TRIPLE HELIX noms.pmd

O-048Ideas production function on regional level: the case of EuropeJoost Heijs, Thomas Baumert, Universidad Complutense Madrid, SpainIntroductionIn the paper we like to present on the conference we estimated a model of the knowledge generation function on regional levelwhich might allow us to establish what are the determining factors of innovation, as well as the relative impact of them on thetechnological result of the EU-27 regions.State of the artOnly a few studies on this subject are published. In our study we make use of a model based on the knowledge generationfunction, initially developed by Griliches (1979), which since then has been successfully applied to a whole series of empiricalworks, both at national and regional level. According to Griliches (1990), the flow of new knowledge (K) depends, on the onehand upon the innovatory effort made and, on the other, on a set of characteristics of the region itself, which would be encapsulatedin a Zr vector, so that:[I]where Zr can be directly substituted by a linear combination of suitable regional indicators. Furman, Porter and Stern (2002)used such a production function on country level and in our case we adapted those models on regional level.MethodologyWe recollect regionalised data -offered by Eurostat- for 207 EU-22 regions for about 100 variables for a period of ten years.Afterwards we used 26 of those variables and based on a Factor Analysis they were merged in five factors or hypothetical nonobservable variables. From our point of view those factors do reflect better the reality of the regional innovation system andelements of the Triple Helix than each of the individual variables. Those new composite variables were used to estimate theideas production function. In which the five factors are the independent variables and the patents are the dependent variables.(Number of Patents: Patents per capita using the total number of patents; high-tech patents and the patents by differenttechnological fields).We have modelled in the present work a knowledge-generating function the explanatory variables of which, unlike what hashappened in previous works, have not been measured by means of individual indicators, but rather, by means of factors ("virtual"variables obtained by factorial analysis). The use of the five factors -specifically (1-RENV) Regional and productive innovatoryenvironment, (2-UNI) Universities, (3-INNFI) Innovatory firms, (4-ADM) Administration and (5-NACENV) National environmenthelpsto solve partially various statistical problems. It permits to simultaneously a broad number of variables without having aproblem of grades of freedom or multi-colinearity.Our knowledge production function is defined by the five previously enumerated factors in accordance with the following equation:K = f (RENV, UNI, INNFI ADM, NACENV)FindingsOur results are according to the theoretical expectations and show a important role of the accumulated knowledge and the firmsand enables the following conclusions. The regional and productive innovatory environment- which measures the size of thesystem and the productive experience-proves to be the factor with the highest incidence on regions´ technological output (orthat of what could be considered as a type or mean European region). Not only in the case of absolute number of patents -whichwould be logical- however also in the case of the number of patents per capita patents, which shows the importance of thecritical mass. Likewise a positive incidence of the National environment is detected, which includes variables referring to capitalinvestment and the penetration of new communication technologies, and which registers those aspects linked to the nationalinnovation system. In turn, it is appreciated that Innovative firms, the University and the Administration combine to aid thecreation of new knowledge with a commensurate intensity with the fact that the latter two are essentially concerned withabstract scientific knowledge -basic research-, whereas Innovatory firms are devoted to production-linked technological knowledge.This fact is reflected in the noticeably greater importance of this latter factor-the second most important-compared to the othertwo agents in the innovation system. In this sense, it is worth highlighting that our findings essentially fit in with the postulatesof the evolutionary approach, since at the beginning all the elements making up the innovation system are significant, as is theinteraction between them, thus confirming the initial hypothesis of this study. This situation is seen particularly in the estimationsfrom the TOBIT panel model, while for the case of the intragroup model estimations, we do not obtain unequivocal findings. Thisis because, depending upon whether we use the dependent variable in absolute or relative terms, either the Administration factoris statistically significant or the University factor is.Madrid, October 20, 21 & 22 - 2010146