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Approaches and Experiences in Projecting Mortality Patterns for the Oldest-Old

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  • Thomas Buettner

Abstract

In 1998 the United Nations Population Division extended the age format of its estimates and projections of population dynamics for all countries and areas of the world from 80 years and above to 100 years and above. The paper is based on experiences made during the implementation of relevant mortality projection methodologies and their application in two rounds of global population projections.The paper first briefly addresses the need for the explicit inclusion of very old population segments into the regular UN estimates and projections. It is argued that since population aging is an important issue for both developed and developing countries, the need for more information regarding the elderly, and the oldest-old in particular, is significant.The paper then documents the methods that have been evaluated and implemented, namely, the relational mortality standard proposed by Himes, Preston, and Condran, the Coale-Kisker extrapolation method for extending empirical age patterns of mortality to very high ages, and the Carter-Lee projection method for projecting model patterns of mortality to very high levels of life expectancy at birth. The methods are critically reviewed, and possible improvements to the methods are discussed.The paper concludes with a discussion of different views regarding the future evolution of mortality at older ages, their regional variability, and the necessity to improve the coverage and quality of data collected in this area.

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  • Thomas Buettner, 2002. "Approaches and Experiences in Projecting Mortality Patterns for the Oldest-Old," North American Actuarial Journal, Taylor & Francis Journals, vol. 6(3), pages 14-29.
    • Handle: RePEc:taf:uaajxx:v:6:y:2002:i:3:p:14-29
    DOI: 10.1080/10920277.2002.10596053
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    Cited by:

    1. Broeders, Dirk & Mehlkopf, Roel & van Ool, Annick, 2021. "The economics of sharing macro-longevity risk," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 440-458.
    2. Pitacco, Ermanno, 2004. "Survival models in a dynamic context: a survey," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 279-298, October.
    3. F. Peters & J. P. Mackenbach & W. J. Nusselder, 2016. "Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 687-702, December.
    4. Jorge Bravo & Carlos Pereira da Silva, 2012. "Prospective Lifetables: Life Insurance Pricing and Hedging in a Stochastic Mortality Environment," CEFAGE-UE Working Papers 2012_01, University of Evora, CEFAGE-UE (Portugal).
    5. Chen, Hua & Cummins, J. David, 2010. "Longevity bond premiums: The extreme value approach and risk cubic pricing," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 150-161, February.
    6. Preeti Dhillon & Laishram Ladusingh, 2013. "Working life gain from gain in old age life expectancy in India," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(26), pages 733-762.
    7. Kirill F. Andreev & James W. Vaupel, 2006. "Forecasts of cohort mortality after age 50," MPIDR Working Papers WP-2006-012, Max Planck Institute for Demographic Research, Rostock, Germany.
    8. Yaser Awad & Shaul K. Bar-Lev & Udi Makov, 2022. "A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach," Risks, MDPI, vol. 10(6), pages 1-17, May.

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