Inference under Staggered Adoption: Case Study of the Affordable Care Act

TL;DR

This paper develops efficient inference methods for staggered adoption policies in panel data, providing confidence intervals for treatment effects, and applies them to assess the impact of Medicaid expansion under the ACA.

Contribution

It introduces new inference procedures for staggered treatment adoption in panel data, enabling accurate confidence intervals for treatment effects and their functionals.

Findings

Medicaid expansion significantly reduced uninsurance rates.

It led to a decrease in infant mortality rates.

No significant effect on healthcare expenditures.

Abstract

Panel data consists of a collection of $N$ units that are observed over $T$ units of time. A policy or treatment is subject to staggered adoption if different units take on treatment at different times and remains treated (or never at all). Assessing the effectiveness of such a policy requires estimating the treatment effect, corresponding to the difference between outcomes for treated versus untreated units. We develop inference procedures that build upon a computationally efficient matrix estimator for treatment effects in panel data. Our routines return confidence intervals (CIs) both for individual treatment effects, as well as for more general bilinear functionals of treatment effects, with prescribed coverage guarantees. We apply these inferential methods to analyze the effectiveness of Medicaid expansion portion of the Affordable Care Act. Based on our analysis, Medicaid expansion has led to substantial reductions in uninsurance rates, has reduced infant mortality rates, and has had no significant effects on healthcare expenditures.