Design-based analysis in Difference-In-Differences settings with staggered adoption

In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the staggered adoption setting where units, e.g, individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences (DID) estimator is an unbiased estimator of a particular weighted average causal effect. We characterize the exact finite sample properties of this estimand, and show that the standard variance estimator is conservative. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper examines the estimation and inference of average treatment effects in panel data, particularly focusing on the staggered adoption setting. The study shows that under random assignment of adoption dates, the standard Difference-In-Differences (DID) estimator is an unbiased estimator of a specific weighted average causal effect, and characterizes the exact finite sample properties of this estimand. Additionally, it is demonstrated that the standard variance estimator tends to be conservative.