Spatial Interaction with eigenfunction. estimated with a Poisson estimator. and Poisson spatial interaction modelling

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1 Appendix A Table A1. Empirical studies analysing the structure of knowledge exchange using spatial interaction modelling Study Dataset Method used Dependent Variable Proximity forms included PONDS et al. (2007) Co-Publications from in the Gravity model approach to Co-Publications between Geographical (pos.) proximity is more impor- Netherlands for different industries analyse the interactions between two places regions tant for collaborations between academic and non-academic organisations FISCHER and GRIF- FITH (2008) FISCHER et al. (2009) HOEKMAN et. al. (2009) SCHERNGELL and BARBER (2009) HOEKMAN et al. (2010) SCHERNGELL and BARBER (2011) Patent citations between 112 European regions ( ) in high-tech firms high-tech patents applied at the European Patent Office between Scientific publications (Web of Science) from and patents (European Patent Office) from , both from 29 European countries for the biotechnology and semiconductor industries Collaborative R&D projects funded by the 5th EU Framework Programme Co-publications between 313 regions in 33 European countries for the period Collaborative R&D projects funded by the 5th EU Framework Programme Spatial Interaction with eigenfunction spatial filtering estimated with a Poisson estimator Case control matching approach and Poisson spatial interaction modelling Spatial interaction model estimated with a zero-inflated negative binomial estimator Poisson and negative binomial spatial interaction model Negative binomial spatial interaction model Negative binomial spatial interaction model Cross-region collaborations (patents) Influences vary for different industries Geographical (neg.), technological (neg.), institutional distance (country borders, neg.) distance Patent citations Geographical (neg.) proximity in the Casecontrol matching approach Geographical (neg.) and technological (neg.) proximity in the Poisson model Cross-region collaborations Geographical distance (neg.) and institutional (patents and co- distance (neg.) (country border) publications) Cross-region R&D collaborations Co-publications between regions in a given time period Cross-region R&D collaborations differentiated between industrial R&D networks and public research R&D networks Technological (neg.) and geographical (neg.) distance Negative effect of institutional proximity decreases over time Physical distance has a stable (over time) negative effect technological distance (neg.) most important for both kinds of networks geographical distance (neg.) more important in industrial R&D networks

2 Table A1 (continued) Study Dataset Method used Dependent Variable Proximity forms included SCHERNGELL and LATA (2011) Cross-region collaborations R&D SCHERNGELL and LATA (2013) Collaborative R&D projects running during the time period from 1999 to 2006 mostly funded by the 5th and 6 th EU Framework Programme European Framework Programme 1999 to 2006 Spatial interaction models (negative binomial specifications) accounting for spatial autocorrelation by using spatial filtering methods Spatial interaction models implementing spatial filtering methods Cross-region collaborations BARBER and SCHERNGELL (2013) Collaborative R&D projects funded by the 5th EU Framework Programme Negative binomial spatial interaction models Cross-region collaborations differentiated between different technology fields LATA et al. (2015) Three data types: Spatial interaction model, Cross-region collaborations project-based R&D networks within accounting for panel data the EU FP and spatial autocorrelation co-patent networks co-publication networks from Note: An increase in proximity induces a decrease (neg.) or increase (pos.) of the dependent variable. Source: Authors own compilation. Geographical distance (neg.), decreasing effect in the period of analysis Decreasing negative effect of geographical distance Negative institutional (country border) effects diminish over time Technological distance has a negative effect Geographical distance (neg.), effect much lower when the partners involved belong to the same technology field Technological distance (neg.) most important Geographical (neg.), technological (neg.) and institutional (country borders, neg.) in all three datasets

3 Table A2. Variables, measurement, sources Variable Measurement Source Number of cross-region collaborations Geographical proximity Number of cross-region collaborations in granted R&D collaboration projects. Euclidian distance between the capitals of two regions. Federal Ministry for Education and Research, Subsidies catalogue (Foerderkatalog, StartAction.do Longitude and latitude of the regions capitals were used. Own calculation. Cognitive proximity One minus the Pearson correlation coefficient between vectors of the relative industry shares between region and. The relative share of an industry is measured by the number of employees working in sector r as a proportion of the total number of employees in region i at time t. The German Federal Employment Agency (BA). Institutional proximity Same as cognitive distance, instead of employees we use the shares of votes in the federal elections in 2005 and The Federal Returning Officer, election statistics. Border region dummy This dummy equals one, if either region or are at the German land border. Own calculations. Neighbouring region dummy Is equal to one if two regions share a common land border. Own calculations. Intra-regional collaboration dummy Number of employees Equals one if an observation represents the number of intra-regional collaborations. The number of employees in the regions under analysis. Own calculations. Federal Statistical Office, Genesis Online Regional, table Number of establishments The number of establishments in the regions under analysis. Source: Authors own illustration. Federal Statistical Office, Genesis Online Regional, table

4 Table A3. Top ten pairs of regions showing the highest number of collaborations Region 1 Region 2 Number of collaborations Munich (city) Munich (district) 900 Munich (city) Berlin 382 Hamburg Berlin 313 Berlin Potsdam 277 Stuttgart Berlin 244 Munich (district) Berlin 237 Stuttgart Munich (city) 231 Berlin Dresden 225 Hanover Berlin 220 Heidelberg Berlin 209 Source: Authors own calculation. Table A4. Descriptive statistics Variable Obs. Mean Std. Dev. Min Max Number of cross-region collaborations 1,131, Geographical Distance 1,131, Cognitive Distance b 1,131, Organizational Distance b 1,131, Institutional Distance b 1,131, Border Region Dummy 1,131, Neighbouring Region Dummy 1,131, Intra-Collaboration dummy 1,131, Number of employees a 1,131, ,754.1 Number of Establishments 1,131,228 9, , , ,654 Notes: a 1,000 employees. b Transformed Pearson s correlation coefficients divided by corresponding standard deviation. Source: Authors own calculation.

5 Figure A1 Histogram Distribution of cross-region R&D collaborations Source: Authors own calculation Figure A2 Histogram Distribution of cross-region R&D collaborations excluding zeros Source: Authors own calculation

6 Figure A3 Average residuals Source: Authors own calculation

7 Appendix B The Choice of Partners in the Context of the Proximity Debate Theoretically, the debate on the choice of collaboration partners relies on Boschma (2005), who presented an integrated view on the different forms of proximity and their impact on interactive learning and innovation processes, focusing on the idea that knowledge flows between actors, generates knowledge spillovers, innovation and, finally, regional growth. These dimensions are geographical, cognitive, social, institutional and organisational proximity. It is assumed that each type of proximity raises the potential for knowledge exchange. Boschma (2005) argues that different forms of proximity may influence each other, whereas potential lock-in effects may reduce knowledge flows. Geographical Proximity The starting point of the ongoing debate on the influence of geographical distance on economic interactions can be traced back to Krugman (1991), Audretsch and Feldman (1996) as well as Jaffe et al. (1993). These authors claim that geographical proximity leads to an increasing amount of positive externalities in form of knowledge spillovers, as it simplifies the transfer of tacit knowledge between actors. According to Maurseth and Verspagen (2002), the concentration of industrial activity and R&D may be endogenously driven by the decision of firms to locate near knowledge producers. This would imply that knowledge externalities are bounded geographically. Face-to-face-interactions can facilitate the transfer of knowledge, although they may be cyberised by modern technologies (Wheeler et al. 2000). Anyhow, excessive proximity can cause spatial and technological lock-in effects. This can occur in very specialised regions (Boschma and Frenken, 2010). Boschma (2005) even assumes that geographical proximity is neither a necessary nor a sufficient condition for learning to take place (Boschma, 2005, p. 62). Hence, other forms of proximity could serve as substitutes for geographical proximity. Bathelt et al. (2004) propose building additional interregional connections. However, geographical proximity may indirectly influence interactive learning, because it can facilitate the other forms of proximity (Broekel and Boschma, 2011; Huber 2012). Cognitive Proximity Boschma (2005) claims that economic agents aim to reduce uncertainty and that the choice for cooperation partners depends on the overlap in their knowledge base. Thereby, routinised behaviour evolves between collaboration partners, leading to path dependences (see Simon,

8 1955; Nelson and Winter, 1982). Therefore, knowledge can be defined as an individual capacity and learning as the recombination of integration process of individual competences, which depends on absorptive capacity (Cassi and Plunket 2014, p 400). Anyhow, Broekel and Boschma (2011) as well as Nooteboom (2000) argue that cognitive proximity may be correlated with an increasing competition, which may reduce the willingness to cooperate. Furthermore, potential lock-in effects may be the results of a substantial overlap in the partners knowledge bases. Therefore, Mowery et al. (1998) and Nooteboom et al. (2007) assume a non-linear relationship between cognitive proximity and innovation. Furthermore, Boschma (2005) considers cognitive proximity in combination with geographical proximity as being sufficient for interactive learning. Organisational Proximity Organisational proximity is defined as the rate of autonomy and the degree of control that can be exerted in organisational arrangements (Boschma, 2005, p. 65). Only minimal control can be exerted on the spot market, so partners cannot influence each other. Maximum control can be found in a hierarchical firm or network. The benefit of a strong organisational boundary is the reduction in opportunism and uncertainty. Furthermore, the transfer of tacit knowledge will be simplified by strong organisational boundaries (Hansen, 1999). The negative effect of too much organisational proximity lies in the hold-up problem. A very powerful partner can demand inefficient cooperation specific investments. Asymmetric distribution of power can further cause a block in novel information and, finally, in innovation (Boschma, 2005). Therefore, Grabher and Stark (1997) claim that the optimal degree of organisational proximity can be realised by loosely coupled networks, where the advantages of autonomous agents and organisational flexibility are combined with weak ties. Social Proximity Social proximity concerns micro-level trust, based on e.g. former experience, kinship or friendship. This may also exert an influence on the probability of collaboration (Uzzi, 1996). Maskell and Malmberg (1999) claim that a high level of trust facilitates the exchange of implicit knowledge and may further reduce opportunistic behaviour in comparison to a marketbased exchange (Boschma, 2005). Furthermore, other scholars consider social proximity as a main influencing factor for knowledge diffusion and therefore interactive learning (see e.g. Sorenson et al. 2006; Agrawal et al. 2008; Breschi and Lissoni 2009). Anyhow, high social proximity may even result in lock-in effects, as new and innovative actors may be excluded from collaboration networks as the partners cling to inefficient connections (Boschma, 2005).

9 Therefore, Uzzi (1996) claims that a mix of embedded ties and arm`s length ties are necessary to generate the optimal level of collaboration probability. Institutional Proximity Institutions can be divided into explicit (laws, enforceable rules) and implicit (habits, routines) institutions (North, 1990). Institutional proximity describes which habits, routines, rules and laws two actors have in common. With rising similarities in institutions, transaction costs will decrease because of an increasing level of trust. However, too much institutional proximity can be a source of inefficient cooperation and local inertia, because of reinforcing effects of complementary institutions (Boschma, 2005). The effectiveness of one institution depends on the establishment of another (Hall and Soskice, 2001). In this system of institutions, changes can be completely hindered (Boschma, 2005).

10 References Agrawal, A., Kapur, D. and McHale J. (2008) How do spatial and social proximity influence knowledge flows? Evidence from patent data, Journal of Urban Economics 64, Bathelt, H., Malmberg, A. and Maskell, P. (2004) Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation, Progress in Human Geography 28, Boschma, R. (2005) Proximity and innovation. A critical assessment, Regional Studies 39, Boschma, R. and Frenken, K. (2010) The spatial evolution of innovation networks: a proximity perspective, in Boschma, R. and Martin, R. (Eds) The handbook of evolutionary economic geography, Edward Elgar, Cheltenham. Breschi, S. Lissoni, F. (2009) Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows. Journal of Economic Geography 9(4): Broekel, T. and Boschma, R. (2011) Knowledge networks in the Dutch aviation industry: the proximity paradox, Journal of Economic Geography 12, Cassi, L. and Plunket, A. (2014) Proximity, network formation and inventive performance: in search of the proximity paradox, The Annals of Regional Science 53, pages Grabher, G. and Stark, D. (1997) Organizing diversity: evolutionary theory, network analysis and postsocialism, Regional Studies 31, Hall, P. and Soskice, D. (2001) Varieties of capitalism: the institutional foundations of comparative advantage. Oxford University Press, Oxford. Hansen, M.T. (1999) The search-transfer problem: the role of weak ties in sharing knowledge across organization studies, Administrative Science Quarterly 44, Huber, F. (2012) On the role and interrelationship of spatial, social and cognitive proximity: personal knowledge relationships of R&D workers in the Cambridge Information Technology. Regional Studies 46(9): Jaffe, A.B., Trajtenberg, M. and Henderson, R. (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics 108, Krugman, P. (1991) Geography and trade, Leuven University Press, Leuven. Maskell, P. and Malmberg, A. (1999) The competitiveness of firms and regions ubiquitification and the importance of localized learning, European Urban and Regional Studies 6, Maurseth, P. B. and Verspagen, B. (2002) Knowledge Spillovers in Europe: A Patent Citations Analysis. The Scandinavian Journal of Economics, 104, Mowery, D. C., Oxley, J. E. and Silverman, B. S. (1998) Technological overlap and interfirm cooperation: implications for the resource-based view of the firm, Research Policy 27, Nelson, R.R. and Winter, S.G. (1982) An evolutionary theory of economic change. Harvard University Press, Cambridge. Nooteboom, B. (2000) Learning and innovation in organizations and economies. Oxford University Press, Oxford. Nooteboom, B., Haverbeke, W. Van, Silverman, G., Gilsing, V. and Oord, A. van den (2007) Optimal cognitive distance and absorptive capacity, Research Policy 36,

11 North, D.C. (1990) Institutions, institutional change and economic performance. Cambridge University Press, Cambridge. Simon, H. A. (1955) A behavioral model of rational choice. The Quarterly Journal of Economics 69, Sorenson, O., Rivkin, J. and W. Fleming, L. (2006) Complexity, networks and knowledge flows. Research Policy 35: Uzzi, B. (1996) The sources and consequences of embeddedness for the economic performance of organizations: the network effect, American Sociological Review 61, Wheeler, J.O., Ayoama, Y. and Warf, B. (2000) Cities in the Telecommunications Age: The Fracturing of Geographies. Routledge, London.