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Spillovers in Crime Reduction: Using Network Data to Measure Social Returns and Improve Targeting of Interventions
Last registered on March 29, 2021

Pre-Trial

Trial Information
General Information
Title
Spillovers in Crime Reduction: Using Network Data to Measure Social Returns and Improve Targeting of Interventions
RCT ID
AEARCTR-0007363
Initial registration date
March 26, 2021
Last updated
March 29, 2021 2:24 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
PI Affiliation
University of Michigan
Additional Trial Information
Status
On going
Start date
2008-09-01
End date
2025-12-31
Secondary IDs
Abstract
Crime is often a group activity, especially among youth. Yet crime-prevention experiments typically ignore the possibility of peer spillovers, which could lead intent-to-treat estimates to either over- or under-state the true impact of a program. Since many more individuals are indirectly treated via their peers than are directly treated, accounting for such spillovers has the potential to substantially change assessments of the overall effect of these programs. Our study will rigorously measure peer spillovers from treatment in four existing crime-prevention experiments. The results will improve our understanding of a set of influential RCTs (and potentially many others), expand our knowledge of how people affect each other's criminal decision-making, and provide guidance to policymakers about how to leverage peer effects to maximize future program impacts.

The study will combine four existing randomized controlled trials that reduced violence in Chicago (total N > 12,000) with multiple administrative measures of social networks (N > 1 million) to estimate how changes in individual criminal behavior spread through connected populations. Using the random variation in exposure to treated peers induced by randomization, we will estimate the causal effect of different kinds of exposure. Totaling these estimates across the entire sample will provide a more complete understanding of the interventions' net effects, and allow us to calculate how biased impact estimates are if they ignore social spillovers onto controls. We will then use heterogeneity in these peer effect estimates to better understand and model how social interactions generate decisions about crime, allowing us to estimate which targeting strategies would be most effective in maximizing the social impact of an intervention.

The study will be a secondary analysis expanding on 4 existing RCTs:
• Becoming a Man
• One Summer Chicago Plus 2012 (AEARCTR-0002222)
• One Summer Chicago Plus 2013 (AEARCTR-0001472)
• Rapid Employment and Development Initiative (https://osf.io/ap8fj/)
External Link(s)
Registration Citation
Citation
Craig, Ashley and Sara Heller. 2021. "Spillovers in Crime Reduction: Using Network Data to Measure Social Returns and Improve Targeting of Interventions." AEA RCT Registry. March 29. https://doi.org/10.1257/rct.7363-2.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
This is a re-analysis of 4 existing RCTs, the descriptions of which can be found in Heller et al. 2017, Heller 2014, Davis & Heller 2020, and the pre-analysis plan for Bhatt et al (READI Chicago)
Intervention Start Date
2009-08-01
Intervention End Date
2021-12-31
Primary Outcomes
Primary Outcomes (end points)
Arrests for violent crime
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Arrests for property, drug, and other crime
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This is a secondary analysis of 4 existing RCTs, so the experimental designs are the same as in the original studies. In this study, we will use multiple administrative measures of social networks to link study participants to other individuals with whom they interact. The pre-existing social network combined with random assignment of treatment will let us overcome the common problems of the endogeneity of ties and common shocks in peer effect estimation.
Experimental Design Details
Not available
Randomization Method
N/A; see prior studies
Randomization Unit
N/A
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
N/A
Sample size: planned number of observations
We expect approximately 2 million people to be in the relevant administrative data sets, a subset of which will be tied to study individuals. The original studies contain about 12,000 people.
Sample size (or number of clusters) by treatment arms
N/A
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Michigan Health Sciences and Behavioral Sciences IRB
IRB Approval Date
2018-01-19
IRB Approval Number
HUM00139925
IRB Name
University of Chicago Social & Behavioral Sciences IRB
IRB Approval Date
2016-12-19
IRB Approval Number
IRB16-1325
Analysis Plan

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