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.
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Notes
Variables that measure institutional characteristics present no missing values, while some missing values are present in two outcomes indicators: the educational equity index and the expected years in tertiary education. See Appendix table with descriptive statistics according to country.
Indicators on investments and differentiation refer to the year 2003, those on autonomy and accountability to the year 2005 and indicators of affordability refer to years between 2000 and 2005, with the exception of Norway. Variables on tracking instead refer to the 1990s, because it is likely that those who entered in the higher education system in the 2000s attended secondary school in that period.
Weighted-average linkage clustering is similar to average-linkage clustering, except that it gives
each group of observations equal weight.
These analyses have been performed using the commands cluster and mds in the statistical software Stata 11 (StataCorp 2009).
The parameter m is defined for real values greater than 1 and the bigger it is the more fuzzy the membership values of the clustered data points are. The user-written routine cmeans within the R environment (version 2.13.2) has been used to perform the fuzzy cluster analysis. We also used the fanny algorithm (Maechler 2008), a generalization of c-means, to check the sensitivity of the results, and the main findings are substantially equivalent.
The values of the original variables are reported, in order to facilitate the interpretation.
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Acknowledgments
I would like to thank Maurizio Pisati, Paolo Trivellato, the participants to the Conference ‘Higher Education and Beyond’ (Monte Verità-Ascona, July 2010) for useful comments to a previous version of this article.
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Appendix
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Triventi, M. Higher education regimes: an empirical classification of higher education systems and its relationship with student accessibility. Qual Quant 48, 1685–1703 (2014). https://doi.org/10.1007/s11135-013-9868-7
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DOI: https://doi.org/10.1007/s11135-013-9868-7