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Improving prisoner re-entry outcomes through large-scale behavioral interventions
Last registered on August 04, 2017


Trial Information
General Information
Improving prisoner re-entry outcomes through large-scale behavioral interventions
Initial registration date
August 03, 2017
Last updated
August 04, 2017 12:47 PM EDT
Primary Investigator
University of Virginia
Other Primary Investigator(s)
PI Affiliation
University of Virginia
Additional Trial Information
In development
Start date
End date
Secondary IDs
As the U.S. moves away from mass incarceration, more inmates are being released from prison each year. Unfortunately, two-thirds of released inmates will be re-arrested within three years. This high recidivism rate signals our failure to help formerly incarcerated individuals build stable lives after prison.

There is little rigorous evidence on how to improve re-entry outcomes for incarcerated individuals. Our project aims to provide evidence on the efficacy of a highly-scalable, technology-based, behavioral science strategy to improve re-entry outcomes.

In partnership with an education technology firm, we will implement and evaluate a tablet-based re-entry module to strengthen inmates’ transition back into society. Before release, the module will help inmates create a personalized transition plan. Post-release, we will provide ongoing information to inmates to keep them on track.

Our proposed approach has proven successful in other contexts, particularly in postsecondary education.

We will implement this program as an RCT to measure the causal effects of the pre-release module and the post-release prompts. We will follow individuals for at least 6 months post-release, using administrative data to measure effects on recidivism. In subsequent years we plan to scale the intervention to additional facilities and investigate mechanisms driving treatment impacts.
External Link(s)
Registration Citation
Castleman, Ben and Jennifer Doleac. 2017. "Improving prisoner re-entry outcomes through large-scale behavioral interventions." AEA RCT Registry. August 04. https://doi.org/10.1257/rct.2371-1.0.
Former Citation
Castleman, Ben, Jennifer Doleac and Jennifer Doleac. 2017. "Improving prisoner re-entry outcomes through large-scale behavioral interventions." AEA RCT Registry. August 04. http://www.socialscienceregistry.org/trials/2371/history/20179.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Recidivism (likelihood of being incarcerated again), and time incarcerated
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Leveraging adaptive tablet technology through our partner, Edovo, our intervention provides inmates with personalized information about accessing transition resources in their community and help them plan a course of action for re-entry. Edovo users will have the opportunity to create a customized transition plan by completing re-entry modules focused on various transition priority areas (e.g. housing, child care, health care). These modules will connect inmates to specific organizations that can provide ongoing support, and will help the users make a plan for following through on accessing transition-related supports.

Edovo users are randomly assigned to one of three equally sized experimental conditions: (1) control; (2) pre-release only; (3) pre-release + post-release. Users assigned to the control group will receive “business-as-usual”; that is, they will continue to access all standard Edovo programs through the tablet, and be given no more or less time with the tablets than users in the treated condition. Users in the pre-release only condition will have access to the re-entry modules and will receive a hard copy of their transition plan at their release. Users in the pre-release + post-release condition will also receive text messages containing their personalized list of re-entry resources as well as the ability to converse with a case-worker via text message.
Experimental Design Details
Randomization Method
The randomization process is computerized: the user’s Edovo user key is fed into a hash function which outputs one of the three experimental groups with equal probability. Put simply, experimental assignment for each user is determined by a virtual flip of a three-sided coin.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
10,000 individuals
Sample size: planned number of observations
10,000 individuals
Sample size (or number of clusters) by treatment arms
3,333 individuals control; 3,333 individuals pre-release treatment; 3,334 pre + post release treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)