Beyond statistics: the economic content of risk scores
In recent years, the increased use of "big data" and statistical techniques
to score potential transactions has transformed the operation of insurance and credit
markets. In this paper, we observe that these widely-used scores are statistical objects
that constitute a one-dimensional summary of a potentially much richer heterogeneity,
some of which may be endogenous to the specific context in which they are applied.
We demonstrate this point empirically using rich data from the Medicare Part D prescription drug insurance program. We show that the "risk scores", which are designed
to predict an individual's drug spending and are used by Medicare to customize reimbursement rates to private insurers, do not distinguish between two different sources
of spending: underlying health, and responsiveness of drug spending to the insurance
contract. Naturally, however, these two determinants of spending have very different
implications when trying to predict counterfactual spending under alternative contracts.
As a result, we illustrate that once we enrich the theoretical framework to allow individuals to have heterogeneous behavioral responses to the contract, strategic incentives
for cream skimming still exist, even in the presence of "perfect" risk scoring under a