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This paper reports the results of two randomized field experiments, each offering different populations of youth a supported summer job in Chicago. In both experiments, the program dramatically reduces violent-crime arrests, even after the summer. It does so without improving employment, schooling, or other types of crime; if anything, property crime increases over 2-3 post-program years. To explore mechanisms, we implement a machine learning method that predicts treatment heterogeneity using observables. The method identifies a subgroup of youth with positive employment impacts, whose characteristics differ from the disconnected youth served in most employment programs. We find that employment benefiters commit more property crime than their control counterparts, and non-benefiters also show a decline in violent crime. These results do not seem consistent with typical theory about improved human capital and better labor market opportunities creating a higher opportunity cost of crime, or even with the idea that these programs just keep youth busy. We discuss several alternative mechanisms, concluding that brief youth employment programs can generate substantively important behavioral change, but for different outcomes, different youth, and different reasons than those most often considered in the literature.

Zonder onderwerpscode: economie

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This paper shows that shootings are predictable enough to be preventable. Using arrest and victimization records for almost 644,000 people from the Chicago Police Department, 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: of the 500 people with the highest predicted risk, almost 13 percent are shot within 18 months, a rate 128 times higher than the average Chicagoan. A central concern is that algorithms may "bake in" bias found in police data, overestimating risk for people likelier to interact with police conditional on their behavior. We show that Black male victims more often have enough police contact to generate predictions. But those predictions are not, on average, inflated; the demographic composition of predicted and actual shooting victims is almost identical. There are legal, ethical, and practical barriers to using these predictions to target law enforcement. But using them to target social services could have enormous preventive benefits: predictive accuracy among the top 500 people justifies spending up to $134,400 per person for an intervention that could cut the probability of being shot by half.

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This paper reports the results of two randomized field experiments, each offering different populations of youth a supported summer job in Chicago. In both experiments, the program dramatically reduces violent-crime arrests, even after the summer. It does so without improving employment, schooling, or other types of crime; if anything, property crime increases over 2-3 post-program years. To explore mechanisms, we implement a machine learning method that predicts treatment heterogeneity using observables. The method identifies a subgroup of youth with positive employment impacts, whose characteristics differ from the disconnected youth served in most employment programs. We find that employment benefiters commit more property crime than their control counterparts, and non-benefiters also show a decline in violent crime. These results do not seem consistent with typical theory about improved human capital and better labor market opportunities creating a higher opportunity cost of crime, or even with the idea that these programs just keep youth busy. We discuss several alternative mechanisms, concluding that brief youth employment programs can generate substantively important behavioral change, but for different outcomes, different youth, and different reasons than those most often considered in the literature.

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Gun violence is the most pressing public safety problem in American cities. We report results from a randomized controlled trial (N = 2, 456) of a community-researcher partnership called the Rapid Employment and Development Initiative (READI) Chicago. The program offered an 18-month job alongside cognitive behavioral therapy and other social support. Both algorithmic and human referral methods identified men with strikingly high scope for gun violence reduction: for every 100 people in the control group, there were 11 shooting and homicide victimizations during the 20-month outcome period. Fifty-five percent of the treatment group started programming, comparable to take-up rates in programs for people facing far lower mortality risk. After 20 months, there is no statistically significant change in an index combining three measures of serious violence, the study's primary outcome. Yet there are signs that this program model has promise. One of the three measures, shooting and homicide arrests, declines 65 percent (p = 0.13 after multiple testing adjustment). Because shootings are so costly, READI generates estimated social savings between $182,000 and $916,000 per participant (p = 0.03), implying a benefit-cost ratio between 4:1 and 18:1. Moreover, participants referred by outreach workers--a pre-specified subgroup--show enormous declines in both arrests and victimizations for shootings and homicides (79 and 43 percent, respectively) that remain statistically significant even after multiple testing adjustments. These declines are concentrated among outreach referrals with higher predicted risk, suggesting that human and algorithmic targeting may work better together.