Using Chicago police data, we train a machine learning model to predict the risk of being shot in the next 18 months. Out-of-sample accuracy is strikingly high. A central concern with using police data is “baking in” bias, or overestimating risk for groups likelier to interact with police conditional on behavior. Our predictions, however, accurately recover risk across demographic groups. Legal, ethical, and practical barriers should prevent using victimization predictions to target law enforcement. But using them to target social services could increase both the potential for interventions to reduce shootings and the available statistical power to detect those reductions.

This paper reports the results of two randomized field experiments, each offering different populations of Chicago youth a supported summer job. The program consistently reduces violent-crime arrests, even after the summer, without improving employment, schooling, or other arrests; if anything, property crime increases over two to three years. Using a new machine learning method, we uncover heterogeneity in employment impacts that standard methods would miss, describe who benefits, and leverage the heterogeneity to explore mechanisms. We conclude that brief youth employment programs can generate important behavioral change, but for different outcomes, youth, and reasons than those most often considered in the literature.