Loss coverage: why insurance works better with some adverse selection

Table of Contents

Part I. Introduction:
1. The central ideas of this book
2. Adverse selection: a history of exaggeration

Part II. Loss Coverage:
3. Introduction to loss coverage
4. Basic mathematics of loss coverage
5. Further mathematics of loss coverage
6. Partial risk classification, separation and inclusivity

Part III. Further Aspects of Risk Classification:
7. A taxonomy of objections to risk classification
8. Empirical evidence on adverse selection
9. Myths of insurance rhetoric
10. Myths of insurance economics
11. Contexts where adverse selection may be stronger
12. Risk classification and moral hazard
13. Risk classification and big data

Part IV. Conclusion:
14. Summary and suggestions
Appendix A. Alternative demand functions
Appendix B. Multiple equilibria: a technical curiosity

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.

https://www.cambridge.org/us/academic/subjects/economics/finance/loss-coverage-why-insurance-works-better-some-adverse-selection?format=PB