Internationalization in Tertiary Education: Intra-European Students Mobility

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1 Internationalization in Tertiary Education: Intra-European Students Mobility Nikos P. Rachaniotis 1 and George M. Agiomirgianakis Hellenic Open University, School of Social Sciences, Bouboulinas Str., Patras, Greece nraxan@unipi.gr, gmagios@eap.gr Abstract European students enhanced internationalization and mobility is reflecting an important aspect of Human Capital investment. This paper examines what determines the probability of European tertiary-education students to move in a country other than their own, focused only on the intra-european market. A Classification and Regression Tree (CART) algorithm is applied, in order to explain the reasoning behind students decisions for long-term studying abroad. The analysis shows that the aforementioned probability depends significantly on whether the tertiary educational system of the destination country is internationally recognized as an advanced one, on geographical affinity to the origin country and on existing immigration networks. Given the oligopolistic nature of the international educational markets these results may suggest some interesting policy implications. Keywords Human Capital investment; Tertiary Education; Students Mobility; CART Algorithm. 1. Introduction Mobility is a basic ingredient of the European political and economic unification process for the creation of a unique European identity. In addition to political and sociological reasons favoring mobility, the case of student mobility contributes directly to the process of human capital formation acquired by young people migrating abroad, which in turn, may result tangible economic effects both in the destination and country of the origin (Agiomirgianakis 2006, Agiomirgianakis et al and Agiomirgianakis and Asteriou 2001). The internationalization of higher education promoted by respective provisions and measures taken by the E.U. is a prominent example of policy that aims to this direction. In 1999 an intergovernmental agreement between E.U. and several non E.U. countries led to the implementation of the Bologna Process. This reform process committed to build a common framework, enabling students to move freely within the European Higher Education Area (EHEA) and to study outside their home countries, obtaining full recognition of their qualifications. 1 Corresponding author, Tel

2 After ten years of developing EHEA, there are still many uncertainties about students mobility and its real incentives and disincentives. The statistical surveys performed until now are mostly descriptive and qualitative, omitting inferential statistical analysis regarding the factors that may affect students mobility. In this paper the internationalization of students in higher education institutions is examined, focused only on the intra-european market and descriptive statistical data are presented. A Classification and Regression Tree (CART) algorithm is used in order to explain students decisions for long-term studying in European Union countries other than their own based on quantitative and qualitative factors. Finally, the paper concludes with some remarks and policy implications. 2. Estimating the probability of a student moving to a European country to study The measurement of students mobility depends to a large extent on countries immigration legislation, mobility arrangements and available data (Lanzendorf and Teichler, 2003). OECD therefore allows countries to define as international students those who are not permanent residents of their country of study or, alternatively, those who received their prior education in another country (regardless of citizenship), depending on the most appropriate operational definition in their national context and as foreign student the non-citizens enrolled in a country (OECD, 2010). Since not all countries are yet able to report data on international students mobility, data on foreign students are the only directly comparable ones, albeit there is a need for caution in interpreting the results. Isolating only the 50 European sovereign states, the number of foreign students in tertiary education by country of origin having as a destination the 23 European OECD countries in 2008 and 3 non-oecd European countries (Estonia, Russian Federation and Slovenia), as well as their respective intra-european foreign students market shares, as percentages of enrolled European foreign students are presented in Table 1 (OECD, 2010 and authors calculations). The reason of examining only these 26 European destination countries is that they are the ones with available relevant data and they can be considered to host the major European educational institutions ( In Table 1 the symbol n is used when the magnitude is either negligible or zero. The symbol a is used when data is not applicable. 2

3 Austria Belgium Czech Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Luxembourg Netherlands Norway Poland Portugal Slovak Republic Republic Spain Sweden Switzerland Turkey United Kingdom NON-OECD countries Total OECD Estonia Russian Slovenia destinations Federation Total all reporting destinations Notes , 5 Country of origin CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT CIT RES CIT CIT CIT OECD countries Austria a Belgium 117 a Czech Republic a n Denmark a Finland a France a Germany a Greece a n Hungary a Iceland a n n n Ireland a n Italy a Luxembourg n n a n n n n n Netherlands a m Norway n 329 a n Poland a Portugal n a Slovak Republic a n Spain a Sweden a Switzerland a Turkey a United Kingdom a T ota l from OECD countrie s N on-oecd countrie s Albania n Andorra n 1 n n n n n n n 1 n n n n 27 n n n n n n n Armenia n 1 40 n n n n Azerbaijan n n 20 n n Belarus n 14 n Bosnia and Herzegovina n Bulgaria Croatia Cyprus n n n n n 71 n Estonia n a Georgia n n Gibraltar n n n n n n n n n n 1 n n n n n n n n n n n n n n 619 Holy See n n n n n 1 1 n n n n 3 n n n n n n 14 n n n 1 20 n n n 20 Kazakhstan Latvia n Liechtenstein 163 n n 1 n 3 25 n n n 2 n 1 n n n 1 n 1 n 580 n n n n 791 Lithuania n Malta 4 4 n n n n n n 1 n Moldova Monaco n 1 n n n 306 n n n n 2 8 n n 1 n n n 1 n 5 n n n n 371 Romania Russian Federation a San Marino n n n n n n n n 778 n n n n n n 1 n 2 n n n n 801 Serbia n n Slovenia n n 7 a The Former Yugoslav Rep. of Macedonia n n Ukraine Europe not specified n n n n n n n Total from non-oecd European countries Total from Europe European Market share, ,3 4,1 3,3 1,8 0,7 7,5 19,5 3,2 1,7 0,1 0,5 4,7 0,2 3,9 1,0 1,4 0,4 0,6 2,8 1,7 4,8 1,1 16,8 87,8 0,3 11,6 0,2 100,0 Note : The proportion of students abroad is based only on the total of students enrolled in countries reporting data to the OECD and UNESCO Institute for Statistics. 1. Excludes tertiary-type B programmes. 2. Excludes data for social advancement education. 3. Reference year Excludes private institutions. 5. Excludes advanced research programmes. 6. Excludes part-time students. Table 1. Number of foreign students enrolled in tertiary education and percentages of all foreign students Europe-wide enrolled in the examined 26 destination countries. From Table 1, it can be deducted that the major European destination countries (in increasing order of intra-european market foreign student shares) are Netherlands, Belgium, Italy, Switzerland, Austria, France, Russia, UK and Germany, having a total share of 79.2%. The developed statistical model has as an objective to estimate the probability of students moving from a European country to another, trying to capture the reasoning behind their decision in a quantitative way. The drivers and barriers of students mobility can be categorized into those that have national-international characteristics and into the ones based on students personal profile features. The paper focuses on the first group, where the examined major underlying factors that either enhance or discourage students in selecting a country of study are: language, financial aspects, immigration policy/migration networks in host countries, perceived academic superiority of the institutions in the host countries and geographical-ideological-cultural affinity (Altbach and Knight, 2007; Guruz, 2008). The utilized methodology is decision trees analysis and more specifically a univariate-split CART (Classification and Regression Trees) algorithm (Breiman et al. 1984). For the 26 destination and the 50 origin European countries, all the possible pairs (origin country, destination country) were formed and after excluding not applicable and unavailable data, a 3

4 sample of 1,109 pairs ( cases ) was created. Then for each case the following variables were selected: Mobility_origin_ordinal: According to Table 1 the numbers of foreign students enrolled in tertiary education by country of origin were transformed to percentages, which in turn were classified to three categories probability levels, namely Low, Medium and High, including approximately 50%, 40% and 10% of the cases, respectively, arranged in non-decreasing order of students percentages enrolled from the origin country. Universities Ranking Normalized: According to (Academic Ranking of World Universities (ARWU)), normalized values (scores) summing up to one were assigned to the 26 countries, quantifying and rating (in an undoubtedly imperfect but worldwide accepted way) their tertiary education systems quality. GDP: Gross Domestic Product (in million euros). Distances: a binary variable indicating if two countries have common geographical borders. Language: a binary variable indicating if two countries have the same official state language. Culture: a binary variable indicating if two countries have significant cultural similarities according to Ronen and Shenkar, 1985 and the authors opinion. Immigration Index: a binary variable indicating if there is a significant population minority (higher than 0.1% of the total population) from the origin country in the destination country (Eurostat 2009a and 2009b). CART algorithm was then implemented with target (dependent) variable Mobility_origin_ordinal and predictor (independent) variables the remaining six from the above list. Statistical analysis was performed using SPSS for Windows (version 17) package. For all the statistical tests, a significance level of 5% was used. The resulting tree diagram is illustrated in Figure 1. Figure 1 about here The tree diagram displays detailed results within each node, which is numbered. The results of the CART tree show 7 sample segments that yield different probabilities for students to move from their origin to a destination country, details for which are displayed in each of the tree s 7 terminal nodes. The largest percentage of high students mobility is obtained from segment 6, defined as pairs of countries with common geographical borders and normalized universities ranking above Terminal node #6 shows that there are a total of 69 pairs of countries in this segment and the percentage of high students mobility between them is 73.9%. This depicts the fact that e.g. many Germans study in Austria, French in Belgium, etc. The next high students mobility segment is obtained from pairs of countries where the destination countries have very high normalized universities ranking, i.e. Germany and UK 4

5 (terminal node #10), and the percentage of high students mobility to these two countries is 54.2%. Segment 4 is also worth mentioning, which contains cases where the destination countries have a tertiary educational system that is not considered of very high quality but they have a significant population minority from the origin country (for example the Russian Federation attracts students from its neighboring former USSR countries, Greece enrolls large numbers of Cypriot and Albanian students, etc.). The percentage of high students mobility in terminal node #4 is 21.5%. The implementation of CART algorithm classifies correctly the foreign students percentages by country of origin in 71.1% of the cases (Table 2). Predicted Low Medium High Percent Correct Low Observed Medium High Overall Percentage Table 2. CART algorithm- observed vs. predicted cases. 3. Conclusions The plethora of statistical surveys published by national governments, specialized agencies, research institutes and international organizations, such as OECD, UNESCO or E.U., often gives the impression that there is no shortage of quality data on foreign/international students mobility, however this is a rather misleading impression since the available data are not (always) the needed ones (Kelo et al., 2006). In addition, there is a large space for inferential statistical analysis, since these surveys are descriptive. This research has shown that students mobility between European countries is not indeed balanced. Several patterns based on geographical affinity and migration networks and a students flow towards large European countries with advanced tertiary educational systems are visible. This inequality could be attributed, to some extent, to the shortages in funds in a part of Europe, but it may also be resulted by a short-sighted vision of the dynamics of cooperation. The aforementioned results have some clear policy implications: First, if a country wants to enhance its share in the foreign student educational market then it should significantly improve the competitiveness of its tertiary education services. Second, foreign students mobility may be affected positively in the near future due to the rapid increase of immigration and generic citizens mobility within Europe, imposed both by institutional measures undertaken by the E.U., as well as by the Economic crisis incurred strongly by several (mostly southern) European countries. 5

6 Finally, it has to be noted that there are several other factors that affect students choices, which could be examined in future research, such as the transparency and flexibility of programs regarding the time spent abroad towards degree requirements, the restrictive university admission policies at origin countries, government policies to facilitate transfer of credits between home and host institutions, etc. Acknowledgements We would like to thank Mr. Dimitrios Spontas, M.Sc., for his valuable assistance in the statistical data collection and analysis. References Agiomirgianakis, G European Internal and External Migration: Theories and Empirical Evidence in Zervoyianni, A., Argiros, G. and Agiomirgianakis, G. (Eds.), European Integration, Palgrave (Macmillan) Press Limited, Hampshire, UK, chapter 10. Agiomirgianakis G., Lianos, T. and Asteriou, D Foreign Universities Graduates in the Greek Labour Market. International Journal of Finance and Economics 9, Agiomirgianakis, G. and Asteriou, D., Human Capital and Economic Growth: Time Series Evidence in the case of Greece. Journal of Policy Modelling, 23, Altbach, P.G. and Knight, J The Internationalization of Higher Education: Motivations and Realities. Journal of Studies in International Education 11, Breiman, L., Friedman, J.H., Olshen, R. and Stone C.J Classification and Regression Tree. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific California. Eurostat, 2009a. The Bologna Process in Higher Education in Europe. Key indicators on the social dimension and mobility: Luxemburg. Eurostat 2009b. Key Data on Education in Europe European Commission: Brussels. Guruz, K Higher Education and International Student Mobility in the Global Knowledge Economy. State University of New York Press, Albany. Kelo, M., Teichler, U., Wächter, B., Toward Improved Data on Student Mobility in Europe: Findings and Concepts of the Eurodata Study. Journal of Studies in International Education 10, Lanzendorf, U. and Teichler, U., Statistics on student mobility within the European Union (European Parliament EDUC 112 EN). Luxembourg City, Luxembourg: Office for Official Publications of the European Communities. OECD, Education at a Glance OECD Indicators: Paris. Ronen, S. and Shenkar, O., Clustering Countries on Attitudinal Dimensions: A Review and Synthesis. Academy of Management Review 10,

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8 Figure 1. CART algorithm tree diagram 8

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