IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i15p1810-d605361.html
   My bibliography  Save this article

Is Longevity Acceleration Sustainable? An Entropy-Based Trial of the Population of Spain vs. Japan

Author

Listed:
  • Amancio Betzuen Zalbidegoitia

    (Department of Economics and Finance I, Economics and Business Faculty, Av/Lehendakari Agirre 83, Campus of Vizcaya, University of the Basque Country, 48015 Leioa, Spain)

  • Amaia Jone Betzuen Álvarez

    (Department of Economics and Finance II, Economics and Business Faculty, Av/Lehendakari Agirre 83, Campus of Vizcaya, University of the Basque Country, 48015 Leioa, Spain)

Abstract

Longevity risk is a major concern for governments around the world as they have to address social benefits, whether in the form of pensions, healthcare, or caring for dependents and providing long-term care, and so forth, which directly impact countries’ budgets. This paper uses a single entropy index to measure this type of risk. This methodology is clearly different from the one traditionally used in the literature, which is nearly entirely based on measuring the evolution of mathematical life expectancy. The authors used the longest-living populations in the world, Japan and Spain, to create a database in order to analyse the virtue of the indicator. The aim was to establish whether the longevity of those populations is accelerating or decelerating, compared by sex, and whether that occurs at the same intensity at different stages of a person’s life in each case. If the indicator showed differences in intensity, it would be a benchmark for the insurance and financial industry, providing it with information to market different products.

Suggested Citation

  • Amancio Betzuen Zalbidegoitia & Amaia Jone Betzuen Álvarez, 2021. "Is Longevity Acceleration Sustainable? An Entropy-Based Trial of the Population of Spain vs. Japan," Mathematics, MDPI, vol. 9(15), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1810-:d:605361
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/15/1810/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/15/1810/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koissi, Marie-Claire & Shapiro, Arnold F. & Hognas, Goran, 2006. "Evaluating and extending the Lee-Carter model for mortality forecasting: Bootstrap confidence interval," Insurance: Mathematics and Economics, Elsevier, vol. 38(1), pages 1-20, February.
    2. Haberman, Steven & Renshaw, Arthur, 2009. "On age-period-cohort parametric mortality rate projections," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 255-270, October.
    3. Haberman, Steven & Khalaf-Allah, Marwa & Verrall, Richard, 2011. "Entropy, longevity and the cost of annuities," Insurance: Mathematics and Economics, Elsevier, vol. 48(2), pages 197-204, March.
    4. Noreen Goldman & Graham Lord, 1986. "A new look at entropy and the life table," Demography, Springer;Population Association of America (PAA), vol. 23(2), pages 275-282, May.
    5. 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.
    6. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454.
    7. Jose Manuel Aburto & Jesús-Adrián Alvarez & Francisco Villavicencio & James W. Vaupel, 2019. "The threshold age of the lifetable entropy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(4), pages 83-102.
    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. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    2. Hunt, Andrew & Villegas, Andrés M., 2015. "Robustness and convergence in the Lee–Carter model with cohort effects," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 186-202.
    3. 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.
    4. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    5. Shang, Han Lin & Smith, Peter W.F. & Bijak, Jakub & Wiśniowski, Arkadiusz, 2016. "A multilevel functional data method for forecasting population, with an application to the United Kingdom," International Journal of Forecasting, Elsevier, vol. 32(3), pages 629-649.
    6. Alvarez, Jesús-Adrián & Kallestrup-Lamb, Malene & Kjærgaard, Søren, 2021. "Linking retirement age to life expectancy does not lessen the demographic implications of unequal lifespans," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 363-375.
    7. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    8. Jesús-Adrián Álvarez & Malene Kallestrup-Lamb & Søren Kjærgaard, 2020. "Linking retirement age to life expectancy does not lessen the demographic implications of unequal lifespans," CREATES Research Papers 2020-17, Department of Economics and Business Economics, Aarhus University.
    9. Henrik Brønnum-Hansen & Juan Carlos Albizu-Campos Espiñeira & Camila Perera & Ingelise Andersen, 2023. "Trends in mortality patterns in two countries with different welfare models: comparisons between Cuba and Denmark 1955–2020," Journal of Population Research, Springer, vol. 40(2), pages 1-28, June.
    10. Oscar E Fernandez & Hiram Beltrán-Sánchez, 2022. "Life span inequality as a function of the moments of the deaths distribution: Connections and insights," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    11. Li, Johnny Siu-Hang, 2010. "Pricing longevity risk with the parametric bootstrap: A maximum entropy approach," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 176-186, October.
    12. Wang, Chou-Wen & Huang, Hong-Chih & Hong, De-Chuan, 2013. "A feasible natural hedging strategy for insurance companies," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 532-541.
    13. 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.
    14. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    15. Aburto, José Manuel & Basellini, Ugofilippo & Baudisch, Annette & Villavicencio, Francisco, 2022. "Drewnowski’s index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Theoretical Population Biology, Elsevier, vol. 148(C), pages 1-10.
    16. Jennifer L. Wang & H.C. Huang & Sharon S. Yang & Jeffrey T. Tsai, 2010. "An Optimal Product Mix for Hedging Longevity Risk in Life Insurance Companies: The Immunization Theory Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(2), pages 473-497, June.
    17. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    18. Jinjing Li & Yogi Vidyattama, 2019. "Projecting spatial population and labour force growth in Australian districts," Journal of Population Research, Springer, vol. 36(3), pages 205-232, September.
    19. Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    20. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.

    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:gam:jmathe:v:9:y:2021:i:15:p:1810-:d:605361. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.