Abstract
Technological innovation has promoted the development of human flourishing. Based on panel data for 30 provinces in China from 2006 to 2022, this study examines the impact of artificial intelligence (AI) on manufacturing employment in China using the two-way fixed-effect model and the instrumental variable method. The study finds that contrary to the traditional impression of “machines replacing humans,” AI technology is correlated with increasing the total number of jobs on the market. Thanks to more efficient labor productivity, capital deepening, and specialized division of labor from integrating digital technology, AI offsets the negative effect of robots on employment and significantly increases manufacturing enterprises’ market size and production scale, with a significant job creation effect. Heterogeneity is evident in the positive impact of AI on employment, which has increased the number of jobs in labor-intensive industries and for female workers. Regions with more complete digital infrastructure construction exhibit stronger job creation effects. Mechanism research reveals that the geographical agglomeration mode of traditional industries are undergoing evolutionary transitions under the transformation of digital technology, and modern industrial agglomeration represented by virtual agglomeration is an indispensable mediating mechanism for AI to create jobs. The study’s conclusions can alleviate citizen’s concerns regarding AI crowding-out jobs, encouraging workers and policymakers to make full use of AI technology to improve employment in the digital economy era.
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The data used in this study are publicly available and have been correctly cited. Datasets used or analyzed in the current study are available from corresponding authors upon reasonable request.
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This work was financially supported by the Natural Science Foundation of Fujian Province (Grant number: 2022J01320).
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Shen, Y. Future jobs: analyzing the impact of artificial intelligence on employment and its mechanisms. Econ Change Restruct 57, 34 (2024). https://doi.org/10.1007/s10644-024-09629-6
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DOI: https://doi.org/10.1007/s10644-024-09629-6