Understanding TFP growth in inland regions of China: an empirical study of the effects of three factors

Abstract

In this paper, we empirically examine the potential effects of international openness, domestic coastal-inland market integration, and human capital accumulation on TFP growth in inland provinces in China. By using a nonlinear technique as our main regression approach as well as an extended GMM method as robustness checks, we show that human capital accumulation plays an important role in promoting TFP growth in the inland provinces. Our results support the argument that the most important contribution of human capital to income growth lies not in its static, direct effect as an accumulable factor in the production function, but in its dynamic role in promoting TFP growth. Our regression results also provide evidence for the positive roles international openness and domestic coastal-inland market integration play in promoting TFP growth in inland provinces in China.

Keywords:

• total factor productivity
• openness
• market integration
• human capital

JEL Classifications:

• F41
• O47
• O53

Notes

1. See also Sisci (2005), World Bank (2005), Fan and Sun (2008), Fan, Kanbur, and Zhang (2009) and Candelaria, Daly, and Hale (2013).

2. For example, foreign trade provides access to technologies embodied in imported goods, and enlarges the market faced by domestic producers so that they could increase their returns to innovations (Harrison 1996).

3. A few exceptions include Madariaga and Poncet (2007), Ouyang and Fu (2012), and Özyurt and Mitze (2012).

4. See Hall and Jones (1999) for a justification for this functional form.

5. Owing to missing data, two inland regions, Tibet and Chongqing, are not included.

6. One coastal province, Hainan, is not included owing to missing data.

7. For example, Hu and Khan (1997) assume an annual rate of capital depreciation as low as 3.6% while Maddison (1998) assumes one as high as 17.0%. Most studies usually assume an annual capital depreciation rate of 4–10%. World Bank (1997) assumes 4%, Perkins (1988), Woo (1998), Meng and Wang (2000) and Wang and Yao (2003) assume 5%, Chow and Li (2002) assume 5.4%, Young (2003) assumes 6.0%, Wu (2004) assumes 7.0%, and Zhang (2008) assumes 9.6% (Wu 2009, 2011).

8. 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.

9. The reported original data were based on the National Sample Survey on Population Changes conducted in each relevant year.

10. Here, in calculating h⁵, we assume that a worker who has completed university or higher level of education has 17 years of schooling on average.

11. We do not need to worry about nationwide change along the time dimension in the quality of education (in terms of its human-capital-enhancing ability). All we need to focus on here in constructing regional human capital intensity is the cross-sectional (cross-region) comparison of regional human capital intensity at any given time. This is because regional human capital intensity enters equation (7) in the log form so that any nationwide time trend in it will be immediately absorbed into the time intercept term in the regression equation.

12. For example, when t in y_it indexes the year 1996, Δlny_it pertains to growth during the sub-period 1996–1999, and so forth.

13. In fact, the size of the state sector, in particular, is important for understanding productivity growth in China. Brandt and Zhu (2010) find that rising productivity in the non-state non-agricultural sector is the key driving force of China’s growth. They also find that the less efficient state sector continues to absorb more than 50% of all fixed investment. The significant misallocation of capital induced by an inefficient institutional environment implies that if capital had been efficiently allocated, China could have achieved the same growth performance without any increase in the rate of aggregate investment (Brandt and Zhu 2010).