Working Papers

The Unintended Consequences of Supply-Side Drug Intervention: Evidence from DEA Chemical Classification (JMP)

Supply-side drug intervention plays a central role in both local and federal anti-drug policy, yet disruption of drug markets may lead to unintended changes in the behavior of individuals who participate these markets. Specifically, addicted users facing increased prices and reduced availability may turn to financially motivated crime in order to continue their consumption patterns. I find evidence that states with high levels of cocaine addiction experienced significant increases in property crime relative to states with lower levels of addiction after a nationwide supply shock in the cocaine market that reduced availability. This effect should be accounted for in both formulation and execution of supply side interventions as well as in the cost-benefit assessment of supply side drug policy as a whole.

Dispersion Weighted Synthetic Controls with Glen Waddell

We propose a new approach to synthetic-control methods, through which we regularize the consideration of variation in available control units and the stability properties of the synthetic control. Specifically, we introduce two penalties directly into the objective function, allowing for the endogenous down-weighting of donors to the synthetic control with outcomes that exhibit different patterns of variation before and after treatment, and donors tending to be distant from the synthetic control’s average each period. While nesting a typical approach, we offer an intuitively appealing method for applied researchers to evaluate the reasonableness of a variety of synthetic controls and consider the sensitivity of results.

Applications of Bayesian Model Averaging and Cross Validation to Synthetic Control

The synthetic control method approaches the identification of a treatment effect by explicitly modeling the untreated counterfactual, and offers a compelling alternative to the differences-in-differences technique in the context of a comparative case study. However, substantive improvements could be made to the model by approaching the method as a forecasting exercise rather than a matching problem. This paper applies a mixture of Bayesian model averaging and cross validation to the method, and demonstrates substantive improvements in the precision from 26% to 51% estimator using simulated data.