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The potential costs of Longevity Risk on Public Pensions. Evidence from Italian data

Author

Listed:
  • Benedetta Frassi

    (IMT School for Advanced Studies Lucca)

  • Fabio Pammolli

    (Politecnico di Milano, Dipartimento di ingegneria gestionale)

  • Luca Regis

    (University of Siena, Department of economics and statistics)

Abstract

In this article, we assess, through an empirical investigation based on Italian data, how uncertainty regarding future mortality may affect public pension expenditure. Based on a representative sample of Italian pensioners from 1985 to 2011, we find a consistent underestimation of improvements seen in mortality and life expectancy when forecasts are based on expectations. The pension expenditure estimated using realized mortality rates is shown to be consistently higher than that obtained by using average forecasted scenarios, produced with well-known stochastic mortality models. The paper highlights the importance of considering the uncertainty regarding future pension benfits, i.e. of evaluating and managing the longevity risk in public pension plans.

Suggested Citation

  • Benedetta Frassi & Fabio Pammolli & Luca Regis, 2017. "The potential costs of Longevity Risk on Public Pensions. Evidence from Italian data," Working Papers 01/2017, IMT School for Advanced Studies Lucca, revised Jan 2017.
    • Handle: RePEc:ial:wpaper:1/2017
    as

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    File URL: http://eprints.imtlucca.it/3628/1/EIC_WP_1_2017.pdf
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    References listed on IDEAS

    as
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    3. Godínez-Olivares, Humberto & Boado-Penas, María del Carmen & Haberman, Steven, 2016. "Optimal strategies for pay-as-you-go pension finance: A sustainability framework," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 117-126.
    4. Emilio Bisetti & Carlo Favero, 2014. "Measuring the Impact of Longevity Risk on Pension Systems: The Case of Italy," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 87-103.
    5. Paola Biffi & Gian Clemente, 2014. "Selecting stochastic mortality models for the Italian population," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 255-286, October.
    6. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    7. 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.
    8. Pitacco, Ermanno & Denuit, Michel & Haberman, Steven & Olivieri, Annamaria, 2009. "Modelling Longevity Dynamics for Pensions and Annuity Business," OUP Catalogue, Oxford University Press, number 9780199547272.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.

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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies

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