人工智能是中国全要素生产率增长的关键驱动力吗？就业、供应链与信息不对称的机制分析

IF 4.2 2区经济学 Q1 ECONOMICS

Economic Modelling Pub Date : 2025-05-07 DOI:10.1016/j.econmod.2025.107126

Ruifeng Xu , Frank M. Song

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引用次数: 0

摘要

利用中国上市公司的人工智能（AI）专利数据和财务记录，我们发现人工智能驱动的创新提高全要素生产率（TFP）的速度是普通专利的40倍。我们的研究结果表明，人工智能在中国是一个关键的全要素生产率驱动因素，可能有助于缓解与中国人口老龄化和潜在的中等收入陷阱相关的风险。这之所以有效，是因为人工智能增强了劳动力教育，优化了供应链，减少了信息不对称和代理成本。异质性分析显示，计算机系统人工智能专利价值最高，而人工智能创新对文化企业全要素生产率的影响最为显著。这些见解为大流行后时代优化人工智能发展提供了宝贵的战略指导。

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Is AI a key driving force for Chinese total factor productivity growth? Mechanistic analysis of employment, supply chain, and information asymmetry

Using artificial intelligence (AI) patent data and financial records from Chinese-listed firms, we find that AI-driven innovations enhance total factor productivity (TFP) at a rate 40 times greater than that of ordinary patents. Our findings suggest that AI serves as a crucial TFP driver in China and may help mitigate the risks associated with the country's aging population and the potential middle-income trap. This works because AI enhances workforce education, optimizes supply chains, and reduces information asymmetry and agency costs. A heterogeneity analysis reveals that computer system AI patents hold the highest value, whereas AI innovation has the most significant impact on the TFP of cultural enterprises. These insights offer valuable strategic guidance for optimizing AI development in the postpandemic era.

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来源期刊

Economic Modelling ECONOMICS-

CiteScore

8.00

自引率

10.60%

发文量

295

期刊介绍： Economic Modelling fills a major gap in the economics literature, providing a single source of both theoretical and applied papers on economic modelling. The journal prime objective is to provide an international review of the state-of-the-art in economic modelling. Economic Modelling publishes the complete versions of many large-scale models of industrially advanced economies which have been developed for policy analysis. Examples are the Bank of England Model and the US Federal Reserve Board Model which had hitherto been unpublished. As individual models are revised and updated, the journal publishes subsequent papers dealing with these revisions, so keeping its readers as up to date as possible.