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Estimating Second Order Probability Beliefs from Subjective Survival Data

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  • Péter Hudomiet
  • Robert J. Willis

Abstract

Based on subjective survival probability questions in the Health and Retirement Study (HRS), we use an econometric model to estimate the determinants of individual-level uncertainty about personal longevity. This model is built around the Modal Response Hypothesis (MRH), a mathematical expression of the idea that survey responses of 0, 50 or 100 percent to probability questions indicate a high level of uncertainty about the relevant probability. We show that subjective survival expectations in 2002 line up very well with realized mortality of the HRS respondents between 2002 and 2010. We show that the MRH model performs better than typically used models in the literature of subjective probabilities. Our model gives more accurate estimates of low probability events and it is able to predict the unusually high fraction of focal 0, 50 and 100 answers observed in many datasets on subjective probabilities. We show that subjects place too much weight on parents' age at death when forming expectations about their own longevity, while other covariates such as demographics, cognition, personality, subjective health and health behavior are underweighted. We also find that less educated people, smokers and women have less certain beliefs; and recent health shocks increase uncertainty about survival, too.

Suggested Citation

  • Péter Hudomiet & Robert J. Willis, 2012. "Estimating Second Order Probability Beliefs from Subjective Survival Data," NBER Working Papers 18258, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18258
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    Cited by:

    1. Foster, Gigi & Frijters, Paul & Schaffner, Markus & Torgler, Benno, 2018. "Expectation formation in an evolving game of uncertainty: New experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 379-405.
    2. Nils Grevenbrock & Max Groneck & Alexander Ludwig & Alexander Zimper, 2021. "Cognition, Optimism, And The Formation Of Age‐Dependent Survival Beliefs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 887-918, May.
    3. Teresa Bago d'Uva & Esen Erdogan Ciftci & Owen O'Donnell & Eddy van Doorslaer, 2015. "Who can predict their Own Demise? Accuracy of Longevity Expectations by Education and Cognition," Tinbergen Institute Discussion Papers 15-052/V, Tinbergen Institute.
    4. Binder, Carola C., 2017. "Measuring uncertainty based on rounding: New method and application to inflation expectations," Journal of Monetary Economics, Elsevier, vol. 90(C), pages 1-12.
    5. Pamela Giustinelli & Matthew D. Shapiro, 2018. "SeaTE: Subjective ex ante Treatment Effect of Health on Retirement," Working Papers wp382, University of Michigan, Michigan Retirement Research Center.
    6. Caliendo, Frank N. & Gorry, Aspen & Slavov, Sita, 2020. "Survival ambiguity and welfare," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 20-42.
    7. Bago d'Uva, Teresa & O'Donnell, Owen & van Doorslaer, Eddy, 2020. "Who can predict their own demise? Heterogeneity in the accuracy and value of longevity expectations☆," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    8. Pamela Giustinelli & Charles F. Manski & Francesca Molinari, 2018. "Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study," NBER Working Papers 24559, National Bureau of Economic Research, Inc.
    9. Alberto Palloni & Beatriz Novak, 2016. "Subjective survival expectations and observed survival: How consistent are they?," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 14(1), pages 187-228.
    10. Péter Hudomiet & Michael D. Hurd & Susann Rohwedder, 2020. "The Impact of Growing Health and Mortality Inequalities on Lifetime Social Security Payouts," Working Papers wp412, University of Michigan, Michigan Retirement Research Center.
    11. Bell, D.N.F. & Comerford, D.A. & Douglas, E., 2020. "How do subjective life expectancies compare with mortality tables? Similarities and differences in three national samples," The Journal of the Economics of Ageing, Elsevier, vol. 16(C).
    12. Vesile Kutlu-Koc & Adriaan Kalwij, 2017. "Individual Survival Expectations and Actual Mortality: Evidence from Dutch Survey and Administrative Data," European Journal of Population, Springer;European Association for Population Studies, vol. 33(4), pages 509-532, October.
    13. Springborn, Michael & Sanchirico, James N., 2013. "A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 609-624.
    14. Frank Sloan & Lindsey Eldred & Tong Guo & Yanzhi Xu, 2013. "Are people overoptimistic about the effects of heavy drinking?," Journal of Risk and Uncertainty, Springer, vol. 47(1), pages 93-127, August.
    15. Rakesh K. Sarin & L. Robin Keller, 2013. "From the Editors ---Group Decisions, Preference Elicitation, Experienced Utility, Survival Probabilities, and Portfolio Value of Information," Decision Analysis, INFORMS, vol. 10(2), pages 99-102, June.
    16. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2017. "The precision of subjective data and the explanatory power of economic models," Journal of Econometrics, Elsevier, vol. 200(2), pages 378-389.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • J1 - Labor and Demographic Economics - - Demographic Economics

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