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The Gender Gap in Attitudes and Test Scores: A New Construct of the Mathematical Capability

Author

Listed:
  • Di Tommaso, Maria Laura

    () (University of Turin)

  • Maccagnan, Anna

    () (University of Exeter)

  • Mendolia, Silvia

    () (University of Wollongong)

Abstract

In most OECD countries, girls outperform boys in all subjects except mathematics. Usually, only test scores are utilised as a measure of mathematical skills. In this paper, we argue that in order to measure children's capability in mathematics we need to include some indicators of the attitudes of children towards the subject. This is particularly important when we analyse gender gaps, because attitudes towards mathematics differ by gender. We first describe the differences by gender both in test scores and attitudes utilising a model including school fixed effects. Next, we estimate a quantile regression in order to analyse how the gender gap varies across the distribution of the attitudes. Lastly, in addition to the test scores in mathematics, we use indicators of attitudes towards maths to estimate a Structural Equation Model, which takes into account that maths capability is a latent construct of which we only observe some indicators (test scores and attitudes). We use data from the Italian National Test (Invalsi) for year 5 and year 10 in 2014 and 2015. Results confirm that when we measure mathematics capability including attitudes in addition to test scores, the gap between boys and girls is even wider with respect to the analysis of test scores alone, and therefore educational policies aimed at reducing the gender gap in mathematics should address both attitudes and test scores.

Suggested Citation

  • Di Tommaso, Maria Laura & Maccagnan, Anna & Mendolia, Silvia, 2018. "The Gender Gap in Attitudes and Test Scores: A New Construct of the Mathematical Capability," IZA Discussion Papers 11843, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp11843
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    References listed on IDEAS

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    1. Marco Paccagnella & Paolo Sestito, 2014. "School cheating and social capital," Education Economics, Taylor & Francis Journals, vol. 22(4), pages 367-388, August.
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    3. Christopher Cornwell & David B. Mustard & Jessica Van Parys, 2013. "Noncognitive Skills and the Gender Disparities in Test Scores and Teacher Assessments: Evidence from Primary School," Journal of Human Resources, University of Wisconsin Press, vol. 48(1), pages 236-264.
    4. James J. Heckman, 2008. "Schools, Skills, And Synapses," Economic Inquiry, Western Economic Association International, vol. 46(3), pages 289-324, July.
    5. Flavio Cunha & James J. Heckman & Susanne M. Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," Econometrica, Econometric Society, vol. 78(3), pages 883-931, May.
    6. Orazio P. Attanasio, 2015. "The Determinants Of Human Capital Formation During The Early Years Of Life: Theory, Measurement, And Policies," Journal of the European Economic Association, European Economic Association, vol. 13(6), pages 949-997, December.
    7. Martha Nussbaum, 2003. "Capabilities As Fundamental Entitlements: Sen And Social Justice," Feminist Economics, Taylor & Francis Journals, vol. 9(2-3), pages 33-59.
    8. Lucifora, Claudio & Tonello, Marco, 2015. "Cheating and social interactions. Evidence from a randomized experiment in a national evaluation program," Journal of Economic Behavior & Organization, Elsevier, vol. 115(C), pages 45-66.
    9. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393 Elsevier.
    10. repec:eee:ecoedu:v:58:y:2017:i:c:p:32-42 is not listed on IDEAS
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    More about this item

    Keywords

    maths gender gap; attitudes; structural equation models; school achievement;

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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