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Influencing Youth Offenders: How Social Status Affects Cheating in Youth Corrections
Last registered on October 26, 2020

Pre-Trial

Trial Information
General Information
Title
Influencing Youth Offenders: How Social Status Affects Cheating in Youth Corrections
RCT ID
AEARCTR-0006658
Initial registration date
October 22, 2020
Last updated
October 26, 2020 8:16 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Fudan University
Other Primary Investigator(s)
PI Affiliation
Nanyang Technological University
PI Affiliation
Fudan University
Additional Trial Information
Status
Completed
Start date
2019-06-01
End date
2019-11-30
Secondary IDs
Abstract
We conducted an experiment with 204 youth inmates to study how the moral cost of cheating that was shaped by peers changed inmates’ cheating behavior. We find that innately dishonest inmates who naively revealed their higher willingness to cheat indeed cheated more in the actual game. When given the chance to observe an imperfect signal of whether a peer cheated, only innately dishonest inmates followed this signal and cheated more. This positive treatment effect increases with the saliency of the signal, and becomes more pronounced when the cheating signal is from an influential peer. No treatment effect was found for all inmates who observed cheating signals from non–influential peers.
External Link(s)
Registration Citation
Citation
Leong, Kaiwen, Huailu Li and Sharon Xuejing Zuo. 2020. "Influencing Youth Offenders: How Social Status Affects Cheating in Youth Corrections." AEA RCT Registry. October 26. https://doi.org/10.1257/rct.6658-1.0.
Experimental Details
Interventions
Intervention(s)
Our study was conducted at the Charlie Housing Unit (CHU) and Prison School where male youth offenders in Singapore are incarcerated. To measure each participant’s dishonesty, we followed Cohn et al. (2015) in asking them to play a coin toss game. Every participant was asked to toss a coin 10 times and to report the outcome after every toss. He will receive 5 reward points for every head he reports and 1 reward point for every tail he reports.We randomize (a) whether a participant sees a peer’s decision, and (b) the actual
number of heads his paired peer reported. This strategy allows us to identify the causal impact of peer effects through the cost of cheating.
Intervention Start Date
2019-09-01
Intervention End Date
2019-10-31
Primary Outcomes
Primary Outcomes (end points)
Every participant was asked to toss a coin 10 times and to report the outcome after every toss. He will receive 5 reward points for every head he reports and 1 reward point for every tail he reports. So the number of heads reported by each participant will be our primary outcome.
Primary Outcomes (explanation)
We will use percentage of heads for the empirical analysis.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our randomization procedure follows standard experiment design. Within each community group, participants were pre–randomized into two groups: the treatment group (T) and the control group (C). In the treatment group, half of the participants were randomly chosen to play the game first (T1) and the remaining half were randomly chosen to play later (T2). Each participant in the T2 group was randomly paired with a participant in the T1 group. For the control group, participants were also randomly split into C1 and C2, in which C1 played first and C2 played later. But, both C1 and C2 did not have the chance to view anyone’s game results.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
The randomization was performed at the community group level. Participants’ peer networks are mostly confined within the community groups. Half of the peers listed by participants are from their current community group.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
About 20 communities
Sample size: planned number of observations
About 200
Sample size (or number of clusters) by treatment arms
About 100 subjects will be assigned into the treatment group and another 100 will be assigned into the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We include all subjects from the facility, so this study is based on the total population.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Nanyang Technological University
IRB Approval Date
2019-02-15
IRB Approval Number
IRB-2018-12- 027
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
November 30, 2019, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
November 30, 2019, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
15 communities
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
204
Final Sample Size (or Number of Clusters) by Treatment Arms
84 in the treatment group
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS