TCI Network
25 April 2017

This monthly selection of articles has been carried out by Philippe Gugler and Damiano Lepori, the Center for Competitiveness, University of Fribourg.The entire selection, carried out since 2013, can be consulted on the academic articles page of our web.


Related variety and the dynamics of European photonic clusters

By: F. Gaschet, M. Becue, V. Bouaroudj, M. Flamand, A. Meunie, G. Pouyanne, D. Talbot. European Planning Studies, DOI: 10.1080/09654313.2017.1306027, 2017.

Abstract : “This article aims at assessing the role of related variety, that is, the relatedness of knowledge bases used by different sectors within a region, as a major driver of clusters’ development. Some recent theoretical papers underline the role of clusters as ‘knowledge platforms’ organizing the recombination of technologies in overlapping industries, following the seminal definition of clusters by Porter as ‘geographic concentrations of linked industries’. In order to investigate the role of related variety in cluster dynamics, we analyse the patterns of development of clusters specializing in photonics in Europe. Photonics constitutes a new and rapidly evolving set of technologies with a high expected degree of technological recombination. However, due to inadequate traditional sectoral classifications, we propose an original method to delineate the perimeter of photonics in patent databases. A two-step algorithm is then used to identify systematically photonic clusters in Western Europe at the local level. In the last part of the paper, a typology of technological trajectories of clusters over the last decades is developed and then correlated with a set of quantitative measures of technological relatedness. The results highly confirm the role of related variety as a major driver of success, particularly for the biggest European clusters.” [ABSTRACT FROM AUTHORS]


Grenoble–GIANT Territorial Innovation Models

By: L. Scaringella, J.-J. Chanaron. HAL Paper, Id: hal-01472878, 2017.

Abstract : “Over the past decades, the EU heavily invested in Research Infrastructures (RI). What are the expected returns of such investments? In the present article we address the question of returns on public funds/public infrastructures. We consider the role of RI and universities from an economic, social, and entrepreneurial perspective from various Territorial Innovation Models (TIMs): (1) Italian industrial districts, (2) innovative milieus, (3) regional innovation systems, (4) new industrial spaces, and (5) regional clusters. We conducted our empirical study on Grenoble Isè€re Alpes Nanotechnologies (GIANT), which is composed of large scientific instruments, universities, and engineering and management schools. Our microeconomic methodology measured the socioeconomic and entrepreneurial effects of GIANT with respect to budget, employment, and spin-off generation. We contribute to the existing body of knowledge on TIMs by (1) comparing the long-term investments to the generation of wealth, the creation of employment, and the development of start-ups; (2) adding new insights to the debate opposing positive and negative impacts empirical studies; and (3) offering recommendations for the use of public resources. In our discussion, we compare the GIANT model as a very localized RI-university club to the Grenoble model as localized cluster.” [ABSTRACT FROM AUTHORS]


The spatial evolution of the Italian motorcycle industry (1893-1993): Klepper’s heritage theory revisited

By: A. Morrison, R. Boschma. Utrecht University, Papers in Evolutionary Economic Geography No. 17.07, 2017.

Abstract : “This paper investigates the spatial evolution of the Italian motor cycle industry during the period 1893-1993. We find support for both the heritage theory of Klepper and the agglomeration thesis of Marshall. Indeed, being a spinoff company or an experienced firm enhanced the survival rates, but we also found a positive effect of being located in the Motor Valley cluster in Emilia Romagna. Interestingly, this beneficial effect of a cluster could not be found outside the Emilia Romagna region. This might indicate the importance of a favourable local institutional environment, as propagated by the Emilian district literature.” [ABSTRACT FROM AUTHORS]


Estimating dynamic localization economies: the inadvertent success of the specialization index and the location quotient

By: A. Fracasso, G. Vittucci Marzetti. Regional Studies, DOI: 10.1080/00343404.2017.1281388, 2017.

Abstract : “Estimating dynamic localization economies: the inadvertent success of the specialization index and the location quotient. Regional Studies. After addressing definitional issues on the concepts of concentration and specialization, the paper reviews the justifications for and the interpretation of some indicators of localization economies used in the empirical literature on agglomeration economies: specialization indexes and location quotients. A simulation exercise shows under what conditions certain specifications lead to biased estimations of dynamic localization (Marshall–Arrow–Romer – MAR) externalities. The results suggest that applied researchers can choose between the size of the local industry, the specialization index and the location quotient to proxy for these externalities as far as they also encompass a correct proxy for the size of the local economy.” [ABSTRACT FROM AUTHORS]


Growth Policy, Agglomeration and (the Lack of) Competition

By : W. J. Brooks, J. Kabowski, Y. Amber Li. HCEO Working Paper No. 2017 – 020, 2017.

Abstract : “Industrial clusters are promoted by policy and generally viewed as good for growth and development, but both clusters and policies may also enable non- competitive behavior. This paper studies the presence of non-competitive pricing in geographic industrial clusters. We develop, validate, and apply a novel test for collusive behavior. We derive the test from the solution to a partial cartel of perfectly colluding firms in an industry. Outside of a cartel, a firm’s markup depends on its market share, but in the cartel, markups across firms converge and depend instead on the total market share of the cartel. Empirically, we validate the test using plants with common owners, and then test for collusion using data from Chinese manufacturing firms (1999-2009). We find strong evidence for non-competitive pricing within a subset of industrial clusters, and we find the level of non-competitive pricing is about four times higher in Chinese special economic zones than outside those zones.” [ABSTRACT FROM AUTHORS]


Innovation and Regional Specialisation in Latin America

By : B. Barroeta, J. Gomez Pieto, J. Paton, M. Palazuelos, M. Cabrera Giraldez. JRC Technical Report No. 28511, 2017.

Abstract : “The Smart Specialisation concept, currently implemented in the European Union, is being widely considered by several countries and regions of Latin-America. The interest towards this approach, highly based on the enhancement of regional innovation capacities, is motivating territorial dialogues, participatory processes and collective vision related to the innovation perspectives of Latin-American regions. This article highlights how policy makers of Mexico, Brazil, Colombia, Peru, Chile and Argentina are considering the smart specialisation concept as an inspirational driver of regional innovation and specialisation. Understanding the socio-economic and contextual differences between EU and Latin-America, this working paper does not seek to elaborate value judgements on the way in which smart specialisation is being (or should be) adapted beyond the EU. Instead, the analysis seeks to emphasise the common tendencies of the concept implementation as a way to frame cooperation between regions of the EU and Latin-America.” [ABSTRACT FROM AUTHORS]


Collective Learning in China’s Regional Economic Development

By : J. Gao, B. Jun, A. Pentland, T. Zhou, C. A. Hidalgo. CompleX Lab, University of Electronic Science and Technology of China, MIT Media Lab, Massachusetts Institute of Technology, Big Data Research Center, University of Electronic Science and Technology of China, 2017.

Abstract : “Industrial development is the process by which economies learn how to produce new products and services. But how do economies learn? And who do they learn from? The literature on economic geography and economic development has emphasized two learning channels: inter-industry learning, which involves learning from related industries; and inter-regional learning, which involves learning from neighboring regions. Here we use 25 years of data describing the evolution of China's economy between 1990 and 2015--a period when China multiplied its GDP per capita by a factor of ten--to explore how Chinese provinces diversified their economies. First, we show that the probability that a province will develop a new industry increases with the number of related industries that are already present in that province, a fact that is suggestive of inter-industry learning. Also, we show that the probability that a province will develop an industry increases with the number of neighboring provinces that are developed in that industry, a fact suggestive of inter-regional learning. Moreover, we find that the combination of these two channels exhibit diminishing returns, meaning that the contribution of either of these learning channels is redundant when the other one is present. Finally, we address endogeneity concerns by using the introduction of high-speed rail as an instrument to isolate the effects of inter-regional learning. Our differences-in-differences (DID) analysis reveals that the introduction of high speed-rail increased the industrial similarity of pairs of provinces connected by high-speed rail. Also, industries in provinces that were connected by rail increased their productivity when they were connected by rail to other provinces where that industry was already present. These findings suggest that inter-regional and inter-industry learning played a role in China's great economic expansion.” [ABSTRACT FROM AUTHORS]


Agglomerations and firm performance: who benefits and how much?

By : J.-L. Hervas-Olivier, F. Sempere-Ripoll, R. Rojas Alvarado, S. Estelles-Miguel. Regional Studies, DOI: 10.1080/00343404.2017.1297895, 2017.

Abstract : “Agglomerations and firm performance: who benefits and how much? Regional Studies. Agglomeration can generate gains. If it does, how does it work and how are those gains distributed across agglomerated firms? The paper examines the effect of localization externalities on innovation. Localization externalities are measured as industry specialization or a firm’s co- location in a relatively high own-industry employment region. By analyzing a large dataset of 6697 firms integrated with another regional agglomeration-related dataset, results show that (1) co-location in an agglomeration has a positive influence on a firm’s innovative performance; and (2) firms benefit heterogeneously from agglomerations, with benefits being distributed asymmetrically. Agglomeration gains exist but not all firms benefit equally.” [ABSTRACT FROM AUTHORS]


Entrepreneurship policies and the development of regional innovation systems: theory, policy and practice

By : H. Lawton Smith. CIMR Research Working Paper No. 36, 2017.

Abstract : “The regional innovation systems (RIS) approach tends to be short in the coverage of the importance of agency in the dynamics of economic change. This paper addresses this by putting the entrepreneur, which Schumpeter (1911/1934) placed at the heart of the analysis of economic change, as the driving force of regional innovation systems and associated policies. This is consistent with work by Feldman and Francis (2006) who identified the entrepreneur as a regional agent of change. The paper provides an appraisal and synthesis of the regional innovation systems approach in relation to entrepreneurship policies. It addresses a number of areas where theoretical, empirical and policy-based issues are currently under-developed in relation to entrepreneurship and entrepreneurs and entrepreneurship policies in an RIS. The second is the rationale for entrepreneurship policies in an RIS. The third relates to what do entrepreneurship policies look like in RIS and how they might be evaluated as contributing towards an RIS.” [ABSTRACT FROM AUTHOR]


Heterogeneous agglomeration

By : G. Faggio, O. Silva, W. C. Strange. Review of Economics and Statistics, ISSN 0034-6535, 2016.

Abstract : “Many prior treatments of agglomeration explicitly or implicitly assume that all industries agglomerate for the same reasons, with the traditional Marshallian (1890) factors of input sharing, labor pooling, and knowledge spillovers affecting all industries similarly. An important instance of this approach is the extrapolation from one key sector to the larger economy, such as the drawing of very general lessons about agglomeration from the specific case of the Silicon Valley. Another is the pooling of data to examine common tendencies in agglomeration even across very different industries. This paper uses UK establishment-level data on coagglomeration to document substantial heterogeneity across industries in the microfoundations of agglomeration economies. The analysis shows that the Marshallian factors interact with the organizational and adaptive aspects of agglomeration discussed by Chinitz (1961), Vernon (1960), and Jacobs (1969). Our findings highlight the importance of treating Marshall’s microfoundations of agglomeration as complements to the analysis of Jacobs and others, rather than as alternatives.” [ABSTRACT FROM AUTHORS]


A novel typology of media clusters

By : M. Komorowsky. European Planning Studies, DOI: 10.1080/09654313.2017.1303823, 2017.

Abstract : “Is the clustering of audio-visual companies in London’s Soho really the same as the clustering of Berlin’s new media industry? The media cluster approach has gained a lot of attention not only in academia, but also in political discourse. But, as appealing as the media cluster concept is, one of the most fundamental issues is the comparability of the phenomenon. This article tackles this issue and an analysis of 43 case studies has been conducted. The case studies have been grouped to find a new typology for media clusters. The research revealed six different types: The Creative Region, the Giant Anchor, the Specialized Area, the Attracting Enabler, the Real Estate and the Pooling Initiative. The typologies showed that they distinguish especially in their geographical scale and specialization in media activities, while at the same time cluster types can be found in the same area. They are driven by four rationales: agglomeration, urbanization, localization economies and artificial formation.” [ABSTRACT FROM AUTHOR]