High-Ability Influencers? The Heterogeneous Effects of Gifted Classmates

Balestra, Simone; Sallin, Aurélien; Wolter, Stefan C.

doi:10.2139/ssrn.3755385

Cited by 4 publications

(6 citation statements)

References 63 publications

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“…Once the difference in ability between the more able student and the peers becomes large, the literature finds negative spillovers (Burke and Sass, 2013;Balestra, Sallin, and Wolter, 2021), as our analysis found for dropouts with field-specific knowledge. This finding is compatible with hypotheses that students who have a very subject-specific knowledge advantage either have a discouraging influence on their fellow students (Rogers and Feller, 2016) or influence the nature of teaching (Duflo, Dupas, and Kremer, 2011;Brodaty and Gurgand, 2016) or grading (Calsamiglia and Loviglio, 2019).…”

Section: Discussionsupporting

confidence: 61%

“…Thus far, peer effects have primarily been studied for compulsory education, such as kindergarten (Chetty et al, 2011), elementary school (Gottfried, 2013), lower-secondary school (Balestra, Eugster, and Liebert, 2020;Balestra, Sallin, and Wolter, 2021) and high school (Lavy, Silva, and Weinhardt, 2012). The impact of grade repeaters on their peers investigated solely in compulsory schooling classes consistently finds negative short-run effects (e.g., Lavy, Paserman, and Schlosser, 2012;Gottfried, 2013;Hill, 2014;Bietenbeck, 2020;Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Sitting Next to a Dropout: Academic Success of Students with More Educated Peers

Goller

Diem

Wolter³

2022

SSRN Journal

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Section: Discussionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Sitting Next to a Dropout: Academic Success of Students with More Educated Peers

Goller

Diem

Wolter³

2022

SSRN Journal

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“…Recently, Jelks and Crain (2020) found, using US data, that over 25 percent of those with an undergraduate degree in STEM did not intend to remain in STEM by age 30. Also using US 1 Many papers study the gender gap in preferences for studying STEM in college with potential factors including differences in comparative advantage in math and English (Speer, 2017;Delaney and Devereux, 2019), peer gender-composition (Brenoe and Zolitz, 2020;Schone et al, 2020), peer ability-composition (Balestra et al, 2020;Cools et al, 2019;Mouganie and Wang, 2020), math rank in high school cohort (Delaney and Devereux, 2021), and role models (Breda et al, 2020). See Kahn and Ginther (2018), McNally (2020), Cavaglia et al (2020), and Delaney and Devereux (2021b) for reviews of this literature.…”

Section: Introductionmentioning

confidence: 99%

“…2020), peer ability composition (Balestra et al . 2020; Cools et al . 2019; Mouganie and Wang 2020), maths rank in high school cohort (Delaney and Devereux 2021a), and role models (Breda et al .…”

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confidence: 99%

Gender Differences in STEM Persistence after Graduation

Delaney

Devereux

2022

Economica

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Much attention is focused on finding ways to encourage females to study STEM in school and college, but what actually happens once women complete a STEM degree? We use the UK Quarterly Labour Force Survey to trace out gender differences in STEM persistence over the career. We find a continuous process whereby women are more likely to exit STEM than men. Among STEM undergraduate degree holders, women are more likely to obtain a non‐STEM master's degree. After entering the labour market, there is a gradual outflow of females during the first 15 years post‐graduation, so that females are about 20 percentage points less likely to work in STEM than men. Conditional on leaving STEM, females are more likely to enter the education and health sectors, while males are more likely to enter the business sector, and this can partly explain the gender pay gap for STEM graduates. Overall, our results suggest that policies that aim to increase the proportion of females studying STEM may have less effect than expected due to the lower attachment of females to STEM after graduation. Such policies may need to be augmented with efforts to tackle the greater propensity of females to exit STEM throughout the career.

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“…As regards the argument of potential selection into schools, I refer to existing literature (Balestra, Eugster, and Liebert, 2020, forthcoming;Balestra, Sallin, and Wolter, forthcoming): student mobility between school is rare in the canton of St. Gallen. Families must move to another municipality if they want to change school (or enroll their students in private schools).…”

Section: Table B1: First Stage Estimatesmentioning

confidence: 99%

Estimating returns to special education: combining machine learning and text analysis to address confounding

Sallin¹

2021

Preprint

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While the number of students with identified special needs is increasing in developed countries, there is little evidence on academic outcomes and labor market integration returns to special education. I present results from the first ever study to examine short-and long-term returns to special education programs using recent methods in causal machine learning and computational text analysis. I find that special education programs in inclusive settings have positive returns on academic performance in math and language as well as on employment and wages. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. However, I find that segregation has benefits for some students: students with emotional or behavioral problems, and nonnative students. Finally, using shallow decision trees, I deliver optimal placement rules that increase overall returns for students with special needs and lower special education costs. These placement rules would reallocate most students with special needs from segregation to inclusion, which reinforces the conclusion that inclusion is beneficial to students with special needs.

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High-Ability Influencers? The Heterogeneous Effects of Gifted Classmates

Cited by 4 publications

References 63 publications

Sitting Next to a Dropout: Academic Success of Students with More Educated Peers

Sitting Next to a Dropout: Academic Success of Students with More Educated Peers

Gender Differences in STEM Persistence after Graduation

Estimating returns to special education: combining machine learning and text analysis to address confounding

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