The determinants of foreign direct investment in ASEAN
MA. PHAM VAN RANG (Faculty of Finance and Accounting, Central Transport College No 6)
ABSTRACT:
This paper examines the significant factors determining the foreign direct investment (FDI) in 10 members of the Association of Southeast Asian Nations (ASEAN), namely Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand and Vietnam (ASEAN10). This paper applies the Bayesian method to estimate the parameters on the constructed panel data starting from 2010 to 2019. This paper’s findings show that there are significantly positive effects of GDP, GDP per capita, Import and Export, and negative effect of inflation on the FDI inflow in ASEAN’s countries. The paper’s results also indicate that the market size (GDP) and the inflation are significant factors affecting the FDI inflow.
Keywords: ASEAN10, developing countries, FDI, Bayesian.
1. Introduction
FDI has become an important source of private external finance for developing countries. It is different from other major types of external private capital flows in that it is motivated largely by the investors' long-term prospects for making profits in production activities that they directly control. Not only can FDI add to investible resources and capital formation, but, perhaps more important, it is also a means of transferring production technology, skills, innovative capacity, and organizational and managerial practices between locations, as well as of accessing international marketing networks.
This study aims to investigate the significant factor determining FDI in ASEAN10. The factors we are interested are labor forces, inflation, gross domestic product (GDP), GDP per capita, import and export. These variables are drawn from “location-specific advantages” in the Bayes Theorem (BT), proposed by Thomas Bayes (1763) and being observable effects (or factors).
2. Literature Review
Hong Hiep Hoang and Duc Hung Bui (2012) analyzed the factors that attract FDI inflows into ASEAN countries in the period 1991 to 2009 by using a panel data model (panle data). The results indicate that market size, trade openness, quality infrastructure, human capital, and labor productivity are the key factors that have a positive impact on FDI inflows. In addition, exchange rate policy, real interest rates, political risk and corruption also affects d Mr. FDI. Surprisingly, cheap labor has the opposite effect of attracting FDI into the region, because foreign investors are particularly concerned with productivity and quality of labor.
Research FDI determinants in ASEAN3 (Cambodia, Laos and Vietnam) and ASEAN5 (Indonesia, Malaysia, Philippines, Thailand and Singapore), Phonesavanh Xaypanya, Poomthan Rangkakulnuwat and Sasiwimon Warunsiri Paweenawat (2014) based on EPT proposed by Dunning (1988). The table data model is applied to annual data from 2000 to 2011 to capture unobserved time and time invariant effects, which have been omitted for most previous studies. The FD method is applied in estimation to obtain unbiased estimation tools proposed by Wooldridge (2009). For ASEAN3 and ASEAN5 we find that the eminence, phone lines and significant trade rates define FDI inflows. In addition, the estimated results of inflation rates and the level of trade openness affecting FDI inflows in these countries are contrary to the hypothesis. With higher inflation rates and lower levels of Openness, foreign investors remain more interested in investing in the region. This can be explained that even through the global economic crisis, foreign investors still see ASEAN5 as an attractive investment area during the research period.
Market size and per capita income are two important factors to attract FDI capital with the aim of seeking markets, expanding production activities, and representing market size as GDP. Nguyen and Hans-Rimbert (2002) based on two regression models for 61 observations and found that GDP and GDP per capita have a strong impact on both registered and implemented FDI. However, the impact of GDP per capita is contradictory between the model of implemented FDI and registered FDI, while GDP has a positive effect on FDI inflows. Bulent and Mehmet's study of FDI into 62 provinces of Vietnam in the period 2006-2009 shows the positive effect of real GDP per capita at 2005 base prices on FDI inflows. Nguyen and Nguyen (2007) see a positive effect on FDI of the growth rate. Empirical research by Dermihan et al (2008); Hussain (2014) shows the strong impact of GDP on FDI. Research Vo Minh Thien (2017) confirms that market size is a positive influence on FDI growth.
From data from 2000 to 2014 of countries in South Asia including Bangladesh, India, Pakistan and Sri Lanka. Countries in Southeast Asia including Indonesia, Malaysia, Philippines, Singapore and Thailand combine OLS regression model, fixed effect (FE), random effect (RE) Shahmoradi, B. and Baghbanyan. , M. (2011) shows the following factors: exchange rate (ER), human capital diary (lnHC), inflation log (lnINF), infrastructure log (lnINFR), log diary Market model (LMS) and trade openness (TO) have the potential to attract FDI in the service sector of developing countries. At the same time, inflation has a negligible and negative relationship with FDI inflows. These countries should maintain growth momentum to improve market size, focus more on education and skills of workers to improve the quality of human capital, and improve infrastructure.
The first basic theory relating to the distribution of FDI between regions in the nations is Popular Accumulative Impact of Krugman (1991). The second one relates to traditional economic advantages. The final theory is institutional factor
This model is established meticulously of Dunning (1977, 1979, 1981, 1988, 1996, 1998, 2000, 2001). According to Dunning, a company decides to invest abroad when it has OLI advantages including Ownership Advantages, Location Advantage and Internalization Incentives.
3. Theory framework
Dunning (1988) first introduced the EPT. This theory states that the extent and pattern of multinational operations are generally determined by three factors: ownership-specific advantages, location-specific advantages, and internalization advantages (1) Ownership-specific advantages (2) Location-specific advantages (3) Internalization advantages.
MacDougall, G. D. A. (1960), think that investment flows will flow from low-interest countries to high interest rates until equilibrium is reached (interest rate two water is equal). The product life cycle theory was proposed by economist Vernon (1966) show that any new product is developed through 4 stages: (1) Stage of invention and introduction; (2) Stage of development and completion; (3) Stage of completion or standardization; (4) Depression phase of the product.
Akamatsu (1962) of "flying swallow" divides the process of product development finished products in 3 stages: (1) The period when products are imported from abroad to serve Domestic demand; (2) Period of increasing domestic production to replace import; (3) Production stage for export. FDI activities are implemented in stages (2) and (3) to face with a change in relative advantage. Ozawa thinks that developing countries have Relative advantage on cheap labor, will attract FDI capital to exploit This advantage [67]. However, the labor cost of the industry will then gradually increase due Local labor resources have been fully exploited and attracted FDI will descend. Meanwhile, domestic companies will invest abroad to exploit The relative advantage of this country, where there is cheaper labor, is that the process is continuous of FDI activities.
As the stated in neo-classical economic theory, labor cost plays an important role in the location decision of FDI, and being measured by the salary and wage paid to the employees 13 Williamson (2011). However, there are no direct historical data available, I use Gross National Income (GNI) per capita, the average income of a country’s citizens, to reflect the average labor cost to companies. The availability of numerous cheap labors in China replaced the positions of employees from Europe and United States for the big wage gap on the same job Sachs (1996). Consequently, Vietnam, a country overall less developed than China with lower national wage level is expected to continually attract foreign investment
Since all the dependent variables involved are in different dimension (unit), such as million dollars, percentage, and kilometers, I transfer them into the natural logarithm form as the way of standard processing Weisberg (2005).
A volatile and unpredictable inflation rate in the host market creates uncertainty and discourages MNEs’ FDI activities Buckley et al (2007). The high inflation rate devalues domestic currency, and reduces the real return on investment as a result. Hence, the government launches policies reducing inflation rate to create an investment environment with less risk Birhanu (1998). Therefore, a low and predictable inflation rate is expected to stimulate the inflow of FDI, and vice versa.
Theoretically, FDI happens for differences in factors endowment between the host and home countries. It can explain why half of FDI in Vietnam has flown to the competitive industries of Vietnam Pham (2001) of which Vietnam is rich. Capital flows from affluent countries to developing countries with abundant and cheap labor in exchange for finished products Nguyen & Nguyen (2007). In Vietnam, contribution of FDI toward export to the total export has increased significantly in the last two decades Schaumburg (2003). Further, FDI in such export-oriented industries has been the main force driving the fast growth of exports.
4. Model and Data
Based on this research framework, the author used the Bayes factor and Bayes test model to choose the most appropriate priors. In addition, we also performed the OLS diagnostic test to inspect the convergence of the MCMC chain. To check how replicated data fit observed data, the author applies the posterior predictive p-value test with the simulation:
Likelihood model: FDI ~ (µ, σ2)
Prior distributions: a|σ2 ~ Invgamma(λ0 2, λ0 σ2 0 2 )
where λ0 is df (prior degree of freedom) and σ2 0 is the residual of MS. The number of dimensions, df, prior mean, σ2 0 and λ0 are obtained from the ordinary least squares (OLS) regression results. Therefore, we have the following prior distributions: a|σ2 ~ Invgamma (11.5,4255).
The author utilizes a panel data covering the 10-year period from 2010 to 2019 of ASEAN 10, collected from World Bank Statistics (2019)’s database. Time frequency indicates the year. Within Bayesian analysis, owing to combining prior information with observed data, inferential results are robust to sparse data, and hence a small sample does not influence MCMC simulation efficiency. Note that the 2010 - 2019 sample period was the after GFC effects, when most of countries all over the world coped with a severe economic crisis, but the Vietnamese economy was much less hit by this global recession. According to statistical figures, the economic growth of Vietnam acquired good performance 6.2% in 2010 (World Bank Data, 2019). The units of GDP, GPP, FDI, Export and Import are a billion USD and of labor force is million employees.
5. Results 5.1. Descriptive Statistics
Table 1. Model summary
Building a Bayesian regression model that measures the impact of macroeconomic variables on foreign indirect investment in 10 Southeast Asian countries. Table 1 illustrates the effects of variables on FDI as follows: GNP, INF variables have strong and positive impacts; while GDP, LB, EX, IM variables have strong and weak effects, sometimes negative positive influence.
The regression model after running on Stata 15 gives the results as shown in Table 1, we can build a regression model for the sample as follows:
FDI = - 8,376.357 + 0.5868995GNP + 2.670167GDP + 100,111INF + 2.352978LB + 0.0885331EX + 0 .0012567IM
5.2. Model Diagnosis
Table 2.
From the sample data, based on the stata tests the probability of variables affecting GDP. Table 2 shows that the probability of impact of terminals on GDP is positive, in which: the most powerful GPP and INF variables is 100% probability, followed by EX variable is 90.4%, GDP variable is 64.6 %, IM variable is 33.6% and weakest is LB with 28.2%.
Table 3. Autocorrelation plots
Autocorrelation plots rapidly decrease (Table 3), while cusum lines are jagged and not smooth, which certainly points to the sign of convergence (Table 4). In sum, MCMC chains of our model mix well. Hence, we can conclude that no serious convergence problem exists.
Table 4. Bayes cusum plots
6. Conclusion
This study investigates the factors determining FDI in ASEAN10 (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand and Vietnam) based on the Bayes Theory proposed by Thomas Bayes (1762). The panel data model is applied in this study in order to capture the time invariant and time variant unobservable effects, which has been ignored in most previous studies. The annual data starting from 2010 to 2019 are used in this paper. Overall, we found that GDP per capital, GDP and inflation significantly determine FDI inflows. This conclusion is also supported by many empirical researches such as Nguyen and Hans-Rimbert (2002), Wooldridge (2009), Dermihan et al (2008), Hussain (2014). Furthermore, these results are consistent with the hypothesis of this study and conform to the fact that even though these countries are the developing countries and least developed countries of Asia.
In addition, after checking for the data series, the estimation results confirm that: Gross Domestic Product has a positive impact on Foreign Direct Investment as found by Nguyen and Hans-Rimbert (2002), Wooldridge (2009), Dermihan et al (2008), Hussain (2014). Export has a positive impact on Foreign Direct Investment as found by Schaumburg (2003). Import has a positive impact on Foreign Direct Investment. Labor forces has a negative impact on Foreign Direct Investment as detected by Akamatsu (1962) and Nguyen & Nguyen (2007). Inflation has a negative impact on Foreign Direct Investment. The results are in compliance with findings by MNEs’ FDI activities Buckley et al (2007) and Birhanu (1998). Gross domestic product per people has a positive impact on Foreign Direct Investment which support the results of Sachs (1996). However, this study has some limitations. First, the small number of observations could influence the finding. Second, the research was limited to the case of in ASEAN 10 countries.
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CÁC YẾU TỐ QUYẾT ĐỊNH
VIỆC THU HÚT DÒNG VỐN ĐẦU TƯ NƯỚC NGOÀI
VÀO KHU VỰC ASEAN
• ThS. PHẠM VĂN RẠNG
Khoa Tài chính - Kế toán, Trường Cao đẳng Giao thông Vận tải Trung ương VI
TÓM TẮT:
Bài viết này nghiên cứu các yếu tố quan trọng quyết định đầu tư trực tiếp nước ngoài (FDI) vào 10 nước thuộc Hiệp hội các quốc gia Đông Nam Á (ASEAN), bao gồm: Brunei, Campuchia, Indonesia, Lào, Malaysia, Myanmar, Philippines, Singapore, Thái Lan và Việt Nam (ASEAN10). Phương pháp Bayes đã được sử dụng để ước tính các thông số trên dữ liệu bảng từ năm 2010 đến năm 2019. Các kết quả cho thấy GDP, GDP bình quần đầu người, xuất nhập khẩu có tác động tích cực đáng kể đến dòng vốn FDI vào các quốc gia thành viên ASEAN; trong khi đó, lạm phát có tác động ngược chiều. Các kết quả nghiên cứu cũng cho thấy quy mô thị trường (GDP) và lạm phát là những yếu tố quan trọng để thu hút FDI.
Từ khóa: ASEAN10, các nước đang phát triển, FDI, Bayes.
