Related work of the team

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.

The paper investigates the gender wage gap among recently graduates, controlling for job and academic variables and for the field of study, as women lag behind in highly remunerative majors. The raw gender differential in hourly wages is 5.6%. Although including academic variables and the field of study—on top of job-related variables—slightly reduces the unexplained gap, the latter still accounts for most of the total difference. Using quantile decomposition, the paper shows that the unexplained gap increases along the wage distribution, indicating a glass ceiling effect. Heterogeneities arise among fields of study: the largest total gap emerges in Law, Political-Social sciences, and Economics-Statistics. In most disciplines, there is a significant unexplained gap—from 3.3% (Medicine), to 8.7% (Law), up to 9.6% (Agriculture)—which constitutes the largest share of the difference, confirming that most of the wage gap remains unexplained also by major. Finally, I use geographical differences to explore the influence of institutional and macro-economic variables, as well as of attitudes towards gender norms. The results indicate that childcare and part-time employment availability are correlated with lower gender wage gaps, while traditional gender norms are associated with higher differentials.

Gender differences in the STEM (Science Technology Engineering and Mathematics) disciplines are widespread in most OECD countries and mathematics is the only subject where girls tend to underperform with respect to boys. This paper analyses 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 latest 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, and analyse the gender gap in mathematics test scores using OLS, school fixed effects, quantile regression, metric free and dynamic pseudo-panel models. Our results show that girls systematically underperform boys, even after controlling for an array of individual and family background characteristics. The average gender gap increases with children's age, is larger among top performing children, and girls keep losing ground relative to boys when progressing in the education system.

The focus of the paper is on the measurement of science education capability with a gender perspective and in the capability approach framework. Measuring science education capability implies going beyond the measurement of children test scores. In the capability approach, we aim at the real opportunities that children can develop later in life and therefore it is important to include some measures of non-cognitive skills. We utilize, therefore, different indicators in addition to test scores in science: enjoyment in science, interest in science, general and personal values of science, self-confidence in performing science related tasks, awareness and perception of environmental issues, and responsibility for sustainable development. We utilize the 2006 PISA survey for Italian 15 years old children because it contains a particular focus on science and we estimate a Structural Equation Model to take into account that capabilities are latent constructs of which we only observe some indicators. We also investigate the determinants of children’s science education capability in Italy taking into account household, individual and school factors. Results confirm that boys outperform girls in science education capability. Our theoretical construct for the science education capability confirms that all the indicators are relevant to measure this capability. School activities to promote sciences improve girls’ capability and interactive methods of teaching improve both girls and boys capability. The household educational resources and the household educational possession are also positively correlated with girls’ and boys’ science education capability.