Modernization in Mainland China: A Comment

By Shillito, Kelly M. | The American Journal of Economics and Sociology, July 1993 | Go to article overview

Modernization in Mainland China: A Comment


Shillito, Kelly M., The American Journal of Economics and Sociology


IN THE JANUARY 1992 ISSUE of this Journal, Chien-Hsun Chen tested the hypothesis that "both domestic capital accumulation and foreign capital inflows affect economic development in China, the latter being an indicator of modernization" ("Modernization in Mainland China: Self-Reliance and Dependence." American Journal of Economics and Sociology, 51 Jan. 1992: 57-68). To test this hypothesis, Chen utilized a linear regression model of the form:

YR = |b.sub.0~ + |b.sub.1~ CAR + |b.sub.2~ FCIR + |b.sub.3~ ET + |Mu~

where YR = the growth rate of provincial income by region, an indicator of modernization; CAR = capital accumulation rate; FCIR = Foreign capital inflows as a percentage of provincial income, by region; ET = Foreign direct investment as a percentage of provincial income by region. ET is a policy variable which represents the degree of openness in China's regional economy. If Chen's hypothesis is correct, then |b.sub.2~ should be positive and significantly related to YR.

In his article, Chen provided data for the dependent and independent variables. The data covered 27 regions over three years. Based on this data, Chen reported the following regression estimate:

YR = 11.916 - 0.03 CAR + 0.20 FCIR + 0.008 ET

(4.83) (-0.37) (2.23) (0.14)

D.W. Stat. = 2.36, |R.sup.2~ = .563.

The numbers in the parenthesis are t statistics. The regression results above indicate that FCIR is significantly related to YR. From this, Chen concluded that foreign capital inflows significantly affected economic development and modernization in China.

Chen, in his article, did not clearly indicate how he utilized the data that he reported to derive the regression results above. However, since the data set consists of a time series over three years and a cross-section of 27 regions, the optimal way to use this data set is to pool the time series and cross- section data. Pooling provides a total of 81 observations. The model above can be estimated using ordinary least squares using the combined time series and cross-section data set. Pooling increases the degrees of freedom, and the ordinary least square estimators are efficient provided that the co-efficients are invariant over time and across the cross-section units.

The least squares regression results using the combined time series cross-section data are reported below. These results were derived from Micro TSP Version 7.0.

YR = 12.266 - 0.02 CAR + 0.216 FCIR + 0.003 ET

(4.52) (-0. …

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