The impact of the artificial intelligence industry on the number and structure of employments in the digital economy environment

Zhuo Zhang

Technological Forecasting and Social Change, 2023, vol. 197, issue C

Abstract: This study endeavors to examine the intricate impact of the industries related to artificial intelligence (AI) on both the quantity of labor employment in terms of quantity and the evolving structure of the job market. The study constructs a mechanism and theoretical model to elucidate labor employment dynamics and structural transformations within the AI industry. The research findings reveal discernible trend in China's labor force characterized by a heightened emphasis on education and increased employment opportunities. This study makes a substantial contribution to the field by constructing innovative mechanisms and theoretical frameworks that facilitate a profound comprehension of the intricacies surrounding labor employment and structural changes in the AI industry. This comprehension is grounded in an analysis of the prevailing employment quantity and structure. This study is underpinned by an examination of employment distribution across diverse industries and a meticulous analysis of the workforce's skill diversity. By thoroughly analyzing China's digital economy policies and the strides made in digital technologies, the study formulates a set of recommendations aimed at fostering national economic growth and achieving successful digital transformation. The primary innovation of this study resides in its exploration of the impact of the AI industry with respect to China's labor employment structure through the lens of a Marxist theoretical perspective. Consequently, the study extends both innovative significance and practical value.

Keywords: The digital economy; AI industry; Employment structure; Labor; AI (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:197:y:2023:i:c:s0040162523005668

DOI: 10.1016/j.techfore.2023.122881

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