Deriving risk adjustment payment weights to maximize efficiency of health insurance markets

Timothy Layton, Thomas G. McGuire and Richard C. van Kleef

Journal of Health Economics, 2018, vol. 61, issue C, 93-110

Abstract: Risk-adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a risk-adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of risk-adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating risk-adjustment weights. We show that when the number of risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate risk-adjustment payment weights in the Netherlands (N = 16.5 million).

Keywords: Health insurance; Risk adjustment; Adverse selection (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Deriving Risk Adjustment Payment Weights to Maximize Efficiency of Health Insurance Markets (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jhecon:v:61:y:2018:i:c:p:93-110

DOI: 10.1016/j.jhealeco.2018.07.001

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