Labor Market Segmentation and Employment Inequality in the Era of Digital Transformation
Keywords:
Labor Market Segmentation, Employment Inequality, Digital Transformation, Digital Economy, Automation, Artificial Intelligence, Gig Economy, Wage Inequality, Skill Polarization, Workforce Development, Employment Opportunities, DigitalizationAbstract
The rapid expansion of digital technologies has fundamentally transformed labor markets across the world, creating new opportunities for productivity growth, innovation, and economic development. At the same time, digital transformation has intensified existing forms of labor market segmentation and contributed to widening employment inequalities among different groups of workers. The integration of automation, artificial intelligence, digital platforms, and information technologies has altered the nature of work, reshaped occupational structures, and changed the demand for skills. While highly skilled workers often benefit from increased access to quality employment, higher wages, and greater career mobility, low-skilled workers frequently face job displacement, precarious employment conditions, and limited opportunities for advancement. These developments have generated concerns regarding the equitable distribution of economic gains associated with technological progress. The analysis demonstrates that digital transformation simultaneously creates opportunities and risks. While digitalization contributes to productivity enhancement and the creation of new occupations, it also reinforces structural inequalities by concentrating benefits among workers with advanced skills and digital competencies. The findings highlight the importance of inclusive labor market policies, continuous workforce training, digital literacy development, and social protection mechanisms in addressing emerging forms of employment inequality. The article concludes that achieving equitable labor market outcomes in the digital age requires coordinated efforts from governments, employers, educational institutions, and other stakeholders to ensure that technological progress promotes inclusive and sustainable economic development.
References
1. Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 4, pp. 1043–1171). Elsevier.https://doi.org/10.1016/S0169-7218(11)02410-5
2. Lu, Z., Li, W., Li, M., & Chen, Y. (2019). Destination China: International students in Chengdu. International Migration, 57(3), 354–372. https://doi.org/10.1111/imig.12464
3. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
4. Liu, Y., Li, Y., & Fan, P. (2020). Digital economy and regional sustainable development in China. Sustainability, 12(18), 7559. https://doi.org/10.3390/su12187559
5. Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. American Economic Review, 99(2), 58–63. https://doi.org/10.1257/aer.99.2.58
6. Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the U.S. labor market. American Economic Review, 103(5), 1553–1597.https://doi.org/10.1257/aer.103.5.1553
7. Li, M., Tu, C., & Zhang, F. (2022). Wage gaps in energy industry: The role of sector. Frontiers in Energy Research, 10, 940637. https://doi.org/10.3389/fenrg.2022.940637
8. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.https://doi.org/10.1016/j.techfore.2016.08.019
9. Li, M., Tang, Y., & Jin, K. (2024). Labor market segmentation and the gender wage gap: Evidence from China. PLOS ONE, 19(3), e0299355. https://doi.org/10.1371/journal.pone.0299355
10. Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries (OECD Social, Employment and Migration Working Papers No. 189). OECD Publishing. https://doi.org/10.1787/5jlz9h56dvq7-en
11. Li, M., & Zhang, F. (2023). The wage structure and gap between public and private sectors: An empirical study in urban China. Economic Research-Ekonomska Istraživanja, 36(2). https://doi.org/10.1080/1331677X.2022.2106276
12. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from U.S. labor markets. Journal of Political Economy, 128(6), 2188–2244.https://doi.org/10.1086/705716
13. Guo, Q., Chen, S., & Zeng, X. (2021). Does fintech narrow the gender wage gap? Evidence from China. China & World Economy, 29(4), 142–166. https://doi.org/10.1111/cwe.12382
14. Dawid, H., & Neugart, M. (2023). Effects of technological change and automation on industry structure and wage inequality. Journal of Evolutionary Economics, 33(1), 35–63. https://doi.org/10.1007/s00191-022-00803-5
15. Li, M., & Wang, J. (2021). Influence of UTCP on the employment of female workers and the supply of labor force. PLOS ONE, 16(11), e0259843. https://doi.org/10.1371/journal.pone.0259843
16. Kiener, F., Eggenberger, C., & Backes-Gellner, U. (2024). The role of occupational skill sets in the digital transformation. Journal of Business Economics, 94(1), 75–111. https://doi.org/10.1007/s11573-023-01153-9
17. Li, M., & Xu, X. (2022). Fertility intentions for a second child and their influencing factors in contemporary China. Frontiers in Psychology, 13, 883317. https://doi.org/10.3389/fpsyg.2022.883317
18. Yang, G., Yao, S., & Dong, X. (2023). Digital economy and wage gap between high- and low-skilled workers. Digital Economy and Sustainable Development, 1(7), 1–20. https://doi.org/10.1007/s44265-023-00009-y
19. Zuo, S., & Feng, J. (2023). Institutional segmentation, inequality in work opportunities, and income in digital labor markets. Emerging Markets Finance and Trade, 59(15), 4125–4137. https://doi.org/10.1080/1540496X.2023.2179874
20. Li, M., Hu, X., & Jin, K. (2025). The return on education and the gender wage gap in China: A sector perspective. SAGE Open, 15(2), 21582440251327015. https://doi.org/10.1177/21582440251327015
21. Fiers, F. (2023). Inequality and discrimination in the online labor market: A scoping review. New Media & Society, 25(12), 3651–3675. https://doi.org/10.1177/14614448221151200
22. Pulignano, V., Grimshaw, D., Domecka, M., & Vermeerbergen, L. (2024). Why does unpaid labour vary among digital labour platforms? Human Relations, 77(9), 1453–1478. https://doi.org/10.1177/00187267231179901
23. Li, M., Hu, X., Jin, K., & Han, J. (2025). Exploring factors influencing entry into the gig economy: A study of Chinese workers. Acta Psychologica, 259, 105301. https://doi.org/10.1016/j.actpsy.2025.105301
24. Rani, U., & Furrer, M. (2020). Digital labour platforms and new forms of flexible work in developing countries. Transfer: European Review of Labour and Research, 26(2), 213–228. https://doi.org/10.1177/1024529420905187
25. Giustini, D. (2025). Digital work across the divide: Japan’s platform economy and labour market dualisation. Contemporary Japan, 37(1), 3–29. https://doi.org/10.1080/18692729.2023.2206339
26. Li, M. (2022). The interindustry wage differentials by sector in China: What is the role of union density? Frontiers in Sociology, 7, 949293. https://doi.org/10.3389/fsoc.2022.94929
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