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Beliefs, Exams and Social Media: A Study of Girls and Boys in the UK


  • Marina Della Giusta

    () (Department of Economics, University of Reading)

  • Sarah Jewell

    () (Department of Economics, University of Reading)

  • Danica Vukadinovic Greetham

    (Centre for the Mathematics of Human Behaviour, University of reading)


Social media diffusion amongst tween and teenagers keeps increasing year on year and involving younger and younger children and studies have begun to appear indicating several changes in adolescent behaviour and mental health corresponding with increased social media use (Twenge, 2017; Twenge et al., 2017). Data derived from social media is also increasingly used to predict a variety of outcomes including personality (Youyou at al., 2014) and mental health (De Choudhury et al., 2013). We investigate the determinants of social media use and the connection between social media and teenagers' beliefs about education, which are known to be strongly connected to educational outcomes. We construct a representative sample of UK teenagers from British survey data and a sample of Twitter data specifically collected around the first national secondary school exam taken at age 16, which have important effects for further educational choices. Building on literature addressing the factors influencing teen's educational expectations (Anders and Micklewright, 2015) and the construction of beliefs (Gennaioli and Schleifer, 2010; Corazzini et al, 2010; Oxoby, 2014; Coffman, 2014; Alesina et al, 2015; Bordalo et al, 2016a; Bordalo et al. 2016b), we model social media use in the representative sample. We identify significant associations between differential usage (at both the extensive and intensive margin) and controls (socio-demographics, parental inputs and children cognitive and non-cognitive skills), particularly indicating that intensive social media usage is indeed associated with a range of negative factors as found in research on US teens (Twenge, 2017). We also find that beliefs become more gender stereotypical with age, and more so the more tweens and teens are in social media. We then use social network modelling to investigate dynamics in the Twitter sample, and identify significant gender differences in social media communication patterns and moods pertaining to scientific subjects, which indicate social media contribute to educational beliefs, potentially biasing them through the propagation of gender stereotypes.

Suggested Citation

  • Marina Della Giusta & Sarah Jewell & Danica Vukadinovic Greetham, 2017. "Beliefs, Exams and Social Media: A Study of Girls and Boys in the UK," Economics & Management Discussion Papers em-dp2017-02, Henley Business School, Reading University.
  • Handle: RePEc:rdg:emxxdp:em-dp2017-02

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    References listed on IDEAS

    1. Jake Anders, 2012. "What's the link between household income and going to university?," DoQSS Working Papers 12-01, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    2. Akerlof, George A. & Snower, Dennis J., 2016. "Bread and bullets," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PB), pages 58-71.
    3. Haroon Chowdry & Claire Crawford & Lorraine Dearden & Alissa Goodman & Anna Vignoles, 2013. "Widening participation in higher education: analysis using linked administrative data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 431-457, February.
    4. Alberto Alesina & Yann Algan & Pierre Cahuc & Paola Giuliano, 2015. "Family Values And The Regulation Of Labor," Journal of the European Economic Association, European Economic Association, vol. 13(4), pages 599-630, August.
    5. Stefano DellaVigna & Ethan Kaplan, 2007. "The Fox News Effect: Media Bias and Voting," The Quarterly Journal of Economics, Oxford University Press, vol. 122(3), pages 1187-1234.
    6. Jake Anders, 2012. "The Link between Household Income, University Applications and University Attendance," Fiscal Studies, Institute for Fiscal Studies, vol. 33(2), pages 185-210, June.
    7. Katherine Baldiga Coffman, 2014. "Evidence on Self-Stereotyping and the Contribution of Ideas," The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1625-1660.
    8. Jake Anders & John Micklewright, 2013. "Teenagers' expectations of applying to university: how do they change?," DoQSS Working Papers 13-13, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    9. Black, Sandra E. & Devereux, Paul J. & Salvanes, Kjell G., 2009. "Like father, like son? A note on the intergenerational transmission of IQ scores," Economics Letters, Elsevier, vol. 105(1), pages 138-140, October.
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    More about this item


    beliefs; social media; education; gender; social networks;

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • 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

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