IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v150y2025ics026499932500121x.html
   My bibliography  Save this article

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

Author

Listed:
  • Xu, Ruifeng
  • Song, Frank M.

Abstract

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.

Suggested Citation

  • Xu, Ruifeng & Song, Frank M., 2025. "Is AI a key driving force for Chinese total factor productivity growth? Mechanistic analysis of employment, supply chain, and information asymmetry," Economic Modelling, Elsevier, vol. 150(C).
  • Handle: RePEc:eee:ecmode:v:150:y:2025:i:c:s026499932500121x
    DOI: 10.1016/j.econmod.2025.107126
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S026499932500121X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2025.107126?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:150:y:2025:i:c:s026499932500121x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.