Adverse selection in health insurance markets leads to two types of inefficiency. On the demand side, adverse selection leads to plan price distortions resulting in inefficient sorting of consumers across health plans. On the supply side, adverse selection creates incentives for plans to inefficiently distort benefits to attract profitable enrollees. Reinsurance, risk adjustment, and premium categories address these problems. Building on prior research on health plan payment system evaluation, we develop measures of the efficiency consequences of price and benefit distortions under a given payment system. Our measures are based on explicit economic models of insurer behavior under adverse selection, incorporate multiple features of plan payment systems, and can be calculated prior to observing actual insurer and consumer behavior. We illustrate the use of these measures with data from a simulated market for individual health insurance.
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 the under-or overcompensation for a particular group is enriching the risk equalization model with risk adjustor variables that indicate membership in that group. For some groups, however, appropriate risk adjustor variables may not (yet) be available. For these situations, this paper proposes an alternative approach to reducing under-or overcompensation: constraining the estimated coefficients of the risk equalization model such that the under-or overcompensation for a group of interest equals a fixed amount. We show that, compared to ordinary least-squares, constrained regressions can reduce under/ overcompensation for some groups but increase under/ overcompensation for others. In order to quantify this trade-off two fundamental questions need to be answered: ''Which groups are relevant in terms of risk selection actions?'' and ''What is the relative importance of underand overcompensation for these groups?'' By making assumptions on these aspects we empirically evaluate a particular set of constraints using individual-level data from the Netherlands (N = 16.5 million). We find that the benefits of introducing constraints in terms of reduced under/overcompensations for some groups can be worth the costs in terms of increased under/overcompensations for others. Constrained regressions add a tool for developing risk equalization models that can improve the overall economic performance of health plan payment schemes.
Many regulated health insurance markets include risk adjustment (aka risk equalization) to mitigate selection incentives for insurers. Empirical studies on the design and evaluation of risk‐adjustment algorithms typically focus on mandatory health insurance schemes. This paper considers risk adjustment in the context of voluntary health insurance, as found in Chile, Ireland, and Australia. In addition to the challenge of mitigating selection by insurers, regulators of these voluntary schemes have to deal with selection by consumers in and out of the market. A strategy for mitigating selection by consumers is to apply some form of risk rating. Our paper shows how risk adjustment and risk rating interact: (1) risk rating reduces the need for risk adjustment and (2) risk adjustment reduces premium variation across rating factors, thereby increasing incentives for consumers to select in and out of the market.
The basic health insurance in the Netherlands includes a mandatory deductible of currently 385 euros per adult per year. Several municipalities offer a group contract for low-income people in which the deductible is reinsured, meaning that out-of-pocket spending under the deductible is covered by supplementary insurance. This study examines to what extent such reinsurance leads to higher pharmaceutical spending. We use a unique dataset from a Dutch health insurer with anonymized individual insurance claims for the period 2014-2017. We run a difference-in-difference regression to estimate the effect of reinsurance on pharmaceutical spending. The treatment group consists of enrollees from three municipalities that implemented reinsurance on January 1 st 2017. The control group includes enrollees from three municipalities that didn't implement reinsurance. We find that the introduction of reinsurance led to a statistically significant increase in pharmaceutical spending of 16% in the first quarter of 2017 and 7% in the second quarter. For the second half of 2017 the effect is small and not statistically significant. Conditional on people with low expected spending we find a statistically significant increase in pharmaceutical spending in all four quarters of 2017 varying from 22% to 30% per quarter.
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