Knowledge flows across European regions

1. Knowledge flows across European regions Raffaele Paci and Stefano Usai CRENoS, University of Cagliari SPRU seminars, 16 May 2008 EU FP7 SSH Project: Intangible Assets and Regional Economic Growth forthcoming in Annals of Regional Science

2. Outline • Motivations and aims • Crenos dataset on patents and citations • Descriptive analysis • The basic model of knowledge flows • Econometric results • Extensions and robustness • Conclusions and future work

3. Motivations /1 • Many economists have attempted to find evidence of the existence of knowledge spillovers or flows either embodied in R&D exchanges, in bilateral trade, in capital goods acquisition or in foreign direct investment…. • These indicators are mainly indirect since technological flows, at the interregional and international levels, are hard to be captured:Krugman (1991) “knowledge flows are invisible and cannot be measured and tracked”

4. Motivations /2 • Jaffe et al (1993): knowledge flows may leave a “paper trail”, in the form of patent citations, which can be measured and used to obtain information on the geographical component of the innovation spillover mechanism • Citations made are a measure of previous knowledge extracted from the cited patent and embodied in the new invention. • They allow us to represent this knowledge flow in the geographical space (by using the inventors’ residence) to have an idea of the knowledge links (network) among cited and citing regions

5. Aims • Contribute to the analysis of knowledge flows and their determinants across European regions • Examine whether geographical distance and spatial contiguity influence knowledge exchanges: i.e. are knowledge spillovers locally bounded ? • Control for the role of other types of “distances”: production structure, economic conditions, technological efforts, national borders • Investigate on the changes along time

6. Background literature • Seminal contributions on the USPTO database: • Jaffe et al, 1993 • Jaffe and Trajtemberg, 1996 and 1998 • Some more recent works on EPO • Maurseth and Verspagen, 1999 and 2002 • Breschi and Lissoni, 2004 • Maggioni et al, 2005 • LeSage et al., 2006 knowledge flows in European regions

7. CRENOS dataset /1 • Patents granted by EPO, 1978 – 2004 • Each patent attributed to one of 175 regions of 17 countries in Europe, the 15 members of the EU15 plus Switzerland and Norway (based on inventors –single and multiple- and not firms HQs). • Citations are linked to patents from both a geographical and an industrial point of view.

8. European Regions CRENoS(ID-CRENoS; Region; Nuts level)

9. CRENOS dataset /2 • Each patent is classified by industrial sector (3 digit) based either on the IPC - ISIC Yale Technology or on the Schmock-OECD concordance. Results on sectors are preliminary and are not reported here. • Note: citation list (both at EPO, USPTO) is completed by the examiner during the granting procedure

10. Some critical features … • Patent is an indicator for both product and process innovations • Long time span • Data on inventor and legal applicant: use of the inventor’s residence instead of applicant’s residence (HQs problem) • Specific treatment of multiple inventors • Use of Yale Technology Concordance. • Such a concordance uses the probability distribution of each IPC across industries of manufacture in order to attribute each patent proportionally to the different sectors where the innovation may have originated (or used) knowledge flows in European regions

11. Critical aspects of citations and other research avenues • Breschi and Lissoni, 2004 • Application of social network analysis to overcome some controversial aspect on the use of patent citation as proxy of knowledge flows… • Maggioni et al., 2005 • Application of network analysis to a set of indicator of knowledge relations: co-patenting, research consortia, educational exchanges and internet hyperlinks… knowledge flows in European regions

12. knowledge flows in European regions

13. knowledge flows in European regions

16. Distribution of patents by region of origin Gini = 0.72 Gini = 0.68

20. The basic model The main hypothesis to test is if knowledge linkages are localized in space and therefore if geographical distance and spatial contiguity influence knowledge flows across regions. Moreover, the use of a set of national and regionaldummies allows to control for other potential influences coming from institutional and cultural differences specific either to the country or to the local area.

21. Dependent variable: patent citations (C) Knowledge flows are proxied by the number of citations between each couple of the 175 European regions considered. 175x175 matrix where the generic element Cij is the number of citations originated from patents granted by EPO to inventors resident in the citing region i and directed to patents granted by EPO to inventors resident in the cited region j.

22. Explanatory variables Geographical distance (GD) 175x175 matrix, the generic element GDij represents the distance in hundreds of kilometers between the centroids of the citing region i and the cited region j. Hypothesis: a higher distance has a negative impact on the strength of knowledge spillovers.

23. Dummy Contiguity (DC) 175x175 dummy matrix, the generic element takes value one where citing and receiving regions share a border (even in different countries) and 0 otherwise Hypothesis: knowledge flows are facilitated by physical proximity between regions which share a common border, irrespective of distance in kilometers (already included in GD). Positive impact

24. Dummy Nation (DN) 175x175 dummy matrix, the generic element is equal to 1 if the citing region i and the cited one j belong to the same nation, or equal to 0 elsewhere Hypothesis: knowledge flows take place more frequently among regions located in the same nation: exchanges are facilitated by language, cultural, institutional homogeneity. Positive impact. Dummy Region (DR) A set of 175 fixed effects for each region i is included to allow for idiosyncratic aspects not appropriately measured by the other explanatory variables, it implies that the model is estimated with the Least Squares Dummy Variable (LSDV) method

25. The estimated basic model Cij = b1 GDij + b2 DCij + b3 DNij + giDRi + eij Estimation method: LSDV Two periods: 1990 and 1998 To reduce the problem of firms self-citations, we exclude the observations within the same region (exclude i = j ) (Maurseth and Verspagen, 2002) Total number of observations: 30450

27. Extensions to the basic model: robustness checks • Estimation methods: Poisson • Intra regional citations (include i = j ) • Structural distance (SD) • Economic distance (ED) • Technological effort (TE) • General specification

28. Robustness /1Estimation methods: Poisson Poisson estimation may be helpful to ensure a control for the presence of zeros in the knowledge exchange matrix, i.e. pair of regions without citation flows.

29. Robustness /2 Intra regional citations Consider citations originated and received by the same regions. Problem: it may also include some intra firm citations. Include also a dummy “within region” (DW) which controls for i = j

30. Robustness /3 Structural distance (SD) Hypothesis: knowledge flows occur with greater intensity between regions with comparable production structure since exchanges are easier within similar sectors. 175x175 matrix: the generic element SDij is : Pijmeasures the similarity between i and j (correlation index) fikrepresents region i share in sector k with respect to the total (measured in terms of patents) The index ranges between: 0 (identical sectoral structure between the two regions) 1 (the production structures are orthogonal)

31. Robustness /3 Structural distance (SD)

32. Robustness /4 Economic distance (ED) Hypothesis: more knowledge exchanges happen among regions which are closer in terms of economic conditions. 175x175 matrix: the generic element EDij is computed as the absolute difference in GDP over population between the origin and the destination region: EDij = (GDP / POP)i – (GDP / POP)j 

33. Robustness /4 Economic distance (ED)

34. Robustness /5 Technological effort (TE) Hypothesis: more knowledge exchanges happen among regions which are characterised by a larger amount of resources allocated to technological activity Include two vectors calculated as the shares of R&D expenditure over GDP both in the origin region i and in the destination region j: TDi = (R&D / GDP)i TDj =(R&D / GDP)j

35. Robustness /5 Technological effort (TE)

36. The general specification Cij = b1 GDij + b2 DCij + b3 DNij + b4 SDij + b5 EDij + b6 TEi + b7 TEj + gi DRi + eij

37. Summary results • Knowledge flows are bounded in space and characterized by a spatial declining effect (due to spatial transaction costs in knowledge exchange). • Flows between neighboring regions are higher. • Flows are more likely when the two regions belong to the same country; national borders constitute an obstacle to knowledge leakages, the national systems of innovation still play a role with respect to a unified European system. • The diffusion of technological spillovers is improved when the origin and destination regions are similar in terms of production / technological structure and economic conditions and allocate more resources to innovative activities. • The importance of such effects is changing along time

38. Future work • Integrate the sample of citations with data provided by OECD on patents at national offices • Analysis of spatial dependence with spatial econometric techniques • Focus on the industrial dimension • Some selected sectors or some macrosectors such as high tech • Deeper analysis of technological networks among firms (applicants) and inventors in specific industries