Positive youth development through sports: Experimental evidence from Chicago

Last registered on September 20, 2021


Trial Information

General Information

Positive youth development through sports: Experimental evidence from Chicago
Initial registration date
March 18, 2018
Last updated
September 20, 2021, 9:19 AM EDT


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Primary Investigator

UChicago Urban Labs

Other Primary Investigator(s)

PI Affiliation
Urban Initiatives
PI Affiliation
UChicago Urban Labs
PI Affiliation
Booth School of Business University of Chicago

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Extracurricular programming, particularly organized sports, are in high demand at the same time that sports participation rates for low-income children are diminishing (Afterschool Alliance, 2014; Aspen Institute, 2017). If participation in high-quality sports programming induces academic, social emotional learning, or health benefits, then the "opportunity gap" during middle childhood may contribute to inequalities later in life. However, there exists little causal evidence on the effects of participation on elementary school sports teams. In this paper, University of Chicago Urban Labs (Urban Labs) in collaboration with Urban Initiatives (UI) examines the causal effects of UI's Work to Play program. Work to Play promotes health and positive youth development through soccer programming. Participants, mostly 2nd-4th grade students, meet before or after school three times a week for 20 weeks during fall and spring seasons at 43 Chicago Public Schools (CPS).

Urban Labs randomized students to Work to Play rosters or a wait list at 19 oversubscribed schools during the 2017-2018 school year. Urban Labs will measure the impact of the intervention using CPS administrative data and primary data collected from students, their parents, and their teachers. Our primary analysis measures the treatment-on-treated effect of Work to Play participation on students' school grades, self-management behaviors, and body mass index percentile. Findings from this research will not only equip UI with a better understanding of the impact of Work to Play across multiple developmental domains, but also provide evidence on whether socioeconomic disparities in access to high-quality sports programming in childhood could induce inequalities later in life.
External Link(s)

Registration Citation

Bertrand, Marianne et al. 2021. "Positive youth development through sports: Experimental evidence from Chicago." AEA RCT Registry. September 20. https://doi.org/10.1257/rct.2643-3.4000000000000004
Former Citation
Bertrand, Marianne et al. 2021. "Positive youth development through sports: Experimental evidence from Chicago." AEA RCT Registry. September 20. https://www.socialscienceregistry.org/trials/2643/history/100082
Sponsors & Partners

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Experimental Details


Urban Initiatives (UI) operates Work to Play (WTP), a sports-based positive youth development program, in 44 Chicago Public Schools. WTP provides students primarily in 2nd-4th grades the opportunity to play on a soccer team at their school. Participation is free and open to all students regardless of skill level. Teams meet before or after school three times a week for 20 weeks split between fall and spring seasons. Teams range in size from 20 to 45 players depending on coach and facility constraints. Each week consists of two practice sessions and one game against a WTP team at another school or team scrimmage. Each session includes at least 60 minutes of physical activity and a healthy snack.

Trained coaches implement a curriculum that incorporates lessons on social emotional learning and health into structured soccer activities. For example, two teamwork activities are making a team cheer and setting expectations and responsibility for players and coaches. Two heart health lesson are learning how to take a pulse and learning about the chambers of the heart through a game where teams are split on the field into four quadrants. Participants earn game time based on teacher reports of their academic engagement and classroom behaviors. These reports also provide coaches with an opportunity to discuss school behaviors with players.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes of interest are post-spring season grade point average (GPA), body mass index percentile, and self-management behaviors.
Primary Outcomes (explanation)
● We will calculate grade point average (GPA) based on students' grades in core courses (Math, Reading, Science, and Social Studies). Letter grades will be converted to a 4.0 scale.
● We will calculate body mass index percentile using the CDC formula to convert measures of height and weight to percentiles based on a child’s sex and age.
● We will create a composite measure of self-management in interpersonal and school work domains based on teacher report. We will scale each construct separately on the same metric (range) and then we will add them together to create an overall self-management measure

Secondary Outcomes

Secondary Outcomes (end points)
● Academic behaviors and performance: CPS administrative data on standardized test scores, daily attendance, disciplinary reports, grade retention, and eligibility for summer school; student-report measures of school interest and competency
● Health: student-report measures of physical activity and self-efficacy to make healthy choices. Parent-report measures of physical activity and food choice.
● Socio-emotional learning: student-report measures of growth mindset, school belonging, interest and competence in peer interactions, self-esteem, internalizing behaviors, externalizing behaviors, and grit; teacher report measures of social awareness and grit; parent-report measures of self-management, social awareness, and grit.
● Parental outcomes: parent-report measures of involvement in their child’s school and extracurricular activities.
● Long-term outcomes: CPS administrative data for two years following the intervention; My Voice My School Survey, collected for 4th grade students and older, for two years following the intervention.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
More interested participants signed up for WTP than available roster spots at 19 schools during the 2017-2018 school year. Roster sizes differed by school based on coaching staff and space constraints ranging from approximately 25 to 45 new and returning participants for the fall season. Since UI guaranteed roster spots to returning students and their siblings, random assignment determined who received the remaining open spots on the roster. Urban Labs (UL) used block random assignment to assign interested new participants to the WTP roster (treatment group) or waitlist (control group) at their school.

Before the fall season, we assigned students to the fall roster until all spots were filled. We assigned all remaining students to the waitlist condition at their school. We added students on the waitlist to the roster over the first two weeks of the season, if UI identified students on the roster who had transferred schools or decided not to participate on the soccer team. We did not adjust rosters to include waitlist students again until the spring season.
Experimental Design Details
Not available
Randomization Method
Urban Labs conducted randomization using Stata and shared the results with Urban Initiatives.
Randomization Unit
Individual student level randomization within school blocks.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Treatment was blocked within in 19 study schools during the 2017-2018 school year. Randomization also occurred at 5 study schools in a pilot feasibility study during the 2016-2017 school year.
Sample size: planned number of observations
In our main analysis, we randomized 530 student across 19 schools. An additional 97 students at 5 schools were randomized during the pilot year.
Sample size (or number of clusters) by treatment arms
In the fall, 220 study students were assigned to treatment and 310 to the wait list condition. Additional students will be treated in the spring season when roster spots become available.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We expect minimal detectable effects sizes (Cohen’s d) of 0.196 for grade point average, 0.179 for body mass index percentile, and 0.212 for self-management under conservative estimates of the variance explained by baseline covariates.

Institutional Review Boards (IRBs)

IRB Name
The University of Chicago
IRB Approval Date
IRB Approval Number
Analysis Plan

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