Embedding network analysis in spatial studies of innovation

Bergman, Edward M.

doi:10.1007/s00168-008-0250-y

Cited by 25 publications

(12 citation statements)

References 21 publications

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“…A body of work has been directed to analyzing the means of appropriation, in particular, the role of mobility, of face to face contacts and more recently to the study of networks (social, scientific, technological, etc.) as witnessed by the special issues of Papers in Regional Science (2007) and Annals of Regional Science (2009). Alongside this, the study of sources likely to generate knowledge flows has given birth to a corpus of work studying diverse sources of externalities, e.g., public/private research programs, intra/inter‐sectoral, and pecuniary versus pure knowledge externalities (Autant‐Bernard et al, 2010).…”

Section: Background Literature and Hypothesesmentioning

confidence: 99%

Quantifying Knowledge Spillovers Using Spatial Econometric Models

Autant‐Bernard¹,

LeSage²

2010

Journal of Regional Science

185

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This paper seeks to develop our understanding of the somewhat diffuse nature of technological externalities and space by associating a geographical dimension with the sectoral dimension. Using a panel data set containing French patents as well as private and public research expenditures by industry and region over the period from 1992 to 2000, this paper estimates a knowledge production function. The region-and industry-specific nature of the sample data allows us to empirically examine spatial spillovers associated with public and private research expenditures in own-and other-industry sectors for our sample of 94 French regions. We find that the largest direct and indirect effects are associated with private R&D activity that spills across industry boundaries. However, since Jacobs externalities decrease more drastically with distance than MAR externalities, our results also point to different optimal strategies for regional versus national officials.

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Section: Background Literature and Hypothesesmentioning

confidence: 99%

Quantifying Knowledge Spillovers Using Spatial Econometric Models

Autant‐Bernard¹,

LeSage²

2010

Journal of Regional Science

185

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“…The regional science literature traditionally has placed great emphasis on the spatial dimension of knowledge production, and in particular the spatial spread of knowledge diffusion. Inter-regional R&D networks are gaining particular interest in this context, given that network arrangements are considered as essential for the successful creation of new knowledge (Autant-Bernard et al 2007;Bergman 2009;Scherngell 2013). Taking a regional perspective, R&D networks may be defined as systems of nodes representing regions that are inter-linked by joint R&D endeavours (see e.g., Wanzenböck et al 2014).…”

Section: Introductionmentioning

confidence: 99%

R&D networks and regional knowledge production in Europe: Evidence from a space‐time model

Wanzenböck

Piribauer

2018

Papers in Regional Science

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ABSTRACT:In this paper we estimate space-time impacts of the embeddedness in R&D networks on regional knowledge production by means of a dynamic spatial panel data model with non-linear effects for a set of 229 European NUTS-2 regions in the period 1999-2009. Embeddedness refers to the positioning in networks where nodes represent regions that are linked by joint R&D endeavours in European Framework Programmes. We observe positive immediate impacts on regional knowledge production arising from increased embeddedness in EU funded R&D networks, in particular for regions with lower own knowledge endowments. However, long-term impacts of R&D network embeddedness are comparatively small.

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“…From a more analytical point of view, there is a need to provide a theoretical framework to represent the decision-making processes that underlie the formation of relationships and their economic implications. (Bergman and Maier, 2009) this volume brought together contributions assessing two main issues: the impact of spatial distance on the diffusion of knowledge relative to other kinds of proximity and especially relative to social or relational proximity on the one hand, and the impact of spatial distance as a driver of knowledge network structure on the other. The first two European Seminars on the Geography of Innovation that were held in Saint Étienne in January 2012 and Utrecht in January 2014 also pointed to an increasing interest in spatial innovation networks.…”

Section: Driving Forces Of Network Formation and Innovative Performanmentioning

confidence: 99%

Editors’ introduction to spatial knowledge networks: structure, driving forces and innovative performances

2014

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Context: knowledge networks and the geography of innovationKnowledge networks have recently received increasing attention in the field of regional science. This is due to two changes in the interaction between geography and knowledge. First, there has been a change in the way knowledge is produced and diffused. Networking processes are increasingly involved in the production of knowledge. Moreover, several studies of scientific publications, patents and R&D collaborations show that these networks are becoming increasingly complex and are characterised by an increasing number of agents, increased density and complexity within networks, and wider geographical distribution

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Embedding network analysis in spatial studies of innovation

Cited by 25 publications

References 21 publications

Quantifying Knowledge Spillovers Using Spatial Econometric Models

Quantifying Knowledge Spillovers Using Spatial Econometric Models

R&D networks and regional knowledge production in Europe: Evidence from a space‐time model

Editors’ introduction to spatial knowledge networks: structure, driving forces and innovative performances

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