Literature DB >> 35707809

Migration and students' performance: detecting geographical differences following a curves clustering approach.

Giovanni Boscaino1, Gianluca Sottile1, Giada Adelfio1.   

Abstract

Students' migration mobility is the new form of migration: students migrate to improve their skills and become more valued for the job market. The data regard the migration of Italian Bachelors who enrolled at Master Degree level, moving typically from poor to rich areas. This paper investigates the migration and other possible determinants on the Master Degree students' performance. The Clustering of Effects approach for Quantile Regression Coefficients Modelling has been used to cluster the effects of some variables on the students' performance for three Italian macro-areas. Results show evidence of similarity between Southern and Centre students, with respect to the Northern ones.
© 2020 Informa UK Limited, trading as Taylor & Francis Group.

Entities:  

Keywords:  62H30; 62J05; Clustering of curves; censored and truncated data; quantile regression; students' performance

Year:  2020        PMID: 35707809      PMCID: PMC9041684          DOI: 10.1080/02664763.2020.1845624

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.416


  3 in total

1.  Parametric modeling of quantile regression coefficient functions.

Authors:  Paolo Frumento; Matteo Bottai
Journal:  Biometrics       Date:  2015-09-22       Impact factor: 2.571

2.  Parametric modeling of quantile regression coefficient functions with censored and truncated data.

Authors:  Paolo Frumento; Matteo Bottai
Journal:  Biometrics       Date:  2017-02-09       Impact factor: 2.571

3.  Reinventing US Internal Migration Studies in the Age of International Migration.

Authors:  Mark Ellis
Journal:  Popul Space Place       Date:  2012-03
  3 in total

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