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Female Classmates, Disruption, and STEM Outcomes in Disadvantaged Schools: Evidence from a Randomized Natural Experiment

Author

Listed:
  • Sofoklis Goulas

    (Brookings Institution, Washington DC, United States)

  • Rigissa Megalokonomou

    (Monash University, Monash Business School, Department of Economics, Australia, University of Queensland, IZA, and CESifo)

  • Yi Zhang

    (University of Queensland, School of Economics, Australia)

Abstract

Recent research has shown that females make classrooms more conducive to effective learning. We identify the effect of a higher share of female classmates on students’ disruptive behavior, engagement, test scores, and major choices in disadvantaged and non-disadvantaged schools. We exploit the random assignment of students to classrooms in early high school in Greece. We combine rich administrative data with hand-collected student-level data from a representative sample of schools that feature two novel contributions. Unlike other gender peer effects studies, a) we use a rich sample of schools and students that contains a large and diverse set of school qualities, and household incomes, and b) we measure disruption and engagement using misconduct-related (unexcused) teacher-reported and parent-approved (excused) student class absences instead of self-reported measures. We find four main results. First, a higher share of female classmates improves students’ current and subsequent test scores in STEM subjects and increases STEM college participation, especially for girls. Second, a higher share of female classmates is associated with reduced disruptive behavior for boys and improved engagement for girls, which indicates an increase in overall classroom learning productivity. Third, disadvantaged students—those who attend low-quality schools or reside in low-income neighborhoods—drive the baseline results; they experience the highest improvements in their classroom learning productivity and their STEM outcomes from a higher share of female classmates. Fourth, disadvantaged females randomly assigned to more female classmates in early high school choose college degrees linked to more lucrative or prestigious occupations 2 years later. Our results suggest that classroom interventions that reduce disruption and improve engagement are more effective in disadvantaged or underserved environments.

Suggested Citation

  • Sofoklis Goulas & Rigissa Megalokonomou & Yi Zhang, 2024. "Female Classmates, Disruption, and STEM Outcomes in Disadvantaged Schools: Evidence from a Randomized Natural Experiment," Monash Economics Working Papers 2024-01, Monash University, Department of Economics.
  • Handle: RePEc:mos:moswps:2024-01
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    References listed on IDEAS

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    1. Michael Dinerstein & Rigissa Megalokonomou & Constantine Yannelis, 2022. "Human Capital Depreciation and Returns to Experience," American Economic Review, American Economic Association, vol. 112(11), pages 3725-3762, November.
    2. Delaney, Judith M. & Devereux, Paul J., 2019. "It's Not Just for Boys! Understanding Gender Differences in STEM," IZA Discussion Papers 12176, Institute of Labor Economics (IZA).
    3. Joshua D. Angrist & Parag A. Pathak & Christopher R. Walters, 2013. "Explaining Charter School Effectiveness," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 1-27, October.
    4. Anne Ardila Brenøe & Ulf Zölitz, 2020. "Exposure to More Female Peers Widens the Gender Gap in STEM Participation," Journal of Labor Economics, University of Chicago Press, vol. 38(4), pages 1009-1054.
    5. Raj Chetty & John N. Friedman & Nathaniel Hilger & Emmanuel Saez & Diane Whitmore Schanzenbach & Danny Yagan, 2011. "How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(4), pages 1593-1660.
    6. Jan Feld & Ulf Zölitz, 2017. "Understanding Peer Effects: On the Nature, Estimation, and Channels of Peer Effects," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 387-428.
    7. Francine D. Blau & Lawrence M. Kahn, 2017. "The Gender Wage Gap: Extent, Trends, and Explanations," Journal of Economic Literature, American Economic Association, vol. 55(3), pages 789-865, September.
    8. Will Dobbie & Roland G. Fryer Jr., 2013. "Getting beneath the Veil of Effective Schools: Evidence from New York City," American Economic Journal: Applied Economics, American Economic Association, vol. 5(4), pages 28-60, October.
    9. Esther Duflo & Pascaline Dupas & Michael Kremer, 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya," American Economic Review, American Economic Association, vol. 101(5), pages 1739-1774, August.
    10. Massimo Anelli & Giovanni Peri, 2019. "The Effects of High School Peers’ Gender on College Major, College Performance and Income," The Economic Journal, Royal Economic Society, vol. 129(618), pages 553-602.
    11. Scott E. Carrell & Mark L. Hoekstra, 2010. "Externalities in the Classroom: How Children Exposed to Domestic Violence Affect Everyone's Kids," American Economic Journal: Applied Economics, American Economic Association, vol. 2(1), pages 211-228, January.
    12. Jere R. Behrman & C. Simon Fan & Xiangdong Wei & Hongliang Zhang & Junsen Zhang, 2020. "After-School Tutoring, Household Substitution and Student Achievement: Experimental Evidence from Rural China," PIER Working Paper Archive 20-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. Atila Abdulkadiroğlu & Joshua D. Angrist & Susan M. Dynarski & Thomas J. Kane & Parag A. Pathak, 2011. "Accountability and Flexibility in Public Schools: Evidence from Boston's Charters And Pilots," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(2), pages 699-748.
    14. Bobby W. Chung & Jian Zou, 2023. "Understanding spillover of peer parental education: Randomization evidence and mechanisms," Economic Inquiry, Western Economic Association International, vol. 61(3), pages 496-522, July.
    15. Angrist, Joshua D., 2014. "The perils of peer effects," Labour Economics, Elsevier, vol. 30(C), pages 98-108.
    16. Robert Bifulco & Jason M. Fletcher & Stephen L. Ross, 2011. "The Effect of Classmate Characteristics on Post-secondary Outcomes: Evidence from the Add Health," American Economic Journal: Economic Policy, American Economic Association, vol. 3(1), pages 25-53, February.
    17. Catherine Buffington & Benjamin Cerf & Christina Jones & Bruce A. Weinberg, 2016. "STEM Training and Early Career Outcomes of Female and Male Graduate Students: Evidence from UMETRICS Data Linked to the 2010 Census," American Economic Review, American Economic Association, vol. 106(5), pages 333-338, May.
    18. Devereux, Paul J. & Delaney, Judith, 2019. "Understanding Gender Differences in STEM: Evidence from College Applications," CEPR Discussion Papers 13558, C.E.P.R. Discussion Papers.
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    1. Goulas, Sofoklis & Gunawardena, Bhagya N. & Megalokonomou, Rigissa & Zenou, Yves, 2024. "Gender Role Models in Education," IZA Discussion Papers 17271, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    gender peer effects; natural e; classroom learning productivit; quasi-random variation; disadvantaged students;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education

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