Synthetic Control Inference for Staggered Adoption

Abstract Many policies are replicated by other policymakers at different times. We introduce a synthetic control methodology to study policies with staggered adoption. 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. Applying the method to intergovernmental coordination reforms that centralize information sharing and enable joint policing across jurisdictions, we find that violent theft and cartel activity fall after adoption. Homicide is largely unchanged for most post-treatment periods, but rises later on.