Using synthetic control methodology to estimate effects of a Cure Violence intervention in Baltimore, Maryland

Abstract

Objective: To estimate the long-term impact of Safe Streets Baltimore, which is based on the Cure Violence outreach and violence interruption model, on firearm violence. Methods: We used synthetic control methods to estimate programme effects on homicides and incidents of non-fatal penetrating firearm injury (non-fatal shootings) in neighbourhoods that had Safe Streets’ sites and model-generated counterfactuals. Synthetic control analyses were conducted for each firearm violence outcome in each of the seven areas where Safe Streets was implemented. The study also investigated variation in programme impact over time by generating effect estimates of varying durations for the longest-running programme sites. Results: Synthetic control models reduced prediction error relative to regression analyses. Estimates of Safe Streets’ effects on firearm violence varied across intervention sites: some positive, some negative and no effect. Beneficial programme effects on firearm violence reported in prior research were found to have attenuated over time. Conclusions: For highly targeted interventions, synthetic control methods may provide more valid estimates of programme impact than panel regression with data from all city neighbourhoods. This research offers new understanding about the effectiveness of the Cure Violence intervention over extended periods of time in seven neighbourhoods. Combined with existing Cure Violence evaluation literature, it also raises questions about contextual and implementation factors that might influence programme outcomes.