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INTERNATIONAL ISLAMIC ECONOMIC SYSTEM CONFERENCE (I-iECONS 2021)
Determinants of Foreign Direct Investment Inflows into Halal Parks in Malaysia
Hartini Mohammad
1Faculty Of Economics And Muamalat, Universiti Sains Islam Malaysia E-mail: [email protected]
1. Introduction
Despite the wide-spread devastation caused by the global COVID-19 pandemic, several notable achievements were record from the performance of halal industry in Malaysia. According to the proprietary data sources and analysis for 2020 by Halal Development Corporation (HDC) a total of 295 companies are currently in operations throughout the 21 halal parks across Malaysia with 14.3 percent (42 companies) being multinational corporations (MNCs) while 85.7 percent (253 companies) are locally-owned corporations. Since 2011 until to date, the cumulative investment was approximately RM16.1 billion and of this value 59 percent (RM9.5 billion) is foreign direct investment while 41 percent (RM6.6 billion) is domestic investment. In terms of sectoral contribution to the halal industry, Halal Food
& Beverages (RM17.40 billion) continues to be the main contributor to Malaysia halal economy, followed by Halal Ingredients (RM8.83 billion), Cosmetic & Personal Care (RM2.67 billion), Palm Oil Derivatives (RM0.89 billion), Industrial Chemicals (RM0.47 billion) and Halal Pharmaceuticals (RM0.30 billion).
According to the ‘OLI paradigm’ foreign direct investment in trading capacity in the case of most goods produced by multinational corporations (MNCs) which are differentiated goods exists under three conditions. Firstly, the firm must enjoy certain ownership advantages in a recipient market and have a competitive advantage over local producers. Secondly, the firm must have certain location advantages in production. Lastly, the firm must have the opportunity to exploit ownership and gain location advantages through internalization. These advantages normally exist in terms of the ability to respond to changes in tastes of the recipient market. The availability of relatively cheap labor and natural resources in the recipient firms should reduce the MNCs’ costs and gives their subsidiaries access to export markets, creating international intra-firm and intra-industry trade and resource relocation. Hence, the main objective of this paper is to investigate the determinants of foreign direct investment inflows into Malaysia halal park industries over the period of 2005-2017. These determinants include production cost, infrastructure, human capital, exchange rate, market size and current account balance.
2. Materials and Methods Hypothesis Development
The literature of the determinants of FDI proposes few important factors that affect FDI, such as production costs, infrastructure, human capital, exchange rate and market size. The lower the costs of production, the more attractive to FDI it becomes. Therefore, the higher the wages costs, the more it is likely to defer FDI and the relationship between FDI and wage costs is expected to be negative. Nevertheless, empirical findings about the significance of the relationship are mixed. Billington (1999), Ewe-Ghee (2001), Cheng and Kwan (2000) find that real wage costs have a significant negative impact on FDI.
The better the infrastructure of the host country, the more attractive it is to FDI. A good infrastructure will facilitate production activities as well as the distribution of output. Therefore, the relationship between FDI and infrastructure is expected to be positive. Nevertheless, there is no catch-all variable for the infrastructure. Instead, proxies are frequently employed for the quality of the transport and communication system. Empirical studies such as by Billington (1999) and Cheng and Kwan (2000) conclude that their infrastructure proxy (or proxies) has a significant positive impact on FDI.
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The better the human capital, the more atrractive it is to FDI. The hypothesis that the human capital in the host country is a determinant of FDI in developing countries has been embodied in the theoretical literature. For example Lucas (1990), Zhang and Markusen (1999), Dunning (1993), Noorbakhsh et. al. (2001) has identified the importance of human capital in attracting the activities of MNS and significantly discourage the inflows of foreign direct investment into the host country. Zhang and Markusen (1999) has present a model which shown the availability of skilled labour in the host country has affects the volume of FDI inflows, while Noorbakhsh et. al. (2001) and Cheng and Kwan have empirically found that human capital has a positive impact on FDI.
Froot and Stein (1991) claims that exchange rate movements can influence FDI by affecting the home currently cost of acquiring an asset abroad. Love and Lage-Hidalgo (2000), Erdal and Tatoglu (2002) states that this is because an appreciation of exchange rate has a negative impact on FDI because it affects the cost of acquiring assets in that country. Erdal and Tatoglu (2002) had empirically found that exchange rate has a significant negative impact on FDI.
Generally, the larger the market size of the host country, the more attractive it is to FDI. A large market size is conducive to an increase in demand for products and services, allows the achievement of economies of scale (Caves, 1971). the net impact of market size on FDI is likely to be positive. Therefore, the relationship between FDI and the market size is expected to be positive. Studies by Billington (1999), Cheng and Kwan (2000), Shatz and Venables (2000) suggest that the market size, proxies by real GDP or real GDP per capita is mostly to have a significant positive impact on FDI.
2 Model Specifications
The discussion of the location-related determinants of FDI in the previous section suggests that the latter could be estimated as a function of production costs, infrastructure, education, exchange rate and market-size in the host country. More specifically, there are two models to be estimated:
ln FDIt = 10 + 11 ln INFt + 12 ln INFRAt + 13 ln EDUt + 14 ln ERt + 15 ln GNIt + u1,t
Eq.1
ln FDIt = 20 + 21 ln INFt + 22 ln INFRAt + 23 ln EDUt + 24 CAt+ 25 ln GNIt + u2,t
Eq. 2
where ln is logarithm; FDIt is foreign direct investment: INFt is inflation, a proxy for production costs; INFRAt is the infrastructure; EDUt is education, a proxy for human capital; ERt the exchange rate; GNIt the market size; CAt the current account balance; and ui,t (i = 1, 2) a disturbance term. The above equations Eq. 1 and Eq. 2 are named Model 1 and Model 2, respectively. Model 2 is the same as Model 1 except that CAt is used as an alternative to ERt. The discussion of the determinants of FDI suggest that INFRAt, EDUt and GNIt are expected to have a positive impact on FDIt. On the other hand, INFt and ERt are expected to have a negative impact on FDI. CAt is expected to have a positive impact on FDIt since an increase in the current account balance is usually viewed as an implication of a healthy economy, therefore it encourage more FDI.
In the study, the Johansen (1988) co-integration method is used to test the number of co-integrating vectors in equations 1and 2. the Johansen (1988) co-integration method can be used to compute two likelihood ratio tests for testing the number of co-integrating vectors in the system, namely the maximum eigenvalues (Max) and trace (Trace) statistics, which are respectively computed as
Max = -T ln (1 - r+1), r = 0,1,2, ... , p-1 (3)
Trace = -T pi=r+1 ln (1 - i), r = 0,1,2, ... , p-1 (4)
where T is the sample size and I (i = 1, 2, ..., p; 1 > 2 > ... > p) is the eigenvalue. The Max test statistics tests the null hypothesis (H0) of r co-integrating against the alternative (Ha) that there are (r + 1) co-integrating vectors.
For the Max test statistics, the null hypothesis to be tested are in a sequence of the following: H0: r = 0 against Ha: r
= 1; H0: r 1 against Ha: r = 2; ... ; H0: r p - 1 against Ha: r = p. For example, if H0: r = 0 is rejected at 95 per cent critical value and H0: r 1, ... and H0: r p - 1 are all not rejected at the same value, then the Max test statistics indicates the existence of one integrating vector. The Trace test statistics test the H0 that has at most r co-integrating vectors in the system. That is the number of co-integrating vectors is less than or equal to r.
For the Trace test statistic, the null hypothesis to be tested are in a sequence of the following: H0: r = 0 against Ha: r 1; H0: r 1 against Ha: r 2; ... ; H0: r p - 1 against Ha: r = p. For instance, if H0: r = 0 is rejected at 95 per cent critical value and H0: r 1, … and H0: r p - 1 are all rejected at the same value, the Trace test statistics implies the existence of at least one co-integrating vector. Critical values of the Max and Trace test statistics can be obtained from
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Osterwald-Lenum (1992). Since the study is interested in determining the long-run relationship, we employ the Philips and Hansen (1990) FMLS estimator. The estimator is consistent and asymptotically efficient, even in the presence of endogeneity (Lynde and Richmond, 1993) and the estimator also work well in finite sample (Phillips and Hansen, 1990).
3. Conclusion
The main aim of this study is to investigate the determinants of foreign direct investment for halal industries by looking at the inflows into the halal parks in Malaysia. According to the Halal Development Corporation (HDC) there are currently 21 halal parks operating across Malaysia. The top 5 MNCs companies in halal parks are Kellogg Asia Products Sdn Bhd, Coca-Cola Bottlers (M) Sdn Bhd, Ajinomoto (M) Bhd, F&N Dairies Manufacturing Sdn Bhd and Kewpie Malaysia Sdn Bhd. While the top 5 LLCs are Ramly Food Industries Sdn Bhd, Mighty Bakery Sdn Bhd, Linaco Manufacturing (M) Sdn Bhd, Polygold Beverages Sdn Bhd and Kawan Food Manufacturing Sdn Bhd. The selected determinants proposed for this study include education, infrastructure, market size, current account balance, inflation and exchange rate.
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