The Impact of Summer Youth Employment on Youth Outcomes
Last registered on January 24, 2020

Pre-Trial

Trial Information
General Information
Title
The Impact of Summer Youth Employment on Youth Outcomes
RCT ID
AEARCTR-0004185
Initial registration date
May 28, 2019
Last updated
January 24, 2020 11:39 AM EST
Location(s)

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Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
Additional Trial Information
Status
On going
Start date
2018-04-01
End date
2022-09-01
Secondary IDs
Abstract
This study examines the impact of participating in a summer youth employment program. The program runs for approximately 6 weeks, during which time participating youth work 20 hours per week in a variety of community service related capacities. To determine the causal impact of the program, we randomly assigned eligible applicants the opportunity to participate in the program in summer 2018. We plan to follow the youth for several years, collecting data on a variety of outcomes via a survey as well as through administrative sources.
External Link(s)
Registration Citation
Citation
Jacob, Brian. 2020. "The Impact of Summer Youth Employment on Youth Outcomes." AEA RCT Registry. January 24. https://doi.org/10.1257/rct.4185-1.1.
Former Citation
Jacob, Brian. 2020. "The Impact of Summer Youth Employment on Youth Outcomes." AEA RCT Registry. January 24. https://www.socialscienceregistry.org/trials/4185/history/61346.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The intervention is a summer youth employment program. The program runs for approximately 6 weeks, during which time participating youth work 20 hours per week in a variety of community service related capacities.
Intervention Start Date
2018-07-09
Intervention End Date
2018-08-01
Primary Outcomes
Primary Outcomes (end points)
Work readiness
Performance in school
Primary Outcomes (explanation)
Work readiness will be a composite variable created from survey responses. Performance in school will include composite variables from survey responses as well as measures of academic achievement and attainment collected from administrative education data.
Secondary Outcomes
Secondary Outcomes (end points)
Criminal justice involvement
Perceptions of the police
Employment and earnings
Secondary Outcomes (explanation)
Criminal justice involvement and perceptions of the police will be composite variables constructed from youth survey responses. Employment and earnings will be created from UI wage record data.
Experimental Design
Experimental Design
The program is regularly oversubscribed. That is, more eligible youth apply than there are spots available. For this reason, we worked with the public agency that administers the program to randomly select a subset of applicants and invited them to apply to the summer program.
Experimental Design Details
Not available
Randomization Method
Researcher generated randomization done via computer, and then provided to agency staff, who invited the treatment youth.
Randomization Unit
individual youth
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Roughly 3,900 eligible youth were included in the study; not clustered
Sample size: planned number of observations
Roughly 3,900.
Sample size (or number of clusters) by treatment arms
2,650 youth were assigned to the treatment group and 1,260 youth were assigned to the control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The sample size (including the proportion of treatment youth) was constrained by the operation of the program. However, we did conduct a power calculation to reassure ourselves that the study would be able to detect meaningful size effects. To determine the minimum detectable effect size (MDES) size for our experiment, we assumed a sample size of 1,800 treatment students and 1,200 control students. Because we are uncertain about the proportion of invited students who will take-up the offer to participate in JPC, we calculated the MDES for three different scenarios: 50% take-up, 70% take-up and 90% take-up. As a conservative assumption, we assume the R-squared of 0.10 for models that include randomization strata fixed effects as well as other student and neighborhood covariates. Based on these assumptions, we estimate our MDES for administrative outcomes will be 0.08 SD and 0.21 SD for survey outcomes. In order to translate these effect sizes into a more useful metric, we made assumptions (informed by some data we have available) about the distribution of outcomes in our population. For example, with 75% compliance, we will be able to detect effects on student attendance as small as 2 days per year and a 5 percentage point impact on the likelihood of having any arrests.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Michigan, Social and Behavioral Sciences
IRB Approval Date
2016-11-16
IRB Approval Number
HUM00120234
Analysis Plan

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