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Using Quantile and Asymmetric Least Squares Regression for Optimal Risk Adjustment
1Department of Economics, University of Trier, Trier, Germany.
Health Economics
|June 14, 2016
Summary
Optimal risk adjustment for direct risk selection (DRS) requires restricted quantile regression, not standard least squares. Insurer incentives and the contest success function (csf) critically influence transfer calculations for health insurance markets.
Area of Science:
- Health economics
- Insurance market dynamics
- Risk adjustment mechanisms
Background:
- Direct risk selection (DRS) is a key behavior in health insurance markets.
- Current risk adjustment practices often rely on least squares regression for transfer calculations.
- Understanding the structure of DRS is crucial for effective risk adjustment.
Purpose of the Study:
- To analyze optimal risk adjustment strategies for direct risk selection (DRS).
- To compare different regression methods for calculating risk adjustment transfers.
- To investigate the impact of contest success functions (csfs) on optimal transfer determination.
Main Methods:
- Integration of insurer DRS activities into a discrete choice model of individual health insurance choice.
- Application of restricted quantile regression and restricted asymmetric least squares regression.
- Empirical analysis using data from German and Swiss health insurers.
Main Results:
- Optimal risk adjustment transfers depend on the specific contest success function (csf) and the nature of DRS (positive or negative).
- Restricted quantile regression is optimal for the Tullock-csf, regardless of DRS type.
- Least squares regression is only optimal under specific conditions for a new csf.
Conclusions:
- Common practice of using least squares regression for risk adjustment transfers is often suboptimal.
- Optimal transfer calculations critically depend on the csf and whether insurers engage in positive or negative DRS.
- The choice of regression method significantly impacts risk adjustment outcomes in health insurance.
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