IDEAS home Printed from https://ideas.repec.org/a/ctl/louvde/v91y2025i2p320-342.html
   My bibliography  Save this article

The network ties behind commercial pension insurance purchase: empirical evidence from China

Author

Listed:
  • Yao HUANG

    (Hunan Agricultural University)

  • Yueru MA

    (Central South University)

Abstract

China is entering a deeply aging society gradually, and individual pension allocation behavior has a profound impact on the practice and effect of national strategies to actively cope with population aging. This paper constructs a dyad model based on the influence path of social network ties in individual commercial pension insurance purchasing decisions, and then validates the path by building a generalized linear mixed model (GLMM) based on Bayesian approach with the national longitudinal sample data of China. The empirical results show that, firstly, strong ties social interaction (e.g., visiting friend's house) positively influences individuals' commercial pension insurance purchase behavior; secondly, the less the frequency of individual social interaction is, the less significant the positive influence of social interaction on individual commercial pension insurance product purchase is; finally, between 2013 and 2018, the intensity and frequency of social interactions among middle-aged and elderly people in China has been changed dramatically by the shock of the popularity and development of digital social tools. The influence of strong ties social interaction on insurance purchase behavior becomes weaker and weaker, while that of weak ties becomes stronger.

Suggested Citation

  • Yao HUANG & Yueru MA, 2025. "The network ties behind commercial pension insurance purchase: empirical evidence from China," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 91(2), pages 320-342, June.
    • Handle: RePEc:ctl:louvde:v:91:y:2025:i:2:p:320-342
    DOI: 10.1017/dem.2024.14
    as

    Download full text from publisher

    File URL: https://doi.org/10.1017/dem.2024.14
    Download Restriction: no
    File URL: https://libkey.io/10.1017/dem.2024.14?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
    ---><---

    More about this item

    Keywords

    Social network ties; individual pension; Commercial pension insurance; Bayesian mixture model;
    All these keywords.

    JEL classification:

    • M59 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Other
    • J32 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

    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:ctl:louvde:v:91:y:2025:i:2:p:320-342. 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: Sebastien SCHILLINGS (email available below). General contact details of provider: https://edirc.repec.org/data/iruclbe.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.