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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Capping risk adjustment?

Patrick Eugster¹, Michèle Sennhauser, Peter Zweifel

¹Allianz Suisse, Bleicherweg 19, Zurich, Switzerland. patrick.eugster@allianz-suisse.ch

Journal of Health Economics

|May 14, 2010

Summary

This summary is machine-generated.

Community-rated insurance premiums use risk adjustment (RA) to prevent insurers from selecting low-risk patients. Capping RA volume can reduce efficiency incentives, but this study explores minimizing risk selection costs.

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Area of Science:

Health economics
Insurance market regulation
Public health policy

Background:

Community-rating and risk adjustment (RA) are used in health insurance markets to balance risk.
RA mitigates insurer incentives for risk selection but can reduce efficiency incentives if not fully prospective.
Capping RA volume introduces financial risk for insurers, potentially counteracting efforts to refine RA formulas.

Purpose of the Study:

To investigate methods for minimizing the opportunity cost associated with capping risk adjustment volume.
To analyze the trade-offs between capping RA and maintaining insurer efficiency incentives.
To evaluate the impact of new RA adjusters on risk selection incentives.

Main Methods:

The study analyzes the economic incentives within a community-rated insurance market with risk adjustment.
It models the effects of capping the volume of risk adjustment transfers.
The analysis considers the introduction of a new adjuster related to hospitalization or nursing home residency.

Main Results:

Capping risk adjustment volume can reduce insurers' incentives for efficiency.
There is an inherent tension between refining RA formulas (increasing RA volume) and capping RA volume.
The introduction of specific adjusters, like hospitalization or nursing home status, needs careful consideration within the capping framework.

Conclusions:

Minimizing the opportunity cost of capping RA requires balancing risk selection and efficiency incentives.
Policy interventions like capping RA must account for their impact on insurer behavior and market efficiency.
Further research is needed to optimize RA mechanisms in regulated insurance markets.