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).

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doi.org/10.1016/j.jhealeco.2018.07.001, hdl.handle.net/1765/109744
Journal of Health Economics
Erasmus School of Health Policy & Management (ESHPM)

Layton, T.J. (Timothy J.), McGuire, T.G. (Thomas G.), & van Kleef, R. (2018). Deriving risk adjustment payment weights to maximize efficiency of health insurance markets. Journal of Health Economics, 61, 93–110. doi:10.1016/j.jhealeco.2018.07.001