Similarity of R&amp;D activities, physical proximity, and R&amp;D spillovers

Deltas, George; Karkalakos, Sotiris

doi:10.1016/j.regsciurbeco.2012.06.002

Cited by 24 publications

(18 citation statements)

References 9 publications

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“…For example, George Deltas and Sotiris Karkalakos investigate regional patent statistics in the European Union and find that an increase in the distance between the originating and recipient region by 500km reduces the positive effects of spillovers by 55-70% [2]. Similar findings were made by other researchers [3,4].…”

Section: Literature Reviewsupporting

confidence: 68%

Cluster analysis of regional innovation activity in Russia in 2010-2015

Pushkarev

2018

R-E

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Section: Literature Reviewsupporting

confidence: 68%

Cluster analysis of regional innovation activity in Russia in 2010-2015

Pushkarev

2018

R-E

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“…24 For a contrasting view, see Deltas and Karkalakos (2013). 25 In a similar context, explain this result in a very convincing way: 'This result may be due to the intrinsic characteristics of the SDM specification, which entails a very complex externalities structure, and puts too strong a requirement on the data, [especially] at the territorial level considered in this study (NUTS 2 regions)'.…”

Section: Resultsmentioning

confidence: 87%

Does intentional mean hierarchical? Knowledge flows and innovative performance of European regions

2014

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The production of scientific and technical knowledge is mostly concentrated in specific locations (high-tech clusters, innovative industry agglomerations, centres of excellence, and technologically advanced regions). Knowledge flows very easily within regions; however, scientific and technical knowledge also flow between regions. The aim of this paper was to analyse how knowledge flows between regions, and the effect of these flows on the innovative performance, measured by patent applications. We estimate a regional knowledge production function, and, using appropriate spatial econometric estimation techniques, we test the effect of both geographical and relational autocorrelation (measured by participation in EU funded research networks as part of Fifth Framework Programme). We model unobservable structure and link value of knowledge flows in these joint research networks. We find that knowledge Previous versions of this paper were presented at the 7th European Meeting on Applied Evolutionary Economics, Pisa, February 2011; Dimetic Conference on 'Regional Innovation and Growth: Theory, empirics and policy analysis', Pècs, March 2011; Eurolio Conference on the Geography of Innovation, Saint Etienne, January 2012. We thank participants for useful comments and suggestions. In depth, discussions with E. Bergman, P. Elhrost, S. Beretta, B. Dettori, K. Frenken, G. Fagiolo, E. Marrocu, S. Usai and the recommendation from three referees for a thorough revision of the paper have substantially improved its structure and the arguments. The usual caveats apply. flows within inter-regional research networks, along non-symmetrical and hierarchical structures in which the knowledge produced by network participants tends to be exploited by the network coordinator. JEL Classification

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“…Positive and significant estimates of coefficients related to UNIRD and GOVRD indicate the relevance of universities and government institutions, respectively, in shaping the geography of patenting activity in Europe. Evidence of interregional research spillovers is associated with an estimate of the WBESRD coefficient that is statistically greater than zero, as is standard in this literature (DELTAS and KARKALAKOS, 2013). Here it is argued that the evidence related to the last coefficient is biased if spatial heterogeneity is not taken into account.…”

Section: Econometric Strategymentioning

confidence: 80%

“…The analysis in this paper continues this line of research and, accordingly, distributions for count data are used to model the number of patent applications. In the majority of cases, interregional research spillovers are accounted for by including spatially lagged R&D; hence, the R&D variable is pre-multiplied by a row-standardized spatial weight matrix (DELTAS and KARKALAKOS, 2013).…”

Section: Econometric Strategymentioning

confidence: 99%