A0533
Title: Studying gender disparities in STEM university credits distribution using quantile regression
Authors: Riccardo De Santis - University of Siena (Italy) [presenting]
Nicola Salvati - University of Pisa (Italy)
Francesco Schirripa - University of Pisa (Italy)
Antonella D Agostino - University of Siena (Italy)
Abstract: The relationship between gender and university performance of students enrolled at a 3-year STEM (Science, Technology, Engineering and Mathematics) degree is investigated. The focus is on gender differences across different quantiles. The statistical modelling of earned credits encounters challenges posed by the discrete and often irregular nature of the observed distribution. Moreover, the hierarchical structure of the data demands an estimation strategy that extends beyond the simplicity of quantile regression. A methodology is implemented based on the jittering approach for counts and on penalized fixed effects in order to deal with these two distinct extensions over standard quantile regression. Data is acquired from two administrative databases made available through an agreement with the Italian Ministry of University and Research (MIUR). In the empirical analysis, data from the cohort of 2018/2019 Italian high school graduates who enrolled in the Italian university system in the academic year 2019/2020 in a STEM field is used.
