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In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011-2016 for Europe and 2012-2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance.

In this article, we contribute to the scant literature covering quantitative studies on the determinants of the non-academic staff incidence in higher education institutions by analysing how the proportion of non-academic staff is related to key features such as size, prestige, year of foundation and financial structure of universities. We apply nonlinear regression analysis to compare HEIs across Europe and the USA, taking into account time and cross-country heterogeneity of the two balanced panel datasets concerning European and American universities over a period of 6 years (2011–2016 for Europe and 2012–2017 for the USA). Evidence suggests that in both Europe and the USA, public and larger (if sufficiently large) as well as more research-oriented units are characterised by a higher proportion of non-academic staff. In Europe, we observe an inverted U-shaped effect of the share of non-personnel expenditure and the foundation year on the proportion of non-academic staff, while the proportion of non-academic staff decreases with the share of core and third-party funding. For the USA, we obtain similar findings except that the share of core funding and third-party funding is characterised by a U-shaped effect, and the impact of the share of non-personnel expenditure has no empirical effect on the proportion of non-academic staff. Additionally, we discover that some factors that contribute to the proportion of non-academic staff may constitute indicators of performance, suggesting the need for further research to extend our knowledge on the complex issue of the role played by non-academic staff in university performance. [ABSTRACT FROM AUTHOR]

In this paper we investigate economies of scale and specialization of European universities. The proposed approach builds on the notion that university production is a multi-input multi-output process different than standard production activity. The analyses are based on an interesting database which integrates the main European universities data on inputs and outputs with bibliometric data on publications, impact and collaborations. We pursue a cross-country perspective; we include subject mix and introduce a robust modeling of production trade-offs. Finally we test the statistical significance of scale and specialization and find that they both have a significant impact on the efficiency of the Humboldt model. Nevertheless, confirming previous findings, specialization has not a significant impact on the efficiency of the research model. [ABSTRACT FROM AUTHOR]

In the last decades of the twentieth century universities in Europe and other OECD countries have undergone a profound transformation. They have evolved from mainly élite institutions for teaching and research to large (public and private) organisations responsible for mass higher education and the production and distribution of new knowledge. Increasingly, new knowledge is produced by universities not only for its own sake but also for potential economic gains. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of characterization and classification of universities in the European system, by using the recently developed Aquameth dataset. Preliminary cluster analysis based on structural variables identifies systematic differences in size. However, this structural distinction is associated with differences in strategic orientation of universities (towards research or towards teaching, respectively) only in a few countries. In most European countries there are no discernible differences across universities along these dimensions. The paper argues that countries in which universities are more differentiated according to research or teaching dimensions have implemented differentiation policies through a variety of policy instruments. In turn, these countries also are ranked high in international rankings of universities. This suggests a structural linkage between the poor performance of European universities in research-based rankings and the lack of differentiation. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs) are crucial to the development of the European Research Area. However, unlike in the USA, the availability of quantitative indicators for individual HEI at the European level is severely limited by several methodological issues, data availability problems and national institutional constraints. The paper discusses strategies for collecting and validating data from different national sources; the limitations of the available data for different categories of indicators and, finally, the influence of the heterogeneity of the national HEIs on comparability at the European level. Based on the experience of two recently completed projects, the paper shows that, despite these problems, it is possible to collect relatively coherent data on European HEIs and to develop a set of consequential indicators. Further, it provides advice on how to exploit them for comparative purposes in a sensible way. It concludes by indicating areas where major improvements are urgently needed, and advocates a European S&T Indicators Platform to maintain and develop these data sets long term. [ABSTRACT FROM AUTHOR]