Abstract
Over the last few years, the emergence of universities’ third mission has significantly affected objectives, sources of funding and financing methods, as well as the management, of universities. Although the university–industry relationships have been widely investigated, several interesting theoretical and empirical issues still remain open in the literature. In this paper we construct an original data set, combining financial information with structural and organizational data on Italian University departments, with a twofold aim. First, to describe the importance and the extent of third-party funding in the Italian system of research as well as the pattern of evolution over the last few years. Second, to investigate the factors that influence both the probability and the intensity of the commitment of departments in third-party activities by building a multi-level framework combining factors at individual, departmental, university and territorial levels. The results obtained suggest a number of policy implications for universities and policy makers. On one hand, universities should explicitly recognize the role of dedicated internal organizations and provide training for professional staff capable of acting as value-added intermediaries. On the other hand, if policy makers wish to improve the relationships between universities and external actors, disciplinary differences across departments as well as regional inequalities in growth levels should be carefully considered, giving up a one-size-fits-all approach.
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Notes
In 1989, Law 168 endorsed the self-regulation principle and increased universities’ administrative autonomy. Law 537 further elaborated on this new institutional framework, in 1993, by introducing greater freedom for universities in the use of funds coming from the Ministry, and in the possibility of attracting external funding. Following the ministerial decree on the 9th of February 1996, which gave full application to Law 168, universities started to elaborate on their own statutes and internal regulations, which gradually expanded to include different possibilities for leveraging their internal resources and competencies.
In addition to the descriptive analysis illustrated above, we performed ANOVA tests in order to evaluate if the average rate of participation in third-party activities significantly differ according to factors such as localization, status and size of the universities. Concerning the localization, we found significant differences among the average rate of third-party funds at 5% level of significance over the 6 year period analyzed. However, when we considered as factor the status and size of the universities, the obtained F statistic did not enabled us to reject the null hypothesis of significant differences among the average values for all the years considered. In particular there are not significant differences in the average values among universities of different size (small, medium and large) in the year 2006 and 2009 while for the other years we can reject the null hypothesis at 10% level of significance. Concerning the status of the universities we rejected the null hypothesis of significance differences for the year 2008 at 1% level of significance. Due to the difficulties in comparing data of the various years at department level, we still referred to data at university level. For this reason the interpretation must be carried out with caution since intra-university compensation effects may be in place.
More in detail information regarding age and disciplinary affiliation of professors and researchers are from the MIUR Teaching Databases managed by CINECA while information concerning PhD courses are from PhD course census archive.
The National Agency for the Evaluation of Universities and Research Activities is now introducing the new Research Assessment Exercise (“Evaluation of the Quality of Research”—VQR—for the period 2004–2010). Results will be available in 2013.
AQUAMETH (Advanced Quantitative Methods for the Evaluation of the Performance of Public Sector Research) was a research project coordinated by A. Bonaccorsi and C. Daraio within the PRIME Network of Excellence. See Daraio et al. (2011) for a European comparative analysis, and Bonaccorsi and Daraio (2007) for preliminary data. The dataset includes 80 universities, from which the distance education ones were excluded for the analysis in this paper.
It is worth noting that being our analyses conducted at department level, the individual variables we considered were summarized with the aim to be included in the data set (i.e. for the age of professors we considered the average age of the professors in each Department).
Firstly we considered the size of the university (in terms of number of students enrolled) by distinguishing in large universities (with a total number of students enrolled greater than 40,000) medium-sized universities (with a total number of students enrolled ranging between 15,000 and 40,000) and small universities (with a total number of students lower than 15,000). Secondly, we considered the geographical localization of the university. Finally we considered a dummy variable accounting for the existence of an Industrial-Liaison Office within the university.
Before considering these factors into the models we firstly evaluated the existing level of correlation among them in order to avoid the presence of redundant information in the models to be estimated.
The Heckman analysis provides us with a null hypothesis stating that “the selection process and the outcome process are independent of each other.” If we accept the null hypothesis we can conclude that it is possible to estimate two separate models.
They rejected the need for the two equations to be estimated simultaneously. Moreover, a comparison between the values of the coefficients estimated with the truncated regression and with the Heckman procedure highlighted the absence of substantial differences between the two estimates.
The distribution of error terms (ui and εi) is assumed to be bivariate normal with correlation equal to ρ. The two equations are related if ρ ≠ 0; in this case estimating only the outcome equation would induce sample selection bias in the estimation of vector β.
For the probit models, estimation results are presented also referring to marginal effects (M.E.) which are more straightforward to interpret as they show, in general, how a one unit change in one of the regressors affects the predicted probabilities leaving the other variables constant (at their mean).
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Acknowledgments
We would like to thank the two anonymous referees for their useful suggestions concerning our study. Comments on a previous version of the study, presented at the 5th International Conference of Education, Research and Innovation (Madrid, November 2011) were also helpful.
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Andrea Bonaccorsi: On leave from Department of Energy and Systems Engineering, University of Pisa.
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Bonaccorsi, A., Secondi, L., Setteducati, E. et al. Participation and commitment in third-party research funding: evidence from Italian Universities. J Technol Transf 39, 169–198 (2014). https://doi.org/10.1007/s10961-012-9268-5
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DOI: https://doi.org/10.1007/s10961-012-9268-5
Keywords
- University–industry relations
- Third-party research
- Italian University department
- Heckman selection model