Synthetic Controls with Staggered Adoption

TL;DR

This paper extends the synthetic control method to handle staggered policy adoption across units, providing bounds on estimation error and proposing a new weighting approach to improve causal inference accuracy.

Contribution

It generalizes SCM for staggered adoption, introduces partially pooled weights, and incorporates covariates, enhancing causal effect estimation in complex observational studies.

Findings

Proposed method reduces bias in staggered adoption settings.

Simulation studies demonstrate improved estimation accuracy.

Application shows minimal impact of teacher bargaining on school spending.

Abstract

Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units that closely balances the treated unit's pre-treatment outcomes. In this paper, we generalize SCM, originally designed to study a single treated unit, to the staggered adoption setting. We first bound the error for the average effect and show that it depends on both the imbalance for each treated unit separately and the imbalance for the average of the treated units. We then propose "partially pooled" SCM weights to minimize a weighted combination of these measures; approaches that focus only on balancing one of the two components can lead to bias. We extend this approach to incorporate unit-level intercept shifts and auxiliary covariates. We assess the performance of the proposed method via extensive simulations and apply our results to the question of whether teacher collective bargaining leads to higher school spending, finding minimal impacts. We implement the proposed method in the augsynth R package.