Numeracy and literacy skills have become increasingly important in modern labour markets. The large gender differences that several studies have identified have therefore sparked considerable attention among researchers and policy makers, as they may have long term effects on educational decisions and labour market outcomes.

Despite major trends pointing towards a closing of the gender gap in educational attainment in favour of males and increasing concerns for emerging gender gaps in favour of females in a variety of educationally relevant outcomes, the academic and career choices of young men and women remain remarkably different. Recent analyses show the persistence of gender inequalities in the acquisition of different competencies too. These studies, which usually focus at the secondary school level, indicate that boys tend to outperform girls in numeracy, while girls tend to do better than boys in literacy. Additionally, Machin and Pekkarinen (2008) show that, in most countries, the skill distribution of 15-years old girls’ is less dispersed than that of their male counterparts.

Strikingly, although several studies document cross-country differences in the size (and sometimes also in the sign) of gender gaps in numeracy and literacy at specific ages, much less is known about how such gaps evolve over time and if such evolution differs across countries. Better knowledge of the timing of events is crucial to identify critical factors and circumstances that shape the evolution of the gaps, and to design effective policies accordingly.

We contribute to the literature by mapping cross-national differences in the magnitude and evolution of gender gaps in numeracy and literacy from childhood (around age 10) into young adulthood (around age 27). In the second part of the paper we then look at the evolution of gender gaps across the skill distribution. Previous evidence has shown that gender gaps differ considerably between low- and high-achieving students, as males tend to be over-represented at both tails of the skill distribution.

In the absence of cross-country comparable longitudinal data, we combine information from existing cross-sectional large-scale international assessments: the Trends in International Mathematics and Science Study (TIMSS) and the Programme in Reading and Literacy Skills (PIRLS) (managed by the IEA), as well as the OECD Programme for International Student Assessment (PISA) and the OECD Programme for the International Assessment of Adult Competencies (PIAAC). In this way, we are able to track the evolution of gender gaps within cohorts of students that participated in different waves of different surveys.

Our findings suggest that the gender gap in numeracy in favour of males tends to grow as individuals age and that such growth is particularly pronounced after males and females leave compulsory schooling and enter post-compulsory education and the labour market. The gender gap in literacy, on the other hand, follows an inverted-U profile, being highest during teenage years and lowest among young adults. Gender gaps evolve in similar ways at both ends of the skill distribution. The variability of performance is always higher among male students, peaking during the teenage years in both numeracy and literacy.

Antecol, H., and Cobb-Clark, D. (2013). Do psychosocial traits help explain gender segregation in young people's occupations? Labour Economics, 21, 59-73. Aronson, J. (2002). Stereotype Threat: Contending and Coping with Unnerving Expectations. In J. Aronson and D. Cordova (Ed.), Improving Academic Achievement (pp. 279-301). Academic Press, New York. Black, S., and Spitz-Oener, A. (2010). Explaining Women's Success: Technological Change and the Skill Content of Women's Work. Review of Economics and Statistics, 92(1), 187-194. Borgonovi, F. and Biecek, P. (2016). An international comparison of students' ability to endure fatigue and maintain motivation during a low-stakes test. Learning and Individual Differences, 49, 128-137. Buser, T., Niederle, M., and Oosterbeek, H. (2014). Gender, Competitiveness, and Career Choices. Quarterly Journal of Economics, 129(3), 1409-1447. Cvencek, D., Meltzoff, A., and Greenwald, A. (2011). Math-Gender Stereotypes in Elementary School Children. Child Development, 82(3), 766-779. Dickerson, A., McIntosh, S., and Valente, C. (2015). Do the maths: An analysis of the gender gap in mathematics in Africa. Economics of Education Review, 46, 1-22. Fryer, R., and Levitt, S. (2010). An Empirical Analysis of the Gender Gap in Mathematics. American Economic Journal: Applied Economics, 2(2), 210-240. Guiso, L., Monte, F., Sapienza, P., and Zingales, L. (2008). Culture, Gender, and Math. Science, 320, 164-165. Halpern, D. (2012). Sex Differences in Cognitive Abilities. Psychology Press, New York. Hedges, L., and Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269(5220), 41-45. Jacob, B., and Rothstein, J. (2016). The Measurement of Student Ability in Modern Assessment Systems. Journal of Economic Perspectives, 30(3), 85-108. Machin, S., and McNally, S. (2005). Gender and Student Achievement in English Schools. Oxford Review of Economic Policy, 21(3), 357-372. Machin, S., and Pekkarinen, T. (2008). Global Sex Differences in Test Score Variability. Science, 322, 1331-1332. Niederle, M., and Vesterlund, L. (2010). Explaining the Gender Gap in Math Test Scores: The Role of Competition. Journal of Economic Perspectives, 24(2), 129-144. OECD (2015). The ABC of Gender Equality in Education: Aptitude, Behaviour, Confidence. OECD Publishing, Paris. Paccagnella, M. (2016). Age, Ageing and Skills: Results from the Survey of Adult Skills. OECD Education Working Papers, 132. OECD Publishing, Paris. Sassler, S., Glass, J., Levitte, Y., and Michelmore, K.M. (2017). The missing women in STEM? Assessing gender differentials in the factors associated with transition to first jobs. Social Science Research, 63, 192-208.