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Econometrics: Foreign Direct Investment in China

Introduction

The main objective of this paper is to look into the Foreign Direct Investment in China. Specifically, the paper looks at Gross Domestic Product, imports, experts, and wages on the Foreign Direct Investment. China is one of the fastest growing economies in the world today. In fact, among the major world economies, China is considered to be the fastest growing one. Measured by Nominal GDP, China is the second largest economy in the world after the United States of America (Sinha 12).

The data, regarding purchasing power parity also shows the same results as the Nominal GDP. Over the past 30 years, the rate of the economic growth of China has been equal to 10%. In terms of export, China is the largest exporter of goods worldwide. It is second after the US in terms of volume of imports it brings in from different parts of the world. The most current data shows that wages in China have been going up in all areas of the country. In the year 2011, the chief Economist of World Bank stressed out the fact that China would become the largest economy in the world by the year 2010, and certainly overtake US if the trends continue to develop as it can be observed today (OECD 15).

For instance, the report done by standard chartered bank in 2011 indicates that China is likely to become the largest economy in the world by the year 2020. A report conducted by OECD in 2007 underlines the fact that China will have overtaken the US by 2015 in terms of purchasing power parity conversions (OECD 13). In 2010, the World Bank former president James Wolfensohn, also emphasized the fact that a big percentage of the world’s middle class will have shifted to live in China by 2030.

All these trends about China make it an interesting economy for research. It would be interesting to look at why the country has been recording a high growth in all spheres. The Foreign Direct Investments have also been increasing in China. 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, it was less than $19 billion in 1990 and over $300 billion by the end of 1999; and it still continues to grow each year. Such a growth in FDI, with the prevailing level of competition in the world by economies to raise their FDI, is worth investigating. The factors which cause a high level of FDI in China are of great interest to contemporary researchers. It is important to investigate the reason for the high level of FDI in China. This is one of the factors that is contributing to a high growth of the country’s economy.

The country’s GDP growth from 1952 to 2005 is shown in the figure below.

The country’s GDP growth from 1952 to 2005

This trend shows a sharp increase from 1980 towards 2005.

Foreign Direct Investments have also showed an interesting trend in China over time. The following graph shows the trend observed from the year 1989 to 2009. The amount of FDI used increased on average during the period covered.

The amount of FDI used increased on average

The amount and trend for imports and exports are shown below:

The amount and trend for imports and exports

The amount and trend for imports and exports

All these variables show an increasing trend. The amounts of each variable have recorded a high rise in the recent years.

The growth of FDI in China seems to have a trend with the increase in GDP, wages, imports and exports. The aim of this research is to investigate whether there is a trend in China or not. In this case, investigation is done on the effects of GDP, imports and wages on the Foreign Direct Investment.

This paper will first discuss the literature regarding the GDP, imports, exports, and wages in China and the effects on the Foreign Direct Investment. The second section will underline the conceptual framework, then data description and gathering, econometric model and estimation, results and conclusion.

Literature Review

FDI in China recently hit the $12.4 billion mark, according to the data, released by the Ministry of Finance in March 2012. The trend has showed an increase in FDI for many years since 1980s. The other variables, such as imports, exports, GDP, and wages have also been increasing at a steady rate (Chen 12). These trends have been used by many researchers to forecast the future economic growth of China. Many researchers conclude that if the current trend in China Economic advancements is maintained, China will soon overtake US and become the largest economy on earth. It is worth to look at the impact of the factors that grow in the same direction as FDI to find out if there is any significant relationship among them.

Conceptual Framework

This framework used lists the possible causes and it also represents the theoretical structure that describes a concept. In this paper, it is assumed that the Dependent variable (FDI) is affected by the independent variables (Wages, GDP, Exports, and Imports).

Independent Variables Dependent Variable.
Independent Variables Dependent Variable.

This shows that all these factors jointly affect FDI. They all have some impact on FDI.

Data Description and Gathering

The data used for this empirical paper is time series data. The data was collected from secondary sources from the year 1995 up to 2011. The data has a sample of 17 years. The data is collected from China Bureau of Statistics. The period covered by the sample was characterized by great improvement of the Chinese economy. The variables covered in this paper showed a great increase during this period. The Foreign Direct Investment showed a steady increase throughout this period.

The same happened to GDP, Imports, Exports, and Wages. They all improved over the period which creates a further need to investigate if the increase recorded by all the variables was related to the increase of the other factors. The data was collected from secondary sources. The Bureau of Statistics of China contains credible information, concerning Chinese Economy growth statistics. The data collected from this source is accurate and free from manipulation. The summary statistics of the variables of interest is presented as follows:

Y X1 X2 X3 X4
Mean 3885.299 188607.7 51356.89 44152.38 21954.39
Median 3311.959 135822.8 36287.90 34195.60 15329.60
Maximum 7181.081 472881.6 123240.6 113161.4 59954.70
Minimum 2322.550 60793.70 12451.80 11048.10 8055.800
Std. Dev. 1514.028 126762.6 38497.83 32973.82 15447.82
Skewness 0.944106 0.942202 0.516889 0.616772 1.162925
Kurtosis 2.624763 2.663134 1.758904 2.152334 3.272909
Jarque-Bera 2.625186 2.595660 1.848054 1.586787 3.884544
Probability 0.269121 0.273124 0.396917 0.452307 0.143378
Observations 17 17 17 17 17

In this case, Y stands for Foreign Direct Investment, X1 for GDP, X2 for Exports, X3 for Imports, and X4 for Wage. The dependent variable is Y, while the independent variables are X1, X2, X3, and X4. The independent variables are also called as the right hand variables.

Econometric model and its method of Estimation

The data in this case is analyzed quantitatively, using regression analysis. Regression analysis was chosen for this study because it shows an effect of an individual independent variable on the dependent variable and that of all variables jointly.

Dependent variable

The dependent variable is Foreign Direct Investment that will be denoted by Y during analysis. This is the variable that is being determined using other variables. The effect that other factors have on this variable is what the study aims to find out.

The independent/Explanatory variables

These are the variables whose effects on the dependent variable are being investigated. Their values are exogenously determined. For this research, the following dependent variables are believed to have some impact on the Foreign Direct Investment.

GDP which will be denoted by X1;

Export in China denoted by X2;

Import in China denoted by X3;

Wage IN China which will be denoted by x4 for the purpose of this analysis.

Regression Model

The econometric model that suggests the possible relationship of these variables is as stated below, FDI = β0 + β 1 GDP growth + β 2 export + β 3 import + β 4 Wage+ u. However, for purposes of simplicity and ease of analysis, the variables will be denoted by Xi as indicated above. Where i = 1, 2, 3, 4. The model, therefore, is presented as follows:

Y = β0 + β 1 X1 + β 2 X2 + β 3 X3+ β 4 X4 + µ.

µ is the error term of the regression model. The parameter βi denotes the coefficients of the regression equation. They either cause a decrease or increase in the dependent variables, being observed by considering the direction of the sign. If the sign is negative, then increase in explanatory variable increases the dependent variable by the value of β and vice versa.

To estimate this model, the data for the variables are collected and used to calculate the values of the coefficients βi. They are then tested for significance. Each variable is tested independently and then the test of overall significance is done. In this case, F-distribution test is used.

A number of assumptions are made concerning this model in regards to variance of the error term and covariance between the regressors and the error term. These assumptions are related to the assumptions of the Ordinary Lease Square method which will be used to estimate the regression model.

The variance of the error term remains the same irrespective of the size of the data collected. That is Var (µi) = δ2µ. Where µi denotes the error terms in periods i = 0, 2, 3…. The symbol δ denotes variance.

The other assumption consists in the fact that the error term and the independent variables (regressors) are independent, in other words, they are not related to one another. This means that Cov (Xi, µi) = 0. In other words, the covariance between regressors (Xi) and the error terms (µi) is zero.

Reason for Inappropriateness of Simple OLS method in Estimating the Model

The OLS method makes a number of 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. Since the data used in this empirical work is time series data, using OLS for estimation assumes that the error terms in different period are not correlated (Rubin 12). They are independent of each other. 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.

Results

The results of OLS determined using Eviews are as sown below:

Dependent Variable: Y
Method: Least Squares
Date: 04/20/13 Time: 21:09
Sample: 1995 2011
Included observations: 17
Variable Coefficient Std. Error t-Statistic Prob.
C 1606.790 125.3156 12.82195 0.0000
X1 0.016803 0.008983 1.870554 0.0860
X2 -0.006624 0.017696 -0.374354 0.7147
X3 0.002956 0.020914 0.141335 0.8900
X4 -0.031015 0.063191 -0.490814 0.6324
R-squared 0.983994 Mean dependent var 3885.299
Adjusted R-squared 0.978658 S.D. dependent var 1514.028
S.E. of regression 221.1809 Akaike info criterion 13.87577
Sum squared resid 587051.9 Schwarz criterion 14.12083
Log likelihood -112.9440 F-statistic 184.4272
Durbin-Watson stat 1.851950 Prob(F-statistic) 0.000000

From these results, the econometrics model is estimated.

The model Y = β0 + β 1 X1 + β 2 X2 + β 3 X3+ β 4 X4 + µ is estimated as follows:

Y = 1606.790 + 0.016803 X1 + -0.006624 X2 + 0.00295 X3+ -0.031015 X4

From this model, it could be observed that the independent variables affect the dependent variable in different ways. For example, the coefficient of X1 shows that when X1 increases by one unit, Y increases by 0.016803 because the coefficient is positive. For X2 and X4, their increase causes a decrease in Y because their coefficients are negative. A unit increase in X3 leads to a 0.00295 increase in Y. The value of coefficient of determination (R2 = 0.978658) shows that the independent variables explains 97.8658% of the variation in Y.

The test of significance of these variables is done at 95% level of confidence. The level of significance (α) = 0.05. The T-distribution test is done to determine the significance of the independent variables as determinants of the dependent variable. The critical value of t-critical at α = 0.05 and 13 degrees of freedom is 1.771. The decision criterion is that if the t-statistic is greater than the t-critical, then the null hypothesis is rejected and the variable is significant.

Variable Coefficient Std. Error t-Statistic
C 1606.790 125.3156 12.82195
X1 0.016803 0.008983 1.870554
X2 -0.006624 0.017696 -0.374354
X3 0.002956 0.020914 0.141335
X4 -0.031015 0.063191 -0.490814

These results show that for X1, t-statistic (1.870554) is greater than the critical t. This means that the null hypothesis (β1 = 0) is rejected. The conclusion is that X1 (GDP) is a significant determinant of Y (FDI). For the other variables, their t-statistics are less than the t-critical value. The null hypothesis in that case cannot be rejected. The conclusion is that β = 0 and the variables are not significant determinants of Y. X2 (Exports), X3 (Imports), and X4 (Wages) are not significant determinants of Y (FDI).

For the test of overall significance, F-test is used. The probability of F-statistic is compared with the level of significance. The criterion followed is that if the probability of F-statistic is less than the level of significance, then the null hypothesis (β1 = β2 = β3 = β4 = 0) is rejected. The conclusion is that β1 ≠ β2 ≠ β3 ≠ β4 ≠ 0, meaning that the variables are jointly significant. In the case above, the variables X1 X2 X3 X4 are jointly significant determinants of Y.

The other method that is considered appropriate is 2 Stage Least Square Method because the variables in the model are related. For example, the wage is related to GDP and consequently related DFI. The output of this model using Eviews is as stated below:

Dependent Variable: Y
Method: Two-Stage Least Squares
Date: 04/15/13 Time: 18:17
Sample: 1995 2011
Included observations: 17
Instrument list: X1 X2 X3 X4
Variable Coefficient Std. Error t-Statistic Prob.
C 1606.790 125.3156 12.82195 0.0000
X1 0.016803 0.008983 1.870554 0.0860
X2 -0.006624 0.017696 -0.374354 0.7147
X3 0.002956 0.020914 0.141335 0.8900
X4 -0.031015 0.063191 -0.490814 0.6324
R-squared 0.983994 Mean dependent var 3885.299
Adjusted R-squared 0.978658 S.D. dependent var 1514.028
S.E. of regression 221.1809 Sum squared resid 587051.9
F-statistic 184.4272 Durbin-Watson stat 1.851950
Prob(F-statistic) 0.000000

Based on the test conducted above, the method of 2SLS also shows the same results as those of OLS. However, it is important to conduct advanced econometrics analysis in order to confirm that OLS is not significantly violated (Keller 15).

Robustness tests

In this case one variable X4 is dropped and effect observed.

Dependent Variable: Y
Method: Least Squares
Date: 04/20/13 Time: 22:22
Sample: 1995 2011
Included observations: 17
Variable Coefficient Std. Error t-Statistic Prob.
C 1641.871 99.88251 16.43803 0.0000
X1 0.012510 0.001986 6.299755 0.0000
X2 -0.001026 0.013128 -0.078158 0.9389
X3 -0.001434 0.018344 -0.078188 0.9389
R-squared 0.983672 Mean dependent var 3885.299
Adjusted R-squared 0.979905 S.D. dependent var 1514.028
S.E. of regression 214.6261 Akaike info criterion 13.77800
Sum squared resid 598836.9 Schwarz criterion 13.97405
Log likelihood -113.1130 F-statistic 261.0670
Durbin-Watson stat 1.919787 Prob(F-statistic) 0.000000

When one variable is dropped, the significance of the other three variables remains the same. However, the coefficient of determination increases up to 97. 99%. The coefficients of the remaining independent variables also increased. This shows that the estimates are sensitive to removal of variables from the right hand side.

Limitations and Caveats

The data is sensitive to addition or removal of some variable. The effects of these changes should be observed because if more variables are removed, variables that were not significant may become significant even when in reality sense they are not.

Conclusion

The research question investigated is stated as follows:

  • Are the following factors significant determinants of Foreign Direct Investments (FDI) in China?
  • Gross Domestic Investment.
  • Exports.
  • Imports.
  • Wages.

The results of the analysis done revealed that Gross Domestic Investment is an important determinant of Foreign Direct Investment. A unit increase in GDP increases FDI by 0.016803 units. The other factors (Exports, Imports, and Wages) we not found to be important determinants of FDI. The variables jointly explained 97.86 of the variation in Y. They were also found to be jointly significant based on F-test.

Works Cited

Chen, Chunlai. Foreign Direct Investment in China: Location Determinants, Investor Differences and Economic Impacts, London: Edward Elgar Publishing, 2011. Print.

Graham, Edward, and E. Wada. 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

OECD. OECD Economic Surveys: China 2013, UK: OECD Publishing, 2013. Print.

Peck, Roxy, and C. Olsen. 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.

Sinha, Swapna.Comparative Analysis of FDI in China and India: Can Laggards Learn from Leaders, New York: Universal-Publishers, 2008. Print.