Using Large-Scale Network Data to Measure Social Returns and Improve Targeting of Crime-Reduction Interventions

许多随机实验已经测试了围绕惩罚、工作和教育的政策是否可以预防犯罪行为。对预防计划的评估通常假设一个人的行为不会影响其他人的行为。如果暴露于犯罪干预的个人将他们在技能，信仰或时间使用方面的改进传递给他们的同龄人，忽略这些溢出效应可能会低估或高估干预的总影响。 本项目将记录犯罪行为的变化是如何通过社交网络传播的，方法是将现有的四个实验与从多个行政数据来源得出的社交网络的测量相结合。它将量化这些减少暴力干预措施对参与者及其同龄人的直接和间接影响。研究结果将使人们更好地了解每个项目的总体效果，以及同行在更广泛地塑造犯罪决策方面的作用。该项目将提供关于如何在未来有效地针对犯罪预防方案的可操作信息，描述方案运营商应该为谁服务，以最大限度地减少净犯罪的好处。 调查结果还将有助于确立美国在减少犯罪政策方面的全球领导地位。 这个项目将评估社交网络对犯罪干预计划的影响。 估计社会溢出效应面临着两个关键挑战：测量社交网络和因果识别同伴效应。本研究通过结合芝加哥人口范围内的行政警察和学校记录来解决第一个挑战，以捕捉不同类型的社会联系。PI通过将网络信息与芝加哥四个现有RCT产生的犯罪和暴力的外源性变化相结合来解决第二个挑战，所有这些RCT都集中在低收入青年身上。使用这些数据，PI将在每个干预措施被随机分配时构建个体之间的社会网络。随机对照试验的随机变化将允许PI测试治疗是否以及如何改变行为-通过直接参与和间接接触治疗的同龄人-以及哪些类型的社会关系重要。PI将根据个人的人口统计学，犯罪历史和网络特征测试同伴效应的异质性，以帮助理解和建模社会互动如何产生犯罪决策。最后，PI将使用该模型来评估未来RCT的替代靶向策略。该研究项目的结果将为减少犯罪的政策提供重要投入，从而改善美国的公共安全。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many randomized experiments have tested whether policies surrounding punishment, work, and education can prevent criminal behavior. Evaluations of prevention programs typically assume that one individual’s behavior does not affect anyone else’s. If individuals exposed to crime intervention transmit their improvements in skills, beliefs, or time use to their peers, ignoring these spillovers could understate or alternatively overstate an intervention’s total impact. This project will document how changes in criminal behavior spread through social networks by combining four existing experiments with measures of social networks derived from multiple sources of administrative data. It will quantify the direct and indirect effects of these violence-reduction interventions on participants and their peers. The results will generate a better understanding of the overall effect of each program, as well as the role of peers in shaping criminal decision-making more broadly. The project will provide actionable information on how to effectively target crime prevention programs in the future by describing whom program operators should serve to maximize net crime reduction benefits. The results will also help to establish the US as global leader in crime reducing policies. This project will estimate the effects of social networks on crime intervention programs. Estimating social spillovers faces two key challenges: measuring social networks and causally identifying peer effects. This research addresses the first challenge by combining population-wide administrative police and school records in Chicago to capture different kinds of social connections. The PIs solve the second challenge by combining the network information with exogenous variation in crime and violence generated by four existing RCTs in Chicago, all focusing on low income youth. Using these data, the PIs will construct the social network between individuals at the time each intervention was randomly assigned. The random variation from the RCTs will allow the PIs to test whether and how treatment changes behavior—both via direct participation and via indirect exposure to treated peers—as well as which types of social ties matter. The PIs will test for heterogeneity in peer effects based on demographic, criminal history, and network characteristics of individuals to help understand and model how social interactions generate criminal decisions. Finally, the PIs will us the model to evaluate alternative targeting strategies for future RCTs. The results of this research project will provide important inputs into policies to decrease crime and thus improve public safety in the US. The results will also help to establish the US as global leader in crime reducing policies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.