Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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A distribution curves comparison approach to analyze the university moving students performance
Giovanni Boscaino, Giada Adelfio, Gianluca Sottile

Last modified: 2018-05-04


Nowadays in Italy we observe a one-directional migration flow ofuniversity students, typically from the South to the North. It represents thenew millennium migration flow: people migrate looking for better job opportunitiesalready during their educational path, believing that northernuniversities may provide more opportunities for being more successful. Thispaper aims to study the performance of those Sicilian students that moveto the northern universities, to take the second level degree, in comparisonwith those remain in Sicily. We want to test the empirical evidence thatshows a similar performance between the two groups of students. We usedifferent measures of performance and follow a new methodology based onthe comparison among the distribution curves. Results seems to confirm ouridea, highlighting some difference.


1. Adelfio, G., G. Boscaino, and V. Capursi. A new indicator for higher education studentperformance. Higher Education 68 (5), 653–668 (2014).

2. Adelfio, G.; Boscaino, G., and Capursi, V. Further considerations on a new indicatorfor higher education student performance. Proceedings XLVIII Scientific Meeting ofthe Italian Statistical Society (2016)

3. Attanasio, M.; Enea, M. La mobilità studentesca In Italia: un’analisi dei flussi dal Sudd’Italia verso il Centro-Nord. Proceedings of Popdays 2017. (2017)

4. Beine, M.; Bertoli, S; and Fernandez-Huertas Moraga, J. A practitioners’ guide togravity models of migration, The World Economy, vol 6, n- 4, 496-512. Wiley Blackwell.(2016)

5. Birch, E. R. and P. W. Miller. Student outcomes at university in Australia a quantileregression approach. Australian Economic Press 45 (1), 1–17.(2006)

6. Boscaino, G.; Vassallo, P.. La migrazione studentesca dalla laurea triennale alla laureamagistrale. Proceedings of Popdays 2017. (2017)

7. Caruso, R.; de Wit, H.. Determinants of Mobility of Students in Europe. EmpiricalEvidence for the period 1998-2009. Journal of Studies in International Education,vol 19, n. 3, 265-282. Sage Publishing. (2015)

8. Faggian, A.; McCann, P.; and Sheppard, S.. Human Capital, Higher Education andGraduate Migration: An Analysis of Scottish and Welsh Students. Urban Studies.Volume: 44 issue: 13, 2511-2528 (2007)

9. Frumento, P., Bottai, M., Parametric modeling of quantile regression coefficient functions,Biometrics, 72, 74-84 (2015)

10. Frumento, P. (2017). qrcm: Quantile Regression Coefficients Modeling. R packageversion 2.0,

11. Kahanec, M.; and Kralikova R.. Higher Education Policy and Migration: The Role ofInternational Student Mobility. CESifo DICE Report 9(4):20-27 (2011)

12. Ordine, P.; Rose, G.. Students’ mobility and regional disparities in quality and returnsto education in Italy. Giornale degli Economisti e Annali di Economia, vol. 66,n. 2, 149-176 (2007)

13. Sà, C., Florax, R.J.G.M.; Rietveld, P.. Determinants of the regional demand forhigher education in the Netherlands: A gravity model approach. Regional Studies,38, 375-392 (2004)

14. Sottile, G., Adelfio, G. (2017). clustEff: Clusters of Effects Curves in QuantileRegression Models. R package version 0.1.2 GPL (General Public Licence):

15. Sottile, G., Adelfio, G. Clusters of effects curves in quantile regression models. Submitted(2018)

16. Van Bragt, C. A. C.; Bakx, A. W. E. A.; Bergen, T. C. M.; and Croon, M. A.. Lookingfor students personal characteristics predicting study outcome. Higher Education61, 59–75. (2011)

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