In this paper we investigate the non-economic determinants of international migration flows. We approach this problem using a Bayesian graphical model methodology which performs variable selection among a set of covariates that are potentially related to migration flows. We consider inflows to Italy from 171 different countries of origin on which we measure demographic and geographic characteristics. While results are coherent with the most recent literature, our method also provides measures of uncertainty around the dependence structure between covariates and migration flows.

Castelletti, F., Furfaro, E., Analysing international migration flows: a Bayesian network approach, in Book of short Papers SIS2019, (Università Cattolica del Sacro Cuore - Milano, 18-21 June 2019), Springer, Milano 2019: 757-762 [http://hdl.handle.net/10807/146933]

Analysing international migration flows: a Bayesian network approach

Castelletti, Federico;Furfaro, Emanuela
2019

Abstract

In this paper we investigate the non-economic determinants of international migration flows. We approach this problem using a Bayesian graphical model methodology which performs variable selection among a set of covariates that are potentially related to migration flows. We consider inflows to Italy from 171 different countries of origin on which we measure demographic and geographic characteristics. While results are coherent with the most recent literature, our method also provides measures of uncertainty around the dependence structure between covariates and migration flows.
2019
Inglese
Book of short Papers SIS2019
SIS2019
Università Cattolica del Sacro Cuore - Milano
18-giu-2019
21-giu-2019
9788891915108
Springer
Castelletti, F., Furfaro, E., Analysing international migration flows: a Bayesian network approach, in Book of short Papers SIS2019, (Università Cattolica del Sacro Cuore - Milano, 18-21 June 2019), Springer, Milano 2019: 757-762 [http://hdl.handle.net/10807/146933]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/146933
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