Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago

• 成果类型: Article
• 署名作者: Bhatt, Monica P.; Heller, Sara B.; Kapustin, Max; Bertrand, Marianne; Blattman, Christopher
• 署名单位: University of Chicago; University of Chicago; University of Michigan System; University of Michigan; National Bureau of Economic Research; Cornell University; University of Chicago; Centre for Economic Policy Research - UK; University of Chicago
• 刊物名称: QUARTERLY JOURNAL OF ECONOMICS
• ISSN/ISSBN: 0033-5533
• DOI: 10.1093/qje/qjad031
• 发表日期: 2024
• 页码: 1-56
• 关键词: YOUTH VIOLENCE; perry preschool; crime; HEALTH; deterrence; PROGRAMS; therapy; designs

摘要

Gun violence is the most pressing public safety problem in U.S. 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, declined 65% (p = .13 after multiple-testing adjustment). Because shootings are so costly, READI generated estimated social savings between $182,000 and $916,000 per participant (p = .03), implying a benefit-cost ratio between 4:1 and 20:1. Moreover, participants referred by outreach workers-a prespecified subgroup-saw enormous declines in arrests and victimizations for shootings and homicides (79% and 43%, respectively) which 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.