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The Increasingly Mixed Proportional Hazard Model: An Application to Socioeconomic Status, Health Shocks, and Mortality

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  • Frijters, Paul
  • Haisken-DeNew, John P.
  • Shields, Michael A.

Abstract

We introduce a duration model that allows for unobserved cumulative individual-specific shocks, which are likely to be important in explaining variations in duration outcomes, such as length of life and time spent unemployed. The model is also a useful tool in situations where researchers observe a great deal of information about individuals when first interviewed in surveys but little thereafter. We call this model the "increasingly mixed proportional hazard" (IMPH) model. We compare and contrast this model with the mixed proportional hazard (MPH) model, which continues to be the workhorse of applied single-spell duration analysis in economics and the other social sciences. We apply the IMPH model to study the relationships among socioeconomic status, health shocks, and mortality, using 19 waves of data drawn from the German Socio-Economic Panel (SOEP). The IMPH model is found to fit the data statistically better than the MPH model, and unobserved health shocks and socioeconomic status are shown to play powerful roles in predicting longevity.
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  • Frijters, Paul & Haisken-DeNew, John P. & Shields, Michael A., 2011. "The Increasingly Mixed Proportional Hazard Model: An Application to Socioeconomic Status, Health Shocks, and Mortality," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 271-281.
    • Handle: RePEc:bes:jnlbes:v:29:i:2:y:2011:p:271-281
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    1. Sarracino, Francesco & Greyling, Talita & O'Connor, Kelsey J. & Peroni, Chiara & Rossouw, Stephanié, 2021. "A Year of Pandemic: Levels, Changes and Validity of Well-Being Data from Twitter. Evidence from Ten Countries," IZA Discussion Papers 14903, Institute of Labor Economics (IZA).
    2. Leive, Adam, 2018. "Dying to win? Olympic Gold medals and longevity," Journal of Health Economics, Elsevier, vol. 61(C), pages 193-204.
    3. Adriaan Kalwij, 2014. "An empirical analysis of the importance of controlling for unobserved heterogeneity when estimating the income-mortality gradient," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(30), pages 913-940.
    4. Akbulut-Yuksel, Mevlude & Khamis, Melanie & Yuksel, Mutlu, 2017. "Women make houses, women make homes," Labour Economics, Elsevier, vol. 49(C), pages 145-161.
    5. Frijters, Paul & Clark, Andrew E. & Krekel, Christian & Layard, Richard, 2020. "A happy choice: wellbeing as the goal of government," Behavioural Public Policy, Cambridge University Press, vol. 4(2), pages 126-165, July.
    6. Steiber, Nadia, 2019. "Intergenerational educational mobility and health satisfaction across the life course: Does the long arm of childhood conditions only become visible later in life?," Social Science & Medicine, Elsevier, vol. 242(C).
    7. Marijana Andrijić & Tajana Barbić, 2021. "When the Going Gets Tough … the Effect of Economic Reform Programmes on National Well-Being," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    8. Schurer, Stefanie, 2014. "Bouncing Back from Health Shocks: Locus of Control, Labor Supply, and Mortality," IZA Discussion Papers 8203, Institute of Labor Economics (IZA).
    9. Virginia Zarulli, 2016. "Unobserved Heterogeneity of Frailty in the Analysis of Socioeconomic Differences in Health and Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 32(1), pages 55-72, February.

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