Identifying dynamic spillovers of crime with a causal approach to model selection

Does crime in a neighborhood cause future crime? Without a source of quasi-experimental variation in local crime, we develop an identification strategy that leverages a recently developed test of exogeneity (Caetano (2015)) to select a feasible regression model for causal inference. Using a detailed incident-based data set of all reported crimes in Dallas from 2000 to 2007, we find some evidence of dynamic spillovers within certain types of crimes, but no evidence that lighter crimes cause more severe crimes. This suggests that a range of crime reduction policies that target lighter crimes (prescribed, for instance, by the 'broken windows' theory of crime) should not be credited with reducing the violent crime rate. Our strategy involves a systematic investigation of endogeneity concerns and is particularly useful when rich data allow for the estimation of many regression models, none of which is agreed upon as causal ex ante.