Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7362641 | Journal of Health Economics | 2018 | 18 Pages |
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 (Nâ=â16.5 million).
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Authors
Timothy J. Layton, Thomas G. McGuire, Richard C. van Kleef,