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The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises

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  • Ke-Liang Wang

    (Ocean University of China)

  • Ting-Ting Sun

    (Ocean University of China)

  • Ru-Yu Xu

    (Ocean University of China)

Abstract

Using the panel data of 938 listed manufacturing companies in China from 2011 to 2020, this paper scientifically examines the impact of artificial intelligence (AI) on total factor productivity (TFP) of China’s manufacturing enterprises by using the fixed effect model, mediating effect model and difference-in-differences model. The results show that AI can significantly improve the TFP of China’s manufacturing enterprises, as confirmed by a series of robustness tests. Technological innovation, human capital optimization and market matching improvement have proved to be three important channels for AI to affect the TFP of China’s manufacturing enterprises. The impact of AI on TFP varies greatly among China’s manufacturing enterprises in different geographical locations, industry characteristics, ownership and life cycle stages. The findings of this paper can provide theoretical insights and empirical evidence at the micro enterprise level for policymakers to give full play to the role of AI in promoting the high-quality development of China's manufacturing industry.

Suggested Citation

  • Ke-Liang Wang & Ting-Ting Sun & Ru-Yu Xu, 2023. "The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises," Economic Change and Restructuring, Springer, vol. 56(2), pages 1113-1146, April.
    • Handle: RePEc:kap:ecopln:v:56:y:2023:i:2:d:10.1007_s10644-022-09467-4
    DOI: 10.1007/s10644-022-09467-4
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