V. Genova, M. Tumminello, M. Enea, F. Aiello, M. Attanasio
{"title":"Student mobility in higher education: Sicilian outflow network and chain migrations","authors":"V. Genova, M. Tumminello, M. Enea, F. Aiello, M. Attanasio","doi":"10.1285/I20705948V12N4P774","DOIUrl":null,"url":null,"abstract":"The Italian public universities are subsidised within a competitive framework that awards excellence, efficiency, and the capacity of universities to attract students from Italian regions other than its own. However, repeated cuts to public spending has increased the well-known Italian North-South divide. The most important student mobility (SM) flow 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 enrolment and students' personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Indeed, our main goal is to eventually reveal the existence of chain migrations, through which students from a particular geographical area move towards a particular destination. We consider 38 clusters aggregating the 390 Sicilian municipalities, based on geographical proximity and socio-economic criteria. 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 suggest the existence and evolution of chain migration patterns over time, which are also gender specific.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"774-800"},"PeriodicalIF":0.6000,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N4P774","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Journal of Applied Statistical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1285/I20705948V12N4P774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 9
Abstract
The Italian public universities are subsidised within a competitive framework that awards excellence, efficiency, and the capacity of universities to attract students from Italian regions other than its own. However, repeated cuts to public spending has increased the well-known Italian North-South divide. The most important student mobility (SM) flow 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 enrolment and students' personal records for three cohorts of freshmen, in order to investigate preferential patterns of SM from Sicily toward universities in other regions. Indeed, our main goal is to eventually reveal the existence of chain migrations, through which students from a particular geographical area move towards a particular destination. We consider 38 clusters aggregating the 390 Sicilian municipalities, based on geographical proximity and socio-economic criteria. 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 suggest the existence and evolution of chain migration patterns over time, which are also gender specific.