Relatedness and the geography of innovation

Balland, Pascale

doi:10.4337/9781784710774.00016

Cited by 22 publications

(22 citation statements)

References 36 publications

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“…In all entry 7 models in Table 1, 8 we find that relatedness density has a positive and significant effect on the probability that a region specializes (RTA > 1) in a new technological field, which is consistent with other findings (Balland, 2016;Boschma et al, 2015;Rigby, 2015). The effect of relatedness is also strong: an increase of 10% in relatedness density is associated with a 23-26% relative increase in the probability of entry.…”

Section: Entry Modelsfull Samplesupporting

confidence: 88%

“…For Frenken and Boschma (2007), diversification is imagined as a branching process that gives rise to new activities within regions. Related diversification of cities and regions is depicted as a higher-order reflection of micro-level dynamics in which individuals and organizations extend the scope of their activities around the technological competencies and the behavioural routines that they accumulate over time (Balland, 2016). Thus, the emergence of new technologies and new sectors within Smart specialization policy in the EU: relatedness, knowledge complexity and regional diversification 1253 regions is not random, rather it reflects the existing collective capacity of agents that produce regions with distinctive technological and industrial profiles.…”

Section: Technological Relatedness and Regional Diversificationmentioning

confidence: 99%

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Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification

et al. 2018

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The operationalization of smart specialization policy has been rather limited because a coherent set of analytical tools to guide the policy directives remains elusive. We propose a policy framework around the concepts of relatedness and knowledge complexity. We show that diversifying into more complex technologies is attractive but difficult for European Union regions to accomplish. Regions can overcome this diversification dilemma by developing new complex technologies that build on local related capabilities. We use these findings to construct a policy framework for smart specialization that highlights the potential risks and rewards for regions of adopting competing diversification strategies.

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Section: Entry Modelsfull Samplesupporting

confidence: 88%

Section: Technological Relatedness and Regional Diversificationmentioning

confidence: 99%

Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification

et al. 2018

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“…To reflect this, it is possible to weight knowledge from every individual pair of varieties by a proximity measure from which firms choose an optimal variety and location , but this additional complexity in a theoretical model of growth is left for future research. For example, Hidalgo et al (2007); Boschma et al (2015) and Balland (2016) estimate measures of the proximity of every pair of industries based on a cooccurrance matrix. Maintaining simplicity, these estimates could also be used to calibrate the related and unrelated industry spillover parameters in this model.…”

Section: Technologymentioning

confidence: 99%

A multi-sector model of relatedness, growth and industry clustering

Bond‐Smith

McCann

2019

Journal of Economic Geography

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This article builds an understanding of regional innovation specialisation by developing a multi-sector model with endogenous growth through quality improving innovations and spillovers from related technologies. The model provides an approach to incorporate the relatedness literature within the mainstream theoretical frameworks of endogenous growth and economic geography. Each firm’s technology sector and the location of other firms play a role in each firm’s ability to improve its own technology. As a result, firms prefer to co-locate in technologically compatible clusters. Without relying on scale assumptions, the model for the first time coherently links related variety knowledge spillovers to mainstream urban economic frameworks and demonstrates that clustering is possible in both core and peripheral areas.

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“…For Frenken and Boschma (2007), diversification is imagined as a branching process that gives rise to new activities within regions. Related diversification of cities and regions is depicted as a higher-order reflection of micro-level dynamics in which individuals and organizations extend the scope of their activities around the technological competencies and the behavioral routines that they accumulate over time (Balland, 2016). Thus, the emergence of new technologies and new sectors within regions is not random, rather it reflects the existing collective capacity of agents that produce regions with distinctive technological and industrial profiles.…”

Section: Technological Relatedness and Regional Diversificationmentioning

confidence: 99%

Smart Specialization Policy in the EU: Relatedness, Knowledge Complexity and Regional Diversification

et al. 2017

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Smart specialization has become a hallmark of the EU's Cohesion Policy. Envisaged as a bottom-up initiative identifying local knowledge cores and associated competitive advantages, the operationalization of smart specialization has been rather limited, as a coherent set of analytical tools to guide the policy directives remains elusive. To tackle the weak underpinning of smart specialization policy, we propose a policy framework around the concepts of relatedness and knowledge complexity. We use EPO patent data to provide evidence on how EU regions develop new technologies in the period 1990-2009. We find that diversifying into more complex technologies is highly attractive but difficult for EU regions to accomplish. Regions can overcome this diversification dilemma by developing new complex technologies that build on local related capabilities. We use these findings to construct a policy framework for smart specialization that highlights the potential risks and rewards for regions of adopting competing diversification strategies. We show how potential costs of alternative strategies in regions may be assessed by making use of the relatedness concept, and how potential benefits of various smart specialization strategies can be derived from estimates of the complexity of technologies. A series of case-studies of different types of regions illustrate the utility of this policy framework.

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Relatedness and the geography of innovation

Cited by 22 publications

References 36 publications

Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification

Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification

A multi-sector model of relatedness, growth and industry clustering

Smart Specialization Policy in the EU: Relatedness, Knowledge Complexity and Regional Diversification

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