Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago
Quarterly Journal of Economics · 2023 · Monica Bhatt、Sara Heller、Max Kapustin、Marianne Bertrand、Christopher Blattman
中文摘要
枪支暴力是美国城市最紧迫的公共安全问题。我们报告了一项针对社区—研究者合作项目“芝加哥快速就业与发展倡议”（READI Chicago）的随机对照试验（N = 2,456）结果。该项目提供为期18个月的工作机会，同时辅以认知行为治疗及其他社会支持。算法与人工转介方法均识别出了枪支暴力减少空间极高的男性群体：在20个月的结果观察期内，对照组每100人中发生了11起枪击和凶杀受害事件。处理组中55%的人开始参与项目，这一参与率与面向死亡风险低得多人群的项目相当。20个月后，作为本研究主要结果指标的、综合三项严重暴力衡量指标而成的指数，未出现统计显著变化。不过，仍有迹象表明该项目模式具有前景。三项指标之一的枪击和凶杀逮捕人数下降了65%（多重检验调整后p = .13）。由于枪击事件成本极高，据估计READI为每名参与者带来了182,000至916,000美元的社会节省（p = .03），意味着收益成本比介于4:1至18:1之间。此外，由外展工作人员转介的参与者——一个预先设定的子群体——其枪击和凶杀相关的逮捕与受害事件分别大幅下降79%和43%，即使在多重检验调整后仍具有统计显著性。这些降幅主要集中在预测风险较高的外展转介对象中，表明人工识别与算法定向识别相结合可能效果更佳。
Abstract
Abstract 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 18:1. 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.