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Why insurance works better with some adverse selection

Tapadar, Pradip, Thomas, R. Guy (2018) Why insurance works better with some adverse selection. In: International Congress of Actuaries, 4-8 Jun 2018, Berlin, Germany. (KAR id:67434)

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Abstract

Regulatory restrictions on insurance risk classification are a common feature of personal insurance markets. Whilst such

restrictions appear motivated by social objectives, they may also induce adverse selection. This is usually perceived as a

disadvantage, both for insurers and for society. We suggest a counter-argument to this perception in circumstances where

modest levels of adverse selection lead to an increase in ‘loss coverage’, defined as expected losses compensated by

insurance for society as a whole. This happens if the shift in coverage towards higher risks more than offsets the fall in

number of individuals insured.

The possibility of this outcome depends on insurance demand elasticities for higher and lower risks. We state elasticity

conditions which ensure that for any downward-sloping insurance demand functions, loss coverage when all risks are pooled

at a common price is higher than under fully risk-differentiated prices. We also discuss some empirical evidence on

insurance demand elasticities, and some limitations of the loss coverage concept. For a more discursive treatment, see our

recent book Thomas (2017) and papers (Hao et al. (2016, 2016a, 2016b)).

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Pradip Tapadar
Date Deposited: 28 Jun 2018 09:24 UTC
Last Modified: 05 Nov 2024 11:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67434 (The current URI for this page, for reference purposes)

University of Kent Author Information

Tapadar, Pradip.

Creator's ORCID: https://orcid.org/0000-0003-0435-0860
CReDIT Contributor Roles:

Thomas, R. Guy.

Creator's ORCID:
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