留学生dissertationDATA AND METHODOLOGY写作指导范文

留学生dissertation写作指导DATA AND METHODOLOGY范文：马来西亚外国直接投资流入的决定因素

The determinant of FDI inflows in Malaysia

 

本文的目的是探讨在马来西亚和其他东盟+ 3个国家的外国直接投资流入的决定因素。东盟+3国包括马来西亚、新加坡、泰国、印度尼西亚、越南、中国、韩国和印度。然而，文莱和东盟的新联合成员，老挝和柬埔寨由于数据的限制不包括在此，并没有太多的外国直接投资在这些国家。之所以选择这些国家，是因为各国吸引外国直接投资流入的国家的表现和国家有哪些可以用来作为例子，以吸引外国直接投资在马来西亚的优势。因此，通过选择这些国家，它是希望外国直接投资的决定因素的差异可以被高亮显示。这些国家的外国直接投资流动的数据可以从UNCTAD和东盟秘书处获得。

为了进行这项研究，面板数据的方法将被使用。这是因为面板数据包括时间序列数据和横截面数据。普通最小二乘法是最好的做法，看到的结果。本文的研究重点将从每年1990到2008个周期，分为两个时间框架是从1999 - 2008年。分离的基本原理是在经济危机之前和之后，研究东盟国家之间的外国直接投资流动的趋势和模式。从研究中，它是希望外国直接投资外国直接投资对这些国家的决定因素的差异可以被确定。

 

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#分页标题#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#分页标题#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#分页标题#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.