The Gender Gap in Mathematics Achievement: Evidence from Italian Data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 10053

Classification and Topics

• Classification: Wirtschaft ; Economics of Gender; Non-labor Discrimination ; Education and Inequality ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
• Subject: math gender gap ; education ; school achievement ; inequalities ; cross-sectional data ; pseudo panel estimation ; quantile regression

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Di Tommaso, Maria Laura ; Mendolia, Silvia ; Contini, Dalit
• Event: Veröffentlichung
• (who): Institute for the Study of Labor (IZA)
• (where): Bonn
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/145187
• Last update: 10.03.2025, 11:43 AM CET