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The Gender Gap in Mathematics Achievement: Evidence from Italian Data

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  • Di Tommaso, Maria Laura

    (University of Torino)

  • Mendolia, Silvia

    (University of Torino)

  • Contini, Dalit

    (University of Torino)

Abstract

Gender differences in the STEM (Science Technology Engineering and Mathematics) disciplines are widespread in most OECD countries and mathematics is the only subject where typically girls tend to underperform with respect to boys. This paper describes the gender gap in math test scores in Italy, which is one of the countries displaying the largest differential between boys and girls according to the Programme for International Student Assessment (PISA), we use data from an Italian national level learning assessment, involving children in selected grades from second to tenth. We first analyse the magnitude of the gender gap using OLS regression and school fixed-effect models for each grade separately. Our results show that girls systematically underperform boys, even after controlling for an array of individual and family background characteristics, and that the average gap increases with children's age. We then study the gender gap throughout the test scores distribution, using quantile regression and metric-free methods, and find that the differential is small at the lowest percentiles of the grade distribution, but large among top performing children. Finally, we estimate dynamic models relating math performance at two consecutive assessments. Lacking longitudinal data, we use a pseudo panel technique and find that girls' average test scores are consistently lower than those of boys at all school years, even conditional on previous scores.

Suggested Citation

  • Di Tommaso, Maria Laura & Mendolia, Silvia & Contini, Dalit, 2016. "The Gender Gap in Mathematics Achievement: Evidence from Italian Data," IZA Discussion Papers 10053, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10053
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    More about this item

    Keywords

    math gender gap; education; school achievement; inequalities; cross-sectional data; pseudo panel estimation; quantile regression;
    All these keywords.

    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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