Papers in Evolutionary Economic Geography

July 1, 2017

# 17.17 Smart Specialization policy in the EU: Relatedness, Knowledge Complexity and Regional Diversification

Pierre-Alexandre Balland, Ron Boschma, Joan Crespo and David L. Rigby

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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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February 18, 2015

# 15.04 Proximity, knowledge base and the innovation process: The case of Unilever’s Becel diet margarine

Filed under: 2015 — Tags: , , , , , — mattehartog @ 6:18 pm

Mila Davids & Koen Frenken

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The proximity concept refers to types of inter-organizational relationships that are expected to facilitate interactive learning and collaborative innovation. Different forms of proximity include geographical, cognitive, social, institutional and organizational proximity. Following an extensive case study of a new diet margarine developed by Unilever, we extent the proximity framework by theorizing how the relative importance of each proximity dimension depends on the type of knowledge being produced, where we distinguish between analytical, synthetic and symbolic knowledge. We argue that our theoretical framework in principle applies to product innovations in all science-based industries.

January 20, 2015

# 15.02 The geography and evolution of complex knowledge

Filed under: 2015 — Tags: , , , , , — mattehartog @ 1:34 pm

Pierre-Alexandre Balland & David L. Rigby

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There is consensus among scholars and policy makers that knowledge is one of the key drivers of long-run economic growth. It is also clear from the literature that not all knowledge has the same value. However, too often in economic geography and cognate fields we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. In this paper we measure the complexity of knowledge across patent classes and we map the distribution and the evolution of knowledge complexity across U.S. cities from 1975 to 2004. We build on the 2-mode structural network analysis proposed by Hidalgo and Hausmann (2009) to develop a knowledge complexity index (KCI) for Metropolitan Statistical Areas (MSAs). The KCI is based on more than 2 million patent records from the USPTO, and combines information on the technological structure of 366 MSAs with the 2-mode network that connects cities to the 438 primary (USPTO) technology classes in which they have Relative Technological Advantage (RTA). The complexity of the knowledge structure of cities is based on the range and ubiquity of the technologies they develop. The KCI indicates whether the knowledge generated in a given city can be produced in many other places, or if it is so sophisticated that it can be produced only in a few select locations. We find that knowledge complexity is unevenly distributed across the U.S. and that cities with the most complex technological structures are not necessarily those that produce most patents.

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