IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/z3qmw_v1.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_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:z3qmw_v1
    DOI: 10.31219/osf.io/z3qmw_v1
    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_v1?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. Marie-Pier Bergeron-Boucher & James E. Oeppen & Vladimir Canudas-Romo & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    2. 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.
    3. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    4. 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.
    5. 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.
    6. 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. Lanfiuti Baldi, Giacomo & NIGRI, ANDREA, 2023. "An Age-Period-Cohort model for gender gap in youth mortality," OSF Preprints z3qmw, Center for Open Science.
    2. 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.
    3. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d_v1, Center for Open Science.
    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. Emanuele Aliverti & Stefano Mazzuco & Bruno Scarpa, 2022. "Dynamic modelling of mortality via mixtures of skewed distribution functions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1030-1048, July.
    6. 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.
    7. Jarner, Søren F. & Jallbjørn, Snorre, 2020. "Pitfalls and merits of cointegration-based mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 80-93.
    8. 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.
    9. 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.
    10. 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.
    11. Bergeron-Boucher, Marie-Pier & Vázquez-Castillo, Paola & Missov, Trifon, 2022. "A modal age at death approach to forecasting mortality," SocArXiv 5zr2k_v1, Center for Open Science.
    12. Benjamin Seligman & Shripad Tuljapurkar & Gabi Greenberg, 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. Kenny Kam Kuen Mok & Jinhui Zhang & Yanlin Shi & Chong It Tan, 2024. "Mortality modelling with arrival of additional year of mortality data: Calibration and forecasting," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(28), pages 797-826.
    15. 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.
    16. Ana Debon & Steven Haberman & Gabriella Piscopo, 2024. "Multipopulation mortality analysis: bringing out the unobservable with latent clustering," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5107-5123, December.
    17. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    18. Bergeron-Boucher, Marie-Pier & Vázquez-Castillo, Paola & Missov, Trifon, 2022. "A modal age at death approach to forecasting mortality," SocArXiv 5zr2k, Center for Open Science.
    19. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    20. Jackie Li & Leonie Tickle & Nick Parr, 2016. "A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes," Journal of Population Research, Springer, vol. 33(4), pages 333-360, December.

    More about this item

    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_v1. 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.