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JACIII Vol.26 No.4 pp. 655-664
doi: 10.20965/jaciii.2022.p0655
(2022)

Paper:

Digital Economy, Intelligent Manufacturing, and Labor Mismatch

Yang Shen and Xiuwu Zhang

Institute of Quantitative Economics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

Received:
February 5, 2022
Accepted:
May 20, 2022
Published:
July 20, 2022
Keywords:
digital economy, labor mismatch, particle swarm optimization, projection pursuit, industrial robot
Abstract
Digital Economy, Intelligent Manufacturing, and Labor Mismatch

Kernel density map of digital economic development index

Factor mismatch is considered to be an important restriction on the growth of total factor productivity. Based on the panel data of 30 Chinese provinces from 2013 to 2019, this work first measures the digital economy development index of each Chinese province by using a particle swarm optimization projection pursuit model, followed by a panel econometric model, to verify the effect of the digital economy and artificial intelligence manufacturing on the labor-resource mismatch. The results show that, from 2013 to 2019, China’s digital economy generally showed a trend of steady progress, with an average annual growth rate of 12.10%. The mismatch index of the labor force dropped by 1.46% every year, and the situation of insufficient or surplus allocation of labor force resources in China was alleviated. The fitting results of the spatial econometric model show that the digital economy can reduce the labor mismatch index, and this conclusion has remained valid after a series of robustness tests. The intermediary mechanism shows that intelligent manufacturing plays a masking role in the process of alleviating labor misallocation in the digital economy. Artificial intelligence cannot alleviate labor mismatches, but it strengthens the corrective function of the digital economy.

Cite this article as:
Y. Shen and X. Zhang, “Digital Economy, Intelligent Manufacturing, and Labor Mismatch,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 655-664, 2022.
Data files:
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Last updated on Aug. 05, 2022