UTI LI ZATI ON THRESHOLDS I N RI SK ADJUSTMENT SYSTEMS

Risk adjustment systems, which reallocate funds among competing health insurers, often use risk adjustors that are based on utilization. The level of utilization that triggers an adjustor— the utilization threshold—is frequently chosen implicitly and uniformly. I study utilization thresholds empirically in the setting of the US Marketplaces. I demonstrate how an explicit choice of such thresholds, tailored to each adjustor, may improve the prediction fit of the risk adjustment system and decrease the incentives to game it. Using simulations, I find that a single alternative threshold may improve the prediction fit in some disease groups by up to 14 percent. A choice of multiple utilization thresholds, guided by a regression tree algo-rithm, may further improve fit while taking into account the effect on gaming incentives.