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Whether government transfer programs increase the human capital of low-income children is a question of first-order policy importance. Such policies might help poor children if their parents are credit constrained, and so under-invest in their human capital. But it is also possible that whatever causes parents to have low incomes might also directly influence children's development, in which case transfer programs need not improve poor children's long-term life chances. While several recent influential studies suggest anti-poverty programs have larger human capital effects per dollar spent than do even the best educational interventions, identification is a challenge because most transfer programs are entitlements. We overcome that problem by studying the effects on children of a generous transfer program that is heavily rationed—means-tested housing assistance. We take advantage of a randomized housing voucher lottery in Chicago in 1997, for which 82,607 people applied, and use administrative data on schooling, arrests, and health to track children's outcomes over 14 years. We focus on families living in unsubsidized private housing at baseline, for whom voucher receipt generates large changes in both housing and non-housing consumption. Estimated effects are mostly statistically insignificant and always much smaller than those from recent studies of cash transfers, and are smaller on a per dollar basis than the best educational interventions.

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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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.