The rapid development of China’s digital economy has promoted industrial structure upgrading and further affected the quality structure of the labor force across industries. This study conducts a theoretical derivation by building a task-based theoretical model and uses panel data of 30 Chinese provinces from 2001 to 2020 to conduct empirical research on the relationship between industrial digitalization, industrial structure upgrading, and structural changes in labor quality. The study results show that industrial digitalization and industrial structure upgrade affect changes in the quality structure of China’s industrial labor. The industrial structure upgrading index plays a mediating role in influencing industrial digitalization on the educational structure of labor employment. When industrial structure upgrading is considered as the threshold variable, the impact of industrial digitalization on the employment and educational structure of different labor forces in China’s industries has diverse threshold characteristics. In the process of digital industrial development, China needs to focus on protecting labor with secondary education at different stages of digital development and accelerate the development of a skilled labor force to drive the high-quality development of China’s industrial economy.

1. [1] J. Tinbergen, “Substitution of graduate by other labour,” Kyklos, Vol.27, No.2, pp. 217-226, 1974. https://doi.org/10.1111/j.1467-6435.1974.tb01903.x
2. [2] D. Tapscott, “The Digital Economy: Promise and Peril in the Age of Networked Intelligence,” McGraw-Hill, 1996.
3. [3] D. H. Autor, L. F. Katz, and A. B. Krueger, “Computing inequality: Have computers changed the labor market?,” The Quarterly J. of Economics, Vol.113, No.4, pp. 1169-1213, 1998. https://doi.org/10.1162/003355398555874
4. [4] D. Acemoglu, “Changes in unemployment and wage inequality: An alternative theory and some evidence,” American Economic Review, Vol.89, No.5, pp. 1259-1278, 1999. https://doi.org.10.1257/aer.89.5.1259
5. [5] D. Acemoglu and D. Autor, “Chapter 12 – Skills, tasks and technologies: Implications for employment and earnings,” D. Cord and O. Ashenfelter (Eds.), “Handbook of Labor Economics,” Vol.4, Part B, pp. 1043-1171, Elsevier, 2011. https://doi.org/10.1016/S0169-7218(11)02410-5
6. [6] D. Acemoglu and P. Restrepo, “The race between man and machine: Implications of technology for growth, factor shares, and employment,” American Economic Review, Vol.108, No.6, pp. 1488-1542, 2018. https://doi.org/10.1257/aer.20160696
7. [7] D. Acemoglu and P. Restrepo, “Automation and new tasks: How technology displaces and reinstates labor,” J. of Economic Perspectives, Vol.33, No.2, pp. 3-30, 2019. https://doi.org/10.1257/jep.33.2.3
8. [8] J. Liebenau, “Labor markets in the digital economy: Modeling employment from the bottom-up,” L. Pupillo, E. Noam, and L. Waverman (Eds.), “Digitized Labor: The Impact of the Internet on Employment,” pp. 71-93, Palgrave Macmillan, 2018. https://doi.org/10.1007/978-3-319-78420-5_5
9. [9] E. Sadovaya, “Digital economy and a new paradigm of the labor market,” World Economy and Int. Relations, Vol.62, No.12, pp. 35-45, 2018. https://doi.org/10.20542/0131-2227-2018-62-12-35-45
10. [10] E. Syarova, “Labor in the digital economy – A challenge or new opportunities,” Nauchni Trudove, Vol.2020, No.3, pp. 245-260, 2020 (in Bulgarian).
11. [11] C. Cheng and H. Peng, “The mechanism of artificial intelligence affecting employment and china’s countermeasures,” China Soft Science, Vol.2018, No.10, pp. 62-70, 2018 (in Chinese).
12. [12] M. Xie et al., “Artificial intelligence, technological change and low-skill employment: Empirical evidence from Chinese manufacturing firms,” Chinese J. of Management Science, Vol.28, No.12, pp. 54-66, 2020 (in Chinese). https://doi.org/10.16381/j.cnki.issn1003-207x.2019.1251
13. [13] L. Yin and R. Wang, “The joys and sorrows of AI development,” People’s Tribune, Vol.2018, No.35, pp. 84-85, 2018 (in Chinese).
14. [14] X. Qu and J. Cheng, “Changes of employment structure in China: Upgrading or polarization?,” Studies in Labor Economics, Vol.3, No.1, pp. 119-144, 2015 (in Chinese).
15. [15] Q. Zhu and M. Li, “The development of artificial intelligence and the trend of the structure of the labor force in the future – Theory, evidence and strategy,” Reformation & Strategy, Vol.33, No.12, pp. 172-177, 2017 (in Chinese). https://doi.org/10.16331/j.cnki.issn1002-736x.2017.12.039
16. [16] X. Li, “The impact of industrial robot technology progress on the employment structure of China’s manufacturing industry,” Modern Business, Vol.2019, No.22, pp. 45-46, 2019 (in Chinese). https://doi.org/10.14097/j.cnki.5392/2019.22.022
17. [17] B. Yang, “Research on the employment of agricultural transfer labor under the wave of intelligence,” J. of Fuzhou University (Philosophy and Social Sciences), Vol.31, No.2, pp. 51-58, 2017 (in Chinese).
18. [18] Z. Sun and Y. Hou, “How does industrial intelligence reshape the employment structure of Chinese labor force,” China Industrial Economy, Vol.2019, No.5, pp. 61-79, 2019 (in Chinese). https://doi.org/10.19581/j.cnki.ciejournal.2019.05.004
19. [19] W. Sun et al., “Analysis on technology upgrade and employment structure in China,” Economic Theory and Economic Management, Vol.2018, No.6, pp. 5-14, 2018 (in Chinese).
20. [20] X. Deng and Z. Huang, “Analysis of the effect of artificial intelligence technology innovation on industry income gap – Empirical evidence from the Chinese industry level,” Soft Science, Vol.33, No.11, pp. 1-5+10, 2019 (in Chinese). https://doi.org/10.13956/j.ss.1001-8409.2019.11.01
21. [21] Y. Cai and N. Chen, “Artificial intelligence and high-quality growth & employment in the era of new technological revolution,” The J. of Quantitative & Technical Economics, Vol.36, No.5, pp. 3-22, 2019 (in Chinese). https://doi.org/10.13653/j.cnki.jqte.2019.05.001
22. [22] D. Acemoglu and J.-S. Pischke, “Why do firms train? Theory and evidence,” The Quarterly J. of Economics, Vol.113, No.1, pp. 79-119, 1998. https://doi.org/10.1162/003355398555531
23. [23] D. Acemoglu and P. Restrepo, “Secular stagnation? The effect of aging on economic growth in the age of automation,” American Economic Review, Vol.107, No.5, pp. 174-179, 2017. https://doi.org/10.1257/aer.p20171101
24. [24] R. M. Baron and D. A. Kenny, “The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations,” J. of Personality and Social Psychology, Vol.51, No.6, pp. 1173-1182, 1986. https://psycnet.apa.org/doi/10.1037/0022-3514.51.6.1173
25. [25] Q. Wang et al., “A research on the effects of industrial intelligence on employment: Based on spatial econometric analysis of laborer’s skills and genders,” J. of Management World, Vol.38, No.10, pp. 110-126, 2022 (in Chinese). https://doi.org/10.19744/j.cnki.11-1235/f.2022.0150
26. [26] X. Zhong, K. Hu, and D. Liu, “Digital economy enabling structural upgrading of service industry: Theoretical mechanism and empirical test,” Statistics & Decision, Vol.2023, No.12, pp. 11-16, 2023 (in Chinese). https://doi.org/10.13546/j.cnki.tjyjc.2023.12.002
27. [27] L. Luo, Z. Zhang, and Q. Zhou, “Industrial intelligence and urban low-carbon economic transformation,” Economic Management, Vol.45, No.5, pp. 43-60, 2023 (in Chinese). https://doi.org/10.19616/j.cnki.bmj.2023.05.003
28. [28] P. Topalova, “Trade liberalization and firm productivity: The case of India,” 2007.