A limited-sample benchmark approach to assess and improve the performance of risk equalization models

A new method is proposed to assess and improve the performance of risk equalization models in competitive markets for individual health insurance, where compensation is intended for variation in observed expenditures due to so-called S(ubsidy)-type risk factors but not for variation due to other, so-called N(on-subsidy)-type risk factors. Given the availability of a rich subsample of individuals for which normative expenditures, Y(NORM), can be accurately determined, we make two contributions: (a) any risk equalization scheme applied to the entire population, Y(REF), should be evaluated through its performance in the subsample, by comparing Y(REF) with Y(NORM) (not by comparing Y(REF) with observed expenditures, Y, in the entire population, as commonly done); (b) conventional risk equalization schemes can be improved by the subsample regression of Y(NORM), rather than Y, on the risk adjusters that are observable in the entire population. This new method is illustrated by an application to the 2004 Dutch risk equalization model.