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Can Price Collars Increase Insurance Loss Coverage?

Chatterjee, Indradeb and Hao, MingJie and Tapadar, Pradip and Thomas, R. Guy (2023) Can Price Collars Increase Insurance Loss Coverage? [Preprint] (Submitted) (doi:10.2139/ssrn.4363818) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:100161)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.2139/ssrn.4363818

Abstract

Loss coverage, defined as expected population losses compensated by insurance, is a public policy criterion for comparing different risk classification regimes. Using a model with two risk-groups (high and low) and iso-elastic demand, we compare loss coverage under three alternative regulatory regimes: (i) full risk-classification (ii) pooling (iii) a price collar, whereby each insurer is permitted to set any premiums, subject to a maximum ratio of its highest and lowest prices for different risks. Outcomes depend on the comparative demand elasticities of low and high risks. If low-risk elasticity is sufficiently low compared with high-risk elasticity, pooling is optimal; and if it is sufficiently high, full risk classification is optimal. For an intermediate region where the elasticities are not too far apart, a price collar is optimal, but only if both elasticities are greater than one. We give extensions of these results for more than two risk-groups. We also outline how they can be applied to other demand functions using the construct of arc elasticity.

Item Type: Preprint
DOI/Identification number: 10.2139/ssrn.4363818
Refereed: No
Other identifier: https://ssrn.com/abstract=4363818
Name of pre-print platform: SSRN
Uncontrolled keywords: Insurance loss coverage, risk classification, price collar, partial community rating, pooling.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Pradip Tapadar
Date Deposited: 21 Feb 2023 16:40 UTC
Last Modified: 26 Sep 2023 10:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/100161 (The current URI for this page, for reference purposes)
Tapadar, Pradip: https://orcid.org/0000-0003-0435-0860
Thomas, R. Guy: https://orcid.org/0000-0003-4745-5849
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