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
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.
-  Z. Yuan and D. Xie, “The Effect of Labor Misallocation on TFP: China’s Evidence 1978–2007,” Economic Research J., No.7. pp. 4-17, 2011 (in Chinese).
-  H. Varian, “Artificial Intelligence, Economics, and Industrial Organization,” National Bureau of Economic Research (NBER), Article No.24839, 2018.
-  X. Xu and M. Zhang, “Research on the Scale Measurement of China’s Digital Economy – Based on the Perspective of International Comparison,” China Industrial Economics, No.5, pp. 23-41, 2020 (in Chinese).
-  N. Negroponte, “Being Digital,” Alfred A. Knopf, Inc., 2000.
-  D. Tapscott, “Growing Up Digital: The Rise of the Net Generation,” McGraw Hill, 1999.
-  A. Goldfarb and C. Tucker, “Digital Economics,” J. of Economic Literature, Vol.57, No.1, pp. 3-43, 2019.
-  P. Zuo and J. Chen, “Digital economy with economic growth from the perspective of high-quality development,” Research on Financial and Economic Issues, No.9, pp. 19-27, 2021 (in Chinese).
-  X. Jiang, “Technology and Culture in the Digital Age,” Social Sciences in China, No.8, pp. 4-34, 2021 (in Chinese).
-  K. Hosono and M. Takizawa, “Do Financial Frictions Matter as a Source of Misallocation? Evidence from Japan,” Research Department, Policy Research Institute, Ministry of Finance, 2012.
-  S. Zhang, J. Xu, W. Chen et al., “Market Distortion, Inter-Provincial Factor Misallocation, and Total Factor Productivity,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.5, pp. 546-553, 2021.
-  K. Munshi and M. Rosenzweig, “Networks and Misallocation: Insurance, Migration, and the Rural-Urban Wage Gap,” The American Economic Review, Vol.106, No.1, pp. 46-98, 2016.
-  P. T. Nguyen and M. K. Nguyen, “Resource Misallocation of SMEs in Vietnamese Manufacturing Sector,” J. of Small Business and Enterprise Development, Vol.26, Issue 3, pp. 290-303, 2019.
-  S. Aoki, “A simple accounting framework for the effect of resource misallocation on aggregate productivity,” J. of the Japanese and Int. Economies, Vol.26, No.4, pp. 473-494, 2012.
-  C.-T. Hsieh and P. J. Klenow, “Misallocation and Manufacturing TFP in China and India,” The Quarterly J. of Economics, Vol.124, No.4, pp. 1403-1448, 2009.
-  L. Yan, L. Tai, X. Xu et al., “Study on the Impact of Housing Price and Labor Spatial Misallocation Under the Background of High-Speed Rail Network,” Soft Science, No.3, pp. 22-28, 2021 (in Chinese).
-  S. Zhou, P. Hai, and L. Zhang, “Does trade liberalization improve the labor misallocation in China’s manufacturing sector,” World Economy Studies, No.9, pp. 3-18+135, 2020 (in Chinese).
-  Q. Liang and S. Wang, “Minimum Wage, Spatial Spillover and Misallocation of Labor Resources,” J. of Social Science of Hunan Normal University, No.4, pp. 83-91, 2019 (in Chinese).
-  W. Zou and L. Hao, “The Improvement Effect and Working Mechanism of Business Environment on Resource Misallocation: An Empirical Analysis at the Industrial Level,” Wuhan University J. (Philosophy & Social Science), No.1, pp. 121-139, 2021 (in Chinese).
-  D. Acemoglu and P. Restrepo, “Robots and Jobs: Evidence from US Labor Markets,” J. of Political Economy, Vol.128, No.6, pp. 2188-2244, 2020.
-  P. Bai and Y. Zhang, “Digital Economy, Declining Demographic Dividends and the Rights and Interests of Low- and Medium-Skilled Labor,” Economic Research J., No.5, pp. 91-108, 2021 (in Chinese).
-  L. Li, X. Wang, and Q. Bao, “The Employment Effect of Robots: Mechanism and Evidence from China,” J. of Management World, No.9, pp. 104-119, 2021 (in Chinese).
-  H. Bathelt, A. Malmberg, and P. Maskell, “Clusters and Knowledge: Local Buzz, Global Pipelines and the Process of Knowledge Creation,” Progress in Human Geography, Vol.28, No.1, pp. 31-56, 2004.
-  X. Ma, J. Shao, and F. Cao, “Comprehensive Performance Evaluation of High Standard Farmland Construction in Mountainous Counties – A Case Study in Dianjiang, Chongqing,” J. of Natural Resources, No.12, pp. 2183-2199, 2018 (in Chinese).
-  Y. Chen and W. Hu, “Distortions, Misallocation and Losses: Theory and Application,” China Economic Quarterly, No.4, pp. 1401-1422, 2011 (in Chinese).
-  J. Bai and Y. Liu, “Can Outward Foreign Direct Investment Improve the Resource Misallocation of China,” China Industrial Economics, No.1, pp. 60-78, 2018 (in Chinese).
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.