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Universities and Regional Innovation Output: A Detailed Study of 19 Technologies in Germany. DIME Workshop „Regional Innovation and Growth: Theory , empirics and policy analysis Pécs , April 1 st , 2011. Charlotte Schlump &Thomas Brenner. Content. Motivation
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Universitiesand Regional Innovation Output: A Detailed Study of 19 Technologies in Germany DIME Workshop „Regional Innovation and Growth: Theory, empiricsandpolicyanalysis Pécs, April 1st, 2011 Charlotte Schlump &Thomas Brenner
Content • Motivation • Theoretical considerations • Mathematical Model • Empirical data • Empirical analysis • Conclusions
1. Motivation: TwoAims • Impact of universities • Direct or indirect impact? • Research or education? • Relevant disciplines and activities • Which disciplines are relevant for which technologies? • Which activities are important?
2. TheoreticalConsiderations • Who areinnovationgenerators? • R&D employees in firms • Employees in firms, in general • Employees at universities and research institutes • ‘Free’ inventors • Hypothesis 1 • Various innovation generators exist • R&D employees are the dominant source
2. TheoreticalConsiderations • Impact ofuniversities: • Empirically proved(Jaffe 1989; Acs et al. 1992/2002; Feldman 1994; Anslin et al. 1997; Blind & Grupp 1999, Autant-Bernard 2001) • Education and research support • Geographic proximity important (?) • Hypothesis 2 • University contributes to regional innovativeness • Different disciplines matter for different technologies
2. TheoreticalConsiderations • Do universitiesgenerateorfacilitateinnovations? • Research creates inventions • University as research partner • Education of future innovation generators in firms • Hypothesis 3 • Universities are mainly innovation facilitators • (Applied) research is more important than education for regional innovativeness
3. Mathematical Model • Three kinds of factors • Innovation generators: G • Innovation attractors: A • Innovation facilitators: F • Mathematical: • Expected number of innovations in a region s:
3. Mathematical Model • Innovation generators: • Potential factors k: Firm employees, R&D employees, public research, inhabitants • Innovation facilitators: • Potential factors f: public research, GDP, unemployment, population density, ...
4. Empirical Approach • Studying 19 technologies separately (correspondence between IPC and NACE) • Regional unit: German labour market regions (270) • Binomial regression with formula for both, potential number and probability • University variables are used for disciplines separately
4. Empirical Data • [Empl] Employment in the relevant industries • [RandD] R&D employees in the relevant industries • [Inhab] Inhabitants • [Uni-Research/Budget] Budget of university per inhabitant • [Uni-Applied/3Funds] Third-party funds per inhabitant • [Uni-Grad/Stud] University graduates per inhabitant • [Highschool] Share of school leavers with a high-school degree • [Unemployment] Unemployment rate • [GDP] GDP per inhabitant • [Dens] Population density
6. Conclusions • Findings: • Innovation generators: Mainlyinhabitants (??) • Variousuniversitydisciplines matter in variouswaysdependent on technology • University israther an innovationfacilitator → University hasonly an impactifeconomicsurroundingfits
Thanks for your attention Questions / Comments ?