Geography of Italian student mobility: A network analysis approach. (February 2021)

• Record Type: Journal Article
• Authors: Columbu, Silvia; Porcu, Mariano; Primerano, Ilaria; Sulis, Isabella; Vitale, Maria Prosperina
• Is Part Of: Socio-economic planning sciences. Number 73(2021)
• Publication Date: 2021-02
• Subjects: Student mobility -- Attractiveness -- Network centrality -- Blockmodeling -- Quantile regression.; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2020.100918
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: Understanding the phenomenon of intra- and international student mobility has become increasingly relevant to the organization of tertiary education systems. Using microdata information provided by the Italian National Student Archive on the cohorts of students enrolled at university in the academic years 2011–12 and 2014–15, we consider a network analysis approach to investigate the incoming and outgoing student flows between territories and universities. More specifically, the paper aims to shed light on the dynamics of Italian student mobility networks at both the bachelor's and master's degree levels by considering attractiveness indicators combined with network centrality measures, clustering techniques for network data and explanatory models. We analyze the partition of the global network structure by means of blockmodeling analysis and we explain the determinants of the differences among universities in attracting students adopting a quantile regression analysis. Highlights: The paper investigates the incoming and outgoing student flows between territories and universities. Microdata on two student cohorts are adopted. Hub and authority scores highlight the most relevant Italian provinces and universities. An asymmetric core–periphery network structure emerges by means of blockmodeling analysis. Quantile regression analysis explains the determinants of the differences in university attractiveness.