Richard C. van Kleef
Institute of Health Policy and Management
Erasmus University Rotterdam
PO Box 1738
3000 DR Rotterdam
NBER Working Papers and Publications
|September 2016||Deriving Risk Adjustment Payment Weights to Maximize Efficiency of Health Insurance Markets|
with Timothy J. Layton, Thomas G. McGuire: w22642
Risk adjustment of payments to health plans is fundamental to regulated competition among private insurers, which serves as the basis of national health policy in many countries. To date, estimation and evaluation of a risk adjustment model has been a two-step process. In a first step, the risk-adjustment payment weights are estimated using statistical techniques, generally ordinary-least squares, to maximize some statistical objective such as the R-squared; then, in a second step, the risk adjustment model is evaluated, usually with simulation methods, but without an explicit framework describing the objective of the model. This paper first develops such a framework and then uses it to replace the two-step “estimate-then-evaluate” approach with a one-step “estimate-to-maximize-the-obje...
|September 2015||Improving Risk Equalization with Constrained Regression|
with Thomas McGuire, Rene van Vliet, Wynand van de Ven: w21570
Several countries rely on regulated health plan competition to combine affordability of health plans with incentives for cost containment and quality improvement. Typically, these policies include premium regulation supplemented with risk equalization to compensate health plans for predictable variation in medical spending. An extensive empirical literature shows, however, that even the state-of-the-art risk equalization models undercompensate some risk groups and overcompensate others, leaving systematic incentives for risk selection. A natural approach to reducing under or overcompensation for a group is to include membership in that group as an indicator in the risk equalization model. For several types of indicators, however, inclusion can be problematic or infeasible. This paper intro...