Abstract
The Erasmus Programme for higher education students is supposed to play an important socio-economic role within Europe. Erasmus student mobility flows have reached a relevant level of two million since 1987, boosted in recent years by the enlargement of the programme to eastern countries. Thereafter, it seems that flows have staggered. In this context, the article analyses the determinants of Erasmus student mobility establishing relevant hypotheses, which arise from the migration theory and gravity models. A panel data set of bilateral flows for all the participating countries has been used in order to test the factors influencing these student flows. Country size, cost of living, distance, educational background, university quality, the host country language and climate are all found to be significant determinants. Results also reveal that there are other determinants, like a country’s characteristics and time effects, which can affect mobility flows. Based on these findings, some general recommendations are put forward to enhance these flows.
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Notes
It is important to bear in mind that international student mobility was very low before the inception of the Erasmus Program (Eurostudent 2009).
However, as it will be made clear below, ESM impulse between EU-16 is facing out since 2000.
The new member countries are: Bulgaria, Czech Republic, Cyprus, Romania, Hungary, Latvia, Lithuania, Slovakia, Slovenia, Estonia, Turkey, Malta, Poland.
The group of old member countries (EU-16) are: Austria, Belgium, Germany, Denmark, Spain, France, Greece, Italy, Ireland, Iceland, Norway, Holland, Portugal, Sweden, Finland, United Kingdom.
This is a plausible hypothesis because, as shown in Fig. 2, this group of new member countries is clearly converging towards the EU-16 countries. In fact, the new group has reached almost exactly the same ratio (0.21) after 9 years (1998–2006) than that reached (0.19) by the old group in 1995, also after 9 years (1987–1995).
ESM budget has increased from 168 million € in 2004, to 201 million € in 2005 and to 204 million € in 2006. For the next 7 years of Erasmus-LLP (2007–2013), the targeted budget is 3.1 billion €.
Students from working-class and non-white backgrounds stand much less chance of engaging in international student mobility because of the financial and linguistic constraints of the environments in which they are embedded and because of the socio-economic and mobility cultures from which they are drawn. They are less likely to opt for, or be admitted to, degree programmes—such as languages—for which a period of study abroad is an integral part (Findlay et al. 2006).
Another finding regarding parents’ occupational background is that Erasmus students exhibit a higher proportion of parents working at executive, professional and technical levels than would be expected for Higher Education students in general, although there is great variation amongst countries and few have economically-inactive or unemployed parents.
For our study neither access to higher education at home, nor tuition fees could be considered determinants of ESM, because the Erasmus programme is for already-enrolled students and because students within this programme do not pay tuition fees to the host university, but to the university that sent them. Although tuition fees could also be viewed like indicators of quality, and hence could prevent from studying abroad when coming from a high tuition fees HEI (i.e. the UK), since a different measure for the quality of HEI has been chosen in the study, tuition fees have not been taken into consideration as quality indicators.
Sá et al. (2004) find that urban attraction is more important than the quality of a university, as regional student mobility determinants. This study also discovers a downward rent effect in student mobility. Almeida et al. (2001) incorporate data on teaching quality (number of laboratories, teachers’ background, number of courses) in order to enhance the basic gravity model.
Most of these articles select the same basic range of explanatory variables for migration flows, but also include other original approaches. For instance, Ashby (2007) or Poston and Zhang (2008) find that climatic variables (number of hot days or precipitation) partially explain migration flows; whereas Shen (1999) employs successful population density and GDP growth as exogenous variables with positive impacts on the migration flow dynamics. On the other hand, Devillanova and Garcia-Fontes (2004) resort to unemployment and wage coefficients to understand migration phenomenon; Cseres-Gergely (2004) underline the unemployment relevance too. Bierens and Kontuly (2008) offer a dynamic model including lagged variables in their estimation.
It is worth noting that students under the Erasmus programme are not required to prove a certain level of language competence by the host university. This is an incentive to use this programme in order to improve their basic knowledge of a foreign language.
We rejected a random effects model because all the countries participate in the Erasmus Programme.
We use the total number of graduates for the whole (home) country as a proxy for the educational background of Erasmus students because there are no specific data for them. We assume therefore that a country where the total number of graduates is high has also Erasmus students with higher educational background than another with a lower number of graduates.
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Acknowledgments
The authors are grateful for financial support from the Department of Education of the Basque Government through grant IT-334-07 (UPV/EHU Econometrics Research Group) and from the Spanish Ministerio de Educación and FEDER (SEJ2007-61362).
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Rodríguez González, C., Bustillo Mesanza, R. & Mariel, P. The determinants of international student mobility flows: an empirical study on the Erasmus programme. High Educ 62, 413–430 (2011). https://doi.org/10.1007/s10734-010-9396-5
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DOI: https://doi.org/10.1007/s10734-010-9396-5