The most important student mobility (SM) flow in Italy is from the Southern to the Central-Northern regions, a phenomenon that has been magnified by an increasing number of outgoing students from Sicily over the last decade. In this paper, we rely upon micro-data of university enrollment and students’ personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Our main goal is to reveal the existence of chain migrations, where students from a particular geographical area move towards a particular destination to follow other students that have previously moved. The paper provides aspects that are innovative under the view of the data, of the application, and of the statistical method. The data from each cohort is represented as a tripartite network with three sets of nodes, namely, clusters of Sicilian municipalities, students, and universities. The tripartite network is projected in a bipartite weighted network of clusters and universities, which is, then, filtered, in order to obtain a statistically validated bipartite network (SBVN). The SBVNs of the three cohorts may suggest the existence and evolution of chain migration patterns over time, which are also gender specific.

Student mobility in higher education: Sicilian outflow network and chain migrations

Fabio Aiello
Writing – Original Draft Preparation
;
2019

Abstract

The most important student mobility (SM) flow in Italy is from the Southern to the Central-Northern regions, a phenomenon that has been magnified by an increasing number of outgoing students from Sicily over the last decade. In this paper, we rely upon micro-data of university enrollment and students’ personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Our main goal is to reveal the existence of chain migrations, where students from a particular geographical area move towards a particular destination to follow other students that have previously moved. The paper provides aspects that are innovative under the view of the data, of the application, and of the statistical method. The data from each cohort is represented as a tripartite network with three sets of nodes, namely, clusters of Sicilian municipalities, students, and universities. The tripartite network is projected in a bipartite weighted network of clusters and universities, which is, then, filtered, in order to obtain a statistically validated bipartite network (SBVN). The SBVNs of the three cohorts may suggest the existence and evolution of chain migration patterns over time, which are also gender specific.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/139341
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