Quantile regression approach to analyze gender disparities in STEM university credit distribution

This study examines the relationship between gender and academic performance across different quantiles among students enrolled in a 3-year STEM (Science, Technology, Engineering, and Mathematics) degree program in Italy. We make use of a unique dataset of linked administrative records, provided through an agreement with the Italian Ministry of University and Research (MUR). The statistical modeling of earned credits presents challenges posed by the discrete and often irregular nature of the observed distribution and the hierarchical structure of our data, which demand an estimation strategy that extends beyond the simplicity of quantile regression. We implement a methodology based on the jittering approach for counts and penalized fixed effects in order to deal with these two distinct extensions over standard quantile regression.