Conference Program

Educational inequalities continue to represent a structural and persistent challenge for contemporary school systems. This issue is especially pronounced in upper secondary education in Italy, where marked disparities in student performance persist across institutions and educational tracks. In this context, learning outcomes are strongly shaped by family background characteristics, such as socioeconomic status, cultural capital, and parental educational attainment, as well as by school composition factors (Giancola & Salmieri, 2020).

In this analysis, educational inequalities are interpreted as the outcome of the interaction between individual and school-level factors: on the one hand, the mechanisms of reproduction of familial cultural capital through selective school processes (Bourdieu, 1971); on the other, differences in social and relational capital across school contexts (Coleman, 1988, 1990).

The aim of this study is to examine the evolution of these inequalities over time by using OECD-PISA test scores in Italian (reading literacy) and mathematics as proxy indicators of learning outcomes. The analysis draws on data from the 2018 and 2022 assessment cycles, corresponding respectively to the pre- and post-pandemic periods, in order to evaluate the effects associated with school closures and the economic crisis following COVID-19. This intertemporal approach makes it possible to move beyond a static perspective and to observe changes in the mechanisms through which educational inequalities are produced over time.

To analyze the effects of transformations over time, an intertemporal dataset was constructed by integrating the original database with time variables and interaction terms. Following a preliminary descriptive analysis, multilevel models and multiple linear regression models (four for reading and four for mathematics) were estimated in order to assess changes over time in the impact of family- and school-level factors (Jaccard & Turrisi, 2003; Giancola & Colarusso, 2020), through the progressive inclusion of variables. The baseline model included the main individual characteristics (gender and migrant background), as well as family and school characteristics, together with territorial controls. In subsequent models, interaction terms between family background, school composition, and time were introduced, first separately and then jointly, so as to estimate variations in their effects across the two waves.

The empirical findings reveal a significant increase in the influence of both family-level factors (e.g., socioeconomic status) and school-level factors (e.g., the sociodemographic composition of classes) on educational inequalities, alongside a widening gap between advantaged and disadvantaged students. The pandemic appears to have intensified these dynamics, generating differential effects for more vulnerable groups.

Overall, the evidence points to the need for targeted educational policies aimed at reducing structural inequalities within the Italian school system, promoting interventions grounded in a stronger equity-oriented perspective.