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Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia

Author

Listed:
  • Pamela Giustinelli
  • Charles F. Manski
  • Francesca Molinari

Abstract

We elicit numerical expectations for late-onset dementia in the Health and Retirement Study. Our elicitation distinguishes between precise and imprecise probabilities, while accounting for rounding of reports. Respondents quantify imprecision using probability intervals. Nearly half of respondents hold imprecise dementia probabilities, while almost a third of precise-probability respondents round their reports. We provide the first empirical evidence on dementia-risk perceptions among dementia-free older Americans and novel evidence about imprecise probabilities in a nationally-representative sample. We show, in a specific framework, that failing to account for imprecise or rounded probabilities can yield incorrect predictions of long-term care insurance purchase decisions.

Suggested Citation

  • Pamela Giustinelli & Charles F. Manski & Francesca Molinari, 2019. "Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia," NBER Working Papers 26125, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26125
    Note: AG EH
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    Cited by:

    1. Gong, Yifan & Stinebrickner, Ralph & Stinebrickner, Todd, 2022. "Marriage, children, and labor supply: Beliefs and outcomes," Journal of Econometrics, Elsevier, vol. 231(1), pages 148-164.
    2. Jane Greve & Morten Saaby & Anders Rosdahl & Vibeke Tornhøj Christensen, 2021. "Uncertain occupational expectations at age 19 and later educational and labour market outcomes," LABOUR, CEIS, vol. 35(2), pages 163-191, June.
    3. Emanuele Ciani & Adeline Delavande & Ben Etheridge & Marco Francesconi, 2023. "Policy Uncertainty and Information Flows: Evidence from Pension Reform Expectations," The Economic Journal, Royal Economic Society, vol. 133(649), pages 98-129.
    4. Pamela Giustinelli & Matthew D. Shapiro, 2024. "SeaTE: Subjective Ex Ante Treatment Effect of Health on Retirement," American Economic Journal: Applied Economics, American Economic Association, vol. 16(2), pages 278-317, April.
    5. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    6. Kirby Nielsen & Luca Rigotti, 2022. "Revealed Incomplete Preferences," Papers 2205.08584, arXiv.org, revised Oct 2022.
    7. Thomas F. Crossley & Yifan Gong & Todd Stinebrickner & Ralph Stinebrickner, 2021. "Examining Income Expectations in the College and Early Post-College Periods: New Distributional Tests of Rational Expectations," CESifo Working Paper Series 8834, CESifo.
    8. Riccardo Scarpa & Claudia Bazzani & Diego Begalli & Roberta Capitello, 2021. "Resolvable and Near‐epistemic Uncertainty in Stated Preference for Olive Oil: An Empirical Exploration," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 335-369, June.
    9. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Yifan Gong & Todd Stinebrickner & Ralph Stinebrickner, 2020. "Perceived and actual option values of college enrollment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 940-959, November.

    More about this item

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • I0 - Health, Education, and Welfare - - General

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