A scaling approach to tackle the heterogeneity of heis

The assessment of the performance of Higher Education Institutions (HEIs) at the micro (institutional), meso (regional) and macro (country) level is an important and recurrent question in the higher education’s policy debate. The modernisation agenda for Higher Education in Europe (European Commission, 2016) identifies the relevance of creating effective governance and funding mechanisms for higher education among the five key priorities for this sector. It is underlined the importance to ensure greater flexibility and autonomy for institutions to specialise more easily, promoting better educational and research performance while fostering excellence within higher education systems. Different models of governance (Agasisti and Catalano, 2006; Capano et al. 2015) are applied by policy makers trying to improve the systemic performance of Higher Education. However, the analysis of the performance of HE systems is far from being easy to deal with. One of the main critical issue to address properly the assessment of the performance, in a multi-level (systemic) perspective, is the consideration of the heterogeneity of the HEIs involved. Among the heterogeneity factors of HEIs, the disciplinary specialization or subject mix is considered one of the most relevant (López-Illescas et al. 2011, Daraio et al. 2011). Recently, Bonaccorsi et al. (2017) extend the results of Ruocco and Daraio (2013) to the evaluation of bibliometric indicators of Social Sciences and Humanities (SSH) and propose a scaling approach as a tool for indirect qualitative-quantitative comparative analysis across heterogeneous disciplines. In this paper we (i) demonstrate that the distributions of total enrolled students (ENR STUDENTS) of European HEIs in four main fields of education (ENG, MED, NAT, SSH) follow a Log-Normal master curve, (ii) estimate the scaling factors that work like rates of substitution to compare the education production across different fields on a common ground and (iii) propose to use the estimated scale factors to make appropriate normalizations before running systemic performance assessment and comparison.