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Competition and diversity in higher education: an empirical approach to specialization patterns of Portuguese institutions

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Abstract

The expansion of higher education systems has often been associated with the need for increasing diversification, namely at the program level, based on the pressures to adapt more general programmes to a more diverse student population and multiple regional, social, and economic needs. This paper explores empirically the question of programme diversity by drawing on the Portuguese higher education’ experience, which presented massive expansion during the last decades and significant institutional competition in recent years. The study provides a longitudinal approach and analyzes in detail the evolution of diversification and specialization of the public and private sectors, as well as the university and polytechnic subsectors. The analysis aims at helping to illustrate the relevance of analysing diversity from an empirical point of view and its contribution to our understanding of the complex relationships between competition and diversity in contemporary higher education.

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Notes

  1. For a complete survey on the main concepts and types or subdimensions associated to diversity and differentiation in higher education see, for instance, Huisman (1997) or van Vught (2009).

  2. In 2008, there were in Portugal 138 HEIs, of which 61 universities (including 14 public universities, 42 private universities, four public military universities and one “distance learning” university) and 77 polytechnics (divided into 20 public polytechnics, one public military polytechnic and 56 private polytechnics).

  3. This capacity of institutions to create most of the programmes they submitted has only changed recently, with the creation of the new accreditation and evaluation agency in 2008, which has signalled a far stricter approach to the creation of new programmes, though its effects will only be felt in the future.

  4. The eight educational fields are those considered in the UOE manual: Education (Teacher training and education science), Humanities and Arts (Arts; Humanities), Social sciences, business and law (Social and behavioural science; Journalism and information; Business and administration; Law), Science (Life sciences; Physical sciences; Mathematics and statistics; Computing), Engineering, manufacturing and construction (Engineering and engineering trades; Manufacturing and processing; Architecture and building), Agriculture (Agriculture, forestry and fishery; Veterinary), Health and welfare (Health; Social services) and Services (Personal services; Transport services; Environmental protection; Security services).

  5. Balassa’s measure of relative export performance by country and industry, defined as a country's share of world exports of a good divided by its share of total world exports. The index for country i good j is RCA ij  = 100(X ij /X wj )/(X it /X wt ) where X ab is exports by country a (w = world) of good b (t = total for all goods).

  6. GPEARI (Gabinete de Planeamento, Estratégia, Avaliação e Relações Internacionais) stands for the office aiming to provide the statistical and policy support to MCTES (Ministério da Ciência, Tecnologia e Ensino Superior). For programs data is available for the period 1998/1999–2006/2007; for faculty data availability refers to the period 2001/2002–2008/2009; and for graduates for the period 1995/1996–2007/2008. As in Rossi (2009), our analysis of programs and graduated students included aggregated data on the different degrees, thus not distinguishing between Bachelor, Master and Doctorate programs or graduates. However, given the low portion of programs and graduates at the Master and Doctorate levels, we believe that their inclusion does not change the results in a significant way, so an analysis based on aggregated data on graduated students or total number of programs offered does not constitute a relevant limitation.

  7. The correlations between HEIs’ total number of programs, faculty and graduates are all around 0.85. When we test these pair-wise correlations by educational field, the coefficients of correlation vary between 0.57 and 0.94, so it seems reasonable to only present the results obtained for one of the three indicators, as the conclusions are roughly the same. However, the results obtained using the remaining indicators are available upon request from the authors.

  8. To return to the specialization index before normalization we must compute: S ji  = (1 + NS ji )/(1 − NS ji ), where S ji is the specialization index of institution j in education field i and NS ji stands for the normalized specialization index.

  9. This summary of results is based on the computations of specialization indexes with data on programs, faculty and graduates, as the results are similar across indicators, allowing identifying this pattern. The conclusions are mainly based on the results obtained for the academic year of 2006/2007, but no significant changes in the patterns of specialization were detected over the period under study.

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Correspondence to Pedro Nuno Teixeira.

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Teixeira, P.N., Rocha, V., Biscaia, R. et al. Competition and diversity in higher education: an empirical approach to specialization patterns of Portuguese institutions. High Educ 63, 337–352 (2012). https://doi.org/10.1007/s10734-011-9444-9

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