B0959
Title: Bayesian binary quantile regression for the analysis of bachelor-master transition
Authors: Cristina Mollica - Sapienza Universita di Roma (Italy) [presenting]
Lea Petrella - Sapienza University of Rome (Italy)
Abstract: The multi-cycle organization of the modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. We propose a binary quantile regression approach to analyse the Bachelor-Master transition adopting the Bayesian inferential perspective. Quantile regression represents a well-established and useful device to gain a more in-depth understanding of the relation between the outcome of interest and the explanatory variables. By using the data augmentation strategy, quantile regression modeling for continuous responses has been recently extended for the treatment of binary response variables. We illustrate the utility of the Bayesian binary quantile regression approach to characterize the non-continuation decision with an application to administrative data of Bachelor graduates at the Faculty of Economics of ``Sapienza'' University of Rome.
