Abstract
The study in this chapter empirically examines the effects of openness and human capital on total factor productivity growth in the Chinese regions. In this chapter we build models of technology diffusion in which follower economies achieve productivity growth by taking advantage of technological spillovers from the world technology frontier. We hypothesize that China’s regional productivity growth is a positive function of regional openness and regional human capital, and a negative function of the current level of regional productivity. By applying panel data fixed effects and GMM regression methods, our analysis shows that human capital has both a growth effect and a convergence effect on regional total factor productivity across the Chinese regions. This result implies that besides its direct, static level effect on output as an accumulable factor input, human capital also exerts indirect, dynamic impacts on output through its growth and convergence effects on total factor productivity. Our analysis also shows that regional openness has a growth effect on regional total factor productivity in China.
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- 1.
In previous chapters (Chaps. 3 and 4), we specified a Cobb-Douglas production function with Harrod-neutral (labor-augmenting) technological progress. This was because technological progress must take the labor-augmenting form in order for the model to have a steady state with constant growth rates. It is clear, however, that with the Cobb-Douglas functional form, labor-augmenting (Harrod-neutral), capital-augmenting (Solow-neutral), and Hicks-neutral technological progress are all essentially the same.
- 2.
These 28 provincial-level regions include provinces, ethnic minority autonomous regions, and three municipalities (Beijing, Tianjin, and Shanghai) in mainland China. Owing to missing data, Tibet, Chongqing and Hainan are not included in our sample.
- 3.
Cross-country studies such as those of Hall and Jones (1999) and Aiyar and Feyrer (2002) assume a value of α that is 1/3, for the reason that this value is broadly consistent with national income accounts data for developed countries. However, given the evidence provided by existing literature, we think that the value 1/3 is too low for the Chinese regions.
- 4.
For more details about regional inequality in productivity change between the coastal and non-coastal provinces, see, for example, Chen et al. (2009).
- 5.
Relevant data on regional foreign trade are also obtained from the Chinese Statistical Yearbooks.
- 6.
However, this GMM method has its problems too. Lagged levels can be weak instruments for first differences, especially when the explanatory variables are highly persistent, and the GMM estimator is likely to be severely biased (Durlauf et al. 2004). To mitigate the problem of weak instruments, Blundell and Bond (2000) show that an extended system GMM estimator, in which lagged first-differences of the series are also used as instruments for the levels equations, can dramatically reduce the potentially large biases induced by the aforementioned first-differenced GMM estimator. However, it is beyond the scope of this analysis to incorporate the use of this more sophisticated system GMM approach into the current analysis.
- 7.
These 28 provincial-level regions include provinces, ethnic minority autonomous regions, and three municipalities (Beijing, Tianjin, and Shanghai) in mainland China. Owing to missing data, Tibet, Chongqing and Hainan are not included in our sample.
- 8.
They are derived by following a simulation process. See Appendix B of Wu (2008).
- 9.
- 10.
For a review of different depreciation rates and initial values of physical capital used in various studies when applying the perpetual inventory approach in the case of China or its regions, see, for example, Wu (2011).
- 11.
The rate for the first 4 years, 13.4 %, corresponds to the average return to an additional year of schooling in sub-Saharan Africa. The rate for the second 4 years, 10.1 %, is the average return to an additional year of schooling worldwide, while that for schooling above the eighth year, 6.8 %, is taken from the average return to an additional year in the OECD.
- 12.
We are forced to perform this five-group division on the regional population aged six and above only because data on the distribution of educational attainment in the regional employed population or working-age population are not available.
- 13.
Here, in calculating h e, we assume that a worker who has completed university or higher level of education has 17 years of schooling on average.
- 14.
In the next subsection, we will examine how a change in the assumed value of α will affect our regression results.
- 15.
In our data, the minimum, maximum, and average values of ln A it are 2.905, 6.477 and 4.687, respectively.
- 16.
The term ln F it ln h it is still insignificant if it is included in the regression equation, so regression (3) excludes this term.
- 17.
In order to save space, the results of the regressions in this subsection are not explicitly reported in tables. However, the results are available from the author upon request.
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Jiang, Y. (2014). Openness, Human Capital, and Productivity Growth in the Chinese Regions. In: Openness, Economic Growth and Regional Disparities. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40666-9_5
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