Tamás Sebestyén DIME Workshop on regional innovation and growth, 1st April, 2011, Pécs

1. Regional productivity and knolwedge transfer through patent co-inventorship - the role for network structure Tamás Sebestyén DIME Workshop on regional innovation and growth, 1st April, 2011, Pécs

2. Motivation • Innovation and spillovers • Why are spillovers local to certain extent? (Jaffe, 1989; Feldman, 1994; Anselin et al. 1997) • How does knowledge flow between economic actors and spatial units? • The role for interpersonal contacts can not be disregarded (Breschi and Lissoni, 2003) • Knowledge mainly flows through social networks • If networks are important, network theory comes into the picture • Network theory emphasizes that the specific structure of networks bears important implications on the overall performance of the system as a whole and that of its elements (Granovetter, 1973; Cowan et al., 2006, Bala and Goyal, Barabási, 2003, Barabási et al. 2000) • In this study these two lines are intertwinned • Spatial knolwedge transfer is examined • Mediated by personal cooperation of patent inventors • And the effect of network structure on regional productivity

3. The research question • Does knowledge transfer through patent cooperation networks contribute to the economic performance of regions? • Economic performance is measured by labor productivity • Knowledge transfer is approximated by patent inventor cooperation • The unit of analysis are NUTS2 regions of Germany, France and the United Kingdom • The analysis covers the high-tech sector (according to the Eurostat classification) • A corollary of the results: • How does the overall network structure affect the results obtained from the previous analysis? • There is an empirical question the answer to which leads to a methodological issue

4. Data sources • Two data sources • Eurostat data for regionalized economic output, labor force and high-tech patenting at the NUTS2 level • EPO data for patent co-inventorship, regionalized at the NUTS2 level • The network database • NUTS2 regions are assigned to each patents according to inventors’ addresses • Complete network is assumed among the inventors of every patent • Interregional link weights are then derived according to the simple principle: • Link weight is increased by one if inventors from the two regions cooperated on a patent

5. An example of network connections R1 R2 I3 I4 I5 I2 1 R3 1 I6 R1 R2 I1 1 2 R3

6. Variables • Three variables • Regional productivity • Calculated from Eurostat data on GDP and high-tech labor force • Regional knowledge stocks • Calculated from Eurostat data on patenting activity • Two methods: simple cumulation and depreciation • Interregional knowledge transfer • Calculated from knowledge stocks and network connection weights • The vector of external knowledge (xt) is obtained from the weighted adjacency matrix (Rt) and regional knowledge stocks (kt): xt= Rtkt

7. Model and results • The following panel model is estimated (96 regions and 3 years) • The results for two knowledge stock generating methods and binary vs. weighted network

8. The results in detail • Regional knowledge stock has a clear positive impact on productivity • Interregional knowledge transfer has a negative effect • Where does this counter-intuitive result stem from? • Regions with higher productivity are connected to • Fewer partners • And/or these links are among those with lower weights • An intuitive resolution of the contradictory result: • Central regions with high productivity typically connect to peripheral regions with lower productivity and knowledge stocks • That is, network structure shows scale-free properties • The analysis may be corrected for this effect with plausible adjustment of the variables • Problem: considerable multicollinearity between the possibly involved correction factors • An alternative method is suggested basedon simulation experiments

9. A simple network model • The model is a modified version of the preferential attachment model (Barabási and Albert, 1999) • First, a random network is generated with Mnodes and average degree d. • A new node is defined in the network which forms dconnections to already existing nodes. The following rule determines this link formation: • With probability rthe new node connects to the potential partner with the highest degree. Potential partners are those nodes with which no connection exists and loops are excluded. If more than one partner can be chosen according to this rule, a random choice decides among them. • With probability 1-rthe new link forms randomly with one of the potential partners. • The previous step is repeated until the number of nodes in the network reaches N.

10. Scale-free structures in the simple model

11. A simulation exercise • The empirically observed values of knolwedge stocks (kt) are taken as the starting point • A 96-element network is generated with the previous method for a given r – this gives the elements of adjacency matrix A • Pick a value for the ‘strength of spillovers’ (θ) between 0 and 1 and calculate the folowing ‘artificial’ productivity levels: • That is, a positive relationship is assumed between productivity and external knowledge • Then, run a single regression as in the empirical analysis: • Where s = Ak

12. Coefficientsinthesimulatednetworks

13. Is there a scale-free structure? • The value of r corresponding to a given empirical network can be derived with the network model presented above • Generate two reference networks with r=0 and r=1 which has the same size and average degree as the empirical network • Calculate the power-law exponent for the empirical and the two reference networks and use them to give the hypothetical r value of the empirical network:

14. Conclusion • Data shows a negative co-movement of network-mediated knowledge transfer andregional productivity • However, this tendency may be ‘illusory’ and generated by the specific structure of the network as a whole • Scale-free characteristics result in central regions linking to less-knowledge intensive regions and vice versa • Therefore interregional knowledge transfer through patent co-inventorship may have a positive effect on regional productivity • But this effect is hidden by the overall structure of the network

15. References • Anselin, L., Varga, A., Acs, Z.J. (1997): Local Geographic Spillovers between University Research and High Technology Innovations. Journal of Urban Economics, 42(3) pp. 422-448. • Bala, V., Goyal, S. (2000): A Noncooperative Model of Network Formation. Econometrica, vol. 68(5), pp. 1181-1230. • Barabási, A.L. (2003): Linked: How Everything is Connected to Everything Else what It Means for Business, Science and Everyday Life. Penguing Group, USA. • Barabási, A.L., Albert, R., Jeong, H. (2000): Scale-free characteristics of random networks: The topology of the world wide web. Physica A, 281, pp. 69-77. • Barabási, A.L., Albert, R. (1999): Emergence of scaling in random networks. Science, 286 pp. 509-512. • Breschi, S., Lissoni, F. (2003): Mobility and social networks: localised knowledge spillovers revisited. CESPRI, Working Paper nr. 142. • Cowan, R., Jonard, N., Zimmermann, J.B. (2006): Evolving networks of inventors. Journal of Evolutionary Economics, 16(1) pp. 155-174. Feldman, M.P. (1994): The Geography of Innovation. Boston, Kluwer Academic Publisher. • Granovetter, M.S. (1973): The Strength of Weak Ties. American Journal of Sociology, 78(6) pp. 1360. • Jaffe, A.B. (1989): Real Effects of Academic Research. American Economic Review, 79(5) pp. 957-970.