Synthetic Control Inference for Staggered Adoption

TL;DR

This paper develops a synthetic control method to analyze policies adopted at different times, providing unbiased estimates and valid inference, demonstrated through a study on gender diversity policies increasing female employment.

Contribution

The paper introduces a novel synthetic control approach tailored for staggered policy adoption, enabling accurate estimation of dynamic treatment effects.

Findings

Regulation on board gender diversity increases female full-time employment.

The method provides asymptotically unbiased estimators.

Valid inference is achieved for staggered policy effects.

Abstract

We introduce a synthetic control methodology to study policies with staggered adoption. Many policies, such as the board gender diversity policies, are replicated by other policy setters at different time frames. Our method estimates the dynamic average treatment effects on the treated using variation introduced by the staggered adoption of policies. Our method gives asymptotically unbiased estimators of many interesting quantities and delivers asymptotically valid inference. By using the proposed method and national labor data in Europe, we find evidence that regulation on board gender diversity leads to an increase in full-time employment for female professionals.