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How social ties affect peer-group effects: a case of university students

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  • Oleg Poldin

    (National Research University Higher School of Economics (HSE), 25/12 Bolshaja Pecherskaja Ulitsa, Nizhny Novgorod 603155, Russia)

  • Dilyara Valeeva

    (Center for Institutional Studies, HSE.)

  • Maria Yudkevich

    (Center for Institutional Studies, HSE, Russia, 101000 Moscow, Myasnitskaya street, 20.)

Abstract

Among the key issues of peer effects estimation is the correct identification of relevant peers. In this study, we explore how the individual performance of university students is influenced by characteristics and achievements of peers from individual’s social network. The analysis uses data from two directed networks: a network of friends and a network of study partners for thirdyear students at a top-tier Russian university. Data on network ties in randomly formed student groups enables us to address the endogeneity problem and disentangle the influence of peers’ performance from the effect that a peer’s background has on students. We show that both the GPA of peers and their ability measures are significant in the estimated regression model. A onepoint increase in the average GPA of peers is associated with an increase in an individual student’s own GPA of approximately one fourth. The regression on the data from the network of study partners has slightly greater explanatory power than the analys is based on data from the network of friends. No effect from a student’s classmates is found in the model that assumes group interactions occur between group mates

Suggested Citation

  • Oleg Poldin & Dilyara Valeeva & Maria Yudkevich, 2013. "How social ties affect peer-group effects: a case of university students," HSE Working papers WP BRP 15/SOC/2013, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:15/soc/2013
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    References listed on IDEAS

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    9. Gregory Androushchak & Oleg Poldin & Maria Yudkevich, 2012. "Peer Effects in Exogenously Formed University Student Groups," HSE Working papers WP BRP 03/EDU/2012, National Research University Higher School of Economics.
    10. Peter Arcidiacono & Gigi Foster & Natalie Goodpaster & Josh Kinsler, 2012. "Estimating spillovers using panel data, with an application to the classroom," Quantitative Economics, Econometric Society, vol. 3(3), pages 421-470, November.
    11. Androushchak, Gregory & Poldin, Oleg & Yudkevich, Maria, 2012. "Peer effects in exogenously formed student groups," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 3-16.
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    Cited by:

    1. F. Aleskerov & N. Meshcheryakova & S. Shvydun, 2016. "Centrality measures in networks based on nodes attributes, long-range interactions and group influence," Papers 1610.05892, arXiv.org.
    2. Ekaterina Krekhovets & Oleg Poldin, 2013. "Students' Social Media: Formation Factors and Influence on Studies," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 4, pages 127-144.
    3. Maria Prosperina Vitale & Giovanni C. Porzio & Patrick Doreian, 2016. "Examining the effect of social influence on student performance through network autocorrelation models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 115-127, January.
    4. Valeeva, Dilyara & Poldin, Oleg & Yudkevich, Maria, 2014. "Student’s social ties and the choice of specialization," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 80-94.

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    More about this item

    Keywords

    peer effects; higher education; student achievement; social networks.;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality

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