<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2019-0009_s_006_w2aab3b7b4b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>Study how economic parameters affect positions in the Academic Ranking of World Universities’ top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions.</p></sec><sec id="j_jdis-2019-0009_s_007_w2aab3b7b4b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>The methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical (OLS) and robust (Huber and Tukey) methods.</p></sec><sec id="j_jdis-2019-0009_s_008_w2aab3b7b4b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>Our results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.</p></sec><sec id="j_jdis-2019-0009_s_009_w2aab3b7b4b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>Using data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings.</p></sec><sec id="j_jdis-2019-0009_s_010_w2aab3b7b4b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>The results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore, minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.</p></sec><sec id="j_jdis-2019-0009_s_011_w2aab3b7b4b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>This study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.</p></sec></abstract>

PurposeStudy how economic parameters affect positions in the Academic Ranking of World Universities’ top 500 published by the Shanghai Jiao Tong University Graduate School of Education in countries/regions with listed higher education institutions.Design/methodology/approachThe methodology used capitalises on the multi-variate characteristics of the data analysed. The multi-colinearity problem posed is solved by running principal components prior to regression analysis, using both classical (OLS) and robust (Huber and Tukey) methods.FindingsOur results revealed that countries/regions with long ranking traditions are highly competitive. Findings also showed that some countries/regions such as Germany, United Kingdom, Canada, and Italy, had a larger number of universities in the top positions than predicted by the regression model. In contrast, for Japan, a country where social and economic performance is high, the number of ARWU universities projected by the model was much larger than the actual figure. In much the same vein, countries/regions that invest heavily in education, such as Japan and Denmark, had lower than expected results.Research limitationsUsing data from only one ranking is a limitation of this study, but the methodology used could be useful to other global rankings.Practical implicationsThe results provide good insights for policy makers. They indicate the existence of a relationship between research output and the number of universities per million inhabitants. Countries/regions, which have historically prioritised higher education, exhibited highest values for indicators that compose the rankings methodology; furthermore, minimum increase in welfare indicators could exhibited significant rises in the presence of their universities on the rankings.Originality/valueThis study is well defined and the result answers important questions about characteristics of countries/regions and their higher education system.

Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeStudy how economic parameters affect positions in the Academic Ranking of World Universities’ top 500 published by the Shanghai Jiao Tong University...

Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Article Category: Research Paper

Publié en ligne: 07 juin 2019

Pages: 56 - 78

Reçu: 05 févr. 2019

Accepté: 25 mars 2019

DOI: https://doi.org/10.2478/jdis-2019-0009

Mots clés
Academic Ranking of World Universities, Socio-economic indicators, Regression analysis

© 2019 Esteban Fernández Tuesta, Carlos Garcia-Zorita, Rosario Romera Ayllon, Elías Sanz-Casado, publsihed by Sceindo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Does a Country/Region’s Economic Status Affect Its Universities’ Presence in International Rankings?

Article Category: Research Paper

Publié en ligne: 07 juin 2019

Pages: 56 - 78

Reçu: 05 févr. 2019

Accepté: 25 mars 2019

DOI: https://doi.org/10.2478/jdis-2019-0009

Mots clésAcademic Ranking of World Universities, Socio-economic indicators, Regression analysis

© 2019 Esteban Fernández Tuesta, Carlos Garcia-Zorita, Rosario Romera Ayllon, Elías Sanz-Casado, publsihed by Sceindo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Academic Ranking of World Universities, Socio-economic indicators, Regression analysis