Competitiveness and FDI Inflows in ASEAN Member Countries

Purpose: The goal of this study is to investigate the empirical effect between competitiveness and FDI inflow in ASEAN member countries over the period 2007-2017. Design/methodology/approach: The effect of competitiveness and foreign direct investment (FDI) was investigated by using Pearson correlation and panel data analysis. The fixed effect and random effect models were applied, followed by the Hausman test, which led us to use the fixed effect model. Finding: The study revealed that the majority of ASEAN countries have a strong and positive association between competitiveness and the FDI inflow. Specifically, variable institutions, market size, health, and primary education had a significant effect on attracting inward foreign direct investment in the region. Research limitations/implications: In order to attract more foreign direct investment, it is highly suggested the ASEAN countries enhance their institution quality and improve the human capital through health and basic education. Originality/value: Our study enriches the literature on globalization and competitiveness by focusing on the regional empirical effect between each variable from the Global Competitiveness Index and the FDI inflow exclusively in countries within the ASEAN region.


Introduction
During the current globalization era, Foreign Direct Investment (FDI) plays a pivotal role in supporting the development of countries' economic development process. As countries develop their economies, they use FDI as an external source to finance their development projects and to increase their economic productivity. For decades, FDI has been an extensive source for developing economies and the most resilient to economic and financial shocks (UNCTAD, 2018:12-13). As reported, the global inflows of FDI declined by 23 percent to $1.43 trillion in 2017. One of the reasons for this is because the value of net cross-border mergers and acquisitions (M&As) decreased from $887 billion to $694 billion in 2016. In 2017, developed economies experienced a decline to $172 billion, whereas developing economies have remained stable at $671 billion. A minor improvement is observed in the Latin American region, with an 8 per cent increase to $151 billion, while the African region has faced a decline to $42 billion (-21 per cent). Furthermore, developing countries from the Asian region have become recognized as the largest FDI recipient by attracting $476 billion. In contrast to the global trend, developing Asia has increased its share in the global FDI from 25 per cent in 2016 to 33 per cent in 2017 (UNCTAD, 2018).
The FDI flow to ASEAN region has risen from $123 billion in 2016 to $137 billion in 2017. This situation had led the ASEAN region to increase its share in the global FDI to developing countries from 18 per cent in 2016 to 20 per cent in 2017. Indonesia has performed extensively well by attracting the inflows up to $23.1 billion, Thailand tripled its inflows to $9.1 billion, the Philippines experienced a 21 per cent increase up to $10 billion, and Vietnam became the third largest recipient, with more than $14 billion. The first place is taken by Singapore, which accounted for 45 per cent of the total FDI among the ASEAN countries with $62 billion. Thus, it comes no surprise that FDI influences Singapore's GDP significantly (Feridun & Sissoko, 2011). Moreover, Singapore also performed extensively well, with investments within ASEAN members up to 69 per cent of the total intra-regional investment. On the other hand, Indonesia has become the largest recipient for intra-regional investment by attracting 45 per cent of the total FDI within ASEAN, with Singapore as the largest investor for Indonesia with $10.7 billion (ASEAN & UNCTAD, 2018). In this context, the benefit from FDI inflows to the ASEAN economy has been confirmed by Uttama and Peridy (2010), who conclude that the FDI in the form of multinational corporations' existence in host countries' economy will increase productivity through backward and forward linkages (Uttama & Peridy, 2010). In light of this phenomenon and the work from Dunning and Zhang (2008), who argue that the locations' competitiveness level influences FDI (Dunning & Zhang, 2008), we pose a central question in this study: how do competitiveness aspects affect the FDI inflow? Therefore, the objective of the study is to examine the empirical effect between competitiveness factors and the inflow of the FDI. We are using the Global Competitiveness Index to represent the competitiveness variable, which consist of 12 pillars: institutions, infrastructure, the macroeconomic environment, health and primary education, higher education and training, goods market efficiency, labour market efficiency, financial market development, technological readiness, market size, business sophistication, and innovation.
The Pearson correlation shows a strong and positive relationship between competitiveness and FDI inflow. Moreover, the fixed effect model shows that institutions, market size, health and primary education have a positive and significant influence on FDI inflow to ASEAN countries; in fact, a higher degree on institutional quality, health and primary education are attracting FDI inflow more than an improvement in market size. Those variables are important for providing a productive, competitive and stable environment for the economy. Moreover, our result supports the view that the location decisions of FDI are influenced by the country's characteristics, especially in quality of institution and human capital. Furthermore, our study complements the literature on FDI and national competitiveness by utilizing the Global Competitiveness Index from the World Economic Forum and concentrating on ASEAN member countries. The rest of the paper is structured as follows: the following section presents the literature on the nexus between FDI and national competitiveness, then continued with panel data analysis in section 3. Moreover, in section 4 and 5 consists of the empirical result and conclusion.

Literature Review
Based on the work of Asghari (2012), foreign direct investment is composed of international credit, transfer of capital, and reinvestment profit between the principal company abroad and its domestic branch (Asghari, 2012). However, Kindleberger argues that "FDI is essentially about transfer of control rather than movement of capital per se." (Krugman, 2000). In support of this definition, Duce (2003) expresses that FDI reflects the purpose from the entities in one economy to engage in a long-term economic relationship and sufficient degree of influence on the management activities toward other entities from other economies (Duce, 2003).
Furthermore, Dunning (2000) created the eclectic paradigm, which is well known as "The OLI Framework," to understand the determinants of FDI and the behaviour of multinational companies. This simple framework is constructed of three sub-paradigms. The first paradigm refers to the ownership (O) of the enterprise, which acts as an investor for a particular advantage. It is formed based on the assumption that the FDI will likely arise if the benefit for making foreign value-adding operations outweighs its own cost. The second paradigm is related to the location of the region (L) where the enterprise would like to operate its foreign production. The third paradigm, called internalization (I), refers to the preference of the investing enterprise to do its own foreign production rather than granting a license to other foreign firms to conduct their business (Dunning, 2000). Initially, the paradigm recognizes the importance of location factors for attracting FDI, which is amplified by the exposure of the knowledge-based world economy and asset exploration through FDI (Dunning, 2000). Location factors such as countries with natural resource advantage will attract FDI, as determined by Hayat (2014). However, Mina (2007), who studied the location determinants of FDI flows in Gulf Cooperation Council (GCC) countries found that a country's institutional quality and infrastructure development can encourage more FDI than oil production and oil reserves (Mina, 2007). Meanwhile, other studies empirically identified that FDI is attracted to countries with great demand or market size, and the study supports other variables such as the quality of formal institutions and the provision of special economic zones within the region to be an advantage for a country to attract more FDI (Nielsen, Asmussen, & Weatherall, 2017). A study by Belascu and Shivarov (2016) identified that, in emerging countries, the country's economic development and access to natural resources become positive attributes that attract FDI (Belascu & Shivarov, 2016).
As the studies reveal, a country's economic competitiveness can potentially be included as location determinants for FDI inflow. Dunning and Zhang (2008) identified resources (machines, land, and natural resources), capabilities (intangible assets, education, and organizational capacity), the market (information on both domestic and foreign market, capability on market exploitation), and institutions (regulations, law, and customs) as factors that enhance a country's competitiveness (Dunning and Zhang, 2008).
This understanding is in line with The Global Competitiveness Report (GCR), 1 which describes "competitiveness as the set of institutions, policies, and the factors that determine the level of productivity of a country." (World Economic Forum, 2008). The capability to enhance the country's productivity will create the possibility not only to increase the country's income level but also becomes the factor of its return on investment, which is one of the important factors to explain economic growth prospects (Ali, 2017). Moreover, the report has considered a set of pillars such as institutions, infrastructure, macroeconomic stability, education, market conditions, policies, and technological readiness. These interrelated variables together are well known as the Global Competitiveness Index (GCI). Therefore, the definition becomes the reason for this study to use the index as representative for a country's competitiveness.
Several studies have used the index; for example, Popovici and Calin (2012) identified a country's competitiveness as a determinant to attract FDI inflow. Using the competitiveness index from European Commission, they found positive connections between competitiveness and FDI in seven countries from Central and Eastern Europe (CEE) (Popovici & Călin, 2012). In addition, the enhancement of a country's competitiveness also has a positive impact for countries to attract more FDI. As shown by Popovici and Calin (2015), who employ GCI for the calculation, enhancement in institution quality, labor market efficiency, and innovation increases the level of FDI per capita for half countries in the CEE region (Popovici & Călin, 2015).
With a similar index, the empirical study by Ali (2017) found that a country's competitiveness level in the ASEAN region has incredible function to attract FDI inflow. Furthermore, the study unveils that countries in the ASEAN region demonstrate weakness regarding institution quality, therefore suggesting that variables beyond economic indicators such as political stability, property rights, and government efficiency should be taken into account as FDI inflow determinants (Ali, 2017). The importance of variables beyond economic indicators as FDI determinants has been confirmed by the study from Harms and Ursprung (2002), who conclude that countries that respect civil and political freedom attract more FDI compared to repressive ones (Harms & Ursprung, 2002).
Amongst the literature provided, our study enriches the literature by focusing on the regional empirical effect between each variable from the Global Competitiveness Index and FDI inflow exclusively in countries within the ASEAN region.

Data
We set the amount of FDI inflow for ASEAN countries taken from the World Bank Dataset as the dependent variable. For the independent variables, this study uses the Global Competitiveness Index from the Global Competitiveness Report, 2018, to measure the level of competitiveness in the region, which consists of 12 pillars: (1) institutions, (2) infrastructure, (3) macroeconomic environment, (4) health and primary education, (5) higher education and training, (6) goods market efficiency, (7) labour market efficiency, (8) financial market development, (9) technological readiness, (10) market size, (11) business sophistication, and (12) innovation. The period of observation is from 2007 to 2017. Since both dependent and independent variables have a different level of measurement, we transform the entire dataset into logarithmic form to stabilize the variance (Suryandaru, 2020). Based on the theories and previous literature review, especially with reference to locational theory from Dunning (2000), we hypothesize that the signs of all independent variables are positive to attract FDI inflow.

Methodology
This study analyses the regional empirical effect between the amount of FDI inflow for ASEAN countries and the level of competitiveness in the region using the unbalanced panel data analysis. Through panel data analysis, we are able to control individual heterogeneity and obtain more reliable estimates from the dataset compared with time series and cross-section observations (Gujarati and Porter, 2009). Our logarithmic functional model for this study is as follows:

LN_FDI Inflow= f(LN_INST,LN_INFRA,LN_ME,LN_HPE,LN_HET,LN_GME,LN_LME,LN_FMD,LN_TR,LN_MS,LN_BS,L N_INOV)
Where In general, the panel data model can be classified into three categories: pooled OLS model, fixed effect model, and random effect model. Pooled data has constant coefficients for both intercepts and slopes. It usually pools all of the data and runs an ordinary least squares model (OLS). The model of a pooled OLS can be specified as follows: The pooled OLS model cannot control the unobserved individual effects since the heterogeneity of the countries under consideration may affect the measurement of estimated parameters. To control the individual heterogeneity, we use the random effect model in which the variations across countries can be captured within the model. By incorporating countries' individual effects, the random effect model can be constructed from equation (2) as follows: Where is a component of the random error term, which consists of between-country error ( ) and withincountry error ( ) over time. The explanation in the random effect model is that a country's error is not correlated with the explanatory variables. In contrast, if a country's error is correlated with the explanatory variables, then we should use the fixed effect model to allow each country to have its own intercept. The fixed effect model can be specified as follows: _ + 4 _ + 5 _ + 6 _ + 7 _ + 8 _ + 9 _ + 10 _ + 11 _ + 12 _ + To test for the possible existence of such a correlation between a country's error and its explanatory variables, we use the Hausman Test. The null hypothesis for this test is that there is no correlation between individual countries' error with its explanatory variable (i.e., random effect). If we reject the null hypothesis, then we prefer the fixed effect model. In contrast, if we do not reject the null hypothesis, we prefer the random effect model. By doing so, this study uses only a one-way error component model; i.e., either fixed effect or random effect.

Empirical Result
As an initial statistical check, we conduct Pearson Correlation test to discover the strength of relationship between competitiveness and FDI inflow for each member of ASEAN countries during the period of observation. The result in Table 1 indicates that the majority of ASEAN countries have a strong positive association between competitiveness and the FDI inflow. Only Laos and Thailand have a negative association. This result suggests that the correlation between competitiveness and FDI inflow is somewhat heterogeneous, depending on the degree of economic development and social-cultural aspects for each member. We now turn to the estimated result of the panel regression, as the result from Table 2 shows that both the F-Test and Wald Test are significant at the 1 percent level. These evidence underpin that all of the independent variables in both models are able to explain the behaviour of the dependent variable (i.e do not reject the null hypothesis). In addition, the Hausman Test indicates that the fixed effect model is statistically preferred over the random effect model. The fixed effect model shows that the variable of institutions (LN_INST) has a positive coefficient and is statistically significant toward FDI inflow. It implies that the institution plays an important role in attracting investment from abroad. When ASEAN members can increase their score in institutional capacity by 1 percent, it leads to an increase of up to 2.354 percent of FDI inflow, ceteris paribus. In general, this finding is in line with another study by Ali et al. (2010), which stated that an institution becomes a robust determinant for FDI. Another study from Ullah and Khan (2017) suggested that the improvement of the institutional quality of ASEAN countries is significant to attract more FDI.
Moreover, health and primary education (LN_HPE) achieves the highest significance level at 1 percent, with a positive coefficient up to 1.668 percent. This means that, when the ASEAN countries manage to increase their score in citizens' health and primary education variable by 1 percent, the FDI inflow will increase up to 1.66 percent. This result is similar to another study conducted by Majeed and Ahmad (2008), who found that health care expenditures in developing countries are a significant factor to attract FDI. Furthermore, as health and education are human capital determinants, this finding resonates with other studies that stated that investment in human capital has a positive and significant effect on FDI inflow (Tsen, 2005: Kumari andSharma, 2017). Lastly, variable market size (MS) is significant at the 10 percent level, with positive magnitude towards FDI inflow up to 1.067 points. In other words, a 1 percent increase in the market size variable leads to an increase in the inflow of FDI up to 1.067 percent. Significance level ***1%; **5%; and *10%. The null hypothesis of the Hausman Test is that there is no correlation between individual countries' error with its explanatory variable. Values in parentheses are the t-value.

Conclusion
The objective of this study is to find the regional empirical effect between a country's competitiveness and the inflow of foreign direct investment by employing Pearson correlation and annual panel data from ASEAN country members from year 2007 to 2017. In order to choose the right model, we did the Hausman test which led us to use fixed effects as the best model in this study.
The result from Pearson correlation shows that the majority of ASEAN countries have a strong positive association between competitiveness and the FDI inflow. From the panel data estimation, variables of institutions (LN_INST) and health and primary education (LN_HPE) are quite elastic to the FDI inflow in ASEAN countries. Besides, the respected parameters are also statistically significant at 5 and 1 percent, respectively. In addition, the variable of market size (LN_MS) is also elastic to the FDI inflow in ASEAN countries despite the significance level is only at the 10 percent.
Based on the estimated results, the following suggestions are found to be important: first, according to our findings, institutions show the highest coefficient value to attract foreign direct investment. Based on the global competitiveness index, this variable matters to create an environment in which individuals, firms, and governments are managed to generate income and wealth within the economy. This means that strengthening both public and private institutions would help create a supportive environment for growth. As economic activities work well in the presence of trust and reliability, thus enhancement on transparency, corporate governance, and government efficiency is beneficial to attract investment into the region.
Second, health and basic education provision are important for the country's productivity and competitiveness. Healthy workers have a better function to fulfil their potential and generate more value in economic activities than ill ones. The latter population hinders the process of knowledge and technology transfers, which discourage the probability of foreign investment for the country (Mirvis, Chang, & Cosby, 2008). Furthermore, decent provision on basic education will help both economic and business activities to enhance its value. This could be an education and training that equip the workers with ICT (Information, Communication and Technology) skills which can increase the efficiency in business activities. Third, it would be beneficial for ASEAN countries to increase their growth both on domestic and foreign markets through international trade. This could be done by exercising more economic interactions between ASEAN members and other countries outside the ASEAN region.
In summary, based on the values of contribution and the level of significance, the main agenda item to be pursued for the ASEAN members should be to improve human capital and the quality of institutions in order to attract FDI inflow into the region.