Loss Coverage: Why Insurance Works Better With Some Adverse Selection, by Guy Thomas, 2017, Cambridge, UK: Cambridge University Press, 274 pages, ISBN: 978‐1‐107‐49590‐6 (Paperback).

• Published date: 01 September 2018
• DOI: http://doi.org/10.1111/jori.12262
• Date: 01 September 2018

BOOK REVIEW

Loss Coverage: Why Insurance Works Better With Some Adverse Selection, by Guy Thomas,

2017, Cambridge, UK: Cambridge University Press, 274 pages, ISBN: 978-1-107-

49590-6 (Paperback).

Reviewer: William L. Ferguson, The University of Louisiana at Lafayette;

ferguson@louisiana.edu

Loss Coverage: Why Insurance Works Better With Some Adverse Selection provides an

interesting critique of the orthodox views many insurance academics and industry

professionals have regarding the “problem” of adverse selection. Guy Thomas opens

by asserting that rather than being a pervasively severe issue always to be avoided or

discouraged, some degree of adverse selection in insurance is not only desirable but

necessary for greater market efficiency. This idea harkens back to early work of Nobel

Laureate (1992) Gary Becker, who essentially proposed similar insight into moral

hazard using the notion that the optimal amount of “crime” is nonzero. Thomas goes

on to effectively incorporate both verbal stories and numerical examples throughout

the book, including exaggerated examples from HIV and genetic testing, as well as

gender- and race-based insurance pricing.

The second section presents the basic theoretical, mathematical and graphical

underpinnings of the book pertaining to the relation between adverse selection, loss

coverage, and demand elasticities, even without the simplifying assumption of zero

moral hazard. Thomasdemonstrates probabilistic loss coverage and socialwelfare are

both enhancedthrough some degree of adverse selection, whetherfrom a public policy

perspective or an insurer premium maximization with proportional profit-loading

perspective. Thomas asserts loss coverage (based on observable claims) is generally

preferable to social welfare(which requires unobservable utilities) as the best measure

to simplify,clarify, and better evaluate policyquestions and outcomes. Further, “weak”

adverse selection is necessary to maximize loss coverage from a public policy

perspective. The key role that separation (and its complement: inclusivity) plays in

binary- and multi-group risk classification schemes is intuitively likened to

maintaining the traditional balance between statistical power and Type II error.

The third section of the book examines various aspects of risk classification needed to

induce some degree of (weak) adverse selection, beginning with why a particular

©2018 The Journal of Risk and Insurance. Vol. 85, No. 3, 865–867 (2018).

DOI: 10.1111/jori.12262

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