IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-23844-4_13.html
   My bibliography  Save this book chapter

Treat-Before-Collapse: Forecasting Change of National Pension Assets in G7 and Republic of Korea by Demographic-Based Machine Learning Approach

In: New Perspectives and Paradigms in Applied Economics and Business

Author

Listed:
  • Young Suh Song

    (Seoul National University
    Korea Military Academy)

  • Jang Hyun Kim

    (Pukyong National University)

  • One-Sun Cho

    (Seoul National University)

Abstract

Future demographic projections have indicated that the low fertility rate problem will put significant pressures on the long-term sustainability of public finance. Nevertheless, among the concerned sustainability of public finance, the depletion of future national pension assets has received little attention. This paper provides numerical projection data by forecasting change of national pension assets in some of OECD countries. Among OECD countries, G7 countries which are leading society of OECD countries and Republic of Korea that has the lowest total fertility rate in OECD countries are analyzed. By adopting demographic-based machine learning (ML) approach, the forecasted results have been demonstrated, and possible future scenarios have been analyzed as variables (future total fertility rate, age when people begin pension receiving) are to be changed in the future. In doing so, possible solutions regarding demographic approach and political approach are suggested to each country.

Suggested Citation

  • Young Suh Song & Jang Hyun Kim & One-Sun Cho, 2023. "Treat-Before-Collapse: Forecasting Change of National Pension Assets in G7 and Republic of Korea by Demographic-Based Machine Learning Approach," Springer Proceedings in Business and Economics, in: William C. Gartner (ed.), New Perspectives and Paradigms in Applied Economics and Business, pages 167-180, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-23844-4_13
    DOI: 10.1007/978-3-031-23844-4_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:prbchp:978-3-031-23844-4_13. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.