26the February, 2014
Comparing the determinants of FDI inflows to China and India
‘FDI’ abbreviated for Foreign Direct Investment, has gained significant importance in global commerce and economic growth. The term has brought a much appreciated change in the industrialized world. In simple meaning, FDI can said to be a saver centered in one nation who obtains a possession in another offshore nation with the objective to work on that property. By the end of 2006, the global supply of FDI at the conclusion of 2006 was $10 trillion, which amounts to GDP of five leading markets of the world, USA, Japan, China, Germany and United Kingdom.
As for the two Asian countries which are developing with the rapid pace is China and India. Both of these economies are increasingly getting integrated with the global economy as they open up their markets to international trade and offshore investments. However, there has been a significant difference in FDI flows to both of these economies. Despite of the good and resourceful prospects for Foreign Direct Investments, the FDI flow is limited to China. Hence, this paper is directed towards comparing the FDI determinant in India and China.
Variables, Data Source and Period of the Study:
In order to compare the determinants of FDI flow in India and China, we shall explore and test the various determinants of FDI, that influence the inflows in India and China. For India, we have used quarterly data from 1990-91 to 2008-09, while, for China, annual data from 1983-2008 has been used. While FDI will be the dependent variable, following will be the independent variables for India to conduct our study, Gross domestic product (GDP), Foreign exchange reserves (RES), Openness (OP i.e, sum of Exports and Imports as a percentage of GDP), Long Term Debt (LTD), Exchange rate (EXCH) and Inflation (INF).
For China, we our independent variables will be:
FDI = the annual stock of real foreign direct investment in China,
RWA = the ratio of real Chinese average wage rates,
RT = the ratio of real Chinese tax,
RGDP = the ratio of real Chinese GDP,
ER = the exchange ratio of Chinese RMB/US $,
VARER= the volatility of exchange rate,
RX = real Chinese exports,
RM = real Chinese imports,
RFX = real exports of foreign funded enterprises in China,
RFM = real imports of foreign funded enterprises in China,
Converting the variables into equation will provide us with:
lnFDIit = α+ β1 RWAit+ β2 RTit+ β3lnRGDPit+ β4 ERit+ β5 VAREX it+ β6 ln RXit+ β7ln RMit+ β8lnRFXit+ β9 lnRFMit+ εt
Description of Variables for India:
Variables Description
LNFDI Natural Log of Foreign Direct Investment
LNEXCH Natural Log of Exchange rate
LNGDP Natural Log of Gross domestic product
LNINF Natural Log of Inflation
LNLTD Natural Log of External indebtedness
LNOP Natural Log of Openness (sum of Exports and Imports as a percentage
of GDP)
LNRES Natural Log of Foreign Exchange Reserves
The source of data is The Handbook of Statistics of Indian Economy published by Reserve Bank of India and from World Development Indicators and World Development Reports published by the World Bank for India. For China, our source is and China respectively.
Hypothesis Testing for India:
The ordinary least square equation and the explanatory variables are regressed so as to test the significance of the above stated variables. The multiple regression analysis was used and the regression results have been reported in the below table:
Regression Results (FDI as Dependent Variable)
(India)
Our analysis indicated that a combination of variables like GDP INF or EXCH are found to be statistically significant in India, while the coefficient of LTD and RES do not have significant t-value. However, the value of F test is found to be significant in all the equations which show the significance of the model, while the value of adjusted R2 is found to be 0.89 which indicates that the percentage variation in FDI due to the combination of variables taken in the study.
Our analysis indicates that the above stated independent variables are statistically significant in India. In conclusion, the variable openness, the inflation rate and Reserve have been found to be the major contributor in explaining 19.83%, 13.57% and 10.84% variation respectively in FDI when there is 1% change in these variables. Thus, while Variables openness, Reserve, GDP, and LTD have estimated to give positive impact to FDI, we have noticed a negative impact of inflation and exchange rate has been analyzed on FDI.
Limitation of the analysis:
Since economic time series move together thus including all the independent variables in the regression equation is most likely to lead to multicollinearity. Thus, while finding the results, the existence of multicollinearity in the variables was also tested by creating Pearson’s Correlation Matrix, which depicted high correlation among the explanatory variables i.e. causing the problem of multicollinearity.
Hypothesis Testing for China:
Just like regression analysis conducted on India, similar regression method is applied to find the impact of variables stated above on Foreign Direct Investment in China. We used ordinary least squares method to regress the independent variables to study their impact, and to find out determinants of FDI. The table below is created to state the results of our regression analysis. Please note that we conducted hypothesis testing for China, both at the National and Regional Level to understand the relationship in a better way.
Regression Results (FDI as Dependent Variable)
(China)
The above results prove that the increasing market size, low labor costs, exchange rate are significant in determining FDI investments. Furthermore, even lower taxation regions are also significant in determining FDI investment. However, the relationship of trade and FDI is ambiguous. Interestingly, the FDI have significant relationship with export but effect of imports is not significant. This indicates that the imports are not significant. However, for regional imports, the results were significant which means that MNC’s will prefer the raw materials from outside China.
Table: Co-Integration Tests Results
