JACIII Vol.24 No.4 pp. 524-531
doi: 10.20965/jaciii.2020.p0524


The Dynamic Correlation Between Capital Deepening and Total Factor Productivity in China

Wuliu Zhang

School of Statistics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

October 25, 2019
January 12, 2020
July 20, 2020
capital deepening, total factor productivity, Bayesian quantile, adaptive Lasso

The impact of capital deepening on total factor productivity (TFP) is a significant and controversial issue. Based on the calculation of relevant indicators, this study adopts a Bayesian time-varying parameter model, Bayesian quantile regression, and adaptive Bayesian quantile models for in-depth statistical analysis. TFP was found to have a complex non-linear structure, and physical and human capital deepening indicators show a significant upward trend. The deepening of physical capital has a negative impact on TFP, while the deepening of human capital has a positive impact. In the capital deepening structure, the level of TFP has been improved and its structure optimized. Primary human and non-production physical capital deepening has no significant effect on TFP, while secondary human capital deepening has some significant effects on TFP. Tertiary and productive human capital deepening of TFP present two different forms of significant effect: the influence coefficient of the former declines in the increasing quantile and the change is larger, while the latter has a stable negative impact. The results of this study provide insights in terms of the improvement of China’s productivity.

Cite this article as:
W. Zhang, “The Dynamic Correlation Between Capital Deepening and Total Factor Productivity in China,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.4, pp. 524-531, 2020.
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