Cultural and Creative Industries and Regional Development: Creativity Where and Why 3031296230, 9783031296239

Citation preview

Contributions to Regional Science

Roberto Dellisanti

Cultural and Creative Industries and Regional Development Creativity Where and Why

Contributions to Regional Science

This book series offers an outlet for cutting-edge research on all areas of regional science. Contributions to Regional Science (CIR) welcomes theoretically sound and empirically robust monographs, edited volumes and handbooks from various disciplines and approaches on topics such as urban and regional economics, spatial statistics, spatial econometrics, geographical information systems, migration analysis, land use and urban development, urban and regional policy analysis, inter-industry analysis, environmental and ecological analysis, and related fields. All books published in this series are peer-reviewed.

Roberto Dellisanti

Cultural and Creative Industries and Regional Development Creativity Where and Why

Roberto Dellisanti Department of Architecture, Built Environment and Construction Engineering Politecnico di Milano Milan, Italy

Contributions to Regional Science ISBN 978-3-031-29623-9 ISBN 978-3-031-29624-6 (eBook) https://doi.org/10.1007/978-3-031-29624-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my little Argo on the way, with the hope you might be proud of your dad

Acknowledgements

I am grateful to the research group in Urban and Regional Economics at the ABC Department, Politecnico di Milano, for their support and suggestions. With their experience, curiosity, and hard work, they contributed to shape a stimulating and instructive working environment. Special gratitude goes to Roberta Capello for believing in me and in this work. Her passion for the discipline and her daily dedication to the research are an example for all of us. This book has been largely enriched thanks to her comments, suggestions, and guidance. I also thank Andrea Caragliu for his enthusiasm and competence that helped me to reinforce this work. Thankfulness goes also to Matteo Laffi, who shared with me the challenging doctoral experience. With mutual support, we faced all the troubles encountered during the journey. I deem it extremely important to thank professors Alessandro Crociata, Charlotta Mellander, and Hans Westlund for their rich and useful comments to previous versions of this book. They helped me to enrich and clarify the work. Finally, the deepest gratitude goes to Caterina and to my family; they always represent my safe harbor where I can take refuge in difficulties.

vii

Contents

1 The Rise of CCIs: Setting the Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Cultural and Creative Industries at the Core of European Regional Competitiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Defining CCIs: Creativity as a Territorial Affair . . . . . . . . . . . . . . . . . 1.3 CCIs’ Location: Static Versus Dynamic Agglomeration Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 CCIs and Regional Growth: The Essential Role of Territories . . . . . 1.5 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part I

1 1 4 5 6 7 11

In Search of a Definition of Cultural and Creative Industries

2 In Pursuit of Creativity in CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Micro-economic Foundations of Creativity . . . . . . . . . . . . . . . . . . . . . 2.1.1 Individual Creativity—Or What Creativity Is and Which Are Its Forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Industrial Creativity—Or How Creativity Turns into Innovation and Economic Value . . . . . . . . . . . . . . . . . . . . 2.1.3 Territorial Creativity—Or How Territory Is a Source of Creativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Diachronic Perspective on the Main Classifications of CCIs . . . . . . 2.2.1 First Phase: The Introduction of the Notion . . . . . . . . . . . . . . 2.2.2 Second Phase: Awareness Period . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Third Phase: The Definitory Period . . . . . . . . . . . . . . . . . . . . . 2.2.4 Fourth Phase: The Institutional Period . . . . . . . . . . . . . . . . . . 2.3 Standing on the Shoulders of Giants: Creative Outputs and Space to Identify Creativity in CCIs . . . . . . . . . . . . . . . . . . . . . . . 2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17 17 17 19 21 24 24 25 28 35 38 43 45

ix

x

Contents

3 An Original Framework for the Identification of Creativity in CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Definition of Creativity in CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 What and Where: Heterogeneous Innovative Capacity of CCIs . . . . 3.3.1 Creative Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Modelling Creativity in Space: Inventive and Replicative CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 The Proposed Framework and the White Paper . . . . . . . . . . . . . . . . . 3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II

53 53 54 58 58 61 64 65 67

Location Patterns of CCIs

4 Location Behaviours of CCIs: Towards New Research Trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Static Agglomeration Advantages of CCIs: The Industrial District Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Static Agglomeration Advantages of CCIs: Cultural Environment Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Not Only Space but Proximity Relations Behind CCIs Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Dynamic Agglomeration Advantages of CCIs: Cognitive Proximity Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Dynamic Agglomeration Advantages of CCIs: Competitive Market Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 A Comprehensive Framework of Location Determinants in CCIs: Innovation and Filière Behind the Scenes . . . . . . . . . . . . . . 4.8 Taxonomy of CCIs’ Prevailing Agglomeration Factors . . . . . . . . . . . 4.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Where Is Creativity? Data and Methodology to Measure CCIs Across EU Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 A Bridge Between Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . 5.2 An Original CCIs Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 IP Data for Creative Intensities in CCIs . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Methodology for Shaping Heterogeneous Innovativeness of CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Measurement of the Filière Structure of CCIs: Input–Output Trade Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 The Geography of CCIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Heterogeneous Inventiveness in Space . . . . . . . . . . . . . . . . . . . 5.6.2 Territorial Versus Industrial Approach . . . . . . . . . . . . . . . . . . . 5.6.3 CCIs by Typology of Territories . . . . . . . . . . . . . . . . . . . . . . . .

73 73 74 77 80 82 84 86 87 90 91 97 97 98 101 102 105 111 111 118 120

Contents

5.6.4 Filière Structure of CCIs—A Key Dimension for Heterogeneous Concentration . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 5.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix 5.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Location of CCIs: Innovation and Filière Behind the Scenes . . . . . . . . 6.1 In Search of Reasons for CCIs’ Clustering . . . . . . . . . . . . . . . . . . . . . 6.2 Agglomeration Factors for CCIs: Indicators . . . . . . . . . . . . . . . . . . . . 6.3 CCIs’ Location Patterns: Static Versus Dynamic Agglomeration Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 CCIs’ Location Patterns: Inventive Versus Replicative Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad of Innovativeness and Filière . . . . . . . . . . . . . . . . . . 6.6 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xi

123 132 134 141 144 147 147 148 154 156 163 174 182 183

Part III CCIs and Local Development 7 The Role of CCIs for Local Development in Europe . . . . . . . . . . . . . . . 7.1 CCIs at the Crossroad of Digitalisation and Culturalisation . . . . . . . 7.2 CCIs and Local Development—An Unresolved Theoretical Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Empirical Attempts to tie CCIs to Regional Growth . . . . . . . . . . . . . 7.4 CCIs and Regional Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusions: Towards an Empirical Analysis . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 CCIs and Local Development: The Role of Creativity Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 The CCIs—Local Development Nexus Under the Lenses of Growth Multipliers, Agglomeration, and Resilience . . . . . . . . . . . 8.2 Methodology and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 The CCIs-Regional Growth Model: The Direct Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 The CCIs-Regional Growth Model: The Growth Multipliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 The CCIs-Regional Growth Model: CCIs’ Agglomeration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 The Regional Resilience Model . . . . . . . . . . . . . . . . . . . . . . . . 8.2.5 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

189 189 191 195 196 199 200 205 205 208 208 209 210 211 212

xii

Contents

8.3 The CCIs-Local Growth Nexus: Empirical Findings . . . . . . . . . . . . . 8.3.1 Direct Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Growth Multipliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 CCIs’ Clustering and Growth . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.4 The Role of CCIs During Economic Turmoil . . . . . . . . . . . . . 8.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

216 216 220 225 225 230 231

9 Creativity Where and Why. Results, Policy Implications, and Future Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 A Creative Journey: In Retrospect . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Creative Policies for Creative Industries . . . . . . . . . . . . . . . . . . . . . . . 9.3 Future Challenges and Opportunities for CCIs . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 236 238 244

Chapter 1

The Rise of CCIs: Setting the Scene

1.1 Cultural and Creative Industries at the Core of European Regional Competitiveness The relevance of Cultural and Creative Industries (CCIs) for socio-economic development is at the core of the debate both for scholars and for policymakers. The European Commission declares that CCIs are ‘important for ensuring the continued development of societies […] and, based on individual creativity and talent, they generate considerable economic wealth’.1 Furthermore, CCIs are so relevant because the value generated is not only economic, but they embed also a social one related to identity, life satisfaction, and accomplishment (Granger 2020; Klamer 2002). As a consequence, CCIs became a target for policy support mostly thanks to the huge Creative Europe programme (European Parliament 2013) fostering artistic creation and innovation in these sectors, helping artists to find inspiration, and exploiting value chains through network cooperation. Moreover, in the new European Agenda for Culture, the focus of the European institutions was even reinforced compared to the past. Indeed, it consists of three strategic areas: social, economic, and external. The first aims at harnessing the power of culture and cultural diversity for social cohesion and well-being, seeking to foster the cultural capability of citizens, encouraging the mobility of professionals in the CCIs, and raising awareness of a strong European identity through the promotion of the cultural heritage as a shared key resource. Second, in order to support culture-based creativity in education and innovation, it aims at promoting the arts at every level of education, improving the economic and legal environment for CCIs, and valorising the skills needed in CCIs including digital, entrepreneurial, and artistic ones. Third, CCIs allow international cultural relationships to be strengthened through the creation of dialogues for peaceful relations and reinforcing cooperation on the cultural heritage (European Commission 2018a). 1

Further information on the approach of the European Commission can be retrieved at: https://ec. europa.eu/culture/sectors/cultural-and-creative-sectors. Date accessed: 08/09/2021. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_1

1

2

1 The Rise of CCIs: Setting the Scene

The debate on the importance of CCIs in supporting socio-economic development is even more important at the territorial scale. Across regions and cities CCIs are also a vital asset for regional economic competitiveness and attractiveness, while cultural heritage is a key element of the image and identity of places and often the focus of city tourism (Panzera et al. 2021). Moreover, creativity is a key mediator in explaining the nexus between culture and local development, meaning that it’s the intangible creativity that fosters the valorisation and exploitation in economic terms of the cultural assets of places (Cerisola 2019). In general, besides the traditional material factors, the performance of a local system depends also on the abundance of its intangible cognitive elements (Capello et al. 2011) such as culture and creativity. In this framework, the New European Agenda for Culture recognises that cities and regions across the EU are at the forefront of culture-led development and constitute natural partners for experimentation, anticipating trends and exploring models of social and economic innovation (European Commission 2018a). This important role played by creativity in shaping local economic development is due to the fact that it is a peculiar territorial asset, shaping the strengths and the weaknesses of places. Culture and creativity, in fact, depend on context conditions (Pratt 2008), i.e. they belong to places and times since historical evolution contributed to this process (Santagata 2002; Serafinelli and Tabellini 2022). Cities and regions identify in their creativity (Sacco and Segre 2009) and creativity shapes their comparative (dis)advantage with respect to other areas. To clarify this key point, the overused example of the Silicon Valley may help the reader in retracing the territorial roots and consequences of creativity. Apple Inc. and Cupertino, California are indissolubly bound, the company is the place and the place is the company, and the creativity expressed by it. When anyone opens the package of a new iPhone, the first thing that draws their attention is the caption Designed by Apple in California. In fact, post Fordist areas and their cultural economies are inclined to exhibit well-developed individual identities, as a consequence of the play of history, agglomeration and locational specialisation (Scott 1997). Hence, creativity and space cannot be studied separately when dealing with CCIs. However, due to their appealing role, CCIs entered the political debate from the beginning and their definition and further conceptualisation suffered this interference dramatically, becoming a bastard concept ever since (Hartley 2021). The list of industries belonging to CCIs is always questioned as with the rapid evolution of new technologies and new fields of application in the arts, newer sectors will become part of CCIs (Hartley et al. 2020). The main challenge is that the Macrosector of CCIs can be metaphorically seen as a basket of fruits: all activities belonging to it have something in common (they are fruits) but, within it, pineapples are extremely different compared to blueberries, for instance. CCIs are like the fruits in the baskets, they all rely on culture and individual creativity but the way in which culture and creativity is translated in and exploited by CCIs may be diametrically opposite. Artistic creation and software development are both industries belonging to CCIs; however, it is natural to imagine that the way in which they operate and the kind of cultural value and creative expression generated are not alike in any dimension.

1.1 Cultural and Creative Industries at the Core of European Regional …

3

This heterogeneity exists in several dimensions related to them. First, workers involved in CCIs are of different species (e.g. craftsmen, musicians, ICT experts, and software developers), ranging from many skills and abilities required for their job, and even within each typology there exist many differences (Baldin and Bille 2021). Second, the output of CCIs is complex as well: symbolic, semiotic, or experience products of CCIs respond neither to the logic of classical manufacturing nor to the knowledge intensive business services with a value that can be seen under many spheres, not necessarily business-oriented (Granger 2020; Klamer 2002; Santagata 2009). For sure, CCIs are innovative actors (Hartley et al. 2013; Müller et al. 2009; Sunley et al. 2008); however, they do so in many different ways so it is important to account not only for classical forms of innovations but also softer forms that better represent the innovativeness of CCIs such as trademarks and copyrights (Stoneman 2010). Third, the creative production process represents another important feature of CCIs and, as well, a source of heterogeneity (Chapain and Sagot-Duvauroux 2020; Santagata 2009; University of Hong Kong 2003). Cultural and creative industries are extremely disaggregated but strongly interconnected (Pratt 1997) and, generally, the creative filière (as wisely described in Santagata 2009) represents the system of interactions between activities, both within the Macrosector of CCIs and among other activities. They are intermediate inputs in the production process also considering support services activities. Taking the film industry as an example, the filière consists in the input–output relationships between activities such as archives, entertainment agencies, digital and analogical special effects, audio pre- and post-production, 3D animation, or scenography. It is important to notice that not all relationships are with other CCIs, depending on the kind of support needed. Whatever the conceptualization used, the creative value chain puts industries in different positions within it. All these aspects are important in defining CCIs but they also create a lot of confusion so that even their name is shaped according to the field of application (Hesmondhalgh 2019; Viswanathan 2019). All this being said, in the last decades the scientific literature has followed two main research paths. On the one hand, scholars tried to find the common traits across CCIs and so to provide a clear definition of the frames within which literature should discuss the topic. On the other hand, the relevance of CCIs in forging the socio-economic development of both countries and regions has been investigated. The nature of the effort in this direction is mostly empirical, with several studies addressing the trigger according to different angles. In this vein, this manuscript provides the reader with a new, comprehensive view on CCIs. Here, CCIs are well-defined and the motivations behind their classification is cautiously discussed. CCIs are undoubtedly innovative sectors, as the literature claims, but here the book considers them according to their innovative capacity, implicitly distancing itself from the idea that they are homogeneously innovative. Furthermore, the book bridges this heterogeneous innovative capacity with the territories in which CCIs settle, stressing the strong relevance of local tangible and non-tangible elements driving the creative genius.

4

1 The Rise of CCIs: Setting the Scene

The next sections provide the reader with a brief overview of the main achievements of this book with respect to the current scholarly debate on CCIs. In this way, before deepening the narration in the following chapters, a comprehensive view on CCIs is presented, to allow a better contextualization of the results.

1.2 Defining CCIs: Creativity as a Territorial Affair When leafing through the book in the attempt to find the main results, the reader will soon understand that the main achievement of the book is not a single outcome but rather a comprehensive view on CCIs through a deep understanding of their innovative behaviour, their locational choices, and their effects on the local economies. The original perspective of this manuscript is based on three aspects. The first is the definition of CCIs, which leads to an original classification of Inventive versus Replicative CCIs according to their innovative capacity. Thanks to such a classification, the book presents a second new perspective on the location choices of these industries, highlighting the local aspects that are really of interest in explaining the clustering of different types of CCIs, irrespective of the sector they belong to. The third, new, perspective is to provide a more thorough interpretation of the local effects of the presence of CCIs, which goes beyond the purely sectoral contribution, but instead emphasising the true innovative capacity of such industries. Defining CCIs is a never-ending task. The literature’s debates on what is the true shape of CCIs are not new in economics. After a long period in which they were labelled as massification industries—whose goods were marked as cultural only to pretend to have a social value-, the literature acknowledged that their relevance has become not negligible. Researchers still struggle to give a shape to the founding nature of CCIs: creativity and discussions around CCIs have acknowledged that this concept is heterogeneous and difficult to capture, especially at the local level. In this work, CCIs’ heterogeneity is analysed in terms of different forms and intensity of creativity, an aspect that finds limited attention in the literature. On the one hand, creativity in CCIs is reflected in the output of these industries, whose heterogeneity is mirrored by the different forms of the creative output. In this sense, the definition has to capture different innovative logics such as technological, symbolic, and artistic creativity. On the other hand, creativity is not only heterogeneous in the forms of the output but in the way it is shaped by the local environment, since, through its features, a territory has an impact on creativity generation. In this book, in fact, a territory is not interpreted as a mere geographical space, but it embraces a definition of economic, relational space in which local societies, traditions, sense of belonging, and collective behaviours play a crucial role in the functioning of the market. A territory represents the locus where the creative genius may flourish, nourished by processes of collective knowledge generation, accumulation, and transmission. It is interpreted as a relational space made of interactions among individuals, private firms, and public institutions

1.3 CCIs’ Location: Static Versus Dynamic Agglomeration Advantages

5

that trigger creativity. New ideas are the result of continuous cross-fertilization and exchanges between actors, spreading both codified and especially tacit knowledge. At the local level, the concept of creative milieu reflects this environment. Defined as ‘the set of or the complex network of mainly informal social relationships in a limited geographical area (…), which enhance the local innovative capability through synergetic and collective learning processes’ (Camagni 1991, p. 4), the milieu favours an agglomeration of actors that, in turn, allows easier creative spillovers. In this vein, CCIs and space are interconnected and their creativity is a territorial affair. In this manuscript, this interaction is taken into account merging two concepts. On the one hand, the potential creative output of CCIs is wider in scope than the usual approaches that would neglect the innovative contribution of some sectors (e.g. music or films). In CCIs, in fact, innovations ‘in goods and services that primarily impacts upon aesthetic or intellectual appeal rather than functional performance’ (Stoneman 2010, p. 22) should be also considered. Hence, as the output of CCIs, this book considers not only classical patents but also trademarks and copyrights, enlarging the possible creative expressions with a solid business orientation. These indicators are measured at the local level so that one can grasp the different creative outputs in each territory. Moreover, according to the territory where each CCI locates, different functions can flourish, attracted by different territorial elements. As an example, the conception phase will be localised mainly in cities where highly educated individuals are more easily available, but also in historical cultural districts where the handicrafts traditions are the basis for the creation of new artistic pieces. In addition, creativity resides not only in well-established functions, such as the conception ones, but also in “traditional” segments like production where new products or processes may emerge spontaneously, thanks to the creative atmosphere of the territory. The new insights in the definition of CCIs are important because it influences both the conditions behind their localisation, trying to explain the conditions that give rise to their clustering, and the stimulus CCIs give to the local socio-economic development. The book, in fact, presents new insights both on CCIs location choices, considering the different agglomeration advantages for Inventive and Replicative CCIs (1.3), and on their contribution to economic growth, accounting for the territorial mediating factors supporting local economies (1.4).

1.3 CCIs’ Location: Static Versus Dynamic Agglomeration Advantages The literature extensively discusses and describes the concentrated distribution of CCIs in space, highlighting the reasonings behind agglomeration forces. Especially cities and cultural districts were found to be the preferred loci for CCIs’ settlement, being the nurseries for creativity generation. The literature investigated the motives behind this pattern and there are several possible explanations such as

6

1 The Rise of CCIs: Setting the Scene

urban hierarchy, settlement structure, industrial relatedness, cultural environment, and cognitive richness, mostly depending on the sectoral features. Although this large branch of studies went to the root of the phenomenon, outlining the most relevant territorial determinants of CCIs concentration and discussing why CCIs belonging to specific sectors benefit more from specific environments, the heterogeneous nature of CCIs finds limited attention from the literature. In fact, the analysis has always been approached by looking at specific sectors, leaving CCIs’ heterogeneous choices unexplained. In this manuscript, the heterogeneous innovative behaviour of CCIs is included as a determining factor for the analysis of their location patterns, explaining most of the reasons explaining clustering. Do Inventive and Replicative CCIs cluster in different areas? Do the territorial characteristics play a different role in stimulating their clustering? In the attempt to answer these questions, the manuscript provides an original perspective introducing a novel taxonomy of agglomeration factors. The conceptual effort put into this part was huge. The taxonomy presents a classification of CCIs’ agglomeration factors following two dimensions. On the one hand, the distinction between Inventive and Replicative CCIs is used to distinguish between static and dynamic factors: the former stimulating the efficiency, the latter stimulating the innovative capacity. On the other hand, due to the complexity of this analysis, it is necessary to consider the differentiated trade relationships of CCIs. In fact, proxying CCIs filières through an analysis of trade partners, the taxonomy considers the different industrial structure of different CCIs in explaining their clustering. Thanks to this analysis, the manuscript presents a new perspective on CCIs concentration, stressing the relevance of CCIs’ heterogeneous innovative capacity and the different filières CCIs belong to. These new perspectives allowed us to refine the interpretation of specific agglomeration factors, distinguishing between static and dynamic agglomeration economies.

1.4 CCIs and Regional Growth: The Essential Role of Territories CCIs’ concentration is only one side of the coin. The literature on the impacts of CCIs cannot neglect the attempts to identify a link between these industries and economic performances. Whether CCIs are important levers of regional growth is still a matter of debate, and empirical investigations are still limited in their results. The creative component in CCIs is expected to trigger positive mechanisms at the local level, thanks to knowledge generation and output diversification, making regions more competitive and, thus, stimulating local growth. Empirical findings presented by the literature have the large merit of opening the discussion about the positive effects along several dimensions (wealth, productivity, resilience); however, they still fail to clarify the channels of transmission of the effect.

1.5 Structure of the Book

7

The takeaways from this book’s perspective on CCIs’ effect on local growth are twofold. On the one hand, the manuscript presents a conceptual framework on the mechanisms through which the local presence of CCIs is expected to drive local growth. More specifically, this work focuses on the territorial growth multipliers enhancing the CCIs-growth nexus. Due to their differences in terms of both a cognitive base and localisation patterns, Inventive and Replicative CCIs are supposed to stimulate local economic growth through different channels. The former drive product quality increase, the latter contribute to growth through a size effect. These two channels are reinforced by specific territorial conditions that, on their turn, stimulate the mechanisms. On the other hand, the empirical findings discuss all these perspectives, adding new insights also for policy design. The results confirm that all forms of CCIs support local growth, despite their large heterogeneity. However, the territorial conditions triggering this process are different, according to the capacity of CCIs to innovate. Furthermore, empirical analysis also offers some other interesting food for thought. More specifically, it considers both the cumulative contribution of CCIs (i.e. whether there exists some agglomerative reinforcement) and the CCIs’ role in explaining resilience. In the former case, the over-representation of CCIs at the local level may cause decreasing returns as in the case of each overexploited resource, leading to positive but decreasing rates. Some reflections are also presented regarding the role of CCIs in stimulating regional resilience, highlighting the importance of the cognitive dimension in triggering the adaptive resilience of regions during and after crises. All this said, the entire discussion is centred around the forms and the intensity of CCIs’ creativity. The heterogeneity of creativity in CCIs is the basis of the novel definition of CCIs presented in this work that, in turn, affects the results and the interpretation of all empirical investigations. This work proposes a territorial perspective on CCIs’ creativity, considering the propulsive role that cities and regions have in the generation of creative goods and services. Territories, in fact, function as catalysts of creativity that, thanks to the synergies among firms and individuals, give rise to innovative outputs. In other words, CCIs act in different ways according to the places where they locate, and the propulsive role of places is reflected in the forms and intensity of the creativity expressed by CCIs. This original, multifaceted, approach to CCIs is organised in this work following a tripartite logic, according to these three main conceptual perspectives just presented. The structure of the book is the subject matter of the following section.

1.5 Structure of the Book The tripartite logic of the book is mirrored by its structure (Fig. 1.1) that follows a step-by-step logic.

8

1 The Rise of CCIs: Setting the Scene

Fig. 1.1 Structure of the book

More in detail, after this introduction, Part A discusses the concept of CCIs and their definition. Chap. 2 is devoted to nestling this research into the literature on the theme. It aims at reviewing in a critical way the main approaches to CCIs, identifying why scholars have been devoting so much attention to the classification of CCIs in the last decades. In this review, the interpretative key is creativity itself, as the way in which it is conceived shapes the approach to the field. However, creativity is a multifaced concept and it is difficult to conceptualise it both in general and in creativity-related topics like CCIs. For this reason, the theory on CCIs focused on two streams of literature to deal with it: the definition of the sectors composing the aggregate and the rationale to be used in order to properly classify them. In other words, on the one hand researchers focused on the identification of sectors that should by definition be part of the aggregate, trying to answer the age-old question “what are the sectors to be included among CCIs?”. On the other hand, some others discussed diverse ways to classify these sectors belonging to the aggregate, due to the large heterogeneity encountered. However, as will emerge more in detail in the diachronic review of these approaches, there is confusion in the theoretical approach to this topic and some scholars underlined that defining boundaries remains an unsolved task and may be even absurd and unnecessary (Buitrago Restrepo and Duque Márquez 2013). The Chapter attempts to identify the pros and cons of each approach, to identify a list of sectors that describes the Macrosector in a coherent and complete way. Then, it discusses the main limitations of the frameworks so far presented, in order to set the starting point for the future development of the work. Building on the literature, Chap. 3 represents the core of the novelty proposed in this work. Once it has presented a novel definition of creativity in CCIs and analysed its intrinsic features, it will go in depth to the description of the ways in which this translates into reality and to the ways it can be measured. In fact, CCIs are extremely

1.5 Structure of the Book

9

diverse and, above all, they express creativity in many different forms. The different expressions of creativity will be used as a criterion for a rational classification of activities and, in this context, three overlapping creative modes will be presented with their underlying features. These modes will allow us to proxy different forms of creativity operating in CCIs, a useful exercise to assess their differentiated impacts on socio-economic development. As many other works did in the past (Howkins 2001; UNCTAD 2010), this work binds creativity and innovation in CCIs. However, it accounts for the fact that creativity is heterogeneous and therefore the innovation forms of CCIs themselves, considering also softer forms of innovation as output of the creative process (Stoneman 2010). Furthermore, one of the substantial innovations of this work resides in the territorialisation of creativity in CCIs. Indeed, creativity is rooted into the territories and the local features play a role in shaping it. Hence, it is not possible to omit that a territory acts as an incubator of new ideas and an attractor of creative talents (Glaeser and Maré 2001) as territories and firms operating in there are not separate entities. Firms tend to internalise most of the determinants of the territory hosting them (Paniccia et al. 2015) in a process often defined of co-evolution (Paniccia et al. 2011). Hence, creativity is part of the territorial assets of cities and regions. Here, the reasoning follows a rational logic: as creativity is expressed by firms that assimilate territorial characteristics, it is supposed that creativity itself incorporates some territorial features. In other words, for each region the theoretical framework will distinguish between Inventive and Replicative CCIs: the former able to intensively innovate in one of the creative modes, the latter not. Then, after the general conceptualisation of CCIs and their intrinsic linkage with territories, Part B is devoted to the discussion of CCIs’ measurement in space. Chapter 4 reviews the existing theories on the agglomerated geographical distribution of CCIs under an innovative perspective. Indeed, the spatial clustering of CCIs has long attracted the interest of both geographers and regional economists since the concentrated geographical distribution of these sectors resulted forthwith to be a constitutive feature especially of sectors embedding cultural and creative determinants. There are several reasons behind the process of clustering and there is a never-ending need to put order between theoretical and empirical studies. Building on the fact that CCIs are heterogeneously able to innovate, the perspective of this review is to add the innovative component to the classical localisation theories. Therefore, starting from different agglomeration advantages, namely localisation and urbanisation economies, always the basis of the analysis of clustering of CCIs, the review reconciles it with the difference between static and dynamic advantages, to provide a more coherent and detailed perspective on the main theories behind the process of spatial concentration of CCIs. However, the review also highlights that the local needs of CCIs change according to the system of industrial linkages in which they are included. In fact, being a retail fashion shop is not the same as being an advertising company, they have a different DNA, and they build on different local features. Hence, the filière of CCIs matters. Then, the theoretical advancements discussed are empirically territorialised in Chap. 5, where the pros and cons of data used for measuring the heterogeneity in CCIs are discussed together with the methodology. This novel empirical setup is

10

1 The Rise of CCIs: Setting the Scene

capable of detecting creative CCIs across European regions, understanding where they prove to be Inventive, i.e. where they are able to intensively innovate in one of the forms identified as creative output (patents, trademarks, copyrights). Hence, in this chapter the innovative and spatial trends in CCIs are described and discussed. Finally, Chap. 6 empirically tests the spatial concentration of CCIs starting from the conceptual framework for the heterogeneous innovativeness. Even though all firms and industries benefit from agglomeration economies that explain why economic activities concentrate in space instead of being spatially dispersed, the sources of agglomeration may considerably differ according to what actors look for. Here, the analysis aims to investigate the static and dynamic agglomeration benefits for CCIs, according to their heterogeneous innovativeness. However, literature on agglomeration economies highlights from the beginning that the main source of localisation is related to the industrial interrelations between actors, comparing Marshallian and Jacobian theories (Beaudry and Schiffauerova 2009; Lorenzen and Frederiksen 2007). Hence, the analysis of localisation patterns of CCIs will also account for this dimension in order to identify the true sources of clustering. The interest in studying the localisation patterns of CCIs is mostly due to the fact that a strong presence of CCIs at the local level is capable of supporting the socio-economic development of places, through a process of regeneration of the built environment, introducing new goods and services, and improving the well-being of people. A deep understanding of the reasons that drive their clustering is beneficial for interpreting how CCIs support the economic development. For this reason, Part C aims to investigate the growth enhancing mechanisms generated by CCIs at the local level. Certainly, the economy of the future and its capacity to develop depend on two main Macrotrends currently in place. First, the 4th industrial revolution is now a reality and it is leading the process of transformation and automation (Arntz et al. 2019; Schwab 2017). This disruptive technological change will impact places unevenly, creating opportunities and challenges for regions (Capello and Lenzi 2021; De Propris and Bailey 2020) towards more and more humanless economies (Low and Clifford Lee 2021). On the other hand, the culturalization of the economic system (Currid 2007; UNCTAD 2010; Venäläinen 2018) is complementary to automation. Culture is not simply a large and important sector of the economy, it is a ‘social software’ that is needed to manage the complexity of contemporary regional societies and economies in all of its manifold implications (Sacco 2013). The intersection between these two processes will shape the society of the future. Through a high degree of digitalization and automation in all aspects of people’s lives, culture and creativity will be key for the socio-economic development of places, becoming the true value of European societies. Starting from this reasoning, Chap. 7 shifts the attention on the final interest of any territorial economist: regional economic development. In this specific case, although there is a wide consensus that knowledge is a key driving force behind economic growth (Acs et al. 2002), the channels which this transmission works through remains still understudied. Specifically, CCIs are nested within this theory as they bundle creativity and innovation into the knowledge economy (OECD 2014). CCIs are performing economically well and they are considered growth drivers at the local level (European Parliament 2013;

References

11

Power 2011) with opportunities for both developed and developing areas (UNESCO 2013). However, the empirical evidence on the impacts of CCIs on the European economy is still very limited and needs much more research (Boix-Domenech et al. 2021; Boix-Domenech and Rausell-Köster 2018). Moreover, even assuming that the impact of CCIs on development is positive, the transmission channels and the territorial meditators remain still unclear. Plus, CCIs proved to be heterogeneous in terms of their innovative capacity and this is expected to play a role in generating economic spillovers. Due to the different knowledge structure, Inventive and Replicative CCIs are expected to stimulate growth through different channels. The results of the empirical analysis on local economic development are presented in Chap. 8, where the role of creativity in CCIs is highlighted. Although CCIs are beneficial for regional economic growth, the channels through which this happens differ according to their capacity to innovate. Some space is devoted also to the concept of regional resilience as, although both theoretical and empirical studies overlook this mechanism, special attention should be paid to the role of the cultural and creative environment of places, influencing the way in which economic systems respond to shocks. In this sense, CCIs may represent a weapon that territories have to fight economic turmoil, due to their capacity of responding to the new needs emerging from the shock. Finally, Chap. 9 concludes. It summarises both the theoretical advancements, the empirical improvements, and the results of the analyses. Moreover, it also discusses the limitations of the work and the research lines that could be investigated in future works. Plus, it attempts at providing an overview of the newest trends in the economic debate, with a particular focus on the disruptive changes to the field that COVID-19 pandemics generated.

References Acs, Z.J., H.L.F. de Groot, and P. Nijkamp (eds.). 2002. The Emergence of the Knowledge Economy. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24823-1. Arntz, M., T. Gregory, and U. Zierahn. 2019. Digitization and the Future of Work: Macroeconomic Consequences. In Handbook of Labor, Human Resources and Population Economics, ed. K.F. Zimmermann, 1–29. Springer International Publishing. Baldin, A., and T. Bille. 2021. Who Is an Artist? Heterogeneity and Professionalism Among Visual Artists. Journal of Cultural Economics. https://doi.org/10.1007/s10824-020-09400-5. Beaudry, C., and A. Schiffauerova. 2009. Who’s Right, Marshall or Jacobs? The Localization Versus Urbanization debate. Research Policy 38 (2): 318–337. https://doi.org/10.1016/j.respol. 2008.11.010. Boix-Domenech, R., B. De Miguel-Molina, and P. Rausell-Köster. 2021. The Impact of Cultural and Creative Industries on the Wealth of Countries, Regions and Municipalities. European Planning Studies 1–21 https://doi.org/10.1080/09654313.2021.1909540 Boix-Domenech, R., and P. Rausell-Köster. 2018. The Economic Impact of the Creative Industry in the European Union. In Drones and the Creative Industry, 19–36. Springer International Publishing. https://doi.org/10.1007/978-3-319-95261-1_2. Buitrago Restrepo, F., and I. Duque Márquez. 2013. The Orange Economy—An Infinite Opportunity. Inter-American Development Bank.

12

1 The Rise of CCIs: Setting the Scene

Camagni, R. 1991. Local Milieu, Uncertainty and Innovation Networks: Towards a New Dynamic Theory of Economic Space. In Innovation Networks: Spatial Perspectives, ed. R. Camagni. Belhaven-Pinter. Capello, R., A. Caragliu, and P. Nijkamp. 2011. Territorial Capital and Regional Growth: Increasing Returns in Knowledge Use. Tijdschrift Voor Economische En Sociale Geografie 102 (4): 385– 405. https://doi.org/10.1111/j.1467-9663.2010.00613.x. Capello, R., and C. Lenzi. 2021. The Regional Economics of Technological Transformations. Routledge. Cerisola, S. 2019. A New Perspective on the Cultural Heritage–Development Nexus: The Role of Creativity. Journal of Cultural Economics 43 (1): 21–56. https://doi.org/10.1007/s10824-0189328-2. Chapain, C., and D. Sagot-Duvauroux. 2020. Cultural and Creative Clusters—A Systematic Literature Review and a Renewed Research Agenda. Urban Research & Practice 13 (3): 300–329. https://doi.org/10.1080/17535069.2018.1545141. Currid, E. 2007. The Warhol Economy (NED-New). Princeton University Press.https://doi.org/10. 2307/j.ctvzsmf4t. De Propris, L., and D. Bailey. 2020. Industry 4.0 and Regional Transformations, ed. L. De Propris, and D. Bailey. Routledge. https://doi.org/10.4324/9780429057984. European Commission. 2018. A New European Agenda for Culture. https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=COM:2018:267:FIN European Parliament. 2013. European Parliament Resolution of 12 September 2013 on Promoting the European Cultural and Creative Sectors as Sources of Economic Growth and Jobs (2012/2302(INI)). Glaeser, E.L., and D.C. Maré. 2001. Cities and Skills. Journal of Labor Economics 19 (2): 316–342. https://doi.org/10.1086/319563. Granger, R (ed.). 2020. Value Construction in the Creative Economy. Springer International Publishing. https://doi.org/10.1007/978-3-030-37035-0. Hartley, J. 2021. Advanced Introduction to Creative Industries. Edward Elgar Publishing Limited. Hartley, J., I. Ibrus, and M. Ojamaa. 2020. On the Digital Semiosphere—Culture. Media and Science for the Anthropocene: Bloomsbury Publishing. Hartley, J., J. Potts, S. Cunningham, T. Flew, M. Keane, and J. Banks. 2013. Key Concepts in Creative Industries. In Key Concepts in Creative Industries, ed. C. Rojek. SAGE Publications, Inc. https://doi.org/10.4135/9781526435965. Hesmondhalgh, D. 2019. The Cultural Industries (4th ed.). SAGE Publications Ltd. Howkins, J. 2001. The Creative Economy—How People Make Money from Ideas. Penguin Global. Klamer, A. 2002. Accounting for Social and Cultural Values. De Economist 150 (4): 453–473. https://doi.org/10.1023/A:1020146202001. Lorenzen, M., and L. Frederiksen. 2007. Why do Cultural Industries Cluster? Localization, Urbanization, Products and Projects. In Creative Cities, Cultural Clusters and Local Economic Development, eds. P. Cooke, and L. Lazzeretti. Edward Elgar Publishing. Low, F.S., and W.L. Clifford Lee. 2021. Developing a Humanless Convenience Store with AI System. Journal of Physics: Conference Series 1839 (1): 012002. https://doi.org/10.1088/17426596/1839/1/012002. Müller, K., C. Rammer, and J. Trüby. 2009. The Role of Creative Industries in Industrial Innovation. Innovation 11 (2): 148–168. https://doi.org/10.5172/impp.11.2.148. OECD. 2014. Creative Industries in the Knowledge Economy. In Tourism and the Creative Economy, 31–49. OECD Publishing. https://doi.org/10.1787/9789264207875-4-en. Paniccia, P., A. Minguzzi, and M. Valeri. 2011. Coevoluzione tra impresa e destinazione turistica: l’esperienza innovativa dell’albergo diffuso. In Creatività, innovazione e territorio: ecosistemi del valore per la competizione globale, ed. L. Pilotti, 405–463. Il Mulino. Paniccia, P., G. Morelli, and A. Cicerchia. 2015. Le imprese creative: dall’approccio per classificazioni ai modelli di management. Economia Dei Servizi 2: 123–150. https://doi.org/10.2382/ 83931.

References

13

Panzera, E., T. Graaff, and H.L.F. de Groot. 2021. European Cultural Heritage and TOURISM flows: The Magnetic Role of Superstar World Heritage Sites. Papers in Regional Science 100 (1): 101–122. https://doi.org/10.1111/pirs.12562. Power, D. 2011. Priority Sector Report: Creative and Cultural Industries. In European Cluster Observatory (Issue March). Publications Office of the European Union.https://doi.org/10.2769/ 95687. Pratt, A.C. 1997. The Cultural Industries Production System: A Case Study of Employment Change in Britain, 1984–91. Environment and Planning a: Economy and Space 29 (11): 1953–1974. https://doi.org/10.1068/a291953. Pratt, A.C. 2008. Creative Cities: The Cultural Industries. Geografiska Annaler: Series B, Human Geography 90 (2): 107–117. Sacco, P.L. 2013. Culture 3.0: The Impact of Culture on Social and Economic Development, & How to Measure It. https://ec.europa.eu/assets/jrc/events/20131024-cci/20131024-cci-sacco.pdf Sacco, P.L., and G. Segre. 2009. Creativity, Cultural Investment and Local Development: A New Theoretical Framework for Endogenous Growth. In Growth and Innovation of Competitive Regions, ed. U. Fratesi, and L. Senn, 281–294. Springer Berlin Heidelberg. Santagata, W. 2002. Cultural Districts, Property Rights and Sustainable Economic Growth. International Journal of Urban and Regional Research 26 (1): 9–23. https://doi.org/10.1111/14682427.00360. Santagata, W. 2009. White Paper on Creativity. In Towards an Italian Model of Development, ed. W. Santagata. Università Bocconi Editore. Schwab, K. 2017. The Fourth Industrial Revolution. Crown Publishing Group. Scott, A.J. 1997. The Cultural Economy of Cities. International Journal of Urban and Regional Research 21 (2): 323–339. https://doi.org/10.1111/1468-2427.00075. Serafinelli, M., and G. Tabellini. 2022. Creativity Over Time and Space. Journal of Economic Growth 27 (1): 1–43. https://doi.org/10.1007/s10887-021-09199-6. Stoneman, P. 2010. Soft Innovation: Economics, Product Aesthetics, and the Creative Industries. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199572489.001.0001. Sunley, P., S. Pinch, S. Reimer, and J. Macmillen. 2008. Innovation in a Creative Production System: The Case of Design. Journal of Economic Geography 8 (5): 675–698. https://doi.org/10.1093/ jeg/lbn028. UNCTAD. 2010. Creative Economy Report. https://unctad.org/en/docs/ditctab20103_en.pdf. UNESCO. 2013. Creative Economy Report—Special Edition. http://www.unesco.org/culture/pdf/ creative-economy-report-2013.pdf. University of Hong Kong. 2003. Baseline Study on Hong Kong’s Creative Industries. https://www. createhk.gov.hk/en/link/files/baseline_study.pdf. Venäläinen, J. 2018. Culturalization of the Economy and the Artistic Qualities of Contemporary Capitalism. In Art and the Challenge of Markets Volume 2: From Commodification of Art to Artistic Critiques of Capitalism, ed. V.D. Alexander, S. Hägg, S. Häyrynen, and E. Sevänen. 37–64. Springer International Publishing. https://doi.org/10.1007/978-3-319-64644-2_2. Viswanathan, B. 2019. Cultivating Copyright: How Creators and Creative Industries Can Harness Intellectual Property to Survive the Digital Age, 1st ed. Routledge. https://doi.org/10.4324/978 1351104807.

Part I

In Search of a Definition of Cultural and Creative Industries

Chapter 2

In Pursuit of Creativity in CCIs

2.1 Micro-economic Foundations of Creativity 2.1.1 Individual Creativity—Or What Creativity Is and Which Are Its Forms The assessment of the phenomenon of CCIs starts with the theoretical conceptualisation of the field that, due to the fleeting nature of creativity itself, remains the starting point of each study. A huge literature on the conceptualisation of CCIs exists, often creating confusion in approaching the field. A first important step is the discussion of the micro-economic foundations of creativity. This step is important since the understanding of this concept allows us to delve into the phenomenon of CCIs and motivate the conceptual and empirical choices made. Thus, the concept of creativity is discussed along three main dimensions: the individual, the industrial, and the territorial level. Creativity is a complex concept and, following Runco and Jaeger (2012), ‘no topic is more central to research on creativity’ than its definition (p. 92). As a matter of fact, not surprisingly, nearly every article in the Creativity Research Journal1 at least briefly defines creativity in some way. However, before defining creativity and understanding its features, it is relevant to discuss the lexicon and the concepts related to it. Andersson (1985) presents four interrelated concepts: information, knowledge, competence and creativity. Information can be seen as the simplest element in a cognitive setting. A set of information constitutes the basis of the system. A rational structured set of information constitutes the knowledge of a given topic. Part of knowledge is composed by ideas. Competence, instead, is the capacity of applying knowledge to interactions, both with other individuals and with machines. It is a relational dimension. Finally, creativity is the capacity of dynamically restructuring the system of information, starting from an existing knowledge environment to fashion 1

https://www.tandfonline.com/toc/hcrj20/current.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_2

17

18

2 In Pursuit of Creativity in CCIs

new knowledge and competence. According to this definition, creativity creates instability in the research process, creating the conditions for the emergence of new paths. Hence creativity embeds the concepts of newness, disruption, and path-break. However, this wise approach leaves creativity unrestricted and it is important to understand how it can be read and measured accordingly. Due to its dynamic nature, creativity can be read under three main dimensions, the three Ps according to Simonton (2011): the process, the person and the product. First, many authors underlined the importance of the trial-and-error process to discover and enable new solutions (Walberg 1988) as the true essence of creativity. Some focused on the complex process of combining ideas, environments, and technologies (Pratt 2005) but also culture (KEA 2006). Regarding culture, the relationship with creativity has been discussed stressing the role of cultural heritage as an input for the creative process resulting in a cultural output (Santagata 2002, 2009). Plus, creativity has been also considered a precondition for innovations to develop, focusing on the journey not on the destination (Landry 2008). Second, creativity has also been related to people. According to this dimension, the focus is on the creative individual generating creative ideas. Psychological literature deeply focuses on the determinants of the genius such as personality, openness, independence, but also a lower tendency towards psychopathology (Simonton 2009a, b). This segment of literature was enlarged by the economics literature on the creative class (Florida 2002; Florida et al. 2008; Westlund and Calidoni 2010). The creative class is a social class rooted in knowledge and mental work, defined mostly by occupations (opposed to industries) that are supposed to better measure the talent of individuals. However, individual creativity is interconnected with the economic environment since, although a genius is a genius everywhere, the knowledge environment he/she belongs to is a determinant in setting the basis for the identification of a new path (Csikszentmihalyi 1999). In the debate on the relationship between creativity and individuals, a long and prosperous literature discussed the correlation between creative class and graduates (Comunian et al. 2021; Comunian and Faggian 2011). Indeed, no common agreement exists on the education sources of the new ideas. Also in terms of impacts, the juxtaposition between graduates and bohemians has not led to straightforward results (Marrocu and Paci 2012a, b). Third, creativity has been seen as the product or output that it is capable of generating. Creativity in the forms of intellectual capital represents a competitive advantage for both firms and local economies, increasing value and catalysing culture and management systems (Andersson et al. 1993; KEA 2006; Lazzeretti 2012; Lazzeretti et al. 2012). Creativity is also the capacity of exploiting knowledge and ideas to produce new ideas (UNCTAD 2010). This approach captures the idea of newness, already discussed in general before. However, if newness is a consolidated and shared feature of the output of creativity, no clear consensus exists on the usefulness that the new output should have to be labelled as creative. Howkins (2001) and Smith (2005) believe that a novel invented product should be considered creative, regardless of its usefulness. Contrarily, the value attached to the output has also been considered a defining factor from many researchers, especially from the psychology literature

2.1 Micro-economic Foundations of Creativity

19

(Boden 1994, 2004; Runco and Jaeger 2012; Sternberg and Lubart 1998) as ‘originality is vital, but must be balanced by fit and appropriateness’ (Runco 1988, p. 4). After the seminal work by Wallas (1926), several psychological studies described a generic creative process. It can be seen as the realisation of the individual creativity in a specific environment, the succession of thoughts and actions that lead to original and adapted ideas, focusing also on the role of the realisation step. However, even failure can be an output of creativity (Andersson 1985). All these micro-level approaches to creativity are mostly conceptual and they try to discuss a complex phenomenon under different lenses. However, especially for economists, there are two aspects that are worthy of consideration. First, it is important to quantify the phenomenon, i.e. to measure the extent of creativity. Second, creativity being embedded in the whole social environment with implication for industries and regions, it is relevant to understand how it translates into a larger aggregate scale. In economics, the concept of creativity has been translated in a more formalised way in order to give it a value. The following two sections describe how creativity enters the discussion at the industrial and territorial level respectively.

2.1.2 Industrial Creativity—Or How Creativity Turns into Innovation and Economic Value In the last two decades, the creative economy has been defined in manifold ways. For instance, Hartley et al. (2013) refer to a creative economy when creative sectors spill over knowledge to the entire economic system or Sung (2015) when businesses promote creativity, knowledge convergence, and advanced scientific technology. However, its first definition dates back to 2001 in the famous The Creative Economy—How People Make Money from Ideas by Howkins (2001). For Howkins, ‘creativity is not new, and neither is economics, but what is new is the nature and the extent of the relationship between them and how they combine to create extraordinary value and wealth’ (p. 8). In the Creative Economy Report, UNCTAD (2010) defines the creative economy as ‘an evolving concept based on creative assets potentially generating economic growth and development.’ It could do so by fostering income generation, promoting social inclusion and cultural diversity. Moreover, UNESCO (2013) assigns to the creative economy also a non-monetary value that leads to an inclusive sustainable development. The key aspect is the way in which creativity translates into economic value and one of the main ways is to understand how it enters into the sectoral structures. Following Andersson and Beckmann (2009), a creative process in science, art, or even design is then embedded into industrial R&D. Nowadays, creativity represents a requirement for industrial R&D due to the increasing complexity of both production systems that need the intervention of creative ideas to innovate (Andersson et al. 2015). Thus, process or product innovation is the natural output of creativity, the realisation of the creative process at the industrial level. Creativity and innovation

20

2 In Pursuit of Creativity in CCIs

can be considered two sides of the same coin since innovation is often perceived as the successful implementation of creative ideas (Amabile 1996). Therefore, innovation and creativity look alike. Hence, this definition of innovation encompasses new products, processes, raw materials, management methods, and markets and the file rouge is the novelty introduced, whatever the form it is expressed through, in line with the discussion on creativity.2 In fact, innovation implies new solutions in products, technology, processes, and marketing (Tushman 1997), new solutions to industrial issues and on the wealth of nations (Harrison and Huntington 2001; Williams and McGuire 2010). It comes from ‘the process of applying a new idea to create a new process or product’ (Galbraith 1982, p. 6). Therefore, considering that creativity is the process through which new ideas are produced and that innovation is the process through which new ideas are produced, creativity is necessary and always precedes innovation (Andersson 2011; Landry and Bianchini 1995). However, this intersection between creativity and innovation may create a paradox. In fact, one could argue that all innovations are somehow creative. But why has literature decided to focus on CCIs and not on all innovative activities? So, why design and not mining? Historically, the interest of researchers on specific creative industries was mostly related to their cultural component, rather than its creativity. In fact, what is arguable is that creativity is linked to culture and it depends on it. In this sense, culture determines the way people think. Mental processes are influenced by the experiences people live in a specific cultural environment as the cultural background has an impact on how people perceive the world, and especially on the way in which they face and solve problems (Baumeister 2005; de Oliveira and Nisbett 2017; Nisbett and Miyamoto 2005). In an analysis of people from different cultures, facing an issue to be solved promptly, researchers found that they also feel emotions, both negative and positive emotions, such as anger or frustration, but also happiness and satisfaction. Their way of taking decisions through potentially creative ideas is different according to their country and culture of belonging. Traditions and values in which one grows up influence problem solving and decision making styles (Güss 2011). The role of culture in explaining creative processes has therefore an implication in the forms of innovation. In fact, innovation in CCIs is the result of a creative process with strong cultural roots (Santagata 2002). In this sense, sectors in CCIs cannot all be innovative ones; rather, they are only those with strong and long-lasting cultural ties. Sectors like book publishing, radio and television are among the main sectors of the cultural industry along with cinema, music (records), and newspapers and periodicals. The outcome of these sectors are cultural both in terms of input and output. In fact, they elaborate and diffuse contents in the fields of literature, music, essay writing, performing arts and information, i.e. based on the existing culture. Moreover, the output is cultural in the sense that it contributes to reshape the cultural mindset for the future. This is the case of great novels, films, and songs. 2

The interrelation of innovativeness, novelty and creativity has also been conceptualised by Helizabeth Hirschman (1980), under the lenses of consumer behaviour.

2.1 Micro-economic Foundations of Creativity

21

In these sectors, the concept of innovation is not the classical one. In fact, although the production of new films or new music may be extremely costly and involve creative ideas, the classical approaches to innovation (e.g. OECD and Eurostat (2005)) would overlook the innovative contribution. For this reason, the innovative component in CCIs is much wider in scope and classical forms of innovation need to be complemented by softer forms where these are innovations ‘in goods and services that primarily impact upon aesthetic or intellectual appeal rather than functional performance’ (Stoneman 2010, p. 22). CCIs are, therefore, peculiar innovative sectors. They for sure innovate (Granados et al. 2017; Müller et al. 2009) but the innovative forms are many and creativity is embedded in their different outputs. This variety of sources and forms of creativity have also affected the way in which creativity was studied. Many are the conceptualisations and the classification proposed and there is not agreement in the literature on how to define creativity in CCIs. After a deep review of the existing approaches, this book will deepen this debate and will propose a novel operative definition of creativity applicable to CCIs (see Sect. 3.2).

2.1.3 Territorial Creativity—Or How Territory Is a Source of Creativity The study of creativity cannot neglect the role of space in explaining the generation of novel ideas. The problem is to define the local socio-economic conditions that can stimulate creative development. There is a tendency of creative actors to concentrate in space, due to the advantages that a concentrated setting allows. In fact, creatives tend to benefit from the organisation into formal groups—such as academies, universities but also firms—in order to stimulate knowledge flows and ideas exchanges, underlying the generation of the new. This happens because creativity is an incremental phenomenon: although novel ideas may flourish everywhere, it is much more likely to see this happen where ideas are already flourishing and where creative actors may fruitfully express their genius. History is full of examples of places where creativity developed and are often known as creative cities. Following Andersson (2011), there exist seven conditions that should be present for a city to be really creative, using the examples of Athens, Florence, London, and Vienna: • a critical mass of accumulated wealth and positive economic growth: both historical and modern creative cities are characterised by economic dynamism, with accumulation of wealthy firms and individuals; • a large and increasing population with a substantial inflow of migrants: the capacity of places to attract people has often been considered a positive factor, as the choice of the place of residence of individuals is endogenous and it depends on the opportunities and the amenities that are available;

22

2 In Pursuit of Creativity in CCIs

• a pool of knowledgeable people: great minds have always shown a concentrated distribution, been attracted by places where other creatives were located (Kandel 2012; Serafinelli and Tabellini 2022) • large and diversified trade and communication flows with other regions: trade exchanges embed knowledge flows and it is not a coincidence that cities in the Low Countries such as Antwerp and Amsterdam stole the sceptre of European creative capitals from Florence after the European colonisation of the Americas when they became the centre of the trade traffic; • an ‘open society’—tolerant and with accessible arenas for exchanging new ideas: creative workers with talents need communities, organisations, and peers that are open to new ideas and different people (Florida 2002); • a situation of excess demand: people require more and novel goods. This is one of the consequences of the population growth, especially of demanding people that ask for new products and new experiences that may flourish thanks to creativity; • structural instability and uncertainty in the development of institutions and of philosophical, scientific and artistic paradigms: uncertainty is a key aspect for research and creativity being not an obstacle but rather a precondition for a creative state (Andersson 1985). Vienna, for instance, was a city of ‘unsatisfied human needs’ with the empire in a permanent state of political repression (Kandel 2012) or Florence whose political instability was at the centre of the famous Historiae Fiorentinae by Niccolò Machiavelli (Boutier and Sintomer 2014). Reading all these aspects in a critical way, everything is related to the interaction among people that stimulates creative genius. The concept of creative milieu perfectly enters this debate. The idea of the creative milieu is an evolution of the innovative milieu, defined as ‘the set of or the complex network of mainly informal social relationships in a limited geographical area, often determining a specific external image and a specific internal representation and sense of belonging, which enhance the local innovative capability through synergetic and collective learning processes’ (Camagni 1991, p. 4). Through the huge mobility of creative individuals, spatial proximity and network relationships feed a virtuous circle of ideas generation— also called collective learning—and territories become sources of creativity. Hence, creative spillovers are favoured by the agglomeration of actors. In the creative milieux, ‘culture is now seen as the magic substitute for all the lost factories and warehouses, and as a device that will create a new urban image, making the city more attractive to mobile capital and mobile professional workers’ (Hall 2000, p. 640) and to attract and retain the creative class (Florida 2002). With a more industrial perspective, after the seminal works by Becattini (1975, 1989), flourishing literature studied industrial clusters, places where knowledge flows fruitfully between firms and individuals thanks to formal and informal networks, resulting in a concentration of creative and innovative activities. That is why specific attention has been put to the localisation choices of creative activities, trying to isolate the specific reasons behind this process (Coll-Martínez et al. 2019; Lazzeretti et al. 2012; Sánchez Serra 2016). The mechanism of spatial concentration of creativity is interesting not only per se, but it has strong implications for economic growth. In

2.1 Micro-economic Foundations of Creativity

23

fact, creative activities being a potential comparative advantage for the places hosting a large amount of them (Lazzeretti 2012; Roger Martin et al. 2015; O’Connor 2004), the economic implications in terms of growth and resilience became an important issue to be studied. Some research started by highlighting that creative industries have relevant direct and indirect effects in the economy (Boix-Domenech et al. 2021; Boix and Soler 2017; KEA 2006) thanks to effects related to the capacity to stimulate technical progress and growth paths of the local economies a (Boix et al. 2013; deMiguel-Molina et al. 2012; Marco-Serrano et al. 2014). However, the literature still gives a limited explanation of the channels and the mediators through which this growth-enhancing process happens. From what has been said so far, it appears that creativity can quite easily be studied and interpreted under different perspectives, each stressing one or more features that creativity brings along. Starting from the individual characteristics that define creativity within a cognitive approach, creativity has been translated and adopted by the economic literature in the industrial and the territorial domains. Cultural and creative industries are one of the most relevant examples of how creativity is a generator of economic value, thanks to its capacity to create the conditions for successful and profitable innovations (Andersson 1985). Furthermore, the linkage with culture binds them with local economies, making creativity a territorial phenomenon. The difficulty in defining creativity is reflected also in CCIs. The definition of CCIs is, in fact, as difficult as the definition of creativity itself. Several studies followed one another with the attempt to answer the age-old question “what are the sectors to be included among CCIs?” and they contributed with more and more articulate definitions that, however, remained confused and unconstrained. The confusion is so deep that some scholars underlined that defining boundaries remains an unsolved task and may be even absurd and unnecessary (Buitrago Restrepo and Duque Márquez 2013). The confusion in defining the sectors also affected the approaches for the classification, and the focus of the debate shifted onto how to deal with these industries that are extremely varied along many dimensions.3 Within the debate, the key motive that pushed scholars and institutions to improve the research on the topic was the necessity to identify and define creativity within CCIs. Not all sectors and sub-sectors are likewise creative and to understand whether creative-driven activities support socioeconomic development, the approach needs to be as precise as possible and able to grasp only those activities that are truly creative. Starting from the critical approach proposed by Adorno and Horkheimer in 1947, until the most recent studies of the last few years, the wide heterogeneity of industries and creative forms is impressive. This is reflected in the evolution of definitions that tried to be more and more precise in capturing the latent heterogeneity. This is the subject matter of the next section. 3

The conceptual confusion generated by different definitions of CCIs has two main reasons according to Wyszomirski (2004). First, the implicit use of different criteria to identify which activities are deemed part of the CCIs; second, the dual objective of the studies: establishing a baseline to perform quantitative analyses and/or constructing a conceptual framework for the design and implementation of policies.

24

2 In Pursuit of Creativity in CCIs

2.2 Diachronic Perspective on the Main Classifications of CCIs 2.2.1 First Phase: The Introduction of the Notion In the history of CCIs classifications, four main conceptual phases can be identified. Figure 2.1 presents the evolution of the notions of CCIs with the main authors. Until the middle of the 90s, CCIs were mostly ignored or sceptically seen by both scholars and institutions. As a matter of fact, the term ‘culture industry’ appeared for the first time as a radical critique of mass entertainment by members of the Frankfurt school led by Adorno and Horkheimer in their book Dialektik der Aufklärung (Dialectic of Enlightenment) of 1947. The authors defined the new forms of culture like radio and music as a ‘mass deception’ as they are infecting everything ‘with sameness’. The process of massification of culture of that period followed a welldefined scheme of business. The cultural activities became uniformly produced and made available for all, indistinctively. This nothing-but-business ideology proposed by the two German sociologists strongly criticised the economic approach of these new industries that, in their view, used the label cultural to ‘legitimize the trash they intentionally produce’ (Adorno and Horkheimer 1947, p. 42). In 1981, Adorno reconsidered his own definition of culture industry in a revision of his main works (Adorno 1981). He disentangled the concept of industry renegotiating the meaning: industry has to be interpreted in a less literal sense but in a more

Fig. 2.1 The evolution of the notions of CCIs

2.2 Diachronic Perspective on the Main Classifications of CCIs

25

sociological one. In fact, the distress emerging from each and every work is linked to the mass reproduction and distribution, and not to the production process that may remain elitist and not contaminated by the masses. In other words, he deemed that the result of commodification and standardisation of the cultural expressions would have destroyed critical thinking and repressed any individual creative instinct (Witkin 2003). However, Garnham (1990) underlined that the main weakness of the Frankfurt School was to deny the irreversibility of industrialisation of culture and if this process had been seen as unproblematic, Adorno’s critique would have received a different doom. Although Adorno and Horkheimer were accused of overreaction and hysteria by their many detractors (Witkin 2003), they had the merit of introducing such an important concept and passionately stimulating the debate around it, not only in the sociological and philosophical environments (seeWaldman 1977; Gunster 2000; Witkin 2003 for more references) which they belonged to. In fact, other disciplines started to discuss the role of these new objects and their implications on everyday lives. The role of the cultural industries in economics became relevant many years later, when Adorno’s negative meaning was mitigated by the continuous growth of the sector in most of the Western economies.

2.2.2 Second Phase: Awareness Period The story went through the first substantial changes in 1994 when the Commonwealth of Australia published the notable Creative Nation where cultural industries have been identified as a policy object to foster economic and social development (Fig. 2.1). Thanks to this policy document, the Government of Australia placed the attention on cultural industries, but now with a positive connotation. They acknowledged that the relevance of cultural industries within the economy and as a share of the national employment became considerable. The cultural industries were identified as bearers of creativity of national culture and some measures to foster their reliance have been identified. For instance, the executive branch set up some policies to enable cultural industries to adapt to new technology, such as multimedia, to keep up the pace with international actors. Furthermore, the government was committed to copyright protection, even with the continuous technological change. In fact, ‘copyright will continue to operate as an effective incentive and reward for creative and intellectual activity’ (Government of Australia 1994, p. 8). This document redefined the idea of culture itself, detaching it from the elitist idea pursued by Adorno, and also introducing a socio-economic dimension. In fact, the cultural policy proposed by the document is a full-fledged economic policy. In this sense, the cultural industries were deemed as a source of potential economic profits, rather than “arts for art’s sake”. Moreover, Creative Nation argued that people need to be involved with the arts as creators and companies of all sizes require support to encourage cultural solutions that may provide answers to social problems (Government of Australia 2013).

26

2 In Pursuit of Creativity in CCIs

In any case, the strong role played by Creative Nation was to acknowledge that the ‘cultural field’ is in a continuous process of transformation and globalisation that may result in the homogenisation of cultural expressions and activities. This trend may be counterbalanced by national and local policies that support the creativity of citizens (Rowe et al. 2016). Following the Commonwealth of Australia, in 1998 the UK Labour government headed by Tony Blair set up a Creative Industries Task Force (CITF), within the new Department of Culture, Media and Sport (DCMS) that substituted the Department of National Heritage (DNH) previously established by the conservative premiership of John Major in 1992. The Task Force had the merit of producing two Mapping Documents (1998, 2001) that determined the course of the literature on CCIs in the following years. Creative industries have been defined as ‘those industries which have their origin in individual creativity, skill and talent and which have a potential for wealth and job creation through the generation and exploitation of intellectual property’ (DCMS 2001, p. 5). Following this logic, 13 sectors have been assigned to this group: Advertising, Antiques, Architecture, Crafts, Design, Fashion, Film, Leisure software, Music, Performing arts, Publishing, Software and TV and radio. When the first Mapping document was released, the Secretary of State for Culture, Media and Sport observed that ‘the role of creative enterprises and their cultural contribution is a key economic issue’ and this sector was becoming one of the most relevant parts of the UK economy (Flew 2012). This study is extremely relevant to understand how the approach on CCIs evolved over time because it was an important turning point for several aspects. First of all, it was the first complete study on the phenomenon within the European boundaries. This indicated the discontinuity with the past because in the EU these sectors did not receive any attention in terms of support policies. Secondly, the study recognised 13 precise sectors that should be part of the analysis, defining more clearly the field of application for future studies on this issue. Finally, it shifted the concept from cultural to creative. All these break points with the state of the art were not neutral, creating tensions between those in favour and those against the change of mindset. In his study on Creative Modernity, Redhead (2004) included many terms in the debate, among which the creative industries play a key role. He highlighted that Adorno’s approach to the ’culture industry’ imploded into the ’social’ form of pervasive information industries thanks to the new course introduced by the DCMS in 1998. Finally, he argued that the State impregnated the rhetoric on creative industries, reinforcing its role as an active player in the game of culture, creativity and society. However, the changes introduced by the Mapping Documents posed some issues in the political and academic discourse that include definitional, statistical and conceptual problems. Hesmondhalgh and Pratt (2005) reviewed the debated tensions and underlined that cultural policy cannot detach itself from the traditional assumptions that for centuries guided the political discussion. In fact, the transition is difficult to enact disruptively, as globalisation and the emergence of new technologies, are posing a new set of challenges for policy makers (Hesmondhalgh 2008). Moreover, Pratt (2005) argued that, although the new definition of creative industries is extremely

2.2 Diachronic Perspective on the Main Classifications of CCIs

27

innovative and powerfully linked to policy actions, it could be challenging to identify a clear demarcation line between what is creative and what is not. Finally, the shift from the cultural to the creative label was not only taxonomy. In fact, it met the need of including in the cauldron also the high-tech industries and this stimulated many critiques in the academic and political debate. Garnham (2005) denounced the dot-com philosophy of the creative industries at the gates of the twenty-first century. He deemed unjustified the claim of the cultural sector to be a key economic growth sector within the global economy and he wished for a re-flourishing of the artist-centred state of cultural policy. Moreover, he claimed that the inclusion of high-tech sectors like Computer and Software is a mediatic strategy to artificially inflate the economic size of the sector and push up statistics. To summarise, by bringing the arts, broadcast and media together within a unique administrative domain, Blair’s government moved towards more integrated approaches to cultural policy and linked once and for all traditional cultural industries with digital technologies, to align British creativity and intellectual capital with new sources of development (Flew 2012). Nonetheless, it opened the discussion about the theoretical and statistical implications of the inclusion of less cultural- and more technological-based activities in the analysis. Finally, this study opened the gates to a wide empirical literature on the role that these activities have in explaining socio-economic development (among others, see de-Miguel-Molina et al. 2012; Boix et al. 2013; Hong et al. 2014; Lee and Rodríguez-Pose 2014; Boix-Domenech and Soler-Marco 2017). At the beginning of the new century, John Howkins published the famous The Creative Economy—How People Make Money from Ideas where he gave birth to the widespread notion of creative economy, which CCIs rightfully belong to. For Howkins, as introduced before, what matters is how creativity combines with economics, creating value and wealth. He identified the heartland of creativity economy, defining a list of core creative industries where creativity is the most relevant resource. These industries qualify themselves as creative because their products exploit some form of intellectual property right such as patents, trademarks, or copyrights. Moreover, he was innovative for the introduction of research and development as a creative sector. He recognised that R&D is a patent business and, although not all R&D leads to a patent, almost all patents are the result of an R&D process (Howkins 2001). Basically, Howkins described a change in the economic paradigm that was already forewarned some years before. This shift towards the “brain power society” or “C-society” has been welcomed by scholars since advanced countries were shifting from the capitalism based on mass production of commodities to the brain power society in which creation of knowledge and information using brain power plays the central role (Andersson 1985; Andersson and Strömquist 1989; Fujita 2007; Thurow 1996). Thus, starting from the ‘exploitation of intellectual property rights’ promoted by the DCMS some years before, Howkins glued together intellectual property rights (IPRs) and creativity, and this choice incorporates both advantages and disadvantages. First of all, the measurability of IPRs allows us to distinguish more clearly the creative sectors from the others. Secondly, this approach circumvents the issue of

28

2 In Pursuit of Creativity in CCIs

the creative occupations posed by Florida (2002) considering that it is not necessary to have creative workers to label an industry as creative. Howkins deemed that all the stages of production in a creative industry are functional to the production of the intellectual property right and, even more so, they are equally part of the creative economy. In his words, in the intangible industries ‘people who print books and build theatre sets are as much a part of the creative economy as those who write and perform on stage’ (p. 4). However, despite these advantages, this work also lured some critics. For instance, Healy (2002a) argued that the creative industries are badly named in Howkins’ definition and they should be called “IP industries” as the interchangeability between creativity and IP right is not necessarily so straightforward. Are all IPRs generated by a creative process? Does a creative process lead to an innovative output? Howkins’ work fails to reply to these questions. Moreover, the relationship between creative industries and IPRs requires a deeper discussion on the ways in which art and culture are consumed and especially how creators are rewarded (Healy 2002b). In this phase, the main aim of scholars was to find similarities between CCIs, i.e. selecting the common logics that allowed some of them to join the élite group of CCIs. However, the recognition of the relevance of CCIs has raised some important questions on the definition of the scope of creative industries and their operative measurement. In fact, net of the commonalities, CCIs are extremely heterogeneous, and this acknowledgement opened the gates to the discussion on what is truly creative and what is not, even within the aggregate of CCIs.

2.2.3 Third Phase: The Definitory Period This third phase is the most comprehensive of all the discussion. It can be labelled “definitory period” because it gathers all the approaches used to refine the existing methodologies and provides a detailed classification of CCIs that accounts for the intrinsic heterogeneity between and within industries. This necessity emerged not only for scientific purposes, i.e. to be more and more precise in classifying CCIs, but also to find an approach that is measurable. With this perspective, scholars started to analyse the ways creativity is embedded in CCIs and they provided some different rationales. As creativity is an intangible concept, what matters is the way in which it is measured. The two most recognised ways are the industry- and occupational-based ones. In the former, the presence of creative industries is considered a consistent proxy of the creativity expressed in each territory. The latter and more recent one builds on the pioneering work of Richard Florida (2002) that introduced the concept of creative class, used (and sometimes abused) in the literature to indicate all creative individuals on the basis of the specific task they perform. Hence, this approach is nestled according to what workers do rather than on what they make (Markusen et al. 2008). There is not unanimous consensus on what is the best approach, both of them present pros and cons in the theoretical and empirical ways in which the phenomenon of creativity in the economy is translated. The following sub-sections discuss how

2.2 Diachronic Perspective on the Main Classifications of CCIs

29

the heterogeneity of sectors has been translated into different theoretical models, starting from the industry-based approach ending up with the recent occupational approach. Industry-based approach: across industry heterogeneity Starting from 2002, in order to study CCIs in a deeper and more precise way through a more and more measurable approach, the need to group similar sectors emerged. Through this arrangement, the studies more or less intentionally paved the way for clustering industries based on different levels of creativity. The first approach was to deepen the analysis of copyright-based industries, through a vast study conducted by the World Intellectual Property Organization. In July 2002, it arranged a Working Group of Experts in Helsinki with the aim of summarising the existing experience in surveying the copyright and related rightsbased industries and proposing recommendation and guidelines for the assessment of the phenomenon. The results of the Working Group of Experts converged into the “Guide on Surveying the Economic Contribution of the Copyright-Based Industries” where the role of copyright is decisive (WIPO 2003). It introduced the idea of categorising the copyright-based industries into four different groups: i.

core copyright industries: industries that are wholly engaged in creation, production and manufacturing, performance, broadcasting, communication and exhibition, or distribution and sales of works and other protected subject matter. ii. interdependent copyright industries: industries that are engaged in production, manufacture and sale of equipment whose function is wholly or primarily to facilitate the creation, production or use of works and other protected subject matter. iii. partial copyright industries: industries in which a portion of the activities is related to works and other protected subject matter and may involve creation, production and manufacturing, performance, broadcast, communication and exhibition or distribution and sales. iv. non-dedicated support industries: industries in which a portion of the activities is related to facilitating broadcasting, communication, distribution or sales of works and other protected subject matter, and whose activities have not been included in the core copyright industries. The Guide recognised that certain industries are more closely identified with copyright than others: some fundamentally exist in order to design intellectual properties while others are mostly supposed to produce and distribute copyright materials. Hence, an enormous value added of this definition is the introduction of the concept of copyright intensity as a way to detect differences among subsectors. Some years later, this idea gave rise to the analysis of creative intensity that will be covered later in the work. Therefore, under the mask of differences in the copyright intensity, the WIPO identified for the first-time different levels of creativity across industries. After the spread of the concept of copyright-based industries, another group of experts was set up in Singapore in October 2008. They studied the evolution of the concept introduced five years before and identified some gaps to fill and some

30

2 In Pursuit of Creativity in CCIs

trends to consider in order to improve the classification. They acknowledged that considering only copyrights was a limitation of the Guide and it did not adapt to the new trends in the content-related industries due to new digital technologies. The Revised Edition of the famous Guide was born in 2015 and made some small but relevant modifications in order to adapt the concept to the evolutions of digital technologies. For instance, it proposed to account for “intellectual property products” previously called “intangible produced assets” (WIPO 2015). This choice cannot be considered neutral: it is the exact response to the need of having more measurable definitions that are also operative and applicable to data. The two versions of the Guide opened the door to many studies about the economic contribution of copyrightbased industries at the national level in partnership between local researchers and WIPO experts in almost 50 countries worldwide.4 Contextually in the Old Continent, the Directorate-General for Employment, Social Affairs and Inclusion of the European Commission commissioned a study to measure the size of its employment in the cultural sector in Europe and to identify new strategies to exploit its potential (MKW Wirtschaftsforschung GmbH 2001). This study reported a significant growth of cultural employment, compared to traditional sectors. However, it highlighted that a correct way for counting the number of workers employed in the cultural sector in the EU is something that has proved to be a very difficult and vague business due to the qualitative way of collecting data on cultural jobs and, especially, due to the fluidity and typicality of the activities carried out in these sectors (MKW Wirtschaftsforschung GmbH 2001). Nonetheless, the MKW study did not provide a genuine definition of CCIs but it stimulated the context discussion at EU level on the emergence and rapid development of the sector. In 2006, this resulted in the famous The Economy of Culture in Europe by KEA, likewise financed by the European Commission. This work is contextualised within the Lisbon Strategy and it aimed at capturing the direct and indirect socioeconomic impacts of the cultural sector in Europe, trying to evaluate its contribution to the Lisbon agenda, as growth and development driver (KEA 2006). Following a concentric model proposed in Throsby (2001) and formalised in Throsby (2008) and in The Work Foundation (2007), the study claimed that creative ideas originate in the core, formed by creative arts and that these ideas diffuse to the surrounding layers with the proportion of cultural to commercial content decreasing as one moves outwards from the core. Although based on different hypotheses, this framework is in line with WIPO (2003) since the rationale of separating industries according to different creative expressions is similar. Here, the concentric circles are the core of the treatise, as they represent the theoretical framework to study CCIs in evolution with previous works. Beyond the conceptual model, the take homes of this work are several and they deserve a short discussion. First, this work is the first one with pan-European outreach. It is the result of the interest placed by European institutions in these activities. After that, many policy documents appeared with the aim of targeting specific support 4

Official links for to the national studies available at: https://www.wipo.int/copyright/en/perfor mance/.

2.2 Diachronic Perspective on the Main Classifications of CCIs

31

policies for CCIs. Secondly, it dedicated some space to the discussion on the issues faced in measuring the extent of employment with available statistics. The official data on sectors and activities related to the world of culture and creativity provided by national and European offices are not harmonised, due to the absence of a uniform definition of CCIs. Moreover, the NACE5 classification is not able to cover the entire cultural sector, creating bias in the statistics. Finally, one of the goals of the study was to measure the relevance of the sectors in the European economy, through the measurement of the economy of culture, focusing on the “value added to the economy”. However, even this study presents some drawbacks. Although the concentric circle approach outlines the conceptual framework to properly classify industries, it statically assigns each of them to one of the circles following an industrial hierarchy. This logic precludes a sector from being more or less creative than both sectors within the same circle and other sectors belonging to surrounding ones. Even in this case, although using different criteria with respect to the WIPO Guide, different industries are supposed to express creativity heterogeneously. Function-based approach: Within industry heterogeneity The tale about the classifications of CCIs underwent another turn of events when some studies acknowledged the need of decomposing each creative sector into different phases. This necessity emerged because some deemed it was not enough to consider different levels of creativity between industries, as heterogeneity has been found also within them. The pioneering study on this topic was the Baseline Study on Hong Kong’s Creative Industries presented by the Centre for Cultural Policy Research of the University of Hong Kong and it dates back to 2003. This report considers content creation as an aggregate part of a more complex production system called Creative Industries Production System, already presented in Pratt (1997) as a way to situate the cultural industries within a broader social and political context. The report recognised that the whole “journey” of a product from its composition to delivery can be divided into different divisions or functions, following the conventional idea of value chain. In this study, the industries have been divided into three groups, according to the phase of the process: • content production industries: those activities directly involved in the creation of contents in all forms including digital, text, image or audio. • industries that provide production /infrastructural input: those industries providing means of production or infrastructural supports for the production process without which the creation would not take place in its present form. • supports, and reproduction and distribution industries: those activities of reproducing original content for consumption, or activities directly enabling accessibility of goods or services to be sold. 5

The Statistical classification of economic activities in the European Community, abbreviated as NACE, is the classification of economic activities in the European Union (EU); the term NACE is derived from the French Nomenclature statistique des activités économiques dans la Communauté européenne. Various NACE versions have been developed since 1970. Source: Eurostat.

32

2 In Pursuit of Creativity in CCIs

This concept builds on the production process and finds different levels of creativity in different phases. In fact, even not explicitly, content production is supposed to gather and express a higher level of creativity compared to support and distribution industries. The conceptual advancement of this study was to acknowledge that it is not the sector itself that is creative or not but it is the conception stage, as a part of the entire industry, that gathers a larger amount of creativity (Santagata 2009). In Europe, the idea behind within-industry heterogeneity needed time to spread. The milestone study was the White Paper on Creativity, edited by Walter Santagata in 2009. This study had a twofold objective: outlining an Italian model of creativity and cultural production and defining the macro sector of the cultural industries, which does not have a clear statistical identity in Italy. In a very detailed analysis, Santagata put the emphasis on dividing up the sectors according to the stages in a common chain of value production, in line with the Hong Kong study. The underlying idea in his approach is that a more detailed assessment of the phases of conception, production and distribution will allow us to better understand the structure of a given sector. Conception, production and distribution are the key steps in the value generation, accompanied by auxiliary activities for the production and the distribution themselves. Naturally, conception is the most creative phase, where products are invented or designed while the creative contribution of those that follow is disputable. Moreover, Santagata had the merit of binding once and for all culture and creativity, as the former is the starting and the ending point of the production process in CCIs. In his idea, cultural heritage is an essential input for triggering creativity and, at the same time, the output of the entire chain. Although the theoretical value added coming from these two works is undeniable, they pose two limits that need to be considered. First, they set boundaries between activities according to the phase occupied in the production process. Secondly, even if it allows for within-industry heterogeneity, the model deems all the activities belonging to the same step to be equally creative and it does not permit any variability in terms of creativity. Therefore, these are strong a priori, since an activity can be extremely creative regardless of the stage it has reached. Moreover, it can be only partially creative, depending on the kind of output produced. Figure 2.2 is the representation that Santagata used for the decomposition of sectors. Thanks to its capacity of balancing input and output side of the creative process, accounting for both culture and creativity, and due to its preciseness in defining the boundaries of the Macrosector, the White Paper on Creativity can be considered the most suitable starting point for this research on CCIs. For this reason, Sect. 2.3 presents a more detailed assessment of the advantages and the limitations of the model, highlighting both pros and cons and setting the scene for the research in the following chapters. Occupation-based approach All the discourse presented so far has had the aim of addressing the issue of classification of CCIs through an industrial approach, i.e. starting from a list of industries belonging to CCIs and assessing them through different theoretical approaches.

2.2 Diachronic Perspective on the Main Classifications of CCIs

33

Fig. 2.2 The creativity value chain in the white paper. Source Author’s elaboration based on Santagata (2009)

These models may lead to extremely different figures about CCIs, as the purpose of each work influenced the assumptions and then the rationale followed for their operationalisation (UNCTAD 2010). Moreover, all the approaches presented so far ignore the dimension of creative occupations in shaping the classifications. At the end of the first decade of this century, especially after the Rise of the Creative Class by Richard Florida (2002), scholars began to reflect around two novel questions: is there something creative outside CCIs that we do not account for? and are CCIs really creative? Basically, the theories on the creative class started to overlap with those on the creative industries, trying to address some doubts left unsolved by previous works. Indeed, the industry-based approach set the boundaries of CCIs and the assessment is made inside those limits. However, creativity can be present also outside the classical industrial limits and, rather than focusing on the output of the production process, new approaches should shed light on the process itself and on the skills and abilities of workers involved (Mellander 2009). The prospective creativity present outside those boundaries is not covered at all. Furthermore, within the boundaries there may be something that is not creative but that is nonetheless analysed. In other words, those questions aimed at cleaning the analysis, trying to detect the true creativity. The two frameworks presented below integrated the occupational approach to the industrial one, in order to find a solution for the two questions. This set of definitions is based on the idea that creativity is an individual characteristic, although deeply rooted in social environments. Only if really creative occupations are needed can an industry be considered creative. In 2008, the former National Endowment for Science, Technology and the Arts, now Nesta, drastically changed the perspective presenting the renowned Creative Trident (Higgs et al. 2008). It considered three types of employment as creative workforce: “specialist” artists or creative individuals in creative industries; support staff in creative industries; and creative individuals working elsewhere. The last part is the most relevant and innovative as it opens the doors of creativity to individuals

34

2 In Pursuit of Creativity in CCIs

not employed in cultural and/or creative industries. Moreover, the introduction of the occupations allowed for more precision in also determining earnings of creative individuals and their features, adding some relevant information to understand the phenomenon. However, although it provided a broader view of the creative economy, the Creative Trident still has some weaknesses. In fact, as underlined by the Centre for International Economics (2009), it is not possible to estimate the creative contribution to the output and the estimates obtained are not easily comparable with traditional industries, due to the issue of double counting.6 Moreover, one of the strengths underlined by the study itself is that it inspects only the core creative value added, excluding deliberately activities in related chains. In light of what has been considered in the previous sections, this choice may lead to misleading conclusions. In fact, recognising that different activities gather creativity heterogeneously, it is not formally correct to exclude some of them. This could overlook some creative expressions present in peripheral sectors. In addition, this approach is only relevant to measure the size of the employment since no reference is made regarding the output of these occupations. ‘It is therefore not possible to estimate the contribution embedded creatives make to the output of the industries in which they are employed’ (p. 20). Having identified that creativity in CCIs is a fuzzy concept, the latest Nesta study (Bakhshi et al. 2013) proposed a new methodological framework for classifying CCIs, based on Freeman (2004) who introduced the concept of creative intensity. In his idea, the creative intensity is the proportion of workers in creative industries employed in creative occupations. This work had the merit of introducing a much more detailed perspective on the topic, with a pioneering approach to the problem of classification. Indeed, it became clear that industries are different even under the umbrella of CCIs and here, for the first time, the authors gave a measurement of the heterogeneous creativity embedded in those sectors. However, the authors deemed it important to provide a more rigorous definition of creative occupations, as the former DCMS (2013) classification is not rigorous enough. For this reason, they define a creative occupation as ‘a role within the creative process that brings cognitive skills to bear so as to bring about differentiation to yield either novel or significantly enhanced products whose final form is not fully specified in advance’ (Bakhshi et al. 2013, p. 24). This definition put the emphasis only on the input side of the creative force, especially on the creative workforce, a pivotal source in the process. Moreover, the approach left completely unrestricted the kind of output generated and this can be misleading as well. The discussion on creative occupations is much larger than the one presented here. In fact, the concept of creative occupation, generated by the creative class, puts at the centre the role of individual skills and abilities to perform specific tasks. Often, these characteristics overlap with education levels and training and there is 6

The Creative Trident mixes the concepts of industries and occupations and its estimation across all industries would result in significant double counting due to overlaps (Centre for International Economics, 2009).

2.2 Diachronic Perspective on the Main Classifications of CCIs

35

great discussion of why creativity is or is not related to education (Comunian and Faggian 2011). For the sake of this work, the discussion has been circumscribed to the definitions of creative occupations in an industrial setting. In conclusion, the occupation-based approach had the merit of introducing some new important questions around the classification of CCIs with the usual goal of identifying what is creative and what is not. However, the outcome was not yet completely satisfactory.

2.2.4 Fourth Phase: The Institutional Period CCIs became the main subject of policy documents of international institutions that extensively contributed to the assessment of the topic ant to the definition of CCIs. In his review of the international models of creative industries policy, Flew bunched the documents born to analyse creative industries in developing countries. Both UNCTAD and UNESCO addressed the topic of CCIs in their reports, under slightly different perspectives: the former focusing on the positive impact that CCIs may have for economic growth in developing countries, the latter highlighting the social impact that CCIs have on inequality (Flew 2012). In 2010, the United Nations presented the UNCTAD Creative Economy Report that acknowledged the main results of the UNCTAD XI Ministerial Conference, held in São Paulo, Brazil, in 2004 (UNCTAD 2004). Its approach substantially enlarged the scope of CCIs considering ‘any economic activity producing symbolic products with a heavy reliance on intellectual property and for as wide a market as possible’ (UNCTAD 2004, 2010 p. 7). Moreover, it focused on measuring the international trade of cultural and creative products and it was truly market-oriented, distinguishing between “upstream activities” and “downstream activities”, according to their closeness to the market, and argues that the latter group derives its commercial value from low reproduction costs and easy transfer to other economic domains. The definition provided can be considered as one of the most influential on the creative and cultural industries due to its multiple-countries approach, it suits good for international comparisons. The point of view presented by the UNESCO in 2013, instead, was built on the ideas and results discussed by the UNCTAD report. In addition to these ideas, UNESCO recognises that CCIs do not only have a positive effect in terms of economic growth and development but also a social one. In fact, CCIs tend to be environmentally friendly, and they generate benefits that are not easily quantifiable such as the affirmation of the distinctive cultural identity of the places where they are located, the improvement of life conditions and the enhancement of the local image and prestige. Moreover, UNESCO proposed a separation between cultural industries, whose principal purpose is the creation, production or reproduction, promotion, distribution and/or commercialisation of products of a cultural-related nature, from creative

36

2 In Pursuit of Creativity in CCIs

industries, applied to a much wider productive set, including goods and services produced by the cultural industries and those that depend on innovation, including many types of research and software development (UNESCO 2013). Meanwhile, at the European level, the interest on such industries grew within the European institutions that deemed it important to put the emphasis on the role of creativity and innovation within the Old Continent. Policy makers considered that in order to stimulate a new and more modern entrepreneurial culture, technological change, creative skills and innovation are pivotal elements in the process of change (Barroso 2009). The new approach of EU institutions converged in the Green Paper—Unlocking the potential of cultural and creative industries (European Commission 2010). This work recognised the importance of CCIs as enablers for European competitiveness in the more and more globalised world. In order to exploit the opportunities of cultural diversity, the EC underlined the importance of sustaining CCIs to innovate and to reinforce the local and regional environments where CCIs operate as the engine for a stronger global presence and stimulating the diffusion of spillovers coming from creative industries. Naturally, the perspective of the EC is the policy, not the academia, and its definition is a working one since the aim is to address some key areas where policies and tools set up at the EU level may unlock the potential of these sectors (European Commission 2010). Moreover, it can be considered as a continuation of the KEA study, remarking the differences between the circles that compose the cultural economy in Europe (Flew 2012). Afterwards, the European Parliament and European Council adopted the Regulation (EU) 1295/2013 that establishes the Creative Europe Programme for support to the European cultural and creative sectors. This is the most important piece of legislation adopted by European institutions to sustain CCIs. This is in line with the 2005 UNESCO Convention to which the Union is a party that stressed the key role played by cultural activities to convey identities, values and meanings. At this stage, it is worth underlining that the discussion at EU level on the new Creative Europe Programme for the framework 2021–2027 has started and some important outcomes have already emerged. First of all, the EU aims at increasing the support for European artists and creators, and so contribute to the further development of European culture and identity. The new Regulation should strengthen the competitiveness of CCIs, with a specific focus on the audio-visual sector considered not competitive enough within the Digital Single Market but strategic for European growth. Furthermore, the European Council has agreed upon the role of the prospective synergies with regional, urban and rural policies as they act as tools for supporting cultural and creative industries. Indeed, the promotion of creativity is supposed to contribute to the competitiveness of EU businesses (European Council 2018). The evolution of the concept of CCIs is a complex one: in each phase conceptual steps forward were undoubtedly achieved, adding however some limits to an already complicated picture (Table 2.1). The result is that CCIs have a still fuzzy definition and whatever study one wants to develop on such industries, a deep reflection on what definition of CCIs is used is absolutely vital. This book is not an exception in

2.2 Diachronic Perspective on the Main Classifications of CCIs

37

this respect and it presents its own perspective on CCIs in the next section and, more in detail, in Chap. 3. Table 2.1 Evolution in the definitions of CCIS: steps forward and limits Authors

Steps forward

Limits

Adorno and Horkheimer (1947)

Rise of the concept

Negative perspective on cultural industries

Australia-DCA (1994)

Cultural industries as a source of economic and social development

Simple list of good intentions for policy makers

DCMS (1998)

Shift from cultural to creative

Challenging distinction between what is creative and what is not

Howkins (2001)

Linkage between creativity and Focus on the output of creative IPRs: a measurable definition industries, neglecting the sources of creativity and innovation

University of Hong Kong (2003)

Shift from industries to functions

Generation of creativity in the conception phases, neglecting novel ideas coming from the making (e.g. process innovation)

WIPO (2003)

Different levels of creativity between industries

Copyrights as the unique dimension to study creativity in the Macrosector

KEA (2006)

Diffusion mechanisms of creative ideas

Ex-ante assignment of the level of creativity according to a mere industrial logic

NESTA—Higgs et al. (2008)

Occupations as creative employment measures

Restriction to core creative activities, neglecting creativity in related chains

UNCTAD (2010)

Multidimensional approach to CCIs, accounting for trade and value chains

Specific focus on developing countries

European Commission (2010)

CCIs as enablers for European regional competitiveness

A pure policy perspective

NESTA—Bakhshi et al (2013)

New concept of creative intensity

No separation between the contribution of creatives and the output of the industries

European Parliament (2013)

Important piece of legislation Lack of a clear definition and adopted by European simple proposal of a framework institutions as a sustain for CCIs for the support of specific sectors

UNESCO (2013)

Clear and applicable distinction Specific focus on social issues between cultural and creative and inequality industries

38

2 In Pursuit of Creativity in CCIs

2.3 Standing on the Shoulders of Giants: Creative Outputs and Space to Identify Creativity in CCIs Once we have re-examined the history of CCIs and the different approaches used for their classification, a simple question can arise: “Do we need another definition of CCIs? Do we need to classify them in another way?”. The main conclusion achievable from what has been discussed until now is that CCIs are an extremely difficult object to handle because they are not homogeneous in many of their features and in the way creativity is present. Moreover, in what was previously said, we did not enter the debate on the list of sectors included in the world of CCIs. In fact, there is no theoretical agreement on the group of sectors to be included in the analysis (Boix-Domenech and RausellKöster 2018; Galloway and Dunlop 2007) and, at this stage, two choices are possible: providing a new definition that can update once more the list of sectors to be included in the sphere of CCIs or improving an already existing list, the most convincing one. The latter option is more attractive when the aim is to provide a clearer interpretative key for the evaluation of the phenomenon. In his White Paper on Creativity in 2009, Walter Santagata presented a wide and detailed conceptualisation. He selected 12 sectors belonging to CCIs according to the spheres in which creativity is translated for the production of culture, summarised in Table 2.2. There are three spheres: material culture; production of content; and historic and artistic heritage. To material culture, he associated Fashion, Industrial Design and Crafts, and the Food and Wine Industry. This choice, especially for Food and Wine denotes the linkage with Italian tradition, as he aimed at assessing the Macrosector of CCIs in Italy. The goods produced by these industries can also be defined as status goods because people identify themselves in the clothes they wear, in the food they eat, and in the crafts they buy. This is one of the essences of CCIs: giving people a way to express themselves. To the production of content, information and communications, he linked the ‘classical’ cultural industries related to the capacity to conserve, reproduce and transmit, often in digital form, sounds and images, i.e. Publishing, TV and Radio, and Film. Plus, in this sphere he included also Computer Software and Advertising, whose products are deemed to be characterised by the creative content of products and the Table 2.2 Division of CCIs according to Santagata (2009) Material culture (status goods)

Production of content (semiotic good)

Historic and artistic heritage (experience goods)

1. Fashion 2. Industrial design and craft 3. Food and Wine industry

4. Computer and Software 5. Publishing 6. TV and Radio 7. Advertising 8. Films

9. Cultural heritage 10. Music and performing arts 11. Architecture 12. Contemporary art

2.3 Standing on the Shoulders of Giants: Creative Outputs and Space …

39

mass distribution of their content (Santagata 2009). Although the content goods are mass consumption goods, they embed signs. Films, advertising campaigns, and TV shows are full of symbols that more or less directly have a strong impact on the behaviour of people, shaping their habits and desires. That is why they can also be called semiotic goods. Finally, as creativity is associated with the historical and artistic heritage, considered as both the remains of past generations and the current cultural and artistical production, he fitted into this sphere the activities related to the Cultural Heritage (e.g. museums or archives), Music and Performing Arts and Contemporary art, considered as the contemporary artistic and cultural creations of modern times, and he concluded with Architecture, as the architect’s intellectual work is also related to the symbolism typical of artistic creations (Santagata 2009). These sectors are those behind the experience economy. Indeed, people are now more and more willing to pay for living strong and touching experiences, and that is what Historic and Artistic Heritage goods do. The subdivision of CCIs into these three clusters follows the heterogeneous nature of the Macrosector. Santagata himself put a lot of effort in trying to provide a logic for a rational classification of CCIs and he reconciled the creative and cultural output with the heterogeneity of goods that can be produced by CCIs. The subdivision that Santagata proposed is relevant for the sake of this work because it reveals two aspects. First, it acknowledges that the spheres in which CCIs can be subdivided are different and heterogeneous, contributing to be precise and polished in the definition of the boundaries of CCIs. Secondly, he rightly identified that the output of CCIs is the main element that deserves attention since the individual creative ideas feed the industries but they result in different outputs, a source of heterogeneity. Three main aspects can be considered for a simple but clear assessment of pros and cons in Santagata’s work: the definition of CCIs, the definition of creativity, and the relationship between creativity and territory. They are discussed in Table 2.3. Table 2.3 Pros and Cons of Santagata’s model PROS

CONS

Definition of CCIs

Fine-tuned identification of CCIs Function-driven distribution of through a creative value chain activities with no reference to creativity

Definition of creativity

Creativity interpreted as a multifaced element

Sector driven definition

Relationship between creativity and territory

Place-related creativity

No conceptualisation of the relationship between creativity and territories

40

2 In Pursuit of Creativity in CCIs

Considering the definition of CCIs, the value added of the White Paper is its extremely well-defined sectoral disaggregation. Santagata and his team selected all the subsectors, expressed through ATECO codes (2002 version)7 at 4 or even 5 digits. This granularity allowed to cluster creative activities according to the phases of a common production chain, creating a distinction between activities related to the conception, to the production, and to the distribution. However, sectoral activities distributed in the value chain according to functions with no reference to creativity. In other words, more or less intentionally, the bespoken creativity of the conception phase is substantially higher compared to the two other ones: production and distribution. In fact, the conception phase is where the invention is supposed to take place, where the potential registration of intellectual property right occurs, while in production- and distribution-related activities the creative content has already been generated. This result has a limitation because an activity can be equally creative even if not related to a specific step in the value chain of production. For instance, creativity may result in a patent obtained thanks to a creative improvement in the production process materialised in activities related to the production, or a trademark might be registered by a retail company working in the distribution phase that aims at protecting its image against the competitors. Considering the definition of creativity, Santagata properly binds creativity and culture and he describes the binding as a multifaced element applied to CCIs. Using his words, ‘creativity can be found in culture, in the territory we live in, in the quality of our everyday life and the products we use. It is not an end in itself, but a process, an extraordinary means for producing new ideas. In this sense, creativity and culture are the pillars of social quality, seen as a context of a free, economically developed, fair and culturally lively community with a high quality of life. Creativity and culture are inextricably bound. They are a successful combination which in periods of strategic transition can position a country in the international process of globalization’ (Santagata 2010, p. 34). This implies that it is important to detect where creativity truly resides, but it remains key to setting the boundaries of the analysis. Here, the boundaries have been set through the identification of activities related to culture. Santagata acknowledges that creativity is not the end of the investigation, it is acceptable only if it produces quality and valuable contents. Due to the intangibility of creativity, it is often considered as unitary, an indefinite object affecting the economic activities. In the White Paper on creativity, Santagata identifies three spheres in which creativity is expressed for the production of cultural contents, used for the identification of the sectors to be analysed just presented above. This is a true novelty in the literature on CCIs because the classification is driven by the different forms of creativity. Moreover, it is a value added because it opens the door to a deeper investigation of the forms through which creativity expresses itself. However, although the model properly recognises the many-sided dimension of creativity, the application presents two important weaknesses. The different forms of creativity apply upstream for the selection of industries but each and every industry belonging to CCIs is composed of 7

ATECO is the Italian classification of economic activities (ATtività ECOnomiche). The 2002 version is based on NACE Rev. 1.1.

2.3 Standing on the Shoulders of Giants: Creative Outputs and Space …

41

heterogeneous activities that express creativity differently. For instance, the model considers that in the industry of Fashion, creativity is expressed only in one way, for the production of material culture. However, inside Fashion there are different activities that may show different creative attitudes that are not accounted for by the model. Then, complementarily the modes themselves are deemed weak to define the ways in which creativity is expressed. Both the material culture, the production of content, and the historic and artistic heritage are related to the kind of output of the entire production process embedded in each industry and not as an output of a creative process, as it should be. All this leads to the consideration that the distinction should not be made according to similarities in the kind of output of the industries but rather through the different expressions of creative ideas. As recalled above, in the field of CCIs, creativity has often been associated to the generation and protection of intellectual property rights like patents, trademarks, and copyrights that, in any case, are forms of innovation, both hard and soft ones (Stoneman 2010). They are not only three different forms of innovation, aiming at protecting different inventions, but they represent extremely different forms of creative expressions. In fact, EPO and EUIPO (2016) highlighted what is required for the protection of the product at issue: the patents need novelty, Inventive step, and industrial applicability; trademarks claim distinctiveness; while copyrights necessitate only the originality of the work, irrespective of its literary or artistic merit. These different forms of innovation are rooted in different creative expressions that a framework aimed at assessing the phenomenon of CCIs and its socio-economic effects should not neglect. In fact, both the input required for the creative process and the output generated are expected to be peculiar to a specific creative model. In other words, what triggers creativity to produce a patent and what are the local spillovers generated by this creative process are supposed to be unequal to what is related to trademarks or copyrights. Finally, the framework presented in the White Paper is built on the innovative idea that creativity is somehow related with territory as the cultural environment impacts the production of creativity. This cultural environment is not only composed of museums or tangible forms of cultural heritage but especially of all the loci that stand in as culture conveyors. In this regard, the districts where a stronger sense of identity and strong social cohesion generating trust and co-operation is in place tend to transmit culture and stimulate creativity at a stronger pace (Santagata 2009). However, local areas are extremely heterogeneous along several dimensions like the different infrastructure endowment, the different quality of the production factors, the different technological knowledge, and culture itself differs substantially as well. The White Paper did not go in depth in investigating the role that different local features had in the flourishing and the development of CCIs either in Italy or in Europe. The role of territories in shaping the intensity and the form of creativity in CCIs has been largely overlooked by theoretical and empirical literature, although it is a key aspect to consider. To better understand this process, this idea builds on a vast theory explaining the capacity of regions and local systems to generate knowledge and innovation. Indeed, ideas are not randomly generated in space but they tend to

42

2 In Pursuit of Creativity in CCIs

concentrate in specific areas, due to the capacity of the systems to stimulate them. Regional Science is rich in theories explaining the ability of regions and local systems to trigger new ideas and the role of space for knowledge creation has been conceptualised in many different ways.8 In the first place, space has been described only in terms of physical distance: knowledge diffusion happens through the pure likelihood of contact between agents who had already adopted an innovation and its potential adopters (Hägerstrand 1966), considering the economic distance among them a paramount factor of interaction (Griliches 1957; Mansfield 1961). The importance of space in explaining the heterogeneity of actors in the process of innovation was also highlighted by studies on the natural tendency of innovative activity to concentrate in space, due to all the advantages typical of an agglomerated setting like easy information exchange, face-to-face exchanges, and the presence of skilled individuals. This idea has been developed and emphasised by the theory on knowledge spillover (among others, see Jaffe 1989; Acs et al. 1994; Audretsch and Feldman 1996). In other words, knowledge spills over its boundaries thanks to a rapid exchange of information and transmission of tacit knowledge, possible through the spatial proximity between actors. Indeed, in a concentrated location, the knowledge generated by a firm’s research and development is not bounded within the plant, but it spills over into the surrounding environment, benefitting the innovative activity of others. However, this approach focused on the sources of knowledge diffusion, rather than the processes favouring the learning. That is why the concept of relational capital was put at the centre by the milieu innovateur theory (Aydalot 1986; Camagni 1991; Maillat Quévit et al. 1993). Relationships, especially the informal ones, were interpreted as the innovation triggers of firms thanks to the reduction of uncertainty typical of innovation processes. In this milieu innovateur theory, the attention shifted to a cognitive-based form of proximity, setting the space as “relational” (Camagni 1980). Relational proximity interprets the capacity of local firms to cooperate, exchange ideas and share the risks and uncertainties linked to creative processes. Knowledge spreads around thanks to longlasting suppliers-customers relationships, spin-offs, and a high mobility of the local labour market. These relationships outline how inextricably bound the local industries are with the territorial industrial base. The process of knowledge acquisition is complex however, as complex as knowledge itself. Hence, theories have put emphasis on a systemic approach, in order to understand how a region learns and innovates (Asheim 1996; Lundvall and Johnson 1994). Learning requires cooperation and interaction between actors either in formal or informal ways resulting in the strong concentration of the innovative activity. Therefore, innovation cannot be understood properly without considering the sociocultural and institutional context in which it takes place (Amin and Thrift 1994), giving rise to the concept of institutional proximity.9 The systemic approach to regional innovation converged into the Regional Innovation Systems (RIS), identifying the interaction between two sub-systems of actors as the determining factor 8

For a detailed review of the theories of innovation in space cf. Capello (2019). Institutions should be read according to North’s perspective, namely as a set of norms and “rules of the game” (North 1990).

9

2.4 Conclusions

43

of innovation. The ‘infrastructure system’ formed by universities and research laboratories creates new knowledge that is adopted by the ‘business system’ formed by local firms. The continuous interaction between these two systems explains the regional capacity to innovate (Cooke 2001; Cooke et al. 2004; Edquist and Johnson 1997). The systemic approach to regional innovation stressed the importance of the interplay between actors explaining the differential Inventiveness. However, another stream of thought has emphasised that, for the creation of new innovations, firms need to have something in common, i.e. they need to figuratively speak the same language. The concept of the common knowledge base is known as ‘related variety’, and it was defined as a variety of interrelated technological solutions with a common knowledge basis (Boschma 2005; Boschma and Iammarino 2009; Frenken et al. 2007; Nooteboom 2000).10 In recent times, all kind of proximities have been applied to account for both informal ways of knowledge diffusion and external sources of knowledge (Capello 2019) through the idea of regional patterns of innovations: they are defined as spatial breakdown of the single, logical phases of the innovation path—from invention based on new knowledge, to innovation, ending up in development—built on the presence/absence of territorial preconditions for knowledge creation, knowledge attraction and innovation (Camagni and Capello 2013; Capello and Lenzi 2013). In simpler words, they represent different models of knowledge creation that can take place in a region, according to the territorial preconditions that allow innovation. However, this literature has been marginally touched by the literature on CCIs. Indeed, it is centred on traditional innovations and not on creativity in general, building on a vast theory explaining the capacity of regions and local systems to generate knowledge and innovation. Moreover, although several studies investigated the reasons behind the process of clustering of CCIs (Boix et al. 2015; Lazzeretti et al. 2012; Lorenzen and Frederiksen 2007; Sánchez Serra 2016; Trippl et al. 2012), the role of space in shaping the innovativeness of these actors has been overlooked by both theoretical and empirical literature. This limitation will be addressed by the novel definition and framework of creativity that will be presented in the next chapter.

2.4 Conclusions This chapter has the aim of finding an interpretative key in the huge literature on the theoretical dimension to study CCIs. In fact, as extensively discussed, a uniform and clear rationale is still missing, and this creates a natural confusion for all those approaching the topic. All the works reviewed until now attempted in some way to detect the most creative sectors within the aggregate of CCIs and the literature proposed several ways to classify CCIs in a proper way to reach the goal. It is the case of the differentiation between 10

The concept of cognitive proximity will be recalled in Chap. 40 when discussing the factors triggering the clustering of CCIs.

44

2 In Pursuit of Creativity in CCIs

creative and cultural industries; the distinction between core copyright industries and the rest; or it is also the difference found within each industry, according to common phases of an ideal creative production chain. All these attempts received either strong support or disapproval by critics for the level of detail of their analysis and for the capacity to be applied more or less properly to data. The focus of the discussion was on how the recognised heterogeneity present within the concept of CCIs has been systematised by the different studies presented in recent years. After a first scepticism from the academic community, the idea of CCIs entered the debate abruptly at the end of the last Millennium. From that moment, the analysis tried to go more and more in depth to create categories to distinguish the core creative activities from all the others. The heterogeneity has been studied mainly between and within industries and according to an occupational logic. Furthermore, from this analysis a confused picture emerges on the forms that creativity take within CCIs with no agreement on a specific idea. Indeed, copyrights, patents, creative tasks and other forms have been introduced indiscriminately into the discussion and they seem complementary types of definitions of creativity, rather than substitute ones. In this sense, although detailed, these approaches presented some limitations. The effort made to retrace the evolution of the theoretical approaches to CCIs was not only an end in itself, but it was also useful to identify the gaps that future works could fill. The title assigned to this chapter is meant to keep in mind what should be the goal of a proper approach to the topic: the presence and the form of creativity within CCIs. Indeed, as recalled throughout all the discussion, it is creativity that drives any analysis on this topic. In this sense, as it has been acknowledged that CCIs are heterogeneous and that creativity can take different forms in the world of CCIs, these different forms of creativity can be used to appropriately detect heterogeneity. Future works aiming at identifying the impacts that CCIs have on socio-economic development should take this discussion into account to develop a framework able to capture the different forms of creativity embedded in each sector belonging to the Macrosector of CCIs. This effort can help to better understand the differentiated impact that various forms of creativity have. This review helped to identify the limitations of the previous approaches used in the literature in order to find a way to fix them in future steps of the work. Under this perspective, balancing advantages and disadvantages, the definition and approach presented in Santagata (2009) are deemed the most suitable as a starting point. In Chap. 3, a new methodology for the evaluation of CCIs will be presented. It will absorb the advantages of the Italian Model and it will attempt to overcome its limitations. Merging industries, creativity and space, it will present a novel theoretical definition of creativity in CCIs, analysing every aspect of it. Secondly, as creativity can take different forms, the model will account for different creative expressions, to understand whether each activity can be considered creative or not. Finally, the territory will play a central role in the model. In fact, creative activities will be found to perform differently in different areas and the model will screen which European regions host them, in order to discover where creativity is rooted and in what forms. Thus, in other words, the idea is to describe the phenomenon of CCIs in a way that adheres as much as possible to reality.

References

45

References Acs, Z.J., D.B. Audretsch, and M.P. Feldman. 1994. R & D Spillovers and Recipient Firm Size. The Review of Economics and Statistics 76 (2): 336–340. https://doi.org/10.2307/2109888. Adorno, T.W. 1981. The Culture Industry—Selected Essays on Mass Culture (1st ed.). Routledge. Adorno, T. W., and M. Horkheimer. 1947. The Dialectic of the Enlightenment, ed. G.S. Noerr. Stanford University Press. Amabile, T. M. 1996. Creativity and Innovation in Organizations, 396–239. Harvard Business School Background Note. Amin, A., and N. Thrift. 1994. Globalization, Institutions, and Regional Development in Europe. Oxford University Press. Andersson, Å. 1985. Creativity and Regional Development. Papers of the Regional Science Association 56 (1): 5–20. https://doi.org/10.1007/BF01887900. Andersson, Å. 2011. Creative People Need Creative Cities. In Handbook of Creative Cities, ed. D.E. Andersson, Å.E. Andersson, and C. Mellander, 14–55. Edward Elgar Publishing. Andersson, Å., D.E. Andersson, B. Hårsman, and Z. Daghbashyan. 2015. Complexity, scientific creativity and clustering. In The Rise of the City, ed. K. Kourtit, P. Nijkamp, and R.R. Stough. 15–32. Edward Elgar Publishing. Andersson, Å., D. Batten, K. Kobayashi, and K. Yoshikawa. 1993. Logistical Dynamics, Creativity and Infrastructure. In The Cosmo-Creative Society, ed. Å. Andersson, D.F. Batten, K. Kobayashi, and K. Yoshikawa, 1–16. Springer Berlin Heidelberg. Andersson, Å., and M.J. Beckmann. 2009. Economics of Knowledge: Theory, Models and Measurements. Edward Elgar Publishing. Andersson, Å., and U. Strömquist. 1989. The Emerging C-Society. In Transportation for the Future, 29–39. Springer Berlin Heidelberg. Asheim, B.T. 1996. Industrial Districts as ‘Learning Regions’: A Condition for Prosperity. European Planning Studies 4 (4): 379–400. https://doi.org/10.1080/09654319608720354. Audretsch, D.B., and M.P. Feldman. 1996. R&D Spillovers and the Geography of Innovation and Production. The American Economic Review 86 (3): 630–640. http://www.jstor.org/stable/211 8216. Aydalot, P. 1986. Milieux innovateurs en Europe. GREMI. Bakhshi, H., A. Freeman, P. Higgs. 2013. A Dynamic Mapping of the UK’s Creative Industries. https://media.nesta.org.uk/documents/a_dynamic_mapping_of_the_creative_industries.pdf. Barroso, J.M. 2009. Political Guidelines for the Next Commission. Baumeister, R.F. 2005. The Cultural Animal. Oxford University Press. https://doi.org/10.1093/acp rof:oso/9780195167030.001.0001. Becattini, G (ed.). 1975. Lo sviluppo economico della Toscana: con particolare riguardo all’industrializzazione leggera. IRPET. Becattini, G. 1989. Riflessioni sul distretto industriale marshalliano come concetto socioeconomico. Stato e Mercato 25 (1): 111–128. Boden, M.A. 1994. Précis of The Creative Mind: Myths and Mechanisms. Behavioral and Brain Sciences 17 (3): 519–531. https://doi.org/10.1017/S0140525X0003569X. Boden, M.A. 2004. The Creative Mind. Routledge. Boix-Domenech, R., B. De Miguel-Molina, and P. Rausell-Köster. 2021. The Impact of Cultural and Creative Industries on the Wealth of Countries, Regions and Municipalities. European Planning Studies 1–21.https://doi.org/10.1080/09654313.2021.1909540. Boix-Domenech, R., and P. Rausell-Köster. 2018. The Economic Impact of the Creative Industry in the European Union. In Drones and the Creative Industry, 19–36. Springer International Publishing. https://doi.org/10.1007/978-3-319-95261-1_2. Boix, R., B. De-Miguel-Molina, and J.-L. Hervas-Oliver. 2013. Creative Service Business and Regional Performance: Evidence for the European Regions. Service Business 7 (3): 381–398. https://doi.org/10.1007/s11628-012-0165-7.

46

2 In Pursuit of Creativity in CCIs

Boix, R., J.L. Hervás-Oliver, and B. De Miguel-Molina. 2015. Micro-geographies of Creative Industries Clusters in Europe: From Hot Spots to Assemblages. Papers in Regional Science 94 (4): 753–772. https://doi.org/10.1111/pirs.12094. Boix, R., and V. Soler. 2017. Creative Service Industries and Regional Productivity. Papers in Regional Science 96 (2): 261–279. https://doi.org/10.1111/pirs.12187. Boschma, R. 2005. Proximity and Innovation: A Critical Assessment. Regional Studies 39 (1): 61–74. https://doi.org/10.1080/0034340052000320887. Boschma, R., and S. Iammarino. 2009. Related Variety, Trade Linkages, and Regional Growth in Italy. Economic Geography 85 (3): 289–311. https://doi.org/10.1111/j.1944-8287.2009.010 34.x. Boutier, J., and Y. Sintomer. 2014. La République de Florence (12e–16e siécle). Revue Française de Science Politique 64 (6): 1055. https://doi.org/10.3917/rfsp.646.1055. Buitrago Restrepo, F., and I. Duque Márquez. 2013. The Orange Economy—An Infinite Opportunity. Inter-American Development Bank. Camagni, R. 1980. Teorie e Modelli di Localizzazione delle Attivita’ Industriali. Giornale Degli Economisti e Annali Di Economia, Nuova Seri(No. 3/4), 183–204. https://www.jstor.org/stable/ 23244153. Camagni, R. 1991. Local Milieu, Uncertainty and Innovation Networks: Towards a New Dynamic Theory of Economic Space. In Innovation Networks: Spatial Perspectives, ed. R. Camagni. Belhaven-Pinter. Camagni, R., and R. Capello. 2013. Regional Innovation Patterns and the EU Regional Policy Reform: Toward Smart Innovation Policies. Growth and Change 44 (2): 355–389. https://doi. org/10.1111/grow.12012. Capello, R. 2019. Theories of Innovation in Space: Path-Breaking Achievements in Regional Science. In Handbook of Regional Growth and Development Theories, ed. R. Capello and P. Nijkamp, Revised an 240–256. Edward Elgar Publishing. Capello, R., and C. Lenzi. 2013. Territorial Patterns of Innovation: A Taxonomy of Innovative Regions in Europe. The Annals of Regional Science 51 (1): 119–154. https://doi.org/10.1007/ s00168-012-0539-8. Centre for International Economics. 2009. Creative Industries Economic Analysis—Final Report. https://opus.lib.uts.edu.au/bitstream/10453/34839/1/CIIC_COLLATERAL_55_original.pdf. Coll-Martínez, E., A.-I. Moreno-Monroy, and J.-M. Arauzo-Carod. 2019. Agglomeration of Creative Industries: An Intra-Metropolitan Analysis for Barcelona. Papers in Regional Science 98 (1): 409–431. https://doi.org/10.1111/pirs.12330. Comunian, R., L. England, A. Faggian, and C. Mellander. 2021. The Economics of Talent—Human Capital, Precarity and the Creative Economy. Springer International Publishing. https://doi.org/ 10.1007/978-3-319-95124-9. Comunian, R., and A. Faggian. 2011. Higher Education and the Creative City. In Handbook of Creative Cities, ed. D.E. Andersson, Å. Andersson, and C. Mellander. Edward Elgar Publishing. Cooke, P. 2001. Regional Innovation Systems, Clusters, and the Knowledge Economy. Industrial and Corporate Change 10 (4): 945–974. https://doi.org/10.1093/icc/10.4.945. Cooke, P., M. Heidenreich, and H.-J. Braczyk (eds.). 2004. Regional Innovation Systems: The Role of Governance in a Globalised World (2nd ed.). Routledge. Csikszentmihalyi, M. 1999. Implications of a Systems Perspective for the Study of Creativity. In Handbook of Creativity, ed. R. J. Sternberg, 313–336. Cambridge University Press. https://doi. org/10.1017/CBO9780511807916.018. DCMS. 1998. Creative Industries Mapping Documents. DCMS. 2001. Creative Industries Mapping Documents. DCMS. 2013. Classifying and Measuring the Creative Industries. https://assets.publishing.service. gov.uk/government/uploads/system/uploads/attachment_data/file/203296/Classifying_and_ Measuring_the_Creative_Industries_Consultation_Paper_April_2013-final.pdf.

References

47

de-Miguel-Molina, B., J.-L. Hervas-Oliver, R. Boix, and M. De-Miguel-Molina. 2012. The Importance of Creative Industry Agglomerations in Explaining the Wealth of European Regions. European Planning Studies 20 (8): 1263–1280. https://doi.org/10.1080/09654313.2012.680579. de Oliveira, S., and R.E. Nisbett. 2017. Culture Changes How We Think About Thinking: From “Human Inference” to “Geography of Thought.” Perspectives on Psychological Science 12 (5): 782–790. https://doi.org/10.1177/1745691617702718. Edquist, C., and B. Johnson. 1997. Institutions and Organizations in Systems of Innovation. In System of Innovation. Technologies, Institutions and Organizations, 41–63. Printer Publisher. EPO, and EUIPO. 2016. Intellectual Property Rights Intensive Industries and Economic Performance in the European Union. European Commission. 2010. Green Paper—Unlocking the Potential of Cultural and Creative Industries. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex:52010DC0183. European Council. 2018. Proposal for a Regulation of the European Parliament and of the Council Establishing the Creative Europe programme (2021 to 2027) and Repealing Regulation (EU) No 1295/2013. https://data.consilium.europa.eu/doc/document/ST-9170-2018-INIT/en/pdf. European Parliament, & European Council. 2013. Regulation (EU) No 1295/2013 of the European Parliament and of the Council of 11 December 2013 establishing the Creative Europe Programme (2014 to 2020) and Repealing Decisions No 1718/2006/EC, No 1855/2006/EC and No 1041/2009/EC. http://data.europa.eu/eli/reg/2013/1295/oj. Flew, T. 2012. The Creative Industries: Culture and Policy. SAGE Publications Ltd. https://doi.org/ 10.4135/9781446288412. Florida, R. 2002. The Rise of the Creative Class: And How It’s Transforming Work, Leisure. Community and Everyday Life: Basic books. Florida, R., C. Mellander, and K. Stolarick. 2008. Inside the Black Box of Regional Development— Human Capital, the Creative Class and Tolerance. Journal of Economic Geography 8 (5): 615– 649. https://doi.org/10.1093/jeg/lbn023. Freeman, A. 2004. London’s Creative Sector: 2004 Update (Issue April). https://www.london.gov. uk/sites/default/files/creative_sector2004.pdf Frenken, K., F. van Oort, and T. Verburg. 2007. Related Variety, Unrelated Variety and Regional Economic Growth. Regional Studies 41 (5): 685–697. https://doi.org/10.1080/003434006011 20296. Fujita, M. 2007. Towards the New Economic Geography in the Brain Power Society. Regional Science and Urban Economics 37 (4): 482–490. https://doi.org/10.1016/j.regsciurbeco.2007. 04.004. Galbraith, J.R. 1982. Designing the Innovating Organization. Organizational Dynamics 10 (3): 5–25. https://doi.org/10.1016/0090-2616(82)90033-X. Galloway, S., and S. Dunlop. 2007. A Critique of Definitions of the Cultural and Creative Industries in Public Policy. International Journal of Cultural Policy 13 (1): 17–31. https://doi.org/10.1080/ 10286630701201657. Garnham, N. 1990. Capitalism and Communication. SAGE Publications Ltd. Garnham, N. 2005. From Cultural to Creative Industries. International Journal of Cultural Policy 11 (1): 15–29. https://doi.org/10.1080/10286630500067606. Government of Australia. 1994. Creative Nation: Commonwealth Cultural Policy. https://apo.org. au/node/29704. Government of Australia. 2013. Creative Australia: National Cultural Policy. https://apo.org.au/ node/33126. Granados, C., M. Bernardo, and M. Pareja. 2017. How do creative industries innovate? A Model Proposal. Creative Industries Journal 10 (3): 211–225. https://doi.org/10.1080/17510694.2017. 1393192. Griliches, Z. 1957. Hybrid Corn: An Exploration in the Economics of Technological Change. Econometrica 25 (4): 501. https://doi.org/10.2307/1905380. Gunster, S. 2000. Revisiting the Culture Industry Thesis: Mass Culture and the Commodity Form. Cultural Critique 45: 40. https://doi.org/10.2307/1354367.

48

2 In Pursuit of Creativity in CCIs

Güss, D.C. 2011. Fire and Ice. Journal of Cross-Cultural Psychology 42 (7): 1279–1298. https:// doi.org/10.1177/0022022110383320. Hägerstrand, T. 1966. Aspects of the Spatial Structure of Social Communication and the Diffusion of Information. Papers of the Regional Science Association 16 (1): 27–42. https://doi.org/10. 1007/BF01888934. Hall, P. 2000. Creative Cities and Economic Development. Urban Studies 37 (4): 639–649. https:// doi.org/10.1080/00420980050003946. Harrison, L.E., and S. Huntington. 2001. Culture Matters: How Values Shape Human Progress. Basic books. Hartley, J., Potts, J., Cunningham, S., Flew, T., Keane, M., and Banks, J. (2013). Key Concepts in Creative Industries. In Key Concepts in Creative Industries, ed. C. Rojek. SAGE Publications, Inc. https://doi.org/10.4135/9781526435965. Healy, K. 2002a. What’s New for Culture in the New Economy? The Journal of Arts Management, Law, and Society 32 (2): 86–103. https://doi.org/10.1080/10632920209596967. Healy, K. 2002b. Digital Technology and Cultural Goods. Journal of Political Philosophy 10 (4): 478–500. https://doi.org/10.1111/1467-9760.00162. Hesmondhalgh, D. 2008. Cultural and Creative Industries. In The SAGE Handbook of Cultural Analysis. SAGE Publications Ltd. Hesmondhalgh, D., and A.C. Pratt. 2005. Cultural Industries and Cultural Policy. International Journal of Cultural Policy 11 (1): 1–13. https://doi.org/10.1080/10286630500067598. Higgs, P., S. Cunningham, and H. Bakhshi. 2008. Beyond the Creative Industries: Mapping the Creative Economy in the United Kingdom. Hirschman, E.C. 1980. Innovativeness, Novelty Seeking, and Consumer Creativity. Journal of Consumer Research 7 (3): 283. https://doi.org/10.1086/208816. Hong, J., W. Yu, X. Guo, and D. Zhao. 2014. Creative Industries Agglomeration, Regional Innovation and Productivity Growth in China. Chinese Geographical Science 24 (2): 258–268. https:// doi.org/10.1007/s11769-013-0617-6. Howkins, J. 2001. The Creative Economy—How People Make Money from Ideas. Penguin Global. Jaffe, A.B. 1989. Real Effects of Academic Research. The American Economic Review 79 (5): 957–970. http://www.jstor.org/stable/1831431 Kandel, E. 2012. The Age of Insight: The Quest to Understand the Unconscious in Art, Mind, and Brain, from Vienna 1900 to the Present. Random House. KEA. (2006). The Economy of Culture in Europe. https://ec.europa.eu/assets/eac/culture/library/ studies/cultural-economy_en.pdf Landry, C. 2008. The Creative City: A Toolkit for Urban Innovators (2nd ed.). Routledge. Landry, C., and Bianchini, F. 1995. The Creative City. Demos. Lazzeretti, L (ed.). 2012. Creative Industries and Innovation in Europe. Routledge. https://doi.org/ 10.4324/9780203112571. Lazzeretti, L., F. Capone, and R. Boix. 2012. Reasons for Clustering of Creative Industries in Italy and Spain. European Planning Studies 20 (8): 1243–1262. https://doi.org/10.1080/09654313. 2012.680585. Lee, N., and A. Rodríguez-Pose. 2014. Creativity, Cities, and Innovation. Environment and Planning a: Economy and Space 46 (5): 1139–1159. https://doi.org/10.1068/a46202. Lorenzen, M., and L. Frederiksen. 2007. Why do Cultural Industries Cluster? Localization, Urbanization, Products and Projects. In Creative Cities, Cultural Clusters and Local Economic Development, ed. P. Cooke, and L. Lazzeretti. Edward Elgar Publishing. Lundvall, B., and B. Johnson. 1994. The Learning Economy. Journal of Industry Studies 1 (2): 23–42. https://doi.org/10.1080/13662719400000002. Maillat, D., M. Quévit, and L. Senn, eds. 1993. Réseaux d’innovation et milieux innovateurs : un pari pour le développement régional. EDES: Neuchâtel. Mansfield, E. 1961. Technical Change and the Rate of Imitation. Econometrica 29 (4): 741. https:// doi.org/10.2307/1911817.

References

49

Marco-Serrano, F., P. Rausell-Köster, and R. Abeledo-Sanchis. 2014. Economic Development and the Creative Industries: A Tale of Causality. Creative Industries Journal 7 (2): 81–91. https:// doi.org/10.1080/17510694.2014.958383. Markusen, A., G.H. Wassall, D. DeNatale, and R. Cohen. 2008. Defining the Creative Economy: Industry and Occupational Approaches. In Economic Development Quarterly. https://doi.org/ 10.1177/0891242407311862. Marrocu, E., and R. Paci. 2012a. Regional Development and Creativity. International Regional Science Review 36 (3): 354–391. https://doi.org/10.1177/0160017612461915. Marrocu, E., and R. Paci. 2012b. Education or Creativity: What Matters Most for Economic Performance? Economic Geography 88 (4): 369–401. https://doi.org/10.1111/j.1944-8287.2012.011 61.x. Martin, R., R. Florida, M. Pogue, and C. Mellander. 2015. Creativity, Clusters and the Competitive Advantage of Cities. Competitiveness Review 25 (5): 482–496. https://doi.org/10.1108/CR-072015-0069. Mellander, C. 2009. Creative and Knowledge Industries: An Occupational Distribution Approach. Economic Development Quarterly 23 (4): 294–305. https://doi.org/10.1177/089124240934 3808. MKW Wirtschaftsforschung GmbH. 2001. Exploitation and Development of the Job Potential in the Cultural Sector in the Age of Digitalisation. https://publications.europa.eu/en/publicationdetail/-/publication/e3e9ef3f-3bfc-41a3-9ba6-9f7baaad6d0e Müller, K., C. Rammer, and J. Trüby. 2009. The Role of Creative Industries in Industrial Innovation. Innovation 11 (2): 148–168. https://doi.org/10.5172/impp.11.2.148. Nisbett, R.E., and Y. Miyamoto. 2005. The Influence of Culture: Holistic Versus Analytic Perception. Trends in Cognitive Sciences 9 (10): 467–473. https://doi.org/10.1016/j.tics.2005.08.004. Nooteboom, B. 2000. Learning and Innovation in Organizations and Economies. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199241002.001.0001/acprof-9780199241002. North, D.C. 1990. Institutions, Institutional Change and Economic Performance. Cambridge University Press. 10.1017/CBO9780511808678. O’Connor, J. 2004. A Special Kind of City Knowledge’: Innovative Clusters, Tacit Knowledge and the ‘Creative City. Media International Australia 112 (1): 131–149. https://doi.org/10.1177/132 9878X0411200111. OECD, and Eurostat. 2005. Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3ed edn. The Measurement of Scientific and Technological Activities (The Measurement of Scientific and Technological Activities). https://doi.org/10.1787/9789264013100-en. Pratt, A.C. 1997. The Cultural Industries Production System: A Case Study of Employment Change in Britain, 1984–91. Environment and Planning A: Economy and Space 29 (11): 1953–1974. https://doi.org/10.1068/a291953. Pratt, A.C. 2005. Cultural Industries and Public Policy. International Journal of Cultural Policy 11 (1): 31–44. https://doi.org/10.1080/10286630500067739. Redhead, S. 2004. Creative Modernity: The New Cultural State. Media International Australia Incorporating Culture and Policy 112 (1): 9–27. https://doi.org/10.1177/1329878X0411200104. Rowe, D., G. Noble, T. Bennett, and M. Kelly. 2016. Transforming cultures? From Creative Nation to Creative Australia. Media International Australia 158 (1): 6–16. https://doi.org/10.1177/132 9878X16629544. Runco, M.A. 1988. Creativity Research: Originality, Utility, and Integration. Creativity Research Journal 1 (1): 1–7. https://doi.org/10.1080/10400418809534283. Runco, M.A., and G.J. Jaeger. 2012. The Standard Definition of Creativity. Creativity Research Journal 24 (1): 92–96. https://doi.org/10.1080/10400419.2012.650092. Sánchez Serra, D. 2016. Location Determinants of Creative Industries’ Firms in Spain. Investigaciones Regionales—Journal of Regional Research 34 (34): 23–48. http://www.redalyc.org/art iculo.oa?id=28945294002.

50

2 In Pursuit of Creativity in CCIs

Santagata, W. 2002. Cultural Districts, Property Rights and Sustainable Economic Growth. International Journal of Urban and Regional Research 26 (1): 9–23. https://doi.org/10.1111/14682427.00360. Santagata, W. 2009. White Paper on Creativity. In Towards an Italian Model of Development, ed. W. Santagata. Università Bocconi Editore. Santagata, W. 2010. The Culture Factory. Springer Berlin Heidelberg.https://doi.org/10.1007/9783-642-13358-9. Serafinelli, M., and G. Tabellini. 2022. Creativity Over Time and Space. Journal of Economic Growth 27 (1): 1–43. https://doi.org/10.1007/s10887-021-09199-6. Smith, G.J.W. 2005. How Should Creativity Be Defined? Creativity Research Journal, 17 (2–3): 293–295. https://doi.org/10.1080/10400419.2005.9651487 Simonton, D.K. 2009a. Genius 101. Springer. Simonton, D.K. 2009b. Varieties of (Scientific) Creativity: A Hierarchical Model of DomainSpecific Disposition, Development, and Achievement. Perspectives on Psychological Science 4 (5): 441–452. https://doi.org/10.1111/j.1745-6924.2009.01152.x. Simonton, D.K. 2011. Big-C Creativity in the Big City. In Handbook of Creative Cities, ed. D.E. Andersson, Å. Andersson, and C. Mellander. Edward Elgar Publishing. Sternberg, R.J., and T.I. Lubart. 1998. The Concept of Creativity: Prospects and Paradigms. In Handbook of Creativity, ed. R.J. Sternberg, 3–15. Cambridge University Press. Stoneman, P. 2010. Soft Innovation. In Soft Innovation: Economics, Product Aesthetics, and the Creative Industries. Oxford University Press. Sung, T.K. 2015. The Creative Economy in Global Competition. Technological Forecasting and Social Change 96: 89–91. https://doi.org/10.1016/j.techfore.2015.04.003. The Work Foundation. 2007. Staying Ahead: The Economic Performance of the UK’s Creative Industries. https://static.a-n.co.uk/wp-content/uploads/2013/11/4175593.pdf. Throsby, D. 2001. Economics and Culture. Cambridge University Press. Throsby, D. 2008. The Concentric Circles Model of the Cultural Industries. Cultural Trends 17 (3): 147–164. https://doi.org/10.1080/09548960802361951. Thurow, L.C. 1996. The Future of Capitalism. William Morrow & Company. Trippl, M., F. Tödtling, and R. Schuldner. 2012. Creative and cultural industries in Austria. In Creative Industries and Innovation in Europe. Tushman, M.L. 1997. Winning Through Innovation. Strategy & Leadership 25 (4): 14–19. https:// doi.org/10.1108/eb054591. UNCTAD. 2004. Creative Industries and Development—TD(XI)/BP/13. https://unctad.org/en/docs/ tdxibpd13_en.pdf. UNCTAD. 2010. Creative Economy Report. https://unctad.org/en/docs/ditctab20103_en.pdf. UNESCO. 2005. Convention on the Protection and Promotion of the Diversity of Cultural Expressions. General Conference of UNESCO, Paris, October. https://doi.org/10.1177/039219210809 2630. UNESCO. 2013. Creative Economy Report—Special Edition. http://www.unesco.org/culture/pdf/ creative-economy-report-2013.pdf. University of Hong Kong. 2003. Baseline Study on Hong Kong’s Creative Industries. https://www. createhk.gov.hk/en/link/files/baseline_study.pdf. Walberg, H.J. 1988. Creativity and Talent as Learning. In The Nature of Creativity, ed. R.J. Stenberg. Cambridge University Press. Waldman, D. 1977. Critical Theory and Film: Adorno and “The Culture Industry” Revisited. New German Critique 12: 39. https://doi.org/10.2307/487755. Wallas, G. 1926. The Art of Thought. Brace and Company: Harcourt. Westlund, H., and F. Calidoni. 2010. The Creative Class, Social Capital and Regional Development in Japan. Review of Urban and Regional Development Studies 22 (2–3): 89–108. https://doi.org/ 10.1111/j.1467-940X.2010.00171.x.

References

51

Williams, L.K., and S.J. McGuire. 2010. Economic Creativity and Innovation Implementation: The Entrepreneurial Drivers of Growth? Evidence from 63 Countries. Small Business Economics 34 (4): 391–412. https://doi.org/10.1007/s11187-008-9145-7. WIPO. 2003. Guide on Surveying the Economic Contribution of the Copyright-Based Industries. WIPO. 2015. Guide on Surveying the Economic Contribution of the Copyright Industries, Revised ed. https://www.wipo.int/edocs/pubdocs/en/copyright/893/wipo_pub_893.pdf Witkin, R.W. 2003. Adorno on Popular Culture. Routledge. Wyszomirski, M.J. 2004. Defining and Developing Creative Sector Initiatives.

Chapter 3

An Original Framework for the Identification of Creativity in CCIs

3.1 Introduction Focusing on a critical review of the literature on CCIs, the previous Chapter posed many questions, rather than solving doubts. In fact, the discussion presented so far aimed at describing how the literature deals with CCIs and how creativity is shaped within them. In this debate, the main limitation that emerged was related to the lack of a clear-cut approach to the theme, due to the huge heterogeneity present in the world of CCIs. Moreover, the absence of a uniform methodology creates uncertainty in interpreting the findings of the empirical investigations as it is unclear whether they are driven by the way CCIs are identified or it is truly a matter of creativity. Indeed, due to the intangibility of the concept of creativity itself, it is difficult to find a common thread to classify CCIs properly. However, as literature assigns to creativity the role of trigger of socio-economic development, it becomes relevant to provide a precise definition of creativity applicable to CCIs. As discussed in Sect. 2.1, creativity is a multifaceted and intangible concept, extremely difficult to pigeonhole into specific domains. Its features encompass many dimensions, ranging from psychology to social sciences. The literature extensively discussed the ways in which it can be interpreted, outlining the features of both the creative process, the creative product, and also the creative people (Simonton 2011) capable of partly giving a shape to such a complex object. However, although all these dimensions are properly able to describe the phenomenon, each of them is generally applicable to different economic contexts such the cultural and creative industries, and will be a focus of this work. In fact, the definitions of CCIs reviewed in the previous chapter adopt specific perspectives to read the issue of creativity into an industrial domain, some considering the output, some the process, and someone else the people. It is possible to affirm that there is not a better theoretical approach, rather each allows a discussion of the phenomenon according to different angles. It is instead necessary to give a shape to an intangible concept such as creativity and,

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_3

53

54

3 An Original Framework for the Identification of Creativity in CCIs

for this reason, the implementation of a creative industrial process will be used to measure such an intangible phenomenon. Importantly, a novel definition of creativity for CCIs should be context-specific, describing what creativity represents within a specific industrial domain, not everywhere. It will not account for the psychological traits required for the generation of new ideas, rather it will provide features of the industrial implications. This Chapter is the core of this book since, after presenting a novel definition of creativity in CCIs and analysed its intrinsic features, it will go in depth to the description of the ways in which this definition translates into an industrial output and to the ways it can be measured. In fact, CCIs are extremely diverse and, above all, they express creativity in many different modalities. The ways creativity manifests itself will be used as a criterion for a rational classification of activities and, in this context, three overlapping creative modes will be presented with their underlying features. These modes will allow us to proxy different forms of creativity operating in CCIs, which is a useful exercise to assess their differentiated impacts on socio-economic development. Furthermore, one of the substantial innovations of this work resides in the territorialisation of creativity in CCIs. In fact, creativity is rooted in the territories and the local features play a role in shaping it. Hence, it is not possible to omit that a territory acts as an incubator of new ideas, attracting the creative talented class (Glaeser and Maré 2001) thanks to the strong linkage existing between firms and territories that operate synergically. Firms tend to internalise most of the determinants of the territory hosting them (Paniccia et al. 2015) in a co-evolution process (Paniccia et al. 2011). Here, the reasoning follows a rational logic: as creativity is expressed by firms that assimilate territorial characteristics, it is supposed that creativity itself incorporates some territorial features.

3.2 Definition of Creativity in CCIs In the field of CCIs, no topic is more central than their definition, as recalled in the dedicated literature review. Indeed, what defines CCIs remains unclear and an unambiguous approach is still missing. Scholars discussed the deep heterogeneity existing among them and they proposed alternative conceptual approaches for their classification. However, all these attempts received either strong support or disapproval by critics for the level of detail of their analysis and for the capacity to be applied more or less properly to data. The focus of the discussion was on how the recognised heterogeneity present within the concept of CCIs has been systematised by the different studies presented in the last few years.

3.2 Definition of Creativity in CCIs

55

This work, instead, keeps the industrial dimension a step behind. It is acknowledged, in fact, that it is not possible to address the issue of CCIs, due to their heterogeneity and complexity, without specifying the features of the main ingredient: creativity. For this reason, this work identifies as a starting point a novel definition of creativity, applied to CCIs, and translates such a definition into the framework for CCIs’ classification. Chapter 2 has been titled “In pursuit of creativity in Cultural and Creative Industries” as the heterogeneity in CCIs is the result of the different translations that this abstract concept has. In general, creativity itself is a complex object. Every time it was studied, it was at least briefly defined (Runco and Jaeger 2012), meaning that also in psychologicaloriented fields the concept is not extremely clear. As creativity is a vague concept, in order to polish whatever analysis on creativity-related topics, the logic is to address the issue in a deductive way. In the case of CCIs, this work presents a novel definition of creativity that applies only to them, in order to discuss and describe the heterogeneity in the creative forms present in CCIs. Indeed, the aim is not to contribute to the psychological literature proposing an additional definition, maybe pointless in that debate. The goal is instead to delineate the features of a particular economic phenomenon. For this reason, this book proposes the following definition of creativity in CCIs (Box 3.1).

Box 3.1 Definition of Creativity in CCIs Creativity in Cultural and Creative Industries represents the engine through which such industries are able to generate novelties, whose economic value reflects the intellectual contribution of creativity. The genesis of creativity is the result of a local process, fed by the territorial socio-cultural conditions. This definition presents some important details that need to be underlined. To describe in detail the features of the definition, each part is analysed separately. A figurative image may be useful to understand how the conceptual definition works: a funnel. Indeed, as a true funnel it takes creativity as a general concept and it steers it towards a concrete object, within the specific field of CCIs. “…IN CULTURAL AND CREATIVE INDUSTRIES…”. First, the definition is typical of CCIs, not a general and vague definition of creativity. Hence, the field of application of this definition is not broad and targets specifically one dimension of the creative economy. In fact, it is now clear how complex it is to address the issue and to provide a better picture it is vital to proceed with a deductive process, from general to particular.

56

3 An Original Framework for the Identification of Creativity in CCIs

“CREATIVITY…REPRESENTS THE ENGINE THROUGH WHICH SUCH INDUSTRIES ARE ABLE TO GENERATE…” Second, one of the main limitations of creativity has always been its abstractness and vagueness. This definition, instead, attempts to provide it with a shape. Here, creativity is considered as an input for CCIs, functioning as an engine that stimulates all phases of the production process, from conception to marketing. Moreover, the concept of generation recalls that at the industrial level, creativity is functional to the process of output generation. Hence, the definition tries to assign a form to creativity, identifying the boundaries of what it can be and, above all, what it cannot be. “…NOVELTIES…”. Third, it conceptualises that creativity follows a theoretical process. At the beginning, the indefiniteness is conceptualised in an input that is still generic. However, it enters the functioning of CCIs as input for the production process. In the brain power society or C-society, creativity plays a decisive role, as the economic system has changed drastically, especially in advanced countries (Andersson and Beckmann 2009; Andersson and Strömquist 1989; Fujita 2007). In fact, the creation of knowledge, represented by goods and services that are the output of an Inventive process of the mind, is the true new asset of modern times. However, a problem of measurement and of boundaries still exists. A single idea is difficult to capture, restrict and especially, measure. However, the creative engine of CCIs takes it and transforms it into concrete novelties that in CCIs do not only take the form of classical product innovations but they possibly embed new brands or new visual artistic creations. This definition is consistent with the heterogeneity identified in CCIs. Indeed, if creativity as input is a unitary concept (idea), it may take different forms on the output side (novelties). In this sense, in order to understand creativity in its whole essence, it is necessary to consider all its forms. Hence, in this work, heterogeneous creativity is the variety of shapes that creativity can take and all of them are important for a correct analysis of the phenomenon. A more detailed analysis of the different forms of creativity is presented in Sect. 3.3.1. “… WHOSE ECONOMIC VALUE REFLECTS THE INTELLECTUAL CONTRIBUTION OF CREATIVITY …” Fourth, the definition clarifies another important point: the output of CCIs is not only novel, but creativity is also embedded into its economic value. The cognitive dimension of creativity is thus reflected in the output of the production process, determining its intellectual component. This is related to the innovative capacity of CCIs, capable of extracting economic value from the creativity expressed. This allows the distance to be shortened between creativity and innovation at the industrial level. In fact, although they are concepts that may not overlap, innovation in its different forms is one of the most direct outputs of creativity.

3.2 Definition of Creativity in CCIs

57

“…THE CREATIVE GENESIS OF THE IDEA IS THE RESULT OF A LOCAL PROCESS, FED BY THE TERRITORIAL SOCIO-CULTURAL CONDITIONS” Finally, the definition creates an indissoluble binding between territory and creativity. In fact, the definition clearly states that creativity is rooted in the local areas that provide the inputs for the generation of novelties. This concept allows for an important division, often neglected in the literature. Each activity performs differently in terms of production of novelties in some places and less in others. This relationship is examined more in depth later in this Chapter and it is contextualised in the broader context of space. The proposed definition of creativity in CCIs clarifies the context where this work operates. It attempts to provide a clear rationale to cope with the inconsistencies and vague approaches detailed in the previous Chapter. Besides technical aspects, the core of the definition is represented by the last two points. Indeed, this definition relates the strong heterogeneity of CCIs, captured by the different forms of novelties produced, to the local roots of creativity that shape it. Hence, creativity is the dimension used to identify differences among CCIs. The next section describes more in detail these two aspects, proposing a conceptual way to frame the heterogeneous creativity and the role of space in the context of CCIs.

Box 3.2: Why Not a Definition of Culture? There is not common agreement on the difference between creative and cultural industries. Some equate the two dimensions; some others create a net boundary between the two and others again find some overlaps but keeping a distinction. As pointed out by Peris-Ortiz et al. (2019), a paradox of the literature is the adjustment between culture and creativity. Indeed, the comparison of these two dimensions frequently appears in the literature about cultural-creative companies as a dichotomy between culture, founded and established in the habits and tradition; and creativity, which should lead to innovation and change. Naturally, some scholars criticised the official notions of creative industries with reference to definitions of both culture and creativity. Indeed, they argued that deliberations on CCIs have failed to adequately consider the differences between cultural and creative activities, and that this is due at least in part to the terminological clutter surrounding the term culture that poses issues in drawing an exact line to decide which industries have to be included (Galloway and Dunlop 2007). In any case, in this work culture represents an ex-ante condition, the starting point of the analysis for all CCIs. Indeed, following the approach proposed in the White Paper on Creativity by Santagata (2009), heritage and traditions represent the trigger for the creative process together with education of human capital; in other words, culture is of strategic importance for the existence and development of the Creative and Cultural Industries (Santagata 2009).

58

3 An Original Framework for the Identification of Creativity in CCIs

In fact, within the Lisbon Strategy,1 culture has been perceived as a catalyst for creativity and growth. Today, within the boundaries of the Bratislava declaration to make the EU a more and more attractive place,2 the New European Agenda for Culture ‘aims to harness the full potential of culture to help build a more inclusive and fairer Union, supporting innovation, creativity and sustainable jobs and growth’ (European Commission 2018a, p. 1). Thus, culture does not need a novel definition as it functions as a catalyst of creativity and innovation in CCIs. Instead, the discussion of this work refers to how to interpret creativity and its declinations in the field.

3.3 What and Where: Heterogeneous Innovative Capacity of CCIs 3.3.1 Creative Modes The definition of creativity in CCIs has two features that deserve more attention than just a brief explanation of the wording used. The first one is the heterogeneous creativity embedded in CCIs, in line with most of the research on them. Indeed, different approaches to assess the phenomenon have been presented and discussed and the common thread present in all works is the substantial heterogeneity among CCIs. Not all sectors and sub-sectors belonging to CCIs follow the same creative logic (KEA 2006; WIPO 2003) and can be considered likewise creative. Here, the approach followed in this work is to reconcile the concept of creative output with innovation, not through the classical patent-based approach but following the original logic presented by Paul Stoneman in his Soft Innovation—Economics, Product Aesthetics, and the Creative Industries. In this book, he provided a critical view of the Oslo Manual (OECD and Eurostat 2005) that defined innovation as the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations. In Stoneman’s idea, there exist other forms of innovation that are not included in the OECD definition. 1

The European Council held a special meeting on 23–24 March 2000 in Lisbon to agree a new strategic goal for the Union in order to strengthen employment, economic reform and social cohesion as part of a knowledge-based economy. For further information: Presidency Conclusions of the Lisbon European Council. 2 The Bratislava Summit of 27 Member States held the 16 September 2016 has been devoted to diagnose together the present state of the European Union and discuss our common future. We all agreed on the following general principles. For further information: Bratislava Declaration and Roadmap.

3.3 What and Where: Heterogeneous Innovative Capacity of CCIs

59

Indeed, the focus is on the change in the functionality as the main feature of innovation. Instead, in the new “weightless economy” it is illogical to exclude activities like films or music from innovative activities. Indeed, these sectors are grounded on creativity and Inventiveness, and considering newness as a requirement, many activities like the writing and publishing of a new book, the development and launch of a new advertising promotion, or the writing, rehearsing, and staging of a new theatre production are now part of the innovative dimension. Stoneman’s idea has a solid basis in the literature. As a matter of fact, Bianchi and Bortolotti (1996) proposed the concept of formal innovation that glorifies the role of the aesthetic or symbolic content of innovation. Cappetta et al. (2006) introduced the idea of stylistic innovation to indicate the change in the sensorial features of a product. Finally, Alcaide-Marzal and Tortajada-Esparza (2007) argue that novelty in terms of sensory attributes can be considered as the true essence of aesthetic innovation. Practically, the classical approaches to innovation mainly focused on patents, deemed as the most relevant form of protection for ideas. However, patents too often represent hard innovations and the main message conveyed by Stoneman was the necessity to extend the analysis to softer forms, like trademarks and copyrights. In fact, they follow different logics and the kinds of idea they are required to protect are different (Stoneman 2010). Therefore, measuring creativity through property rights assimilates the concept of innovation to creativity. Indeed, the exploitation and deployment of intellectual property is perceived as the way in which originality, expressiveness and imagination are disclosed in a business dimension (University of Hong Kong 2003). This discussion is not an end in itself but it opens the gates for a practical measurement of the creative output in CCIs. Indeed, a wider definition of innovation is in line with a more general approach to the different forms of creative outputs. Here, since intellectual property rights (IPRs) have been widely considered as the output of CCIs (DCMS 1998; Howkins 2001; WIPO 2003), in this work different forms of novelties, output of the creative process in CCIs, are proxied through patents, trademarks and copyrights. The idea of IPR is also present in the UNCTAD Creative Economy Report 2010 where the logic around the output of CCIs is taken on. Indeed, it was underlined that the production requires human creativity as an input, while the output is protected through some forms of IPRs, like the definition states. Moreover, the UNCTAD Report states that these goods and services also embed a cultural, more than a utilitarian value. This point is not stressed in the definition because it is too vague to be measured in any way. The cultural dimension of the output of CCIs is set by the choice of a list of activities, as in Santagata (2009). Indeed, he and his group of experts made a huge and deep analysis of the industries potentially belonging to the Macrosector of CCIs and they selected those activities that are related to the production of culture and whose products also share a symbolic value. Moreover, the importance of IPRs in explaining the dynamics of creativity is due to the process of dematerialisation of products, especially the ones of CCIs. Indeed, compared to the past, it is the intellectual component that determines the value of the products. Hence, the ideas behind new products need to be protected in order to

60

3 An Original Framework for the Identification of Creativity in CCIs

gain monetary value from the commercialisation of these products. For this reason, IPRs ‘take command’ as they are ‘the principal means of protecting ideas’ (Santagata 2010, p. 66). Behind the three forms of IPRs, there exist three different creative logics that are rooted in the wider theory of the differentiated knowledge bases by the Norwegian Bjørn Asheim. The theory starts from the idea that new knowledge generation—i.e. creativity in the words of Andersson (1985)—and its consequent economic exploitation necessitate the transformation of existing knowledge (both tacit and codified), also through a strong system of interactions among people (Nonaka and Hirotaka 1995). Thus, the knowledge processes are becoming more and more complex and industrially interdependent, with sector-specific peculiarities (Asheim and Gertler 2006). For this reason, Asheim indicated ‘different mixes of tacit and codified knowledge, codification possibilities and limits, qualifications and skills, required organizations and institutions involved, as well as specific innovation challenges and pressures from the globalizing economy’, namely knowledge bases (Asheim 2007, p. 225), required for innovation. However, he associated industries related to CCIs only to the symbolic domain as ‘the increasing significance of this type of knowledge is exemplified by the dynamic development of cultural industries’ (Asheim 2007, p. 226). Nonetheless, associating the huge Macrosector of CCIs with only a single knowledge base may seem restrictive. In fact, creativity is expected to take different forms and given that different sectors are associated to different knowledge bases, this requires a further discussion on the possibility that the knowledge bases differ also within CCIs. It is translated into three creative modes, summarised as follows: • technological creativity: based on scientific and technological ideas, generally industrially applicable; • symbolic creativity: based on the symbols and to the image that firms have on the market, capturing the economic value at the industrial level; • artistic creativity: based on imagination and novel views of the world in the form of text, sound and images. It is important to evaluate these three forms of creativity because of the complexity of the concept of creativity itself. In this way, it is possible to consider a wide range of potential creative expressions. Technological creativity is the most developed innovative mode of the different industries, like hardware and software industries. It is generally measured through patent production. Based on images, symbolic creativity is generally mirrored by trademarks, typical of the retail sector and manufacturing of status goods. Lastly, artistic creativity involves various sectors such as the publishing, the printing and the music industry. They are based on the artistic creations made by local artists, protected through copyrights. Hence, creativity in CCIs can be proxied also by softer forms of innovation like trademarks and copyrights (Stoneman 2010), and not only patents. Therefore, the recognition that creativity may take different forms enriches the theory of differentiated knowledge bases (Asheim 2007), considering that also within CCIs the knowledge bases may be substantially different.

3.3 What and Where: Heterogeneous Innovative Capacity of CCIs

61

These forms of creativity are mirrored also in the more generic taxonomy of the already mentioned UNCTAD Creative Economy Report 2010, albeit with some differences. There, creativity is divided into three forms: artistic, scientific and economic. Artistic creativity is related to imagination and to interpreting the world in a novel way. Scientific creativity, instead, involves ‘curiosity and a willingness to experiment and make new connections in problem-solving’. Finally, economic creativity should lead to innovative practices in business-related fields like marketing or advertising. Moreover, as recalled also in KEA (2006), all these forms of creativity are fed by technological creativity that is interrelated with all of them. The conceptual division in these three forms of creativity is remarkable because it started to outline a framework to explain a complex object in a more detailed way. However, the classes are still vague and do not allow us to properly understand how the knowledge base is rooted in each activity. More in detail, artistic creativity and economic creativity can overlap the artistic and symbolic ones proposed here. Indeed, the logic leading to a copyright is the capacity to generate a novel piece of art, through sounds, images, words; while the idea behind a trademark is the necessity of differentiating in order to gain a potential competitive advantage in the economy. However, the definition of scientific creativity is too wide, and it does not fit any classification. Indeed, the willingness to experiment and solve problems is typical of all CCIs and of all kinds of IPRs. In this sense, a stricter idea of technological creativity is complementary to the others, not a prerequisite. It is important to distinguish the three ways of expressing creativity, since they are expected to follow different logics, as the knowledge bases which they are rooted in are substantially different and the territorial determinants driving both the locational decisions and their socio-economic impact are expected to be different. Therefore, the three creative modes proposed in this section make it possible to better proxy the differentiated creative base in CCIs. Creativity takes different forms and the innovative output differs according to the originating creativity in a purely industrial perspective. However, creativity was defined as a local element, determined by and determining the territorial elements. The next section describes how creativity has been conceptualised not only as an industrial feature, but adding its spatial scale.

3.3.2 Modelling Creativity in Space: Inventive and Replicative CCIs Until now, creativity in CCIs has been mostly treated as an industrial feature. It was deemed to depend only on the industrial structure and on the intellectual component that some sectors have more than others. However, as discussed in the previous Chapter, territories may act as generators of creativity. In fact, territories and firms coevolve, and the ones are functional to the others and vice-versa (Paniccia et al. 2011). The territory is not external to the firm, since the firm assimilates some of the territorial

62

3 An Original Framework for the Identification of Creativity in CCIs

features (Paniccia et al. 2015). Therefore, the behaviour and the performance of economic actors are strictly dependent on the territorial features. In this sense, it is possible to distinguish Inventive from Replicative CCIs. This distinction has roots in the literature of the regional life-cycle theory proposed by Norton and Rees (Norton and Rees 1979) and based on Vernon’s well-known product life-cycle theory (Vernon 1979). Within a given industry, there exist different phases of product development, starting from the infant period to the diffusion in the wide economy. According to this theory, technological development has three stages associated—through analysis of demand, production and innovative processes—with three specific locations of innovation. The first stage is the take-off of a new product. In this stage production processes have not yet been standardized, and the strategic factors necessary for the innovation are research and innovation capabilities and the quality of labour, whose natural location is an urban and metropolitan area (Duranton and Puga 2001). The second stage is product maturity, in which incremental process innovations predominate. Production processes require large-scale plants because they are now highly capital-intensive. The more peripheral areas of advanced countries, where land costs less, are the best locations for manufacture of the innovative product. The third stage is the standardised production of the innovative good. The strategic factor is now the cost of labour, and the optimal location is a developing country. In this sense, ideas are expected to be generated in the leading regions and only after some initial developments are diffused in follower regions (Forslund and Johansson 1995). In other words, following the lexicon introduced in this work, Inventive activities could be found more frequently in large urban areas functioning as the sender of ideas while Replicative ones may be found in receiving regions, using ideas generated elsewhere. This combines the innovative component with the geographical distribution pattern. More in detail, Inventive activities are those intensively capable of producing at least one of the three forms of creativity; the latter not. Specifically, local culture and creativity affect the way in which companies act and innovate. Among the pros of Santagata’s approach described in Chap. 2, creativity and culture are defined as bound, and they move along the same line feeding one another reciprocally. In this mindset, CCIs are the result of local customs and traditions, of the cultural heritage and of the folkloristic myths and beliefs. Local culture fuels the creativity and the inventiveness of economic actors, stimulating territorial innovation. This process follows a virtuous cycle as cultural environments nourish CCIs that, in turn, contribute to the development of the creativity and culture of the place. It is therefore of primary interest and importance to take the location of CCIs, with its socioeconomic features, into account to explain their creativity, and their capacity to influence economic performance of the regions where they are located. Although the linkage between creativity and space has a long history, there are two important conceptual approaches that may guide the shaping of the relationship between creativity and space: the industrial district theory and the milieu innovateur theory (Camagni 2009). Thanks to the seminal works by Becattini (1987, 1989) on Italian industrial clusters, the relationship between innovation and local districts has been approached by the literature, highlighting the triggering role of space in

3.3 What and Where: Heterogeneous Innovative Capacity of CCIs

63

stimulating cooperation among actors, leading to the creation of an “industrial atmosphere”. Socio-economic proximity, intended as social (non-written) rules and norms of behaviours inhibits opportunistic behaviours through social sanctions. In such conditions, private interests coincide with collective ones (Becattini 1989). When applied to CCIs, the exchange and cooperation among firms is interpreted as a channel for knowledge exchange and explains their location behaviour. This cannot be understood neglecting territorial forces (Sforzi and Lorenzini 2002), but by focusing on the socio-cultural roots of the phenomena (Becattini 2002), which is not only an industrial but also a territorial phenomenon (Boix and Trullén 2010). Agglomeration forces, especially the Marshallian ones, act among actors in whatever sector by stimulating Inventiveness (Boix and Galletto 2009). The relationship between creativity and territories has been put forward by the milieu innovateur theory which underlines the formation of collective learning processes in areas with a high relational proximity. In such areas, firms share knowledge, even against their will, through mobility within the local labour market, through spin-off and long lasting relationships between suppliers and customers (Camagni 1991). Firms in specific territories (milieux innovateurs) are more prone to be innovate thanks to the common knowledge base available in the area. Fed by local knowledge, creativity is part of what has been defined as territorial capital, i.e. “all the tangible and intangible assets of a public and private nature, that constitute the development potentials of an area” (Camagni 2009). Introduced for the first time by the OECD Territorial Outlook in 2001 (OECD 2001), territorial capital gained a strong relevance in the European debate as a tool for local policy. Each place has a specific capital that is distinct from the one of other areas (OECD 2001) and it contributes to determine the uniqueness of each single area. The different characteristics are extremely diverse, ranging from geo-morphological aspects to socio-cultural ones. Among the various spillovers, the combination of them is a stimulus for the creativity and innovation of economic actors operating in the place (OECD 2001). From this perspective, creativity is not only an industrial concept but also a territorial one. The new framework deriving from the definition of creativity in CCIs should account for this dual nature of creativity. First, it should consider that CCIs innovate in different ways, according to the different creative modes based on different creative outputs. Second, this feature is not only industrial, but it strongly depends on territories that create the conditions for CCIs to be more or less creative. A regionindustry framework is therefore required as the heterogeneous innovativeness of CCIs depends on both aspects. The two dimensions in this work are merged as Fig. 3.1 shows. Within each region (the territorial dimension), each industry belonging to CCIs is evaluated according to its capacity of generating different innovative forms (industrial dimension). If it is able to intensively generate at least one of the innovative outputs, it is assigned to the group of Inventive, otherwise to the Replicative one. The former are those intensively capable of producing at least one of the aforementioned types of creativity; the latter, instead, do not contain any form of creativity.

64

3 An Original Framework for the Identification of Creativity in CCIs

in region Replicative

Inventive Technological creativity Symbolic creativity Artistic creativity

Fig. 3.1 Novel conceptual framework for the evaluation of CCIs

Creativity Where and Why—the subtitle of the book—mirrors this reasoning. The model is built to understand where truly creative CCIs are located and according to which logic they may be considered creative or not and why. Through an operational and measurable approach, the framework is in this way able to refine the classification of CCIs according to a concrete measure of creativity. Industries belonging to CCIs are heterogeneously creative along two dimensions: at the industrial level, i.e. they produce differentiated creative outputs; and at the regional one, i.e. each activity is found to be creative only where the result of the creative process is more significant.

3.4 The Proposed Framework and the White Paper The conceptual logic put forward in this work to study CCIs and their effects on local areas has been designed to improve the logic presented in the White Paper on Creativity by Walter Santagata (2009) and to overcome some of its weaknesses. His approach has been identified as the starting point for the study for its several pros in explaining the dynamics of CCIs. Our conceptual framework emphasises and reinforces them. First, the bond between creativity and culture remains unchanged, as the list of sectors used is the same. Indeed, the activities selected are all related to the production of culture and creativity plays a role, stimulating the quality and value of contents. Likewise, the sectoral disaggregation is as fine as it was in the White Paper, using NACE 4-digits activities, according to the official European classification. Second, our approach conceptually accepts, and even proposes, that creativity takes different forms, proxied in a way that is also measurable. Finally, it indissolubly links creativity with the territory where it is generated. Finally, this framework is built with the exact aim of identifying a way in which creativity translates into socio-economic development. However, together with the pros, the White Paper also contains some drawbacks, and the approach presented here proposes a way to solve them. In fact, in the White Paper the steps of the creative production chain, central in the discussion, mirrored different levels of creativity.

3.5 Conclusions

65

However, the White Paper’s reasoning failed in two main dimensions. First, it was vague in defining the different forms that creativity can take and, especially, they affect an entire industry, not a specific activity. Second, the rationale according to which these activities are assigned to a phase of the creative production chain is not complete. An objective criterion that is also related to the production of creativity is in fact missing. In the conceptual framework presented here, instead, an activity belonging to the sphere of the production of culture, whatever its task is, whatever its place in the production chain is, it is potentially creative or un-creative. Using the framework presented in this chapter, each activity may be Inventive or Replicative only if it is high performing in the generation of a new creative output in one of the possible forms identified. These forms are precise and measurable. Moreover, our approach concretely structures the relationship between creativity and space. In fact, each single activity is not only creative if it extensively creates new knowledge, but it is such only where the Inventive activity achieves more. In this way, the territory expresses itself in a powerful way. It becomes a determining dimension to practically discuss and debate the relationship in place between creativity and space, often presented only at a conceptual level. Finally, the new framework is no longer an Italian model, it does not use any national precondition, but it applies to the entire EU in a much wider scope. The European Union is extremely interconnected, especially regarding the dynamics of production and consumption. The labels Inventive and Replicative go exactly in this direction. Indeed, the disaggregation of the production chain and the international trade policies allowed the geography of the knowledge-intensive sectors to be reshaped. It is likely that the activities involved in the generation of new knowledge and activities involved in their replication will display different (and possibly divergent) spatial paths.

3.5 Conclusions This Chapter represents the core of the conceptual innovation proposed in this work. After a deep review of the literature, the relevance of CCIs in the scientific economic debate has been highlighted and, especially, the limitations of the proposed approaches so far stressed. The relevance of CCIs in the economic debate is not new and scholars continue to investigate this phenomenon because of the different behaviour that these activities have, compared to the rest of the economy. The actors involved in the creative activities are extremely heterogeneous, ranging from craftsmen, designers, artists to ICT experts, software developers, and researchers. What CCIs produce is difficult to define, since symbolic, semiotic, or experience products of CCIs respond neither to the logic of classical manufacturing neither to the knowledge intensive business services (Santagata 2009) with a value that can be seen in many spheres, not necessarily business-oriented (Granger 2020; Klamer 2002, 2017) and extremely difficult to be capture through classical economic modelling (Hartley 2021). In any case, what it is clear is the strong ability of CCIs to innovate (Müller et al. 2009), although heterogeneity exists also in this dimension. Indeed, the

66

3 An Original Framework for the Identification of Creativity in CCIs

forms of innovation output of CCIs can be considered neither as hard nor soft forms of new knowledge (Hartley et al. 2013; Stoneman 2010; Sunley et al. 2008). That is why the first part of the work aimed to detect a fil rouge in the literature on CCIs: in pursuit of creativity.3 Indeed, all definitions and classification of CCIs aimed at shaping more or less intentionally the forms and the intensity of creativity in these particular industries. For this reason, an original definition of creativity has been proposed. This definition has several elements of novelty compared to the literature but two of them are the most relevant ones for future discussions. First, creativity in CCIs can be measured through the output of the creative production process, using intellectual property rights as a coherent proxy. In this way, innovation and creativity are bound and creativity becomes measurable at the industrial level. The theoretical definition of creativity in CCIs creates the conditions for a further conceptualisation of sectors, according to their heterogeneous capacity to innovate. This approach is novel in the literature because it combines both the heterogeneity in the output of CCIs and the fact that this depends on the place. The classification of CCIs based on the creative modes proposed, allows creative CCIs to be detected in a more polished way. The second novelty is the binding between CCIs and space. Indeed, the definition states that creativity, whatever the form assumed, has strong territorial roots and that intensity in creativity generation cannot neglect the geographical scale. Although new in the field of CCIs, these theories that interpret knowledge creation as an endogenous process stemming from local culture, social atmosphere and socioeconomic and relational proximity among economic actors find space in the regional economics literature (Breznitz and Noonan 2018; Santagata 2002; Santagata and Bertacchini 2011). Cooperation among actors, guaranteeing the (voluntary or involuntary) cooperation among firms is, according to the industrial district theory, fed by socio-economic conditions that inhibit opportunistic behaviours through social sanctions, and reduce risks associated to production processes (Becattini 1989). By the same token, the milieu innovateur theory stresses the role of relational proximity as the main source of spillover of knowledge from the firm, even against the will of the firm itself, to the entire local area, generating collective learning (Camagni 1991). The importance of knowledge as a local growth-enhancing factor is witnessed by its being mentioned as part of “territorial capital”, defined as all tangible and intangible assets of public and private nature, that constitute the development potentials of an area. Finally, it is important to underline that this framework represents a starting rather than an end point. Indeed, the relationship between CCIs and space is not only in terms of providing a better picture of the size of CCIs in different regions but also in terms of spillovers that these actors generate for local economies. In this sense, although the geographical pattern of CCIs has been widely investigated (Boix et al. 2016; CollMartínez et al. 2019; Comunian et al. 2010; Lazzeretti et al. 2008; Lorenzen and 3

Cf. Chap. 2.

References

67

Frederiksen 2007; Mateos-Garcia et al. 2018; Power 2011), the literature neglects the heterogeneity of CCIs in terms of location choices. As we shall see in the next part of the book, the literature gives for granted that CCIs look for co-location, so as to take advantage of knowledge spillovers from other CCIs. Instead, it is our idea that location choices largely depend on the creative process developed. The theoretical expectation is that the local conditions stimulating the spatial concentration of these actors vary according to the intersection between creativity and space. The factors theoretically associated to the spatial concentrations, indeed, differ according to both dimensions because different types of CCIs need different territorial factors to cluster. Replicative CCIs may benefit more from pure productivity gains, while Inventive ones look for territorial factors that are able to support their innovativeness, what in the literature are called dynamic agglomeration economies. Moreover, this distinction is not enough. Even Inventive CCIs do not follow the same location patterns. Each type of creativity calls for different local conditions. Moreover, in a context of knowledge economy theory, our approach allows for a deeper analysis of the contribution that CCIs give to socio-economic development. Vastly studied in the economic debate, the role of knowledge creation remains paramount for the explanation of regional growth patterns and regional resilience. The heterogeneity of CCIs in terms of their different forms of creativity is in this respect still largely neglected. A better understanding of which kinds of CCIs have the capacity to generate economic growth is of paramount importance.

References Alcaide-Marzal, J., and E. Tortajada-Esparza. 2007. Innovation assessment in traditional industries. A proposal of aesthetic innovation indicators. Scientometrics 72 (1): 33–57. https://doi.org/10. 1007/s11192-007-1708-x Andersson, Å. 1985. Creativity and Regional Development. Papers of the Regional Science Association 56 (1): 5–20. https://doi.org/10.1007/BF01887900. Andersson, Å., and M.J. Beckmann. 2009. Economics of Knowledge: Theory. Models and Measurements: Edward Elgar Publishing. Andersson, Å., and U. Strömquist. 1989. The Emerging C-Society. In Transportation for the Future, 29–39. Springer Berlin Heidelberg. Asheim, B.T. 2007. Differentiated Knowledge Bases and Varieties of Regional Innovation Systems. Innovation: The European Journal of Social Science Research 20 (3): 223–241. https://doi.org/ 10.1080/13511610701722846. Asheim, B.T., and M.S. Gertler. 2006. The Geography of Innovation: Regional Innovation Systems. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199286805.003.0011. Becattini, G. 1987. Mercato e forze locali: il distretto industriale. Il Mulino. Becattini, G. 1989. Riflessioni sul distretto industriale marshalliano come concetto socioeconomico. Stato e Mercato 25 (1): 111–128. Becattini, G. 2002. Industrial Sectors and Industrial Districts: Tools for Industrial Analysis. European Planning Studies 10 (4): 483–493. https://doi.org/10.1080/09654310220130194. Bianchi, G., and F. Bortolotti. 1996. On the Concept of Formal Innovation.

68

3 An Original Framework for the Identification of Creativity in CCIs

Boix, R., F. Capone, L. De Propris, L. Lazzeretti, and D. Sanchez. 2016. Comparing Creative Industries in Europe. European Urban and Regional Studies 23 (4): 935–940. https://doi.org/ 10.1177/0969776414541135. Boix, R., and V. Galletto. 2009. Innovation and Industrial Districts: A First Approach to the Measurement and Determinants of the I-District Effect. Regional Studies 43 (9): 1117–1133. https://doi. org/10.1080/00343400801932342. Boix, R., and J. Trullén. 2010. Industrial Districts, Innovation and I-district Effect: Territory or Industrial Specialization? European Planning Studies 18 (10): 1707–1729. https://doi.org/10. 1080/09654313.2010.504351. Breznitz, S.M., and D.S. Noonan. 2018. Planting the Seed to Grow Local Creative Industries: The Impacts of Cultural Districts and Arts Schools on Economic Development. Environment and Planning a: Economy and Space 50 (5): 1047–1070. https://doi.org/10.1177/0308518X1877 6327. Camagni, R. 1991. Innovation Networks: Spatial Perspectives. Belhaven-Pinter. Camagni, R. 2009. Territorial Capital and Regional Development. In Handbook of Regional Growth and Development Theories, ed. R. Capello and P. Nijkamp, 118–132. Edward Elgar Publishing. Cappetta, R., P. Cillo, and A. Ponti. 2006. Convergent Designs in Fine Fashion: An Evolutionary Model for Stylistic Innovation. Research Policy 35 (9): 1273–1290. https://doi.org/10.1016/j. respol.2006.02.009. Coll-Martínez, E., A.-I. Moreno-Monroy, and J.-M. Arauzo-Carod. 2019. Agglomeration of Creative Industries: An Intra-Metropolitan Analysis for Barcelona. Papers in Regional Science 98 (1): 409–431. https://doi.org/10.1111/pirs.12330. Comunian, R., C. Chapain, and N. Clifton. 2010. Location, Location, Location: Exploring the Complex Relationship Between Creative Industries and Place. Creative Industries Journal 3 (1): 5–10. https://doi.org/10.1386/cij.3.1.5_2. DCMS. 1998. Creative Industries Mapping Documents. Duranton, G., and D. Puga. 2001. Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products. American Economic Review 91 (5): 1454–1477. https://doi.org/10.1257/aer. 91.5.1454. European Commission. 2018. A New European Agenda for Culture. https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=COM:2018:267:FIN. Forslund, U.M., and B. Johansson. 1995. The Mälardalen—A Leading Region in Scandinavia and in Europe? In Territorial Competition in an Integrating Europe, ed. P.C. Cheshire, and I.R. Gordon. Ashgate Publishing Limited. Fujita, M. 2007. Towards the New Economic Geography in the Brain Power Society. Regional Science and Urban Economics 37 (4): 482–490. https://doi.org/10.1016/j.regsciurbeco.2007. 04.004. Galloway, S., and S. Dunlop. 2007. A Critique of Definitions of the Cultural and Creative Industries in Public Policy. International Journal of Cultural Policy 13 (1): 17–31. https://doi.org/10.1080/ 10286630701201657. Glaeser, E.L., and D.C. Maré. 2001. Cities and Skills. Journal of Labor Economics 19 (2): 316–342. https://doi.org/10.1086/319563. Granger, R (ed.). 2020. Value Construction in the Creative Economy. Springer International Publishing. https://doi.org/10.1007/978-3-030-37035-0. Hartley, J. 2021. Advanced Introduction to Creative Industries. Edward Elgar Publishing Limited. Hartley, J., J. Potts, S. Cunningham, T. Flew, M. Keane, and J. Banks. 2013. Key Concepts in Creative Industries. In Key Concepts in Creative Industries, ed. C. Rojek. SAGE Publications, Inc. https://doi.org/10.4135/9781526435965. Howkins, J. 2001. The Creative Economy—How People Make Money from Ideas. Penguin Global. KEA. 2006. The Economy of Culture in Europe. https://ec.europa.eu/assets/eac/culture/library/stu dies/cultural-economy_en.pdf. Klamer, A. 2002. Accounting for Social and Cultural Values. De Economist 150 (4): 453–473. https://doi.org/10.1023/A:1020146202001.

References

69

Klamer, A. 2017. Doing the Right Thing: A Value Based Economy. Ubiquity Press.https://doi.org/ 10.5334/bbb. Lazzeretti, L., R. Boix, and F. Capone. 2008. Do Creative Industries Cluster? Mapping Creative Local Production Systems in Italy and Spain. Industry & Innovation 15 (5): 549–567. https:// doi.org/10.1080/13662710802374161. Lorenzen, M., and L. Frederiksen. 2007. Why do Cultural Industries Cluster? Localization, Urbanization, Products and Projects. In Creative Cities, Cultural Clusters and Local Economic Development, ed. P. Cooke, and L. Lazzeretti. Edward Elgar Publishing. Mateos-Garcia, J., J. Klinger, and K. Stathoulopoulos. 2018. Creative Nation—How the Creative Industries are Powering the UK’s Nations and Regions. Müller, K., C. Rammer, and J. Trüby. 2009. The Role of Creative Industries in Industrial Innovation. Innovation 11 (2): 148–168. https://doi.org/10.5172/impp.11.2.148. Nonaka, I., and T. Hirotaka. 1995. The Knowledge Creating Company. Oxford University Press. Norton, R.D., and J. Rees. 1979. The Product Cycle and the Spatial Decentralization of American Manufacturing. Regional Studies 13 (2): 141–151. https://doi.org/10.1080/095952379001 85121. OECD. 2001. OECD Territorial Outlook. OECD. https://doi.org/10.1787/9789264189911-en. OECD, and Eurostat. 2005. Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data (3rd edn). In The Measurement of Scientific and Technological Activities (The Measurement of Scientific and Technological Activities). https://doi.org/10.1787/9789264013100-en Paniccia, P., A. Minguzzi, and M. Valeri. 2011. Coevoluzione tra impresa e destinazione turistica: l’esperienza innovativa dell’albergo diffuso. In Creatività, innovazione e territorio: ecosistemi del valore per la competizione globale, ed. L. Pilotti, 405–463. Il Mulino. Paniccia, P., G. Morelli, and A. Cicerchia. 2015. Le imprese creative: dall’approccio per classificazioni ai modelli di management. Economia Dei Servizi 2: 123–150. https://doi.org/10.2382/ 83931. Peris-Ortiz, M., J.A. Gomez, and M. López-Sieben. 2019. Cultural and Creative Industries: An Overview, 1–13. https://doi.org/10.1007/978-3-319-99590-8_1. Power, D. 2011. Priority Sector Report: Creative and Cultural Industries. In European Cluster Observatory (Issue March). Publications Office of the European Union.https://doi.org/10.2769/ 95687 Runco, M.A., and G.J. Jaeger. 2012. The Standard Definition of Creativity. Creativity Research Journal 24 (1): 92–96. https://doi.org/10.1080/10400419.2012.650092. Santagata, W. 2002. Cultural Districts, Property Rights and Sustainable Economic Growth. International Journal of Urban and Regional Research 26 (1): 9–23. https://doi.org/10.1111/14682427.00360. Santagata, W. 2009. White Paper on Creativity. In Towards an Italian Model of Development, ed. W. Santagata. Università Bocconi Editore. Santagata, W. 2010. The Culture Factory. Springer Berlin Heidelberg.https://doi.org/10.1007/9783-642-13358-9. Santagata, W., and E. Bertacchini. 2011. Creative Atmosphere: Cultural Industries and Local Development (No. 4). https://doi.org/10.1111/j.1467-629X.1980.tb00220.x. Sforzi, F., and F. Lorenzini. 2002. I distretti industriali. In Istituto per la promozione industriale (IPI), L’esperienza italiana dei distretti industriali, 20–33. Simonton, D.K. 2011. Big-C Creativity in the Big City. In Handbook of Creative Cities, ed. D.E. Andersson, Å. Andersson, and C. Mellander. Edward Elgar Publishing. Stoneman, P. 2010. Soft Innovation: Economics, Product Aesthetics, and the Creative Industries. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199572489.001.0001. Sunley, P., S. Pinch, S. Reimer, and J. Macmillen. 2008. Innovation in a Creative Production System: The Case of Design. Journal of Economic Geography 8 (5): 675–698. https://doi.org/10.1093/ jeg/lbn028. UNCTAD. 2010. Creative Economy Report. https://unctad.org/en/docs/ditctab20103_en.pdf.

70

3 An Original Framework for the Identification of Creativity in CCIs

University of Hong Kong. 2003. Baseline Study on Hong Kong’s Creative Industries. https://www. createhk.gov.hk/en/link/files/baseline_study.pdf. Vernon, R. 1979. The Product Cycle Hypothesis in a New International Environment. Oxford Bulletin of Economics and Statistics 41 (4): 255–267. https://doi.org/10.1111/j.1468-0084.1979. mp41004002.x. WIPO. 2003. Guide on Surveying the Economic Contribution of the Copyright-Based Industries.

Part II

Location Patterns of CCIs

Chapter 4

Location Behaviours of CCIs: Towards New Research Trajectories

4.1 Introduction The spatial clustering of CCIs has long attracted the interest of both geographers and regional economists. Indeed, the concentrated geographical distribution of these sectors resulted forthwith to be a constitutive feature especially of sectors embedding cultural and creative determinants (Caves 2006; De Vaan et al. 2013; Heebels and Boschma 2011). Although it has ancient roots, this strong tie between creativity and places found its greatest relevance in academic literature because of the concentrated behaviour of CCIs. The concentration in space of CCIs has empirically shown an extremely uneven distribution, with some areas that gained the lion’s share attracting the highest number of creative workers and firms. Usually, the geographical pattern has a bias towards urban environments, preferred loci for the settlements of CCIs (Power 2011; Scott 1997), although there exist some exceptions due to the presence of historical non-urban cultural districts (Bertacchini and Borrione 2013; Santagata 2002). This feature is the result of some centripetal forces that tend to attract these actors towards dense and large locations for capturing benefits that exceed the huge costs of central locations (e.g. rents, congestion, and pollution). The evolution of scientific literature on the agglomerative benefits for CCIs followed the main theories of regional science, trying to identify similarities and differences. There have been many important attempts to describe the agglomerative benefits for CCIs (Branzanti 2015; Flew and Cunningham 2010; O’Connor 2010), mainly interpreting the phenomenon under the lenses of efficiency gains purposes, labelled also static agglomeration economies (Gong and Hassink 2017; Lorenzen and Frederiksen 2007), focusing on the traditional factors that CCIs look for at the local level in order to improve their efficiency. The reasons behind the process of clustering are several and there is a never-ending need to put order between theoretical and empirical studies. However, static localisation and urbanisation economies are not the only key aspects to consider when dealing with spatial clustering of CCIs. Indeed, recalling from the previous chapter, CCIs are heterogeneously capable to innovate: i.e. they do not all innovate following © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_4

73

74

4 Location Behaviours of CCIs: Towards New Research Trajectories

Table 4.1 Theoretical approaches to CCIs clustering

Static economies Dynamic economies Localisation economies

Industrial Cognitive proximity district approach approach

Urbanisation economies

Cultural environment approach

Competitive market approach

the same mode and they innovate unevenly in space. For this reason, the literature review on the factors of clustering of CCIs will add another dimension of interest: the difference between static and dynamic agglomeration advantages. Indeed, these aspects are often misinterpreted and mingled by recent reviews although they hide different reasons for CCIs behaviour. These are analysed in depth in this chapter with a logic presented in Table 4.1. In particular the chapter aims at disentangling the different ways the literature explained the process of clustering of CCIs. The starting point for this review is represented by agglomeration advantages, namely localisation and urbanisation economies, always the basis of the analysis of clustering of CCIs (Coll-Martínez et al. 2019; Gong and Hassink 2017; Lorenzen and Frederiksen 2007). However, the innovative component of CCIs is largely overlooked by both theoretical and empirical studies. For this reason, the review reconciles the dimension of agglomeration economies with the difference between static and dynamic advantages, to provide a more coherent and detailed perspective on the main theories behind the process of spatial concentration of CCIs. Table 4.1 presents the logic of the discussion: at the intersection between the two dimensions, four conceptual approaches can be identified.

4.2 Static Agglomeration Advantages of CCIs: The Industrial District Approach CCIs proved to be extremely spatially concentrated within urban areas (Power 2011; Scott 1997), although there are some relevant exceptions of more rural industrial cultural districts (Bertacchini and Borrione 2013; Santagata 2002). Although both cases build on the physical closeness among actors, district localisation economies and urbanisation economies refer to different theories and different factors for clustering. The review on static factors promoting the spatial clustering of CCIs can be divided into two approaches: the industrial district approach and the cultural environment approach. The former explains the static forces related to localisation economies; the latter the static forces related to urbanisation economies. The Industrial District approach is used to describe localisation economies. The name of this approach comes from the nature of the place where the benefits of agglomeration arise. Indeed, the emergence of cultural districts was connected to

4.2 Static Agglomeration Advantages of CCIs: The Industrial District …

75

the presence of specific spatial and productive features that have been analysed in the literature on localisation economies in CCIs (Boix and Trullén 2010; Cooke and Lazzeretti 2007; Lazzeretti et al. 2012; Mommaas 2004).1 District economies are external to the firm but closely linked to the local industrial specialisation. They mirror the combination of various endogenous components, shaping ‘the overall scale of activity in an area, thereby affecting the productivity of all firms’ (Capello 2002, p. 388). The components range from specialised suppliers and labour force to local know-how and technical competences. Hence, here the focus is on the factors promoting efficiency gains for CCIs within cultural districts. In general, localisation economies are all the advantages coming from the concentration of different actors belonging to the same industry. Marshall claimed that historically there existed two ‘chief causes’ for the co-localisation: physical conditions and patronage of a court and the arts. The physical conditions are supply-side oriented as the presence of specific resources (e.g. climate, soil, and veins) allowed the emergence of specific industries that require this endowment for production. For instance, it is the case of the iron industry that tended to locate close to charcoal mines like the industrial areas within the Upper Silesian Coal Basin between Poland and Czech Republic. On the other hand, patronage historically represented one of the strongest vehicles of demand. In fact, courts had the monopoly of consumption of several products and there the highest-skilled workers clustered to produce everything needed and desired by the courtiers. As an example, the Florentine court during the Medici’s dynasty created a huge pole of artistic and architectural production during the Renaissance. Indeed, financing artistic and architectural production through patronage was considered a way to stretch out towards the virtue of Magnificence (Jenkins 1970). Thus, the chief causes may be summarised in the “specialisation history” of a place as it captures the reasons why a given industry firstly settled in a given location. However, these causes do not explain the advantages coming from the stay in a given locality in the long run; that is why it is important to also understand the agglomeration advantages. First, an agglomerated setting is able to reduce transaction and transportation costs thanks to physical proximity, as the intensity of networking and face-to-face interactions facilitate communication (Becattini 1987; Brusco 1982) and reduce the time and costs of transportation. However, the application of the notion of transport costs reduction to the context of CCIs is largely overlooked by the literature. Indeed, transport costs do not represent a particular matter of interest for the sector due to the extensive use of new technologies (Branzanti 2015). Moreover, considering transaction costs, some relevance is also assigned to industrial or also creative atmosphere. Promoted by social ties and close proximity among companies operating in the same industry, this concept is based on a set of intangible assets that shape a common industrial culture made of entrepreneurial propensity and cooperation (Capello 2015). That is also able to give the place a brand, able to valorise the activities of CCIs, as ‘the place of origin is itself often part of commercial constructions 1

Cf. Branzanti (2015) and Chapain and Sagot-Duvauroux (2020) for reviews on the spatial concentration of CCIs from a district economy’s perspective.

76

4 Location Behaviours of CCIs: Towards New Research Trajectories

cultural industries weave to support their competitiveness’ (Jansson and Power 2010, p. 901). The association between cultural and creative firms and places is important, as these actors appropriate and use the place as part of their image, reinforcing the positioning of the firms.2 The atmosphere is enhanced by the sense of belonging to a specific community and it is of key importance for CCIs shaping new ways of managing old and new knowledge (Pratt 2000). This builds on physical proximity as is based on face-to-face interactions occurring through physical encounters (Felton et al. 2010; Storper and Venables 2004), facilitating the transferring of experiences and knowledge. Second, the spatial concentrations of firms within the same sector stimulates the emergence and the growth of a specialised labour force through the cumulation of sector-specific skills. This process is known as labour pooling. Becattini defined the industrial district as a ‘socio-territorial entity which is characterised by the active presence of both a community of people and a population of firms in one naturally and historically bounded area. In the district, unlike in other environments, such as manufacturing towns, community and firms tend to merge’ (Becattini 1989, p. 112). Famous examples of Marshallian industrial districts of CCIs with a specialised workforce are: the Lancashire Cotton Industry (Sunley 1992), the jewellery sector in Birmingham (De Propris and Lazzeretti 2007), and the textile in Prato (Ceccagno 2012; Smyth and French 2009). Moreover, labour pooling is relevant especially for manufacturing in CCIs due to the specific knowledge required for the production. Indeed, as wisely highlighted in Santagata (2009), many cultural and creative goods are the result of ancient traditions, handed down from generation to generation in the several districts.3 Third, within localisation economies, an important role is played by input sharing. Indeed, the specialisation of an area in each sector triggers the rise of some activities, either on top or at the bottom of the production chain, capable of valorising the local production. These benefits differ according to the kind of inputs which an industry depends upon4 and by the tendency of sectors of intermediate supply to concentrate themselves. Indeed, a sector clusters in space to benefit from this sharing mechanism if the suppliers do the same.5 Therefore, for the case of CCIs, localisation economies seem to affect manufacture-related industries more strongly as they often concentrate in dense networks, exploiting these advantages coming from spatial clustering with similar activities (e.g. leather industry in Prato and ceramic district in Sassuolo). Concerning CCIs, the role of the filière in explaining their clustering has been weakly 2

Notice that also the reverse may be true. CCIs act as attractors for people, improving the quality of the place-brand (Capone and Lazzeretti 2016). 3 Santagata (2009) also underlined the threat faced by cultural districts due to the difficulty of the intergenerational transition, especially in the Era of globalisation and delocalisation. 4 Rosenthal and Strange (2001) identified that a reliance on manufactured inputs contributes to agglomeration more in comparison with other segments. 5 Overman and Puga (2010) highlighted that, although the meat processing industry buys a lot from intermediates, it has no reason to concentrate in space, as its main suppliers (farms and plastic film industry) do not cluster.

4.3 Static Agglomeration Advantages of CCIs: Cultural Environment …

77

touched by the literature. Indeed, although the creative filière is considered theoretically important for the description of the behaviour of CCIs (Santagata 2002, 2009), it was largely overlooked by economic literature (see Boix 2013 for an analysis of publishing and printing filière). All the points raised so far build on the theory of industrial districts themselves, focused on the gains that economic actors within the same industry obtain from a clustered localisation (Becattini 1989). Cultural industrial districts are a specific form of district. Indeed, because of tacit knowledge, easy networking and the cost-free diffusion of information, in these local communities the cultural traditions foster both cost savings and the profitability of creative actors. However, this district economy’s perspective is only partly able to explain the heterogeneity of factors contributing to the clustering of CCIs.

4.3 Static Agglomeration Advantages of CCIs: Cultural Environment Approach CCIs tend to cluster in urban areas, where they can take advantage from urbanisation economies. As such, these industries largely benefit from dense as well as large locations, because creative activities may flourish in cities, thanks to urban cultural environments that stimulate this process. The set of factors that the literature identified as a trigger of static efficiency for CCIs are summarised in this section, called Cultural Environment Approach for the aforementioned reasons. Economists grasped the ideas of Jane Jacobs to formalise the externalities generated at the urban level, known as Jacobs externalities. Indeed, she deemed that the diversification of economic activities is a source of benefit for actors in that area, improving their efficiency. In her words, ‘the greater the sheer numbers and varieties of divisions of labour already achieved in an economy, the greater the economy’s inherent capacity for adding still more kinds of goods and services’ (Jacobs 1969, p. 59). Economically, the diversification is often associated to urban size as larger cities are also more diverse cities and vice-versa. In a context of urbanisation economies, the kind of advantages coming from urbanisation can be divided into different classes of externalities (Camagni 1993). Hereafter, the focus will be put on those benefits that are more relevant for CCIs according to the literature. First, the concentration of the public investment is important because there are some infrastructures that may exist only when a minimum threshold of users is reached. That is the case of metropolitan lines, airports, advanced telecommunication networks that can be rightfully located only in large areas with a significant catchment area. Plus, public services offered are more efficient if the costs are shared among many actors, like hospitals, universities, or sewer systems that generally offer a costadvantage for users in large areas. As far as CCIs are concerned, large metro areas

78

4 Location Behaviours of CCIs: Towards New Research Trajectories

allow CCIs to reduce the gap with the institutions.6 Indeed, institutions matter for the development of creative project-based work and this work is influenced by national policy environments. Specific cultural policies create the capacities for solving problems and improve the overall quality of the work (Christopherson and van Jaarsveld 2005). In his wide studies on Hollywood and the concentration of creative actors, Scott attributed to the presence of a supportive institutional environment the stimulus for the competitive advantages in the form of increasing returns to scale and scope, functioning as ‘a seedbed of creativity and innovation for the industry’ (Scott 1997, 2002, 2005). In other words, this reasoning refers to the lobbyism that creative and cultural actors may employ towards institutions that are necessary for the flourishing and prospering of CCIs.7 Second, the nature itself of larger markets, typical of urbanised areas, is linked to a greater self-sufficiency of larger cities as it has been proved that the ratio between the internal and the external market increases steadily with urban size (Camagni 1993). This offers great advantages to urban actors since transport and transaction costs decrease in a unique economic environment. Moreover, a wide urban market allows the emergence of the so-called specialisation niches, that are specialised market alcoves of some activities that do not produce commodity goods or services but mainly products in small demand. As an example, the actors’ outfits for theatrical pieces are a niche and the demand for these specific goods intersects the supply only in large areas, where large theatres are located and where companies are mostly based. For CCIs, this is relevant because for sure the proximity to a large plethora of consumers is beneficial for CCIs (Yusuf and Nabeshima 2005), reducing transportation costs and deepening the profitability also for early-stage firms (Campbell-Kelly et al. 2010). Starting from the seminal work by Alonso (1971), the concept of urban size has been discussed extensively, trying to identify how big is big enough. Many are the empirical investigations to detect a suitable threshold (e.g. Alonso 1971; Mera 1973; Segal 1976) but there is general agreement on the fact that agglomeration economies are sources of productivity increase (Camagni et al. 2016).8 However, it is clear that not only top-tier cities grow and benefit from economic externalities and many midand small-sized cities performed as positively as large ones. This gave rise to the theory of borrowed size: while exploiting the advantages of their limited size such

6

This differs from institutional proximity, that is a much wider concept. The idea of institutional proximity emerged from the acknowledgement that innovative processes are strongly concentrated in space, and this is the result of a system of traditions, norms, habits, and more in general codes that has been nicely defined as institutional thickness. Cf. Amin and Thrift (1994) and Lundvall and Johnson (1994) for a wider perspective. 7 The presence of institutions, often proxied by the presence of political power in the area, is collinear with the degree of urbanisation in general. For this reason, some argued that it is the urban–rural typology a big driver of concentration and not the political ranking of the city (Ženka and Slach 2018). 8 For instance, it has been found that the average labour productivity is greater in American cities with more than 5 million inhabitants (Alonso 1971).

4.3 Static Agglomeration Advantages of CCIs: Cultural Environment …

79

as lower congestion, smaller cities borrow the advantages of larger centres located nearby (Alonso 1973). In the context of the creative economy, this benefit should be read in light of the creative class theory (Florida 2002). It was argued that creatives, namely bohemians and artists, move to city centres in search of inspiration and for their work. In this way, their presence contributes to asperse fun, joy, and a novel creative lymph to places. In this way, the process of gentrification of some places was found to be driven by them (Florida 2002).9 Nowadays, creative and cultural activities represent the heart of cities and they are businesses that demonstrate their essence especially in large urban locations. A known example is New York that, after 9/11, is now rich in cultural districts like DUMBO in Brooklyn or Williamsburg (Indergaard 2009, 2013). This is true also for places on the other side of the Pacific like Hong Kong’s West Kowloon Cultural District that is one of Asian largest culture-led urban development projects and it is another example of cultural and creative districts within city centres (Raco and Gilliam 2012). This phenomenon of spatial clustering of CCIs in urban locations is primarily the result of urban amenities as well as the location decision of the creatives, looking for large markets and diversified environments to feed their creativity (Lazzeretti et al. 2012; Turok 2003). The role of urban size for CCIs has not been fully exploited by the literature and there is yet a lot to discuss about the possibility of borrowing size, related to cultural events. The literature highlights that there exists evidence of borrowed size in the domain of cultural functions, stretching the classical concept (Meijers and Burger 2017). Possibly, there may be the opportunity of borrowing also temporary size for festivals, fairs, or more in general for touristic reasons. This could be an interesting research line, not yet explored. Finally, the urban nature of production factor incubator and input market represents another advantage for both workers and firms. Indeed, a wider labour market with the presence of different and more advanced skills offered is definitely advantageous. Moreover, the urban labour market is also richer in managerial and directorial skills attainable through the higher education institutions. Even if some outliers exist and occupational or industrial structures may push specific clustering, the number of workers and aggregate incomes in highly paid occupations (e.g. knowledge economy and supporting occupations) are correlated with the urban scale (Sarkar et al. 2020). In this context, the role of geographical economies of scope is relevant. Economies of scope are not only limited within companies’ boundaries but they have also a spatial reach. They build on the idea that a close geographical co-location of related capabilities is expected to trigger efficiency in production, like in the case of the entertainment industry (Florida et al. 2012). Hence, cities allow cultural industries to benefit from the synergies generated by close proximity to related skills, inputs and capabilities. 9

However, this path led to the emergence of extremely unequal cities that became victims of their own success, with huge inequality reaching its peaks, perversely, in the most liberal and creative areas since “Knowledge-based places don’t just reflect inequality, they help create it” (Florida 2017). In turn, artists may move and change their location, leaving the old “creative” cities at the mercy of the ghosts they contributed to create.

80

4 Location Behaviours of CCIs: Towards New Research Trajectories

Moreover, as urban areas tend to host both high-level functions, like universities or research centres, and specialised activities, like market niches, economic actors benefit from the easier access to them. With regard to CCIs, the literature underlined that industrial diversity helps and supports the creation and promotion of cultural contents, like the case of financial and legal support services for the registration and valorisation of IPRs. In fact, innovation is a product of interactions between sites and a form of organisational creativity. Innovative designs are not just a function of highly talented individuals, or the systemic outcome of a particular firm’s routines; rather they result from the integration of key creatives within a distinctive industrial architecture (Sunley et al. 2008). Hence, the generation and exploitation of property rights (DCMS 1998), a distinctive activity of CCIs, requires specific support of professional activities more easily available within urban boundaries. Therefore, cities incubate diversified firms and workers, allowing for an easy allocation of creative workers and an easy match between firms for support activities. This has also been confirmed empirically as the industrial diversity represents a driver for clustering of CCIs (Lazzeretti et al. 2012; Sánchez Serra 2016). CCIs cluster to benefit from diversification of local industries and the exchange of ideas, boosting spillovers and increasing productivity growth, making diversified and dense environments key for clustering (Tao et al. 2019).

4.4 Not Only Space but Proximity Relations Behind CCIs Clustering Agglomerative forces, however, do not only function as bearers of static benefits. The localisation of creative activities responds also to a dynamic stimulus, i.e. specific areas (urban or not) stimulate the innovative capacity of creative actors, reducing the dynamic uncertainty and the business risk, and increasing entrepreneurial creativity and innovation (Camagni 1993). The relevant aspect is what is behind and before the process of innovation, identifying the determinants that stimulate the flow of knowledge and the emergence of creative ideas (Potts 2019). Due to the ease of information exchanges and face-to-face interactions, physical proximity has also been seen as the explanation of knowledge exchange in concentrated environments. This has great consequences on territorial analysis, as most of the forces stimulating the innovative process are embedded within local economies. Concerning CCIs, although most of the literature is focused on the stimulus that CCIs themselves give to the innovativeness of the economic system (Innocenti and Lazzeretti 2019; Müller et al. 2009) contributing not just to value-added and jobs, but to the evolutionary process by which economic systems grow (Potts 2009), still limited evidence exists on the dynamic drivers that places generate for their innovativeness. What it is important to highlight here is that if innovative activity has a natural tendency to concentrate in space in general, this is even more relevant for CCIs.

4.4 Not Only Space but Proximity Relations Behind CCIs Clustering

81

Indeed, among all the advantages that a dense and concentrated location generate— frequent face-to-face encounters, the presence of R&D, and the availability of creative labour—most of them are also relevant for the enhancement of the creative process. Hence, these places can be recognised as ‘incubators’ of creativity. The innovation process in general displayed a cumulative character, even at the local level, denoting an incremental structure (Capello 2019). Likewise, creative ideas are supposed to be fed by specific urban environments that, thanks to their openness to diversity, wellestablished technology base, and access to creative talents, create the conditions for the emergence of the creative economy (Acs and Megyesi 2009). The flow of knowledge and the stimulus to innovation do not only require physical proximity but they stem also from other proximity forms. On the one hand, due to spatial closeness, agents more easily share tacit knowledge that foster imitative innovative behaviours. Closeness creates favourable conditions for people to stay together, supporting information contacts and promoting the exchange of tacit knowledge. Spatial proximity is associated to the theory of knowledge spillovers that flourish in concentrated environments. In fact, the dynamic interactions, synergies, and tacit knowledge flows are at the basis of the retomber that innovative activities have on the neighbourhood. In addition, the density of economic environments is expected to stimulate a virtuous cycle of demand and supply of innovative factors. Indeed, innovative firms diffuse their know-how and inventions in the local area that, in turn, feed other firms and support their innovative behaviours. However, this is not enough. Indeed, on the other hand, economic actors innovate if they share a common knowledge base that creates the preconditions for constructive interactions. This idea is rooted in the wider theory of evolutionary economic geography which stresses the importance of time and history to explain the evolution of regional economies (Boschma and Frenken 2006; Storper 1997). In this view, the evolutionary approach considers innovations as the result of a creative process around existing knowledge, within specific technological trajectories. In this sense, cognitive proximity captures the common starting point. There exists ease of communication and collaboration among actors, due to a common or a similar cognitive base that foster co-localisation of actors and their innovative performance. Logically, if innovation is incremental and is an improvement of the existing, localisation choices of firms acting in the same cognitive dimension are influenced by the presence of other similar firms. Hence, agglomeration forces act pushing firms together that will all benefit from a concentrated environment. In this sense, territories differ in terms of their cognitive capability to transform knowledge into innovation (Lundvall and Johnson 1994), exploiting easier market interactions generated by the similar cognitive base. The well-known concept of related variety enters the debate to provide a measure of the interconnected cognitive base of different places. Furthermore, besides the cognitive proximity, it is important to add the so-called relational proximity, focal point of the deep review on the milieu innovateur theory.10 10

During the 80s the GREMI (Groupe de recherche européen sur les milieux innovateurs) identified the system of formal and informal relationships among individuals and firms as one of the main sources of benefits for innovative performances and success of places (Camagni 1991).

82

4 Location Behaviours of CCIs: Towards New Research Trajectories

According to this new concept, the innovative stimulus generated by closeness among actors is the result of a system of social interactions, interpersonal networks, and collective actions that generate collective learning processes, and give rise to mechanisms that reduce uncertainty and risks associated to innovation processes. The relationships can be of two kinds, formal and informal, according to the degree of codification of the information exchanged. The former refers to all the kinds of cooperative agreements based on technological development: the language of science is formalised. The latter, instead, relate to the “untraded interdependencies” for the transfer of uncoded knowledge.11 The milieux are ideal places, they do not necessarily exist in reality; however, they represent an economic archetype to understand the local features that may operate as preconditions for innovation (Camagni and Capello 2002). The collective learning is fostered by the mobility of people and innovative interactions that spontaneously emerge due to the structured and deep collaborations that exist among players (Maillat et al. 1993). In other words, it is a matter of interpersonal linkages with the right actors that stimulate the generation of new ideas. In this sense, the process of learning is the key aspect behind the milieu, as it allows us to explain how knowledge flows in a dynamic way, outdoing some other proximity dimensions that failed to explain this link.12 Therefore, for stimulating the innovativeness, the geographical proximity is a necessary but not sufficient condition. The mutual understanding and the extent of personal relationships are vital for fuelling creativity. This discussion opens the gates for a further deepening of the analysis of the agglomerative benefits for CCIs. Indeed, although their innovative capacity is considered as a distinctive feature, the determinants of innovation are often neglected. The reader should keep in mind that the factors identified in this second part are naturally not traditional ones and, thus, the treatment by the literature is rougher. The idea of this part is to try to stress the most important aspects and highlight where there are the shortcomings.

4.5 Dynamic Agglomeration Advantages of CCIs: Cognitive Proximity Approach As mentioned above, the benefits that economic actors gain from concentrated locations with similar industries are several. Previously, however, the focus was put only on the static efficiency gains, but they are by no means exhaustive. Indeed, the logic of localisation is linked to positive externalities mainly for CCIs that benefit from tacit knowledge spillovers, acquired through a process of learning based on making 11

For a deeper analysis of untraded interdependencies, cf. Storper (1995), Keeble and Wilkinson (1999), and Vicente (2018). 12 For this discussion, only three forms of proximity have been mentioned. However, regional science theory builds on other forms of proximity. Among others, cf. Boschma (2005), Capello (2009), Torre and Wallet (2014) for further references on proximities.

4.5 Dynamic Agglomeration Advantages of CCIs: Cognitive Proximity …

83

and on interacting (Santagata 2009). In other words, the flow of knowledge, especially tacit, is fostered within creative clusters of similar industries. The cognitive proximity approach aims at describing the set of dynamic benefits for CCIs generated in a system of interrelated industries. The idea is to identify the reasons that push CCIs to innovate when concentrated with similar actors. The name of the approach suggests that, in order to learn and to exchange knowledge, companies should not be too cognitively far away from their potential partners, otherwise they would not be able to learn from each other. In other words, in a context of incremental innovation, they should be cognitively not too far and not too close to grasp the potential benefits of learning. This concept is known as related variety approach and it has been employed to study the role of proximate diversity on growth at the level of regions and nations (Boschma and Iammarino 2009; Frenken et al. 2007; van Oort et al. 2015). This idea builds on the contributions of Marshall, Arrow and Romer (MAR) and it is related to knowledge spillovers between firms belonging to the same sector, whose specific knowledge fazzcilitates the exchange of both codified and especially tacit knowledge (Beaudry and Schiffauerova 2009; Marshall 1920). The theory of tacit knowledge flow has been extensively developed in the debate on industrial districts (Becattini et al. 2009) and it puts the accent on how the dynamic externalities are maximised in the geographical areas that are characterised by a strong presence of small and medium-sized specialised enterprises. Indeed, actors in specialised areas have the capacity of mutually understanding, allowing a fruitful knowledge flow. However, this idea of cognitive relatedness is also nestled within the relational proximity discourse. Indeed, if it is true that the geographical distance still matters, and that knowledge flow is easier between similar actors, it is the system of relationships with cognitively proximate agents that foster the learning. Hence, it is a collective learning type of phenomenon (Camagni and Capello 2002). In the context of CCIs, building to the notion of ‘untraded interdependencies’ (Storper 1995), places that constitute a ‘nexus of untraded interdependencies’ can allow local actors to benefit from place-specific conventions, rules, norms and practices. This is extremely relevant for CCIs because the process of knowledge flow constitutes a key element for the development of creative ideas. Similarly, O’Connor (2004) argued that the success of a CCIs cluster is due exactly to tacit knowledge diffusion, opposed to codified knowledge. In any case, the strong pattern of clustering in CCIs is due to the disproportionate advantages that they experience from co-location, transforming the cluster into a ‘creative field’ (Scott 2006). Moreover, it is not only a matter of knowledge flowing that determines the advantages. Indeed, it is the ability to understand each other that determines the success of learning (Capello 2009). For CCIs, their ability to grow depends upon the area’s industrial variety, and particularly its related variety. The concept of related variety has been applied to CCIs to explain the process of cross-fertilisation and cognitive bonds among different industries. In other words, related variety is expected to trigger creativity and innovation both in CCIs and in the wider economy (Innocenti and Lazzeretti 2019; Lazzeretti 2009). For the interest of this review, related variety has been tested as a trigger for the clustering of CCIs at the local level. Empirical studies found out that related variety promotes creativity due to transversal processes of innovation in other sectors, resulting in the

84

4 Location Behaviours of CCIs: Towards New Research Trajectories

clustering of these actors (Lazzeretti et al. 2012; Sánchez Serra 2016). However, the discourse on the way in which knowledge flows in CCIs and how it promotes the innovativeness of these actors misses an important point. As highlighted in the previous chapter, CCIs are heterogeneous both in the forms of innovations generated and in the places where they do so, and the literature largely overlooks this aspect. The expectation is that cognitive proximity is relevant for those who use knowledge as an input, i.e. those who innovate. Otherwise, if only devoted to replication, CCIs do not necessarily need an environment that favours knowledge diffusion. Empirical literature should test this aspect because the economic implications may be many.

4.6 Dynamic Agglomeration Advantages of CCIs: Competitive Market Approach As seen in the Cultural Environment Approach, it is clear for CCIs that positive externalities arise from the concentration in the territory of enterprises belonging to different sectors. These economies of scope are based on the idea that, besides productivity growth, the diversity and variety of businesses that are close together in space can also stimulate the transfer of knowledge. In this sense, regional specialisation typical of localisation economies may also represent a threat for innovation of economic agents in general, especially CCIs (Klement and Strambach 2019). Thus, it is possible to state that there are some other factors behind the innovativeness of CCIs when nestled within vast and diversified (usually urban) environments. The name of the approach used here (Competitive Market) introduces the logic behind it. Indeed, interactions in general and competition effects stimulate the innovativeness of creative actors that innovate in order to distinguish themselves with respect to the rest of the market. Hence, what matters is the ability to differentiate from competitors and this happens when the variety of inputs available within the market is high and possibly heterogeneous. Therefore, CCIs benefit from dense urbanised environments for many reasons. First, cities allow the exploitation of the economies of variety, that is the set of benefits that result from variation, meaning that trying a variety of things improves the creative capacity distributed throughout the system (Rao 2016). In what Currid defines as Warhol Economy, CCIs are key drivers of modern economies13 and the variety of cultural activities such as clubs, galleries, music venues, and fashion shows is fuel for the valorisation of CCIs (Currid 2007). Only in large and densely built environments, is it possible to enjoy those positive externalities triggered by cultural experiences expressed in various art languages (Santagata 2009). New ideas emerge more strongly if the cultural environment is lively. Landry (2008) sees a place as a ‘creative milieu’ as long as it contains the necessary predictions to generate a flow of ideas and inventions. The notion of creative 13

She focused on New York City, but the reasoning can be easily extended to other built environments.

4.6 Dynamic Agglomeration Advantages of CCIs: Competitive Market …

85

milieu has been widely discussed in association with the idea of creative city, where ‘culture is now seen as the magic substitute for all the lost factories and warehouses, and as a device that will create a new urban image, making the city more attractive to mobile capital and mobile professional workers’ (Hall 2000, p. 640) and to attract and retain the creative class (Florida 2002) also due to experience economy (Lorentzen 2012). Hence, creative milieux are usually located in cities, and they are the physical settings where ‘a critical mass of entrepreneurs, intellectuals, social activists, artists, administrators, power brokers or students can operate in an open-minded, cosmopolitan context and where face to face interaction creates new ideas, artefacts, products, services, and institutions’ (Landry 2008, p. 11). What is expected to matter for CCIs is a varied and stimulating environment, where the economic and personal relationships may generate ideas also due to creative cross-fertilisation. In this sense, compared to the cognitive approach, CCIs may also benefit from varied and unrelated domains. There is not only a need of understanding, but new disruptive innovations may come from the application of ideas generated in other fields. Finally, in cities, consumption is gaining a more and more relevant weight in explaining the forces behind spatial clustering (Glaeser et al. 2001). Hence, the future of cities increasingly depends on their attractiveness for consumers. CCIs are important actors in this respect because their products do not respond to a classical logic, but they refer to the field of experience of consumers. First, they are created and produced to respond to a symbolic need, thanks to their ascriptive features, to convey a sign or a message that is genuinely personal. In this sense, they can be also seen as semiotic goods, as they carry a specific meaning for people who consume them (e.g. a fashion good is a perfect example of this as it represents the owner and, in some way, it identifies her in society). Second, these products are centred on the experience that consumers have when enjoying them. For this reason, they are often called experience goods. In this peculiar context, consumers’ tastes and preferences are dynamically formed and, above all, they are fast changing, adapting to the new trends. Finally, within the Macrosector, a key role is also played by the historical and artistic heritage that form the cultural capital of a country. Indeed, the cultural heritage is often perceived as a creativity-enhancer as, by feeding the cultural environment, it stimulates innovation (Santagata 2009). Moreover, it promotes cultural activities such as conservation, enhancement and economic management of these resources (Camagni 2012). The role of the Cultural Heritage in promoting the flourishing of CCIs has also been confirmed empirically, although the effect may differ across countries (Lazzeretti et al. 2012). This setting creates the conditions for a harsh competition among CCIs that struggle to offer newer and newer experiences to consumers. However, compared to other approaches that are somehow saturated, the literature presents some conceptual and empirical gaps in this respect. Indeed, scholars are often sceptical in considering distance (also cognitive distance) as a positive driver of concentration. What emerges here, instead, is that innovative CCIs may have some benefits from an unrelated and varied cognitive environment. In other words, creative ideas are not only the fruit of existing knowledge but they are also disruptive ideas, coming from the unexpected.

86

4 Location Behaviours of CCIs: Towards New Research Trajectories

4.7 A Comprehensive Framework of Location Determinants in CCIs: Innovation and Filière Behind the Scenes In order to interpret in depth the location choices of CCIs, the understanding of the type of advantage needs to interact with the degree of creativity. Replicative CCIs may benefit more from static agglomeration economies, i.e. all those factors that improve their efficiency while Inventive ones look for territorial factors that are able to support their innovativeness, namely dynamic agglomeration economies. However, this distinction is not enough. Another crucial aspect, ignored so far by the literature, is the position of a particular CCI in its creative value chain, that strongly affects the kind of interactions that CCIs have with other firms and that might drive their location behaviours. Some industries may be more “upstream” due to a larger connection to manufacturing and others may be more “downstream”, closely linked to final consumers. Nested in this idea, the reader can figure out the importance of trade relationships among firms as a coherent proxy for the kind of creative process followed by the industry of interest. This system of interconnections is often called filière and the analysis of the positioning of each industry in the value chains is paramount to analyse possible location factors and possible interdependencies in location behaviours of CCIs and their trade partners. Although the word filière can have different meanings based on the field of application, it is generally defined as a sequence of conception, R&D, sourcing, production and distribution phases (Bianchi and Labory 2013) and it describes how firms and sectors interact. At the industrial level the filière mirrors the intensity of market transactions with other segments of the economy, either other businesses or the consumers (Montaigne and Coelho 2012; Raikes et al. 2000). The concept of filière is useful to address CCIs’ clustering because it helps highlight different agglomeration factors CCIs are in search of. CCIs, in fact, position at different steps of the filière according to the counterparts in trade and, especially, according to the needs to be satisfied through exchanges. In this perspective, three main types of linkages, or filières, can be highlighted: • Creative filière: when CCIs are mostly engaged in trade with other creative and cultural industries; • Vast filière: when CCIs are mostly engaged in trade with unrelated industries; • Short filière: when CCIs are mostly market-oriented. These three filières mirror the heterogeneity of trade exchanges. As an example, if comparing the retail of fashion goods with advertising, the former are naturally market-oriented, i.e. with short filière; while advertising is pre-eminently businessto-business (B2B), i.e. with a filière that is either creative or vast. The heterogeneity of trade relationships is reflected on a heterogeneity of location factors CCIs are in search of, and of interdependences of location choices with their trade partners from which they obtain agglomerative benefits.

4.8 Taxonomy of CCIs’ Prevailing Agglomeration Factors

87

Market oriented (short filière): Most of its trade is with the market

Sector

Creative filière: Most oft he B2B trade is with CCIs

B2B oriented: Most ofi ts trade is with other companies

Vastf ilière: Most oft he B2B trade is with the Rest oft he Economy

Fig. 4.1 Logic for the classification of sectors according to trade relationships

In this work, for the first time, the heterogeneity of trade relationships is taken into consideration in a location choice analysis of CCIs. This aspect is of paramount importance. Let us imagine comparing the retail trade of fashion goods and advertising. Both activities belong to CCIs but they participate in different value chains. Retail activities are naturally market oriented, as their aim is to sell goods to final consumers. Their position in the value chain is very close to the final market, and for this reason one can say that they pertain to a short filière. Their location choice is influenced by the size, quality and wealth of local markets. On the other hand, retail activities are predominantly characterised by their business-to-business (B2B) activities, as they are mostly linked to other companies that exploit their consultancy to improve the image of the company or to launch a new market. The closeness to their customers might strongly influence the location choice of such CCIs. In this study, the type of industrial relationships that characterise each CCI industry is taken into consideration to explain location choices. The conceptual framework separates out the market-oriented CCIs from the B2B-oriented ones (Fig. 4.1). But this is not all. In order to account for the complexity of the systems of relationships, the market-oriented category is further divided into two sub-categories that might explain location choices. In particular, a distinction is made between those B2B trade relationships that take place among creative CCIs (labelled creative filière), and those that trade with all sectors of the economy (labelled a vast filière) (Chesnel et al. 2013). Only colocation of the first group represents a real CCI cluster (Fig. 4.1). This tripartition of filière relationships—short, creative and vast—will be used to go in depth into the location choices of CCIs.

4.8 Taxonomy of CCIs’ Prevailing Agglomeration Factors This framework represents an added value to the analysis of CCIs for the analysis of the localisation choices. The heterogeneous innovativeness will interact with the

88

4 Location Behaviours of CCIs: Towards New Research Trajectories

Table 4.2 Taxonomy of CCIs’ prevailing agglomeration factors by inventiveness and types of filière Creativity intensity Filière

Replicative

Inventive

Creative

Sectoral relatedness

Cognitive relatedness

Vast

Sectoral unrelatedness

Cognitive unrelatedness

Short

Labour pooling/market size

Creative local knowledge/tech. in retail

dimension of the creative process because the theoretical expectation is that the local conditions stimulating the spatial concentration of these actors vary according to the intersection between the two dimensions. In other words, the interpretation of the geographical pattern calls for an analysis of these two aspects, since they are distinguishing features of the activities belonging to the Macrosector. Especially for the localisation choices, different filières will indeed mirror different spatial behaviours. Here, it is important to underline that the filière logic is not necessarily global, but it remains intrinsically territorialised. Indeed, although the value chains are now global in scale, the different trade needs go hand-in-hand with the process of clustering. Indeed, behind different filières, CCIs have different DNAs (e.g. retail with short filière vs. advertising with longer B2B linkages) and they will settle where they can keep strong trade relationships. The reasoning converges into a theoretical taxonomy presented in Table 4.2. The intersection between the two dimensions allows us to identify six key factors that are the basis for the interpretation of the localisation behaviours of CCIs. The table presents on the rows the three forms of filière identified, following the logic of Fig. 4.1, and in the columns the contraposition between Inventive and Replicative CCIs, as described and presented in Fig. 3.1 in the previous chapter. Considering activities under the umbrella of the Creative filière, the comparison is made between sectoral and cognitive relatedness. The former refers to the abundance within the districts of activities—and thus employees—belonging to the same industrial domain. This variable is expected to drive the localisation of less innovative CCIs: the cost of matching the related labour supply for less innovative CCIs with a creative filière is reduced if the workforce of the district is industrially related. On the other hand, cognitive relatedness refers to the presence of sectoral activities that are related thanks to complementary competences that trigger interactive learning among industrial domains. This should reinforce industrial innovativeness. Thanks to an easier learning, actors are more likely innovate in a domain if already familiar with it. Hence, considering the first row of the taxonomy, the expectation is to find a positive association to spatial concentration of both forms of relatedness: cognitive for Inventive and sectoral for Replicative CCIs. Second, CCIs belonging to the Vast filière are made of sectors that are interrelated with different types of businesses and they are more logically influenced in their location choices by the presence of a large set of industries and more affected by unrelatedness. The sectoral unrelatedness is the entropy of the sectoral distribution and it measures how “messy” the sectoral composition of CCIs is within the area.

4.8 Taxonomy of CCIs’ Prevailing Agglomeration Factors

89

Cognitive unrelatedness, instead, refers to a variegated cognitive setting, based on knowledge stemming from different CCIs’ firms. Although unrelatedness is often considered a negative driver for productivity and innovation of firms (Aarstad et al. 2016), the cognitive unrelatedness is expected to be a trigger of the clustering of innovative CCIs, if they have a vast filière. Indeed, vast and diversified economic environments may stimulate innovation, due to cross-fertilisation processes stemming from other sectors. Similarly to the previous one, cognitive unrelatedness is also a form of collective learning as it considers the cognitive knowledge base. In this case, CCIs may innovate if the environment in which they are nested in is heterogeneous, creating the conditions for the generation of ideas. Sectoral unrelatedness, instead, is expected to be beneficial for the clustering of Replicative activities with a vast filière, as the diversity of the labour force may represent a factor of attraction to be closer to support manufacturing and services. Both cognitive and sectoral unrelatedness are related to the diversity of the economic environment as it should represent the nourishment for the Macrosector of CCIs. Finally, the bottom row is related to activities that have fewer interactions with other sectors of the economy and concentrate most of their trade relationships with final consumers, both private households and public bodies. The location factors attracting this type of CCIs are no longer related to the composition of knowledge and/or activities. Replicative manufacturing CCIs that do not trade a lot with other segments but are directly oriented to the market can be also defined “self-relying” due to the fact that most of the value is produced in-house and not thanks to many steps exchanging raw materials towards final products. They will look for a favourable and dynamic labour market. That is why labour pooling—representing the availability of professionals within the district—helps reduce costs due to specific skills and knowledge present at the local level. Labour pooling is deemed extremely relevant for manufacturing CCIs due to the specific knowledge required for the production. Indeed, as wisely highlighted in Santagata (2009), many cultural and creative goods are the peculiar result of ancient traditions, handed down from generation to generation in the districts.14 Furthermore, labour pooling has mostly been associated to manufacturing industries in the empirical literature (Overman and Puga 2010; Rigby and Brown 2015). Instead, Inventive manufacturing CCIs are expected to cluster where the industry-specific local knowledge is rich and diffused, thanks to a cumulative process of knowledge accumulation and learning (Caragliu and Nijkamp 2012; Cohen and Levinthal 1990). In the case of Replicative services CCIs with a short filière, instead, the market potential is expected to be a relevant dimension for them as their business is reaching the largest number of consumers. Finally, Inventive services CCIs, in their turn, are expected to follow a peculiar path. Indeed, it is no more a matter of market size as a driver of innovation and concentration of these activities that are extremely peculiar in terms of innovation triggers. Indeed, retail companies are considered as the least innovative sectors in the economy, adopting in most of the cases novel ideas coming 14

Santagata (2009) also underlined the threat faced by cultural districts due to the difficulty of the intergenerational transition, especially in the Era of globalisation and delocalisation.

90

4 Location Behaviours of CCIs: Towards New Research Trajectories

from other domains to distinguish themselves against the competitors in a logic of market competition. Nonetheless, the literature placed the attention on the role of consumers in stimulating innovation in this segment, thus considered demand-driven (Pantano 2014). In this context, retailers introduce innovation, questioning whether consumers would exploit and appreciate it. Hence, the acceptance of new technology by users has been used to predict market trends and innovative behaviours thanks to the so-called Technology Acceptance Model (TAM) (Davis 1989). New forms of experience in the retail sector follow users’ innovativeness and propensity to novelty (Pantano and Di Pietro 2012). For this reason, the taxonomy introduces the concept of technology in retail as a trigger to spatial concentration of Innovative service CCIs in this segment. The taxonomy proposed opens up to some expectations. First of all, it is reasonable to expect that Replicative activities are in search of locational advantages different from Inventive ones. The former are expected to look for efficiency gains, while the latter for dynamic efficiency advantages. However, this straightforward distinction is probably not enough to understand the real location behaviour. This one is for sure influenced by the type of trade relationships that CCIs have, i.e. on the type of filière they belong to. Being characterised by similar sectors, creative filière CCIs are expected to be attracted by localisation economies, while for vast and short filières, urbanisation economies may probably explain their location choices. All these reflections lead to the following research questions: Do CCIs cluster? Do Inventive and Replicative CCIs cluster following different types of agglomeration advantages? Do CCIs belonging to different types of filières benefit from different agglomeration advantages? These research questions will be addressed in the next chapters.

4.9 Conclusions The first consideration is that the literature does not provide a uniform and synthetic way to describe the approaches to location choices of CCIs, despite the very good attempts (e.g. Chapain and Sagot-Duvauroux 2020; Gong and Hassink 2017). The logic presented in this chapter was to detect two aspects that may drive the treatise. The factors triggering the clustering of CCIs are several and in most of the cases follow the general literature on agglomeration economies. However, there is a lack of a systematic approach to the phenomenon, disentangling the drivers of static and dynamic agglomeration economies. Moreover, within the latter, the literature overlooked the importance of the difference between cognitive proximity and competitive market factors. Indeed, although they are both deemed innovation drivers, they mirror different conditions and different environments. Cognitive proximity helps CCIs in building on existing knowledge, to generate incremental innovations. Cognitive unrelatedness, instead, allows CCIs to create disruptive innovations, thanks to the crossfertilisation of ideas coming from unrelated domains. Finally, there is a last literature gap: the structure of the filière in describing CCIs clustering, was presented in some

References

91

discussions (Santagata 2009; University of Hong Kong 2003) and in some empirical studies (Boix 2013) but it has not yet received the space it deserves. Indeed, in the list of factors determining the needs of actors when choosing a place, the trade linkages they have are expected to explain a great part of the story. Firms and industries more embedded in a system of B2B relationships will look for different locations compared with industries that are naturally projected towards the market. All that has been said requires empirical validation, which is the subject matter of the next chapters.

References Aarstad, J., O.A. Kvitastein, and S.-E. Jakobsen. 2016. Related and Unrelated Variety as Regional Drivers of Enterprise Productivity and Innovation: A Multilevel Study. Research Policy 45 (4): 844–856. https://doi.org/10.1016/j.respol.2016.01.013. Acs, Z.J., and M.I. Megyesi. 2009. Creativity and Industrial Cities: A Case Study of Baltimore. Entrepreneurship & Regional Development 21 (4): 421–439. https://doi.org/10.1080/089856 20903020086. Alonso, W. 1971. The Economics of Urban Size. Papers of the Regional Science Association 26: 66–83. Alonso, W. 1973. Urban Zero Population Growth. Daedalus 102 (4): 191–206. Amin, A., and N. Thrift. 1994. Globalization, Institutions, and Regional Development in Europe. Oxford University Press. Beaudry, C., and A. Schiffauerova. 2009. Who’s Right, Marshall or Jacobs? The Localization Versus Urbanization Debate. Research Policy 38 (2): 318–337. https://doi.org/10.1016/j.respol.2008. 11.010. Becattini, G. 1987. Mercato e forze locali: il distretto industriale. Il Mulino. Becattini, G. 1989. Riflessioni sul distretto industriale marshalliano come concetto socioeconomico. Stato e Mercato 25 (1): 111–128. Becattini, G., M. Bellandi, and L. De Propris. 2009. A Handbook of Industrial Districts. Edward Elgar Publishing. http://www.elgaronline.com/view/9781847202673.xml. Bertacchini, E., and P. Borrione. 2013. The Geography of the Italian Creative Economy: The Special Role of the Design and Craft-Based Industries. Regional Studies 47 (2): 135–147. https://doi. org/10.1080/00343404.2011.628652. Bianchi, P., and S. Labory. 2013. Structural Transformations in Industry and Filières. Revue d’Économie Industrielle 144: 177–199. https://doi.org/10.4000/rei.5718. Boix, R. 2013. Creative Industries in Spain: The Case of Printing and Publishing. In Creative Industries and Innovation in Europe, ed. L. Lazzeretti, 65–85. Routledge. Boix, R., and J. Trullén. 2010. Industrial Districts, Innovation and I-District Effect: Territory or Industrial Specialization? European Planning Studies 18 (10): 1707–1729. https://doi.org/10. 1080/09654313.2010.504351. Boschma, R. 2005. Proximity and Innovation: A Critical Assessment. Regional Studies 39 (1): 61–74. https://doi.org/10.1080/0034340052000320887. Boschma, R., and K. Frenken. 2006. Why Is Economic Geography Not an Evolutionary Science? Towards an Evolutionary Economic Geography. Journal of Economic Geography 6 (3): 273– 302. https://doi.org/10.1093/jeg/lbi022. Boschma, R., and S. Iammarino. 2009. Related Variety, Trade Linkages, and Regional Growth in Italy. Economic Geography 85 (3): 289–311. https://doi.org/10.1111/j.1944-8287.2009.010 34.x.

92

4 Location Behaviours of CCIs: Towards New Research Trajectories

Branzanti, C. 2015. Creative Clusters and District Economies: Towards a Taxonomy to Interpret the Phenomenon. European Planning Studies 23 (7): 1401–1418. https://doi.org/10.1080/096 54313.2014.935930. Brusco, S. 1982. The Emilian Model: Productive Decentralisation and Social Integration. Cambridge Journal of Economics. https://doi.org/10.1093/oxfordjournals.cje.a035506. Camagni, R. 1991. Innovation Networks: Spatial Perspectives. Belhaven-Pinter. Camagni, R. 1993. Principi di Economia Urbana e Territoriale, 1st ed. La Nuova Italia Scientifica. Camagni, R. 2012. Creativity, Culture and Urban Milieux. In Sustainable City and Creativity: Promoting Creative Urban Initiatives, ed. L. Fusco Girard, T. Baycan, and P. Nijkamp. Ashgate Publishing Limited. Camagni, R., and R. Capello. 2002. Milieux Innovateurs and Collective Learning: From Concepts to Measurement. In The Emergence of the Knowledge Economy, ed. Z.J. Acs, H. de Groot, and P. Nijkamp. Advances in Spatial Science, 15–45. Springer Berlin Heidelberg. Camagni, R., R. Capello, and A. Caragliu. 2016. Static Vs. Dynamic Agglomeration Economies. Spatial Context and Structural Evolution Behind Urban Growth. Papers in Regional Science 95 (1): 133–158. https://doi.org/10.1111/pirs.12182. Campbell-Kelly, M., M. Danilevsky, D.D. Garcia-Swartz, and S. Pederson. 2010. Clustering in the Creative Industries: Insights from the Origins of Computer Software. Industry & Innovation 17 (3): 309–329. https://doi.org/10.1080/13662711003790593. Capello, R. 2002. Entrepreneurship and Spatial Externalities: Theory and Measurement. The Annals of Regional Science 36 (3): 387–402. https://doi.org/10.1007/s001680200106. Capello, R. 2009. Indivisibilities, Synergy and Proximity: The Need for an Integrated Approach to Agglomeration Economies. Tijdschrift Voor Economische En Sociale Geografie 100 (2): 145–159. https://doi.org/10.1111/j.1467-9663.2009.00525.x. Capello, R. 2015. Regional Economics, 2nd ed. Routledge. https://doi.org/10.4324/9781315720074. Capello, R. 2019. Theories of Innovation in Space: Path-Breaking Achievements in Regional Science. In Handbook of Regional Growth and Development Theories, ed. R. Capello and P. Nijkamp, Revised ed., 240–256. Edward Elgar Publishing. Capone, F., and L. Lazzeretti. 2016. Fashion and City Branding: An Analysis of the Perception of Florence as a Fashion City. Journal of Global Fashion Marketing 7 (3): 166–180. https://doi. org/10.1080/20932685.2016.1166715. Caragliu, A., and P. Nijkamp. 2012. The Impact of Regional Absorptive Capacity on Spatial Knowledge Spillovers: The Cohen and Levinthal Model Revisited. Applied Economics 44 (11): 1363–1374. https://doi.org/10.1080/00036846.2010.539549. Caves, R.E. 2006. Organization of Arts and Entertainment Industries. In Handbook of the Economics of Art and Culture, vol. 1, ed. V.A. Ginsburgh and D. Throsby, 533–566. Elsevier B.V. https:// doi.org/10.1016/S1574-0676(06)01017-9. Ceccagno, A. 2012. The Hidden Crisis: The Prato Industrial District and the Once Thriving Chinese Garment Industry. Revue Européenne Des Migrations Internationales 28 (4): 43–65. https://doi. org/10.4000/remi.6211. Chapain, C., and D. Sagot-Duvauroux. 2020. Cultural and Creative Clusters—A Systematic Literature Review and a Renewed Research Agenda. Urban Research & Practice 13 (3): 300–329. https://doi.org/10.1080/17535069.2018.1545141. Chesnel, S., Molho, J., Raimbeau, F., Morteau, H., and Sagot-Duvauroux, D. 2013. Les clusters ou districts industriels du domaine culturel et médiatique: revue du savoir économique et questionnement. [Rapport de recherche] Département des études, de la prospective et des statistiques du ministère de la Culture. pp.58. hal-02502563. Christopherson, S., and D. van Jaarsveld. 2005. New Media After the Dot.com Bust. International Journal of Cultural Policy 11 (1): 77–93. https://doi.org/10.1080/10286630500067846. Cohen, W.M., and D.A. Levinthal. 1990. Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly 35 (1): 128. https://doi.org/10.2307/2393553.

References

93

Coll-Martínez, E., A.-I. Moreno-Monroy, and J.-M. Arauzo-Carod. 2019. Agglomeration of Creative Industries: An Intra-Metropolitan Analysis for Barcelona. Papers in Regional Science 98 (1): 409–431. https://doi.org/10.1111/pirs.12330. Cooke, P., and L. Lazzeretti. 2007. Creative Cities, Cultural Clusters and Local Economic Development. Edward Elgar Publishing. https://doi.org/10.4337/9781847209948. Currid, E. 2007. The Warhol Economy (NED-New). Princeton University Press. https://doi.org/10. 2307/j.ctvzsmf4t. Davis, F.D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13 (3): 319. https://doi.org/10.2307/249008. DCMS. 1998. Creative Industries Mapping Documents. De Propris, L., and L. Lazzeretti. 2007. The Birmingham Jewellery Quarter: A Marshallian Industrial District. European Planning Studies 15 (10): 1295–1325. https://doi.org/10.1080/096543107 01550785. De Vaan, M., R. Boschma, and K. Frenken. 2013. Clustering and Firm Performance in Project-Based Industries: The Case of the Global Video Game Industry, 1972–2007. Journal of Economic Geography 13 (6): 965–991. https://doi.org/10.1093/jeg/lbs038. Felton, E., M.N. Gibson, T. Flew, P. Graham, and A. Daniel. 2010. Resilient Creative Economies? Creative Industries on the Urban Fringe. Continuum 24 (4): 619–630. https://doi.org/10.1080/ 10304312.2010.485675. Flew, T., and S. Cunningham. 2010. Creative Industries After the First Decade of Debate. The Information Society 26 (2): 113–123. https://doi.org/10.1080/01972240903562753. Florida, R. 2002. The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. Basic Books. Florida, R. 2017. The New Urban Crisis: How Our Cities Are Increasing Inequality, Deepening Segregation, and Failing the Middle Class—And What We Can Do About It. Basic Books. Florida, R., C. Mellander, and K. Stolarick. 2012. Geographies of Scope: An Empirical Analysis of Entertainment, 1970–2000. Journal of Economic Geography 12 (1): 183–204. https://doi.org/ 10.1093/jeg/lbq056. Frenken, K., F. van Oort, and T. Verburg. 2007. Related Variety, Unrelated Variety and Regional Economic Growth. Regional Studies 41 (5): 685–697. https://doi.org/10.1080/003434006011 20296. Glaeser, E.L., J. Kolko, and A. Saiz. 2001. Consumer City. Journal of Economic Geography 1 (1): 27–50. https://doi.org/10.1093/jeg/1.1.27. Gong, H., and R. Hassink. 2017. Exploring the Clustering of Creative Industries. European Planning Studies 25 (4): 583–600. https://doi.org/10.1080/09654313.2017.1289154. Hall, P. 2000. Creative Cities and Economic Development. Urban Studies 37 (4): 639–649. https:// doi.org/10.1080/00420980050003946. Heebels, B., and R. Boschma. 2011. Performing in Dutch Book Publishing 1880–2008: The Importance of Entrepreneurial Experience and the Amsterdam Cluster. Journal of Economic Geography 11 (6): 1007–1029. http://www.jstor.org/stable/26162582. Indergaard, M. 2009. What to Make of New York’s New Economy? The Politics of the Creative Field. Urban Studies 46 (5–6): 1063–1093. https://doi.org/10.1177/0042098009103855. Indergaard, M. 2013. Beyond the Bubbles: Creative New York in Boom, Bust and the Long Run. Cities 33: 43–50. https://doi.org/10.1016/j.cities.2012.07.001. Innocenti, N., and L. Lazzeretti. 2019. Do the Creative Industries Support Growth and Innovation in the Wider Economy? Industry Relatedness and Employment Growth in Italy. Industry and Innovation 26 (10): 1152–1173. https://doi.org/10.1080/13662716.2018.1561360. Jacobs, J. 1969. The Economy of Cities. Random House, Inc. Jansson, J., and D. Power. 2010. Fashioning a Global City: Global City Brand Channels in the Fashion and Design Industries. Regional Studies 44 (7): 889–904. https://doi.org/10.1080/003 43400903401584.

94

4 Location Behaviours of CCIs: Towards New Research Trajectories

Jenkins, A.D.F. 1970. Cosimo de’ Medici’s Patronage of Architecture and the Theory of Magnificence. Journal of the Warburg and Courtauld Institutes 33: 162. https://doi.org/10.2307/ 750894. Keeble, D., and F. Wilkinson. 1999. Collective Learning and Knowledge Development in the Evolution of Regional Clusters of High Technology SMEs in Europe. Regional Studies 33 (4): 295–303. https://doi.org/10.1080/00343409950081167. Klement, B., and S. Strambach. 2019. Innovation in Creative Industries: Does (Related) Variety Matter for the Creativity of Urban Music Scenes? Economic Geography 95 (4): 385–417. https:// doi.org/10.1080/00130095.2018.1549944. Landry, C. 2008. The Creative City: A Toolkit for Urban Innovators, 2nd ed. Routledge. Lazzeretti, L. 2009. The Creative Capacity of Culture and the New Creative Milieu. In A Handbook of Industrial Districts, ed. G. Becattini, M. Bellandi, and L. De Propris. Edward Elgar Publishing. https://doi.org/10.4337/9781781007808.00035. Lazzeretti, L., F. Capone, and R. Boix. 2012. Reasons for Clustering of Creative Industries in Italy and Spain. European Planning Studies 20 (8): 1243–1262. https://doi.org/10.1080/09654313. 2012.680585. Lorentzen, A. 2012. Leisure, Culture and Experience Economy as a Creative Strategy in the Periphery. In Creative Industries and Innovation in Europe. Lorenzen, M., and L. Frederiksen. 2007. Why Do Cultural Industries Cluster? Localization, Urbanization, Products and Projects. In Creative Cities, Cultural Clusters and Local Economic Development, ed. P. Cooke and L. Lazzeretti. Edward Elgar Publishing. Lundvall, B., and B. Johnson. 1994. The Learning Economy. Journal of Industry Studies 1 (2): 23–42. https://doi.org/10.1080/13662719400000002. Maillat, D., O. Crevosier, and B. Lecoq. 1993. Réseaux d’innovation et dynamique territoriale: Le cas de l’Arc Jurassien. In Réseaux d’innovation et milieux innovateurs: Un pari pour le développement régional, ed. D. Maillat, M. Quévit, and L. Senn, 17–51. Neuchâtel: EDES. Marshall, A. 1920. Principles of Economics, 8th ed. Palgrave Macmillan UK. https://doi.org/10. 1057/9781137375261. Meijers, E.J., and M.J. Burger. 2017. Stretching the Concept of ‘Borrowed Size’. Urban Studies 54 (1): 269–291. https://doi.org/10.1177/0042098015597642. Mera, K. 1973. On the Urban Agglomeration and Economic Efficiency. Economic Development and Cultural Change 21 (2): 309–324. Mommaas, H. 2004. Cultural Clusters and the Post-Industrial City: Towards the Remapping of Urban Cultural Policy. Urban Studies 41 (3): 507–532. https://doi.org/10.1080/004209804200 0178663. Montaigne, E., and A. Coelho. 2012. Structure of the Producing Side of the Wine Industry: Firm Typologies, Networks of Firms and Clusters. Wine Economics and Policy 1 (1): 41–53. https:// doi.org/10.1016/j.wep.2012.12.002. Müller, K., C. Rammer, and J. Trüby. 2009. The Role of Creative Industries in Industrial Innovation. Innovation 11 (2): 148–168. https://doi.org/10.5172/impp.11.2.148. O’Connor, J. 2004. ‘A Special Kind of City Knowledge’: Innovative Clusters, Tacit Knowledge and the ‘Creative City’. Media International Australia 112 (1): 131–149. https://doi.org/10.1177/ 1329878X0411200111. O’Connor, J. 2010. The Cultural and Creative Industries: A Literature Review, 2nd ed. Creativity, Culture and Education Series. https://doi.org/10.1007/978-3-642-13358-9_9. Overman, H.G., and D. Puga. 2010. Labor Pooling as a Source of Agglomeration: An Empirical Investigation. In Agglomeration Economics, ed. E.L. Glaeser, 133–150. The University of Chicago Press. Pantano, E. 2014. Innovation Drivers in Retail Industry. International Journal of Information Management 34 (3): 344–350. https://doi.org/10.1016/j.ijinfomgt.2014.03.002. Pantano, E., and L. Di Pietro. 2012. Understanding Consumer’s Acceptance of Technology-Based Innovations in Retailing. Journal of Technology Management & Innovation 7 (4): 1–19. https:// doi.org/10.4067/S0718-27242012000400001.

References

95

Potts, J. 2009. Why creative industries matter to economic evolution. Economics of Innovation and New Technology 18 (7): 663–673. https://doi.org/10.1080/10438590802564592. Potts, J. 2019. Innovation Commons: The Origin of Economic Growth. Oxford University Press. https://doi.org/10.1093/oso/9780190937492.001.0001. Power, D. 2011. Priority Sector Report: Creative and Cultural Industries. In European Cluster Observatory (Issue March). Publications Office of the European Union. https://doi.org/10.2769/ 95687. Pratt, A.C. 2000. New Media, the New Economy and New Spaces. Geoforum 31 (4): 425–436. https://doi.org/10.1016/S0016-7185(00)00011-7. Raco, M., and K. Gilliam. 2012. Geographies of Abstraction, Urban Entrepreneurialism, and the Production of New Cultural Spaces: The West Kowloon Cultural District, Hong Kong. Environment and Planning A: Economy and Space 44 (6): 1425–1442. https://doi.org/10.1068/ a44411. Raikes, P., M. Friis Jensen, and S. Ponte. 2000. Global Commodity Chain Analysis and the French Filière Approach: Comparison and Critique. Economy and Society 29 (3): 390–417. https://doi. org/10.1080/03085140050084589. Rao, V. 2016. Fat Thinking and Economies of Variety. https://www.ribbonfarm.com/2016/07/28/ fat-thinking-and-economies-of-variety/. Rigby, D.L., and W.M. Brown. 2015. Who Benefits from Agglomeration? Regional Studies 49 (1): 28–43. https://doi.org/10.1080/00343404.2012.753141. Rosenthal, S.S., and W.C. Strange. 2001. The Determinants of Agglomeration. Journal of Urban Economics 50 (2): 191–229. https://doi.org/10.1006/juec.2001.2230. Sánchez Serra, D. 2016. Location Determinants of Creative Industries’ Firms in Spain. Investigaciones Regionales—Journal of Regional Research 34 (34): 23–48. http://www.redalyc.org/art iculo.oa?id=28945294002. Santagata, W. 2002. Cultural Districts, Property Rights and Sustainable Economic Growth. International Journal of Urban and Regional Research 26 (1): 9–23. https://doi.org/10.1111/14682427.00360. Santagata, W. 2009. White Paper on Creativity. Towards an Italian Model of Development, ed. W. Santagata. Università Bocconi Editore. Sarkar, S., E. Arcaute, E. Hatna, T. Alizadeh, G. Searle, and M. Batty. 2020. Evidence for Localization and Urbanization Economies in Urban Scaling. Royal Society Open Science 7 (3): 191638. https://doi.org/10.1098/rsos.191638. Scott, A.J. 1997. The Cultural Economy of Cities. International Journal of Urban and Regional Research 21 (2): 323–339. https://doi.org/10.1111/1468-2427.00075. Scott, A.J. 2002. A New Map of Hollywood: The Production and Distribution of American Motion Pictures. Regional Studies 36 (9): 957–975. https://doi.org/10.1080/0034340022000022215. Scott, A.J. 2005. On Hollywood. Princeton University Press. Scott, A.J. 2006. Creative Cities: Conceptual Issues and Policy Questions. Journal of Urban Affairs 28 (1): 1–17. https://doi.org/10.1111/j.0735-2166.2006.00256.x. Segal, D. 1976. Are There Returns to Scale in City Size? The Review of Economics and Statistics 58 (3): 339–350. https://doi.org/10.2307/1924956. Smyth, G.J.R., and R. French. 2009. Living Outside the Walls: The Chinese in Prato. Cambridge Scholars Publishing. Storper, M. 1995. The Resurgence of Regional Economies, Ten Years Later. European Urban and Regional Studies 2 (3): 191–221. https://doi.org/10.1177/096977649500200301. Storper, M. 1997. The Regional World. Territorial Development in a Global Economy. Guilford. Storper, M., and A.J. Venables. 2004. Buzz: Face-to-Face Contact and the Urban Economy. Journal of Economic Geography 4 (4): 351–370. https://doi.org/10.1093/jnlecg/lbh027. Sunley, P. 1992. Marshallian Industrial Districts: The Case of the Lancashire Cotton Industry in the Inter-War Years. Transactions of the Institute of British Geographers 17 (3): 306. https://doi. org/10.2307/622882.

96

4 Location Behaviours of CCIs: Towards New Research Trajectories

Sunley, P., S. Pinch, S. Reimer, and J. Macmillen. 2008. Innovation in a Creative Production System: The Case of Design. Journal of Economic Geography 8 (5): 675–698. https://doi.org/10.1093/ jeg/lbn028. Tao, J., C.-Y. Ho, S. Luo, and Y. Sheng. 2019. Agglomeration Economies in Creative Industries. Regional Science and Urban Economics 77: 141–154. https://doi.org/10.1016/j.regsciurbeco. 2019.04.002. Torre, A., and F. Wallet. 2014. Introduction: The Role of Proximity Relations in Regional and Territorial Development Processes. In Regional Development and Proximity Relations, ed. A. Torre and F. Wallet, 1–44. Edward Elgar Publishing. https://doi.org/10.4337/9781781002896. 00006. Turok, I. 2003. Cities, Clusters and Creative Industries: The Case of Film and Television in Scotland. European Planning Studies 11 (5): 549–565. https://doi.org/10.1080/09654310303652. University of Hong Kong. 2003. Baseline Study on Hong Kong’s Creative Industries. https://www. createhk.gov.hk/en/link/files/baseline_study.pdf. van Oort, F., S. de Geus, and T. Dogaru. 2015. Related Variety and Regional Economic Growth in a Cross-Section of European Urban Regions. European Planning Studies 23 (6): 1110–1127. https://doi.org/10.1080/09654313.2014.905003. Vicente, J. 2018. Untraded Interdependencies and Cluster Formation. In Economics of Clusters, 21–46. Palgrave Pivot. https://doi.org/10.1007/978-3-319-78870-8_3. Yusuf, S., and K. Nabeshima. 2005. Creative Industries in East Asia. Cities 22 (2): 109–122. https:// doi.org/10.1016/j.cities.2005.01.001. Ženka, J., and O. Slach. 2018. How Do Various Types of Regions Attract Creative Industries? Comparison of Metropolitan, Old Industrial and Rural Regions in Czechia. Hungarian Geographical Bulletin 67 (3): 239–257. https://doi.org/10.15201/hungeobull.67.3.3.

Chapter 5

Where Is Creativity? Data and Methodology to Measure CCIs Across EU Regions

5.1 A Bridge Between Theory and Practice All that has been said so far aimed at discussing the importance of CCIs in the scholarly debate, highlighting that additional research is needed to deepen our understanding of their location behaviours. However, one of the most important limitations faced by researchers in this field is the lack of deep data from official sources. The lack and inaccuracy of quality data creates serious problems of credibility and undermines the possibility of conducting deep analyses and interpreting the few results obtained in limited contexts as externally valid. This chapter aims at bridging the gap between the conceptual discussions of the previous chapters and the rigorous empirical investigations of the coming ones. In fact, both the analysis of CCIs’ location behaviours and their impacts on socioeconomic growth require supporting data. This data should quantify the size of CCIs and their capacity to innovate at different scales. Thus, the first two sections aim at describing the data sources for CCIs employment (5.2) and innovative behaviours of these industries (5.3), with a deep discussion of the main challenges faced related to the data collection and cleaning. Then, the chapter deals with the empirical measurement of two key dimensions of interest related to CCIs in space. On the one hand, Sect. 5.4 outlines the methodology used for CCIs classification based on their heterogeneous inventiveness, necessary to distinguish between Inventive and Replicative CCIs. On the other hand, Sect. 5.5 presents the methods employed for the measurement of different filières in CCIs. Finally, Sect. 5.6 is entirely devoted to the first descriptive analyses. More specifically, CCIs are mapped across all European regions and some interesting patterns are presented and discussed. This section opens the door to the more comprehensive econometric analysis presented in the next chapter and in the final part of the book.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_5

97

98

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

5.2 An Original CCIs Database Since the emergence of the Mapping Documents of the DCMS (1998), the aim to quantify the size of the creative economy was quite evident. The most relevant aspect was and still is to count the creative employment, considered a proper indication of the amount of creatives in a given location, so to be able to address questions like Where do CCIs locate? Which types of CCIs are located where? To reply to these questions, a robust database is required, a database that does not exist in official data sources. The primary interest of this study is to build such a database, to quantify the employment in CCIs, using an industrial perspective, and classify it according to different creative performances. Considering the industrial disaggregation, the choice of the list of sectors to be included in the analysis rests on the classification proposed by Santagata in 2009. Among the several pros previously discussed, this list is extremely subtle as it uses the 4 and 5 digits ATECO classification.1 However, this classification presents two limitations. First, it is old, as Santagata used the 2002 version to list CCIs. Second, it is Italian and it is difficult to apply at the European scale. To address both issues, each ATECO 2002 code has been firstly converted into the latest version through specific concordance tables.2 Then, the latest version of ATECO being derived from the NACE Rev. 2 classification, applied at the European scale, this is easily converted (ISTAT 2009). The final conversion from Santagata’s classification into NACE Rev. 2 is presented in Appendix 5.1, the representation of which contains the exact NACE codes that enter this analysis. Hence, for the sake of this work, activities will be selected according to the NACE 4 digits classification (classes) divided into NUTS3 regions. The choice of the geographical scale was data driven, according to the finest possible disaggregation level.3 For this reason, the analysis looks at the NUTS3 regions of the European Union, covering at least few years for the empirical analysis. The source of this information is the Orbis database, product of Bureau van Dijk. Bureau van Dijk (BvD) is a Mood’s Analytics Company that is a world leader in capturing and handling data on private companies. It shares information on more than 365 million companies around the globe, about what they do, their performances, their leaders, financial and legal data, and corporate structures and entities. BvD Orbis is the world platform for exploring data on companies. The most relevant value of this platform is the comparability. Indeed, the information provided by

1

ATECO is the Italian classification of economic activities (ATtività ECOnomiche). The 2002 version is based on NACE Rev. 1.1. 2 The correspondence table (tavola di raccordo tra ATECO 2007 e ATECO 2002) is available through the ISTAT website. 3 The database allows mitigation of the possible trade-off between industrial and spatial disaggregation that may emerge in studies like this one. In fact, because of privacy issues, it is usual for firms not to disclose at the same time details on industrial and spatial details. In that case, it would be possible to identify firms that produce a very specific type of good.

5.2 An Original CCIs Database

99

Orbis comes from a wealth of sources. Thus, it is instantly comparable and searchable. To make sure that Orbis’s data are as comprehensive, wide-ranging and detailed as possible, data were collected from more than 160 providers, as well as hundreds of our own sources, around the world. When the download of the data was done, at the beginning of 2018, Orbis was in a process of restructuring due to the recent acquisition of Bureau van Dijk by Moody’s.4 At that moment, Orbis offered the Historical platform for huge downloads connecting remotely to the Orbis servers. Besides the possibility of downloading data from several years in the past, the platform had a major pro. Indeed, compared to the simple user-access, the remote one enabled a huge amount of data to be downloaded in a single click, avoiding unpacking them into several downloads. To clarify the scale of this discourse that may seem not relevant, before any kind of cleaning of data, filtering for the 95 Santagata NACE 4-digits codes and the NUTS3 regions of the EU, the download refers to more than 2 million companies. It takes days to perform this download from the Historical platform; the same procedure performed through the single-user access would require months if not years.5 Therefore, the download of employment data has been performed through Historical Orbis, referring to 2008– 2015 as the timespan. Despite these numerous pros, the Orbis database presents drawbacks and limitations about the reliability of the data provided. The main recognised concern is that, although BvD does deep harmonisation work, different countries have different legislations about publicity obligations, and this influences the reliability of information. For instance, data on employment presents some concerns. Indeed, the number of employed persons is not “public” information from the financial statement and in some cases the values can be either missing or inconsistent. For this reason, in order to build up a representative database from Orbis, some caution is required. Following Kalemli-Ozcan et al. (2015), the consolidation codes will be used as a criterion for filtering only companies with consistent information. Orbis provides financial statements according to different consolidation codes (C1, C2, U1, U2). C1 and C2 consider the company headquarter of a group, aggregating all companies belonging to the group, with the difference that the headquarter in the former case has a consolidated account and, in the latter, it is unconsolidated. U1 and U2, instead, consider “simple” companies whose accounts are unconsolidated and consolidated, respectively. All companies with a C2 consolidation code have been dropped from the sample, to avoid double counting. Moreover, due to very extreme data, Kalemli-Ozcan et al. (2015) also dropped C1 companies from some countries where inconsistencies were present.6 4

Official news on the acquisition of BvD available at Moody’s Corporation—Press Releases. To avoid misconducts, the access to the Historical Orbis platform was allowed only to a dedicated PC at the Technology Transfer Office of the Politecnico di Milano. I thank Massimo Barbieri for his support during this process. 6 In most of the cases, each record found in Orbis refers to a specific establishment, even if it belongs to a larger company. For instance, this is the case of national-level companies belonging to a larger group (e.g. Adidas has national branches, each of them representing a single entity). Dropping C2 records from the sample has exactly the aim of preventing the data from containing both the 5

100

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Fig. 5.1 Share of firms and employees missing the georeference (before and after the cleaning). Source Author’s elaboration of Orbis data

As far as the geographical disaggregation of data is concerned, in many cases the exact NUTS3 region of the companies was not available from ORBIS and this would have created a poor database, especially if the region-less firms were located in the same areas. In order to cope with this issue, the postcodes provided by ORBIS have been matched with the correspondence tables developed by TERCET—Eurostat7 that contain a lookup-list of European postal codes and their corresponding NUTS codes. Before the cleaning, around 5% of companies were not geographically mapped. This percentage refers to around 100,000 companies all across Europe. This procedure substantially improved the quality of data, assigning them to a region. Otherwise, these companies would not have been included in the analysis. This polishing method applied allows the analysis to include a high share of employment that would have been otherwise lost. Over a total of around 21 million employees in CCIs in Europe, the region-less employment represents a non-negligible share of 8.2% before the cleaning. Once several companies had found their spot in the world, the missing employment became only 0.8% of the total. Figure 5.1 plots the extent of cleaning for both firms and employees. Finally, few words need to be devoted to the case of part-time workers in CCIs. Creative and cultural employees are often self-employed or with part-time contracts and they may not have a permanent job. In fact, as underlined by Platman (2004), holding company, embedding all employees from all branches, and branches themselves. Therefore, through this methodology the database contains all the different establishments of a group if the information is separated and available; otherwise, if only the data for the headquarter is available, this is considered alone. 7 Available at http://ec.europa.eu/eurostat/tercet/flatfiles.do.

5.3 IP Data for Creative Intensities in CCIs

101

the general tendency towards less stable working conditions is even stronger in CCIs with workers much more willing to accept precarious situations (Lee 2012) or part-time and freelance contracts (Jeffcutt and Pratt 2002). The database used for this work only partly accounts for them as it considers registered workers at the moment of the yearly fiscal declaration, so that part-time workers can be included in the count, while freelance ones may not be. Especially with so large a geographical scale, encompassing various countries with differentiated fiscal laws, having a perfect coverage of the number of employees with non-canonical contracts is extremely complicated.

5.3 IP Data for Creative Intensities in CCIs Employment data are key for the analysis of CCIs since it allows the size of the Macrosector to be studied at the local level. However, the theoretical framework regarding the heterogeneous Inventiveness requires more detailed information on the sectors and on their capacity of producing new knowledge. As described in previous chapters, the different creative modes follow the logic of IPRs: patents, trademarks, and copyrights. To evaluate the sectoral intensity in producing one or more forms of IPRs, it is important to have data that are granular enough to make comparisons. Furthermore, usually, data on IPRs can be easily retrieved with a good geographical precision. However, a wide sectoral disaggregation is often a problem for researchers. Indeed, patents and trademarks especially follow specific classifications based on the area of technology rather than the sectors that generate the innovation.8 Being a firm-level database, Orbis contains information on the amount of IPRs produced at the establishment level that can be grouped at the region-industry level. However, the restructuring of the data following the acquisition by Moody’s created some limitations that need to be clarified. First, Historical data did not contain trademark data that completely disappeared from the transition towards a dedicated platform named Orbis Intellectual Property. Hence, firm-level data on patents and trademarks could be retrieved only through the single-user licence. From a computational perspective, this download was found to be much lighter as, filtering only for companies that produced either a patent or a trademark, the size of the extraction was drastically reduced. However, this extraction was completed before the end of 2018, when many companies had not published the balance sheet of 2017. For this reason, the reference year of this extraction can reasonably be 2016. This information is important because the data on patents and trademarks is cumulative, i.e. the number of patents and trademarks associated to each firm and, consequently, to each sector is the cumulated value of all “active” rights in the company’s possession. This can be considered a limitation of the data because full data on employment refer to 2010–2015. 8

The international classifications for patents (IPC) and trademarks (NCL) can be consulted through the WIPO.

102

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

The information on IPRs obtained in this way represents a huge value added for the study of Inventiveness of CCIs, despite the limitations. Indeed, although many studies focused on the intensity in producing IPRs of different sectors (EPO and EUIPO 2016; ESA and USPTO 2016), one of the main drawbacks is the lack of geography in these analyses. Indeed, industries are often considered containers that behave similarly all around the world. However, especially considering their capacity to innovate, this concept needs to be reshaped, accounting for the differentiated role of places. In Sect. 5.6 a visual representation of this will be presented. Finally, here only patents and trademarks data have been mentioned. However, copyrights have not been neglected but, referring to authors’ rights not imputable to companies and sectors, is extremely difficult to find data at this level of granularity. Thus, unfortunately, for copyrights the list of intense sectors provided by EPO and EUIPO (2016) will be employed.

5.4 Methodology for Shaping Heterogeneous Innovativeness of CCIs All the debate is around the identification and measurement of the different groups. At the beginning, the model separates Inventive from Replicative activities. In order to do so, according to the theoretical conceptualisation presented, patents, trademarks and copyrights have been selected as proxies for the different forms of creativity. By construction, an activity i in a region r is Inventive if it is intense in at least one of the three creative modes identified. Future extensions of this work may discuss less strict requirements for an activity i to be either Inventive or Replicative, for instance considering quartiles. The specific geographical distribution of innovative ability is spontaneously concentrated in few places, due to the nature of innovation itself that is increasingly local, concentrated in a limited number of areas (WIPO 2019). The case of CCIs is not an exception and all industries belonging to the Macrosector follow an asymmetric distribution: each sector will display low productivity levels in most of the regions and medium or high values in élite places. This long tail shape is typical of the Pareto distribution in which there is a tall “head” to the left and a long “tail” to the right: small values are very common and large ones are very rare. Just to provide an overview, Table 5.1 shows the descriptive statistics of the number of employees, number of patents and trademarks, and patents and trademarks per employee in CCIs across regions. In other words, for the first three columns the sums of employees, patents and trademarks for each region have been computed (i.e. summing all industries) and then described. For the last two, instead, the industrial intensities of patents and trademarks per employee have been averaged by region and then described.9 9

These variables are in an industry-region scale. The average indicated here refers to the average productivity measured across industries, for each region.

5.4 Methodology for Shaping Heterogeneous Innovativeness of CCIs

103

Table 5.1 Descriptive statistics of number employees, number of patents and trademarks in CCIs across regions Statistic

Empl.

Patents

Trademarks

Patents per employee (average)

Trademarks per employee (average)

Mean

13,864.111

159.772

114.793

0.05

0.04

Median

3519

29

26

0.01

0.02

Mode

922

0

0

0.00

0.00

Standard deviation

58,089.855

899.178

368.119

0.20

0.09

329.42

88.00

Kurtosis

236.314

304.836

108.431

Skewness

13.702

16.136

9.188

Minimum

7

0

Maximum

1,279,764

20,657

15.21

7.52

0

0.00

0.00

6264

5.01

1.56

As expected, there is high skewness of the distribution for all variables, denoting that the distribution is largely asymmetric with most of the values concentrated in the left part of the distribution. In other words, CCIs in most of the regions do not innovate and in few regions do. Regarding patents, the distribution is even more asymmetric than trademarks. This result describes the slight difference among the two forms of IPR. Indeed, patents are found to be more concentrated in few places, compared to their counterpart. Trademarks are a form of knowledge that tends to be relatively more widespread. Largely patenting regions are Helsinki, Western Paris, and Erlangen-Höchstadt (Bayern); while for trademarks the highest values are found in Madrid, Central Paris, and Westminster. Similarly, intensity in patent production shows a skewness that is double compared to trademarks. This confirms that patents and trademarks are different and the former is more concentrated in space, while trademarks more diffused. Due to data limitation, these descriptions are made only within CCIs and do not consider other sectors. This does not allow an appreciation of the unicity of CCIs compared to other industries. However, the final aim of the study is to make an assessment of different creative/innovative behaviour within the Macrosector. This peculiar behaviour of intensities, described only considering mean values, is the same for all the sectors with only minor differences. For this reason, a sector i in a region r can be considered patent- or trademark-intense if its productivity in the region is higher than the mean. In this way, a sector is intense only in the regions that graphically are located in the right part of the tail, that is where it outperforms. One could argue whether the value should not be compared with the median, given this skewed behaviour. This is a choice made to “reward” only very intense situations, generating Inventive activities such as the élite ones compared to all the others. However, there may exist industrial differences that need attention. Indeed, within the Macrosector, it is possible to identify different aggregations of industries: e.g. manufacturing and services.

104

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 C (Low-Tech)

C C (Med-Low-Tech) (Med-High-Tech)

G Pat/E

C: manufacturing G: wholesale and retail trade I: accommodation and food service activities

I

J

M

R

TM/E

J: information and communication M: professional, scientific and technical activities R: arts, entertainment and recreation

Fig. 5.2 Comparison of the mean of patents and trademarks per employee, according to industrial classes. Source Author’s elaboration of ORBIS data

Following the division into industrial classes, Fig. 5.2 compares the mean of patents and trademarks per employee. Different industrial classes have naturally different innovation performances in both forms. Manufacturing (C) displays a high level of intensity in patent production especially for medium and high-tech codes; for trademarks, instead, the value is comparable with service activities.10 Within services, there still exists some heterogeneity. Indeed, as foreseeable retail (G) and accommodation services (I) are the least innovative activities while ICT (J) and professional activities (M) are much more performing. Finally, Arts, Entertainment and Recreation (R) are somehow innovative, especially concerning trademarks. This description does not add novel insights and can be considered common knowledge for an economist. However, for the sake of this work it is important to highlight these industrial differences to justify once again the choice of a location quotient. In fact, the performance is considered within the specific activity and sector of interest and not compared with all CCIs from different domains. For this reason, the intensity of an activity i is considered within its industrial boundaries but made between regions. In this way, the industrial peculiarities remain and the territories themselves shape different intensities. Otherwise, if the logic was reversed, in a single region only highly intense activities would emerge and, probably, only those that are so, due to the industrial structure. In other words, retail and accommodation activities would never be Inventive because they innovate less. The aim of this study is to unravel the role of places in shaping different innovativeness of CCIs and this procedure works in this direction. 10

The subdivision of manufacturing codes according to high-tech propensity follows the criteria provided by Eurostat: Indicators on High-tech industry and Knowledge—intensive services.

5.5 Measurement of the Filière Structure of CCIs: Input–Output Trade …

105

For copyrights, instead, it is more complicated to find data at this extremely fine granularity. In fact, it is not possible to identify the sector to which copyrights refer and to assign each copyright to a specific region, as it is an author’s right not that of a firm that is generally located in a specific place.11 Thus, in this work the list of copyright-intense sectors provided by EPO and EUIPO (2016) is used and applied to the list of CCIs presented above. In this way, copyright intensity remains an absolute characteristic, not relative to the place. In other words, if a sector i is copyrightintense, it is such in every region. The copyright-intense sectors within CCIs are listed in Table 5.2. Hence, this methodological approach is the core of the framework applied in this study, whose focus is about differences in CCIs’ innovative behaviour across space rather than across industries. The analysis lies in fact on a regional industrial location quotient, which stresses the differences of innovation performance (of the three types analysed before) within the same sector across regions, and not within a specific region across sectors. In the latter case, the innovative differences among CCIs would only belong to the mix of (more or less) innovative sectors present in the region. In the former case, instead, by construction, the region may have a differential innovative performance of a specific sector. Each industry included in the analysis may be innovative in some places and not in others. This is consistent with the assumption that all activities included in CCIs are in some way creative, otherwise they would not pertain to the CCI sector. Finally, the framework leaves open the possibility of overlaps among creative modes. In fact, although the knowledge bases are different, the world of CCIs remains a fleeting concept, difficult to restrict within rigid boundaries. There is no restriction on the possibility of an activity to be intense in more than one single creative mode, as no conceptual restrictions exist. Therefore, in this way, CCIs are classified according to the creative output generated, the modes of creative expression are heterogeneous, accounting for a host of possible forms, and the territory plays a role in shaping and stimulating them.

5.5 Measurement of the Filière Structure of CCIs: Input–Output Trade Relationships One of the main reasons driving the clustering of CCIs was found to be the relationship with other industries that may explain a lot of the dynamic and the logic behind their agglomerative behaviour. Since Input–Output relationships of CCIs are heterogeneous, a measure of such linkages is thus required. For this purpose, Input– Output Tables (IOTs) have been analysed. The interest of this work are industryby-industry matrices that present the trade exchanges of one industry with all other industries (intermediate goods) and end-consumers (final demand). In other words, 11

The ownership of works of art, literature, music, multimedia and other protectable works in general resides in their creators (see the EUIPO website for further information).

106

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.2 Copyright intense CCIs according to EPO and EUIPO (2016)

Copyright intense industries 1811 Printing of newspapers 1812 Other printing 1813 Pre-press and pre-media services 1814 Book-binding and related services 1820 Reproduction of recorded media 4761 Retail sale of books in specialised stores 4762 Retail sale of newspapers and stationery in specialised stores 4763 Retail sale of music and video recordings in specialised stores 5811 Book publishing 5813 Publishing of newspapers 5814 Publishing of journals and periodicals 5821 Publishing of computer games 5829 Other software publishing 5911 Motion picture, video and television programme production activities 5912 Motion picture, video and television programme post-production activities 5913 Motion picture, video and television programme distribution activities 5914 Motion picture projection activities 5920 Sound recording and music publishing activities 6010 Radio broadcasting 6020 Television programming and broadcasting activities 6201 Computer programming activities 6202 Computer consultancy activities 6209 Other information technology and computer service activities 6391 News agency activities 7311 Advertising agencies 7312 Media representation 7410 Specialised design activities 9001 Performing arts 9002 Support activities to performing arts 9003 Artistic creation 9004 Operation of arts facilities 9101 Library and archives activities (continued)

5.5 Measurement of the Filière Structure of CCIs: Input–Output Trade … Table 5.2 (continued)

107

Copyright intense industries 9329 Other amusement and recreation activities Source Author’s elaboration on EPO and EUIPO (2016)

these matrices outline how the domestic production and import of goods and services are used for intermediate consumption and final use. Starting from this information, we can operationalise the logic presented in the previous chapters, by classifying CCIs according to the main trade partner, in order to detect different types of filières. Recalling the conceptual subdivision, CCIs may belong to three filières: (a) Creative filière CCIs are mostly engaged in trade with other creative and cultural industries; (b) Vast filière CCIs are mostly engaged in trade with unrelated industries; and (c) Short filière ones are mostly market-oriented. FIGARO experimental tables12 are used as a data source, being a useful tool for the analysis of CCIs’ relationships with other industries. FIGARO produced experimental EU-Inter Country Supply, Use and Input–Output Tables (EU-IC-SUIOTs) with a high level of detail (European Commission 2018) that allows us to understand the relationships among industries of subtle classification. In practice, from the industry-by-industry table, it is possible to understand how the supply matches uses, through the identification of the linkages of each industry with others (supplying the products of use). Applied to CCIs, it highlights their transactions with all the segments of the economy. The level of industrial detail of the FIGARO tables imposes a larger aggregation of CCIs with respect to the one used to identify them at NACE 4-digits, but deep enough to broadly capture the phenomenon (Table 5.3). The methodology to group CCIs based on their type of filière is the following. From the IOTs, the Total Use, the Total Final Use and the Total Intermediate Consumption are collected. The Total Final Use refers to the market consumption, i.e. to the amount of trade of a given industry with final consumers capturing the businessto-consumers (B2C) relationships. Total Intermediate Consumption, instead, represents the intermediate goods traded between the sector and all the other sectors of the economy, capturing the business-to-business (B2B) relationships. Finally, the Total Use is the sum of Total Final Use and Total Intermediate Consumption. To allocate each CCI to a group, the following methodology is applied. If the share of the Total Final Use over the Total Use exceeds the mean of all CCIs, the industry is assigned to the “Short filière”. Otherwise, if the share of Total Intermediate Consumption with CCIs over the Total Intermediate Consumption as a whole exceeds the mean of all CCIs, the sector is assigned to the “Creative filière”. Residually, it is assigned to the “Vast filière”.13 Table 5.4 describes the assignment of industries to the relative filière. 12

FIGARO—Experimental statistics—Eurostat. The FIGARO tables were born to analyse the socioeconomic and environmental effects of globalisation also through global value chain relationships. 13 The logic can be replicated using the median and not the mean, but minor changes would apply with no relevant theoretical implications.

108

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.3 List of CCIs considered in the IOTs Code

Label

C10T12

Manufacture of food products; beverages and tobacco products

C13T15

Manufacture of textiles, wearing apparel, leather and related products

C16

Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials

C18

Printing and reproduction of recorded media

C23

Manufacture of other non-metallic mineral products

C25

Manufacture of fabricated metal products, except machinery and equipment

C26

Manufacture of computer, electronic and optical products

C27

Manufacture of electrical equipment

C31_32

Manufacture of furniture; other manufacturing

G46

Wholesale trade, except of motor vehicles and motorcycles

G47

Retail trade, except of motor vehicles and motorcycles

I

Accommodation and food service activities

J58

Publishing activities

J59_60

Motion picture, video, television programme production; programming and broadcasting activities

J62_63

Computer programming, consultancy, and information service activities

M71

Architectural and engineering activities; technical testing and analysis

M73

Advertising and market research

M74_75

Other professional, scientific and technical activities; veterinary activities

R90T92

Creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling and betting activities

R93

Sports activities and amusement and recreation activities

To summarise, CCIs can be grouped based on trade relationships as follows: . Short filière, when Total Final Use (i.e. trade with the final consumers) represents the core of trade of the industry; . Creative filière, when most of the Total Intermediate Consumption (i.e. trade with other industries) is with other CCIs; . Vast filière, when most of the Total Intermediate Consumption (i.e. trade with other industries) is with other sectors of the economy.

5.5 Measurement of the Filière Structure of CCIs: Input–Output Trade …

109

Table 5.4 Classification of CCIs according to I/O trade relationships (in italic manufacturing) Industrial activity

Trade with CCIs (% of P2_TC)

Trade with RoE (% of P2_TC)

Filière

Manufacture of wood 21.1 and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials (C16)

45.0

55.0

Creative

Printing and reproduction of recorded media (C18)

13.6

51.7

48.3

Creative

Motion picture, video, television programme production; programming and broadcasting activities (J59_60)

45.2

56.2

43.8

Creative

3.7

50.6

49.4

Creative

Manufacture of other 18.7 non-metallic mineral products (C23)

27.4

72.6

Vast

Manufacture of fabricated metal products, except machinery and equipment (C25)

25.6

33.1

66.9

Vast

Computer programming, consultancy, and information service activities (J62_63)

45.4

35.8

64.2

Vast

Architectural and engineering activities; technical testing and analysis (M71)

25.6

30.2

69.8

Vast

Other professional, scientific and technical activities; veterinary activities (M74_75)

31.4

37.2

62.8

Vast

Advertising and market research (M73)

TFU (% of TU)

(continued)

110

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.4 (continued) Industrial activity

TFU (% of TU)

Trade with CCIs (% of P2_TC)

Trade with RoE (% of P2_TC)

Filière

Manufacture of food 63.5 products; beverages and tobacco products (C10T12)

73.8

26.2

Short

Manufacture of textiles, wearing apparel, leather and related products (C13T15)

64.5

67.0

33.0

Short

Manufacture of computer, electronic and optical products (C26)

65.4

42.6

57.4

Short

Manufacture of electrical equipment (C27)

50.9

35.7

64.3

Short

Manufacture of furniture; other manufacturing (C31_32)

72.2

34.6

65.4

Short

Wholesale trade, except of motor vehicles and motorcycles (G46)

50.4

40.0

60.0

Short

72.1 Retail trade, except of motor vehicles and motorcycles (G47)

35.9

64.1

Short

Accommodation and food service activities (I)

80.5

29.0

71.0

Short

Publishing activities (J58)

51.1

46.0

54.0

Short

Creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling and betting activities (R90T92)

77.9

62.4

37.6

Short

Sports activities and amusement and recreation activities (R93)

68.5

56.2

43.8

Short

Mean

47.4

44.5

55.5 (continued)

5.6 The Geography of CCIs

111

Table 5.4 (continued) Industrial activity

TFU (% of TU)

Trade with CCIs (% of P2_TC)

Trade with RoE (% of P2_TC)

Median

50.6

41.3

58.7

Filière

5.6 The Geography of CCIs 5.6.1 Heterogeneous Inventiveness in Space The richness of the database created allows maps on the geography of CCIs to be produced according to their different degrees of innovation intensity. Map 5.1 shows the intensity of Inventive and Replicative CCIs employment as a share of the total regional employment,14 showing a discrete heterogeneity. Interestingly enough, there is a quite clear distinction in the location of the two different groups, with the innovative activities in CCIs preferring Western and more central locations, compared to the replicative ones that seem to be more spatially diffused, preferring more peripheral— especially Eastern—areas. Moreover, regions hosting large and populated cities seem to have a stronger intensity of Inventive activities. This is the case of Madrid and Lisbon in the West, Stockholm, Helsinki and Riga in the North, and Milan, Turin and Barcelona in the South. This should be reflected also in the level of wealth of the places where different CCIs cluster. With regard to Replicative CCIs, there is a clear preference towards Eastern areas. Croatia, Hungary, Romania, and Bulgaria host large clusters of Replicative CCIs. Concerning Western countries, the map shows that large clusters of Replicative CCIs are not hosted in capitals or in large metropolitan areas. Instead, they prefer other locations like the Algarve, Ave, and Tâmega e Sousa in Portugal, the Dumfries and Galloway, Leicestershire, and Dorset in the United Kingdom, and the Tyrolean lands in Austria. Tables 5.5 and 5.6 present the list of NUTS3 regions with the largest share of Inventive and Replicative employment by year. It emerges that it is a stable and structural regional feature. For instance, Novara NUTS3 region (Piedmont, Italy) is always among the regions with highest employment in Inventive CCIs, hosting the large plant of De Agostini, a company active in the publishing sector in 30 countries with publications in 13 languages.15 Moreover, it is relevant to notice that British regions are always among the most Inventive and Replicative regions. This confirms the UK’s strong tradition in creative industries and their relevance also in terms of size (DCMS 2013). However, it is interesting to note how Inventive regions are mostly located in the area of London while the rest of the country is mostly Replicative, imposing a deeper discussion on the spatial distribution of these activities. 14

The share is calculated using the overall regional employment as denominator. The source can be either Eurostat or ARDECO. The spatial distribution does not present major changes in either of the two cases. 15 For an overview of the company, refer to http://www.gruppodeagostini.it/.

112

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Map 5.1 Intensity of inventive and replicative CCIs in European NUTS3 regions (2015)

2014 NUTS3 code

NL339

UKI32

2013

NUTS3 code

4.

5.

Rank

Novara

ITC6

UKI31

UKI75

ES532

2.

3.

4.

5.

Mallorca

Hounslow and Richmond upon Thames

Camden and City of London

A Coruña

ES111

1.

NUTS3 label

Westminster

Groot-Rijnmond

Hounslow and Richmond upon Thames

15.19

17.74

19.69

30.78

32.47

Inventive empl. (%)

13.52

13.95

17.40

ES111

ES532

UKI75

UKI31

ITC6

ES111

UKI32

UKI75

DE946

UKI31

UKI75

23.72

3.

A Coruña

Camden and City 19.61 of London

ES111

UKI31

1.

2011

Inventive empl. (%)

NUTS3 code

NUTS3 label

2010

NUTS3 code

2.

Rank

Mallorca

Hounslow and Richmond upon Thames

Camden and City of London

Novara

A Coruña

NUTS3 label

Westminster

Hounslow and Richmond upon Thames

Ammerland

Camden and City of London

A Coruña

NUTS3 label

Table 5.5 List of NUTS3 regions with largest share of inventive employment, by year

16.04

18.82

19.89

29.55

34.71

Inventive empl. (%)

14.59

17.78

17.82

19.06

26.65

Inventive empl. (%)

2012

UKI75

UKI32

UKI31

ITC6

ES111

NUTS3 code

2015

UKI75

UKI31

DE946

ES111

ITC15

NUTS3 code

Hounslow and Richmond upon Thames

Westminster

Camden and City of London

Novara

A Coruña

NUTS3 label

Hounslow and Richmond upon Thames

Camden and City of London

Ammerland

A Coruña

Novara

NUTS3 label

16.31

17.61

19.60

30.01

37.79

Inventive empl. (%)

17.36

19.19

20.13

29.90

35.04

Inventive empl. (%)

5.6 The Geography of CCIs 113

UKJ12

HR032

ITG29

3.

4.

5.

Leeds

42.52

UKE42

Leeds

Blagoevgpad 21.64 (Blagoevgrad)

Ave

HR032

BG413

PT119

4.

5.

Liˇcko-senjska županija

21.03

25.83

32.11

3.

Barnet

PT119

BG413

HR032

UKI71

Ave

2015

BG413

PT119

HR032

UKI71

UKE42

NUTS3 code

2012

Blagoevgpad (Blagoevgrad)

Ave

Liˇcko-senjska županija

Barnet

Leeds

NUTS3 label

25.34

31.62

40.86

21.64

PT119

BG413

UKE42

HR032

UKI71

25.39

26.72

29.78

Inventive empl. (%)

19.22

19.39

23.60

32.34

42.62

Inventive empl. (%)

Ave

22.07

Blagoevgpad 24.50 (Blagoevgrad)

Leeds

Liˇcko-senjska županija

Barnet

Inventive empl. NUTS3 code NUTS3 label (%)

18.22

20.11

22.33

32.99

42.84

Inventive empl. (%)

Blagoevgpad 22.63 (Blagoevgrad)

Liˇcko-senjska županija

Barnet

Leeds

UKI71

UKE42

2.

Leeds

41.80

South Hampshire

Milton Keynes

Liˇcko-senjska županija

Barnet

UKE42

2014

UKJ35

UKJ12

HR032

UKI71

1.

20.67

20.72

22.34

34.48

Inventive empl. NUTS3 code NUTS3 label (%)

Olbia-Tempio

Liˇcko-senjska županija

Milton Keynes

Barnet

NUTS3 label

NUTS3 code NUTS3 label

Rank 2013

UKE42

UKI71

1.

NUTS3 code

Inventive empl. (%)

NUTS3 code

NUTS3 label

2011

2010

2.

Rank

Table 5.6 List of NUTS3 regions with largest share of replicative employment, by year

114 5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

5.6 The Geography of CCIs

115

Plus, everybody knows the famous brand Zara. Zara and other companies of the Inditex group such Pull&Bear, Bershka, and Stradivarius have reinvented the world of fashion through the production and the commercialisation of fast fashion goods, ranging from clothing, accessories, shoes, swimwear, to beauty and perfumes. Its efficiency is so high that only one week is needed to develop a new product and get it to the stores, compared to the six-month industry average.16 Zara’s headquarter is located where its story began in 1990: Arteixo, a small town in the NUTS3 region of A Coruña on the northwest coast of Spain. More than five thousand employees in different departments including design, photography, sales, and e-commerce work there, where the largest Zara distribution centre is also located, where products are boxed and sent to 96 countries around the world. The company has permeated the life of the city, attracting many creatives from different corners of the world and most of the other companies present in the area work as support for Inditex’s activities.17 The presence of large companies such Inditex are able to explain the overperformance of some regions and the time dependency found in Tables 5.5 and 5.6. Furthermore, maps seem to suggest some degree of spatial autocorrelation, i.e. highly Inventive (Replicative) regions tend to be closely in line with conspicuous literature on spatial co-location of CCIs (Boix 2013; Boix et al. 2015; Coll-Martínez et al. 2019; Cruz and Teixeira 2015). In order to check more rigorously the validity of this visual inspection, the Moran’s Is (Moran 1950) have been computed for the share Inventive and Replicative employment in CCIs. This measure of spatial autocorrelation is characterised by the correlation of a variable among nearby locations in space. Being multi-dimensional and multi directional, the spatial autocorrelation is a more complex measure compared to the classical autocorrelation. The Moran’s I can be calculated as in (5.1) ∑ ∑ n i j wi, j z i z j ∑ 2 I = (5.1) S0 i zi where n is the number ∑ of observations (regions) indexed i and j, wi, j represent the ∑ spatial weights, S0 = i j wi, j , and z i and z j are deviations from the sample mean of the variable of interest (Fig. 5.3; Table 5.7). In both cases, the Moran’s I suggests that there is a positive spatial autocorrelation. This means that regions where CCIs are highly clustered tend to be geographically close. This result is even stronger considering Replicative CCIs that are mostly located in Eastern European regions while Inventive ones are co-located but less structurally. In line with this discussion, following Anselin (1995) it is possible to extend the reasoning computing the local indicators of spatial association (LISA) to provide

16

Cf. https://www.theguardian.com/business/2012/aug/17/zara-inditex-profits, accessed 31/01/2022. 17 Cf. https://www.businessinsider.com/zara-transforms-life-in-la-coruna-2018-9?r=US&IR=T? utm_source=copy-link&utm_medium=referral&utm_content=topbar, accessed 31/01/2022.

116

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

-4

-2

0

2

4

6

Replicative 8

6

6

4

4

2

2

0

0 -2

-2 -4

-2

0

2

4

6

8

W.Standardized Replicative empl. share

W.Standardized Inventive empl. share

Inventive -4

-2

0

2

4

6

8

6

6

4

4

2

2

0

0 -2

-2

Standardized Inventive empl. share

-4

-2

0

2

4

6

8

Standardized Replicative empl. share

Fig. 5.3 Graphical representation of Moran’s Is

Table 5.7 Moran’s I statistics of the share of inventive and replicative employment Variable

Moran’s I

E(I)

SE(I)

Z(I)

p-value

Empl. inventive (standardized)

0.19473

− 0.00075

0.00978

19.98422

0.000

Empl. replicative (standardized)

0.28851

− 0.00075

0.00989

29.23980

0.000

insights about the tendency of CCIs to cluster or not in neighbouring regions (Bertacchini and Borrione 2013).18 The treatment of the bivariate Local Moran’s I follows its global counterpart and it captures the relationship between the value for one variable at a given location, and the average of the neighbouring values for its spatial lag. In terms of interpretation, ‘the LISA for each observation gives an indication of the extent of significant spatial clustering of similar values around that observation’ (Anselin 1995, p. 94) and it should be used cautiously because this is a descriptive tool that suggests which are the interesting locations, not allowing any further inference on them. Regions highlighted in red in Map 5.2 display high values of the share of CCIs (Inventive in the former map and Replicative in the latter) and have neighbours that also have high values (high-high). Blue areas, instead are low-low in the same scheme, while indigo regions are low-high and pink areas are high-low. It is quite evident how Inventive and Replicative CCIs tend to cluster in different areas. Especially, Inventive CCIs are typical of urban and metropolitan areas, as the red areas are concentrated in the areas of Paris, London, Central and Northern Italian Cultural and Creative clusters, without forgetting the Vienna area, the districts of Spain, and some areas in the North Rhine-Westphalia and Bavaria in Germany. Contrarily, considering Replicative CCIs, co-location patterns seem to mostly affect Eastern European areas with significant country effects.

18

LISA are constructed by local Moran’s Is using the toolkit present in GeoDa (Anselin et al. 2002, 2010).

5.6 The Geography of CCIs

Map 5.2 LISA cluster maps of inventive and replicative CCIs

117

118

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

5.6.2 Territorial Versus Industrial Approach As explained in Part I of the book, the territorial perspective to creativity in CCIs is the value added of this work. In the traditional industrial approach, each activity can be either Inventive or Replicative, according to whether it belongs to an industry with high or low intensity in IP production (cf. EPO and EUIPO 2016).19 Through the classical industrial approach the identification of inventive CCIs can be based on the share of employment in CCIs defined as inventive by EPO on total CCIs employment. The consequence of such an approach is the lack of regional variance concerning the capacity of a sector to innovative. Each sector is indeed either Inventive or Replicative regardless of the place where it is located. This work applies another logic, looking at the regional variance in inventive capacity of each CCI, claiming that each industry may be inventive or replicative according to the place where it is located. In fact, to highlight the territorial dimension, the analysis is developed through an indicator that allows each industry to have a regional variance in the degree of invention within each CCI. The share of inventive CCIs is obtained through the share of employment in CCIs that show a higher than EU average innovative activity. In this way, one sector can be inventive in one region and not in another, as the result of an inventive atmosphere at the local level, and of dynamic agglomeration economies. For this reason, we call this a territorial approach. An interesting message comes from the comparison between the industrial and the territorial approach. Map 5.3 shows the differences of employment intensity of Inventive CCIs obtained with the two methodologies. The value assigned to each region on the map is the difference between the share of Inventive CCIs obtained by applying the territorial approach and the share of Inventive CCIs following the industrial one. Red colours highlight regions that would be characterised by inventive CCIs with an industrial approach, and that, on the contrary, are found to host low inventive activities in such sectors. Blue colours underline instead regions that would host low-inventive sectors by looking at the degree of industrial invention, and that instead are loci of inventive activities of such low-inventive industries. The difference between the two approaches is not a small one, ranging from − 25 to + 27%. Indeed, the choropleths in Map 5.3 show major differences in terms of spatial pattern between the two groups, assigning a premium to Western and Southern European countries at the expense of Eastern ones. In the case of Western and Southern countries, therefore, the territorial approach highlights the presence of dynamic inventive CCIs in a mix of relatively low inventive sectors. For Eastern regions and some scattered ones in France, UK, Spain, Italy and most German ones, on the contrary, the map shows the presence of a mix of low-inventive CCIs that are instead dynamic. The case of the Spanish A Coruña region is evident in this respect. It hosts the famous innovative brand Zara, which is classified as belonging to an inventive sector; in reality, the degree of inventiveness of this region is rather 19

Usually, international bodies dealing with IPRs prefer an industrial classification of sectors according to the intensity in producing patents, trademarks, or copyrights (EPO and EUIPO 2016; ESA and USPTO 2016; USPTO 2012).

5.6 The Geography of CCIs

119

Map 5.3 Difference in the distribution of inventive activities according to the territorial versus industrial approach (2015)

limited, due to the presence of administrative and headquarter activities, rather than of creative functions, like design. Even within each country, differences occur. In UK, for instance, most of the Inventive activity is concentrated in the neighbourhood of London while the rest of the country is the location of inventive activities of replicative CCIs. In Spain, Inventive functions of Replicative CCIs are concentrated in the capital city, in the Barcelona area, and in the Valencian one. Conversely, without a territorial perspective, the Inventiveness of CCIs in part of Extremadura and Castilla y Leon would be overestimated; the inventive sectors hosted in these regions are instead characterised by replicative functions. The same reasoning can be applied to all other countries that present a disproportion of CCIs representation across regions. In this sense, it is possible to state that Inventive and Replicative activities tend to cluster in different parts of the continent, separating the process of generation of new ideas from the existence of mere CCIs. That is why it is extremely important to further improve this analysis with a detailed assessment of the reasons behind the process of clustering of inventive activities, trying to detect the different territorial forces driving

120

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

the phenomenon. Indeed, looking at Map 5.3, it is clear that localisation patterns of Inventive CCIs do not reflect the localisation patterns of their inventive activities and, although the phenomenon of clustering of CCIs tout court is not new in the literature (Coll-Martínez et al. 2019; Lazzeretti et al. 2012; Lorenzen and Frederiksen 2007), a further discussion of the role of innovative activities and their territorial drivers could add important insights to the debate. This will be the subject matter of the next chapter.

5.6.3 CCIs by Typology of Territories As widely highlighted by the literature, and as shown in the previous sections of this chapter, CCIs cluster in space. They generally choose locations with specific features such as urban and rich areas where creativity may flourish and innovations may be easily adopted. In other words, CCIs select their locations according to the type of territory, preferring both economic-rich environments that accept and stimulate creativity and cultural-rich environments where the creative class finds the suitable conditions for their stay. A first statistical analysis can be run in this respect through an ANOVA,20 measuring the statistical difference in the intensity of CCIs in different regional contexts. The latter are identified in terms of: . settlement structure. As usually proved by the literature, urban and metropolitan areas are the most attractive loci for CCIs, since they stimulate the flourishing of new ideas to be developed; . economic environment. Economically rich areas attract CCIs because of both the presence of a suitable end-market for their products and the availability of innovative and advanced sectors that sustain their activity (e.g. professional support services for the protection of IPRs); . cultural environment. Culturally advanced areas attract creative individuals: these actors prefer lively areas where they can enjoy cultural consumption (e.g. museums, monuments, …) and where they can share experiences with similar people. The intensity of CCIs is measured by quintiles of the share of CCIs employment in the different groups of territories. The latter are measured through the following variables. For the settlement structure a variable built by Eurostat is applied, and it takes values 0: non-metropolitan region; 1: metropolitan region; 2: capital metropolitan region. The economic environment gathers classical variables in regional economics such the development stage of the region (Dummy for Eastern European countries), level of wealth (GDP in PPS per capita), and innovativeness of the area (patents per capita). Finally, the cultural environment can be captured by the number of UNESCO World Heritage Sites, and education levels, measured through the share 20

Technical aspects related to ANOVA are presented in the Appendix 5.2 to this chapter.

5.6 The Geography of CCIs

121

of people with tertiary education. All these variables describe the local contexts that CCIs select for their settlement. Table 5.8 reports the summary statistics for CCIs as a whole, and for the variables used to identify the different types of territories while Table 5.9 presents the mean values for regional variables according to type of CCIs and quintiles. To better clarify the logic, 0.498 associated to the 1st quintile of Replicative CCIs associated to the variable Metropolitan area represents the mean of Metropolitan area in areas whose employment in Replicative CCIs is the lowest (1st quintile). Table 5.10 reports the first group of ANOVA results. For each type of territory, the statistical significance of the difference in the intensity of Inventive versus Replicative CCIs is measured for each quintile within each type of territory. Interesting results emerge. The negative and significant difference between Inventive and Replicative CCIs in the 5th quintile considering Eastern countries, witnesses that the largest populated clusters in Eastern countries are characterised by Replicative activities, while in these countries Inventive CCIs cluster in relatively smaller groups than in Western countries. Instead, large clusters of Inventive CCIs are definitely present in urban, rich, inventive and cultural areas. Moreover, interestingly enough, large clusters of Inventive CCIs prefer rich and culturally lively areas compared to large clusters of Replicative. Plus, it is worth underlining that if clusters are small, Replicative CCIs tend to be associated to innovative areas more than considering Inventive Table 5.8 Descriptive statistics Territorial variables

Source

Obs.

Mean

Std. dev.

Min.

Max.

CCIs

Regional empl. in inventive CCIs (%)

Orbis

1332

0.021

0.025

0.000

0.378

Regional empl. in replicative CCIs (%)

Orbis

1332

0.025

0.031

0.000

0.298

Settlement structure

Metropolitan area

Eurostat

1332

0.475

0.641

0.000

2.000

Economic environment

New EU country – (after 2004)

1332

0.181

0.385

0.000

1.000

GDP per capita PPS

ARDECO

1332

26,537

17,519

6,194.74

404,997.40

Patents per capita

OECD RegPat

1332

0.533

1.258

0.000

23.087

Popul. with tertiary education (%)

ESPON

1332

0.289

0.089

0.116

0.695

1332

0.255

0.556

0.000

4.000

Cultural environment

UNESCO World UNESCO Heritage Sites (WHS)

Cultural environment

UNESCO WHS

Popul. with tertiary education (%)

Patents per capita

GDP per capita PPS

0.251 0.169

Inventive

0.281

Inventive Replicative

0.310

0.208

Inventive Replicative

0.406

Replicative

19,350.145

Inventive

0.371 24,570.985

Replicative

Inventive

New EU country (after 2004)

Economic environment 0.247

0.303

Inventive Replicative

0.498

Replicative

Metropolitan area

Settlement structure

1st quintile

Type of CCIs

Territorial variable

Territorial field

Table 5.9 Mean values for regional variables according to creative cluster and quintiles

0.154

0.203

0.277

0.296

0.312

0.695

23,650.949

28,592.815

0.192

0.019

0.380

0.534

2nd quintile

0.285

0.277

0.277

0.283

0.444

0.881

25,576.071

29,211.117

0.150

0.011

0.412

0.479

3rd quintile

0.222

0.241

0.294

0.286

0.766

0.493

28,404.036

27,161.563

0.102

0.075

0.538

0.429

4th quintile

0.444

0.301

0.313

0.268

0.937

0.191

35,732.853

23,144.281

0.090

0.553

0.744

0.436

5th quintile

122 5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

5.6 The Geography of CCIs

123

ones. This means that there exist small clusters of CCIs dedicated to replication that exploit the high innovativeness of areas. Table 5.11 presents the second group of ANOVA analyses. In this case, the difference is calculated for Replicative or Inventive for different size of CCIs clusters (quintiles) by type of territory. In particular, the table reports the results only of the largest group (5th quintile) with respect to the smallest (1st quintile), while the total overview of the single values are presented in Fig. 5.4 (and Table 5.13 in Appendix 5.2 to this chapter). The large Inventive CCIs clusters are, with respect to the small ones, typical of Western countries, large urban areas, rich areas, and inventive and culturally developed environments. Instead, large Replicative CCIs clusters are typical of Eastern countries, less cultural areas, and less inventive areas. These results witness once again that Inventive activities in CCIs require the traditional local conditions emphasised by the literature. In order to be creative, one needs wealthy, advanced, inventive and culturally rich environments. Cities are therefore the major loci of these Inventive CCIs since they embed all crucial elements: richness, culture, inventive atmosphere. Moreover, the results underline that the higher the inventive activity, the higher the clustering, suggesting that localisation and urbanisation economies are key localisation factors since they play a role in the performance of these activities. A completely different story characterises the Replicative activities of CCIs. While the richness of the area does not make any difference, they strongly cluster in areas at an early stage of development. Moreover, it remains much more difficult to picture the reasoning for clustering of this group of CCIs activities. All this has to be proved through a more advanced and finer empirical analysis, which will be presented in the next chapter.

5.6.4 Filière Structure of CCIs—A Key Dimension for Heterogeneous Concentration In the previous section we have analysed the location of CCIs according to their replicative versus inventive nature. We have shown how location patterns strongly differentiate across the two types of CCIs. In this section, we add an additional dimension of the complex story of location choices. The last one can in fact differ according to the kind of trade relationships that characterise a creative industry. There are industries, like museums, and historical activities, that are oriented towards the final market (the so-called short filières), while others that have a more business-tobusiness trade nature, where their intermediate trade is more in favour of other CCIs (the so called creative filières) rather than other economic sectors (the so-called vast filières). Map 5.4 presents the geographical distribution of CCIs, divided between replicative and inventive activities belonging to ‘creative’ w.r.t. vast filières. Some interesting results emerge. First, the maps confirm the difference in location patterns between

UNESCO WHS

Popul. with tertiary education (%)

Patents per capita 0.000 − 0.019*** 0.495 − 0.049

0.108 − 0.029*** 0.844 − 0.082* 0.052

Difference

Levene’s test (p-value)

Difference

Levene’s test (p-value)

0.207

− 0.382***

− 0.198*

Difference

Levene’s test (p-value)

− 4941.866*** 0.199

− 5220.840***

0.000

0.173***

0.004

− 0.154***

2nd quintile

0.066

Levene’s test (p-value)

Difference

0.124*** 0.002

Difference

Levene’s test (p-value)

0.000

Levene’s test (p-value)

1st quintile − 0.195***

Measure

Difference

0.887

0.007

0.22

− 0.006

0.003

− 0.437**

0.181

− 3635.046**

0.000

0.139***

0.18

− 0.067

3rd quintile

If the null hypothesis of Levene’s test for equality of variances is rejected, the Welch’s correction is applied for the t-test *** p < 0.01, ** p < 0.05, * p < 0.1

Cultural environment

New EU country (after 2004)

Economic environment

GDP per capita PPS

Metro area

Settlement structure

Territorial variable

Table 5.10 Post-hoc t-test of pairwise comparisons among inventive and replicative CCIs within each quintile

0.665

− 0.019

0.087

0.008

0.007

0.274***

0.661

1242.473

0.286

0.026

0.046

0.109**

4th quintile

0.015

0.143**

0.146

0.045***

0.000

0.746***

0.64

12,588.572***

0.000

− 0.462***

0.000

0.308***

5th quintile

124 5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

5.6 The Geography of CCIs

125

Table 5.11 ANOVA comparisons of 5th quintile with respect to the 1st quintile by type of territory and type of CCIs Inventive

Territorial variable Settlement structure

Metro area

Sectoral environment

New EU country (after 2004) GDP per capita PPS

Cultural environment

0.441*** − 0.281*** 16,382.708***

Replicative − 0.062 0.305*** − 1,426.704

Patents per capita

0.729***

− 0.215**

Popul. with tertiary education (%)

0.032***

− 0.042***

UNESCO WHS

0.275***

0.050

*** p < 0.01, ** p < 0.05, * p < 0.1

Inventive and Replicative. Inventive activities, especially when their trade is mostly with other CCIs, tend to cluster in space (Map 5.4b). The high concentration of Replicative CCIs in Eastern countries is due to CCIs that have a strong trade relationship with sectors other than CCIs, demonstrating once more that the creative cluster of creative input–output local firms is not typical of such countries. These kinds of creative CCIs input–output clusters are typical of a few areas in Europe (Map 5.4b), all large cities, also thanks to institutions and lobbying, possible only where political power is settled (Scott 2005) (Maps. 5.5 and 5.6). A similar result is obtained concerning the Inventive service activities in short filières. Large urban areas host creative service activities offered to the local population (Map 5.7b) while manufacturing activities with a short filière tend to ignore metropolitan areas, confirming the initial intuition of considering them as “self-relying”. These intuitions obtained by looking at the maps can achieve a statistical confirmation in Fig. 5.5 that presents the pairwise significant correlations (p-value < 0.05) between the share of CCIs in each region (variable plotted in the maps) and the context conditions used also in the previous ANOVAs. Figure 5.5 reveals important messages. First of all, just by looking at the figure in general, a strong differentiation in the importance of local characteristics for the different types of CCIs emerges, reinforcing our idea that the different trade relationships add value to the interpretation of location patterns. More in particular, all types of replicative CCIs are detached from the local characteristics, underlining their footloose nature compared to inventive. Replicative activities are present in Eastern countries, irrespective of their trade relationship, and are mostly located in non-urban areas. Instead, inventive CCIs differ with respect to the type of trade relationship. Inventive CCIs with either a vast or creative filières reflect the traditional CCIs clusters that look for specific rich, urban, cultural environments. Instead, inventive manufacturing CCIs oriented towards the final market (e.g. food and textile manufacturing) seem to follow a different location logic with respect to the other inventive CCIs. They do not look for educated environments as well as are attracted by non-urban areas. The different location behaviour between manufacturing and services inventive CCIs

126

5 Where Is Creativity? Data and Methodology to Measure CCIs Across … Settlement structure a) Metropolitan Area

Economic environment b) New EU Country (after 2004)

c) GDP per capita PPS

d) Patents per capita

Cultural environment e) Popul. Tertiary education (%)

f) UNESCO WHS

Fig. 5.4 Distribution of group means across quintiles of inventive and replicative activities

5.6 The Geography of CCIs

127 a. Replicative ‘creative filière’ CCIs

b. Inventive ‘creative filières’ CCIs

Map 5.4 CCIs’ geographical distribution by trade relationships: creative filière

128

5 Where Is Creativity? Data and Methodology to Measure CCIs Across … a. Replicative ‘vast filières’ CCIs

b. Inventive ‘vast filières’ CCIs

Map 5.5 CCIs’ geographical distribution by trade relationships: vast filière

5.6 The Geography of CCIs

129 a. Replicative manufacturing ‘short filière’ CCIs

b. Inventive manufacturing ‘short filière’ CCIs

Map 5.6 CCIs’ geographical distribution by trade relationships: manufacturing short filière

130

5 Where Is Creativity? Data and Methodology to Measure CCIs Across … a. Replicative services ‘short filière’ CCIs

b. Inventive services ‘short filière’ CCIs

Map 5.7 CCIs’ geographical distribution by trade relationships: services short filière

oriented to the final market suggest that the intuition of splitting the two categories was a right one. The main comments and preliminary conclusions that can be drawn from this exercise are the following. First, maps confirm the conceptual spatial subdivision between Inventive and Replicative, especially for activities belonging to the Short

5.6 The Geography of CCIs

131

Fig. 5.5 Pairwise correlations between share of CCIs and context conditions21

filière. As a confirmation of this, the new EU country dummy is positive only for Replicative activities, corroborating the idea that CCIs innovate mostly in regions located in the Western part of the continent. Secondly, Inventive activities mostly seek for denser metropolitan places if they belong to non-market-oriented filières. Indeed, density of actors stimulates the interactions creating the occasions for the exchange of ideas through goods and services. The same reasoning applies for services activities with a short filière since dense areas are also places where consumers concentrate, favouring the access to them for sellers. On the contrary, manufacturing activities with a short filière tend to ignore metropolitan areas, confirming the initial intuition of considering them as “self-relying”. This reasoning confirms the role of urban areas where higher functions and higher size may stimulate the industrial relationships and favour trade exchanges, also thanks to institutions and lobbying, possible only where political power is settled (Scott 2005). However, this seems more important for Inventive CCIs, i.e. those who bear the cost of cities for the stimuli to innovation. As far as the economic environment is concerned, all variables included go in line with the idea that richer places are preferred by activities that innovate. Finally, a few lines on the cultural environment are needed. Indeed, the correlations provide interesting insights. Overall, highly educated societies are positively correlated with a concentration of Inventive activities. The generation of new knowledge requires professional and specialised people in the fields of interest. However, the dichotomy between Inventive and Replicative as regards education levels does not hold for services activities with a short filière. Recalling the previous section, within this group 21

Only significant correlations are shown (p-value < 0.05).

132

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

it is possible to find retail activities and cultural services like museums. The fact that both Inventive and Replicative activities cluster where there is a high education level suggests that the “cultivated demand” of cultural and creative services is important to sustain those businesses. Creative individuals enjoy these services also if they do not innovate in a strict sense. All these preliminary results allow us to draw some considerations, linking them with existing literature on the topic. First, CCIs are confirmed to be highly clustered in space as was found in most of the previous empirical analyses (Boix et al. 2015; Yusuf and Nabeshima 2005). Second, although there is a strong urban (metropolitan) pattern in the phenomenon, there is strong heterogeneity in the behaviour between industries and between groups of industries (Bertacchini and Borrione 2013; deMiguel-Molina et al. 2012). The spatial behaviour of CCIs seems to be linked to the form and intensity of industrial relations. In fact, according to the typology of filière, CCIs tend to locate in different areas that also have different features. The literature stresses the importance of industrial relations for CCIs (Madudová 2017; Santagata 2009) but this component is not exploited in detail when dealing with clustering. Third, considering the regional features, there exist some common determinants in attracting CCIs at the local level (Lazzeretti et al. 2012) in terms of typology of the territory, the economic and cultural environment playing a key role. Finally, the differences found by comparing Inventive and Replicative CCIs have few antecedents in the literature. In fact, although the diversity of geographical patterns of CCIs is evident when comparing different groups like knowledge-intense CCIs and traditional ones (Cruz and Teixeira 2015), the fact that each specific industry may be heterogeneously innovative is still overlooked. Finally, the discussion confirms that location patterns of CCIs are a regional phenomenon, spatial heterogeneity exists both within and between countries.

5.7 Conclusions This chapter has the aim of presenting the bridge between the conceptual and the empirical part of the analysis. The main value added of this approach is the attention devoted to measuring creativity at the regional level. Classical works on creative CCIs address the measurement of creativity by relying on the industrial IPRs intensity. In this way, however, since they consider the intensity only as an inter-sectoral concept, they completely neglect the territorial dimension of creativity, considering each industry as a unitary object in a broad reference area (e.g. the EU). Indeed, they compare industries, identifying as intense only those above a certain threshold across industries. The empirical framework proposed, instead, has strongly innovative features. It accounts for the fact that the roots of creativity are local, due to the indissoluble bond among firms and places (Paniccia et al. 2011). Then, it considers that a specific creative activity is really creative only where it produces a consistent amount of new knowledge, whatever the form is. In fact, the intensity in producing intellectual

5.7 Conclusions

133

property rights is considered only at the industrial level (as in WIPO 2003 or EPO and EUIPO 2016). Furthermore, it deems the intensity as an intra-sectoral dimension that accounts for different intensities within the same industry, exploiting the regional differences. Thus, this reasoning presents some relevant elements that will play a role in the interpretation of the empirical results. First of all, in contrast with all the previous models, the membership of an activity within a specific group, whatever it is, is not pre-determined but it derives from an empirical investigation on data. In other words, the groups of sectors are endogenously determined by the method itself. In order to understand the reach of this conceptual change, a brief comparison with the Santagata’s work is worthwhile. The Italian scholar meticulously divided each industry into sub activities and he assigned them to the phase of the creative production chain that conceptually better fitted their daily work. Thanks to the approach applied in this work, it is possible to avoid the mistake coming from selection. In fact, activities related to distribution may emerge as more creative than activities related to the conception or vice versa, no restrictions are needed at the beginning. The same reasoning is applied to the other models that attempted to outline different levels of creative expressions within CCIs (KEA 2006; WIPO 2003). Second, this narrative is consistent with the storyline that downgrades the role of sectors in explaining the dynamics of innovation (and creativity) substituted by the territories that mitigate any industrial specificity (Boix and Trullén 2010). The framework presented in this work attempts to merge the two dimensions, the sectoral and the territorial, in order to identify the places that, better than others, have produced knowledge. In this way, CCIs and space are bound, and it is no longer important to distinguish creative places and creative industries, they are merged in a unique concept: places where CCIs are on the edge of creativity. The chapter has also presented the original database on the presence of different types of CCIs built from the Orbis databank. The Orbis database has been laid out, describing its strengths and also the limitations and the methods employed to fix them. Moreover, the chapter has entered the indicators on the intensity of CCIs built thanks to the rich georeferenced data. Results from such an interesting database have been presented in interesting maps representing the intensity of different types of CCIs in European NUTS3 regions. Interesting results have emerged. Replicative and inventive CCIs have differentiated location patterns, the latter much more concentrated than the former ones, and more prone to culturally and economically rich urban locations. By adding a further element in the description of location behaviour, namely the trade relationship, reflecting the type of value chain that characterises each CCI, the geographical patterns are differentiated too. The descriptive analysis of the location patterns presented in this chapter calls for an interpretative analysis for the assessment of localisation choices. This is the subject matter of the next chapter.

134

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Appendix 5.1 See Table 5.12. Table 5.12 Conversion of Santagata’s codes according to NACE Rev. 2 classification ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

Fashion 74875

Design e styling relativo a tessili, abbigliamento, calzature, gioielleria, mobili e altri beni personali

7410

Specialised design activities

1771

Fabbricazione di articoli di calzetteria

1431

Manufacture of knitted and crocheted hosiery

1772

Fabbricazione di pullover, cardigan ed altri articoli simili a maglia

1439

Manufacture of other knitted and crocheted apparel

1920

Fabbricazione di articoli da viaggio, borse, marocchineria e selleria

1512

Manufacture of luggage, handbags and the like, saddlery and harness

1930

Fabbricazione di calzature

1520

Manufacture of footwear

1810

Confezione di vestiario in pelle 1411

Manufacture of leather clothes

1822

Confezione di abbigliamento esterno

1413

Manufacture of other outerwear

1823

Confezione di biancheria intima

1414

Manufacture of wearing apparel

1824

Confezione di altri articoli di abbigliamento ed accessori

1419

Manufacture of other wearing apparel and accessories

1711

Preparazione e filatura di fibre tipo cotone

1310

Preparation and spinning of textile fibres

1760

Fabbricazione di tessuti a maglia

1391

Manufacture of knitted and crocheted fabrics

1712

Preparazione e filatura di fibre tipo lana cardata

1310

Preparation and spinning of textile fibres

1713

Preparazione e filatura di fibre tipo lana pettinata

1310

Preparation and spinning of textile fibres

1714

Preparazione e filatura di fibre tipo lino

1310

Preparation and spinning of textile fibres

1715

Torcitura e testurizzazione della seta e di filamenti sintetici o artificiali

1310

Preparation and spinning of textile fibres

1716

Fabbricazione di filati cucirini

1310

Preparation and spinning of textile fibres (continued)

Appendix 5.1

135

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

1717

1310

Preparazione e filatura di altre fibre tessili

Preparation and spinning of textile fibres

1721

Tessitura di filati tipo cotone

1320

Weaving of textiles

1722

Tessitura di filati tipo lana cardata

1320

Weaving of textiles

1723

Tessitura di filati tipo lana pettinata

1320

Weaving of textiles

1724

Tessitura di filati tipo seta

1320

Weaving of textiles

1725

Tessitura di altre materie tessili 1320

Weaving of textiles

1910

Preparazione e concia del cuoio

1511

Tanning and dressing of leather; dressing and dyeing of fur

1830

Preparazione e tintura di pellicce; confezione di articoli in pelliccia

1420

Manufacture of articles of fur

1730

Finissaggio dei tessili

1330

Finishing of textiles

5116

Intermediari del commercio di 4616 prodotti tessili, abbigliamento, calzature e articoli in cuoio

Agents involved in the sale of textiles, clothing, fur, footwear and leather goods

5124

Commercio all’ingrosso di pelli, anche per pellicceria, e cuoio

4624

Wholesale of hides, skins and leather

5141

Commercio all’ingrosso di prodotti tessili

4641

Wholesale of textiles

5142

Commercio all’ingrosso di abbigliamento e di calzature

4642

Wholesale of clothing and footwear

51478

Commercio all’ingrosso di articoli in cuoio e articoli da viaggio

4649

Wholesale of other household goods

5242

Commercio al dettaglio di articoli di abbigliamento

4771

Retail sale of clothing in specialised stores

5241

Commercio al dettaglio di tessili

4751

Retail sale of textiles in specialised stores

Industrial design and craft 74875

Design e styling relativo a tessili, abbigliamento, calzature, gioielleria, mobili e altri beni personali

7410

Specialised design activities

1751

Fabbricazione di tappeti e moquette

1393

Manufacture of carpets and rugs (continued)

136

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

2051

Fabbricazione di altri prodotti in legno

1629

Manufacture of other products of wood; manufacture of articles of cork, straw and plaiting materials

2052

Fabbricazione di articoli in sughero e materiali da intreccio

1629

Manufacture of other products of wood; manufacture of articles of cork, straw and plaiting materials

2630

Fabbricazione di piastrelle in ceramica per pavimenti e rivestimenti

2331

Manufacture of ceramic tiles and flags

2812

Fabbricazione di porte e finestre in metallo

2512

Manufacture of doors and windows of metal

2861

Fabbricazione di articoli di coltelleria e posateria

2571

Manufacture of cutlery

3150

Fabbricazione di apparecchiature per illuminazione e di lampade elettriche

2740

Manufacture of electric lighting equipment

3350

Fabbricazione di orologi

2652

Manufacture of watches and clocks

3611

Fabbricazione di sedie e divani 3109

Manufacture of other furniture

3612

Fabbricazione di mobili per uffici e negozi

3101

Manufacture of office and shop furniture

3613

Fabbricazione di mobili per cucina

3102

Manufacture of kitchen furniture

3614

Fabbricazione di altri mobili

3109

Manufacture of other furniture

3622

Fabbricazione di gioielleria e oreficeria

3212

Manufacture of jewellery and related articles

3630

Fabbricazione di strumenti musicali

3220

Manufacture of musical instruments

3650

Fabbricazione di giochi e giocattoli

3240

Manufacture of games and toys

17545

Fabbricazione di tulle, pizzi, 1399 merletti- fabbricazione di tulle e di altri tessuti a maglie annodate, di pizzi in pezza, in strisce o in motivi

Manufacture of other textiles n.e.c.

17546

Fabbricazione di ricami

1399

Manufacture of other textiles n.e.c.

20301

Fabbricazione di porte e finestre in legno

1623

Manufacture of other builders’ carpentry and joinery

26152

Lavorazione di vetro a mano e a soffio- fabbricazione di articoli di vetro a pressa

2319

Manufacture and processing of other glass, including technical glassware (continued)

Appendix 5.1

137

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

26210

Fabbricazione di prodotti in ceramica per usi domestici e ornamentali

2341

26702

Lavorazione artistica del 2370 marmo e di altre pietre affini, lavori in mosaico- taglio, modellatura e finitura di pietre per monumenti funerari, ecc

Cutting, shaping and finishing of stone

51471

Commercio all’ingrosso di mobili di qualsiasi materiale

4647

Wholesale of furniture, carpets and lighting equipment

52441

Commercio al dettaglio di mobili

4759

Retail sale of furniture, lighting equipment and other household articles in specialised stores

52442

Commercio al dettaglio di utensili per la casa, di cristallerie e vasellame

4759

Retail sale of furniture, lighting equipment and other household articles in specialised stores

52443

Commercio al dettaglio di articoli per l’illuminazione

4759

Retail sale of furniture, lighting equipment and other household articles in specialised stores

52444

Commercio al dettaglio di altri 4759 articoli diversi per uso domestico

Retail sale of furniture, lighting equipment and other household articles in specialised stores

52453

Commercio al dettaglio di strumenti musicali e spartiti

Retail sale of furniture, lighting equipment and other household articles in specialised stores

52483

Commercio al dettaglio di 4777 orologi, articoli di gioielleria e argenteria

Retail sale of watches and jewellery in specialised stores

15512

Produzione dei derivati del latte

1051

Operation of dairies and cheese making

15130

Lavorazione e conservazione di carne e di prodotti a base di carne

1013

Production of meat and poultry meat products

4759

Manufacture of ceramic household and ornamental articles

Taste industry

1131

Colture Vitivinicole

–

–

15931

Produzione di vini da tavola e v.q.p.r.d. (DOC, DOCG, IGT)

1102

Manufacture of wine from grape

55235

Agriturismi

5520

Holiday and other short-stay accommodation

55301

Ristorazione con somministrazione

5610

Restaurants and mobile food service activities (continued)

138

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

5225

Commercio al dettaglio di bevande (vini, birra ed altre bevande)

4725

Retail sale of beverages in specialised stores

52271

Commercio al dettaglio di latte 4729 e di prodotti lattiero-caseari

Other retail sale of food in specialised stores

52220

Commercio al dettaglio di carni e di prodotti a base di carne

4722

Retail sale of meat and meat products in specialised stores

5821

Publishing of computer games

5829

Other software publishing

Computer and software 7221

Edizione di software

72600

Altre attività connesse all’informatica

6209

Other information technology and computer service activities

7222

Altre realizzazioni di software e consulenza informatica

6201

Computer programming activities

6202

Computer consultancy activities

2233

Riproduzione di registrazioni informatiche

1820

Reproduction of recorded media

5184

Commercio all’ingrosso di computer

4651

Wholesale of computers, computer peripheral equipment and software

Publishing 2211

Edizione di libri

5811

Book publishing

2212

Edizione di giornali

5813

Publishing of newspapers

2213

Edizione di riviste e periodici

5814

Publishing of journals and periodicals

9240

Attività delle agenzie di stampa

6391

News agency activities

2221

Stampa di giornali

1811

Printing of newspapers

2222

Altre stampe di arti grafiche

1812

Other printing

2223

Legatoria, rilegatura di libri

1814

Book-binding and related services

2224

Lavorazioni preliminari alla stampa

1813

Pre-press and pre-media services

Attività radiotelevisive

5911

Motion picture, video and television programme production activities

6010

Radio broadcasting

TV and radio 9220

(continued)

Appendix 5.1

139

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label 6020

Television programming and broadcasting activities

2630

Manufacture of communication equipment

32201

Fabbricazione e montaggio di apparecchi trasmittenti radio televisivi

3230

Fabbricazione di apparecchi 2640 riceventi per la radiodiffusione e la televisione

Manufacture of consumer electronics

51431

Commercio all’ingrosso di 4643 elettrodomestici, di apparecchi radiotelevisivi e telefonici e altra elettronica di consumo

Wholesale of electrical household appliances

52451

Commercio al dettaglio di elettrodomestici, apparecchi radio, televisori, lettori e registratori di dischi e nastri-commercio

4743

Retail sale of audio and video equipment in specialised stores

4719

Other retail sale in non-specialised stores

Studi di promozione pubblicitaria- ideazione e realizzazione di campagne pubblicitarie

7311

Advertising agencies

7410

Specialised design activities

Agenzie di concessione degli spazi pubblicitari

7311

Advertising agencies

7312

Media representation

Advertising 74401

74402 Cinema 9211

Produzioni cinematografiche e 5911 di video

Motion picture, video and television programme production activities

9212

Distribuzioni cinematografiche 5912 e di video

Motion picture, video and television programme post-production activities

5913

Motion picture, video and television programme distribution activities

9213

Gestione di sale di proiezione cinematografiche

5914

Motion picture projection activities

2232

Riproduzione di registrazioni video

1820

Reproduction of recorded media

Cultural heritage 9251

Attività di biblioteche e archivi 9101

Library and archives activities (continued)

140

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

Table 5.12 (continued) ATECO 2002 ATECO 2002 label

NACE Rev. 2 NACE Rev. 2 label

Gestione di musei e del patrimonio culturale

9102

Museums activities

9103

Operation of historical sites and buildings and similar visitor attractions

9253

Gestione degli orti botanici, dei parchi naturali e del patrimonio naturale

9104

Botanical and zoological gardens and nature reserves activities

5510

Alberghi

5510

Hotels and similar accommodation

5521

Ostelli

5520

Holiday and other short-stay accommodation

Creazioni e interpretazioni artistiche e letterarie

9001

Performing arts

9002

Support activities to performing arts

92723

Scritturazione di attori

9329

Other amusement and recreation activities

2214

Edizione di registrazioni sonore

5920

Sound recording and music publishing activities

9252

Music and performing arts 9231

92342

Circhi

9001

Performing arts

2231

Riproduzione di registrazioni sonore

1820

Reproduction of recorded media

92341

Sale da ballo e simili

9329

Other amusement and recreation activities

9232

Gestione di teatri, sale da concerto e altre sale di spettacolo

9004

Operation of arts facilities

51432

Commercio all’ingrosso di supporti, vergini o registrati, audio, video, informatici

4643

Wholesale of electrical household appliances

52452

Commercio al dettaglio di dischi e nastri

4763

Retail sale of music and video recordings in specialised stores

Studi di architettura e di ingegneria-attività di consulenza in campo architettonico

7111

Architectural activities

7112

Engineering activities and related technical consultancy

Servizi di ingegneria integrata

7112

Engineering activities and related technical consultancy

9003

Art creation

Architecture 74201

74202

Contemporary art

Appendix 5.2

141

Appendix 5.2 The first of the two ANOVA tests presented in this chapter is a multiple comparison of means across quintiles of Inventive and Replicative CCIs. Table 5.13 presents the results of both the F-test of equality of group means and the Bartlett’s test for equal variances among the groups. Both tests are important because they guide the following steps. In fact, accepting the null hypothesis of the F-test means that the model is not able to detect any differences among the quintiles. Moreover, the response of the Bartlett’s test is even more important. In case of unequal variances, a correction method should be applied in the post-hoc test for the differences.22 In case of unequal variances, Tamhane’s T2 post-hoc tests for pairwise comparisons of means are used. Indeed, in case of unequal variances among groups, classical adjustments (e.g. Bonferroni, Scheffé) are not suitable options and it is necessary to correct for this (Tamhane 1979).23 Instead, the second analysis was performed to compare Inventive and Replicative by the size of these clusters (i.e. each quintile of the distribution). A t-test is conducted as it involves only two groups. More specifically, a Levene’s test is used to assess the equality of variances for a variable calculated for two or more groups (Levene 1960). When the null is rejected, a more generalised version of the t-test should be performed. The most used and applied in this work is Welch’s correction (Welch 1947). Results of this analysis are presented in Table 5.10. Looking at the results, there emerge substantial differences among the localisation patterns of different CCIs. First, although both low Inventive and low Replicative regions are largely located in the Eastern part of the continent, for high quintiles this is true only for Replicative. Furthermore, it is possible to state that Replicative CCIs largely prefer Eastern regions. Second, considering population levels and GDP, the comments are specular. Indeed, for both variables, it is not possible to detect any difference among quintiles of Replicative CCIs while for Inventive differences exist and are relevant. The more Inventive employment is hosted in a region the higher is the population and the economic size of the region itself. This indicates that Inventive CCIs may have a strict preference for large urban areas, where generally new ideas emerge more easily and, reversed, only strongly creative CCIs may bear the cost of urban locations. This reasoning is also supported by the results for the dummy Metropolitan area that follows the same scheme. Third, the regional knowledge environment, proxied by the patents per capita and the education levels, goes in the direction of the previous results. Indeed, these variables go hand in hand with concentration of Inventive CCIs, while understanding the correlation with Replicative employment is much less straightforward. In other words, innovations call for innovations and Replicative CCIs are looking for something else. Summing up the

22 Post-hoc tests are conducted after the fact, i.e. after a significant ANOVA, and they are used to evaluate among which groups the significant differences exist. 23 In Stata, the pwmc command allows to perform multiple comparisons, using methodologies to correct for unequal variances.

New EU country (after 2004)

Sectoral environment − 0.102***

− 0.090**

12,081.904***

− 5448.534*

0.534***

0.365***

− 0.098

2827.965**

− 2049.554

− 0.048

0.064***

0.126

− 0.051

10,156.782***

− 6066.836**

− 0.060

0.541***

0.332***

− 0.043

5 versus 3

0.116*

− 0.014

Inventive 4 versus 3

0.026

− 0.048

5 versus 2

− 0.004

− 0.005

Inventive Replicative

− 0.027***

0.236

0.475***

− 0.014

0.104

Inventive

6225.926***

Replicative

0.288*

Replicative

4300.804***

Inventive

− 0.221*** 4640.132***

− 0.179*** 4021.830***

Inventive Replicative

0.109 − 0.236***

0.076

7328.817***

− 4017.282

− 0.011

0.477***

0.000

0.000

0.000

0.000

0.000

0.289

p-value (F-test)

0.275***

0.050

0.032***

− 0.042***

(continued)

0.000

0.000

0.000

0.000

0.000

0.042

p-value (Bartlett-test)

0.131**

0.074

0.001

− 0.013

0.132

0.187

− 0.215** 0.729***

1925.122

618.302

− 0.042

− 0.008

0.032

− 0.054

3 versus 2

16,382.708***

− 1426.704

− 0.281***

0.305***

0.441***

− 0.062

5 versus 1

0.207***

0.008

5 versus 4

0.053

− 0.010

0.013

− 0.024**

0.558***

0.086

9053.891***

2590.578

− 0.269***

− 0.172***

0.234***

− 0.070

− 0.019

− 0.228***

4 versus 1

3 versus 1

Replicative

Inventive

2 versus 1 0.036

Types of CCIs Replicative

0.056**

0.158**

− 0.105

GDP per capita − 1431.252 PPS 4753.087***

Metro area

4 versus 2

UNESCO WHS

Popul. with tertiary education (%)

Patents per capita

Settlement structure

Territorial variable

Cultural environment

New EU country (after 2004)

Sectoral environment

GDP per capita PPS

Metro area

Settlement structure

Territorial variable

Table 5.13 Tamhane’s T2 post-hoc test of pairwise comparisons among quintiles

142 5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

UNESCO WHS

Popul. with tertiary education (%) 0.098 0.289***

0.068

0.037***

0.038

− 0.028***

0.018

0.625***

− 0.503***

5 versus 2

− 0.010

0.454***

− 0.202

4 versus 2

*** p < 0.01, ** p < 0.05, * p < 0.1

Cultural environment

Patents per capita

Territorial variable

Table 5.13 (continued)

0.024 0.159*

− 0.063

0.036***

− 0.015

0.493***

− 0.690***

5 versus 3

− 0.037

0.017

0.003

0.322***

− 0.389**

4 versus 3

0.222***

0.060

0.019

− 0.018

0.171

− 0.301***

5 versus 4

0.000

0.314

0.000

0.000

0.000

0.000

p-value (F-test)

0.000

0.000

0.000

0.000

0.000

0.000

p-value (Bartlett-test)

Appendix 5.2 143

144

5 Where Is Creativity? Data and Methodology to Measure CCIs Across …

ANOVA and post-hoc results, it is possible to say that Inventive CCIs follow a wellestablished scheme in regional sciences: as they are driven by innovations, they tend to cluster where there exist the conditions for the flourishing and the valorisation of new knowledge. These places are mostly Western cities and areas with highly developed knowledge environments. Instead, Replicative CCIs are much more difficult to pigeonhole as their clustering is more chaotic and less explained by classical variables of regional economic literature. The idea is that, if not innovative, these activities may cluster according to static local conditions that improve their efficiency. On the contrary, Inventive CCIs are expected to follow a dynamic logic of clustering, looking for factors that may stimulate their innovative capacity.

References Anselin, L. 1995. Local Indicators of Spatial Association-LISA. Geographical Analysis 27 (2): 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x. Anselin, L., I. Syabri, and O. Smirnov. 2002. Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows. In New Tools for Spatial Data Analysis, ed. L. Anselin and S. Rey. Proceedings of the Specialist Meeting. University of California, Santa Barbara: Center for Spatially Integrated Social Science (CSISS). Anselin, L., I. Syabri, and Y. Kho. 2010. GeoDa: An Introduction to Spatial Data Analysis. In Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, ed. M.M. Fischer and A. Getis, 73–89. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-64203647-7_5. Bertacchini, E., and P. Borrione. 2013. The Geography of the Italian Creative Economy: The Special Role of the Design and Craft-Based Industries. Regional Studies 47 (2): 135–147. https://doi. org/10.1080/00343404.2011.628652. Boix, R. 2013. Creative Industries in Spain: The Case of Printing and Publishing. In Creative Industries and Innovation in Europe, ed. L. Lazzeretti, 65–85. Routledge. Boix, R., and J. Trullén. 2010. Industrial Districts, Innovation and I-District Effect: Territory or Industrial Specialization? European Planning Studies 18 (10): 1707–1729. https://doi.org/10. 1080/09654313.2010.504351. Boix, R., J.L. Hervás-Oliver, and B. De Miguel-Molina. 2015. Micro-Geographies of Creative Industries Clusters in Europe: From Hot Spots to Assemblages. Papers in Regional Science 94 (4): 753–772. https://doi.org/10.1111/pirs.12094. Coll-Martínez, E., A.-I. Moreno-Monroy, and J.-M. Arauzo-Carod. 2019. Agglomeration of Creative Industries: An Intra-Metropolitan Analysis for Barcelona. Papers in Regional Science 98 (1): 409–431. https://doi.org/10.1111/pirs.12330. Cruz, S.S., and A.A.C. Teixeira. 2015. The Neglected Heterogeneity of Spatial Agglomeration and Co-Location Patterns of Creative Employment: Evidence from Portugal. The Annals of Regional Science 54 (1): 143–177. https://doi.org/10.1007/s00168-014-0649-6. DCMS. 1998. Creative Industries Mapping Documents. DCMS. 2013. Classifying and Measuring the Creative Industries. https://assets.publishing.service. gov.uk/government/uploads/system/uploads/attachment_data/file/203296/Classifying_and_ Measuring_the_Creative_Industries_Consultation_Paper_April_2013-final.pdf. de-Miguel-Molina, B., J.-L. Hervas-Oliver, R. Boix, and M. De-Miguel-Molina. 2012. The Importance of Creative Industry Agglomerations in Explaining the Wealth of European Regions. European Planning Studies 20 (8): 1263–1280. https://doi.org/10.1080/09654313.2012.680579. EPO and EUIPO. 2016. Intellectual Property Rights Intensive Industries and Economic Performance in the European Union.

References

145

ESA and USPTO. 2016. Intellectual Property and the U.S. Economy: 2016 Update. European Commission. 2018. EU Inter-Country Supply, Use and Input-Output Tables (FIGARO Project)—Methodological Note. https://ec.europa.eu/eurostat/documents/7894008/8749273/ Methodological_note.pdf. ISTAT. 2009. Classificazione delle attività economiche Ateco 2007. https://www.istat.it/it/files/ 2011/03/metenorme09_40classificazione_attivita_economiche_2007.pdf. Jeffcutt, P., and A.C. Pratt. 2002. Managing Creativity in the Cultural Industries. Creativity and Innovation Management 11 (4): 225–233. https://doi.org/10.1111/1467-8691.00254. Kalemli-Ozcan, S., B. Sorensen, C. Villegas-Sanchez, V. Volosovych, and S. Yesiltas. 2015. How to Construct Nationally Representative Firm Level Data from the ORBIS Global Database. https:// doi.org/10.3386/w21558. KEA. 2006. The Economy of Culture in Europe. https://ec.europa.eu/assets/eac/culture/library/stu dies/cultural-economy_en.pdf. Lazzeretti, L., F. Capone, and R. Boix. 2012. Reasons for Clustering of Creative Industries in Italy and Spain. European Planning Studies 20 (8): 1243–1262. https://doi.org/10.1080/09654313. 2012.680585. Lee, D. 2012. Precarious Creativity: Changing Attitudes Towards Craft and Creativity in the British Independent Television Production Sector. Creative Industries Journal 4 (2): 155–170. https:// doi.org/10.1386/cij.4.2.155_1. Levene, H. 1960. Robust Tests for Equality of Variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, ed. I. Olkin, S.G. Ghurye, W. Hoeffding, W.G. Madow, and H.B. Mann, 278–292. Stanford University Press. Lorenzen, M., and L. Frederiksen. 2007. Why Do Cultural Industries Cluster? Localization, Urbanization, Products and Projects. In Creative Cities, Cultural Clusters and Local Economic Development, ed. P. Cooke and L. Lazzeretti. Edward Elgar Publishing. Madudová, E. 2017. Creative Industries Value Chain: The Value Chain Logic in Supply Chain Relationships. Marketing and Branding Research 4 (3): 227–235. https://doi.org/10.33844/mbr. 2017.60236. Moran, P.A.P. 1950. Notes on Continuous Stochastic Phenomena. Biometrika 37 (1/2): 17–23. https://doi.org/10.2307/2332142. Paniccia, P., A. Minguzzi, and M. Valeri. 2011. Coevoluzione tra impresa e destinazione turistica: l’esperienza innovativa dell’albergo diffuso. In Creatività, innovazione e territorio: Ecosistemi del valore per la competizione globale, ed. L. Pilotti, 405–463. Il Mulino. Platman, K. 2004. ‘Portfolio Careers’ and the Search for Flexibility in Later Life. Work, Employment and Society 18 (3): 573–599. https://doi.org/10.1177/0950017004045551. Santagata, W. 2009. White Paper on Creativity. Towards an Italian Model of Development, ed. W. Santagata. Università Bocconi Editore. Scott, A.J. 2005. On Hollywood. Princeton University Press. Tamhane, A.C. 1979. A Comparison of Procedures for Multiple Comparisons of Means with Unequal Variances. Journal of the American Statistical Association 74 (366a): 471–480. https:// doi.org/10.1080/01621459.1979.10482541. USPTO. 2012. Intellectual Property and the U.S. Economy: Industries in Focus. Welch, B.L. 1947. The Generalization of ‘Student’s’ Problem When Several Different Population Variances Are Involved. Biometrika 34 (1–2): 28–35. https://doi.org/10.1093/biomet/34.1-2.28. WIPO. 2003. Guide on Surveying the Economic Contribution of the Copyright-Based Industries. WIPO. 2019. World Intellectual Property Report 2019: The Geography of Innovation: Local Hotspots, Global Networks. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_944_2019.pdf. Yusuf, S., and K. Nabeshima. 2005. Creative Industries in East Asia. Cities 22 (2): 109–122. https:// doi.org/10.1016/j.cities.2005.01.001.

Chapter 6

Location of CCIs: Innovation and Filière Behind the Scenes

6.1 In Search of Reasons for CCIs’ Clustering Spatial concentration is not a new aspect of the literature on CCIs, also thanks to the several examples of areas in which these actors cluster to take advantage of the suitable environments. These areas where the condition for the flourishing and the concentration of CCIs are favourable forge the creative expressions and stimulate the emergence of creative ideas and innovations. These places set the conditions for both local and international business can develop through the constant engagement of local actors, stimulating the creation of a strong territorial identity, bounding firms and places like the Brera Design District in the centre of Milan. Leaving Italy and moving to Germany, the Prenzlauer Berg district in Berlin results to be a rich and diverse melting pot of styles and people arriving from different places and bringing together ideas and knowledge. The sharing of knowledge is made possible thanks to the presence of cafés, clubs, restaurants, and galleries that embed a strong cultural and creative atmosphere to feed the creative genius. Moreover, the small Provençal village of Mougins offers another inspiring example of CCIs concentration. In fact, with the aim to maintain and preserve its artistic heritage forged by Picasso and other creators, it set the conditions for the clustering of over 30 art galleries and studios that make the place really fashinating.1 The simple mapping of CCIs across European regions (Chap. 5) shows that the location choices of CCIs are rather articulated and more complex than the traditional literature on CCIs claims. The innovative capacity and the trade relationships that characterise each CCI, in fact, strongly influence the location choice of CCIs that turn out to be a very heterogeneous group of industry, calling for heterogeneous territorial conditions.

1

https://www.theguardian.com/travel/2011/jul/24/france-mougins-art-picasso. 20.09.2022.

Accessed

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Dellisanti, Cultural and Creative Industries and Regional Development, Contributions to Regional Science, https://doi.org/10.1007/978-3-031-29624-6_6

on 147

148

6 Location of CCIs: Innovation and Filière Behind the Scenes

In particular, as explained in Chap. 4, there is no common reason for the clustering of CCIs. The expectations are that the sources of location choices differ according to the Replicative or Inventive nature of each activity in the area, and to their type of trade relationships. The aim of this chapter is to go in depth into the explanations of the choices and, through a rigorous empirical test, to prove whether our conceptual expectations are true. More in depth, as underlined in Chap. 4, we expect that the reasons for CCIs’ clustering depend on two key dimensions: their creativity and their trade relationships. In fact, these two dimensions mirror the distinction between static and dynamic agglomeration economies on the one hand, and the distinction between localisation and urbanisation economies. In other words, according to the trade exchanges they engage and to the degree of innovativeness of the local functions, it is expected that CCIs will cluster in different areas to exploit different agglomeration factors. As an example, CCIs activities oriented to the market are expected to benefit more from the presence of a vast market compared to activities mostly engaged in B2B trade. The chapter is structured as follows. Before entering the empirical analysis in detail, the next Sect. 6.2 presents the indicators for each type of agglomeration advantage, in which the measure of concentration together with all variables of the taxonomy (Table 4.2) are described. Then, Sects. 6.3–6.5 present the results of the empirical investigations, stressing the importance of considering both creativity and filière for a clear description of the phenomenon. In fact, the analysis presents first the different contribution of static and dynamic agglomeration factors for the clustering of all CCIs (6.3). Second, the role of creativity in CCIs is discussed, trying to understand whether Inventive CCIs benefit more for one of the two groups of factors (0). Third, the analysis includes also the filière dimension, studying the determinants of CCIs’ concentration at the crossroad of creativity and filière itself (6.5). Discussions on the results and robustness checks (6.6) precede the conclusions (6.7).

6.2 Agglomeration Factors for CCIs: Indicators CCIs’ location choices are analysed combining data from different sources, with the aim of assessing the relationship between the spatial concentration of CCIs, split into the two different categories of Inventive and Replicative CCIs, and their different trade relationships. Chapter 5 describes in detail the data sources with a specific focus on CCIs’ presence in EU NUTS3 regions through Orbis. The intensity of CCIs in NUTS3 calculated as the employment in each CCIs sector (sector i) in each EU region (region r) over the total employment in region r: empl shar e CC I si,r =

empli,r emplr

(6.1)

6.2 Agglomeration Factors for CCIs: Indicators Table 6.1 Static and dynamic indicators of agglomeration factors by type of filière

149

Type of filière

Static

Dynamic

Creative filière

Sectoral relatedness

Cognitive relatedness

Vast filière

Sectoral unrelatedness

Cognitive unrelatedness

Short filière

Labour pooling Market size

Creative local knowledge Technology in retail

acts as the dependent variable, while controls and few explicative territorial characteristics are obtained by Eurostat. The geographical scope of this analysis is represented by the NUTS3 regions of the EU plus the UK in 2015, a period in which the UK was still part of the Union. The richness of the database allows a location choice by single sector of CCIs to be built. The sectoral regional variance in location patterns can find in this way profound interpretations. Normally, data at this extreme, granular level (NACE 4-digits and NUTS3 regions) are unavailable through classical channels. For this reason, is made possible through the combination of two data sources, the ORBIS database for the numerator and Eurostat for the denominator.2 The list of indicators is recalled, distinguishing between static and dynamic factors, and presented in Table 6.1. Static and dynamic indicators of relatedness and unrelatedness are the first indicators built. The methodology for their construction comes from an established literature that lies in the concept of entropy, and its measurement, applied to sectoral distribution (Boschma 2005; Boschma and Iammarino 2009; Frenken et al. 2007; Nooteboom 2000). Relatedness and unrelatedness refer conceptually to the degree of similarity or dissimilarity of the economic system. The two measures are generally computed using the related and unrelated variety indicators (Frenken et al. 2007). Conceptually, static related variety is willing to capture the variety of sectors within a certain sectoral specialisation, with the idea that sharing services and competences generates localisation advantages. In the same vein, dynamic related variety conceptually captures the presence of complementary knowledge within a similar knowledge base, which helps the exchange of ideas and stimuli for new inventions. On the contrary, static unrelated variety aims at capturing the diversity of the sectoral distribution, with the idea that CCIs benefit also from the presence of supportive activities from different industrial domains, improving their efficiency. Dynamic unrelated variety, instead, mirrors the idea that ideas generation may be stimulated by unrelated domains thanks to a creative cross-fertilisation. In formulas, the Unrelated Variety (UV) is the entropy of the two-digits distribution, and it is computed as in Eq. (6.2).

2

Besides Eurostat, robustness checks have been performed using Cambridge Econometrics data (ARDECO) for regional employment. No major changes emerge.

150

6 Location of CCIs: Innovation and Filière Behind the Scenes

UV =

∑

( Pg log2

g∈G

1 Pg

) (6.2)

where the entropy Pg is the share of the 2-digit sector over ∑ the total economy, obtained as the sum of the 4-digit shares belonging to it: Pg = i∈Sg pi . Each NACE 4-digit code i belongs to only one 2-digit sector Sg , with g ∈ G the set of all two-digits codes. Then, the Related Variety (RV) can be obtained as the weighted sum of entropy within each two-digit sector, and it results as in Eq. (6.3): RV =

∑

Hg Pg

(6.3)

g∈G

( ) ∑ P where the weight Hg = i∈Sg Ppgi log2 pgi . The relatedness and unrelatedness among economic activities can be calculated using different variables and measures, influencing the interpretation. Referring to the theoretical taxonomy, the indicators of this work are calculated in two different ways. On the one hand, both RV and UV on the left-hand side of Table 6.1, the one related to Replicative CCIs, are computed through a sectoral entropy, using the employment levels in CCIs at the two different scales. On the other hand, the two cognitive indicators of the right-hand side of the table are computed using cumulated patents. In other words, pi and Pg are computed using employment levels and patents for sectoral and cognitive (un)relatedness respectively. The main different interpretation of the two indicators is that in the former case, the concept of relatedness refers to the workforce in general, describing the degree of (un)relatedness in the employment in the Macrosector. In other words, it is a sort of probability of finding employees in a related or unrelated industrial base in a place. On the other hand, using patents, the indicators are more related to the concept of cognitive proximity.3 The choice of patents to calculate both RV and UV instead of other forms of innovation builds on some reasonings. First, data availability of patents at a very granular level allows proper identification of cognitive similarity and differences in a given place. Second, the nature of patenting activity goes in line with the conceptual idea behind cognitive measures. In fact, patenting is a complex activity given that 3

As a technical note, the indicators of related and unrelated variety presented in this analysis are computed only within the Macrosector of CCIs for two reasons, one conceptual and one practical. First, the aim of the study is to understand the dynamics of CCIs, within CCIs. In this way, the measures of (un)relatedness are creative in nature, indicating the sectoral and cognitive (dis)order of the regional creative system, with a conceptual impact on the interpretation. Secondly, for the sectoral measures a so wide and detailed dataset was not available. The Orbis download would have made it necessary to cover the entire EU for all the NACE 4-digits of the economy with an unmeasurable time expense. For symmetry, the same methodology has been applied for both sectoral and cognitive measures. Regarding cognitive relatedness and unrelatedness, the indicators have also been calculated using the entire economy and included in the models. Results and interpretations (available upon request) do not present substantial changes.

6.2 Agglomeration Factors for CCIs: Indicators

151

the inventors need to make substantial improvements in the existing technology. Formally, according to the European Patent Convention, there must be an “invention”, belonging to any field of technology, susceptible of industrial application that is also new and involves an innovative step.4 In other words, patents mix knowledge from both similar and also different fields, and it is possible to say that they intrinsically embed knowledge. Therefore, in an analysis of cognitive (knowledge) proximity, patents represent a proper indication of the degree of similarity of the knowledge in a given territory. Trademarks would have represented a valid alternative in terms of data availability. However, they are surely innovative outputs; they are innovations in the image of the company but they do not necessarily introduce new knowledge. That is why patents have been preferred. As far as the indicators used in the bottom row of the theoretical taxonomy are concerned, the one related to CCIs with a short filière, they differ considering manufacturing and services separately. Starting for the former, the spatial concentration of these activities is tested against the following variables, presented in Eqs. (6.4) and (6.5). | | ∑ | | est∈i %ΔEmpl est(i ),r − %ΔEmpli,r (6.4) L Pi,r = n C K i,r =

patentsi,r patentsr

(6.5)

The availability of a workforce in a specific place with the necessary skills and knowledge is also known as labour pooling (LP). This phenomenon is not easy to measure and, in this work, the indicator used is originated by the Labour Pooling model proposed by Krugman (1991). The model considers that establishments in an economy adapt their size (production and employment) to idiosyncratic productivity shocks that naturally happen. If the labour supply in a place has an abundance of workers in the domain, the expected profits do not change in response to it due to adjustments in local wages. Hence, the model suggests that firms will concentrate in these places, as a response to the risk of shocks. Formally, the model predicts that the larger the heterogeneity of the shocks the higher the pooling effect at the local level. The variable proposed in Eq. (6.4) has been proposed by Overman and Puga (2010) and re-adapted by de Almeida and de Moraes Rocha (2018). It captures the pooling effect, measuring the heterogeneity of the shocks comparing each establishment with the entire industry containing it. The absolute value is the difference between the growth rate of employment in an establishment and the growth rate of employment in the specific industry i. The value is large if establishments and industries adapt differently to shocks, creating heterogeneity. Being this an establishmentspecific measure, the value for the industry is obtained through an average of all establishments (de Almeida and de Moraes Rocha 2018).

4

Cf. Art. 52 of the European Patent Convention.

152

6 Location of CCIs: Innovation and Filière Behind the Scenes

Moreover, a measure for the creative knowledge cumulated at the local level is constructed through the share of patents of a specific sector over the total number of patents. In this way, the indicator of Eq. (6.5) measures the intensity of creative knowledge in sector i at the local level. Changing the point of view and focusing on services, the market size is proxied through the Added Value generated at the local level. Indeed, the European Commission calculates the value added (at basic prices) from the production value plus subsidies on products less the purchases of goods and services (other than those purchased for resale in the same condition) plus or minus the change in stocks of raw materials and consumables less other taxes on products which are linked to turnover but not deductible (European Commission 1998). In other words, it is equivalent to revenues minus intermediate consumption, i.e. a good proxy for final consumption. For the sake of this analysis, it is a suitable indicator for the market potential since the higher the VA generated in a place the larger the consumption for final goods. Finally, the dynamic indictor used to proxy the technology in retail is the population purchasing holiday services online obtained from Eurostat. It is a good proxy for how much technology permeates the retail market in a given region. However, Eurostat publishes this information only at the NUTS2 level and this may result in low variance for explaining the spatial concentration of CCIs at NUTS3 regional level.5 This has to be considered when reading and interpreting the significance of the results. Heretofore, the main indicators used for the empirical analysis have been presented. However, in order to reinforce the empirical investigation, a set of control variables is included. These variables are extremely useful when running empirical analyses from both a technical and a conceptual point of view. Starting with the latter, they are needed to adjust the results obtained from the predictors, accounting for some bias coming from external conditions. It is a way to refine the results, that is why they are often called confounding effects. In econometrics, the failing in including control variables is known as omitted-variable bias (OVB).6 Control variables are used to strongly mitigate this issue, controlling for potential correlation 5

To cope with this issue, a NUTS3 weight has also been considered: younger regions receive a premium compared to older ones, using the median age of the NUTS3 region r w.r.t. the median age of the NUTS2 region s containing it. The indicator would result as: T ech. r etailr = onlinesaless ∗ (median ager ∈s /median ages )−1 . Although this choice does not find unanimous support in the literature as older people have a larger purchasing capacity also online (Sorce et al. 2005) and they are becoming more and more engaged in e-commerce (Lian and Yen 2014), younger people still represent the most active category. Furthermore, the indicator aims at measuring the pervasiveness of technology in retail as a measure of acceptance by consumers, as most tech-oriented consumers are expected to influence the innovativeness of firms in this segment (Pantano and Di Pietro 2012). For this reason, the “premium” generated by a relatively younger society is coherent due to the kind of innovations that market-oriented sectors generate such as digital signage, mobile apps, and ubiquitous computing (Pantano 2014). The results, available upon request, do not substantially differ from those obtained using the “original” variable and, for this reason, they are not shown here. 6 Technically, in any classical regression model, the OVB results if the error term produces some noise that is correlated with the regressor of interest, creating possible misunderstandings. In formulas, considering a classical OLS regression: y = X β + ε, the OVB arises if one of the

6.2 Agglomeration Factors for CCIs: Indicators

153

between regressors and unexplained terms. In this study, the controls included are the following, in line with literature on localisation choices of CCIs: • • • • • • •

Population Density (ln): Metropolitan area Number of UNESCO World Heritage Sites Total Population (ln) Dummy for Eastern EU countries (i.e. joined EU after 2004) GDP per capita (ln) Education (Share of population with tertiary education).7

Furthermore, the inclusion of these regional control variables make it possible to polish the potential effect of the main regressors from more classical agglomeration forces coming from densely and largely populated areas, such as capital cities, where educated creative individuals settle (Florida 2002). More specifically, the variables related to population (total population and population density) aim at controlling for the urban dimension and the total size of the area. Especially, density is often used as a proxy for controlling urbanisation economies in the area. Another important control is the level of wealth of the regions, measured through the GDP per capita. Indeed, the settlement of creative activities may be lured by rich environments thanks to their higher demand of goods of the latest generation or thanks to the presence of already advanced firms that support the development of these goods.8 The capital city, instead, controls for the presence of the political power in the area. Indeed, the proximity with institutional houses allows the lobbying activities that may favour the emergence of creative activities (Scott 1997, 2005). The inclusion of the number of UNESCO World Heritage Sites as a control variable has a twofold nature. First, it widely accounts for the attraction of the place as a touristic destination. Second, it measures the cultural endowments of the place. Both meanings can represent a factor of attraction of firms working in the Macrosector of CCIs, as cultural heritage is considered either a precondition for the development of the creative process (Santagata 2009) or, in any case, culture promotes creativity through emotional, symbolic, and inspirational mechanisms, changing the way in which people think and work (Cerisola 2019a, b). Finally, the level of education of the region, measured through the share of population with tertiary education, is a proxy for the presence of the aforementioned creative class à la Florida. Indeed, an educated environment is a condition for the settlement of industries that, by definition, are knowledge-based. Thus, all these effects may contribute to bias the results of the variables of interest,

Gauss–Markov assumptions fails. More specifically, the assumption that does not hold in the presence of an OVB is the strict exogeneity: E[ε|X ] = 0. This assumption is important because if it fails, the interpretation of both the sign and the magnitude of the results is potentially misleading. 7 Tertiary education refers to levels 5–8 of the International Standard Classification of Education (ISCED). 8 Summary tables also display values related to GDP in levels because this variable has been explored as a control and as a substitute for GDP per capita but its Variance Inflation Factor (VIF) always reported high values, denoting probable collinearity.

154

6 Location of CCIs: Innovation and Filière Behind the Scenes

that is why they are included in the model as a control. The source of all these variables is Eurostat. Furthermore, although the use of this practice is controversial in the literature (Bellemare et al. 2017; Reed 2015), the explanatory variables used have been timely lagged (if possible), to cope with potential endogeneity coming from simultaneity. Moreover, the model will also control for industry and country fixed effects. These controls are extremely important in this framework for several reasons. First, the spatial concentration may be higher in some countries and lower in others only due to data-related issues and, without controlling for country specific effects this may bias the results. Second, most of the controls are only at the regional level to account for specific territorial characteristics. However, in this way the industrial dimension remains uncontrolled, and the industry fixed effects help to capture any noise coming from industrial specificities. In Table 6.2, the summary statistics of all the variables in the model are presented, together with the source and the reference year. Table 6.3, instead, presents the correlation table among all the variables. The correlations can provide the reader with first insights that will be further investigated in the full regression model. Especially regarding the correlation between the share of CCIs and all the other explanatory variables, the coefficients and the attached significance may be extremely misleading as there is not a distinction between Inventive and Replicative CCIs. In fact, there are some unexpected results such as the negative coefficients associated to population, GDP and GDP per capita that seem to suggest that CCIs are not attracted by wealthy and large places. Plus, also the coefficients associated to cognitive relatedness and unrelatedness are either not significant or negatively correlated with the share of CCIs. Instead, there are also some coefficients that satisfy preliminary expectations such labour pooling, creative knowledge environment, population density, and education levels. All these considerations suggest the need to further investigate the agglomeration factors of CCIs, trying to test the hypothesis of differentiated territorial conditions behind spatial clustering of CCIs. With such an aim, the first step is to test the contribution of static and dynamic factors and this is the subject matter of the next section.

6.3 CCIs’ Location Patterns: Static Versus Dynamic Agglomeration Factors The literature generally discussed the importance of both static and dynamic agglomeration factors behind CCIs’ concentration (Lazzeretti et al. 2012; Lorenzen and Frederiksen 2007) but without stressing on the different impact they can have. The first empirical investigation is, therefore, to understand whether CCIs rely differently on static and dynamic agglomeration factors. Being at the core of the knowledge

6.3 CCIs’ Location Patterns: Static Versus Dynamic Agglomeration Factors

155

economy, CCIs are expected to concentrate in areas where dynamic factors are more strongly present. Equations (6.6) and (6.7) present this first empirical attempt. The share of regional employment in CCIs is tested against all the static and dynamic factors respectively: empl shar e CC I si,r =

∑

β j static j + X γ + ϕi + µc + εi,r

(6.6)

j

Table 6.2 Summary statistics and data sources Variable

Obs.

Mean

Std. dev.

Min

Max

Source

Year9

Share of regional 49,352 employment in sector i (dep. var)

0.12

0.46

0.00

34.82

ORBIS

2015

Inventive dummy

49,352

0.49

0.50

0

1

ORBIS

2016

Sectoral relatedness (RV empl.)

49,352

5.08

2.38

0.00

12.97

ORBIS

2013

Cognitive relatedness (RV pat.)

49,352

0.72

1.02

0.00

7.11

ORBIS

2016

Sectoral unrelatedness 49,352 (UV empl.)

11.08

3.18

0.00

17.34

ORBIS

2013

Cognitive unrelatedness (UV pat.)

49,352

2.82

2.11

0.00

12.79

ORBIS

2016

Labour pooling

49,352

0.40

0.65

0.00

6.86

ORBIS

2013–2015

Creative local knowledge (%)

49,352

0.27

2.10

0

100

ORBIS

2016

Market size (GVA)

49,352

0.13

0.20

0.00

1.81

Eurostat

2013

Technology in retail (online sales)

49,352

0.88

0.56

0.03

2.25

Eurostat

2008–2016

Population density (ln) 49,352

5.20

1.40

0.69

9.96

Eurostat

2013

Metropolitan area

49,352

0.52

0.67

0

2

Eurostat

–

UNESCO World Heritage sites

49,352

0.36

0.66

0

4

UNESCO

–

Population (ln)

49,352

12.76

0.85

9.27

15.68

Eurostat

2013

Eastern EU country

49,352

0.19

0.39

0

1

Eurostat

–

GDP (ln)

49,352

8.88

1.10

5.18

12.23

Eurostat

2013

GDP per capita (ln)

49,352

9.96

0.69

7.97

12.82

Eurostat

2013 (continued)

9

Variables obtained through the patents in Orbis refer to 2016 as they are cumulated values. Sections 5.1 and 5.3 presented in detail the data collection process and the pros and cons of the dataset.

156

6 Location of CCIs: Innovation and Filière Behind the Scenes

Table 6.2 (continued) Variable

Obs.

Education levels: (population with tertiary education)

49,352

Mean

empl shar e CC I si,r =

0.27

∑

Std. dev.

Min

Max

Source

Year

0.09

0.11

0.68

Eurostat

2013–2015

β j dynamic j + X γ + ϕi + µc + εi,r

(6.7)

j

where i is a generic industry belonging to CCIs (NACE 4-digits), r a generic region (NUTS3), and j is a counter index for the static and dynamic factors. The model is an industry-region framework and it is estimable through a panel regression. Usually, panels can be either fixed or random effects and an Hausmann test is employed to select the best-fitting model. However, in this case, the choice of a Radom Effect is a forced one. The fixed effects model does not support variables that are constant in one of the two dimensions. In this framework, some of the regressors vary only across regions (e.g. the GVA) and a fixed effects model, being an in-differences model, would automatically drop them. In any case, all equations control for industry fixed effects (ϕi ) and country fixed effects (µc ). Table 6.4 presents the results of this first analysis. It emerges quite clearly that CCIs prefer locations where dynamic agglomeration factors are present. In fact, all the coefficients of the main regressors in column (2) are positive and significant, with the exception of cognitive relatedness that are found to be virtually 0. Instead, no statistical significance can be identified in the static factors outlined in column (1), with the exception of sectoral unrelatedness that is even negatively associated with the spatial concentration of CCIs. Hence, this simple result allows us to describe a general phenomenon. Overall, CCIs tend to prefer areas where the cognitive and dynamic environment is stronger, giving them the trigger for the generation of new knowledge. However, this result calls for further discussion. One could argue whether these results are driven by the heterogeneous capacity of CCIs to innovate. Does the clustering of innovative CCIs rely more on dynamic factors? Does the clustering of less innovative CCIs rely more on static factors? Therefore, in the next section, CCIs’ inventiveness is introduced as a discerning factor to identify whether Inventive CCIs look for different factors compared to Replicative ones.

6.4 CCIs’ Location Patterns: Inventive Versus Replicative Activities The role of creativity as a reinforcing factor for CCIs’ spatial concentration is tested following the specifications in (6.10) and (6.11). These equations recall those in (6.6) and (6.7) but including the dummy inventive (I nvi,r ) both as a level and as an

0.097***

0.013***

Metropolitan area

0.126***

0.217***

− 0.122*** 0.213***

0.160*** − 0.118***

0.021***

− 0.001

Eastern EU country

GDP per capita (ln)

0.207***

− 0.033***

GDP (ln)

0.176*** 0.211***

0.383***

0.059*** 0.146***

− 0.011** − 0.043***

UNESCO World Heritage sites

0.172***

− 0.221***

0.363***

0.581***

0.478***

0.281***

0.335***

0.380***

− 0.219*** 0.112***

0.544***

0.202***

0.015*** − 0.022***

0.053***

0.784***

0.277***

1.000

Cog. rel.

− 0.043***

0.249***

Population (ln)

0.121***

0.014*** 0.022***

0.153***

Technology in retail

− 0.002

Population density (ln)

Market size

0.036***

0.019*** 0.104***

Labour pooling

Creative local knowledge (%) 0.083***

0.197***

− 0.013***

Cognitive unrelatedness

0.777***

0.279***

0.150*** 0.063***

0.002 − 0.074***

Cognitive relatedness

Sectoral unrelatedness

1.000

1.000 0.035***

− 0.020***

Sect. rel.

− 0.047***

Inventive dummy

Inventive dummy

1.000

Share (dep. var)

Sectoral relatedness

Share of regional employment in sector i (dep. var)

Table 6.3 Correlation table

0.289***

− 0.134***

0.342***

0.567***

0.260***

0.173***

0.048***

− 0.300***

0.293***

− 0.054***

0.076***

0.394***

1.000

Sect. unrel.

(continued)

− 0.312***

0.472***

0.766***

0.632***

0.323***

0.362***

0.385***

0.268***

0.646***

− 0.031***

0.021***

1.000

Cog. unrel.

6.4 CCIs’ Location Patterns: Inventive Versus Replicative Activities 157

0.110***

Inventive dummy − 0.086***

Sect. rel. 0.202***

Cog. rel.

− 0.007

Population density (ln)

0.245***

1.000

0.411***

0.743***

0.408***

0.737***

− 0.027**

0.051***

GDP (ln) 0.003

0.645*** − 0.051***

− 0.039***

0.092***

Population (ln) − 0.030***

0.417*** − 0.075***

− 0.012***

0.020***

0.239***

0.461***

− 0.015***

0.032**

UNESCO World Heritage sites

0.303***

1.000

Metropolitan area

0.446***

0.012*** − 0.010**

− 0.020***

0.047***

Market size

1.000

− 0.053***

0.005

Technology in retail

1.000

Creative local knowledge (%)

GDP per capita (ln)

− 0.159***

Sect. unrel.

0.255***

Cog. unrel.

0.470***

0.540***

0.333***

0.046***

0.563***

1.000

(continued)

0.159***

0.308***

0.287***

0.171***

1.000

Labour pooling Creative local know. Market size Tech. in retail Popul. density (ln) Capital city area

0.031***

Share (dep. var)

Labour pooling

Cognitive unrelatedness

Sectoral unrelatedness

Cognitive relatedness

Sectoral relatedness

Inventive dummy

Share of regional employment in sector i (dep. var)

Education levels

Table 6.3 (continued)

158 6 Location of CCIs: Innovation and Filière Behind the Scenes

0.077***

0.021***

*** p < 0.01, ** p < 0.05, * p < 0.1

Education levels

0.026***

− 0.013***

Eastern EU country

0.791*** 0.066***

0.333*** 0.043***

GDP (ln)

GDP per capita (ln)

1.000

1.000 0.409***

Population (ln)

Popul. (ln)

UNESCO World Heritage sites

Metropolitan area

Population density (ln)

Technology in retail

Market size

Creative local knowledge (%)

Labour pooling

Cognitive unrelatedness

Sectoral unrelatedness

Cognitive relatedness

Sectoral relatedness

Inventive dummy

0.003

− 0.022***

UNESCO WHS

− 0.035***

0.029***

0.364***

− 0.445***

0.661***

1.000

GDP (ln)

0.310***

0.299***

− 0.262***

0.513***

− 0.752***

1.000

GDP per capita (ln)

0.688***

− 0.200*** − 0.565***

− 0.313***

1.000

New EU member

0.398***

0.063***

Labour pooling Creative local know. Market size Tech. in retail Popul. density (ln) Capital city area

Share of regional employment in sector i (dep. var)

Education levels

Eastern EU country

Table 6.3 (continued)

6.4 CCIs’ Location Patterns: Inventive Versus Replicative Activities 159

160 Table 6.4 Comparison of static and dynamic agglomeration factors for CCIs’ clustering

6 Location of CCIs: Innovation and Filière Behind the Scenes Variables

(1)

(2)

All CCIs—static factors

All CCIs—dynamic factors

Sectoral relatedness

0.0022 (0.002)

Sectoral unrelatedness

− 0.0154*** (0.003)

Labour pooling

0.0034 (0.003)

Mkt. size

0.0329 (0.024)

Cognitive relatedness

0.0006 (0.003)

Cognitive unrelatedness

0.0101*** (0.003)

Creative local knowledge

0.0188*** (0.003)

Tech. in retail

0.0442* (0.025)

Population density (ln)

0.0128*** (0.004)

0.0123*** (0.004)

Metropolitan area

0.0024 (0.010)

0.0053 (0.005)

UNESCO World Heritage sites

0.0028 (0.004)

0.0052 (0.004)

Population (ln)

0.0023 (0.008)

− 0.0366*** (0.008)

Eastern EU country

0.1176*** (0.021)

0.1124*** (0.030)

GDP per capita (ln) 0.0460*** (0.009)

0.0210* (0.011)

Population with tertiary education (%)

0.1820** (0.072)

0.2677*** (0.095)

Constant

− 0.2522* (0.145)

0.2782 (0.181)

Observations

49,352

49,352

Number of region

1327

1327

Model

Random effect

Random effect

Industry FE

Yes

Yes

Country FE

Yes

Yes

R2

0.282

0.233

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

6.4 CCIs’ Location Patterns: Inventive Versus Replicative Activities

161

interaction with all relevant explanatory factors. In other words, this element allows us to investigate whether the relative importance of static and dynamic factors in explaining the spatial concentration of CCIs differ for Inventive and Replicative CCIs. Results are presented in Table 6.5. ⎛ empl shar e CC I si,r = I nv i,r ∗ ⎝β1 + +

∑

∑

⎞ β j static j ⎠

j

β j static j + X γ + ϕi + µc + εi,r

(6.8)

j

⎛ empl shar e CC I si,r = I nv i ,r ∗ ⎝β1 + +

∑

∑

⎞ β j dynamic j ⎠

j

β j dynamic j + X γ + ϕi + µc + εi,r

(6.9)

j

The coefficients estimated from these two equations suggest that, overall, the heterogeneous capacity of CCIs to innovate does not call for different locations. In fact, the interaction coefficients are rarely significant, mirroring that Inventive CCIs do not benefit differently from local conditions compared to Replicative ones. The only very relevant exception is the interaction coefficient with the technology in retail variable. As previously discussed, this variable represents how digital and innovative is the final demand, measuring how technology permeates the consumers. In this case, the results clearly show how this is important for Inventive CCIs that innovate also thanks to the pressure of demanding consumers that are capable of absorbing innovations. Although these results convey the message that, in general, CCIs’ creativity does not matter substantially for their agglomeration, they call for a further discussion. In fact, innovativeness is only one of the relevant aspects determining the location choices of this huge group of actors. CCIs set their location not only based on the degree of innovativeness but also on the trade relations they engage with other segments of the economy. In other words, localisation and urbanisation economies should be considered together with static and dynamic ones for a clear understanding of the phenomenon. As an example, activities mostly engaged in trade relations with final consumers will be more interested in the size of the market rather than in the composition of the sectoral base. Again, innovative activities oriented to the market will rely more on the consumers’ capacity to embrace innovation rather than on the cognitive knowledge base of the innovations in the area. Therefore, in the next section, both dimensions of the taxonomy (inventiveness and type of filière) are considered in the attempt to evaluate the different contribution of the agglomeration factors.

162

6 Location of CCIs: Innovation and Filière Behind the Scenes

Table 6.5 Comparison of static and dynamic agglomeration factors for CCIs’ clustering—inventive versus replicative CCIs (1)

(2)

All CCIs—static factors interaction

All CCIs—dynamic factors interaction

Inventive dummy

0.0253 (0.045)

0.0239** (0.011)

Sectoral relatedness

0.0055** (0.002)

Sectoral unrelatedness

− 0.0182*** (0.004)

Labour pooling

0.0041 (0.004)

Mkt. size

0.0073 (0.030)

Inventive dummy * Sectoral relatedness

− 0.0066** (0.003)

Inventive dummy * Sectoral unrelatedness

0.0048 (0.005)

Inventive dummy * Labour pooling

− 0.0019 (0.005)

Inventive dummy * Mkt. size

0.0239 (0.025)

Variables

Cognitive relatedness

0.0012 (0.005)

Cognitive unrelatedness

0.0071** (0.004)

Creative local knowledge

0.0469*** (0.012)

Tech. in retail

0.0197 (0.025)

Inventive dummy * Cognitive relatedness

− 0.0013 (0.007)

Inventive dummy * Cognitive unrelatedness

0.0038 (0.003)

Inventive dummy * Creative local knowledge

− 0.0334*** (0.012)

Inventive dummy * Tech. in retail

0.0417*** (0.010)

Population density (ln)

0.0129*** (0.004)

0.0120*** (0.004)

Metropolitan area

0.0033 (0.004)

0.0058 (0.005) (continued)

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad …

163

Table 6.5 (continued) (1)

(2)

All CCIs—static factors interaction

All CCIs—dynamic factors interaction

UNESCO World Heritage sites

0.0032 (0.004)

0.0053 (0.004)

Population (ln)

0.0011 (0.008)

− 0.0376*** (0.008)

Eastern EU country

0.1255*** (0.021)

0.1092*** (0.030)

GDP per capita (ln)

0.0450*** (0.009)

0.0205* (0.011)

Population with tertiary education (%)

0.1757** (0.075)

0.2596*** (0.095)

Constant

− 0.2240 (0.141)

0.3010* (0.182)

Observations

49,352

49,352

Number of region

1327

1327

Model

Random effect

Random effect

Industry FE

Yes

Yes

Country FE

Yes

Yes

R2

0.288

0.230

Variables

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad of Innovativeness and Filière In order to account for the dimension of trade relationships among CCIs, the different filières are included in the framework. In order to recall the distinction already presented in the previous chapters, CCIs are subdivided into the three following filières: • Creative filière: CCIs mostly engaged in intermediate trade with other CCIs; • Vast filière: CCIs mostly engaged in intermediate trade with other businesses; • Short filière: CCIs mostly engaged in trade with the final market. Having set the frame to subdivide CCIs into the different filières based on trade exchanges, the taxonomy of the prevailing agglomeration factors (Table 4.2) can be tested thanks to the indicators of Table 6.1 through the following regression model. Basically, for each filière (i.e. each row of the taxonomy) the share of employment in industry i in region r is tested against the variables indicated in the taxonomy as presented in the set of equations (6.10).

164

6 Location of CCIs: Innovation and Filière Behind the Scenes

empl shar e CC I si,r ) ⎧ ( f Sect Relr ; Cog Relr ; I nvi,r ; X + ϕi) + µc + εi,r ⎪ ⎪ ⎨ ( f ( SectU nr elr ; CogU nr elr) ; I nvi,r ; X + ϕi + µc + εi,r = ⎪ f L P ; C K i,r ; I nvi,r ; X + ϕi + µc )+ εi,r ⎪ ⎩ ( i,r f Mkt Si zer ; T ech. r etailr ; I nvi,r ; X + ϕi + µc + εi,r

if if if if

i i i i

∈ Cr eati ve f ilier e ∈ V ast f ilier e ∈ Shor t f ilier e − Man. ∈ Shor t f ilier e − Ser v.

(6.10) In this case, the object of interest is to evaluate whether the intensity of the agglomeration of Inventive and Replicative activities within each filière is affected by different agglomeration factors. Hence, both a baseline regression and an augmented one have been estimated for each equation in (6.10). The baseline regression includes both measures proposed in each row of the taxonomy, while the augmented one considers also the interaction between the Inventive dummy with the variables of interest so to assess the prospective differentiated role played by these variables in explaining the clustering of Inventive and Replicative CCIs. Taking the second equation as an example, the specification takes the following form: empl shar e CC I si,r = β1 SectU nr elr + β2 CogU nr elr + X γ + ϕi + µc + εi,r empl shar e CC I si,r = β1 SectU nr elr + β2 CogU nr elr + I nv i ,r ∗ (β3 + β4 SectU nr elr + β5 CogU nr elr ) + X γ + ϕi + µc + εi,r

(6.11)

All the other equations referring to other rows of the taxonomy take the same form, changing only the relevant explanatory variables. Once all regressions have been run, it is possible to carefully read the results. Table 6.6 collects the results of all regressions presented in Eq. (6.10). Columns (1) and (2) presents the estimated coefficients of the baseline and the augmented specifications associated to the Creative filière; columns (3) and (4) refer to CCIs associated to a Vast filière; columns from (5) to (8), instead, consider Short filière CCIs subdivided between manufacturing and services. Considering Creative filière CCIs, the coefficient associated to sectoral relatedness shows negative correlation with concentration of CCIs while the coefficient of the cognitive relatedness is positive and significant, instead. In the augmented regression, the sectoral relatedness results negatively correlated with both Inventive and Replicative CCIs. This result suggests the presence of a congestion effect generated by the presence of actors with a similar background. A related employment structure represents a deterrent for the settlement of other, similar, industries. Contrary, the cognitive relatedness respects conceptual priors. It represents a powerful driver of concentration only of Inventive CCIs within this filière. This result suggests that in presence of an environment characterised by cognitive proximity, CCIs that are characterised by a creative filière, i.e. exchanging goods and services mostly with other creative activities, tend to concentrate in space to benefit from the supportive cognitive environment. Given that this consideration was not possible only looking at

Creative filiere

Share of employment in CCI i within the region r

0.0075** (0.004)

Cognitive unrelatedness

Inventive dummy * Sectoral unrelatedness

− 0.0108*** (0.003)

Vast filiere

(3)

Sectoral unrelatedness

0.0293*** (0.005)

Inventive dummy * Cognitive relatedness

− 0.0217*** (0.005)

0.0023 (0.003)

0.0045* (0.002)

Cognitive relatedness

− 0.0057** (0.002)

0.0059 (0.018)

Creative filiere

(2)

Inventive dummy * Sectoral relatedness

− 0.0040*** (0.001)

Sectoral relatedness

Inventive dummy

(1)

Dep var.

Table 6.6 Results of the regression analyses of Eq. (6.10)

0.0042 (0.004)

− 0.0064† (0.004)

− 0.0130*** (0.004)

− 0.0883** (0.042)

Vast filiere

(4) Short filiere—man

(5)

0.0617*** (0.014)

Short filiere—man

(6) Short filiere—ser

(7)

(continued)

0.0575*** (0.017)

Short filiere—ser

(8)

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad … 165

Creative filiere

(2)

(3)

(4)

(5) Short filiere—man

0.0541 (0.042)

Tech. in retail

Inventive dummy * Mkt. size

0.0516* (0.028)

− 0.0072 (0.020)

Inventive dummy * Creative local knowledge

Short filiere—ser

(7)

Mkt. size

− 0.0469*** (0.015)

0.0407*** (0.016)

0.0335*** (0.010)

Short filiere—man

(6)

Inventive dummy * Labour pooling

0.0369*** (0.009)

0.0210*** (0.004)

Vast filiere

Creative local knowledge

Vast filiere

0.0217*** (0.008)

Creative filiere

Labour pooling

Inventive dummy * Cognitive unrelatedness

(1)

Dep var.

Share of employment in CCI i within the region r

Table 6.6 (continued)

(continued)

− 0.1124*** (0.038)

0.0312 (0.043)

0.1493*** (0.046)

Short filiere—ser

(8)

166 6 Location of CCIs: Innovation and Filière Behind the Scenes

Creative filiere

(2)

0.0058† (0.004)

− 0.0143** (0.006)

0.0060† (0.004)

− 0.0136** (0.006)

0.0081 (0.025)

0.0253*** (0.007)

0.1382** (0.065)

0.0656 (0.110)

UNESCO World Heritage sites

Population (ln)

Eastern EU country

GDP per capita (ln)

Population with tertiary education (%)

Constant

0.0800 (0.109)

0.1316** (0.064)

0.0256*** (0.007)

0.0120 (0.025)

0.0033 (0.004)

Metropolitan area 0.0032 (0.004)

Population density (ln)

0.0038† (0.003)

Creative filiere

0.0034 (0.003)

Inventive dummy * Tech. in retail

(1)

Dep var.

Share of employment in CCI i within the region r

Table 6.6 (continued) (3)

(4)

0.3840*** (0.083) − 1.4473*** (0.318)

− 1.4800*** (0.313)

0.1321*** (0.022)

0.3110*** (0.045)

0.3962*** (0.082)

0.1301*** (0.022)

0.3103*** (0.045)

0.0109 (0.014)

− 0.0016 (0.008)

− 0.0017 (0.008) 0.0104 (0.014)

0.0184** (0.008)

0.0115* (0.006)

Vast filiere

0.0174** (0.008)

0.0115* (0.006)

Vast filiere

(5)

0.9872*** (0.164)

1.0935*** (0.171)

0.0635 (0.105)

− 0.0507*** (0.014)

− 0.0456*** (0.014) 0.0722 (0.105)

0.0521* (0.032)

− 0.0401*** (0.008)

0.0518† (0.032)

− 0.0348*** (0.008)

0.0103 (0.010)

− 0.0264*** (0.010)

− 0.0282*** (0.010) 0.0099 (0.010)

0.0148*** (0.005)

Short filiere—man

(6)

0.0145*** (0.005)

Short filiere—man

0.0045 (0.005)

0.0142* (0.007)

0.0143** (0.007)

0.0429** (0.018)

Short filiere—ser

(8)

− 0.1600 (0.237)

0.2582† (0.172)

0.0409** (0.017)

0.0717† (0.048)

(continued)

− 0.0097 (0.238)

0.2291 (0.172)

0.0345** (0.017)

0.0718† (0.048)

− 0.0276*** − 0.0355*** (0.009) (0.010)

0.0046 (0.005)

0.0135* (0.008)

0.0147** (0.007)

Short filiere—ser

(7)

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad … 167

Creative filiere

7818

1254

Random effect

Yes

Yes

0.184

Observations

Number of regions

Model

Industry FE

Country FE

R2

(2)

0.191

Yes

Yes

Random effect

1254

7818

Creative filiere

Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1, † p < 0.15

(1)

Dep var.

Share of employment in CCI i within the region r

Table 6.6 (continued) (3)

0.236

Yes

Yes

Random effect

1275

7872

Vast filiere

(4)

0.235

Yes

Yes

Random effect

1275

7872

Vast filiere

(5)

1249

10,728

Short filiere—man

(6)

1321

22,934

Short filiere—ser

(7)

0.168

Yes

Yes 0.174

Yes

Yes

0.148

Yes

Yes

Random effect Random effect Random effect

1249

10,728

Short filiere—man

0.151

Yes

Yes

Random effect

1321

22,934

Short filiere—ser

(8)

168 6 Location of CCIs: Innovation and Filière Behind the Scenes

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad …

169

the results of the previous section, this first important result stresses the importance of subdividing CCIs according to their predominant trade exchanges. Moving towards the second line of the taxonomy to consider CCIs with a vast filière, sectoral unrelatedness shows significant but negative coefficients both in the baseline and in the augmented regression specification. Moreover, results show no differences comparing Replicative and Inventive activities in this regard. An economic setting characterised by an heterogeneous (or vast) creative workforce does not represent an attraction factor for CCIs. Instead, considering the cognitive unrelatedness, the interpretation becomes more fascinating. Indeed, although no correlation emerges considering the baseline specification, subdividing CCIs based to their Inventiveness allows us to detect a dynamic a benefit for Inventive ones. This group of CCIs increase its spatial clustering in presence of a diverse and industrially variegated cognitive environment. In other words, spillovers from unrelated sectors exist. Although this consideration mirrors to the one obtained for cognitive relatedness, it is more significant from a conceptual perspective. In fact, the literature discussed the importance of unrelatedness as a limit to the innovative capacity of firms (Aarstad et al. 2016) and, in positive terms, it was often considered as a shield against unemployment rise (Frenken et al. 2007). In the context of creativity, instead, it assumes a novel and even more important meaning. The diversity of the cognitive environment is a benefit for those who innovate if belonging to a Vast filière. Inventive CCIs prefer locations where they can benefit from heterogeneous knowledge, even if generated industrial (non-creative), thanks to the vast trade relations they embrace in the economy. The validity of this result will be reinforced thanks to the robustness checks presented in the next section. As a preliminary summary, what emerges for B2B industries is the strong and paramount role of the cognitive dimension in explaining the spatial concentration of Inventive CCIs, opposed to the employment-related dimension that was found to be a factor of de-agglomeration of CCIs. Finally, the analysis of the third line of the taxonomy needs to be split between manufacturing and services activities. Starting from the former, the results are presented in columns (6) and (7). Coherently with theoretical expectations, labour pooling results more beneficial for the clustering of Replicative manufacturing activities, corroborating the conceptual prior that considers it as a static benefit (Combes and Duranton 2006; Duranton and Puga 2004; Rosenthal and Strange 2001). Instead, the role of the creative local knowledge on spatial concentration is unexpected. Indeed, due to the dynamics of geographical knowledge accumulation (Balland et al. 2020; Capello 2017), the expectation was to observe this pattern also when considering CCIs. Instead, a knowledge-rich local environment creates positive conditions for all, both those who replicate (mainly through knowledge adoption) and those who invent thanks to cumulative processes. If the results are clear for Inventive CCIs, some lines need to be spent on the Replicative ones. To provide an example of manufacturing activities with a short filière that are not particularly intense in the production of new knowledge, this is the case of the manufacture of textiles, wearing apparel, leather and related products that show a concentration of non-innovative actors where knowledge of that kind is strong. This effect can have a twofold interpretation. First,

170

6 Location of CCIs: Innovation and Filière Behind the Scenes

there can be a “brand” effect, i.e. places with a high share of knowledge of a specific sector may identify themselves in that sector and, for this reason, Replicative actors concentrate there to benefit from the reputation, as the place of origin is itself often part of commercial constructions cultural industries weave to support their competitiveness (Jansson and Power 2010). Secondly, there could be an effect of knowledge adoption. Indeed, companies may decide to adopt existing knowledge and apply it to the mass production goods, benefitting from the creation made by others. Theoretically, both explanations seem sound. However, a high share of patents of a given sector may also mean that other activities do not patent in that place. In other words, there can be a bias due to size. In this case, some places may host few patents, all belonging to the same sector. This is mostly the case of Eastern European countries and, to cope with this issue, country fixed effects included in the model should have helped in reducing the bias. Therefore, concluding with services activities with a short filière, the analysis focuses on retail and cultural services activities. As foreseen in the taxonomy, the market potential turns out to be beneficial for service Replicative activities as the estimated coefficient is positive and significant only for this group. Market-oriented CCIs concentrate to sell products and offer cultural services where the market potential is large enough for their profitability. On the dynamic side, considering the technology in retail (i.e. how the demand of the consumers is capable of embracing innovations) a deeper discussion is needed. Innovation in Market-oriented activities is demand driven, that is consumers trigger the generation of new knowledge in this domain through their acceptance and propensity for technology (Davis 1989; Pantano and Di Pietro 2012). However, research with consumers showed that the demand of innovation happens on-site like entertaining experiences or digital stores (Pantano 2014; Pantano and Viassone 2014). Moreover, the online sales variable only measures how much people do not go physically to stores, buying products comfortably from their sofas. In this way, they do not stimulate on-site innovation either for stores or for cultural institutions like museums. For this reason, the variable needs to be adjusted and the attention was placed on the geographical contexts that may (dis)favour the technology adoption. As also displayed in the correlation table, online sales are strongly correlated with the education levels and economic size and the magnitude is strong as well. This means that places where both the education level and the size of the economy in general is high are also places where people buy substantially online, in line with research on the determinants of e-shopping (Naseri and Elliott 2011). Therefore, these determinants may confound the effect of technology adoption on the higher innovativeness of market oriented CCIs. Their adoption of technology is due to their high education and, reasonably, high income levels. Although the model controls for these determinants, including them as controls in all equations, the variable of interest remains spurious, without accounting for this effect. The interest is to measure the technology adoption by consumers as a stimulus for innovation and the online sales may work if cleaned from other effects. To try to fix this issue, the online sales variable has been firstly regressed on education levels and on regional economic size (Eq. 6.12), then the residuals of this first regression have been used as explanatory variable in place

6.5 CCIs’ Location Patterns: Agglomeration Factors at the Crossroad …

171

of the original one.10 onlinesalesr = f (education r ; economic_si zer ) + εr

(6.12)

The results of this analysis are presented in col. (8) of Table 6.6. The interpretation of the coefficient can be read as a technology adoption cleaned from the typology of context, as the effect of highly educated and wealthy contexts was removed. In this way, a higher propensity of people for technology in retail (measured through the online sales) is associated to a higher spatial concentration of marketoriented services activities that are also Inventive, meaning that expert and demanding consumers stimulate the development of new innovations. With this procedure, only the online sales that cannot be explained through education and economic size are used as explanatory variable. For the sake of clarity, the output of the regressions displayed so far may be misleading and it requires consideration of the Average Marginal Effects (AMEs),11 summarised in Table 6.7 and Fig. 6.1. To conclude the comments on the results, a few lines need to be dedicated to the control variables. Basically, the inclusion of these variables allows us to control for other agglomeration economies that are not captured by the main variables of interest, cleaning potential bias coming from OVB. First, both wealth (GDP per capita) and total population were included to control for the economic well-being and the size of the area respectively. Looking at the correlation table, the former shows positive correlation with the concentration of CCIs while the latter is negatively associated to their concentration. However, looking at the regression coefficients, the division into groups suggests differentiated interpretations. Indeed, for both variables there emerges a difference between market- and non-market-oriented filières, although with different signs. The average wealth of the place matters for those activities that are based on B2B relationships. In fact, rich places are also those where more developed activities are located, favouring exchanges. However, considering marketoriented activities, richness is not necessarily a driver for concentration of CCIs. Manufacturing activities do not need wealthy areas while services (retail and cultural services) prefer rich places for the sale of their products. On the contrary, population in general represents a deterrent for the concentration of CCIs. This result is not as straightforward as one could expect. Indeed, large areas (usually urban) have 10

Though this procedure is controversial in literature (Freckleton 2002), residuals of a first-stage regression are often used for the purpose of controlling for unwanted effects in multivariable framework. In this way, the effect of education and wealth on technology adoption are “cleaned” and only the “true” adoption of technology in retail remains. 11 In any regression output that includes interaction terms, the coefficient of the interaction between the dummy and the variable of interest shows the difference in slope between the two groups (dummy = 0 vs. dummy = 1). Hence, the actual coefficient of the variable of interest in the group with dummy = 1 is the sum between the coefficient of the variable not interacted and the coefficient of interaction. In formulas, given a regression model yi = α + β xi + γ di + ϑ xi ∗ di + εi , β represents the slope of the regression line if di = 0 while β + ϑ is the slope if di = 1. However, the significance of the coefficient of the interaction refers to the difference, not to the magnitude.

172

6 Location of CCIs: Innovation and Filière Behind the Scenes

Table 6.7 Average marginal effects of the main variables of interest AMEs

Variable

Replicative

Inventive

Sectoral relatedness

− 0.0057 (0.0024)

**

− 0.0035 (0.0014)

**

Cognitive relatedness

− 0.0215 (0.0051)

***

0.0077 (0.0025)

***

Sectoral unrelatedness

− 0.0130 (0.004)

***

− 0.0088 (0.0034)

***

Cognitive unrelatedness

− 0.0064 (0.0042)

0.0145 (0.0041)

***

Labour pooling

0.0337 (0.0096)

***

− 0.0133 (0.0112)

Creative local knowledge

0.0406 (0.0158)

***

0.0336 (0.0113)

Market size

0.1362 (0.0474)

***

0.0340 (0.0267)

Technology in retail (residuals)

0.0280 (0.0440)

0.0713 (0.0413)

***

*

*Only statistically significant coefficients (p