Study topic and purpose
Foreign Direct Investments in China has been a concept of interest to many researchers. The trend of FDI has been increasing over time. The impact of Gross Domestic Product, Wages, Imports, and Exports on the Foreign Direct Investment in China is the topic of interest in this paper.
Reasons for selecting the topic and why it is interesting
China is one of the fastest-growing economies in the world. The rate of growth has been of interest by all countries in the world including the developed economies like the US and UK. According to Graham and Wada, “by almost all accounts, foreign direct investment (FDI) in China has been one of the major success stories of the past 10 years” (1). The trend of the growth of FDI in China has been very high, having a base of less than $19 billion in 1990 to over $300 billion by the end of 1999 and more than that in recent years (Chen 5). Such growth in FDI, with the prevailing level of competition in the world by economies to raise their FDI, is worth investigating. The factor which causes a high level of FDI is an area that is not fully investigated. It is important to investigate the reason for the high level of FDI in China. This is one of the factors that are contributing to the high growth of the country’s economy.
The regression model, Dependent and independent variable and a variable of interest
The following model shows how the variables used in this research are related.
FDI= α0 + α 1 GDP growth + α 2 export + α 3 import + α 4 Wage+ u. In this empirical paper, the variables on the right-hand side of the regression model will be denoted by Xi where i = 1, 2, 3, 4. The model, therefore, is transformed to the following:
Y = α 0 + α 1 X1 + α 2 X2 + α 3 X3+ α 4 X4 + µ.
The term µ in the model denotes the stochastic term or the error term (Rubin 34).
The key Variable of interest
The four independent variables are all of the interest because they show interesting trends in China. The Gross Domestic Product (X1), for example, has shown an increasing trend in China the same way as the Foreign Direct Investment. China is also the second-largest economy in the world based on GDP.
Dependent and independent variables
The variable Y (Foreign Direct Investment) appearing on the left-hand side of the model is the dependent variable. Its variation is believed to be caused by the variables appearing on the right-hand side of the regression model.
The variables on the right-hand side of the model are the independent variables. They are believed in this empirical paper to be responsible for the variation in Y.
- X1 denotes GDP
- X2 denotes Exports
- X3 represents import
- x4 presents wage
Hypotheses being tested
The following hypotheses will be tested:
H0 denotes the null hypothesis while H1 denotes the alternative hypothesis.
H0: α1 = 0, GDP growth (X1) is a significant determinant of FDI (Y) in China
H1: α1 ≠ 0, GDP growth (X1) is not a significant determinant of FDI (Y) in China
H0: α2 = 0, Export (X2) is a significant determinant of FDI (Y) in China
H1: α2 ≠ 0, Export (X2) is not a significant determinant of FDI (Y) in China
H1: α3 ≠ 0, Imports (X3) are not significant determinants of FDI (Y) in China
H1: α3 ≠ 0, Imports (X3) is not a significant determinant of FDI (Y) in China
H1: α4 ≠ 0, Wages (X4) are not significant determinants of FDI (Y) in China
H1: α4 ≠ 0, wages (X4) are not significant determinants of FDI (Y) in China
Expectations of the signs of the coefficients
Since the data collected shows an increasing trend on all variables, the signs of all the coefficients are expected to be positive.
The problem of using Simple OLS in estimating the model
The OLS method makes several assumptions which may lead to inaccurate estimates or estimation of the model. Firstly, the method assumes that the error term has a constant variance which may not be the case (Keller 23). The error term is also assumed to be normally distributed with a mean of zero and constant variance. Since the data used in this empirical work is time-series data, using OLS for estimation assumes that the error terms in the different periods are not correlated. They are independent of each other (Black 97). The other assumption is that the error term is not correlated with the independent variables. All these assumptions may not always hold (Peck and Olsen 213). Other methods that do not rely on these assumptions should be used to ensure no such mistakes are committed.
To deal with this problem, a 2 Stage Least Square Method will be used. It involved the use of OLS in two stages. It helps eliminate the problem of having the variables on the right-hand side correlated.
The descriptive statistics of the variables
OLS Table.
2SLS Method Table.
Works Cited
Black, Ken. Business Statistics: For Contemporary Decision Making, New York: John Wiley & Sons, 2011. Print.
Chen, Chunlai. Foreign Direct Investment in China: Location Determinants, Investor Differences and Economic Impacts, London: Edward Elgar Publishing, 2011. Print.
Graham, Edward and Wada, Erika. Foreign Direct Investment in China: Effects on Growth and Economic Performance, Oxford: Oxford University Press, 2001. Print.
Keller, Gerald. Statistics for Management and Economics, New York: Cengage Learning, 2011. Print
Peck, Roxy and Olsen, Chris. Introduction to Statistics & Data Analysis: Enhanced Edition, New York: Cengage Learning, 2008. Print.
Rubin, Allen. Statistics for Evidence-based Practice and Evaluation, London: Cengage Learning, 2010. Print.