We investigate the mobility of Italian academic students among geographical areas (i.e., provinces) to attend university. The study relies on data collected by the Italian National Student Registry and concerns students enrolled in the academic year 2011–2012 in a bachelor degree program or a five-years degree program of any Italian university. The methodological approach we adopt is based on the analysis of the flows of students among provinces through spatial autoregressive gravity models. The gravity component of this type of models accounts for the deterrence effect due to the distance among province of origin and province of destination. Instead, the spatial autoregressive component is introduced to capture homogenous behaviours among contiguous geographical areas. In particular, we focus on alternative ways to specify the spatial weight matrix that characterises the spatial autoregressive component of the models at issue.

Spatial autoregressive gravity models to explain the university student mobility in Italy / Silvia Bacci, Bruno Bertaccini, Chiara Bocci. - ELETTRONICO. - (2020), pp. 79-84. (Intervento presentato al convegno 50th Scientific Meeting of the Italian Statistical Society).

Spatial autoregressive gravity models to explain the university student mobility in Italy

Silvia Bacci
;
Bruno Bertaccini;Chiara Bocci
2020

Abstract

We investigate the mobility of Italian academic students among geographical areas (i.e., provinces) to attend university. The study relies on data collected by the Italian National Student Registry and concerns students enrolled in the academic year 2011–2012 in a bachelor degree program or a five-years degree program of any Italian university. The methodological approach we adopt is based on the analysis of the flows of students among provinces through spatial autoregressive gravity models. The gravity component of this type of models accounts for the deterrence effect due to the distance among province of origin and province of destination. Instead, the spatial autoregressive component is introduced to capture homogenous behaviours among contiguous geographical areas. In particular, we focus on alternative ways to specify the spatial weight matrix that characterises the spatial autoregressive component of the models at issue.
2020
Book of Short Papers SIS2020
50th Scientific Meeting of the Italian Statistical Society
Goal 4: Quality education
Silvia Bacci, Bruno Bertaccini, Chiara Bocci
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1214143
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