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Can price collars increase insurance loss coverage?

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  • Chatterjee, Indradeb
  • Hao, MingJie
  • Tapadar, Pradip
  • Thomas, R. Guy

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.

Suggested Citation

  • Chatterjee, Indradeb & Hao, MingJie & Tapadar, Pradip & Thomas, R. Guy, 2024. "Can price collars increase insurance loss coverage?," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 74-94.
    • Handle: RePEc:eee:insuma:v:116:y:2024:i:c:p:74-94
    DOI: 10.1016/j.insmatheco.2024.02.003
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    More about this item

    Keywords

    Insurance loss coverage; Risk classification; Price collar; Demand elasticity; Marginal revenue-to-cost ratio; Arc elasticity; Partial community rating;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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