metadata: authors: 0: author: Dinarte Diaz,Lelys Ileana
1: author: Egana-delSol,Pablo
2: author: Martinez A.,Claudia
3: author: Rojas A.,Cindy
count: El Salvador,Honduras,Guatemala
docna: 0: docna: When Emotion Regulation Matters : The
            Efficacy of Socio-Emotional Learning to Address School-Based
            Violence in Central America
docty: Policy Research Working Paper
subtopic: Effective Schools and Teachers,Educational Institutions & Facilities,Education for Development (superceded),Education For All,Educational Populations,Crime and Society
teratopic: Social Development,Education
abstracts: cdata!: After-school programs (ASP) that keep
            youth protected while engaging them in socio-emotional
            learning might address school-based violent behaviors. This
            paper experimentally studies the socio-emotional-learning
            component of an ASP targeted to teenagers in public schools
            in the most violent neighborhoods of El Salvador, Honduras,
            and Guatemala. Participant schools were randomly assigned to
            different ASP variations, some of them including
            psychology-based interventions, which constitutes the ASP
            socio-emotional-learning component. Results indicate that
            including psychology-based activities as part of the ASP
            increases by 23 percent-age points the probability that
            students are well-behaved at school. The effect is driven by
            the most at-risk students. Using data gathered from
            task-based games and AI-powered emotion-detection
            algorithms, this paper shows that improvement in emotion
            regulation is likely driving the effect. When comparing a
            psychology-based curriculum aiming to strengthen
            participants&#x2019; character and another based on
            mindfulness principles, results show that the latter
            improves violent behaviors while reducing school dropout.
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repnme: repnme: When Emotion Regulation Matters : The
            Efficacy of Socio-Emotional Learning to Address School-Based
            Violence in Central America
display_title: When Emotion Regulation Matters :
            The Efficacy of Socio-Emotional Learning to Address
            School-Based Violence in Central America
listing_relative_url: /research/2022/03/33751538/emotion-regulation-matters-efficacy-socio-emotional-learning-address-school-based-violence-central-america
topicv3: Teachers Management m1326119 1523,Education, Skills Development and Labor
            Market m1326135 1484,Soft Skills m1327287 2137,Homicide m1439222 HOM-3046,Psychiatry, Psychology m1504686 3613
publishtoextweb_dt: 2022-03-07T00:00:00Z
dois: 10.1596/1813-9450-9957
historic_topic: Social Development,Education
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 88
author_keys: 0: author_key: 1668999
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model_extractions: Policy Research Working Paper 9957

# When Emotion Regulation Matters

The Efficacy of Socio-Emotional Learning to Address School-Based Violence in Central America _Lelys Dinarte-Diaz Pablo Egana-delSol Claudia Martinez A. Cindy Rojas A._ Development Economics Development Research Group March 2022
Policy Research Working Paper 9957

# **Abstract**

After-school programs (ASP) that keep youth protected while engaging them in socio-emotional learning might address school-based violent behaviors. This paper experimentally studies the socio-emotional-learning component of an ASP targeted to teenagers in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala. Participant schools were randomly assigned to different ASP variations, some of them including psychology-based interventions, which constitutes the ASP socio-emotional-learning component. Results indicate that including psychology-based activities as part of the ASP increases by 23 percent-age points the probability that students are well-behaved at school. The effect is driven by the most at-risk students. Using data gathered from task-based games and AI-powered emotion-detection algorithms, this paper shows that improvement in emotion regulation is likely driving the effect. When comparing a psychology-based curriculum aiming to strengthen participants’ character and another based on mindfulness principles, results show that the latter improves violent behaviors while reducing school dropout.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ldinartediaz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
When Emotion Regulation Matters: The Efficacy of Socio-Emotional Learning to Address School-Based Violence in Central America<sup>*</sup> Lelys Dinarte-Diaz<sup>†</sup> Pablo Egana-delSol<sup>‡</sup> Claudia Martinez A.<sup>§</sup> Cindy Rojas A.<sup>¶</sup> Originally published in the Policy Research Working Paper Series on _March 2022._ This version is updated on _February 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**JEL Classification:** I29, K42, I25, D87

*We appreciate the feedback from Sofia Amaral, Samuel Berlinski, Chris Blattman, Christina Brown, Julian Cristia, Andy De Barros, Andrew Dillon, Taryn Dinkelman, Gemma Dipoppa, Rafael Di Tella, Valentina Duque, Claudio Ferraz, Francisco Gallego, Sebastian Gallegos, Felipe Gonzalez, James Heckman, John Eric Humphries, Caro Lopez, Jeanne Lafortune, David McKenzie, Daniel Mejia, Ernesto Schargrodsky, Manisha Shah, Mica Sviatschi, Roman Zarate, and participants in different seminars at which we presented this work. Egana-delSol acknowledges the support and generosity of Rosalind Picard and the Affective Computing Group at MIT and Affectiva, especially Maksat Amangeldiyev for helping construct the digital platform to collect data in the field. We are truly grateful for the support of Glasswing International as an implementer partner. We thank Carlos Espinosa, Tatiana Mojica, David Quiroz, and Steffanny Romero for their research assistance. Dinarte-Diaz acknowledges support from the Templeton World Charity Foundation, Inc., Grant Proposal TWCF0362 and the Research Support Budget (RSB) from the World Bank. Egana-delSol acknowledges the support provided by the Centre for Social Conflict and Cohesion Studies (COES ANID/FONDAP/1523A0005) and the ANID/NCS2021033 Millennium Nucleus on The Evolution of Work (MNEW). Martínez A. acknowledges the support from Fondecyt 1201305 and ANID/Millennium Nucleus on Intergenerational Mobility: From Modelling to Policy (MMOVI) [NCS2021072]. The research project’s protocol was reviewed and approved by the Institutional Review Board at the PUC Chile under ID Protocol 190129001. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. This study was registered in the AEA RCT Registry with a unique identifying number: AEARCTR-0003976

> †The World Bank, IZA, CESifo, HiCN. Email: ldinartediaz@worldbank.org

> ‡Adolfo Ibanez University School of Business, EGAP, MNEW. Email: pablo.egana@uai.cl.

> §Corresponding author. Inter-American Development Bank and Pontificia Universidad Católica de Chile, J-PAL. Email: claudiam@iadb.org

> ¶Inter-American Development Bank. Email: cindyro@iadb.org
# **1 Introduction**

Children living in gang-controlled areas of Latin America are at high risk of being exposed to or engaging in violent activities. These outcomes can affect adolescents’ well-being and economic opportunities, including worsening labor market outcomes or increasing involvement in crime (Sviatschi, 2022; Caudillo and Torche, 2014; Monteiro and Rocha, 2017; Heckman et al., 2006). For at-risk individuals, their inability to regulate their emotions can increase the likelihood that they will respond violently to some stimuli (Peterson and Seligman, 2003; Heller et al., 2017).

One to reduce this to and in violence is to limit these strategy exposure engagement students’ unstructured time. After-school programs (ASPs) are an attractive approach since they can _protect_ children by keeping them occupied and off the streets when they might otherwise be left unsupervised and exposed to external risks; they thereby prevent children’s victimization and delinquent behavior (Gottfredson et al., 2004). ASPs may also directly invest in students’ human capital. When these programs include a curriculum designed to foster socio-emotional skills and help students control their impulsive responses, they also offer an alternative source of _socio-emotional learning_ (SEL) (Taheri and Welsh, 2016; Durlak et al., 2010), which translates into improvements in adolescents’ violent behaviors and their ability to regulate their emotions (Dinarte-Diaz and Egana-delSol, 2023).

But whether the ASP SEL component affects adolescents’ behaviors and academic outcomes and which ASP curricula (targeting different skills) are more effective are unexplored questions. On the one hand, evidence from the education and crime literature shows that protecting youths through lengthening school days (Berthelon and Kruger, 2011; Jacob and Lefgren, 2003) or providing summer jobs (Heller, 2014) without offering violence-reduction curriculum can reduce adolescents’ violent and criminal behav- any iors. On the other hand, more recent evidence suggests that programs targeted at adolescents and young adults that include a psychology-based curriculum can reduce violencerelated and criminal outcomes (Heller et al., 2017; Blattman et al., 2017; Dinarte-Diaz and 1
Egana-delSol, 2023; Bhatt et al., 2024) and improve socio-emotional skills (Cook et al., 2014; Barker et al., 2021) and academic outcomes (Cook et al., 2014; Heller et al., 2017).

This paper experimentally evaluates the role SEL plays in the effectiveness of ASPs on violent behaviors and academic performance by conducting a simultaneous randomized control trial (RCT) in three of the most violent countries in the world. Specifically, we make three contributions. First, we experimentally evaluate the bundling SEL with ASPs. Second, we evaluate two different SEL curricula which target different skills. Third, we develop methods for directly measuring growth in socio-emotional skills. In combination, this allows us to not only evaluate the effectiveness of offering SEL as part of ASPs, but to understand how the choice of SEL curriculum may matter in the development of specific socio-emotional skills and later outcomes.

The ASP (and the variations that we explain below) was implemented in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala. The program was targeted at students aged 12 to 19 by the implementing NGO. Within the Central American context, this age range is significant because gangs are most likely to recruit adolescents of this age (International Crisis Group, 2017). All the ASP variations we study were implemented in school facilities after school, two days per week for seven months (between April and October) during the 2019 academic year. Each meeting lasted approximately 1.5 hours; 69% of students attended at least 70% of sessions. On average, each ASP was implemented in groups of 13–15 participants and was run by adult volunteers who had no formal training in social work or psychology, which matters for scalability.

We enrolled 897 students between 12 and 19 years of age from 21 public schools to participate in the study. We conducted a stratified randomization at the country (El Salvador, Honduras, and Guatemala) and school-risk level (high or low risk, proxied by the homicide rate in the school’s municipality),<sup>1</sup> and randomly assigned the 21 participating schools (with equal probability)<sup>2</sup> to one of three treatment arms.<sup>3</sup> The first group received

> 1A school was denoted “high risk” if it was located in a municipality with a homicide rate greater than the mean rate at the country level in 2018.

> 2We estimate using wild clusters to correct standard errors, given the small number of schools.

> 3Because of constraints affecting the implementing NGO, we do not have a pure control group (a group

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the implementing NGO’s ASP ( _Clubs_ ), which includes extracurricular activities such as dance, sports, and art. The instructors of _Clubs_ played a limited role as facilitators (they registered participants’ attendance, distributed materials, and led the activities). The other two groups were assigned to a variation of the ASP that, in addition to the extracurricular activities of _Clubs_ , also contained supplementary activities from a psychology-based curriculum.<sup>4</sup> In these groups, the first half of the session consisted of the activities from the psychology-based curriculum and the second half included extracurricular activities from _Clubs_ .

We selected the psychology-based curriculum to test two programs that differ in design and cost. The first, the Character Strengths Development Program ( _Virtue_ ), aims to strengthen participants’ character and increase their psychological well-being. It was designed for the Central American context through a consultation process, as documented in Vásquez and Dinarte-Diaz (2021), and inspired by Peterson’s (2004) model of character strength and virtue. The curriculum includes 32 training and self-reflection activities distributed across the sessions. It employs an active-learning methodology, which places students at the center of the learning experience and motivates participation in individual and group activities.

The second psychology-based curriculum we study is Calm Classroom® ( _Mindful_ ), a mindfulness-based and relaxation-response program. The program includes 16 standard breathing, stretching, relaxation, and focusing activities. It aims to help participants to develop self-awareness, control of automatic responses, mental concentration, and emotion that does not receive any intervention) in our experimental design, which limits our ability to measure the pure protection component—that is, the impacts on outcomes of interest of being assigned to _Clubs_ relative to receiving no intervention. In designing the RCT, we aimed to address this by selecting similar schools through a propensity score matching approach. However, the result of the matching was that selected schools were not comparable in several student-level baseline characteristics (whether the student was enrolled in the afternoon shift, travel time, household composition (living with both parents or only with the mother), and mother’s education) and in most of the outcomes and mechanisms measured at the student level (including behavior at school, perseverance, self-control, risk aversion, and arousal). Therefore, we did not pursue this analysis.

> 4We acknowledge that participating in the _Clubs_ can strengthen students’ social learning, due to the opportunity to interact with other children, among other channels. In this sense, our hypothesis is that any impact we may find from the SEL component would be driven by the specific activities included in psychology-based curriculum, beyond the social learning that may come from the participation in the _Clubs_ .

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regulation. We used the standard curriculum, which is homogeneous across contexts and demographics. Thus, we can think of this curriculum as a blueprint that is cheaper and easier to replicate and scale up than other psychology-based programs, including _Virtue_ .

To measure the impacts of the ASPs on students’ outcomes and explore potential mechanisms, we collected data before the start of the intervention (baseline) and right after its completion (follow-up). To measure the main outcomes—namely, behavior at school, school dropout, and academic performance (math scores)—we rely on administrative records submitted as printed reports by teachers from the three countries. Data on behavior at school and math grades were collected at baseline and endline, whereas reports on school dropout are available only at endline.

Moreover, we undertook an innovative and exploratory investigation to provide evidence on three potential mechanisms underlying the effects of psychology-based interventions on students’ main outcomes—namely, socio-emotional skills, fluid intelligence,<sup>5</sup> and emotion regulation.<sup>6</sup> To gather measures of socio-emotional skills, we used taskbased games available in an application developed and used by Danon et al. (2023). We collected measures to proxy for perseverance, self-control, and risk-averse behaviors. To assess emotion regulation, we examined the impact of our intervention on indicators of emotional reactions, which we refer to as _emotion regulation_ markers. These markers were estimated using a software platform based on computer vision and AI called Reactiva (Amangeldiyev and Egana-delSol, 2018) and a platform designed to conduct research on behavioral economics in the field.<sup>7</sup> Last, we use a progressive matrices test (similar to a

> 5Fluid intelligence is consistent with Mullainathan and Shafir’s (2013) idea of mental bandwidth. The ASPs’ psychology-based curriculum may increase concentration capacity and, thus, improve fluid intelligence, which proxies for logical thinking.

> 6The literature defines _emotion regulation_ as a mixture of cognitive and emotional processes that affect a person’s disposition to act (Salzman and Fusi, 2010). Drawing from Kahneman’s (2011) theory of automaticity, which posits that individuals often act automatically and unthinkingly because of habitual responses, we argue that participants in both _Virtue_ and _Mindful_ may increase their emotion regulation by reducing their automatic emotional reactions to both positive and negative stimuli.

> 7 For the former, we leverage a software development kit, developed by a spin-off of MIT’s Affective Computing Group (Picard, 1995; McDuff et al., 2015), that measures different features of emotions and has been trained on millions of subjects. Regarding the latter, we construct an online platform with researchrelated protocols such as the ability to randomly show emotionally laden stimuli and present cognitive tests.

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Raven (1936)) test to assess the effect of emotion regulation on fluid intelligence<sup>8</sup> .

We document four main results. First, we find that compared to students in _Clubs_ , participants in the _Virtue_ and _Mindful_ curricula (henceforth, psychology-based interventions or curricula) are 23 percentage points (pp) more likely to be well-behaved at school ( _p-value_ =0.015). This improvement is equivalent to an increase of 36% relative to the share of students in _Clubs_ with an above-the-median behavior score. Moreover, we find no statistically significant effects of the SEL component on school dropout and math grades.

Second, we study the differential impacts of the SEL component on behavior at school for different student characteristics using a machine learning approach (Athey and Wager, 2016, 2019; Athey et al., 2019), following Wang (2022) and Carlana et al. (2022). Our results indicate that the most at-risk students are benefiting the most from the ASP’s SEL component. That is, the net effect of this ASP component is larger for students with greater exposure to risks (those living in high-homicide urban municipalities or engaging in worse behavior at school at baseline), students likely to have less adult supervision (not living with both parents, living with mothers with more years of education,<sup>9</sup> or enrolled in the afternoon shift at school), students whose demographic characteristics are positively correlated with violent behaviors (male and older students), and students who have lower fluid intelligence and higher alertness or stress at baseline. Therefore, the psychologicalbased curricula have a positive impact on those who might be more in need of the ASP SEL component.

Third, among the three potential mechanisms we explore, we only find evidence of emotion regulation as a mechanism driving the effects of the SEL component on behavior at school. Our results show that students who participated in the variant of the ASP that included psychology-based interventions reduced their emotion-regulation score (called ”valence”) by 0.24 standard deviations (sd) relative to students assigned to the variant that includes only a protection component ( _p-value_ =0.050). This reduction in emotion reg-

> 8We found, through correlation analysis of our study sample, that our progressive matrices tests correlate strongly with math scores. Results available upon request

> 9In Central America, women with higher levels of education are more likely to be employed in formal (non-domestic) full-time jobs, and, thus, their children are more likely to be alone after school.

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ulation can be interpreted as saying participants became emotionally and behaviorally calmer and less We rule out that the net effects of the SEL are impulsive. component driven by changes in socio-emotional skills or fluid intelligence. A potential explanation for these two mechanisms seem not to be at is that we are why play estimating average effects from two interventions that, by design, are targeting different skills.<sup>10</sup> Considering the above, our last result is that the type of curriculum for the SEL component matters. The net SEL component can complement the pure protection component’s effectiveness by including either of the psychology-based curricula. Compared to students in _Clubs_ , those who participated in _Virtue_ or _Mindful_ are better behaved at school (23 pp ( _p-value_ =0.035), and 24pp. ( _p-value_ =0.020), respectively). When we look at other main outcomes, we find that although the mindfulness curriculum produces a nonsignificant reduction in dropout rates of 4pp relative to _Clubs_ ( _p-value_ =0.12), the observed disparity between the two curricula is noteworthy ( _p-value_ =0.020).<sup>11</sup> Despite similar behavioral improvements, the underlying mechanisms of the curricula differ because of their distinct content. The virtue-based curriculum enhances behavior by decreasing risk-taking behaviors (0.25 sd, _p-value_ =0.020) and improving emotion regulation (a 0.27 sd decrease in the valence index, _p-value_ =0.030). The former effect is consistent with an excess of risk-taking in highly vulnerable and violent contexts, as in the three countries in our study. Meanwhile, the mindfulness-based curriculum affects both behavior and dropout rates by improving emotion regulation, which translates into an increase in logical-thinking (that is, fluid intelligence) scores after the subjects are disrupted by negative stimuli (0.22 sd, _p-value_ =0.010). This suggests a heightened resilience in youth, enabling better focus and improved performance on standardized tests such as the Raven’s matrices test. These findings highlight the different benefits of both curricular approaches. While both are effective in behavioral enhancement, the mindfulness approach offers additional advantages in reducing dropout rates and enhancing youth

> 10 _Mindful_ is expected to improve fluid intelligence, self-control, and emotion regulation, whereas _Virtue_ is expected to improve perseverance, self-control, risk-taking behaviors, and emotion regulation.

> 11The greater effects of _Mindful_ relative to _Virtue_ or _Clubs_ are not driven by differences in ASP attendance rates. On the contrary, we show that attendance in the _Virtue_ ASP was higher compared to the other two ASPs.

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resilience and focus, making it a particularly compelling option in educational settings.

This contributes to three strands of the literature. The strand includes paper first studies concerning how school-based programs targeted at adolescents can improve participants’ behaviors, academic outcomes, emotion regulation, and socio-emotional skills (Alan and Kubilay, 2024; Dinarte-Diaz and Egana-delSol, 2023; Jackson et al., 2020; de Chaisemartin and Navarrete, 2019; Levine and Zimmerman, 2010; Fleming et al., 2008; Vandell et al., 2007).<sup>12</sup> Some of these studies measure the total effects of programs that mostly just protect students for a certain period. Others measure the effect of combining protection and SEL elements on participants’ outcomes. We contribute to this literature by providing experimental evidence of the net SEL component of an ASP. This evidence has important policy implications related to the design of programs targeted at adolescents: since the SEL component provides an impact beyond the protection component, policy makers should invest in including psychology-based curricula in ASPs.

Second, this paper contributes to the growing economics literature on the impacts of psychology-based interventions on outcomes of at-risk adolescents and young adults.<sup>13</sup> Our findings demonstrate the efficacy of psychology-based interventions in highly violent environments, particularly benefiting the most at-risk children. Our study is innovative in comparing different types of psychology-based curricula that aim to decrease bad behavior in teenagers. Our results provide critical information on how to foster good behavior and emotion regulation through school-based interventions that differ in intensity and suitability. Moreover, since the type of curriculum matters, resources should be allocated to the most cost-effective curriculum. Last, we show that these programs can be implemented in a cost-effective manner. These programs are relatively cheap: the average cost per student of the respective ASPs is US$296.5 (US$269.4 for _Clubs_ , US$292.5 for _Mindful_ ,

> 12For meta-analysis of ASPs implemented in high-income countries, see Cappella et al. (2018); Kremer et al. (2015).

> 13 For research concerning cognitive behavioral therapy as a particular psychology-based curriculum, which has proven highly effective to improve violent behaviors and academic performance, see Heller et al. (2017) and Cook et al. (2014) for Chicago, Blattman et al. (2017) for Liberia, Barker et al. (2021) for Ghana, and Dinarte-Diaz and Egana-delSol (2023) for El Salvador. From the psychology evidence, evaluations of self-control strategies or grit show positive effects on achievement of academic goals (Duckworth et al., 2016) and perseverance (Santos et al., 2022).

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and US$327.6 for _Virtue_ ), which is only one-seventh<sup>14</sup> the cost of similar programs for atrisk youth in the United States (Heller et al., 2017). Remarkably, our back-of-the-envelope calculation shows a benefit–cost ratio that ranges from 11.3 to 45.2. Hence, from a public policy perspective, this program is worth investing in because it is likely to pay for itself in the short run and can even generate large additional welfare gains in the long run.

Finally, our paper makes an important contribution related to measurement of socioemotional skills and emotion regulation in the field. Most of the existing evidence on these outcomes has been obtained through self-reported questionnaires. However, self-reported responses always entail the risk of reference or self-reporting bias, an issue that becomes more salient when evaluating programs aimed at improving these outcomes since there is no way to assess the direction of the bias.<sup>15</sup> Recent papers have been using task-based games to avoid bias and obtain more objective measures of outcomes.<sup>16</sup> In addition to such games, we use an AI-powered emotion-detection experimental protocol in the field, which allows us to collect emotion-regulation and fluid-intelligence measures in school contexts with minimal infrastructure.

Unlike in other studies, our interventions take place within the unique violent environments of three Central American countries. During the last decade, these three nations have been the world’s 10 deadliest for The rate re- among places young boys. mortality lated to personal violence for adolescents was almost 10 times as high as the global average. Moreover, adolescent boys in Honduras are 1.9 times more likely to die by homicide than from any other cause. In El Salvador and Guatemala, one-third of the deaths of adolescent boys are due to homicide (UNICEF, 2017). In addition, during the last decade, these countries have experienced a 13% average reduction in the educational enrollment

> 14Alternatively, the PPP-adjusted cost of these at-risk programs in the United States would be 3.1 times the cost of the programs under analysis in this study.

> 15For example, Lira et al. (2022) shows that self-reporting questionnaires are prone to reference bias. Moreover, as argued in Egana-delSol (2016a), Egana-delSol (2016b), and Egana-delSol et al. (2023), participants may underreport their skill level because they may have become more aware of their skill level during the intervention and may feel that they have not achieved proficiency. Alternatively, they may overreport their skill level to prove that they have learned something from the intervention.

> 16See Danon et al. (2023). Also, see Kautz et al. (2014) for a discussion of a task-based framework for identifying and measuring noncognitive skills.

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rate, with over 18% of students reporting that they dropped out of school because of delinquency.<sup>17</sup> The grave situation in these countries points to the urgent need to find solutions to such violence.

# **2 Research Design**

## **2.1 Interventions**

To experimentally estimate the net impacts of the SEL component of an ASP, beyond the effects of the protection component, on behavioral and academic outcomes, we study three variations of an ASP: a traditional ASP with extracurricular activities (hereafter _Clubs_ ) and two psychology-based curricula, the Character Strengths Development Program ( _Virtue_ ) and Calm Classroom® ( _Mindful_ ). We describe the structure and curriculum of each of these three variations below.

As noted, in 2019, Glasswing International implemented these three ASP variations in public schools in the most violent neighborhoods of El Salvador, Honduras, and Guatemala.<sup>18</sup> The ASPs targeted students between 12 and 16 years old in the three countries.<sup>19</sup> In Central America, targeting programs at adolescents is important because they are more likely to be forcibly recruited or decide voluntarily to enroll in gangs (Cruz et al., 2016; International Crisis Group, 2017).

The three ASPs were implemented in school facilities after class time, two days per week for seven months in the 2019 academic On each ASP was year. average, implemented in groups of 13–15 participants. The ASPs were run by adult volunteers who had no formal training in social work or psychology.<sup>20</sup> During the sessions, the volunteers

> 17Moreover, in El Salvador, 66% of detained unaccompanied children cited violence on the part of organized criminal groups as their main motivation for seeking asylum in the United States (UNHCR, 2014).

> 18Glasswing International started operations in El Salvador in 2007 and has implemented projects throughout Central America, Mexico, Colombia, and the Caribbean. More information about Glasswing International and its work can be found here: https://glasswing.org/.

> 19As we explain below, the NGO requested that we allow a few students between 17 and 19 years of age to enroll. Yet they represent only 2.45% of our total sample.

> 20Three types of volunteers supported this ASP: community volunteers, who were tutors living in the

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supervised the adolescents and protected them from risky contexts for approximately 1.5 hours.

_Clubs_ , which serves as our curriculum of comparison for the SEL component, consists of extracurricular activities including sports, science, and arts.<sup>21</sup> To keep the structure of _Clubs_ as close to a pure protection ASP as possible, the instructors of this ASP variation played a restricted role, serving only as supervisors or facilitators who registered participants’ attendance, distributed materials, and led the activities. Instructors were not allowed to use any activities from the two curricula we describe below.

The structures of _Virtue_ and _Mindful_ groups were similar to _Clubs_ , but they also contained supplementary activities related to their specific curricular aims. That is, in each of the sessions, students participating in _Virtue_ and _Mindful_ first received the psychologybased curriculum during the first part of each session (more details below). During the second part of each session, they received the _Clubs_ intervention. The number of volunteers, types of facilities, and number of sessions scheduled per week were the same across all ASPs. We argue, therefore, that any difference in the effects of these interventions was driven by variations in the psychology-based curriculum instead of by how the ASP was implemented.

The first psychology-based curriculum we study, _Virtue_ , aimed to strengthen participants’ character and increase their psychological well-being.<sup>22</sup> In coordination with local experts and psychologists, Glasswing International developed the _Virtue_ program, which community and stood out because of their leadership skills; corporate volunteers, who were associated with a firm involved in a social project with Glasswing; and independent volunteers, who were usually college students involved in social work. Unfortunately, we do not have information on which type of volunteers facilitated each program. Yet we know that each volunteer was in charge of one and only one group; thus, contamination is not a concern.

> 21More details about this ASP can be found here: https://glasswing.org/program/ after-school-programs/. The type of extracurricular activity was fixed for each group. For example, if a group were focused on sports, then they were doing the same activity during the academic year—that is, they did not do any science-related activity. Dinarte-Diaz and Egana-delSol (2023) experimentally evaluated a version of this ASP in El Salvador and presents a detailed description of the program. Overall, they find that ASP participants improved their academic performance and behavior at school relative to students in the control group (no ASP).

22Evidence from psychology shows that character development can improve peer relations and measures of school grades and engagement (Park et al., 2017).

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was inspired by Peterson et al.’s (2004) model of character strengths and virtues.<sup>23</sup> These virtues are perseverance, self-control, perspective, courage, social intelligence, creativity, and hope. The curriculum includes 32 activities distributed across all of the sessions.<sup>24</sup> The _Virtue_ program included both training and self-reflection activities. The training sessions while the sessions invited to assess presented concepts, reflection participants their personal history and environment. To achieve the desired level of reflection, volunteers used an active-learning methodology, which placed the students at the center of the learning experience and motivated participation in individual and group activities. Participants actively practiced their strengths, reflected on how they had applied them in their daily lives, and acquired tools to adopt them easily.<sup>25</sup> For example, the _Virtue_ curriculum included four sessions (one training and three selfreflective) to develop perseverance. In the training session, participants discussed the definition of perseverance and how perseverance can be beneficial to them. Then, the selfreflection sessions included three main activities: The Backpack, My Map, and Fighting for the Puzzle. In The Backpack, participants were invited to think about two goals and to describe what tools they would carry in their backpacks to have ready at hand to achieve these goals. In My Map, participants discussed the path they would take to achieve the goals they defined in the previous activity, the potential obstacles they may face, and how the tools they included in their backpacks would help them overcome obstacles.<sup>26</sup> Finally, in Fighting for the Puzzle, participants had to earn the individual pieces of a puzzle and then it. Students had to some tasks such as in complete accomplish physical push-ups order to obtain each piece of the puzzle. Then, they discussed how achieving the goal (completing the puzzle) required them to exert effort.

The second curriculum we study, _Mindful_ , is a mindfulness-based and relaxation-

> 23Vásquez and Dinarte-Diaz (2021) describe how the _Virtue_ curriculum was developed. This process includes validation of the most relevant character strengths of the group of students from the three countries in our sample. The program was piloted with a group of adolescents in each country before it was implemented in participant schools in this study.

> 24For details on the structure of the program and sessions, see Appendix Table A1.

> 25 The instructor’s manual and support materials for _Virtue_ are available in Spanish here.

> 26The activity sheets for these two modules can be found in Appendix Figures A1 and A2, respectively.

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response program. This curriculum was adapted by the NGO to the Central American context from the program developed by the Luster Learning Institute after the NGO conducted a pilot program among 200 Salvadoran students in 2017.<sup>27</sup> The program included directed meditation to reduce stress and anxiety and to control automatic responses. It also used thought techniques to help participants develop self-awareness, mental concentration, and inner calm. Through a series of activities, _Mindful_ provided students with tools to manage their stress more effectively and to regulate their emotions.

The _Mindful_ program included 16 breathing, stretching, relaxation, and focusing activities, which were implemented throughout all of the sessions. During each session, the volunteers explained each technique for about three minutes and then demonstrated and led the students in the practices throughout the session. These activities were supplemented with the _Clubs_ activities. For example, in the I am Calm activity, students used and consciousness such as and sun and breathing techniques deep breathing butterfly breathing to try to calm themselves. During the Sun Breathing activity, participants were invited to sit comfortably in their chairs with their feet on the floor. Then they were asked to breathe for 10 seconds while being conscious of their breathing. They were instructed to inhale through their noses, stretch their arms above their heads, and then lower their arms slowly after 5 seconds. They repeated this exercise 10 times, after which they inhaled and exhaled for 20 to 30 seconds, opened their eyes, and discussed how they felt.<sup>28</sup>

# **2.2 Conceptual Framework**

Our analysis is organized around the conceptual framework we present in Figure 1, which was preregistered at the AEA RCT Registry. This framework approaches the incipient problem of high incidence of school-based violent behaviors in low- and middle-income countries and how an ASP can help to address it. We expect that the SEL component can affect at least three primary outcomes. By learning new skills and techniques from the

> 27For more details on the original program, visit www.calmclassroom.com.

> 28This activity is presented in Figure A3 in the Appendix. A manual covering the complete intervention, including support materials along with the activity schedule for every academic year, is available in Spanish here.

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two psychology-based curricula included in the ASP ( _Virtue_ and _Mindful_ ), participant students could improve their behavior at school, school dropout, and academic performance, relative to students who attended the protection-based ASP ( _Clubs_ ).<sup>29</sup> In terms of potential mechanisms, we hypothesize the SEL component could affect the main outcomes through improving socio-emotional skills, fluid intelligence, and emotion regulation. We also hypothesize that because of the focuses of the curricula, the curricula can affect different mechanisms. On the one hand, _Mindful_ cultivates a greater consciousness among students, helping them foster attitudes of respect, kindness, and regulation toward themselves and others within their school environment (Keng et al., 2011). Considering the essential elements of _Mindful_ , this curriculum is expected to improve self-control, fluid intelligence, and measures of emotion regulation. These hypotheses are consistent with evidence of the positive effects of practicing mindfulness on emotion regulation, the capacity to perform tasks, memory, and attention.<sup>30</sup> On the other hand, following existing empirical evidence (Park et al., 2017; Linley et al., 2007; Shimai et al., 2006), we hypothesize that the _Virtue_ ASP increases perseverance and self-control, reduces risk-taking behaviors, and improves emotion-regulation measures. We preregistered these outcomes in the PAP and collected data to measure these dimensions.

# **2.3 Experimental Design**

In January 2019, we identified and recruited 21 public schools to test the SEL component of our ASP.<sup>31</sup> Our selection process took into account four school criteria: (i) the school is located in one of the most violent municipalities in El Salvador, Honduras, or Guatemala, (ii) it has not participated in a Glasswing intervention in the past five years, (iii) it has both the physical and technological infrastructure to implement the ASP, and (iv) school

> 29Studies have shown that psychological interventions targeted at adolescents or young adults reduce criminal and violence-related outcomes (Heller et al., 2017; Blattman et al., 2017; Dinarte-Diaz and EganadelSol, 2023), improve socio-emotional skills (Cook et al., 2014; Barker et al., 2021), and improve academic outcomes (Cook et al., 2014; Heller et al., 2017; Dinarte-Diaz and Egana-delSol, 2023).

> 30See Galla et al. (2020); Kral et al. (2018); Saltzman and Goldin (2008); Zelazo and Lyons (2011); Huppert and Johnson (2010); Van de Weijer-Bergsma et al. (2012); and Meiklejohn et al. (2012).

> 31Figure 3 in the Appendix presents a timeline of the intervention divided into stages.

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principals supported the study and intervention. The schools that met these criteria were chosen and randomized across the three interventions described above.

As we depict in Figure 2, we conducted a stratified randomization at the country (El Salvador, Honduras, and Guatemala) and school-risk levels (high or low risk, proxied by the homicide rate in the school’s municipality). A school was considered high risk if it was located in a municipality with a homicide rate greater than the mean rate at the country level in 2018. The design includes a total of six strata based on three values for country and two for school-risk level. Then, we randomly assigned the 21 participating schools to the three treatment arms— _Clubs_ , _Virtue_ , and _Mindful_ (7 schools per arm). We used a self-coded randomization procedure in Stata to randomly assign schools to the ASP variations. Then, we informed our implementing partner about the treatment allocation. The NGO distributed materials, trained mentors, and supervised implementation accordingly. Treated schools were aware of their treatment status and the research project’s evaluation goals, but the schools were not aware that another treatment group existed.

We test the SEL component by comparing the main outcomes (that is, behavior, school dropout, and academic performance) of students enrolled in schools that were randomly assigned to any psychology-based intervention (either _Virtue_ or _Mindful_ ) to the same outcomes of the students enrolled in schools assigned to _Clubs_ .<sup>32</sup> Moreover, we test which curriculum ( _Virtue_ or _Mindful_ ) more efficiently improves participants’ outcomes by using the experimental variation and comparing the average outcomes of the students who were randomly assigned to _Virtue_ with the average outcomes of those assigned to _Mindful_ . Both outcomes are then compared to the average outcomes of students allocated to _Clubs_ . Last, to study the mechanisms driving these effects, we conduct similar comparisons to those described above but using the variables that measure the potential channels, as specified in our conceptual framework—that is, students’ socio-emotional skills, fluid intelligence, and emotion regulation.

> 32In other words, we pooled students assigned to these two treatment arms together and compared them to students assigned to _Clubs_ .

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## **2.4 Recruitment of Participants**

Between mid-January and February 2019, before collecting baseline data and implementing the ASP, the implementing partner visited the 21 schools in our sample. It advertised the ASP and provided informational brochures and videos in the 21 schools, excluding ref- _Virtue_ or . erences to any specific activity related to _Mindful_ During its visit to any school, the NGO gave consent forms to both students and their parents to confirm their interest in participating in both the intervention and the study.

In March 2019, the NGO and research team returned to the schools to register and enroll children in the ASP. Any child was allowed to self-enroll as long as they and their parent or legal guardian signed a consent form. During the registration process, students used tablets to the enrollment form with and information as complete personal family well as completing the application to participate in a particular type of activity (sports, dance, or arts) at school. More details on the enrollment process are presented in the next section. We recruited and enrolled 897 students between 12 and 19 years of age (341 in _Clubs_ , 294 in _Virtue_ , and 262 in _Mindful_ ).

# **3 Data and Summary Statistics**

## **3.1 Data Collection**

In this section, we describe the stages during which we collected information from schools and participants and the procedures we followed. Figure 3 presents a timeline indicating when we collected the data.

**Baseline data collection.** As mentioned before, during the enrollment process, students interested in participating in the study completed an enrollment form. It includes 21 questions and takes approximately 10 minutes to complete and was completed using tablets (see Table A2). The staff of the NGO oversaw registration. Once registered, each student received a unique identification number, which enabled us to track them through all data sets.

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Two weeks after the registration process and before program implementation, enumerators went to schools to collect baseline information on social-emotional skills, fluid intelligence, and emotion regulation. To reduce the risk of fatigue among students, we collected these data over two days. On the first day, we asked students to complete the tasks used to collect data on social-emotional skills on a tablet. On the following day, the students completed the tasks used to collect fluid-intelligence and emotion-regulation data. In each school, we organized several classrooms in which students were scheduled to arrive in groups at specific times. Once the groups arrived, we gave the instructions to them and each student a tablet. Enumerators followed with students who had gave up questions.

Before the intervention commenced, we were given access to students’ administrative records, which included behavior at school and academic performance (specifically, math grades). Teachers submitted this information as printed reports for their students enrolled in the school. We collected, digitized, and cleaned all these paper reports for the students registered in the program and enrolled in the participating schools in El Salvador, Guatemala, and Honduras. One field coordinator per country was responsible for checking that the digitized data were consistent. Because of ethical protocol, we were unable to keep the records of students who did not consent to participate in the study.

We collected data on the characteristics of the participant schools from the 2018 Educational Censuses for El Salvador and Guatemala and from a school-level survey conducted by the authors for Honduras. We were able to obtain information on school location, enrollment, equipment available, and infrastructure, among other aspects. Last, we collected data on homicide rates at the municipality level from each country’s national police, which we use as a stratification variable.

**Short-term follow-up data collection.** As presented in the project timeline in Figure 3, the implementation of the ASP and intervention curricula was completed by the end of September 2019. We started short-term follow-up data collection in October 2019, while the students were still enrolled in school and before they took final exams, to maximize our ability to locate them. During this process, we gathered the same data (on social-

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emotional skills, fluid intelligence, and emotion regulation) and followed identical procedures as at baseline. In addition, we collected teachers’ reports on students’ behavior at school, academic performance (specifically, math grades), and school dropout in February 2020. We followed the same procedures to digitize and clean all these paper reports in the three countries.

As we reported in our PAP, we planned a midterm data-collection process in July and August 2020 (six months after the intervention ceased) to study whether the impacts found remained stable or changed over time. Unfortunately, we were unable to collect such data at that time because COVID-19 hit and all schools were functioning virtually and were not systematically collecting student data. As of November 2021, we were still unable to conduct because of the outbreak. this time it was fieldwork Considering gap, no longer useful to conduct this follow-up.

**Focus groups.** To understand other potential mechanisms driving the short-term results and to obtain reports from parents and teachers, we implemented 24 virtual focus groups between February and March 2021. In each country, 4 focus groups were conducted with teachers and 4 with parents. These focus groups were separated by treatment arm so that we could inquire into each treatment effect. Parents and teachers were recruited from the list of participants. For methodological reasons, we recruited between 6 and 8 participants per group.

During these discussions, we gathered information that would allow us to complement the quantitative results from our analysis and to obtain information about potential mechanisms using parents’ and teachers’ input. For example, we included the following questions: (i) “Have parents/teachers observed the same or different behaviors in their child as those identified in the short-term results?” (ii) “How easy/difficult is it for teachers/parents to teach skills such as perseverance, self-control, etc.?” (iii) “What are the parents’/teachers’ perceptions of and expectations for students?” In Appendix 1, we provide further details on the methods used and all the questions included in the qualitative component of our study.

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# **3.2 Data Sources, Instruments, and Outcomes**

All the outcomes measured in this were selected based on the frame- paper conceptual 2.2. work presented in Section A summary of outcomes, data source, and type of outcome is shown in Appendix Table A3.

## **A. Main Outcomes**

We use administrative data provided by schools to measure the three primary outcomes: behavior at school, school dropout, and academic performance (math grades). As described in Section 3.1, we collected data on behavior at school and on math grades at baseline and endline. School-dropout data were collected at endline only.

- _a. Behavior at school._ To proxy for bad behavior at school, we use teachers’ reports. Each country has a different way of assessing behavior:

- El Salvador evaluates behavior using a Likert scale that ranges from Good, Very Good, and Excellent.

- Honduras provides an evaluation of behavior using a Likert scale that includes the following four categories: Good Behavior, Very Good Behavior, Excellent Behavior, and Outstanding Behavior.

- Guatemala does not conduct a behavior evaluation, but schools participating in the treatment were to evaluate behavior the same Likert scale as in El requested using

- Salvador.

Teachers submitted a printed report for each student enrolled in the school, which we subsequently digitized. We transformed the reports into numerical variables on a scale from 1 to 3 for El Salvador and Guatemala, and a scale from 1 to 4 for Honduras. In both cases, a higher value indicates better behavior. To standardize the measure of behavior reports among countries and schools, we define the outcome as a dummy indicator that equals 1 if the student has a behavior score above the median of the school they attend.<sup>33</sup>

> 33In El Salvador and Honduras, teachers were already collecting these data for school reports. In

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_b. School dropout._ We referred to teacher reports to measure whether the participants dropped out and created a dummy indicator that equals 1 if the student abandoned school in 2019.<sup>34</sup> We collected dropout data from 868 students in the follow-up only.

_c. Academic performance._ We use students’ grades as a proxy for academic performance. Since we do not have a standardized test for the three countries, we use math grades because the mathematics curricula in these three countries are similar and, therefore, comparable.<sup>35</sup> In all cases, a higher value indicates better math grades. To standardize the measure of math-grade reports among countries and schools, we define the outcome as a dummy indicator that equals 1 if the student has a behavior score above the median of the school they attend.

A potential concern in using teachers’ reports to measure the main outcomes is that this reporting may be affected by teachers’ knowing which students are participating in the ASP. We argue that this occurrence is unlikely because we requested the records of all students, without informing the teachers that we were going to use only the data from students participating in the ASP.

# **B. Mechanisms**

According to the conceptual framework and existing evidence summarized in Heller et al. (2017) and Dinarte-Diaz and Egana-delSol (2023), behavioral outcomes correlate in a statistically significant way with skills such as self-control, emotion regulation, persistence, and prudence. For this reason, we collected data to measure proxies for these skills using the SoftGames (to measure socio-emotional skills) and Reactiva (to measure fluid intelligence and emotion regulation) applications.

# **Social-Emotional Skills**

SoftGames was developed and used by Danon et al. (2023). It can be played on a tablet and

> Guatemala, we asked teachers to do it for this study. As for 2023, data were collected for school reports in all three countries.

> 34We are able to measure school dropout only for students who (i) were not enrolled in the same school (or in any other school in our sample) or (ii) reported they were going to abandon the educational system (due to migration to another country, for example).

> 35Teachers in Honduras and Guatemala score performance between 0 and 100, and teachers in El Salvador report math grades with a score between 0 and 10.

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includes three task-based games that allowed us to collect information to measure the following three social-emotional skills, which we aggregate to generate a social-emotionalskills index for our estimations.<sup>36</sup> _a. Perseverance._ This skill is defined as a continued effort to do or achieve something despite difficulties or obstacles. To proxy for this skill, we estimate a measure of short-term perseverance using the Additions Game<sup>37</sup> , similar to the Alan and Ertac Grit Task Alan and Ertac (2019). In this game, participants are given a tablet showing a set of additions that are easy or difficult to solve. After each round, the participants are asked to choose the level of difficulty of the next set. The outcome is measured as a dummy that equals 1 if a participant persists after failing in round 1, a round of high difficulty level as predetermined by the tablet.<sup>38</sup> _b. Self-control._ This trait is defined as the tendency to avoid acting suddenly without thinking carefully about the consequences of an action.<sup>39</sup> We estimate this trait using the GoNoGo task-based game, which measures the player’s ability to inhibit an inappropriate response as determined by a Go-NoGo rule<sup>40</sup> . Specifically, the participant is presented with a square on the screen for a very short period. If the square is not black, the participant must touch the screen as quickly as possible (the “Go” stimulus). If the square is black (the “NoGo” stimulus), then the respondent must refrain from responding and touching the screen. A total of 72 trials are presented (48 “Go,” 24 “NoGo”). The outcome is the number of times a participant responds correctly to the “NoGo” stimulus. The data are standardized relative to the comparison ( _Clubs_ ) group.

_c. Risk-averse behavior._ A behavior is or con- risk-taking any consciously unconsciously

> 36The advantages of summary indexes are that they are more robust to overtesting and potentially more powerful than individual-level tests because they reduce random error in each outcome measure (Anderson, 2008). This summary index is a weighted mean of the three standardized skills measured, where the weights are calculated to maximize the amount of information captured in the index using an efficient Generalized Least Squares estimator.

> 37For papers related to the use or validation of this instrument, refer to Sule et al. (2019) and Duckworth and Quinn (2009).

> 38Unlike in Danon et al. (2023), our respondents are more likely to possess the required math ability (oneand two-digit addition).

> 39Alternatively, it is defined as the capacity to regulate attention, emotion, and behavior in the presence of temptation (Duckworth and Gross, 2014).

> 40For papers related to use or validation of this instrument, refer to Bezdjian et al. (2009) and Patton (1995).

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trolled behavior with a perceived uncertainty about its benefits or detriment to the wellbeing of oneself or others (Trimpop, 1994). To measure risk-taking behavior, we used the Balloon Analogue Risk Task (BART)<sup>41</sup> . Participants were asked to maximize the number of points they could earn from pumping a balloon. They earned points for every pump but could lose all of their points if the balloon popped. Risk-taking was measured as the number of for the balloons that did not We the values of average pumps pop. adjusted this variable to reflect a different orientation of risk attitudes, so that risk-taking, reversed, defines the risk-aversion outcome.<sup>42</sup> If originally high scores indicated high risk-taking, reversing them means now lower scores indicate higher risk-taking. Thus, the greater the risk-taking reversed score, the more risk-averse the individual is. The data are also standardized relative to the comparison group for consistency.

# **Emotion Regulation**

This trait is defined as all of the conscious and nonconscious strategies we use to increase, maintain, or decrease one or more components of an emotional response (Gross, 2001). To measure this skill, we use the Reactiva application (Amangeldiyev and Egana-delSol, 2018). This platform synthesizes the pioneering technology developed by MIT’s Affective Computing Group (Picard, 1995; McDuff et al., 2015) with a bespoke platform tailored for conducting field research in behavioral economics. Specifically, we used the _affdex_ software development kit created by Affectiva, a spin-off of Affective Computing Group. Affectiva’s technology analyzes videos captured from the front camera of smartphones or tablets to proxy for emotions. It measures emotional reaction to different emotionally laden stimuli (negative and positive videos) based on face detection, facial-feature extraction, and expression classification. Via the front cameras of tablets, the computer vision algorithm identifies key landmarks on the face. The machine learning algorithm then an-

> 41For papers related to the validation of this instrument, refer to Lejuez et al. (2002).

> 42In Stata, _revrs_ is a module to reverse variable value order. Reversing with the _revrs_ command in Stata specifically means taking a variable’s values and changing their order so the highest becomes the lowest and vice versa, effectively flipping the scale. For example, if a scale originally runs 1 to 5, reversing it would make 1 correspond to 5, 2 to 4, and so on, altering the dataset to reflect the inverse value for each entry. We used it to reverse the order of the risk-taking variable values so that it reflects risk-averse behavior, our outcome of interest.

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alyzes pixels to classify facial expressions. The accuracy level of these algorithms (that is, success rate in predicting emotional state) is around 60%–80% (McDuff et al., 2015; Stockli, 2018). Affectiva’s metrics indicate when individuals manifest a specific emotion or expression (for example, a smile) along with the degree of confidence.<sup>43</sup> The Reactiva application, in particular, follows a protocol similar to that of EganadelSol (2016a), Egana-delSol (2016b), and Egana-delSol et al. (2023) to construct arousal (proxy of alertness and stress) and valence (proxy of emotional self-regulation) indices at the onset of positive and negative stimuli using emotionally laden videos from the Geneva Affective Picture Database (GAPED) (Dan-Glauser, 2011). Valence is a composite emotional metric that measures overall emotional experience. Valence values from 0 to 100 indicate a neutral to positive experience, while values from -100 to 0 indicate a negative to neutral experience. We interpret a reduction of valence, either positive or negative, as a proxy of a decrease in the emotional reaction to a stimulus or, put differently, an increase in emotion regulation.

Arousal is defined as the physiological and psychological state of being awake or reactive to stimuli. Arousal values range from 0 to 100. We interpret a reduction of arousal as a proxy of a decrease in emotional reaction to a stimulus, or, in other words, as an increase in emotion regulation. We present more details about the rationale for using these metrics, . our collection procedure for these measures, and how we analyzed them in Appendix 3 Both outcomes, valence and arousal, were coded by averaging the 10 highest arousal or valence scores recorded with the Reactiva application during the interval in which the student is watching the emotionally laden videos from GAPED (Dan-Glauser, 2011). Based upon the confidence variable provided by Reactiva with each video observation, we keep only measures that are above the 30th percentile of the standardized AI confidence for the group of observations. First, for each country separately, and with respect to the control group’s confidence, we standardize the confidence variable in the baseline; second, we trim observations that stem from videos that are below the 30th percentile of detection

> 43See Figure 4 in Appendix 3 for an illustration.

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confidence;<sup>44</sup> finally, we standardize the remaining observations relative to the comparison group.

## **Fluid Intelligence and Fluid Intelligence after Stimuli**

Fluid intelligence refers to the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. To collect a proxy for fluid intelligence, we use a tool similar to Raven’s Progressive Matrices (Raven, 1936).<sup>45</sup> This nonverbal assessment tool presents a series of visual puzzles missing a piece, and the individual is required to identify the correct piece from a set of options. Each puzzle is designed to be increasingly complex, requiring more abstract reasoning, pattern recognition, and problem-solving skills, which are key components of fluid intelligence. Before the participant solves each matrix, we randomly show negative and positive stimuli using emotionally laden videos from GAPED (Dan-Glauser, 2011). The outcome is measured by calculating the percentage of correct answers after the positive and negative stimuli; and we also calculate the total percentage of correct answers, irrespective of which stimuli were shown before the matrix. Then, these percentages are standardized with respect to the _Clubs_ group. Thus, an increase in the score after the negative stimuli can be thought of as an increase in emotion regulation, but also in Mullainathan and Shafir (2013)’s concept of “mental bandwidth”, which translates directly into better reasoning.

# **3.3 Baseline Summary Statistics**

Table 1 reports the average characteristics of the participant schools, and Table 2 shows the average characteristics of participant students. In both tables, column (1) exhibits statistics for the total sample, column (2) for the students in _Clubs_ , column (3) for any psychology-based intervention, and columns (4) and (5) for the _Virtue_ and _Mindful_ groups,

> 44To reduce the number of variables and ease the analysis, we only present in our tables the results with trimming at the 30th percentile, but the analyses conducted with these variables trimmed at the 10th, 20th, and 40th percentiles follow those with the variables trimmed at the 30th percentile.

> 45The Progressive Matrices are widely recognized for their ability to assess cognitive ability without being influenced by language and cultural background, making them a valuable tool in the study of fluid intelligence.

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respectively. On average, more than half of participating schools are located in very violent communities, and around 71% are in urban areas. The schools are midsized in terms of enrollment (an average of 410 students in grades 1-9). Most schools have their own building (81%), are connected to a water supply (86%), and have computers within the facilities (76%). Finally, around 81% of schools have a food program for students, but less than 50% of schools provide health services.

Moreover, Table 2 shows that, of the total sample of 897 participant students, 49% are girls and the average age is 14 years. The students’ average travel time from home to school is 14.5 minutes. On 37% of these adolescents are not with both average, living parents, which is typical for households in these three countries. Only 2% of students report having tried to emigrate to the United States, and 13% report currently working. In terms of the main outcomes, 81% of students have a behavior-report score that is above the school median, and 57% have a math-score report that is higher than the school median.

Regarding mechanisms, 30% of students persisted after failing the first round in the Additions Game, indicating low perseverance. The average student responded correctly to 86% of the “NoGo” stimuli, which indicates high self-control. The average score in the risk-aversion measure was 32 out of 38 points. Thus, students in our sample have a low tendency to take risks. In terms of fluid intelligence, our sample shows that 61% of the time students answered correctly the progressive matrices of the Raven-like test. This result remains similar when measuring the percentage of correct answers after positive (62%) or negative (58%) stimuli. Regarding arousal and valence, we find that students have relatively high arousal at baseline (68 out of 100) and a positive valence index (21 in the range -100 to 100).

# **3.4 Balance and Attrition**

We test whether there are differences across ASP variations by comparing the means of the baseline variables at the school and student levels. We present unadjusted _p_ -values and sharpened two-stage _q_ -values to account for multiple hypothesis testing in Tables A4 and A5 for the school and student characteristics, respectively. Overall, our balance 24
tests indicate that schools assigned to the different ASPs are statistically similar in their average characteristics, except for school location: all schools in the _Mindful_ ASP are in urban areas. Moreover, most of the individual and household characteristics are balanced across treatment arms, except students’ age, students’ course, student’s travel time from home to school, and whether students are enrolled in the evening shift.<sup>46</sup> Moreover, some outcomes and mechanisms were imbalanced at baseline, including behavior at school, perseverance, risk-aversion behavior, fluid intelligence, arousal, and valence. We account for these differences by including the characteristics as controls and by using a differencein-differences approach as the main estimation model, as we describe in the next section.

A potential concern is that our results might be driven by differences in matching rates with administrative data on the main outcomes or in survey response rates for the data on the mechanisms. To test whether there is differential attrition among treatment arms, we create an indicator that takes the value of 1 for students for whom we were not able to collect administrative data on the main outcomes (behavior, school dropout, or math grades) or whom we were unable to contact for collecting mechanism data (social-emotional skills, and emotion at endline. Our estimations indicate an aver- fluid intelligence, regulation) attrition rate of 18% and an rate with administrative data age survey average matching of 99%. These rates are only slightly higher than in previous studies conducted in similar contexts (Dinarte-Diaz and Egana-delSol, 2023). As presented in Appendix Table A6, the estimations indicate that the matching rates with administrative data and attrition rates in the survey are balanced across all treatment arms.

46Because of limited infrastructure, and to meet most of the demand for education, schools in El Salvador operate in two shifts, one in the morning and another in the afternoon.

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# **4 Empirical Strategy**

## **4.1 Empirical Methods**

Given our randomized experimental design, it is straightforward to measure the intent-totreat effects of the interventions on the outcomes of interest using Ordinary Least Squares. However, since differences exist in the means of our outcomes of interest measured at baseline, we measure the net effects of the SEL component of the ASP (beyond the effects of the protection component) on our main outcomes using a difference-in-differences approach and estimate the following equation: Here, _Yist_ is the outcome for student _i_ at school _s_ during period _t_ . _AnyTis_ is a dummy indicating that a student is enrolled in a school that was randomly assigned to treatment _Virtue_ or _Mindful_ . _Post_ is the post-intervention indicator variable.<sup>47</sup> _Xisj_ is a vector of baseline control variables that includes the _Post_ indicator, an indicator for whether the baseline outcome was missing,<sup>48</sup> and the variables that are not balanced between treatment arms at the student level: student’s age, course, travel time from home to school, _π_ and whether the student is enrolled in the evening shift. _j_ captures stratum fixed effects for the six strata—the interaction between country and whether the school is located in a highly violent community. Because of our small number of classrooms, we use wild cluster bootstrap at the classroom level (Cameron et al., 2008) in all our estimations. We also conduct Least Absolute Shrinkage and Selection Operator (LASSO) analysis to identify variables with strong relationships with _Yist_ , to assess their suitability for inclusion as controls in equation (1) following Bruhn and McKenzie (2009), and to confirm the stability

> 47For the dropout outcome, we estimate the model only with one difference, as we do not have baseline data.

> 48To reduce the number of observations lost because of missing data at baseline, we impute the value of the mean for each outcome at the school level and include an indicator (a dummy) of imputation in all estimations. We do not impute values in follow-up data.

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of our estimated coefficients after including the control variables selected by LASSO for each of our main outcomes.

In this model, _μ_ ˆ2 captures the _net_ SEL component of the ASP—that is, the short-term effect on dropout, behavior at school, and math grades of being assigned to participate in _Virtue_ or an ASP including a psychological curriculum ( _Mindful_ ) compared to only being protected ( _Clubs_ ).

To measure the SEL component of each type of psychology-based intervention, we modify specification (1) to capture the effects of each treatment arm ( _Virtue_ and _Mindful_ ) relative to _Clubs_ . Specifically, instead of including the indicator _AnyTis_ , we add two dummies ( _V irtueis_ and _Mindfulis_ ) that equal 1 if the student _i_ is enrolled in a school _s_ randomly assigned to _Virtue_ or _Mindful_ , respectively, and interact these indicators with the _Post_ variable as follows: _Yist_ = _θ_ 0+ _θ_ 1 _V irtueis_ + _θ_ 2 _Mindfulis_ + _θ_ 3 _V irtueis×Post_ + _θ_ 4 _Mindfulis×Post_ + _γXisj_ + _πj_ + _εisjt_ All variables are defined as before. In this model, _θ_<sup>ˆ</sup> 3 and _θ_<sup>ˆ</sup> 4 capture the SEL component of each psychology-based curriculum relative to the effects from the _Clubs_ ASP. Moreover, we test whether _θ_<sup>ˆ</sup> 3 = _θ_<sup>ˆ</sup> 4 to provide evidence of the differential impacts by type of psychology-based curriculum on the main outcomes of interest.

# **4.2 Heterogeneity Analysis**

For the heterogeneity analysis of the main treatment effect, we use machine learning tools following the recent literature on heterogeneous treatment effects (Athey and Wager, 2019; Davis and Heller, 2017). This approach allows us to capture a high-dimensional combination of covariates that the researcher-specified interactions may miss. To this end, we estimate the first-difference conditional average treatment effect (CATE), using the first-difference causal forest (FDCF) algorithm (Athey and Wager, 2016, 2019; Athey et al., 2019), following Wang (2022). We train 100,000 trees using the honest approach (Athey 27
and Wager, 2016; Athey et al., 2019). We include in the causal forest all the individual and household characteristics available at baseline and other school and neighborhood characteristics we collected.

We also follow Athey and Wager (2019) and Carlana et al. (2022) in using the predictions of the expected treatment effect for each student, given the set of covariates, to categorize participants into two groups, the _"Strong"_ and _"Weak"_ groups. This classification is achieved by categorizing the CATEs into three tiers: "High," "Medium," and "Low." The _“Strong"_ group refers to the subgroup whose CATEs fall within the “High" category (that is, above the 66th percentile) of all CATEs when switching from _Clubs_ to the _Mindful_ or _Virtue_ treatment. In contrast, the _"Weak"_ group consists of participants with "Medium" and "Low" CATEs (that is, positioned at or below the 66th percentile). Then, to understand what types of students are more likely to see an increase in the main outcomes due to the interventions, we characterize the groups using a balance test.

# **5 Results**

## **5.1 Main Results**

Table 3 presents the main results of our paper. We show the net impact of the SEL component—beyond the regular protection component—estimated using equation (1) on the primary student outcomes measured using administrative data: an indicator for whether the behavior at school report was above the school median (column (1)), an indicator for whether the students abandoned school during the 2019 academic year (column (2)), and an indicator for whether the math-score report was above the school median (column (3)).

The impact estimates show positive and significant effects on students’ behavior at school. Compared to students in _Clubs_ , participants in the psychology-based interventions ( _Virtue_ and _Mindful_ ) are 23 pp more likely to be better behaved at school. This improvement is equivalent to an increase of 36% relative to the share of students in the ASP’s protection component with an above-the-median behavior score. We find no statistically significant effects of the SEL component on school dropout and math grades. These re28
sults are robust to the selection of control variables using LASSO (Table A7). Moreover, these estimated impacts align with focus group feedback from parents and teachers, who perceived that program participants improved their behavior at school and tendencies toward violence outside school.

Taken together, these results are consistent with the evidence that adding psychologybased curricula to interventions such as traditional ASPs (in this case, _Clubs_ ) is essential to increase the impact of the interventions on students’ behavior at school (Dinarte-Diaz and Egana-delSol, 2023). Note that these effects are on top of the effects of _Clubs_ . Our finding of positive effects from the net SEL component on behavior at school aligns with existing evidence of the positive effects of other socio-emotional-skills curricula used in previous high-quality interventions for adolescents (Vandell et al., 2007; Heller et al., 2017; DinarteDiaz and Egana-delSol, 2023). For example, Vandell et al. (2007) find that high-quality ASPs can improve behavior among disadvantaged students in the USA. Similarly, Heller et al. (2017) find that the Becoming A Man program for youth in Chicago reduced violentcrime arrests. In El Salvador, Dinarte-Diaz and Egana-delSol (2023) estimate positive effects on participants’ violent behaviors of an ASP that includes components of cognitive behavioral therapy, using teachers’ reports of behavior at school and students’ self-reports of violent behaviors.

We argue that the finding of no impact of SEL on school dropout or math grades is not surprising since we are evaluating the net effect of the SEL of the ASP, which does not target academic performance directly. It may be expected, however, that some of the skills learned through the psychology-based intervention, such as perseverance, indirectly affect academic performance. Yet the evidence from psychology has found that grit, a measure of perseverance in effort, is only modestly correlated with teacher ratings in math scores (r _≤_ 0.25) (Usher et al., 2019). Moreover, evidence of positive effects of interventions on academic comes psychology-based performance through perseverance from programs based on the concept of mindset (Bettinger et al., 2018) or based on mathspecific self-perceptions of perseverance, which capture students’ perceived tendency and ability to persevere at challenging math problems (Miele et al., 2022). Thus, we may need either interventions with a focus on or different stronger math-specific perseverance ap29
proaches from the ones explored in this study.

# **5.2 Heterogeneous Effects of the ASP’s Socio-Emotional-Learning Component**

We study the differential impacts of the SEL component on behavior following the machinelearning approach discussed in Section 4.2. Table 4 presents the first-difference CATE for behavior at school, for the _“Strong"_ and _"Weak"_ groups, as defined in Section 4.2. Additionally, we characterize the groups using a balance test for the baseline variables. We present the differences shown by the balance tests (column (3)) along with the corresponding _p_ -values adjusted for the Benjamini-Hochberg procedure for multiple-hypothesis testing for that difference across these groups (column (4)).

Overall, the results indicate that the most vulnerable students are benefiting the most from the SEL component—that is, they are overrepresented among students with a higher CATE. For example, the program has a larger impact on male and older students, on those with worse behavior at school before the intervention, and on those enrolled in a higherlevel course (which is related to age). Moreover, students with greater exposure to risks are also benefiting more from the intervention. Specifically, students who are enrolled in the afternoon shift (and thus less likely to have adult supervision during the morning), or who are less likely to be working<sup>49</sup> are overrepresented in the Strong CATE group.

Household and school/neighborhood characteristics support the result that the most vulnerable students benefit the most from this intervention. First, the _Strong_ group is overrepresented by students living only with their mothers (and underrepresented by students living with both parents), who are less likely to have adult supervision compared to those living with both parents. This result aligns with existing evidence from Chile that the effects of an ASP are larger for children who at baseline spent after-school hours at home alone (Martínez and Perticará, 2020). Second, the average number of years of education

> 49Based on our discussions with the NGO, we interpret this result as follows: if students are working, they are busier (have less unsupervised time) and probably are less exposed to risks. Also, working students attend fewer sessions, which makes them less likely to benefit from the curricula.

30
of the mother is larger in the _Strong_ group relative to the _Weak_ group. In Central America, women with higher levels of education are more likely to be employed in formal (nondomestic) full-time jobs and thus their children are more likely to be alone after school. Additionally, the _Strong_ group is overrepresented by students attending schools located in urban communities or communities affected by higher crime. These results also point to a larger impact in contexts in which socio-emotional skills or emotion regulation might be more important.

Last, we find statistically significant differences in fluid intelligence and arousal between both groups, which indicate that students with worse scores and higher stress/alertness, as proxied by arousal, at baseline are more likely to be more responsive to the psychologybased interventions (that is, they are in the _Strong_ group).<sup>50</sup>

# **5.3 Mechanisms**

We explore three potential mechanisms for the SEL component of the ASP: socio-emotionalskills, fluid-intelligence, and emotion-regulation measures. Table 5 shows the net SEL effects on these potential channels.

We only find evidence of emotion regulation as a mechanism driving the effects of the SEL component. Our results show that students who participated in the variation of the ASP that included a psychology-based intervention reduced their valence score by 0.24 sd relative to students assigned to the variation that includes only a protection component.<sup>51</sup> The is most the to be aware of emotions and be less program likely affecting capacity affected by and impulsive about emotionally laden triggers. We argue that a decrease in valence indicates that participants became emotionally and behaviorally calmer and less impulsive. Such a calming effect is particularly beneficial to individuals who live in a

> 50We observe a larger share of students from El Salvador and Honduras in the _Strong_ group relative to the _Weak_ group, which may indicate more local capacity of the implementing partner in those two countries relative to Guatemala. This greater capacity may translate into more efficient identification of good volunteers and more trust on the part of the communities, which may increase the overall effectiveness of the intervention.

> 51These results are robust to the inclusion of control variables selected using a Double-LASSO procedure, as we show in Table A8.

31
violent environment and are prone to violence. These results are consistent with previous estimates of the impact of an ASP based on cognitive behavioral therapy, a well-studied psychology-based curriculum, on valence using biomarkers in El Salvador (Dinarte-Diaz and Egana-delSol, 2023).

We rule out that, in this context, the net effects of the average SEL components are driven by changes in socio-emotional skills or fluid intelligence. A potential explanation of this finding is that we are estimating average effects from two interventions that, by design, are targeting different skills, as discussed in Section 2.2. On the one hand, _Mindful_ is expected to improve fluid intelligence, self-control, and emotion regulation. On the other hand, _Virtue_ improves perseverance, self-control, risk-taking behaviors, and emotion regulation. Pooling the effects of both curricula may be biasing the average effect on these measures toward zero. We explore this hypothesis in the next section.

In sum, important policy implications regarding the implementation of these interventions arise from these results. Early findings in Davidson et al. (2003) suggested that impulsive aggression and violence are positively associated with lack of emotion regulation. More recent work finds a similar association between maladaptive cognitive-emotionregulation strategies and violence-related behaviors among adolescents in the US (Bao et al., 2016). Thus, psychology-based curricula should be incorporated into ASPs to ensure the greatest impacts, particularly for at-risk adolescents.

# **5.4 Which Curriculum with a Social-Emotional-Learning Component Is More Effective?**

As demonstrated previously, the psychology-based ASP improves behavior at school and reduces student valence. To understand which of the two psychology-based curricula is driving these effects, we measure the impacts of each curriculum separately using equation (2). We present the impacts on the main outcomes in Table 6 and the effects on potential mechanisms in Table 7.

We document three main results. First, we find that relative to the pure ASP ( _Clubs_ ), both curricula have similar impacts on behavior at school (23 pp). We are not able to re32
ject the null hypothesis of equality of the two estimated impacts ( _p-_ value = 0.868). Thus, the net SEL component can add to the effectiveness of the pure protection component by including either psychology-based curriculum. When we look at other main outcomes, however, we find that the _Mindful_ curriculum is more effective at reducing school dropout compared to students in the _Virtue_ variation. We find a reduction of 4.0 pp in school dropout relative to the _Clubs_ ASP ( _p-_ value = 0.120, Table 6) and 5.6 pp ( _−_ 4 _._ 0 pp _−_ 1.6 pp) relative to the _Virtue_ ASP ( _p-_ value = 0.020, Table 6). The effect relative to the control is sensitive to the controls included, but the difference between the larger effect ( _Mindful_ ) and the _Virtue_ effect with respect to the control remains when controls are selected using LASSO ( _p-_ value = 0.008, Table A9). The positive effect of a mindfulness-based curriculum on school dropout is consistent with existing evidence of an association between mindfulness and adolescents’ intention to drop out of school in the US (Carsley et al., 2017) and with a meta-analysis showing that interventions implemented in schools promise to improve children’s and youths’ resilience and academic performance in general (Zenner et al., 2014).

Second, we explore the effects of these two curricula separately on the socio-emotionalskills index and fluid-intelligence and emotion-regulation measures. Moreover, since each curriculum may affect different skills included in the socio-emotional-skills index, we estimate the main effects of each curriculum on these skills separately. We find that the effects of each type of curriculum on behavior at school, relative to the _Clubs_ ASP, are driven by effects on different mechanisms. The effects from the _Virtue_ ASP may be driven by reduction in risk taking (0.025sd) and valence (0.27 sd), whereas the effects of the _Mindful_ ASP seem to be driven by improvements in fluid intelligence after exposure to negative stimuli (0.22 sd).

Last, we document that the differential impacts of _Mindful_ , relative to _Virtue_ , on school dropout may be driven by improvements in fluid intelligence after negative stimuli (0.34 sd, _p_ -value = 0.005). In other words, if the mindfulness-based program can increase concentration capacity, measured as a greater score in fluid intelligence, that would increase the mental bandwidth of the individual and reduce their probability of abandoning 33
school.<sup>52</sup> Overall, these results contribute to the existing body of research that shows the positive effects of mindfulness on an array of behaviors and outcomes of adults (Keng et al., 2011). The main feature of the _Mindful_ intervention is that it is low cost, which is an attractive feature of public policy. Based on the impacts estimated from the ASP, an ASP should also include an evidence-based curriculum oriented toward improving student outcomes. We find that the _Mindful_ intervention is the best curriculum to add to _Clubs_ to help vulnerable youth in poor and highly violent countries.

# **5.5 ASP Attendance**

We first explore the differences in ASP attendance by ASP variation using data obtained from reports from volunteers. Table 8 shows the results from estimating a version of specification (2), which excludes the interactions with the _Post_ variable. Column (1) presents the effects on attendance (as a percentage of the total sessions scheduled), column (2) shows the effects on an indicator of whether the student dropped out of the ASP, and column (3) presents the effects on the number of sessions scheduled. We find that, on average, students assigned to _Clubs_ attended 61% of the scheduled sessions. The attendance rate was greater for the _Virtue_ intervention relative to both _Clubs_ and _Mindful_ (column (1)). Moreover, students in the _Virtue_ ASP are 11 pp less likely to abandon the intervention relative to students assigned to _Clubs_ (column (2)). We show that these differences in attendance are not driven by differences in number of sessions scheduled in each type of ASP variation (column (3)). Considering the ASP attendance and dropout results together, we show that the _Virtue_ ASP was more attractive to the students assigned to it since they attended a greater share of sessions relative to both _Mindful_ and _Clubs_ and they were less likely to abandon the intervention relative to _Clubs_ .

> 52 Overall, these results are robust to the inclusion of control variables selected using a Double-LASSO procedure, as we show in Table A10.

34
## **5.6 Cost-Benefit Analysis**

Cost estimates provided by Glasswing International indicate that the average cost of a seven-month-long ASP intervention per student is US$296.5. The cost is US$269.4 for _Clubs_ , US$292.5 for _Mindful_ , and US$327.6 for _Virtue_ .<sup>53</sup> Based on the literature that estimates the between human interventions and on adult earn- relationship capital impacts ings (Holla et al., 2021; Ganimian et al., 2021; Galasso and Wagstaff, 2019) and following the framework of Hendren and Sprung-Keyser (2020) for estimating the marginal value of investments using public funds, we conducted a calculation of the program’s approximate benefit–cost ratio.<sup>54</sup> Our estimates indicate that the present discounted value of earning gains expected to result from ASP-induced improvements in behavior at school and an indirect potential reduction in school dropout yields a benefit–cost ratio that ranges from 11.3 to 45.2. Consequently, this program should be encouraged as a public policy, as the intervention is likely to pay for itself even in the short run and has the potential to generate large additional welfare gains in the long run.

# **6 Conclusions**

This paper provides an experimental evaluation to understand the effect of different ASP curricula implemented in three developing and highly violent countries on adolescent behavior at school, academic performance, socio-emotional skills, and emotion regulation. To our knowledge, this is the first study to use AI technology and task-based games to estimate proxies for the difficult-to-measure competencies of socio-emotional skills and emotion regulation.

To achieve our end, we created exogenous variation by randomly assigning partici-

> 53Similar interventions involving at-risk youths in the USA (Heller et al., 2017) are 6.8 times more expensive per participant (in nominal terms, or 3.1 times more expensive in PPP terms) than the average of the interventions involved in this study.

> 54For more details of our approach, see Appendix 4 and Table A12.

35
pant schools to three ASP interventions: one based only on recreational activities such as sports, art, and dancing ( _Clubs_ ) and two psychological interventions based, respectively, on a curriculum that aims to strengthen character and virtues ( _Virtue_ ) and a mindfulnessand-relaxation program ( _Mindful_ ). Every _Virtue_ and _Mindful_ meeting was immediately followed by _Clubs_ activities. By comparing the average outcomes of students enrolled in schools assigned to either of the two psychology-based curricula with students enrolled in schools assigned to _Clubs_ , we measure the net socio-emotional-learning channel. We provide evidence that the psychology-based interventions improve students’ behavior at school and decrease valence, a proxy for emotion regulation.

We also find policy-relevant heterogeneous effects. First, the psychology-based curriculum has larger effects on students more vulnerable to violence: boys, students older than 14 years, those with bad or average school behavior at baseline, those in urban schools within high-homicide-rate municipalities, and those not living with both of their parents. Second, the type of psychology-based curriculum matters. On average, the _Virtue_ curriculum affects student behavior, risk-taking, and emotion regulation (that is, valence). Meanwhile, the _Mindful_ curriculum improves behavior at school, decreases school dropout relative to _Virtue_ , and increases fluid intelligence after negative stimuli. Therefore, we conclude that _Mindfulness_ , which is a standard intervention, is more effective in improving students’ outcomes.

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45
**7 Tables and Figures**

Table 1: **Mean School Characteristics by Treatment Group**

||(1)<br>Full<br>Sample|(2)<br>_Clubs_|(3)<br>_Virtue_or<br>_Mindful_|(4)<br>_Virtue_|(5)<br>_Mindful_|
|---|---|---|---|---|---|
|_School characteristics_||||||
|School is located in a highly violent community|0.52|0.43|0.57|0.43|0.71|
|School is in an urban area|0.71|0.57_†_|0.79+|0.57|1.00|
|Total school enrollment (1st to 6th courses)|306.62|269.86|325.00|302.86|347.14|
|Total school enrollment (7th to 9th courses)|104.29|81.43|115.71|145.00|86.43|
|School has its own building|0.81|0.71|0.86|0.86|0.86|
|School is connected to a water supply|0.86|0.86|0.86|0.86|0.86|
|School has computers|0.76|0.86|0.71|0.71|0.71|
|School has a health program for students|0.43|0.29|0.50|0.43|0.57|
|School has a foodprogram for students|0.81|0.71|0.86|0.71|1.00|
|Observations|21|7|14|7|7|

_Notes:_ This table shows descriptive statistics of the available variables at baseline for the sample of participant schools. All information was obtained at the school level. Data were obtained from the Educational Censuses for El Salvador and Guatemala (2018) and self-collected by the authors for Honduras. Column (1) presents the mean characteristics for the full sample of schools and column (2) for the schools assigned to the _Clubs_ group. The average characteristics for schools assigned to either of the psychology-based interventions are presented in column (3). Characteristics for schools in each of the interventions ( _Virtue_ or _Mindful_ ) are presented in columns (4) and (5), respectively. Total school enrollment refers to the enrollment in the school, not to the ASP or study. “Observations” indicates the number of schools within each group. The symbols representing unadjusted _p_ -values in the balance tests for all available variables are as follows: Clubs vs (Mindful + Virtue): *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Clubs vs Mindful: _† † †_ p _<_ 0.01, _††_ p _<_ 0.05, _†_ p _<_ 0.1. Clubs vs Virtue: _§§§_ p _<_ 0.01, _§§_ p _<_ 0.05, _§_ p _<_ 0.1. Mindful vs Virtue: +++ p _<_ 0.01, ++ p _<_ 0.05, + p _<_ 0.1. For more information on balance tests, exact _p_ -values, see Table A4 in the Appendix.

46
# Table 2: **Mean Student Characteristics at Baseline by Treatment Group**

||(1)<br>Full<br>Sample|(2)<br>_Clubs_|(3)<br>_Virtue_or<br>_Mindful_|(4)<br>_Virtue_|(5)<br>_Mindful_|
|---|---|---|---|---|---|
|_Individual Characteristics_||||||
|Female|0.49|0.50|0.49|0.52|0.45<br>|
|Student’s age|13.55<sup>_∗_</sup>|13.87|13.36|13.35|13.36<sup>_††_</sup>|
|Student’s course|7.33|7.37|7.30|7.29|7.32<sup>_†_</sup>|
|Student is enrolled in the afternoon shift|0.40<sup>_∗∗∗_</sup>|0.64|0.25|0.08<sup>_§§§_</sup>|0.44<sup>_††_</sup>|
|Travel time (minutes from home to school)|14.50<sup>_∗∗_</sup>|16.41|13.32<sup>++</sup>|14.88|11.57<sup>_††_</sup>|
|Student has tried to immigrate to USA|0.02|0.03|0.01|0.01|0.02|
|Student works|0.13<sup>_∗_</sup>|0.15|0.11|0.11|0.11|
|_Household Characteristics_||||||
|Student’s household composition||||||
|<br>Student lives with both parents|0.63|0.62|0.63|0.66|0.60|
|Student lives only with mother|0.29|0.30|0.28|0.26|0.31|
|<br>Student lives only with father|0.02|0.01|0.02|0.02|0.02|
|<br>Student lives with other relatives|0.07|0.06|0.07|0.05|0.08|
|Student lives with unrelated adult|0.00|0.01|0.00|0.00|0.00|
|Mother’s education level||||||
|Years of education|7.60|7.42|7.71|7.96|7.45|
|_Main Outcomes_||||||
|Behavior at school|0.81<sup>_∗_</sup>|0.86|0.78|0.74<sup>_§_</sup>|0.82|
|Math grades|0.57|0.56|0.57|0.55|0.59|
|_Mechanisms_||||||
|SES Index|0.83|0.86|0.81|0.73|0.89|
|Short-term perseverance|0.30|0.32|0.29|0.29|0.28<sup>_†_</sup>|
|<br>Self-control|0.86|0.85|0.86|0.85<br>|0.88|
|Risk-aversion|31.88<sup>_∗_</sup>|32.07|31.76|31.61<sup>_§§_</sup>|31.93|
|Fluid intelligence|0.61|0.62|0.60|0.61|0.59<sup>_††_</sup>|
|<br>Fluid intelligence after negative stimuli|0.58|0.60|0.57|0.59|0.55<sup>_†††_</sup>|
|<br>Fluid intelligence after positive stimuli|0.62|0.63|0.61|0.62|0.60<sup>_††_</sup>|
|Arousal|68.08|66.40|69.08<sup>+</sup>|65.86|71.99<sup>_††_</sup>|
|Valence|20.94|18.90|22.16<sup>++</sup>|18.69|25.28<sup>_††_</sup>|
|Observations|897|341|556|294|262|

_Notes:_ This table shows average student characteristics and outcomes at baseline for the sample of participants. Column (1) presents mean characteristics for the full sample. Columns (2) and (3) show mean characteristics for the pure-control and _Clubs_ groups. Average characteristics for participants assigned to either of the psychologybased interventions are presented in column (4) and for each of the interventions, _Virtue_ and _Mindful_ , in columns (5) and (6), respectively. A description of individual and household characteristics is presented in Appendix 2. Details on the outcomes and mechanism variables are discussed in Section 3.2. All data on individual and family characteristics and outcomes were collected by the authors during the enrollment phase and baseline data collection using the different instruments as summarized in Table A3. “Observations” indicates the number of students within each group. The symbols representing unadjusted _p_ -values in the balance tests for all available variables are as follows: Clubs vs (Mindful + Virtue): *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Clubs vs Virtue: _§§§_ p _<_ 0.01, _§§_ p _<_ 0.05, _§_ p _<_ 0.1. Clubs vs Mindful: _† † †_ p _<_ 0.01, _††_ p _<_ 0.05, _†_ p _<_ 0.1. Mindful vs Virtue: +++ p _<_ 0.01, ++ p _<_ 0.05, + p _<_ 0.1. For more information on balance tests, exact _p_ -values, and adjusted sharpened two-stage _q_ -values, see Table A5 in the Appendix.

47
Table 3: **Effects of ASP Social-Emotional Learning on Behavior and Academic Outcomes** _(Intention-to-Treat Estimates)_

||Behavior<br>at School<br>(1)|School<br>Dropout<br>(2)|Math<br>Grades<br>(3)|
|---|---|---|---|
|Psychology-based curricula|0.232***|-0.010|-0.017|
||(0.078)|(0.023)|(0.076)|
||[0.015]|[0.640]|[0.860]|
|Mean pure club group|0.650|0.037|0.587|
|MDE|0.068|0.037|0.099|
|Observations|887|868|855|
|R-squared|0.076|0.076|0.019|

_Notes:_ This table shows the estimated impacts of the ASP’s socialemotional learning on behavioral and academic outcomes. _Psychologybased intervention_ is a dummy equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, and 0 if assigned to _Clubs_ . _Mean pure club group_ is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. We present the estimated coefficient _μ_ ˆ2 from specification (1). The description of dependent variables is available in Section 3.2. _Behavior at school_ is a dummy that equals 1 if the student’s behavior report is above the school median. _School dropout_ is a dummy variable that equals 1 if a student dropped out of any school in our study in the 2019 academic year. _Math grades_ is a dummy that equals 1 if the student’s behavior report is above the school median. All outcomes were obtained from administrative data sources (namely, teachers’ reports). Sample size in each specification varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is an imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located. Wild bootstrap standard errors are shown in parentheses, and adjusted _p_ -values are in brackets. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01 _, p <_ 0 _._ 05 _, p <_ 0 _._ 1 48
Table 4: **Conditional Average Treatment Effect (CATE): Behavior at School**

||“Strong"<br>Group<br>(1)|“Weak"<br>Group<br>(2)|Diff.<br>(3)|MHT.<br>_p_-value<br>(4)|
|---|---|---|---|---|
|ITT behavior at school|0.259|0.212|0.047||
|_Individual Characteristics_|||||
|Female|0.420|0.525|-0.105|0.006|
|Student’s age|13.730|13.475|0.255|0.014|
|Bad or regular behavior at school at baseline|0.904|0.723|0.181|0.000|
|<br>Student is enrolled in the afternoon shift|0.573|0.320|0.253|0.000|
|Student’s course|7.659|7.164|0.495|0.000|
|Travel time (minutes from home to school)|15.181|14.197|0.984|0.349|
|Student has tried to immigrate to USA|0.031|0.015|0.016|0.214|
|Student works|0.099|0.144|-0.045|0.068|
|_Household Characteristics_|||||
|Student’s household composition|||||
|Student lives with both parents|0.519|0.682|-0.163|0.000|
|Student lives only with mother|0.406|0.231|0.175|0.000|
|<br>Student lives only with father|0.007|0.022|-0.015|0.068|
|Student lives only with other relatives|0.075|0.063|0.012|0.572|
|Mother’s education level|||||
|Years of education|8.563|7.116|1.447|0.000|
|_Mechanisms_|||||
|SES Index|-0.069|-0.104|0.035|0.627|
|Fluid Intelligence|-0.207|-0.052|-0.155|0.074|
|Arousal|0.169|-0.012|0.181|0.028|
|Valence|0.114|0.070|0.044|0.622|
|_School and Neighborhood Characteristics_|||||
|School is urban|0.956|0.569|0.387|0.000|
|School is located in a highly violent community|0.853|0.145|0.708|0.000|
|_Country_|||||
|El Salvador|0.372|0.287|0.085|0.021|
|Guatemala|0.191|0.632|-0.441|0.000|
|Honduras|0.437|0.080|0.357|0.000|
|Observations|293|585|||

_Notes: “Strong Group"_ refers to subgroups whose conditional average treatment effect (CATE) is above the 66th percentile of all CATEs when switching from _Clubs_ to the _Mindful_ or _Virtue_ treatment, and equal or below to the 66th percentile for the “Weak group". A positive number in the “Difference” column indicates that the average covariate value for the “Strong" subgroup is higher. _P_ -values for the difference between groups are shown in the fourth column (using Benjamini-Hochberg correction for multiple hypothesis testing).

49
Table 5: **Effects of ASP Social-Emotional Learning on Social-Emotional Skills and Emotion Regulation** _(Intention-to-Treat Estimates)_

||SES<br>Index|Fluid<br>Intelligence|Fluid<br>Intelligence<br>after<br>Negative<br>Stimuli|Fluid<br>Intelligence<br>after<br>Positive<br>Stimuli|Arousal|Valence|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Psychology-based curricula|0.003|-0.037|0.051|-0.027|-0.014|-0.245*|
||(0.013)|(0.088)|(0.086)|(0.094)|(0.117)|(0.129)|
||[0.845]|[0.700]|[0.600]|[0.830]|[0.910]|[0.050]|
|Mean pure club group|0.000|0.000|0.000|0.000|0.000|0.000|
|MDE|0.233|0.219|0.221|0.217|0.233|0.230|
|Observations|755|733|733|733|622|622|
|R-squared|0.043|0.045|0.032|0.030|0.034|0.024|

_Notes:_ This table shows the estimated impacts of the ASP’s social-emotional learning on social-emotional-skills (SES) and emotion-regulation outcomes. _Psychology-based intervention_ is a dummy equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, and 0 if assigned to _Clubs_ . _Mean pure club group_ is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. We present the estimated coefficient _μ_ ˆ2 from specification (1). The description of dependent variables is available in Section 3.2. All dependent variables are measured in standard deviations relative to the comparison group. Sample size in each specification varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is an imbalance at baseline. All regressions include randomizationblock (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located. Wild bootstrap standard errors are shown in parentheses and adjusted _p_ -values in brackets. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01 _, p <_ 0 _._ 05 _p <_ 0 _._ 1 50
# Table 6: **Which Curriculum Is the Most Effective? - Behavior at School and Academic Performance** _(Intention-to-Treat Estimates)_

|||Estimated|Standard|_P-_value|_P_-value|Mean|P-value|Observations|
|---|---|---|---|---|---|---|---|---|
|||Coeffcient|Error||Wild Bootstrap|Clubs Group|_Virtue_=_Mindful_||
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Behavior at school|_Virtue_|0.229***|(0.083)|(0.007)|[0.035]|0.650|0.868|887|
||_Mindful_|0.236***|(0.079)|(0.004)|[0.020]||||
|School dropout|_Virtue_|0.016|(0.029)|(0.573)|[0.640]|0.037|0.020|868|
||_Mindful_|-0.040*|(0.023)|(0.085)|[0.120]||||
|Math grades|_Virtue_|-0.054|(0.089)|(0.545)|[0.635]|0.587|0.447|855|
||_Mindful_|0.023|(0.090)|(0.802)|[0.840]||||

_Notes:_ This table shows the estimated impacts of each type of psychology-based curriculum with a social-emotional-learning component. Descriptions of all dependent variables are available in Section 3.2. _Virtue_ and _Mindful_ are dummies equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, respectively, and 0 if assigned to _Clubs_ . In column (1), we present the estimated coefficients of the interaction between the treatment dummies and the _Post_ indicator. Clustered standard errors, unadjusted _p_ -values, and _p_ -values adjusted using a wild bootstrap procedure (adjusted for a small number of clusters) are shown in columns (2), (3), and (4), respectively. _Mean Clubs Group_ in column (5) is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. Column (6) presents the _p_ -value for the test for differences between estimated coefficients for _Virtue_ and _Mindful_ presented in column (1). Sample size in each specification presented in column (6) varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗_</sup> _p <_ 0 _._ 1
# Table 7: **Which Curriculum Is the Most Effective? - Social-Emotional Skills and Emotion Regulation** _Intention-to-Treat Estimates_

|||Estimated<br>Coeffcient<br>(1)|Standard<br>Error<br>(2)|_P-_value<br>(3)|_P_-value<br>Wild Bootstrap<br>(4)|Mean<br>Clubs Group<br>(5)|P-value<br>_Virtue_=_Mindful_<br>(6)|Observations<br>(7)|
|---|---|---|---|---|---|---|---|---|
|SES index|_Virtue_|-0.000|(0.014)|(0.996)|[0.995]|0.000|0.381|755|
||_Mindful_|0.007|(0.014)|(0.608)|[0.685]||||
|Short-term perseverance|_Virtue_|0.004|(0.010)|(0.736)|[0.725]|0.000|0.902|759|
||_Mindful_|0.005|(0.008)|(0.576)|[0.580]||||
|Self-control|_Virtue_|-0.008|(0.012)|(0.497)|[0.530]|0.000|0.469|776|
||_Mindful_|-0.013|(0.011)|(0.233)|[0.230]||||
|Risk aversion|_Virtue_|0.025**|(0.012)|(0.032)|[0.020]|0.000|0.066|773|
||_Mindful_|0.004|(0.011)|(0.709)|[0.715]||||
|Fluid intelligence|_Virtue_|-0.116|(0.080)|(0.152)|[0.210]|0.000|0.244|733|
||_Mindful_|0.037|(0.132)|(0.780)|[0.815]||||
|Fluid intelligence - after negative stimuli|_Virtue_|-0.119|(0.095)|(0.217)|[0.280]|0.000|0.005|733|
||_Mindful_|0.220**|(0.089)|(0.016)|[0.010]||||
|Fluid intelligence - after positive stimuli|_Virtue_|-0.068|(0.091)|(0.456)|[0.555]|0.000|0.531|733|
||_Mindful_|0.009|(0.132)|(0.944)|[0.930]||||
|Arousal|_Virtue_|0.027|(0.111)|(0.811)|[0.845]|0.000|0.617|622|
||_Mindful_|-0.059|(0.171)|(0.731)|[0.720]||||
|Valence|_Virtue_|-0.270**|(0.106)|(0.013)|[0.030]|0.000|0.792|622|
||_Mindful_|-0.211|(0.219)|(0.338)|[0.360]||||

_Notes:_ This table shows the estimated impacts of each type of psychology-based curriculum with a social-emotional-learning component. Descriptions of all dependent variables are available in Section 3.2. _Virtue_ and _Mindful_ are dummies equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, respectively, and 0 if assigned to _Clubs_ . In column (1), we present the estimated coefficients of the interaction between the treatment dummies and the _Post_ indicator. Clustered standard errors, unadjusted _p_ -values, and _p_ -values adjusted using a wild bootstrap procedure (adjusted for small number of clusters) are shown in columns (2), (3), and (4), respectively. _Mean Clubs Group_ in column (5) is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. Column (6) presents the _p_ -value for the test for differences between estimated coefficients for _Virtue_ and _Mindful_ presented in column (1). Sample size in each specification presented in column (7) varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗_</sup> _p <_ 0 _._ 1
Table 8: **ASP Attendance and Dropout**

||Attendance<br>(as % of sessions)<br>(1)|Dropped<br>the ASP<br>(2)|Sessions<br>scheduled<br>(3)|
|---|---|---|---|
|_Virtue_|0.195***|-0.110**|0.235|
||(0.048)|(0.046)|(3.548)|
|_Mindful_|0.064<br>(0.043)|-0.058<br>(0.040)|0.034<br>(2.871)|
|Psychology-based curricula|0.131***<br>(0.042)|-0.084**<br>(0.039)|0.137<br>(2.723)|
|P-value for_Virtue_=_Mindful_|0.0046|0.1598|0.9542|
|<br>Mean_Clubs_group|0.6142|0.2258|42.7689|
|<br>Observations|897|897|897|
|height||||

_Notes:_ This table shows the ASP attendance rates. The estimation sample includes all students treated in _Clubs_ , _Virtue_ , or _Mindful_ . It compares the attendance of participants randomly assigned to _Virtue_ or _Mindful_ to those treated in _Clubs_ . Column (1) shows the attendance rate as a percentage of the number of total sessions conducted (column (3)). Column (2) presents the share of students who dropped out of the ASP. “Mean Clubs is the mean of the outcome for the that in _Clubs_ . The co- group” group participated efficients for both the _Virtue_ and _Mindful_ groups are derived from a unified regression the on the broader of cur- analysis. Conversely, coefficient category psychology-based ricula is obtained from a distinct regression that employs a dummy variable set to 1 for students who participated in any such curriculum _(Virtue or Mindful)_ . All estimations include the relevant control variables at baseline. Bootstrapped standard errors at the course level are in parentheses.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗_</sup> _p <_ 0 _._ 1 53
<!-- Start of picture text --> IMPACT Improvement of students’ behavior at school<br>Primary Outcomes:<br>OUTCOMES Behavior at school, School dropout, and Academic performance<br>Virtue: Mindful:<br>i Perseverance, risky behavior, self-control | Self-control, fluid intelligence, and<br>POTENTIAL eee, | eee<br>MECHANISMS [ Improvement or learning of Social- | Improvement of Enhancement of fluid |<br>emotional skills (perseverance, risk- emotional self-regulation { intelligence<br>taking behavior, and self-control) i | i<br>INTERVENTION Social-Emotional Learning Component of an After-School Program that includes a<br>psychology-based curriculum (Virtue or Mindful)<br>PROBLEM High incidence of school-based violence in public schools in<br>El Salvador, Guatemala, and Honduras<br><!-- End of picture text -->
<!-- Start of picture text --> Random allocation of 21 public schools<br><!-- End of picture text --> Strata: Country (Guatemala, Honduras, El Salvador) and crime-related risk level (homicide rate of the community is higher than the average rate at the national level) <!-- Start of picture text --> 7 schools to 7 schools to 7 schools to<br>Clubs Virtue Mindful<br><!-- End of picture text -->
Recruitment of schools, ; agreements with principals, Focus groups with IRB submission Enrollment of students Program implementation teachers and parents Jan. 2019 Early Mar. 2019 Apr. — Oct. 2019 Feb. - Mar. 2021 ; eee ow Recruitment of students Baseline data collection Endline data collection Mid-Jan. — Feb. 2019 Mar. 2019 Survey: Oct. - Nov. 2019 Administrative data: February 2020