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

Ke-Liang Wang (), Ting-Ting Sun and Ru-Yu Xu
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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

Economic Change and Restructuring, 2023, vol. 56, issue 2, No 17, 1113-1146

Abstract: 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.

Keywords: Total factor productivity (TFP); Artificial intelligence (AI); Manufacturing enterprises; Transmission mechanism; Heterogeneity; Exogenous policy shock (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10644-022-09467-4

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