Innovation in Russia: The Territorial Dimension

Crescenzi, Riccardo; Jaax, Alexander

doi:10.1080/00130095.2016.1208532

Cited by 53 publications

(64 citation statements)

References 70 publications

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“…Since the publication of the seminal work of Griliches (), knowledge production has been an important topic in economic geography and regional science (Audretsch & Keilbach, ; Charlot, Crescenzi, & Musolesi, ; Crescenzi & Jaax, ; Jaffe, ; Lee, ; Miguelez & Moreno, ; Ó hUallacháin, & Leslie, ; Ponds, Oort, & Frenken, ). The knowledge production model assumes that the functional relationship between knowledge output and knowledge input is linear, the former mainly depends on a set of inputs, such as research and development expenditure (RDE) or human capital (Charlot et al, ; Lee, ).…”

Section: Introductionmentioning

confidence: 99%

Does network position foster knowledge production? Evidence from international scientific collaboration network

Gui

Liu

2018

Growth and Change

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There is consensus among scholars that social networks are important mechanisms of knowledge spillovers. By occupying a central and advantageous network position in the collaboration network, it is easy for actors to access intangible external resources. However, empirical studies of the impact of network structure on knowledge production remain scarce. Based on copublication data from the Web of Science database (WoS) from 2000 to 2015, we construct eleven international scientific collaboration networks and empirically estimate the impacts of multiple network properties, comprehensively measured by degree centrality, structure holes, and small‐world quotient, on national knowledge output. Empirical results based on fixed effect negative binomial models suggest positive effects of the three facets of network properties, that is, higher degree centrality, structural holes, and small‐world quotient are beneficial for facilitating and improving national knowledge production, which in turn encourages international academic collaboration.

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Section: Introductionmentioning

confidence: 99%

Does network position foster knowledge production? Evidence from international scientific collaboration network

Gui

Liu

2018

Growth and Change

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“…The relatively stable position of the largest high R&D city-regions suggest there is considerable path-dependence in the global research and innovation system. However the rise of certain cities in Asia suggests path-creating factors are present there, either due to public policy interventions, foreign investment in R&D, or more likely, a combination of the two (Crescenzi and Jaax 2017;Dicken 2007).…”

Section: Resultsmentioning

confidence: 99%

“…While there is ambiguity over the influence of spatial concentration on R&D activity, there appears to be strong path dependence, whereby spatial concentrations of R&D activity persist over long periods of time (Boschma and Frenken 2006;Crescenzi and Jaax 2017;Crescenzi and Rodríguez-Pose 2011). Path-creating factors, which could lead to significant shifts in the spatial concentration of R&D activity are public policy intervention and foreign direct investment in R&D (Crescenzi and Jaax 2017;Dicken 2007). Investment in higher education and basic research, as well as targeted public-private sector R&D collaboration, play an important role in establishing and attracting high technology industries to a particular location (Lee and Lim 2001;Lee et al 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Mapping high R&D city-regions worldwide: a patent heat map approach

Stek

2019

Qual Quant

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A methodology for identifying high R&D city-regions worldwide using patent data is presented. A heat map (kernel density estimation) approach is used which allows city-regions to be identified in areas with a high patenting intensity, a proxy for high R&D expenditure. The methodology is independent of any pre-existing administrative boundaries and it can therefore be applied to identify sub-national concentrations of R&D expenditure worldwide. This is an important advantage compared to other statistical data which is often only available at the national level. The results provide insight into the changing worldwide spatial distribution of R&D expenditure between 1997 and 2011, including the rapid rise of Asian R&D city-regions as well as less dramatic shifts among European and North American city-regions. The results also highlight some challenges of identifying high R&D cityregions and estimating R&D expenditure using patent data, and the existence of very large high R&D city-regions which encompass multiple cities. Some suggestions for improvement and further research are also proposed.

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“…Path dependency is another factor explaining the uneven geography of innovation (Martin and Sunley, ; Simmie et al ., ; Crescenzi and Jaax, ). This is particularly evident in the case of India, which has tended to suffer less from historical upheavals––at least in terms of the location of innovation and industrial activity––than China.…”

Section: The Drivers Of the Geography Of Innovation In China And Indiamentioning

confidence: 99%

The Geography of Innovation in China and India

Crescenzi

Rodríguez‐Pose

2017

Int J Urban Regional Res

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The BRICS countries in general, and China and India in particular, are now widely regarded as the areas of the world likely to challenge the economic leadership of the United States (US) and the European Union (EU). A large part of this challenge will come from rapid technological catch-up by China and India. Yet, despite a recent rise in interest, there is limited knowledge about how and where innovation takes place in these two leading emerging countries and to what extent the Chinese and Indian territorial systems of innovation differ from those in the EU or the US. In this article we explore the geography of innovation in China and India, concentrating on understanding key territorial-level innovation trends by country, region and technology field, using the US and the EU as benchmarks. We find significant contrasts between the geography of innovation in China and India and that of the US and the EU. First, the degree of concentration of innovative activities in both countries is extremely high. Levels of agglomeration of innovation in the coastal provinces of China, as well as in Delhi and the South of India, significantly exceed the levels of agglomeration found in the USA and the EU. Secondly, China has witnessed a more rapid increase in the degree of concentration of innovation than India. We posit that the differences in the geography of innovation between, on the one hand, China and India and, on the other hand, between these countries and the developed world are rooted in different institutional settings, different systems of innovation and different national innovation strategies.

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Innovation in Russia: The Territorial Dimension

Abstract: The debate on Russia

Cited by 53 publications

References 70 publications

Does network position foster knowledge production? Evidence from international scientific collaboration network

Does network position foster knowledge production? Evidence from international scientific collaboration network

Mapping high R&D city-regions worldwide: a patent heat map approach

The Geography of Innovation in China and India

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