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Demography

, Volume 51, Issue 1, pp 51–71 | Cite as

Mortality Deceleration and Mortality Selection: Three Unexpected Implications of a Simple Model

  • Elizabeth Wrigley-FieldEmail author
Article

Abstract

Unobserved heterogeneity in mortality risk is pervasive and consequential. Mortality deceleration—the slowing of mortality’s rise with age—has been considered an important window into heterogeneity that otherwise might be impossible to explore. In this article, I argue that deceleration patterns may reveal surprisingly little about the heterogeneity that putatively produces them. I show that even in a very simple model—one that is composed of just two subpopulations with Gompertz mortality—(1) aggregate mortality can decelerate even while a majority of the cohort is frail; (2) multiple decelerations are possible; and (3) mortality selection can produce acceleration as well as deceleration. Simulations show that these patterns are plausible in model cohorts that in the aggregate resemble cohorts in the Human Mortality Database. I argue that these results challenge some conventional heuristics for understanding the relationship between selection and deceleration; undermine certain inferences from deceleration timing to patterns of social inequality; and imply that standard parametric models, assumed to plateau at most once, may sometimes badly misestimate deceleration timing—even by decades.

Keywords

Mortality deceleration Mortality selection Heterogeneity Methods Logistic models 

Notes

Acknowledgements

This research was supported by a graduate research fellowship from the National Science Foundation, a dissertation fellowship from the Ford Foundation, and core grants to the Center for Demography and Ecology (R24 HD047873) and Center for Demography of Health and Aging (P30 AG017266) at the University of Wisconsin–Madison. The author thanks, for helpful feedback, Felix Elwert, James Montgomery, Jenny Conrad, Michal Engelman, Duncan Gillespie, Jeffrey Grigg, Paul Hanselman, Anna Haskins, Michelle Niemann, Jenna Nobles, and Sarah Zureick-Brown, along with excellent reviewers and the Editor; and, for technical assistance, Russell Dimond, David Siegel, and the Social Science Computing Cooperative at the University of Wisconsin–Madison.

Supplementary material

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Copyright information

© Population Association of America 2014

Authors and Affiliations

  1. 1.MadisonUSA

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