Abstract
In higher education, the mobility of students across a country to take a degree is receiving increasing attention in recent years, mainly because of the effects on the economic development and competitiveness of the territorial areas where universities are located. In Italy, an important source of information is represented by the National Student Registry. Among others, the Registry allows us to analyze the student mobility across the country both at the moment of the first enrollment in a bachelor or 5-years degree program and, then, at the enrollment in a master degree program. In the former case, students may attend university in the area of residence or move somewhere else (first-level mobility); in the latter case bachelor graduates may prosecute the academic career with a master degree in the same university or move in another higher academic institution (second-level mobility). In this contribution we propose a set of instruments to analyze student mobility. These instruments will be applied to the information gathered by the National Student Registry. We first define two indicators of the capability of retention and attraction of students computed at the first- and the second-level mobility; we also illustrate how these indices may be synthesize through a composite indicator and how they can be used to produce a graphical display of the ego-centric network generated by the flow of incoming and outgoing movements. Then, we formulate a gravity model to detect how the mobility is affected by university characteristics, such as quality of structures and quality of life of the territorial area of influence of the university.
Similar content being viewed by others
References
Anderson, J. E. (2010). The gravity model. Working Paper 16576. Cambridge, MA: National Bureau of Economic Research.
Beine, M., Noël, R., & Ragot, L. (2014). Determinants of the international mobility of students. Economics of Education Review, 41, 40–54.
Bradley, R. A., & Terry, M. E. (1952). The rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39, 324–345.
Bruno, G., & Genovese, A. (2012). A spatial interaction model for the representation of the mobility of university students on the Italian territory. Networks and Spatial Economics, 12, 41–57.
Casacci, S. (2019). Classifying Italian students by mobility. In M. Bini, P. Amenta, A. D’Ambra, & I. Camminatiello (Eds.), Statistical methods for service quality evaluation (pp. 221–224). Napoli: Cuzzolin.
Ciriaci, D. (2014). Does university quality influence the interregional mobility of students and graduates? The case of Italy. Regional Studies, 48, 1592–1608.
Columbu, S., Porcu, M., Primerano, I., Sulis, I., & Vitale, M. P. (2019). Exploring the Italian student mobility flows in higher education. In M. Bini, P. Amenta, A. D’Ambra, & I. Camminatiello (Eds.), Statistical methods for service quality evaluation (pp. 46–49). Napoli: Cuzzolin.
D’Agostino, A., Ghellini, G., & Longobardi, S. (2019). Out-migration of university enrolment: the mobility behaviour of Italian students. International Journal of Manpower, 40, 56–72.
Dotti, N., Fratesi, U., Lenzi, C., & Percoco, M. (2013). Local labour markets and the interregional mobility of Italian university students. Spatial Economic Analysis, 8(4), 443–468.
Dotti, N. F., Fratesi, U., Lenzi, C., & Percoco, M. (2014). Local labour market conditions and the spatial mobility of science and technology university students: Evidence from Italy. Review of Regional Research, 34, 119–137.
Enea, M. (2018). From south to north? Mobility of Southern Italian students at the transition from the first to the second level university degree. In C. Perna, M. Pratesi, & A. Ruiz-Gazen (Eds.), Studies in theoretical and applied statistics (pp. 239–249). Berlin: Springer.
Enea, M., & Attanasio, M. (2019). La mobilità degli studenti universitari nell’ultimo decennio in Italia. In G. De Santis, E. Pirani, & M. Porcu (Eds.), Rapporto sulla popolazione. L’istruzione in Italia (pp. 43–58). Bologna, IT: Il Mulino.
Fotheringham, A. S., & O’Kelly, M. E. (1989). Spatial interaction models: Formulations and applications. Dordrecht: Kluwer Academic.
Genova, V. G., Tumminello, M., Enea, M., Aiello, F., & Attanasio, M. (2019). Student mobility in higher education: Sicilian outflow network and chain migrations. Electronic Journal of Applied Statistical Analysis, 12, 774–35.
Giambona, F., Porcu, M., & Sulis, I. (2017). Students mobility: Assessing the determinants of attractiveness across competing territorial areas. Social Indicator Research, 133, 1105–1132.
Griffith, D., & Fischer, M. (2013). Constrained variants of the gravity model and spatial dependence: Model specification and estimation issues. Journal of Geographical Systems, 15, 291–317.
Goldstein, H., & Healy, J. R. (1995). The graphical presentation of a collection of means. Journal of the Royal Statistical Society Series A (Statistics in Society), 158, 175–177.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Ishikawa, Y. (1987). An empirical study of the competing destinations model using Japanese interaction data. Environmental Planning A, 19, 1359–1373.
Kolaczyk, E. D. (2009). Statistical analysis of network data: Methods and models. New York: Springer.
Mazziotta, M., & Pareto, A. (2012). A non-compensatory approach for the measurement of the quality of life. In F. Maggino & G. Nuvolati (Eds.), Quality of life in Italy (pp. 27–40). Berlin: Springer.
Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127, 98–1003.
Mazziotta, M., & Pareto, A. (2017). Synthesis of indicators: The composite indicators approach. In F. Maggino (Ed.), Complexity in society: From indicators construction to their synthesis (pp. 159–191). Berlin: Springer.
Nguyen, N., & LeBlanc, G. (2001). Image and reputation of higher education institutions in students’ retention decisions. International Journal of Educational Management, 15, 303–311.
OECD. (2008). Handbook on constructing composite indicators. Methodology and user guide. Paris, FR: OECD Publications.
Perry, B. L., Pescosolido, B. A., & Borgatti, S. P. (2018). Egocentric network analysis. Cambridge, MA: Cambridge University Press.
Ravenstein, E. G. (1889). The laws of migration. Journal of the Royal Statistical Society, 52, 241–305.
Sà, C., Florax, R. J. G. M., & Rietveld, P. (2004). Determinants of the regional demand for higher education in The Netherlands: A gravity model approach. Regional Studies, 38, 375–392.
Shields, R. (2013). Globalization and international student mobility: A network analysis. Comparative Education Review, 57, 609–636.
Simini, F., Gonzàlez, M. C., Maritan, A., & Barabàsi, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484, 96–100.
Sung, M., & Yang, S. (2008). Toward the model of university image: The influence of brand personality, external prestige, and reputation. Journal of Public Relations Research, 20, 357–376.
Tosi, F., Impacciatore, R., & Rettaroli, R. (2018). Individual skills and student mobility in Italy: A regional perspective. Regional Studies, 53, 1099–1111.
Tinbergen, J. (1962). Shaping the world economy: Suggestions for an international economic policy. New York, NY: The Twentieth Century Fund.
Acknowledgements
The authors acknowledge the financial support provided by the “Dipartimenti Eccellenti 2018–2022” ministerial funds.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bacci, S., Bertaccini, B. Assessment of the University Reputation Through the Analysis of the Student Mobility. Soc Indic Res 156, 363–388 (2021). https://doi.org/10.1007/s11205-020-02322-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-020-02322-x