Person:

McGuire, Thomas

The Supreme Court recently held that in reverse payment settlements of drug patent disputes, anticompetitive effects can be inferred if the reverse payment exceeds the patent holder’s anticipated litigation costs, absent some offsetting justification. Application of this standard is problematic because defendants usually (a) obscure the amount of the reverse payment and (b) claim their settlement was justified by risk aversion. Further, even if a net reverse payment can be proven, it is little help in estimating the period of delay or damages. This Article offers another type of evidence that demonstrates and quantifies anticompetitive effects. An otherwise unexplained bump in the patent holder’s stock price shows that the settlement created new future profits by extending the period without generic competition beyond what the stock market expected. The stock market test has several advantages: it rebuts the risk aversion claim (which cannot explain the stock price rise); it more effectively (though still conservatively) captures damages than the magnitude of the reverse payment; and, finally, it relies on the behavior of objective traders rather than deal makers with well-understood incentives to obscure the presence of a payment. We conduct a stock market event study on one of the early instances of a reverse-payment settlement to illustrate how the method works.

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 under- and 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.