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

Young Suh Song, Jang Hyun Kim and One-Sun Cho ()
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Young Suh Song: Seoul National University
Jang Hyun Kim: Pukyong National University
One-Sun Cho: Seoul National University

A chapter in New Perspectives and Paradigms in Applied Economics and Business, 2023, pp 167-180 from Springer

Abstract: 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.

Keywords: Low fertility rate; National pension assets; Pension depletion; Machine learning; Artificial intelligence; Aging; OECD; G7 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-23844-4_13

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DOI: 10.1007/978-3-031-23844-4_13

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