Spatial Econometrics of Innovation: Recent Contributions and Research Perspectives

Autant‐Bernard, Corinne

doi:10.1080/17421772.2012.722665

Cited by 45 publications

(13 citation statements)

References 62 publications

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“…Second, a space-time perspective is implemented in order to show how effects of R&D network embeddedness unfold within and across European regions and over time. As dynamic spatial panel data models are rather new to the field (see e.g., Autant-Bernard 2012;Parent 2012), the current study will shed new light on the role of spatio-temporal spillover effects between European regions in a regional knowledge generation framework. Third, we augment basic space-time impact measures (see Debarsy et al 2012) by specifying an interaction effect in the set of knowledge production determinants.…”

Section: Introductionmentioning

confidence: 97%

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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Section: Introductionmentioning

confidence: 97%

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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“…Endogenous growth theory considers that knowledge is partially a public good (Autant‐Bernard ). In the context of regional economic studies, knowledge is also a regional public good and can spill over to other regions.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the seminal work by Griliches () and Jaffe (), the knowledge production function has been introduced for quantifying regional knowledge spillovers (Greunz ). Spatial econometric tools have gained popularity in the analysis of regional innovation in the past two decades, and the advances of new spatial analytical methods have promoted the development of regional knowledge spillover research (Autant‐Bernard ).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Space–Time Dynamics of Innovation in China: ESDA and Spatial Panel Approaches

Wang¹,

Cheng²,

Ye³

et al. 2015

Growth and Change

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Through applying exploratory spatial data analysis (ESDA) and confirmatory spatial data analysis (CSDA), this paper examines the space–time dynamics of innovation at the provincial level in China from 2000 to 2011. We first investigate the trend of regional inequality in innovation in China and then use ESDA methods to visualize the space–time relationship between patent applications and innovation determinants. Impacts of innovation determinants on the space–time dynamics of innovation are also examined by analyzing the knowledge production function with the spatial panel Durbin model. Knowledge spillovers from both the R&D and the innovation outcome perspectives are highlighted in CSDA. We have found an increasingly intensified coastal–interior divide with growing spatial clusters and more innovative activities in the coastal region. We have also found a close space–time relationship between innovation and its determinants. Lastly, innovation spillovers cross province boundaries, and knowledge from a province's neighboring provinces has substantial effects on innovation activities of the province. This paper highlights positive knowledge spillover effects between regions, the close relationship between innovation and its determinants, and the importance of spatial externalities in innovation activities.

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“…In particular, as it is difficult to identify whether the network structure implies the outcome or the reverse, we have to consider both aspects together. To this respect, the contributions of spatial econometrics to the field of network analysis may extend beyond gravity models as suggested by (Autant-Bernard 2012). Spatial tools can indeed provide valid instruments allowing endogenous effects to be separated from exogenous ones (see for instance Bramoullé et al 2009).…”

Section: Discussionmentioning

confidence: 99%

Network Formation and Geography: Modelling Approaches, Underlying Conceptions, Recent and Promising Extensions

Autant‐Bernard

Hazir

2013

The Geography of Networks and R&D Collaborations

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Due to the strong polarisation of economic activities in space and rise in collaborative behaviour, increasing attention has recently been devoted to the relationship between geography and network formation. The studies conducted on this topic reveal a high variation in terms of methodologies. Putting special emphasis on R&D networks, the aim of this chapter is to review the different methods and assess their ability to address the issues raised by the relationship between network and space. We first discuss the different facets of the relationship between geography and networks. Then, we detail the methodological approaches and their capability to test each effect of geography on network formation. We argue that the effect of distance on dyads have received the major attention so far, but the development of block modelling and top-down approaches opens new research perspectives on how distance or location might affect formation of more complex structures. Moreover, recent improvement in temporal models also offers opportunities to better separate spatial effects from that of influence over time.

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Spatial Econometrics of Innovation: Recent Contributions and Research Perspectives

Cited by 45 publications

References 62 publications

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

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

Analyzing the Space–Time Dynamics of Innovation in China: ESDA and Spatial Panel Approaches

Network Formation and Geography: Modelling Approaches, Underlying Conceptions, Recent and Promising Extensions

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