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Rectangularization of the survival curve reconsidered: The maximum inner rectangle approach

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  • Marcus Ebeling
  • Roland Rau
  • Annette Baudisch

Abstract

Rectangularization of the survival curve—a key analytical framework in mortality research—relies on assumptions that have become partially obsolete in high-income countries due to mortality reductions among the oldest old. We propose refining the concept to adjust for recent and potential future mortality changes. Our framework, the ‘maximum inner rectangle approach’ (MIRA) considers two types of rectangularization. Outer rectangularization captures progress in mean lifespan relative to progress in maximum lifespan. Inner rectangularization captures progress in lifespan equality relative to progress in mean lifespan. Empirical applications show that both processes have generally increased since 1850. However, inner rectangularization has displayed country-specific patterns since the onset of sustained old-age mortality declines. Results from separating premature and old-age mortality, using the MIRA, suggest there has been a switch from reducing premature deaths to extending the premature age range; a shift potentially signalling a looming limit to the share of premature deaths.

Suggested Citation

  • Marcus Ebeling & Roland Rau & Annette Baudisch, 2018. "Rectangularization of the survival curve reconsidered: The maximum inner rectangle approach," Population Studies, Taylor & Francis Journals, vol. 72(3), pages 369-379, September.
  • Handle: RePEc:taf:rpstxx:v:72:y:2018:i:3:p:369-379
    DOI: 10.1080/00324728.2017.1414299
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    Cited by:

    1. Henrik Brønnum-Hansen & Juan Carlos Albizu-Campos Espiñeira & Camila Perera & Ingelise Andersen, 2023. "Trends in mortality patterns in two countries with different welfare models: comparisons between Cuba and Denmark 1955–2020," Journal of Population Research, Springer, vol. 40(2), pages 1-28, June.
    2. Aburto, José Manuel & Basellini, Ugofilippo & Baudisch, Annette & Villavicencio, Francisco, 2022. "Drewnowski’s index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Theoretical Population Biology, Elsevier, vol. 148(C), pages 1-10.
    3. Jos'e Manuel Aburto & Ugofilippo Basellini & Annette Baudisch & Francisco Villavicencio, 2021. "Drewnowski's index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Papers 2111.11256, arXiv.org.
    4. 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.
    5. Wen Su & Vladimir Canudas-Romo, 2024. "Cross-sectional Average Length of Life Entropy ( $${\mathcal{H}}_{\text{CAL}}$$ H CAL ): International Comparisons and Decompositions," European Journal of Population, Springer;European Association for Population Studies, vol. 40(1), pages 1-23, December.

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