1. Agglomeration and interregional network effects on European R&D productivity Attila Varga University of Pécs, Pécs Dimitrios Pontikakis European Commission JRC-IPTS, Seville Georgios Chorafakis European Commission DG-RTD, Brussels and University of Cambridge

2. Research question: Agglomeration and network effects in R&D productivity Geography and technological development –Increasing awareness of the significance of the regional dimension both in economics and policy –Debate about specialisation in the EU Geography of innovation - empirical research: –Spatial proximity and innovation (e.g., Jaffe, Trajtenberg, Henderson 1993, Anselin, Varga, Acs 1997) –Interregional networks and innovation (e.g., Maggioni, Nosvelli, Uberti 2006, Ponds, Oort, Frenken 2009, Varga, Parag 2009) –Agglomeration and the productivity of research in regional innovation (e.g., Varga 2000, 2001)

3. Main contributions –agglomeration and interregional network effects on research productivity estimated in an integrated framework –static and dynamic agglomeration effects tested –policy impact analysis

4. Outline Introduction Empirical model, data and estimation Estimation results Discussion: relation to policy

5. - H A : research input – number of researchers - A: the total stock of technological knowledge (codified knowledge component of knowledge production in books, patent documents etc.) - dA: the change in technological knowledge - φ: the „codified knowledge spillover parameter” -  : scaling factor - : the “research productivity parameter” Starting point: Romer-Jones KPF

6. Empirical model, data and estimation

7. Output of knowledge production (K) –Competitive research (Patents) –Pre-competitive research (Publications)

8. The index of agglomeration where -EMPKI is employment in knowledge intensive economic sectors (high and medium high technology manufacturing, high technology services, knowledge intensive market services, financial services, amenity services – health, education, recreation) - i stands for region - j stands for the jth KI sector - EU stands for the respective EU aggregate

9. Measurement of network effect Log(NET) is measured by: total of Log(R&D expenditures) in regions with which FP5 partnership is established (calculated via a non-row standardized FP5 collaboration matrix)

10. Data sources: –EUROSTAT New Cronos database (PAT, RD, δ, PATSTOCK) –EC DG-Research FP5 database (NET) –Regional Key Figures Publications database (PUB) Estimation: –Pre-competitive and competitive research productivity effects are tested –Panel with temporally lagged dependent variables (1998-2002) –spatial econometrics methodology Empirical model, data and estimation

13. Our contribution Clear distinction between competitive and pre- competitive research: –Competitive (innovation-oriented research proxied by patenting): local agglomeration important in R&D productivity out-of regional spatially mediated knowledge transfers important (spatial multiplier is 1.33) National level codified technological knowledge important network effects absent in R&D productivity clear spatial regime: PATHCORE

14. Our contribution Clear distinction between competitive and pre- competitive research: –Pre-competitive (science-oriented research proxied by journal publications): no effect for local agglomeration on R&D productivity no effect of national level codified technological knowledge network effects important in R&D productivity additional (spatially mediated) interaction among spatial units not found Clear spatial regime: PUBCORE

15. Estimated regional productivity of research in innovation and scientific output

16. Regional R&D productivity in competitive research

17. Siginificant clusters of most R&D productive regions in competitive research

18. Regional R&D productivity in pre-competitive research

19. Cumulative process empirically tested by: Empirical model, data and estimation

22. Our contribution Changes in regional R&D is positively related to both competitive and pre- competitive research productivity patenting productivity plays a more intensive attraction force spatial regime: R&D high core regions follow a different pattern

23. Our contribution agglomeration is strongly path dependent this path dependency is influenced by the level of R&D in the region spatial regime effect: R&D core regions follow a different pattern

24. Our contribution Eqs. 5 and 6 indicate the presence of a cumulative feedback-effect of agglomeration

25. R&D promotion: Regional and interregional effects Regional effects: –R&D is positively related to both patents and publications –Increasing R&D positively affects agglomeration which increases regional research productivity Cumulative process

26. Interregional (spillover) effects: –Regional R&D promotion increases research productivity of FP partner regions as well contributing to a cumulative agglomeration process in the network partner regions –Regional R&D promotion positively affects patenting not only in the region targeted but also in other regions with distance decay Contributing to a cumulative agglomeration process in closely located regions R&D promotion: Regional and interregional effects

27. Agglomeration and network dynamism in R&D promotion