NBER Publications by Anirban Basu

Features of Part D gave rise to broad concern that the drug benefit would negatively impact prescription utilization among the six million dual eligible beneficiaries, either during the transition from state Medicaid to Part D coverage, or in the long-run. At the same time, Part D contained other features, such as its auto-enrollment and premium subsidization policies, which were designed to safeguard utilization for this vulnerable group. Using national retail pharmacy claims, we examine the experience of dual eligibles during the first 18 months of Part D. We find no evidence that Part D adversely affected pharmaceutical utilization or out-of-pocket expenditures in the transition period, or in the 18 months subsequent to Part D implementation.

Under the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the combination of both as in doubly-robust estimators. However, comparison of these alternative methods is sparse in the context of data generated via non-linear models where treatment effects are heterogeneous, such as is in the case of healthcare cost data. In this paper, we compare the performance of alternative regression and propensity score-based estimators in estimating average treatment effects on outcomes that are generated via non-linear models. Using simulations, we find that in moderate si...