A Framework for Synthetic Control Methods with High-Dimensional, Micro-Level Data

Evaluating a Neighborhood-Specific Crime Intervention

by Michael Robbins, Jessica Saunders, Beau Kilmer

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Synthetic control methods are an increasingly popular tool for analysis of economic programs. Here, they are applied to a neighborhood-specific crime intervention in Roanoke, VA, and several novel contributions are made to the synthetic control toolkit. We examine high-dimensional data at a granular level (the treated area has several cases, a large number of untreated comparison cases, and multiple outcome measures). Calibration is used to develop weights that exactly match the synthetic control to the treated region across several outcomes and time periods. Further, we illustrate the importance of adjusting the estimated effect of treatment for the design effect implicit within the weights. A permutation procedure is proposed wherein countless placebo areas can be constructed, enabling estimation of p-values under a robust set of assumptions. An omnibus statistic is introduced that is used to jointly test for the presence of an intervention effect across multiple outcomes and post-intervention time periods. Analyses indicate that the Roanoke intervention did decrease crime levels, but the estimated effect of the intervention is not as statistically significant as it would have been had less rigorous approaches been used.

The research described in this report was conducted by RAND Justice, Infrastructure, and Environment.

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