IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30707.html
   My bibliography  Save this paper

How do Workers and Households Adjust to Robots? Evidence from China

Author

Listed:
  • Osea Giuntella
  • Yi Lu
  • Tianyi Wang

Abstract

We analyze the effects of exposure to industrial robots on labor markets and household behaviors, exploring longitudinal household data from China. We find that a one standard deviation increase in robot exposure led to a decline in labor force participation (-1%), employment (-7.5%), and hourly wages (-9%) of Chinese workers. At the same time, among those who kept working, robot exposure increased the number of hours worked by 14%. These effects were concentrated among the less educated and larger among men, prime-age, and older workers. We then explore how individuals and families responded to increased exposure to robots. We find that more exposed workers increased their participation in technical training and were significantly more likely to retire earlier. Despite the negative impact on wages and employment, we find no evidence of an effect on consumption or savings, which is explained by an increase in borrowing (+10%). While there is no evidence of an effect on marital behavior, we document that robot exposure led to a small decline in the number of children (-1%). Finally, we find that robot exposure increased family time investment in the education of children (+10%) as well as the investment in children’s after-school academic and extra-curricular activities (+24%).

Suggested Citation

  • Osea Giuntella & Yi Lu & Tianyi Wang, 2022. "How do Workers and Households Adjust to Robots? Evidence from China," NBER Working Papers 30707, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:30707
    Note: CH LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30707.pdf
    Download Restriction: Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    2. Jie Zhou, 2024. "The Impact of the Digital Economy on Employment Scale in the Yangtze River Delta Region," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(5), pages 1-5.

    More about this item

    JEL classification:

    • J0 - Labor and Demographic Economics - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:nbr:nberwo:30707. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.