Agglomeration and interregional network effects on European R&D productivity

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. Starting point: Romer-Jones KPF - HA: 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”

6. Empirical model, data and estimation

7. Empirical model, data and estimation • 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. Empirical model, data and estimation • 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

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

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. R&D promotion: Regional and interregional effects • 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

27. Agglomeration and network dynamism in R&D promotion