The role of R&D collaboration networks on regional knowledge creation: Evidence from information and communication technologies

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1 doi: /pirs The role of R&D collaboration networks on regional knowledge creation: Evidence from information and communication technologies Cilem Selin Hazır 1, James LeSage 2, Corinne Autant-Bernard 3 1 Université Côte d Azur, CNRS, GREDEG, France and OFCE Sciences Po, France ( cilemselin@gmail.com) 2 Fields Endowed Chair in Urban and Regional Economics, McCoy College of Business Administration, Texas State University, San Marcos, Texas 78666, USA ( james.lesage@txstate.edu) 3 Université de Lyon, Lyon, F-69007, France; CNRS, GATE Lyon Saint-Etienne, Ecully, F-69130, France; Université Jean Monnet, Saint-Etienne, F-42000, France ( corinne.autant@univ-st-etienne.fr) Received: 29 February 2016 / Accepted: 4 August 2016 Abstract. We investigate how R&D networks impact regional innovation, considering alternative connectivity structures based on co-publications, co-inventions and projects supported by the EU-FP. Patent activity impacts on ICT during for 213 European regions are quantified using a spatial Durbin model. Findings indicate that local knowledge flows to proximate regions are influenced by: proximate regions that are not collaboration partners, proximate collaboration partners, and distant collaboration partners. Evidence on the role of distant collaboration partners is found only for co-invention networks. JEL classification: C21, L14, O31, O52, R12, R15 Key words: R&D collaboration networks, knowledge creation performance, spatial econometrics, ICT 1 Introduction Collaboration in research and development (R&D) takes place in different forms and result in overlapping networks involving different types of knowledge actors and innovation activities in different extents. Some of these networks are much more oriented towards enhancing the knowledge base and disseminating new knowledge as in the case of co-publication networks, where we observe a dominant presence of higher education and public research institutions. Some others are targeting a useful (industrial application) invention to be appropriated like co-patenting networks, where private enterprises are extensively involved. Public policies foster creation or development of these networks directly or indirectly. For instance, the policy considering collaborative research as a performance measurement criterion for higher education or public research institutions has an impact on development of co-publication networks. A more direct effect arises from direct funding policies, a prominent example of which are European Union Framework 2016 The Author(s). Papers in Regional Science 2016 RSAI

2 2 C.S. Hazır et al. Programmes (EU-FP). As in the case of EU-FP, we also observe R&D collaboration networks being created by means of formal project consortiums among a variety of knowledge actors in response to a direct funding tool. Hence, as a matter of fact these networks co-exist, facilitating knowledge flows among actors and in space in complex ways, and public policies may shape them all. The theoretical and applied economic literature suggests several reasons to consider crossregional collaborations as a tool to foster regional knowledge creation and growth. First, most links are established within geographical areas (Almeida and Kogut 1999; Singh 2005) indicating a higher level of local knowledge diffusion (Breschi and Lissoni 2009). However, repeated local ties may reduce creativity although they reduce transactions costs. Therein, the virtue of global ties is widely acknowledged (Boschma 2005; Guiliani and Bell 2005) as cross-regional collaborations can enable regions to avoid territorial lock-in. The literature on industrial clusters identify leading actors in the regional innovation system as gatekeepers who facilitate entry of external knowledge (Guiliani and Bell 2005). Second, combining such dense sets of local ties and a few distant ties are theoretically shown to be efficient structures for diffusion of knowledge (Cowan and Jonard 2004; Kastelle and Steen 2010). These structures, called small worlds, improve the efficiency of linking together heterogeneous communities of actors who possess diverse mental models, routines and cognitive maps (Kastelle and Steen 2010). Efficient diffusion of knowledge in space, in turn becomes important for economic growth as theoretical growth models suggest that cross-regional knowledge flows lead to a reduction in uneven growth (Baldwin and Forslid 2000; Baldwin et al. 2001). Early applied economic literature produced many efforts to quantify the impact of R&D collaboration networks on firm knowledge creation (see Pippel 2013 for a meta-analysis), but only few recent studies addressed the issue at the regional level. While some of these studies compare the impact of within region ties and cross regional ties formed by local agents (Broekel 2012; Breschi and Lenzi 2016); only few studies address the issue at a more global level by considering the region as the unit of analysis and analysing the role of cross-regional collaborations along with spatial interaction. These studies consider either one type of R&D collaboration network as an alternative to the traditional spatial interaction between proximate regions (Maggioni et al. 2007); or take both interactions into account simultaneously (Ponds et al. 2010; Sebestyen and Varga 2013). However, since R&D collaborations often take place locally, these approaches fail to disentangle spatial interaction from network interactions. In this study, we suggest a novel spatial approach to investigate the relative role played by distant and proximate direct learning partners on regional knowledge creation. As R&D collaboration networks enable both spatially proximate and/or distant partners to become direct learning partners, we decompose neighbouring regions into three sets: spatially proximate regions that are not collaboration partners; spatially proximate regions that are collaboration partners; and distant collaboration partners. This allows us to suggest a methology to identify the proper combination of various interactions driving knowledge diffusion. Moreover, we do not focus on a single network but take into account co-existence of different R&D collaboration networks and investigate how knowledge flows mediated by these overlapping networks affect regional knowledge creation alongside more traditional spatial interaction between proximate neighbours. We explore whether there is a difference between networks created in response to direct public support and more spontaneous networks, indirectly shaped by public policies. To this aim, we study patenting activity of 213 European regions in the field of ICT during We employ the knowledge production function (KPF) framework (Griliches 1979) to relate innovation inputs to patents. Since the data spans a short period,

3 Collaboration networks and regional knowledge creation 3 we use a static modelling strategy that relies on a spatial Durbin model specification. 1 Such an expression enables us to study marginal and joint effects of spatial distance and collaborations on regional knowledge creation. To do that we grid on values associated with the three components of the weight matrix and choose the combination yielding best model fit as we move from a purely space-based to a purely network-based specification of the dependence structure. We repeat this analysis three times, each time using different data to express R&D collaboration partners. We make use of data on R&D collaborations supported by the European Union s Framework Programmes (FP) to study inter-regional R&D collaboration networks created in response to a direct public support programme. Scientific publications and patents are used to represent networks that are more indirectly shaped by public policy and undergo a more spontaneous development process. In Section 2, we first provide a review of existing evidence regarding the effect of R&D collaboration networks on regional knowledge creation. Some shortcomings are discussed in order to position our contribution in this literature. Section 3 sets forth the model and explains our empirical implementation. We present empirical findings and interpret them in Section 4. A final section summarizes our conclusions and discusses possible future research directions. 2 The effect of R&D collaboration networks on regional knowledge creation The effect of R&D collaboration networks on knowledge creation is much less studied at the regional level as compared to the firm level. Existing literature addressing firm level knowledge creation is not only larger but also richer. Thanks to data availability at the firm level, these studies consider alternative aspects of performance, such as quality and the quantity of output, or the efficiency in converting knowledge inputs to outputs (Fritsch 2004; Schilling and Phelps 2007; Frenken et al. 2010), as well as different metrics for innovation performance. 2 These studies provide partial insights regarding the effect of interregional R&D collaboration on aggregate regional performance, since they take into account spatial scale of collaborations as well as perhaps local knowledge externalities. Among these, Fornahl et al. (2011) assess the impact of R&D on patent activity of German firms and focus on whether this impact is greater for firms that are embedded in knowledge networks. They show that neither the number of partners nor the average geographical distance to partners has an impact on patenting. However, they find that who a firm is connected to makes a difference in patent performance. A more interesting result comes out of comparing the effect of being located in a cluster and the effect of inter-regional network ties. It appears for German biotech firms, that being co-located fosters patenting via local knowledge flows, whereas interregional connections do not matter. Conversely, for Canadian biotech firms, Gertler and Levitte (2005) suggest that innovative firms make more use of collaborative arrangements and the presence of a foreign partner in these collaborations is more strongly associated with patenting. Frenken et al. (2010), however, address the effect of collaboration at different spatial scales on the quality of the research output, which is measured by citations received by co-publications. They compare the effect of regional, national and inter-regional collaborations on citation impact of the resulting co-publication. They find that for life sciences and physical sciences the spatial scale of collaboration plays different roles. While international collaborations matter most in both technological domains, regional collaborations matter more than national collaborations in life sciences, whereas the reverse holds for physical sciences. 1 LeSage and Pace (2009) as well as Elhorst (2010) argue this particular specification has numerous advantages in applied work. We construct neighbourhood structure specifications (i.e., the weight matrix), as a convex combination of: (i) spatially proximate regions that are not collaboration partners; (ii) spatially proximate regions that are collaboration partners; and (iii) distant collaboration partners. 2 See Pippel (2013) for a meta-analysis.

4 4 C.S. Hazır et al. Conclusions from these results regarding the effect of collaborations on knowledge creation are that: impacts may change from one technology domain to another; impacts may depend on the spatial level of the interaction; and impacts may work in conjunction with local knowledge externalities arising among spatial agglomeration of knowledge actors. Several studies extend these results by taking an aggregate approach and focusing on the region as the unit of analysis. They compare the impact of intra-regional and inter-regional collaborations on the aggregate knowledge creation of the region. Among these Boschma and Ter Wal (2007) study a footwear district in the South of Italy and conclude that flows that occur though deliberate interactions with both local and global scope affect knowledge creation. Similarly, using patent data, Breschi and Lenzi (2016) explore the impact of network spatial structure on invention in US cities. Their results point to complementarity between local and global. In the same vein, Broekel (2012) adopts a non-parametric approach to study the relationship between collaboration intensity and the efficiency at which knowledge inputs in 270 German regions are related to knowledge outputs in the electronics industry. He concludes that this relationship takes the form of an inverted-u shape, where regions with extremely high or low and unbalanced regional and inter-regional collaboration intensities tend to be innovation inefficient. These studies take the region as the unit of analysis, but when focusing on collaborations, they neglect less intentional knowledge flows occurring among regions. Few studies have taken into account local knowledge flows crossing regional borders along with network mediated knowledge flows. Maggioni et al. (2007) study the effect of knowledge flows from network partners and spatial neighbours on regional knowledge creation in separate models and then compare the results. In their analysis they make use of data on R&D collaborations supported by EU-FP and study patent activity of European regions located in France, Germany, Italy, Spain and United Kingdom. In both models, they obtain evidence on the positive effect of external knowledge on regional knowledge creation, but the effect of flows from spatial neighbours is found to be larger. Sebestyen and Varga (2013) investigate the effect of collaboration networks and spatial knowledge flows together on regional research productivity. They express embeddedness of a region in R&D collaboration networks by means of the ego quality index and account for the effect of knowledge flows from neighbouring regions using spatial econometrics. The ego quality index summarizes three aspects of the network: (i) knowledge potential, which measures the knowledge accumulated in the neighbourhood by means of the number of partners and their knowledge; (ii) local connectivity, measures the strength of ties and interaction intensity; and (iii) global embeddedness which measures the level and quality of knowledge of indirect partners. They study the impact of ego network quality in co-patenting networks and knowledge flows from spatial neighbours on regional patenting and conclude that both affect inventive performance positively. They also investigate the effect of FP collaborations on research productivity measured by scientific publications. They show that again ego network quality has a positive effect on productivity in scientific publications, but spatial knowledge flows have a negative effect. While Sebestyen and Varga (2013) include network-mediated flows by means of an index, Ponds et al. (2010) combine the two models from Maggioni et al. (2007) into a single model, and incorporate network-mediated flows through a spatial econometric approach. They use copublications data to represent inter-regional collaboration networks and find statistical evidence of a positive effect arising from the network on patent activity of Dutch regions. In addition to network-mediated knowledge flows they show that knowledge flows from academic research activities in spatially proximate regions have a positive effect on patenting. Their approach however does not allow disentangling the respective role played by local and distant ties. In their approach, local ties are by construction assumed to matter more than distant ones.

5 Collaboration networks and regional knowledge creation 5 To summarize, these findings point to a positive role played by collaboration networks on knowledge creation at both the firm and regional level. However, they do not provide us with immediate conclusions regarding how inter-regional collaboration ties impact different spatial scales, or how local knowledge flows crossing regional borders impact aggregate performance of firms located in a region. Indeed, such an understanding regarding the global picture would be helpful in designing policies targeting economic convergence. This requires however being able to disantangle the role played respectively by purely local knowledge flows, by local ties and by distant ties. To this aim, this paper provide a methodological contribution that goes beyond spatial weight matrix comparison and synthetic indicators. Spatial weight matrix comparison techniques have been developed, but they tend to focus on choice of a single best matrix, rather than allow for simultaneous effects arising from different interaction structures. Two recent contributions by Debarsy and Ertur (2016) and Mur Lacambra et al. (2013) compare various specifications of the weight matrix. In their Schumpetarian growth model, Debarsy and Ertur rely on the minimum J-test suggested by Hagemann (2012) to choose between spatial, ethnic, commercial and linguistic weight matrices. Mur Lacambra et al. (2013) compare the performance of four different tools to select among several weight matrices. They show that Akaike information criterion and Bayesian tools perform better than entropy and J-test under some conditions. However, selecting a single best interaction matrix implies that only a single interaction structure matters. Based on the literature reviewed above, we believe that in our case this could generate misleading results. Selecting a single weight matrix would lead us to neglect possibly important channels of knowledge flows. Our approach is to identify a proper combination of the various interactions that may drive knowledge diffusion and creation. We therefore propose an alternative method that avoids potential bias that could arise from selecting a single type of interaction matrix. Our approach further avoids use of ad hoc assumptions about respective weights assigned to various types of interaction structures, such as that employed in the case of synthetic indexes. Based on this methodological contribution, this paper assesses the respective role played by distant versus local-based collaboration, while allowing for non-collaboration-based local knowledge diffusion. To our knowledge, this is the first time that the role played by these three dimensions is jointly estimated. Based on past studies, we expect positive knowledge flows emanating from these three sources. A prevailing role would appear for local collaborations if the complementarity effect of various types of proximity pointed out by past studies occurs. However, distant ties may also play a crucial part if they provide privileged access to key knowledge produced by leading regions. In addition, this study compares different collaboration networks, in an effort to explore whether public incentives significantly shape knowledge diffusion channels through collaboration networks. In this regard, one can expect a larger role played by distant ties in FPnetworks compared to more spontaneous networks such as co-publication and co-inventor networks. Direct incentives are given though the EU-FP policy to develop collaborations between distant partners (at least three different countries must be involved in each project). The extent to which these incentives translate into more effective distant knowledge flows remains however to be assessed. In addition, co-inventorship may be more effective in favouring distant knowledge flows, since some of these collaborations take place within multi-plant firms. In this case, distant knowledge flows may be strengthened by organizational proximity. This is less likely to be the case within FP and co-publication networks. Our FP data includes records at the organization-level hence intra-organization collaborations are not observed. For co-publications, we rely on authors data. We therefore include intra-organization collaborations, but due to the national boundaries of public organizations, distant intraorganization collaborations may occur within countries only, which reduces the spatial scope of such ties.

6 6 C.S. Hazır et al. 3 The model To study the effect of R&D collaboration networks on regional knowledge creation, we work on a spatially extended KPF framework. The KPF framework first proposed by Griliches (1979), leaves detailed events taking place during the knowledge creation process aside, and provides an overall assessment by linking own and external knowledge inputs to knowledge outputs. KPF is assumed to have a Cobb-Douglas form and a baseline specification can be expressed as follows: Inn i = α(characteristics i ) β1 (OwnResearch i ) β2 (ExternResearch i ) β3. (1) Autant-Bernard and LeSage (2011) use theoretical reasoning related to the fact that external effects of research might arise from both observable and unobservable inputs. They show that this results in a spatial regression extension to KPF taking the form of a spatial Durbin model (SDM) specification. Using this result, we work with the following model, which is in log-linear form: y t = λwy t + X t 3 β + WX t 3 δ + c + α t l + v t, (2) y t is a column vector of size n 1 with entries showing the knowledge output in region i S at time t τ, W is an n n weight matrix as defined in (3). In (3), W 1, W 2,andW 3 are all row-normalized, hence W also has row sums of unity, λ is the parameter showing the strength of dependence among regions knowledge outputs, X t 3 is a n k matrix of individually and time varying non-stochastic regressors representing regional inputs to knowledge creation, β is a k 1 vector of coefficients associated with k innovation inputs in X t, δ is the parameter showing the strength of dependence among regions knowledge inputs, c is an n 1 column vector of individual effects, α t is the t th element of the m 1 column vector of fixed time effects, l is a n 1 column vector of ones, v t is an n 1 column vector of identically and independently distributed error terms with zero mean and variance σ 2 0. In this model, we assume a time delay (three years) with which knowledge outputs (the dependent variable) responds to variation in knowledge inputs (explanatory variables) as generation of new knowledge and filing a patent takes time. Essentially, the model is still static in the sense that the time-lags of the dependent variable are not involved. Hence, despite this time delay, we can still say that the model allows us to study the effects of intra-temporal knowledge flows among regions, hence the effects of the current knowledge on the current innovative activities. In other words, the model enables quantifying the static effects of knowledge. However, knowledge might have also dynamic effects (Glaeser et al. 1992; Henderson 1997), which stem from the cumulative property of knowledge and refer to the effects of prior accumulated knowledge on the current invention activities. Despite this, we do not work on a dynamic model because dynamic effects occur with a lag and the temporal scope of our data set is not large enough to observe and quantify lagged effects properly. To this model, we introduce an extension by decomposing W, which enables us to distinguish between the relative roles of knowledge flows from three types of neighbours: spatially proximate regions that are not collaboration partners, spatially proximate regions that are collaboration partners, and distant collaboration partners. In the following subsections, we explain the decomposition and estimation strategy. 3.1 Decomposition of the weight matrix Let S = {1, 2,, n} be the set of regions and W N N (W from now on) be the weight matrix indicating the structure of interaction among regional knowledge creation processes, in other

7 Collaboration networks and regional knowledge creation 7 words, learning partners. We express W as a convex combination of three mutually exclusive components: W 1, W 2, W 3. Among these, W 1 indicates the strength of interaction among spatially proximate region pairs that are not collaborating. W 2 indicates the strength of interaction among spatially proximate region pairs that are collaborating; whereas, W 3 shows strength of interaction among distant region pairs that are collaborating. Letting λ 1, λ 2,andλ 3 be the corresponding scalar weights for W 1, W 2, W 3, respectively, W can be expressed as follows: W = λ 1 W 1 + λ 2 W 2 + λ 3 W 3 λ 1 + λ 2 + λ 3 = 1. (3) Such a definition of W suggests nice properties in two aspects. First, from an interpretative viewpoint, it allows assessing the relative strength of different dependence structures and it fits well with the research objectives of this study discussed in Section 1. Second, it suggests some computational advantages in optimization of the log-likelihood function. These advantages are further explained below Interpretative advantages LeSage and Pace (2011) set forth a host of interpretive issues that arise from use of multiple weight matrices in spatial autoregressive models. A major point they raise is that one cannot correctly interpret partial derivatives from multiple weight matrix models, such as: y = ρ 1 W 1 y+ρ 2 W 2 y+ρ 3 W 3 y+zδ +ε.wherez =(X WX) for the SDM specification. The problem is that one cannot separate out direct and indirect impacts arising from the three different types of connectivity structure. This means that the relative strength of the various types of dependence cannot be assessed. (They also point to other issues such as the stationary region for the parameters ρ 1,ρ 2,ρ 3 which are very complicated.) Our approach on the other hand does allow for an assessment of the relative contributions associated with the three different types of dependence/connectivity through an examination of the λ i, i = 1,, 3 weights. Also, the stationary region for the single dependence parameter obeys the usual range for spatial autoregressive models. Moreover, interpretation of direct and indirect effects based on conventional matrix derivatives for the own-and cross-partials is preserved by our approach. By allowing us to distinguish relative contributions of the three connectivity structures, the decomposition serves the two key objectives of the study. First, it allows us to specify networkmediated flows in the presence of local knowledge flows. Here we use the term local knowledge flows instead of the term knowledge externalities purposefully, to account for the fact that local diffusion of knowledge may not necessarily be in the form of spillovers. The literature suggests that knowledge flows more from geographically proximate regions and less from distant regions as spatial proximity affects the different mechanisms, by which knowledge flows, at different extents. For instance, in spite of trade globalization, buying and selling goods is still significantly influenced by physical distance as shown by the gravity models (Anderson and van Wincoop 2004). Similarly, labour mobility and face-to-face contacts are mainly local processes (Zucker et al. 1994; Almeida and Kogut 1999; Balconi et al. 2004; Breschi and Lissoni 2009). Hence, the matrix W 1 and the scalar parameter λ 1 give us an overall assessment of the role played by different mechanisms accounting for local knowledge flows. The matrix W 2 and the scalar parameter λ 2, however, helps distinguishing the specific role played by the R&D collaboration network at the local scale. Second, the specification allows distinguishing the role played by local versus distant collaboration partners by including weight matrices W 2 and W 3, with associated parameters λ 2,λ 3. All in all, estimates of the scalar parameters λ 1,λ 2,λ 3 that assign weights to the different types

8 8 C.S. Hazır et al. of connectivity can be used to draw inferences that are informative about: the design of network policies; and the role played by different types of regional collaboration networks on regional knowledge creation Computational advantages In terms of the computational advantages, the log likelihood function with β,σ concentrated out for the SDM model takes the form in (4) (see Anselin 1988). ln L = (n 2) ln(πσ 2 )+ln I n ρw (1 2σ 2 )(e e), e = y ρwy Zδ, δ =(Z Z) 1 Z (I n ρw)y, σ 2 = e e n. (4) Pace and Barry (1997) provide the expression in (5) for the concentrated likelihood, where C is a constant that does not depend on the parameter ρ,and I n ρw represents a scalar expression representing the determinant of this n by n matrix. The advantage of this representation of the concentrated log-likelihood function is that it expresses the likelihood as a vector over a grid of values for the parameter ρ. We use the notation e(ρ) to indicate that this vector depends on values taken by the parameter ρ, as does the scalar log likelihood function value ln L(ρ). ln L(ρ) =C + ln I n ρw (n 2) ln(e(ρ) e(ρ)), e = e o ρe d, e o = y Zδ o, e d = Wy Zδ d, δ o =(Z Z) 1 Z y, δ d =(Z Z) 1 Z Wy. (5) To simplify optimization of the log likelihood with respect to the scalar parameter ρ, Pace and Barry (1997) proposed expressing the log likelihood as a vector for a grid of q values of ρ, in the interval (ρ min,ρ max ), which we label ρ 1,,ρ q in (6). ln L(ρ 1 ) ln I n ρ 1 W ln(φ(ρ 1 )) ln L(ρ 2 ) ln I n ρ 2 W ln(φ(ρ (n 2) 2 )), (6) ln L(ρ q ) ln I n ρ q W ln(φ(ρ q )) φ(ρ i )=e o e o 2ρ i e d e o + ρ 2 i e d e d i = 1,, q. (7) Given a sufficiently fine grid of q computed log likelihoods, interpolation can supply the intervening points with and desired accuracy (which follows from the smoothness of the log likelihood function). Use of the convex combination weight structure W = λ 1 W 1 + λ 2 W 2 + λ 3 W 3 in this formulation preserves the smoothness of the log likelihood function. The formulation also allows direct examination of smoothness in the case of our convex combination spatial weight matrix.

9 Collaboration networks and regional knowledge creation Estimation strategy By means of gridding, we compare how model fit changes as we move from a purely proximitybased specification to a purely collaboration network-based specification of the dependence structure. We start with the case where λ 1 = 1, and λ 2 = λ 3 = 0 and evaluate the log-likelihood for the model presented in (2). In other words we estimate the model where dependence among regional knowledge creation processes is assumed to have a purely spatial structure. Then, we use a step size of 0.1, increment λ 2 and λ 3 using the following looping procedure to evaluate the log-likelihood for each case (there are 66 possible combinations): while λ 1 0 set λ 3 = 1 λ 1 while λ 3 0 set λ 2 = 1 λ 1 λ 3 evaluate the likelihood setλ 3 = λ set λ 1 = λ Empirical application: Patenting performance of European regions in ICT As an empirical application, the model presented in the previous section is implemented to quantify the effect of knowledge flows on patent activity of European regions in the field of information and communication technologies (ICT). As a matter of fact, patents as indicators of knowledge creation have some limitations. If we refer to the definition by Mitchell and Boyle (2010): the generation, development, implementation and exploitation of new ideas, patent fail to cover all the dimensions of knowledge creation. Patents focus on knowledge creation defined as an output, whereas according to the above definition, knowledge creation can also be measured as a process and as an outcome (Mitchell and Boyle 2010). As an output, knowledge creation refers to the development of new ideas that reflect a significant elaboration or enrichment of existing knowing. To this respect, patents do not account for the method or means through which knowledge is created (process). Since patenting does not necessarily imply that the new knowledge is diffused, adopted and embedded as new products, services and system, they also do not inform us on the impact of knowledge on the organisational system (outcome). In addition, as knowledge output, patents focus on knowledge directed towards competitive ends only. Despite these limitations, patents are used as a proxy for knowledge creation in this study due to the lack of process and outcome indicators of knowledge creation at the regional level. The focus on a single knowledge field is based on the rationale that in different fields various mechanisms through which knowledge flows (such as labour mobility, buying and selling of goods, collaborations, buzz, etc.) might have roles at different extents, and hence the relative role played by geographically proximate and distant regions as sources of external knowledge may differ. There are two motivations for our focus on ICT. First, ICT is a horizontal field as developments in ICT have impacts on a number of other fields. ICT knowledge production triggers not only product innovations in other fields but can also lead to restructuring of production processes, and changes in organizational forms or marketing methods. Second, ICT is one of the few fields for which data on R&D collaborations created via EU-FP could be harmonized over time, enabling us to compare the role played by different collaboration networks.

10 10 C.S. Hazır et al. 4.1 Variables and data Concerning availability of external data at the regional level, the study covers regions located in 26 countries (EU-27 members except Bulgaria and Greece, and Norway). Regions are defined by means of a modified NUTS 2 classification. Some NUTS 2 regions, such as islands far from the mainland were excluded. For Belgium, Denmark and United Kingdom some NUTS 2 regions are replaced with their NUTS 1 counterparts. The rationale behind this replacement is that NUTS classification based on population as a criterion results in large variation in the spatial dimension of NUTS 2 regions. In Belgium, Denmark and United Kingdom NUTS 2 regions are so small that they can hardly be compared with their counterparts in other EU countries. To illustrate, in the United Kingdom, London belongs to several NUTS 2 regions. Thus, based on earlier work by EuroLIO regions are covered in the empirical analysis. By making use of several data sources we prepared a panel data set for these 213 regions and for 7 years. The time window for the dependent variable and the explanatory variables are not the same due to the fact that research inputs are not immediately converted to inventions, and filing an invention in a patent office takes time. In this research we assumed a lag of three years. Thus, the time window for the dependent variable spans ; whereas, for the explanatory variables it spans The dependent variable, which we label Patents, represents the number of patents in a region at a point in time. It is measured by the number of patent applications to the Euporean Patent Office (EPO), localized with respect to the inventor s region of residence, in the field of ICT. Data on regional patents is obtained from OECD Regpat Database (June 2012). Explanatoryvariables consist offour main knowledgeinputs. The first, BERD refers to financial inputs to research. It is defined as the amount of regional R&D expenditures (in million PPS) in the field of ICT performed by the business enterprise sector (Source: Eurostat). Nevertheless data on sectoral breakdown of regional R&D expenditures is not available. Hence, an approximation is made for each region by multiplying the total regional R&D expenditures performed by the business enterprise sector (in million PPS) by the ratio of ICT publications to publications in all domains. The second explanatory variable HRST measures human resources as another input to the knowledge production process. It is defined as the number of people either having successfully completed education at the third level in an S&T field of study; or, although not formally qualified as in the first case, employed in an S&T occupation where qualifications in the first case are normally required (Source: Eurostat). The third and the fourth variables aim at capturing the content of the knowledge base in the region, namely, to what extent the region is specialized in ICT or not. Among these, FP-ICT projects is defined as the number of ICT projects that involve region i as a partner in the consortium and are supported by the FP in year t. On the other hand, FP-other projects stands for the number of all projects (excluding ICT) that involve region i as a partner in the consortium and get supported by the FP in that year. 4.2 Construction of weight matrices As explained in subsection 3.1, we express W as the weighted sum of three weight matrices W1, W2, W3. To build these matrices we first determine contiguous regions using the rook criterion. Next, an R&D collaboration network among regions was constructed. We note that R&D collaboration networks are dynamic since the set of collaboration partners for regions changes over the seven year time period for which we have data. Since our empirical model is static, we made use of a collaboration network constructed for a single year. To choose the year to build the 3 European Localized Innovation Observatory (

11 Collaboration networks and regional knowledge creation 11 network, we computed the Mantel statistics to check to what extent annual states of the collaboration network are correlated with each other. The results show that all annual network states are correlated with each other. The correlation becomes very strong (80% 89%) between consecutive years, and gradually decreases as we move apart in the time window and reaches to 50 per cent between 2000 and 2006 network states. Then, we select the first year, namely 2000, taking into account that network ties do not die out as soon as a research project is finished. 4 To build the collaboration connectivity structure, we make use of three different data sets, and we analyse the impact of each connectivity structure separately. Below these data sets and the procedure used to build the connectivity structure is explained in detail: 1. European Commission records on trans-national collaborative ICT projects that received a grant through the Framework Programmes (FP) in 2000: two regions i and j (i j)aresaid to be connected if there exists at least one collaborative R&D project running in 2000, containing at least one participant organization from each of i and j. 5 Participant organizations are geo-localized with respect to the applicant s address, which does not necessarily show the place of R&D. Hence, if an organization has a headquarter as well as several R&D sites in different locations and reports the headquarter s address as the applicant s address for all FP projects, then all collaborations carried out in its different R&D sites are counted as collaborations involving the region where the headquarters is located PASCAL (INIST-CNRS) database for scientific publications: two regions i and j (i j) are said to be connected if there exists at least one co-publication including authors from both i and j in The addresses of authors institutions are used to geolocalize co-publications and the counts are smoothed over three years, meaning the number of co-publications between two regions in 2000 is the average of values recorded in 1999, 2000 and OECD Regpat Database for patents January 2014: two regions i and j (i j) are said to be connected if there exists at least one co-invention including inventors from both i and j in The inventor s address is used to geolocalize co-inventions and counts are smoothed over three years, meaning the number of co-inventions between two regions in 2000 is an average of values recorded in 1999, 2000 and These three collaboration networks co-exist and overlap to some extent. To illustrate, research projects funded by FP might lead to joint patents or joint publications. Nevertheless, they also have some distinguishing features meaning that they describe different aspects of the research collaboration phenomenon. For instance, co-invention networks involve knowledge actors and research activities that are oriented towards a useful outcome to be appropriated. On the other hand, co-publication networks involve actors and research activities more oriented towards extending the knowledge base and disseminating knowledge. As both motivations might underlie FP collaborations, FP networks can be viewed as more all encompassing compared to co-invention or co-publication networks in at least two respects: they represent different 4 We also explored the sensitivity of econometric results to the weight matrix by using the 2003 (mid-year), and 2006 (last-year). As suggested by LeSage and Pace (2014), who discuss how perturbations in the spatial weight matrix impact estimates and inferences; our results are insensitive to the choice on the year. 5 Our W matrix and the FP-ICT variable do come from the same database, but the FP-ICT variable counts the number of projects in which the region is involved whereas the W matrix measures who are the partners involved in these projects. Both might be correlated in some cases (more projects implying potentially more partners), but as explained in the result section, our results hold for non-fp weight matrix or without the FP explanatory variables. 6 To identify organizations that belong to the same region, the address of the applicant organization is used because the address of the department at which the research is conducted is not available for some years covered in the analysis (FP5 and FP6). This information has been reported since FP7, and it should be noted that comparison of these two addresses for FP7 yields two different spatial distributions of innovative activity.

12 12 C.S. Hazır et al. research actors as well as different research activities that arise as part of the overall knowledge creation process. 4.3 Results Table 1 displays the highest ten likelihood values (in descending order) and corresponding weights (λ 1,λ 2,λ 3 ) used to express W. The table reveals that regardless of which collaboration network is used to express the connectivity structure among regions, the best model fit is obtained when a large weight (at least 70%) is assigned to spatially proximate collaboration partners (W 2 ). In the case of FP and publication networks, no weight is given to distant collaboration partners (W 1 ); whereas, when R&D collaborations are defined by means of co-inventions, distant collaboration partners (W 3 ) also receive a small non-zero weight. Despite differences across FP, co-publication and co-invention networks regarding the scope of knowledge creation activities and knowledge actors, the finding that spatially proximate collaboration partners receives the principal weight in each case highlights the importance of intentional interactions taking place at short distances on transmission of knowledge in the ICT field. This, in turn, corroborates the criticism by (Breschi and Lissoni 2001) that what has been quantified so far as the effect of local knowledge externalities might indeed involve the effect of deliberate knowledge flows. The weight of (W 2 ) being much larger than the weight of (W 1 ) implies that the strategic interactions that take place in any of these collaboration networks have a greater impact on regional knowledge creation relative to all other types of local interactions, which may include non-strategic face-to-face interactions, labour mobility, or other types of deliberate interactions. Hence, our approach tries to open up the so called black-box of local knowledge externalities (Autant-Bernard et al. 2007). We manage to demarcate the role played by a particular network of strategic interactions (via W 2 ), but at the same time even our approach leaves open the question of whether W 1 reflects pure knowledge externalities. The mixed finding on the effect of distant collaboration partners (W 3 ) for different networks might have several explanations. First, in this analysis a region is considered as a single entity, disregarding the fact that actors involved in inventive activities may not be the same as actors involved in cross-regional collaborations. An implicit assumption is that once external knowledge is acquired from other regions, it diffuses within the region and contributes to the invention processes. In the case of co-invention networks, the actor who collaborates is by definition also involved in an inventive activity. However, for FP networks and co-publication networks this is not necessarily true, since the collaborating agent may be indirectly linked to the invention process. Hence, it may be the case that on the average there is a missing interaction among those Table 1. Log-likelihood for different compositions of W FP network Co-publications network Co-invention network rank λ 1 λ 2 λ 3 log-lik λ 1 λ 2 λ 3 log-lik λ 1 λ 2 λ 3 log-lik

13 Collaboration networks and regional knowledge creation 13 Table 2. Estimation results FP network Co-publications netw. Co-invention netw. Variable Coef. z-prob. Coef. z-prob. Coef. z-prob. BERD HRST FP-ICT projects FP-other projects W*BERD W*HRST W*FP-ICT projects W*FP-other projects W*Patent log-likelihood Table 3. Average marginal effects (W is built using FP collaboration network) Variable Direct effects Indirect effects Total effects BERD 0.126*** *** HRST ** 0.819*** FP-ICT projects *** 0.145** FP-other projects *** 0.468*** Notes: *** 99% confidence level, ** 95% confidence level. actors who are capable of establishing FP collaborations or co-authorship relations with distant regions and actors involved in inventions. Second, in the case of FP, design of the support program might also suggest a partial explanation for zero weight assigned to distant collaboration partners. FP aims at developing trans-national collaboration and hence participation rules favour partners located in different countries. 7 Hence, some distant collaborations may not be very functional in terms of knowledge generation or flows but rather serve to fulfil participation criteria or increase the probability of obtaining a grant. Third, for the case of a co-invention network, one may still suspect an endogeneity problem despite use of a time lag, since patent performance can affect who collaborates with whom. In the analysis, the dependent variable measures level of patents for ; whereas the co-invention network structure is built using data on joint patents in Current performance cannot determine past collaboration choices, but if current performance is correlated with past performance, an endogeneity problem may still exist. When we interpret the weights assigned to W 2 and W 3 as both capturing effects of collaboration partners, we conclude that our findings go hand in hand with former evidence on the impact of R&D collaboration networks on regional knowledge creation (Maggioni et al. 2007; Ponds et al. 2010; Sebestyen and Varga 2013). However, if we consider the (estimated) weights assigned to all three components, we may have evidence that impacts found by these studies reflect mainly an interaction effect, namely, R&D collaborations with spatially proximate regions. In Tables 2, 3, 4 and 5, however, we report coefficient estimates and direct, indirect, and total effects estimates. Direct effects refer to the impact of a unit change in one of the research inputs in a typical region (say i) on the dependent variable Patents in the typical region i. Inthe context of our spatial Durbin model (SDM) specification, this impact is slightly different from 7 Article 4 in Council Decision of 22 December 1998 concerning rules for participation of undertakings, research centres and universities and for the dissemination of research results for the implementation of the fifth framework programme of the European Community ( ).

14 14 C.S. Hazır et al. Table 4. Average marginal effects (W is built using co-publications network) Variable Direct effects Indirect effects Total effects BERD 0.128*** ** HRST ** 0.882*** FP-ICT projects *** 0.169*** FP-other projects *** 0.437*** Notes: *** 99% confidence level, ** 95% confidence level. Table 5. Average marginal effects (W is built using co-invention network) Variable Direct effects Indirect effects Total effects BERD 0.131*** ** HRST *** 1.421*** FP-ICT projects *** 0.369*** FP-other projects ** 0.341*** Notes: *** 99% confidence level ** 95% confidence level. the maximum likelihood estimate β in (2), because an endogenous interaction model such as the SDM produces a small feedback effect. 8 Indirect effects measure changes in patenting in other regions j i that arise from changes in region i inputs. Since these are formally defined as cross-partial derivatives, y j x i, we can also view indirect effects as reflecting y i x j. These cross-partial derivatives can be expressed as off-diagonal elements of an n n matrix, since changes in one region (i) inputs can (potentially) impact all other (n 1) regions output as these effects work their way through the n region network. The matrix of partial derivatives records these responses to the change in a single region (i) input as an n 1 1 vector, where n is the number of regions. As is typical of regression coefficient estimates, we average over changes in all regions, i = 1,, n, producing n columns of n 1 1 vectors of output responses, or off-diagonal elements of an n n matrix. We report scalar summary measures set forth in LeSage and Pace (2009) for the total, direct and indirect effects in Tables 3 5. Direct effect scalar summaries are based on an average of the n different own-partial derivatives: y i x i, i = 1,, n, whereas indirect effect scalar summaries are calculated as the cumulative sum of off-diagonal elements in each row of the n n matrix, which are then averaged to produce a scalar. The indirect effects in our specification capture knowledge flows arising from spatial proximity as well as collaborative networks. When focusing on total effects (the sum of direct plus indirect effects), our three interregional collaboration measures provide some recurring results. The four explanatory variables produce positive and significant impacts on regional patenting activity, and a prominent role is assigned to human resources in science and technologies. Considering the FP network specification for interregional interaction, Table 3 shows that a one per cent change in human resources in science and technologies investment increases patents in region i by per cent (an elasticity response because of the log-transformation). A similar elasticity is obtained with the co-publications network Table 4, whereas the highest impact is observed using co-invention networks Table 5. This stems fromthe fact thatw used in this specification accords non-zero weight to distant collaboration partners, hence the connectivity among 213 European regions under study is higher/denser in this case. The content of the regional knowledge base, approximated by the number of ICT projects and non-ict projects supported by the FP, is also a significant 8 Feedback arises because changes in region i inputs result in region i output changes, and spillover impacts on output of neighbouring regions, say j. These impacts on neighbours output in turn produce spillover impacts on output of regions neighbouring j, and region i is one such neighbouring region (see LeSage and Pace 2009 for details).