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α: calibrated so average coauthorship-adjusted count equals average raw count
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.