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Higher education regimes: an empirical classification of higher education systems and its relationship with student accessibility

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  • Moris Triventi

Abstract

This article proposes a multidimensional empirical classification of higher education systems on the basis of several institutional characteristics, which are likely to affect student participation and social inequality (tracking, expenditures, structural differentiation, institutional autonomy and accountability, affordability for students, graduates’ occupational returns). We develop a theoretical framework in which higher education systems are related to four main institutional domains: school system, State, labour market, students and their families. In the second part, an empirical analysis of the institutional profiles of higher education systems of 16 Oecd countries is performed. An empirical classification of higher education systems is elaborated applying hierarchical cluster analysis and multidimensional scaling on macro-indicators. The analyses identify four clusters, that have been labelled the Continental, Nordic, Anglo-Saxon and North-American regime. Fuzzy cluster analysis is used to assess the robustness of the results and to identify systems with an hybrid institutional configuration, which are difficult to classify. At the end, a detailed description of the four higher education regimes is provided and the relationship with student access is analysed. Copyright Springer Science+Business Media Dordrecht 2014

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  • Moris Triventi, 2014. "Higher education regimes: an empirical classification of higher education systems and its relationship with student accessibility," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1685-1703, May.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:3:p:1685-1703
    DOI: 10.1007/s11135-013-9868-7
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    References listed on IDEAS

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    1. Romina Boarini & Joaquim Oliveira Martins & Hubert Strauss & Christine de la Maisonneuve & Giuseppe Nicoletti, 2008. "Investment in Tertiary Education: Main Determinants and Implications for Policy," CESifo Economic Studies, CESifo Group, vol. 54(2), pages 277-312.
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    8. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    9. Romina Boarini, 2008. "Investment in Tertiary Education: Main Determinants and Implications for Policy," CESifo Economic Studies, CESifo Group, vol. 54(2), pages 277-312, June.
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    Cited by:

    1. Patricia A. Lowe, 2019. "Cross-National Comparison between U.K. and U.S. Higher Education Students in Test Anxiety," Higher Education Studies, Canadian Center of Science and Education, vol. 9(3), pages 88-97, September.
    2. Triventi, Moris, 2014. "Does working during higher education affect students’ academic progression?," Economics of Education Review, Elsevier, vol. 41(C), pages 1-13.
    3. Hao Wang & Sanhong Deng & Xinning Su, 2016. "A study on construction and analysis of discipline knowledge structure of Chinese LIS based on CSSCI," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1725-1759, December.
    4. Kristina Lindemann & Markus Gangl, 2018. "Parental Unemployment and the Transition into Tertiary Education: Can Institutions Moderate the Adverse Effects?," SOEPpapers on Multidisciplinary Panel Data Research 972, DIW Berlin, The German Socio-Economic Panel (SOEP).
    5. Giulia Corti & Stefani Scherer, 2021. "Mating Market and Dynamics of Union Formation," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 851-876, November.

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