IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/z3qmw.html
   My bibliography  Save this paper

An Age-Period-Cohort model for gender gap in youth mortality

Author

Listed:
  • Lanfiuti Baldi, Giacomo
  • NIGRI, ANDREA

Abstract

In this paper, we introduce a novel framework in longevity study, operating on the statistical approach of the Age--Period--Cohort framework by leveraging the skew-normal distribution and Bayesian estimation. We propose a specific application to gender gap analysis and forecasting. By employing mortality data from the Human Mortality Database in the USA, our study contributes a two-fold advancement. First, we present a novel perspective on gender gap analysis and forecasting, improving the current literature. Second, we contribute an improvement to the statistical framework for Age--Period--Cohort analysis. The proposed model offers invaluable insights applicable to healthcare planning and public interventions, providing a comprehensive snapshot of the gender gap across the population, and indispensable information for devising healthcare strategies.

Suggested Citation

  • Lanfiuti Baldi, Giacomo & NIGRI, ANDREA, 2023. "An Age-Period-Cohort model for gender gap in youth mortality," OSF Preprints z3qmw, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:z3qmw
    DOI: 10.31219/osf.io/z3qmw
    as

    Download full text from publisher

    File URL: https://osf.io/download/652f9300164d32081ca5e106/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/z3qmw?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. Lucia Zanotto & Vladimir Canudas-Romo & Stefano Mazzuco, 2021. "A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 1-27, March.
    2. Stefano Mazzuco & Bruno Scarpa & Lucia Zanotto, 2018. "A mortality model based on a mixture distribution function," Population Studies, Taylor & Francis Journals, vol. 72(2), pages 191-200, May.
    3. Shripad Tuljapurkar & Nan Li & Carl Boe, 2000. "A universal pattern of mortality decline in the G7 countries," Nature, Nature, vol. 405(6788), pages 789-792, June.
    4. Andrea Nigri & Elisabetta Barbi & Susanna Levantesi, 2022. "The relay for human longevity: country-specific contributions to the increase of the best-practice life expectancy," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4061-4073, December.
    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. Ainhoa-Elena Léger & Stefano Mazzuco, 2021. "What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 769-798, November.
    2. Duncan Gillespie & Meredith Trotter & Shripad Tuljapurkar, 2014. "Divergence in Age Patterns of Mortality Change Drives International Divergence in Lifespan Inequality," Demography, Springer;Population Association of America (PAA), vol. 51(3), pages 1003-1017, June.
    3. 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.
    4. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    5. 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.
    6. Heather Booth & Rob J Hyndman & Leonie Tickle & Piet de Jong, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Monash Econometrics and Business Statistics Working Papers 13/06, Monash University, Department of Econometrics and Business Statistics.
    7. Chia, Ngee-Choon & Tsui, Albert K.C., 2005. "Medical savings accounts in Singapore: how much is adequate?," Journal of Health Economics, Elsevier, vol. 24(5), pages 855-875, September.
    8. S. Haberman & A. E. Renshaw, 2009. "Measurement of Longevity Risk Using Bootstrapping for Lee–Carter and Generalised Linear Poisson Models of Mortality," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 443-461, September.
    9. Jackie Li, 2014. "An application of MCMC simulation in mortality projection for populations with limited data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(1), pages 1-48.
    10. 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.
    11. Patrizio Vanella & Ugofilippo Basellini & Berit Lange, 2020. "Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19," Working Papers axbhmxrs-o0viyh9z07m, French Institute for Demographic Studies.
    12. 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.
    13. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    14. 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.
    15. Michel Denuit, 2009. "Life Anuities with Stochastic Survival Probabilities: A Review," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 463-489, September.
    16. Hatzopoulos, P. & Haberman, S., 2009. "A parameterized approach to modeling and forecasting mortality," Insurance: Mathematics and Economics, Elsevier, vol. 44(1), pages 103-123, February.
    17. Wong, Jackie S.T. & Forster, Jonathan J. & Smith, Peter W.F., 2018. "Bayesian mortality forecasting with overdispersion," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 206-221.
    18. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    19. Jarner, Søren Fiig & Kronborg, Morten Tolver, 2016. "Entrance times of random walks: With applications to pension fund modeling," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 1-20.
    20. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:osfxxx:z3qmw. 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: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    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.