IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v58y2024i6d10.1007_s11135-023-01723-7.html
   My bibliography  Save this article

Functional data analysis approach in population studies: an application to the gender gap in life expectancy

Author

Listed:
  • Alessandro Feraldi

    (Sapienza University of Rome
    Max Planck Institute for Demographic Research)

  • Virginia Zarulli

    (CPOP University of Southern Denmark)

  • Stefano Mazzuco

    (University of Padua)

  • Cristina Giudici

    (Sapienza University of Rome)

Abstract

This work analyses the contribution of ages and causes of death to gender gap in life expectancy in 20 European and non-European countries between 1959 and 2015, using Functional Data Analysis. Data were retrieved from the WHO Mortality Database and from the Human Mortality Database. We propose a Functional Principal Component Analysis of the age profiles of cause-specific contributions, to identify the main components of the distribution of the age-specific contributions according to causes of death, and to summarize them with few components. Our findings show that the narrowing gender gap in life expectancy was mainly driven by decreasing differences in cardiovascular diseases. Additionally, the study reveals that the age cause contributions act almost entirely on only two dimensions: level (extent of the cause-specific contribution to the overall mortality gender gap) and age pattern (location of the curves across ages). Notably, in the last period, it is not the "quantum" of the cause-specific contributions that matters, but the "timing", i.e. location across the age spectrum. Moreover, our results show that in the most recent period the gender gap in life expectancy is affected by composition of the causes of death more than it was in previous periods. We emphasise that Functional Data Analysis could prove useful to deepen our understanding of complex demographic phenomena.

Suggested Citation

  • Alessandro Feraldi & Virginia Zarulli & Stefano Mazzuco & Cristina Giudici, 2024. "Functional data analysis approach in population studies: an application to the gender gap in life expectancy," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5125-5150, December.
  • Handle: RePEc:spr:qualqt:v:58:y:2024:i:6:d:10.1007_s11135-023-01723-7
    DOI: 10.1007/s11135-023-01723-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-023-01723-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11135-023-01723-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:cai:poeine:pope_201_0157 is not listed on IDEAS
    2. John Bongaarts, 2005. "Long-range trends in adult mortality: Models and projection methods," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 23-49, February.
    3. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    4. Samuel Preston & Haidong Wang, 2006. "Sex mortality differences in The United States: The role of cohort smoking patterns," Demography, Springer;Population Association of America (PAA), vol. 43(4), pages 631-646, November.
    5. Aline F. Désesquelles & Michele Antonio Salvatore & Marilena Pappagallo & Luisa Frova & Monica Pace & France Meslé & Viviana Egidi, 2012. "Analysing Multiple Causes of Death: Which Methods For Which Data? An Application to the Cancer-Related Mortality in France and Italy [Analyse des causes multiples de décès: quelles méthodes pour qu," European Journal of Population, Springer;European Association for Population Studies, vol. 28(4), pages 467-498, November.
    6. Virginia Zarulli & Ilya Kashnitsky & James W. Vaupel, 2021. "Death rates at specific life stages mold the sex gap in life expectancy," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(20), pages 2010588118-, May.
    7. Vladimir Canudas-Romo & Michel Guillot, 2015. "Truncated cross-sectional average length of life: A measure for comparing the mortality history of cohorts," Population Studies, Taylor & Francis Journals, vol. 69(2), pages 147-159, July.
    8. 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.
    9. Yang, S. & Khang, Y.-H. & Harper, S. & Smith, G.D. & Leon, D.A. & Lynch, J., 2010. "Understanding the rapid increase in life expectancy in South Korea," American Journal of Public Health, American Public Health Association, vol. 100(5), pages 896-903.
    10. Shiro Horiuchi & Nadine Ouellette & Siu Lan Karen Cheung & Jean-Marie Robine, 2013. "Modal age at death: lifespan indicator in the era of longevity extension," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 11(1), pages 37-69.
    11. Vladimir Canudas-Romo, 2008. "The modal age at death and the shifting mortality hypothesis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(30), pages 1179-1204.
    12. José Manuel Aburto & Francisco Villavicencio & Ugofilippo Basellini & Søren Kjærgaard & James W. Vaupel, 2020. "Dynamics of life expectancy and life span equality," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(10), pages 5250-5259, March.
    13. Eduardo Arriaga, 1984. "Measuring and explaining the change in life expectancies," Demography, Springer;Population Association of America (PAA), vol. 21(1), pages 83-96, February.
    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. Suryakant Yadav, 2021. "Progress of Inequality in Age at Death in India: Role of Adult Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 523-550, July.
    2. Pinheiro, Pedro Cisalpino & Queiroz, Bernardo L, 2018. "Regional Disparities in Brazilian Adult Mortality: an analysis using Modal Age at Death (M) and Compression of Mortality (IQR)," OSF Preprints t2ey3, Center for Open Science.
    3. Pinheiro, Pedro Cisalpino & Queiroz, Bernardo L, 2018. "Regional Disparities in Brazilian Adult Mortality: an analysis using Modal Age at Death (M) and Compression of Mortality (IQR)," OSF Preprints t2ey3_v1, Center for Open Science.
    4. Konstantinos N. Zafeiris, 2023. "Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis," Journal of Population Research, Springer, vol. 40(1), pages 1-31, March.
    5. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & Marcus Ebeling, 2015. "Decomposing changes in life expectancy: Compression versus shifting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(14), pages 391-424.
    6. Dustin Brown & Mark Hayward & Jennifer Montez & Robert Hummer & Chi-Tsun Chiu & Mira Hidajat, 2012. "The Significance of Education for Mortality Compression in the United States," Demography, Springer;Population Association of America (PAA), vol. 49(3), pages 819-840, August.
    7. Ediev, Dalkhat M. & Sanderson, Warren C. & Scherbov, Sergei, 2019. "The inverse relationship between life expectancy-induced changes in the old-age dependency ratio and the prospective old-age dependency ratio," Theoretical Population Biology, Elsevier, vol. 125(C), pages 1-10.
    8. Chiara Micheletti & Francisco Villavicencio, 2024. "On the relationship between life expectancy, modal age at death, and the threshold age of the life table entropy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 51(24), pages 763-788.
    9. 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.
    10. Jinwook Bahk & Kyunghee Jung-Choi, 2020. "The Contribution of Avoidable Mortality to the Life Expectancy Gains in Korea between 1998 and 2017," IJERPH, MDPI, vol. 17(18), pages 1-10, September.
    11. Eun, Sang Jun, 2019. "Avoidable, amenable, and preventable mortalities in South Korea, 2000–2017: Age-period-cohort trends and impact on life expectancy at birth," Social Science & Medicine, Elsevier, vol. 237(C), pages 1-1.
    12. Ting Li & Yang Yang & James Anderson, 2013. "Mortality Increase in Late-Middle and Early-Old Age: Heterogeneity in Death Processes as a New Explanation," Demography, Springer;Population Association of America (PAA), vol. 50(5), pages 1563-1591, October.
    13. Shiro Horiuchi & Nadine Ouellette & Siu Lan Karen Cheung & Jean-Marie Robine, 2013. "Modal age at death: lifespan indicator in the era of longevity extension," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 11(1), pages 37-69.
    14. Matthias Börger & Martin Genz & Jochen Ruß, 2018. "Extension, Compression, and Beyond: A Unique Classification System for Mortality Evolution Patterns," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1343-1361, August.
    15. Vladimir Shkolnikov & Evgeny Andreev & Zhen Zhang & James Oeppen & James Vaupel, 2011. "Losses of Expected Lifetime in the United States and Other Developed Countries: Methods and Empirical Analyses," Demography, Springer;Population Association of America (PAA), vol. 48(1), pages 211-239, February.
    16. Jorge M. Uribe & Helena Chuliá & Montserrat Guillen, 2018. "Trends in the Quantiles of the Life Table Survivorship Function," European Journal of Population, Springer;European Association for Population Studies, vol. 34(5), pages 793-817, December.
    17. Ana Debón & Steven Haberman & Francisco Montes & Edoardo Otranto, 2021. "Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    18. 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.
    19. Joel E. Cohen & Roland Rau & Christina Bohk-Ewald, 2018. "Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(29), pages 773-842.
    20. Annette Baudisch & Jose Manuel Aburto, 2024. "How lifespan and life years lost equate to unity," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(24), pages 643-666.

    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:spr:qualqt:v:58:y:2024:i:6:d:10.1007_s11135-023-01723-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.