The shadow prices implied by individuals’ predictions and a risk adjustment policy are contained in Table 4. Three information assumptions are combined with three risk adjustment policies to produce nine sets of profit-maximizing shadow prices. The q for the “other” category is normalized to 1.00 in all cases, so each entry in the table needs to be read as the shadow price relative to this numeraire.
Begin with the first three columns of results, computed for the assumption that individuals can forecast health costs based only on their own age and sex. The very first column shows the consequences of no risk adjustment with this informational assumption. Individuals cannot forecast very well at all, so the incentives plans have to distort are small, even with no risk adjustment. All estimated q’s are close to 1.00 with the exception of birth-related expenditures. Birth-related expenses are more predictable with age and sex, so plans will have an incentive to ration these services more tightly. Interestingly, the two risk-adjustment systems tested, ADGs and HCCs, each exacerbate the distortion in birth expenses.Apparently the risk adjusted payments under these systems are relatively less correlated with the predictable expenses, and magnify plans’ incentives to discriminate against these categories of expenditures.
The second set of three columns reports q’s when individuals can predict based on 25 percent of the information contained in prior spending. Specifically, predicted spending for each person was figured as a weighted average of the prediction based on age-sex and the prediction based on age, sex, and prior year spending in that category. When we say “can predict based on 25% of the information contained in prior spending,” we mean, operationally, that the weight on the prior spending prediction is 25%, and the weight on the age-sex prediction is 75%.
Estimated q’s diverge quite a bit from 1.00 when individuals know only 25% of the information contained in prior use and there is no risk adjustment. The highest estimated q is mental health and substance abuse; three categories are together for the lowest. Recall that the q’s presented here are relative to the numeraire. “Other” services too might be distorted, and indeed, in this case it is evident that other services with their q of 1.00 are above the average q for all services.
The third panel of three columns presents calculated q’s, assuming individuals can predict spending based on forty percent of the information contained in prior spending. Note that with no risk adjustment, mental health and substance abuse services are quite distorted as evidenced by the q of 3.73. Figure 2 graphs these results. Risk adjustment attenuates the distortions, moving all q’s toward unity. The mental health and substance abuse are continues to have the largest service-specific q.
The two risk adjustment systems studied, ADGs and HCCs, have very similar effects on incentives. For some services, notably birth-related expenditures, risk adjustment improves matters, moving the profit-maximizing q closer to the overall average. But a favorable effect of risk adjustment is not uniform. The incentives to overprovide care for hypertension are exacerbated by risk adjustment. Mental health and substance abuse changes from a service that tends to be underprovided to one much closer to the average with either risk adjustment system. In the next section, we describe a summary index that shows that overall, risk adjustment helps, even if for some services, it makes matters worse. help with payday loans