A Framework for Synthetic Control Methods with High-Dimensional, Micro-Level Data: Applications to Neighborhood-Specific Crime Interventions

• Activity Number: 362
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Michael Robbins*
• Companies: RAND Corporation
• Keywords: Synthetic control methods ; Crime intervention ; Calibration ; Omnibus statistics ; High-dimensional ; Micro-level

Abstract

Synthetic control methodologies are applied to a neighborhood-specific crime intervention in Roanoke, VA. In the process, 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 that is used to jointly test for the presence of an intervention effect across multiple outcomes and post-intervention time periods is proposed. Analyses indicate that the Roanoke crime intervention did result in a decrease in crime levels, but the estimated effect of the intervention is not as statistically significant as it would have been had more naive approaches been used.