Information asymmetry in fire insurance: a frontier approach
Donald Vitaliano
Journal of Economics and Finance, 2021, vol. 45, issue 4, No 10, 764-773
Abstract:
Abstract The composed error stochastic frontier model is used to separate random variations in fire insurance losses from systematic unexpected losses due to adverse selection or moral hazard. Net premiums are an ex-ante predictor of losses, based on information available to insurers. Losses due to information unknown to the insurer are picked up by the half-normal part of the error term. Loss data for 275 stock fire companies operating in 51 states and territories in 1917 are analyzed in a random effects panel data model. The mean cost per insurer of adverse selection and moral hazard amounts to 15% of fire losses. This is an upper bound estimate because some portion of this cost may be due to failure to operate efficiently rather than incomplete information.
Keywords: Fire insurance; Asymmetrical information; Stochastic frontier (search for similar items in EconPapers)
JEL-codes: C10 D82 G22 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s12197-021-09547-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:jecfin:v:45:y:2021:i:4:d:10.1007_s12197-021-09547-7
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/12197/PS2
DOI: 10.1007/s12197-021-09547-7
Access Statistics for this article
Journal of Economics and Finance is currently edited by James Payne
More articles in Journal of Economics and Finance from Springer, Academy of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().