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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

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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

Suggested Citation

  • 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. Michela Nardo & Michaela Saisana & Andrea Saltelli & Stefano Tarantola & Anders Hoffman & Enrico Giovannini, 2005. "Handbook on Constructing Composite Indicators: Methodology and User Guide," OECD Statistics Working Papers 2005/3, OECD Publishing.
    2. Romina Boarini, 2008. "Investment in Tertiary Education: Main Determinants and Implications for Policy," CESifo Economic Studies, CESifo, vol. 54(2), pages 277-312, June.
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    Cited by:

    1. Triventi, Moris, 2014. "Does working during higher education affect students’ academic progression?," Economics of Education Review, Elsevier, vol. 41(C), pages 1-13.
    2. 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.

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