Synthetic control inference for staggered adoption
提出一种适用于政策在不同时间点被不同地区采纳的合成控制方法，估计动态平均处理效应，并应用于政府间协调改革，发现暴力盗窃和卡特尔活动减少，凶杀案后期上升。
Summary 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.