Mortality Risk Perceptions: A Bayesian Reassessment
Jahn K Hakes and
W Viscusi
Journal of Risk and Uncertainty, 1997, vol. 15, issue 2, 135-50
Abstract:
This paper uses a Bayesian learning model to assess the respective influence of different risk measurements on mortality risk perceptions. People form risk beliefs using several sources of information, including the actual population mean death risk level the discounted lost life expectancy, and the age-specific hazard rate considered by Benjamin and Dougan (1997). The appropriate criterion for judging the validity of risk perceptions is not the perfect information case, but rather whether people form their risk beliefs in a rational manner given a world of costly and limited risk information. Although the statistical results support the overall conclusion that the learning process is rational, the character of the learning process differs depending on the risk level. Risk-related variables are much better predictors of larger risks than of small risks which reflects the role of information costs and the benefits of learning about larger risks. Copyright 1997 by Kluwer Academic Publishers
Date: 1997
References: Add references at CitEc
Citations: View citations in EconPapers (33)
Downloads: (external link)
http://journals.kluweronline.com/issn/0895-5646/contents link to full text (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kap:jrisku:v:15:y:1997:i:2:p:135-50
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/11166/PS2
Access Statistics for this article
Journal of Risk and Uncertainty is currently edited by W. Kip Viscusi
More articles in Journal of Risk and Uncertainty from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).