IDEAS home Printed from https://ideas.repec.org/p/zbw/kdifoc/v69y2016p1-9.html
   My bibliography  Save this paper

Longevity Risk in Korea

Author

Listed:
  • Choi, Yongok

Abstract

Korea's unprecedented rapid growth in life expectancy at birth is mainly attributable to a decline in elderly mortality rates. Indeed, the unanticipated increase in life expectancy and elderly population could complicate the government's long-term consolidation efforts and present a serious obstacle to formulating and streamlining policies. Accordingly, the next crucial step in the face of mounting longevity risk is to conduct preemptive research on Korea's elderly mortality rates. The government must acknowledge the exposure to longevity risk and make every efforts to compile accurate data on life expectancy and population projections in order to build a consensus on the gravity of the impending risks and seek a solution which could include a fiscal automatic stabilizer. - OECD statistics show that Korea's life expectancy has risen at the fastest pace among member nations. - It is highly likely that life expectancy estimates based on the period life table are underestimated compared to the actual average remaining life span. - Although unexpected longevity is prevalent across the globe, in terms of magnitude, Korea will experience a much bigger shock than any other developed country. - Despite reaching 80 years in 2008, Koreans' life expectancy continues to rise. - Analysis found that 80% of the increase in life expectancy after 2000 is attributed to the decrease in the mortality rates of those aged 50 years and over. - The elderly population has been consistently underforecast by Statistics Korea. - It has been found that the forecasting error for the 65 years and over population is approximately -10% on average in the previous population projections for for 15 years in the future. - Projections that take into account the recent improvement in mortality rates find that the 65 years and over population will reach 21.34 million by 2060, 21.1% more than Statistics Korea's estimates. - Accurate population projections are particularly important as huge social welfare expenditure with regards to aging is expected in the decades ahead. - Under-forecasting the elderly population could impose a serious obstacle to streamling existing policies by weakening the necessity to reform the social security system. - Recognition and mitigation efforts to tackle longevity risk should be of the utmost urgency. - Due to the consistently changing improvement patterns in Korea's mortality rates, Lee-Carter type models, which assume a universal pattern for mortality rate improvements, may not be suitable. - Longevity risk is an undiversifiable, systematic risk that cannot be shouldered by the government alone. The government should inform the public of the actual conditions of longevity risk, which would help to create an environment wherein economic agents can reach a consensus on burden-sharing.

Suggested Citation

  • Choi, Yongok, 2016. "Longevity Risk in Korea," KDI Focus 69, Korea Development Institute (KDI).
  • Handle: RePEc:zbw:kdifoc:v:69:y:2016:p:1-9
    DOI: 10.22740/kdi.focus.e.2016.69
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/200869/1/kdi-focus-69.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22740/kdi.focus.e.2016.69?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
    ---><---

    References listed on IDEAS

    as
    1. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    2. Choi, Yongok, 2015. "A Study on Measuring and Managing Longevity Risk," KDI Policy Studies 2015-18(K), Korea Development Institute (KDI).
    3. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongok Choi, 2020. "Impact of Longevity Risks on the Korean Government: Proposing a New Mortality Forecasting Model," Korean Economic Review, Korean Economic Association, vol. 36, pages 201-225.
    2. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    3. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.
    4. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Koissi, Marie-Claire & Shapiro, Arnold F., 2006. "Fuzzy formulation of the Lee-Carter model for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 39(3), pages 287-309, December.
    6. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    7. French, Declan, 2014. "International mortality modelling—An economic perspective," Economics Letters, Elsevier, vol. 122(2), pages 182-186.
    8. Schinzinger, Edo & Denuit, Michel M. & Christiansen, Marcus C., 2016. "A multivariate evolutionary credibility model for mortality improvement rates," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 70-81.
    9. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    10. Feng, Lingbing & Shi, Yanlin & Chang, Le, 2021. "Forecasting mortality with a hyperbolic spatial temporal VAR model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 255-273.
    11. Jevtić, Petar & Regis, Luca, 2019. "A continuous-time stochastic model for the mortality surface of multiple populations," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 181-195.
    12. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2016. "Coherent modeling of male and female mortality using Lee–Carter in a complex number framework," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 130-137.
    13. Jens Robben & Katrien Antonio & Sander Devriendt, 2022. "Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model," Risks, MDPI, vol. 10(2), pages 1-33, January.
    14. Ayuso, Mercedes & Bravo, Jorge M. & Holzmann, Robert, 2021. "Getting life expectancy estimates right for pension policy: period versus cohort approach," Journal of Pension Economics and Finance, Cambridge University Press, vol. 20(2), pages 212-231, April.
    15. Benjamin Seligman & Gabi Greenberg & Shripad Tuljapurkar, 2016. "Convergence in male and female life expectancy: Direction, age pattern, and causes," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(38), pages 1063-1074.
    16. Liu, Yanxin & Li, Johnny Siu-Hang, 2016. "It’s all in the hidden states: A longevity hedging strategy with an explicit measure of population basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 301-319.
    17. Bozikas, Apostolos & Pitselis, Georgios, 2020. "Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 353-368.
    18. David Blake & Andrew Cairns & Guy Coughlan & Kevin Dowd & Richard MacMinn, 2013. "The New Life Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 501-558, September.
    19. Rui Zhou & Johnny Siu-Hang Li & Ken Seng Tan, 2013. "Pricing Standardized Mortality Securitizations: A Two-Population Model With Transitory Jump Effects," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 733-774, September.
    20. Søren Kjærgaard & Vladimir Canudas-Romo, 2017. "Potential support ratios: Cohort versus period perspectives," Population Studies, Taylor & Francis Journals, vol. 71(2), pages 171-186, May.

    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:zbw:kdifoc:v:69:y:2016:p:1-9. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/kdiiikr.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.