In this paper we take a very different approach to address the question of how to monitor selection-related quality distortions in the market for health insurance with managed care. We start from the assumption that plans maximize profit. We show that to do so, each plan rations by, in effect, setting a service-specific “shadow” price for each service. We interpret the shadow price as characterizing the incentives a plan has to distort services away from the efficient level. The shadow price captures how tightly or loosely a profit maximizing plan should ration services in a particular category in its own self-interest. Services that the plan should restrain will be characterized by higher shadow prices than services that the plan should provide generously. The shadow price is an operational concept, measurable with data from a health plan. We take the ratio of the shadow price for a particular service in relation to some numeraire service to create a “distortion index.”
After developing the shadow price measure of selection distortions and discussing the properties of services that will be over and underprovided (Section II), we illustrate how these shadow prices can be calculated with data from a health plan (Section III). Our purpose at this stage is not to draw conclusions about which services are distorted. To do so one needs data, just now emerging, on the behavior of managed care plans. Our purpose here is to illustrate how to calculate the shadow prices with health plan data, and to confront the issues involved in an empirical application. We go on to illustrate how our measures can be used to evaluate the efficiency properties of various strategies to deal with adverse selection, such as risk adjusting payments to managed care plans, and segmenting insurance markets with “carve outs.”
An analogy might be helpful at this point. Another question about the efficiency of markets is more familiar: Which firms’ outputs are most distorted by monopoly power? The direct approach to answering this would be to compare the existing price of each firm to an estimate of what the price would be in a competitive market. But since hypothesized competitive prices cannot be easily observed, the more common, indirect approach is to examine each firm’s elasticity of demand.
Following Lemer (1934), we could use firms’ elasticities to rank firms according to where output is likely to be distorted most. Demand elasticity does not directly measure the distortion, it simply is a measure of how bad the distortion would be, assuming the firm acts so as to maximize profit. In the market for managed care, the condition for profit maximization involves more than an elasticity-driven markup, but the method we use for exposing distortions is analogous to Lemer’s for flagging monopoly. We do not measure the distortion directly, but we do measure the strength of the economic forces creating the distortion.
Our analysis is based on a model of a profit maximizing managed care plan competing for enrollees. We assume that the plan cannot select enrollees on the basis of their future health care costs, either because the plan does not have this information or because there is an “open enrollment” requirement. Consumers, however, have some information about their future health care costs. The plan sets the quality of services in light of its beliefs about consumers’ knowledge. We analyze the incentives of the plan to distort quality in order to attract “good” enrollees – those with low expected future health care costs in relation to the capitated payment plans are paid. cheap payday loans