IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v30y2023i13p1780-1783.html
   My bibliography  Save this article

A dynamic game model for the study of food safety regulation based on sampling probability and penalty intensity

Author

Listed:
  • Lingbo Tan
  • Yuan Zhou
  • Yuan Yuan
  • Jiapeng Liu

Abstract

By constructing a dynamic game model, this article explores the ‘optimal regulation’ and the food safety regulatory mechanism to be adopted when the food safety regulatory authority has to implement a certain level of regulation. In the short term, the actual quality of food can be improved by increasing the probability of sampling or the intensity of punishment. However, in the long term, the optimal path is to improve the production technology of sellers. A moderate level of supervision makes regulation more effective, while a threshold level of regulation can reduce welfare.

Suggested Citation

  • Lingbo Tan & Yuan Zhou & Yuan Yuan & Jiapeng Liu, 2023. "A dynamic game model for the study of food safety regulation based on sampling probability and penalty intensity," Applied Economics Letters, Taylor & Francis Journals, vol. 30(13), pages 1780-1783, July.
    • Handle: RePEc:taf:apeclt:v:30:y:2023:i:13:p:1780-1783
    DOI: 10.1080/13504851.2022.2082368
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2022.2082368
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2022.2082368?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.

    More about this item

    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:taf:apeclt:v:30:y:2023:i:13:p:1780-1783. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.