IDEAS home Printed from https://ideas.repec.org/a/eee/chieco/v86y2024ics1043951x24000695.html
   My bibliography  Save this article

Economic geography of talent migration and agglomeration in China: A dual-driver framework

Author

Listed:
  • Gu, Hengyu
  • Wang, Junhui
  • Ling, Yingkai

Abstract

The long-standing debate on the role of economic factors and amenities on talent migration and redistribution foreshadows the importance of spatial economic theoretical thinking in resolving the effects of duality, a point yet to be conclusively addressed in the existing literature. Situated within the framework of new economic geography (NEG), this article introduces an innovative theoretical model – the Dual-Driver (DD) framework– aiming at comprehending the nuanced dual impact of economic and amenity factors on talent movements between regions. While the DD framework retains the mechanisms of increasing returns to scale, iceberg transportation cost, and talent agglomeration presented in the NEG models, it depicts, for the first time, the self-reinforcing mechanisms between non-traded goods (i.e., amenities) and talent agglomeration. The model describes a logic of talent movements influenced by the dual drivers of regional economic factors (nominal wage, the diversity of local manufacturing products) and amenities (the diversity and quality of amenities, transfer payment for talent), among which the economic effects play a predominant role versus the amenity effects. Empirical evidence has been given by the geographical analysis of China's internal talent migration between 2000 and 2015 and the corresponding econometric analysis using the Poisson pseudo-maximum likelihood (PPML) estimation. Our findings furnish a theoretical perspective for comprehending talent geography and offer policy insights into China's logic of talent migration.

Suggested Citation

  • Gu, Hengyu & Wang, Junhui & Ling, Yingkai, 2024. "Economic geography of talent migration and agglomeration in China: A dual-driver framework," China Economic Review, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:chieco:v:86:y:2024:i:c:s1043951x24000695
    DOI: 10.1016/j.chieco.2024.102180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1043951X24000695
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chieco.2024.102180?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:chieco:v:86:y:2024:i:c:s1043951x24000695. 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/chieco .

    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.