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  • Cited by 11
Publisher:
Cambridge University Press
Online publication date:
May 2017
Print publication year:
2017
Online ISBN:
9781316178843

Book description

Most academic and policy commentary represents adverse selection as a severe problem in insurance, which should always be deprecated, avoided or minimised. This book gives a contrary view. It details the exaggeration of adverse selection in insurers' rhetoric and insurance economics, and presents evidence that in many insurance markets, adverse selection is weaker than most commentators suggest. A novel arithmetical argument shows that from a public policy perspective, 'weak' adverse selection can be a good thing. This is because a degree of adverse selection is needed to maximise 'loss coverage', the expected fraction of the population's losses which is compensated by insurance. This book will be valuable for those interested in public policy arguments about insurance and discrimination: academics (in economics, law and social policy), policymakers, actuaries, underwriters, disability activists, geneticists and other medical professionals.

Awards

Winner, 2019 Kulp-Wright Book Award, American Risk and Insurance Association

Reviews

'Guy Thomas challenges the orthodox views held by the insurance industry, actuaries, and economists concerning the problem of adverse selection. He makes his case that a little adverse selection is actually a good thing in a sensible, pragmatic, and compelling manner. His critical insights about the debates on restricting risk classification in insurance should be essential reading for policy makers.'

Michael Hoy - University of Guelph, Canada

'Despite dramatic warnings, insurance companies continue to prosper despite bans on gender rating, genetic testing, racial and other discriminations in setting policy terms. This thought-provoking book explains why. The author makes a convincing case for even tighter regulation of allowable risk classifications to enhance the welfare of society of a whole - especially timely now as 1-in-5 proposers for life insurance are not accepted at standard rates.'

Shane Whelan, FFA, FSAI - Former Managing Editor of the British Actuarial Journal

'Actuaries traditionally see nothing but danger in adverse selection. Guy Thomas, an actuary himself, sees opportunity. Using the concept of loss coverage, Thomas challenges the conventional wisdom of how economists model insurance markets, much of which, he sets out to show, is more myth than reality. Lucidly written and sure to get the reader thinking afresh.'

Angus Macdonald - Heriot Watt University, Scotland

'This is a serious book which challenges some of the conventional thinking of actuaries and economists about adverse selection in insurance, and does so with justification; they would do well to take the author’s views into account. It can also be read with profit by others including insurance managers, academics, and those responsible for public policy.'

David Wilkie - InQA Limited and Heriot Watt University, Scotland

'This is a book that is full of common sense. Thomas provides important and, what will be to many, controversial recommendations to curtail [insurers'] use of certain characteristics of individuals for purposes of differential pricing. … It is important to take seriously the criticisms of both insiders and outsiders to strengthen both the application and development of economics or any other social science. His criticisms are very well thought out.'

Michael Hoy Source: Annals of Actuarial Science

'In summary, Loss Coverage offers policymakers, academics, professionals, students, and other interested parties useful insight into the 'problem' of adverse selection. Thomas employs simple and timely real-world examples to make the concepts of adverse selection, loss coverage, and risk classification more understandable and relevant for policy decisions, offering a path toward mitigating concerns over unfair discrimination while increasing insurance market efficiency.'

William L. Ferguson Source: Journal of Risk and Insurance

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Contents

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