留學生dissertation寫作指導DATA AND METHODOLOGY范文：馬來西亞外國直接投資流入的決定因素

The determinant of FDI inflows in Malaysia

 

本文的目的是探討在馬來西亞和其他東盟+ 3個國家的外國直接投資流入的決定因素。東盟+3國包括馬來西亞、新加坡、泰國、印度尼西亞、越南、中國、韓國和印度。然而，文萊和東盟的新聯合成員，老撾和柬埔寨由于數據的限制不包括在此，并沒有太多的外國直接投資在這些國家。之所以選擇這些國家，是因為各國吸引外國直接投資流入的國家的表現和國家有哪些可以用來作為例子，以吸引外國直接投資在馬來西亞的優勢。因此，通過選擇這些國家，它是希望外國直接投資的決定因素的差異可以被高亮顯示。這些國家的外國直接投資流動的數據可以從UNCTAD和東盟秘書處獲得。

為了進行這項研究，面板數據的方法將被使用。這是因為面板數據包括時間序列數據和橫截面數據。普通最小二乘法是最好的做法，看到的結果。本文的研究重點將從每年1990到2008個周期，分為兩個時間框架是從1999 - 2008年。分離的基本原理是在經濟危機之前和之后，研究東盟國家之間的外國直接投資流動的趨勢和模式。從研究中，它是希望外國直接投資外國直接投資對這些國家的決定因素的差異可以被確定。

 

DATA AND METHODOLOGY

The purpose of this paper is to investigate the determinant of FDI inflows in Malaysia and other ASEAN+3 countries. ASEAN+3 countries consist of Malaysia, Singapore, Thailand, Indonesia, Vietnam, China, Korea and India. However, Brunei and newly joint member of ASEAN which are Laos and Cambodia are not included due to limitation of data and not much FDI in these countries. The reason for choosing these countries is because of the countries performance in attracting FDI inflows to the countries and advantages that the countries have which can be used as example in order to attract FDI in Malaysia. Therefore, by choosing these countries it is hope that the differences in determinants of FDI can be highlighted. Data of FDI flows for these countries can be obtained from UNCTAD and ASEAN Secretariat.

 

In order to carry out this study, panel data approach will be used. This is because panel data consist of times series data and cross sectional data. Ordinary Least Square method is the best practices to see the results. This study will focus for a period from the year 1990 to 2008 and separated into two time frames which are from 1990-1998 and 1999 - 2008. The rationale of the separation is to examine the trends and patterns of FDI flows among ASEAN countries before and after the economic crisis. From the studies it is hope that the differences in determinants of FDI to these countries can be identified.#p#分頁標題#e#

 

3.1 Model Specification

In order to proceed with the studies, the Gravity Model will be used. The Gravity Model is based on an analogy of Newton's Law of Gravitation and is used to predict movement of information and commodities between different places related to the distance between them (Erlander, 1980; Rosenberg, 2004).

 

In 1962, Jan Tinbergen for the first time applied gravity model to economics by using equation to explain international trade. After few years, the equation has been applied to a wide range of social interactions such as migration, foreign direct investment (FDI) and tourism. The typical gravity equation in log form (linear) is as follows:

 

ln(Xij) = a + b ln(Yi) + c ln(Yj) + d ln(Dij)

 

where;

 

Yi : Gross Domestic Product of Country i represents the total potential supply for exports of country i

 

Yj : Gross Domestic Product of Country j presents the total potential supply for exports of country j

 

Dij : Distance between country i and j

 

The equation has been improved for the last 4 decades by many economist until now has become concrete and solid theoretical basis. The Gravity Theory has been applied to a wide variety of goods, trades and services moving across regional and national borders (Anderson, 1979: Deardorff, 1995; Pelletiere and Reinert, 2004). However, in the last few years the Gravity Model has become very popular in explaining FDI, including the flow of FDI (Stone and Jeon, 1999), the effects of distance over FDI (Egger and Pfaffermayr, 2004a, 2004b) and the relationship between the FDI and trade in a bilateral context (Gopinath and Echeverria, 2004). The Gravity Model can capture the relative market sizes of two economies and their distance from each other. Distance can be viewed as a measure of the transaction cost in undertaking foreign activities for instance, costs of transportation and communication, costs of dealing with cultural and language differences, costs of sending personnel oversea and the informational costs of institutional and legal factors, for example property rights, regulations and tax systems (Bevan and Estrin, 2004; Deardorff, 1995; Portes and Rey, 2005)

 

Therefore, in order to look in depth the source and determinants of FDI in ASEAN countries, Gravity Model will be used in this paper. This is because the Gravity Model is really useful in explaining FDI flows, the effects of distance over FDI (Egger and Pfaffermayr, 2004). According to Gravity Model, market size and distance are important determinants in the choice of the location of source countries for direct investment. This study will follow semi-gravity model and some of the variables that can be used as proposed by Normaz, 2009. However, this study will be extended by including other variables which are unemployment rate and labor force as determinants of FDI. This is because, from my opinion unemployment rate and quantity of labor force plays significant roles in determining FDI. Investors now are very particular with the quality and efficiency of host country labor before they decided to make an investment. The model is as follows:#p#分頁標題#e#

 

ln(FDIit) = β0 + β1ln(GDPit) + β2ln(GDPjt) + μXij + λZjt + εijt

 

X

 

(log DISTij + LANGij + BORDERij)

 

Z

 

(Capital-labor ratio, inflation rate, real exchange rate, Government Budget balance, Openness, Trade Policy, Education, Infrastructure and Communication, Economic Freedom Index, Transparency, unemployment rate and labor force)

 

FDIit

 

The real FDI inflow host country in time (t)

 

GDPit

 

Real GDPs in US dollars

 

3.1.1 Market size

 

The possible correlation between the market size of a host country or region (GDP, GDP Per capita and GDP Growth Rate) with the volume of inward investment should take into consideration in many FDI empirical studies (Anderson, 1979; Buch et al, 2003; Dunning, 1980; Kim, 2000). For example, studies by Kim (2000) showed that GDP was significant to the determinants of FDI in the host countries when he test to Japan and US. Therefore, an increased in GDP will represent the overall patterns of distribution of FDI among countries.

 

3.1.2 Geographical distance

 

In this study, geographical distance represents the navigable distance between capital of ASEAN+3 countries with the source countries. Bougheas et al (1999) stated that distance can be indirectly give impact to the transaction flow by increased in transportation and other transaction costs. Therefore, distance can have direct and indirect effects on the investment climate between ASEAN+3 countries with their other investing partner. For Portes and Rey (2005), suggested that distance is the most important determinant of transaction flows in foreign investment and should be included in Gravity Model.

 

3.1.3 Common language and borders

 

Language and borders also play an important role in determining FDI. It is belief when the neighbouring partner or other countries shared common language and borders, therefore it will be much easier to dealt with as easy to make transaction.

 

3.1.4 Macroeconomic indicators

 

Almost all macroeconomic indicators are important in determining FDI. For example, exchange rate will give impact to the foreign investment. This is because volatile exchange rate might reduce investment. Exchange rate movements are relevant and significant to FDI in spite of exchange rate volatility. This is because the volatility of exchange rate directly contributes to uncertainty on the returning transaction plan from the investing countries (Guerin, 2006; Hubert and Pain, 2002; Rose, 2000).#p#分頁標題#e#

 

Openness to trade also can be determinant of FDI. The higher level of the openness of the ASEAN+3 countries economy, the easier it is for the investors to invest in and trade with the countries.

 

3.1.5 Non-economic Indicators

 

Non-economic indicators can come from various forms such as policy, political stability, transparency, education, infrastructure and many other forms. These variables indirectly will give impact to the volume of investment in the countries. For example, level of education will represent the quality of human capital that the country have. Besides that, good quality of infrastructure will promote growth and attract more investors using the facilities.

 

3.2 Expected sign

It is expected that the coefficients of the real GDP of the source and destination countries to both be positive.

 

A common border and a common language are expected to be positively related to FDI especially for intra-regional FDI since foreign investors from neighboring countries might take the opportunity to invest in a country which shares a common culture, language and border.

 

Real interest rate and inflation rate are expected to be negative.

 

Exchange rate, many studies (Kohlagen 1977, Cushman 1985; Froot and Stein 1991) concluded that devaluation in the host country's currency induces a reduction in local production costs in term of foreign currency and therefore stimulates the inflows of FDI. Therefore, the exchange rate is expected to be positively related with FDI.

 

Total trade, is a proxy for openness measuring how a country has liberalized trade to the world market and is expected to exhibit a positive relationship with FDI.

 

The telecommunications act as a proxy for infrastructure which encourage more investors to operate business. It is expected to be positive with FDI.

 

Transparency International Corruption Index is measured on a scale ranging from 0 to 10 with higher values representing the cleanest and most transparent countries. This variable is expected to be positively correlated with FDI flows.

 

The Economic Freedom Index is a grading based on as score from 1 to 5 with lower scores representing greater economic freedom. Therefore, the relationship with FDI is expected to be negative, indicating that the freer a host country is, the greater is the flow of FDI that it attracts.