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Risk ratings that do not measure probabilities

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  • W. Kip Viscusi
  • Jahn Hakes

Abstract

Survey questions regarding assessed survival chances are an often-used example of a risk rating scale for eliciting a probability assessment. The responses to such questions do exhibit several properties of probabilities, but differ in some key respects, resulting in relationships which are not only inconsistent with accurate beliefs, but also in which precision is sacrificed for ease of use. The Health and Retirement Study, for example, uses a 0 to 10 scale to measure self-assessed survival probabilities to a particular age. Transformation of these responses for use as a probability results in some patterns that are consistent with a model of imperfect information, or a monotonic transformation of imperfectly perceived risks, but more subtle analysis reveals inconsistencies with either of these theories, suggesting the scale is inappropriate for use as a probability measure. The age-related effects for female respondents are the most salient results that are inconsistent with use of the survey's response scale as representing a probability.

Suggested Citation

  • W. Kip Viscusi & Jahn Hakes, 2003. "Risk ratings that do not measure probabilities," Journal of Risk Research, Taylor & Francis Journals, vol. 6(1), pages 23-43, January.
  • Handle: RePEc:taf:jriskr:v:6:y:2003:i:1:p:23-43
    DOI: 10.1080/1366987032000047789
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    1. Michael D. Hurd & Kathleen McGarry, 1995. "Evaluation of the Subjective Probabilities of Survival in the Health and Retirement Study," Journal of Human Resources, University of Wisconsin Press, vol. 30, pages 268-292.
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    Cited by:

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    3. Jin-Tan Liu & Meng-Wen Tsou & James K. Hammitt, 2007. "Health Information and Subjective Survival Probability: Evidence from Taiwan," Journal of Risk Research, Taylor & Francis Journals, vol. 10(2), pages 149-175, March.
    4. Joan Costa-Font & Cristina Vilaplana-Prieto, 2022. "Biased survival expectations and behaviours: Does domain specific information matter?," Journal of Risk and Uncertainty, Springer, vol. 65(3), pages 285-317, December.
    5. Yokoo, Hide-Fumi & Arimura, Toshi H. & Chattopadhyay, Mriduchhanda & Katayama, Hajime, 2023. "Subjective risk belief function in the field: Evidence from cooking fuel choices and health in India," Journal of Development Economics, Elsevier, vol. 161(C).
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    7. Henrik Hammar & Olof Johansson‐Stenman, 2004. "The value of risk‐free cigarettes – do smokers underestimate the risk?," Health Economics, John Wiley & Sons, Ltd., vol. 13(1), pages 59-71, January.
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    9. Fumihiro Yamane & Kyohei Matsushita & Toshio Fujimi & Hideaki Ohgaki & Kota Asano, 2014. "A Simple Way to Elicit Subjective Ambiguity: Application to Low-dose Radiation Exposure in Fukushima," Discussion Papers 1417, Graduate School of Economics, Kobe University.
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    11. Katherine Carman & Peter Kooreman, 2014. "Probability perceptions and preventive health care," Journal of Risk and Uncertainty, Springer, vol. 49(1), pages 43-71, August.
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    13. Shimokawa, Satoru & Kito, Yayoi & Kudo, Haruyo & Yamaguchi, Michitoshi & Niiyama, Yoko, 2021. "Distinguishing Attitude and Belief Expressions from Economic Preferences in Long-Lasting Aversion in Food Choice," 2021 Conference, August 17-31, 2021, Virtual 315249, International Association of Agricultural Economists.
    14. Jocelyn Raude & Patrick Peretti-Watel & Jeremy Ward & Claude Flamand & Pierre Verger, 2018. "Are Perceived Prevalences of Infection also Biased and How? Lessons from Large Epidemics of Mosquito-Borne Diseases in Tropical Regions," Medical Decision Making, , vol. 38(3), pages 377-389, April.
    15. Khwaja, Ahmed & Silverman, Dan & Sloan, Frank & Wang, Yang, 2009. "Are mature smokers misinformed?," Journal of Health Economics, Elsevier, vol. 28(2), pages 385-397, March.
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    20. Evans, Mary F. & Smith, V. Kerry, 2006. "Do we really understand the age-VSL relationship?," Resource and Energy Economics, Elsevier, vol. 28(3), pages 242-261, August.

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