Theoretical Framework - LITERATURE REVIEW AND THEORETICAL FRAMEWORK

CHAPTER III LITERATURE REVIEW AND THEORETICAL FRAMEWORK

3.3 Theoretical Framework

Those determinants stated above have been emerged in various empirical researches. In

this section, we begin with a brief overview of FDI determinants literature over the past decades.

The works of Barrell and Pain (Barrell & Pain, 1996), Rodrig (Rodrig, 2007) and Chakrabartim

(Chakrabarti, 2001) suggest the following theoretical model of foreign direct investment:

Y = f(X, I, Z) (1)

Where Y denotes for net FDI; X represents for a set of variables related to aggregate

demand; I is a set of interest variables, often include the host country’s wage, openness, and real

exchange rate; Z is set of other factors that measure different phenomenon from I, yet might

influence the firm’s level of production.

The variables’ units are not always the same, so that a log-linear multiple regression is

often used for this kind of specification model. The use of log-linear might help to reduce

extreme values or transform non-linear relationship into a linear one (Wei, 2005). Therefore, the

equation (1) can be written as:

Ln(Y) =α+ β1LnX + β2LnI+β3LnZ+ε (2)

where Ln represents natural logarithms.

A vast empirical literature has developed surrounding the issue of relationship between

FDI and its determinants, yet still there is a lack of consensus about it. The aggregate demand or

the size of the market of the country, often measured by Gross Domestic Product (GDP), is

received the most widely accepted as a significant determinants of FDI flows. In order to utilize

resources and exploit economies efficiently, the large market-size is a crucial factor. Janicki and

Wunava in their analysis of determinants of FDI among 15 EU nations in 1997 demonstrates a

significant relationship between FDI and market size (Janicki & Wunnava, 2004). The results

obtained by Grosse & Trevino also reveals a significant positive relationship between the amount

of FDI and market size which is used as a proxy to pursue international expansion (Grosse &

Trevino, 1996; Rodrig, 2007)

Another factor which has been very popular as well as controversial part in an

explanation of FDI is the wage differentials between the source and host countries. Theoretically,

a rise in wage rate often associates with a fall in FDI or in other words, a lower wage rate of the

host country encourages firms to invest in order to reallocate production and obtain cheaper cost,

and hence FDI will rise (Janicki & Wunnava, 2004). There are, however, some studies find the

positive effect of wage in attracting FDI (Nankani, 1979). One of alternative explanation is that a

rise in wage might result from the development of economy which leads to the changes of the

factor price-ratio. The economy demands more capital as a substitution of labor, and hence FDI

increase. Another explanation is wage rate might not fully reflect labor cost. A rise in wage rate

could imply for a fall in labor cost when it is possible for productivity are not associated with

labor and increases far from wage rises (Yang, Groenewold, & Tcha, 2000).

A variable that also plays very important influence in determining FDI is the degree of

openness. Theoretically, openness measures the degree to which an economy is open to foreign

trade and integrated with the world economic system, so that the more openness the economy is,

the better and larger domestic market are offered for market seeking firms. On the other hand,

some researchers argue that FDI inflows might be a substitution of trade, so that these two

variables would be negatively correlated or in the other words a fall or a rise in FDI flows will be

associated with a rise or a fall in trade flows (Yang, et al., 2000).

Another critical determinant of FDI in empirical work is related to exchange rate. And

there is unclear evidence regarding the significant of exchange rate as well. The theoretical

expectation is that there is a strong negative correlations between host countries’ exchange rate

and FDI inflow because a depreciation of the host country’s currency allows foreign firms to

purchase cheaper assets and technology so lowers relative cost of capital then increases the

relative wealth position of foreign firms (Chakrabarti, 2001; Dees, 1998; Froot & Stein, 1991;

Grosse & Trevino, 1996; Liu, Song, Wei, & Romilly, 1997). However, others have found a

positive relationship (Froot & Stein, 1991; Thomas & Grosse, 2001; Wang & Swain, 1995). The

main argument is the rate of return. When the host country’s currency depreciates relative to

others, the return of the asset in the foreign currency will go down because the profit will be

converted back into the home country’s currency. In addition, foreign firms’ assets might engage

in export or import activities, so that the effect of exchange rates on the value of assets is still

32 questioning.

As stated above, there has been a multitude of research focusing on foreign direct

investment, yet literature studying FDI’s determinants at macroeconomic level by using

comparison of two different characteristic groups of investors is rather sparse. This study draws

methodologies from both principal studies Zhang (Zhang HongLin, 2005) and Wei (Wei, 2005)

that analyzed FDI in China, and then it is applied to Vietnam case.

Zhang uses a comparative analysis between FDI from Hong Kong-Taiwan (HKTDI) and

from the European Union, the US, and Japan (the Triad) goes to Mainland China. The paper

presents evidences that cannot be fully appreciated without understating China’s location

characteristic and differences between HKTDI and the Triad FDI. Four determinants are

identified: labor cost, economic growth, trade barriers in which tariff rates are used and political

instability which is employed as dummy variables defined by the author. The result reveals low

labor cost as the primarily determinant which motivates HKTD investors. However, this paper

just investigates the effects of individual determinants by measuring relatively the host country’s

conditions in comparison with other potential host countries, yet not with other home countries.

Therefore, only series data, not cross-section data, is adopted (Zhang HongLin, 2005).

Wenhui Wei explores the determinants of inward FDI in China by using time-series

cross-sectional panel data regression model for 15 OECD countries in 12 years. The findings of

this study’s model suggest that the major driving force of attracting FDI in China is its domestic

market, rather than the low labor cost. However the paper studies the effects of determinants by

pulling all home countries together, yet not by comparative analysis which divides home

countries into different groups of investors (Wei, 2005).

Based on the above discussion of theories and empirical work, a panel data set is used and

a time-series cross-sectional model is developed to analyze the major determinants of FDI from

Asian and non-Asian countries into Vietnam. Four important determinants: market size as the

ratio of the host country to the home country, degree of openness as the ratio of trade (import plus

export) of host country to the home country, labor cost, and exchange rate between home and

host country are included in this study.

Regarding Vietnam, not any detailed analysis on FDI’s contribution to growth has been

conducted. Although some empirical works have been done on the determinants of FDI inflows

into Vietnam, the numbers of these works are still very little, and most of the analyses are at

sectoral or industrial level (Hoang; Ngoc & Ramstetter, 2004; Nguyen, et al., 2007; Vu, 2008; Vu,

et al., 2008). Furthermore, to the best of our knowledge there is no published empirical study on

FDI that has been conducted and paid a particular attention to the differences in determinants of

inward FDI into Vietnam from Asian countries and non-Asian countries. Therefore, it calls for a

research on these aspects to understand more determinants of FDI into Vietnam and helps to

34 answer characteristics of Vietnam’s industry.

CHAPTER IV

HYPOTHESIS, MODEL SPECIFICATION, AND METHODOLOGY

4.1 Hypothesis

Given that the literature review and the theoretical framework has been discussed and set

up in chapter III, specific hypotheses can be formulated for testing the framework as following.

H1 – Market Size: The higher the relative host countries’ GDP to home countries’

GDP is, the higher is FDI inflows to host countries

GDP may be seen as a measure of the future potential of the host country’s domestic

market or the economic development. Therefore, as for market-seeking investors, the bigger the

market size is, the better infrastructure and the larger potential market the host countries have, so

that the better for them to set up, expand their production and sell their products in domestic

markets.

H2 - Openness: The higher the openness (exports and imports) between host countries

and home countries, the higher FDI inflows is to host countries

The openness is often seen as the relationship between a country and the others.

Therefore, the higher the openness of host countries are, the more open they are to foreign

countries. That expresses the host countries promote FDI more and so that attach more foreign

36 investors.

H3 – Wage: The higher the relative host countries’ real wage to home countries’ real

wage, the lower is FDI inflows to host countries.

A cheaper relative real wage of host countries to other countries means a cheaper

production in host countries, so that the host countries attach more investors.

H4 – Exchange rate: Exchange rate of host countries relative to that of home

countries can have both positive and negative effects on FDI

The higher exchange rate of host countries relative to that of home countries expresses

depreciation in host countries’ currency that means investors can sell their products abroad with a

higher return. However, if investors sell their products in domestic market then convert profit to

their currency or they have to import materials from abroad, then they get a lower return or more

expensive in production.

To estimate the determinants of FDI into Vietnam, the data from 2000 to 2006 was

employed. Top fourteen investors were chosen based on the availability of data and continuity

of investment. These investors are South Korea, Singapore, Taiwan, Japan, Hong Kong,

Thailand, China, Australia, Canada, Germany, UK, USA, Netherlands, and France.

We chose the period between 2000 and 2006 which appeared not to have serious

economic fluctuation affecting on investment - the year 2000 was just after the Asian financial

crisis and the year 2006 was just before Vietnam joined World Trade Organization (WTO). This

period may give us more objective results studying determinants of Vietnam’s FDI inflow.

The data was collected from General Statistical Office (GSO) in Vietnam. Although

there is a concern that the data from Vietnamese government tends to be overstated, it is not

easy to find data about Vietnam in other sources. According to the data from GSO, there were 21

countries which had a continuous investment in Vietnam from 2000 to 2006. Table 4 shows the

amount of FDI from these countries. Among them, British Virgin Islands had no trade with

Vietnam. We also found out that there is little information available about Malaysia and no wage

information available for Denmark, so that we excluded these three countries from the country

set. Three outlier countries (Belgium, Italy, and New Zealand) were taken away because they

had significantly smaller amount of investment compared to other countries. Russia used to

have a special relationship with Vietnam so their investment may not represent typical FDI. So,

Russia was also excluded from our data.

For the analysis of difference depending on region, we divided countries into two

groups considering the important influences of geographic distance and cultural distance on FDI

inflows- Region 1 (called Asian countries) consisting of 7 countries such as South Korea,

Singapore, Taiwan, Japan, Hong Kong, Thailand, and China, and Region 2 (called Non-Asian

countries) consisting of 7 countries such as Australia, Canada, Germany, UK, USA, Netherlands,

and France.

Summary statistics of the variables of the two investor groups and Satterthwaite method

of t test used to classify the data are reported in Table 1

4.2 Data