This paper aims at studying the increasing use of output measures in the decisions of allocating public funds to universities and other research institutions, by analysing in particular the attempts done in Italy to introduce new rules for the evaluation, incentives and funding mechanisms.
Academic research in Europe experienced several changes during the last decades. As a consequence to this, several changes have occurred in the rationale for university research funding, in particular a shift from ex-ante rationale of university funding to contractual-based mechanisms of resource allocation: such mechanisms are based on ex-post evaluations and on quasi-market schemes, thus increasing competitiveness. This results in a short-term increase in efficiency of the research institutions, but also some long-term unintended negative effects. During the past years there has been an increasing interest in Europe on the design of new funding mechanisms for universities and public research institutions based on evaluation of output measures.
Science and technology indicators are widely used for policy purposes, e.g. to promote exchanges, the creation of centre of excellence, for specialization towards PhD students, etc., but they have been contested because of the central role of inputs rather than outputs and because they focus mainly on the economic dimension of science and technology. The use of indicators of innovation and statistics on science is based on accounting methodology within an input-output framework: inputs are invested in research activity, producing outputs. The origin of this approach can be traced on economic literature, in particular the analysis of growth of science, technological change and factor productivity is based on econometric methods applied to the production function. This particular type of function is often called the knowledge (or technology) production function. The use of the production function to produce numerical indicators for input-output relation in the R&D process is still the dominant model, and such indicators are considered as an important political instrument for public budget decisions; although the knowledge production function model has its limitations (for instance numbers do not tell us enough about the relation between innovation and its impacts), the problem in developing better tools, given the long lag of time required, makes it an analytical instrument still widely used.
A model of knowledge production function is employed in order to study the academic research evaluation methods in Italy during the last years, thus establishing a relation between inputs and outputs (the latter being measured by scientific publications, patents, prototypes, etc.). A positive correlation between output quality (and quantity) and input factors (especially number of FTE researchers) is observed; technological specialisation is an important factor that increase knowledge productivity. On the other hand, the lack of a certain critical mass and dispersion of R&D into several technological areas yields results that are lower than the average rating; this is especially true for small and medium organizations.