Internationalization in Tertiary Education: Intra-European Students Mobility

Internationalization in Tertiary Education: Intra-European Students Mobility Nikos P. Rachaniotis 1 and George M. Agiomirgianakis Hellenic Open University, School of Social Sciences, 57-59 Bouboulinas Str., 26222 Patras, Greece E-mail: 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. 2004 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. +30-2104142150 1

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 (http://www.arwu.org/europe2009.jsp). 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

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 1 2 5 6 5 4, 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 46 24 45 39 492 6 419 32 122 15 42 181 5 248 50 51 20 71 133 127 1 064 36 1 416 10 678 1 8 16 10 703 Belgium 117 a 7 48 28 2 763 963 22 10 1 58 197 95 2 193 35 10 80 3 357 54 325 21 2 475 9 862 6 15 2 9 885 Czech Republic 622 58 a 71 42 751 2 016 10 44 13 29 181 10 143 51 536 34 2 632 115 62 165 3 1 301 8 889 n 18 7 8 914 Denmark 104 37 5 a 48 200 492 8 5 68 21 52 4 165 838 20 8 4 70 742 99 8 1 516 4 514 7 4 1 4 526 Finland 180 41 7 210 a 284 721 16 21 43 41 79 6 197 300 8 12 3 92 2 958 121 2 1 666 7 008 551 55 1 7 615 France 517 16 650 41 236 153 a 5 784 60 63 50 448 1 013 241 822 174 94 823 4 1 884 388 4 690 37 12 685 46 857 6 87 6 46 956 Germany 17 464 675 286 1 461 423 6 918 a 393 1 640 103 467 1 591 240 16 554 756 469 310 222 1 830 1 340 10 960 391 13 625 78 118 24 196 11 78 349 Greece 300 435 151 74 58 1 926 5 627 a 166 1 52 4 537 19 670 25 22 28 381 184 241 356 875 12 626 28 754 n 218 5 28 977 Hungary 1 391 103 41 166 115 584 2 212 15 a 8 21 169 12 261 43 63 16 88 73 113 203 10 1 026 6 734 1 27 12 6 774 Iceland 24 5 5 1 747 21 36 89 1 62 a 6 7 1 79 267 5 n 2 11 369 21 n 340 3 098 2 5 n 3 105 Ireland 63 56 48 44 31 392 358 2 127 3 a 35 3 125 19 19 10 17 74 94 43 2 15 261 16 826 1 1 n 16 828 Italy 6 733 1 757 30 223 173 5 009 7 318 91 41 29 233 a 63 640 113 54 215 22 3 566 326 4 906 23 5 607 37 171 11 58 100 37 340 Luxembourg 537 1 614 n 4 3 1 551 2 562 2 2 n 10 41 a 58 3 2 42 n 14 6 288 n 834 7 573 n n n 7 573 Netherlands 204 4 056 15 237 80 652 1 544 19 13 10 62 118 8 a 211 11 55 3 291 249 363 31 3 024 11 256 5 m 3 11 264 Norway 74 17 259 2 411 76 324 489 3 700 34 90 69 n 329 a 1 014 9 205 82 1 190 78 n 2 797 10 250 4 7 1 10 262 Poland 1 637 494 279 817 190 3 260 13 891 114 44 31 209 1 430 17 844 230 a 160 92 782 564 494 7 8 572 34 158 3 41 13 34 215 Portugal 116 770 369 56 31 2 612 1 519 9 19 n 33 130 236 282 43 69 a 10 2 783 97 1 155 1 2 828 13 168 1 2 2 13 173 Slovak Republic 1 470 67 18 621 65 24 399 1 415 6 2 178 8 16 205 8 101 38 119 16 a 126 30 165 3 1 116 26 196 n 20 8 26 224 Spain 473 886 24 193 122 3 905 4 692 27 50 32 187 504 24 812 182 93 613 13 a 326 1 520 8 5 739 20 425 5 28 5 20 463 Sweden 175 62 112 1 796 532 441 612 25 331 48 71 123 1 201 1 290 725 14 49 207 a 276 7 3 194 10 292 10 26 1 10 329 Switzerland 708 105 12 81 26 1 613 2 235 33 13 12 20 1 143 1 175 55 6 171 10 319 70 a 10 1 892 8 710 1 6 3 8 720 Turkey 2 346 293 53 373 94 2 270 23 881 148 133 4 36 465 4 874 73 89 29 7 56 251 913 a 2 370 34 762 4 345 5 35 116 United Kingdom 243 241 410 472 200 2 519 1 723 102 104 38 1 421 247 7 827 306 105 99 41 721 525 402 104 a 10 857 7 20 1 10 885 T ota l from OECD countrie s 35 498 28 468 20 799 10 830 2 509 38 901 86 563 1 138 5 888 551 3 573 12 517 1 005 26 600 5 102 3 584 2 764 3 879 13 770 10 122 28 607 1 579 101 910 446 156 650 1 187 203 448 196 N on-oecd countrie s Albania 218 108 49 20 24 437 767 5 940 11 2 7 11 787 4 56 25 88 2 2 79 25 187 590 226 20 654 n 86 5 20 745 Andorra n 1 n n n 143 1 n n n n 1 n n n n 27 n 1 074 n n n 10 1 257 n n n 1 257 Armenia 49 87 52 18 11 482 422 154 5 n 1 40 n 24 8 88 n 7 126 29 54 n 54 1 711 3 3 348 n 5 062 Azerbaijan 47 19 36 8 6 164 445 4 18 n n 20 n 29 22 25 1 2 3 30 19 2 014 178 3 090 3 3 689 n 6 782 Belarus 141 70 355 56 25 517 2 096 n 14 n 12 259 2 67 84 1 922 10 9 99 120 72 6 156 6 092 23 21 972 3 28 090 Bosnia and Herzegovina 2 742 23 67 272 26 145 2 906 25 10 1 6 374 10 43 149 2 1 12 78 164 219 495 101 7 871 n 25 223 8 119 Bulgaria 1 161 233 123 186 75 2 322 10 552 625 32 9 25 803 18 737 97 104 43 14 890 108 283 1 179 1 251 20 870 5 290 7 21 172 Croatia 1 440 29 72 24 23 122 4 476 12 146 1 11 1 270 3 76 53 14 8 16 29 47 356 12 215 8 455 1 16 707 9 179 Cyprus 32 18 172 3 3 242 209 14 377 307 n 14 106 n 36 2 11 n 37 21 7 14 n 9 795 25 406 n 71 n 25 477 Estonia 50 17 3 220 681 128 691 8 10 10 13 51 1 65 77 12 3 n 81 237 24 1 658 3 041 a 590 1 3 632 Georgia 143 36 54 13 6 409 2 705 182 25 n 4 74 2 32 9 50 1 3 56 29 44 230 173 4 280 7 2 510 n 6 797 Gibraltar n n n n n n n n n n 1 n n n n n n n n n n n 618 619 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 51 93 332 14 27 200 989 37 18 5 11 36 1 35 19 426 3 2 18 26 32 709 1 178 4 262 3 35 531 1 39 797 Latvia 62 24 9 239 61 165 848 6 5 11 18 66 3 125 95 50 5 1 20 135 53 n 1 145 3 146 187 797 3 4 133 Liechtenstein 163 n n 1 n 3 25 n n n 2 n 1 n n n 1 n 1 n 580 n 14 791 n n n 791 Lithuania 94 39 11 490 99 237 1 577 3 4 22 40 199 3 107 166 543 8 n 92 196 64 9 1 968 5 971 57 841 3 6 872 Malta 4 4 n 2 2 17 22 n 1 1 6 55 1 7 2 n n 2 73 2 8 n 820 1 029 n 1 n 1 030 Moldova 100 42 79 24 11 794 794 98 37 2 8 685 1 25 24 89 68 5 152 22 55 165 76 3 356 4 3 771 2 7 133 Monaco n 1 n n n 306 n n n n 2 8 n n 1 n n n 1 n 5 n 47 371 n n n 371 Romania 897 420 43 285 134 3 844 3 859 163 3 134 5 72 3 151 32 324 169 48 114 86 2 424 193 565 66 1 179 21 206 5 26 10 21 247 Russian Federation 588 575 1 405 386 1 291 3 347 12 501 351 204 24 75 949 19 450 868 459 96 64 817 583 757 524 2 646 28 978 1 190 a 32 30 200 San Marino n n n n n 1 5 1 n n n 778 n n n n n n 1 n 2 n 13 801 n n n 801 Serbia 1 497 103 127 21 12 479 2 177 123 1 310 4 n 209 26 48 28 32 15 225 17 24 870 242 124 7 713 n 158 131 8 002 Slovenia 653 23 20 18 17 98 606 1 31 n 8 328 5 71 4 9 8 4 35 15 41 2 285 2 282 n 7 a 2 289 The Former Yugoslav Rep. of Macedonia 343 24 63 n 6 124 866 58 7 1 n 355 3 71 8 19 4 5 19 36 237 332 89 2 670 1 26 202 2 899 Ukraine 707 184 907 225 115 1 307 8 787 252 1 372 6 14 737 1 220 203 2 877 127 105 606 241 268 183 495 19 939 102 12 101 25 32 167 Europe not specified 25 6 4 173 n 741 603 3 11 4 10 33 n 54 152 n n 6 37 2 2 193 n 232 4 289 n n 2 4 291 Total from non-oecd European countries 11 206 2 179 3 983 2 698 2 655 16 775 58 931 22 423 6 712 108 360 22 377 136 2 702 2 265 6 868 545 607 6 863 2 271 7 002 6 759 23 747 210 172 1 591 85 856 1 357 298 976 Total from Europe 46 704 30 647 24 782 13 528 5 164 55 676 145 493 23 561 12 600 659 3 933 34 894 1 141 29 302 7 367 10 452 3 309 4 486 20 633 12 393 35 609 8 338 125 657 656 328 2 241 87 043 1 560 747 172 European Market share, 2008 6,3 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 2007. 4. 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

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 http://www.arwu.org/europe2009.jsp (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 0.0164. 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

(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 401 146 2 73.0 Observed Medium 79 292 54 68.7 High 3 36 96 71.1 Overall Percentage 43.6 42.7 13.7 71.1 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

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. 2006. 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. 2004. Foreign Universities Graduates in the Greek Labour Market. International Journal of Finance and Economics 9, 151-164. Agiomirgianakis, G. and Asteriou, D., 2001. Human Capital and Economic Growth: Time Series Evidence in the case of Greece. Journal of Policy Modelling, 23, 481-489. Altbach, P.G. and Knight, J. 2007. The Internationalization of Higher Education: Motivations and Realities. Journal of Studies in International Education 11, 290-305. Breiman, L., Friedman, J.H., Olshen, R. and Stone C.J. 1984. 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 2009. European Commission: Brussels. Guruz, K. 2008. Higher Education and International Student Mobility in the Global Knowledge Economy. State University of New York Press, Albany. Kelo, M., Teichler, U., Wächter, B., 2006. Toward Improved Data on Student Mobility in Europe: Findings and Concepts of the Eurodata Study. Journal of Studies in International Education 10, 194-223. Lanzendorf, U. and Teichler, U., 2003. 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, 2010. Education at a Glance 2010. OECD Indicators: Paris. Ronen, S. and Shenkar, O., 1985. Clustering Countries on Attitudinal Dimensions: A Review and Synthesis. Academy of Management Review 10, 435-454. 6

Web-pages (available on 20/01/2011) http://www.arwu.org/europe2009.jsp 7

Figure 1. CART algorithm tree diagram 8

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