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

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  • 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
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    References listed on IDEAS

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    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. 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.
    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.
    4. 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.
    5. 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.
    6. 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.
    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.
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