, 2000) presents a comprehensive and integrated approach that combines traditional trade theories with internalization theory, which emphasizes that foreign direct investment (FDI) decisions are influenced by three crucial factors: ownership-specific competitive advantages (O) within multinational corporations, locational advantages (L) within host countries, and internal firm-specific advantages (I) that offer superior commercial benefits Specifically, the locational advantages aspect of the eclectic paradigm indicates the significance of a host country's infrastructure facilities in shaping the FDI decisions made by multinational enterprises (MNEs) Likewise, enhanced infrastructure facilities have also been proved to have the ability to enhance productivity and foster long-term profitability, thereby encouraging the inflow of foreign direct investment (FDI) into a country (Kaur et al., 2016) Moreover, improved transportation systems encompassing road, rail, and air networks have been found to attract FDI (Bellak, Leibrecht, & Römisch, 2007; Leibrecht & Riedl, 2010; Riedl, 2010) These upgraded transportation facilities not only lead to reduced domestic freight costs but also contribute to lower expenses associated with imports and exports This can be explained by the fact that high-quality infrastructure creates a favorable investment climate for investors to operate within (Wekesa et al., 2016) Therefore, our team would like to propose the third hypothesis: H3: Infrastructure positively affects the FDI inflows 2.2.4 Inflation The relationship between Foreign direct investment (FDI) and inflation has long been investigated in recent years This is because while FDI is widely recognized as a crucial driver of economic growth and development in countries, it is proved that high inflation rates exerts a highly adverse impact on FDI inflows in developing economies, impeding their progress (Mustafa, 2019) Thus, it is imperative to empirically investigate the causal relationship between inflation and FDI Not to mention, higher levels of inflation brings about more uncertainty and risk, creating discouragement for foreign investors and therefore negatively affecting their investment decisions (Udoh & Egwaikhide, 2008) However, it is worth noting that diversifying investment across countries and financing options can help mitigate the negative effects of inflation on investment fluctuations (Sayek, 2009) Later in this research field, Djokoto (2012) also on the Breusch and Pagan Lagrangian multiplier test and Hausman test to find the most suitable method among the three above methods Table 4.4: Results of estimated models for determinants of FDI Dependent variable: FDI inflow Variable FEM REM OLS Constant -27783.7*** -8881.6 -30814.9*** (-4.77) (-1.42) (-4.35) 3.002*** 1.632*** 0.583*** (10.35) (8.94) (5.63) 71.94* -18.33 118.2*** (2.18) (-0.65) (5.44) -20.86 -30.36 -18.89 (-0.84) (-1.09) (-0.57) -2.915 72.16 207.4*** (-0.07) (1.74) (3.68) 0.6352 0.6010 0.7240 Market size Trade openness Infrastructure Inflation R2 18 Breusch-Pagan test 0.0000(239.75) Hausman test No of 0.0000(26.9) 120 120 120 observations ***, ** and * denote 1%, 5% and 10% of significance levels, respectively Value in parenthesis denote the t-values Source: The authors’ calculation The pooled OLS is a restricted model as it assumes that countries are homogenous It does not explain the existence of a country's specific effects such as the differences in technology, resource endowments and institutions However, the random and fixed effects models acknowledge the heterogeneity among countries by introducing intercepts and other parameters, which are likely to vary across different countries Based on Breusch-Pagan test of homogeneity, the result favored this hypothesis, suggesting that these countries are heterogeneous This means that REM is the more consistent estimator in this case At the same time, the Hausman specification test (Hausman, 1978) was used to test random effect models versus fixed effect models The results show the fixed effect model is preferred to the random effect model However, it is noticeable that certain complications that may arise from the fixed effect model such as serial correlation or heteroskedasticity For this reason, we first performed tests to check for the correlation between error terms and independent variables The presence of insignificant p-value (p-value = 0.2132 > 0.05) indicates the inexistence of autocorrelation in our specified model Next, we check the likelihood of heteroskedasticity by a modified Wald test, then the result shows that there’s the case of heteroskedasticity in our specified model (p-value = 0.0000 < 0.05), and we have to fix it 19

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