Abstract
The present European higher education policy and research policy can be characterized as emphasizing external financing of universities, competition between and within universities, and the need for a more practical and economically profitable output from research and education. A theoretical framework of analysing the impacts of this new rationale can be constructed on the following two premises. First, the funding structure of universities and university research is a main factor that influences the situations in which universities and their members make their decisions on teaching, research and administering. Second, universities consist of various groups of personnel each having and developing objectives and preferences of their own. This theoretical framework is applied to the Finnish science university system. On the basis of the analysis it can be seen that although the new policy probably has clarified the division of labour between universities, there have emerged some negative unintended consequences of the new funding structure. Indications of weakening performance in research and education can be identified in the empirical analysis.
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Notes
Finland serves as a representative example of the small north-west European and Scandinavian countries who invest much in academic research and have comparable large outputs (Pavitt 2001, pp. 5–6). This is accompanied by higher than the OECD and EU averages in investments in higher education measured as percentage of GDP (OECD: Education at a glance 2006. Table B2.1b (www.oecd.org/edu/eag2006)).
They are ETH Zurich and University of Basel in Switzerland; University of Twente in The Netherlands; University of Bristol in Great Britain; and MIT and UT Austin in the United States.
See also Campbell and Slaughter (1999), who analyze the differing objectives and preferences both between faculties and between faculties and administrators.
The notion of negative effects of the decline in research publishing is not clear and it lacks systematic empirical evidence. One line of argumentation would lean on the social rate of returns of investing publicly funded research (see Salter and Martin 2001).
Termed in the KOTA database as ‘domestic companies’.
Tekes (Finnish Funding Agency for Technology and Innovation) is the major public funding organization for research and development in Finland. It finances industrial projects as well as projects in research organizations, and especially promotes innovative, risk-intensive projects. It has a more applied research profile than the Academy of Finland.
Smeby (2003, p. 136) reports on a Norwegian data showing that the lack of uninterrupted time was the greatest problem of faculty members in a 2000 survey. That is, 57% of the respondents (n = 1436) reported that the lack of uninterrupted time caused many problems for their possibility for undertaking research.
Because the number of observations in the Finnish case is small (n = 16) we were not able to run the principal component analysis. Instead, a simple K-means analysis was conducted for descriptive classifying purposes. Therefore the study is not able to replicate Geuna’s model but it assess, nevertheless, the Finnish case with the same key variables as Geuna’s model (see Appendix 1 for the technical details of the clustering).
See also the analysis of Australia’s university clustering by Valadkhani and Worthington (2005).
The remaining two are (4) the establishing of the Finnish research school system in the late 1990s and the use of (5) research personnel inputs to teaching.
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I am grateful to the anonymous referees for making helpful comments.
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Appendices
Appendix 1: The clustering of Finnish science universities
The clustering of Finnish science universities (n = 16) was based on K-means clustering which is a multivariate statistical technique that uses the following Euclidean distance as a dissimilarity measure (used in minimizing the total intra-cluster variance):
where X ia and X ib are values of universities a and b in variable I, respectively. Thus, the smaller is D(a, b) the more similar are the universities a and b.
The clustering partitioned Finnish science universities into three clusters on the basis of three variables (publications per academic person, researchers per student, and share of external research financing). The variables measure the means of 2 years values in 1994–1995 and in 2004–2005. The clustering variables were transformed into Z-score variables.
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Tammi, T. The competitive funding of university research: the case of Finnish science universities. High Educ 57, 657–679 (2009). https://doi.org/10.1007/s10734-008-9169-6
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DOI: https://doi.org/10.1007/s10734-008-9169-6