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A population level analysis of the gender gap in mathematics: Results on over 13 million children using the INVALSI dataset

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  • Giofrè, D.
  • Cornoldi, C.
  • Martini, A.
  • Toffalini, E.

Abstract

Whether males outperform females in mathematics is still debated. Such a gender gap varies across countries, but the determinants of the differences are unclear and could be produced by heterogeneity in the instructional systems or cultures and may vary across school grades. To clarify this issue, we took advantage of the INVALSI dataset, that offered over 13 million observations covering one single instructional system (i.e., the Italian system) in grades 2, 5, and 8, in the period 2010–2018. Results showed that males outperformed females in mathematics (and vice versa in reading), with gaps widening from the 2nd through to the 8th grade. The gender gap in mathematics was larger in the richer northern Italian regions (also characterized by greater gender equality) than in southern regions. This was not explained by average performance or fully accounted for by economic factors. No such north-south difference of the gap emerged in reading. Results are discussed with reference to the literature showing that the gender gap varies across world regions.

Suggested Citation

  • Giofrè, D. & Cornoldi, C. & Martini, A. & Toffalini, E., 2020. "A population level analysis of the gender gap in mathematics: Results on over 13 million children using the INVALSI dataset," Intelligence, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:intell:v:81:y:2020:i:c:s0160289620300453
    DOI: 10.1016/j.intell.2020.101467
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    References listed on IDEAS

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    2. Oberleiter, Sandra & Fries, Jonathan & Schock, Laura S. & Steininger, Benedikt & Pietschnig, Jakob, 2023. "Predicting cross-national sex differences in large-scale assessments of students' reading literacy, mathematics, and science achievement: Evidence from PIRLS and TIMSS," Intelligence, Elsevier, vol. 100(C).
    3. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    4. Feraco, Tommaso & Cona, Giorgia, 2022. "Differentiation of general and specific abilities in intelligence. A bifactor study of age and gender differentiation in 8- to 19-year-olds," Intelligence, Elsevier, vol. 94(C).
    5. Daniel Doz & Mara Cotič & Darjo Felda, 2023. "Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades," Mathematics, MDPI, vol. 11(19), pages 1-19, September.

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