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Non-Cognitive Skills Development and School-Based Violence Reduction in Central America.
Last registered on May 26, 2019

Pre-Trial

Trial Information
General Information
Title
Non-Cognitive Skills Development and School-Based Violence Reduction in Central America.
RCT ID
AEARCTR-0003976
Initial registration date
March 07, 2019
Last updated
May 26, 2019 11:11 AM EDT
Location(s)

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Primary Investigator
Affiliation
Massachusetts Institute of Technology
Other Primary Investigator(s)
PI Affiliation
PUC Chile and JPAL
PI Affiliation
World Bank
Additional Trial Information
Status
In development
Start date
2019-03-08
End date
2020-03-31
Secondary IDs
Abstract
Literature in psychology –particularly positive psychology—has extensively studied non-cognitive skills as a measure of wellbeing of individuals and as a “buffer” to reduce psychological disorders among young people (Park, 2004, Jordan and Rand, 2018).

From the behavioral economic perspective, enhancing non-cognitive skills can be a relevant policy because these skills may influence decision making and economic behavior (Haushofer and Fehr, 2014; DellaVigna, 2009; Loewenstein, 2000). Particularly, in the context of poverty, stress and emotional instability may lead to short-sighted and risk-averse decision-making. In that sense, instead of adopting behaviors that may generate greater returns, individuals end up favoring habitual low yield ones. This, in turn, can generate vicious cycles or psychological poverty traps (Haushofer and Fehr, 2014).
Moreover, exposure to high violence can exacerbate inefficiency in individuals’ economic decisions. Existing evidence indicates that exposure to risky environments may have unwanted results on socio-emotional skills and further welfare outcomes (Peterson and Seligman, 2003; Baysan et al., 2018; Card and Dahl, 2011; Loewenstein, 2000). For at-risk people, the inability to regulate their emotions can make them more susceptible to respond to some stimuli with violence.

How can we tackle young people exposure to and participation in crime? After-school programs (ASP) are a type of intervention that can protect children, keeping them busy and off the streets during a time when they might be left alone and exposed to external risks, to preventing victimization and delinquent behavior (Gottfredson et al., 2004; Jacob and Lefgren, 2003; Newman et al., 2000). These programs can also act as an alternative source of learning and social development when they include a specific curriculum oriented to foster socio-emotional skills and impulsive responses control (Taheri and Welsh, 2016; Durlak et al., 2010; Eccles and Templeton, 2002).

This project aims to contribute to the economic literature and policy in two ways. First, it provides experimental evidence of the impact of three types of interventions: (i) extracurricular activities (EA, i.e. sports at school), (ii) mindfulness and EA, and (iii) Cognitive Behavioral Therapy and EA. We measure those interventions’ impacts on academic performance, cognitive and non-cognitive development.

Second, this project aims to separate these learning and protection services from the ASP. We argue that the effects we can find from EA are determined by children’s protection, while those from the other two interventions are mainly driven by the specific additional curriculum they are learning from the program.

The first intervention consists to participate in extra-curricular sports activities. In this treatment, children are not learning a specific curriculum, but are under adult supervision and protected from their risky contexts during a couple of hours. The second intervention is a combination between extra-curricular activities and a curriculum that promotes character strengths. In this program variation, students are protected and learn how to enhance their character or socio-emotional skills. The third intervention is a combination between extra-curricular clubs and a mindfulness program, including directed meditation for stress and anxiety reduction and control of automatic responses. Schools in the comparison group will not receive any of the three interventions.

Participants will meet once per week during one academic year (between 7-8 months). Each of the sessions is implemented after-school time, with an approximate duration of 1-1.5 hours. For methodological reasons, club sizes are between 13-15 participants on average.

External Link(s)
Registration Citation
Citation
Dinarte, Lelys, Pablo Egana del Sol and Claudia Martinez. 2019. "Non-Cognitive Skills Development and School-Based Violence Reduction in Central America.." AEA RCT Registry. May 26. https://doi.org/10.1257/rct.3976-2.0.
Former Citation
Dinarte, Lelys, Pablo Egana del Sol and Claudia Martinez. 2019. "Non-Cognitive Skills Development and School-Based Violence Reduction in Central America.." AEA RCT Registry. May 26. https://www.socialscienceregistry.org/trials/3976/history/47113.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
This project aims to evaluate the impact of three interventions oriented to enhance participants’ non-cognitive skills and character. Our target audience will consist of students enrolled in participants and comparison schools in Central America, with the ages between 12 and 16 years—second and third educational levels.

The first intervention consists to participate in extra-curricular activities that include dance, sports, art, among others. In this alternative, children are not learning a specific curriculum, but remain under adult supervision and protected from their risky contexts during a couple of hours. The second intervention is a combination of extra-curricular activities and a curriculum that promotes character strengths. In this program variation, students are protected and learn how to enhance their character or socio-emotional skills. The third intervention is a combination of extra-curricular clubs and a mindfulness program, including directed meditation for stress and anxiety reduction and control of automatic responses. Schools in the comparison group will not receive any of the three interventions.

Participants will meet once per week during one academic year (between 7-8 months). Each of the sessions is implemented after-school time, with an approximate duration of 1-1.5 hours. For methodological reasons, club sizes are between 13-15 participants on average.
Intervention Start Date
2019-03-11
Intervention End Date
2019-10-31
Primary Outcomes
Primary Outcomes (end points)
(i) Strengths and Virtues (Peterson and Seligman, 2004), (ii) non-cognitive games using SoftGames App (Danon et al, 2018), (iii) Emotional reaction and regulation.
Primary Outcomes (explanation)


For each strand of data that we mentioned before, we will use the following instruments:

● Strengths and Virtues. We will employ the Values in Action Inventory of Strengths (Peterson and Seligman, 2004). It is a catalog of positive psychological traits, consisting of 240 items, ten for each of 24 different character strengths. Following Jordan and Rand (2018), we will use an adapted version that consists of only 24 items that ask directly about each character strength. Participants will be asked to respond, “It is natural and effortless for me to express my X CS”. The scale is [Completely true; Very true; Somewhat true; Not true/untrue; Somewhat untrue; Very untrue; Completely untrue]. This instrument will be available in tablets.

● Non-cognitive skills. As a complement of the above, we will ask participants to run and complete the SoftGame App, a game-based digital instrument (in tablets) developed by Danon et al. (2018) that measures five non-cognitive traits:
○ GRIT: Grit Scale (Duckworth et al., 2007), Additions game, frustration task.
○ BigFive Scale
○ Locus of Control Scale
○ Impulsiveness: Barratt Impulsiveness Scale, Go-noGo Task, STEP
○ Risk taking behavior: Balloon Analogue Risk Task (BART).

● Emotions. We will proxy emotions using the artificial intelligence-based algorithm developed by Affectiva and the MIT Media Lab’s Affective Computing Group. It uses the analysis of videos captured from the front camera of smartphones or tablets to proxy for emotions. The level of accuracy of this methodology is around 75-80%. Then, we will use the specifications of Egana-delSol (2016) to construct arousal (proxy of stress) and valence indices (proxy of emotional regulation) at resting state and in the onset of a positive and negative stimuli.
Secondary Outcomes
Secondary Outcomes (end points)
(iv) Administrative data on misbehavior and school attendance, and (v) academic performance using school records.
Secondary Outcomes (explanation)
For each strand of data that we mentioned before, we will use the following instruments:



● Misbehavior at school and attendance. It will be measured from the student behavior reports in the schools, that we will obtain through administrative reports of the teachers. They are in scale [excellent; very good; well; average; below average]. These categories can be transformed into scores that can go from 0-10 points.

● Academic performance. Reports of math and student science notes on scales of 0-10 points will be standardized at the course level. These data will be collected from the administrative bases of the schools. In coordination with Ministries of Education, we will also develop a short quiz for math, to have a standardized test for all schools as in Rao (2018).
Experimental Design
Experimental Design
The randomized control trial that will generate the data and evidence we are looking for will be implemented in 21 schools, including 7 in El Salvador, 7 in Honduras, and 7 in Guatemala. Our randomization unit will be educational-level. In El Salvador, each educational level includes three courses: the first level includes first to third grades, the second level includes fourth to sixth courses, and the third level is constituted by grades seventh to ninth. Each level attends school either in the morning or evening shift. By stratifying at the country, school and school's risk level, all participating educational levels will be randomly assigned to three treatments:

● T1: Clubs: 1/3 of educational levels (14 levels) will be randomly assigned to the intervention of extracurricular clubs. Schools in this treatment will receive specific activities of the clubs including sports, languages, among others.

● T2: T1 + CS: 1/3 of educational levels (14 levels) will be randomly assigned to the intervention of CS + extracurricular clubs (T1). In each of the sessions, schools in this treatment will first receive the curricula that encourage CS and in the second part of the session, the specific activities of the clubs are developed.

● T3: T1 + Mindfulness: The remaining 1/3 of educational levels will be randomly assigned to the intervention of Mindfulness + extracurricular clubs (T1). In each of the sessions, schools in this treatment will first receive the curricula of Mindfulness and in the second part of the session, the specific activities of the extracurricular clubs.

In addition, we will identify 7 schools, and their respective 14 educational levels, that will serve as a comparison group. Exploiting information from National Educational Censuses, we will use matching in propensity score approach to identify schools that are more like to those treated. From these comparison schools, we will gather information to characterize the potential demand for the Clubs.
Experimental Design Details
Not available
Randomization Method
Randomization Method: randomization done in office by a computer,
Randomization Unit
Unit of randomization: School

Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
28 schools in total.
Sample size: planned number of observations
The final sample should be 2552 students, 638 from comparison schools and 1582 from treated schools, that accounts for an average of 92 students per school.
Sample size (or number of clusters) by treatment arms
Assuming power of 0.80 and a reliability of 0.95, from a total of 28 schools we will require a sample with size of 2320 students, 580 from comparison and 1740 from treated schools. On average, we must recruit 83 students per school. However, estimating an attrition of 10%, the final sample should be 2552 students, 638 from comparison schools and 1582 from treated schools, that accounts for an average of 92 students per school.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using previous evidence of Dinarte and Egana-Del Sol (2018), the expected effect of an intervention aimed at improving socio-emotional skills of adolescents participating in these interventions is between 0.17 and 0.10 standard deviations on behavior in school and academic performance, respectively. From that data, we also use mean and standard deviations of these two outcomes from the control group, correlations between baseline and follow-up of 0.81 and an intra-school correlation of 0.03. Assuming power of 0.80 and a reliability of 0.95, from a total of 28 schools we will require a sample with size of 2320 students, 580 from comparison and 1740 from treated schools. On average, we must recruit 83 students per school. However, estimating an attrition of 10%, the final sample should be 2552 students, 638 from comparison schools and 1582 from treated schools, that accounts for an average of 92 students per school.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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