Handbook of Industrial Development 1800379080, 9781800379084

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HANDBOOK OF INDUSTRIAL DEVELOPMENT

In Memory of Keith Cowling (1936–2016) Colleague, Mentor, Friend, Inspiration

Handbook of Industrial Development Edited by

Patrizio Bianchi Professor of Applied Economics, Department of Economics and Management, University of Ferrara, Italy

Sandrine Labory Professor of Applied Economics, Department of Economics and Management, University of Ferrara, Italy

Philip R. Tomlinson Professor of Industrial Strategy, School of Management, University of Bath, UK

Cheltenham, UK • Northampton, MA, USA

© Patrizio Bianchi, Sandrine Labory and Philip R. Tomlinson 2023

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2022948498 This book is available electronically in the Economics subject collection http://dx.doi.org/10.4337/9781800379091

ISBN 978 1 80037 908 4 (cased) ISBN 978 1 80037 909 1 (eBook)

EEP BoX

Contents

List of contributorsvii Foreword by Annalisa Primi: why talking about production means talking about developmentxvi Foreword by Richard Kozul-Wrightxviii INTRODUCTION 1

Shaping sustainable industrial development paths Patrizio Bianchi, Sandrine Labory and Philip R. Tomlinson

PART I

2

HISTORICAL AND SOCIOECONOMIC PERSPECTIVE ON INDUSTRIAL DEVELOPMENT

2

Industrial revolutions in a globalizing world, 1760–present Bas van Leeuwen, Ulbe Bosma and Meimei Wang

18

3

Latin America: learning and fictional expectations in industrial development Clemente Ruiz Durán and Moisés Balestro

37

4

Murmurs of an industrial revolution in Africa: is it time for Africa? Horman Chitonge

54

5

Industrialization, economic and political power Graham Brownlow

75

6

The transformation of work: changing employment governance regime Valeria Pulignano

90

7

Sustainable human development, capabilities and the new trajectories of industrial policy Mario Biggeri, Andrea Ferrannini, Santosh Mehrotra, Marco R. Di Tommaso and Patrizio Bianchi

PART II

106

INDUSTRIAL DEVELOPMENT IN REGIONS

8

Place and industrial development: paths to understanding? Peter Sunley and Ron Martin

133

9

Innovation, industrial dynamics and regional inequalities Ron Boschma, Martina Pardy and Sergio Petralia

151

10

Evolutions in industrial districts and local productive systems Marco Bellandi, Maria Chiara Cecchetti and Erica Santini

165

v

vi  Handbook of industrial development 11

External collaboration for innovation: firms, industry, regions and policy Mariachiara Barzotto, Carlo Corradini, Felicia Fai, Sandrine Labory and Philip R. Tomlinson

12

Governing industrial policy: the scope and limits of the ‘good governance’ agenda Pedro Marques and Kevin Morgan

182

200

PART III SECTORS 13

Spatial implications of the platform economy: cases and questions Martin Kenney, John Zysman, Dafna Bearson and Camille Carlton

14

Consumer goods: from mass consumption to servitization 232 Juan Carlos Monroy-Osorio, Marco Opazo-Basáez and Ferran Vendrell-Herrero

15

The car industry as a laboratory of transformations induced by industrial development David Bailey, Dan Coffey, Lisa De Propris and Carole Thornley

248

16

The propulsive role of the space industry in industrial development: evaluating the case of spaceports Leslie Budd and Davide Villani

268

17

The energy sector: an industrial perspective on energy transitions Tuukka Mäkitie and Markus Steen

287

18

Industry, innovations and transition to the green and circular economy Massimiliano Mazzanti and Emy Zecca

302

215

PART IV THE ROLE OF THE STATE IN INDUSTRIAL DEVELOPMENT 19

Industrial policy beyond market failure: structural dynamics, innovation and economic governance for industrial development David Bailey, Sandrine Labory and Philip R. Tomlinson

20

Stages of industrial development and appropriate industrial policy Murat Yülek and K. Ali Akkemik

338

21

Platform oligopolies, anti-trust policy and sustainable development Eleni E.N. Piteli and Christos Pitelis

357

22

States of innovation: how the state shapes production transformation Antonio Andreoni and Rainer Kattel

382

23

Industrial development and the growth process: a structural framework Ivano Cardinale and Roberto Scazzieri

403

322

Index425

Contributors

K. Ali Akkemik is Professor of Economics at the Faculty of Commerce, Fukuoka University in Japan. He holds a PhD from Nagoya University and a BS from Middle East Technical University. His research interests include development economics, industrial policy, applied general equilibrium models, and behavioural economics. He is the author of Industrial Development in East Asia: A Comparative Look at Japan, Korea, Taiwan, and Singapore (World Scientific, 2009). Antonio Andreoni is Professor of Development Economics at the Department of Economics, SOAS University of London. He is also Visiting Professor at the South African Research Chair in Industrial Development, University of Johannesburg and Honorary Professor at the Institute for Innovation and Public Purpose, University College London. Antonio is a co-Editor of the European Journal of Development Research. He has published extensively on production dynamics, technological change, and social conditions of innovation; structural transformation, GVCs, industrial ecosystems; financialisation and corporate governance; political economy of industrial policy; energy transition and sustainable industrial restructuring; competition policy, digitalisation, platform economy. His books include Structural Transformation in South Africa (Oxford University Press, 2021) and From Financialisation to Innovation (Cambridge University Press, 2022). Antonio advised multilateral organisations including UNIDO, UNCTAD, ILO, UNDP, World Bank and OECD as well as national governments in industrial policy making including the European Commission, UK, Finland, South Africa, Tanzania, Uganda and Mauritius. David Bailey is Professor of Business Economics at Birmingham Business School, University of Birmingham, UK, and an Economic and Social Research Council (ESRC) ‘UK in a Changing Europe Senior Fellow’. He has written extensively on industrial and regional policy, especially in relation to manufacturing and the auto industry. He has been involved in a number of recent major projects including the Horizon 2020 RISE project MAKERS, where he led the Work Package on Industrial Policy. He is Editor-in-Chief of the journal Regional Studies and Chair of the RSA Europe think-tank and policy forum. Recent books include Industry 4.0 and Regional Transformations (with Lisa De Propris), published by Taylor & Francis (2020), and Carmageddon? Brexit & Beyond for UK Auto, published by Bite-Sized Books (2020). David is a regular media commenter and newspaper columnist. Moisés Balestro is Associate Professor at the University of Brasilia, Brazil in the Graduate Program on Comparative Studies in the Americas in Social Sciences. His research is on comparative political economy in state and development, comparative capitalism, and upgrading in latecomers. He was a visiting scholar at Goethe University, Germany in 2014 and a member of the Society for the Advancement of Socio-Economics (SASE) since 2008 and the World Interdisciplinary Network for Institutional Research (WINIR). His recent articles and book chapters are about industrial policy, growth models and innovation.

vii

viii  Handbook of industrial development Mariachiara Barzotto is an Associate Professor in International Management at the University of Bath, UK. Prior to this, Mariachiara worked as an Assistant Professor at Newcastle University Business School and Essex Business School, UK. She held a post-doctoral Marie Skłodowska-Curie Research Fellowship at Birmingham Business School, UK. Her research focuses on skills, labour markets, technological changes, new working spaces and regional development. Her research has been published in international journals, such as: the Journal of Economic Geography and Work, Employment and Society. Dafna Bearson is a doctoral student in the Strategy Unit at Harvard Business School (HBS), USA. Prior to joining HBS, she served as a research analyst at the Berkeley Roundtable on the International Economy, Center for Information Technology Research in the Interest of Society and the Banatao Institute, and University of California, Berkeley Haas School of Business. Marco Bellandi is Full Professor of Applied Economics at the University of Florence, Italy. He was Dean of the School of Economics and Management from 2019 to 2022, and Vice-Chancellor for Technology Transfer from 2009 to 2015. He has co-organized national and international projects and conferences, participated on boards of scientific associations and journals, and co-founded research centres. He has published extensively on local industrial development and policy, and innovation systems. Patrizio Bianchi is Full Professor of Economics at the University of Ferrara, Italy, and since February 2021 has been Italian Minister of Education. He was Minister for Education and Research of the Regional Government of the Emilia-Romagna region from 2010 to 2020. He has been an advisor to international institutions such as the European Commission, the Interamerican Development Bank and the United Nations Industrial Development Organization (UNIDO). Patrizio has more than 200 publications, including books and articles in scientific journals. Mario Biggeri is Full Professor of Applied Economics at the Department of Economics and Management, University of Florence, Italy, Scientific Director of ARCO (Action Research for CO-development) and Director of the Scientific and Ethical Committee of Yunus Social Business Centre, University of Florence, Italy. Moreover, he is Fellow of the Human Development and Capability Association and of the National University Centre for Applied Economic Studies (CiMET), Italy. His research activities focus on the capability approach, sustainable human development, industrial policy, international development cooperation, social economy, local development, well-being and poverty measurement, and impact evaluations. Ron Boschma is Full Professor in Regional Economics at the Department of Human Geography and Planning of Utrecht University, the Netherlands. He is also Professor in Innovation Studies at UiS Business School of Stavanger University, Norway, and he holds the Bernard Maris Chair in Economics at the University of Toulouse, France. He is board member of the International Regional Studies Association. Boschma has published on evolutionary economic geography, regional diversification, smart specialization policy, geography of innovation, regional resilience, economic complexity, and spatial networks. Ulbe Bosma is Senior Researcher at the International Institute of Social History and Professor at the VU University Amsterdam, the Netherlands. Among his recent publications are The Sugar Plantation in India and Indonesia: Industrial Production 1770–2010 (Cambridge

Contributors  ix University Press, 2013) and The Making of a Periphery: How Island Southeast Asia Became a Mass Exporter of Labor (Columbia University Press, 2019). Graham Brownlow is Senior Lecturer in Economics at Queen’s Management School (QMS), Northern Ireland, UK. He is also a Visiting Professor at Birmingham City University, UK. Since 2012 he has been Editor/Co-Editor of Irish Economic and Social History. In 2019, Graham was the Plenary Lecturer for the Economic History Society annual conference. His research has involved historical and contemporary topics as wide and varied as Brexit, Douglass North, DeLorean, New Zealand’s recent economic history, partition and the history and method of economics. Leslie Budd is Professor of Regional Economy at The Open University Business School, UK. He is an economist who has published extensively on urban and regional economies in relation to a number of global and regional issues. He is a Fellow of the Academy of Social Sciences and was Special Economic Advisor to the Committee for Enterprise Trade and Investment (CETI) and Technical Lead for evaluating the socioeconomic benefits of the key European Space Agency human-robotic exploration project. Ivano Cardinale is Reader in Economics at Goldsmiths, University of London, UK, where he co-initiated and directs the Economics programmes. He also lectures on the history of economic thought at the Faculty of Economics, University of Cambridge, UK. He was previously the Mead Fellow in Economics (Research Fellow) at Emmanuel College, Cambridge. His research combines economic theory, political economy and social theory, providing a new understanding of how political-economic actions, including public policies, unfold within economic and social structures. Camille Carlton is the Senior Policy and Communications Manager at Center for Humane Technology. She previously worked on issues related to artificial intelligence and the rise of technology platforms at the Berkeley Roundtable on the International Economy. She holds a master’s degree in Economic Development from the London School of Economics and Political Science. Maria Chiara Cecchetti recently graduated in MSc Economic Sciences at the University of Florence, Italy, with a thesis on ‘Digital transformation and new development trajectories for small manufacturing companies’. Horman Chitonge is Professor of African Studies at the Centre for African Studies, and a researcher fellow at PRISM, School of Economics, University of Cape Town, South Africa. He is also a Visiting Professor at the African Studies Center, Tokyo University of Foreign Studies, Japan and in the Global Justice Program, Yale University, USA. His research interests include economic development in Africa, agrarian political economy, water supply, social welfare, poverty and inequality, and alternatives for Africa’s economic growth. Dan Coffey is an economist at Leeds University Business School, UK, where he was previously Graduate School Director. He is author of The Myth of Japanese Efficiency: The World Car Industry in a Globalizing Age (Edward Elgar Publishing, 2006), co-author of Globalization and Varieties of Capitalism (Palgrave Macmillan, 2009), and co-editor of Global Economic Crisis and Local Economic Development (Routledge, 2016). He researches

x  Handbook of industrial development all aspects of the global car industry and sits on the Steering Committee of GERPISA (‘the international research network of the automobile’). Carlo Corradini is Professor of Regional Economics at Henley Business School, University of Reading, UK and an Associate Editor for the journal Regional Studies. His research interests and expertise encompass areas such as regional innovation systems and economic development, industry and technological change, as well as evolutionary economic geography. He is also working on spatial big data science and intangible dimensions of regional dynamics. Lisa De Propris is Professor of Regional Economic Development at Birmingham Business School, UK. She has expertise in manufacturing, Industry 4.0, technological change, service clusters/districts, creative industries, regional economic development, industrial and EU cohesion policy. She led a Horizon 2020 project on Industry 4.0 called MAKERS. She has published extensively on these themes in top academic journals such as Regional Studies, Research Policy, Cambridge Journal of Economics, Journal of Economic Geography and Cambridge Journal of Regions, Economy and Society. She sits on the editorial board of Regional Studies. Marco R. Di Tommaso is Full Professor of Applied Economics at the University of Bologna, Italy, Clare Hall Life Member at Cambridge University, UK, Honorary Professor at South China University of Technology, Director of the National University Centre for Applied Economic Studies (CiMET), Italy and Editor-in-Chief of the journal L’Industria published by Il Mulino. His research activities focus on industrial development and policy, structural change, innovation, US, China and regional development. Felicia Fai is Associate Professor in International Business and Innovation at the University of Bath, School of Management. She has published work that considers multinational corporations, industrial development and regional innovation-based evolution in leading journals such as Industrial and Corporate Change, Technovation, Management International Review, and Regional Studies. She has also advised regional and national government bodies on place-based policy issues relating to innovation. Andrea Ferrannini is coordinator of the Strategic Unit on Local Development at Action Research for CO-development (ARCO) and post-doc researcher at the Department of Economics and Management, University of Florence, Italy. He is also Fellow of the National University Centre for Applied Economic Studies (CiMET), Italy. His research activities focus on sustainable human development, industrial policies and social progress, international development cooperation, Sustainable Development Goals (SDGs) and local economic development and participatory methods. Rainer Kattel is Deputy Director and Professor of Innovation and Public Governance at the University of London (UCL) Institute for Innovation and Public Purpose, UK. Professor Kattel’s research focusses on organizational and institutional aspects of innovations and innovation policies. He has published extensively on innovation policy, its governance and specific management issues. In 2013, he received Estonia’s National Science Award for his work on innovation policy. Martin Kenney is a Distinguished Professor of Community and Regional Development at the University of California, Davis and a Co-Director at the Berkeley Roundtable on the

Contributors  xi International Economy, USA. He is a Senior Advisor to the Research Institute of the Finnish Economy. He was the Arthur Andersen Distinguished Visitor at the University of Cambridge, UK. He has written extensively on the impacts of platforms on entrepreneurship and technological competition in the West and China. He has consulted for various international agencies on the impacts of platforms on the economy and society. Sandrine Labory is Professor of Applied Economics at the University of Ferrara, Italy. She has an MSc in Economics from University College London, UK and a PhD from the European University Institute in Florence, Italy. Her research is focused on industrial economics and policy, including comparative analysis of national and regional industrial policies, innovative and productive processes at firm and territorial levels, structural changes and industrial development. She has numerous publications in Italian and international journals, as well as books such as Industrial Policy after the Crisis: Seizing the Future (Edward Elgar Publishing, 2011) and Industrial Policy for the Manufacturing Revolution: Perspectives on Digital Globalisation (Edward Elgar Publishing, 2018) with Patrizio Bianchi. Tuukka Mäkitie is a Research Scientist at SINTEF Digital, Department of Technology Management, Norway and has a PhD in Innovation Studies from the University of Oslo, Norway. Tuukka’s research has focused on green industrial transformation and innovation processes towards more sustainable patterns of production and consumption in sectors such as energy, shipping, and wastewater management. Pedro Marques is a Research Fellow at INGENIO, in Valencia (Spain). He is also Visiting Fellow at the Centre for Urban and Regional Development Studies (CURDS), Newcastle University, UK and an affiliated member at the Centre for Innovation Research (CIRCLE), Lund University, Sweden. Pedro has a PhD in Economic Geography from CURDS, an MA from the same institution and a BA in Sociology from ISCTE-IUL, Lisbon, Portugal. Previously he worked at Cardiff University and Newcastle University, UK, and University of Kiel, Germany. His research interests are in regional development, inequality, politics and development, and innovation. Ron Martin is Emeritus Professor of Economic Geography at the University of Cambridge, UK. His main research interests include regional economic development, productivity and competitiveness; geographies of finance; evolutionary economic geography; the economic resilience of cities and regions; and spatial economic policy. He has published 30 books and more than 250 articles. He is a former President of the Regional Studies Association and in 2016 he was awarded the Royal Geographical Society’s Victoria Gold Medal for Outstanding Contributions to Economic Geography. Massimiliano Mazzanti (UNIFE SEEDS) is Full Professor in Economic Policy, University of Ferrara (UNIFE). Currently, he is the Director of the Department of Economics and Management, University of Ferrara, and Prorector for the PNRR at UNIFE. He is director of SEEDS (Sustainability, Environmental Economics and Dynamics studies), an interuniversity research centre, and Vice-Coordinator of the PhD Environmental Sustainability and Wellbeing. He has collaborated and published policy-oriented reports under research contracts with the Organisation for Economic Co-operation and Development (OECD), United Nations Industrial Development Organization (UNIDO) and The World Bank. Main research compe-

xii  Handbook of industrial development tences revolve around applied environmental economics and policy issues, with a focus on eco-innovation themes. Santosh Mehrotra is Visiting Professor at the Centre for Development Studies, University of Bath, UK, and former Professor of Economics and Chair at Centre for Labour Studies, Jawaharlal Nehru University, India. He was the Director General (2009–14) of the National Institute of Labour Economics Research, Planning Commission, in the rank of Secretary to the Government of India. His research activities focus on human development economics, industrial policy, informal sector and labour, employment, skill development, child poverty, and the economics of education. Juan Carlos Monroy-Osorio is a lecturer (Assistant Professor) in Marketing at EAFIT University, Colombia. He is currently studying for a PhD in Business and Territorial Competitiveness, Innovation, and Sustainability at Deusto, Spain. His work focuses on business analytics, digital economy and data visualization for strategic business decisions related to prescriptive analytical models. His research interests include servitization, digital consumer, user-centred business models and behavioural economics. Kevin Morgan is Professor of Governance and Development in the School of Geography and Planning at Cardiff University, UK, where he is also the Dean of Engagement. The theory, policy and practice of place-based innovation policy is one of his main research interests and in this domain he has worked with the European Commission, the Organisation for Economic Co-operation and Development (OECD) and a wide range of regional governments and development agencies throughout Europe. Marco Opazo-Basáez is a lecturer (Assistant Professor) in Marketing at the University of Deusto, Deusto Business School, Spain. His work focuses on firm’s leading edge practices, analysing drivers that shape strategies, models and mechanisms for value creation based upon data-driven analysis. His research interests include servitization, digitalization, sustainability and innovation. He has published in internationally renowned journals such as Journal of Service Management, Technovation, Journal of Business Research, Annals of Operations Research and Journal of Knowledge Management, among others. Martina Pardy is a PhD student at the Department of Geography and Environment of the London School of Economics and Political Science, UK. Her PhD is on globalization, FDI and regional structural change. Previously, she conducted research on regional inequality, globalization and migration and completed a research project on gender inequality for the United Nations Development Programme. Martina holds an MSc in Economics and a BSc in Economics and Social Sciences from the Vienna University of Economics and Business (WU), Austria. Sergio Petralia is an Assistant Professor at the Department of Human Geography and Planning of Utrecht University, the Netherlands. Petralia worked as a post-doctoral researcher at the London School of Economics and Political Science, and he has been Visiting Fellow at the Center for International Development’s Growth Lab at Harvard, USA. Sergio works on issues related to technological change and innovation, such as the identification of opportunities for technological development in developing economies, and the impact of disruptive technological change on income and wages.

Contributors  xiii Eleni E.N. Piteli is Assistant Professor in International Business (IB) at the University of Sussex, UK. A recipient of the Onassis Public Benefit Foundation scholarship, she holds a PhD in IB from the University of Leeds, UK, and has published in journals such as the Journal of World Business and Journal of International Management. She has worked at the United Nations Conference on Trade and Development (UNCTAD), and EU-funded projects at the Universities of Cambridge, Leeds and Athens. She teaches and consults on IB, entrepreneurship and strategy, migration, and sustainable development. Christos Pitelis is Professor of International Business and Sustainable Competitiveness and Head of the Department of International Business at the University of Leeds, UK. He is also a Life Fellow at Queens’ College, University of Cambridge, UK. He is co-editor of the Cambridge Journal of Economics and literary executor of the works of Edith Penrose. He has published in journals such as the Academy of Management Review. He has taught, coordinated projects, consulted, trained, and designed policies for international organizations, national governments and regions, businesses and NGOs and was visiting professor in leading universities worldwide. Valeria Pulignano is Professor in Sociology at the Centre for Sociological Research (CeSO) – KU Leuven, Belgium. She has published extensively on the sociology of work, comparative European industrial (employment) relations, labour markets and inequality, working conditions, job quality and workers’ voice. She serves as Principal Coordinator of the Research Network 17 Work, Employment and Industrial Relations within the European Sociological Association and Co-Researcher at the Interuniversity Research Centre on Globalization and Work (CRIMT) at the University of Montreal and Laval in Canada. She is Principal Investigator of the European Research Council AdG ResPecTMe. Clemente Ruiz Durán is Professor at the Graduate School of Economics at the National Autonomous University of Mexico, where he is the head of the Regional Studies Program. His research has focused on industrial policy in Mexico and comparative studies with emerging countries of Latin America and Asia. He has worked with the International Labour Organization (ILO), mapping global value chains in Mexico and its impact on labour, and has discussed the impact of uneven integration with the US and Canada within the Free Trade Agreement. Erica Santini is Assistant Professor of Management at the University of Trento, Italy. She has been visiting researcher at the Business School of the University of Birmingham, UK, and post-doc in innovation and new manufacturing at the University of Neuchâtel, Switzerland. Her research focuses on innovation, clusters, ecosystems, digitalization of manufacturing, servitization strategies, and product-service innovation. She has published in international journals such as Regional Studies, Cambridge Journal of Economics and International Journal of Production Economics. Roberto Scazzieri is Professor of Economic Analysis, University of Bologna, and Fellow of the Italian National Academy (Accademia Nazionale dei Lincei). He is also Senior Member of Gonville and Caius College, and Life Member of Clare Hall, Cambridge, UK. He is a Member of the Babbage Policy Forum, University of Cambridge. His research deals with production theory and the dynamics of production networks, the structural analysis of economic systems, and the transformations of political economies.

xiv  Handbook of industrial development Markus Steen is a Senior Research Scientist at SINTEF Digital, Department of Technology Management, Norway. He holds a PhD in Economic Geography from the Department of Geography at the Norwegian University of Science and Technology. Markus’s research has focused on regional development, industrial transformation, sustainability transitions, innovation and policy, notably in sectors that provide energy, transport and food. Peter Sunley is Professor of Economic Geography and formerly Director of Research and Enterprise at the University of Southampton, UK. His research has focused on geographies of labour and labour market policy, business clusters and venture capital, design and creative industries, regional and urban economic change and resilience, and manufacturing in industrial regions. He is a member of the Research Committee of the Regional Studies Association and a Fellow of the Academy of Social Sciences. Carole Thornley is an economist and Emeritus Professor of Employment and Public Policy at Keele Business School, Keele University, UK. She is co-author of Globalization and Varieties of Capitalism (Palgrave Macmillan, 2009) and co-editor of Globalization and Precarious Forms of Production and Employment (Edward Elgar Publishing, 2010) and Global Economic Crisis and Local Economic Development (Routledge, 2016). She works on globalization, industrial organization, employment and public policy, and industrial relations. She is on the Steering Committee of GERPISA (‘the international research network of the automobile’). Philip R. Tomlinson is Professor of Industrial Strategy and Deputy Director of the Centre for Governance, Regulation and Industrial Strategy (CGR&IS) in the School of Management at the University of Bath, UK. His research explores the interplay between economic governance, innovation, regional development and place-based industrial strategy. He has published widely and extensively, with over 100 career research publications in leading academic journals, books and book chapters, industry reports and media outlets. Professor Tomlinson has held several external appointments, including with the UK All-Party Parliamentary Manufacturing Group (APMG), the Independent Local Industrial Strategy Review Panel for Swindon and Wiltshire LEP (SWLEP) and he is currently a member of the West of England’s Skills Advisory Panel (SAP). Bas van Leeuwen is Senior Researcher at the International Institute of Social History, Utrecht University, the Netherlands. Among his recent publications are British Economic Growth 1270–1870 (Cambridge University Press, 2015) and the edited volume An Economic History of Regional Industrialization (Routledge, 2020). His current research focusses on inventions and long-run growth in Europe and Asia. Ferran Vendrell-Herrero is Senior Lecturer in International Business at University of Edinburgh Business School, UK. Ferran’s research aim is to uncover innovation, digitalization and internationalization dynamics of small and large organizations in manufacturing and creative industries. He has made a distinctive contribution through publications in top academic journals, including the Journal of International Business Studies, Journal of World Business, International Journal of Production Economics, Regional Studies, Industrial Marketing Management and International Business Review, among others. Davide Villani is a researcher at the European Commission Joint Research Centre (JRC), where he works for the Centre for Advanced Studies (CAS) on the project ‘Social Classes in the Digital Age’ (DIGCLASS). Before joining the JRC, Davide worked as lecturer in

Contributors  xv Economics at Goldsmiths College (University of London, UK) and as researcher in different research projects at The Open University, UK. His research interests include a range of topics within economics, such as macroeconomics, labour relations, international economics and corporate finance. Meimei Wang is Junior Researcher at the Institute of Economics at the Chinese Academy of Social Sciences. Among her recent publications are Education in China, ca. 1840–present (Brill, 2020) and ‘Fenjiashu: Economic development in the Chinese countryside based on household division inventories, ca. 1750–1910’ (Australian Economic History Review, 2021). Her current research focusses on long-run growth in China. Murat Yülek is a Professor of Economics and President of Ostim Technical University, Turkey. A former IMF economist, he has been a Visiting Scholar at Columbia University and a Professor at Georgetown University, USA. He has been a consultant at institutions such as the European Bank for Reconstruction and Development (EBRD), The World Bank and the United Nations Conference on Trade and Development (UNCTAD). His research has concentrated on economic development, in particular on industrial and financial policy. He is the author of the book How Nations Succeed: Manufacturing, Trade, Industrial Policy and Economic Development (Palgrave Macmillan, 2018). He holds an MBA degree from Yale University, USA, PhD and MA degrees from Bilkent University, Turkey, and an MSM degree from Boston University, USA. Emy Zecca (UNIFE SEEDS) obtained a PhD in Economics & Finance at the Doctoral School of Economics of the Sapienza University of Rome, and she spent a period as a visiting student at Tilburg University during the Master’s degree. She currently covers the role of Researcher University of Ferrara (UNIFE), and previously she held the position of Research Fellow at University of Ferrara and Research Associate at University of Urbino “Carlo Bo”. She is research affiliate of Sustainability Environmental Economics and Dynamic Studies (SEEDS) and she was adjunct professor of Micro and Macroeconomic Foundations of Circular Economy at the University of Tuscia and previously adjunct professor of Environmental Economics at the Faculty of Environmental and Territorial Engineering of the Sapienza University of Rome. Her research interests vary across Environmental Economics, Eco-innovation and Circular Economy. John Zysman is Professor Emeritus at the University of California Berkeley, USA. He is co-director and co-founder of the Berkeley Roundtable on the International Economy.

Foreword by Annalisa Primi: why talking about production means talking about development1

The global economy is under major stress. It faces simultaneous transitions, from managing the digital transition to accelerating the ecological transition. This requires a deep shift in modes of production, trade and consumption, as well as a reorganization of social and spatial interactions, with local and international networks increasingly intertwining. The unfolding of the pandemic affected a world economy that was already on the verge of an unsustainable balance.2 Globalization and the organization of production around complex and geographically widespread supply chains did not deliver a fast track to development, as had been expected. With the exception of a handful of locations around the world, few countries have been able to fully transform their economies and to reap the benefits of globalization and digitalization in terms of a strong and vibrant local industrial development base and a dynamic local domestic entrepreneurial class. The pandemic exposed the fragilities of a fragmented industrial system with limited resilience and adaptive capacity, and made visible the importance of having manufacturing capacities ‘within reach’, meaning at reasonable physical distance, but also within trusted trade and investment networks that continue to operate even under major global stress or national emergencies. Production – what a country produces and trades – shapes the generation of rent and distribution capacity, the capacity to interact with others and build alliances, the capacity to innovate and, ultimately, the quality of life of citizens, as it shapes jobs and territorial organization. This is why when we talk about production we are ultimately talking about development. And this is why it is not only a private sector concern. Firms and private investment are, of course, essential, but they are steered and shaped (for the good and bad) by public policies and public institutions, or the lack of them. This Handbook puts forward three elements that, in my view, summarize a fundamental lesson in understanding global industrial dynamics and that will be important in shaping better industrial dynamics in the years to come. These issues also lie at the heart of the Production Transformation Policy Reviews that are implemented in the framework of the OECD Initiative for Policy Dialogue on Global Value Chains (GVCs), Production Transformation and Development: ● State capacity and state action (or inaction) are key to shaping production dynamics, and therefore national development pathways. Governments play an important role in determining market dynamics and in creating the conditions for learning and innovation. This is particularly relevant to steering investments to foster an ecological transition and innovations to address climate change. In this context, private investment and initiatives remain key to making industrial development happen. A major challenge for the future will be reorienting interests between governments (which by definition have a more local reference point) and firms (which nowadays have global aspirations). ● Understanding and shaping industrial development paths cannot occur without a place-based approach. Industries flourish and perish in given locations, and national industrial stratexvi

Foreword by Annalisa Primi  xvii gies need to come to terms with effective coordination and alliance with regional dynamics. Taking into account the territorial perspective in industrial development is relevant not only from a compensation logic within a nation state, but especially as a way to identify untapped development potentials and to support the search for sustainable development pathways. ● Industrial activities and sectors are heterogeneous and ever-changing. Digitalization is reshaping businesses across all industries in deep ways, increasingly challenging the traditional characterization of industrial activities. The platform economy is prompting the core business of some manufacturers to shift from products to services (the case of the automotive and the mobility industry is a case in point) and this is changing competition dynamics, often favouring large businesses. The Handbook of Industrial Development is a timely read. It provides the reader with a global tour of industrial development pathways and challenges from Africa to Latin America. It recalls that history often repeats itself, with industrial development happening through a mix of emulation and innovation. When industrial development is successful in a given location or country, it changes the geopolitical and power relations within a territory and with international markets, creating subsequent waves of adjustment, some foreseeable, others unexpected. This Handbook rests on the premise that while there is no unique best pathway for industrial development, there is a common goal for the industry of the future: it will need to be inclusive, capable of delivering good jobs and pay for workers, and it will need to be compatible with planetary needs and environmentally sustainable. Coordination will be essential between firms and governments, and among countries at the multilateral level. This Handbook is a key reference for academics, policy advisors and shapers to inspire new and updated policy approaches to foster a people-centred industrial development that can contribute to achieve the world we all want: one that is inclusive, sustainable and capable of pushing the innovation frontier forward.

NOTES 1. Annalisa Primi is Head of the Economic Transformation and Development Division, Organisation for Economic Co-operation and Development (OECD) Development Centre. The opinions expressed in this Foreword are those of the author and do not necessarily reflect those of the OECD. 2. Primi, A. and M. Toselli (2020), ‘A global perspective on Industry 4.0 and development: new gaps or opportunities to leapfrog?’, Journal of Economic Policy Reform, 23(4), 371–89.

Foreword by Richard Kozul-Wright1

In a recent address to the Economic Club of New York, Brian Deese, the Director of President Biden’s National Economic Council, outlined the case for a modern American industrial strategy, noting that: ‘For much of the last half century, even uttering the words “industrial policy” was met with something between derision and concern. But our economy has changed, and the world has changed too’. Structural change is hardly a new feature of capitalist development and, no country, including the United States, has gone from widespread rural poverty to post-industrial wealth without drawing on the resources and capacities of the state to target and support favoured sectors, firms and products, remove infrastructure bottlenecks, develop needed technological capabilities and coordinate the creative (and mitigate the destructive) forces unleashed by such change. Even at the high point of market fundamentalism under the presidency of Ronald Reagan, industrial policy was not so much abandoned in the United States as ‘hidden’ behind what political commentator Robert Reich dubbed ‘the ideological drift of the times’. Still, Deese’s remarks are a welcome recognition that policy circles in advanced economies are facing a set of big challenges that require them to do much more than deliver some basic elements of a business-friendly market economy. Whether it is the looming climate crisis and accompanying environmental degradation, or industrial decline and growing economic polarization, or the breakdown of global supply chains and attendant inflationary pressures, or the spread of zoonotic diseases and resulting health pandemics, changing the way we produce and consume the goods and services that shape everyday life will require what the Financial Times2 calls ‘radical reforms’ that reverse the prevailing policy direction of the last four decades and allow governments to assume ‘a more active role in the economy’. The struggle is now on to define exactly what those reforms will contain. As the editors of this Handbook rightly point out, the place governments should begin that exercise is with the dominant model of socio-economic development, driven by an unsustainable pattern of industrial development, which is generating systemic imbalances and inequalities that threaten not only its own sustainability but also life on the planet itself. Accordingly, and as the chapters in this Handbook highlight, policymakers will need to embrace a more transformative approach to their craft, with industrial policy as an integral component and ‘higher well-being for the whole population’ a principal goal. A great deal has been written about industrial policy tools and experiences in recent decades, with much of the discussion revolving around a sterile debate about whether governments can ‘pick winners’. Even a passing familiarity with the history of industrial policy reveals a much more nuanced account of successes and failures. This story reflects the historical path-dependent and dynamic relationship between the state and industry, due to the fact that industrial development is a structural transformation process involving changes in the sectoral composition of the economy and the interdependencies linking sectors, as well as the ever-present contestation between different economic stakeholders seeking to shape policy in line with their particular interests and aspirations. Moving the policy debate forward in light of contemporary challenges requires a systemic approach that embraces the multidimensional nature of structural change and industrial evoxviii

Foreword by Richard Kozul-Wright  xix lution, and which can cut across isolated policy silos to establish a more encompassing and coordinated policy framework that not only aligns trade, competition, labour and macroeconomic policies with industrialization imperatives but also ensures that complementary welfare and other social policies reinforce the goal of higher well-being for the whole population. The Handbook frames this as an ‘industrial development policy’, which connects measures directly aimed at industry to those aimed at adapting the socio-economic system in which industrial development is always embedded so that there is a transformation along sustainable paths. This suggests a second goal for policymakers, which is to provide the conditions and implement actions to facilitate transformative industrial pathways. Such an evolutionary approach is inherently forward-looking, but history still matters. Policymakers must work with existing institutional capacities, means of resource mobilization, corporate structures, learning capabilities, cultural norms, patterns of international integration and so on. These historical legacies inevitably structure and constrain the space available to governments in which to design and implement policies. These, however, are not fixed but emerge from acquisition, accumulation and learning strategies. Crucially, having the room to experiment and adapt policy measures to local circumstances is key to establishing a successful transformative path. The important lesson to draw is that there is no ‘one-size-fits-all’ approach to managing structural transformation. The employment of industrial policies in the successful East Asian experiences, including most recently China, embraced these principles. As such, and as the remarks by Deese suggest, policymakers in the advanced economies are beginning to catch up. Indeed, recent efforts to recalibrate industrial policy is, in part, a belated response to the global transformation that is accompanying the rapid economic progress of the East Asian countries. Further progress in this regard will, no doubt, hinge on their willingness to drop the myths and shibboleths of neoliberal policymaking. Accordingly, the emphasis is not on whether to have an industrial policy, but on how to design and implement it effectively in coordination with the wider set of economic and social policies that together determine the success of a transformative development path. Managing this process effectively requires the state, at various levels, to engage in a certain amount of experimentation in seeking the configuration of institutions, policies and behavioural conventions that work best in their national conditions. Moreover, adapting those features to changing economic circumstances and evolving political and social preference would appear to be a critical element in distinguishing a successful development pathway. The Handbook provides a wealth of material from different sectors and regions that policymakers can look to when designing policies in line with their own particular circumstances. There is no doubting that the scale and urgency of the climate crisis is changing those circumstances across the world and in profound ways. Still, effective responses cannot come at the expense of longstanding development goals. A climate-conscious industrial development path will need industrial policy to adapt. This will include a variety of measures to affect the transition to zero-carbon energy systems and to support the circular economy, issues discussed in this Handbook. But the adaptation challenge, long neglected in climate discussions, requires building more diversified and resilient economies that can manage the climate shocks that are already damaging development prospects in the Global South and are set to intensify even if the Paris climate goals are met. In light of these challenges, governments everywhere need to be both ambitious and realistic. Their ambition involves striving for a high development road by creating new sources and

xx  Handbook of industrial development patterns of growth and dynamism that go beyond doing the best with what is currently in place. Small and incremental steps can be useful, but more radical measures will be needed to shift towards higher value-added and employment-generating activities that are consistent with the required transition in our energy, transport and agricultural systems and that create synergies along the evolving industrial pathways through technological advances and knowledge creation, increasingly linked to the expanding digital space. A green industrial policy will need to use a mixture of general and targeted subsidies, tax incentives, equity investments, loans and guarantees, as well as accelerated investments in research and development, along with a new generation of intellectual property rules that support the transfer of appropriate technologies, particularly to developing countries. In all countries, regardless of their level of development, this ambition will depend on facilitating access to long-term investment finance at reasonable cost, especially for firms (public as well as private) in targeted sectors, as well as in those activities that can benefit from linkages with firms in those sectors. Here the design of industrial policy must complement efforts to address the distortions in contemporary financial systems. Over recent decades, financial markets have acquired unprecedented global reach. As obstacles to the free movement of capital have been dismantled, the power of financial actors, from banks to asset managers, has strengthened accordingly while new rules (on financial services provision, investment, and intellectual property rights) in trade and investment treaties have only amplified that power on the global stage. Greater efficiency and prosperity was promised. In reality, unrestrained finance has aimed less at boosting investment, productivity and jobs, and more at extracting rents through a whole new range of pyramid schemes, toxic products and the buying and selling of existing assets for quick returns. Rethinking the links between finance and industry serves as a reminder that no country can solve their contemporary development challenges by acting alone. But a renewed multilateralism will be required to support climate-conscious industrial development pathways, particularly in developing countries, by cooperating and coordinating policy initiatives that demand collective action, mitigating common risks, ensuring that no nation’s pursuit of these broader goals infringes on the ability of other nations to pursue them, and providing the global public goods needed to deliver shared prosperity and a healthy planet. Elsewhere we have set out the ‘Geneva Principles for a Global Green New Deal’ that can guide an urgent reform agenda for a new multilateralism to recalibrate the global economy in support of a 21st-century vision of stability, shared prosperity, and environmental sustainability. This Handbook provides an invaluable collection of ideas, experiences and policy lessons that can help advance such an agenda.

NOTES 1. Richard Kozul-Wright is Director of the Globalisation and Development Strategies Division in the United Nations Conference on Trade and Development (UNCTAD). 2. Financial Times (2020, 3 April), ‘Virus lays bare the frailty of the social contract’ (editorial), accessed 15 May, 2022 at https://​www​.ft​.com/​content/​7eff769a​-74dd​-11ea​-95fe​-fcd274e920ca.

INTRODUCTION

1. Shaping sustainable industrial development paths Patrizio Bianchi, Sandrine Labory and Philip R. Tomlinson

Industry around the world is going through a process of deep transformation following not only the fourth Industrial Revolution (Industry 4.0) but also other societal challenges such as climate change and the need to preserve the environment (or prevent further damage to it), pandemics and rising inequalities. Each of these challenges have separate and very complex effects, but they also are intertwined and interdependent, making it extremely difficult to derive a comprehensive vision of where the economy and society will head towards. These challenges are also changing the conditions within which industry develops, making it necessary for companies to design new products, new production processes and new strategies. In this context of deep structural change, the Handbook provides an analysis of industrial development from different perspectives – economic, sociological and historical – to provide insights into the likely directions of these transformations. Industrial development has always been considered a key factor for the growth of territories, be they nation-states or regions. Yet, today, both economic growth and industries are the subject of heated debates as growth has been stagnating in many countries – raising concerns about ‘secular stagnation’ – and industrial development has not always brought about progress in the sense of improvement in the well-being of the whole population, in terms of both income enabling decent living conditions for all and ensuring appropriate living and working conditions for future generations. These concerns have been expressed in the Sustainable Development Goals, defined by the United Nations in 2015 as an urgent call for all countries on the planet to collaborate and take action to ensure the well-being of current and future generations. In other words, the current model of industrial development is not sustainable, since social inequalities are rising and the environmental damage caused by human activities puts life on the planet at risk.

INDUSTRIAL DEVELOPMENT AND INDUSTRIAL REVOLUTIONS This leads to the question of what is meant by industrial development. Defining industrial development as the building and growing of industries within an economy is useful for socioeconomic systems only insofar as it provides both jobs for people who can make a living thanks to this employment, and goods and services that the population can buy and use in their everyday life. As Van Leeuwen et al. recall in Chapter 2, industrial production was as much part of human existence in 500 BCE as it is today. The main difference is that the organization of production and the use of machines and technology have substantially changed across centuries, and especially after the first Industrial Revolution, which represents a rupture from this point of view, a structural break where production organization begins to be performed in the factory system. 2

Shaping sustainable industrial development paths  3 Adam Smith showed in The Wealth of Nations (1776) that the organization of production in the factory allows a division of labour, each worker specializing in specific tasks, which realization improves through time as the worker learns, thereby inducing improvement in quality as well as productivity. The overall factory has a higher productivity, but also a higher quality and higher capacity to innovate since workers implementing their ‘skills, dexterity and judgement’ in performing their tasks can suggest improvements. From a social point of view, the situation improves as the workers receive regular wages and can send their children to school so that they can develop knowledge and skills that further benefit the economy and society. This process of industrial development therefore leads to an increase in the wealth of nations, not only in terms of financial wealth, but also in terms of civil and human development.

A BROAD PERSPECTIVE ON INDUSTRIAL DEVELOPMENT In his analysis of industry, Smith, in fact, considers the whole socioeconomic system in which it is embedded and shows how development in industries drives the former. For this purpose, the invisible hand is insufficient, but, according to Smith, the state must intervene on different aspects: justice, property rights, education, infrastructure, and regulation favouring competition (against monopolies and state monopolies in particular). Adam Smith was particularly concerned about poverty and inequalities, a concern that has returned today, especially after the financial crisis and the COVID-19 pandemic. In other words, industrial policy in the sense of policy favouring industrial development must act on all these aspects in a coherent manner for the whole socioeconomic system to increase its wealth. In a way, what the contributions of this Handbook highlight is that we should draw on the lessons from Adam Smith to make today’s industrial development more sustainable, and hence deliver on the UN’s Sustainable Development Goals. All contributions argue for broad vision and perspectives that take the context into account in its various dimensions: historical, social, political and cultural, at various levels of government (international, national and regional). It seems that only such broad perspectives can resolve the multiple, complex and interdependent challenges of today. For this purpose, what the contributions to this Handbook suggest is that rather than an industrial policy, we need an industrial development policy, comprising both measures directly aimed at industry and measures aimed at adapting the socioeconomic system in which industries are embedded so that the whole expands coherently along specific sustainable paths.

INDUSTRIAL DEVELOPMENT POLICY In fact, the primary objective of industrial policy is generally presented as that of economic growth, through rising productivity. However, in the last few decades, although industrial sectors have expanded, this has not been translated into higher economic growth rates in many countries. This has prompted a debate about the limits to growth or the problem of de-industrialization. Adam Smith argued that the division of labour in the factory system led to increases in the volume of production, hence productivity. However, the more important effect he identified was the learning processes generated by this organization of production, leading to innovation

4  Handbook of industrial development and an increase in the well-being of people in general – what he called the ‘wealth of nations’ – in a much broader perspective than income alone. It might be argued therefore that the primary effect of industrial development and hence aim of industrial policy should be development in a broader sense, leading to higher well-being for the whole population. This is what happened in the previous industrial revolutions: Van Leeuwen et al. in Chapter 2 show, for instance, that the first Industrial Revolution did not induce a huge rise in growth. However, per capita income substantially rose after some time, in the second half of the 19th century, leading to more people with access to decent food and clothing and higher well-being, so that mortality rates reduced. Industrial development takes different forms in that distinctive industrial development paths are possible, depending on a mix of factors and conditions existing in countries such as the knowledge base, skills, business models and production systems, more or less inclusive institutions, and so on. In fact, the contributions to this Handbook agree that there are different industrial development paths and that the purpose of policy should be to provide the conditions and implement actions to try to facilitate particular paths. Brownlow, for instance, in Chapter 5 argues that industrialization paths vary across countries and regions, as shown by different scholars through different approaches (Best, 2018; Iverson and Soskice, 2019). Industrial policies intended to support the manufacturing sector cannot be one size fits all, especially if they are envisaged in the broader concept of industrial development policies as we suggest here – namely, policies aimed at orientating industrial development so that it coherently contributes to the development of the whole socioeconomic system, preferably in a sustainable manner. This broad vision resonates with the concept of fictional expectations that Ruiz Durán and Balestro consider in their analysis of the history of industrial development of Latin America (Chapter 3). Inspired by List (1909), this literature argues that common fictional expectations towards imagined futures are important determinants of cumulative development, especially as nation-states affirm themselves (Beckert, 2016). The state thus becomes an agenda-setter, to frame the future towards what it could be. This might be important to the country embarking on specific industrial development paths. It also resonates with the idea that governments can mobilize actors of the socioeconomic system towards the goals attached to specific industrial development policies, hence towards specific industrial paths. For instance, this aspect has been stressed as important in the literature on regional industrial paths: Grillitsch and Sotarauta (2020) in their concept of ‘place leadership’ (expectations and visions shared by all regional stakeholders) as a key pillar of industrial development; Bianchi and Labory (2019) in their analysis of industrial policy in the Emilia Romagna region in Italy where the government was able to mobilize regional stakeholders towards the chosen industrial development path. This concept also resonates with the importance of ‘industriousness’ highlighted by Chitonge in Chapter 4. A government that creates fictional expectations about imagined futures mobilizes citizens because it creates trust and confidence that they can improve their well-being. Some scholars have argued that such industriousness has preceded each industrial revolution (de Vries, 1994), acting as a trigger. In this way, people and entrepreneurs look for new solutions and become creative. Chitonge sees the premises for the development of such industriousness today in Africa. It is probably also necessary in more advanced countries to find solutions to the multiple challenges they are faced with, particularly the twin digital and green transitions and the promotion of people’s health and well-being.

Shaping sustainable industrial development paths  5

POLITICAL ECONOMY APPROACH TO INDUSTRIAL DEVELOPMENT In the last few decades, industrial policy has been implemented without this broad vision of the whole socioeconomic system in which industry is embedded. The focus has often been on innovation (Bailey et al., Chapter 19), with the assumption that innovation induces the development of some sectors, which would draw in the development of the other sectors to the benefit of the whole of society. However, Pulignano in Chapter 6 shows us that such an approach can have negative effects if the other parts of the system move in a different direction: thus, firms have had to adapt to globalization, which has put pressure on them to focus on shareholder value and minimize costs wherever possible. One strategy has been to shift some production phases and primary assembly towards low-cost countries, with a resulting general pressure to reduce wages in all countries. Governments have tended to underscore this tendency by making the labour market more flexible and diffusing precarious forms of work. According to Pulignano, ‘precarious employment has been exacerbated by the growing power and reach of global capital, which has exceeded the ability of nations and labour unions to regulate it’. Industrial development policy is defined as taking the features of the wider socioeconomic system in which industry is embedded into account. Hence it accounts for the social needs and the social foundations of industrial and economic development and growth, and therefore leads to more sustainable development paths. It is in this sense that structural changes and economic dynamics must be addressed. Hence, a political economy approach is necessary because it is more encompassing, starting from production but also considering the demand side of socioeconomic systems. Cardinale and Scazzieri propose a theoretical framework for this purpose in Chapter 23, starting from an analysis of production. They stress that industrial development involves changes in the interdependencies between sectors, in that new manufacturing technologies transform the interdependencies between production activities. Different scholars have pursued this line of inquiry and argued that the transformation of production structures has represented the basis of the historical dynamics of capitalist economies since the first Industrial Revolution, so that production processes are the principal loci of structural economic dynamics along increasing and decreasing returns trajectories, triggered by structural opportunities and constraints embedded in production systems (Andreoni and Scazzieri, 2014; Pasinetti 1981, 1993; Scazzieri, 2014). Andreoni and Kattel (Chapter 22) agree with this approach, since they argue that state and industry are linked by a mutually constitutive, historical path-dependent and dynamic relationship, due to the fact that industrial development is a structural transformation process involving changes in the sectoral composition of the economy, and in the structural interdependencies linking sectors. The roots of structural change are not individuals allocating resources according to their preferences but the dynamics of production technology, with division and specialization of labour. Improvements in industrial production, which Adam Smith considered to be a central feature of a progressing economy, are still today, in this age of complex green and digital transition, the key factors leading to the transformation of institutions and social structures at large. Structural changes generate economic dynamics that constantly evolve along development paths, or sometimes to new paths. This notion of path has become crucial in analyses of industrial development, at different levels, starting from the regional/local one where territorial-specific conditions can favour the creation of firms and new sectors.

6  Handbook of industrial development

INDUSTRIAL DEVELOPMENT PATHS The notion of path captures the recursive and cumulative interactions between technologies, routines, working and business practices (Sunley and Martin, Chapter 8), and the systemic relationships between firms and their institutional contexts and how these interactions change over time. Sunley and Martin in Chapter 8, together with Kenney et al. in Chapter 13, highlight that globalization and technological revolution driven by digitalization and AI have considerably changed the geographical distribution of industry within and across nations and will continue to do so in the future. In this context, an important branch of the regional studies literature has extensively analysed industrial paths, addressing issues such as how they emerge, what are their determinants, their effects, and how they can be orientated. In Chapter 9, Boschma et al. address the complexity of regional industrial development and path development, showing that the literature has now deeply analysed the factors for path development. Factors are both structural (related variety and other conditions) and behavioural in the sense of resulting from agency – namely, the decisions of actors of the regional ecosystems – both individually and collectively. However, the wider effects on the socioeconomic system and the interrelationship of the industrial path with the regional socioeconomic system must be further analysed because there is evidence that regional inequalities have risen in the past decades. The reasons for this have yet to be systematically examined, but we suggest, given the evidence provided by the contributions to this Handbook, that it depends on the wider institutional framework in which regions are embedded, at national or higher levels. In fact, the literature on path development has started to address this issue by highlighting the multiscalar dynamics and interactions between firms and public institutions at different scales (Binz, Truffer and Coenen, 2016). In fact, Sunley and Martin in Chapter 8 stress that the study of regional industrial paths must broaden their focus and consider the wider financial and institutional contexts, the social and political arrangements in which places are inserted. This is also what Barzotto et al. stress indirectly in Chapter 11, since the focus on innovation and high-tech sectors tends to leave some regions behind – the regions that are less developed – and a broader view is needed that also takes account of the possible synergies that can be exploited by developing extra-regional links between firms and other actors. In fact, the literature on industrial paths at regional level has stressed the importance of extra-regional agents such as firms leading global production networks that may create links with regional firms, thereby creating a ‘strategic coupling’ (Coe et al., 2004). The embeddedness of industrial development in local territories or better, local production systems, is a key aspect of the literature on industrial districts. Thus, Bellandi et al. (Chapter 10) stress that industrial districts and firms are embedded in local production systems in various ways and this again generates different possible development paths.

INDUSTRIAL DEVELOPMENT AND SECTORS Part III of this Handbook on sectors show that the disruption caused by the Industrial Revolution, together with the necessary green transition, affect all sectors, although in different ways. The car industry has constituted a laboratory of structural changes in the second and the third Industrial Revolutions, primarily through the production systems (mass production

Shaping sustainable industrial development paths  7 system and flexible production system; see Bianchi and Labory, 2019) that this industry developed and that were subsequently used in other sectors. Today, it is completely disrupted since the product is deeply transforming, from a mechanical-electronic good using internal combustion engine and petrol, hence linked to the oil industry, to an electronic-software product using electrical energy and literally driven by artificial intelligence (AI) (Bailey et al., Chapter 15). Incumbents have reacted by pooling resources in mergers and acquisitions (e.g., Stellantis created by merging car giants FCA and Groupe PSA), while new protagonists have entered the sectors, such as Tesla, as well as Chinese producers that are entering this oligopolistic market with high barriers to entry by focusing on the new market – electric cars. Similarly, the space sector, historically high-tech using frontier technologies, is now more so than ever (Budd and Villani, Chapter 16). Another common feature of sectoral evolution is the tendency towards servitization, which is analysed in this Handbook by Monroy-Osorio et al. (Chapter 14). Servitization means that, increasingly, consumers buy the services of physical products rather than products themselves. For instance, consumers acquire mobility services, such as drivers in cars rather than owning their own automobile. In other words, consumers use material products via services rather than owning them. This practice is diffusing across industries, from aircraft engines (for instance, Rolls Royce now primarily flying hours in airplanes carrying its engines rather than the physical engines; Michelin sells kilometres driven using its tyres rather than the tyres). According to Monroy-Osorio et al., this allows firms to reach both price and operational optimality. However, servitization may also have important consequences on the ecological transition. If consumers find owning and accumulating material goods less attractive, the accumulation of waste as well as energy consumption will reduce. This is one aspect of the ecological transition, as stressed by Mäkitie and Steen in Chapter 17. In fact, both the chapter on sustainability transition in the energy sector (Chapter 17) and Chapter 18 on the greening of sectors through the adoption of the circular economy model (Mazzanti and Zecca) align with the above idea that only a broad and holistic approach to industrial development can favour the transition to sustainable models of production and development, hence resolve the global challenges. Thus, Mäkitie and Steen show that the transition is complex because all the dimensions of the sectoral system must coherently evolve in a context of high uncertainty and risk, and power struggle since old powers (incumbents) may strongly resist change. This confirms the need for a political economy approach to the analysis of industrial development. Mazzanti and Zecca also outline that the green transition requires a systemic rethinking of the whole economy, with a broad policy approach, encompassing economic, social, industrial and labour policies designed in a coherent manner. Mazzanti and Zecca’s findings are in line with the evolution of the literature on industrial policy. Thus, Bailey et al. (Chapter 19) show that industrial policy is effectively moving towards a holistic approach focused on the multidimensional nature of structural changes and industry evolution. Industrial development policy must consider the whole socioeconomic system in which industry is embedded, primarily at local and regional levels, but also adopting a multiscalar perspective, as outlined above. This is a complex endeavour, but it is possible by jointly and coherently designing and implementing industrial, social, economic and labour policies, paying particular attention to the governance of economic structures, so that industrial development is orientated towards sustainable paths. In particular, the multiscalar perspective is crucial for the ‘good governance’ of policies (Chapter 12), because the quality of institutions at lower levels can be improved by actions at higher levels, in an incremental process. Good

8  Handbook of industrial development governance and appropriate industrial development policies depend on the specific conditions of territories, the resources and capabilities accumulated through time and the institutional framework, so that one-size-fits-all policies are impossible (Chapter 20). Governance structures are important when considering in particular the deep structural transformation of the digital economy with the rise of digital platforms. Kenney et al. show in Chapter 13 that digital platforms are having a profound impact on the spatial organization of value creation and capture, at the macro, meso and micro levels. In fact, Google, Amazon, Facebook, Apple and Microsoft – namely, the GAFAM or Big Techs – are centred on the development of the infrastructure to collect and analyse big data. Digitalization implies that most businesses in all sectors depend on these infrastructures. Thus, the infrastructure of cloud services is becoming an essential facility: all businesses need them for their operation, and while companies initially tried to have their own infrastructure, they are now increasingly using Big Tech’s infrastructure. GAFAM have reached enormous levels of market value, never before attained on the global market, thanks to their global reach and enormous market power. The huge market power of digital platforms and their pervasiveness in many other sectors have raised issues of competition policy. Competition authorities have started to investigate specific cases involving these Big Techs, especially in the EU. The EU is also defining new regulations for the Digital Single Market. Piteli and Pitelis address this issue in Chapter 21 and suggest that competition policy should promote competition that favours sustainable development from environmental and social points of view rather than social welfare only. They also raise the issue of limited state jurisdictions, at national level, when the market is global. Hence, at the very least, international coordination would be necessary. Overall, more research is needed on industrial development paths to shed new light on industrial development policies. In particular, we need to better understand how paths emerge and evolve according to the variations in the socioeconomic system (in which an industry is embedded) at different levels – regional, national and global. A key issue is how the wider national and international institutional framework influences local and regional development, and how this institutional framework should be shaped to favour sustainable industrial development paths that build fictional expectations of imagined futures that mobilize people to take action accordingly, especially regarding industriousness. Sustainable industrial development in the sense of both environmentally and socially sustainable (or sustainable and inclusive)1 requires a transformation of industry, but also requires markets – namely, demand for the new products and processes – thereby transmitting impulses for change and creating more resources by widening the space for specialization and the division of labour. Sustainable industrial development will not take place if there is no demand for the green products. In this sense, the inequalities existing within and between regions and countries today (highlighted, for instance, by Piketty, 2014) are supportive not only of inclusive development but also of development tout court. This is a matter for macroeconomic policy, especially fiscal policies, which determines the distribution of wealth within societies, of economic and hence political power, and democracy, as highlighted by different contributions in this Handbook. It is also a matter of competition policy and regulation, as argued by Piteli and Pitelis in this Handbook (Chapter 21). Such considerations are included in a political economy approach that appears necessary to ensure more even development and balanced growth, through sustainable and inclusive industrial development paths. Following these considerations, the Handbook is structured as follows.

Shaping sustainable industrial development paths  9

OVERVIEW OF THE HANDBOOK Part I: Historical and Socioeconomic Perspective on Industrial Development This Handbook comprises four parts and 23 chapters, including this introductory chapter. Part I explores historical and socioeconomic perspectives on industrial development. History matters for industrial development, since changes in industrial structures and the stimulation and diffusion of new ideas and dynamic processes, and the concomitant development trajectories in economies therein, build upon (and are shaped by) what has occurred in the past. This is captured in Chapter 2 by Bas van Leeuwen, Ulbe Bosma and Meimei Wang (Chapter 2), who explore the sociopolitical impacts of industrial revolutions across the globe, from the 1760s to the present day. They note that the level of mechanization and the organization of production have changed fundamentally since (and before) that time. These changing forms of industrialization have spread across the globe, beginning with Western Europe, the Western offshoots (Australia, New Zealand, Canada and the United States), and Japan, and followed by the colonial commodity frontiers and finally by other countries. Factors driving this spread were cultural susceptibility, material incentives, government intervention, and the capacity to embed new technologies and organization of production in societies that were not in the world’s industrial vanguard. However, in many countries, industrialization was less successful. The authors concur that this can be explained by the locking in of the world economy, preventing these countries from developing capital-intensive production. Clemente Ruiz Durán and Moisés Balestro (Chapter 3) seek to unravel the puzzle of why industrializing countries in Latin America moved from being in a pioneer position among latecomers in the 1930s and 1940s, to a stagnant position in the 1980s. The chapter emphasizes the role of fictional expectations and capitalist dynamics and draws on the German historical school’s contribution and the more recent literature on historical institutionalism. According to the authors, the import substitution industrialization (ISI) strategies adopted in the continent had two main shortcomings: it ignored the changing economic landscape in the 1970s towards trade openness and the role of exports in industrial competitiveness, not enough attention was paid to the financial system in industrial development. The authors then discuss the paradigm of global value chains (GVCs) and their implications in Latin America, and conclude that the increase in world trade – through GVCs – was a double-edged sword for industrial development in the continent. Fictional expectations on imagined futures resonate with the concept of industriousness – namely, the individual expectation that the situation can be changed and well-being improved, driving action. Horman Chitonge (Chapter 4) offers an African perspective, focusing on the impact of demographic changes on the continent’s industrial development. It is argued the ongoing demographic transition, amid low levels of productive employment, high levels of poverty, rapid urbanization and the growing effects of climate change, constitutes the underlying force for transforming the continent. It is envisaged that these dynamic forces are generating a diffused ‘industriousness’ that can trigger an industrial transformation in Africa. However, it is argued that the African industrial transformation will be different, in many respects, from past industrial transformations, precisely because of the difference in underlying conditions, both within Africa and globally. The question arises of the extent to which and how this transformation can be sustained, and in a way that delivers for the African people.

10  Handbook of industrial development Graham Brownlow (Chapter 5), focuses on the issues of economic and political power in relation to path dependencies and industrialization. Beginning with Mancur Olson’s (1996) notion of ‘big bills left on the sidewalk’, he examines the importance of institutions and industrial policy in steering development paths, asking why it is difficult for countries to successfully imitate best practice (and policy) from elsewhere. The chapter seeks answers through long-run case studies of industrialization, policy and industrial performance in both the UK and India. The conclusions drawn are that efforts to reform institutions to deliver successful industrial policy outcomes will depend on concentrations of economic and political power; these can hinder or enhance industrial performance. The lesson here is clear – industrial policy to drive industrial development cannot be easily replicated from one country context to another. In Chapter 6, Valeria Pulignano examines the transformation of work since the early 20th century. The governance and nature of employment evolves with industrial transformations and development, and again (political) actors can shape the ‘social order’ around these processes. During the 20th century, this was evident as employment governance shifted from a Fordist style ‘welfare state capitalism’ to post-Fordist notions of ‘flexible capitalism’. Consequently, there has been a shift from relatively stable/secure employment regimes (with employment protections) to more precarious (and casual) forms of employment; the latter have become even more prevalent during periods of crises, such as the recent COVID-19 pandemic, exacerbating inequities and fragmentation in the labour market. However, as Pulignano argues, the ethics of these new labour platforms – characterized by precarious employment practices – and their prevalence in the consumer-centric and capitalist ‘gig economy’, run anathema to recent European Union goals around the Green New Deal and a ‘social Europe’. The chapter concludes by arguing for a new social contract to reward ethical employment practices, alongside promoting the circular economy. Mario Biggeri, Andrea Ferrannini, Santosh Mehrotra, Marco R. Di Tommaso and Patrizio Bianchi (Chapter 7) also consider the issue of sustainable human development. Acknowledging the twin digital and green transitions driving the current structural transformations of economies and societies, the authors seek to reframe a role for industrial policy to smooth their disruptive nature of these transformations, and to tackle societal challenges such as climate change and rising inequalities. In particular, the chapter highlights how the four pillars of sustainable human development could provide a new lens through which to analyse the industrial policy practice across the world – in China, United States of America, the European Union and India – to shed new insights on the nexus between industrial development, human capabilities and sustainability. Part II: Industrial Development in Regions Part II examines the territorial dimension of industrial development, emphasizes the importance of place and economic geography in the industrial development process and highlights how distinct spatial characteristics, capabilities and network linkages can shape regional growth path trajectories and outcomes. Much of the recent literature in this field has adopted an evolutionary path-based approach – the implication being that places develop along largely deterministic paths (i.e., they are ‘path’ or ‘place’ dependent). In providing a critical review of the ‘path approach’, Peter Sunley and Ron Martin (Chapter 8) explore the role of firms and other agents, emerging trends/processes and adaptation within

Shaping sustainable industrial development paths  11 place contexts. Their analysis suggests a more complex interplay between industrial processes and place in shaping industrial development. Such complexities are evident in the relationship between innovation, industrial dynamics and regional inequalities, which is considered by Ron Boschma, Martina Pardy and Sergio Petralia (Chapter 9). Recent evidence points to an increasing tendency for innovation and complex activities to concentrate in a few places, especially in advanced urban regions. Technological advances – such as AI – can reinforce these patterns (especially via regional labour markets), which in turn can exacerbate existing intra- and inter-regional inequalities. However, as Boschma et al. point out, these dynamic processes also highlight a possible contradiction in policies that simultaneously seek to promote (1) ‘smart’ growth based on upgrading activities, building upon existing local capabilities; and (2) well-intentioned policies geared towards inclusive and balanced regional growth. There are no obvious solutions to easing this policy dilemma. The authors call for new data and systematic evidence on the impact of industrial dynamics on intra- and inter-regional inequalities, and how the complexity levels of industries and new technologies such as AI affect these, as the basis for a better understanding of this complex issue. In Chapter 10, Marco Bellandi, Maria Chiara Cecchetti and Erica Santini examine the recent evolution of local production systems (LPSs), and through the lens offered by three exemplificative cases they consider the impact of contemporary digital transformation. The chapter offers a conceptual framework to identify the main multilevel structural dimensions featuring different forms of LPS (i.e., the industrial organization, the sociocultural and territorial structure, and the institutional and governance support). A classification of LPS models is extracted from this review, from classical industrial districts to poles of large firms. Focusing on the industrial organization dimension, Bellandi et al. propose an updated classification of types of firms according to their possible relations with LPSs. This allows the authors to associate compositions of such types to the LPS classification, which is then applied to an exemplificative set of cases of different types of small to medium-sized firms (SMEs), localized in LPSs of different forms and which are investing in Industry 4.0 technologies. The case analysis confirms the heterogeneity of paths entered by LPSs in the absorption of such technologies. The chapter concludes by deliberating on the implications for future research on rerouting paths (with LPSs) and policies within and among different forms of LPS that face disruptive technological challenges. Mariachiara Barzotto, Carlo Corradini, Felicia Fai, Sandrine Labory and Philip R. Tomlinson (Chapter 11) also consider innovation, focusing on external collaborations arising between firms and other actors (such as universities and research institutes) within the innovation process. Such external collaboration facilitates the exchange and coordination of resources and information in the value chain, and is observed in local and global supply chains, open innovation strategies and/or in regional clusters/industrial districts. The linkages across these different levels define synergies in production, organization and knowledge transfer. Moreover, as Barzotto et al. point out, by pursuing external ties, resource-constrained SMEs can potentially access a wider set of technological opportunities through information sharing and resource pooling. Consequently, external collaboration can enhance firms’ innovative performance and their host region’s innovative dynamic. The chapter offers a comprehensive review of the literature (and evidence) on external collaboration at firm, industry and regional levels, before considering the efficacy of recent policy programmes directed at nurturing and

12  Handbook of industrial development supporting the development of inter-firm cooperative ties through supply chain fora and in regional and industry bodies. One of the most ubiquitous mantras in industrial policy circles is that ‘good governance’ is critical to the success of industrial development. This issue is explored in depth by Pedro Marques and Kevin Morgan (Chapter 12), who offer a critical review of the ‘good governance agenda’ at the subnational level. Here, conventional wisdom has often attributed the shortcomings of regional institutions to the regions themselves. The chapter explores this in the context of two case studies: Valencia in Spain, where a coalition of regional banks and national political parties propelled the region into a prolonged crisis; and post-Brexit Britain, where the central state is undermining the devolution settlements agreed with the Celtic nations, triggering an unprecedented crisis of governance in the UK’s multilevel polity. The analysis here points to the need for researchers and policymakers to take multiscalar political relationships more seriously, not only in terms of the coordination in the design and delivery of place-based industrial policy, but also with regard to their political dynamics, especially the multilevels of governance, where one scale of government can constrain or enable action at another scale. Moreover, as the authors point out, ‘good enough governance’ and small incremental change may be the optimal and potentially only strategy to improve the quality of institutions in the medium to long term. Part III: Sectors Part III considers specific sectors that have become critical to modern industrial development: automotive, energy, consumer goods, circular economy and space. The modern economy is also the subject of Martin Kenney, John Zysman, Dafna Bearson and Camille Carlton’s chapter (Chapter 13), which considers the rise and dominance of the platform economy, paying particular attention to its spatial consequences. Online platforms have become the modern architecture of our economic and social lives. The chapter examines the concentration of large-platform firms in terms of their (1) location on the US West Coast; and (2) market share in various services, such as search, maps and online sales. As the authors point out, this has some troubling implications. Platforms are simultaneously intermediaries, two-sided markets and data aggregators, which creates synergies for platform owners and contradictions for those using the platform. Drawing on the cases of Amazon and Google Maps, the chapter examines the extensive reach of these platforms in terms of the markets they serve and shape, while also exploring how these online platforms are reorganizing the geography of economic activity. Looking ahead, the growth of the platform economy and the continuing dominance of some platform firms is likely to be a contentious issue for industrial development. Over the last decade or so, one of the major transitions in the advanced industrial nations has been the change in consumption and manufacturing patterns and especially the growth in servitization. This is where manufacturing firms add services to foster closer and richer relationships with customers and digital technologies to improve logistics and inventory management in the upstream supply chain. Juan Carlos Monroy-Osorio, Marco Opazo-Basáez and Ferran Vendrell-Herrero (Chapter 14) explore this relatively recent phenomenon and propose a framework that integrates the literature on firm strategy with operations management that successfully combines operations (cost-efficiency) and pricing. The authors then demonstrate how servitization can enable first-degree price discrimination to be established via product customization, while also achieving production efficiency via built-in digital production

Shaping sustainable industrial development paths  13 facility and product capabilities. This then leads to a conceptual discussion on the evolution of different production models and how these relate to markets and consumer demand, which in turn raises questions that may help to better understand the significance and relevance of such models at strategic historical points in the industrial development of firms. David Bailey, Dan Coffey, Lisa De Propris and Carole Thornley (Chapter 15) explore the car industry through the lens of a laboratory of experiments at times of disruptive technological change. Beginning with a brief précis of historical transformations, this chapter goes on to provide an overview of the current debate on Industry 4.0 and the impact of new technologies on the nature of manufacturing, on the reorganization of old industrial spaces, as well as the emergence of new ones. This industrial transformation is considered specifically in the context of the automotive sector, which, as the authors point out, has historically been at the forefront of technological innovation and adoption – indeed, few other mass consumption goods have embodied such a complexity and variety of technologies. The chapter then examines how the automotive industry is a testing ground, not only for the adoption of automation and AI to increase production efficiency and flexibility in new cyber-physical systems of production, but also on the fundamental transformations, including the green transition and new business models. Leslie Budd and Davide Villani (Chapter 16) examine the contribution of the space sector for industrial development. Over the last two decades, space exploration and technologies have become increasingly the focus of European policy. For instance, the European Space Agency (ESA) has become a central player in funding science in relation to the International Space Station. In partnership with the European Union, the ESA has created ‘Towards a United Space in Europe’ as the strategy for Europe’s space industry, of which Space 4.0 is its application of the concept and practice of Industry 4.0, whose development cross-cuts old and new technologies and their application. The embrace of the Industry 4.0 discourse and its underlying technologies in the form of Space 4.0 industrial strategies have contributed significantly to the development of the space economy. Moreover, Space 4.0 tends to underpin a wider set of socioeconomic benefits than mainstream Industry 4.0 manufacturing: direct and indirect, upstream and downstream, as well as spillovers and externalities due to the range of segments created by the industry. The chapter explores this general context to evaluate the trajectory of the space economy and industry by providing different theoretical and conceptual insights by drawing upon input–output (I–O) analysis and Pierre Bourdieu’s notion of economic capitals. A case study of the fast-growing satellite industry is also used to analyse the socioeconomic benefits of Scotland’s proposed Spaceport programme. Powering future industrial transformations will require an energy transition towards lowand zero-carbon sources to mitigate the impacts of climate change. As Tuukka Mäkitie and Markus Steen (Chapter 17) note, such a transformation relies upon substantial technological innovation and change processes in energy production, distribution and consumption. By drawing on the sustainability transition literature, the authors provide an overview of the sociotechnical transformation dynamics and processes associated with this ongoing energy transition. In doing so, the chapter reviews the multilevel perspective and technological innovation system frameworks, and the recent elaborations outlining an industrial perspective on sustainability transitions. Two cases from Norway – transformations in offshore energy extraction/production and maritime transport – are also provided to illustrate the challenges and opportunities arising from sustainability transitions. The chapter concludes by suggesting that more attention to industrial perspectives in energy transitions is warranted to (1) better

14  Handbook of industrial development understand the crucial industrial upscaling processes necessary for the acceleration of energy transitions; and (2) to explore policy perspectives that may contribute to a sustainable industrial transformation and socially ‘just’ transitions. Massimiliano Mazzanti and Emy Zecca (Chapter 18) explore the implications of the transition to the circular economy, particularly the impact of the technological, organizational and social innovations and their adoption and diffusion for socioeconomic well-being and industrial development. Eco-innovation involves different actors at different levels, and its effects will vary across countries and regions. Firms especially will have to adapt their business models to the new sustainability paradigm and make innovative choices to ensure the necessary changes occur. In this context, the knowledge of firms’ strategic choices over eco-innovation becomes crucial to support them in their eco-decision-making processes, alongside appropriate policy instruments. The chapter then investigates the path of eco-innovative schemes of SMEs in a circular context by comparing European firms and, in doing so, highlights some possible gaps and elements of novelty. Part IV: The Role of the State in Industrial Development The final section of the Handbook, Part IV, deals with the role of the state in industrial development, and in particular industrial strategy and/or policy. This is important because industrial policy is back on the political agenda, especially in Western economies, which have struggled with the challenges of deep structural industrial changes, globalization and the diffusion of global value chains, digitalization and Industry 4.0, and environmental concerns. David Bailey, Sandrine Labory and Philip R. Tomlinson (Chapter 19) offer a review of the evolution of modern industrial policy that goes beyond the traditional market failure and neoclassical approaches, and the tainted statist interventions of supporting ‘national champions’ and bailing out failing firms. The chapter deliberates on some of the salient issues, including the innovation agenda, structural change and economic governance, that are shaped by and affect the efficacy of industrial policy. The authors argue that what emerges from theoretical reflections and the case evidence is that orientating industrial development towards specific trajectories is possible by using a wide array of instruments at micro, meso and macro levels – critical here for success is the coherence of interventions actions across policy fields and policy levels. This requires a more holistic and wide-ranging approach embracing a wider set of interdisciplinary perspectives that cut across isolated policy silos and offer a more encompassing and coordinated policy framework. Murat Yülek and K. Ali Akkemik (Chapter 20) argue that a one-size-fits-all type, industrialization-stage-proof industrial policy is ineffective, and examine appropriate industrial policies in conjunction with the different stages of industrialization. In conducting their analysis, the chapter develops an index to rank and classify countries into stages of industrialization. The authors demonstrate that (1) overall, there is a positive relationship between the level of industrialization and per capita income; (2) the within-stage relationship is positive but less powerful in relatively less advanced stages – that is, industrial policy is effective in raising incomes and productivity in these stages; and (iii) for the most advanced countries, the relationship turns to negative – that is, the effectiveness of traditional and focused industrial policy vanishes and newer modes of industrial policy, based upon science and innovation, are more appropriate. The authors also underline the important link between education and the efficacy of industrial policy. These insights suggest that policymakers need to be careful in their choice

Shaping sustainable industrial development paths  15 of policy instruments and overarching industrial strategy, and – for success – ensure they align with the appropriate stage of development. In Chapter 21, Eleni E.N. Piteli and Christos Pitelis focus on the role of competition policy in the context of contemporary concerns around platform-based global oligopolies. As the authors argue, extant antitrust policy is not designed to address today’s realities of platform-based global oligopolies and that a new approach is required that accounts for their specificities and helps foster world-wide sustainable industrial development. This requires addressing constraints to sustainability and fostering workable and healthy competition and coopetition. It also requires international coordination, a level playing field between firms, nations and peoples, and the fostering of diversity, pluralism and innovation. There is a call here for policymakers to co-create the conditions that thwart regulatory capture and review the whole gamut of options to manage global monopolies and introduce fair and workable innovation-promoting competition and cooperation. Critically, though, it is important to do so without undermining the value-creating aspects of the operations of platform monopolies and their incentives, and those of others, to continue introducing value creating innovations. As the chapter concludes, this is a fine balance and will require smart and yet common-sense policies. Antonio Andreoni and Rainer Kattel (Chapter 22) argue that state formation and industrialization have been historically linked by a mutually constitutive relationship, and that industrialization itself has been shaped by the state via interventionist (or lack thereof) industrial and innovation policy. This process has involved state institutions, governance and bureaucratic structures that have mediated the continuously evolving relationship between state, industry and markets. At the same time, industrialization and the emergence of new powerful organizations (and interests) have shaped the political economy of the state and policymaking. The authors discuss three historical forms of the state – developmental, entrepreneurial and innovation-driven – and focus on the evolution of the state–industrialization relationship. In doing so, they consider three comparative historical cases – Germany, USA and China – to explore the different configurations of ‘states of innovation’, as well as the evolution in policy framing, instruments and challenges. The chapter highlights the importance of a political economy perspective and the role of states (and state actors and institutions) in the innovation and industrialization process. Finally and as a conclusion, Ivano Cardinale and Roberto Scazzieri (Chapter 23) offer a structural framework of industrial development, but also with a political economy dimension. Their chapter focuses on the systemic nature of industrial transformations and the importance of networks of interdependent production activities in manufacturing. They argue that types of interdependencies can have very different consequences for growth and development. Moreover, fundamental changes in manufacturing technologies have implications for the division of labour, which has grown in complexity, and for the transition between different manufacturing regimes. Such changes can lead actors to switch between different patterns of increasing returns to scale (that may or may not be sustained), leading to a variety of technologies and/or specialisms being promoted (or blocked), and hitherto very different trajectories of industrial development. As both Cardinale and Scazzieri document, a political economy understanding of these production dynamics and industrial transformations is critical to gauging their impact on the economy.

16  Handbook of industrial development

NOTE 1.

Sustainable development is often referred to as development that does not damage the environment, while inclusive development refers to development that increases the well-being of the population, ‘leaving no one behind’ in the words of the European Commission in its 2030 Agenda.

REFERENCES Andreoni, A. and R. Scazzieri (2014), ‘Triggers of change: structural trajectories and production dynamics’, Cambridge Journal of Economics, 38(6), 1391–408. Beckert, J. (2016), Imagined Futures: Fictional Expectations and Capitalist Dynamics, Cambridge, MA: Harvard University Press. Best, M. (2018), How Growth Really Happens, Princeton, NJ: Princeton University Press. Bianchi, P. and S. Labory (2019), ‘Regional industrial policy for the manufacturing revolution: enabling conditions for complex transformations’, Cambridge Journal of Regions, Economy and Society, 12(2), 233–49. Binz, C., B. Truffer and L. Coenen (2016), ‘Path creation as a process of resource alignment and anchoring: industry formation for on-site water recycling in Beijing’, Economic Geography, 92(2), 172–200. Coe, N., M. Hess and H.W. Yeung et al. (2004), ‘“Globalising” regional development: a global production networks perspective’, Transactions of the Institute of British Geographers, 29(4), 468–84. de Vries, J. (1994), ‘The Industrial Revolution and the industrious revolution’, Journal of Economic History, 54(2), 249–70. Grillitsch, M. and M. Sotarauta (2020), ‘Trinity of change agency, regional development paths and opportunity spaces’, Progress in Human Geography, 44(4), 704–23. Iversen, T. and D. Soskice (2019), Democracy and Prosperity: Reinventing Capitalism through a Turbulent Century, Princeton, NJ: Princeton University Press. List, F. (1909), The National System of Political Economy, London: Longmans, Green and Co. Olson, M. (1996), ‘Distinguished lecture on economics in government: big bills left on the sidewalk: why some nations are rich, and others poor’, Journal of Economic Perspectives, 10(2), 3–24. Pasinetti, L.L. (1981), Structural Change and Economic Dynamics: A Theoretical Essay on the Dynamics of the Wealth of Nations, Cambridge, UK: Cambridge University Press. Pasinetti, L.L. (1993), Structural Economic Dynamics: A Theory of the Economic Consequences of Human Learning, Cambridge, UK: Cambridge University Press. Piketty, T. (2014), Capital in the 21st Century (trans. A. Goldhammer), Cambridge, MA: The Belknap Press of Harvard University Press. Scazzieri, R. (2014), ‘Increasing returns: towards a structural theory’, Structural Change and Economic Dynamics, 29(C), 75–88. Smith, A. (1776), An Inquiry into the Nature and Causes of the Wealth of Nations, London: W. Strahan and T. Cadell.

PART I HISTORICAL AND SOCIOECONOMIC PERSPECTIVE ON INDUSTRIAL DEVELOPMENT

2. Industrial revolutions in a globalizing world, 1760–present1 Bas van Leeuwen, Ulbe Bosma and Meimei Wang

1 INTRODUCTION When speaking of ‘industrialization’, defined in its widest sense as the development of industries, we must not forget that industrial output has always existed. After all, people needed clothes and many kinds of tools in 500 BCE just as much as they do today. This output is visible in existing estimates of national income. For example, Foldvari and Van Leeuwen (2012) found that the share in manufacturing (the creation of new, non-agricultural products from factors of production – generally representing the largest share of industrial output) in the total economy of various empires around 1 CE ranged from between 5 and 10 per cent in rural areas and up to 40 to 50 per cent in urban locations. Roughly a millennium later, this had not changed much: for 980 CE China, the share of industry in gross domestic product (GDP) was found to be 5.5 per cent (Broadberry, Guan and Li, 2021), with the figure for 1381 Britain being much higher at 28.8 per cent (Broadberry, Campbell and Van Leeuwen, 2013). Moving forward yet another millennium to circa 1800, this pattern had changed little, with industry in China making up 8.1 per cent of GDP, while in Great Britain the figure was 32.7 per cent. Changes in industries’ share of GDP, however, have occurred in the last two centuries. While China in 2019, with a 27.9 per cent industry share of GDP, has witnessed strong industrial growth, Great Britain,2 with its 10 per cent share, has witnessed a structural, economic transformation away from agriculture and industry to mainly services (United Nations, 2021).3 These patterns of industrialization are subjects of an extensive literature. As this literature focuses on the aforementioned major changes in the 18th–20th centuries, the term ‘industrialization’ has been limited to its main underlying factors. Indeed, the focus in the literature has been primarily on mechanization (e.g., the introduction of the steam engine) and production organization (e.g., a rise in factory employment) being direct causes of industrial growth. These measures arguably initially accelerated in Britain in the period 1760–1830, often referred to as the ‘first Industrial Revolution’ (see Section 2 below), and the process gradually spread across the globe (Section 3). Logically, this process of global development was subject to economic networks and also international interactions, which are empirically analysed in Section 4 by means of the concept of commodity frontiers. These frontiers seem to lead to global convergence, with industries increasingly located in developing economies. Nevertheless, this process of economic convergence and divergence, caused by industrialization, varies both between and within countries, which we touch upon in Section 5. We end with a brief conclusion.

18

Industrial revolutions in a globalizing world, 1760–present  19 Table 2.1  

Compound annual GDP per capita growth in Britain, ca. 1700–1850 (percentages) Deane and Cole (1962)

Crafts (1983)

Clark (2010)

Broadberry, Campbell et al. (2015a)

1700–60

0.45

0.31

0.36

0.29

1760–80

–0.04

0.01

–0.13

0.07

1780–1800

1.08

0.35

–0.02

0.42

 

 

0.78

0.61

1800–50

2

THE ‘INDUSTRIAL REVOLUTION’ AND ITS CAUSES

Popularized by Toynbee in the late 19th century (Wilson, 2014), the characterization of the economic change that had occurred in Britain around 1760–1830 as a ‘revolution’ has had a major impact on scholarship ever since (e.g., Hobsbawn, 1968; Wrigley, 2018). However, since the 1970s, the question has arisen whether the so-called Industrial Revolution truly was a revolution. Looking at compound annual growth rates per capita in national income (Table 2.1), the revisionists argue in general for low per capita growth during the phase of the first Industrial Revolution. More recently, counter-revisionists have emerged who claim that some forms of change did indeed occur in the 18th century, without necessarily claiming it to have been a ‘revolution’. For example, Broadberry, Campbell et al. (2015a), based on extensive reworking of the available data, located the beginning of industrialization in the mid-18th century (Table 2.1). Based on more descriptive data, this counter-revisionist view is also advanced by various other authors (e.g., Berg and Hudson, 1992). Only for the period after 1850 is there a consensus that there were markedly rising per capita incomes. A similar debate also exists on development in China. The well-known starting point is Pomeranz (2000; see also Broadberry et al., 2018, 2021), who claimed there was an economic divergence between China and Britain at the time of the first Industrial Revolution, with China moving on a downward trajectory because it lacked factors such as access to energy (coal) and consumer markets (colonies), both of which Great Britain had in abundance. But if economic growth in Britain was indeed minimal during the first Industrial Revolution, this leaves open the question of how China could have entered economic decline for the same reasons that apparently played at best a minor role in 18th-century Britain. A possible answer is provided by Solar (2021), who claims that Broadberry et al.’s per capita GDP estimate for China contains a peak around 1700, caused by an underestimate of population (see also Williamson, 2012 on terms of trade). Xu et al. (2017), although agreeing that there was a peak in income around 1700, find it to be smaller. Hence, the decline in the 18th century is mainly considered to have been a correction of the earlier rise in income, rather than caused by a lack of certain industrial fundamentals. Without this correction, income would have been stable much like that of Britain in the late 18th century. A similar observation was made by Allen et al. (2011) on wages and by Pomeranz (2005) on the basis of more qualitative material. Regardless of whether there were changes in growth rates during the first Industrial Revolution, the fact remains that, in British eyes, the world in 1900 looked very different from that of 1760. The direct causes of this rise in output per capita were technical change and a shift towards factory production. Starting with technical change, this can be proxied by total factor productivity (TFP). When looking at TFP in the British economy, most authors agree on slowly accelerating TFP growth (e.g., Antras and Voth, 2003; Crafts and Harley, 1992; Feinstein, 1981). One argument why this occurred might be that technological innovation at

20  Handbook of industrial development Table 2.2  

Compound annual TFP growth in China, 1730–1830 TFP Growth (%)

1730–80

0.2

1782–99

–0.4

1800–30

–0.4

a given time t results in economic growth only several decades later. A second argument is that economic growth does not necessarily apply to a whole economy. For example, where Temin (1997) argues for economy-wide change, Crafts and Harley (1992) limit this primarily to changes in textiles, iron and agriculture, while Clark (2009) limits it further to textiles only. Even though slow and late, significant TFP growth did occur in Britain and differed quite substantially from that in developing economies. For example, Van der Eng (2008) found an insignificant long-term TFP contribution to economic growth in Indonesia since 1881, with the exception of a few periods directly following economic decline and a reduction of the capital stock. One might expect a similar finding for China. Indeed, Fu (2014) estimated the long-term TFP in China during the period 980–1840 and found a negative TFP growth in the period 1782–99. We also performed a rough price dual TFP analysis here in this study, based on the method used by Antras and Voth (2003), which resulted in negative growth in the late 18th/early 19th century (Table 2.2). Only after this period can one identify a rise. This accords with Van Leeuwen, Didenko and Foldvari (2015) and Van Leeuwen, Li and Foldvari (2017), who found low TFP growth for China since the 1920s, with a marginal increase during the reforms that started in 1978. Second, this period after 1780 was characterized by a move away from small-scale, often rural, hand production to factory-based, mechanized production (see e.g., Mendels, 1972). This process varied by sector, beginning, inter alia, in textiles (Broadberry, Custodis and Gupta, 2015b; Landes, 1969), iron, chemicals and steam power. Some writers have argued that both factors (factory employment and mechanization) were related: workers had to perform their labour closely with the new machinery, which required a great deal of worker discipline to reach the right coordination (e.g., Landes, 1986; Lipson, 1953). While this coordination theory has drawn much attention, yet others have seen it rather as a matter of incentive: by employers coercing people into labour discipline, labourers could gain substantial wage premiums (Clark, 1994). However, this difference between ‘coordination’ and ‘coercion’ is somewhat artificial, since in handicraft-type workshops no technology was available that would make coercion profitable. Hence, in both the coordination and the coercion theories it was ultimately the introduction of new technologies that made factory-type work profitable. A clear example is textiles, where spinning became mechanized to a high degree and increasingly located in factories, but pre-tailored clothing continued to be made at home and became mechanized only in the mid-19th century. In China, the change from handicraft to factory industry occurred much later. According to Wang and Yan (2007), private factory industry developed from about the 1860s–70s, but the total output of these modern factories remained almost negligible compared with total output in the following several decades. Indeed, in 1914, modern industrial production accounted for approximately only 9 per cent of all industrial output value. Given a faster growth of modern factories compared with traditional handicraft production during the period 1914–36, the proportion of modern industrial production rose to 28.93 per cent in 1936. However, handicraft production was still dominant, and the expansion of factory production was limited mainly

Industrial revolutions in a globalizing world, 1760–present  21 to mining (primarily coal and iron) and metallurgy, cotton textiles and chemical industries. The output value of modern industrial output did not exceed that of handicraft industry until the 1940s (56.5 per cent modern versus 43.5 handicraft industry in 1949; see Institute of Economics in Chinese Academy of Sciences, 1957). The reasons these changes in mechanization and organization of the production process began in Britain are widely debated. Following Crafts (2011) and Clark (2012), one can distinguish the ‘idealist’ approaches, where ideology and social norms open up existing potential for economic growth (e.g., Mokyr, 2010), and the ‘incentive’ approaches, where the appearance of material incentives – such as institutions, the presence of coal, a preceding agricultural revolution, or human capital – enhanced growth (e.g., Acemoglu and Robinson, 2012; Clark, 1999; Lewis, 1978; McCloskey, 1972; Squicciarini and Voigtländer, 2015).

3

THE GLOBAL SPREAD OF INDUSTRIES

Even though it is difficult to disentangle all the various theories, the fact remains that, in Britain, output per worker increased strongly since the mid-18th century (Figure 2.1), leading to a jump in per capita income. The British Industrial Revolution soon spread over the globe, starting with Europe and North America and followed later by other regions such as Japan (Figure 2.1). This obviously led to a strong shift in the share in world industrial output, with an initial increase in Britain and the United States combined, and decreasing shares in China (Figure 2.2).

Sources: China: Wu (2014) and Xu et al. (2017); UK: Broadberry, Campbell et al. (2015a); Japan: Bassino et al. (2018) and Fukao et al. (2015).

Figure 2.1

GDP per capita for the United Kingdom, USA, Japan and China, 1600–2018 (in 2011 USD)

22  Handbook of industrial development

Source:

Xu and Van Leeuwen (2016).

Figure 2.2

World value-added shares in industry (in 2005 USD), 1850–2010

However, the patterns of industrialization differed among countries (e.g., Fremdling, 2004), which may be explained by three arguments. First, similar to Britain, the above-mentioned ‘incentive’ and ‘idealist’ approaches are often also mentioned as reasons for the global industrial spread into later industrializing countries. It is important to stress, however, that this applies less to ‘idealist’ factors such as culture, since in countries where industrialization came later it is not just their start in economic growth that requires explanation but also why these cultural factors occurred later than in Britain. When dealing with the question of why a culture is less conducive to growth than European culture, the approach quickly becomes Eurocentric. Hence, for later followers, more focus is placed on material incentives such as high wages, low capital costs and increases in human capital (e.g., Allen, 2009). For example, Chenery, Robinson and Syrquin (1986) argued for the importance of the differences in trade, population scale and resource endowments in different countries. Similarly, Clark (1987) added to this list the importance of inefficient labour. A similar focus on material incentives is offered by Strulik (2014), who focused on a modified version of the neoclassical growth model, capturing the, arguably slow, growth during the start of the industrial revolution by adding an interaction effect of human and physical capital. These types of theories about economic development come close to what today is known as the ‘Washington Consensus’ – that is, a set of development policies followed by the International Monetary Fund and World Bank for developing economies (Williamson, 1989). Because of their advocacy of free markets and a limited role for governments, critics have labelled these policies as neoliberalism. Economists have increasingly agitated against the Washington Consensus. Some have argued that the Consensus should be abandoned in exchange for proactive government policies of the mid-20th century (Cimoli, Dosi and Stiglitz, 2009), while others have argued for stronger local policies instead of a one-size-fits-all policy (e.g., Rodrik, 2005).

Industrial revolutions in a globalizing world, 1760–present  23 A second argument for differences in global industrialization is that the further a country is from the industrial frontier, the more it uses state policies to catch up (Amsden, 2001; Gerschenkron, 1962). This is logical since, as we noted above, the focus was on material incentives such as wages, capital and trade. To catch up, these factors had to be implemented simultaneously rather than be implemented gradually – and one by one – as had been mostly the case in Britain (see, e.g., Allen, 2011), hence creating a task for the government. For example, Japan caught up through government investment in education, banks and infant industry protection (ibid.). South Korea started to develop an export-oriented industrial economy, which was pushed by the government from around 1963 (Song, 1990). For other countries, an even larger push was necessary. An example is the Soviet Union, which, according to Allen (2003), was quite efficient compared with other lagging economies in forced capital accumulation. Nonetheless, economic growth in the USSR declined in the 1970s because of inefficient investments; indeed, the technical efficiency of the factors of production decreased considerably (Van Leeuwen et al., 2015). In addition, social tensions required a shift from producer to consumer goods, which – as pointed out by Van Leeuwen et al. (2015) – should lead to a reduction in the physical-to-human capital ratio, thus reducing growth. Essentially, the same occurred in China, with two differences: on the one hand, human capital in China rose after the reforms initiated in 1978 rather than that physical capital declined, allowing more efficient use of the existing factors of production; on the other hand, the Chinese production frontier was shifted outwards – meaning that, even without becoming more efficient, production could still rise (ibid.). A third factor concerns the creation of a country’s own development strategy. For example, Wilkins (1987), who complains that neoclassical economics sees firms as a black box, argues that economic divergence follows from the absence, and lack of import, of effective management skills. Likewise, according to Hanson (1988), differences in economic development are caused by technology differences among countries, while Wolcott (1994) argues that Indian labour is less flexible in weighing lifetime employment versus technology than Japan. Sugihara (2003) argues for a labour-intensive development path and suggests such a path for Japan, leading to the adoption of labour-intensive technologies. However, this argument is difficult to apply in practice, as various technologies can be simultaneously both labour and capital saving. In addition, by increasing capital intensification in developed countries, they may push their less capital-intensive industries towards less developed countries. As these latter countries have a low capital intensity, these new industries may increase their capital intensity. Also, growing wages may make more capital-intensive investment profitable. Hence, although it has a slower growth of capital intensity, there is no a priori reason to classify Asian development as labour intensive.

4

GLOBAL LINKS BETWEEN INDUSTRIALIZATION AND COMMODITY FRONTIERS

If the aforementioned three strategies on global spread of industrialization (incentives, government policy and local development strategies) failed, it was often argued that there was a market failure, an inefficient government, or an inefficient local development policy. Of course, many studies (often non-Western) disagreed, and proposed various alternative reasons to explain why following these development strategies was unsuccessful, usually by

24  Handbook of industrial development arguing for an unequal structure of global production patterns and for military and economic imbalances. First, as regards the global economic structure, there were problems with forward and backward linkages that were created intentionally, or unintentionally, by Western economies. For example, Myrdal (1957) argued that the development of a specific industry in an area may create positive feedbacks, such as an increasing demand for consumption goods from labourers in this developing industry and for inputs for this industry, and the creation of a more highly skilled labour force, which in turn was helpful in attracting more new producers in other industries – that is, backward linkage (e.g., Hirschman, 1958; Krugman, 1991). With an increasing number of industries in place, the price of many consumption goods declined and more labourers began to live in the area (forward linkage). This combination of forward and backward linkages caused industry to concentrate in a region, while other regions became industrial peripheries. Second, there are political, economic and military factors that lock in the current level of industrial development at a particular level. For example, Furtado ([1974] 2020) claimed that the current economic superstructure locks poor countries in poverty. More specifically, he pointed to the existence of declining prices of raw materials (especially sugar) and to closed European markets for Brazilian industrial products. Likewise, Parthasarathi (2011) argued that, while, historically, India had been far from backward, it was the dual British policy of blocking imports from the Global South and opening up – often by violent means – markets for their industrial goods, while investing in cash crop production, that exploited the Indian economy and hence led to a lack of industrialization. A similar argument is put forward for China in the ‘sprouts of capitalism’ debate. In this literature, it is essentially argued that in the Ming and Qing dynasties, some economic and policy elements developed that, just as in Europe, could have led to an industrial revolution. The reasons, put forward in this literature, that it ultimately failed are diverse, including domestic decline (Myers and Wang, 2002), the 17th-century Manchu invasion, and the 19th-century Opium Wars with the Western powers (Pomeranz, 2000). For the more recent period, Beckert (2014), elaborating on the aforementioned studies on political, economic and military factors, argued that in the 19th and 20th centuries, Europe exerted, via its advantages in state violence and capital accumulation, a negative effect on the global commodity chains by concentrating low value-added production in developing economies and high value-added processing predominantly in Europe (see also Furtado [1974] 2020). This became worse the larger the economic gap was between the developed and developing economies. This means, as Beckert states, that ‘it was this nexus of social and political power that together animated industrial capitalism’ (Beckert, 2014, p. 63). In other words, economic growth in Europe accelerated because it had an advantage in state violence and means of capital accumulation, thus leading to further industrialization by creating ‘new ways of raising capital, new ways of inserting capital into production, new forms of labour mobilization, new forms of market making, and, last but not least, new forms of the incorporation of land and people in the global capitalist economy’ (ibid., p. 173). This argument is taken one step further by Williamson (2012), according to whom the deindustrialization in the periphery was accelerated because of the immense demand for commodities by industrial countries. These two structural impediments in the way of developing economies (link-in and imbalanced capital and power factors) affected the global pattern of industrialization – that is, commodity chains. One way to analyse the role of these global economic chains is by

Industrial revolutions in a globalizing world, 1760–present  25 looking at the role of Western countries in the development of commodity frontiers.4 These commodity frontiers can be defined as processes and sites of the incorporation of resources (land, energy, raw materials, knowledge and labour) that have shaped the expanding capitalist world economy, which have moved at ever-accelerating speed across vast areas of the globe, incorporating ever more land, labour and natural resources (Beckert et al., 2021). It is important to note that while at a global scale the commodity frontiers extracted crops, minerals and energy from the global periphery on behalf of the industrializing countries, this process could engender industrialization in the frontier zones themselves. The mineral or energy frontiers have been, since the late 19th century, dominated by highly mechanized activities, with some room for small-scale and artisanal mining (Verbrugge and Thiers, 2021). Even within the realm of cash crops, industrialization processing can take place at the commodity frontiers. One example is sugar. The Caribbean region massively imported steam-driven cane crushers in the early years of the 19th century. Cuba was one of the first countries in the world where railway construction started, but this mechanization and industrialization hardly produced any backward and forward linkages in the commodity frontiers. Both machine building and the refining/processing and retail of the tropical products took place in the core economies of the world, not in Cuba and the Caribbean. Another important example is cotton production. Using the cotton textile industry as an example, Hobsbawm (2016) emphasized the role played by the government in Britain. In addition to internal economic driving forces, he believed that market monopoly in colonies and some other underdeveloped countries, coupled with a protectionist import ban, played a crucial role in triggering the first Industrial Revolution in Britain. Even though these factors certainly played a role, exceptions did exist. For example, in China circa 1933, cotton was grown in many regions. Due to data issues, here we have analysed only five provinces (Jiangsu, Anhui and Shanghai around the Yangtze River, and Guangdong and Guangxi at the Zhujiang River).5 Our main finding is that, as argued above for Caribbean sugar, those prefectures with a substantial manufacturing sector had little or no cotton cultivation (Figure 2.3). Even though manufacturing and growing were separated, technological development within cotton growing did occur. For example, the introduction and expansion of American upland cotton from the 1860s helped to improve cotton growing in China (Yuan, 2010). In addition, many modern agricultural technical schools and their affiliated farmlands were established. They carried out research and experiments on the technological development of cotton growing, and their successful experiences spilled over to individual cotton growers. The little cotton manufacturing in most of the cotton planting areas was mainly due to the lack of backward and forward linkages. It is also important to note that, although often no backward and forward linkages existed in either sugar or cotton growing, exceptions did exist. For example, the Malaysian rubber sector successfully upgraded its rubber exports by enhancing the quality of its processing and the production of semi-final products. However, the case of Malaysia was a seemingly unique situation, as there are only a few producers of natural rubber in the world. In most cases, tropical cash crops can be produced in many places of the world, while the global commodity trade and processing has become increasingly oligopolistic. Yet, this existence of backward and forward linkages in markets with more producers is arguably less unique than might be assumed. For example, in our Chinese data, Shanghai and Nantong prefectures in Jiangsu province are found to be exceptions, hosting both cotton growing and cotton manufacturing. As these regions border each other, we can essentially

Figure 2.3

Cotton growing (in picul) in 1933 (left) and cotton manufacturing (no. of labourers) in 1933 (right)

Note: The unit of measurement used on the left side of the figure, picul, is a traditional Asian unit of weight, defined by the Oxford English Dictionary as ‘as much as a man can carry on a shoulder pole’. Source: See main text.

26  Handbook of industrial development

Industrial revolutions in a globalizing world, 1760–present  27 speak about the Tong-Hai area. In the Ming–Qing dynasties, this region increasingly developed a commercial economy, witnessed a rapidly growing population pressure and a heavy head and land tax burden, and possessed good soil. In addition, at that time, Shanghai was almost at the northern frontier of cotton planting, making it a main cotton supplier to the northern market. Similarly, good soil conditions in especially Haimen county and Qidong county (Nantong prefecture), and migrants from Shanghai, advanced cotton growing to Nantong (Zhang, 1931). This placed a premium on crops with a high market value in the Tong-Hai area, involving a shift from the growing of rice to cotton and mulberries. As reported by Gao Jin, a governor general of the whole Liangjiang area in the 1770s, in some coastal counties of the Tong-Hai area – such as in Shanghai, Songjiang, Taicang, Chongming and Haimen – around 70–80 per cent of farmers were cotton growers; no more than 20–30 per cent of peasants grew rice.6 This, in turn, increased the handicraft of spinning and weaving such that it became a main part of household income in agricultural areas of the Yangtze delta. Hence, abundant supply of raw material and abundant market demand maintained a virtuous cycle. This virtuous cycle continued in the 19th and the beginning of the 20th centuries. Around 70 per cent of cultivable farmland in Nantong was used to grow cotton (Yin, 1936) and the majority of raw cotton exported from China to Japan and other countries in South Asia and Europe was also produced in Nantong (Lin, 1984). In addition, increased demand for raw cotton – caused by the replacement of modern mechanized large-scale cotton textile production for handicraft cotton weaving and spinning nearby – also stimulated the growth of cotton planting in Tong-Hai area. Shanghai was a frontier influenced by advanced Western technologies and modern factory systems. From the 1880s, a large quantity of imported machine-made cotton yarn of higher quality and lower price entered China from Shanghai and soon became dominant in the market (Shi, 2005). As a response to the impact of the inflow of foreign products, the Qing government decided to import spinning and weaving machines and establish domestic modern cotton mills. Combined with the convenience of transportation to both domestic and international markets, the Tong-Hai area became the first preferred location. In 1890, supported by the Qing government, the first modern cotton weaving and spinning factory of China was established in Shanghai (Wu, 1997). Around 1895, owing to the opening up of the cotton industry, modern cotton mills funded by foreign capital appeared in the Tong-Hai area. The majority of these were funded by the British and Japanese, such as Yihe Cotton Mill and Dachun Cotton Mill (Yu, 1995). Domestic private factories followed. In 1899, the first private modern cotton mill, founded by Zhangjian in Nantong, was put into operation (Shi, 2005). To guarantee the increasing supply of raw material, some large cotton mills were set up to support cotton planting bases in those areas suitable for the growth of cotton. Tonghai Farming & Husbandry Company, founded in 1901, was a representative example. Organized production facilitated technological development of cotton planting much more than scattered individual production (Qiang, 2010).

5

INDUSTRIALIZATION AND GLOBAL INEQUALITY

5.1

Between-country Inequality

So far, we have discussed trends in industrialization under the presumption that only one industrial revolution took place between 1760 and the present. However, many scholars dis-

28  Handbook of industrial development tinguish, after the first Industrial Revolution (1760–1870), a second one in electrical power (1870–1960), a third one in ICT (1960–2010), and a fourth – and present – one in artificial intelligence (AI) and robotization (2010–). Within the context of ever-changing demand for technology, one can question whether these were true ‘revolutions’ or rather longer periods of slightly elevated growth levels. In any case, it seems likely that, as noted for the first Industrial Revolution (Section 2), technological growth levels during these ‘revolutions’ affected GDP only a decade or so later (e.g., Bergeaud, Cette and Lecat, 2016). The effects of these revolutions on the spread of industries over the globe can also be debated. Even though, in theory, ‘backward’ countries should catch up owing to cheaper labour and lower costs of (copied) technology, in practice this often did not take place, as, historically, it was usually richer countries that showed the fastest growth rates over extended periods (Rodrik, 2013) – though certain countries, such as South Korea and Japan, did manage to catch up. This pattern of industrialization can be due, as pointed out by Rodrik (2016), to a shock in capital-intensive production in industry, thus reducing the labour share in industry in advanced economies. Whether the income share in industry also declines depends on whether the positive effect of technology shocks or the negative effect of trade shocks predominates. This leads to a rise in the share of industrial output (and possibly to a rising labour share in industry) in countries with a comparative advantage in industry (mostly Asian). Yet, in countries without such an advantage (mostly Latin American and African), a decline in both labour share and income share will occur. One such shock might be found in the present (fourth) Industrial Revolution, in AI and robotization (e.g., Van Ark, 2016). Robotization in industry in the Global North replaces labour, causing a decline in the industrial labour share in GDP, as discussed above. Initially, this depressed wages (e.g., Acemoglu and Restrepo, 2017), allowing for decreases also in the share of industry in GDP. But, as robotization increases labour productivity, later it increases wages again (Kromann et al., 2020). This means that, as mentioned above, for Western countries it is unclear whether the share of industry in GDP increases remains stable or declines. It also increases wages in the Global South by robot-induced specialization (Artuc, Bastos and Rijkers, 2018), thus increasing the share in GDP. Basically, the North increases the Southern trade of intermediate goods, meaning a shift back to the North of high-value-added industry, with still rising incomes in the South. Indeed, as predicted in the above example, between 1991 and 2019, Britain7 (a developed country) and Brazil (a developing country without comparative advantage in industry) witnessed declines of 12.3 and 3.4 percentage points, respectively, in the share of the industrial labour force, while China (a country with a comparative advantage in industry) witnessed a rise in industrial labour force share of 6 percentage points (Table 2.3). This discussion shows that technological development in industry leads to a shift of employment out of industry mainly in developed countries and in less developed countries without a comparative advantage in industry. These shifts away from industry in parts of the world moved largely into services and, to some extent, into agriculture. Indeed, while China has a 47.3 per cent service share in labour force in 2019, this is 80.8 per cent for the United Kingdom (Table 2.3) and 70.9 per cent for Brazil. Looking at other less-developed countries without a comparative advantage in industry, we find similar figures. For example, Argentina has 78.1 per cent labour share in services. In Central Africa, with only 23.9 per cent labour share in services and no less than 69.9 per cent working in agriculture, industrial employment is negligible.

Industrial revolutions in a globalizing world, 1760–present  29 Table 2.3

Labour force shares in China and Britain, 1770–2019 (percentages)

   

 

China 

 

 

Britain

 

Agriculture

Industry

Services

Agriculture

Industry

Services

1770

77.0

6.0

17.0

36.8

33.9

29.3

1850

83.0

8.0

9.0

23.5

45.7

30.9

1911

76.0

9.0

15.0

16.1

46.5

37.4

1933

80.0

7.0

13.0

13.2

32.1

54.7

1952

77.1

7.1

14.3

9.9

34.8

55.2

1991

59.7

21.4

18.9

2.2

30.4

67.4

2019

25.3

27.4

47.3

1.0

18.1

80.8

Sources: Broadberry et al. (2013); Guo et al. (2019); Mitchell (1988, p. 104); World Bank (2019).

This rise of services was widely considered to be an inevitable stage of economic growth (Rostow, 1960). Even though, as outlined above, the share of services in GDP in some developed countries has approached a very high level, in recent years there has also arisen an increasing awareness of the necessity to reindustrialize their economies. The dominance of services does not automatically mean a decline in the importance of manufacturing, as manufacturing is a key driving force of services, especially of producer services and R&D services. In addition, high-tech services such as cloud and smart technologies can help in the automation and upgrade of some manufacturing capacities in traditional industrial sectors. The integration of high-tech manufacturing and modern services is now becoming a new economic growth mode pursued by many countries. The question is whether this development causes global income convergence. The standard view is that since industries move to less-developed countries with a comparative advantage in industry, and industries have a higher innovation potential than services, these poorer countries will catch up economically and eventually overtake, with increasingly more modern technology, the Western world (Lederman and Saenz, 2005; Levitt, 1972). However, there are three arguments why this will not necessarily be the case. First, Gordon (2016), who links economic growth to consecutive industrial revolutions, argues that after the first technological revolution circa 1760–1870, in a second revolution, circa 1870–1960, science improved these technologies, inter alia by introducing electrification. After 1960, information technology arose and became an industrial driver. Nevertheless, each consecutive revolution increased labour productivity less than the previous one, and hence innovation potential in industry becomes less over time. This point has been empirically shown by Bergeaud et al. (2016) for a set of 13 developed economies. Second, notwithstanding innovation potential in industry, as pointed out by Levitt (1972), services also have high innovation potential. Indeed, services, possibly via different technologies, remain important for innovation (e.g., Hipp and Grupp, 2005). But we cannot simply equate this with innovation in industry, if only because it leads to factory work in services with a (at times bad), working environment (such as meal-delivery work, package deliveries and the like). Third, rising industrial wages in developing economies with a comparative advantage, such as in China, made manufacturing move to other countries, such as to Indonesia and Mexico. Indeed, looking at the value added per labourer in services, divided by the value added per labourer in industry, the most advanced developing regions such as China experienced the highest industrial wages (relative to services). Indeed, service production per labourer in

30  Handbook of industrial development China was, with only 51 per cent of that of industry, at the global bottom, while Mexico, with 93 per cent, was close to the top. Hence, industrial wages in China were high. This does not mean, however, that China will experience a decline in industrial share as witnessed by, inter alia, Britain. It can still focus on specialized sectors such as consumer electronics. In addition, services may stimulate industrialization (Eswaran and Kotwal, 2002). 5.2

Industrialization and Within-country Inequality

We have witnessed that countries can undergo various types of industrialization, ranging from a slow development towards mechanization and factory work (Western Europe), to a fast, forced industrialization (e.g., Japan, Korea, USSR, China), and to deindustrialization (many Asian countries). There is an extensive literature on the effect of industrialization on well-being (see e.g., Prados de la Escosura, 2015; Rijpma, 2014, 2021; Van Zanden et al., 2021). The main theory states that starting industrialization may result in rising income inequality. Various authors have made this point. For example, Kuznets (1955) argued that, during economic growth, per capita income inequality first increases, and once more than 50 per cent of the population has moved to high-wage sectors, decreases again. Lewis (1954) held the similar opinion that industrialization usually led to increasing inequality until the surplus agricultural labourers were completely absorbed by modern industries. Another reason for rising inequality during the first phase of industrialization, as mentioned in the previous section, is that a shock in technology may initially result in rising inequality up to the point that wages start increasing. Looking at the first Industrial Revolution, Komlos (1998) found that human height decreased in times of rapid expansion of industrial output per capita in the first half of the 19th century in Britain and the USA, thus suggesting physical deprivation and declining per capita income. If we follow Williamson (2000) in arguing that labour income divided by GDP per capita reflects an index of equality, a decline in heights implies a decline of this index, which, in turn, suggests rising inequality in the first Industrial Revolution. Similar observations have been made for later industrializing countries. For example, Baten et al. (2010) found that in China, the rising incomes in the late 19th century were also accompanied by declining height. For countries that experienced stagnation or even deindustrialization, no such rises in inequality are apparent. For example, small increases/stagnation in human height around the turn of the 20th century were found for India (Brennan, McDonald and Shlomowitz, 1994; Guntupalli and Baten, 2006), combined with declining GDP per capita (Broadberry, Custodis and Gupta, 2015b), thus implying declining inequality. Similar trends of rising inequality are also found for periods of industrialization in the 20th and 21st century. An obvious example is forced industrialization in the 1920s–30s in the USSR (Baten and Blum, 2012), as well as the Chinese forced industrialization where we find, in a sample for Inner Mongolia (Jining city), declining human height from the Great Leap Forward (1958–62) onwards (Inner Mongolia 1977–92). The above-mentioned increases in inequality during initial phases of industrialization were thus quite common. Depending on which theory of inequality was prevalent, inequality could be naturally reduced by economic development, yet this is a long historical process within which social upsets may occur. Therefore, in many countries it became a common practice for the government to make significant efforts to diminish the income gap in the middle or late stages of industrialization. For instance, the American government issued its Agricultural

Industrial revolutions in a globalizing world, 1760–present  31 Adjustment Law in 1933 to intervene in and protect peasants’ incomes, and the Japanese government issued its Agricultural Basic Law in 1961 to improve agricultural productivity and decrease inequality.

6 CONCLUSION Industrial production was as much part of human existence in 500 BCE as it is today. The main difference is that the level of mechanization and the organization of production have changed fundamentally over time. For most of human history, mechanization was predominantly the introduction of animal, wind and water power. The first Industrial Revolution, as occurred in Britain at the end of the 18th century, was characterized by the introduction of steam technology and a rise in the importance of factory-based output. These forms of industrialization spread across the globe, beginning with Western Europe, the Western offshoots, such as Canada and Australia, and Japan, and followed by the colonial commodity frontiers and finally by other countries. Factors driving this spread were cultural susceptibility, material incentives, government intervention, and the capacity to embed new technologies and organization of production in societies that were not in the world’s industrial vanguard. Some countries, such as Czarist Russia/the USSR, quite successfully introduced industrialization under strong state guidance. However, in many countries, industrialization was less successful. This can be explained by the locking in of the world economy, preventing these countries from developing capital-intensive production. Testing lock-in in the global economy, we looked at commodity frontiers. Technological development occurred in raw material production without, however, backward and forward linkages – even if there are some exceptions, such as rubber in Malaysia and cotton in the Shanghai–Nantong area. This pattern had strong effects on income inequality. In the case of between-country inequality, we find a shock in technology decreases labour share in industry in developed and underdeveloped countries, while labour share increases in developing economies with a comparative advantage, such as in China. Hence, while China industrializes, developed countries (e.g., Britain) and less-developed countries without an industrial comparative advantage (e.g., Brazil) move to services. In theory, this may lead countries like China to catch up with the West; however, various factors, such as a high innovation potential in services, will limit this process. As a general rule, for within-country inequality in both developed and less-developed countries that exhibit industrial growth, we witness increasing inequality in the first phase of industrialization. Although in the long-run, this effect of industrialization should ebb away to yield more equality, this is a long process during which countries may experience social unrest. Hence, many countries have established laws specifically to reduce inequality during the initial phase of industrialization.

NOTES 1. The research leading to the results in this chapter received funding from the European Research Council under the European Union’s Horizon 2020 Programme/ERC-StG 637695 – HinDI, as part

32  Handbook of industrial development

2. 3.

4. 5. 6. 7.

of the project ‘The Historical Dynamics of Industrialization in Northwestern Europe and China ca. 1800–2010: A Regional Interpretation’. As proxied by the United Kingdom. These figures must not be taken at face value. Besides various measurement issues, we also face the issue of relative prices, with prices of industrial products decreasing strongly since the Industrial Revolution as opposed to those of agriculture. Yet, it is probably best to view the figures as current prices. The term commodity frontiers covers whether/why these factors – that is, land, labour, ecological and technological regimes in particular times and places (Schneider 2020) – supported (or blocked) industrial catch-up. These data were extracted from Xu, Van Leeuwen and Zhuang (2020) and Chinese Cotton Statistics Association (1938). See the memorial to the throne written by Gao Jin [高晋] in 1775 about keeping balance of the planting of both rice and cotton in the coastal lower Yangtze area [奏请海疆禾棉兼种疏] recorded in Huangqing Zouyi [皇清奏议], Vol. 61. Proxied by the United Kingdom.

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3. Latin America: learning and fictional expectations in industrial development Clemente Ruiz Durán and Moisés Balestro

1 INTRODUCTION This chapter aims to provide a broader institutional and historical explanation for the industrial development in Latin America – how the industrializing countries in the continent moved from a pioneer position in the 1930s and 1940s within the latecomers at the onset of their industrialization to a stagnant position in the 1980s, followed by early deindustrialization, except for Mexico. In addition, drawing on comparative lessons from other regions, the chapter presents an explanation for the lack of spillover from the industrialized economies to other countries in the region. Contrary to former explanations from structuralist and neostructuralist schools, the chapter lays emphasis on the role of fictional expectations and capitalist dynamics (Beckert, 2016; Beckert and Bronk, 2018) and the contributions from the German historical school, as well as more recent contributions of historical institutionalism. The chapter argues that the import substitution industrialization (ISI) paradigm had two shortcomings concerning fictional expectations of industrial capitalist development. One was missing the opportunity of the changing economic landscape in the 1970s towards trade openness and the role of exports in industrial competitiveness; another was not paying enough attention to the role of credit and the financial system in industrial development. The chapter is divided into five sections besides the final considerations. Stemming from Gerschenkron (1962) and List (1909), Section 2 discusses the intertwining between the idea of nation and economic development in latecomers previously as an imagined future. Section 3 addresses the industrialization project as well as the invention of new institutions as fictional expectations (Beckert, 2016) in the transformation of agrarian countries into industrial nations. Another often-neglected institutional innovation was the establishment of development banks in Mexico, Chile, Brazil and Argentina between the 1930s and the 1950s. The creation of these banks together with the burgeoning industrialization process was a critical juncture to transform agrarian nations and commodity exporters into industrial nations during the ISI period. Critical junctures are one-time episodes with lasting consequences. Their effects and the institutions created because of them continue after the juncture has passed. In this sense, a critical juncture helps to establish the trajectory of a society and is contingent on preceding events, but yields unexpected outcomes of these events (Gibb, 2011). Another criterion with which to identify a critical juncture is its crucial impact on future outcomes (Capoccia, 2016). The enduring nature of changes produced by critical junctures provides a historical or institutional legacy. Section 4 explores the missing link between industrial development and technological development because of passive and rent-seeking protectionism by Latin American industrial elites. The chapter argues that by setting export goals for manufactured goods back in the 1960s and in the 1970s, industrial policy would have forced the creation of 37

38  Handbook of industrial development institutions supporting technological development as well as technological catching-up as part of the next move in the fictional expectations of industrial development. The resilient protectionism undermined the openness of the industrial future of Mexico, Brazil and, to a lesser extent, Argentina. Short-termism and rent-seeking prevailed over an imagined future. Section 5 covers a critical juncture in the industrial development of Latin American countries with the debt crisis at the beginning of the 1980s and the creation of a new path leading to stagnation and further deindustrialization for Brazil and Argentina and structural dependence for Mexico. Section 6 brings to the fore the paradigm of global value chains and its implications for industrial development in Latin America. The increase in world trade through global value chains was a double-edged sword for industrial development in the continent.

2

IMAGINED FUTURES IN LATIN AMERICAN INDUSTRIALIZATION

List (1909) was the first classical economist to emphasize a unifying role in lasting prosperity. He argues that only where the individual’s interest (we might say individual capitalist’s interest) has been subordinated to that of the nation, has there been a harmonious development of their productive forces. According to List (1909, p. 132), there is little prosperity from private industry without the united efforts both of the individuals who are living at the time, and of successive generations directed to one common object. Therefore, cumulative development depends on sharing common fictional expectations towards imagined futures (Beckert, 2016; Beckert and Bronk, 2018). In this vein, Mannheim ([1936] 1979) makes a clear-cut distinction between attempting to predict the future with calculative devices and recognizing the future as an array of possibilities from which to choose. As the radical uncertainty of societal and economic development prevents an organized and rationalized approach towards the future, there is a need to deliberately choose our course in connection with utopian or fictional expectations to drive human action forward (ibid.). Corroborating the role of economic utopias in capitalist dynamics, Mannheim claims that, without utopias, human beings would lose their will to shape history, and history itself would cease to be a human creation. As former colonial countries, the emergence of a shared view and the nation-making process had played a decisive role in the building of the continent’s national institutions and imagined futures. The concept of nation for Latin American countries intertwines with the independence process and with the struggles among the different countries in the continent (Bethell, 1985). However, breaking away from colonial rule was not enough to build state capacities and imagined futures. As Anderson (2006) puts it, the formation of nations in Latin America was far removed from encouraging the lower classes to take part in political life as in 18th- and 19th-century republican movements in Europe. On the contrary, the independence of Venezuela, Mexico and Peru from Madrid stemmed from the fear of mass political mobilizations and uprisings (ibid.). By the end of the 1870–1910 period, the United States had managed to supersede Britain in terms of industrial development. Again, as in colonial times, Latin America continued to be primarily an exporter of raw materials and an importer of manufactured goods. In the 1890s, with the creation of the United Fruit Company, a US multinational corporation that traded in tropical fruit grown on Latin American plantations, a perverse relation began with the setting

Latin America: learning and fictional expectations  39 of quotas of production in Central and South America where the US supported puppet governments, which could ensure the provision of fruit. The Latin American experience shows that industrialization of undeveloped countries to overcome the barriers of stagnation is a far from spontaneous and inevitable trajectory. As in the words of Gerschenkron (1962), breaking through these barriers requires the ignition of human imagination beyond the better allocation of resources. Industrialization in these countries is an extremely complex and uncertain enterprise that needs more stimulus than the classical audacious and innovating entrepreneur (ibid.). As individual economic actors cannot match the challenges of structural transformation alone, the state is an agenda-setter and has a strong influence on the definition of the situation (Gao, 2002). By defining the situation and forging a paradigm, the state plays a pivotal role in the formation of fictional expectations, to frame the future towards what it could be. Beckert (2016) claims that capitalism is a socioeconomic system oriented towards the future. The dynamics of the capitalist system implies expanding the credit system, investing in new products, and new forms of production to achieve higher rates of productivity. This is a radical break with the agrarian and traditional societies in the Latin American continent. In these societies, the action is not essentially oriented towards an open future, which is distinct from either past or present. An open future entails a radical uncertainty and, despite the rational calculative devices of capitalism, the future expectations are fictional rather than rational. Due to widespread innovation and complex interdependencies, the economic actors make decisions based on a shared narrative and fictional expectations that structure beliefs and behavior (Beckert and Bronk, 2018). Largely, Latin American industrialization was an imagined future based upon fictional expectations resting on the building blocks of capitalist dynamics – fictional expectations towards investment, credit, innovation and consumption. This approach contrasts with Ricardian comparative advantage. In opposition to an imagined future, Ricardian comparative advantage is self-referential, static, with no progress, nothing to emulate and no narrative for the future (Reinert, 2007). According to comparative advantage theory, it would be meaningless to begin industrialization in Latin America. In turn, Beckert (2016) argues that fictional expectations undergird the building blocks of capitalist dynamics – credit, investment, innovation and consumption. Credit requires confidence in the results of entrepreneurial activity under radical uncertainty, and it calls for an imagined future. The same holds for investment, where the calculative devices and forecasting techniques are no substitute for fictional expectations because such devices and techniques cannot rule out the radical uncertainty. In this sense, it is more of a narrative and persuasive communication about future scenarios and states of the world to convince investment decision-makers. Innovation is far from developing what existing demand requires but results from vague ideas about possible worlds where the entrepreneurs act as if the new combinations, new products and technologies were already here in the present. Consumption stems from rising expectations towards what a desirable lifestyle would appear to be. This lifestyle as a future state creates the symbolic value of the products and services comprising it. In the early 20th century, Mexico’s agrarian revolution redefined the rules of the game, incorporating both the state and the masses in economic development. The interwar period helped redefine Latin American relations with the US and Europe, increasing the role of the US in trade relations mainly through agricultural products, raw materials, and light manufac-

40  Handbook of industrial development turing of textiles that was a traditional activity from ancient times in the region. In the 1920s, car assembly plants spread in Latin America, Mexico, Argentina, Brazil and Peru.1 Latin American countries are not only latecomers in their capitalist economic development but also in their state-building process. As Love (2018) reminds us, taxation and government revenues with a rising modern bureaucracy only took place between the late 1920s and the late 1950s, more than a 100 years after the leading Latin American countries became independent. The obligations concerning social spending and the minimum levels of social protection took place in the 1930s for Brazil, Chile and Uruguay, and in the 1940s in Argentina and Mexico (ibid.). The introduction of educational and health systems led to a new labor force with rising expectations. These could only be satisfied through higher earnings and better working conditions. As Brearley puts it: Southern Cone countries such as Argentina, Brazil, Chile, and Uruguay were the first to introduce insurance schemes in the early 1920s for urban elites such as civil servants, public enterprise employees, the military, and private-sector workers. The government provided these occupations with disability pensions, survivorships, old-age pensions, and in some cases, health insurance… In the 1940s, Colombia, Costa Rica, Mexico, Paraguay, Peru, and Venezuela widened the availability of social insurance to a larger share of the formal labor force. For Central America and the Caribbean, this only took place in the mid-1950s and 1960s. Though all welfare systems in the region had an urban bias, some were more ‘advanced’ than others. (Brearley, 2016, p. 56; added emphasis)

Throughout Latin America, there was a push for the emergence of social security to create a safety net for the new labor force stemming from the industrialization process. Latin America’s early development of social protection distinguishes itself from other developing regions where the social protection system developed after industrialization took place (Cook, 2007). In Latin America, the state shaped industrial relations pari passu with the formation of a capitalist labor market (Roxborough, 1981). A growing labor force and regulations contributed to the emergence of unions across several Latin American countries. There were also general unions across the region, including the Confederación General del Trabajo (1930) in Argentina; Confederação Operária Brasileira (1906) in Brazil (Miranda, 2011, p. 1); Confederación Regional Obrera Mexicana (1910) and later the Confederación de Trabajadores de México in Mexico (1936); as well as the Confederación Sindical in Colombia and the Confederación de Trabajadores de Chile, also in 1936. Together with the emergence of business associations, the corporatist arrangements played a part in shaping ISI in the region, with the more complex business associations developed in Mexico and Brazil (Schneider, 2002). Notwithstanding the existence of rent-seeking behavior and particularistic interests, the business associations helped to coordinate with the government and foreign investors by building fictional expectations towards investment projects. In countries without strong business associations, the particularistic and clientelist prevailed over collective action and fictional expectations for credit and investment. Moreover, the weak governments gave in to short-term interests from businesspeople and admitted them to their cabinets. This kind of amalgamation of private interests and government hampered the process of state-building and reduced degrees of freedom for state action. After World War II, the imagined futures of industrial development in Latin America were reinvigorated by the creation of the United Nations and its Economic and Social Council. There were five regional economic commissions to collaborate with the Latin American govern-

Latin America: learning and fictional expectations  41 ments. Such commissions carried out research and analysis of regional and national economic issues. They resulted in the creation of the Economic Commission for Latin America (ECLA), established by Economic and Social Council Resolution 106 (VI) of February 25, 1948, adopted the same year. Much later, the Commission’s studies and recommendations included the countries of the Caribbean. With Resolution 1984/67 of July 27, 1984, the Commission changed its name to Economic Commission for Latin America and the Caribbean (ECLAC, or CEPAL in Spanish and Portuguese). Under the mandate of Raúl Prebisch as the Executive Secretary, the Commission set up a Latin American school of economic thought within the United Nations Conference on Trade and Development (UNCTAD) in the 1960s. In his structuralist manifesto from 1949, ‘The Economic Development of Latin America and its Principal Problems’, Prebisch spoke of an industrial, hegemonic center and an agrarian, dependent periphery as a framework for understanding the international division of labor. He claimed that the combination of both contributed to explaining the process of unequal exchange. Prebisch’s claim is pivotal to grasping how this asymmetric relationship undermines the Latin American countries’ means of accumulation and growth (Da Silva, 2018). Assuming a greater rate of technological innovation in industrial countries, he argued that there were different responses to the business cycle behavior of primary exporters and manufacturers, resulting in secular effects. This process occurred primarily because of organized labor power to maintain high wages, and therefore high export prices, in the industrial countries, and second because of oligopoly in markets for manufactured goods (and its near absence in those for primary commodities). Thus, there was a tendency for the terms of trade of agriculture-exporting countries to deteriorate. (A similar explanation came from another UN economist, Hans W. Singer, and the thesis became known as the Prebisch-Singer argument.) In addition, Prebisch emphasized (1) structural unemployment, owing to the inability of traditional export industries to grow and therefore to absorb excess rural population; and (2) external disequilibrium, because of higher propensities to import industrial goods in Latin America than to export traditional agricultural and mineral goods. The school focused on structures, blockages and imbalances, hence the name ‘structuralism’. The neglect of the role of manufacturing exports in the ISI period was also an intellectual shortcoming. Prebisch himself was an export pessimist despite being very aware of the need for a Latin American common market (Irwin, 2021). In the 1960s, there was an effort to develop an integration process in the region by creating the Latin American Free Trade Association (LAFTA). As a result, the Montevideo Treaty entered into force in June 1961 between nine countries – Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru and Uruguay. The member countries instituted a free trade zone for 12 years (later extended until 1980). The country members were to integrate their economies through tariff cuts and differentiated taxes on the products that would make up the national lists and the list common to all countries. The tariff cuts would go through annual negotiation, and the national lists would gradually change every three years to include all the products that had significant participation in the trade among the country members. However, in practice, the tax reduction process stalled after December 1964, with the fourth round of negotiation of the national lists. Focusing on the periphery’s problems, CEPAL emphasized structural unemployment due to traditional export industries’ inability to grow and, therefore, absorb excess rural population. There was persistent external disequilibrium with problems in the balance of payments due to Latin America’s greater propensity to import industrial goods at a higher level than its export

42  Handbook of industrial development of traditional agricultural and mineral goods. A correctly implemented policy of industrialization could smooth the deteriorating terms of trade. In the early 1970s, CEPAL presented another essential feature of its doctrine, implicit in Prebisch’s early writings: the characterization of underdevelopment as ‘structural heterogeneity’, in which economic processes of vastly different productivities coexisted in the same economy. The most enthusiastic proponent of CEPAL’s recommendations was probably Brazilian President Juscelino Kubitschek (1956–61). With his slogan ‘Fifty years of progress in five’, Kubitschek announced in his 1956 presidential address that CEPAL and the Brazilian National Development Bank (BNDES) had devised a five-year development plan (Love, 2018).

3

DEVELOPMENT BANKING: INNOVATION TO FINANCE INDUSTRIALIZATION

State-building in Latin America in the first decades of the 20th century led to the emergence of national policies in most countries, backed by labor demands that requested a new structure of the economy to solve employment problems and increase the share of wages in national income, backed by the modernization of institutions to preserve health. A key component in the emergence of this process was the consolidation of a financial system centered on the creation of development banks to foster domestic entrepreneurship. Latin America was a pioneer in development finance to enhance productive activities. In 1934, Mexico created the Nacional Financiera (Nafinsa) bank as an instrument to promote the stock market and the mobilization of financial resources towards productive activities (Nacional Financiera, 2021), providing liquidity to the national financial system through the confiscation of properties awarded as collateral in the revolutionary stage. In 1937, the first public debt securities on the stock market began to strengthen government securities. Likewise, and for the first time, Nafinsa captured national savings by issuing its financial securities, strengthening the incipient stock market of that time, with which it soon acquired importance within the economic system. As part of the institutional framework created by the Mexican state to promote the country’s economic development, it became the central instrument to finance the development of Mexico’s economic infrastructure and support the growth of small and medium-sized enterprises (SMEs) to become large businesses. The development of its operations during the period 1934–40 contributed to the formation of the Mexican financial system and its mixed economy in a broader context. In the 1940s, there were two pressing concerns in the economic policy of the Mexican government: the industrialization of the country and the creation of an effective mechanism to mobilize massive savings towards the promotion of productive investment. With the idea of creating an official institution that could operate in these two directions, an act was issued on December 30, 1940 that defined Nafinsa as a development bank, granting it power to promote, create and financially assist targeted companies from priority industries, form a national stock market, and act as a financial agent for the federal government. During this phase of Mexican industrial development, the financial endowments to long-term capital investment were scant. Therefore, Nafinsa filled this gap by granting the necessary means to promote the investment of capital in infrastructure and light industry works. The institution fully met the critical objectives to spur on industrialization, create the stock market, and consolidate the financial system. A decade after its establishment as a development bank, Nafinsa took risks with patient (long-term) capital, nurturing a series of industrial projects that

Latin America: learning and fictional expectations  43 would result in the creation of critical strategic companies for the economic development of the country: Nafinsa’s support of the industrialization process in Mexico was fundamental and took place in multiple primary manufacturing sectors. Its timeframe is different from the short-term horizon from private banks. As a developmental institution, it also played an essential role in advising, formulating, and evaluating projects, selecting technology, commissioning, operating, and selling large projects and industrial companies. Simultaneously, Nafinsa stood out as the pioneer in promoting and developing the stock market. (Nacional Financiera, 2021, n.p.)

In 1939, Chile established La Corporación de Fomento a la Producción (CORFO) to provide aid for a natural disaster – Chilean earthquake victims – and promote the infrastructure of the country (Biblioteca Nacional de Chile, 2021). In its early years as a development bank, CORFO accounted for the funding of over 30 percent of Chilean investment in machinery and equipment and 25 percent of public investment (Griffith-Jones, Martínez and Petersen, 2018). In its initial years, CORFO also played a pivotal role in public enterprises in Chile like electricity (ENDESA), steel (CAP), oil (ENAP), and the national airline (LAN) (ibid.). However, during and after the Pinochet regime, CORFO’s contribution dropped substantially, greatly lagging behind what it was as a development bank. In 1944, Argentina founded the Banco de Crédito Industrial to finance the substitution of imports by local production during the development of World War II. Later, in 1969, during the de facto government of General Roberto Marcelo Levingston, it changed to Banco Nacional de Desarollo (BANADE). BANADE terminated its operations amidst the neoliberal period of the Menem government in 1993. After the end of World War II, other Latin American countries managed to create development banks to support productive activities. Colombia created the Financiera de Desarrollo Nacional and the Financiera de Desarrollo Territorial; Peru, the Corporación Financiera de Desarrollo (COFIDE); in Brazil, the largest development bank, Banco Nacional de Desenvolvimento Econômico e Social (BNDES), and Minas Gerais Development Bank; and in El Salvador, Banco de Desarrollo de El Salvador (BANDESAL). Founded in 1952, BNDES was probably the most crucial developmental institution in Brazil. It accomplished different tasks in the structural transformation of Brazil during the following decades (Studart and Ramos, 2018). In the 1960s, it funded the expansion of the consumer goods industry. In the next decade, BNDES was critical in the import substitution programs, strengthening several industrial input-producing sectors like the petrochemical industry and capital goods. Thus, BNDES was a developmental institution capable of shaping the most diversified manufacturing industry in Latin America (ibid.). Development banking in Latin America became an integral part of industrial policy and helped overcome problems caused by scarcity of capital endowments owing to a private financial system unwilling to take risks in project finance.

4

LATIN AMERICAN MANUFACTURING AS FICTIONAL EXPECTATIONS AND LEARNING PROCESS

Industrialization emerged in the region in the 19th century in the aftermath of wars of independence, intertwined with indigenous practices of spinning and weaving textiles, carpentry and carving, breweries, coffee grinding, smithies, paper and the introduction of railroads and

44  Handbook of industrial development shipyards. At the turn of the 20th century, Mexico, in 1903, started up the first metallurgical coke blast furnace in Latin America, 43 years ahead of any other country. In the rest of the region there were small plants in some countries that were only marginal in the supply of steel. Latin America had to import virtually all steel and the rest of its industrial goods. World War I did not prompt industrial policies; it was the Great Depression that pushed Latin American countries to put together measures to reduce its effects, through a pioneer fiscal expansion that preceded the US New Deal. In the 1930s, President Cárdenas of Mexico pushed for massive land reform, and in 1938 signed an order of expropriation of foreign-owned oil companies. In 1933 was the First Sexennial Plan, stating that the state should: ‘stimulate the creation of new industries that must advantageously substitute imports, or that represent the use of unexploited or exploited resources efficiently’ (Carmona, 2021a, n.p.). It is worth noting that this planning was a relevant institutional innovation back in the 1930s. (This kind of planning only spread throughout Europe after World War II.) The Second Sexennial Plan, formulated in 1940 by President Ávila Camacho, was a plan for the general industrialization of the country. The plan aimed ‘to achieve a better location of industrial establishments and avoid unnecessary cost surcharges, to prevent overcapitalization of specific industries, to regulate the necessary renewal of industrial equipment and, in general, to satisfy better way the economic needs of the country’ (Carmona, 2021b, n.p.). Argentina, under President Agustín Justo, also promoted a National Economic Action Plan in 1933. The purpose of this plan was to expand the economy and have greater control over foreign trade (Rougier, 2021). A pioneer response to the Great Depression also came from Brazil. After the 1930 revolution, President Vargas spurred fiscal expansion to recover from the Great Depression, and set forth the import substitution strategy. At the end of the 1930s, the Vargas government embarked on a program of growth in the automotive industry, focused on the production of foreign brands and other imported materials for the war. Some Latin American countries also reacted to the crisis by gradually raising tariffs, increasing domestic demand, and implementing control over capital flows and foreign currency. Recovery from the Great Depression combined with World War II (1940–45) forced Latin American countries to develop strategies to substitute goods and at the same time increase raw materials exports. At the end of the war, there was a foreign exchange crisis in all Latin American regions as all countries resumed imports delayed by the conflict. As the exports fell, trade deficit increased enormously and there was therefore a balance of payment crisis pushing for alternative solutions. The timeframe between 1950 and 1980 was a period of active institutional learning for the region. There was the building of new industrial areas, the modernization of old factories, and a joint effort between the state and business engaged in industrial development. As a result, the economic growth rates in Brazil, Chile and Mexico were very high. Consumer goods industries flourished to meet growing demand from urbanization, an expanding labor force, and rising middle classes. It was the birth of a prosperous era for the whole region. Manufacturing in the region rose to account for one-fourth of gross domestic product (GDP) (Table 3.1), manufacturing employment reached around 1 million in the 1980s in Argentina, Brazil and Mexico, and half a million in Colombia. By the end of the 1980s, the main industries were chemicals and chemical products in Argentina, Brazil, Chile, Colombia and Mexico; food and beverages in all countries; basic metal production in Brazil, Chile and Mexico; and motor vehicles, trailers and semi-trailers in Argentina, Brazil and Mexico. It was a period of restructuring, pushing for additional infrastructure, and, under the growing labor force, there were

Latin America: learning and fictional expectations  45 more demands from unions for better education and health. It was the beginning of a higher educational system across the region. Technical schools multiplied, research centers were opened to diffuse technologies in agricultural production, and the region became a producer of vaccines led by Mexico. Health improvements brought about a growth in the population and the labor force, unfortunately not matched by employment, pushing for the growth of informal employment. Transformation took place in large countries of the region, leaving behind Central America and Caribbean countries, as well as some countries in South America – Ecuador, Bolivia and Paraguay, among others. Governments learned from and adapted entrepreneurs’ strategies to local conditions, which led to the success of the first stages of import substitution for consumer goods, but furthering industrialization into capital goods lacked support from transnational corporations. In general, governments had a short view on the subject, with the exception of Brazil where there was an upgrading process. Support for industrialization also came from foreign direct investment (FDI), which in the period 1970–81, reached US$184 million, focused on Brazil, which received 42 percent of the funds, and Mexico with 26 percent. Two of the pitfalls were the diminished manufacturing exports and the neglect of technological development. With hindsight, it is now clear that the challenges posed by the need to compete internationally were beneficial for East Asian latecomers. Bussell (2018) reminds us of the criticism of protectionism in the Latin American ISI, and how the successful export-led strategies in East Asia later supported this criticism. Nevertheless, it is necessary to mention that inefficient protectionism stems from weaker states, trapped by rent-seeking behavior from some business sectors. Moreover, the political and social instability was much greater in Latin American countries than in the East Asian authoritarian developmental regimes. Finally, one may argue that rent-seeking behavior stems from short-termist gains and contrasts with imagined futures. In each country, the opportunity to expand industrial production often depended on variations in these social and political constraints. ISI was most successful in countries with large populations and higher income levels, which allowed for the consumption of locally produced products. Latin American countries such as Argentina, Brazil and Mexico (and to a lesser extent Chile, Uruguay and Venezuela) had the most success with ISI. While the investment to produce cheap consumer products may be profitable in small markets, the same cannot be said for capital-intensive industries, such as automobiles and heavy machinery, which depend on larger markets to survive. As lower middle-income countries were unable to broaden their consumer markets, industrialization as a structural change did not take place, and they remained traditional agrarian societies with prevailing rural labor. Another missed opportunity was the lack of trade interactions between the large and medium/small economies of the region. In the 1960s, there was an attempt to push regional integration through the creation in 1960 of the Latin American Free Trade Association (LAFTA, or ALALC in Spanish) under the expectation that it would allow trading products between countries with special tariffs, but the lack of compromise by large economies in intraregional trade meant it failed. ISI, however, was successful in large economies. As Hoogvelt (1997) states: ‘[B]y the early 1960s, domestic industry supplied 95% of Mexico’s and 98% of Brazil’s consumer goods. Between 1950 and 1980, Latin America’s industrial output increased six times, keeping well ahead of population growth. Infant mortality fell from 107 per 1,000 live births in 1960 to 69 per 1,000 in 1980, and life expectancy rose from 52 to 64 years. In the mid-1950s, Latin America’s economies were growing faster than those of the industrialized West’ (p. 243, citing Green, 1995, p. 17).

46  Handbook of industrial development Table 3.1  

Share of manufacturing in GDP (%) Argentina

Brazil

Chile

Colombia

Mexico

Latam

USA

1920

17.4

N.D

N.D.

N.D.

N.D.

10.0

40.2

1930

22.8

11.7

7.9

6.2

14.2

8.9

42.1

1940

22.7

15.0

11.8

9.1

16.6

12.1

42.2

1950

23.7

21.2

23.1

13.5

18.3

18.7

39.8

1960

26.5

26.3

24.8

16.7

19.5

21.3

38.7

1970

28.8

28.4

27.2

17.5

22.8

25.1

38.3

1980

25.3

30.2

24.2

18.3

19.1

25.4

35.6

1990

21.6

27.9

21.7

22.1

22.8

23.4

33.7

2000

20.1

25.2

17.1

19.6

19.7

22.3

30.1

2010

15.8

14.0

13.6

14.6

17.8

15.4

15.9

2020

13.7

10.5

10.0

11.8

15.2

11.8

14.0

Note: Source:

N.D. = no data. Own elaboration based on ECLAC (2021).

It was a period of great projects in the region. Brazil was able to assemble its own aircraft in Embraer, a company founded by the military in the 1940s. Privatized in the 1990s, Embraer became a dynamic aircraft company competing with Boeing and Airbus, becoming the third largest world producer of commercial aircraft, and designing specific products with the Brazilian air force (Rodengen, 2009). Brazil and Argentina developed shipyards; Argentina developed large pharmaceutical complexes (in 1934, Sebastián Bagó created a pharmaceutical company that became a global company); and Mexico developed large business groups in the iron and steel industry – for example, Hylsa (1942) and Tamsa (1952) – that were essential for import substitution, in home appliances Mabe (1946) and in the cement industry (Cementos Mexicanos 1930). All of them became global companies. The structural transformation resulting from the ISI strategy in Latin America was very successful. Manufacturing rose from 10 to 25 percent as a percentage of GDP between 1920 and 1980 (Table 3.1), but it ran out of steam and was severely affected by the debt crisis in the 1980s. State and society went through a rich learning process fueled by fictional expectations where industrial development was the path towards becoming fully developed nations. Amid this process, the share of manufacturing rose from 9 percent of GDP in 1930 to 25 percent in 1980. This industrialization process pushed growth in Brazil, Colombia and Mexico. In the early 1980s, employment in the manufacturing sector reached more than 5 million in Brazil, followed by Argentina with 1.1 million, and Mexico with 983 000 employees. The transformation process led to a new manufacturing specialization beyond food and textiles. There was the development of new industries such as refined petroleum, chemicals and chemical products and motor vehicles, trailers and semi-trailers in Argentina, Brazil, Chile, Colombia and Mexico. The ISI period can be acknowledged as a process of learning and transformation of the region, promoting manufacturing as a source of growth. Industrialization was linked to two different forces. On one side was a push from local businesses and state enterprises. On the other side, public policy got government credit support through development banks. According to World Bank World Development Indicators, the other source of growth came from FDI, which, in the period 1970–80, was US$32 260 million. This process of industrialization has different facets. One was the traditional industrialization that helped to bring together clusters of production in big cities such as São Paulo in Brazil, Buenos Aires in Argentina, and Ciudad de México in Mexico. New industries

Latin America: learning and fictional expectations  47 revamped their development. Amid the process was the development of micro, small, and medium-sized enterprises (MSMEs), which was the underlying structure to large business operations (Rougier, 2021). In 2016, over 95 percent of the approximately 17 million estimated businesses in the region were MSMEs. These businesses employed a significant portion of the population – over 50 percent of total employment in many countries in the region (Dini and Stumpo, 2020). Yet, the contribution of MSMEs to GDP does not match the weight of these enterprises in the business structure and in employment, as there is a large gap between MSMEs and large enterprises in productivity and competitiveness. A different facet of industrialization emerged in the border cities of Mexico with the US – for example, Ciudad Juárez, Laredo and Tijuana – where the ‘Border Industrialization Program’ (BIP) was established. This program began back in 1965 and its main goal was to attract FDI through tax breaks and other fiscal incentives for assembling plants, with the main goal of attracting labor to avoid migration. It became known as the Maquila Program, and was the root of industrialization based on low wages and low value added established in Mexico, Central America (Guatemala, Honduras, El Salvador, Costa Rica, Nicaragua and Panama) and Dominican Republic (Gamboa, 1991).

5

DEBT CRISIS AND THE END OF CAPITALIST FICTIONAL EXPECTATIONS OF INDUSTRIAL DEVELOPMENT

One of the main weaknesses of the ISI strategy was public finance. In the 1980s, Latin American countries had on average a public expenditure of only 18.8 percent of GDP and incomes of 16.6 percent of GDP. The public sector did not have enough resources to deploy the required infrastructure, training, education and technological research required to support industrialization. To undergird ISI strategy, governments built a protectionist path, severely hampering technological development, and failed to foster manufacturing exports. The ISI growth model was dependent on imports financed by raw materials exports (agriculture, mining and oil) and FDI. However, both sources were insufficient to finance the external deficit, so the strategy was to obtain debt financing in the expectation that new industries could provide new sources of foreign exchange in the future to repay indebtedness. At the beginning of the 1980s, the oversupply of oil led to a collapse of raw materials prices, hitting Latin American export earnings and leading countries into a severe debt crisis, which meant a critical juncture in industrial development with the exhaustion of the ISI model. In this period, Mexico and Brazil declared they would no longer be able to service their debt. Moreover, as much of Latin America’s loans were short term, the shortage of capital and devalued currencies triggered a crisis, notably when the international financial institutions refused to refinance billions of dollars. As the ISI model was inward oriented rather than export oriented and had become less competitive under inefficient protectionism, manufacturing was not a major source of foreign currency for Mexico, Brazil and Argentina. Rather than contributing to overcoming the debt crisis through increasing exports, several firms were indebted in dollars, but with most of their sales in the domestic market. With debt default, GDP growth plummeted, and the 1980s became the lost decade of Latin America. Policies during that period focused on solving the financial crisis and stabilizing the economy, cutting budgets across the border. Ideationally, it represented a dramatic shift from a macroeconomic approach supportive of development

48  Handbook of industrial development to a static Walrasian equilibrium where growth and development vanished from the policy agenda. It implied the abandonment of several unfinished economic projects, contributing to infrastructural problems. The crisis also created greater inequality and stagnation of per capita income. It was the onset of what will later be known as the middle-income trap. The debt crisis caused a collapse in mass consumption in the domestic market that was the pillar of import substitution model. This led to a restructuring of the ISI paradigm, pushing the countries to redesign their foreign exchange dilemma through an export-oriented paradigm, linked to the consolidation of the emerging global value chains.

6

THE GLOBAL VALUE CHAINS PARADIGM AS A DOUBLE-EDGED SWORD FOR INDUSTRIAL DEVELOPMENT IN LATIN AMERICA

Debt crisis pushed Latin America into an economic restructuring that, instead of getting to the root of the problem, put the burden of the adjustment on the public finance equilibrium. This formula led to a reduction of public expenditure, with negative effects for the industry and for the well-being of the population (Felix, 1990). There was a fall in per capita GDP of the region and the debt burden remained high. This pushed countries to look for alternatives to continue its industrialization. It was clear that the debt financing formula would not be viable to maintain industrialization growth. Amid the debt crisis, some governments in the region began to discuss the need for a change in the industrialization strategy. One option was to look to export-oriented industrialization, as was happening in some countries in East Asia. Government, business and academia began to discuss the need to set export targets, as was the case for Korea (Haggard, Kim and Moon, 1991). Debt default also affected government organization and, in most cases, produced political turmoil that led to a political reorganization of most countries in the region (Remmer, 1991). There was no homogeneous response. Little by little, each country defined an imagined future. Mexico, as mentioned earlier, which successfully developed the Border Industrialization Program, chose to extend this project and formalize the link with the US and Canada through the North America Free Trade Agreement, signed in 1992 under the expectation that it would foster industrialization through the auto and electronics industries. Central America decided to deepen industrialization through maquila, and in some South American countries – Colombia, Ecuador, Peru, Chile, Bolivia and Paraguay – the number of maquila increased with the arrival of new ones from East Asia. At the turn of the century, Mexico deepened its industrialization path by joining producer-driven global value chains (GVCs) (Gereffi, 2001). Argentina has been successful in food products, beverages and tobacco; Brazil in food products, beverages and tobacco, wood and paper products, and basic metals/fabricated metal products; Chile in food products, beverages and tobacco, wood and paper products and printing; and Mexico in food products, beverages and tobacco, textiles, clothing apparel, leather and related products, basic metals and fabricated metal products, computers, electronics and electrical equipment, transport equipment, and in repair and installation of machinery and equipment (Table 3.2). A force that has been hampering industrialization in South America has been the emerging demand for raw materials in the world, mainly from China. This has led Latin American countries rich in natural resources to increase this type of export, weakening its industrial base.

0.28

0.25 0.34 0.39 0.04 0.52

Wood and paper products, printing

Chemicals and non-metallic mineral products

Basic metals and fabricated metal products

Computers, electronic and electrical equipment

Transport equipment

Other manufacturing, repair and installation of machinery and equipment 0.03

Source: #access.

1.23

0.24

Textiles, clothing apparel, leather and related products

Brazil

2.48

Chile

0.03

0.04

0.02

0.19

0.18

2.06

0.08

1.06

Colombia

0.05

0.02

0.04

0.28

0.31

0.18

0.25

0.31

Costa Rica

0.19

0.00

0.03

0.02

0.04

0.06

0.04

0.25

Mexico

1.52

4.74

4.17

1.61

0.89

0.59

1.16

1.23

Peru

0.04

0.00

0.01

0.31

0.19

0.10

0.30

0.66

World

100

100

100

100

100

100

100

100

OECD Statistics, Trade in Value Added indicators (2018). Accessed September 2021 at https://​www​.oecd​.org/​sti/​ind/​measuring​-trade​-in​-value​-added​.htm​

0.29

1.68

0.95

2.52

0.77

4.62

Argentina

Food products, beverages and tobacco

GVCs in Latin America (% share in world exports, 2005–15)

 

Table 3.2

Latin America: learning and fictional expectations  49

50  Handbook of industrial development Table 3.3

Latin America: external and intraregional merchandise trade (2019)

Country/Region

Millions of Dollars Latam

USA

China

% Share Europe

Total

Latam

USA

China

Europe

exports  

Exports Argentina

20 984

4 110

6 818

11 277

43 189

48.6

9.5

15.8

26.1

Brazil

37 246

29 860

63 358

40 371

170 835

21.8

17.5

37.1

23.6

Chile

10 534

9 494

22 571

8 855

51 454

20.5

18.5

43.9

17.2

Colombia

12 677

12 266

4 565

4 988

34 495

36.7

35.6

13.2

14.5

Mexico

21 093

358 661

6 930

21 396

408 080

5.2

87.9

1.7

5.2

Peru

6 885

5 748

13 546

8 720

34 900

19.7

16.5

38.8

25.0

Central America

17 039

20 305

1 256

8 750

47 350

36.0

42.9

2.7

18.5

Other South America

20 127

9 705

8 541

10 485

48 857

41.2

19.9

17.5

21.5

 

 

 

 

 

 

 

 

 

 

 

Imports Argentina

16 021

6 274

9 259

9 689

41 243

38.8

15.2

22.4

23.5

Brazil

32 289

30 417

35 271

40 975

138 952

23.2

21.9

25.4

29.5

Chile

16 753

13 451

16 555

11 367

58 126

28.8

23.1

28.5

19.6

Colombia

11 687

13 375

10 967

8 930

44 959

26.0

29.8

24.4

19.9

Mexico

14 221

206 142

83 052

54 669

358 084

4.0

57.6

23.2

15.3

Peru

11 174

8 809

10 265

5 342

35 590

31.4

24.8

28.8

15.0

Central America

25 732

29 127

11 665

7 672

74 196

34.7

39.3

15.7

10.3

Other South America

24 051

10 659

10 420

7 821

52 950

45.4

20.1

19.7

14.8

Note: Central America excludes Mexico; Other South America excludes Argentina, Brazil, Chile, Colombia and Peru. Source: UNCTADSTAT, Data Center, International Merchandise Trade (2021). Accessed September 2021 at https://​unctadstat​.unctad​.org/​wds/​ReportFolders/​reportFolders​.aspx​?sCS​_ChosenLang​=​en.

By failing to develop intra-trade and productive integration across different countries in the continent, Latin America missed the opportunity to reach industrial complementarity among countries, obtain economies of scale, and reduce its dependence on developed economies. As Baer (1972) puts it, an export-oriented ISI depended on the possibility of Latin American economic integration and the willingness of the US and Europe to receive Latin American manufactured imports. However, the willingness of the US and Europe shifted to East and Southeast Asian countries. The construction of regional value chains (RVCs), including medium and small economies in the Caribbean, Central and South America, may fuel fictional expectations towards industrial development in natural resources industries. Today, large countries like Brazil and Mexico have only a small share of trade with Latin America (Table 3.3). Undoubtably, an effort to increase it could help foster development of small economies in the region. As mentioned by Chitonge in Chapter 4 in this Handbook, RVCs constitute an opportunity for further industrialization in Africa, which is confirmed by a recent study on industrialization in Sub-Saharan Africa that reveals substantial growth in manufacturing jobs despite the prevailing low skills and the export of primary products (Abreha et al., 2021). However, the integration of African manufacturing into GVCs may have less to offer than the strengthening of RVCs with more room for backward and upward integration. Although suffering from premature deindustrialization, Latin American productive integration also has vast potential. For example, there is still integration in the automotive supply chain between Brazil and Argentina. The Latin American multinationals in the transformation

Latin America: learning and fictional expectations  51 industry are primarily concentrated in countries such as Mexico – for example, the home appliance company Mabe – in Brazil – for example, the steel company Gerdau – and in Argentina, with Techint, which provides engineering services for the oil and gas industry, energy and mining. The supply chains from these manufacturing companies located in Latin American countries are pivotal to strengthening the RVCs. Nevertheless, since the 2008 financial crisis, there has been ‘stop-and-go’ economic growth, with direct consequences for the Latin American multinationals in the manufacturing industry. According to the BCG report on these ‘multilatinas’, there were 51 companies in industrial goods in 2009 but this number fell to 30 in 2018 (Aguilar et al., 2018). In addition, there is persistent underinvestment from these countries abroad. Between 2005 and 2020, the average FDI outflow from Mexico, Brazil and Argentina was US$9.2 billion, US$8 billion and US$1.4 billion, respectively. In the same period, the average gross fixed capital formation was 21.7 percent for Mexico, 18 percent for Brazil, and 16.2 percent for Argentina. Despite the problems related to meager growth rates and low investment for developing economies, with an average of 41.2 percent of South American exports to other Latin American countries and 36 percent from Central America, the intraregional trade is far from negligible in the continent (Table 3.3).

7

FINAL CONSIDERATIONS

As the future holds an open-ended array of possibilities, Latin America faces an opportunity to recast imagined futures. Furthermore, similar to the Great Depression, the issue of subsistence due to climate change with the warming of the oceans causing intense tropical storms is also a critical juncture to build imagined futures for Latin American countries. In this sense, dealing with climate change can strengthen the development of alternative energy sources and create new paths of economic growth. Climate change and the urgent need for a green transition by developing renewable energy and sustainable agriculture require complex, knowledge-intensive solutions. In addition, there is growing global political and economic pressure towards a reset in the natural resources industries, opening up new paths for industrial development in Latin America. Climate change also creates the need to innovate in durable consumer goods. It is already an ongoing process with the conversion of the Mexican automotive plants to manufacture of electric cars. In addition, the fact that Mexican exports are geographically concentrated in the US market implies a growing awareness from consumers towards environmental issues. Brazil and Mexico, the most industrialized countries in Latin America, have similar productive structures but diverging paths, with the former integrated into the value chain of natural resources and the latter in manufacturing. However, both exports of manufacturing and natural resources may engender opportunities to move up the value chain. Furthermore, the imagined future of a Latin American Union can fuel the fictional expectations about an economic integration, unleashing industrialization in non-industrialized countries of the region that could foster industrial development in countries that need to move up the value chain of manufacturing goods. It implies moving beyond traditional globalization where maquila and commodity exports prevail, pushing down high-tech industrialization and hindering a structural transformation of the region. From a geopolitical perspective, a looming multipolar world can create more room for maneuvering to the Latin American area by

52  Handbook of industrial development increasing its bargaining power with global players. Industrial development in Latin America also has renewable fictional expectations through ‘frugal innovation’ and overcoming secular endemic inequality.

NOTE 1.

In Mexico, Buick in 1921; in Brazil, General Motors in 1925.

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Latin America: learning and fictional expectations  53 Gamboa, L. (1991). Desarrollo de la industria en Guatemala (1870–1959). Revista Estudios No. 9, 93–109. Gao, B. (2002). Economic Ideology and Japanese Industrial Policy: Developmentalism from 1931 to 1965. Cambridge, UK: Cambridge University Press. Gereffi, G. (2001). Las cadenas productivas como marco analítico para la globalización. Problemas del Desarrollo, 32(125), 9–37. Gerschenkron, A. (1962). Economic Backwardness in Historical Perspective: A Book of Essays. Cambridge, MA: The Belknap Press of Harvard University Press. Gibb, R. (2011). Critical juncture. In G. T. Kurian (editor-in-chief), The Encyclopedia of Political Science. Washington, DC: CQ Press. Green, D. (1995). Silent Revolution, the Rise of Market Economics in Latin America. London: Cassell and Latin America Bureau. Griffith-Jones, S., Martínez, M.L. and Petersen, J. (2018). The role of CORFO in Chile’s development: achievements and challenges. In S. Griffith-Jones and J.A. Ocampo (eds), The Future of National Banks (pp. 136–66). New York: Oxford University Press. Haggard, S., Kim, B.-k. and Moon, C.-i. (1991). The transition to export-led growth in South Korea: 1954–1966. The Journal of Asian Studies, 50(4), 850–73. Hoogvelt, A. (1997). Globalization and the Postcolonial World: The New Political Economy of Development. London: Palgrave Macmillan. Irwin, D.A. (2021). The rise and fall of import substitution. World Development, 139(C). List, F. (1909). The National System of Political Economy. London: Longmans, Green and Co. Love, J.L. (2018). CEPAL as idea factory for Latin American development: intellectual and political influence, 1950–1990. In M.A. Centeno and A.E. Ferraro (eds), State and Nation Making in Latin America and Spain (pp. 29–50). Cambridge, UK: Cambridge University Press. Mannheim, K. ([1936] 1979). Ideology and Utopia: An Introduction to the Sociology of Knowledge (4th edition). Thetford, UK: Routledge & Kegan Paul. Miranda, Maria B. (2011). História do sindicalismo no Brasil. Revista Virtual Direito Brasil, 5(1), 1–4. Nacional Financiera (2021). Historia. Accessed September 2021 at https://​www​.nafin​.com/​portalnf/​ content/​sobre​-nafin/​historia​.html. Reinert, E. (2007). How Rich Countries Got Rich… and Why Poor Countries Stay Poor. London: Constable & Robinson Ltd. Remmer, K.L. (1991). The political impact of economic crisis in Latin America in the 1980s. The American Political Science Review, 85(3), 777–800. Rodengen, J.L. (2009). The History of Embraer. Fort Lauderdale, FL: Write Stuff Enterprises, Inc. Rougier, M. (2021). La Historia Argentina en su Tercer Siglo: Una Historia Multidisciplinar (1810–2020). Buenos Aires: Ministerio de Desarrollo Productivo de la Nación. Roxborough, I. (1981). The analysis of labour movements in Latin America: typologies and theories. Bulletin of Latin American Research, 1(1), 81–95. Schneider, B.R. (2002). Why is Mexican business so organized? Latin American Research Review, 37(1), 77–118. Studart, R. and Ramos, L. (2018). The future of development banks: the case of Brazil’s BNDES. In S. Griffith-Jones and J.A. Ocampo (eds), The Future of Development Banks (pp. 86–111). New York: Oxford University Press.

4. Murmurs of an industrial revolution in Africa: is it time for Africa? Horman Chitonge

INTRODUCTION Industrial development in Africa is widely known to be low and fragile. Many analysts would agree that Africa, as a whole, is yet to undergo an industrial revolution of some kind. There are a few countries on the continent (South Africa, Egypt, Morocco, Kenya, and now Nigeria) with relatively sophisticated industrial activities, but even these are still waiting for broad-based transformation of their industrial sectors and societies. For the continent as a whole, the picture we see is that of a region that is yet to revolutionize its industrial activities. The now growing body of literature on industrialization in Africa has not only shown that industrial development remains weak, it has also produced a consistent list of challenges believed to be holding back industrial transformation on the continent (see Lall, Henderson and Venables, 2017; Mytelka, 1989; Newman et al., 2016; Page, 2012, 2015). Some analysts have even questioned whether Africa can industrialize (Newman et al., 2016), or become ‘a supplier of manufactured goods’ to the world (Aiginger and Rodrik, 2020). In this chapter, I proceed from the premise that an industrial revolution in Africa is possible. Therefore, the question is not whether Africa can industrialize, the question is whether the seeds of an industrial revolution in Africa exist today. However, an industrial revolution in Africa does not entail inventing a ‘steam engine’ or the ‘spinning jenny’; an African industrial revolution (AIR) will be a product of the underlying conditions, including the current stock of knowledge, technological, industrial practices and material conditions. In other words, an AIR will be a product of the underlying conditions both within Africa and globally. It is important at the beginning to point out that the term industrial revolution is highly contested (see Allen, 2009; Mokyr, 1990). Here I use the term to mean rising and sustained levels of productivity across economic sectors, leading to widening the scope of production, and the spread of growth effects throughout society. While industrial revolutions in the past have been triggered and dominated by different things, one of the common features among them is the ability to spread the effects of rising productivity throughout society, not just in the economic sphere. When an industrial revolution occurs in Africa, it should be manifested in the transformation of the broader society, not just the economic sector. The common triggers of industrial revolutions everywhere are changes in the underlying conditions that create pressure within society on people to slowly adapt to the changing circumstances. This change in underlying conditions can lead to change of mindset, which enables individuals and households to be more creative as they strive for better and higher levels of well-being. There are visible signs of these strivings becoming more widespread among the youth in Africa, who are faced with debilitating levels of unemployment and underemployment, especially in urban areas (ADB/OECD/UNDP, 2017; Filmer and Fox, 2014; Fox et al., 2013; Newfarmer, Page and Tarp, 2018a). When these strivings become secular, 54

Murmurs of an industrial revolution in Africa: is it time for Africa?  55 they contribute to what has been called the ‘industrious revolution’, which has always been the precursor and trigger of industrial revolutions everywhere (de Vries, 1994). Seen from this angle, it becomes evident that an industrial revolution in Africa will not be triggered by Chinese, American, European or Japanese investments, or goodwill; it will be triggered by what lies within Africa. This chapter highlights some of the underlying conditions for an industrial revolution in Africa. Rapid urbanization, rising domestic demand (especially for processed food), growth of regional value chains (particularly in agro-industries) supported by regional trading arrangements including the African Continental Free Trade Area (AfCFTA), revival of interest in industrial policy, and the improving quality of human capital, are some of the enablers of an AIR. Landry Signé (2018), with specific reference to the potential for transforming African manufacturing, has identified low labour costs, availability of abundant natural resources, improving human capital and rising domestic demand as key drivers of manufacturing growth and industrialization in Africa, going forward. While these are important enablers, in this chapter I focus on the potential immediate triggers of an AIR. The purpose of this chapter is not to predict when an industrial revolution in Africa will occur, but to identify the seeds of an industrial revolution on the continent.

INDUSTRIAL REVOLUTION: PAST AND PRESENT What is an Industrial Revolution? To locate this discussion in its broader context, it is important to look at past and current industrial revolutions to highlight the important role underlying conditions play in any industrial revolution. There is an unsettled debate on what constitutes an industrial revolution (IR). Some authors suggest that the term is misleading because in the case of the British Industrial Revolution (BIR), there was no abrupt break or acceleration of change in society – the changes were gradual (Clark, 2001). It is not only the meaning of the term that is contested; even explaining the factors that played an important role in IRs has proved to be controversial. If we take, for instance, the BIR, which has been widely recognized as a turning point not just in British society, but in human history (Allen, 2009), economic historians have for centuries argued about which factors played an influential role. It has thus been argued that: [e]xplaining the industrial revolution is a long-standing problem in social science, and all manner of prior events have been adduced as causes. Recent research has emphasized non-economic factors like the British constitution or British culture which have been alleged to be superior. The matter, however, is controversial: while certain legal arrangements and cultural predispositions may have favoured economic development, it is not at all clear that Britain was alone in possessing them. (Allen, 2011, pp. 357–8)

As Mokyr (1990) argues, abruptness or acceleration of change is a relative concept: ‘Revolutions do suppose an acceleration of the rate of change, but how much does the rate have to change in order for it to qualify?’ (Mokyr, 1990, p. 3). It is not necessary here to go into a lengthy debate about what constitutes IR; suffice to say that analysts emphasize different things on what defines it. Some analysts have emphasized political factors, particularly the role institutions played in creating conditions that gave rise to the BIR (North and Weingast,

56  Handbook of industrial development 1989); others point to the technological inventions of the time (Ashton, 1948); still others highlight the influence of Newtonian science (Mokyr, 1990); while others have highlighted the role of commercial incentives as well as the role played by higher wage levels and the low cost of energy in Britain at the time (Allen, 2009, 2011). What all these different views highlight is that several factors (underlying conditions) contributed to and enabled the occurrence of the BIR at the time it did. Reading through the massive literature on the BIR, it becomes clear that it is not just the immediate causes of IR in Britain that are contested; even the period when the revolution began is a matter of controversy among historians (Clark, 2001; de Vries, 1994). But when we look at the different IRs, some common basic characteristics emerge. Common Features of IRs One of the common features in all IRs is ‘industriousness’ or simply striving for something better. All revolutions are rooted in people’s striving to improve themselves or their societies by changing their existing situation. The other common feature in all IRs is the element of surprise and unpredictability. Indeed, revolutions of any kind – political, economic, industrial, technological, social and so on – have an element of randomness. No one plans a revolution; key protagonists of revolutions only capitalize on the momentum that is already underway. While we can at the moment analyse the underlying conditions and how they can trigger and drive change in society, it is not possible to predict the exact path, time and place of how the changes will occur in future. Just two decades ago, no one was able to predict that we would be Facebooking, Twittering, Google Mapping, Zooming, Instagramming and so forth. All these have revolutionized society. Surprise is very much part of the process of change, even now when we have a lot of information about what type of technology is likely to be dominant in future society. A senior Organisation for Economic Co-operation and Development (OECD) education advisor underscores the unpredictability surrounding digitalization: ‘I don’t think anybody confidently predicts what the implications are for the labour market in terms of the jobs that will be available, still less in terms of the types of knowledge, skills and attitudes that will be important in it’ (cited in The EIU, 2018, p. 21). For example, just 20 years ago, few people would have foreseen that Amazon, Google, Facebook, LinkedIn, Twitter, Workday would belong to the ‘Super-Unicorn Club’ today (see Lee, 2013). The other common feature of IRs is that they bring about irreversible change in the manner, scope and levels of production, largely induced by inventions of new tools, new ways of organizing production, new ideas and products, and technology (Kaldor, 1966; Schumpeter, 1939). Revolutions in the past occurred in various areas of human life, and they have all been marked by an irreversible change in the way things are done or society is organized. These changes, however, are triggered by different underlying conditions. For example, in the context of the BIR, Clark (2001) has shown that the period often regarded as the time when the BIR occurred (1760–1840) was actually characterized by slow gross domestic product (GDP) per capita and low productivity growth. He attributes the rapid spread of industrial activities and inventions during this period to the demographic factor: ‘The seeming dramatic industrialization of the British economy in these years was the result just of the unusual demographic experience of England compared to the rest of Western Europe. This population growth combined with rapid productivity growth in small parts of the English economy spurred rapid structural change and urbanization’ (Clark, 2001, pp. 4–5). As has been observed, ‘[t]he key concept is an increase

Murmurs of an industrial revolution in Africa: is it time for Africa?  57 in the rate of change, not the occurrence of change itself’(Mokyr, 1990, p. 3). Sustained productivity growth is critical because it revolutionizes production and the character of people in society, how they relate to one another, and the type and quality of institutions they build (Abramovitz, 1986; Rodrik, 2015). It is for this reason that IR is often associated with a dramatic rise in economic growth rates (Steinsson, 2020). Phases of IR Although subsequent IRs are built on earlier inventions and discoveries, they are different from the older ones in fundamental ways. Drucker (1993) has observed that later revolutions in industry are characterized by faster diffusion of both ideas and technology because they are increasingly driven by a knowledge-based system and not a tools-based one, as was the case in the past. From the time of the BIR, there have been four IRs so far: the first (IR 1.0, or more appropriately BIR), second (IR 2.0 or the American Industrial Revolution), third (IR 3.0, the German/Japanese Industrial Revolution) and the fourth industrial revolution (IR 4.0). Later IRs have been building on the advances made during earlier revolutions, but each subsequent IR has been characterized by new inventions driven by specialized knowledge, sophistication and quicker diffusion of ideas and techniques (ibid.). IR 1.0 was marked by a transition from cottage industries (localized production that dominated the pre-industrial era) to the establishment of factories and mass production (de Vries, 1994). IR 2.0 was built on the technology developed in the first, leading to the emergence of the steel, chemical, pharmaceutical industrial complex, and the use of electricity in the manufacturing process. Other important technologies developed during IR 2.0 include the internal combustion engine that replaced the steam engine, the invention of the telephone, radio and aeroplanes. IR 3.0 was characterized by further advancement of information and communication technology (ICT), starting from the 1960s up to the end of the 1990s. The key inventions include the development of the electronic industries (the Silicon Valley era), the Internet and the World Wide Web (WWW), and increased automation of production lines. IR 4.0 (currently underway) is characterized by the spread of what is often referred to as the digital economy, supported by recent advances in technology, including computer-aided design (CAD), 3-D printing, robotics, artificial intelligence (AI), cloud computing, smart platforms, wireless communication and so on (Banga and te Velde, 2018). Peter Drucker used a different framework to map out the course of industrial transformation over the last two centuries. He argues that modern society has undergone three major revolutions. The first phase, which he refers to as the industrial revolution, began when ‘knowledge was applied to tools, processes and products’ (Drucker, 1993, p. 53; original emphasis). The second phase, which according to Drucker started around 1880, involved the application of knowledge to work, giving rise to what he calls the productivity revolution. The third phase, which is estimated to have started after World War II, involved the application of knowledge to knowledge itself, giving rise to the management revolution (ibid.). If we extend this framework beyond the 1990s, one can add the digital revolution, which is underpinned by mass production, processing, sharing and storing of knowledge and information using digital platforms – the big data phenomenon (Banga and te Velde, 2018). The mainstream view of IR has tended to emphasize the production of machines and technology as decisive elements of the transformation, when in fact the decisive factor is knowledge. Putting an emphasis on knowledge helps to highlight the critical factor behind

58  Handbook of industrial development all transformations in society – people. One of the weaknesses in the orthodox view on IR is that it tends to glorify machines, tools, gadgets, and now robots, to the extent that the people inventing and using these become invisible. People are central to any process of transformation in any society.

POTENTIAL TRIGGERS OF IR IN AFRICA To illustrate the prospect of an IR in Africa, it is important to demonstrate that the underlying conditions are significantly changing. The question then becomes, what are these changes occurring on the continent that support the view that an IR in Africa is attainable? There are several fundamental changes occurring on the continent, while at the same time there are conditions that have not significantly changed. One of the most important underlying conditions that has been changing is the mindset of Africans. The Industrious Revolution Shifts in the way people think and their vision of the world are important aspects of any process of transformation. When people’s mindsets change, the nature and type of decisions they make, including the goals and vision they set for themselves, change too. In his analysis of the BIR, de Vries (1994) argues that the most important change that gave rise to its various manifestations was the change in people’s behaviour and attitudes. He calls this change the industrious revolution, and he argues that this is what triggered the BIR. The most important change, according to de Vries (ibid., p. 256), was the change in people’s aspirations, which, in turn, changed household behaviour, particularly the way they allocated household resources between consumption and production. According to de Vries, the industrious revolution ‘preceded and prepared the way for the Industrial Revolution’ (ibid.). The point de Vries is making is that the most important factor in any revolution is a change of mindset, which leads to a change of behaviour, decisions people make, and aspirations. In the current African context, there are ample signs of an industrious revolution well underway. One example of this is the growing positive attitude towards entrepreneurship, which two or three decades ago was widely negatively perceived (Chitonge, 2019). The Global Entrepreneurship Monitor (GERA, 2017) reports that Africa has the highest proportion (22 per cent) of working age population starting a business, with 42 per cent indicating that they intend to start a business. While the high rate of entrepreneurship in Africa can be attributed to the absence of productive employment, with starting a business being the only option to earn a living for many people, the spreading positive attitude towards entrepreneurship does point to a shift in people’s mindset, which is an important feature of an industrial revolution. A former Kenyan minister of industrialization and enterprise development captured this when he spoke about a mindset shift among young Africans who now increasingly see themselves not as prospective employees but as employers (Obonyo, 2016, p. 17). Many of the youth who in the past would wait for employment are now thinking in terms of self-employment, and in the process are creating jobs for others (Bhorat et al., 2017; Fox and Thomas, 2016). Today, many African youth see entrepreneurship not as an activity for desperate people, but as a respectable and preferred career option (ADB/OECD/UNDP, 2017; GERA, 2017). It has been observed that ‘[t]oday, entrepreneurship is seen as one of the most

Murmurs of an industrial revolution in Africa: is it time for Africa?  59 sustainable job generation ventures in Africa’ (Obonyo, 2016, p. 16). The issue is not so much about whether entrepreneurship offers a viable source of income, it is about the change in the decisions people are making and what they aspire for. This is an important seed of a revolution. The material condition in which African youth live is part of a set of underlying conditions that are creating a fertile ground for this shift in mindset. Newfarmer, Page and Tarp (2018b, p. 428) capture this when they argue that ‘[r]apid population growth interacting with extremely low productivity in traditional agriculture has led to a wave of urbanization at unprecedently low levels of average per capita income’. The growing ‘employment crunch’ across the continent, especially among the youth (Fox and Thomas, 2016), is an important underlying condition responsible for the mindset change we are now seeing. The Demographic Transition The other related underlying condition is demographic dynamics. For the African continent, the current demographic changes are remarkable. When IR is seen from the vantage point of it being a product of change in human behaviour, the demographic dimension becomes vital. The demographic dividend and IR in Africa The relationship between population and economic growth has been explored in economic development literature from the early days of development economics. In recent years, the debate has been framed in terms of the demographic dividend (DD) (Lam, Leibbrandt and Allen, 2019). The DD refers to the potential gains a country or region can experience as a result of the simultaneous decline in mortality and fertility rates (Bloom, Kuhn and Prettner, 2017). When these two transitions occur, they eventually lead to a fall in the dependency ratio in society, and a growth in the working age population (WAP) (Canning, Raja and Yazbeck, 2015). The economic importance of these two dynamics is that when the child dependency ratio declines, families are able to save more, thereby contributing to stronger capital formation (both human and physical), which is critical to economic development (Fang and Yang, 2013). Not only do households tend to save more when they have fewer dependents, they also dedicate more resources to providing better education and health services for children or dependents. The increase in the WAP plays an important role in ensuring that there is a steady and cheaper supply of labour (Bloom et al., 2017). Williamson (1998) elaborates more on the complex dynamic between demographic transitions and their impact on economic growth and other development outcomes.1 While DD is an automatic outcome of two related demographic dynamics, reaping the benefits of this transition is not automatic. It depends on several other factors such as investment in a range of public services, including education, healthcare, early childhood development and empowerment of women. Creation of productive employment and addressing inequality of opportunity are also important prerequisites. ‘While speeding up the demographic transition can help to deliver more and higher-quality workers, the full economic benefits can be achieved only if there is strong demand for labour: the supply of labour is not enough in the absence of sufficient demand’ (Canning et al., 2015, p. 3). Bhorat et al. (2017), with specific reference to the African context, have highlighted the failure to generate more productive employment opportunities to cater for the rapidly expanding WAP as a risk to reaping the DD in Africa. They argue that African ‘countries need to experience both economic growth and high levels of job creation to realize the dividend that comes with an expansion of the labour

60  Handbook of industrial development force’ (Bhorat et al., 2017, p. 4). In the African context, the fact that most economies are failing to create productive jobs poses a major challenge to reaping DD (Bhorat et al., 2017; Fox and Thomas, 2016). It is not just the absence of productive employment that may prevent Africa from reaping DD benefits. Weak healthcare systems, relatively low quality of education and low levels of women and youth empowerment can prevent Africa from taking advantage of DD. However, it has been observed that Africa can exploit DD benefits if policies are implemented in good time, and there is optimism that this can be done (Canning et al., 2015). Bloom et al. (2017) come to a similar conclusion, arguing that while Africa’s DD is potentially large, reaping the dividend would require coordinating different policies to promote complementarity. Reaping DD benefits can trigger rapid expansion of industrial activities on the continent. The African Tsunami Attention to Africa’s demographic dynamics as a game changer was sparked by the UN’s 2010 World Population Prospects Revision report. The report observed that the African population reached 1 billion people in 2000, and projected that, by 2050, Africa will double its population to 2 billion people, under the low birth rate scenario, and 2.19 billion under the medium growth rate scenario, and 2.5 billion under the high birth rate scenario (UNDESA, 2011).2 When we look at the long-term population trend, it becomes apparent that the continent has been sparsely populated after the debilitating effects of the slave trade and colonialism, with the population in 1950 estimated at 227 million (Table 4.1). Since then, the continent has been experiencing a steady increase in population, in terms of both its share in global population and the absolute number of people. If we look at the 100 years between 1950 and 2050, Africa’s population would increase 9 times, compared to the world population’s 3.2 times, Latin America 3.6 times, Asia 3 times, Europe 1.2 times and North America by 2.1 times. Some commentators have referred to this as the ‘African Tsunami’ (Rotberg, 2013). It is also important to note that Africa will be the only region that will experience substantial population growth after 2050. Lam et al. (2019, p. 5) show that between 2020 and 2100, Africa will account for 87 per cent of growth in world population. Even at the current levels of education, health and investments in physical and human capital, the momentum that this size of population growth can generate is likely to be significant. WAP and the DD Dominant views on the prospects of Africa benefiting from the demographic transition have focused on the size of the WAP, which is the main factor in the DD discussion (Bloom et al., 2017). Bloom et al. (2017, p. 65) have decomposed the DD into what they call the accounting effects (which capture the mechanical consequences of a declining ratio of children in the population – the declining child dependency ratio), and the behavioural effects (which refer to the change in behaviour of families in decisions made around the number of children [fertility rate], the level of investment in children’s education and health, and savings level within the household). The WAP dynamic for Africa is depicted in Figure 4.1. Africa’s WAP has been growing steadily from around 328 million in 1990 to about 750 million in 2020 and this is expected to double to above 1.5 billion in 2050.

0.5

19.8

6.6

7.1

55.1

9.1

3700.4

20.3

233.2

Author, based on data from UN Population Database.

0.5

Source:

21.9

European

6.8

6.9

Latin America & Caribbean

North America

Oceania

9.0

56.6

Africa

Asia

Regional share in world population (%)

3034.9

15.7

World

201.3

North America

Oceania

293.7

661.1

599.5

214.6

Europe

Latin America & Caribbean

2194.5

276.8

1671.7

Africa

373.0

1960

Asia

1950

0.5

17.9

6.3

7.9

59.3

10.1

4380.5

23.6

256.4

369.3

696.5

2701.2

490.0

1970

0.5

15.9

5.9

8.4

61.7

11.2

5327.2

27.7

282.6

450.9

722.8

3282.9

647.3

1980

World population by region (million) and percentage share

World population by region (millions)

 

Table 4.1

0.5

13.6

5.3

8.5

61.6

12.2

6143.5

31.8

315.6

529.2

725.8

3789.3

830.9

1990

0.5

11.8

5.1

8.6

61.7

13.5

6956.8

37.5

346.3

598.0

737.9

4255.2

1066.4

2000

0.5

10.6

5.0

8.6

61.2

15.3

7379.8

40.4

359.5

630.1

744.3

4476.6

1213.0

2010

0.5

10.1

4.9

8.5

60.7

16.4

7794.8

42.7

368.9

654.0

747.6

4641.1

1340.6

2020

0.5

9.6

4.7

8.4

59.5

17.2

8571.0

50.0

396.0

717.0

751.0

4943.0

1714.0

2030

0.6

7.4

4.4

7.9

53.3

25.3

9852.0

64.0

437.0

783.0

730.0

5253.0

2487.8

2050

Murmurs of an industrial revolution in Africa: is it time for Africa?  61

62  Handbook of industrial development

Source:

Author, based on data from UN Population Database.

Figure 4.1

Africa’s WAP and share in total population (1950–2050)

While WAP has been growing since 1960, its share in total population had been declining until the 1990s, when it started to rise, leading to what is now being called the youth bulge (ADB/OECD/UNDP, 2017). Lam et al. (2019) estimate that Africa will be the only continent with the growing WAP after 2050. Since 1995, WAP has accounted for more than half of Africa’s population, reaching 56 per cent in 2020 and expected to rise to 60 per cent in 2050. The current trends in Africa’s WAP imply that there are more people entering the labour market today than a decade before, and, as indicated earlier, this has the potential to contribute significantly to higher growth rates if the labour is productively employed. The large size of the WAP is economically important in terms of wage cost advantage and is likely to be a key driver of manufacturing growth in Africa going forward (Signé, 2017; Sun, 2017). Some studies estimate that ‘increasing the growth rate of the ratio of the working‐age population to the total population by 1 percentage point leads, on average, to an increase in the per capita GDP growth rate of 1.394 percentage points’ (Bloom et al., 2017, p. 8). While gross national income (GNI) per capita in Africa has been growing since the later 1990s, the levels have remained low at an average of around US$1500 for this period, and per capita income growth has slowed down since 2016 (Figure 4.2).3 The seemingly positive correlation between the WAP and the growth of GNI per capita income between 2000 and 2015 can be accounted for largely by the commodity boom that occurred around the same period. The collapse of commodity prices in 2015 has led to slower GNI per capita growth rates in subsequent years. This is where an IR can make a difference. The growth of the entrepreneurial culture that is linked to the WAP dynamic in Africa can contribute to promoting industrial growth as more young people opt to start their own businesses instead of waiting to be employed (ADB/OECD/UNDP, 2017). However, for the WAP dynamic to deliver these benefits, African countries should be able to create productive jobs to ensure that the growing workforce participates meaningfully in the economy (Bhorat et al., 2017). Lam et al. (2019) estimate that African countries will need to create 1.5 million jobs per month in 2020 and 2 million per month in 2037 to cope with the

Murmurs of an industrial revolution in Africa: is it time for Africa?  63

Source:

Author, based on data from World Development Indicators Online Database.

Figure 4.2

Sub-Saharan Africa GNI per capita levels and per capita growth rate (constant 2010 US$)

rising number of new entrants to the labour market. This is a huge challenge in most countries given that the rate of productive employment creation has been dismal, with the continent as a whole reported to have created only 1.3 million jobs in 2015 (Bhorat et al., 2017), and possibly far less after 2015. A rapidly growing WAP can also induce other dynamics, particularly the entrepreneurship vigour that in the continent has been growing as young people strive to find the means of survival, given that formal jobs are not easy to find (Fox, Thomas and Haines, 2017). The growing youth population can also contribute to propping up domestic demand, especially as average income in many countries rises steadily (Shimeles and Ncube, 2015).

Source:

Author, based on data from UN Population Database.

Figure 4.3

Child, adult and total dependency ratios (1950–2050)

64  Handbook of industrial development Dependency ratio The other important aspect of the DD is around the ratio of dependent children and adults to WAP (dependency ratio). In the African context, the rise in WAP ratio since 1990 has translated into falling child and total dependency ratios (Figure 4.3). Child and total dependency ratios are expected to decline sharply from 2020 onwards, although the adult dependency ratio is expected to rise as the share of pensioners in the population increases, but this will have little impact on total dependency ratio.

BEYOND THE DEMOGRAPHIC DIVIDEND In the African context, the critical point is not that some demographic ratios have begun to move in the right direction; it is the massive increase in population on the continent that is a force for change. The more than 2.5 billion people projected to live on the African continent by 2050, even if other things remain the same, implies doubling the current levels of skills and talent in the population, entrepreneurial levels and industriousness. Many young people who are increasingly getting educated and are aware of the challenges the continent is facing and are striving to make a difference in demanding and driving change in various aspects of human life (Signé, 2018). As people strive to meet their daily needs under the extremely challenging African circumstances, the levels of industriousness and entrepreneurship are expected to rise, and that is an important change factor. Pressure on Land The rapidly growing population, and the pressure this creates on resources including land, can trigger change in behaviour and mindset. Different indicators of land availability in Africa are showing that pressure on arable land is building up fast. One example of this is the arable-land-to-labour ratio. If we look at Figure 4.4, the pressure on land is evident, with arable-land-to-labour ratio in 2020 dropping to only one-third of what it used to be in 1960. Africa’s arable-land-to-labour ratio was about two-thirds of the global average in 1960, but this declined to only half in 2020. Pressure on arable land is even more evident when we look at the average arable land available per rural resident over time. Despite Africa urbanizing quite rapidly in recent years (Lall et al., 2017; UN-Habitat, 2014), the size of arable land available per rural resident has fallen significantly from one-third of a hectare (3340m2) in 1960 to less than 4 per cent of a hectare (400m2) in 2020 (Figure 4.4). While these aggregate figures hide the huge variations between and within countries, they do highlight the growing pressure on arable land in general. Pressure on arable land is most acute in countries such Rwanda, Malawi, Burundi, Mauritania, Mauritius, Egypt, Djibouti, Ethiopia and Somalia. This pressure is likely to increase in the next three decades as the population increases. Further, additional pressure on arable land will be exerted by the effects of climate change, which ultimately reduces arable land available. These pressures have the potential to induce change in fundamental ways, including efficient use of land. Increased efficiency in land use can induce and lead to productivity growth. Rising productivity in the agricultural sector can in turn contribute to revolutionizing other industrial activities. Indeed, it is widely believed that sustained economic growth and the transformation

Murmurs of an industrial revolution in Africa: is it time for Africa?  65

Sources: Author, based on data from UN Population Database and FAO Statistics. Arable land figures are for 2019 from the FAO online database.

Figure 4.4

Arable land/labour trends (1960–2020)

of African economies will be boosted by rising productivity in the agricultural sector (ACET, 2017; ADB/OECD/UNDP, 2017). Agriculture sector productivity trends in Africa have increased steadily, though the levels are still below the global average and way below developed countries. If we take, for instance, maize yield per hectare, it is clear that the yields have steadily risen from just under 1 ton in 1960 to 2 tons in 2020 (Figure 4.5). The interesting thing to note in Figure 4.5 is the relative growth in yields over time. While Africa’s yield per hectare relative to the global average was about half in 1960, the continent

Source:

Author, based on FAO online database.

Figure 4.5

Maize yield per hectare by region (kg) (1961–2019)

66  Handbook of industrial development has fallen behind in 2020 to just a third of the global average. Africa’s average maize yield per hectare has been falling relative to other regions, including developing regions such as South Asia and South America. We see a similar trend when we look at soybeans and wheat yields (Table 4.2). While these low yields point to low productivity of both land and labour, this presents an opportunity to raise productivity sharply.

POTENTIAL ENABLERS OF AN AIR Urbanization One of the direct outcomes of the growing WAP is urbanization, which is steadily underway on the continent (Fox, 2017; UN-Habitat, 2014). As Figure 4.6 shows, although the continent today still has more people in rural than in urban areas, population projections show that three out of five people in Africa will live in urban areas by 2050 (see also Lam et al., 2019, p. 20). An expanding urban population has the potential to enable sustained economic growth (Fox, 2017). The potential for growth is not just in terms of increasing the size of the domestic market, but also in terms of raising the levels of industriousness, especially among the youth. Although African cities have been described as ‘closed’ to world markets, crowded, disconnected and, as a result of these features, costly (Lall et al., 2017), urbanization in general creates strong forces for transformation. Urban areas are places where new ideas, shift in mindset and attitudes begin and spread quickly to the rest of society. When this change in

Source:

Author, based on UN Population Database.

Figure 4.6

Urban population (million) and share in total population

Soybeans

Wheat

Soybeans

Wheat

Soybeans

Wheat

Soybeans

Wheat

 

South America

 

America

 

World

 

Source:

Wheat

South Asia

1215.1

1227.9

1633.4

1641.0

1244.6

1249.8

884.3

465.6

828.7

Author, based on FAO online database.

1088.9

1128.7

1298.1

1680.9

1135.8

1145.8

854.2

497.7

693.0

348.0

1494.1

1480.0

1870.6

1750.3

1249.5

1186.7

1110.6

487.1

870.3

436.6

1570.1

1657.4

1869.7

1882.0

1252.6

1676.6

1245.5

1060.1

1100.4

555.2

1975

 

375.8

1970

Soybeans

Africa

1965

 

 

1961

Cereal yield by region (kg/hectare) (1961–2019)

Table 4.2 1980

1855.4

1600.1

1981.0

1764.6

1315.3

1699.5

1381.8

761.1

1098.8

802.9

1985

2171.9

1906.3

2194.1

2126.2

1629.0

1833.0

1668.5

799.4

1247.6

1018.6

1990

2562.6

1895.8

2416.0

2111.2

1717.2

1865.5

1870.8

1017.9

1598.1

677.8

1995

2515.3

2031.2

2343.0

2295.1

2098.0

2173.4

2251.4

1019.4

1621.2

715.7

2000

2721.8

2170.8

2666.0

2474.9

2355.0

2369.7

2468.4

828.3

1752.0

1052.1

2005

2828.7

2317.5

2755.3

2596.9

2501.1

2380.8

2457.2

1088.3

2135.3

1089.6

2010

2972.1

2579.5

3029.9

2889.6

2828.8

2870.3

2585.3

1340.3

2301.3

1458.7

2015

3321.3

2674.1

2882.5

3081.7

2623.2

3012.6

2637.6

748.4

284.07

1236.9

2019

3546.8

2769.0

3370.5

3147.1

3090.0

3148.6

3090.2

1202.5

2756.8

1253.7

Murmurs of an industrial revolution in Africa: is it time for Africa?  67

68  Handbook of industrial development mindset diffuses widely among young people, it can contribute to transforming industrial activities on the continent. Currently, huge opportunities from a growing urban population exist in the food sector particularly. The fact that an increasing number of people on the continent will be living in urban areas means that more people will have to buy food – they will not be able to grow the food they eat. To meet the food needs of the growing urban population, food production (including food processing) will have to increase tremendously. Currently, the continent is a net importer of food, and the value of imported food has been rising rapidly since the early 2000s (Figure 4.7).

Source:

Author, based on FAO online database.

Figure 4.7

Value of food import trends: Africa and the world (1961–2019)

Although Africa’s share in world food imports is still small, about 7 per cent, the value of food imports has risen rapidly from US$41 billion in 2005 to US$76 billion in 2019. This has been attributed to the growing middle class (ACET, 2017). Growing demand for processed food creates opportunities for the continent to expand its agro-processing industrial activities. In the context of IR, the production of food has always been a stepping-stone on the path to more sophisticated industrial activities (Syrquin and Chenery, 1989). Regional Value Chains The other enabler of an AIR is the emerging regional value chains (RVCs) and the gradually growing network of input suppliers (Signé, 2018). The debate on industrialization in Africa has largely focused on strategies that enable countries to migrate from low to high value-added activities in global value chains (GVCs) (Djafar and Milberg, 2020; Newfarmer et al., 2018a; Page, 2015; UNIDO, 2016). But the difficulties involved in migrating from low to high value-added nodes in GVCs have confined many African firms to low value-added activities for a long time, and most of them have little power to change this configuration (Davies, Kaplinsky and Morris, 2018). However, the challenges of moving up the ladder of value addition in GVCs is gradually forcing local firms to create RVCs. Most of the

Murmurs of an industrial revolution in Africa: is it time for Africa?  69 emerging RVCs on the continent concentrate around agro-processing industries, particularly processing of food and other natural resources (UNCTAD, 2019). These emerging RVCs are positioning themselves to take advantage of the growing regional markets, particularly in processed foods. Given the rising urban population and the launch of the AfCFTA, there is great potential to increase the production and processing of food on the continent. The 2017 Africa Transformation Report (ACET, 2017) reports that RVCs on the continent are emerging in several value chains, including cotton, cassava and oil palm. Other growing RVCs include horticultural (mainly fresh fruits), vegetables, cut flowers, rubber and beverages (UNCTAD, 2019). Backbone Services Earlier IRs are widely associated with the growth of the industrial sector, and much less with services. Although services are sometimes seen as ‘alternative sources of growth’ (Hallward-Driemeier and Nayyar, 2018), they are an integral component of any long-term industrialization. Fricke and Dettmer (2014) have observed that a set of ‘backbone services’ is a critical enabler of competitiveness and contributes to increasing the country’s ability to participate in GVCs.4 Under the current global economic conditions where the digitalization of economies has widened the scope of services even into what traditionally could have fallen under manufacturing, services have become a central enabler of industrial transformation and economic growth generally. It is now impossible to achieve any significant level of industrial development without the support of a suite of backbone services. For example, an electronic manufacturing firm that sources inputs from other firms, even within the same country, relies on efficient marketing, sales, logistical, transport and Internet services to be able to function optimally. In cases where these enabling services are inefficient or inadequate, the levels of productivity and competitiveness in economy is low. In the African context, weak and inefficient services due, largely, to inadequate infrastructure, have been recognized as one of the factors constraining economic growth (Lall et al., 2017; Newman et al., 2016; Page, 2015). The gradual improvement in the stock and quality of infrastructure in the last decade is slowly improving the quality of backbone services provided, and this is expected to contribute to creating an environment where ‘pockets of efficiency’ can emerge and spread throughout the economy. Chitonge (2019) gives an example of how the contracting of farmers to supply farm produce to international hotels in Africa’s big cities has led to improved operations along the supply chain, as suppliers strive to meet the service standards required by the buyer (the hotels). These buyer-induced efficiencies are slowly transforming various activities across different sectors of the economy on the continent. Industrial Policy The other important enabler of industrial transformation in Africa is the resurgence of interest in industrial policy (IP). IP is supposed to create an environment where industrial activities can thrive. In this sense, IP is essentially an enabler of industrial development (Aiginger and Rodrik, 2020). While in the past, African governments were advised to abandon IP (Soludo and Ogbu, 2004), we have seen a strong resurgence of interest across the continent, with several countries have formulated IPs in the last decade (Oqubay, 2015; UNECA, 2014). The importance of IP in the context of IR is that, if implemented effectively, it can create condi-

70  Handbook of industrial development tions where the momentum triggered by the underlying conditions referred to above can be sustained. The different triggers and enablers of IR discussed earlier can only lead to radical change if there are policy strategies to facilitate and sustain the transformative momentum. There are, of course, several other triggers and enablers of IR in Africa today; the ones discussed above only serve to illustrate the changes occurring on the continent, creating opportunities for an IR.

AN INDUSTRIAL REVOLUTION IN AFRICA? Low levels of industrialization in Africa, particularly in the manufacturing sector,5 has left many analysts sceptical about the possibility of Africa experiencing sustained industrial transformation. In most countries, the manufacturing sector is not only small, but also concentrated in the processing of raw materials using low technology (Signé, 2018; UNIDO, 2016). South Africa, Morocco, Egypt and Nigeria produce two-thirds of Africa’s manufactured value added (MVA), and only 11 countries out of 54 produce 80 per cent of MVA in Africa (KPMG, 2015).6 Given this situation, talk of an AIR appears to be in the same league as the story of unicorns.7 However, in recent years, some analysts and observers are beginning to recognize the growing potential for industrialization on the continent (Fox et al., 2017; Newfarmer et al., 2018a; Signé, 2017). Between 2015 and 2020, there have been several volumes with the titles or subtitles, ‘Made in Africa’, which signals a positive perception of the prospect of industrial development in Africa. Arkebe Oqubay’s 2015 book, Made in Africa: Industrial Policy in Ethiopia, speaks about an ‘African Renaissance’ underpinned by industrial development. Although this book draws from the Ethiopian experience, it makes a general argument that industrializing Africa is very much possible, and that this industrialization process will diverge from the conventional wisdom and trajectories we have seen in other regions of the world (Oqubay, 2015). Oqubay’s main reason for believing that an AIR is possible is that successful transformation of African economies and societies only require committed leadership to implement an aggressive industrial policy. A second book, Made in Africa: Learning to Compete in Industry (Newman et al., 2016), also argues that industrial development in Africa is possible though for this to happen, African governments must address several enduring challenges such as poor infrastructure, the skills gap, policy and institutional failures, and building capabilities by intensifying and diversifying exports through the growth of non-traditional exports, and promoting regional integration. The book argues that the key to boosting Africa’s industrialization is to implement strategies that can improve the business climate on the continent. A third volume with the title Made in Africa is the United Nations Conference on Trade and Development’s Economic Development in Africa Report 2019 (UNCTAD, 2019). This volume focuses mainly on the potential arising from the creation of the AfCFTA. It sees AfCFTA as a possible game changer, with tremendous potential for job creation and poverty reduction. Irene Sun’s (2017) article is another example of a recent commentary that points to the growing potential for Africa to become the ‘World’s next great manufacturing center’. The reason she gives for this is that:

Murmurs of an industrial revolution in Africa: is it time for Africa?  71 Chinese companies are making more than 150 investments in the manufacturing sector in Africa… In Nigeria, Chinese businesses smelt steel to fuel the construction boom in Africa’s largest economy. In tiny [sic] Lesotho, Chinese and Taiwanese companies churn out Kohl’s yoga pants, Levi’s jeans and Reebok athletic wear destined for the US shopping malls. They have made the clothing industry the largest economic sector in the country. In Ethiopia, just as the British pharma Giant GSK was scrapping its plans to build a drug production plant, Humanwell, a Chinese pharmaceutical company broke ground on a $20 million production site outside Addis Ababa. (Sun, 2017, p. 122)

Overall, the theme of ‘Made in Africa’ is now featuring more prominently in scholarly discussions, suggesting that researchers are beginning to recognize the potential for industrial transformation on the continent. Several analysts are now able to see some fundamental changes occurring on the African continent that make industrialization more likely than before. What most of these analysts are highlighting is that the underlying conditions to trigger an AIR lie within Africa itself. It is not foreign direct investments from China, Europe, America, Brazil, India, Australian or Japan that will spark an AIR; the ‘seeds of the revolution’ are within Africa. Foreign investments are a familiar activity in Africa. The European capitalists energetically partitioned Africa among themselves, and (like the Chinese) melted steel, logged timber, mined diamonds and gold, forced Africans to grow tea and tobacco instead of maize and cassava, yet the continent has not experienced anything that resembles rapid and sustained productivity growth. The Europeans have been investing in Africa for the past 135 years (and they have continued to today, now competing with the Chinese), but we have not seen the expansion of industrial activities and sustained growth. If Africa’s past experience is anything to go by, there is no justification that the Chinese investments that Sun is celebrating will trigger an IR. The potential trigger of an IR in Africa lies in the dynamism created by the underlying conditions within Africa and by Africans. The IR in Africa will certainly borrow and build on past IRs, but there is no reason to expect it to resemble them. The only thing we should look for, in common with past IRs, is rapid and sustained growth in productivity. When this occurs in all major sectors of African economies, it is only then that we can safely say, ‘It is time for Africa!’

CONCLUSION This chapter has argued that an IR is possible in Africa, and that the underlying conditions for an IR are changing rapidly. The nature of industrial transformation that will occur in Africa will certainly be different from what has been experienced in other regions of the world in the past. This is simply because Africa’s IR will occur under different underlying conditions (globally and locally). The chapter has highlighted the importance of change of mindset, people’s aspirations and the goals they set for themselves. It is this change of mindset within the population that triggers events that lead to the transformation of society. These changes have been underway on the continent for some time now, though few people realize this. As the former Nigerian Finance Minister, Ngozi Okonjo-Iweala (now WTO Director General), once observed, ‘Africa is changing, but only the smart ones are noticing!’ Several factors such as the growing population, stubborn unemployment and underemployment, high levels of poverty, low productivity, rising urbanization, increasing pressure on land and the effects of climate change, are creating pressure for change in mindset, which is a key

72  Handbook of industrial development precursor of any IR. Although the change in mindset is more evident in urban areas among the youth who are becoming more industrious as they seek to navigate the challenging circumstances in which they find themselves, these changes are gradually spreading to the rural areas as well. However, for these changes to take root and revolutionize industrial activities on the continent, there should be enablers that can sustain the momentum of change. Urbanization, the emergence and growth of RVCs, the launch of the AfCFTA, improvement in backbone services, and the revival of interest in industrial policy are some of the potential enablers of an IR in Africa.

NOTES 1.

In terms of demographic dynamics, Williamson (1998) speaks of the three bulges: the rising number of child dependents, rising number of labourers (working age population), and the third bulge characterized by the rising adult (elderly) dependents. 2. Africa here includes North Africa, not just Sub-Saharan Africa, which is often reported as Africa. 3. Even if we use purchasing power parity to take into account the differences in the dollar purchasing power in different regions, per capita income in Africa is still low at around US$4600. 4. Fricke and Dettmer (2014) identify six backbone services: energy supply, communication, distribution, transport, finance and insurance. 5. The industrial sector as a whole has actually not been very bad, mainly due to the large extractive industries and the natural-resource-based manufacturing. The industrial sector share in GDP has averaged above 32 per cent for the period 1980 to 2015 for resource-rich countries, and 23 per cent for non-resource-rich countries over the same period (Chitonge, 2019, p. 101). It is the manufacturing sector that has been weak, with an average of just about 9 per cent of GDP in 2017. 6. This includes the four countries plus Tunisia, Kenya, Côte d’Ivoire, Angola, Ghana, DRC and Zambia (see KPMG, 2015). 7. A unicorn is a mythical animal in Greek mythology, but recently the term has been used to refer to the fastest-growing start-ups with market capitalization of over US$1 billion. The term is used to describe the short ‘time to market cap’ demonstrated mainly by software-based companies founded in the last decade (Harvard Business Review, 2016). Aileen Lee (2013), who coined the term ‘Unicorn Club’ argues that that every major tech discovery leads to the emergence of one or two ‘super-unicorns’: Facebook in the 2000s; Google and Amazon in the 1990s; Cisco in the 1980s; Apple in the 1970s; and Oracle and Microsoft in the 1960s.

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74  Handbook of industrial development Mytelka, L. (1989). ‘The unfulfilled promise of African industrialization’. African Studies Review, 32(3), 77–137. Newfarmer, R., J. Page and F. Tarp (2018a). ‘Industries without smokestacks and structural transformation in Africa: overview’. In R. Newfarmer, J. Page and F. Tarp (eds), Industries without Smokestacks: Industrialization in Africa Reconsidered. London: Oxford University Press, pp. 1–25. Newfarmer, R., J. Page and F. Tarp (2018b). ‘Widening the options: implications for public policy’. In R. Newfarmer, J. Page and F. Tarp (eds), Industries without Smokestacks: Industrialization in Africa Reconsidered. London: Oxford University Press, pp. 411–31. Newman, C., J. Page and J. Rand et al. (2016). Made in Africa: Learning to Compete in Industry. Washington, DC: Brookings Institution Press. North, D. and B. Weingast (1989). ‘Constitution and commitment: the evolution of institutions governing public choice in seventeenth century England’. Journal of Economic History, XLIX(4), 803–32. Obonyo, R. (2016). ‘Africa looks to its entrepreneurs’. Africa Renewal, 30(1), 16–17. Oqubay, A. (2015). Made in Africa: Industrial Policy in Ethiopia. Oxford: Oxford University Press. Page, J. (2012). ‘Can Africa industrialise?’ Journal of African Economies, 21(2), 86–124. Page, J. (2015). ‘Structural change and Africa’s poverty puzzle’. In L. Chandy, H. Kato and H. Kharas (eds), The Last Mile in Ending Extreme Poverty. New York: Brooklyn Institute Press, pp. 219–48. Rodrik, D. (2015). ‘Premature deindustrialisation’. NBER Working Paper, No. 20935. Accessed 16 September 2017 at http://​www​.nber​.org/​papers/​w20935. Rotberg, R. (2013). Africa Emerges. Cambridge, UK: Polity Press. Schumpeter, J. (1939). Business Cycles. New York: McGraw-Hill. Shimeles, A. and M. Ncube (2015). The making of the middle-class in Africa: evidence from DHS data. The Journal of Development Studies, 51(2), 178–93. Signé, L. (2017). Innovating Development Strategies in Africa: The Role of International, Regional and National Actors. Cambridge, UK: Cambridge University Press. Signé, L. (2018). The Potential of Manufacturing and Industrialization in Africa: Trends, Opportunities and Strategies. Africa Growth Initiative at Brookings. Accessed 3 August 2021 at https://​www​ .brookings​.edu/​wp​-content/​uploads/​2018/​09/​Manufacturing​-and​-Industrialization​-in​-Africa​-Signe​ -20180921​.pdf. Soludo, C. and O. Ogbu (2004). ‘A synthesis of major themes in the political economy of trade and industrialization in Africa’. In C. Soludo, O. Ogbu and H. Chang (eds), The Politics of Trade and Industrial Policy in Africa. Trenton, NJ/Asmara, Eritrea: Africa World Press Inc., pp. 1–42. Steinsson, J. (2020). ‘How did growth start? The Industrial Revolution and its antecedents’. Accessed 11 August 2022 at https://​eml​.berkeley​.edu/​~jsteinsson/​teaching/​originsofgrowth​.pdf. Sun, I.Y. (2017). ‘The world’s next great manufacturing center’. Harvard Business Review, May–June, 122–9. Syrquin, M. and H.B. Chenery (1989). ‘Three decades of industrialization’. World Bank Economic Review, 3(2), 145–81. The Economist Intelligence Unit (EIU) (2018). The Automation Readiness Index: Who is Ready for the Coming Wave of Automation? London: EIU. UN Department of Economic and Social Affairs (UNDESA) (2011). World Population Prospects: The 2010 Revision (Volume 1). New York: UNDESA. UN-Habitat (2014). The State of African Cities: Reimagining Sustainable Urban Transition. Nairobi: UN-Habitat. United Nations Conference on Trade and Development (UNCTAD) (2019). Economic Development in Africa Report 2019: Made in Africa – Rules of Origin for Enhancing Intra-Africa Trade. Geneva: UNCTAD. United Nations Economic Commission for Africa (UNECA) (2014). Dynamic Industrial Policy in Africa: Innovative Institutions, Effective Processes and Flexible Mechanisms. Addis Ababa: UNECA. United Nations Industrial Development Organization (UNIDO) (2016). Industrialization in Africa and Least Developed Countries: Boosting Growth, Creating Jobs, Promoting Inclusiveness and Sustainability. Vienna: UNIDO. Williamson, J.G. (1998). ‘Growth, distribution, and demography: some lessons from history’. Explorations in Economic History, 35(3), 241–71.

5. Industrialization, economic and political power Graham Brownlow

1

INTRODUCTION: ‘BIG BILLS ON THE SIDEWALK’, PATH DEPENDENCE AND INDUSTRIALIZATION

In this chapter, we are concerned with the economic history of industrialization and its relationship to economic and political power. Mancur Olson’s argument regarding ‘big bills on the sidewalk’ has abiding relevance in examining this relationship (Olson, 1996). Olson argued that if Coasean bargains really were possible then there would not be so many inefficient institutions, with the accompanying poverty, in the real world (ibid.). Olson observed that such bargains were often prevented, as gaps existed between what was individually and socially rational (ibid.). Olson concluded that Coasean bargains would not always occur, the most efficient institutional arrangements need not automatically emerge, and that inefficient ones could persist. Hence, he saw ‘big bills’ as abiding features in economic life (Olson, 1996, p. 23). Variations of this ‘big bills’ pessimism can be found in the old and new institutionalisms (Acemoglu and Robinson, 2013; Hodgson, 2001; North, 1990; Veblen, 1915).1 Veblen, influenced as he was by evolutionary thinking, rejected the idea that all kinds of economic system should converge into one (‘natural’) type, any more than all species would evolve into a single one (Hodgson, 2001, p. 69). Veblen, by rejecting the Marshallian focus on a single equilibrium, as well as related Marxian arguments concerning profit maximization and strategy, instead invoked cumulative causation under which multiple outcomes were possible (ibid.). The idea of cumulative causation relates to the possibility of economies or technologies getting ‘locked into’ inferior paths of development: hence there will be ‘path dependency’ rather than convergence to a given equilibrium (Arthur, 1989; David, 1985; Hodgson, 2001, p. 69). Path dependence involves contingent, non-reversible dynamic (including evolutionary) processes (David, 2007). History matters. In theory, once ‘locked in’, a system cannot escape except through some external force that alters the underlying structural relationships among the agents (David, 2007, p. 131).2 Similar insights regarding path dependence apply when we discuss the determinants of long-run economic performance. Nicholas Crafts has contended, for instance, that the pursuit of political and economic power as well as path-dependent mechanisms may offer an explanation of the UK’s long-run underperformance: Economic historians often stress that institutions are persistent and this means that ‘history matters’. Institutional reform is often difficult even when existing arrangements have plainly become dysfunctional – switching costs are high and the resistance of supporters of the status quo is hard to overcome… An implication of persistence is that growth performance can be affected by institutional legacies. At the same time, it is important to recognize that history also matters through its legacy of constraints on the policy choices of vote-seeking politicians. (Crafts, 2018, p. 125)

An important aspect of the economic history of industrialization is how it has related to industrial policy. Industrial policy has been defined in a variety of ways by both its supporters 75

76  Handbook of industrial development and detractors (Bailey, Cowling and Tomlinson, 2015; Shackleton and Zuluaga, 2016). For the purposes of this chapter, we present it as involving any targeted microeconomic policy aimed at promoting manufacturing (Aiginger and Rodrik, 2020). Industrial policies in theoretical terms are often concerned with applying the classic Pigouvian analysis to structural issues (Coyle, 2020; Crafts, 1993).3 There is a range of such market failures that may give rise to the need to intervene. However, once we acknowledge that, theoretically, markets can fail, we must equally acknowledge that there is a danger of committing the ‘Nirvana fallacy’ of downplaying the possibility that interventions may be more damaging than the market failure they aim to correct (Demsetz, 1969). In theory, interventions, such as industrial policy, can promote rent-seeking activities that are closely related to crony capitalism (Krueger, 1974; Tullock, 1967). Limiting entry of employees or rivals, directing subsidies to incumbents, and regulatory capture more generally, are some of the ways in which theory suggests political and economic power may promote rent-seeking (Coyle, 2020, p. 261). More narrowly, economic historians have found evidence of rent-seeking besetting industrial policy and of institutional reforms being able to offset these problems (Brownlow, 2007; Crafts, 1995). The structure of the remaining sections of the chapter is as follows. In the next section, we provide a survey of some of the ways the issue of industrialization paths have been addressed. In Section 3, discussion turns to evidence from economic history in explaining why imitation of successful paths has proved and remains difficult. In Section 4, we consider the long-run UK industrialization path since the Industrial Revolution and its relationship to explaining poor performance. Section 5 discusses the long-run Indian industrialization experience. Section 6 concludes by summarizing the findings and discussing recent work regarding paths of deindustrialization.

2

INDUSTRIALIZATION PATHS AND INDUSTRIAL POLICY: A BRIEF SURVEY

In this section, we will survey some of the ongoing attempts at investigating industrialization paths. A first argument suggests that industrialization follows a single series of stages, while an alternative argument, traceable back to the old institutionalism, suggests that at least two or more paths have existed. For those who claim that a single pathway exists in economic history, as a matter of interpretation it follows that successful outcomes involve relatively easy processes of imitation. In contrast, if different pathways have existed, then more pessimistic conclusions are implied. The older institutionalists, such as Veblen and Commons, held this more pessimistic view. They argued that late-start economies would develop differently from the early industrializers and that any success for late starters was not guaranteed. Veblen, for instance, argued that Japan’s industrial success was far from inevitable (Veblen, 1915). Instead, he argued that it was a result of its unique combination of assimilating technical knowledge with its ceremonial institutions (Hodgson, 2001, p. 70; Veblen, 1915).4 Written against the backdrop of the Cold War, W.W. Rostow’s take-off analysis, which was billed as providing a ‘non-communist manifesto’, was an optimistic response to the Marxist model (Rostow, 1960, 1963). Rostow’s analysis suggested that a common pathway to development existed. Rostow started with preconditions, went onto take-off, then the drive to maturity, and a final stage of high-level mass consumption (Kindleberger, 1997, p. 13;

Industrialization, economic and political power  77 Rostow, 1960). Some distinguished economists and economic historians, including two future Nobel laureates, have lined up to identify problems with Rostow’s model (Kindleberger, 1997; Kuznets, 1963; North, 1963). There are at least three major problems with Rostow’s approach. A first aspect of Rostow’s model that justifiably drew criticism was his claim that the stage of take-off was so predictable as to be dated fairly precisely. In the case of Britain, 1783 was the year he ascribed to its take-off (Rostow, 1960, p. 38). Other scholars point out that such precision is somewhat spurious to say the least (Kindleberger, 1997, p. 13). Rostow’s claim that certain leading sectors – such as railways in Britain, the USA, France and Germany – spurred economies along this preordained path was another of his arguments that has not fared well (Rostow, 1960, pp. 61–2). Indeed, pioneers of cliometrics went out of their way to demonstrate that railroads were far from indispensable for American economic growth, for example (Fogel, 1964). Rostow’s claim that all capitalist, and perhaps even the socialist, economies would in time follow the stages in a fairly uniform fashion was a third argument that has drawn criticism (Kindleberger, 1997, p. 13; Rostow, 1960, pp. 149–56). It soon ran into empirical problems when the detailed historical records were compared with Rostow’s predictions (Rostow, 1963). Rostow and his students eventually conceded that some countries such as Argentina, for example, were subject to a ‘long delay’ in terms of reaching take-off, but such arguments look like ad hoc justifications (Kindleberger, 1997, p. 13). In short, Rostow’s stage view of industrialization had far less generality than he asserted. Gerschenkron, in contrast to Rostow, argued that Britain’s ‘early start’ during the Industrial Revolution was not simply imitated by successful followers (Gerschenkron, 1962). Gerschenkron, for example, by drawing comparisons between English and Russian industrialization, argued against Rostow that there was no unique set of preconditions (Gerschenkron, 1963). More generally, he suggested that one precondition could substitute for another (Gerschenkron, 1963; Kindleberger, 1997, p. 13). Gerschenkron suggested by way of illustration that banks and government could substitute for private sector entrepreneurs if the latter were missing from a society. Gerschenkron expanded the antecedent conditions into a theory of backwardness, in which he argued that the more backward an economy was at the start of industrialization, the more it was necessary that industrial banks step in to provide capital (as he claimed was the case with German universal banking) (Fohlin, 2016, p. 420). In cases of even more extreme underdevelopment, as in the Russian case, government intervention would be required (Fohlin, 2016, p. 420; Kindleberger, 1997, pp. 13–14). Empirical support has, however, proven hard to find for Gerschenkron’s predictions (Fohlin, 2016, pp. 422–3). Critics of what Gerschenkron himself termed ‘the big spurt’, for instance, note that Gerschenkron eventually felt compelled to explain why Austria failed to fit his model (Gerschenkron, 1977; Kindleberger, 1997, p. 14). In short, Gerschenkron suggested that industrialization could follow a range of successful paths. While this part of his argument is plausible, it is also highly doubtful whether he was successful in providing a truly general model. Another framework for thinking about industrialization, and which blends economic history and theory, is that associated with Kaldor (Kaldor, 1966, 1967, 1970). Kaldor produced a range of arguments regarding ‘stylized facts’, stages, cumulative causation and comparative growth rates (Kaldor, 1966; see also Abad and Khalifa, 2015; Eatwell, 1982). Unlike Rostow and Gerschenkron’s arguments, Kaldor’s continues to influence economists’ thinking about industrial policy and growth (Dosi, Riccio and Virgillito, 2021; Felipe, Mehta and Rhee, 2019;

78  Handbook of industrial development Rodrik, 2016). Kaldor, following his analysis of relatively poor UK growth, suggested that it had reached a state of ‘premature maturity’ (Kaldor, 1966, p. 4). According to Kaldor, the UK economy, thanks to its ‘early start’, had arrived at a mature stage of development earlier than in other advanced economies. However, in Kaldor’s view, it had done so prematurely; it had not attained particularly high productivity or income per head (ibid., p. 31). Kaldor’s analysis was concerned with what he called the ‘rhythm of development’ as it related to stages in an economy (ibid., pp. 21–2). Kaldor’s analysis provided a rationale for activist industrial policy because he identified manufacturing as the historical engine of growth (Kaldor, 1966, p. 18; Stafford, 1981). He suggested that the growth rate depended on the growth of output per worker in manufacturing and that this in turn was determined by the rate of growth of demand for manufactures (Kaldor, 1966; see also Eatwell, 1982, p. 59).5 Moreover, and again invoking cumulative causation, Kaldor (1970) explained why regional living standards within a country could diverge rather than converge. Cumulative causation implied that there was a need for both automatic financial transfers – in the form of risk pooling – as well as discretionary industrial policies, in order to try to rectify regional inequalities (ibid.). Kaldor’s analysis hence provides an explanation for the UK’s relative economic underperformance as well as suggesting there could be growing regional inequality unless governments intervened. Kaldor’s predictions regarding the size of the manufacturing base or trade have not fared well (Crafts, 1993, pp. 27–9). Nor do Kaldor’s predictions regarding diverging regional economic paths find much empirical support: the most authoritative empirical studies of British regional gross domestic product (GDP) between 1911 and 2001 suggest a U-shaped curve rather than persistent divergence (or indeed convergence) (Geary and Stark, 2015, 2016). Kaldor’s argument that income per head would determine the level of inter-regional fiscal transfer also appears wanting. The empirical evidence suggests that different UK regions with very similar income per head have ended up with very different fiscal transfer situations. A ‘corridor’ of possible outcomes seems a more historically accurate description (Brownlow, 2017). Even if we think that cumulative causation explains the economic differences between regions, it is political economy differences that better explain the observed public finance data (Brownlow, 2017, p. 566). Kaldor’s analysis was weak on the institutional determinants of public finance. Historically, different regions have different lobbying powers, and this aspect is crucial in examining why spatial institutional geography varies so much around the world (Rodríguez-Pose and Gill, 2005). A more recent attempt at trying to build an economic history-based account of comparative industrialization paths can be found in Michael Best’s How Growth Really Happens (2018) (Best, 2018). Best rejected the idea of trying to create a single, formal model of industrialization paths analogous to a Keynesian model. Instead, he argued that historical case studies better serve as an analytical starting point (Best, 2018, p. 127). Best thus rejected much conventional modelling in order to build an explanation of what he termed ‘economic transformative experiences’ (Best, 2018, p. 3).6 Best, by drawing on what he termed the ‘capability triad’, which he argued was characterized by skill formation, business models and production systems, surveyed the long-run economic histories of the USA, UK, Germany, Ireland, China and Japan. Best acknowledged that each of these experiences differed from each other. He noted that Ireland used low corporate taxes to promote inward investment and a more R&D-based approach was found in Japan. Best hence suggested that, as long as they were

Industrialization, economic and political power  79 built on interconnections within the triad, very different industrial policy approaches could be successful (Best, 2018, pp. 4, 22–3).7 Another recent discussion of the diversity of industrialization paths can be found in Iverson and Soskice’s Democracy and Prosperity (2019). Their discussion of the variety of economic history is particularly interesting when thinking about economic and political power because they connect economic performance to the variety of political paths found internationally (Iverson and Soskice, 2019, pp. 53–101). Iverson and Soskice argue that modern industrialization differs from earlier periods because clusters of skills in urban environments makes it relatively immobile (ibid., p. 29). They contrast this new regime with Fordism and suggest the latter was fairly egalitarian in its wage-setting implications. So, when centralized manufacturing wage bargaining collapsed, so did this source of wage restraint. In addition, reliance on Keynesian demand management (which had previously prioritized full employment) was, according to Iverson and Soskice, another victim of the collapse of Fordism. Macroeconomic instability and deindustrialization are therefore connected in this interpretation of economic and political power (ibid., pp. 102–36). According to Iverson and Soskice, the economic model of the contemporary world, which they termed ‘advanced capitalist democracies’ (ADCs), has ramifications for regional economic divergence and political polarization. Within ADCs, the economically powerful workers from these immobile clusters become aspirational voters concerned with preserving their high living standards. Electoral demand for economic competence hence tends to dominate elections within such prosperous clusters. To gain votes, successful politicians will have to offer growth-enhancing policies. However, disillusioned voters in poorer locations will turn towards more populist electoral offerings (ibid., pp. 216–57). Those voters with and without political-economic power will end up living in places looking very different from each other. It is on increased educational opportunities rather than activist industrial policy that Iverson and Soskice place their hopes for reversing such economic divergence and political polarization (ibid., p. 250).

3

INDUSTRIAL POLICY AND THE IMITATION OF SUCCESSFUL INDUSTRIALIZATION PATHS

Even supporters of activist industrial policy concede that if lobbying allows it, industries may succeed at appropriating returns while socializing risk (Mazzucato, 2013). It follows that, as different sets of constraints vary in their fruitfulness for pursuing industrial policy, then the imitation of successful policies may depend on the similarity between the institutional-historical contexts in which successful industrial policy first emerged and the contexts that exist in the followers attempting to imitate previous successes. This line of argument returns us to Veblen’s insights regarding varieties of development paths (Hodgson, 2001, p. 69). Considerations of space restrict the historical detail or range of examples we can cover; however, a number of examples have been presented of countries that successfully formulated and implemented activist industrial policies. Best’s discussion of the capability triad, discussed previously, provides an informative historical overview (Best, 2018). Coyle identifies postwar Japan as an example of industrial policy success (Coyle, 2020, pp. 131–2). She notes that in the wake of the asset bubble collapse in the 1990s, the emphasis of policy shifted towards building regional clusters and liberalization of finance and away from the

80  Handbook of industrial development earlier export and R&D support measures. Nevertheless, a governmental commitment to industrial policy of some form has been an ongoing feature (ibid.). A number of authors coming from a variety of perspectives present South Korea’s industrial policy as a success (Amsden, 1989; Crafts, 1999; Rodrik, Grossman and Norman, 1995; Wade, 2004). Proponents of the ‘developmental state’ thesis suggest that a range of interventions (including subsidies, trade restrictions, administrative guidance, public enterprises or credit allocations) explains the South Korean success (Amsden, 1989; Wade, 2004). Those more sceptical of that statist interpretation, view aspects of its institutional and historical legacy as vital in explaining South Korea’s success (Crafts, 1999; Rodrik et al., 1995). As discussed in the introduction to this chapter, there is a danger that one pitfall of activist industrial policy is that it can descend into crony capitalism through rent-seeking. Yet, South Korea’s high level of equality arguably avoided the need for large-scale redistribution; thus, it reduced the risk that redistribution could degenerate into rent-seeking. Furthermore, South Korea’s dedicated bureaucracy further restrained any rent-seeking risks. Crafts has observed that the political-economic preconditions in South Korea, which enabled it to successfully pursue industrial policy, may not be replicated elsewhere (Crafts, 1999). The ability to formulate and implement successful industrial policy may therefore rest on institutional-historical legacies. It may be reasonable to assume that shared institutional legacies are more likely to exist in regions within a country than between different countries. This assumption deserves some historical qualification. Pollard’s classic analysis of European industrialization between 1760 and 1970, while accepting some of the spread was a result of successfully imitating the British model, also placed regional histories at the centre of his discussion (Pollard, 1981). Pollard noted that economic development in Europe did not necessarily correspond with national political boundaries, and he argued that attempts at explaining European economic history by applying a ‘national’ view of industrialization thus represented ‘an inadmissible backward projection from a differently organized world on to an earlier Europe’ (Pollard, 1981, p. viii). In Pollard’s view, what separated those neighbouring localities that industrialized from those that did not, regardless of political boundaries, could have been due to small shifts that were able to convert vicious circles into virtuous ones and vice versa. Pollard’s discussion implies that historical accidents of the sort that may give rise to path dependence might explain Europe’s uneven industrial geography.

4

‘EARLY START’, INSTITUTIONAL LEGACIES AND INDUSTRIALIZATION: INTERPRETING THE UK SINCE THE INDUSTRIAL REVOLUTION

Nicholas Crafts in his 2018 book Forging Ahead, Falling Behind and Fighting Back summarizes his decade-long research on comparative economic performance. The hypothesis of the book, which covers UK economic history from the Industrial Revolution to the financial crisis, is that the observed underperformance was connected to the ‘early start’ of weak productivity performance during the Industrial Revolution (Crafts, 2018, pp. 11–37). The productivity disadvantages of this ‘early start’ were, according to Crafts, most acutely felt during the Golden Age of economic growth (c. 1950–73).8 Crafts demonstrates that it was during these years that the UK, despite experiencing its fastest ever growth rate, was overtaken (rather than merely caught up) by France and Germany (ibid., pp. 79–85). For Crafts then, institutional

Industrialization, economic and political power  81 arrangements traceable back to the early start went much of the way in explaining long-run underperformance: The main thrust of my argument, simply put, is that growth in the United Kingdom was undermined to a significant extent by institutional legacies which can be traced back to the early start and which interacted with weak competition in product markets to impair productivity performance. (Crafts, 2018, p. 127)

Crafts argues that the evidence also points to government failure, linked to ongoing rentand vote-seeking, which were common in attempts at remedying supply-side weaknesses (ibid., p. 90). Following the distinction developed by Hall and Soskice (2001), Crafts contends that the UK settled on a liberal market economy (LME) rather than a coordinated market economy (CME) (ibid., pp. 86–7). Crafts observes that CMEs provide for higher and more patient investment settlements. He suggests that the UK settling on an LME was a mixed blessing. While an LME allowed for greater flexibility in response to shocks, it equally provided a less stable (and hence inferior) investment environment. For the UK, however, a major ingredient in a successful LME was missing during much of the Golden Age: strong competition in product markets was not a cornerstone of policy (ibid., pp. 91–2). Crafts presents LMEs as involving more than one path of industrialization. His discussion also suggests that policy errors in the UK explain partly why the American LME variant has enjoyed greater long-run success. Indeed, he describes the UK economy during the Golden Age as representing what he terms ‘a malfunctioning LME’ (ibid., pp. 94–7). Furthermore, c. 1950, the UK was disadvantaged in formulating successful industrial policy because CMEs are better suited to exploiting the opportunity for catch-up growth (Crafts, 2018). The evidence produced by Crafts regarding CMEs and LMEs reinforces two arguments we have seen earlier in the chapter. First, the idea that there is more than one industrialization path is one supported by the evidence Crafts produces. Second, his evidence lends further support to the observation that imitation of successful industrial policies from one institutional-historical context (as we could interpret the American LME model) cannot be painlessly applied to another (in this case, the UK LME model). Policy design regarding subsidy was especially flawed in the UK. Crafts observes that investment was supposed to be encouraged by a variety of subsidies, including depreciation allowances or grants; the evidence is that there was a large deadweight cost due to this untargeted approach (Crafts, 2018, p. 91). He also discusses that selective subsidies tended to go to a few sectors (such as aircraft, shipbuilding and motor vehicles) as well as ailing industries that dominated the focus of the National Enterprise Board (NEB), the pattern of tariff protection and in the creation of British Leyland. Selective interventions in the high technology area during the period were equally unsuccessful. Crafts speculates that a more ‘horizontal’ emphasis on measures such as R&D tax credits would have been more appropriate than selective attempts at nurturing ‘national champions’ (ibid.). During the Golden Age, the attainment of microeconomic efficiency – and with it the potential for better productivity outcomes – was further undermined because competition policy was toothless (ibid., pp. 92–3). Crafts illustrates that there were few investigations, few mergers were prohibited and any penalties were weak or non-existent, ‘public interest’ defences for anti-competitive behaviour were upheld and the process was politicized with neither big business nor trade unions supporting measures to moderate supernormal profits or wages. The toothlessness of competition policy was due in part to a failure of contemporaries

82  Handbook of industrial development to understand the connections between market power and productivity (Broadberry and Crafts, 2001; Crafts, 2018, p. 92). The contrast between the UK’s often concentrated market sectors, with their associated ossified business practices, with the outcomes of West Germany’s more adaptive Mittelstand business system is a theme others have noted (Best, 2018, p. 132; see also Chapter 22 in this book). Relatedly, Crafts presents British delay in entering the EEC as a further missed opportunity in terms of promoting greater competition and benefitting from lower trade costs. He found, with clear implications for the post-Brexit economy, that EEC membership, via its impact on trade volumes and greater competition, raised UK GDP by about 8–10 per cent (Crafts, 2016). Crafts is far from arguing that laissez-faire should have been the order of the day. He notes that market failures hindered appropriate policy formulation c. 1950–73: the importance of promoting competition and funding civil R&D was undervalued. Likewise, he notes that the role of poor industrial relations and corporate governance made raising productivity more difficult. The result of this unhappy settlement was that it would only be in the 1980s that a sustained (albeit confrontational) approach to reforming industrial relations would occur, with mixed results (Crafts, 2018, pp. 114–17). The publication of an explicit industrial strategy by the UK’s Conservative government in 2017 has even been presented as both a revival of interventionist thinking and illustrating an example of ‘the links between events, politics, and economic thought’ (Coyle, 2020, p. 127).9 Whatever the accuracy of these claims, it is nevertheless the case that the track record of explicitly stated industrial policies in the UK case has been decidedly mixed (Brownlow, 2020). Furthermore, in recent decades, other countries have had greater success with industrial policy than the UK (Bailey et al., 2015). It has been noted that in the UK more recently, a mix of policy tools were chosen in what can charitably be described as an ad hoc manner. Furthermore, in some sectors, an implicit or informal selective industrial policy has come to the fore even if it was never legislated as such (Coyle, 2020, p. 129). The role of government funding in basic medical research and NHS procurement, deregulation and public investment in transport infrastructure in London have been highlighted as examples of ‘accidental’ (or implicit) selective industrial policy in the UK during the 2010s, serving pharmaceuticals and the finance sector, respectively (ibid., pp. 129–30). Echoing the earlier findings of Crafts, even within LMEs the British approach remains distinctive in the 2020s.

5

LONG-RUN INDIAN INDUSTRIALIZATION: FROM EAST INDIA COMPANY TO IBM

India’s economy over the last two centuries to the present day has been one dominated by agriculture (Roy, 2002, p. 113). More recently, it has been suggested that services have been the driver of Indian economic growth in recent decades. Furthermore, the composition of Indian manufacturing, relative to China, may not have the same growth potential: since the 1991 liberalization it has been more reliant on light industry sectors such as food and textiles, with smaller technical spillovers and less focused on electronics production (Ohara and Lin, 2011). Nevertheless, despite these qualifications, as we will demonstrate, the long-run industrialization experience of India is one that should be of interest to scholars interested in the relationships between economic and political power, institutional legacies and industrial policy.

Industrialization, economic and political power  83 India’s, and indeed South Asia’s more generally, formal institutional arrangements still bear the hallmarks of its colonial history (1858–1947), as well as even longer-standing informal institutions linked to inequalities of economic and political power. It was a century between the East India Company establishing its supremacy in Bengal (1757) and 1858 when British Crown rule was established over India (Roy, 2002, p. 110). British rule in India has been presented as a series of unsuccessful interventions to create property rights in order to promote agricultural productivity while simultaneously trying to maintain stability. These twin objectives were to prove incompatible (McCartney, 2019, p. 22). India’s economy before independence was characterized by vast spatial and structural differences.10 It is accurate to claim that there were some sectoral winners before 1947; it was also true that success was unevenly distributed. Political agitation was focused on the existence of losers and the challenges of the interwar period made it easier to highlight supposed economic disadvantages of British rule. An influential nationalist argument before 1947 was that India’s development was hindered by the supposed ‘drain’ effects of colonial rule (Roy, 2002). A range of arguments, involving a range of mechanisms (e.g., trade, remittances, public debt interest) draining India’s resources were produced to suggest that the Empire worked to the economic benefit of Britain rather than India (Roy, 2002; 2018, p. 295). The counterfactual – as claimed by nationalists – was that without colonialism or trade, India would industrialize as fast as Britain. The emerging nationalist movement consequently insisted that India, after political independence, would have to protect its economy from trade and foreign investment (Roy, 2018, p. 295). This nationalistic view coalesced with socialist views on the virtues of economic planning and this synthesis was to prove crucial in economic policy until 1991 (Roy, 2002).11 Independent India, of course, remained influenced by the British model. When political independence arrived in 1947, these influences continued to shape Indian industrialization. The complex system of legal property rights created by the British after the 1793 Permanent Settlement ensured that efficient land transfer remained very difficult when India achieved independence (McCartney, 2019, p. 23). Such legal complexities, despite the creation of compulsory acquisition under the terms of the Land Acquisition Act 1894, provided an obstacle to both consolidating larger-scale agriculture and for transferring land to manufacturing use. The result of this was that Indian governments have, since independence, had to resort to extending compulsory acquisition laws (ibid.). These extensions provide further evidence of the influence of unequal economic and political power on industrialization. Evidence for such inequalities is not hard to find. For instance, the 2005 Special Economic Zone (SEZ) Act enabled state governments to acquire land for industrial estates. While by 2008, 404 such SEZs had been created, many others were held up by combinations of political protest and lengthy court cases. These obstacles have hindered potential manufacturing investments (ibid., pp. 23–4). The response of central governments to reform these long-standing land acquisition issues have led to legislation making compensation more generous. However, business interests as late as 2019 had continually attempted to dilute these compensation provisions (ibid., p. 24). This example, more than seven decades after independence, of an institutional inheritance traceable back to British rule still having political and economic ramifications, provides another example of path dependence. The Nehru-Mahalanobis model after 1947 was an interventionist one. It reflected the influence of nationalist and socialist economic thinking mentioned earlier (ibid., p. 58).12 State-led initiatives were to build up manufacturing investment in the public sector. The private sector

84  Handbook of industrial development was to be regulated in line with economic planning needs. Foreign direct investment (FDI) was regarded with suspicion under the import substitution regime. Futhermore, the Industrial Licensing Act of 1951 regulated a range of business activities. Moreover, licences in practice could be allocated to those who had already installed capacity rather than those who could use them most efficiently (ibid., p. 69).13 More positive assessments present such interventions as promoting industrialized economic growth in a way that had not been possible before 1947 (Nayyar, 2006). Those sympathetic to such interventions have conceded, however, that licensing created costly administrative delays and discouraged industrial investment (McCartney, 2019, pp. 68–9). Those less sympathetic to the Nehru-Mahalanobis model have argued that it contributed to an inefficient system based on patronage and subsidies (Bardhan, 1984). After 1947, more than half of all investment was conducted by the state under central Planning Commission direction. Furthermore, state governments before 1991 had to compete with each other regarding central funding for projects like airports or steel mills (McCartney, 2019, p. 46). Any party holding the central levers of power had considerable discretion over investment (ibid., pp. 45–6). The Indian constitutional settlement since independence makes state governments responsible for health and educational expenditure. The tax revenue streams are controlled by the central government. The settlement is hence tilted towards state governments having to lobby for transfers from the centre with any strings attached. Again, this adds further layers of costs into the system. Since 1991, states have tended to compete to attract FDI without interventions from the centre. However, central government patronage over infrastructure remains an important feature of the Indian investment environment, although lobbying is now dominated by business (ibid., p. 46). Inter-state regional inequalities have widened since liberalization as richer states have proven more successful at securing domestic and FDI projects (ibid., p. 47). The response of multinational enterprises (MNEs) to the changing Indian policy environment provides an insightful way to think about MNEs and the evolving policy environment as it relates to industrialization (Choudhury and Khanna, 2014). In the Indian case, it shut down to MNEs in the 1970s and then opened up to them in 1991. For instance, IBM refused to comply with local regulations in 1977 and this led it to exit from India; it re-entered in the 1990s after liberalization. After 1947, India regulated FDI inflows and even had they welcomed a firm before the 1970s it would have to receive permissions from up to 80 agencies before it could produce in India. Moreover, even for those able to surmount these obstacles, the central government interfered over all kinds of product characteristics (including quantity and pricing), as well as creating entry and exit barriers. Securing licences rather than product or process innovation became the focus of MNEs (ibid., p. 138). In the early 1970s, the Indian government made the ‘regulatory noose’ on MNEs even tighter (ibid.). This was a deliberate effort to dislodge MNEs from India. Tools used to dislodge MNEs included exchange controls and reduction of patent protection. A policy U-turn on MNEs occurred as part of the 1991 liberalization. Evidence suggests that firms reacted to these policy shifts in a variety of ways (Choudhury and Khanna, 2014). However, regardless of these sectoral or corporate issues, insofar as MNEs tend to diffuse modern technologies and improve managerial practices, India’s manufacturing productivity performance must have been affected by policy shifts towards MNEs in both directions.

Industrialization, economic and political power  85

6 CONCLUSIONS In this chapter, we have focused on the historical variation of industrialization paths. This chapter began by noting the relevance of Olson’s ‘big bills’ insight for thinking about the relationship between political and economic power and industrialization. Discussion of the ‘Medici vicious circle’ more generally reinforces the idea that socially inefficient outcomes may not easily be reversed when cumulative causation kicks in (Zingales, 2017). Those benefitting from concentrations of economic and political power, conversely, may do well even if society as a whole is locked into an inefficient (‘big bills’) state of the world (ibid.). The evidence Crafts (2018) produced regarding the long-run consequences of Britain’s ‘early start’ and the problems that emerged during the Golden Age and the evidence produced regarding India before and after liberalization in 1991, all provide concrete reminders of the relationship between economic and political power in explaining industrialization paths. Industrialization is not the entire story of contemporary economic history, however. The shrinking share of manufacturing employment and/or output in developed countries, a process that began during the Golden Age, reflects the type of deindustrialization process that Kaldor discussed (Kaldor, 1966). Economists have noted, however, that just as paths of industrialization have differed internationally, so paths of deindustrialization now also differ.14 Since the 1980s, with the exception of some Asian countries, deindustrialization has spread throughout the developing world (Felipe et al., 2019). This is not a case of developing countries following the same process as found in the developed, however. The turning point arrived more quickly in the developing world and it arrived at much lower levels of income (Felipe et al., 2019; Rodrik, 2016, p. 2). Rodrik, reflecting Kaldor’s (1966) arguments regarding manufacturing and economic growth, refers to this as ‘premature deindustrialization’. Rodrik’s analysis updates Kaldor’s analysis, however, as he relates these paths to the rise of globalization in recent decades. More recently, Dosi et al. (2021) have conducted an empirical analysis that breaks down the industries by sector and technology. This analysis finds that the premature deindustrialization experience differs internationally and that such differences are driven by the variety of sectoral compositions.15 They conclude that industrial policies are needed for developing countries to develop; they acknowledge that complex product specification, entry barriers and appropriability conditions make this more difficult than it was in the 1990s (ibid.). The evidence presented in this chapter suggests that moving up the industrial ladder will prove easier for some countries than others. The ability to reform institutions differs between countries because concentrations of economic and political power vary. Furthermore, industrial policies successful in one context may not be easily replicated. ‘Big bills’ outcomes cannot be simply assumed away in formulating and implementing industrial policy.

NOTES 1. In Zingales (2017), for instance, a cumulative causation process drives ‘big bills’ outcomes. Zingales refers to this process as the ‘Medici vicious circle’. He suggests that, as the ability to influence political power increases with economic power, so does the need to do so, because the greater the firm’s market power, the greater the fear of expropriation by the political power (Zingales, 2017, pp. 119–20).

86  Handbook of industrial development 2. For a more optimistic discussion of lock-in based more on economic geography than economic history, in which lock-in is not inevitable, see Martin and Sunley (2011). 3. In a Pareto-optimal resource allocation, the division of output between services and manufacturing would reflect consumers’ preferences and real resource costs; for each item, the marginal benefit of consumption would be equal to the marginal costs of production. Any situation involving a switch of output into manufacturing, and away from services, would not be regarded as welfare-improving. Once the efficiency conditions are not met, there are, however, grounds for intervention (Crafts, 1993, p. 65). 4. Likewise, Commons argued that America’s distinctive economic history would force it along an industrialization path quite different from that found elsewhere (Commons, 1934). The American trade union model certainly fits a distinctive pattern (Hodgson, 2001, p. 70). 5. Kaldor also suggested that in the UK case, a balance of payments constraint (related to manufacturing performance) might have been at work in limiting the range of feasible policy options (Kaldor, 1966, pp. 23–5; see also Crafts, 1993, p. 28). Kaldor’s interpretation of the UK’s weak balance of payments position, and related advocacy of cumulative causation, led him to be supportive of import controls. Kaldor, for the same reasons, was equally sceptical about the benefits of European economic integration. He objected to the UK joining the Common Market (and during the 1975 referendum he supported the withdrawal campaign) (Thirlwall, 1989, pp. 133–4). Kaldor’s analysis of the role of manufacturing in economic growth led to his major role in the promotion of the selective employment tax (SET) (Reddaway, 1989). 6. Best acknowledged that Kaldor’s approach came the closest to his own ‘production-centric’ approach, but he argued that Kaldor’s conclusions were too reliant on the role of scale economies (Best, 2018, pp. 172–3; Kaldor, 1970). 7. Best discussed the relatively poor performance of industrial policy in the UK – particularly when compared to the German experience – as providing an example of a less well-integrated triad (Best, 2018, pp. 144–76). He attributed the skills component of the UK’s triad as being particularly inadequate (ibid., p. 169). 8. In terms of quantitative evidence, he suggests that the UK’s growth underperformance was approximately 0.8 per cent per year, with the result that the level of GDP per person was around 20 per cent lower than would have been reasonable to expect by 1973 (Crafts, 2018, p. 99). 9. Many of the UK’s national high-tech champions of the 1960s and 1970s ultimately proved to be economic dead ends (Coyle, 2020, p. 125). Coyle mentions nuclear power and De Lorean Motor Company Limited (DMCL) as examples of what she terms ‘explicit industrial policy’ commercial failures (ibid., p. 130). For more on DMCL, see Brownlow (2015). 10. Port cities like Bombay and Calcutta were stimulated and benefitted most. Long-distance trade grew from 1 million tonnes in 1840 to 160 million in 1940 (Roy, 2018, p. 297). The profits of this trade were reinvested and India became the developing world’s leading producer of cotton textiles and iron and steel (Roy, 2018, p. 297). 11. Since the 2000s, there has been a revival of academic debate in interpreting Indian economic history (Roy, 2002). The recent literature, moreover, questions the idea that the British Industrial Revolution was a useful template for thinking about India’s economic potential anyway. The Industrial Revolution relied on relatively abundant fossil fuel and capital, while monsoon Asia needed a model of industrialization that was less energy dependent, more trade dependent and more labour intensive (Roy, 2002; 2018, p. 298). Even if India’s institutional arrangements between 1858 and 1947 were identical to Britain’s, which they were clearly not, the relative factor prices found in India would have eliminated the ability to imitate the British path. 12. Of course, India was far from unique in the influence of interventionism: similar policies could be found in a range of more developed European economies after 1945. What was unique was the role of central planning, import substitution and hostility to inward investment, endured longer than in far richer economies. 13. India was not the only Asian country to restrict FDI flows as part of an industrial strategy. In 1950, Japan passed the Foreign Investment Law. This legislation restricted investment flows more than licences, and this reflected that Japan’s industrial development strategy was more export orientated and market based than India’s import-substitution approach. For more detail on the Japanese approach to technology and trade, see Ozawa (1973).

Industrialization, economic and political power  87 14. In terms of the developed world, there is a recognition that in some cases, such as the United States, the employment share in manufacturing since the 1950s has fallen faster than the manufacturing value-added (MVA) share. In other cases, such as Britain since the 1970s, both output and MVA have shrunk (Rodrik, 2016). 15. Furthermore, Dosi et al. (2021) find evidence that technical upgrading has been made more difficult by globalization. In short, the divergent deindustrialization paths reflect that some industries offer more technical spillovers than others. As Dosi et al. note, microchips and potato chips offer very different learning regimes.

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Industrialization, economic and political power  89 Ohara, M. and H. Lin (2011), ‘Competition and management in the manufacturing sector in China and India: a statistical overview’, in M. Ohara, M. Vijayabaskar and H. Lin (eds), Industrial Dynamics in China and India, London: Palgrave Macmillan, pp. 19–39. Olson, M. (1996), ‘Distinguished lecture on economics in government: big bills left on the sidewalk: why some nations are rich, and others poor’, Journal of Economic Perspectives, 10(2), 3–24. Ozawa, T. (1973), ‘Technology imports and direct foreign investment in Japan’, Journal of World Trade Law, 7(6), 666–79. Pollard, S. (1981), Peaceful Conquest: The Industrialization of Europe 1760–1970, Oxford: Oxford University Press. Reddaway, B. (1989), ‘Tax reform in the United Kingdom’, Cambridge Journal of Economics, 13(1), 141–54. Rodríguez-Pose, A. and N. Gill (2005), ‘On the “economic dividend” of devolution’, Regional Studies, 39(4), 405–20. Rodrik, D. (2016), ‘Premature deindustrialization’, Journal of Economic Growth, 21, 1–33. Rodrik, D., G. Grossman and V. Norman (1995), ‘Getting interventions right: how South Korea and Taiwan Grew Rich’, Economic Policy, 10(20), 53–107. Rostow, W.W. (1960), The Stages of Economic Growth: A Non-Communist Manifesto, Cambridge, UK: Cambridge University Press. Rostow, W.W. (1963), ‘Leading sectors and the take-off’, in W.W. Rostow (ed.), The Economics of Take-Off into Sustained Growth, London: Macmillan, pp. 1–21. Roy, T. (2002), ‘Economic history and modern India: redefining the link’, Journal of Economic Perspectives, 16(3), 109–30. Roy, T. (2018), ‘South Asia’, in M. Blum and C.L. Colvin (eds), An Economist’s Guide to Economic History, London: Palgrave Macmillan, pp. 293–9. Shackleton, J.R. and D. Zuluaga (2016), Balancing the Economy: The Hand of Government or the Invisible Hand? London: Institute of Economic Affairs. Stafford, G.B. (1981), The End of Economic Growth? Growth and Decline in the UK since 1945, Oxford: Martin Robertson. Thirlwall, A.P. (1989), ‘Kaldor as policy advisor’, Cambridge Journal of Economics, 13(1), 129–39. Tullock, G. (1967), ‘The welfare costs of tariffs, monopolies and theft’, Western Economic Journal, 5(3), 224–32. Veblen, T.B. (1915), Imperial Germany and the Industrial Revolution, New York: Macmillan. Wade, R. (2004), Governing the Market: Economic Theory and the Role of Government in East Asian Industrialization (2nd edition), Princeton, NJ: Princeton University Press. Zingales, L. (2017), ‘Towards a political theory of the firm’, Journal of Economic Perspectives, 31(3), 113–30.

6. The transformation of work: changing employment governance regime1 Valeria Pulignano

INTRODUCTION For social scientists, examining the transformation of work is a challenging but not new intellectual exercise. The regulatory institutions of work and employment have undergone profound changes within capitalist societies. Globalization has undermined the relationships between employers, labour and the state at the national, sector (industry) and local (firm) level by creating instability in the form of wage competition, the decentralization of collective bargaining and the deregulation of labour standards. Some comparative political economy literature argues that this has coincided with structural changes generating congruence in national outcomes in relation to the institutions of industrial relations affecting working conditions rather than convergence in patterns within (and across) different countries (Baccaro and Howell, 2017). Together, processes of Europeanization have contributed to amplifying the effects of these changes for work and employment while increasing the complexity of the industrial relations map. In particular, new horizontal and vertical relationships and interdependencies among company, sectoral, national and transnational public and private stakeholders have resulted from the complex European project, adding new levels, players and institutions for social governance (Keune and Marginson, 2013). The European project has also opened up space for capital and labour transnational mobility, which has had profound disintegrative implications for work and employment relations as the 2007–08 financial crisis first (Arrowsmith and Pulignano, 2013), and the COVID-19 pandemic later (Scambler, 2020) have clearly revealed. Although several studies have examined the challenges that structural changes at different national, industry, local and transnational levels have created for work and employment, and analysed its socio-political and socio-economic dynamics and assessed its effects, a compelling picture of the evolutions surrounding these changes still needs to be fully developed. This chapter is an attempt to address this deficiency. Thus, we focus on work and explain its evolution by assessing the structural changes underpinning the employment governance regime. In particular, we examine how ‘social order’ is guaranteed within changing socio-economic and socio-institutional contexts by indicating the transformations and analysing the evolutions in how employment is governed. Employment here encompasses the organization and the management of the employment relationship as well as the welfare dimension. Hence, we point to the shift from the ‘welfare state capitalism’ regime during Fordism to the emergence of the post-Fordist ‘flexible capitalism’ regime, and explain how this shift has caused much precarious employment, whose conditions have been exacerbated during the COVID-19 pandemic. In doing so, the chapter provides a sociological and historical analysis of the structural changes that have accompanied the transitions of employment while assessing their antecedents and driving forces. Thus, it reveals the processes and examines the mecha90

The transformation of work: changing employment governance regime  91 nisms nurturing the way in which these changes have affected work and employment conditions from the 20th-century capitalist societies onwards, with a particular focus on Europe. The first section of this chapter deals with work, employment and change and it presents the different analytical approaches and theoretical perspectives within the literature. The second section illustrates the evolutions and examines the transformations in the employment governance regime in its shift from welfare state capitalism to the flexible capitalism regime. Thereby, it explains the way in which social order around work and employment has been socially constructed and guaranteed. The third section discusses the implications of the emergence of the flexible capitalism regime for precarious employment. It examines precarious employment, its forms, nature and social outcomes in the light of the 2007–08 financial and economic crisis and the current COVID-19 pandemic. It also adds some reflections on the emergency of the environmental situation within the scope of the European Green Deal, and finally concludes.

WORK, EMPLOYMENT AND CHANGE Work is a core activity in society. It is central to individual identity, links individuals to each other, and locates people within the social stratification system. Employment is work that produces pay or income. Equating work with pay is, of course, a limited view, as there are many activities that create value but are unpaid, such as those that take place in the household. Given the focus largely on institutional and regulatory settings, we emphasize employment in the formal economy. Work also reveals much about how social order is achieved in society, how it is changing, and the problems and issues that people – and their national governments – must address. Accordingly, the study of work has long been a central field in sociology, beginning with classical sociologists such as Durkheim (in his Division of Labour in Society), Marx (in his theories of the labour process and alienation), and Weber (in his conceptualizations of bureaucracy and social closure). Since the last few decades, however, there has been a change in the way in which social order has been constructed and how regulatory settings and mechanisms for the creation and the maintenance of social order – that is, social governance – have been guaranteed. The idea of social governance, particularly in employment (and industrial) relations studies, has benefited from different theories. On the one hand, theories of production regimes concerned with the causes and the consequences of the differences in the organization of capitalist market economies within comparative political economies have conceptualized institutions as part of distinct societal configurations (Maurice and Sorge, 2000). Examining changes in work and employment through the lens of social order has largely rested on stylized typologies of capitalist market economies based on the varieties of capitalism (VoC) approach (Hall and Soskice, 2001). This framework mainly focuses on the national level, although the binary categorization extends to key issues facing organizations, including management and labour strategies and practices (Estevez-Abe, Iversen and Soskice, 2001; Soskice, 1999). The VoC perspective draws a broad distinction between coordinated market economy and liberal market economy as ideal types of capitalist society. Although there are different formulations of the VoC approach, which remains much debated (Palier and Thelen, 2010), a core principle is that institutional variations emerge on the basis of deeply established cultural and institutional

92  Handbook of industrial development patterns – an implicitly ‘consensual’ emphasis that tends to minimize or even obscure the role played by class divisions, especially those between capital and labour. In essence, the VoC approach views welfare states and labour regulation as efforts at societal coordination, and thus as nationally rooted production systems or regimes that solve the institutional needs (e.g., for the maintenance of asset-specific skills) that business organizations confront. On the other hand, power resource theory (Fligstein and Byrkjeflot, 1996; Korpi, 1978, 1983; Streeck, 2009) has focused on agency- and strategy-based perspectives as a useful corrective to stylized capitalism typologies. At the core of the power resource perspective is the resurgence of interest in how social choices and strategies are shaped, mediated and channelled by institutional arrangements, thereby converging to shape employment systems. Accordingly labour and employer choices and practices at different levels are often examined through a focus on – and different combinations of – power resources, which are available in particular contexts and whose changing balance–unbalance informs policymaking. This is because how actors wield the available resources when responding to flexibilization, marketization and deregulation pressure can be used to explain variation (O’Brady, 2021). Moreover, the magnitude of power resources, combined with the strength of management and labour interests, affect how both parties interact and influence each other through negotiations (Kochan, McKersie and Cappelli, 1983; Walton, Cutcher-Gershenfeld and McKersie, 1994). This yields to a theorization of the interactions between political economy institutions and the strategies of organized interests within different institutional systems (Doellgast, Lillie and Pulignano, 2018). It also explains how local dynamics of interest (and sometimes identity) construction can account for strategy, thereby how it contributes to shaping subnational variation (Tapia, Ibsen and Kochan, 2015) and segmentation (Emmenegger et al., 2012; Thelen, 2014). Employment systems have exhibited massive pressures for change throughout the advanced industrial world, and earlier featuring the evolution from an agricultural to an industrialized era. As we will illustrate in the next section, beneath the pressures for change there has always been the attempt to re-establish a sort of social order that came under threat because of the pressures that characterized the evolution, such as globalization, new information and communications technologies including digitalization, and the dominance of (neoliberal) economic policies.

EMPLOYMENT REGIMES IN TRANSITION: FROM WELFARE STATE TO FLEXIBLE CAPITALISM A more rigid and stable phase of production and distribution came to fruition in the period after the Second World War, with the extension of various forms of welfare state capitalism. At the heart of welfare state capitalism was an orientation to improve distributive justice through the labour market by guaranteeing security backed by regulation, and institutions oriented to the perceived need of the ‘labouring man’ (Standing, 2011). Grounded upon the ‘male breadwinner’ industrial model, in which men earn a family wage while women do domestic (unpaid) labour and care for family members, employment stability subordinates the male worker to the dictates of mass production and large-scale organizations. In particular, the employment regime under Fordism involved centralized bureaucratic structures and rules that sheltered many (but not all) workers from labour market uncertainty by organizations providing internal job ladders that ensured enduring support for orderly careers, and thus for meaningful life nar-

The transformation of work: changing employment governance regime  93 ratives (Sennett, 1998). Bureaucratic rules within firms served the goal of reducing uncertainty by increasing predictability in the use of skill through, for instance, anticipating promotions within pay scales and job ranks (Cappelli et al., 1997; Doeringer and Piore, 1985) or enhancing cooperation in production (Stark, Taylor and Yitzhaki, 1986). Macro-economic instability during the 1980s coincided with a shift of the employment regime. The welfare state capitalism based on a system of employment stability and regulation ran up against a series of socio-economic and political crises, which culminated with the defeat of the Swedish wage earner funds that were established in Sweden in 1983. Inflationary pressures built up the perception that the capacity of the state to achieve redistributive objectives was ineffectual. As anti-Keynesian critics argued, democratic governments would always be inflationary in their operation of macro-economic policy because they would wish to boost the economy by cutting interest rates in the year before elections. The crisis of the employment stability system resonated in the rise of a post-bureaucratic account of change that yields to the growth of flexible capitalism, which was supported by a flexibility-based employment system. Flexible employment in the form of part-time work, temporary and fixed-term work, agency work and flexible scheduling and so forth was regarded as combining both paid work and family work by reducing the gap between paid and unpaid (domestic) labour. Thereby, it was seen to break with the early ‘male breadwinner’ model by creating a ‘dual-earnings’ family model where both partners contribute to the financial support of their household through their work outside the home. However, flexible employment, which is mainly concentrated amongst women, is not usually associated with individual success in the labour market, and flexible female workers often tend to be in lower-level positions (Crompton, 2006). Two distinct strains of flexibility theory can be identified in post-Fordist accounts of structural change (Vallas, 1999). The first is the ‘flexible specialization’ approach, which emphasizes the nature of inter-firm relationships and stresses extra-organizational influences, such as the continuous shifting of product markets and consumer tastes, considering them to be major sources of workplace change. From this vantage point, the main objectives reveal the arbitrary nature of the constrained notion of Fordism while specifying the conditions necessary for overcoming it. The second and possibly most important strain of flexibility theory under the heading of post-Fordism is the post-hierarchical, or post-bureaucratic, model of work. In this orientation, firms are considered capable of forging new organizational structures that fully engage the skills of their employees because managerial decisions about production are decentralized. That is, the main feature of the new employment regime is the supposed paradigm shift from technical, or bureaucratic, modes of workplace regulation to a decentralization, flexibilization and de-bureaucratization of the labour process. This is witnessed in the tropes of work reorganization, teamwork, quality circles and new profit centres that have contributed much to the design of lean production or lean manufacturing techniques across different sectors and industries, such as automobile manufacturing (Womack, Jones and Roos, 1990) and logistics and retail services (Pulignano et al., 2021). The constant focus is on the ending of (supposedly) inflexible work rules and formality and, therefore, on the increase of more informal arrangements for the governance of production and work organization. The idea is that cooperation occurs through corporate culture and values acting as the glue holding the decentralized parts of the new organization together. A clear example of this would be the possibility that long-term strategic planning, including detailed monitoring of schedules, occurs lower down the corporate structure, and finally at the plant level as part of the organization of work. The assumption is that this increases workers’ trust in

94  Handbook of industrial development management while enhancing intrinsic rewards from work. The consequence of this, of course, is that trust and intrinsic rewards are seen to have strong positive effects on job satisfaction and organizational commitment. Specifically, workplace relations are seen to rely primarily on the motivational benefits of the activity of work organization (Pulignano and Stewart, 2006). Moreover, while financial and economic incentives are seen as important, they are not believed to be prominent in regulating employment. Productivity is supposedly to be driven by workers’ psychological (and emotional) commitment more than by rigid bureaucratic rules (Barker, 1993; Wilkinson and Willmott, 1995). Broadly, the assumption is that for post-bureaucratic organizations, managerial discourses and organizational cultures serve to generate employee involvement together with employment regulation. Control through organizational culture is seen to depend upon viewing people as emotional, symbol loving, and needing to belong to a superior entity (Ray, 1986). Employees follow self-disciplinary values and symbols rather than rules (Willmott, 1993). To some extent, this is supposed to work where individual identity is positively tied to participation in what are interpreted as discursive practices providing a sense of belonging (Thompson and Findlay, 1999). In consequence, improvements in productivity and quality, it is argued, are the result of corporate cultures that systematically recognize and reward individuals for identifying their sense of purpose with values designed into the organization (Kunda, 1992). This is seen to be preferable to rigid rules and lends shape to the new ‘hegemonic’ factory regime (Burawoy, 1985). The reorganization towards a ‘flexible’ model of production at the organizational level is in tandem with a marked increase in the autonomy of financial capital when compared to industrial capital at the level of the circuit of capital (Thompson, 2013). Capital has spread throughout the world, opening the way for mass delocalization and relocation; capital is less fixed and is growing more flexible not only at the plant level as part of the way work is organized but also at the higher level of the circuit of capital. This transformation in capital brings about a management offensive that seeks to adapt workers to the new conditions of capital development (Supiot, 2001). It is here that the deregulation of employment, which is based on the destruction of welfare state capitalism and the rise of flexible capitalism, comes into play. Deregulation involves dismantling the barriers that capital faces on the labour market (e.g., regulation inherited from the employment stability model during Fordism) and restoring to capital its discretionary power. This involves the destruction of the social protections that have often surrounded the employment stability (or security) model during Fordism (Crouch, 2015). In other words, the protections offered by the welfare state capitalism become subordinated to the new conditions of capital development (Hassel and Palier, 2021), which feature a new way to establish social order through the introduction of flexibility as the device for the governance of the employment system. The result is the widening of precarious employment, in which expanding segments of the labour force encounter a condition of economic liminality – a condition that excludes individuals from full citizenship in the firm and, by corollary, in civil society (Vallas, 2015).

The transformation of work: changing employment governance regime  95

TRACKING THE GROWTH OF PRECARIOUS EMPLOYMENT IN ADVANCED CAPITALIST SOCIETIES Understanding the changes capital has undergone in the attempt to establish a new social order for the governance of employment is key to explaining the transformations occurring in work processes and their effects on the employment relationship (Kalleberg, 2011) and the work and lives of individuals in society (Kalleberg, 2018). Individual attributes have been much explored to understand the changes underpinning the growth of precarious employment and its consequences for job quality and the working conditions of workers (Holman, 2013; Prosser, 2015). The impact of cultural (symbolic), ideological and political constructs on subjective experiences and individuals’ acceptance of precarious work have also been pointed out (Vallas and Prener, 2012). Ho (2009), for example, illustrates how financial institutions such as investment banks and private equity funds mobilized new meanings and ideologies at work, underwriting the ‘shareholder conception of the firm’ that has in turn increased precarious work. Likewise, Lane (2011) explains how in the United States a new corporate culture has arisen to produce a new definition of employment in which all jobs are temporary, therefore contractually precarious, and all workers are, or should be, independent ‘companies of one’. This is in line with other studies emphasizing the capacity of organizations to frame the precarious experiences of job seekers through discourses (Sharone, 2014). The central threat underlying all these studies is that both cultural and ideological influences embraced in different organizations have played a major role in the legitimation of precarious work (Vallas, 2015), which in turn arise from the structural changes that have accompanied the shift from welfare state capitalism to flexible capitalism. In particular, precarious employment has been exacerbated by the growing power and reach of global capital, which has exceeded the ability of nations and labour unions to regulate it. Precarious employment is work that is uncertain, unpredictable, unstable, vulnerable and risky from the point of view of the worker (Kalleberg, 2014). Examples include work that lacks continuity of employment and social protection and/or uncertain levels of pay and working hours (see also Rodgers and Rodgers, 1989). Literature on precarious work is extensive and it is not the intention of this chapter to recall it. However, several analytical questions emerge from this literature and general state of the field. One addresses the complex relations and factors that condition the experience of precarious employment, which varies across different groups and individuals. Because precarious employment at least partly involves reactions to ongoing events, its meaning and significance for individuals are generally framed by the relevant social backgrounds and conditions of individual workers (Della Porta et al., 2015). Needed here is an integrated analytical framework that might explore the relation between individual experiences, institutional ties and the broader socio-economic, cultural and political contexts in which labour markets are viewed. One important part of this task is that of identifying the broad social mechanisms that underpin precarious employment within a new phase of capital development and that alter the horizon in which workers perceive the labour market and their positions within it. Elsewhere, I have adopted a power resources perspective to explore these mechanisms within the context of Europe (see Pulignano, 2018). In particular, I have identified a ‘regime of competition’ that helped explore how changes in power relationships have affected the way in which precarious employment becomes legitimatized, institutionalized and governed within a national and transnational European context in which social policy must increasingly

96  Handbook of industrial development be subordinated to market competition, and where nation-states have experienced rising pressure to ‘redefine social policy as public provision for private competitiveness for the re-commodification of labour’ (Streeck, 2016, p. 22). Thereby, I have explored how change in power resources, as evident in economic, political and ideological elements of society, has affected the working conditions and (work) experiences of people. Implied here, as Crouch (2015) points out, was the need to investigate unions’ strength and labour rights in shaping employment policy and regulation, thereby helping to contextualize the transformations that occurred in employment relationships as capitalism developed. For example, Gonos (1997) argues that, beginning in the 1950s and 1960s, a series of social struggles ensued over the laws governing the temporary employment industry in the United States, eventually setting the stage for what proved to be far-reaching changes in the employment relationship. In particular, the meagre power resources available to labour within a context characterized by feeble participation rights allowed temporary help for firms to exercise unrivalled influence over major shifts in employment law. These shifts made possible the growth of temporary work ‘without significant social conflicts and difficulties’ (Gonos, 1997, p. 104). My argument around the ‘regime of competition’ is built upon the analytical and empirical evidence that increasing insecurity for citizens in Europe has corresponded to a socio-political change in power resources, placing organized labour in an ever-more unfavourable position, a change that became particularly evident during the 2007–08 financial crisis and whose effects were magnified by the recent COVID-19 pandemic. I argue that this shift can be traced back to the original process of European (economic) integration and several policies implemented by the EU member states as the way to increase employment levels as far as possible. Although the degree of labour participation in employment regulation and policymaking has varied widely across different European countries, it is the fundamental shift in power relationships between capital and labour that has emerged as a common trend, and it is this shift that underlies the proliferation of growing precarious employment in Europe. These policies and ideologies have underpinned a subtle yet significant shift from the idea of an occupation, defined by a qualification, toward that of a job for which the only benchmark is money. This trend is clearly visible, for example, when we look at the criteria governing unemployment, which increasingly emphasize the recipients’ earning capacity and less the previous occupation, with the ultimate objective to place the unemployed back on the labour market at any price – also defined by some as the duty to work (Supiot, 2001, p. 32). As said, all this has occurred within a context characterized by major changes in the institutions of industrial relations, particularly the decline in trade union density, the trend toward uncoordinated and decentralized forms of collective bargaining, and cutbacks in welfare spending. These changes have affected, although at a different pace, the process of negotiation of wages, working conditions, and the quality of employment in different European countries. These negotiations have delivered various outcomes, which have been contingent upon different combinations of unions’ strength, including participative and protective labour standards, in different European countries (Thelen, 2014). However, the evolution of these employment conditions has followed a broadly shared set of political and policy directions, often informed by a ‘supply-side diagnosis of Europe’s job crisis’ (Streeck, 2016, p. 22). Hence, the regime of competition corresponds to a regime where competitiveness and economic growth through employment at any price are viewed as constituting the key factor of a healthy and vibrant social-economic system of employment. These, then, are the conditions that labour is required to adopt as Europe envisions its post-crisis future. Streeck (2008) made

The transformation of work: changing employment governance regime  97 a similar argument when claiming that citizenship and social policy expectations are being reformulated until labour is required to accept all manner of risks just to get employment. Several studies illustrate that these risks often reflect the erosion of existing institutions for the protection of employment. Although not all employment institutions within Europe have lost their protective functions to the same degree (Bosch, Lehndorff and Rubery, 2009; Gautié and Schmitt, 2010), there has been a discernible shift toward ever-more competitive relations producing precarious employment as they are governed by market and not social needs (Bosch and Weinkopf, 2017). As we shall indicate in the following section, the COVID-19 pandemic has magnified the distortions of a socio-economic system of ‘precarious’ employment based on flexible capitalism, which has underpinned the changes imposed by the regime of competition under the pressure of an emerging neoliberal ideology.

PRECARIOUS EMPLOYMENT UNDER THE COVID-19 PANDEMIC AND FURTHER: THE EUROPEAN GREEN DEAL Precarious workers have been the most at risk under the COVID-19 pandemic as they are the ones who have experienced low and/or the absence of social protection, which covers employment and adequate health and social benefits (e.g., sickness), due to the lack of social and human rights, including collective bargaining and participation rights. Although the focus of a sustainable society should be on guaranteeing social health, social protection and social insurance for all, this was not the case for both those who were not in employment (i.e., unemployed) and those who were in employment under a non-standard and precarious employment arrangement when COVID hit. Non-standard employment is an umbrella term for different employment arrangements that deviate from standard employment. They include temporary employment; part-time and on-call work; temporary agency work and other multiparty employment relationships; as well as disguised employment and dependent self-employment (Eurofound, 2020). Although not all non-standard employment arrangements are necessarily precarious, precarious employment usually consists in not having a standard employment contract, and therefore having guaranteed a specific amount of work and/or working hours (e.g., on-call and zero-hours contracts), as well as all those who work under low pay, and whom in the majority of the cases are migrants, women and the young segregated in specific sectors of the economy (e.g., cleaning, hospitality and food service, and retail). These sectors were particularly affected by the COVID-19 pandemic. Amongst all forms of non-standard employment arrangements, the case of the self-employed arguably represents one of most vulnerable and precarious groups under the emergency circumstances of the COVID-19 pandemic. This is because, while during the pandemic people in salaried employment have benefited from job-retention schemes (such as short-time work schemes) and were able to access unemployment schemes, this was not the case for several categories of non-standard workers, and especially for the self-employed (Spasova et al., 2021). Consequently, a salaried employee and a bogus self-employed or a dependent self-employed person who do the same job in the same enterprise may well have very different levels of entitlement (or, in fact, no entitlements whatsoever) to, for instance, unemployment and sickness benefits (access to which was essential during the pandemic). In a context of increasing fragmentation of labour market statuses, some categories of non-standard workers,

98  Handbook of industrial development and more traditionally the self-employed, have only restricted access, or even no access at all, to certain social protection schemes, notably unemployment benefits, sickness benefits and schemes covering occupational diseases and accidents at work (Matsaganis et al., 2016; Spasova et al., 2017). Although there have been some reforms improving access for the self-employed (Spasova et al., 2019; Spasova and Wilkens, 2018), significant gaps remain. Non-standard workers and the self-employed, therefore, were particularly affected during the pandemic, which has impacted people’s health (access to sickness benefits) and jobs (access to unemployment benefits) (Pulignano et al., 2021). Furthermore, while in the past independent self-employed workers were found in mostly high-class occupations, such as attorneys, private doctors, architects and the like, today this rank is a lot larger and more varied, and with many struggling to make ends meet. This is mainly because the income of the self-employed is based on a client–provider relationship (as opposed to an employer–employee connection). If the client withdraws the order, the worker loses the pay. Moreover, by virtue of the independent employment status, the self-employed are required to shoulder the responsibility for such inconvenience. Quite consistently across Europe, the governmental incentives to shift to self-employment have flourished post-2007–08 financial crisis, and they have been mainly related to digitalization in the form of the emergence of labour platforms. Labour platforms are defined as digital networks that coordinate labour service transactions in an algorithmic way (Lehdonvirta, 2018). The nature of the relationship between self-employed contractors and labour platforms is often termed independent contracting, freelancing or on-demand work. This is because the platform provides tools to bring together the supply of and demand for labour (Graham and Woodcock, 2018), including the app, digital infrastructure and algorithms for managing the work (Griesbach et al., 2019; Srnicek, 2017), but it does not engage in a contractual relationship of employment with the worker. Although there is currently no accepted standard definition of platform work (OECD, 2019) when performed without an employment relationship, nevertheless, platform work is considered as an example of non-standard precarious employment (European Commission, 2020, p. 37). This is because, usually, platform work has no employer giving the platform worker instructions in the traditional sense, remuneration is typically paid per task rather than as a wage or salary, and work can take place on more than one platform (ibid.). Work that is offered on digital platforms can be casual and contingent work in the sense that it does not imply a permanent and stable employment relationship with neither the platform nor the client. Platform work also has variable hours and it provides little social protection and security to the worker because the worker is outside an employment relationship and their work depends on the demand available on the platform and the good performance and good reputation of the independent contractor (Gandini, Pais and Beraldo, 2016). Moreover, platform work may involve payment on a piecework basis, and it lacks serious options for career development. The work is typically short, temporary and the hours of work unpredictable. Platform workers use an app or a website to match with customers in order to provide a service in return for money. Services cover a wide range, such as ride hailing, coding, and writing product descriptions. Platform work may be a worker’s main job, or occasional secondary work to supplement a worker’s income (OECD, 2019, p. 5). With bars, restaurants and other catering services shut down as a consequence of the COVID-19 lockdown measure, for example, we aided the proliferation of precarious employment as many of the activities offered by labour platforms turned to home delivery through

The transformation of work: changing employment governance regime  99 food delivery platforms (e.g., Deliveroo, Uber Eats, Foodora, Glovo, etc.) as a way to keep their businesses going even in times of shutdown. Ironically, though, the food does not get delivered by a robot but by an individual who is put in danger – of catching the disease, of becoming the vehicle of its spread, and so on – often for just a few euros per delivery, and (usually) no health insurance provided by the platform. Together, and although national government policy measures aimed at cushioning the impact of COVID-19 on platform workers’ livelihoods, particularly freelancers, these measures proved necessary but insufficient to guarantee long-term protection because the eligibility criteria for such support measures have excluded many freelancers offering their services on labour platforms. Moreover, those who have been guaranteed access to national government support have often been confronted with the complexity and length of the administrative procedures that accompany the implementation of these measures within their national and local jurisdiction. Finally, career development and employability are vulnerable areas for platform workers and freelancers due to there being a lack of (or insufficient) national funds dedicated to these areas (Pulignano et al., 2021). Hence, universal national protection measures remain critical for freelancers today and probably critical (or less feasible) to be implemented in a near future. One of the main impediments we can envisage from a policy perspective may come directly from those who benefit most from the absence of such universal measures, and who indeed lobby against their introduction. We should not forget that expanding social insurance for freelancers by incorporating social contributions into general taxation may reduce companies’ incentives to resort to freelancers instead of paid employees, as is mainly the case with digital platforms. So, if we pose the question ‘Is there a policy window for the idea of establishing a universal protection for freelancers?’, the answer would probably be ‘Not at this moment’. But what the future holds will much depend on how far and quickly power (social) forces will turn around. So far, however, we have seen happening what we probably would not have expected to happen a short while ago. I refer here to two illustrative recent examples. First, the 2021 Spanish Congress’s approval of the ‘riders law’– which entered into force in August 2021 – in accordance with which riders are recognized as ‘employees’ and therefore entitled to access to work-regulating algorithms. Second, the Court of Bologna (Italy) has ascertained the discriminatory nature of both the conditions of access to work sessions through the digital platform and the company conduct and practices put into practice by the company Deliveroo Italia. Both these initiatives intersect with several other cases where digital platforms have been challenged in a variety of countries, such as France, Italy, the Netherlands and the United Kingdom. In Italy, for example, in September 2021, the Court of Milan established that riders working for different digital platforms (Foodinho, Glovo, Uber Eats, Just Eat and Deliveroo) cannot be considered as occasional independent workers but must be reclassified as employees. A recent court case initiated by the Netherlands Trade Union Confederation (FNV) concluded that Uber is an employer and hence its workers are employees and that the collective agreement of the taxi service should be applied.2 Furthermore, and moving a little further, the path of change will probably deepen by the moving to a carbon-neutral future that can be expected to change everything: what we eat, how we dress, how we travel, the way we live. The European Union is seeking to lead the world in its political will to set goals to save the planet and humanity, to develop radically new tools to make the pursuit of these goals feasible, and to reconcile aggressive public policy and radical corporate innovation. As such, the Commission has set out a roadmap for a new ‘European Green Deal’ designed to impact not only on the climate, but also agriculture, trans-

100  Handbook of industrial development port, industry – the whole of society. The European Parliament approved two resolutions: one declaring a ‘climate and environmental emergency’ in Europe and globally, and calling on the Commission to ensure that all relevant legislative and budgetary proposals are fully aligned with the objective of limiting global warming to under 1.5°C; and a second resolution urging the EU to submit its strategy to reach climate neutrality as soon as possible, and by 2050 at the latest, to the UN Framework Convention on Climate Change, along with a 55 per cent reduction target of greenhouse gas emissions by 2030 as part of the European Green Deal in the meantime. Following this, the Commission launched its proposed new European Green Deal. These measures are intended to launch a wave of policy-driven ‘growth’, which will transform not only the building industry, transport and food sectors, and many others, but also, and more importantly, our current lifestyles with cheap flights, wasteful food and disposable clothing and other products – in short, what is the very essence of ‘consumer capitalism’ as we have known it for many decades could be fixed and/or deeply transformed. The aim is to replace a consumer society with a genuine circular economy that seeks to reuse most of the materials we use – glass and paper, but increasingly plastics, clothing and a longer life for domestic equipment. This ambitious set of policies will challenge society to look for the scientific and economic benefits and to set priorities that attain the maximum carbon abatement for the best price. Housing is an obvious early target – especially the social housing stock that needs to be renewed and made more energy efficient through better insulation of roofs and walls, double glazing of windows, and solar panels that provide energy, heating and cooling. A balance will have to be found in the incentive effects of new regulation that sets targets without describing narrow paths to better outcomes, thereby leaving more space for innovation and disruptive technologies. Difficult choices will have to be made between extensive agriculture with low yields at the price of less biodiversity, and intensive agriculture that is sustainable. The objectives are not only about carbon sequestration through planting trees on a massive scale, but they also look at entire systems of production and sustainable agriculture. To really succeed, we need to build and nurture our human, social and natural capital stocks as higher priorities and also place a new emphasis on ethics – for example, the ethics of artificial intelligence.

CONCLUSIONS This chapter has offered a synthesis to account for the transformation of work by examining the changes that have accompanied the shift from welfare state capitalism during Fordism, which is based on an employment regime that guarantees stability, to the flexible capitalism in the post-Fordist era, which relies on flexible labour. It has also illustrated that precarious employment is best understood as rooted in this dynamic shift, and that it accounts for the unbalance in power relationships between capital and labour. This is done by connecting our synthesis to an analysis of the social mechanism, which refers to the regime of competition. This regime underpins the policies and practices that have elicited growing precarious employment in Europe, especially since the financial crisis, and whose effects have been magnified during the COVID-19 pandemic. In particular, under the processes of European economic integration, national policymakers, employers and trade unions, in their local and national negotiations, were not called upon to directly safeguard employment by strengthening employment protection law or social entitlements for those not in work. Rather, good working

The transformation of work: changing employment governance regime  101 conditions and social protection came to be seen as constraining the capacity of financial capital to operate freely and as inhibiting corporate ability to adapt quickly to market imperatives. At the same time, and under the influx of flexible capitalism, social protection through welfare and social entitlements has come to be viewed as an unaffordable luxury, or as an obstacle to further economic growth. As the chapter has explained, this has resulted in the growth of precarious employment, particularly in the making of the financial 2007–08 crisis, as confined not only to jobs, but also to a wider canvas of deregulatory measures. These measures have entailed deterioration in working conditions and wage suppression, whose distortive effects were exacerbated by the pandemic. In particular, structural changes and policy reforms underpinning the shift from welfare state capitalism to flexible capitalism account for the erosion of the labour’s capacity to mobilize resources on the one hand, and the retreat of public policy with cuts in social protection and investments in human capital development as the outcome of workfare on the other. If a lack of increase in human capital is combined with reduced income protection, unemployed people risk becoming more precarious and poorer, without having improved their labour market chances. This is why Stone and Arthurs (2013) argue that precarious employment in Europe manifested itself not only through an increase in (often involuntary) temporary work but also through an imposition of reduced working hours, low wages and less advantageous working conditions, all of which workers have had to accept if they were to remain employed at all. In this light, self-employment, informal work and casual work have also increased. These forms of work are commonly associated with precariousness as they offer workers levels of employment characterized by lower entitlement for protection, which often reflects relatively lower power and a stronger relation of dependency. For example, as indicated, false or bogus self-employment describes a relationship of unequal power, where the offer of work is dependent on a relationship with a single source, rather than a range of clients, and where individuals are hired for work only where they are prepared to declare themselves as self-employed. Self-employment (without employees) is central within contemporary new forms of work, following the introduction of digital technologies. In comparison to the post-industrial contractual worker, self-employed workers working in conjunction with labour platforms may be more highly exposed to the effects of the regime of competition since they must handle all the risks that employers had previously assumed. Moreover, labour platforms accompany the emergence of new work structures, which are based on the monetization (commodification) of human efforts and consumer assets as the result of the arbitrage between the practices adopted by platform firms with regard to other firms. If Europe is to succeed in its social and more generally sustainability policies in the near future, we will need to make sure that the political and socio-economic actors pursuing these goals are made more accountable both within the firm and between firms and society. In other words, a new regulatory ‘social contract’ is needed, in accordance with which companies that show they are ethical, competent and reliable will be rewarded and those who do not will be penalized and sanctioned. While cooperative approaches are needed not only to multi-stakeholder deliberation, but also to business delivery, assessments through measurement are the key not only to effective business delivery but to better regulation, increased accountability and grounds for public trust within the ‘Social Europe’ of the future.

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NOTES 1. This chapter draws upon previous and current work by the author (sometimes co-authored): Pulignano, V. (2018). ‘Precariousness, regime competition and the case of Europe’. In A. Kalleberg and S. Vallas (eds), Precarious Work: Causes, Characteristics, and Consequences (Research in the Sociology of Work, Volume 31) (pp. 33–60). Bingley, UK: Emerald; Pulignano, V. and Stewart, P. (2006). ‘Bureaucracy transcended? New patterns of employment regulation and labour control in the international automotive industry’. New Technology, Work and Employment, 21(2), 90–106; Pulignano, V. (2019). ‘Work and employment under the gig economy’. Partecipazione e Conflitto, 12(3), 629–69. Among the funding on which this research is based, we mention the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 833577). 2. Voet, L. (2021, 7 October). Trade unions take on platform companies in the struggle for decent work. Social Europe. Accessed 13 August 2022 at https://​socialeurope​.eu/​trade​-unions​-take​-on​ -platform​-companies​-in​-the​-struggle​-for​-decent​-work.

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7. Sustainable human development, capabilities and the new trajectories of industrial policy Mario Biggeri, Andrea Ferrannini, Santosh Mehrotra, Marco R. Di Tommaso and Patrizio Bianchi

1 INTRODUCTION We are living in times of deep structural transformation as the COVID-19 pandemic has been exacerbating several problems of our economies and societies, all of which are pressing for more sustainable, inclusive and resilient forms of development. Above all, the twin digital and green transitions are driving the structural transformation of economies and societies across the world, opening up new trajectories of industrial development and new rationales for government intervention. These rationales refer not only to the objectives related to the pursuit of the Fourth Industrial Revolution (Industry 4.0) (Bianchi and Labory, 2018; Schwab, 2016), but also to tackling societal challenges such as climate change and rising inequalities. A renewed centrality is thus clearly assigned to industrial policy, to deal with all elements of contemporary production dynamics (Di Tommaso, Rubini and Barbieri, 2013; Di Tommaso and Schweitzer, 2013; Ferrannini et al., 2021) and requiring a whole-of-government approach to holistically integrate it with other complementary policy strands, such as competition, education and training, environment, research and innovation (R&I), health, employment, territorial cohesion, and so on (Aiginger and Rodrik, 2020; Barbieri et al., 2020; Barbieri, Di Tommaso, Tassinari et al., 2019; Bianchi, Biggeri and Ferrannini, 2021; Bianchi and Labory, 2011; Di Tommaso, Spigarelli et al., 2020). In this regard, the Organisation for Economic Co-operation and Development (OECD, 2021, p. 1) argues that governments must design industrial policy tools to foster the business sector’s contribution to the UN Sustainable Development Goals (SDGs) to support the continued innovation of sustainability frontrunners, the development and adoption of alternative business models for all firms, the design of new products, new production processes and new strategies in line with sustainability concerns. Indeed, according to both theoretical advancements and empirical evidence, it is clear that, on the one side, sustainable human development (SHD) is deeply affected – both positively and negatively – by production dynamics (Mehrotra and Biggeri, 2007), and, on the other side, expanding human capabilities, agency and empowerment requires dealing with industrial development processes. In particular, industrial development and industrial policies are not neutral in terms of direct and indirect SHD outcomes, as they deeply affect – either positively or negatively – the expansion of human capabilities (Andreoni, Chang and Scazzieri, 2019; Ferrannini et al., 2021). This inextricable relation points to a normative perspective on industrial development, where the increase of value-added and productivity should be primarily directed towards expanding human capabilities and ensuring environmental protection, towards societal objectives that go beyond economic growth. In this regard, the 2030 Agenda for Sustainable Development (UN, 2015) represents the current integrated, multidimensional and universal framework based on 17 SDGs and 169 106

Sustainable human development, capabilities and new trajectories  107 targets, fostering policy and entrepreneurial and human action on planet, people, prosperity, peace and partnership (i.e., the 5Ps). The 2030 Agenda has strong implications for industrial development and policy, especially SDG#8 ‘Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all’; SDG#9 ‘Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation’; and SDG#12 ‘Ensure sustainable consumption and production patterns’, among others. They push for a rethink of the vision, objectives, targets and tools of industrial development – in other words, its direction, drivers, engine and fuel. Nevertheless, evidence from the literature (Barbier and Burgess, 2019; ICSU, 2017; Nilsson, Griggs and Visbeck, 2016) reports not only synergies among goals and targets, but also trade-offs, especially between production goals (prosperity) and environmental goals (planet) (Biggeri et al., 2019). In this chapter, we argue that industrial development should simultaneously integrate value-added generation and productivity enhancement along with inclusiveness, social cohesion and environmental protection. In other words, we highlight the need for a decisive turning point on industrial policy to break the vicious circle characterized by rising social inequalities, worsening job conditions, never-ending environmental damage and climate change, and lagging productivity growth, which past industrial policies across the world have both caused and not been able to tackle. Thus, we show that promoting SHD and its four pillars – that is, productivity, equity, participation/empowerment, and sustainability – means devising integrated policies for industrial development as a more general driver of the transformation of the economy and society in the long run. These arguments may also provide a new lens through which to analyse the experiences of industrial policy across the world. For this reason, in this chapter we explore the current (rather than simply the past) practice of industrial policy in four key case studies – China, United States, European Union and India, representing 46 per cent of the world’s population and 55.4 per cent of the world’s gross domestic product (GDP) (Eurostat, 2020) – through the lens of this interpretative perspective, to shed light on the nexus between industrial development, human capabilities and sustainability. And, in line with the spirit of this Handbook, we aim to identify different trajectories of industrial development based on the interrelation between the four pillars of SHD and to better frame the current and future role of industrial policy in a context characterized by both deep and complex transformations as well as multiple and interrelated challenges and crises. The rest of the chapter is structured as follows. In Section 2, we highlight the nexus between industrial policy and SHD. In Section 3, we briefly analyse the historical and current experience of industrial policy in China, United States, European Union and India. In Section 4, we discuss industrial development processes from an SHD perspective, and then in Section 5 we provide some final remarks.

2

INDUSTRIAL POLICY FROM A SUSTAINABLE HUMAN DEVELOPMENT PERSPECTIVE

Today, one of the main issues the pandemic puts centre stage is the societal vision underlying government intervention, including the design and implementation of industrial policy, in order to ‘embed the structural changes that have long been necessary to develop more sustainable, dynamic and inclusive economies’ (WEF, 2020a, p. 10). Indeed, in recent times before

108  Handbook of industrial development COVID-19, industrial policy has been increasingly related to wider societal goals (Aiginger and Rodrik, 2020; Altenburg and Assmann, 2017; Bianchi and Labory, 2011; Chang and Andreoni, 2020; Di Tommaso and Schweitzer, 2013), in order to harness the full potential of industry’s contribution for lasting prosperity for all, in line with the 2030 Agenda for Sustainable Development (UN, 2015). Indeed, the connection between industrial policy and complex societal challenges has been enhanced – and even more today due to the pandemic – by concerns over the perceived weaknesses and fragilities of both mature and emerging economies, ridden with the productivity puzzle, systemic imbalances, public and private debt burden, economic stagnation (and recent collapse), rising inequality and environmental degradation. For instance, there is a robust discussion about the enhanced connection with the notion of inclusive growth (World Bank, 2009) and green growth (Rodrik, 2014). There is also a discussion about the role of industrial policy in building a ‘good jobs economy’ (i.e., targeted at expanding productive good-quality employment opportunities in the dynamic sectors) (Cramer, Sender and Oqubay, 2018; Ngo et al., 2021; Rodrik and Sabel, 2019). Bailey et al. (2019) discuss whether developing participative rights and democratic practices in policy formulation should itself be an aim of industrial policy. Moreover, R&I policies – which can be conceived as part of, and strictly linked to, industrial policy – have been recently framed to focus on grand societal challenges and on the transition of sociotechnical systems towards sustainable development, including not only new innovations and new technologies but also new models for partnership and governance, market creation, along with behavioural, organizational, infrastructural, regulatory and governance changes (Biggeri and Ferrannini, 2020; Schot and Steinmueller, 2018). This evolution within the recent international debate – further enhanced by the pandemic – has been pointing to a shift in academic and political thinking about industrial policy, including (1) being conceived through a systemic approach that extends far beyond the correction of market failures (Aiginger, 2007; Di Tommaso and Schweitzer, 2013; Peneder, 2017); (2) being designed to combine both horizontal policies to promote an enabling and competitive environment for business growth along with vertical policies geared towards supporting specific sectors, leading towards a more integrated perspective (Barbieri et al., 2020; Di Tommaso, Tassinari et al., 2020; Di Tommaso et al., 2017; Peneder, 2017); and (3) being steered by societal goals for the sake of the long-run collective interest (Aiginger and Rodrik, 2020) to benefit society as a whole. Nevertheless, the debate on the nexus between industrial policy and its effects in terms of human development, social cohesion and sustainability – conceived in economic, social and environmental terms – is still at an early stage (Ferrannini et al., 2021). Adam Smith (1776) had already outlined an approach to a strict dynamic interaction between economic/industrial development and civil/human development, recognizing also that increasing the wealth of nations may have social costs, such as the negative impact of the vertical division of labour on the quality of life of workers (Guarini, 2022). Therefore, countering and offsetting these negative effects requires the opportune interventions of public institutions. According to Amartya Sen (1999), who is strongly inspired by Adam Smith (1759), the dual role of human beings as agents, beneficiaries and adjudicators of progress, as well as – directly or indirectly – primary means of all production, provides a fertile ground for unintended confusion of ends and means in planning and policymaking. In Sen’s words: ‘it can – and frequently does – take the form of focusing on production and prosperity as the essence of progress, treating people as the means through which that productive progress is brought about

Sustainable human development, capabilities and new trajectories  109 (rather than seeing the lives of people as the ultimate concern and treating production and prosperity merely as means to those lives)’ (Sen, 2003, p. 3). In this regard, the capability approach and human development paradigm provide a robust theoretical background to distinguish between the means and goals of development and to question the societal vision that growth and development processes are steering towards. The capability approach proposes a fundamental shift from concentrating on the means of living (income and its growth) to the actual opportunities of living in itself – that is, human flourishing in terms of expanding the capabilities of people to lead the kind of life they have reason to value. Moreover, capability scholars have strongly reconciled sustainability arguments about future generations’ opportunities of leading worthwhile lives, with attention to present life opportunities, especially for vulnerable or excluded social groups (Anand and Sen, 2000; Neumayer, 2012; Pelenc et al., 2013), along with arguing for greater attention to groups and collectivities, their capabilities, the structures of living together, in favour of a more communal ethos (D’Amato, 2020; Deneulin and McGregor, 2010; Ibrahim, 2006; Stewart, 2005). All in all, conceptualizing human flourishing and sustainable development in multidimensional terms provides a vision and direction for the structural transformation of the society that industrial policy may contribute to. Indeed, SHD is deeply affected – both positively and negatively – by production dynamics, which can increase and/or reduce freedoms and capabilities – concerning, for instance, fair working conditions and environmental pollution, among others – depending on the ways in which production processes are structured (Andreoni et al., 2021). Similarly, expanding human capabilities, agency and empowerment also means dealing with industrial development processes, which can lead to the provision of better goods, services and jobs. In other words, the development of production capabilities is constitutive of human beings’ flourishing, but involvement in production activities may be detrimental for human capabilities (Andreoni et al., 2021, p. 189). For instance, evidence from UN SDG data in 2018 for almost all countries highlights that the prosperity goals/targets of the Agenda 2030 – such as SDG#8 (on growth/development) and SDG#9 (on industrial development) – are negatively and significantly correlated with the planet goals/targets – SDG#14 (on marine ecosystems) and SDG#15 (on terrestrial ecosystems) – and even with segments of prosperity targets (on decent jobs within SDG#8 or inequality within SDG#10).1 Therefore, industrial development and industrial policies are not neutral in terms of SHD outcomes, as they deeply affect – either positively or negatively – the expansion of human capabilities (Ferrannini et al., 2021; Mehrotra and Biggeri, 2007), thus the need to bring production back to the core of the human development agenda (Andreoni et al., 2021). This paves the way for arguing that industrial policy should be directed towards contributing to the four pillars of SHD (Haq, 1995): ● productivity, such as the extent to which economic, human and infrastructural resources are efficiently used within production systems to create value-added; ● sustainability, such as the extent to which natural resources and environmental ecosystems are protected to ensure equal intergenerational opportunities for industrial development; ● equity, such as the extent to which socio-economic opportunities are expanded through industrial development, also paying attention to distribution and cohesion; and ● participation and empowerment, such as the extent to which all stakeholders and actors within production systems are enabled to act as individual and collective agents within industrial development processes.

110  Handbook of industrial development In line with these pillars, the recent international debate highlights that a holistic view on the industrial environment, comprising all drivers of value-added generation and productivity enhancement, accounts for both growth and SHD. In a nutshell, increasing value-added generation and productivity is necessary, especially for the provision of goods and services ensuring and expanding human capabilities, for increasing the standards of living of all people and for a sustainable use of natural resources. Nevertheless, continuous attention should be devoted to what happens in the ‘real world’ and specifically to the analysis of industrial policy practices promoted by governments in a long-term perspective (Ferrannini et al., 2021).

3

LEARNING FROM REAL-WORLD POLICY EXPERIENCES: CHINA, USA, EU AND INDIA

Going beyond the theoretical debate and focusing on ‘real-world’ practices, it is common today to find industrial policy associated with a long list of heterogeneous goals, including meta-economic goals (unemployment, deindustrialization, protection of domestic productions, territorial unbalances, social inequalities and environmental issues etc.) (Barbieri et al., 2020; Di Tommaso and Schweitzer, 2013; Di Tommaso, Tassinari et al., 2020; Ngo et al., 2021; Oqubay et al., 2020). Although this trend undoubtedly signals that a broader notion of industrial policy has been gaining momentum in the last decade, it may not automatically lead to conceiving the current practice of industrial policy around the world as consistently directed towards a vision of SHD. For this reason, here we expand the preliminary insights raised by Ferrannini et al. (2021) by briefly analysing the historical and current practice of industrial policy in China, United States, European Union and India (based on information available up to January 2022) through the interpretative lens of an SHD perspective. Analysis of the industrial policy practices in these four cases is relevant for three reasons: first, they are illustrative of the role of government intervention on industrialization as a general driver of the structural transformation of the economy; second, their undeniable centrality in global production networks, in financial and investment flows, in research and innovation processes and in consumption patterns; third, their weight and influence in the international policy debate on government intervention and industrial policy. It is important to clarify that our analysis is more forward (rather than backward) looking and we are not focusing our attention on past industrial policies to (albeit correctly) blame them for increasing social inequalities, worsening the quality of jobs, and environmental damage, as this is very clear and extensively discussed in the literature. Rather, we are taking these societal challenges as the starting point to call for a stronger connection between industrial policy and SHD, and we are analysing (for the very first time in the literature) whether and to what extent current industrial policies across the world – for example, by the Biden administration in US, in the 13th and 14th Five-Year Plans in China, in the New Industrial Strategy for Europe by the European Commission, by the Modi administration in India) – are moving in a new direction.

Sustainable human development, capabilities and new trajectories  111 3.1

Industrial Policy in China

In 1949, the era of its foundation, the socio-economic system in the People’s Republic of China was devastated by war, and regional economic distribution was strongly unbalanced in favour of the coastal region. During the First Five-Year Plan (FYP), the Chinese Communist Party followed the ‘Soviet model’, based on self-reliance and regional autarky, for economic, political and ideological reasons. Industrial policy was based on collectivization of the production system (especially in steel and grain) and mainly on large state-owned enterprises. Despite mistakes (Tsui, 1993), the Maoist period allowed China to gain some important economic results as well as to achieve some important social progress in several well-being domains. After Mao’s death in 1978, the strategy of Deng Xiaoping and his successors consisted in changing those elements of the ‘Maoist system’ that reduced personal incentives and hampered the potential growth of the economy. Chinese leaders believed that the fast economic and technological development of coastal provinces would have enhanced the economic development of interior provinces and considered the regional differences and disparities as necessary to ‘socialist modernization’. The great reforms of the 1980s, relaunched by Deng Xiaoping during his travel to South China in 1992, allowed the economic transition to a ‘market socialist economy’ and brought impressive economic performances and various resounding social achievements (Lin, 2011; Nolan, 2004). The transition from a planned economy to a market economy has produced spectacular changes in the development of the Chinese economy. Since the early 1980s, step by step, China has gradually attained a predominant position in the production of goods and services at the global level. In this economic transition, China has implemented several industrial policy interventions that played a crucial role (Barbieri, Di Tommaso, Pollio et al., 2019, 2020). From 1979, the ‘Open Door’ policy was pursued, starting with the opening of the first four Special Economic Zones (SEZs) with the aim of attracting foreign direct investment (FDI) and technology in some coastal provinces (Barbieri, Di Tommaso, Pollio et al., 2019, 2020; Biggeri, 2003; Di Tommaso et al., 2013; Zheng et al., 2016) and then opened to cities and all provinces in the whole coastal area. The coastal region (eastern provinces), which already had the advantages of location, human capital, infrastructure and FDI, had to develop its industrial and technological capabilities, and increasing investments were planned in this direction. The central and western regions had to specialize in low-technology production, raw materials, energy production and agricultural products. Given the territorial disparities in terms of opportunities, the relaxation of migration rules produced a massive migration phenomenon to find ‘better’ jobs from the central and western regions to the coastal region (Liang and White, 1997). This industrial policy tool contributed to driving a relevant and gradual (compared to other socialist economies) structural adjustment that has led part of the country to shift from an agriculture-based economy to becoming a global manufacturing centre for a wide variety of goods and services (Nolan, 2004). Manufacturing in China has also experienced a further shift towards higher value-added and research-intensive production, driven by several industrial policy actions (Barbieri, Di Tommaso, Pollio et al., 2019, 2020; Di Tommaso et al., 2013). However, markets were not suddenly opened and foreign companies were not allowed in the country overnight (Rodrik, 2010). Industrial policy tools were envisaged to accompany a gradual shift of the economy and society in the long run, and state-owned enterprises started to be privatized in 1997. The

112  Handbook of industrial development industrial growth was based on the new private sector and FDI creating joint ventures with Chinese companies. As suggested by Ferrannini et al. (2021), two keywords might summarize the Chinese approach to industrial policy: experimentation and conditionality. Experimentation was made possible through tools such as the SEZs and state-owned companies in key industrial sectors, in which the government tested capitalism and the entry of foreign capital (Barbieri, Di Tommaso, Pollio et al., 2019, 2020; Biggeri, 2008; Di Tommaso et al., 2013; Rodrik, 2010). Conditionality was often attached to such entry: foreign companies were first allowed only in joint ventures with Chinese firms to guarantee knowledge and technology transfer. Despite the success, the reforms also revealed intense contradictions in terms of environmental degradation and pollution, and increasing inequalities with highly differentiated growth and human development performances across provinces (Barbieri et al., 2020; Biggeri, 2003, 2008; Biggeri and Bortolotti, 2020; Herrerías and Monfort, 2015; Rolf, 2021; Yao and Liu, 1998; Zhao and Tong, 2000). In other words, Chinese development is a multifaceted phenomenon that cannot be reduced to economic growth and development recorded at the national level (Goodman and Segal, 2002; Shue and Wong, 2007). Under the leadership of Hu Jintao (2004–12) and Xi Jinping (from 2012), the concern about uniform development and non-monetary aspects of well-being have resurfaced with the commitment to build a ‘Harmonious Society’, officially adopted in 2005 as the vision underlying its development strategy (Li et al., 2016). Indeed, the Chinese leadership attempted to include a broader set of targets (related to environment, health, education, energy, demography etc.). Xi Jinping’s leadership continues to embrace an inclusive and widespread concept of development, which is part of his project named ‘Chinese Dream’ or ‘Great Rejuvenation of the Chinese Nation’. The FYPs still represent fundamental moments for industrial policy in China to establish the main pillar industries and in the definition of strategic sectors and structural changes that the country needs. In particular, China’s 13th FYP 2016–20 (adopted in March 2016) focused on five main principles – innovation, coordination, green economy, openness and inclusiveness – through a package of action comprising a scientific and technological innovation plan, a poverty alleviation plan, a guideline on environmental improvements and other related plans. According to Xue, Weng and Yu (2018), China’s 13th FYP closely reflects the 2030 Agenda for Sustainable Development: each SDG is linked to one or more chapters, showing to what extent they both focus on poverty, social welfare, environmental protection, financing and technology innovation. China’s 14th FYP 2021–25 (released in March 2021) identifies strategic arrangements for driving China towards becoming a modernized socialist country in a multifaceted way, referring to new-type industrialization, informatization, urbanization and agricultural modernization (National People’s Congress, 2021) to shift away from export-led growth. It includes 20 main indicators of economic and social development, including five binding targets related to the environment, as compared to 25 main targets in the 13th FYP with ten environment-related targets (UNDP, 2021). As for the productivity pillar from an SHD perspective, the 14th FYP points to the elevation of science, research and development (R&D), and technological innovation, including digitalization, to a matter of national strategic importance (aiming to increase R&D expenditure of society by more than 7 per cent annually) to further optimize the economic structure and significantly improve the capacity for innovation (National People’s Congress, 2021).

Sustainable human development, capabilities and new trajectories  113 Regarding the sustainability pillar, a strategic priority in the 14th FYP is green development, with environmental priorities being integrated throughout the plan (Vaughan, 2021). For instance, the FYP envisages enhancing green production and green consumption by 2035 in its shift to a consumption-based economic model; it identifies multiple actions to advance green urbanization (e.g., electrification of public transport, expansion of urban green spaces) along with making cities more resilient to climate change; it calls for a market-oriented green technology innovation system to achieve energy and natural resource efficiency targets in key sectors, such as iron and steel, petrochemicals, building materials, mining and agriculture, among others (ibid.); and it sets out an ambitious and comprehensive strategy to protect nature, anchored first around the expansion of a system of national parks that prohibit non-ecological development (ibid.). However, relevant concerns remain about how China will reconcile this drive toward carbon neutrality with the expansion of coal, oil and gas, and also the lack of an explicit emissions cap and practical elements needed to put China firmly on a road to fulfil its 2030–60 pledge and the Paris Agreement’s commitments (UNDP, 2021). With regard to the equity pillar, the 14th FYP aims to expand people’s well-being by achieving more fulfilling and higher-quality employment, by synchronizing the growth of per capita disposable income of residents, and by significantly increasing equitable access to basic public services (e.g., education and health). Attention is also paid to the rural revitalization strategy, to allow people as a whole to take solid steps towards greater prosperity (National People’s Congress, 2021). As for the participation pillar, the 14th FYP sets an ambitious objective to fully ensure people’s rights to equal participation and equal development, driven not only by the commitments discussed above, but also by making China a cultural, educational, talent and sports powerhouse (ibid.). Nevertheless, it is clear that the centralized system clearly reduces the possibility to make priority-setting (in terms of strategic industries and targets) a transparent and participatory process. 3.2

Industrial Policy in the United States

Despite the continuous emphasis on the strengths of free markets in guiding the country’s destiny, in reality, government policies have been far more interventionist throughout US history (see, for instance, Block, 2008; Di Tommaso and Schweitzer, 2013; Di Tommaso, Tassinari and Ferrannini, 2019, 2020; Mazzucato, 2013; Ngo et al., 2021; Tassinari, 2019). Indeed, in the long-run experience of American industrialization, different administrations – despite deploying different systems of societal values – have similarly promoted and sustained an American model of industrialization, economy and society. This role has been actively exercised in many historical moments at social, military and economic turning points, with surprising continuity until recent times, marked first by a prolonged economic crisis and second by wide societal challenges. From the end of the eighteenth century to the first decades of the twenty-first century, the government funded and supported American companies in ‘strategic’ sectors, and these companies have been protected from foreign competition. Protection of infant industries occurred through centuries of US industrialization. World War I presented an opportunity to consolidate the industrial system due to public demand, while during the Great Depression of the 1930s, the Roosevelt administration financed public work programmes and bailed out many industries, and then redirected the

114  Handbook of industrial development economy towards war production. The Cold War justified government support of industries that promoted national defence. The Reagan years were a time of deregulation but, in continuity with the past, the government supported science and technology, and trade agreements became a central feature of US industrial policy. To save the US economy during the 2008 economic crisis, the Obama administration used many tools from past US history – bailouts, public works programmes, stimulus packages, and ‘Buy American’ campaigns. These interventions have been driven by short-term necessity, but they can also be seen as attempts to look at the long run by trying to promote strategic change in the US economy. Even in the Trump era, a special relationship between industry and government was evident, especially with regard to the automotive, steel and defence sectors. The top budget priority of the Trump administration was increasing national security funds by relying on budget cuts in non-defence spending, reforming the US welfare and health systems (e.g., repealing Obamacare) and also downplaying the development of the green industries, in contrast to the strong efforts (and vision) of the previous administration. Moreover, the reduction of income and business taxes was a cornerstone of its industrial policy, along with a strong interventionist approach on trade through ‘Buy American’ acts, the new United States–Mexico–Canada Agreement (USMCA) and the long trade disputes with European countries and, above all, with China. Nevertheless, the Trump administration was characterized by certain elements of discontinuity compared to the last 30 years concerning trade and – more broadly – foreign policy. This was especially true of his rhetoric, which was increasingly accompanied by neo-protectionist slogans and actions. Nowadays, the approach on government intervention by the Biden administration can be clearly conceived as a new industrial policy, mainly centred on the following actions: ● the American Jobs Plan, which combines investments in quality of infrastructure, quality of housing, caregiving infrastructure, labour force participation (especially women), R&D manufacturing and training, in order to strengthen US global competitiveness and create the well-paid union jobs of the future; ● the Infrastructure Investment and Jobs Act (which operationalizes the main elements of the American Jobs Plan), which foresees around $550 billion in new federal investment in roads and bridges, passenger rail, water infrastructure, Internet, electric grid, clean energy transmission and electric vehicle infrastructure, in order to make the US economy more sustainable, resilient and just; ● Executive Order on Promoting Competition in the American Economy, which establishes a whole-of-government effort including 72 initiatives by more than a dozen federal agencies in order to tackle some of the most pressing competition problems in the US (especially in labour markets, agricultural markets, healthcare markets, tech sector, transportation, Internet service, banking and consumer finance); ● Executive Order on Ensuring the Future is Made in All of America by All of America’s Workers, which launches a whole-of-government initiative to strengthen the use of federal procurement to support American manufacturing, also establishing the ‘Made in America Office’; ● the United States Innovation and Competition Act, which expands the role for the federal government in fostering advanced manufacturing in ‘strategic sectors’ and vital technologies (including semiconductors, drones, wireless broadband, robotics, artificial intelligence [AI], lithium batteries and electric vehicles) with increased funding in R&D, subsidies and

Sustainable human development, capabilities and new trajectories  115 incentives for domestic production, supervision and regulation through a new ‘Directorate for Technology and Innovation’, along with several higher-education provisions to facilitate R&D in the key technology focus areas and trade provisions to restrict the flow of Chinese goods and services; and ● the Made in America Tax Plan, a planned action to reward investment and job creation at home instead of shifting production and/or profits overseas. Overall, Biden’s industrial policy focuses on enhancing those cutting-edge capabilities that are critical to US national security, global competitiveness, sustainability and resilience, making relevant steps towards embracing – yet not completely – an SHD perspective. As for the productivity pillar, the current industrial policy is about strengthening the public systems that connect manufacturers and researchers and workers and small businesses through public investment in (1) infrastructure (e.g., transport and logistics, digital network, power grid, schools); (2) government-supported research for breakthrough technologies (e.g., robotics, AI, advanced energy sources, pharmaceutics); and (3) building supply-chain capacity, sustainability and resilience in strategic sectors (e.g., semiconductor industry), tying innovation to domestic manufacturing and employment. Regarding the sustainability pillar, the pace of climate change is conceived as a threat to the industrial base (and more broadly to the security and stability of economies and regions of the world), requiring a fundamental shift in production patterns and the enhancement of supply-chain resilience to environmental shocks. For this reason, according to US National Economic Council Director Brian Deese (2021, n.p.), the ‘approach to rebuilding industrial strength puts investments in decarbonization at the forefront in the power sector, in the transportation sector, in the industrial sector, and the built environment’ through support for R&D and supply-side production incentives. This is evident, for instance, in the plans to build a national network of electric vehicle charging stations, to speed the switch to electric for the government fleet and to strengthen the electrical grid towards carbon neutrality. With regard to the equity pillar, Biden’s industrial policy acknowledges that persistent inequality is not only slowing economic growth but also risks fracturing the social and democratic stability upon which the sustainability of the structural change of the economy and society depends. Central attention is devoted to jobs creation through the American Jobs Plan, including a targeted (e.g., in terms of racial and gender equity) and sectoral-based approach to workforce development and increased attention to ensuring labour standards for all. This is also accompanied by investments in new schools and childcare facilities that allow people and parents to work. Finally, territorial disparities are also taken into account by investing particularly in those regions that have suffered from decades of deindustrialization to unlock their innovative capability. Regarding the participation pillar, the current industrial policy only generally points at incorporating worker voice into the process and promoting new models of public–private collaboration. This seems to be inspired by Rodrik’s approach (2004), where the coordination between the public and the private sector is critical to solving asymmetries of information, getting the right investment outcomes and spurring the right kind of private sector innovation. All in all, US industrial policy over time comprised actions motivated by short-term economic, social and political necessity, along with more ambitious interventions aiming to achieve more complex structural adjustments and consolidate an American model of society, as defined by economic powers and interests. In this respect, the recent politics of ‘greening

116  Handbook of industrial development of industrial policy’ in the US, strongly favoured by the Obama administration and then slowed down by the Trump administration, is illustrative of the extreme complexity of public decision-making in the field of industrial policy, with a wide impact on investment decisions in particular sectors and technologies, on the cost structure of companies and on the structural adjustment of the US economy (Tassinari, 2019). Moreover, US industrial policy is also conceived to ensure the social endurance of these dynamics, keeping under control those social fractures, vulnerabilities and inequalities that may hamper the sustainability of industrialization development itself. 3.3

Industrial Policy in the European Union

Talking about industrial policy in the EU is complicated due to the undeniable importance of competition policy within the single market, the interplay of responsibilities and competences between the supranational, national and regional levels, and the multiplicity of strategies, programmes, frameworks and regulations that are discussed, announced and – to a different extent according to conditions in each member state – implemented and made operational (Landesmann and Stöllinger, 2020). Overall, industrial policy in the EU is complex because there is only a strategy with broad objectives and targets and a few tools defined at the common European level and implemented by the European Commission, while the main competence for industrial policy in the EU remains at national level, as indicated in the Treaties, with each member country having its own industrial policy (Pelkmans, 2006). However, the European level has a strong influence directly on regional industrial policies through the structural funds provided to regions through the Cohesion Policy. Nevertheless, common concerns over production dynamics and deindustrialization in the EU at aggregate European, member state and regional levels within a changing global context have always deserved central attention, also reinforced by the uneven impact of the financial and economic crisis after 2008. Despite the variation in policy frameworks on industrial policy embraced by the EU over time (see, for instance, Aiginger, 2014; Eder and Schneider, 2018; Mosconi, 2015; Pianta, 2014; Pianta, Lucchese and Nascia, 2016) three features seem to represent a unifying theme: (1) the mixed approach, incorporating both horizontal and sector-specific measures given that purely horizontal industrial policy appeared to be either inadequate or impossible in the EU setting; (2) the centrality of the EU institutions in designing and implementing industrial policy, due to both their importance to key challenges (e.g., deindustrialization, technological leadership, environmental transformation) that member states individually cannot realistically meet, as well as to the inextricable synergies with the European Commission’s regulatory power in competition and trade; and (3) the importance of an effective implementation of EU’s sophisticated industrial strategies, whose difficulties arise from policy incoherence (with rival or even contradictory objectives), overlapping responsibilities among governance levels, separation among interconnected policy fields and over-multiplication of industrial programmes and initiatives often lacking in critical mass (Landesmann and Stöllinger, 2020). Recent industrial policy at European level has embraced – at least in its mandate and statements – a wider societal perspective. First, the Europe 2020 Strategy (EC, 2010a) approved in 2010 was designed to pursue three mutually reinforcing priorities: smart growth – developing an economy based on knowledge and innovation; sustainable growth – promoting a more resource-efficient, greener and more competitive economy; and inclusive growth – fostering

Sustainable human development, capabilities and new trajectories  117 a high-employment economy delivering social and territorial cohesion. In the same year, An Integrated Industrial Policy for the Globalisation Era (EC, 2010b) was released based on the premise that a vibrant and highly competitive EU manufacturing sector can provide the resources and many of the solutions for the societal challenges facing the EU. Today, the overarching vision and goals for industrial policy in Europe are surely given by the commitment towards a prosperous and sustainable Europe by 2030 and by the political guidelines issued by the new European Commission 2019–24, which focus on six headline ambitions for Europe over the next five years and well beyond: (1) a European Green Deal; (2) an economy that works for people; (3) a Europe fit for the digital age; (4) protecting our European way of life; (5) a stronger Europe in the world; and (6) a new push for European democracy. Such commitments were further reinforced in the Next Generation EU plan approved in May 2020 (EC, 2020a) to support EU member countries in their recovery strategies from the health, economic and social crisis triggered by the COVID-19 pandemic. In particular, the European Green Deal – approved in December 2019 (EC, 2019) – and its Investment Plan (EC, 2020b) shape the new European industrial strategy to transform EU’s economy for a sustainable future, aimed at simultaneously becoming the world’s first climate-neutral continent, ensuring a ‘just transition’ (i.e., leave no one behind), and opening up opportunities, creating jobs and offering a competitive edge to European industries. For instance, to redesign how EU industry and economy work, pivotal importance is assigned to mobilizing research and fostering innovation to advance knowledge, new technologies, sustainable solutions and disruptive innovation. Last but not least – and central to our analysis – on March 2020, the European Commission approved its New Industrial Strategy for Europe (EC, 2020c) as a plan for how the EU’s world-leading industries could lead the twin transitions towards climate neutrality and digital leadership, to enhance its global competitiveness in the world economy, its strategic autonomy based on its economic and technological sovereignty, and its resilience to external shocks (CEPS, 2021). This strategy was later updated in May 2021 (EC, 2021) to account for the needs and challenges raised by the pandemic and tackle structural vulnerabilities of industrial ecosystems in Europe. In particular, the updated strategy focuses on the need to (1) uphold the free movement of persons, goods, services and capital in the Single Market and strengthen its resistance to disruptions; (2) analyse and address strategic dependencies, both in technological and industrial terms; and (3) accelerate the twin green and digital transitions. From an SHD perspective, it was argued that the New Industrial Strategy for Europe lies at the centre of the future of the EU and its ability to meet the SDGs (SDSN and IEEP, 2020), also by consistently linking with the Green Deal, the European Education Area, the Digital Agenda for Europe, the European Research Area, the European Skills Agenda, and other overarching policy areas. With regard to the productivity pillar from an SHD perspective, it undoubtedly represents the central concern, as the New Industrial Strategy identifies digital and innovation leadership as the defining challenge and opportunity for enhancing productivity and value-added to secure global competitiveness. Indeed, productivity growth (based on labour productivity), public and private investment and R&D investments represent the core key performance indicators defined to monitor the progress achieved, focussing on competitiveness, Single Market integration, small and medium-sized enterprises (SMEs), the twin transition, and economic resilience.

118  Handbook of industrial development Regarding the sustainability pillar, the Commission clearly assigns a leading role to European industry and R&I in helping the EU achieve climate neutrality by 2050 and decouple economic growth from resource use, in line with the set of deeply transformative policies identified in the Green Deal. Indeed, all industrial value chains and innovation ecosystems are involved in this transition, including existing ones and others to be launched through proactive policy (e.g., the European Battery Alliance and the European Clean Hydrogen Alliance) aimed at boosting lead markets (Renda, 2021). However, this also requires enhanced corporate orientation and R&D towards the circular economy and mitigating climate impacts, among others. As for the equity pillar, the strategy acknowledges that women, youth and low-income workers were particularly affected by the crisis, and thus that particular attention must be paid to equal rights and opportunities for an inclusive recovery across all sectors. In this regard, the enhancement and upgrading of skills is assigned a central role to support individuals in labour market transitions and ease skills transfers from declining to expanding sectors and occupations, as well as across regions, and to allow industry to count on a talented and skilled workforce during the transitions. In other words, as argued by Alcidi, Baiocco and Corti (2021, p. 142), ‘having skills among the priorities of an EU industrial policy means recognising the crucial role of human capital for the EU industry to thrive, as well as endorsing people to benefit from industrial development’. However, limited attention is paid to both fairer value distribution in value chains (Renda, 2021) and to job quality for a competitive industry and a resilient society in the three dimensions related to wages, social protection and social dialogue that represent prominent features of the European social model (Alcidi et al., 2021) and European Pillar of Social Rights. Finally, with regard to the participation pillar, the New Industrial Strategy intends to set in motion a new policy approach focused on better connecting the needs and support provided to all players within each value chain or industrial ecosystem. This approach is centred on the EU’s open and inclusive Industrial Forum, where industry, public authorities, social partners and other stakeholders can ‘co-create’ transition pathways, promote best practices and solutions across ecosystems and identify cross-border and cross-ecosystem investment needs and cooperation opportunities (EC, 2021). All in all, it is possible to argue that the New Industrial Strategy in Europe represents a substantial step forward to fully embrace an SHD perspective, especially if analysed within the wider overarching policy framework of the Green Deal and of the Next Generation EU plan. As compared to the past, the current industrial policy seems able to take into account and integrate objectives affecting the different pillars of SHD (despite concerns over the limited focus on job quality), as well as overcoming traditional boundaries among policy fields towards a ‘whole-of-government’ approach. However, as stressed by SDSN and IEEP (2020), it still needs to develop clear roadmaps and investment programmes for key industries and technology areas, identifying clear ways to increase private and public investments to finance strategic industrial initiatives for the SDGs. Moreover, the key performance indicators set to monitor the implementation of the EU Industrial Strategy are not yet able to fully embrace its economic, social, environmental and governance dimensions, as they uniquely focus on competitiveness indicators. Here again, the Strategy needs to set clear and measurable targets coherent with the overall objective of simultaneously pursuing productivity, sustainability, equity and participation. Last but not the least, it is important to recall that the European level has a strong influence on regional industrial policies. Indeed, the Smart Specialisation Strategy (S3) has been

Sustainable human development, capabilities and new trajectories  119 representing the new approach since the 2010s to induce regions to define and implement more effective regional industrial policies based on the entrepreneurial discovery process, identification of strengths and weaknesses and industrial activities so as to promote those new activities that are related to existing ones and can lead to new development paths for the regional economy (Bianchi and Labory, 2019). The S3 prioritizes domains, areas and economic activities where regions or countries have a competitive advantage or have the potential to generate knowledge-driven growth and to bring about the economic transformation needed to tackle the major and most urgent challenges for society and the natural and built environment (Foray, Eichler and Keller, 2021). Recently, the European Commission proposed a new and stronger directionality in the use of the unprecedented EU investment for local jobs creation in the post-COVID recovery through an advanced ‘Smart Specialisation Strategies for Sustainability (S4+)’ approach, aiming ex ante at improving sustainability and inclusiveness through a place-based and innovation-driven policy. The S4+ would mobilize transformative innovation in a systemic approach for cross-sectorial solutions and whole-of-government approach, focusing on synergies between innovation, sustainability, infrastructure and skills while leaving no one behind (Interreg Europe, 2021). 3.4

Industrial Policy in India

As the sub-sections on the US and Europe show, explicit industrial policy has come into its own in at least 100 countries after the economic shock of the Global Economic Crisis after 2008. The paradox is, however, that despite being a developing economy, and despite having had an industrial policy prior to 1991, in the last three decades of development, industrial policy has barely found favour in India. It was only in 2011 that the federal government formulated an industrial policy, but which never really took off because the government went into policy paralysis in the last two years of its rule (which ended in May 2014). The new right-wing nationalist government has not formally adopted an industrial policy either. This absence of an industrial policy, combined with a poor focus on human capital since economic reforms of liberalization and economic reform began in 1991, has meant that while the GDP growth rate has picked up significantly compared to the pre-1990 period, averaging 6.5 to 7 per cent per annum, the pace of structural transformation has stalled in the last seven years or so. There are two distinct periods into which the three decades from 1991 to 2021 can be divided: the period until 2014, and the period under the new regime since 2014 (whose term runs till May 2024). The first period was characterized by faster economic growth, structural change in output and employment terms, and significant poverty reduction; in that sense, it is consistent with the SHD framework of this chapter. The second period was marked by slower economic growth, a stalled process of structural change, and an actual increase in the absolute number of the poor (although the incidence of poverty has remained similar) as defined by the national poverty line. The two periods are similar in that there was no explicit industrial policy (Gandhi, 2019; Mehrotra, 2020). However, in the first, there was an implicit industrial policy, with an explicit objective in national policy of ‘inclusive growth’, as stated in the 11th and 12th FYPs of India (Planning Commission, 2007, 2013). Unfortunately, post-2014, planning itself was abandoned and the Planning Commission abolished (Mehrotra, 2020). There was, however, an implicit industrial policy, essentially drawing from the Statement on Industrial Policy of 1991, as follows:

120  Handbook of industrial development 23. …industrial licensing will henceforth be abolished for all industries, except those specified, irrespective of levels of investment. These specified industries will continue to be subject to compulsory licensing for reasons related to security and strategic concerns, social reasons, problems related to safety and overriding environmental issues, manufacture of products of hazardous nature and articles of elitist consumption… 25. In order to invite foreign investment in high priority industries, requiring large investments and advanced technology, it has been decided to provide approval for direct foreign investment up to 51 per cent foreign equity in such industries… 26. Promotion of exports of Indian products calls for a systematic exploration of world markets possible only through intensive and highly professional marketing activities. To the extent that expertise of this nature is not well developed so far in India, Government will encourage foreign trading companies to assist us in our export activities… 28. With a view to injecting the desired level of technological dynamism in Indian industry, Government will provide automatic approval for technology agreements related to high priority industries within specified parameters… 34. Government will strengthen those public enterprises which fall in reserved areas of operation or are in high priority areas or are generating good or reasonable profits. … Competition will also be induced in these areas by inviting private sector participation. In the case of selected enterprises, part of Government holdings in the equity share capital of these enterprises will be disinvested in order to provide further market discipline to the performance of public enterprises… 37. …emphasis will be on controlling and regulating monopolistic, restrictive and unfair trade practices rather than making it necessary for the monopoly houses to obtain prior approval of Central Government for expansion, establishment of new undertakings, merger, amalgamation and takeover and appointment of certain directors. (Government of India, 1991, pp. 3–5)

In addition to these policies, which did give a new dynamism to the Indian economy post-1991, there was a new emphasis in the first decade of the new millennium on two new areas of policy: (1) new free trade agreements were signed with a number of countries (Japan, South Korea and ASEAN) with a view to promoting greater foreign trade and some participation in global production networks; and (2) a new initiative to increase investment in infrastructure for industry, by not relying on public investment alone, but rather promoting private–public partnerships in areas of roads and highways, airport development and power-generating plants and power distribution. The overall effect of these actions did lead to a higher than ever GDP growth rate of 6.4 per cent per annum over the 1990s, and even faster growth of 8 per cent per annum over 2003–04 to 2013–14. As a result, the latter period saw an unprecedented improvement in the poverty elasticity of growth, with 140 million rising above the national poverty line in seven years until 2011–12 (Mehrotra, 2016; Planning Commission, 2013). However, several structural weaknesses remain. First, manufacturing never became the lead sector in these 30 years since economic reforms began. Non-manufacturing industry (mining, utilities and its most important constituent, construction) and manufacturing industry did enable India’s growth to pick up, but it was services that were driving growth: services share in GDP shot up to about 57 per cent, while industry is still stuck at around 22 per cent of GDP (of which manufacturing is barely 15 per cent). Manufacturing growth stalled post-2014, and the process of structural transformation was stalled, even though the share of agriculture in GDP fell to 15 per cent and its share in employment to 40 per cent in 2019. Second, manufacturing share of GDP was 17 per cent in 1992, but in 2017 was still 17 per cent; worse, its share fell in 2019 to 15 per cent. Third, manufacturing’s share in employment was barely 10.5 per cent in 1999–2000, had risen to 12.8 per cent in 2012, but fell back to 11.5 per cent in 2019–20 (Mehrotra, 2020). The causes of manufacturing never becoming a lead sector in India’s growth lie not only in the absence of serious and explicit industrial policy since 1991 (Mehrotra, 2016), but also in

Sustainable human development, capabilities and new trajectories  121 the sheer neglect of investment in human development, especially in India’s most populous northern and eastern states. School education was so neglected until the mid-1990s that India managed to universalize primary schooling (grades 1–5) only in 2007. Thereafter, enrolment has grown sharply, so that secondary education (grades 9–10) was nearly universalized by 2015, with gender parity. Meanwhile, however, the delays in this achievement have meant that vocational education and training remained neglected until a decade ago. At the same time, the neglect of public health and low public expenditure on health (barely 1.3 per cent of GDP in 2019) exposes India’s millions of poor and the lower middle-class to rely on dubious-quality private healthcare. This Achilles’ heel was brutally exposed during the COVID-19 pandemic. Despite India being a global generics pharmaceutical production giant, and the vaccine manufacturer of the world (60 per cent share), India’s public health system failed to protect its population. All in all, the effects of the absence of a manufacturing strategy are noticeable in their outcomes. The value and volume of manufacturing output/employment grew, but slower than was the potential. India is still a labour surplus economy, with 40 per cent of its workforce still in agriculture. Manufacturing output grew because most of the industry that emerged since the economic reforms post-1991 was relatively capital-intensive, and hence did not generate jobs on the scale required in an economy, while post-2013 more than 5 million young people are joining the labour force each year, and are looking for non-farm work (as they are better educated than an earlier generation). Had there been an explicit focus on labour-intensive manufacturing sectors (leather/footwear, textiles, garments, food processing, woodwork and furniture), which account for half of all manufacturing jobs, the employment elasticity of industrial growth would have been higher. What we have instead is still a country, as noted by the World Inequality Report 2022 (Chancel et al., 2021), that is quite poor but very unequal. However, there is a new focus on renewable energy sources; India is one of the world’s major producers of wind energy and solar power. Going forward, the challenges are as follows. India is already the sixth largest economy in the world in 2022, and yet its per capita emissions are very low compared to China, let alone the US or Europe. However, given that 60 per cent of commercial energy use comes from coal, the latter will have to be replaced within the next few decades or so by greater reliance on renewable sources of energy. Second, it will have to focus much more on public health and education investments, especially vocational education and skill development, to better prepare its growing workforce if it wants to meet its manufacturing goals and the challenges of Industry 4.0 technologies. Third, even prior to the pandemic, the structural transformation had stalled; it can be reversed with a comprehensive industrial policy based on a labour-intensive strategy and renewed emphasis on cluster development targeted at micro and SMEs. Only then can the SDGs be achieved, and the four pillars of SHD – productivity, equity, participation/ empowerment, and sustainability – be attainable.

4

INDUSTRIAL DEVELOPMENT PROCESSES FOR SUSTAINABLE HUMAN DEVELOPMENT

As shown, industrial development and policies are complex and varied over time, being characterized by multidimensional, multilevel and dynamic processes and shaped by multiple public, private and societal interests. Moreover, they are place based and historically deter-

122  Handbook of industrial development mined, as socio-institutional features, distribution of power, class relations and community structures in each time and place enable or disable firms (i.e., individual agents) and sectors (i.e., collective aggregations) to develop and upgrade, also affecting SHD and human capabilities to flourish (Biggeri and Ferrannini, 2014). In line with this reasoning, the consolidated literature on clusters and industrial districts (Becattini, Bellandi and De Propris, 2009; Lazzeretti, Sedita and Caloffi, 2014; Pyke and Sengenberger, 1992) has identified two ideal-typical roads that industrial development can follow: a low road or high road. The low road is characterized by limited interaction, specialization and cooperation between firms, a scarcely dynamic government and a modest level of investments and institutional changes, exploiting a low-wage and unskilled workforce as well as hampering the environment and natural resources. The high road is based on a wide and intersectoral participation of different actors able to compete and cooperate at the same time, stimulating investment in innovative technologies and in R&D, employing skilled workers and safeguarding the environment in order to pursue a solid systemic efficiency, greater innovation capacity and competitiveness. According to the literature (Altenburg and Meyer-Stamer, 1999; Nadvi and Schmitz, 1994; Schmitz 1995), this distinction does not depict the different degrees of cooperation and competition within industrial development processes as well as their nexus with sustainability and SHD (Mehrotra and Biggeri, 2007). Therefore, Mehrotra and Biggeri (2007) introduce a further option: the dirt road, characterized by the large presence of micro and small enterprises in the informal sector, no or insufficient social protection, low wages and labour rights, poor environmental and working conditions (e.g., poor lighting and ventilation, non-availability of safety devices, exposure to toxic substances, maltreatment and so on). This road is typical of developing countries but can also be found in the so-called emerging and developed countries (Biggeri, 2020). For instance, several industrial clusters in BRICS countries do not appear to be in line with the decent work goal of the International Labour Organization (ILO), while productive employment and decent work are key elements to achieving SHD. Indeed, some of these industrial clusters can be the product of fierce international competition in the global market, an expression of subcontracting (within international value chains), often deriving from FDI by multinational corporations (MNCs) that escapes social/environmental costs in their home countries (social dumping). To capture the nexus between industrial development, human capabilities and sustainability, expanding from Mehrotra and Biggeri (2007) and Biggeri and Ferrannini (2014), we propose a more general framework taking into consideration two dimensions that shape the strategic routes that countries and places may follow in their industrial development processes towards SHD. In particular, we distinguish between productivity and collective efficiency for value addition, and the other SHD outcomes concerning sustainability, equity and participation/ empowerment. Collective efficiency for value addition is produced by both competition and cooperation between firms. The empirical evidence, in general, emphasizes that potential for value addition originates not only from knowledge diffusion, technological upgrading and skills enhancement, but also from stakeholders’ participation, social environment and trust, joint public–private and collective actions, and public interventions, in particular with the provision of public goods and services (Bellandi and Sforzi, 2003; Rabellotti, 1997; Schmitz and Nadvi, 1999). A ‘healthy’ (positive, i.e., non-disruptive) competitive environment gives the firms the right incentive to innovate, while a ‘healthy’ cooperative environment enables firms to pool

Sustainable human development, capabilities and new trajectories  123 resources – for example, to provide public services through collective action and public and private interaction. The other SHD outcomes concern those characteristics that influence it positively such as health, basic education, social insurance, equity, environmental protection and social integration (a mixture of equity/cohesion), and which must be part of the analysis. The nine cells in Figure 7.1 illustrate the combination of these two dimensions.

Source:

Adapted from Mehrotra and Biggeri (2007, p. 368).

Figure 7.1

High road, low road and dirt road for sustainable human development

It is evident that countries and places in the same SHD category can pursue different strategic routes for upgrading along the two dimensions. For instance, the dirt SHD road represents a case where industrial development follows a path opposed to SHD and decent work, constraining at the same time the growth of the industrial system itself as productivity enhancement and value addition are limited. Therefore, behind the routes pursued by a specific industrial system is the interrelation among the four pillars of SHD themselves – productivity, sustainability, equity, participation and empowerment – and government intervention (in terms of both budget resources for investments and institutional reforms) determines the SHD trajectories pursued by an industrial system. For both the academic and policymaking debate, this framework may facilitate the understanding of the potential strategic routes to upgrade industrial systems towards SHD, disentangling the nexus between industrial development, human capabilities and sustainability. The transition from one stage of SHD to a better one is not an easy task, and deterioration can

124  Handbook of industrial development occur. Therefore, we argue that the potential for evolving from a dirt SHD road to a high SHD road exists, and it follows, for instance, a ‘two-synergies strategic route’. In other words, it needs joint/collective actions carried out by the entrepreneurs and workers in their collective interest and the support of public intervention, as well as favourable macroeconomic and external conditions. This strategy would also reinforce the resilience of industrial systems – that is, the capacity to respond positively to external shocks and recession periods – as strongly required in these times of uncertainty. In terms of industrial policy design, governance, management and implementation, this would depend on how the power relationships and conflicts (among social groups, firms, forms of capital, governments) will create and support ‘productive coalitions’ willing to invest in the enhancement of collective productive capabilities to make development processes more sustainable and inclusive (Chang and Andreoni, 2020) for the sake of both systemic and collective interest (Ferrannini et al., 2021).

5

FINAL REMARKS

In this chapter, the combined attention to theoretical insights and ‘real-world’ practices point to some main arguments that may lead the current and future research and policy agenda on industrial development and policy. The starting point here is that the scope of industrial policy has definitively gone beyond primary or unique attention to the manufacturing sector, and it has been centred on a stronger and holistic integration with other complementary policy strands, such as competition, education and training, environment, R&I, health, employment, territorial cohesion and so on. Moreover, the rationales supporting government interventions on industrial development have gone well beyond the simple correction of market or system failures (Chang, 1994; Di Tommaso and Schweitzer, 2013; Di Tommaso et al., 2022; Ngo et al., 2021; Peneder, 2017) towards more widely promoting and governing structural change to attain a few different economic and societal goals ((Biggeri and Ferrannini, 2014; Di Tommaso, Tassinari et al., 2020; Ferrannini et al., 2021). In this regard, the analysis of the historical and current practice of industrial policy in China, United States, European Union and India provides relevant insights. On the one side, their long-lasting experience is characterized by a strong reliance on total factor productivity and technological innovation to promote industrialization as a general driver of the structural transformation of the economy, but missing a necessary equal focus on environmental sustainability, equity and social cohesion, participation and empowerment, which often appear to have worsened over the past decades. On the other side, their current industrial policy (except for India) is increasingly associated with government interventions on production dynamics to promote broader development objectives: not only growth, competitiveness, productivity or innovation, but also employment, territorial and social rebalancing, and environmental sustainability, among others. All in all, future industrial development processes will follow different routes, based on the capacity by government institutions, industrial actors and societal stakeholders – in the form of ‘productive coalitions’ – to combine collective efficiency for value addition with the pursuit of SHD outcomes. Two final remarks are in order at this stage. First, industrial development processes do not take place in a vacuum, but rather within an institutionally organized society, with dynamic

Sustainable human development, capabilities and new trajectories  125 power relations and political mobilization shaping collective arrangements for decision-making (Andreoni, Chang and Scazzieri, 2019; Biggeri and Ferrannini, 2014; Ferrannini et al., 2021), and it appears fundamental to illuminate the ways in which productive forces shape human and power relations and are in turn shaped by them (Bagchi, 2011). In other words, the core of this perspective is neither the state, the market, the industrial system nor the individual, but rather the society (at any level of understanding) with its multiplicity of interests, political and economic relations and challenges. Therefore, designing industrial policy towards an SHD vision should not be misconceived as a new ‘one-size-fits-all’ recipe or ‘Holy Grail’ (Rodrik, 2010). Rather, a normative societal vision would have to express the systemic interest and the society’s fundamental constitutive values and meaning of development, which are its vital sources that can change over time due to a combination of factors (Ferrannini et al., 2021). This would not simply be the result of different individuals or social groups getting together in a social contract, but it must also be the result of different partial interests being able to compromise on suitable weights within the range of configurations compatible with systemic interest (Cardinale, 2018; Cardinale and Scazzieri, 2020), along with influences external to the system – such as a global development paradigm, global flows of resources and ideas, geopolitics relations – that shape it from the outside. These arguments are valid not only for industrial development processes at national (and subnational) level, but also globally, strongly pointing to the need for international coordination – instead of competition – to promote economic policies that focus both on growth of gross domestic product as well as on social resilience, universal access to services, and equity across genders, social groups and generations, building a global community of a shared future and less vulnerable humankind (SSH20, 2021). Second, government failures can be even more dangerous when industrial policy is conceived in a more comprehensive way (Di Tommaso, Tassinari et al., 2020) and directed towards an SHD vision. In general terms, it appears necessary to combine top-down and bottom-up processes in industrial policy design and implementation, centred in the collective determination of both a systemic interest (for its viability) and a normative vision based on the expression of different instances. More concretely, this perspective on industrial policy highlights the need for a comprehensive diagnostics of – and contextualization to – socio-economic conditions and challenges, along with sectoral opportunities and priorities; investing in governments’ capabilities to embrace the complexity affecting the reality of production dynamics and societal challenges and to learn consciously from successes and failures of different interventions and tools; deploying robust and evidence-based processes for the selection of strategic targets and appropriate tools; and a better understanding of the strengths and vulnerabilities of key and new forms of collaboration across companies, industries and governments to ensure industrial resilience (Di Tommaso et al., 2022) and capacity to respond to external shocks (OECD, 2020; WEF, 2020b). These remarks appear essential in order to ultimately succeed in designing and implementing industrial policies to pursue a multilevel high-road strategy for SHD and expand human capabilities, leaving no one behind.

NOTE 1. For instance, in China, in 2019, the correlation between SDGs#8–9 is negatively and significantly correlated with SDG#14 (–0.20 and –0.22, respectively) and SDG#15 (–0.43 and –0.73, respectively).

126  Handbook of industrial development

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130  Handbook of industrial development Rabellotti, R. (1997). External Economies and Co-operation in Industrial Districts: A Comparison of Italy and Mexico. London: Macmillan. Renda, A. (2021). The EU Industrial Strategy: towards a post-growth agenda? Intereconomics, 56(3), 133–8. Rodrik, D. (2004). Industrial policy for the twenty-first century. Harvard Kennedy School Faculty Research Working Paper, No. RWP04-047. Rodrik, D. (2010). Diagnostics before prescription. Journal of Economic Perspectives, 24(3), 33–44. Rodrik, D. (2014). Green industrial policy. Oxford Review of Economic Policy, 30(3), 469–91. Rodrik, D. and Sabel, C.F. (2019). Building a good jobs economy. Harvard Kennedy School Faculty Research Working Paper, No. RWP20-001. Rolf, S. (2021). China’s Uneven and Combined Development. Cham, Switzerland: Springer Nature/ Palgrave Macmillan. Schmitz, H. (1995). Collective efficiency: growth path for small-scale industry. Journal of Development Studies, 31(4), 529–66. Schmitz, H. and Nadvi, K. (1999). Clustering and industrialization: introduction. World Development, 27(9), 1503–14. Schot, J. and Steinmueller, W.E. (2018). Three frames for innovation policy: R&D, systems of innovation and transformative change. Research Policy, 47, 1554–67. Schwab, K. (2016, 14 January). The Fourth Industrial Revolution: what it means, how to respond. Accessed 13 August 2022 at https://​www​.weforum​.org/​agenda/​2016/​01/​the​-fourth​-industrial​ -revolution​-what​-it​-means​-and​-how​-to​-respond/​. Sen, A.K. (2003). Development as capability expansion. In S. Fukuda-Parr and A.K. Shiva Kumar (eds), Readings in Human Development: Concepts, Measures and Policies for a Development Paradigm. Oxford: Oxford University Press, pp. 3–16. Sen, A.K. (1999). Development as Freedom. Oxford: Oxford University Press. Shue, V. and Wong, C. (eds) (2007). Paying for Progress in China: Public Finance, Human Welfare and Changing Patterns of Inequality. London: Routledge. Smith, A. (1759). The Theory of Moral Sentiments. Oxford: Clarendon Press. Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. Oxford: Oxford University Press. SSH20 (2021). Crises: economy, society, law and culture – towards a less vulnerable humankind. SSH20 Academies Joint Statement, July 2021. Stewart, F. (2005). Groups and capabilities. Journal of Human Development, 6(2), 185–204. Sustainable Development Solutions Network and Institute for European Environmental Policy (SDSN and IEEP) (2020). The 2020 Europe Sustainable Development Report: Meeting the Sustainable Development Goals in the Face of the COVID-19 Pandemic. Paris and Brussels: SDSN and IEEP. Tassinari, M. (2019). Capitalising Economic Power in the US: Industrial Strategy in the Neoliberal Era. London: Palgrave Macmillan. Tsui, K.-Y. (1993). Decomposition of China’s regional inequalities. Journal of Comparative Economics, 17, 600–627. United Nations (UN) (2015). Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly of the United Nations (A/RES/70/1), 25 September 2015. United Nations Development Programme (UNDP) (2021, 23 July). China’s 14th Five-Year Plan: spotlighting climate and environment. Issue Brief. UNDP China. Vaughan, S. (2021, 10 March). A new plan ahead. SDG Knowledge Hub. Accessed 13 August 2022 at https://​sdg​.iisd​.org/​commentary/​guest​-articles/​a​-new​-plan​-ahead/​. World Bank (2009). What Is Inclusive Growth? Washington, DC: World Bank. World Economic Forum (WEF) (2020a). Emerging Priorities and Principles for Managing the Global Economic Impact of COVID-19 – Chief Economists Outlook: April 2020. Geneva: WEF. World Economic Forum (2020b). How to Rebound Stronger from COVID-19: Resilience in Manufacturing and Supply Systems. Geneva: WEF. Xue, L., Weng, L. and Yu, H. (2018). Addressing policy challenges in implementing Sustainable Development Goals through an adaptive governance approach: a view from transitional China. Sustainable Development, 26(2), 150–58.

Sustainable human development, capabilities and new trajectories  131 Yao, S. and Liu, J. (1998). Economic reforms and regional segmentation in rural China. Regional Studies, 32(8), 735–46. Zhao, X.B. and Tong, S.P. (2000). Unequal economic development in China: spatial disparities and regional policy reconsideration, 1985–1995. Regional Studies, 34(6), 549–61. Zheng, G., Barbieri, E., Di Tommaso, M.R. and Zhang, L. (2016). Development zones and local economic growth: zooming in on the Chinese case. China Economic Review, 38, 238–49.

PART II INDUSTRIAL DEVELOPMENT IN REGIONS

8. Place and industrial development: paths to understanding? Peter Sunley and Ron Martin

1 INTRODUCTION The speed and extent of change in the geographical distribution of industry in recent history, both within and between nations, has been unprecedented. The growth of globalization has reordered the global distribution of manufacturing and the continuing development of digitalization, and artificial intelligence (AI) may well change the spatial and global distribution of both manufacturing and service industries in a similarly profound way (Baldwin, 2019). Among these radical spatial shifts, however, place continues to matter for industrial development. The development and evolution of firms and places are deeply entangled, so that location continues to shape the capabilities and resources available to industries, sometimes positively, in other cases negatively. Indeed, in an effort to capture and stabilize this shifting industrial landscape, modern official industrial strategy in many countries increasingly seeks to use ‘place’ as a platform on which to base policy measures that respond to industries’ place-specific needs (see, for example, Bailey, Pitelis and Tomlinson, 2018; Barca, McCann and Rodríguez, 2012; Beer et al., 2020). The continuing significance of place in industrial development is not easy to explain, however, as it involves the locally varying and locally specific interaction of a series of complex processes that together produce the emergence and development of the assets and capabilities of firms and institutional actors (Scott, 1998). Recent years have seen the growth of an evolutionary path-based approach to understanding these effects and the significance of place for industrial development, and this chapter provides a critical review of the ‘path approach’ and its progress so far. The chapter begins by explaining why the notion of industrial ‘paths’ has become central to the analysis of the spaces of economic development. It explains how the notion of economic paths emerged as a flexible way of capturing the recursive and cumulative interactions between technologies, routines, working and business practices. The use of a path metaphor promised to provide a systemic view that captured the interactions of continuity and change in places. We examine how the notion of paths has been used and theoretically refined, and how research has identified an increasing variety of types of trajectory, shifting from explanations of continuity to explanations of change and transition. We outline the advantages and value of paths as abstractions, before turning to their ambiguities and unresolved questions. Our intention is to consider how far and in what ways the path-based vision has advanced our understanding of place and its key roles at different stages of industry development. We conclude by arguing that while a path focus has undoubtedly been useful for raising questions about meso-level investigations of industrial changes in places, our understanding needs a firmer empirical and operational base to build on the many efforts at categorisation and classification. Many studies have identified different categories of paths and types of action, but there has been less progress in understanding processes and mechanisms of change. In particular, paths 133

134  Handbook of industrial development have assumed a certain degree of misplaced concreteness, which has substituted for in-depth analysis of the relations between firms’ dynamic and absorptive capabilities and institutional agencies – in particular places. Firms’ dynamic capabilities and their historical formation in specific places remain unclear. Path studies have also been somewhat self-contained and disconnected from broader structural and systemic changes and patterns in the geography and history of capitalism. Studies of regional industry paths have certainly broadened their focus, but the analysis of broader technological shifts, financial and institutional contexts, and social and political arrangements are underdeveloped. The understanding of the role of place in industrial development has undoubtedly been improved by a path perspective, but future research needs to be both deeper and more contextual.

2

WHAT IS AN INDUSTRIAL PATH?

The origins of recent conceptions of industrial paths lie in neo-Schumpeterian approaches to innovation and technology that portray technologies as progressing in certain directions, and in historical phases of emergence, development, maturity and replacement (or displacement). Schumpeter himself attributed the emergence of new industrial-technological paths as driven by the spontaneous behaviour of entrepreneurs as the endogenous initiators of change. He viewed industrial evolution as a continuous process of ‘mutation’, although he also argued that innovations also have a discontinuous dimension in that they tend to ‘swarm’ periodically, involving a series of inter-related innovations that stimulate significant shifts in both the industrial structure and the rhythm of capitalist development. In such phases, some industries, sectors and markets disappear, or are superseded by new industries, sectors and markets (his ‘gales of creative destruction’). There have been numerous critiques of Schumpeter’s account – for example, that it is too supply-side orientated (the overwhelming focus on the entrepreneur) and ignores the role of demand in shaping industrial development paths; that it largely ignores the socio-institutional context within which innovation and industrial-technological change occurs; and that it is not the number of innovations (size of swarms) that matters but the inter-relationship between the new technologies. Neo-Schumpeterian responses have sought to address these issues. Nelson and Winter (1982), for example, argued that innovation tends to have a logic of its own and follows ‘natural trajectories’. These, they argued, are determined by ‘regimes’, understood as sets of beliefs about what is technologically feasible and possible that guide the focus, and the boundaries, of innovation strategies and tactics. Dosi (1982) further developed such ideas by directly linking technological paradigms with directions of innovation search and the closure of some directions. In this view, technological paradigms are defined as combinations of outlooks, definitions of problems and sets of procedures that lead to a focus on some technological and industrial paths and the exclusion of others. Again, once a technological path is established, Dosi (1982) argues that it shows a cumulative momentum of its own.1 Perez (2010) added that just as individual technologies show growth of a path from exploration to maturity, so broader techno-economic paradigms, which involve sets of interrelated industries, show changes of rhythm that tend to follow a logistic curve as technological revolutions are assimilated and diffused across industries. The notion of a place-based industrial path contends that these technological paths condense in both time and space, are unavoidably spatialized, and are uncertain and unpredictable rather

Place and industrial development: paths to understanding?  135 than following set routes. As Storper and Walker (1989, p. 69), wrote: ‘We call the erratic tracks of growth industry development paths, because the word path suggests the sinuous, unsteady and idiosyncratic course of industrial expansion through time (and space)’ (our emphasis). They argued that technological pathways are embedded in particular territories as their spillovers and knowledge frameworks exhibit distance decay and are often strongest in specific locations. As Glasmeier (2000) demonstrated in the case of the Swiss watch industry, technological paradigms can fuse with the business cultures and practices of certain regions, so that the two become locally embedded and inseparable. The adoption of this path metaphor was in part a reaction to more reductive approaches in evolutionary industrial studies. Generalized Darwinism, for instance, highlights the variation, selection, replication and retention of firm routines as the core processes that determine the competitive viability and rise and fall of places (Essletzbichler and Rigby, 2010). The notion of industrial paths offers a more systemic and holistic view in which firm routines and technologies are only one part of a broader local or regional system that includes the formal and informal institutions, conventions and governance systems that surround and support industries. In this definition, paths are partly firm based but also inherently socio-institutional. Storper (1997), for example, described how product-based ‘technology districts’ are reliant on conventions and relations that define their collective identity and participation. Paths and contexts are integral to each other as hard technologies fuse with their surrounding ‘soft’ infrastructures and cultures (Walker, 2000). Nevertheless, despite being somewhat broader than a preoccupation with firm routines, most path-based thinking imbibed the micro-, supply-side focus and technological optimism of neo-Schumpeterian economics. The abstraction of industry paths has been valuable in highlighting the significance of the co-evolution between firm capabilities and the local cultures and institutions in which firms are embedded. Industrial paths are defined broadly as technologically based trajectories of development and change that are the emergent outcomes of a place-based combination of systems of beliefs, practices, routines, conventions, firms, institutions and policy measures. The path idea suggests that there is some functional coherence or interactions between these elements, or even contestation and conflict between them, that affects the direction of change. Thus, examining the collective path is assumed to give us some insight into what has happened to individual firms and agents, and vice versa. Some organizational theorists argue that if it is to retain a specific and distinct meaning, then the concept of a path should be process based, and refer to the self-reinforcing processes and cumulative dynamics summarized by path-dependence theory (Sydow, Schreyogg and Koch, 2020). However, as we will see, economic geographers have tended to define paths in a much looser way, more akin to institutional pathways that may follow different logics of change.2 The range of processes seen as driving industrial paths has been broadened and diversified beyond self-reinforcing dynamics. However, freed from strict or ‘canonical’ path dependence (Martin, 2010; Martin and Sunley, 2006), the path concept when applied to geography is beset by a degree of fuzziness and a lack of clear definition, and there are many questions over exactly how the path abstraction should be operationalized, and evidenced by empirical phenomena. The empirical definition of industry paths is often not directly explained and there is a clear danger that we assume that paths are marked by a concrete but spurious homogeneity, which would lead to ecological fallacies. In reality, the idea of place-embedded paths is shorthand for a complex set of outcomes that, when aggregated, produce a particular course of change. Of course, it may be the variability within paths rather than their shared features that really matters. In fact,

136  Handbook of industrial development most studies have shown that firms, and even sectors, are typically marked by a high degree of heterogeneity in their capabilities and ability to adapt and change, and the ways in which firm heterogeneity relates to the courses of industry change in particular places requires much more attention. The path metaphor can, of course, be applied to economic units at different scales. Paths can be imagined at the scale of individuals, firms, agglomerations and clusters, and even larger territorial units, each with different patterns of change. Most studies define paths as localized collections of firms that share a technological paradigm or industrial culture, but precisely how we should identify and measure paths is often sidestepped. The abstraction is used loosely as corresponding to established industry, sector, subsector or even product definitions, but these may not be the most relevant for identifying the evolutionary paths of economies. This definitional challenge is important as cities and regions are typically compound and multi-layered systems, and typically include several paths, some interrelated and interdependent, others unrelated. There has, however, only been a limited amount of research into the notion of multiple paths in a region and how they may interact and influence each other (see Bergek and Onufrey, 2013). As a result of these difficult issues, Henning, Stam and Wenting (2013) conclude that the empirical study of industrial paths has been difficult, and that different studies have used different methods and definitions so that the empirical results on industrial paths present a rather disjointed cacophony instead of a coherent body of explanatory insight. Certainly, there is a need for more thought on how the abstraction of industrial paths should be translated into empirical research, and evidenced examination. A further complication is that some have argued that the constellation of industries in a region produces an urban or regional path formed by shared conventions or institutions that span and infuse different industries and production systems. There may be complementarities or shared conventions between industries in a place that shape their (co-)development, and these regional systems have emergent features that exercise downward causation on their constituent industries and firms. As Storper (1997, p. 41) argued: ‘the ensemble of conventions and relationships that come into existence in a territorially defined economy may cut across the array of production systems and activities found there, affecting the evolutionary pathways of a variety of sectors in a regionally or nationally common way’. Storper et al. (2015), for example, contrast San Francisco’s high knowledge path with Los Angeles’s less knowledge-intensive route and declining relative performance. They trace the causes of these urban paths to the set of informal institutions, networks and conventions that shape economic governance in both cities. However, according to Storper (2009), to a large degree such institutional differences remain a ‘dark matter’ as they are hard to study empirically, and they are more often invoked than substantiated. In summary, the notion of place-based industry paths is a useful abstraction in the way it foregrounds questions of industrial history and varied change over time in particular places. And it has provided a basis for understanding systemic relationships between firms and their institutional contexts and how these interactions change over time. However, there continues to be a degree of fuzziness about the empirical identification, constitution and tracking of paths that needs to be resolved by further work. We need more careful attention directed towards the constitution and definition of paths and how they become embedded and disembedded in places over time. Of course, this has not prevented a rapid expansion of expositions and theories on how local industry paths evolve over time.

Place and industrial development: paths to understanding?  137

3

HOW DO INDUSTRIAL PATHS EVOLVE AND WHY DO SOME DECLINE?

Initial work on the evolution of place-based industry paths relied heavily on increasing returns as an engine of growth. This recognized that the competitive advantages of industrial clusters and regions have become less based on natural resources and static cost differentials, and more on increasing returns effects, firm capabilities and local externalities that are cumulative through time.3 Thus, those cities and regions that are ‘early movers’ in a particular technological pathway or industry may reinforce their dynamic advantage over time and secure a leading role in a particular sector. The significance of agglomeration effects is clearly a fundamental reason why local industrial development follows a cumulative trajectory. However, the increasing returns growth perspective soon led to the idea of path dependence. Scott (1998, p. 97), for example, argued that ‘[t]his kind of growth is typically associated with lock-in, in the sense that many indurated and mutually reinforcing relationships within the regional economy will ensure that its trajectory acquires a marked dependence on its past’. As we have argued elsewhere (Martin, 2010, 2012; Martin and Sunley, 2006), the classic or ‘canonical’ model of path dependence describes a process in which small, contingent and accidental events in a preformation stage trigger a process in which self-reinforcing mechanisms and processes select one option among many and act over time to narrow options for change. These processes gradually become a source of ‘lock-in’ and a loss of adaptability.4 This classic type of path dependence resembles a punctuated equilibrium model in which long periods of endogenously driven incremental change and lock-in are disrupted by short periods of external disruption and radical change. However, this type of path may be the exception rather than the rule in urban and regional economic development (Hassink, 2010; Martin, 2010). There are many cases in which the past provides the basis for enabling and incremental positive changes so that lock-in is avoided and these on-path incremental changes may add up over time to produce a switch to a radically new path. This suggests that there are two ideal types of path dependence – one of which is enabling and allows paths to be modernized, and another that leads to lock-in and decline, as well as varieties on the continuum between these two.5 This critique chimed closely with others which stressed that a classic path-dependence approach overstated the effect of historical structure and downplayed the importance of reflexive and future-oriented human agency in which knowledgeable agents make efforts to break past habits and practices, and deviate into new paths based on visions of the future. Entrepreneurial agents show mindful deviation from paths and have capabilities to act in ways not prescribed by existing technological paths, as well as imaginations to perceive ‘opportunity spaces’. On this basis, recent work has identified a series of different industrial paths of change, of which classic path dependence is only one variety. Recent research has certainly made some progress in developing a useful set of categories (see Figure 8.1 for some stylized examples). Some include paths with little path dependence. Grillitsch, Asheim and Trippl (2018), for example, argue that industry paths fall into five different types. At one end of a continuum is path extension, involving incremental innovations and existing knowledge, and often leading to stagnation and decline (Isaksen, 2016). This definition of path extension is in effect a re-description of the classic strong version of path dependence (see note 5). It has tended to be associated with strongly specialized industrial regions with an industrial monoculture that does not foster entrepreneurialism. Path upgrading refers to more substantial on-path changes and this could be through the infusion of major new technologies, organizational changes,

138  Handbook of industrial development

Figure 8.1

Some stylized industrial paths

or through improvement of firm positions in global production networks (GPNs) and value enhancement, or the development of niches in mature industries (Grillitsch et al., 2018; Isaksen and Trippl, 2014). A third type of trajectory is path importation, which describes the attraction of established industries often through inward investment. Path branching and diversification describe the emergence of new industries by building on the capabilities of existing industries, often at the interfaces between existing sectors (Boschma and Iammarino, 2009), or by combining existing capabilities with unrelated knowledge. Path creation is the most radical form of change as it involves the growth of new industries based on new technological or organization knowledge (Grillitsch et al., 2018; Martin and Sunley, 2006; Tödtling and Trippl, 2013). Boschma (2017) has rightly argued that it is important to distinguish between ‘new to the world’ and ‘new to the region’ path creation (Binz and Gong, 2022). Yet further, some have identified what they have called ‘path plasticity’, a situation where a process of more or less continuous innovation occurs along a technological-industrial path, so distinguishing this type from strong path dependence (lock-in) on the one hand and new path creation on the other (Strambach, 2010). The distinction between these various types is not always clear, however, and there is a need for empirical substantiation of these categories and how we can consistently identify them, given that all paths are complex and mixed outcomes. Path creation tends to be associated with more radical innovations, but we cannot assume that there is a simple correspondence between the type of innovation and consequences for constellations and clusters of

Place and industrial development: paths to understanding?  139 firms in a particular place. Radical innovation may lead to transformation rather than creation (Baumgartinger-Seiringer, Miörner and Trippl, 2021). Notwithstanding these issues, there has been some progress on understanding the causal processes and mechanisms that underlie these various outcomes. There has been much research into related variety and the notion that combinations and exchanges of knowledge and capabilities between cognitively proximate, or input–output related, industries provide the basis for path upgrading, diversification and creation. There is a considerable body of empirical work that demonstrates that there are some important aggregate associations between the presence of a set of related industries in a region and positive economic outcomes in terms of indicators of diversification and entrepreneurship (Content, Frenken and Jordaan, 2019; Neffke, Henning and Boschma, 2011). In some cases, a set of exchanges between industries, and recombinant innovations, can reshape and reset their development paths. Unrelated knowledge exchanges have been found to be more unusual, but perhaps more significant for novel path creation (Castaldi, Frenken and Los, 2015; Frenken, Van Oort and Verburg, 2007). However, as many reviews have pointed out, there are still limitations and ambiguities both to demonstrate this effect and to identify its causal mechanisms.6 In addition, there are issues concerning both how ‘related variety’ is measured empirically, and in relation to its causal role. Relatedness has typically been defined as either knowledge flows within some cognitive limits or as a degree of similarity between firm capabilities, and it has, rather too easily, tended to be assumed that these two definitions correspond. Both these definitions are hard to measure and test empirically, so measures have relied on proxies such as industry classifications, tendency to co-produce exports in a product space, labour market flows, or patent citations, but it is not clear that these proxy measures are all effectively capturing the same phenomena. Research on relatedness using patents is certainly more direct evidence on knowledge flows, but there are, of course, limits to the types of knowledge shown by patenting. More generally, as Boschma (2017) has argued, distinguishing relatedness from unrelatedness is harder than it first appears, as the two will often be blurred. Unrelated capabilities may become related as innovation occurs and technologies and opportunity spaces change. The line between related and unrelated assets changes over time. Indeed, relatedness may be more an outcome than a determinant of local industrial and technological dynamics. To that extent, causal models that use a measure of sectoral relatedness as an independent variable to explain local economic development, innovation and growth may be mis-specified since they ignore the endogeneity of relatedness itself. Further, the work on related variety has in some ways been curiously non-historical in the sense that history should refer not just to past events or conditions but to the interpretation and narrative constitution of the past by agents. One entrepreneur may well interpret past assets as related and learn from them, while another does not, but this does not seem to be recognized in much related variety theory, which takes past assets as objective and fixed realities. The research on relatedness as a source of diversification has also primarily concentrated on firm capabilities. It has not really evaluated the role of institutional relatedness, whereby capabilities developed by institutions in older paths can be transferred forward and allow path branching. If paths are to be understood systematically and holistically, then institutional capabilities may well be as important as firm capabilities. While the typologies of regional industrial paths have underlined the importance of recombinant innovations to industry trajectories, many questions on path evolution remain. In some ways, research into the operation of path dependence has appeared to stop prematurely

140  Handbook of industrial development and there has been much more enthusiasm for studies on path creation. There is an evident imbalance in that the processes of creation and diversification have received far more attention than the decline and stagnation of some industry paths. The focus on more successful cases of industrial growth reflects the general technological optimism that underlies much of neo-Schumpeterian economics (Evangelista, 2021). To some degree, of course, if we understand the capabilities and processes that drive path creation and regeneration, we may be able to transfer the lessons to those geographical places where these processes are absent. The relative lack of research on the latter may also reflect that progress in work on path dependence has been hindered by the assumption that path dependence has been thoroughly studied and that lock-in is well understood. In fact, there has only been a limited advance on Grabher’s (1993) classic study of the economic, political and cultural mechanisms of lock-in in the Ruhr Valley. This has been a longstanding concern in economic geography. As Phelps, Atienza and Arias (2017) have argued, the ‘dark side’ of industry paths has received far less attention. While there are some valuable recent exceptions (see Blažek et al., 2020; Hassink, 2010), there has been a surprising lack of research in economic geography on the disintegration of some industry paths and the associated emergence of left-behind places.7 Thus, there has been only limited progress in terms of understanding industry decline and how institutional reproduction and maintenance can lead to a lack of adaptability (Fredin, 2016). While work on industry paths has highlighted the centrality of firm capabilities, there has been little engagement with the notions of firm dynamic capabilities and why these vary in different firm populations (Labory and Bianchi, 2021). Consequently, we need to know much more about the origins and achievement of dynamic capabilities and how they enable economic adaptability. A key part of the process is undoubtedly the co-evolution between firms and local institutions, but despite the growing appreciation of the importance of institutional innovation and change, many aspects of the influence of institutions on paths and trajectories of economies remain a mystery. As Rodríguez-Pose (2020) explains, there has been little research into the exact transmission mechanisms through which institutions, and especially informal institutions, affect economic outcomes. In his words: ‘There is very limited work on how institutional dynamics and institutional change impinge on regional and urban development paths, leaving a gap in our understanding of the relationship between institutions and the economic fortunes of cities and regions’ (p. 375). Economic processes and capabilities and institutional quality are, of course, highly endogenous and difficult to disentangle and analyse empirically, but such research is a crucial agenda for studies of industry paths. Both firm capabilities and institutional quality can easily be used as catch-all black boxes where explanation ends, and there is a clear need for more research into these characteristics and their mechanisms of co-evolution over time.

4

WHY AND HOW DO NEW PATHS ARISE?

The enormous growth of studies on the origins of new industry paths connects closely with the large and growing interest in innovation, and especially in innovation in carbon reduction and environmental technologies. Following the early work of Cooke (2010) on Jutland, there has been an explosion of studies focused on green industry path creation and many of these have made productive use of ideas from transitions studies, especially the notions of technological niches and regimes (Boschma et al., 2017; Truffer and Coenen, 2012).

Place and industrial development: paths to understanding?  141 Early attempts to explain path creation focused on the conditions available in different places and particularly the role played by regional innovation systems in blocking or encouraging new paths. New path creation tends to occur more where ‘thick’ and organizationally diverse innovation systems have already been consolidated and are able to translate and switch their expertise, networks and experience to new sectors. Where regional innovation systems are thin or highly specialized on a narrow set of activities, they may struggle to support new path creation (Isaksen and Trippl, 2016). Of course, such institutional variations are reproduced when they are drawn on and activated by different types of agent (Trippl et al., 2020). Grillitsch (2019), for example, argues that path creation requires experienced agents who are well embedded within social networks and able to build coordinated efforts around a new industrial path. Grillitsch and Sotarauta (2020) identify three broad categories of agency that need to be brought together and aligned for successful path creation. These are innovative entrepreneurship, which refers to the emergence of new entrepreneurs from both private and public sectors, and the birth of new firms and start-ups, which can be seen as set within an entrepreneurial ecosystem. The second type is institutional entrepreneurship, and refers to the roles of individuals, groups who originate change processes in institutions, leading to the creation of new regulations, frameworks and conventions and/or the transformation of existing ones. Such institutional shifts are often needed to support the mobilization of actors and legitimization of new activities (Chlebna and Simmie, 2018). In the early stages of path creation, institutional agency is argued to be often unplanned, highly personal and intuitive (Sotarauta and Suvinen, 2018). The final type of essential agency is local place leadership, which involves local policies and initiatives that define shared visions of the future and produce collective actions in a place. These include activities and schemes to orientate multiple actors and coordinate their efforts around the emerging pathway. The key goal is to construct a shared vision and collective commitment around the opportunities offered by the new industrial path. Public policies and investments are clearly crucial both in building some of the key assets and capabilities for new industrial paths but also in coordinating and mobilizing diverse actors and groups (Tödtling and Trippl, 2018). The role of governments at various scales has become crucial in capturing and shaping path creation, especially through strategic coupling with GPNs and the attraction of other extra-regional agents (Dawley, MacKinnon and Pollock, 2019; Hassink, Isaksen and Trippl, 2019; MacKinnon et al., 2019). However, there continues to be a relative lack of research into these policies, and the ways in which regulation and surrounding geopolitical contests and interest groups may hinder and block the emergence of transition paths (Westgard-Cruice and Aoyama, 2021). The focus of explanations of place-based path creation has rightly shifted beyond regionally endogenous resources and capabilities to examine the attraction of exogenous resources and knowledge, especially in peripheral regions (Isaksen and Trippl, 2017; Trippl, Grillitsch and Isaksen, 2018). Such studies have revealed some of the multi-scalar dynamics and interactions between firms and public agencies (Binz, Truffer and Coenen, 2016). Path creation may depend on strategic coupling with GPNs to transplant and anchor facilities and inward investment. Understanding how combinations of multiscale agency shape trajectories of industrial change remains relatively underexplored, however (Njǿs et al., 2020). It is now difficult to understand path formation without understanding the global innovation systems that exist in many sectors and the ways in which sets of firms in different regions play different roles in these global networks (Binz and Truffer, 2017; Hipp and Binz, 2020).

142  Handbook of industrial development The explosion of studies on path creation and renewal has certainly made considerable progress in showing the key interactions between types of agency and local preconditions (Grillitsch and Hansen, 2019). This has involved a wider appreciation of the types of non-firm actors involved in path creation and given greater attention to the importance of public actors and investments (Dawley, 2013). There have been several recent calls for a renewed micro-focus on agents and how different agencies may be involved at different stages of path creation and consolidation (Boschma, 2017; Jolly, Grillitsch and Hansen, 2020). While this has produced a wide range of descriptive examples and specific insights, there has been an apparent move away from causal models, and generalizable theoretical explanation of why path creation varies so markedly over space remains in short supply. However, we would argue that research into path creation needs not only to be deeper and to investigate dynamic capabilities among changing populations of firms and other agents more thoroughly, but that it also needs to be more contextual and explain how place-based contexts are interpreted and used by actors in new types of industrial and technological path creation. As has been widely argued, agency and historical context are not alternatives but are deeply and recursively embedded in each other, and this relationship is essential to understanding paths as emergent outcomes. In the next section, we discuss some broader contextual influences.

5

PATH INTERACTION WITH ECONOMIC LANDSCAPES

Despite this explosive growth of literature, partly because of its micro- and supply-side origins, the study of paths in places has yet to fully examine the importance of the broader economic and institutional contexts and their historical evolution. One of the paradoxes of path-focused work is that, despite the way in which it was inspired by notions of technological paradigms and pathways, and by Schumpeterian studies on ‘gales of creative destruction’ unleashed by the bunching together of radical innovations to produce critical junctures, much of this research has seemed reluctant to consider the implications of these broader technological waves or any crises and junctures. After all, not just Schumpeter but also numerous other economists, and business and technology scholars, have argued that it is possible to discern a number of major industrial-technological ‘revolutions’ throughout the history of capitalism, each such revolution acting as the catalyst for the development of a new technological regime, involving one or more ‘general-purpose technologies’, a range of new industries and a new historic phase or ‘wave’ of economic development. An intriguing question is how far and in what ways each such technological-industrial wave reconfigures the economic landscape. Some see these innovative industrial revolutions as a key factor that selects those regions and places where new industries will emerge, and equally, where they will not. The latter type of place, it is argued, will be those that are unable to reorientate and adapt their inherited structures, capabilities and assets to those required by the new industries, so that ‘tomorrow’s industries are not going to be born in yesterday’s regions’ (Hall, 1981, p. 53). Yet, in opposition to this view, there are numerous examples of yesterday’s places – cities and regions – that were once leading centres of ‘old’ industries, which have managed to ‘reinvent’, phoenix-like, their economies around such new industry-technological configurations (Agtmael and Bakker, 2016; Christopherson, 2009; Power, Plöger and Winkler, 2010). Although most studies have focused on particular industrial paths, some recent studies have begun to consider how historic changes to technological regimes reshape paths in dif-

Place and industrial development: paths to understanding?  143 ferent regions (Taalbi, 2019). For example, De Propris and Bailey (2021) argue that different technological regimes and new niches are territorialized and embedded in local technology systems. They argue that local technological systems show four paths: those that remain nested in mature regimes tend to obsolescence; others show endogenous transformation and generate niches; a third set of systems specialize in several of the new technologies and display a hyper-transformative path that can spawn diverse niches in different spheres; and a fourth set can import and attract niches that have started elsewhere. Regional moves between these four paths depend on three crucial capabilities: innovation capabilities to generate niches; docking capabilities to attract niches started elsewhere; and translational abilities to access and absorb new technologies. Such greater attention to the diffusion and absorption of new innovations is overdue. A key question, however, about the significance of major new ‘general-purpose technologies’, such as the emerging ‘Fourth Industrial Revolution’ (Industry 4.0), is whether a path metaphor that tends to distinguish separate industries is most appropriate and helpful. Many would argue that to properly understand the spread and diffusion of new digital and AI technologies, for example, we should be looking at upstream and downstream connections and networks in ensembles of sectors, and whether technologies are moving and permeating through these supply chains and market relationships. Paths may not therefore be the most appropriate unit of analysis for understanding these technological transformations, unless the notion of ‘paths’ can be widened significantly. While the place and path literature has moved towards a more systemic and contextual view and has argued that we need to understand not just knowledge capabilities, but also markets, financial resources and legitimation of paths (Binz et al., 2016), nevertheless, only limited progress has been made on these issues. For instance, while path creation studies have recognized the importance of market construction and have started to depart from an exclusive supply-side focus, market construction has been studied more in terms of cognitive agreement among groups of actors, and support for niches. There has been little consideration of the timing of critical changes to industry paths, and how this relates to the demand for their products. Questions about consumer behaviour and demand for products have tended to be beyond the scope of path studies. While there has been recognition that major shocks and crashes may stimulate path formation through the release of resources (Bathelt and Boggs, 2003), most of the path-creation literature has not considered the effects of major disruptions and shocks to demand. In an adaptive cycle perspective, however, a gradual loss of resilience in a place-based industry path may well make this path vulnerable and susceptible to external demand and resource shocks, which then release resources and potentially enable path creation (Martin and Sunley, 2011). We still know relatively little about the significance and prevalence of the role of shocks in shaping the evolution of local industry paths. Similarly, a few path studies have mentioned the importance of finance for early-stage firms and the need for actors to secure and align capital investment. However, there have been very few studies that have engaged in depth with the ways in which different types of financial markets impact on and shape new path creation. Boschma and Capone (2015) suggest that different types of ‘varieties of capitalism’ may have different potential to nurture new paths, and they argue that liberal market capitalism may be more conducive to radical and unrelated path creation, especially in ‘new to the world’ industries. However, the financialization of liberal market economies has increasingly meant that their financial systems have focused on trading securities and assets rather than investing in start-up firms. As a result, such systems

144  Handbook of industrial development have often failed to supply start-up firms with long-term patient capital. Moreover, firm capital has been increasingly devoted to share buybacks and investor rewards rather than in investment to raise innovation and productivity (Mazzucato, 2013). There is little recognition of the way in which financialization and the changing imperatives of banks have constricted path creation in some sectors where the venture capital model has not been appropriate (Lerner and Nanda, 2020), and much private equity focuses on management buyouts and mergers and acquisitions rather than supporting new, more riskier firms and activities. The path literature has yet to engage seriously with financial investment and how this varies in different national and regional political economic contexts and how this is a key determinant of the fortunes of industries over time. One of the concerning aspects of the emergence of new technological systems and their associated new industries is their impact on local labour markets. As the urbanist Jane Jacobs (1969) once remarked, the pressing issue at such times is less that of a conflict between capital and labour than of a conflict between those groups of workers and localities in the old industries and those in the new industries. To the extent that workers in the former lack the skills to fill the jobs in the latter, inequalities in job opportunities and incomes are likely to widen. As the iconic example of Silicon Valley shows, many new paths in high-technology and service sectors have led to dramatic increases in inequality: in many cases, high-skilled groups have been the primary beneficiaries, while low-skilled groups have either been excluded or have only been included in low paid and insecure service work. These labour market effects are not supplementary or incidental to the trajectories of industry paths. Instead, strongly rising inequality will feed back through a low political legitimacy, a lack of collective public support and profound constraints on market stability and consumer demand. Indeed, increasing spatial inequality appears to be a defining feature of the current economic turmoil and wave of creative destruction associated with the growth and spread of digital technologies and activities (see Chapter 13 by Kenney et al. in this Handbook) – the more so as, thus far, these new technologies and activities appear to be concentrating in just a few select cities and regions that are becoming the ‘innovation superstars’ or ‘hyper-transformative’ regions (Feldman, Guy and Iammarino, 2021; Muro and Liu, 2021). In this view, there is a cumulative feedback as new paths attract skilled labour, which then leads to the formation and renewal of further industries in these regions. In this way, skilled labour mobility becomes a key reason for path decline and lock-in in peripheral regions. This circular process of ‘winner take all’ has become a key hypothesis in a possible explanation for the selective and discriminatory nature of contemporary industrial geographies (see Florida, Mellander and King, 2020). A related issue requiring more research concerns the role of monopolistic firms in shaping these new industrial-technological pathways and ‘innovation superstar’ places, how that monopolistic power ‘locks out’ other firms, and poses a challenge to policymakers.

6

SOME CONCLUDING THOUGHTS: INDUSTRIAL PATHS AND PLACE DEPENDENCE

This chapter has argued that the notion of ‘industrial path’ has frequently been used in studies concerned with how industries (and their technologies) develop across geographical space. Advances have been made in establishing conceptual taxonomies of different stages of industry growth and decline, which provide the bases for causal accounts. However, there is

Place and industrial development: paths to understanding?  145 still a lack of a generally agreed clear and consistent definition of what exactly constitutes an ‘industrial path’, why there are different types of path, and how best to theorize those paths. Our focus here has been on a related series of questions concerning the extent to which the emergence and evolution of industrial paths is a place-dependent process – that is, why particular industries emerge in certain places rather than others, and how the subsequent developmental paths of those industries in those places are shaped by locally based and emergent feedback processes on the one hand, and external factors and processes on the other. The ideas of path dependence and path creation have had an obvious relevance to such questions. While analyses have moved beyond a strong version of path dependence, they have reaffirmed the importance of place dependence, although direct attention to the strength of place dependence in different paths, and its continuing tensions with the mobility of firms and the geographical stretching of production networks and supply chains, has been surprisingly limited. Extended and stretched supply chains and production have undoubtedly been used by firms as a way of managing and escaping place dependence, yet some degree of place dependence is inevitable, especially for higher-skilled and higher-quality capabilities. Place dependence arises not only because the various resources, conditions and agencies required for industry path formation and adaptability are constituted and shaped by their sequence and history in place, but also because a significant portion of their interactions and synergies are inherently place based. There are at least three key sets of conditions involved in producing place dependence: political agency and formal institutional regulations; interaction spaces and informal conventions in regions and localities, in which firms compete and cooperate; and the organizational strategies and practices of firms themselves (Berndt, 1998). None of these are spatially bounded and discrete, but the ability to learn, attract and absorb resources, assets and knowledges from elsewhere is paradoxically constructed and deployed in place, and more easily in some places than in others. All three of these conditions and processes are relational and vary strongly over space, but they typically produce supervenient effects that define the characters of urban and regional economies. Furthermore, their interactions over time produce downward emergent effects that affect the future decisions and behaviour of local agents. As we have argued, in an emergent understanding of regional economies, these downward effects are not deterministic but depend on the memory, interpretation and selection exercised by agents (see Martin and Sunley, 2012). Place dependence thus underlines both the importance of studying industry paths and the difficulties of fully understanding their evolution. Industry paths are typically influenced by many regional and urban emergent effects, at the same time as they are reacting to changes in their economic environments at multiple scales. Understanding how industry paths evolve and change ultimately depends on better knowledge of firms’ and other agents’ capabilities and intangible institutional qualities, and how these interact with place-based effects. This remains a key research challenge. Likewise, while several studies have addressed the question of how new or related industrial paths can develop from existing local paths – through branching, for example – there has, in fact, been fewer studies of why and how existing local industrial paths can atrophy, break down and decline. Standard path-dependence theory stresses the role of ‘external shocks or disruptions’ in such instances. To be sure, the atrophy and undermining of a local industrial path may well be caused by external events and developments, such as the rapid rise of more efficient or technologically superior competitors elsewhere, but this then simply raises the question of why the local industry fails to adapt to such pressures and undertake appropriate

146  Handbook of industrial development changes required to restore its competitiveness. This slowness or failure to adapt may itself reflect the degree and nature of the industry’s embeddedness in the locality’s industrial and institutional system. Yet another way that a local industrial path might break down or decline is where its major firm(s) decides to relocate its activities elsewhere (this is not uncommon, for example, where such firms are acquired or bought out by foreign competitors, which then rationalize their international operations, and close down the original local firm). The key point about these examples is that they point to the complex interplay between industrial paths and local places. How far it is possible, and what constitutes the most fruitful approach, to construct general theories of the place-dependent (and place-forming) relationship between places and industrial paths are consequently important issues. The rise of ‘history-friendly’ models of industrial evolution, which use appreciative theorizing based on detailed research of case-study industries and firms (see Capone et al., 2019; Garavaglia, 2010; Malerba et al., 1999, 2016) is instructive in this regard. Similarly, appreciative theorizing based on detailed historical studies of individual industrial evolutions in particular places would seem to be an equally useful mode of enquiry. Indeed, explaining industry paths entails a theoretical framework that is not just ‘history friendly’ but ‘geography friendly’ too.

NOTES 1. Such ideas rehearsed and formalized the ideas of economic historians such as Rosenberg (1963), who had emphasized that ‘[m]any aspects of technological change, in order to be properly understood, must be examined in terms of particular historical sequences, for in technological change as in other aspects of human ingenuity, one thing often leads to another’ (Rosenberg, 1963, p. 442). 2. In a similar way, institutional studies have sought to distinguish different pathways of institutional change based on variations in both the scope and pace of change (see Micellota, Lounsbury and Greenwood, 2017). 3. This perspective was, of course, greatly encouraged by the work of economic theorists such as Brian Arthur and Paul Krugman, who applied increasing returns processes to industrial locations. 4. There is, of course, some debate about the meaning of ‘lock-in’. David (see, for example, 1994, 2005) and others tended to see lock-in as equivalent to equilibrium or stasis, but organizational theorists prefer to argue that ‘lock-in’ is actively reproduced by agents (see Sydow et al., 2020). However, both approaches agree that significant change is highly difficult to achieve in a state of lock-in, and has to be triggered by a crisis or shock. 5. In recent business theory, these two versions of path dependence have been described as ‘strong’ and ‘weak’. Strong path dependence refers to the classic model in which self-reinforcing processes lead to lock-in, while weak path dependence involves actors who consciously manage and manipulate an evolving path-dependent process. In the latter view, paths are a resource rather than a constraint, as actors can interrupt and disrupt the logic of a path’s self-reinforcing mechanisms (Fortwengel and Keller, 2020). 6. The issue is further complicated by those recent reviews which propose that it is a combination of both relatedness and complexity (typically measured by the ubiquity and connections of an industry or technology) that provides the formula for successful regional development (see Balland and Rigby, 2016). In this case, there is much more emphasis again on specialization in complex activities rather than variety per se. 7. There are some signs of this changing, although many studies focus on the political and electoral outcomes of long-run economic decline, described as the growth of discontent.

Place and industrial development: paths to understanding?  147

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148  Handbook of industrial development David, P. (2005). Path dependence in economic processes: implications for policy analysis in dynamical systems contexts. In K. Dopfer (ed.), The Evolutionary Foundations of Economics. Cambridge, UK: Cambridge University Press, pp. 151–94. Dawley, S. (2013). Creating new paths? Offshore wind, policy activism, and peripheral region development. Economic Geography 90(1), 91–112. Dawley, S., MacKinnon, D. and Pollock, R. (2019). Creating strategic couplings in global production networks: regional institutions and lead firm investment in the Humber region, UK. Journal of Economic Geography 19, 853–72. De Propris, L. and Bailey, D. (2021). Pathways of regional transformation and Industry 4.0. Regional Studies 55(10–11), 1617–29. Dosi, G. (1982). Technological paradigms and technological trajectories: a suggested interpretation of the determinants and directions of technical change. Research Policy 11(3), 147–62. Essletzbichler, J. and Rigby, D. (2010). Generalized Darwinism and evolutionary economic geography. In R. Boschma and R. Martin (eds), The Handbook of Evolutionary Economic Geography. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 43–61. Evangelista, R. (2021). Technology and economic development: the Schumpeterian legacy. Review of Radical Political Economics 50(1), 136–53. Feldman, M., Guy, F. and Iammarino, S. (2021). Regional income disparities, monopoly and finance. Cambridge Journal of Regions, Economy and Society 14(1) 25–49. Florida, R., Mellander, C. and King, K.M. (2020). Winner-Take-All Cities. London: Routledge. Fortwengel, J. and Keller, A. (2020). Agency in the face of path dependence: how organizations can regain scope for maneuver. Business Research 13, 1169–201. Fredin, S. (2016). Breaking the cognitive dimension of local path dependence: an entrepreneurial perspective. Geografska Annaler Series B Human Geography 98(3), 239–53. Frenken, K., Van Oort, F. and Verburg, T.N. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies 41, 685–97. Garavaglia, C. (2010). Modelling industrial dynamics with ‘history-friendly’ simulations. Structural Change and Economic Dynamics 21, 258–75. Glasmeier, A. (2000). Manufacturing Time: Global Competition in the Watch Industry, 1795–2000. New York: The Guildford Press. Grabher, G. (1993). The weakness of strong ties: the lock-in of regional development in the Ruhr area. In G. Grabher (ed.), The Embedded Firm: On the Socioeconomics of Industrial Networks. London: Routledge, pp. 255–77. Grillitsch, M. (2019). Following or breaking regional development paths: on the role and capability of the innovative entrepreneur. Regional Studies 53(5), 681–91. Grillitsch, M., Asheim, B. and Trippl, M. (2018). Unrelated knowledge combinations: the unexplored potential for regional industrial path development. Cambridge Journal of Regions, Economy and Society 11, 257–74. Grillitsch, M. and Hansen, T. (2019). Green industry development in different types of regions. European Planning Studies 27(11), 2163–83. Grillitsch, M. and Sotarauta, M. (2020). Trinity of change agency, regional development paths and opportunity spaces. Progress in Human Geography 44(4), 704–23. Hall, P. (1981). The geography of the fifth Kondratieff cycle. New Society, 26 March. Hassink, R. (2010). Locked in decline? On the role of regional lock-ins to in old industrial areas. In R. Boschma and R. Martin (eds), Handbook of Evolutionary Economic Geography. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 450–68. Hassink, R., Isaksen, A. and Trippl, M. (2019). Towards a comprehensive understanding of new regional industrial path development. Regional Studies 53(11), 1636–45. Henning, M., Stam, E. and Wenting, R. (2013). Path dependence research in regional economic development: cacophony or knowledge accumulation. Regional Studies 47, 1348–62. Hipp, A. and Binz, C. (2020). Firm survival in complex value chains and global innovations systems: evidence from solar photovoltaics. Research Policy 49(1), Article 103876. Isaksen, A. (2016). Industrial development in thin regions: trapped in path extension? Journal of Economic Geography 15, 585–600.

Place and industrial development: paths to understanding?  149 Isaksen, A. and Trippl, M. (2014). Regional industrial path development in different regional innovation systems: a conceptual analysis. Papers in Innovation Studies, No. 2014/17. Lund University, CIRCLE – Centre for Innovation Research. Isaksen, A. and Trippl, M. (2016). Path development in different regional innovation systems: a conceptual analysis. In M.D. Parilli, R.D. Fitjar and A. Rodríguez-Pose (eds), Innovation Drivers and Regional Innovation Strategies. London: Routledge, pp. 66–84. Isaksen, A. and Trippl, M. (2017). Exogenously led and policy-led new path development in peripheral regions: analytical and synthetic routes. Economic Geography 93, 436–57. Jacobs, J. (1969). The Economy of Cities. New York: Random House. Jolly, S., Grillitsch, M. and Hansen, T. (2020). Agency and actors in regional industrial path development: a framework and longitudinal analysis. Geoforum 111, 176–88. Labory, S. and Bianchi, P. (2021). Regional industrial policy in times of big disruption: building dynamic capabilities in regions. Regional Studies 55(10–11), 1829–38. Lerner, J. and Nanda, R. (2020). Venture capital’s role in financing innovation: what we know and how much we still need to learn. Journal of Economic Perspectives 34(3), 237–61. Mackinnon, D., Dawley, S., Pike, A. and Cumbers, A. (2019). Rethinking path creation: a geographical political economy approach. Economic Geography 95(2), 113–35. Malerba, F., Nelson, R.R., Orsenigo, L. and Winter, S.G. (1999). ‘History-friendly’ models of industry evolution: the computer industry. Industrial and Corporate Change 8, 3–40. Malerba, F., Nelson, R.R., Orsenigo, L. and Winter, S.G. (2016). Innovation and the Evolution of Industries: History Friendly Models. Cambridge, UK: Cambridge University Press. Marshall, M. (1986). Long Waves of Regional Development. London: Macmillan. Martin, R.L. (2010). Roepke Lecture in Economic Geography. Rethinking regional path dependence: beyond lock-in to evolution. Economic Geography 86(1), 1–27. Martin, R.L. (2012). (Re)Placing path dependence: a response to the debate. International Journal of Urban and Regional Research 36, 179–92. Martin, R.L. and Sunley, P. (2006). Path dependence and regional economic evolution. Journal of Economic Geography 6, 395–437. Martin, R.L. and Sunley, P. (2011). Conceptualising cluster evolution: beyond the life cycle model? Regional Studies 45, 1299–318. Martin, R.L. and Sunley, P. (2012). Forms of emergence and the evolution of economic landscapes. Journal of Economic Behavior & Organization 82, 338–51. Mazzucato, M. (2013). Financing innovation: creative destruction vs. destructive creation. Industrial and Corporate Change 22(4), 851–67. Micelotta, E., Lounsbury, M. and Greenwood, R. (2017). Pathways of institutional change: an integrative review and a research agenda. Journal of Management 43, 1885–910. Muro, M. and Liu, S. (2021). The Geography of AI: Which Cities will Drive the Artificial Intelligence Revolution? Washington, DC: Brookings Metropolitan Policy Programme, Brookings Institution. Neffke, F., Henning, M. and Boschma, R. (2011). How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Economic Geography 87(3), 237–65. Nelson, R. and Winter, S. (1982). An Evolutionary Theory of Economic Change. Cambridge, MA: The Belknap Press of Harvard University Press. Njøs, R., Sjøtun, S., Jakobsen, S. and Fløysand, A. (2020). Expanding analyses of path creation: interconnections between territory and technology. Economic Geography 96(3), 266–88. Perez, C. (2010). Technological revolutions and techno-economics paradigms. Cambridge Journal of Economics 34, 185–202. Phelps, N., Atienza, M. and Arias, M. (2017). An invitation to the dark side of economic geography. Environment and Planning A: Economy and Space 50(1), 236–44. Power, A., Plöger, J. and Winkler, A. (2010). Phoenix Cities: The Fall and Rise of Great Industrial Cities. Bristol: Policy Press. Rodríguez-Pose, A. (2020). Institutions and the fortunes of territories. Regional Science Policy and Practice 12, 371–86. Rosenberg, N. (1963). Technological change in the machine tool industry, 1840–1910. The Journal of Economic History 23(4), 414–43.

150  Handbook of industrial development Scott, A.J. (1998). Regions and the World Economy: The Coming Shape of Global Production, Competition, and Political Order. Oxford: Oxford University Press. Sotarauta, M. and Suvinen, N. (2018). Institutional agency and path creation: institutional path from industrial to knowledge city. In A. Isaksen, R. Martin and M. Trippl (eds), New Avenues for Regional Innovation Systems. Cham, Switzerland: Springer, pp. 85–104. Storper, M. (1997). The Regional World: Territorial Development in a Global Economy. New York: The Guilford Press. Storper, M. (2009). Regional context and global trade. Economic Geography 85(1), 1–21. Storper, M., Kemeny, T., Makarem, N. and Osman, T. (2015). The Rise and Fall of Urban Economies: Lessons from San Francisco and Los Angeles. Stanford, CA: Stanford University Press. Storper, M. and Walker, R. (1989). The Capitalist Imperative: Territory, Technology, and Industrial Growth. Oxford: Blackwell. Strambach, S. (2010). Path dependency and path plasticity: the co-evolution of institutions and innovation – the German customized business software industry. In R. Boschma and R. Martin (eds), Handbook of Evolutionary Economic Geography. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 406–29. Sydow, J., Schreyogg, G. and Koch, J. (2020). On the theory of organizational path dependence: clarifications, replies to objections, and extensions. Academy of Management Review 45, 717–34. Taalbi, J. (2019). Origins and pathways of innovation in the third industrial revolution. Industrial and Corporate Change 28(5), 1125–48. Tödtling, F. and Trippl, M. (2013). Transformation of regional innovation systems: from old legacies to new development paths. In P. Cooke (ed.), Re-framing Regional Development: Evolution, Innovation and Transition. London: Routledge, pp. 297–317. Tödtling, F. and Trippl, M. (2018). Regional innovation policies for new path development – beyond neo-liberal and traditional systemic views. European Planning Studies 26(9), 1779–95. Trippl, M., Baumgartinger-Seiringer, S. and Frangenheim, A. et al. (2020). Unravelling green regional industrial path development: regional preconditions, asset modification and agency. Geoforum 111, 189–97. Trippl, M., Grillitsch, M. and Isaksen, A. (2018). Exogenous sources of regional industrial change: attraction and absorption of non-local knowledge for new path development. Progress in Human Geography 42(5), 687–705. Truffer, B. and Coenen, L. (2012). Environmental innovation and sustainability transitions in regional studies. Regional Studies 46(1), 1–21. Walker, R. (2000). The geography of production. In E. Sheppard and T. Barnes (eds), A Companion to Economic Geography. Oxford: Blackwell, pp. 113–32. Westgard-Cruice and Aoyama, Y. (2021). Variegated capitalism, territoriality and the renewable energy transition: the case of the offshore wind industry in the Northeastern USA. Cambridge Journal of Regions, Economy and Society 14, 235–52.

9. Innovation, industrial dynamics and regional inequalities Ron Boschma, Martina Pardy and Sergio Petralia

INTRODUCTION The EU aims to combine smart and inclusive growth in Europe (Lee, 2016). The objective of smart growth means regions aim to develop new and upgrade existing activities that build on local capabilities. However, not every region has the same capacity to diversify into more complex activities (Balland et al., 2019; Hidalgo and Hausmann, 2009). While more advanced regions tend to show a stronger capacity to do so, less advanced and peripheral regions lack such capacity to a greater or lesser extent (McCann and Ortega-Argilés, 2015). As a consequence, smart growth could lead to an increase in income disparities between regions in Europe. What is more, there is evidence that smart growth in more advanced regions contributes to an increase in intra-regional inequality. For instance, the innovative and economic success of Silicon Valley has been accompanied by a rise in socio-economic inequality, soaring housing costs and low-wage employment (Lee and Clarke, 2019). This would imply that smart and inclusive growth may not necessarily go together. The objective of this chapter is to shed light on this complex relationship between smart and inclusive growth by outlining and discussing the literature that focuses on the relationship between innovation, industrial dynamics and regional inequality. The aim is to take stock of this literature and to highlight promising avenues for future research. We restrict our attention to two recent literatures on industrial dynamics: (1) the literature on regional diversification claiming that the rise (and also the fall) of industries in regions is conditioned by local capabilities (Boschma, 2017); (2) and the literature on the geography of artificial intelligence (AI) (Balland and Boschma, 2021a), with a special focus on the role of automation for regional labour markets (Felten, Raj and Seamans, 2021; Muro, Maxim and Whiton, 2019). We discuss briefly how the current literature addresses possible links between industrial dynamics and inequalities within and between regions. We identify a number of research gaps that need to be tackled to unravel the complex relationship between innovation, industrial dynamics and regional inequality. In the next section, we briefly present the literature on industrial dynamics that focuses on regional diversification and the spatial implications of AI. The third section will outline the literature on inequalities both within and between regions. We draw particular attention to the question of how innovation and industrial dynamics contribute to intra- and inter-regional inequalities. The fourth section explores some unresolved issues that need to be taken up by future research on the relationship between innovation, industrial dynamics and regional inequality. The fifth section concludes.

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THE GEOGRAPHY OF INDUSTRIAL DYNAMICS There is overwhelming evidence that innovation agglomerates in space (Audretsch and Feldman, 1996; Asheim and Gertler, 2005). Knowledge spillovers are often geographically bounded, and knowledge diffusion is therefore limited across space (Jaffe, Trajtenberg and Henderson, 1993). Regions tend to accumulate specific knowledge and specialize in certain industries over time, as the innovation process is often subject to cumulative, localized and path-dependence tendencies (Dosi et al., 1988). More advanced regions often act as research and innovation hubs that are well endowed with human capital, industrial diversity and a well-established knowledge infrastructure that tend to reinforce the uneven geography of innovation (Moreno, Paci and Usai, 2005). There is a long tradition in economic geography of investigating the relationship between innovation, industrial and regional dynamics. Scholars have described cases of regions that experienced the rise of new industries that boosted their economies for a long period of time (Hall and Preston, 1988), such as micro-electronics in Silicon Valley (Lecuyer, 2006). Scholars also documented cases of regions that were confronted with a structural decline of their mature industries, leading to de-industrialization (Marshall, 1987). Apart from these industrial dynamics in regions, scholars have investigated the extent to which such intra-regional dynamics had far-reaching consequences for the wider spatial system, in terms of changing centre–periphery patterns (Perez and Soete, 1988). A well-known example is the rise of the Sunbelt and the decline of the Rustbelt states in the US that started to unfold in the 1970s (Scott, 1988). This example illustrates how the rise and fall of industries can have their own geographies, resulting in regional inequalities. The literature struggled for some time to understand the relationship between industrial and regional dynamics. When explaining the rise of new industries in some places, scholars had a tendency to conceptualize such territories as ‘places of luck’, dubbing them as ‘historical accidents’ or ‘chance events’, as if new industries had been built from scratch, or could have developed anywhere (Arthur, 1994). The exception were old industrial regions that were perceived as ‘places of constraints’ in which new industries were unlikely to develop due to lock-in processes (Grabher, 1993). Instead, the ‘window of locational opportunity’ approach (Boschma, 1997; Storper and Walker, 1989) perceived regions as ‘places of opportunities’, some of which provided favourable conditions that agents could turn into supportive structures to host new industries. This shifted the attention to the conditions that make such a transformation process more feasible; what makes regions successfully develop new activities; why this capacity to do so differs across regions; and who are the agents of change that set that in motion. Regional Diversification In the last decade or so, a vast body of literature on new growth paths and regional diversification has taken up these questions (Boschma, 2017). In a nutshell, it refers to the importance of local capabilities that consist of specific sets of knowledge, skills and institutions that new activities need as inputs for their development. The ‘product space’ concept (Hidalgo et al., 2007) developed tools to accumulate systematic evidence that history matters when countries shift into new export products. The concept allowed for the identification of products that require similar capabilities. This relatedness measure was used as input to explain diversifi-

Innovation, industrial dynamics and regional inequalities  153 cation opportunities of countries: a country is more likely to develop new products related to existing products because these provide capabilities on which new activities can build. In economic geography, work on proximity (Boschma, 2005; Torre and Rallet, 2005) and related variety (Frenken, Van Oort and Verburg, 2007; Kuusk and Martynovich, 2021) aimed to identify which activities could be considered proximate in various dimensions, such as activities sharing similar knowledge requirements (Breschi, Lissoni and Malerba, 2003). Cognitive proximity between local activities was perceived to enhance knowledge spillovers between them. This idea of proximity has been extended to the question of how regions diversify (Neffke, Henning and Boschma, 2011). A large body of literature has provided empirical evidence that related diversification is most common (Boschma, 2017; Hidalgo et al., 2018). Local capabilities condition which new activities emerge and prosper in cities and regions. Neffke et al. (2011) showed that emerging industries are more likely to enter a region when technologically related to local industries. Similar findings have been documented for regions in the US (e.g., Essletzbichler 2015), the EU (e.g., Cortinovis et al., 2017; Xiao, Boschma and Andersson, 2018), China (e.g., He, Yan and Rigby, 2018) and Latin America (e.g., Alonso and Martín 2019). Relatedness has also been found to be an important factor explaining the decline and fall of industries in regions. Neffke et al. (2011) found that the exit probability of an industry in a region is lower when related to existing activities. Essletzbichler (2015) found a similar result for US cities. In other words, exits of industries in regions are less likely to occur when strongly embedded in other local activities. Farinha et al. (2019) found the same results for exits of jobs in cities. The overall net effect of entries in more related industries and exits in more unrelated industries tends to make the industrial portfolio of regions more coherent over time (Neffke et al., 2011; Quatraro, 2010). Essletzbichler (2015) found for cities in the US that the positive effect of exit on cohesion is greater than the negative effect of entry. This was further reinforced by higher employment growth in those industries that are more closely related to the industrial structure of a region. Neffke et al. (2011) found that this tendency towards more coherent structures due to industrial dynamics is more pronounced at the regional than the national scale. This tendency to more coherence has some analogy in literature describing that old industrial regions get locked in specializations, which hampers their ability to diversify into something new (Grabher, 1993). In a similar vein, Pinheiro et al. (2022) observed that low-income regions may get trapped in ‘low-complexity’ economies, meaning that their industrial structure consists primarily of simple activities that are easy to copy, after the concept of economic complexity introduced by Hidalgo and Hausmann (2009). As expected, there is evidence that low-complexity regions have lower economic performance, such as lower gross domestic product (GDP) growth (Lo Turco and Maggioni, 2020) and lower employment growth (Rigby et al., 2022). The low-complexity trap can be attributed to the fact that low-income regions have a hard time diversifying into complex industries because their diversification opportunities are primarily geared towards low-complexity activities. Pinheiro et al. (2022) found that potential and actual entries in low-income regions tend to be primarily in low-complexity activities, as these are most related to existing local activities. This could imply that relatedness may have a tendency in low-income regions to trap them in low-complexity activities.

154  Handbook of industrial development The Geography of AI Technological change has a pervasive impact on industries and regional labour markets. The rise and fall of industries is directly related to the tasks and occupations in each industry being substituted or complemented by technological progress. AI is no exception to that rule (Laffi and Boschma, 2022). Studies explore how automation may affect skills and labour markets (Nedelkoska and Quintini, 2018). They show that a wide range of jobs and tasks may be automated, including non-routine tasks (Ménière, Rudyk and Valdes, 2017). Focus is often on negative effects, like jobs that might be displaced, but certain jobs or tasks might also be augmented (Acemoglu and Restrepo, 2019; Autor, 2015; Bessen et al., 2019; Brynjolfsson and McAfee, 2014; Frey and Osborne, 2017). However, the (empirical) literature on the geography of AI and in particular its consequences for regional labour markets is still evolving. There is substantial evidence that automation of routine tasks has played a major role in the economic and industrial development of regions, particularly for those with a strong prevalence of the manufacturing sector, where employment and wages have been substantially reduced (Autor, 2019). While at the beginning of the twentieth century, hubs of manufacturing were mostly located in major cities, they have gradually shifted to less crowded locations due to the development of transportation networks (Glaeser, 2011). Instead, knowledge-intensive sectors concentrate in cities to an increasing extent, where high-skilled supply of labour is abundant (Moretti, 2004). The decline in employment and wages in the manufacturing sector has been explained by an increase in trade and automation pressure, as well as the widespread use of computers that has reduced the number of clerical and administrative staff (Autor and Dorn, 2013; Autor, Dorn and Hanson, 2013). A similar pattern has been observed in many Western European countries, as shown by Goos, Manning and Salomons (2014). Recent studies are making an effort to identify jobs that are most exposed to automation to map their geographical distribution, especially in the US. Muro et al. (2019) showed there are differences between metropolitan regions and between rural and urban communities in the US with regard to their exposure to automation. At the state level, they find little variation in task exposure to automation. Instead, at the county level, they observed that the exposure to potential automation-driven task replacement is substantially higher in more rural communities and smaller metropolitan areas. According to Muro et al. (2019), the least affected in the group of the largest metropolitan areas are those with the highest educational attainment such as San José and New York that are well endowed with advanced services occupations. Felten et al. (2021) constructed an AI exposure measure for the US. They found that, on average, urban counties on the East Coast (e.g., Boston, New York City, Philadelphia, Washington, DC) and the West Coast of the US (Bay Area) are more highly exposed to AI than rural counties. PwC (2021) made a study of the UK showing that AI is expected to have higher positive net effects in terms of number of jobs in the more affluent regions such as London and the South East, and higher negative net effects in several cities in the Midlands and Northern England.

INTRA- AND INTER-REGIONAL INEQUALITIES So, technological change and industrial dynamics in regions have consequences for regional development, but how about their effects on intra- and inter-regional inequalities? This is a key

Innovation, industrial dynamics and regional inequalities  155 topic, as scholars have documented widening income disparities and increasing inequalities across and within regions in recent years (Feldman, Guy and Iammarino, 2021; Iammarino, Rodríguez-Pose and Storper, 2019). In many developed countries, inter-regional inequalities have increased. This is the case for the EU, where income inequality across NUTS2 regions has substantially risen since the 2000s (Rosés and Wolf, 2018), but also for the US, where inequality in income per capita between metropolitan areas has increased by 30 per cent from 1980 to 2016 (Ganong and Shoag, 2017; Iammarino et al., 2019). While the observed surge in regional disparities has been the result of the interaction of complex processes and drivers, scholars tend to attribute the lion’s share to technological change and globalization (Iammarino et al., 2019; Moretti, 2012; Rosés and Wolf, 2018). The interaction of these forces has generated the so-called ‘great inversion’ or ‘new geography of jobs’ (Moretti, 2012; Storper, 2013), leaving previously prosperous small and medium-sized cities in economic and population decline, while large metropolitan areas are growing strongly in terms of employment and wages. The increase in inter-regional inequality in the EU observed since the 1970s is largely the result of two main drivers: structural change and regional path dependencies (Iammarino et al., 2019). Structural change describes long-run dynamics stemming from major disruptions in technological change and globalization. Technological change has decreased costs of trade and communication between businesses as well as within their value chains, encouraging industries to become increasingly geographically distributed and located in large cities (Cavailhes et al., 2007; Levy and Murnane, 2005). These technological and economic hotspots provide higher-paid, higher-skilled and non-routine jobs, pulling in talented workforce from other regions (Diamond, 2016; Iammarino et al., 2019). In contrast, many regions are stagnating or shrinking in their economic performance, while others are characterized by high productivity. Automation of routine tasks as well as trade have affected the economic development of regions, especially in those regions with a strong manufacturing sector presence, where employment and wages have decreased to a considerable degree (Autor, 2019). Instead, knowledge-intensive sectors are increasingly concentrated in cities, where a large supply of high-skilled labour is present (Moretti, 2004). In many advanced economies, the increasing agglomeration of economic and knowledge-intensive activities in large cities in combination with the extensive spread of low-value activities to less developed locations has led to an increase of inter-regional inequalities (Kemeny and Storper, 2020; Puga, 1999). The second driver of inter-regional inequality is very much influenced by evolutionary thinking, which involves the unique capabilities of every region that determine the region’s potential to engage in change – that is, local endowments and resources, including skills, people, firms, institutions, knowledge and industries. These become intertwined with the forces of structural change, creating a region’s economic development path (Iammarino et al., 2019). While before the 1970s, this interplay provided lagging regions with the possibility to move up in the development process, this trend has been reversed in more recent times, leading to rising regional divergence in terms of wages, skills and opportunities (Ganong and Shoag, 2017; Moretti, 2012). With the persistence of agglomeration economies in certain locations and lacking spatial redistribution of prosperity, rural and de-industrialized areas are becoming increasingly left behind due to economic decline and lack of opportunities (Kemeny and Storper, 2020). The latter areas are more likely to become susceptible to anti-system movements, like the rise of populism (Rodríguez-Pose, Lee and Lipp, 2021).

156  Handbook of industrial development In addition to a focus on inter-regional inequalities, studies have also focused on intra-regional inequalities, and how these have been affected by innovation processes. What studies tend to observe is that concentration of economic and innovative activity in certain locations often goes hand in hand with intra-regional inequalities (Lee, Sissons and Jones, 2016; Lindley and Machin, 2014; Rodríguez‐Pose and Tselios, 2009). According to Florida (2006), those US cities that are the most innovative are also those that are the most unequal. Breau, Kogler and Bolton (2014) found this positive relationship between innovation and inequality in earnings for Canadian cities. Also for European regions, Lee (2011) and Lee and Rodríguez-Pose (2013) confirm a positive link between innovation and wage inequality. Educated workers tend to group themselves according to their individual skills in cities with high employment densities (Combes, Duranton and Gobillon, 2008). In turn, they raise the demand for local services and goods and thus create an employment multiplier for low-wage jobs (Moretti, 2010). Large cities draw in high- and low-skilled workers and are often characterized by thicker tails in skill distribution, which Eeckhout, Pinheiro and Schmidheiny (2014) refer to as extreme-skill complementarity. For the UK, Lee and Clarke (2019) found substantial local employment multiplier effects for high-tech jobs. While low-skilled workers profited from new employment opportunities, they were also often in poorly paid service jobs and coping with high and rising local housing costs (see also Florida, 2017). Structural forces such as technological change and globalization have been found to profoundly change occupation and wage structures within a region. In the 1990s, the skill-biased technological change (SBTC) hypothesis was applied to understand the rising demand for higher-educated workers and the rise of wage inequality (Autor and Katz, 1999; Johnson, 1997) but failed to explain the rise in labour demand for low-skilled occupations happening at the same time. Goos and Manning (2007) refer to this hollowing out of jobs in the middle of the skill distribution as job polarization, such as the loss of manufacturing or routine office jobs. These developments have been mostly explained by the routine-biased technological change (RBTC) hypothesis proposed by Autor, Levy and Murnane (2003), describing how manual and routine tasks can be substituted by computers. On the other hand, jobs with more complex tasks, which are characterized by non-routine problem solving, are complemented by technological change. Webb (2020) made an attempt to assess the impact of AI and found that high-skill occupations are most exposed. According to this study, AI is expected to reduce wage inequality. In many advanced economies, including Europe and the United States, the RBTC hypothesis has been backed up by empirical evidence (Goos, Manning and Salomans, 2009). Acemoglu and Restrepo (2021) find that the dynamics in the wage structure over 40 years are mostly due to automation and offshoring of routine tasks, explaining between 50 per cent and 70 per cent of the changes observed within the US. Kogan et al. (2021) found that, while technological innovation has been linked to higher labour productivity, it is associated with worsening labour market outcomes, such as wages and employment, in the US between 1850 and 2010. The study shows that particularly older, higher-paid workers with lower educational attainment have experienced a greater reduction in their average earnings, which is linked to technological progress. According to Autor et al. (2013), globalization through import competition has a similar effect, decreasing employment and wages in the US manufacturing sector. They found a strong decline in US wages and employment in local labour markets that were strongly exposed to import competition between 1990 and 2007, with 25 per cent of US manufacturing employment declining due to Chinese import competition. For European regions,

Innovation, industrial dynamics and regional inequalities  157 trade had a substantial job displacement effect but also created new employment, leading to net positive employment growth in the period between 1999 and 2010 (Gregory, Salomons and Zierahn, 2022). Task displacement due to mostly automation technologies and offshoring is thus a key driver of increased wage inequality as it reduces wage and employment in the middle of the wage and skill distribution. Besides these drivers of intra-regional inequality, other determinants have been identified. Some point towards the role of institutions, emphasizing the role of institutional changes in the form of economic deregulation, minimum wage changes and de-unionization (Fortin and Lemieux, 1997). Others have looked at the role of firms, explaining wage inequality due to heterogeneity between firms (Criscuolo et al., 2020) or within firms (Mueller, Ouimet and Simintzi, 2017). Autor et al. (2020) show that the decrease in the labour share in the US is linked to increasing product market concentration, with technological change and globalization pushing sales towards highly productive firms, which they call ‘superstar firms’. However, many developed countries have experienced low productivity growth and increasing wage inequality at the same time. Criscuolo et al. (2020) show that firm productivity gaps play a key role in 14 OECD countries, with around 50 per cent of the changes in overall wage inequality being attributed to the average dispersion between firms. Two-thirds of these overall changes in wage inequality can be attributed to productive firms paying higher wages, and one-third to the spatial sorting of high-skilled workers into these firms.

SOME FUTURE RESEARCH AVENUES So far, we have discussed possible links between innovation and regional inequalities. Much of the recent literature shares the view that innovation has a tendency to increase both inter-regional and intra-regional inequality. At the same time, more systematic research is needed to unravel this complex relationship between innovation and regional inequalities. In the remainder of the chapter, we will discuss a few research topics that are still underdeveloped. First, there is a need to collect systematic evidence on whether industrial dynamics increase or decrease inequalities across regions, and how. The regional diversification literature has not yet investigated how industrial dynamics may affect regional inequality (Boschma, 2017). Studies show that the most complex activities concentrate in the richest cities, and there is a positive association with their economic performance (Antonelli, Crespi and Quatraro, 2020; Balland et al., 2020; Balland and Rigby, 2017; Davies and Maré, 2021; Mewes and Broekel, 2020; Rigby et al., 2022). This could imply that inter-regional inequality is likely to increase, as high-income regions would have a greater capacity to diversify into more complex activities that bring higher economic benefits than low-income regions. Pinheiro et al. (2022) seem to suggest that this is the case in Europe. They found that diversification opportunities in more complex technologies and industries tend to be higher in high-income regions than low-income regions. However, they did not test whether this could explain rising inter-regional inequality in Europe. Some studies have looked at the relationship between economic complexity and intra-regional inequality. These have been conducted at the country level, such as Hartmann et al. (2017, 2020), showing that the complexity of economies is negatively associated with income inequality. To our knowledge, there exists only one study – Morais, Swart and Jordaan (2021) – at the subnational scale. They found an inverted-U-shaped relationship between income

158  Handbook of industrial development distribution and economic complexity for Brazilian states, implying that with higher levels of complexity, inequality first increases and then decreases in Brazilian regions. However, it remains unknown what the mechanisms are behind this, and in particular how industrial dynamics may affect intra-regional inequality. One could hypothesize that both entry and exit of industries are likely to contribute to increasing levels of wage inequality in regions (Cortinovis, Zhang and Boschma, 2022). Entries will mainly occur in more complex industries that are primarily related to existing industries in a region (Balland et al., 2019). These entries are expected to generate high-wage jobs and increase levels of wages in related industries in the region, and so might increase intra-regional wage inequality. Exits are expected to occur mainly in less complex industries that are more unrelated to local industries (Neffke et al., 2011), meaning that affected people will end up either unemployed or find alternative jobs in local skill-unrelated industries, which would lower their wage level. This would contribute to a further increase in intra-regional wage inequality. Second, there are few studies that have looked at the impact of AI and automation on the evolution of regional inequality at the subnational scale. Many studies have made attempts to estimate the impact of AI on labour markets (e.g., PwC, 2021), how automation affects routine and non-routine tasks (e.g., Acemoglu and Restrepo, 2019), and how AI may impact wage inequality in countries in particular (e.g., Webb, 2020). In many of these studies, the main focus is on ‘jobs under threat’, ‘jobs at risk’ or on ‘AI exposure’ but it is not entirely clear what AI exposure means in terms of positive or negative impacts on regional economies. In the case of automation, when technology replaces labour, the number of tasks, employment and market size contract (Autor et al., 2022). This has been observed in less complex industries, as these are mostly characterized by manual and routine tasks and therefore have a higher risk of becoming automated and offshored. These often involve middle-paid jobs in the manufacturing industry, as well as many clerical occupations. Therefore, with increasing automation and offshoring, certain tasks and occupations become substituted by technology, and labour becomes displaced, followed by a decline in employment and wages. This capital–labour substitution can be empirically observed by a drop in the labour share (Autor et al., 2020; Autor and Salomons, 2018). While employment reduces in less complex industries, at the same time it expands in other industries and could lead to a net positive employment effect (Gregory et al., 2022; PwC, 2021). Industries where occupations and their tasks can be complemented or augmented by technological innovation do not experience labour displacement to the same extent. Innovation tends to augment output in complex industries that are characterized by cognitive and non-routine tasks, and higher knowledge-intensity (Autor et al., 2022). These industries tend to experience higher growth and generate higher economic returns for the region. These opposing dynamics are likely to induce wage inequalities between different occupations and skill levels, but in what way, and how this will affect different types of regions, are still open questions. Investigating AI exposure in regions is one thing, another is to investigate which regions have the capacity to bounce back once negatively affected by AI. This implies that we have to investigate the capacity of regions to provide alternative job opportunities for those tasks and jobs that can be considered under pressure from AI. This also implies that we need to identify the extent to which skill-related jobs or tasks are present that can absorb the jobs or tasks that are at risk. When those types of jobs or tasks under threat would be concentrated in regions where there is a shortage of such job opportunities, as in peripheral regions or old industrial regions, negative economic consequences would prevail. Urban regions might be in a more

Innovation, industrial dynamics and regional inequalities  159 advantageous position, despite the fact that they might be hit hard by automation, especially in non-routine activities. These regions might have potential to bounce back, as they are expected to have more job opportunities in activities that are skill-related to their activities most exposed to AI. This still needs to be empirically tested. For future research, there are many opportunities to understand the complex interplay between industrial change and regional inequalities. While it has been shown that complex industries and non-routine tasks have been complemented by technological innovation, it is still not clear how they will develop in the future. With shifting demand and new technologies, new tasks and new occupations will emerge, changing the labour markets and the relationship between labour and technology. Understanding this phenomenon is particularly important because a new series of disruptive innovations in robotics, AI or biotechnologies could accelerate and reinforce the unequal agglomerations of high-skilled workers we have seen in the past, without having an analogous ‘great levelling’ period for the last 40 years. If so, then the contemporary geography of regional inequality may be a prelude to an additional round of increase in inequality. Another area of research that should be explored further is the role of firms and their influence on wage differentials and regional inequalities. While there are studies such as Mueller et al. (2017) and Criscuolo et al. (2020) that attribute a major role to firms contributing to changes in wage inequality, there is still a lot to be done.

CONCLUDING REMARKS We have explored the complex relationship between innovation, industrial dynamics and regional inequality. First, technological change and globalization have contributed to an increasing tendency of innovations to concentrate in a few places. Complex activities have also become increasingly concentrated in the more advanced urban regions (Balland and Rigby, 2017). Since these complex activities also bring higher economic benefits, it is not unlikely that these regions will continue to prosper. Second, there is increasing evidence that these innovative hotspots are also confronted with an increase in intra-regional inequality. The innovative activities tend to increase wages and living costs (Moretti, 2012), which means that low-income people are crowded out due to high housing costs in these ‘superstar cities’ (Gyourko, Mayer and Sinai, 2013) and are forced to move. At the same time, there is the hollowing-out of the middle segment of the labor market due to technological change, which increases the level of intra-regional inequality especially in innovative hotspots. Third, technologies like automation substitute for routine-task based jobs, which tend to concentrate in old industrial regions and less developed regions. At the same time, technologies augment certain occupations and tasks, especially in complex industries (Autor et al., 2022), which tend to concentrate in the more advanced regions. These tendencies are likely to contribute to an increase in regional inequality. However, we also identified a number of research gaps that need to be taken up on the relationship between innovation and regional inequalities. First, we have to collect more systematic evidence on how industrial dynamics increase or decrease inter-regional and intra-regional inequalities. There is still little understanding of whether entries and exits of industries affect regional inequality, and how complexity levels of industries also have an effect. Second, there is a lack of studies that systematically investigate the impact of AI and automation on regional inequality at the subnational scale. It is not entirely clear what AI exposure means in terms of

160  Handbook of industrial development positive or negative impacts on regional economies. For instance, AI is affecting non-routine tasks, and these tasks or jobs are often performed in major cities. However, it is still uncertain what type of consequences this might have for the most advanced regions in the future. Third, studies have focused on the relationship between innovation and intra-regional inequality (e.g., Breau et al., 2014; Lee, 2011). However, few studies have investigated the impact of industrial dynamics on intra-regional inequalities (Pinheiro et al., 2022). Fourth, there is little high-quality data at a detailed spatial scale with which to compare regional inequality levels across countries. Research tends to focus on a single country, in particular the US. Because the US is very different from Europe, it is not necessarily the case that what is found in the former would also apply to the latter, if only because the institutional context is very different. Due to the lack of subnational data on wage or income inequalities in a comparative setting, comparative studies on intra-regional inequality in Europe are also lacking. An exception is Lee and Rodríguez-Pose (2013), but the data they used were available only at the NUTS1 level and restricted to a selective number of countries. Fifth, we did not discuss explicitly what might be the effect of inter-regional linkages on regional inequalities, such as flows of capital, labour and knowledge (Balland and Boschma, 2021b). Given the many empirical challenges outlined here, how to tackle the possible negative links or tensions between smart and inclusive growth at the regional level that have been identified remains a challenge. From policy circles, there is a strong need to acquire more understanding of how to combine both objectives, and how to align smart specialization and cohesion policies in the EU in particular (Balland et al., 2019; Boschma, 2022; McCann and Ortega-Argilés, 2015). It is still uncertain under what circumstances this can be accomplished, what policy can do, and how. The same applies to the challenges related to AI and automation and their possible effects on regional labour markets. There is a huge demand from policymakers for more understanding of the effects of AI on labour markets, how regions will be affected differently, and what consequences it will have for inter-regional and intra-regional inequality. These insights are needed to develop the right mix of labour market, innovation, industrial and regional policies that aim to align smart and inclusive growth (Rodrik and Stantcheva, 2021).

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Innovation, industrial dynamics and regional inequalities  163 Hidalgo, C.A., P.A. Balland and R. Boschma et al. (2018). The principle of relatedness. In A.J. Morales, C. Gershenson and D. Braha et al. (eds), Unifying Themes in Complex Systems IX (Springer Proceedings in Complexity). Cham, Switzerland: Springer, pp. 451–7. Hidalgo, C.A. and R. Hausmann (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences 106(26), 10570–75. Hidalgo, C.A., B. Klinger, A.L. Barabási and R. Hausmann (2007). The product space conditions the development of nations. Science 317(5837), 482–7. Iammarino, S., A. Rodríguez-Pose and M. Storper (2019). Regional inequality in Europe: evidence, theory and policy implications. Journal of Economic Geography 19(2), 273–98. Jaffe, A., Trajtenberg, M. and Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics 108(3), 577–98. Johnson, G.E. (1997). Changes in earnings inequality: the role of demand shifts. Journal of Economic Perspectives 11(2), 41–54. Kemeny, T. and M. Storper (2020). Superstar cities and left-behind places: disruptive innovation, labor demand, and interregional inequality. LSE III Working Paper, No. 41. London School of Economics. Kogan, L., D. Papanikolaou, L.D. Schmidt and B. Seegmiller (2021). Technology–skill complementarity and labor displacement: evidence from linking two centuries of patents with occupations. NBER Working Paper, No. w29552. National Bureau of Economic Research. Kuusk, K. and M. Martynovich (2021). Dynamic nature of relatedness, or what kind of related variety for long‐term regional growth. Tijdschrift voor Economische en Sociale Geografie 112, 81–96. Laffi, M. and R. Boschma (2022). Does a local knowledge base in Industry 3.0 foster diversification in Industry 4.0 technologies? Evidence from European regions. Papers in Regional Science 101(1), 5–35. Lecuyer, C. (2006). Making Silicon Valley: Innovation and the Growth of High Tech, 1930–1970. Cambridge, MA: MIT Press. Lee, N. (2011). Are innovative regions more unequal? Evidence from Europe. Environment and Planning C: Government and Policy 29(1), 2–23. Lee, N. (2016). Growth with inequality? The local consequences of innovation and creativity. In R. Shearmur, C. Carrincazeaux and D. Doloreux (eds), Handbook on the Geographies of Innovation. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 419–31. Lee, N. and S. Clarke (2019). Do low-skilled workers gain from high-tech employment growth? High-technology multipliers, employment and wages in Britain. Research Policy 48(9), Article 103803. Lee, N. and A. Rodríguez-Pose (2013). Innovation and spatial inequality in Europe and USA. Journal of Economic Geography 13(1), 1–22. Lee, N., P. Sissons and K. Jones (2016). The geography of wage inequality in British cities. Regional Studies 50(10), 1714–27. Levy, F. and R. Murnane (2005). How computerized work and globalization shape human skill demands. IPC Working Paper Series, No. MIT-IPC-05-006. Industrial Performance Center, MIT. Lindley, J. and S. Machin (2014). Spatial changes in labour market inequality. Journal of Urban Economics 79, 121–38. Lo Turco, A. and D. Maggioni (2020). The knowledge and skill content of production complexity. Research Policy, https://​doi​.org/​10​.1016/​j​.respol​.2020​.104059. Marshall, M. (1987). Long Waves of Regional Development. London: Macmillan. McCann, P. and R. Ortega-Argilés (2015). Smart specialisation, regional growth and applications to EU cohesion policy. Regional Studies 49(8), 1291–302. Ménière, Y., I. Rudyk and J. Valdes (2017). Patents and the Fourth Industrial Revolution: The Inventions Behind Digital Transformation. Munich: European Patent Office. Mewes, L. and T. Broekel (2020). Technological complexity and economic growth of regions. Research Policy, https://​doi​.org/​10​.1016/​j​.respol​.2020​.104156. Morais, M.B., J. Swart and J.A. Jordaan (2021). Economic complexity and inequality: does regional productive structure affect income inequality in Brazilian states? Sustainability 13(2), Article 1006. Moreno, R., R. Paci and S. Usai (2005). Geographical and sector clusters of innovation in Europe. The Annals of Regional Science 39, 715–39.

164  Handbook of industrial development Moretti, E. (2004). Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data. Journal of Econometrics 121(1–2), 175–212. Moretti, E. (2010). Local multipliers. American Economic Review 100(2), 373–7. Moretti, E. (2012). The New Geography of Jobs. Boston, MA: Houghton Mifflin Harcourt. Mueller, H.M., P.P. Ouimet and E. Simintzi (2017). Wage inequality and firm growth. American Economic Review 107(5), 379–83. Muro, M., R. Maxim and J. Whiton (2019, 24 January). Automation and Artificial Intelligence: How Machines Are Affecting People and Places. Metropolitan Policy Program at Brookings. Nedelkoska, L. and G. Quintini (2018). Automation, skill use and training. OECD Social, Employment and Migration Working Papers, No. 2018/3. Organisation for Economic Co-operation and Development. Neffke, F., M. Henning and R. Boschma (2011). How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Economic Geography 87(3), 237–65. Perez, C. and L. Soete (1988). Catching up in technology: entry barriers and windows of opportunity. In G. Dosi, C. Freeman and R. Nelson et al. (eds), Technical Change and Economic Theory. London/ New York: Pinter, pp. 458–79. Pinheiro, F.L., P.A. Balland, R.A. Boschma and D. Hartmann (2022). The dark side of the geography of innovation: relatedness, complexity, and regional inequality in Europe. Regional Studies, forthcoming. Puga, D. (1999). The rise and fall of regional inequalities. European Economic Review 43(2), 303–34. PwC (2021). BEIS Research Report Number 2021/042. The Potential Impact of Artificial Intelligence on UK Employment and the Demand for Skills: A Report by PwC for the Department for Business, Energy and Industrial Strategy. Quatraro, F. (2010). Knowledge coherence, variety and productivity growth: manufacturing evidence from Italian regions. Research Policy 39, 1289–302. Rigby, D.L., C. Roesler and D. Kogler et al. (2022). Do EU regions benefit from smart specialization principles? Regional Studies, https://​doi​.org/​10​.1080/​00343404​.2022​.2032628. Rodríguez-Pose, A., N. Lee and C. Lipp (2021). Golfing with Trump: social capital, decline, inequality, and the rise of populism in the US. Cambridge Journal of Regions, Economy and Society 14(3), 457–81. Rodríguez‐Pose, A. and V. Tselios (2009). Education and income inequality in the regions of the European Union. Journal of Regional Science 49(3), 411–37. Rodrik, D. and S. Stantcheva (2021). Fixing capitalism’s good jobs problem. Oxford Review of Economic Policy 37(4), 824–37. Rosés, J.R. and N. Wolf (2018). Regional economic development in Europe, 1900–2010: a description of the patterns. Economic History Working Papers, No. 278/2018, London School of Economics. Scott, A. (1988). New Industrial Spaces: Flexible Production Organization and Regional Development in North America and Western Europe. London: Pion. Storper, M. (2013). Keys to the City: How Economics, Institutions, Social Interaction, and Politics Shape Development. Princeton, NJ: Princeton University Press. Storper, M. and R. Walker (1989). The Capitalist Imperative: Territory, Technology and Industrial Growth, New York and Oxford: Blackwell. Torre, A. and A. Rallet (2005). Proximity and localization. Regional Studies 39(1), 47–60. Webb, M. (2020). The impact of artificial intelligence on the labor market. Working paper. Stanford University. Xiao, J., R.A. Boschma and M. Andersson (2018). Industrial diversification in Europe: the differentiated role of relatedness. Economic Geography 94(5), 514–49.

10. Evolutions in industrial districts and local productive systems Marco Bellandi, Maria Chiara Cecchetti and Erica Santini

INTRODUCTION Various streams of regional and industrial organization studies in the second half of the last century focused on models and cases of local productive systems (LPSs) – that is, delimited places (local systems, small regions) featured by localized industries that, in different degrees, express a local division of labour, embed in the local socio-cultural and institutional context, and liaise with wide (national and international) trade and production networks (Garofoli, 2002). Among the several LPS models, some have been more extensively discussed, such as the Marshallian industrial districts (MIDs), the hub-and-spoke districts, and the growth poles led by large firms. These models intersect with other units of investigation of regional development, such as regional innovation systems (RISs), business clusters, innovative milieux and so on (Asheim, 1996; Becattini, Bellandi and De Propris, 2009; Cooke, Uranga and Etxebarria, 1997; Maillat, 1998; Markusen, 1996). From the end of the century, with a new phase of rampant globalization, the founding features of LPSs have increased their heterogeneity and complexity across sectors and regions (Belussi and Sedita, 2009). Moreover, this tendency has been further accelerated by the mounting wave of digital and green transformations. This chapter paves the way for a comprehensive assessment of LPS models starting from a systematic literature review. We proceed following an argumentation in three steps: 1. First, we expand a conceptual framework that identifies the main forms of LPS according to the features of their structural dimensions: the industrial organization; the socio-cultural and territorial structure; and the institutional and governance support. 2. Second, we collect a set of types of firms in relationships with LPSs and associate various compositions of these types with the classification of LPS models. 3. Third, this association is applied to an exemplificative set of cases of different types of small to medium-sized firms (SMEs) associated with various forms of LPSs. The common theme of the different cases is the way in which such SMEs are trying to absorb Industry 4.0 technologies. The conclusions discuss some viewpoints on the change of LPS models, their resilience and robustness, and their relative role for the creation and distribution of value within and across regions and sectors. This is, of course, also strictly related to policies of productive development and transformation that have an impact on creating different scenarios also depending on a wide range of agency mechanisms (Bellandi, Plechero and Santini, 2021; Hassink, Isaksen and Trippl, 2019; Sotarauta and Beer, 2017). 165

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PRODUCTIVE SYSTEMS: A MULTIDIMENSIONAL CONCEPT In many industrialized regions, factors of competitiveness, economic growth and development are also related to local systems of industrial organization. In the so-called post-Fordist age, where the models of flexible production emerged as an alternative to the dominant mass production methods (Salais and Storper, 1992), territories and communities all over the world showed patterns of agglomeration and specialization following such an alternative in a variety of industries, including not only advanced sectors but also more traditional, labour-intensive ones. Various European territories, such as Watch Valley in Switzerland, Baden-Württemberg in Germany, Småland in Sweden, and areas of Central and North-East Italy, were characterized by constellations of independent SMEs specialized in specific phases of complex manufacturing sectors (see e.g., Garofoli, 2002; Maillat, 1998; Pyke, Becattini and Sengenberger, 1990). These manufacturing sectors largely dominated the local economic structure, so much so that in many cases such territories were recognized worldwide by the name of their main industry. The stories of older districts dominating the first industrial revolution in the United Kingdom re-emerged as well, such as, for example, ‘Steel City’ in the case of Sheffield, ‘Cottonopolis’ for Manchester, and ‘The Potteries’ for the area of Stoke-on-Trent (Popp and Wilson, 2009). Some of them even had a new lease of life (Tomlinson and Branston, 2014). The populations of specialized SMEs localized in many European territories symbolized a slow and cumulative process of division of labour and processes of adjustment at a local level. This self-reinforcing and evolving model of production and competition therefore shows a systemic dimension that simultaneously affects the actions of SMEs, their performances, and the local organization of production. This idea has been made clear and formalized in various LPS models such as, first, the MID that Giacomo Becattini proposed to international attention in the 1980s (see Becattini, 2004). The place-based and systemic features of LPSs are also shown, however, by cases that cannot be easily read as simple variations of MIDs. The Three Dimensions of Place Models of Development, the MID, and the Cluster Indeed, various LPS models have been identified by the literature in the 1990s and in the following decades. They may be understood by looking at the same three structural dimensions of place-based development that the MID helped to make explicit. We consider first the canonical and general content of these dimensions in MIDs, following and referring to Bellandi et al. (2021) for further references: 1. Industrial organization with its market and cognitive sides. An expanding population of specialized SMEs interact in the local value chain with independent but interrelated strategies of value creation and redistribution. Here, a diffused entrepreneurship couples with systemic opportunities of both Marshallian external economies and business specialization at the micro level and maintains a low degree of business concentration. From the cognitive side, industrial districts usually host a ‘multiplicity’ of productive know-how nuclei that refer to both the main manufacturing value chain characterizing the LPS and complementary and substitutive specializations. 2. Socio-cultural relationships and territorial structure. Three main trades (i.e., entrepreneurs/capitalists, skilled artisans, and skilled workers) represent a local well-integrated social capital and a sense of local/community belonging. Dynamic entrepreneurs/capitalists

Evolutions in industrial districts and local productive systems  167 invest in absorptive capacity, high quality standards and versatile network organizations. Artisans are ready to co-invest in technologies and human capital within business networks led by dynamic entrepreneurs. The skilled workers, who have possibly evolved from unskilled positions, aspire either to be engaged in co-management positions or to spinout their own enterprise. This socio-cultural structure allows a diffused trust within evolving games of competition and collaboration, therefore decreasing transaction costs and fostering the realization of potential Marshallian external economies (Dei Ottati, 2018). These relationships are mirrored by the territorial structure of the place, with a non-segregated location of dwellings, and a distributed infrastructure of mobility and social services supporting the mixture of work and non-work life experiences (Brusco, 1982). 3. Institutional and governance support. A set of formal and informal institutions such as norms, regulations, beliefs and cultural habits defines the ‘rules of the game’ in the place (Brusco, 1999). A set of public and collective organizations favour the identification and provision of specific collective and public goods that reduce opportunistic behaviours within a decentralized industrial organization and support an efficient provision of the services for high-indivisibility technical structures (Bellandi, 2011; Brusco, 1992; Crouch et al., 2004). Problems of institutional lock-in would emerge if institutions were not able to avoid the dominance of myopic behaviours that discourage the network adaptations required of local agents by the changes in the cognitive structure (Bellandi et al., 2021; Forrer et al., 2022; Hassink et al., 2019). From a structural point of view, such a framework of general features is compatible with many different real-world configurations, spanning classical manufacturing districts; types with a high presence of craft and smaller firms; types where leading small firms activate broad delocalization strategies; types led by medium-sized firms with local roots and trans-local investments also acting as knowledge integrators; cases with the (non-dominant) presence of some entities of large firms; and types where the main manufacturing specialization is coupled with an important presence of other industries, even in resource or service sectors (see Boix, Sforzi and Hernandez, 2015; Buciuni and Pisano, 2018; Canello and Pavone, 2016; Coltorti, 2013; Rabellotti, Carabelli and Hirsch, 2009; Sammarra and Belussi, 2006). From a dynamic point of view, the same features may combine in virtuous auto-reproductive circles, expressing quasi-steady paths of local development (Bellandi, De Propris and Santini, 2019), by which the system changes while preserving its deep place-based identity (Becattini, 2004). Before moving further in the exploration of the alternative models that explicitly consider variants in all three dimensions, it is important to recall the different models proposed from the 1990s, focusing on one or other of the dimensions touched upon. Specifically, the business cluster (Porter, 1998) and the local ‘production’ system (Torre, 2019) models mainly look at the industrial organization of the LPS in dimension 1; the local system as a local labour market (Boix et al., 2015; Canello and Pavone, 2016) and the innovative milieux (Camagni, 1995; Crevoisier, 2004; Kebir et al. 2017; Maillat, 1998) focus instead on dimension 2 – the socio-cultural relationships and territorial structure; in dimension 3, it is possible to find the RIS (Asheim, 1996; Cooke et al., 1997) and the triple helix organization of university/organization of research, business, and government relationships for innovation (Cai and Etzkowitz, 2020). At the worldwide level, the most popular of the models recalled above is the cluster, considered by many as a generalized form of industrial district. Indeed, the cluster includes many

168  Handbook of industrial development forms of place-based systemic industrial organizations, but it is not a place, and it is defined in generic terms with regard to the socio-cultural, territorial and institutional dimensions. It is true, however, that the same empirical studies of industrial districts, even when they assume the MID as the guiding interpretative model, often focus on the structure and dynamics of the main localized industry of the district, which is precisely a type of cluster. Furthermore, the studies on clusters may contribute to the analytical understanding of aspects of industrial districts and more in general of LPSs. Other LPS Models Looking at the holistic LPS models expressing substantial differences with respect to the MID in some essential features, at least in principle, we would propose an enlargement of the list proposed by Bellandi and Sforzi (2003) with the help of other older or more recent contributions: ● The ‘new forms of industrial district’ include the research-centric industrial district (Patton and Kenney, 2009), the new social network system (Iammarino and McCann, 2006), and the industrial district ‘Mark 3’ (see Bellandi and De Propris, 2017). They are on the borders of species of MID. However, a different relationship of their business clusters with the place and a different systemic nature of the same clusters are implied by features such as the leading role played here by triple helix relationships for the advancement and first diffusion of radical innovations; the significant dynamics of university spinoff and business spinout becoming trans-local medium- to large-sized firms; the great importance of global networks of knowledge; and the related sustained emergence of local know-how nuclei of new industries. ● The ‘local systems based on natural resources’ include various types of LPS whose main industry develops around the valorisation of a local endowment of natural resources and together with a cultural and landscape heritage – for example, local rural systems, local tourist systems, harbour cities and so on. Different craft, manufacturing and service sectors may spinout from the same endowment, while the vertical division of labour is relatively low. When such systems are not dominated by large firms (see below), they express strong cooperative attitudes that support the preservation of the local endowment but may undermine possibilities of radical innovation and change (Lazzeretti, 2007; Santagata, 2002; Tregear et al., 2007). ● The ‘industrial regions’ include the great dynamic city (Jacobs, 1969), the system area (Garofoli, 2002), and the Marshallian industrial regions (Becattini, 2000; Bellandi, 2011). They correspond to regions larger or more urbanized than the usual empirical proxies of the MID, include more than one business cluster, and preserve a common field or nexus of socio-cultural and institutional-political relationships, supporting proximity and complementarities among the different clusters. Sometimes, a polycentric organization of a large industrial region is consistent with the inclusion of a set of smaller LPSs that, although interlinked, have their own territorial identity. Services, and specifically knowledge-intensive services (KISs), play an important and nowadays growing role in these LPSs, sometimes expressing specialized clusters. They also tend to correspond to the territorial support of innovative milieux and thick, diversified RISs (Crevoisier, 2004; Isaksen and Trippl, 2016).

Evolutions in industrial districts and local productive systems  169 ● The ‘agglomerations of dependent SMEs’ include various forms of LPS where local firms do not form strong networks and depend largely on actors and factors external to them, such as the strategies of external multinational enterprises (MNEs) or governmental agencies and local urban markets (Garofoli, 2002; Iammarino and McCann, 2006; Isaksen and Trippl, 2017). Here, we are at the border of the same meaning of an LPS because the systemic features are feeble and depend on a common experience of relation with the external actors and factors. However, in an evolutionary perspective, these weak relationships can be the basis for the emergence of more autonomous and specialized capabilities, and sometimes of new clusters and districts (Biggeri and Ferrannini, 2014). ● The ‘industrial poles’ include the Perrouxian growth pole (Caldari, 2018), the local industrial complex (Iammarino and McCann, 2006), and the hub-and-spoke district (Markusen, 1996). All are characterized by large firms playing a leading role. These strategically anchored actors of the LPS not only shape the structure and dynamics of the main business cluster of the place, but also give a corporate orientation to the local socio-cultural and institutional-political life of the place (De Propris and Crevoisier, 2011). In the last decades, with the growth of MNEs, foreign direct investments, and global (or just international) value chains (GVCs), the presence of anchored entities of large firms has also increased in canonical districts (Belussi, 2018; Hervás-Oliver and Albors-Garrigos, 2014). However, in classical districts, such entities do not play a leading role even if they contribute to fruitful intersections of local and global networks of trade and knowledge. The Role of LPSs, the Balance of Place-based and Place-blind Forces of Development The classification of LPS models provides a theoretical basis for investigating some important real-world questions, spanning the economy, the geography, the history and the policies of industry, trade, innovation and development. Of course, the first question concerns the relative role of local or place-based forces with respect to global or place-blind forces (Barca, McCann and Rodríguez-Pose, 2012). The two types of forces may combine in different equilibria. Nonetheless, a leading role played at a certain time and in a certain country by MIDs, new forms of industrial districts, natural-based LPSs, and industrial regions will point to a significant strength of local forces, in a world that has neither become a global village nor a flat homogeneous space (Pisano and Shih, 2009). Vice versa, the prevalence of global forces and footless business strategies would be assessed by the dominance of industrial poles, agglomerations of dependent SMEs, and sets of productive activities variously localized without the support of significant local systemic factors, such as, for example, in the case of satellite industrial platforms (Markusen, 1996). Another interesting but more delimited question concerns the interpretative strength of the MID model within the set of alternative LPS models. In a sense, the MID model is the more direct expression of local forces. Therefore, waves of industrial development characterized by a clear role of districts proxying MID forms suggest contexts of relative strength of such forces (Bellandi and De Propris, 2017). On the other side, it may happen that in some contexts and at certain times, other LPS models represent the stronger place-based engines of development (Iammarino and McCann, 2006; Markusen, 1996). This is one reason for debates on the decline of the traditional forms of industrial districts, as both an ideal-typical model (MID) and an empirically significant phenomenon, which have emerged periodically in the last decades (Brusco, 1992; Dei Ottati, 2018). It should be distinguished from another reason,

170  Handbook of industrial development the more-or-less explicit scorn for place-based collaborative models of development, which is sometimes based on an actual surge of dominance of global forces, and sometimes just on free market or state dirigiste-oriented preconcepts (Becattini, 2004). Finally, the classification of LPS models represents a conceptual field for reasoning on local/regional paths of development, transformation and rerouting, and related policies. These themes intercept various strands of literature developed in the last decades as well, such as new economic geography, socio-technical transitions and the multilevel approach, cluster life cycles, and the agent-based models of change, including place leadership (PL) models (Hassink et al., 2019). It is possible that phases of crises will have as an outcome either the decline of the local forces of the place, or a new lease of life for traditional models, or alternatively a path transformation or rerouting towards different LPS models (Bellandi et al., 2019). We will not present here a discussion of such studies. However, we can hint at the meaning of their intersection with the LPS classification, considering, for example, the case of LPSs that have successfully followed a path of development according to the MID model, and which come up against a period of disruptive challenges. The rerouting potential relies on a combinative transformation of all three structural dimensions referred to above. The industrial organizational structure may experience opportunities for internal economies of scale and vertical integration, but also for the emergence of new industries based on the fertilization of the traditional related variety of nuclei of know-how within the main local cluster and around it. In the last case, the district could reroute to a new path of MID development. In the first case, the district moves from a canonical MID to some hybrid form, or possibly to a model led by a few large firms. These trajectories are not new in economic history (Cooke, 2009). The transformation in the industrial organization dimension is either triggered or even triggers change in the social-cultural formation of the district and the related territorial structure. For example, coupling with a surging role of larger firms, the social groups of the district may recompose towards more corporatist configurations and the territorial structure could become more fragmented or segregated (Sabel and Zeitlin, 1997; Torre, 2019). In addition, the set of district institutions may be subjugated to dominant privately driven forces controlling key resources, or fragment when new disruptive challenges make the validity of the past consolidated rules obsolete. Here, in particular, the role of PL becomes crucial to drive path transformation and its different possible outcomes (Sotarauta and Beer, 2017). The rerouting of the district to a new MID path or to some new forms of LPS, exposing strong local forces, is supported when the traditional social and business constituencies of the district are able to share a vision of the future, also thanks to an RIS that strengthens the relation with providers of KISs (Buciuni and Pisano, 2018; Cooke, 2008; Lafuente, Vaillant and Vendrell-Herrero, 2017). This would support investments in public goods specific to research-based innovation, as well as to social welfare, education, environmental safety, labour protection and a new territorial mix. However, PL might be unable to guarantee effective collective solutions, and the negotiating power of some local players might lead to oligopolistic elites consolidating a dominant role in the system (Bellandi et al., 2021). Policies Aimed at the Productive and Territorial Development of LPSs The remarks concluding the previous subsection suggest the general features and contents of policies of development in and/or for LPSs. The literature investigates them in order to suggest possible actions for policymakers and practitioners. Expanding further (Beer et al.,

Evolutions in industrial districts and local productive systems  171 2020; Bellandi et al., 2021; Bianchi, Durán and Labory, 2019; Cooke, 2009; Gebhardt, 2019; Grundel and Dahlström, 2016; Hassink et al., 2019; Sabel, 2002; Solvell, 2015; Torre, 2019), such policies reflect the following key points: ● They are system-based policies, their direct objects of promotion or support being sets of material and immaterial goods with public-like qualities. Agents of the LPS should be able to easily take advantage of such public-like qualities, that expose a certain degree of specific complementarity to the needs of the LPS or parts of it. ● Some of these types of goods point to business needs, others to socio-cultural and institutional needs, others to the interrelations between the three dimensions in the territory of the LPS. Some have a broad span or a high indivisibility, being infrastructures that include support to more than one LPS, implying multi-scalar coordination problems. ● The methodology of promotion, provision or support by policies cannot be reduced to pure bureaucratic command and control, given the need to mobilize local dynamic capabilities. Multi-scalar governance schemes and public–private partnership, pointing to triple, quadruple, and even quintuple helix models, need to be developed and implemented. ● It is experiential governance, complemented by structured-learning territorial laboratories, given the complexity implied by its system-based, multi-scalar, multi-actor nature. Learning includes the accumulation and application of local development and cluster initiative toolboxes within and between national and international practitioner networks. ● The governance schemes and partnerships should confront conflicts of interests, cognitive and ownership asymmetries, and power struggles, but they are usually also part of them. Political leaders specifically, and PL in general, therefore move within conditions of autonomy or capture, strength or weakness, visionary ambitions or practicalities. When autonomy, strength and vision have a larger space, the possibility of leaders influencing evolutionary forces increases, and vice versa. The significant presence or potentiality of different LPS models in an historical context and the possibility for LPSs in such context to reroute from one model to another provide some qualifications of policies of development in and/or for LPSs. For example, let us return to the case of industrial districts that, after a period of development according to the MID model, meet some disruptive challenges. The fact that other place models of development are possible enlarges the windows of opportunity for a district to employ in a successful if adapted way a large number of the local factors accumulated in the previous phase. This decreases the risks of dramatic losses and conflicts that the leaders should try to manage. On the other side, the plurality of potential constructive outcomes tends to reduce the urgency of an entrepreneurial drive against the status quo, inertial attitudes being strengthened by the difficulty of choosing (the paradox of Buridan’s ass). We will suggest other qualifications of policies in the concluding section.

LPS MODELS, INDUSTRIAL ORGANIZATION, AND HETEROGENEOUS FIRMS’ POPULATIONS This section proposes to go more deeply into the first structural dimension – that is, the industrial organization – in the different LPS models, pivoting on their heterogeneous composition of different types of firms’ populations. The starting point is a classification presented in

172  Handbook of industrial development Table 10.1

LPS models and types of industrial organization

LPS Models

Featuring Types of Firms

Secondary Types

MID

a

b, c, d, e, g

New forms of industrial districts

b

a, g, e

Resource based local systems

c

d, h, g

Industrial regions

b, d, g

a, c, e, h

Agglomerations of dependent SMEs

e, f

h

Industrial poles

g, h

b, e, d

Source:

Authors’ elaboration based on Bellandi and Sforzi (2003).

Bellandi and Sforzi (2003), to which we refer for the original table of associations. We obtain by this a clearer glimpse of differences and similarities between the different LPS models, as well as the basis for an empirical exemplification of contemporary LPS paths in the face of the digital transformation challenges. The types of firms’ populations that may be associated with LPSs are defined according to a set of variables: embedding, anchoring, dependent or footless relation with the main local clusters; constructive relationships or lack of access to the social capital and the community of people; the size of the firms; their strategic autonomy or dependency; and the relation with traditional or emerging specializations. A possible list includes: a. district firms, small or medium sized, with a place-based trade or manufacturing specialization related to the main LPS clusters, possibly extending their business networks in other places; b. small, new, high-tech or KIS firms, anchored in the LPS and its main clusters, with a high growth potential, either pointing to type g or to variations related to new business models; c. firms specialized in the valorisation of local natural resources; d. craft laboratories or creative firms intensive in historical and/or cultural content; e. local firms related to the main LPS clusters and dependent on the demand outsourced by firms of types a, b, g or h; f. firms dependent on the local demand for services and goods for the families; g. large firms with anchored entities localized in the LPS and related to its main clusters; and h. large firms with footless entities localized in the LPS, more or less related to its main clusters. Specific compositions of the different types feature different LPS models. Consider Table 10.1, where the LPS models in the second section above are related to the structural presence of different types of firms. Following Bellandi and Sforzi (2003), we distinguish between the types at the core of an LPS model, and the secondary types that, when present in an LPS, and complementary to the main type, contribute to the LPS’s internal business heterogeneity and possibly help define sub-classes or variations of the same model. Being one of the dimensions of an LPS’s multiplicity of know-how nuclei, such business heterogeneity is also a possible lever for path transformation and rerouting (Bellandi et al., 2019). A large number of the associations proposed in Table 10.1 descend quite directly as implications of the above definitions of LPS models and types of firms. Nonetheless, to exemplify with the MID, we would recall that this model is featured precisely by a main cluster of firms with complementary specialization and capitals embedded in the socio-cultural and

Evolutions in industrial districts and local productive systems  173 political-institutional features of the place. The variations include the secondary but not insignificant presence of other types of embedded or anchored firms.

CASES OF TRANSFORMATION OF CONTEMPORARY INDUSTRIAL DISTRICTS Case Study Selection Now we present the result of a systemic literature review aimed at identifying cases of SMEs variously investing in the introduction of Industry 4.0 technologies1 and localized in LPSs of different forms but possibly expressing place-based forces. The context is defined by the opportunities and risks for SMEs and LPSs posed by the digital transformation and the need for industrial policies suitable for the digital age (Bianchi et al., 2019; De Propris and Bailey, 2020). The analysis covers publications from 2010 to 2021. The review follows a ‘theoretical and targeted sampling approach’, as illustrated, for example, by Najmaei (2016). Our selection of papers is based on the presence of keywords related to four features that suggest the inclusion of case studies relevant to the ‘theoretical’ focus expressed just above, at the beginning of the section: ● firms specialized in activities within or related to manufacturing; ● embeddedness or anchoring of the firms in types of LPS representative of place-based forces; ● strategic business investments in Industry 4.0 technologies; and ● relationship between these strategic investments and opportunities of LPS rerouting. Papers are identified through the analysis of a set of academic journals, volumes and databases. Table 10A.1 in the Appendix summarizes the sources. A first screening identified 1044 documents as potentially relevant to our research, showing keywords related to one or more of the above four features. A second screening, looking for cases related to all the four main features at the same time, discarded almost all those documents. This boiled down to a selection of seven papers that provided a set of case studies representative of the requirements above. Clearly, there would have been room and necessity for fresh empirical investigations, but we must leave that to future research. Table 10.2 at the end of the next sub-section summarizes the case studies selected for the in-depth analysis. Results and Discussion Let us start with three studies pointing to cases of independent specialized SMEs of the district type a, operating within industrial districts proxying the MID model. Those SMEs try to take advantage of the opportunities created by the technological transformation. Specifically, in the case of the shoe district of Riviera del Brenta, Veneto, Italy, the population of specialized SMEs enlarge their value-creation process by involving customers in the design of products and services. Here, digital platforms support a fruitful combination of craft skills, together with processes of co-innovation and enlargement of markets, opening up the local network to more global networks (Bettiol et al., 2020). Other cases, such as the mechanics districts of Emilia-Romagna in Italy (Freddi and Rizzo, 2016), and the toy district of Ibi, Valencia, Spain

174  Handbook of industrial development (Hervás-Oliver, 2021), show that the absorption of new technologies takes place thanks to collective actions combining traditional know-how and research-based innovation, the population of specialized SMEs preserving independency and strong socio-cultural relationships. For example, in the case of Emilia-Romagna, the emergence of local mechatronics specializations relies on a strong collective initiative driven by policymakers, the local universities, the local business association, and some high-profile local companies. The authors highlight the co-evolution of the industrial organization and the higher-education system of the area, referring to the degree in engineering and mechatronics offered at the University of Modena and Reggio Emilia (Freddi and Rizzo, 2016). All these cases describe the possibility for district firms and MID forms to absorb Industry 4.0 technologies with renewed managerial and network solutions pointing to some features of type b firms and new forms of districts. Despite the openness of such SMEs to global and digital pipelines, they seem able not only to strengthen their independency but also preserve their relationships with the local socio-cultural and territorial structure. However, the border between embeddedness and anchoring becomes fuzzier. Another study addresses instead the important role played by big global players in the digital transition of the LPS. Fontefrancesco (2018), exploring the jewellery district of Valenza Po, Piedmont, Italy, underlines that a new path of development was opened in 2016 when Bulgari, an anchored large jewellery firm – type g– opened a new large and technologically advanced plant for production in the district area. The industrial organization took on a new configuration, possibly pointing to the transition to a hub-and-spoke form of district. Indeed, despite the quite radical change in the industrial sphere, the socio-cultural relationships were not weakened. On the contrary, the paper stresses the thickness of the local networks, even if many SMEs tend to translate from the district type a to the satellite type e. An interesting adjustment has been recorded in the institutional and governance support to SMEs, probably to help the upgrading of competences that both reproduce the anchoring advantages for the large firm and preserve some sphere of autonomy for the SMEs. Specifically, policymakers implemented a set of initiatives to improve the competencies specialized in the integration of digital technologies. An example is the Fondazioni Mani Intelligenti,2 established in 2017 to support and promote new educational paths aiming at training goldsmiths. Three studies present cases of firms anchored in business clusters of industrial regions. The cases described by Orazi (2016) show agglomerations of micro and small fablabs mainly localized in metropolitan areas, such as Rome and Milan, which can be assimilated here with models of industrial regions. The digital craft laboratories support the dynamicity of the city, and new entrepreneurship is fostered by a particular societal structure. Additive manufacturing is at the core of the value-creation process of the local craftspeople, who can combine traditional competencies with knowledge related to 3D printing. Such diffused competences support strong cooperative dynamics at a local and extra-local level and allow innovation as a collective output. The thick but not strictly localized networks rely mainly on informal institutions. The cases described by Rylands et al. (2016) refer to Northwest England, which has been a traditional Marshallian industrial region with a polycentric structure represented by cities (such as Liverpool and Manchester) and types of industrial districts. It hosts specializations in biotechnology, pharmaceuticals and chemicals, aerospace and advanced manufacturing (ibid., p. 975). Despite many papers underlining phenomena related to the replacement of traditional methods by additive manufacturing (AM), the two companies in this study show how local and

Evolutions in industrial districts and local productive systems  175 Table 10.2 Cases

Selected case studies Original LPS Model

Traditional

Industry 4.0

Types of Firms

Technologies

Rerouting

a

Cloud and advanced

From MID to new

manufacturing

form of industry

Types of Firms Featuring Rerouting

Bettiol et al. (2020)

Shoe district of Riviera del Brenta,

district

Veneto, Italy Hervás-Oliver (2021) Toy district of Ibi,

b, a

a

All types

Valencia, Spain

From MID to new

b, a

form of industry district

Freddi and Rizzo

Mechanics districts

(2016)

of Emilia-Romagna,

a

Advanced

From MID to new

manufacturing

form of industry district

Italy Fontefrancesco

Jewellery district

(2018)

of Valenza Po,

a

Additive

From MID to

manufacturing

hub-and-spoke

Rome and Milan,

b, d, g, h

Italy Rylands et al. (2016) Northwest England,

g, a, e

industry district

Piedmont, Italy Orazi (2016)

b, a

b, a, g

UK

Additive and advanced Renewal in manufacturing

metropolitan areas

Additive

Renewal in

manufacturing

Marshallian industrial

b b

regions Lazzarotti and

Brianza, Lombardy,

Visconti (2018)

Italy

Source:

a, b, g

All types

Renewal in system

New b, h

areas

Authors’ original elaboration.

traditional SMEs ‘were able to increase their capabilities and presence in the market, whilst retaining their traditional methods’ (ibid., p. 982) by developing AM value streams next to the traditional methods of production. This strategy rests on the establishment of new local and non-local relationships at the core of the innovation process. Specifically, the manufacturing networks tend to integrate knowledge suppliers, such as AM knowledge centres, fabrication laboratories, and the university within the region, while the co-creation networks with customers create larger territorial scale. The specialized manufacturing SMEs maintain their independency and size, and the technological capabilities take advantage of the university’s assets, in terms of knowledge and machineries related to Industry 4.0 technologies. Finally, a case described by Lazzarotti and Visconti (2018) considers, among others, a manufacturing SME adapting its strategy under the push of a young second-generation entrepreneur. The firm is localized within an old system area – Brianza – which hosted clusters of district-type SMEs, but the firm’s territorial relationships refer more properly to a larger industrial region centred again on Milan (Lombardy). This case contemplates a broad absorption of Industry 4.0 technologies, thanks to significant R&D relationships with a local university and the entry of venture capital in the finance and governance structure of the firm. Though not a large firm, the managerial structure is quite specialized to support an articulated architecture of innovation teams within the firm and across its knowledge and trade networks. The co-innovation networks, supported by digital technologies, span nationally and internationally. This case exemplifies local SMEs incorporating high-tech capabilities and possibly preserving access to the complex territorial resources of an industrial region, while extending their activity and value-creation process to large networks outside the place. Therefore, these

176  Handbook of industrial development SMEs may be referred to as innovative type b firms. Recalling firm type a turning to type b in industrial districts discussed above, we see in the last case the forking trajectories that this type of firm exemplifies. On one side, a trajectory is from embeddedness, to anchoring, to footless location and high value creation without local development feedback; on the other side, an alternative trajectory points to an anchored growth of such firms, which could help the rerouting to new high-development forms of industrial districts, possibly within industrial regions.

CONCLUSIONS Focussing on one or a few LPS models, many studies have underlined the alternative scenarios between rerouting and decline confronting LPS hit by disruptive technological waves and globalization forces (Dei Ottati, 2018). The empirical section above has specifically surveyed studies on cases facing the challenges of Industry 4.0. LPSs confronting disruptive challenges show that local resilience can indeed rely on different technological, organizational and societal dynamics, therefore on a different depth of rerouting (Chaminade et al., 2019; Isaksen and Trippl, 2016). Some studies have explored the key factors moving an LPS from one model to another. Indeed, rerouting may imply a proper path transformation, where the models of value creation and value capture would absorb the response to the fundamental opportunities and threats related to societal challenges (Bailey, Pitelis and Tomlinson, 2018), such as digital transformation, and allow the growth of globally competitive, sustainable, and place-based new paths of development (Cooke, 2008). A more organic type of change sees rerouting corresponding to just path renewal, where the LPS tries to absorb an appropriate range of new business, social and territorial solutions – for example, related to digital technologies, with renewed business and productive solutions that preserve large parts of the traditional model of development, starting from the local productive specializations (Bettiol et al., 2020). At an intermediate level, somewhere between path renewal and path transformation, rerouting may absorb system innovation and new specializations spinning out of the traditional ones while the traditional models of value creation and capture are largely preserved (Belussi and Sedita, 2009). Policies of development of/for LPSs should be defined and applied in a specific way to each LPS depending on its internal heterogeneity and its prospects of rerouting. Considering specifically the different scenarios of LPS rerouting in the face of the challenges of digital transformation, we would recall, in conclusion, a couple of qualifications that add to the list of LPS policy features presented before. First, even when it is just path renewal, the adaptation of the business models in populations of SMEs needs the engagement of sets of technology mediators according to well-tuned complementary functions that entice multi-scalar and place-based dynamic capabilities (Labory and Bianchi, 2021; Larrea and Estensoro, 2021). Second, a deeper reaction implies more radical changes in business models, specifically from a primary positioning within specialized productive value chains to one in platform-based market ecosystems (Jeannerat and Theurillat, 2021). This is the core of what we have called here, with a variation on De Propris and Bailey (2020), an innovative Industry 4.0 model of SMEs, a new type b firm that possibly either features renewing MID forms, or propels new forms of industrial districts, or complements other

Evolutions in industrial districts and local productive systems  177 LPS models as well. However, without appropriate place-based multi-scalar governance, possibly driven by autonomous, strong and visionary leaderships (Bellandi et al., 2021), aiming at anchoring such new models of SMEs to local forces of development, it seems probable that either the value they generate will be largely lost by the LPSs where they localize, eventually in favour of capture by global digital monopolies (Bailey et al., 2018), or their creation will be limited to some market niches and the developmental potential will remain under-expressed.

NOTES 1.

2.

In this regard, we use the classification developed by the Italian Ministry of Economic Development in 2017 to identify the Industry 4.0 technologies: advanced manufacturing, additive manufacturing, virtual and augmented reality, simulation, horizontal and vertical integration, Internet of Things, cloud, cybersecurity, big data and analytics. See https://​www​.mise​.gov​.it/​images/​stories/​documenti/​ Piano​_Industria​_40​.pdf. Accessed 17 August 2022. ‘Intelligent Hands’; see http://​www​.maniintelligenti​.it/​it/​. Accessed 17 August 2022.

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Evolutions in industrial districts and local productive systems  179 Freddi, D. and Rizzo, U. (2016). Cambiamento tecnologico, upgrading e istituzioni di alta formazione: modalità e canali di coevoluzione in un distretto tradizionale. L’Industria: Rivista di economia e politica industriale, 37(1), 101–18. Garofoli, G. (2002). Local development in Europe: theoretical models and international comparisons. European Urban and Regional Studies, 9(3), 225–39. Gebhardt, C. (2019). The impact of participatory governance on regional development pathways: citizen-driven smart, green and inclusive urbanism in the Brainport Metropolitan Region. Triple Helix, 6, 69–110. Grundel, I. and Dahlström, M. (2016). A quadruple and quintuple helix approach to regional innovation systems in the transformation to a forestry-based bioeconomy. Journal of Knowledge Economy, 7, 963–83. Hassink, R., Isaksen, A. and Trippl, M. (2019). Towards a comprehensive understanding of new regional industrial path development. Regional Studies, 53(11), 1636–45. Hervás-Oliver, J.L. (2021). Industry 4.0 in industrial districts: regional innovation policy for the Toy Valley district in Spain. Regional Studies, 55(10–11), 1775–86. Hervás-Oliver, J.L. and Albors-Garrigos, J. (2014). Are technology gatekeepers renewing clusters? Understanding gatekeepers and their dynamics across cluster life cycles. Entrepreneurship & Regional Development, 26(5–6), 431–52. Iammarino, S. and McCann, P. (2006). The structure and evolution of industrial clusters: transactions, technology and knowledge spillovers. Research Policy, 35(7), 1018–36. Isaksen, A. and Trippl, M. (2016). Path development in different regional innovation systems: a conceptual analysis. In M.D. Parrilli, R.D. Fitjar and A. Rodríguez-Pose (eds), Innovation Drivers and Regional Innovation Strategies. London and New York: Routledge, pp. 82–100. Isaksen, A. and Trippl, M. (2017). Exogenously led and policy-supported new path development in peripheral regions: analytical and synthetic routes. Economic Geography, 93(5), 436–57. Jacobs, J. (1969). The Economy of Cities. New York: Vintage Books. Jeannerat, H. and Theurillat, T. (2021). Old industrial spaces challenged by platformized value-capture 4.0. Regional Studies, 55(10–11), 1738–50. Kebir, L., Crevoisier, O., Costa, P. and Peyrache-Gadeau, V. (eds) (2017). Sustainable Innovation and Regional Development: Rethinking Innovative Milieus. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Labory, S. and Bianchi, P. (2021). Regional industrial policy in times of big disruption: building dynamic capabilities in regions. Regional Studies, 55(10–11), 1829–38. Lafuente, E., Vaillant, Y. and Vendrell-Herrero, F. (2017). Territorial servitization: exploring the virtuous circle connecting knowledge-intensive services and new manufacturing businesses. International Journal of Production Economics, 192, 19–28. Larrea, M. and Estensoro, M. (2021). Governance of Industry 4.0 policies: making knowledge services accessible for SMEs. Regional Studies, 55(10–11), 1839–50. Lazzeretti, L. (2007). The cultural districtualization model. In P. Cooke and L. Lazzeretti (eds), Creative Cities, Cultural Clusters and Local Development. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 93–120. Lazzarotti, V. and Visconti, F. (2018). L’innovazione nelle imprese familiari: ce la faranno i giovani imprenditori? Quaderni di ricerca sull’artigianato, 3, 339–72. Maillat, D. (1998). Innovative milieux and new generations of regional policies. Entrepreneurship & Regional Development, 10(1), 1–16. Markusen, A. (1996). Sticky places in slippery space: a typology of industrial districts. Economic Geography, 72(3), 293–313. Najmaei, A. (2016). How do entrepreneurs develop business models in small high-tech ventures? An exploratory model from Australian IT firms. Entrepreneurship Research Journal, 6(3), 297–343. Orazi, F. (2016). Manifattura digitale e sviluppo locale: nuove opportunità per il sapere artigiano. Quaderni di ricerca sull’artigianato, 4(1), 97–116. Patton, D. and Kenney, M. (2009). The university research-centric district in the United States. In G. Becattini, M. Bellandi and L. De Propris (eds), A Handbook of Industrial Districts. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 549–65.

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Evolutions in industrial districts and local productive systems  181

APPENDIX Table 10A.1

Sources and screening methods of the literature review 2010–20 on SMEs and LPSs facing investments in Industry 4.0 technologies

Sources

Screening Method

De Propris and Bailey (2020)

Complete analysis of the book

Journal of Small Business Management

Analysis of articles published between 2010 and 2019

L’industria: Rivista di economia e politica industriale

Analysis of articles published between 2010 and 2019

Quaderni di ricerca sull’artigianato, Rivista di economia,

Analysis of articles published between 2013 and 2019

cultura e ricerca sociale Business Source Premier database

Additive manufacturing, small business, case study

Search for articles published between 2010 and 2019, and some

Advanced manufacturing, small business, case study

follow-ups in 2020, using keywords →

Artificial intelligence, small business, case study Cloud computing, small business, case study Virtual and augmented reality, small business, case study Big data analytics, small business, case study Big data, small business, case study Cybersecurity, small business, case study IoT or Internet of Things, small business, case study Horizontal and vertical integration, small business, case study Horizontal integration, small business, case study Customer relationship management (CRM), small business, case study Enterprise resource planning (ERP), small business, case study Supply chain management (SCM), small business, case study Product lifecycle management (PLM), small business, case study

Source:

Authors’ elaboration based on Cecchetti (2021).

11. External collaboration for innovation: firms, industry, regions and policy Mariachiara Barzotto, Carlo Corradini, Felicia Fai, Sandrine Labory and Philip R. Tomlinson

1 INTRODUCTION Innovation is the cornerstone of industrial development. The innovation process itself is often complex and multifaceted. This complexity is reflected in the increasingly collaborative nature of innovation activities (Van der Wouden, 2020). Over the last three decades, the strategic management, innovation studies and economic geography literature has coalesced around a consensus that a firm’s external ties and relationships are important conduits for innovation. External collaborations allow companies to organize and synthesize knowledge coming from a variety of sectors, locations and cultural settings (Malecki, 2010). When there are complementarities between internal (to the firm/industry/region) and external resources and capabilities, complex innovative outputs may be generated (Hervás-Oliver and Albors-Garrigos, 2009). Extending seminal insights on external linkages in technological partnerships, inter-firm integration and networking (Coombs et al., 1996; Rothwell, 1977; Teece, 1986), the role of external collaboration is inherent to Chesbrough’s (2003, 2007) notion of ‘open innovation’. Within an open innovation framework, a firm’s network is itself an innovation resource because firms can purposively exploit knowledge from it (through formalized partnerships, or intellectual property licensing, purchasing components, or informally through reverse engineering, imitation etc.) as inputs to stimulate and accelerate their own internal innovation activities and subsequent market exploitation. Conversely, a firm’s knowledge outflows may be used by others to do likewise. These networks include a firm’s value chain (its suppliers, main customers, retail outlets and end consumers). Another key channel for firms is represented by collaborations with universities and research institutions (Lee, 1996; Tether, 2002). Occasionally, collaborations may also take place with competitors through a process known as co-opetition, where partners compete and cooperate simultaneously (Brandenburger and Nalebuff, 1996). This chapter reviews the importance of external collaboration for industrial innovation and development. In particular, the chapter connects earlier literature that focuses solely on firms (e.g., Hoang and Antoncic, 2003) or economic geography to provide an overview of external relationships occurring across firm, industry and regional levels. We consider the rationales behind inter-firm collaboration as well as the potential drawbacks of over-embedded relationships. We then examine the case for regional collaboration, both within regional clusters and across regions (extra-regional collaboration). We conclude by highlighting some recent policy initiatives to enhance inter-firm and extra-regional collaboration in the context of the European Union’s (EU) Research and Innovation Strategies for Smart Specialisations (RIS3) framework. 182

External collaboration for innovation  183

2

FIRMS, INDUSTRY AND SUPPLY CHAINS

2.1

Transaction Cost Perspectives

The rationale for fostering external collaboration for ‘open innovation’ arises from several strands of the literature. For instance, transaction cost economics (TCE) highlights issues relating to opportunism and appropriability that occur when firms undertake investments in specific technologies (whose value in alternative use is low), or where investments are under monopolistic ownership and upon which there is heavy reliance (perhaps along a supply chain). Both give rise to risks of opportunistic behaviour that can undermine innovative endeavour (Williamson, 1985). Thus, in technologically orientated supply chains, the risks of knowledge spillovers and free riding in technological development may lead to dulled incentives to innovate in the first place – in the extreme, firms may be unwilling to invest (in new technologies) due to their inability to appropriate the full return of their own endeavours (Katz, 1986; Klein, Crawford and Alchian, 1978). While establishing a position in complementary assets may allow firms to protect their innovation from imitation (Teece, 1986), strong and close cooperative ties can safeguard against such behaviour by acting as a form of network governance among partner firms (Jessop, 1998; Pisano, 1990). In this regard, there is now a wide literature espousing the virtues of social capital formation, highlighting the role of social norms, trust, reciprocity and informal codes of conduct among networked firms, which reduces the costs associated with inter-firm monitoring. The latter is a costly activity that yields few, if any, additional benefits, but investment in the former, whilst also costly, does yield other benefits such as reputational effects, as reliability and trust might be rewarded through enhancing a partner’s credibility within the network (see Aldrich and Fiol, 1994; Dyer and Singh, 1998; Granovetter, 1992; Jessop, 1998; Parkhe, 1993; Uzzi, 1996). Additionally, the implicit threat of collective sanctions from within the network against any single firm also alleviates opportunism. Indeed, in networks with strongly embedded close collaborative ties, there is likely to be a greater degree of resource pooling and joint actions on technological development, which, in turn, will collectively improve the appropriability of supply chain innovations (Harabi, 1998; Negassi, 2004). Long-term and ongoing contracting, a high volume of exchange, and the frequency of interaction between partner firms, are likely to be critical in building trust, reducing relational stress and establishing sustainable collaborative networks for innovation (Dyer and Singh, 1998; Henke and Zhang, 2010). 2.2

Resource-based View

Beyond governance issues, the resource-based view suggests that firms may pool their differentiated resources and specialized capabilities for genuine mutual benefit (see Langlois and Robertson, 1995; Teece, Pisano and Shuen, 1990). In this regard, Richardson (1972) was an early advocate of firms developing ‘network competencies’ in which they coordinate their own activities and utilize the expertise of partners within their network. Within the network, focal firms can engage in strategic outsourcing and discourse with suppliers and clients, which can enhance innovation (Håkansson, 1987; Von Hippel, 1976, 1988). For example, the success of Japanese manufacturers in penetrating Western markets during the 1970s and 1980s was widely accredited to their long-established keiretsu supplier networks that aided both product and process innovation (see Gerlach, 1992; Sako, 1994; Smitka, 1991). Critical to this success

184  Handbook of industrial development was the repeated interaction and enduring relationships among firms (see Poldolny and Page, 1998), which particularly facilitated high-technology sharing (Kotabe, Martin and Domoto, 2003). More generally, close collaboration within supply networks facilitates knowledge transfer and technical know-how between users and suppliers and expands search capabilities, while feedback loops allow for adjustments and refinement of products and/or processes. It also enables standard setting, and ‘appropriate practice’ while enhancing the adoption rate of new technologies through demonstration effects (see Bessant, Kaplinsky and Lamming, 2003; Tether, 2002). A similar rationale has been put forward for establishing collaborations with universities and public research organizations, in order to access complementary knowledge, creation capabilities and new ideas, especially amongst firms engaged in open innovation and R&D activities (Laursen and Salter, 2004; Lee, 1996). While access to a variety of knowledge sources widens the set of a firm’s innovative opportunities, a critical adjunct is whether a firm has sufficient absorptive capacity to combine and exploit external knowledge resources and align these with its own internal innovative capability (Cohen and Levinthal, 1990; Kogut and Zander, 1992). Higher levels of absorptive capacity enable firms to reduce their search and assimilation costs and more effectively process external knowledge, which, in turn, enhances innovation (see Barge-Gil, 2010; Negassi, 2004). Moran and Ghoshal (1996) note that more innovative firms are able to combine new knowledge sources with novel redeployments of their existing resources to create new products and production processes. The reality is that firms are heterogeneous within and across industries (as well as regions, as we will explore later), and differ in their internal capabilities to process and exploit external knowledge, and this is reflected in their relative innovation performance (Inkpen and Tsang, 2005; Sammarra and Biggiero, 2008). 2.3

SMEs and Collaboration

For small and medium-sized firms (SMEs), engaging in collaborative networks is especially important and can provide them with a competitive edge (De Propris, 2002; Rogers, 2004). For instance, they can allow resource-constrained SMEs access to a wider set of technological opportunities and complementary knowledge through information sharing and resource pooling (Chesbrough, 2003, 2007). While engaging with external sources may generate higher coordination and organizational issues for SMEs (Van de Vrande et al., 2009), this may enable them to obtain the advantages often associated with larger established firms (Nooteboom, 1994). By collaborating with other partners – possibly larger firms – which either own relevant assets or through a co-financing arrangement and sharing those existing assets, SMEs can gain access to a set of intrinsic value generating assets that are beyond their reach in factor markets, thus overcoming their internal resource deficiencies (Ahuja, 2000). By being part of a large contractor network – such as an established supply chain – SMEs might also benefit from securing closer links with regulators and state funded bodies, which in turn, may enhance their innovative endeavour (Fountain, 1998). Recently, entrepreneurship and economic geography literatures have converged on the study of common issues (Autio, Kenney and Mustar, 2014). The concept of entrepreneurial ecosystems has been proposed, supporting the idea that SMEs’ collaboration extends to the local context and different institutions, including universities and public authorities. SMEs inserted in entrepreneurial ecosystems benefit from cultural, social and material attributes that favour entrepreneurship and innovation (Alvedalen and Boschma, 2017; Spigel, 2017). For

External collaboration for innovation  185 this purpose, learning processes are essential and have started to be more explicitly studied (Pugh et al., 2021). 2.4

Empirical Evidence – Firm and Industrial Studies

The interest in ‘open innovation’ has spawned a significant body of empirical research on the cooperation–innovation relationship. This empirical literature has tended to utilize unique survey data, in part due to a lack of secondary data on collaboration, but also to capture different measures of innovation and the nature and extent of cooperative ties. In this regard, some researchers exploit the number of cooperative ties a firm establishes within their network and examine how this affects innovative performance. For example, Shan, Walker and Kogut (1994) find that in US biotech, the higher number of external collaborative ties that a firm establishes is positively correlated with their patent count (a proxy measure for innovation). Similarly, in US chemicals, Ahuja (2000) also finds a positive association between the number of ties a firm establishes (within its network) and patent counts. Ahuja’s (2000) study is novel in distinguishing between direct and indirect ties. Direct ties refer to those exchanges involving a direct exchange of knowledge and resource (e.g., in a supply chain), while indirect ties are more distant, less transparent and knowledge flows are generally tacit (e.g., in a cluster environment or extended network). The study reveals that direct ties have a greater positive impact on innovation. Quintana-García and Benavides-Velasco (2004) examine a five-year panel of European SME biotech firms to explore the impact of co-opetition on innovation, looking at inter-firm collaboration and the number of new product lines and a measure of technological diversity as innovation indicators. Their findings suggest that co-opetition is important, with cooperation between direct competitors being positively and significantly correlated with a firm’s innovative capacity. Indeed, the authors also find downstream alliances with firms who are also engaged in co-opetitive strategies are also more likely to improve a firm’s innovative capacity. This is indicative of wider knowledge diffusion effects where networks are relatively open (see also Lado, Boyd and Hanlon, 1997). Studies of supply chain relationships are especially prominent in the ‘open innovation’ literature. For instance, Squire et al. (2009) show that, in downstream buyer–supplier relationships, cooperation was positively correlated with levels of knowledge transfer between firms. Tsai et al. (2013) find that strong social capital in buyer– supplier relationships has an indirect and positive impact upon innovation performance, via the mediation of commitment to innovation and customer knowledge development. Kühne, Gellynck and Weaver (2013) focus on the quality of supply chain relationships in the food industry; they find that trust and social satisfaction enhanced the quality of relationships along the value chain and this, in turn, led to greater innovative activities. In their seminal empirical contribution, Laursen and Salter (2006) show that both the breadth of different forms of collaboration as well as the strength of the connections play a positive role on product innovation. Tomlinson (2010) and Tomlinson and Fai (2013) offer further findings using a wider set of measures of inter-firm cooperation that are (1) multi-scalar to capture the strength (or depth) of inter-firm collaboration; and (2) multi-dimensional to capture the breadth of inter-firm collaboration over a range of different activities (e.g., product development, labour training, marketing). Utilizing this format, their research across five UK manufacturing sectors indicates a significant positive association between the degree of cooperation and both product and process innovation over a range of supply chain activities.1 This

186  Handbook of industrial development would suggest that strongly embedded ties are important – though, only up to a point, as the cooperation–innovation relationship may be curvilinear (see Section 2.5). 2.5

Diminishing and Negative Returns

While the extant literature has largely expounded the benefits of external collaboration for innovation, this ought to be qualified by the realization that positive returns are not infinite. Indeed, some studies have begun to acknowledge that in over-embedded cooperative relationships, further cooperation may not necessarily be matched by increases in innovative performance. The relationship may exhibit diminishing and even negative returns (Granovetter, 1973) or in technical terms, the cooperation–innovation relationship is curvilinear (or an inverted ‘U’ shape). In short, there is likely to exist a ‘dark side’ to embeddedness in collaborative relationships (Hagedoorn and Frankort, 2008; Villena, Rivella and Choi, 2011). The message here is that in long embedded relationships there is less to share, which is novel (Cowan, Jonard and Zimmermann, 2006). In this regard, and at the organizational level, Edelman et al. (2004) show that strong ties binding sub-organizational-level groups were significant barriers to new knowledge and idea generation. Similarly, in an ethnographic study of a network of New York apparel firms, Uzzi (1997) finds several negative traits of over-embeddedness: over-socialization, ‘feelings of obligation’ and managers exhibiting ‘negative emotions of spite and revenge’ (ibid., p. 59), which in turn reduced performance. In their study of Valencian industrial districts, Molina-Morales and Martínez-Fernández (2006) and Molina-Morales, Martínez-Fernández and Torlo (2011) demonstrate that ever higher levels of trust among networked firms had a negative impact upon innovative performance. The authors suggest that a high reliance on the district firms can lead to complacency, and this, in turn, inhibits opportunities for new talent to emerge and/or participate in district activities, thus reducing experimentation and innovation. Indeed, while familiarity and experience can be a recipe for firms to collaborate over innovation, it can simultaneously reduce previously complementary knowledge to ‘duplicative knowledge’ and dampen future innovative activity (Simard and West, 2006). For Christensen and Bower (1996), an over-focus on collaborating with existing clients can mean a focal firm inadvertently acquiring a ‘closed mindset’ and missing out on new innovation opportunities in adjacent markets. This view is partially supported in Bonner and Walker’s (2004) study of consumer involvement in open product development processes, which build upon existing knowledge bases. In incremental innovation projects, Bonner and Walker (2004) show a strong positive relationship between the degree of embeddedness (with existing lead product users) and product development, yet this was not the case in highly innovative projects. Similarly, Laursen and Salter (2006) underline that both search breadth and depth across different sources of external collaboration are curvilinear. The situation in supply chains is, however, quite nuanced. Tomlinson and Fai (2016), for instance, only find evidence of a curvilinear relationship existing where suppliers cooperate over product innovation, and not in process innovations; buyer collaboration is not subject to negative returns. Yet, a critical adjunct is the stage of product development. Johnsen et al. (2006) argue that engaging with buyers is critical at the beginning, fluid stages of new product development and important (although not critical) throughout the remainder of the lifecycle. Supplier input is really only sought when the product is brought to market (especially if sup-

External collaboration for innovation  187 plying tangible components). However, if suppliers are providing intangible knowledge, then their role may be more important in earlier, more fluid stages of the product lifecycle.

3

COOPERATION AND INNOVATION IN THE REGIONAL CONTEXT

3.1

Cooperation in Industrial Districts and Clusters

While strategic management and innovation scholars have largely explored the cooperation– innovation relationship through units of analyses (firms and industries) in a demarcated non-territorial way, economic geographers have followed a systemic perspective emphasizing the importance of co-location (in regional clusters or industrial districts) for innovation. Several terms have been used to describe the regional cooperation–innovation nexus, including so-called ‘learning regions’, ‘innovative milieu’ and/or ‘regional innovation systems’ (Boekema et al., 2000; Camagni, 1991; Cooke and Morgan, 1994, 1998; Maillat, 1995). The regional studies literature has a long history, dating from Marshall’s (1919) notion of an ‘industrial atmosphere’ in industrial districts to Florence’s (1948) study of Birmingham’s manufacturing enclaves and Becattini’s (1990) Italian industrial districts (for a review, see Belussi and Caldari, 2008). The core idea is that co-located firms benefit from tacit knowledge flows and specific know-how and these are transmitted through the mobility of local labour (including managers) bringing their ideas and experiences to different firms (and new start-ups), local public research activities and industry fora (including trade associations) that arise through being spatially proximate (Hudson, 1999; Molina-Morales and Martínez-Fernández, 2006). Yet, a critical ingredient in facilitating wider interaction and knowledge exchange in clusters is the extent to which firms collaborate. In this regard, spatial proximity opens up wider possibilities for collaboration over the exchange of resources and knowledge through both formal and informal channels, especially in local buyer–supplier relations (Cooke, Gómez-Uranga and Etxebarria, 1997). The classical Italian industrial districts were a myriad of criss-crossing ties existing between firms, which engaged in co-opetition simultaneously on several levels. Cooperation between district firms in knowledge sharing and resources was a source of innovation and competitive advantage, while competition stimulated technical advance (for instance, Bellandi, 2003; Lado et al., 1997). The most fruitful relationships are what Cooke and Morgan (1998) term ‘associative’ – that is, they involve a two-way interchange between firms. This is essentially the relational capital that exists between firms – the cooperative and trusting attitudes that aid collective learning processes (Molina-Morales and Martínez-Fernández, 2006). Spatial proximity is critical here for building relational assets, especially with regard to facilitating face-to-face communication, trust building and reciprocity between firms, which provide the foundations for a ‘collective learning’ environment for knowledge creation and innovation (Morgan, 2004). 3.2

Empirical Evidence – Regional-level Studies

We now consider within-region (intra-regional) empirical studies of the cooperation– innovation nexus. In this regard, De Propris (2002) undertakes a cross-sectoral study of 435 manufacturers in the West Midlands region of the UK, considering (1) firms that engage/do

188  Handbook of industrial development not engage in cooperation with their suppliers/clients over innovation; and (2) ‘incremental’ (i.e., changes to existing products/processes) and ‘radical’ (i.e., new products/processes) innovations. The results suggest that product innovation is enhanced where firms engage in cooperation with both buyers and suppliers, while only supplier collaboration is significant in relation to process innovation. In addition, ‘radical’ innovators are also more likely to cooperate with both upstream and downstream partners, while those engaged in predominantly ‘incremental’ innovation tend only to benefit from upstream collaboration. Freel and Harrison (2006) conduct a larger regional survey of 1300 SMEs in Northern England and Scotland. For the manufacturing sector, they find that ‘novel’ product innovators are more likely to engage in cooperation with buyer firms with regard to ‘non-innovators’, although in process innovation, supplier cooperation is significantly correlated with ‘novel’ process innovations relative to both ‘incremental’ innovators and ‘non-innovators’. The authors uncover similar patterns in the service sector, although supplier cooperation is also important in explaining ‘novel’ product innovation relative to ‘non-innovators’. The study, however, finds no empirical support for co-opetition being important. Moreover, the authors also note that a significant proportion of innovators (between 30 and 40 per cent) in their sample did not engage in any form of external cooperation. This is consistent with the view that, for most firms, internal R&D activity is the primary source of innovation, with external cooperation being complementary. Fitjar and Rodríguez-Pose (2013) provide evidence that collaboration with consultants, universities and research centres as well as within the supply chain may support firm innovation. One focus for researchers has been the industrial district. For instance, Tomlinson and Jackson (2013) study the UK ceramics industry and find that ceramics firms based inside the UK’s North Staffordshire ceramics district are far more likely to engage and benefit from external collaboration in innovation relative to non-district firms. Molina-Morales and Martínez-Fernández (2006) use survey data from five industrial districts (food, textiles, furniture, ceramic tiles and leather) in the Valencian region of Spain to examine factors that encapsulate the ‘cooperative atmosphere’ and then its impact on innovation within these districts. These include measures (or constructs) of ‘shared values’, the ‘internal mobility’ of district managers/employees and ‘trusting cooperation’ within the district. These factors, in turn, facilitate the combination and exchange of resources/knowledge/information flows between firms. The study finds that these relational variables are all important in explaining the level of innovation in district firms, suggesting that a ‘cooperative atmosphere’ in districts may be important for innovation. Other studies, however, suggest that the impact of cooperation and innovation (at the regional level) is overstated. In this regard, Fritsch and Franke (2004) examine the relationship within three German regions for the 1990s. They find that while the more innovative regions – Baden and Hanover – appear to benefit from a higher degree of tacit knowledge flows between firms and public institutions, the role of inter-firm cooperation is ‘largely insignificant’ in explaining innovation. Additionally, the authors also argue that cooperative ties are only a minor conduit through which knowledge flows (between firms) arise. In a related study covering 11 European regions, Fritsch (2004) examines data from the European Regional Innovation Survey and again finds little evidence of links between cooperation and innovation. He concludes that a high level of R&D cooperation between firms within a region is not necessarily ‘conducive to innovative activity’ (p. 844).

External collaboration for innovation  189 3.3

Extra-regional Collaborative Links

In the modern global economy, firms engage in a variety of collaborative networks that span (and are organized) beyond their own regional boundaries. Examples include global value chains and/or establishing research links between regional actors and universities located in other regions. Such extra-regional linkages are important in widening a firm’s (and their home region’s) access to knowledge and expertise. Indeed, they may act as a substitute for the benefits associated with regional agglomeration (Johansson and Quigley, 2004). Accordingly, Fitjar and Rodríguez-Pose (2013) suggest that extra-regional collaboration may be more conducive to innovation than collaboration with local partners, especially when related to supply chain interactions. This is particularly the case for firms located in lagging regions, who might be able to exploit extra-regional collaborations (with distant partners) to compensate for a weak local knowledge pool, and therefore gain quicker access to new technologies, while opening up new possibilities for learning and knowledge transfer (Asheim, Boschma and Cooke, 2011). It may also provide these firms with access to a wider set of business networks, from which new opportunities may arise (Barzotto et al., 2020). In a study of Swedish firms, Grillitsch and Nilsson (2015) find this to be especially the case for larger high-tech firms located in lagging regions – they were far more likely than their counterparts in leading regions to engage in extra-regional networks to access external knowledge. Tödtling, Grillitsch and Höglinger’s (2012) study of Austrian ICT firms suggests that those located in weak regional innovation systems are more likely (compared to firms based in stronger regional innovation systems) to engage in international (and by implication, extra-regional) R&D collaborations to compensate for weak local knowledge exchange. Using patent data, Barzotto et al. (2019a) find that firms based in lagging regions and engaged in extra-regional collaborations with partners elsewhere have higher levels of innovation. Firms may also establish extra-regional research collaborations with universities located in other regions due to a lack of adequate scientific competencies in their local universities, or a lack of willingness (from their local universities) to collaborate. Moreover, in regions characterized by low absorptive capacities, but with a strong science base, it has been found that researchers may seek to collaborate with external universities or industrial partners located outside their home region (Azagra-Caro, 2007). Likewise, extra-regional collaboration between firms on training and skills programmes and/or through temporary work teams can enhance human capital and skills. Indeed, De Noni, Orsi and Belussi (2018) find that firms based in European lagging regions are more innovative when they work closely with prolific inventors located in knowledge-intensive regions. Yet, it is important to be aware that the incentives for firms – located in leading and lagging regions – to participate in extra-regional collaboration differ. Firms in lagging regions may perceive positive benefits from collaborating with counterparts in leading regions, though the converse is not necessarily the case. Weak capabilities and technological expertise in lagging regions can act as a barrier for firms in leading regions to participating in extra-regional collaboration with actors from lagging regions. Extra-regional collaboration over knowledge and technology sharing are easier to establish and have a higher probability of success when firms operate in similar technological domains (Boschma and Iammarino, 2009). This means that knowledge and innovation networks are usually highly selective, with participants chosen based on their absorptive capacity to engage in/contribute to interactive learning and knowledge transfer (Gilsing, Lemmens and Duysters, 2007; Giuliani, 2006). Indeed, evidence from

190  Handbook of industrial development 255 NUTS2 European regions suggests that extra-regional knowledge links based on firms with related and complementary technologies may be more likely to support radical innovations (Miguelez and Moreno, 2018).2

4

POLICY TO SUPPORT COLLABORATION AND INNOVATION

4.1

Supply Chains and Networks: Policy Issues

Given the empirical evidence on the benefits of ‘open innovation’, policymakers have sought to introduce initiatives to promote greater inter-firm networking and collaboration. Since the 1990s and across the Organisation for Economic Co-operation and Development (OECD) and more broadly, EU innovation policy has shifted from a predominantly firm-subsidy model towards funding projects that promote collaborative ties between firms (Aranguren, Larrea and Wilson, 2010; Bougrain and Haudeville, 2002). In the UK, this approach continues to permeate policy thinking, as evident in a series of central government directives/initiatives on innovation policy, through the work of Innovate UK and especially its Knowledge Transfer Network and Smart Manufacturing (see also Bailey and Tomlinson, 2017).3 There is some evidence that such initiatives have had a positive impact. For example, Huggins, Johnson and Thompson (2012) find that SME innovation performance is positively related to public funding for regional networks in three European regions (UK: Northern England; Greece: Thessaloniki; Turkey: Istanbul metropolitan). The effectiveness of this policy support, however, differs across these regions and reflects the national/regional and cultural contexts and attitudes to collaboration and open innovation (see also Dachs, Ebersberger and Pyka, 2008). Yet, while policy support for networking might be fruitful, engineering closer collaboration and new networks is fraught with barriers and challenges. External collaboration relies upon establishing joint commitments, mutual trust and reciprocity among firms – these are not always forthcoming and can easily break down. Coordinating resources between firms can be problematic, especially if resource synergies are not transparent, while firms’ inertia to change can halt such initiatives (Jessop, 1998). Indeed, over 20 years ago, Huggins (2001) noted the deep-rooted scepticism among UK firms with regard to the idea of open innovation and its perceived benefits. SMEs are particularly sceptical and are less likely to participate in innovation networks than larger firms (Asheim et al., 2003) – possibly due to a lack of trust or fear of intimidation by the larger partner (Hanna and Walsh, 2002). Zeng, Xie and Tam’s (2010) survey of Chinese SMEs highlighted the lack of technical experts; lack of financial capital (in relation to R&D); lack of technical information regarding new technologies; and a lack of suitable partners as barriers to collaboration and open innovation. These internal resource constraints are relatively germane and likely to extend beyond the Chinese context (Huizingh, 2011). To address some of these issues, research has focused on the socialization of the supply chain – especially, to improve inter-firm dyads. At the practitioner level, initiatives such as developing more open communication systems, the facilitation of joint workshops and reciprocal firm visits and discussions over (joint) problems to build relational capital and engineer greater goodwill/understanding between parties have had some success (see Cousins et al.,

External collaboration for innovation  191 2006). Such arrangements can be supplemented with social rules and appropriate sanction mechanisms to reward reliability and commitment in the network and punish opportunistic behaviour among participating firms (Dyer, 1996). To assist this process, there may be a greater role for industry bodies such as trade organizations to act as conduits for building relational capital among firms (Zeng et al., 2010). Finally, Aranguren et al. (2010) have also suggested a role for information exchanges. Such bodies could provide SMEs with lists of appropriate partners, with data on strengths, weaknesses and capabilities on each to facilitate network development (see Lee et al., 2010). These types of initiatives – which act to widen network opportunities – may also help to counter the negative impacts of over-embeddedness (see Section 2.5). 4.2

EU Policy Initiatives on Extra-regional Collaboration

Over the last decade, the European Union’s innovation policy has been dominated by smart specialization strategies (S3) under the Research and Innovation for Smart Specialisations Strategies (RIS3) agenda. The idea behind S3 is that of public–private partnerships, in which state funding is prioritized to support ‘activities’ in specific technological fields at the regional level that have the potential for innovation, ‘entrepreneurial discovery’ and commercial exploitation (Foray, 2015). This is to be achieved through local and regional stakeholder (i.e., businesses in conjunction with local development agencies) collaboration that identifies new opportunities that build upon a region’s existing and historic advantages and capabilities. In this regard, a region’s existing assets may be combined with new general-purpose technologies (GPTs) (such as ICT, electronics and digitalization) that are driving the emergence of Industry 4.0 to stimulate knowledge and innovation opportunities. This has led to S3 being labelled as ‘place based’ and a return to a more selective mode of industrial policy (Barca, McCann and Rodríguez‐Pose, 2012). While RIS3 puts an emphasis on ‘place-based’ interventions, there is explicit recognition across several EU programmes that supporting extra-regional collaboration can aid smart specialization and the innovation process, and indeed may assist firms (and other actors) in lagging regions (see Section 3.3). Indeed, the principle of policy support for extra-regional collaboration is longstanding in EU institutions and programmes – being evident, for example, in the European Cooperation in Science and Technology (COST) programme established in 1971. COST funds international, multidisciplinary networking and collaboration in science and technology – the projects are led by academics, with deliberate scientific exchange and dissemination with business across Europe. A core principle of COST is to foster collaboration between leading scientists and partners from less research-intensive regions.4 In addition, and since 1984, the EU’s Framework Programmes are geared towards supporting research and innovation. Horizon Europe is the latest incarnation, with an anticipated budget of €100 billion (for 2021–27).5 Like its predecessors, a key facet of the programme is to foster extra-regional collaboration across member states (and affiliated members) through vehicles such as Marie Skłodowska-Curie Actions (MSCAs) and Joint Technology Initiatives (JTIs). Likewise, the EU’s Structural and Investment Funds, most notably the European Regional Development Fund and Cohesion Funds, have sought to promote stronger pan-European extra-regional collaboration for technological enhancement and knowledge diffusion. The European Territorial Cooperation (ETC) programme known as Interreg was established in 1990 and has evolved to operate in three separate categories of cooperation: cross-border,

192  Handbook of industrial development transnational and extra-regional.6 Interreg is aimed at addressing common challenges through collaboration between different cross-regional actors (i.e., firms, researchers and third sector) across a diverse range of fields from sustainable energy, health to transport. Projects seek to foster and exploit synergies between RIS3, clusters and network collaboration, and industrial and social innovation. In this regard, Barzotto et al. (2019b) highlight the ¡VAMOS! Project, which was funded to create a new underwater, environmentally viable mining system to facilitate new mining possibilities. The project mapped onto a wider EU challenge to better exploit discarded European deposits of high-grade minerals and thus to reduce the EU’s high dependency on imported minerals. The project itself involved a diverse set of partners collaborating from across science, industry and academia based in nine EU countries.7 In the UK, there were several partners from Cornwall – a lagging region – and the development of the prototype has opened up a new set of opportunities to nurture new smart specialization in environmental marine mining and technologies (with potential global application) that build upon Cornwall’s traditional mining expertise. Participation in these EU funding programmes and networks can enhance a region’s international profile and policy learning, along with building institutional capacity. This can be critical to the success and implementation of RIS3. For example, in Apulia – one of Italy’s less developed regions – local government has been blighted by poor governance, weak social capital and the presence of criminal organizations, which in turn impairs innovation. In recent years, Apulia has sought to break out of this cycle through engaging in extra-regional networks such as Interreg. These include involvement in NEREUS, a European inter-regional space industry initiative (which includes the European Space Agency, business and local governments), and ADRION, which includes regions bordering the Adriatic and Ionian seas (that covers eight regions from Croatia, Greece, Italy, Slovenia, Albania, Montenegro, Serbia, and Bosnia and Herzegovina). Within ADRION, Apulia participates in the Blue_Boost project to promote innovation and knowledge exchange in the maritime economy, covering fisheries, shipbuilding and so-called blue technologies (i.e., new materials, green shipbuilding and robotics). Initiatives include innovation hubs, fablabs and co-working spaces that are open to partner firms, researchers and the third sector to explore and exploit technological synergies and develop new specialisms at regional and extra-regional levels (Barzotto et al., 2019b). In short, these EU programmes seek to deliberately connect actors from dissimilar regions and offer the opportunity to widen scientific, technological and organizational learning. The aim is to enhance innovation activity and, in some cases, revive lagging regions while delivering a higher level of cohesion. Extra-regional collaboration may also contribute to overcome over-embeddedness issues and ‘closed thinking’, and network biases that may exist among similarly minded actors with regard to cooperation (see Section 2.5). Moreover, the new networks that arise from these types of projects may persist beyond the funding cycle and create a long-lasting legacy of co-created innovation and development.8 However, despite the growth in EU programmes, significant barriers remain with regard to fostering extra-regional collaboration with the RIS3 framework. For instance, drawing on a survey of regional policymakers from a sample of EU regions (and two associated countries), Uyarra, Marzocchi and Sorvik (2018) find a lack of political commitment, relational inertia (among firms and policymakers) and the difficulty in adapting to vagaries of EU funding rules and procedures especially prohibitive for RIS3. The authors conclude that such barriers

External collaboration for innovation  193 ought to be lowered if the efficacy of RIS3 to promote better and more fruitful extra-regional connectedness is to be realized. A further caveat relates to lagging regions, which tend to be characterized by low- to medium-tech sectors. Such sectors largely rely upon practice-based innovation enabled through ‘learning by doing’ and local inter-firm collaboration; however, regional capacities to generate and sustain local knowledge spillovers are often weak (Asheim, 2012). To succeed in an RIS3 framework, the quality of extra-regional links is critical. In this sense, for lagging regions it may be that the foci of diversification should, perhaps, be quite broad – that is, the implementation of ‘smart specialization’ should not lead lagging regions to focus on diversifying into those technologies that are similar to what they already possess, since this would only lead to incremental changes and be insufficient to transform their development trajectory. In this sense, regional businesses may find greater benefit from engagement in the building of new complementarities and synergies with partners (in other regions) who utilize different types of technologies. This process of explorative technological search that may lead to completely new trajectories emerging (Castaldi, Frenken and Los, 2015). The new initiative of the European Commission regarding inter-regional innovation investments, to be implemented in the 2021–27 period, partly responds to the above challenges. A new ‘Component 5’ has indeed been added to the new EU Cohesion Policy, and more in particular in the European Territorial Cooperation strand regarding inter-regional innovation investments. The initiative aims at encouraging close-to-market investments involving innovative products and services through the deployment of new technologies or processes. It provides financial support for collaboration between actors in the different regional ecosystems so that complementarities in their activities along value chains in priority sectors can be exploited.9 The next step should be to provide support to regional actors so that they could identify opportunities for collaboration – namely, potential partner regions – which might be particularly difficult for less developed regions.

5

CONCLUDING COMMENTS

This chapter has summarized some of the key issues in the literature on the external cooperation–innovation relationship. At the firm level, closer cooperative ties were initially thought of as an alternative governance structure to circumvent the issues of transaction costs and appropriability. The literature has evolved to widely regard them as being an essential source of innovation. The fostering of closer ties across a range of activities between firms along the value chain, as well as collaboration with research institutions, facilitates knowledge transfer and organizational learning and can be mutually beneficial for innovation. Indeed, for resource-constrained SMEs, such external ties can provide access to a wider set of technological opportunities through information sharing and resource pooling. Nevertheless, firms need to be careful not to over-invest in specific relationships, due to concerns about over-embeddedness and ‘closed thinking’. Within national and supra-national policy circles, there has been a tendency to nurture and support the development of inter-firm cooperative ties through supply chain fora and industry bodies. Such initiatives are now also being viewed as opportunities to build extra-regional collaborative links, particularly between firms and actors in leading and lagging regions. Several EU programmes (e.g., Horizon 2020, Interreg) are a case in point, and offer the opportunity

194  Handbook of industrial development to promote more balanced regional development by allowing lagging regions to develop new industrial activities and ‘catch up’. This is not an easy process, and several barriers remain, but a cautious approach may deliver dividends.

NOTES 1. Tomlinson (2010) also finds a positive relationship in the aerospace industry, where competitor firms collaborate over process innovation (co-opetition). 2. Extra-regional collaborations involving cross-sectoral collaborations based upon largely dissimilar knowledge bases can and do prosper and may lead to radical innovation (for example, see Grillitsch, Asheim and Trippl, 2018). 3. See Innovate UK KTN at https://​ktn​-uk​.org/​about/​. Accessed 8 July 2021. 4. The latest COST template (2018) requires new proposals to include at least of 50 per cent of proposers based in designated ‘Inclusiveness Target Countries’; these are largely countries in Southern and Eastern Europe where research capacity is low (see http://​www​.cost​.eu/​about​_cost/​strategy/​ excellence​-inclusiveness; accessed 18 August 2022). 5. See https://​ec​.europa​.eu/​info/​designing​-next​-research​-and​-innovation​-framework​-programme/​what​ -shapes​-next​-framework​-programme​_en. Accessed 7 December 2018. 6. See https://​www​.interregeurope​.eu/​. Accessed 7 December 2018. 7. Fields included robotics, geology and mining technologies. 8. For instance, collaborations originated within the Horizon 2020 framework create links between actors taking part in the project. Such links usually outlive the formal Horizon 2020 collaboration, and they can potentially be reactivated for future joint projects. 9. See https://​ec​.europa​.eu/​regional​_policy/​it/​policy/​themes/​research​-innovation/​i3/​. Accessed 30 June 2021.

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External collaboration for innovation  197 Hervás-Oliver, J.L. and Albors-Garrigos, J. (2009). The role of the firm’s internal and relational capabilities in clusters: when distance and embeddedness are not enough to explain innovation. Journal of Economic Geography, 9(2), 263–83. Hoang, H. and Antoncic, B. (2003). Network-based research in entrepreneurship: a critical review. Journal of Business Venturing, 18, 165–87. Hudson, R. (1999). The learning economy, the learning firm and the learning region: a sympathetic critique of the limits of learning. European Urban and Regional Studies, 6(1), 59–72. Huggins, R. (2001). Inter-firm network policies and firm performance: evaluating the impact of initiatives in the United Kingdom. Research Policy, 30, 443–58. Huggins, R., Johnston, A. and Thompson, P. (2012). Network capital, social capital and knowledge flow: how the nature of inter-organizational networks impacts on innovation. Industry and Innovation, 19(3), 203–32. Huizingh, E.K.R.E. (2011). Open innovation: state of the art and future perspectives. Technovation, 31, 2–9. Inkpen, A.C. and Tsang, E.W.K. (2005). Social capital, networks and knowledge transfer. Academy of Management Review, 30, 146–66. Jessop, B. (1998). The rise of governance and the risks of failure: the case of economic development. International Social Science Journal, 155, 29–45. Johansson, B. and Quigley, J.M. (2004). Agglomeration and networks in spatial economics. Papers in Regional Science, 83, 165–76. Johnsen, T., Phillips, W., Caldwell, N. and Lewis, M. (2006). Centrality of customer and supplier interaction in innovation. Journal of Business Research, 59, 671–8. Katz, M.L. (1986) An analysis of co-operative research and development. RAND Journal of Economics, 17, 527–43. Klein, B., Crawford, R.G. and Alchian, A.A. (1978). Vertical integration, appropriable rents, and the competitive contracting process. Journal of Law and Economics, 21, 297–326. Kogut, B. and Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3, 383–97. Kotabe, M., Martin, X. and Domoto, H. (2003). Gaining from vertical partnerships: knowledge transfer, relationship duration and supplier performance improvement in the US and Japanese automotive industries. Strategic Management Journal, 24, 293–316. Kühne, B., Gellynck, X. and Weaver, R.D. (2013). The influence of relationship quality on the innovation capacity in traditional food chains. Supply Chain Management: An International Journal, 12, 52–65. Lado, A.A., Boyd, N.G. and Hanlon, S.C. (1997). Competition, cooperation and the search for economic rents: a syncretic model. Academy of Management Review, 22(1), 110–41. Langlois, R.N. and Robertson, P.L. (1995). Firms, Markets and Economic Change. London: Routledge. Laursen, K. and Salter, A. (2004). Searching high and low: what types of firms use universities as a source of innovation? Research Policy, 33(8), 1201–15. Laursen, K. and Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management Journal, 27(2), 131–50. Lee, S., Park, G., Yoon, B. and Park, J. (2010). Open innovation in SMEs – an intermediated network model. Research Policy, 39(2), 290–300. Lee, Y.S. (1996). ‘Technology transfer’ and the research university: a search for the boundaries of university–industry collaboration. Research Policy, 25(6), 843–63. Maillat, D. (1995). Territorial dynamic, innovative milieus and regional policy. Entrepreneurship and Regional Development, 7, 157–65. Malecki, E.J. (2010). Global knowledge and creativity: new challenges for firms and regions. Regional Studies, 44, 1033–52. Marshall, A. (1919). Industry and Trade. London: Macmillan. Miguelez, E. and Moreno, R. (2018). Relatedness, external linkages and regional innovation in Europe. Regional Studies, 52, 688–701. Molina-Morales, F.X. and Martínez-Fernández, T.M. (2006). Industrial districts: something more than a neighbourhood. Entrepreneurship and Regional Development, 18, 503–24. Molina-Morales, F.X., Martínez-Fernández, T.M. and Torlo, V.J. (2011). The dark side of trust: the benefits, costs and optimal levels of trust for innovation performance. Long Range Planning, 44, 118–33.

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12. Governing industrial policy: the scope and limits of the ‘good governance’ agenda Pedro Marques and Kevin Morgan

INTRODUCTION A significant body of theoretical and empirical research has emerged over the last few decades that argues that ‘good governance’ is fundamental for long-term economic growth, innovation and development (Di Cataldo and Rodríguez-Pose, 2017; Marques and Morgan, 2021). This argument has been made both for national and subnational levels, triggering a renewed interest in institutions, public policy and power dynamics. We argue that this is a welcome development as research has hitherto focused on economic, cultural or social variables as explanations for uneven development, while the issue of institutional quality remained less explored (Gertler, 2010). However, there are some serious limitations to what has been called the ‘good governance agenda’, particularly at the subnational level. One involves the tendency to analyse regional institutions as autonomous entities, which can then be used to explain outcomes in particular territories. Drawing on the seminal work of Chubb (1982) on Southern Italy, we argue that regions with weak (or low-quality) institutions are in a vulnerable position in their relationships with regions that have stronger (or higher-quality) institutions. This vulnerability tends to lead to a loss of resources (human and others) from the weakest regions, which further reinforces their negative lock-in. In some cases, this weakness can also be deliberately exploited to the benefit of the strongest region. Another limitation involves the lessons and prescriptions that are drawn from this work. As discussed by Grindle (2004, 2011), expecting territories with fragile institutional settings to improve their institutions across the board is unrealistic, and even unnecessary, considering the urgency of dealing with wicked issues such as growing inequality, social exclusion or climate change. The goal then should be to identify targeted institutional improvements that are realistic and can help regions to develop place-based strategies that are context-specific and therefore more consequential (Montero and Chapple, 2019). These governance problems are further exacerbated when there is little or no agreement between levels of government within the multilevel polity (Marques and Morgan, 2021). To illustrate the above theoretical arguments, we focus on two empirical case studies. The first is of Valencia in Spain, which illustrates the Chubb argument (1982) through an analysis of how a coalition of regional banks and national political parties launched the region into a prolonged crisis. This story also demonstrates how institutional change can incrementally improve governance. The second is from post-Brexit Britain, where the central state is reserving for itself the powers transferred from Brussels and plans to deploy these industrial policy powers in domains that have been formally devolved to the Celtic nations, triggering an unprecedented crisis of governance in the UK’s multilevel polity. 200

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THE CHALLENGES OF GOOD GOVERNANCE Over the last couple of decades, the issue of institutional quality has moved to the forefront of academic and policy agendas (OECD, 2012; World Bank, 2017). After a period when this topic was either ignored or actively avoided (Amsden, 2001; Rodrik, 2005), a body of research has emerged that shows how institutions shape economic growth, inclusion, inequality and other factors, particularly in the medium to long term (Hickey, Sen and Bukenya, 2014; Rodrik, Subramanian and Trebbi, 2004; Teichman, 2016). At the subnational level, Gertler (2010) lamented that institutions remained relatively underexplored. However, since then, a body of research has emerged demonstrating that regional quality of governance is correlated with a variety of development outcomes (Di Cataldo and Rodríguez-Pose, 2017; Montero and Chapple, 2019; Nistotskay, Charron and Lapuente, 2015; Peiró‐Palomino, Picazo‐Tadeo and Rios, 2020). This has been accompanied by a variety of related research, such as that which focuses on institutional leadership (Sotarauta and Beer, 2017) or on agency (Grillitsch and Sotarauta, 2020; Gunko, Kinossian and Pivovar, 2021), and that demonstrates how individuals acting in coordination can improve institutional conditions and development outcomes. Despite the many positive contributions made by these authors, some questions remain about the good governance agenda. One is the tendency to analyse institutional quality as detached from the wider territorial context. In a multi-scalar framework, the different levels of government interact in processes of coordination and conflict, consensus and dissension to shape institutional quality at the regional level (Hooghe and Marks, 2001; Schakel, Hooghe and Marks, 2014). Furthermore, understanding governance as the product of multi-scalar forces is not exclusively a technical or a coordination issue, which can be addressed with better policymaking. Relationships between scales are permeated by power dynamics, and in some cases, regions with stronger institutions can actively undermine those with more fragile institutions and contribute to their continuing peripheralization. An example of the latter is provided by the case of Southern Italy, one of the enduring topics within regional studies, due to the persistence of territorial disparities within this country. Chubb (1982) offered a historical take on the political, economic and social evolution of this part of Italy through the case studies of Palermo and Napoli. The author argued that since the reunification of Italy in the mid-nineteenth century, economic and political agents in Northern Italy took advantage of the weaknesses in Southern Italian society and exploited them to their advantage. These weaknesses included the persistence of a rural, scattered population that prevented the rise of popular worker movements; the concentration of rentier elites in the cities, who due to their distance and detachment from the land did not value reinvestment; and the presence of criminal organizations, which was useful to the economic elites as a means of disciplining the population, and at the same time generated economic activity in an underdeveloped territory. When the country reunited, rather than promote industrialization and development in the South, Northern politicians allied with Southern political elites to placate labour movements in Northern Italy, by offering patronage to the South in return for votes in the national assembly. Similar arrangements have persisted through this country’s modern history, which effectively pushed the South to maintain its ‘feudal’ socio-economic structures, in a situation that some authors have called ‘growth without development’ (Fukuyama, 2015). Of course, one can consider this to be an extreme example of multi-scalar power dynamics conditioning the development of a peripheral region. However, there are other ways in which power dynamics between territories can negatively affect the development opportunities for

202  Handbook of industrial development less developed regions. On a macro scale, the evolution of EU institutions is a good example of such dynamics. As detailed by Dyson and Featherstone (1999), discussions regarding the creation of an Economic and Monetary Union (EMU) for the EU were overwhelmingly dominated by the geopolitical and economic considerations of core countries (especially France and Germany, but also Italy). This led to a set of macroeconomic rules that favoured advanced regions specialized in high-value-added activities, such as those found predominantly in parts of Northern Europe (with a few exceptions), and hindered the competitiveness of countries where medium- to low-tech manufacturing continues to be dominant (Hadjimichalis, 2018). Furthermore, the EU’s legal limitations to industrial policy, which in the case of Southeast Asian countries was central to their structural economic transformation (Amsden, 2001; Rodrik, 2005), are once again built on a logical reasoning that may make sense for regions that are already highly developed, but limit what can be done to assist those that are not. In both these examples, our argument is not that agents in the most developed territories actively work to undermine opportunities for the European periphery, but rather that their strong institutions allow them to shape debates and decision-making in a way that produces that outcome, even if it is unintended. We also argue that place-based policies by themselves are insufficient to change institutional settings or economic outcomes, without the support of national and European instruments (Morgan and Marques, 2019). A second concern looks at the lessons extracted from the good governance agenda and rests on the following question: is it realistic to ask that regions improve their entire institutional settings as a precondition to achieving development, especially when one considers the constraints discussed in the previous paragraphs? In a series of contributions, Grindle (2004, 2010, 2011) challenged this assumption in the context of developing countries, arguing instead that incremental institutional change is sufficient to achieve development goals, and that it is furthermore the only realistic approach – that is, good enough governance. From a different but relevant perspective, Chang (2011) also questioned the issue of causality and argued that the expectation that better institutions lead to better socio-economic outcomes does not necessarily occur in that order. This is especially true when one considers ‘narrow approaches’ to institutional improvement, which very much focus on liberal market reforms. Milanovic (2019) made a similar argument when he argued that it is possible to achieve economic development with supposedly ‘bad institutions’, with China as the most obvious example. What these arguments imply for the purposes of this chapter is that the good governance agenda should not be designed around the notion that regions must achieve high-quality institutions before they can strive for better socio-economic outcomes. Instead, one can think of how incremental institutional change, alongside investments that can offer tangible improvements in economic conditions, can provide the foundations for further development. These institutional improvements can be based, for instance, on building or strengthening networks between agents committed to development, an approach that can generate results even in ‘fragile territories’ (Montero and Chapple, 2019). These improvements should also involve better recruitment practices and the training of human resources in public administration, which is a fundamental dimension of institutional quality (Fukuyama, 2015). Finally, and considering the argument made previously about multi-scalar power dynamics, these institutional improvements would benefit from a commitment from national or international institutions to assist in the implementation of these changes. This chapter will now use two examples as illustrations of its theoretical arguments. The first uses a bottom-up approach and looks at institutional degradation and subsequent

Governing industrial policy  203 improvements in the region of Valencia, Spain. It will place this analysis in the national and European contexts. The second example uses a top-down approach and examines the evolution of regional policy in the UK – namely, in the relationship between the national and subnational scales, against the backdrop of withdrawal from the European Union.

CORRUPTION AND REDEMPTION: THE EVOLUTION OF VALENCIA IN THE SPANISH CONTEXT The region of Valencia1 offers a vivid illustration of how regional institutions, shaped by national and European dynamics, can enter periods of decline, with negative consequences for economic development, but also how small changes can lead to significant improvements. Valencia, with a population of nearly 5 million, hosts a strong manufacturing base based on low- and medium-tech industries, such as ceramics, shoemaking, agro-food and retail, which continue to be an important source of employment, investment and exports (IvieLAB, 2019; López-Estornell et al., 2014; Zabala-Iturriagagoitia, Gutiérrez-Gracia and Jiménez-Sáez, 2008). These industries are supported by 11 technological centres, employing around 1750 researchers and support staff, and five public universities. The region is also the headquarters for several major retailers, including Mercadona, the largest retailer in Spain, and some sizeable foreign-owned plants, of which the Ford plant, with around 7000 direct employees, is an example. One could argue that, a few decades ago, Valencia was on a similar path to that of regions such as Catalonia or the Basque Country (Boira, 2012). In fact, in the late 1960s and early 1970s, income in the region rose above the national average and its exports accounted in some years during these decades for 20 per cent of the Spanish total (ibid.). However, contrary to those two regions, Valencia did not build on its manufacturing and agricultural strengths to develop new, highly productive sectors of economic activity (Morgan, 2016; Navarro et al., 2014). Especially since 2001, Valencia’s gross domestic product (GDP) per capita has diverged from the country’s average. In 2001, it stood at 96.9 per cent of the Spanish average, vs 87.8% in 2018 (IvieLAB, 2019). For this reason, Iammarino et al. (2020) classified this region as one trapped at a middle-income level. The explanations for this divergence are complex, but at the core of it is the inability of the Valencia region to generate new high-value-added economic activities. Before 2001, there was already a decline of productivity in its manufacturing base and a lack of strategic investments in innovation, especially in the private sector, coupled with inadequate public policies (López-Estornell et al., 2014). It should also be noted that the region has suffered from persistent underinvestment, according to authors such as Boira (2012), because the transfer of public funds from the national government has been lower in comparison to other Spanish regions. Since 2001, this situation has worsened due to various economic and institutional forces. At the EU level, of particular importance has been the introduction of the euro currency and the emphasis of the European Central Bank on macroeconomic stability, in order to deliver on its mandate of keeping inflation below 2 per cent (Bibow, 2013). In contrast, the USA Federal Reserve has to deal with the dual mandate of inflation and employment targets, which in some contexts justifies looser monetary policy despite inflation threats. Coupled with wage deflation policies in Germany in the early 2000s, the management of the euro currency has led to an exacerbation of competitiveness differences between regions specialized

204  Handbook of industrial development in knowledge-intensive, high-value-added activities, and those that rely on the growth of labour-intensive industries, such as tourism and retail (Bibow, 2013; Hadjimichalis, 2018). More importantly for the purposes of this chapter, however, are the institutional characteristics of this region. As documented by various authors after the financial crisis of 2008 (Boira, 2012), the solution of the regional government, working together with municipalities, was to use EMU to fuel a spending boom on infrastructure and big events, which was meant to offset declining competitiveness. The construction boom was, of course, not exclusive to Valencia, and was, in fact, part of a national policy to turn rural land into urban land, especially in coastal areas, a policy supported by virtually all major political parties and the municipalities involved (Burriel, 2011). In the Valencian case, the construction boom was supported by a complex multi-scalar architecture: at the regional level, the dominant political party, the centre-right PP, used their influence to take over a mutualist bank that became a major source of funds for local politicians. At the same time, through a network of corruption that extended across Spain, these investments were used to fund and cement the regional and national operations of this party, as demonstrated by several high-profile corruption cases (Edwards, 2019; Fortes and Urquizu, 2015). Funding for these investments was provided to a great extent by foreign European banks – namely, German regional banks, which accumulated liquidity due to the export boom in Germany but which, due to strict banking regulations in this country, were limited in their opportunities to invest within their own nation-state. This savings glut, as the movement of excess liquidity across borders has been called, was facilitated by the existence of the EMU and the lack of oversight over bank leveraging and complex financial transactions. The result, in the late 2000s, was a highly indebted economy, the bankruptcy of the local mutualist bank, and several high-profile corruption cases that led Valencia to be classified by its own population as one of the most corrupt in Spain (Boira, 2012; Charron, Dijkstra and Lapuente, 2013). Since then, there have been several important institutional changes. Corruption cases brought down the regional government, and eventually the national government led by the same political party, and led to a new regional government that has, at least nominally, committed itself to a new development strategy based on innovation, human capital development and knowledge transfer between universities and businesses. This is evidenced, for instance, by the creation of the Valencian Innovation Agency, which was created with the explicit purpose of generating new economic activities by drawing on the science base that exists in the region. Though it would be difficult to draw a direct causal relationship between these changes and major regional outcomes, because of the short time elapsed since their introduction, two recent results are noticeable. First, on the Regional Innovation Scoreboard produced by the European Commission, Valencia was the Spanish region that most improved its relative performance (European Commission, 2021). We use this result with caution, because all Spanish regions improved, and therefore one cannot discount national effects. Furthermore, the two areas where Valencia is strongest are ‘trademark applications’ and ‘design applications’, which do not represent areas that easily lead to unique knowledge resources, such as those found in the most advanced European regions (Balland et al., 2019). An important second result is the fact that in the latest European quality of governance survey, Valencia now appears as one of the regions with the highest value for Spain, compared to a region with one of the lowest scores in the first survey that took place ten years before (Charron, Lapuente and Bauhr, 2021). It should also be noted that the Valencian economy

Governing industrial policy  205 stopped diverging from the Spanish average, and even recovered slightly from the trough of 2012 when it reached 86.9 per cent of the national average. However, it would be a mistake to conclude that this can be attributed to the institutional changes described here, considering the complexity of factors that explain productivity and the limited time span elapsed. Overall, these elements taken together suggest a slight shift in a pattern of decline that started in 2001 and was likely made worse by a public investment strategy geared towards infrastructure, which, as has been shown before, has no significant impacts on economic growth in contexts of low institutional quality (Barca, 2009). Importantly, despite some positive dynamics, there are also some persistent conditioning factors. Innovation policy, especially the instruments that rely on cohesion funds, remains highly centralized in the Spanish context, and, as such, the opportunities for Valencia to adapt it to its regional context are limited (Marques and Morgan, 2021). The region has tried to counteract this centralization by increasing the amount of own funds directed to innovation, but the continuing dominance of cohesion funds leads to several important bureaucratic hurdles. One of them is its use of yearly funding cycles, which makes it impossible for a company or organization to apply for multi-annual funding, thereby adding to the burden of applying for resources and reporting. The key point though is that this pattern where institutional change and continuity occur alongside, and are influenced by actions at various geographical scales, is probably unavoidable. A pragmatic attitude to this issue would need to recognize this complexity and focus on the small-scale improvements that can in the medium to long term have lasting effects on socio-economic outcomes.

CONTESTED GOVERNANCE IN A MULTILEVEL POLITY: INDUSTRIAL POLICY IN POST-BREXIT BRITAIN The re-emergence of industrial policy in the UK, the country that pioneered the backlash against the state under Thatcherism, is arguably the most compelling testament to the failure of the neoliberal approach to industrial policy. In the wake of the Thatcher experiment, a succession of Labour and Conservative governments flirted with a more active industrial policy in the hope of addressing some of the UK’s perennial problems – the most prominent being low productivity and technological innovation, and uneven regional development. In recent years, the British debate about the new industrial policy has undergone significant sectoral and spatial changes. On the sectoral front, the conception of industrial policy has gradually become more capacious, breaching the narrow range of R&D-intensive sectors that have dominated the political agenda for the past half-century to include foundational economy sectors such as health, social care, food, housing and utilities – sectors that account for a large share of total employment and that collectively underwrite everyday civilized life (Berry, Froud and Barker, 2021). On the spatial front, the most notable change has been the ever-growing significance of the place-based approach to industrial policy, partly due to the influence of EU regional policy and partly a consequence of political devolution, a process that spawned a more polycentric polity with the advent of devolved polities in the Celtic nations and in city-regions throughout the UK (Beel, Jones and Rees Jones, 2021; McCann, 2016; Morgan, 2007). But the most momentous change of all has been Brexit, signalling the end of the UK’s 47-year membership of the EU. Often perceived as a purely economic threat, Brexit also poses a political threat to the territorial integrity of the UK. While it obviously changes the external relationship between the UK and the EU, what is not so obvious is that it also changes the

206  Handbook of industrial development internal relationship between the four nations of the UK. Although it is not widely appreciated outside legal circles, this constitutional inter-dependence of the two unions – the European Union and the United Kingdom of Great Britain and Northern Ireland – is deeper than it might appear because the EU legal framework furnished the constitutional basis for the devolution settlements in Scotland, Northern Ireland and Wales. Under the ‘reserved powers’ model of devolution, there is a presumption that the devolved legislatures can legislate on any matter that is not formally reserved to Westminster, which seems to imply a binary relationship between the devolved administrations and the Westminster Parliament. But the reality is more complicated because the devolution settlements were framed in the context of the UK’s pre-existing EU membership and therefore they reflected the supremacy of EU law. So much so that, far from being a binary one-to-one relationship between the UK and the devolved institutions, the UK union in effect consisted of a three-sided relationship in which many powers were exercised neither in Westminster nor the Celtic nations – but in Brussels. In other words, the EU was ‘the glue holding together the United Kingdom’s single market’ (House of Lords, 2017, Ch. 2, para. 26.). The repatriation of these powers from the EU to the UK raises the issue of where they will be located – in Westminster or the devolved administrations. Under the ‘reserved powers’ model on which all the UK devolution settlements are predicated, the most prominent examples of matters that are the reserve of the UK Parliament – and which are not therefore devolved – are relations with the EU, defence of the realm, banking, immigration, and policing. Although taxation mostly remains the responsibility of the UK Government and Parliament, some limited taxation powers have been devolved – for example, to raise landfill tax and land transaction tax, to set council tax and business rates and to vary income tax rates. Responsibility for fiscal and macroeconomic policy and public expenditure allocation across the UK lies with the central government. We need to understand this division of powers because (1) it illustrates the scope and limits of devolution in the UK; (2) it underlines the inter-dependence between central and devolved governments; and (3) it serves as a sobering reminder of the fragility of the ‘good governance agenda’. One of the hallmarks of the good governance agenda in the UK was the Sewel Convention,2 embodied in a formal memorandum of understanding that stated that the UK Parliament would not normally legislate on devolved matters without the consent of the devolved legislatures, a convention designed to foster good inter-governmental relations between central and devolved administrations. But this ‘good governance’ convention was one of the casualties of Brexit. Taking industrial policy in the broad, multidimensional sense of the term (Berry et al., 2021; Rodrik, 2005), a harbinger of new forms of contested governance surfaced in the arcane field of sanitary and phytosanitary (SPS) standards setting. Anticipating the likely impact of Brexit, a leaked document from the Department for Environment, Food and Rural Affairs (DEFRA) said that the US would press the UK to relax measures to protect humans, animals and plants as a condition of a trade deal. DEFRA acknowledged that relaxing SPS regulation in the UK could have damaging repercussions on reserved policy domains (like trade) as well as on devolved policy domains (like public health and agri-food), saying: ‘Weakening our SPS regime to accommodate one trade partner could irreparably damage our ability to maintain UK animal, plant and public health, and reduce trust in our exports’ (Pickard, 2019, n.p.). The leaked document also highlighted the constitutional implications by noting that, as SPS policy is a devolved matter, the governments in Scotland and Wales could ‘fight any moves by London to relax SPS regulations’ (ibid.).

Governing industrial policy  207 But the anticipated problems in the SPS field have been dwarfed by the scale of contested governance problems in two other fields – post-Brexit regional policy and the UK Internal Market Act – which have triggered real concerns for the territorial integrity of the UK state. Post-Brexit Regional Policy As we have seen, as well as signalling a new external relationship with the European Union, Brexit also signals a new internal relationship because it disrupts the inter-governmental arrangements between the nations of the UK, raising questions about the integrity of the devolution settlements. Indeed, the First Ministers of Scotland and Wales were so concerned about the perceived centralization of political power that they issued a joint response to the EU (Withdrawal) Bill, saying it was ‘a naked power-grab, an attack on the founding principles of devolution and could destabilise our economies’ (Welsh Government, 2017a, n.p.). In short, the regional development debate became inextricably linked to the Brexit debate because the latter affected the former in two ways. First, Brexit involves the repatriation of powers from the EU to the UK and some of these powers – with respect to regional policy, agri-food policy and environmental policy, for example – could be transferred to the central state even though they are all critical to place-based economic development policy, which is a devolved competence. Second, these political conflicts over Brexit have soured relations between the devolved administrations and central government to such an extent that the level of inter-governmental trust has plummeted to an all-time low, making it that much more difficult to secure the multilevel partnerships and information-sharing arrangements on which multi-scalar regional development policy is predicated. The UK Shared Prosperity Fund (SPF) is a case in point. Ostensibly, the replacement for EU Structural Funds, the SPF was supposed to be the subject of a consultation exercise before the end of 2018, but it has been subject to successive delays on account of the Brexit crisis, so much so that the devolved administrations have been kept in the dark about the scale of the fund, how the money will be allocated and who will control its deployment. A potentially momentous change was first signalled by Boris Johnson, during his Conservative Party leadership campaign, when he said that he wanted Whitehall to have a strong hand in managing the fund, a position that was quickly rejected by the Welsh Government, which said: ‘We explicitly and vigorously reject any notion of a UK centralisation of regional economic development policy, including the creation of a Whitehall-managed UK Prosperity Fund’ (Welsh Government, 2017b, p. 21). Being more dependent on EU Structural Funds than other parts of the UK, the Welsh Government took the lead in the campaign for the funds to be devolved when they were repatriated from Brussels to London, and its political demands were succinctly captured in the slogan ‘Not a penny less, not a power lost’. But these demands came to nought when the UK Government unilaterally decided that it would retain full control of the funding schemes that were to replace the EU Structural Funds. Two new schemes were introduced as stop gaps before the flagship SPF was to be launched in 2023 and both schemes were branded as part of the ‘levelling-up’ agenda that Boris Johnson’s team had designed to placate the ‘red wall’ constituencies that the Tories had won from Labour in the 2019 general election. Although much has been made of the rhetoric of the levelling-up policy agenda, a sober analysis of its substance concluded by saying: ‘It is diffi-

208  Handbook of industrial development cult to avoid the gravamen that policy is being driven, rather nakedly, by political calculation rather than a real concern to address longstanding problems’ (Tomaney and Pike, 2020, p. 4). This criticism was widely endorsed when the two schemes – the Community Renewal Fund and the Levelling Up Fund – were rolled out for the period 2021–22. Taking Wales as an example, the first point to note is the scale of the funding compared to the EU funding it is intended to replace. Wales was receiving around £375 million a year from EU Structural Fund programmes. The Community Renewal Fund, which is intended as a pilot for the SPF has just £220 million (90 per cent revenue) for the whole of the UK in 2021/22. If Wales attracted 5 per cent of this, it would amount to around £11 million. For its part, the Levelling Up Fund has £4.8 billion (capital) up to 2024, with £800 million of this earmarked for Wales, Scotland and Northern Ireland. For the first round, the UK Government has said at least 9 per cent of total UK allocations will be set aside for Scotland, 5 per cent for Wales, and 3 per cent for Northern Ireland. With £600 million expected to be released in 2021–22, a 5 per cent share would amount to some £30 million (Welsh Local Government Association, 2021). Aside from the scale of the new funding schemes, local government leaders in Wales were also concerned by a series of other issues, especially (1) the opacity of the methodology that established which places were eligible for funding; (2) the wholly inadequate timescales by which projects were to be designed and delivered; and (3) the political marginalization of the Welsh Government as the UK Government sought to deal bilaterally with individual local authorities, a stance that undermined the previous emphasis on building regional consortia of local authorities for city deals and growth deals and destroyed the trilateral dialogue between the UK Government, Welsh Government and local government on which such deals were based in Wales (Waite and Morgan, 2019). Local government leaders were alarmed by these new forms of contested governance because ‘the importance of maintaining a three-way dialogue stands out from the points made above. It could become very confusing if different tiers of government are giving different messages, developing competing initiatives or, worse still, contradictory ones’ (WLGA, 2021, p. 4). The changing governance of regional policy funds – from Brussels to London – might be construed as a simple change of form, a spatial shift of little consequence to the substantive nature of the funds. But nothing could be further from the truth because the EU funds were allocated on the basis of socio-economic need, while the UK funds are allocated on the basis of a competitive bidding process in which the principle of need has been displaced by the principle of excellence for allocative purposes. In other words, while the EU regional policy regime explicitly acknowledged uneven regional development, the new UK regional policy regime is in effect treating regional unequals equally, which runs counter to any serious definition of territorial justice (Morgan, 2006). The United Kingdom Internal Market Act The United Kingdom Internal Market Act (UKIMA) came into effect on 18 December 2020 and it represents the lowest point of inter-governmental relations in the entire history of democratic devolution in the UK. Not surprisingly, the Welsh and Scottish Parliaments refused to give their legislative consent to the Internal Market Act. The key provisions of the UKIMA are the following: new rules on how legislatures and governments in the UK can legislate to regulate goods and services in future; the regulation of professional qualifications in the UK; the implementation of the Protocol on Ireland-Northern Ireland; giving UK ministers new

Governing industrial policy  209 spending powers in devolved areas; and reserving powers for the UK Government related to subsidy control. Of all these provisions, perhaps the most controversial are the mutual recognition principle (Section 2) and the financial assistance powers (Section 50). The mutual recognition principle for goods means that goods made, or imported into, one part of the United Kingdom that comply with relevant legislative requirements in that part, can be sold in the other parts of the United Kingdom without having to comply with any relevant legislative requirements in those other parts. This principle in effect means that the devolved administrations cannot regulate the supply of goods in the Celtic nations if they are deemed to comply with regulations in England, thereby neutering their policies in all the devolved areas. Section 50 of the Act gives the UK Government wide powers to provide financial assistance to any person for, or in connection with, a wide range of specified purposes. These purposes include promoting economic development, providing infrastructure, supporting cultural activities and events, and supporting educational and training activities and exchanges. The financial assistance powers extend to funding activities in policy areas devolved to the Celtic nations. It was under these financial powers that the UK Government launched the Community Renewal Fund and the Levelling Up Fund (Welsh Parliament, 2021). The UKIMA has proven to be so combustible that it triggered an unprecedented joint letter of protest from the three devolved administrations, part of which is summarized in Box 12.1.

BOX 12.1 MINISTERS CALL FOR AN END TO BYPASSING OF DEVOLVED GOVERNMENTS As Ministers in the Devolved Governments of Wales, Scotland and Northern Ireland, we wish to register our shared concerns about the UK Government’s decision to bypass democratically agreed devolution arrangements to deliver the Levelling Up and Community Renewal Funds announced at Budget 2021. We share the aim to spread inclusive economic growth more widely and take the opportunity to simplify systems post EU exit. For that reason, we believe monies to replace EU funds should be allocated in full through the Devolved Governments and successful structures that already exist specifically to deliver economic development to address the needs and opportunities of the people of Wales, Scotland and Northern Ireland rather than through a new, separate layer of bureaucracy. The UK Government ignored the Devolved Governments’ efforts and requests to input to the development process for these funds for almost three years and is now using powers under the UK Internal Market Act to bypass us completely. It is ignoring our respective devolution arrangements, delivering funding to meet Whitehall’s priorities rather than those of the people of Wales, Scotland and Northern Ireland. This must be addressed before further policy development takes place on the Shared Prosperity Fund. Denying us any meaningful input, harms the effectiveness of these funds, will duplicate resources, and risks value for money and the achievement of better, fairer outcomes, which our communities and people deserve. Source: Welsh Government (2021).

The UKIMA embodies a new centralism in the UK Government and this has triggered new concerns for the future territorial integrity of the UK. Significantly, these concerns have come not so much from Scotland, where Scottish independence is part of the SNP government’s manifesto, but from Wales, where the Labour Government has always been viscerally commit-

210  Handbook of industrial development ted to the UK’s territorial integrity. But the First Minister of Wales, Mark Drakeford, has led the fight against the new centralism by criticizing the UK government for being ‘aggressively unilateral’ and for its ‘belief that the best way to deal with [devolution] is to bypass it, to marginalise it, to act as if devolution didn’t exist’ (Drakeford cited in Zorzut, 2021). Such has been the unprecedented scale of the opposition to UKIMA in Wales that the Welsh Government has sought a judicial review of the Act on two grounds: (1) that it implicitly repeals parts of the Government of Wales Act 2006 in a way that diminishes the Welsh Parliament’s legislative competence; and (2) that the Act confers power on the UK Government that could be used by UK ministers to substantively amend the Government of Wales Act in a way that cuts down the devolution settlement. The politics of inter-governmental relations have arguably never been so toxic in the 21-year history of devolution and this is one of the new challenges of post-Brexit Britain: how to pursue regional and industrial policies in the context of a multilevel polity where central and devolved governments are mired in such an adversarial relationship.

CONCLUSIONS This chapter sought to address two concerns within the good governance agenda. First, the need to take multi-scalar relationships seriously, not just from the technical point of view that argues that they should be better coordinated (Barca, 2009), but also in terms of their political dynamics, and how one scale of government can constrain or enable action at another scale (Chubb, 1982; Marques and Morgan, 2021). Second, this chapter also brought to the discussion the concept of good enough governance (Grindle, 2004, 2011), and how small incremental change may be the best (potentially the only) strategy to improve the quality of institutions in the medium to long term. It drew on two significantly different examples of how these two dimensions interact. In the Valencian case, we adopted a bottom-up strategy and examined the institutional evolution of a region over the past couple of decades. In the UK case, we adopted a top-down view to analyse how relationships between the national and European levels are affecting the competencies, capabilities and resources of devolved governments. We would argue that within the emerging body of research on subnational quality of governance, the two issues raised here remain important blind spots. The complex and often confusing multi-scalar governance architectures (Hooghe and Marks, 2001; Hooghe et al., 2016), render comparison across countries or jurisdictions difficult. Even studying different policy arenas within the same country (e.g., health and innovation policy) may lead to significantly different conclusions in terms of how different scales interact and the opportunities for institutional improvement. Our suggestion is that studies of regional governance should make a greater effort to engage with political science (e.g., Weible and Sabatier, 2017), which offers a panoply of theories on the policy process that could be adopted and modified to shed light on these matters. This would also contribute to a more comprehensive and informed conception of how policy processes unfold – for instance, in terms of the power asymmetries between different actors or the importance of both state and non-state actors for the design and implementation of regional policy.

Governing industrial policy  211

NOTES 1.

We use the term region to refer to the autonomous community of Valencia, governed by a devolved parliament. This is distinct from the province of Valencia, a smaller administrative unit and one of the three provinces that form this autonomous community, and from the city of Valencia, the major city within the autonomous community. 2. See https://​www​.parliament​.uk/​site​-information/​glossary/​sewel​-convention/​. Accessed 19 August 2022.

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212  Handbook of industrial development Grindle, M.S. (2004). Good enough governance: poverty reduction and reform in developing countries. Governance, 17(4), 525–48. Grindle, M.S. (2010). Good governance: the inflation of an idea. Faculty Research Working Paper Series, No. RWP10-023. Harvard University, Center for International Development. Grindle, M.S. (2011). Good enough governance revisited. Development Policy Review, 29(S1), s199–s221. Gunko, M., Kinossian, N. and Pivovar, G. (2021). Exploring agency of change in small industrial towns through urban renewal initiatives. Geografiska Annaler: Series B, Human Geography, 103(3), 218–34. Hadjimichalis, C. (2018). Crisis Spaces: Structures, Struggles and Solidarity in Southern Europe. London: Routledge. Hickey, S., Sen, K. and Bukenya, B. (2014). The Politics of Inclusive Development: Interrogating the Evidence. Oxford: Oxford University Press. Hooghe, L. and Marks, G. (2001). Types of multi-level governance. European Integration Online Papers (EIoP), 5(11). Accessed 19 August 2022 at https://​papers​.ssrn​.com/​sol3/​papers​.cfm​?abstract​_id​=​ 302786. Hooghe, L., Marks, G. and Schakel, A.H. et al. (2016). Measuring Regional Authority: A Postfunctionalist Theory of Governance, Volume I. Oxford: Oxford University Press. House of Lords (2017, 19 July). Chapter 2: Devolution, the UK and the EU. In European Union Committee, House of Lords, Brexit: Devolution: 4th Report of Session 2017-19. London: House of Lords. Iammarino, S., Rodríguez-Pose, A., Storper, M. and Diemer, A. (2020). Falling into the Middle-Income Trap? A Study on the Risks for EU Regions to be Caught in a Middle-Income Trap. Brussels: European Commission. IvieLAB (2019). La productividad de la Comunitat Valenciana en el contexto regional: determinantes. Valencia: IvieLAB. López-Estornell, M., Barberá Tomás, D., García-Reche, A. and Mas Verdú, F. (2014). Evolution of innovation policy in Emilia-Romagna and Valencia: similar reality, similar results? European Planning Studies, 22(11), 2287–304. Marques, P. and Morgan, K. (2021). Getting to Denmark: the dialectic of governance & development in the European periphery. Applied Geography, 135, Article 102536. McCann, P. (2016). The UK Regional-National Economic Problem. London: Routledge. Milanovic, B. (2019). Capitalism, Alone: The Future of the System that Rules the World. Cambridge, MA: Harvard University Press. Montero, S. and Chapple, K. (2019). Fragile Governance and Local Economic Development: Theory and Evidence from Peripheral Regions in Latin America. London: Routledge. Morgan, K. (2006). Devolution and development: territorial justice and the North–South divide. Publius: The Journal of Federalism, 36(1), 189–206. Morgan, K. (2007). The polycentric state: new spaces of empowerment and engagement? Regional Studies, 41(9), 1237–51. Morgan, K. (2016). Collective entrepreneurship: the Basque model of innovation. European Planning Studies, 24(8), 1544–60. Morgan, K. and Marques, P. (2019). The public animateur: mission-led innovation and the ‘smart state’ in Europe. Cambridge Journal of Regions, Economy and Society, 12(2), 179–93. Navarro, M., Valdaliso, J.M., Aranguren, M.J. and Magro, E. (2014). A holistic approach to regional strategies: the case of the Basque Country. Science and Public Policy, 41(4), 532–47. Nistotskay, M., Charron, N. and Lapuente, V. (2015). The wealth of regions: entrepreneurship and wealth in Europe. Environment and Planning C, 33(5), 1125–55. Organisation for Economic Co-operation and Development (OECD) (2012). Promoting Growth in All Regions. Paris: OECD Publishing. Peiró‐Palomino, J., Picazo‐Tadeo, A.J. and Rios, V. (2020). Well‐being in European regions: does government quality matter? Papers in Regional Science, 99(3), 555–82. Pickard, J. (2019, 7 October). Warning of threat to UK-EU trade from US food demands. Financial Times. Accessed 19 August 2022 at https://​www​.ft​.com/​content/​778b2d6c​-e830​-11e9​-a240​-3b065ef5fc55.

Governing industrial policy  213 Rodrik, D. (2005). Growth strategies. In P. Aghion and S.N. Durlauf (eds), Handbook of Economic Growth (Vol. 1A). Amsterdam: North-Holland, pp. 968–1014. Rodrik, D., Subramanian, A. and Trebbi, F. (2004). Institutions rule: the primacy of institutions over geography and integration in economic development. Journal of Economic Growth, 9(2), 131–65. Schakel, A.H., Hooghe, L. and Marks, G. (2014). Multilevel governance and the state. In S. Leibfried, E. Huber and M. Lange et al. (eds), The Oxford Handbook of Transformations of the State. Accessed 19 August 2022 at https://​garymarks​.web​.unc​.edu/​wp​-content/​uploads/​sites/​13018/​2016/​12/​2014​ -schakel​-marks​-hooghe​-Multilevel​_Governance​_and​_the​_State​.pdf. Sotarauta, M. and Beer, A. (2017). Governance, agency and place leadership: lessons from a cross-national analysis. Regional Studies, 51(2), 210–23. Teichman, J.A. (2016). The Politics of Inclusive Development: Policy, State Capacity, and Coalition Building. London: Palgrave Macmillan. Tomaney, J. and Pike, A. (2020). Levelling up? Political Quarterly, 91(1), 43–8. Waite, D. and Morgan, K. (2019). City deals in the polycentric state: the spaces and politics of Metrophilia in the UK. European Urban and Regional Studies, 26(4), 382–99. Weible, C.M. and Sabatier, P.A. (eds) (2017). Theories of the Policy Process (4th edition). New York: Routledge. Welsh Government (2017a, 13 July). Joint statement from First Ministers of Wales and Scotland in reaction to the EU (Withdrawal) Bill. Cardiff: Welsh Government. Accessed 19 August 2022 at https://​gov​ .wales/​joint​-statement​-first​-ministers​-wales​-and​-scotland​-reaction​-eu​-withdrawal​-bill. Welsh Government (2017b). Securing Wales’ Future: Transition from the EU to a New Relationship with Europe. Cardiff: Welsh Government. Welsh Government (2021, 24 March). Ministers call for an end to bypassing of devolved governments. Accessed 19 August 2022 at https://​gov​.wales/​ministers​-call​-for​-an​-end​-to​-bypassing​-of​-devolved​ -governments. Welsh Local Government Association (WLGA) (2021, 27 May). Wales and Levelling Up/Community Renewal Funds [Internal report]. Cardiff: WLGA. Welsh Parliament (2021, August). UK Internal Market Act 2020. Cardiff: Welsh Parliament. World Bank (2017). World Development Report 2017: Governance and the Law. Washington, DC: World Bank. Zabala-Iturriagagoitia, J.M., Gutiérrez-Gracia, A. and Jiménez-Sáez, F. (2008). Benchmarking innovation in the Valencian Community. European Urban and Regional Studies, 15(4), 333–47. Zorzut, A. (2021, 5 March). Welsh first minister says UK ‘is over’ in blistering attacks on No 10’s handling of devolution. The New European.

PART III SECTORS

13. Spatial implications of the platform economy: cases and questions Martin Kenney, John Zysman, Dafna Bearson and Camille Carlton

INTRODUCTION Digital online platform firms are reorganizing the geography of capitalist accumulation. This chapter explores some instances of the impact of platforms on space from three perspectives. At the macro level, key platform firms are shifting the locus of economic activity to specific regions (e.g., US West Coast). We will show this in detail at the meso level, where Amazon is reorganizing the logistics industry. Next, we demonstrate this at the micro level, where Google Maps (GM) is changing the ways in which people discover and decide upon local service providers. At each level, the intermediation of a platform changes the geography of value creation and capture. In sum, the impact of these global platforms is that they extract value across geographies but centralize it in a very few locations. The implications of this observation are profound, as these platforms can be understood as gigantic machines for organizationally and spatially centralizing value and thus power (Kenney and Zysman, 2020). Online digital platforms can be considered a new organizational form that consists of a relationship between the platform and its ecosystem of complementors and users (Stark and Pais, 2020; Thomas, Autio and Gann, 2014; Tiwana, 2013). The organization and geography of the economy is being reorganized by this organizational form in the same way as, almost a century earlier, the Chandlerian firm became the dominant organizational form (Chandler, 1993). Indeed, the migration of increasingly large sectors of economic activity to digital platforms is propelling an economic shift that is as transformative as the rise of Fordism in the early to mid-20th century (on Fordism, see, e.g., Aglietta, 1979; Lipietz, 1982; on platforms, see Kenney and Zysman, 2016; Srnicek, 2017). The rise and maturation of the ‘platform economy’ is already having profound effects on labor and competition (Bearson, Kenney and Zysman, 2021; Kenney, Bearson and Zysman, 2021; Kenney and Zysman, 2016; Thelen, 2018). Importantly, for this chapter, and by historic analogy, the Fordist mass production paradigm had a powerful impact on the geography of economic activity – not simply on the rise of the industrial Midwest but also on the design of the US city. Fordism reshaped the spatial relations of capitalism, thereby creating, for its era, a ‘spatial fix’ (Harvey 1982). Similarly, the platform economy is recasting spatial relationships that will certainly generate and, perhaps, has already generated a new spatial fix. One overt indicator is the recognition and discussion around the powerful effects that digitalization will have on ‘smart cities,’ which conceive of urban dwellers as being embedded in a digitally defined landscape (e.g., Kitchin, 2015; Richardson, 2020). Even more concretely, perhaps echoing the efforts by the automobile firms in the postwar era to close down streetcar systems, is Google Sidewalk Labs effort to build a new data-intensive, Google-compatible neighborhood on the Toronto waterfront. More to the point, consider how Uber and Lyft are reconfiguring transportation patterns 215

216  Handbook of industrial development (Gehrke, 2020) and Airbnb is reconfiguring the nature and use of housing (see, for example, Wachsmuth and Weisler, 2018). Platform firms are creating new spatial arrangements and relationships, from housing hotspots to city arrangements, which further their value creation and accumulation goals (Kenney and Zysman, 2020; McNeill, 2021). The geographic implications of the emergence of the digital platform are significant because digital platforms have become the intermediary for an increasing proportion of all economic activity (Kenney et al., 2021). We therefore emphasize the power that digital platforms have accumulated over the last approximately 15 years as they have intermediated between ever-increasing segments of social and economic life. We next explore these questions further in case studies of Amazon and GM – effectively, these platforms exemplify many of the critical issues raised by the emergence of a platform economy and their consequences for spatial organization.

DIGITAL PLATFORMS AND ECONOMIC GEOGRAPHY Before turning to our core argument, we situate the current understanding in economic geography of the impact of digital technology on place. Until very recently, the majority of research by economic geographers on the impacts of digital technologies was undertaken during or in the aftermath of the 1990s’ Internet bubble (Castells, 2000; Malecki, 2002; Zook, 2000).1 With the collapse of the Internet bubble, interest in the geographic consequences of digital technologies waned and has only been reborn recently as geographers became interested in the impacts of digital platforms (one early contribution was Langley and Leyshon, 2017). In 2011, Rob Kitchin and Martin Dodge revived the discussion when they argued that software, through its ubiquity and indispensability in an increasing number of activities, was blurring or even determining the use of space. Moriset and Malecki (2009, p. 271) concluded that the ‘main effect of IT-enabled informational ubiquity is to provide individuals, enterprises, and communities, wherever on Earth, with a greater choice for shaping an enterprising future.’ This conclusion was prescient and suggested that the digital technologies allowed a greater dispersion of economic activity and increased the ability of producers to reach ever-more distant consumers. The prevailing view regarding the constitutive powers of the software and code was that, while important, the changes driven by the Internet reinforced the existing business structures and arrangements (see Lessig, 2009). As with many scholars, they did not foresee the rise to a monopoly position of the online platform firms. Some labor researchers also shared the view that the changes the Internet has caused are incremental. For example, in 2018, the International Labour Organization concluded that ‘work on these platforms resembles many long-standing work arrangements, merely with a digital tool serving as an intermediary’ (Berg et al., 2018, p. xv). This conclusion understands platforms as being merely an ‘intermediary,’ rather than gatekeepers, data aggregators, and, in fact, untrammeled powers in their particular markets (see Cutolo and Kenney, 2020 for a more realistic evaluation). Their conclusion is true, in the same way that the introduction of the moving assembly line did not change the fact that workers in factories were employed to produce and received payment for the work. Such a conclusion would not take into account, however, that the context for work had changed profoundly, as the assembly line allowed the reorganization of production, created entirely new work categories, and led to a new geography of capitalist accumulation and competition while transforming consumption patterns.

Spatial implications of the platform economy: cases and questions  217 The debate has only recently begun to comprehend the geographical consequences of the rise of the platform economy. While economic geographers have made progress in analyzing the relationship between space, digitalization and the role of networks, they have focused far less attention on the fact that certain key Internet firms are not just websites or even massive multinationals, but rather they are online platforms, serving as intermediaries and gatekeepers connecting enormous numbers of users and customers with service providers, advertisers and others. In other words, they have been less concerned with the power that these platforms wield and, consequently, have missed the impacts of this power on the spatial organization of this new way of organizing the economy (for an important exception, see Grabher and Van Tuijl, 2020). More recently, scholars have advanced the proposition that platforms are a new institutional form that conforms neither to market nor hierarchical logic (Frenken et al., 2018; Stark and Pais, 2020; Van Dijck, Poell and De Waal, 2018). Whether the form should be understood as new institutional logic or the economic-technical base of a new regime of accumulation, there is increasing evidence that the platform economy is reshaping the geography of economic activity (Grabher and König, 2020; Kenney and Zysman, 2016). Before we turn to the spatial implications, let us re-emphasize the emerging centrality of these digital platforms, as this is fundamental to grasping their spatial implications. The apex online platform giants such as Amazon, Apple, Facebook, Google, and Microsoft2 are now central firms in capitalist economies. In August 2021, these five platforms were the most valuable firms in the world. The two Chinese platform giants, Tencent and Alibaba, had tumbled because the Chinese government launched a far-reaching crackdown on them (McKnight, Kenney and Breznitz, 2021).3 Along with a number of sectoral platform firms, such as Airbnb, Expedia, Priceline, Saleforce, Shopify, Uber, and so on, these have become the intermediaries organizing, reorganizing or even transforming a host of industries (Parker, Van Alstyne and Choudary, 2016; Van Dijck, 2013). Not only are platforms organizing markets by disintermediating incumbents and providing opportunities for new entrants, in many respects, but they are also private regulators of commerce.

SPACE AND POWER IN THE PLATFORM ECONOMY The implications of platforms for geography have been underestimated despite the fact that, as online intermediaries and connective agents, the geographic reach of these platform firms is staggering. It is perhaps only rivaled by the petrochemical giants such as Standard Oil, Royal Dutch Shell, and British Petroleum at the peak of their power. This reach is illustrated by the fact that Amazon, Facebook, Messenger, and WhatsApp and Google Chrome, Drive, Gmail, Maps, and Search have a billion or more monthly active users. Consequently, the implications of these platforms as a space for social and economic activity is enormous. Online digital markets are characterized by network dynamics and a winner-take-all nature, as argued by Shapiro and Varian (1998), where the overarching goal of these firms is to ‘tip the market.’ This concentration provides an opportunity for the dominant platform to extract value from the other actors in the ecosystem. Certainly, there is an ideological aspect to this, as Silicon Valley firms, in particular, nurtured a culture, as articulated by Mark Zuckerberg, of ‘moving fast and breaking things’ (Taplin, 2017), while venture capitalists suggested that entrepreneurs should ‘not ask permission, but rather forgiveness’ (Davies, 2019).4

218  Handbook of industrial development The winner-take-all nature of online platform competition means that they are not simply intermediaries; rather, they are monopolistic or oligopolistic intermediaries. That is, platforms become the sole or one of a limited number of digital intermediaries between users and sellers, granting them immense power to channel transactions and extract value from them. To illustrate, if advertisers wish to connect with potential customers, there are only a few paths – predominantly advertising goes through the Google, Facebook, or Amazon networks. In 2020, Google dominated the US advertising market with 28.9 percent, Facebook with 25.2 percent, and Amazon with 10.3 percent of the total revenues (Bruell, 2021). Because the global telecommunications infrastructure already exists and, in particular, the availability of smartphones, the rapidity with which online platforms can add users is astonishing. For example, Google Drive was launched in 2012 and by 2018 had 1 billion users. When compared to the titans of the past, the markets in which digital platform firms operate are, by contrast, even more concentrated. To provide a few comparisons, despite enormous consolidation, there are still 14 significant-sized auto makers (ex-China), at least six large private petroleum industry firms (and many more if one considers the national oil firms), and an even larger number of steelmakers. Outside China, there is only one major search engine, one or two social media sites, one e-book seller, one or, perhaps two online merchants, one mapping program, two smartphone operating systems, and two online travel sites (though Google Travel is threatening the current market leaders, Expedia and Booking.com). The levels of concentration in platform-organized markets is remarkable. If the sectoral concentration is remarkable, the geographic concentration of the mega platforms is even greater, as the headquarters for these firms are almost entirely concentrated on the West Coast of the United States. As noted, the most important exception, the largest single market in the world, China, is closed to these firms. The few platform markets within which non-West Coast firms are significant are vertical markets, such as travel and music. Yet, even the travel and music sectors are experiencing encroachment from Apple, Google, and Amazon. For example, Google Travel has become the largest online travel agency (McBride, 2019). Crucial to an examination of the spatial content of platforms is that, often, these digital platforms have replaced activities that were previously local, and centralized them onto a platform in the ‘cloud.’ Consider, for example, one of the earliest platforms, Craigslist, which absorbed classified advertisements from newspapers. While it only charged for employment advertisements, it destroyed classified advertising – one of the mainstay income sources for local newspapers (Seamans and Zhu, 2014). Similarly, online travel agencies such as Expedia and Booking.com control approximately 39 percent of all online bookings (Kelly, 2017), thereby replacing local travel agents. Amazon, which we discuss later in more detail, is directly leading to an ongoing shake-out in bricks-and-mortar retail globally (Mitchell and LaVecchia, 2016). Finally, Google, the global giant, is increasingly important in finding merchants locally, forcing merchants to advertise on its search platform and thereby extracting value from the local market and centralizing it. The geography of value creation and capture are in fundamental flux, as the integration of ever-more businesses into the various platform’s ecosystems continues. At the local level, ever-more firms are dependent upon Google Search and Maps, Yelp, and Facebook to entice customers, and for this must buy advertisements, thereby transferring value from the local economy to the platform.5 Built upon the ubiquitous networks that Castells (2000) documents, the scale, pervasiveness, and reach of the platforms is paradoxically wedded by a remarka-

Spatial implications of the platform economy: cases and questions  219 bly granular localness created by user-generated local content. The value transfer produces ever-greater spatial inequality, as the platform accumulates ever-more users and data. The next two sections examine some features of two mega platforms, Amazon and GM, to indicate tendencies and developments of the platform economy more generally.

AMAZON – THE ECONOMIC GEOGRAPHY OF A PLATFORM GIANT The Amazon case has been examined in depth by many, most famously by Lina Khan, but also by us (Kenney et al., 2021; Khan, 2017). In this chapter, our concern is the geographic implications of the case, which have not been a focus in most other literature. We utilize the Amazon case as a dramatic example that heightens our understanding of how, in general, platforms reshape geography. While Amazon’s expansion methods are not intrinsically different from bricks-and-mortar institutions practicing e-commerce, Amazon’s importance is dramatically greater due to the unique logic of platforms. Through this case, we demonstrate several geographical development implications: ● first, the transformation of the physical shop-based retail sales model; ● second, the borderless nature of Amazon vendors; ● third, the increased reliance on geographically dispersed contractors for fulfillment processes, which ensures lower labor costs than existing firms; and ● fourth, the pioneering of a logistics system that is a digital Taylorist work process. Each of these contributes to putting pressure on wages and working conditions in traditional retailers, logistics firms, and the entire economy. Amazon is reshaping geography to suit its business model. Importantly, it also gains advantage by operating across geographically situated regulatory regimes, creating a form of regulatory arbitrage – this was particularly true during its early growth. In one sense, but only in a limited sense, Amazon confirms Cairncross’s (1997) claim that the Internet results in the death of distance, as an increasing number of people order online, and have items delivered rapidly. But Amazon is also building a logistics infrastructure in ways that have significant implications for the spatial economy of cities (on code/space, see Kitchin and Dodge, 2011). Before turning to the geography of fulfillment, let us remind ourselves of the basics. Amazon was established in Seattle in 1994 as an e-commerce online bookseller, and has since become the largest online retailer. After 2000, when Amazon opened its website to third-party vendors it became possible for any merchant anywhere to sell through Amazon. This enabled anyone to become a retailer – there was no need for a store or even normal place of business – a spare bedroom in any city in the US or anywhere else could become a ‘shop.’ By 2021, Amazon overtook Walmart as the largest US retailer in terms of gross merchandise value, responsible for approximately 40 percent of all online retail sales (Deagon, 2021). The movement of sales online reorganized the process and locations for fulfilling customers’ orders. This, in turn, is changing the location of employment and types of employees needed. Instead of customers coming to physical stores, for most goods, purchases can be made online and delivered from a warehouse normally located outside town. This brings us to the geography of fulfillment and its implications.

220  Handbook of industrial development The Geography of Fulfillment The Amazon website was established as a virtual bookstore accessible to anyone with an Internet connection. Initially, Amazon used Ingram Books, a book distributor, to handle all logistics. Four years after it was founded, Amazon began selling compact disks and video cassettes, as they had similar physical characteristics to books that made expanding logistics simple. In 1999, toys and electronics were added to the firm’s inventory. The company continued its rapid expansion into selling other products, gradually outgrowing its relationship with distributors. For Amazon, adding and extending product lines was relatively easy as it simply entailed building a new catalog and placing a new tab on its home page. This constant expansion of product offerings meant that the fulfillment system was obliged to grow and indeed innovate. Amazon’s computer system, warehouses and later in-house logistics system, and management team, not only grew in number and size, but as importantly, in capability to handle an ever-greater variety of products of varying sizes and shapes. In 1997, Amazon opened its first warehouses: one in Seattle to serve the West Coast and one in Delaware to serve the East Coast. In 1999, it opened warehouses in a number of other states, including Fernley, Nevada, largely to serve the rapidly growing California market (MWPVL International, 2019). To avoid paying taxes, Amazon exploited differences in state regulation in its location decisions, taking advantage of a feature of the US federal system – namely, that the shipping firm is not required to collect state sales taxes on goods shipped interstate due to the interstate commerce clause in the Constitution. This regulatory arbitrage was a powerful subsidy to the online retailer, as the unpaid taxes largely covered the shipment costs (Einav et al., 2014). Effectively, federal law provided a subsidy and also shaped the initial location of Amazon’s warehouses, as having a point of presence within a state meant that the firm would then have to collect taxes on all products shipped into that state. As Amazon grew and was shipping ever-more merchandise, more distribution centers were needed. Amazon faced a conundrum – namely, it could expand in the states where it already had distribution centers (Washington, Delaware, Georgia, Kansas, Kentucky, and Nevada) or it could expand into another new state. However, expansion into another state meant that it would have to begin charging sales tax for all sales that went to that state. Eventually, in response to regulatory pressure from states and local vendors as well as ever-increasing volumes, the decision was made to no longer avoid sales taxes. The strategic decision to establish dispersed facilities rather than focus on a few major centers came in 2005, when Amazon launched Amazon Prime. Amazon Prime, which promised free two-day delivery anywhere in the US, locked in customers and drove even higher volumes. However, it meant that Amazon faced a new logistics challenge. Fulfillment now became the key cost for Amazon’s business. No longer constrained by local tax evasion, the location of Amazon’s distribution centers changed dramatically. Facilities were soon established outside all major populations. Rapid, free delivery replaced tax savings and Amazon shifted its concentration to lowering the cost of logistics. The significance of this logistics shift, which dramatically accelerated in the 2010s, was that with the enormous success of Amazon Prime, Amazon needed to deliver products more rapidly, while containing the resulting cost. To meet the demands of Amazon Prime, Amazon created an entirely new logistics system that included warehousing, fulfillment, long-haul trucks, and even an air freight fleet. Amazon rapidly increased its warehousing footprint, nationally and globally, but last-mile delivery was contracted to the US Postal Service (UPS) and FedEx in the US (and their equivalents

Spatial implications of the platform economy: cases and questions  221 abroad). As volume grew, Amazon was able to extract ever-better rates from these firms. Due to its scale, the shipping rates Amazon negotiated were lower than the rates Amazon Marketplace sellers could get independently from shippers, allowing Amazon to eventually launch ‘Fulfillment by Amazon’ (FBA, see below).6 Second, fulfillment remained one of Amazon’s greatest costs. In 2015, Amazon introduced Amazon Flex, which engaged ‘independent contractors’ that used their own automobiles to deliver packages from Amazon or Amazon-contracted warehouses. This allowed it to put further pressure on its logistics suppliers and force them to take Amazon’s expensive peak load deliveries. To further extend this contractor-based delivery system, in 2018, Amazon signed a contract to purchase 20 000 Mercedes Benz delivery vans that it ‘sold’ to local ‘entrepreneurs’ who wished to start local delivery businesses (Stevens, 2018). These contractors then ‘hired’ or contracted subcontractors to staff the delivery vans, thereby removing the ‘contracting’ responsibility from Amazon. Despite this contractual separation, Amazon monitored all these contractors in real time (Hempstead 2019). Third, Amazon’s effort to build a supply chain expanded to include directly contracting long-haul trucks to move goods. It also leased planes and established a delivery hub in Hebron, Kentucky. The Amazon pilots are employed by a contractor that pays less than other airlines. Finally, in 2016, Amazon received a license from maritime authorities to become an importer-shipper from China (Chamlou, 2018). Coordinating this ever-expanding network of ‘captive’ contracted logistics operations and its own warehouses was accomplished through the application of enormous computational power and specialized software. Fourth, once in place, the logistics system with two-day and often same-day delivery was wielded as a competitive advantage against competitors such as Walmart, eBay, and others. And yet, even though Amazon has built its own logistics system, it continues to contract with FedEx, UPS, and USPS for delivery, taking advantage of other organizations’ geographical strengths or relative costs. Similarly, even as it contracts with warehouse logistics providers like Dynamex, it also competes with them and resells their services to Marketplace sellers. But the key to its expansion is that Amazon has a structural advantage because it has more supply chain data than any other retailer. This provides it with a god-like view into the physical and virtual dimensions of its logistics chain. Fifth, the final important geographical impact was that Amazon began offering fulfillment to its Marketplace vendors – it labels these offerings as having ‘Fulfillment by Amazon’ (FBA). By providing the fulfillment service to its vendors, it further increased its warehouse and delivery volume, thereby decreasing logistics costs per item. This offering had another important effect as the growth of Chinese sellers was facilitated by FBA, as it allowed their products to have the same two-day Prime shipping from an Amazon warehouse as domestic sellers. The Chinese sellers would ship their products to the Amazon warehouse in the US or Europe from where customer orders were fulfilled, thus concealing the fact that the seller’s business location was in China. According to Marketplace Pulse (2019), ‘almost all top Chinese sellers use FBA, while only 75% of top US-based sellers do.’ In addition, firms that used FBA had higher rankings than those that did not.7 As Amazon increases the throughput in its logistics system, it will increase its economies of scale and scope, magnifying its considerable advantages, enabling it to enter yet other markets, further squeezing competitors. The local and regional development implications of the movement of sales online are difficult to capture because there has been little study of the local employment effects. One obvious result of the movement of sales online, a tendency that was reinforced by the COVID-19

222  Handbook of industrial development pandemic, is the transformation of the physical shop-based retail sales model that is leading to the ‘hollowing out’ of many shopping centers and main street shops (Semeuls, 2018). The jobs in these shops are being partially replaced with warehousing and delivery workers, many of whom are contractors, whose place of employment is on the urban periphery. Here again, the COVID-19 pandemic appears to have accelerated this trend. In some cases, the geographical implications are even greater, as seasonal workers may travel to temporarily reside in the towns with Amazon warehouses. A second result is that vendors can operate from anywhere in the world or from their homes to sell online through Amazon, as was shown clearly in the case of Chinese exporters. Third, the Amazon-owned logistics system utilizes large numbers of contractors that ensure that it has lower labor costs than established firms such as UPS or FedEx, which are being replaced. As part of this, the Amazon logistics system has pioneered an all-encompassing digital Taylorist work process. Finally, this combination of ‘innovations’ puts pressure on wages and working conditions at retailers and logistics firms. In regional development terms, Amazon is likely to decrease local employment and contribute to a further hollowing out not only of downtown retail, but also suburban shopping centers as their retail anchor tenants collapse into bankruptcy. Summary Amazon is changing the geography of retailing, logistics, and also production as it replaces stores with home delivery, and the Amazon Marketplace allows vendors from anywhere in the world easy access to customers. As it has expanded, Amazon or vendors on its Marketplace now compete with nearly every retailer, online and offline. The continual entry into new markets, drive to automate, and lower costs result in constant pressure on prices and thus wages in ever-more industries and geographies. It has exploited the fact that as it increasingly became the website upon which consumers searched for products, it could sell advertising, forcing vendors to bid for the all-important ‘Buy Box’, even as it demoted and hid vendors with the lowest prices – something understandable as it gets a commission from every sale and the advertising was a lucrative source of greater profits. Amazon offers remarkable convenience, competitive low prices, and the ability for consumers to purchase from vendors anywhere in the world; however, while not yet demonstrated empirically, the business model appears to be a powerful engine for increasing spatial inequality. The effects of this spatial inequality on labor are multi-faceted. First, clerks in shopping malls are being displaced by workers augmented by robots in warehouses and drivers for last-mile delivery. In the logistics system, UPS and FedEx drivers are being replaced by poorly paid contractors. As local businesses are displaced, as was the case with Walmart in an earlier retail revolution, consumer spending and control is transferred from the community to the headquarters and centers of control to an even greater degree. Second, at the global level, the powerful national and, even, international retailers now have a global competitor that benefits from winner-take-most economics, contributing to greater global concentration in all the countries within which it operates. To summarize, the spatial inequality prompted by Amazon’s business model occurs through the destruction of local retailing, the inherent structure of its Marketplace to put downward pricing pressure on its third-party vendors, its logistics chain using third-party vendors that are paid far less than incumbents, and the relentless warehouse and logistics automation ensuring

Spatial implications of the platform economy: cases and questions  223 ever fewer jobs per dollar of retail sales. Be that as it may, we are only now beginning to understand the social, labor and geographic implications of Amazon’s success.

GOOGLE MAPS While Amazon has reshaped the physical geography of work, Google Maps (GM) has reshaped our mental models of place and space. Google’s mapping tools shift the ways in which we locate ourselves, activities, and places in the physical world. GM is transforming the lived experience of geographic space as well as the competitive dynamics in a wide variety of industries. As a result, GM has become a powerful platform for reorganizing social and economic activity to capture value. However, before diving into the spatial consequences of GM, we begin by exploring the business of GM as an owned digital platform. Maps, while important in the desktop era, have become a vital service in the mobile era – for both users and service providers. Maps are a representation of geography and, as Craig Dalton (2013) observes, ‘maps have a long-running association with sovereign power in the exercise of state programs such as empires, defense, land tenure, and administration’ (Dalton 2013, p. 264). With 80 percent of the US market, GM has become both a platform and a reference. To paraphrase Dalton, in spatial terms, the map denotes existence, and to be ‘on the map’ is mandatory for any entity wishing to be found – overstated perhaps, but today, GM is the manifestation of this. Google Maps Begins and Spreads The key to GM’s success as a business was that almost immediately upon introduction, users began creating applications that rested on top of the GM platform. Google managers quickly realized that the user-generated content from these spontaneous customizations of GM created enormous value. As a result, in June 2005, Google began allowing users to integrate the GM application programming interface (API) into their websites and applications. With the move to the mobile era, the ability of firms and individuals to integrate GMs into their own platforms made businesses such as Uber, DoorDash, Instacart, and many other location-based services, possible. For the gig-based firms, it was vital because now any person with a car could find places without having special knowledge or passing a test (Edwards, 2015), thereby allowing them to use less knowledgeable workers. Initially, GM was free, thereby helping it gain market share against other map applications. However, in June 2018, it was announced that all users of the GM API would have to have a Google billing account to continue to use it, though small-scale users would get a $200 per month credit – a clever strategy as it allowed entrepreneurs and users to experiment. If they created a commercially successful entity, they would have to begin paying Google for its use. The initially generous terms meant GM was quickly adopted. By 2013, the GM API was the most used API in the world, with over 1 million websites using it (Google Maps Platform, 2013). Effectively, every website with a Google Map embedded in it was transformed into a potential Google customer. These design-and-deployment decisions were critical, as users began to innovate on GM and integrate it into their websites. This user involvement was vital for its transition to becoming a powerful platform. Consequently, the degree to which our sense of place and space hinged on these maps expanded.

224  Handbook of industrial development As is typical for a platform, GM has constantly evolved by adding more features and often learning from innovations made by the users in its ecosystem. For example, GM has added features such as Street View, aerial maps, public transport schedules, pedestrian information, hiking trails, the ability to hail an Uber or follow a package, and so forth, even as it increased its knowledge of locales. Today, GM is what Gawer (2021) would denote as a hybrid platform. A combination of a transaction platform (serving as an intermediary between businesses looking to be found and searching customers) and an innovation platform (upon which others have developed complementary innovations by integrated GM to serve as a locational function), GM has reorganized markets and societal behaviors around spatial relationships. Thus began the shift from the professional to anyone being able to contribute to and ‘create’ maps. Originally, the ability of users to interact directly with online mapping tools such as GM led some geographers to argue that a ‘neogeography,’ synonymous with a ‘bottom-up’ democratization of mapping, was emerging (Turner, 2006).8 In some respects, traditional map-making has been overwhelmed by online user-generated content in roughly the same way that Wikipedia overwhelmed encyclopedias. Mapping, with GM being the frontrunner, has become a platform that has winner-take-most dynamics, multi-sided characteristics, lock-ins, user-generated data, and the formation of powerful ecosystems. We suggest that the lack of recognition that maps are now platforms leads to an underestimation of the power of GM – and, most remarkably, a power that is only increasing as lock-in becomes greater and AI is applied to extract more value from the resultant user-generated geographical data. The Ubiquity of GM GM, with its winner-take-most dynamics, multi-sided characteristics, and lock-ins, has formed powerful ecosystems that have fueled its ubiquitous spread, making it an essential feature of daily life. The reach of GM can be measured in terms of volume, where the greatest reach and largest amount of information, almost certainly, is all Android users (and iPhone users that use GM) and basically all desktop users. Complementary to mobile mapping, by being embedded in so many websites, Google receives information every time someone goes to a website and accesses their map – be that an Uber customer or driver, someone searching Booking.com for hotels, or someone using Yelp to search for a nearby restaurant. This provides data regarding potential consumer interest that grants Google two revenue streams – the merchant pays for the click on the map and because of the evidence of consumer interest, advertisements can be served to that consumer. The behavioral change that GM has catalyzed – the widespread habitual practice of referencing GM for directions, restaurant recommendations, and even activities – is even more extraordinary than the pervasiveness of GM across devices, as remarkable as that is. In economic and competitive power terms, GM’s embeddedness in the applications and operations of other firms may be a greater indicator of its ubiquity and power. Consider that Google’s competitors (as Google now has direct travel booking and local business rating systems), Yelp, Booking.com, Expedia, and others all use GM, thereby providing data on map searches directly to Google, while paying Google for usage – effectively, Google can see directly into the core of their businesses. What is significant is that GM is a vital module in the business models of these firms and Google is able to extract value and data – and, should it wish to, can enter their markets, such as lodging or travel, armed with significant prior understanding.

Spatial implications of the platform economy: cases and questions  225 Maps are also becoming an important input for incumbents in other industries. For example, today’s automobiles are sensor-laden connected vehicles with cameras, mechanical, temperature, and numerous other sensors. Initially, most automakers resisted the integration of GM as their default in-automobile navigation systems. And yet, Fiat Chrysler, General Motors, Renault–Nissan–Mitsubishi Alliance, Ford, and other automakers are using GM as a bow to consumer wishes (Narayanan, 2020). The German automakers, BMW, Mercedes, and Volkswagen as a consortium, purchased Nokia Maps in 2015 for $3 billion in an effort to preserve an alternative to GM (Kiley, 2015). As an increasing number of automakers adopt GM, it could become the de facto standard for auto mapping, with a lock-in providing Google with yet another opportunity to generate revenue.9 The generativity of Internet platforms, their ability to be repurposed and integrated into new uses, is what allows the innovative use of the platform resources while integrating new actors into the ecosystem (Zittrain, 2008). This is very evident in the case of GM. To illustrate, insurance claims adjusters use Google Street View to reconstruct an automobile accident scene without visiting the location, thereby viewing locational details digitally and saving time. GM and Street View are integral to Pokémon Go and other real-world, place-based games and thus give rise to greater innovation that straddles our mental and physical interactions with geography (Holly, 2018).10 Yet another application on GM is ‘Plane Finder,’ which maps planes in the skies worldwide in real time. Each of these uncompensated innovators make GM more ubiquitous and more valuable, and, if the innovations become monetizable, they pay for the use of maps. GM’s revenue streams are as diverse as its use cases. First, and fundamentally, GM profits from the data it collects from users, both end users and business users. For instance, when a potential customer interacts with a Google Map that is embedded in a website or application, Google receives two revenue streams – the merchant pays for the click on the map and because of the evidence of consumer interest, advertisements can be served to that consumer. Second, GM monetizes large-scale partnerships with other firms wanting to integrate GM with their own products or services. Between January 2016 and December 2018 alone, Uber paid GM approximately $58 million for use by its drivers and for route visualization for customers (Lyft also uses GM). Here again, the data collected while the car is driving is shared with Google. This approach to monetization expands beyond the tech industry as automakers enable their vehicles to sync with GM, per the demand of customers. Third, as GM has become the intermediary connecting customers in their search for local service providers, it has developed a digital advertising model in which local service providers pay Google for advertisements to generate customers. Finally, the generativity of GM has enabled ever-expanding revenue streams for Google, grounded significantly, in unpaid innovations and content, as discussed above. Still, the implications of GM go beyond the monetization of unpaid contributions; GM’s power in determining digital geography and spatial relationships has real-world effects across social and economic activities. Implications for Geography The ubiquity of GM has had profound spatial consequences. As previously noted, GM, like traditional maps and, in fact, as is the case with Google Search for virtual places, the ability to find something denotes existence. Similarly, but with greater reach than traditional mapping, the platform dynamics of GM has nurtured an uneven playing field when it comes to firms

226  Handbook of industrial development being mapped. For local businesses appearing on the map, in particular, Google’s Map, is becoming critical to social and economic participation. This proof of existence also necessarily means that a business must provide unpaid content, such as pictures and information about the business establishment, to GM. As a restaurant, for example, this increasingly also includes a menu with prices, but as Google also provides customers with the ability to review the local business, it also includes user-generated content from customers. More recently, Google introduced the Google Guaranteed program, which features and guarantees local service providers, thereby directing customers to them for a referral fee. While GM may enable new businesses to be easily found, it can also reinforce existing inequalities. For instance, a larger coffee chain is likely to have greater resources that allow it to correctly set up and market a GM listing, while a small, family business may not. As consumers utilize maps to find businesses, they are more likely to find organizations that can utilize the platform well, as opposed to one that cannot, regardless of consumer preference. On GM, existence can also be guaranteed in the form of advertising. GM has become part of the hegemonic local information package that allows Google to more tightly integrate local firms into its advertising machine. Because, increasingly, searching for local service providers such as plumbers, electricians, locksmiths, and so on, is through Google, it has become the intermediary for service provision, replacing newspapers, television, radio, Yellow Pages, and so forth. The result is that local service providers must pay Google for advertisements to generate customers, and this revenue is extracted from the community and community news media. Effectively, this serves as a counterpart to Amazon, removing retail sales from community stores, as Google Local Services displaces local advertisers and extracts that revenue from local vendors and service providers. Also important is that when a potential customer searches for a specific establishment online, if that establishment does not advertise against that search, Google will sell the advertisement to the establishment’s competitor. This is done in two ways. Google runs ‘keyword’ ads as well as ‘competitor’ ads. The keyword ad allows a business to place an ad based on a keyword search (e.g., ‘flat white coffee’), while competitor ads allow firms to directly target people searching for a competing business (i.e., with two Thai restaurants in the same area, Thai A can pay to come up on the search when people look for Thai B) – essentially ‘hijacking’ the restaurant’s potential customers. Furthermore, restaurants may feel that they have to ‘purchase’ reviews by providing customers with free or discounted products. While putatively illegal from Google’s perspective, it is a common practice among merchants as a way to increase ‘organic’ advertising. In each of these examples, existing inequalities can be reinforced, as those who can afford advertising on GM, either directly or through reviews, direct more customers to their businesses, often regardless of location or preference. Of course, a new entrant could invest in Google advertising to build their business. Regardless, as the intermediary, Google always wins. The geographical implications of GM extend beyond the extent to which individuals or firms can participate in mapping, to whether or not individuals, firms, and even places are granted the choice to participate. Spatial knowledge, as Harvey, Kwan and Pavlovskaya (2005) noted, is influenced by identity, power, and socioeconomic status. It is unsurprising, therefore, that the global development of GM has reflected the perspectives and priorities of the Silicon Valley elite. In 2014, for instance, a year after becoming the most used smartphone app in the world (Richter, 2013), entire townships in South Africa were still unmapped, left as blank open spaces despite the communities that existed within them (Wan, 2014).

Spatial implications of the platform economy: cases and questions  227 This brief discussion of the increasing hegemony of GM over ‘location’ itself suggests that mapping is becoming a powerful platform in its own right and is being integrated into other products and services. While GM provides a remarkable service to users, for place-based establishments it effectively operates as a tax upon them. Moreover, GM continues to amplify existing inequalities as a discerner of existence. The dominance of GM has effectively subsumed the function of finding things and being found into a dual monetization model that continues to reshape spatial relationships, online and offline. As an affordance, GM is built into a myriad of other applications, thereby extending Google’s access to information and data. Finally, it has had extraordinary importance as a social phenomenon that affects how we understand and experience space.

CONCLUDING REMARKS It is now widely accepted that online platforms are transforming economic and social life. In this chapter, we have used two case studies to explore the ways in which online platforms are reorganizing the geography of economic activity. First, the reach and power of the online platform giants is such that, at the global scale, value is created by firms and individuals scattered around the world. However, the capture of that value is remarkably concentrated in a few firms located on the US West Coast. In the previous era, nations had national champions in the key sectors such as steel, automobiles, chemicals, and so on. In the platform economy ex-China, this is not the case; most other nations are relevant only as consumers or content providers to be intermediated by the platform. Synchronically, their sectors such as retail, logistics, publishing, advertising, entertainment, and others are being challenged directly by the platform giants, even while their local firms can reach global markets through the platforms. At the meso level, the increasing share of online sales, which has only been accelerated by the COVID-19 pandemic, and, in particular, by Amazon, is resulting in a new geography of retail. This geographical shift has replaced sales clerks in stores with non-unionized workers in warehouses and delivery that are constantly monitored in real time by algorithms and, as a result of Amazon’s digital Taylorism, face the omnipresent threat of replacement by robots. One result is the demise of the shopping center, once emblematic of the mass consumption society, to be replaced by warehouses on the outskirts of cities. The political economic significance of online maps and its effect on local business and communities has received little sustained attention. Our brief discussion suggested that GM, because it is able to locate things and people in space, is remarkably important for all manner of services. We showed that GM’s ability to act as an intermediary between those looking for something and those that have it, allows it to extract value, frequently amplifying inequalities. It is already altering the use of economic space, as GM enabled untrained drivers to replace professional taxi cab drivers through the Uber/Lyft apps. Home buyers can use Street View to look at neighborhoods and individual homes without using real estate agents or having to drive through a neighborhood. Lenders can ‘tour’ a neighborhood through Street View and decide its ‘quality’ by observing the conditions of homes and even the infrastructure such as the streets. GM increasingly is treated not as a depiction of a place, but actually as an unbiased view of the place, with little consideration given to its political, economic implications. Online platforms and society’s reaction to them and their impacts will be one of the most contentious struggles in the next few decades (Cioffi, Kenney and Zysman, 2022; McKnight

228  Handbook of industrial development et al., 2021). This will include how online platforms such as Uber, Airbnb, Instacart, and many others use public space for private purposes. Amazon and online sales are reconfiguring work and the location of work. And, finally, online maps are proving a powerful tool for extracting value from local businesses. The articulation of the local and its integration into the global have received far too little attention from geographers and political economists.

NOTES 1. For recent important exceptions, see Fields (2022). 2. Microsoft was, of course, the platform owner during the personal computer era, but its dominance never reached beyond the personal computer itself. Of course, more recently, its cloud computing platform, Azure, has become increasingly significant. Its increased importance in the platform economy is through its purchase of platforms such as Skype, LinkedIn, and, most recently, GitHub. 3. In the case of China, the crackdown by the Chinese government, which is trying to control their power, has dramatically decreased their value (McKnight et al., 2021). 4. While this aphorism is widely believed to have originated in Silicon Valley, it has a far longer history and has been traced back to Admiral Grace Hopper among others (Quoteinvestigator.com, 2018). 5. In addition to buying advertisements, ‘purchasing reviews’ is a common practice among merchants. Restaurants may offer a discount to patrons for leaving a Yelp review, or Amazon sellers (although, according to the Terms and Conditions, not allowed) may offer free or discounted products in exchange for a positive review. There are obviously implications for inequality, as platform familiarity and the ability to forgo income for self-promotion become necessary for marketplace participation. 6. FBA enabled Amazon sellers to send their products directly to Amazon fulfillment centers where the firm would ‘pick, pack, ship, and provide customer service’ for orders. 7. As Cutolo and Kenney (2020) point out, using FBA separates the third-party vendor from its customers and thus strengthens Amazon’s control of the customer and prevents disintermediation. For the Chinese vendor, this is not important. For US vendors wishing to decrease Amazon’s power as an intermediary, FBA is a double-edged sword (see also Cutolo, Hargadon and Kenney, 2021). 8. For a more skeptical view of neogeography, see Haklay (2013). 9. Most cars permit Apple Play as an alternate mapping system to show up on the primary navigation screen, even though it is not integrated into the additional services such as the heads-up display. 10. More recently, Niantic swapped the in-game map system in Pokémon Go to OpenStreetMap (OSM) to weaken Google’s hold over its games.

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14. Consumer goods: from mass consumption to servitization Juan Carlos Monroy-Osorio, Marco Opazo-Basáez and Ferran Vendrell-Herrero

1 INTRODUCTION The transition from consuming goods to consuming services is a subject of great interest to academics and has been examined from various perspectives. The vast majority of management research traditionally adopts a manufacturing, and therefore goods-based, perspective (Lee, Yoo and Kim, 2016). However, economies around the world have long reached the age of service-driven economic growth. Services are now indisputably significant to economies, determine corporate and personal well-being, and are increasingly edging toward traditional goods consumption domains (Martin, Schroeder and Bigdeli, 2019). As a result, consumption is increasingly shifting from mere goods-related transactions toward service-related transactions (Spring and Araujo, 2013). This development has recently been boosted by technological advancements and innovative production models, enabling consumers to use material products via services without the need for ownership (Frank et al., 2019). The growing industrial concern about sustainability and the development of better practices of manufacturers has encouraged servitization to compete by integrating technology and services into the firm’s productivity, contributing to the necessary development of the industry ecosystem (Opazo-Basáez, Vendrell-Herrero and Bustinza, 2018). Understanding how production has evolved to contribute to this transition is imperative. Its impact on modern history has been enormous, since it has given rise to the spread of goods and products across societies, countries and regions (Grundy, 2006). These models can be encapsulated by craft production, mass production, segmentation and servitization. Understanding their connections, strengths and weaknesses in historical and present-day contexts allows each model’s significance to be interpreted in business scenarios (Argyres et al., 2020; Gomes et al., 2021). However, the integration of operations and price strategies, a model first introduced by Porter (1997), is not widely explored, and normally seen as separate. There are calls for the convergence of different strategic viewpoints or levels (Bailey, Pitelis and Tomlinson, 2020). So, can industry combine these strategies with the transition from products to services in new production models? The chapter presents a review of operations and price strategies and their impact on business growth. The objective is to put forward a proposal for a framework that allows Porter’s price strategies and production models to be integrated via operations strategies by using the servitization product model, which successfully combines operations (cost-efficiency) and pricing. This analysis will lead to conceptual discussions on how the evolution of different production models relates to demand, the market and therefore the consumer, and will raise questions that help to better understand the significance and relevance of such models at strategic historical points in the industrial development of firms. The research will also raise questions that will 232

Consumer goods: from mass consumption to servitization  233 allow industry to expand its horizons, entailing important implications for practitioners and policymakers.

2

THEORETICAL BACKGROUND

2.1

Porter’s Competitive Strategies

Competition has driven industry to advance and innovate in different scenarios, and is caused, according to Porter, by two competencies: operations and price strategies (Porter, 2008; see also Grundy, 2006), which can build an ecosystem still under discussion in the academic community. Porter simplifies the description of strategic orientations by limiting it to cost leadership, differentiation and market segmentation (or focus). Market segmentation is narrow in scope, while both cost leadership and differentiation are relatively broad in market scope, and increase the profit impact of strategies (Lavoie and Liu, 2007). Empirical research on profit impact indicates that firms with high market share are often profitable, but many firms with low market share have the same advantage (Hefley and Murphy, 2008). The least profitable firms are those with moderate market share. This is sometimes referred to as the ‘hole-in-the-middle’ problem. Porter explains that firms with high market share are successful. Nevertheless, they must pursue a pricing strategy. According to Porter, firms in the middle are less profitable because they do not have a viable generic strategy – that is, to combine the firm’s product and cost (supply) with the characteristics of target market segments (demand) (Porter, 2008). However, different pricing strategy combinations, such as market segmentation with product differentiation, cannot be performed due to the potential conflict between cost minimization and the additional cost of value-added differentiation (Björkdahl and Holmén, 2013). According to Porter, an operations strategy is crucial to differentiation by placing emphasis on the efficient production of high volumes of standardized products so that the firm can possibly take advantage of economies of scale, and experience curve effects (Porter, 1997). The product is often essentially a no-frills product produced at relatively low cost and made available to an extensive, broad customer base (Grundy, 2006). 2.2

Price Strategies as a Way of Competing

Understanding and defining the main price strategies as a whole is an approach that academics have developed in recent research (Stole, 2007). Moreover, the studies show that most academics agree on defining price discrimination as one of the most common, effective and traditional actions that companies take when implementing market strategies for business growth (Grundey and Griesiene, 2011). Discrimination strategies have been developed in different dimensions of study, including the financial dimension, whose main component is profit maximization; economic dimension, focusing on the market and its properties; and marketing dimension, where price discrimination definitions reside in the ability that companies acquire to compete by means of price strategies in different markets with high or low segmentation (Ekelund, 1970). Table 14.1 shows the definitions accepted by the literature according to the above dimensions.

234  Handbook of industrial development Table 14.1

Description of price discrimination strategies (a selection of dimensions)

Source

Description of Price Discrimination Strategies

Dimension

Phlips (1983, p. 5)

Price discrimination occurs when the same commodity is sold at different prices to

Economic

different consumers Bishop and Colwell

One kind of behavior that is consistent with profit maximization is called price

(1989)

discrimination. Price discrimination is the practice of charging different buyers

Financial

different prices according to how responsive consumers of the particular good or service are to a change in its price OECD (2003)

Price discrimination occurs when customers in different market segments are charged

Financial

different prices for the same good or service for reasons unrelated to costs. Price discrimination is effective only if customers cannot profitably re-sell the goods or services to other customers Dibb and Simkin

Price discrimination: a policy whereby different prices are charged in order to give

(2004, p. 159)

a particular group of buyers a competitive edge. It is important that a marketer

Marketing

ascertains that such discrimination does not break any laws Drake (2005, p. 4)

Price discrimination is the practice of charging different consumers different (marginal) Economic prices for the same economic good. These price differences cannot be explained by the difference in marginal cost of making the goods available for the various consumers

Lancaster and Withey

Segmented/differential pricing (price discrimination) – companies will often adjust

(2007, p. 153)

their basic prices to allow for differences in customers, products, location, time/season

Marketing

and so on. Essentially, the company sells its products via two or more processes, even though price difference is not always based on cost differences. Often known as price discrimination, this approach to price adjustments can be very effective at maximizing demand and company revenue Armstrong (2006, p. 1)

In broad terms, it can be said that price discrimination exists when two ‘similar’

Financial

products that have the same marginal cost of production are sold by a firm at different prices Farrell and Hartline

Price discrimination occurs when firms charge different customers different prices.

(2008, p. 247)

Price discrimination is very common in business markets, where it typically occurs

Marketing

between different intermediaries in a supply chain. In general, price discrimination is illegal unless the price differential is based on the actual cost differences of selling products to one customer in relation to another Mankiw, Quah and

It has been assumed that monopolies charge all customers the same price. Yet, in

Wilson (2009, p. 326)

many cases, firms sell the same good to different customers at different prices, even

Economic

though the production costs for all customers are the same. This practice is called price discrimination

Source:

Grundey and Griesiene (2011).

Although these dimensions help to clarify the definition objectives set by different academics, their strategic implementation has led to a historical breakdown that is allowing them to be shared and applied, cutting across different levels. The first level is first-degree price discrimination, whose aim is to differentiate price according to perceived value in a limited market, such as highly personalized luxury products with limited demand. Second-degree price discrimination strategies can lead to exponential business growth in global markets, with standardized products primarily aimed at mass purchase volume. They employ strategies such as discounts, enabling quicker inventory turnover. However, this sacrifices personalization for the sake of a wider market. Furthermore, these dimensions’ third cross-cutting level is third-degree price discrimination, whose aim is business growth in markets with widespread

Consumer goods: from mass consumption to servitization  235 Table 14.2

Defining degrees of price discrimination according to dimension

Degree of Price Discrimination

Financial Definition

Marketing Definition

Economic Definition

First-degree price discrimination

A different price for each

Separating the entire market

Identical goods are sold

customer depending on

into each individual consumer

at different prices to each

demand intensity

and charges the price they are

individual consumer

willing and able to pay Second-degree price discrimination The seller charges bulk buyers less

Selling off product packages

Charging lower prices for larger

considered better value

quantities. This degree also

for money than previously

includes early-bird discounts

published/advertised prices Third-degree price discrimination

The seller charges different Charging different prices for types of buyers different

the same product in different

amounts

market segments. The market

Results in the most sales in each segmented consumer ‘group’

is usually divided in two ways: according to time or geography

Source:

Grundey and Griesiene (2011).

segmentation. Table 14.2 summarizes first-, second- and third-degree price discrimination strategies in their academic dimensions. 2.3

Operations Strategies

Primarily studied by academics and industry itself, numerous production paradigms have emerged throughout history that have proven to be key in society’s economic and industrial progress. However, four models have emerged to lead product innovation and deliver to a market in need – namely, craft production, mass production, segmentation and servitization. Their operations strategies can be broken down into three ecosystems: manufacturing, services and product-service systems (PSSs). The first model was craft production, the standard approach to manufacturing in the pre-industrialized world, centered around high quality, personalization and exclusiveness based on skilled manual labor (Solomon and Mathias, 2020). It does, however, entail a collateral effect. While the product may be of extremely high quality, exclusivity can be detrimental to a wider market. A second model, called mass production, was therefore developed to create standard goods for a mass market, transforming businesses throughout the 20th century by concentrating their efforts on the undisputed aspiration of industry – namely, industrial efficiency (Hara, Sato and Arai, 2016; Hu, 2013). The fact that technological development focused on heavy machinery and increasing the capacity of large firms to switch production rapidly from product to product (Meier, Roy and Seliger, 2010; Zabihi, Habib and Mirsaeedie, 2013) was one of the most discordant aspects of the mass production model. Hence, in the late 1970s, segmentation emerged as a solution to mass production. The third model, mass customization, centers on growing consumer demands, whilst benefiting from global production by using the latest technology. It was brought about by several essential concepts and technologies, which include product-family architecture, reconfigurable manufacturing systems and delayed differentiation (Tomlinson, 2010). While the goals of mass production and mass customization can be described as economies of scale and economies of scope, the consumer’s role changes from that of ‘buyer’ to ‘chooser,’ which calls for different approaches capable of yielding more responsive

236  Handbook of industrial development Table 14.3

Operations strategies, benefits and challenges

Operations Strategies in

Operations Strategies in

Operations Strategies in

Operations Strategies in

Craft Production

Mass Production Models

Segmentation

Servitization

Benefits Alignment of strategy and

Alignment of strategy and target Alignment of strategy and target Merging of operations strategies

target market

market

market

in manufacturing and services

Clear definition of

Clear definition of competitive

Focus on sets of competitive

Intense focus on customer and

competitive priorities

priorities

priorities

human resources

Focus on quality

Focus on sets of competitive

Technology

Good alignment with suppliers

Service adaptation to market

priorities

Good alignment with suppliers

Cost efficiency

segments

Technology

Hard-to-measure performance Environmental and social issues   Challenges Appropriate technological choices Good alignment of competitive priorities, business strategies and operations strategies Strategic alignment with the target market Good alignment with suppliers Balancing the roles of manufacturing and services Financial risk

manufacturing systems (Stole, 2007). It is in this scenario that the four production models emerge. Servitization is determined by how the increased offering of more comprehensive market packages or ‘bundles’ of customer-focused combinations of goods, services, support, self-service and knowledge can add value to core product offerings (Vandermerwe and Rada, 1988). The literature has identified three general reasons for servitization: economic reasons, user needs and competitive reasons (Rabetino et al., 2021). Economic reasons include the pursuit of higher profit margins and income stability due to the services’ resilience to economic cycles (Opazo-Basáez, Vendrell-Herrero and Bustinza, 2019). Changes in user needs relates to the fact that consumers increasingly demand a variety of different services. In the business-to-business (B2B) context, this involves focusing on core competencies, and is an additional reason for external services (Vendrell-Herrero and Wilson, 2017). Servitization makes it economically advantageous for firms to extend the product’s useful life, enabling constant revenue to be gained throughout the product life cycle, not simply from the specific transactions (Vendrell-Herrero, Gomes et al., 2021). The differences between manufacturing and service firms arise in relation to perishable, complex and multifunctional service activities. Becoming an industrial service provider is not, therefore, simply a question of offering adjustments, but rather an entire organizational change in focus of attention and managerial approach (Brax, 2005; Rajala et al., 2019). Vandermerwe and Rada (1988) describe the progression of how companies understand the servitization in industrial development by first considering the differentiation in goods or services, and then moving to offer goods combined with closely related services, and finally to a position where firms focus on the combinations of goods, services, support, self-service and knowledge. Servitization offering calls for a new way of thinking in relation to business strategy, business model and manufacturing model. Moreover, the company needs to broaden its definition of the value chain, shifting its focus from operational excellence to alliances with consumers (Kowalkowski et al., 2015). Table 14.3 shows the main operations strategies according to production model.

Consumer goods: from mass consumption to servitization  237

3

INTEGRATIVE FRAMEWORK

3.1

Operations and Price Strategies

Understanding how these dimensions are implicit in production model strategies is essential to understanding the rise of servitization in the industrial development (Vandermerwe and Rada, 1988). Craft production possessed limited customer reach, as sales were mainly restricted to customers who discovered craft products at small local shops, and through a few other channels. Growth thus involved activities such as building more storefronts (Solomon and Mathias, 2020). However, two critical convening factors altered this landscape. First, technology dramatically changed the growth opportunities available to artisan entrepreneurs. The rise of online marketplaces and social media marketing provided artisan entrepreneurs with new channels to display their products to a wider market (ibid.). Second, social movements fostered increased demand for handmade goods. The 21st century has ushered in a shift in consumer values, paving the way for the rise of an artisanal movement (i.e., makers). Hence, it is common to find first-degree price discrimination strategies based on personalization and high segmentation in craft production. Companies expect exponential growth in this scenario, where mass production has historically had its greatest strengths, competing in small production and customization strategies in markets with homogeneous characteristics (Hu, 2013). Recent research shows that the mass production model has provided abundant access to mass consumer goods without discriminating markets, needs, geographies or publics. Nevertheless, it has triggered heavy consumption and given rise to concepts such as fast fashion, planned obsolescence and other strategies to the detriment of product quality, while, at the same time, it has increased production (Duguay, Landry and Pasin, 1997; Raddats et al., 2016; Sabel and Zeitlin, 1985). Be that as it may, today’s world, and industrial firms’ development, are difficult to understand without the benefits of mass production related to its strategies for volume and availability and resulting accessibility to different markets. Many academic communities have spoken of concepts such as the democratization of consumption (Küçük, 2020), mass consumer goods, and the rise of the global market (Bianchi and Labory, 2006; Coveri et al., 2020; Matyushok et al., 2021), recognizing that there would be no simplification of the supply chain and availability in different markets without mass production. However, it would always need strategies based on broader price ranges than those offered by craft-type models and would no longer rely on the product and its components for value. Thus, second-degree price discrimination emerged as a strategic complement to this mode of production (Cortiñas, Chocarro and Elorz, 2019), whose main difference from the first degree was that it introduced volume, discount and promotion strategies. Hence, the connection between price, product and market entered into a previously unseen definition – namely, the price war – where the value is not perceived in relation to the product but rather to the end price associated with the market (Wang et al., 2020). In this scenario, mass production reaches a zenith in terms of availability, production and simplification. By the time most companies fulfill their main objective of delivering mass consumer products to the global mass market, and the strategies associated with second-degree price discrimination contemplate new products within a standardized view of consumption, mass scale stagnation will not allow the firm to grow any further (ibid.). Mass customization as a production model begins with clear product, price, and market differentiation. It is thus trans-

238  Handbook of industrial development muted into what has subsequently been called mass customization, which develops product personalization within mass production (Hu, 2013). Traditionally, segmentation or customization production models use strategies that cover more markets with fewer products whilst maintaining its characteristics adapted to consumption, among other variables. Third-degree price discrimination then becomes the basis of many segmentation strategies. An example of the use of this price strategy in segmentation can be observed in technology firms, where companies such as Apple, Microsoft and Dell satisfy the needs of different markets and segments via a portfolio of limited, differentiated products that, to a lesser or greater extent, adapt to geographies and consumption trends accordingly. As mass production grows and more products are included in the portfolio, mass customization must not take over. There must also be noticeable market and consumer differentiation enabling price discrimination based on outstanding value (Wang et al., 2017). Recent investigations into the degree of price discrimination have revealed remarkable variations, due mainly to the entry of technologies enabling connections between individual or segmented markets at global level, such as social media, digital shopping channels, and digital banking (Cortiñas et al., 2019; Jenkinson, 2009; Stole, 2007; Vendrell-Herrero et al., 2018). In addition, traditional production models are increasingly exposed to these technologies, giving rise to mixed models that challenge theoretical concepts and encourage the development of new strategies yet to be defined and appropriated. Examples can be seen in Table 14.3, highlighting the most widely used price discrimination strategies in the past few decades. Nonetheless, the inclusion of new media and technologies evidenced the need for a new production model offering a broader spectrum of product and market competition (Gomes et al., 2021; Qi et al., 2020; Wang et al., 2017). The paradigm of service as a product or as part of its portfolio has been worked separately in industrial development history. However, and for the advance of new business and production strategies, it requires a model whose center is not simply the beneficial relationship between the tangible and the consumer. 3.2

The Value of Servitization: The Benefit of Connected Working

Over the past two decades, academic and industry interest in services accompanying different manufacturing industries is growing and gathering constant momentum in the development and growth of different theories and fields (Qi et al., 2020; Sousa and da Silveira, 2019). As a theoretical concept, servitization has enabled industry and business portfolios to be increased, providing knowledge in business models and research that industry has yet to explore (Rabetino, Kohtamäki and Gebauer, 2017; Raddats et al., 2019; Vandermerwe and Rada, 1988). Some servitization experiences at global and local level have highlighted its potential, disentangling the elements shaping a product. These range from the different models involved in the product and its potential value to expertise gained by the roles and individuals developing the process into an industry focused on the pooling of experience, seen, for example, in knowledge-based business theory (Pistoni and Songini, 2017; Raddats et al., 2016). Servitization has created bridges between product, production and different roles, knowledge and experiences (Bustinza et al., 2018; Valtakoski, 2017). To appreciate and comprehend how the servitization production model has evolved, it is essential to understand the role of service-dominant orientation (Valtakoski, 2017; Visnjic Kastalli and Van Looy, 2013). Nevertheless, whilst focusing on services, instead of integrating products and services, service-dominant orientation tends to ignore aspects relating to product

Consumer goods: from mass consumption to servitization  239 development, competence and pricing. Servitization overcomes this problem of integrating products and services via product servitization or service productization according to the situation (Bustinza et al., 2018; Opazo-Basáez, Cantín and Campos, 2020). For several academic researchers, understanding this competitive scenario proves key to understanding the rise of servitization as a production model (Luoto, Brax and Kohtamäki, 2017; Rabetino et al., 2021). Servitization adds value from the moment the service or product design is conceptualized and consequently adapted by consumers and their context (Opazo-Basáez, Vendrell-Herrero and Bustinza, 2022). Degrees of price differentiation and price discrimination possess a dynamism in servitization that has been little used in other production models and, in some cases, is unthinkable (Vendrell-Herrero and Wilson, 2017). Thanks to its flexibility, enabling the integration of services with products and value, competition between firms has been transformed, to the extent, for example, that alliances are being formed in specific processes requiring knowledge in order to gain pole position in differentiation strategies. An example of this is how Spotify, a music streaming service, connects with other firms such as Facebook, Google and Amazon to identify variables exogenous to its platform in order to build omnichannel profiles aimed at multimedia, virtual and face-to-face consumption of content. This would have previously been unthinkable in the music industry, whose segmentation was more limited to audio products (Jovanovic, Sjödin and Parida, 2021; Tian et al., 2022). Servitization can therefore be a mechanism enabling firms to simultaneously deploy first-degree price and operations strategies based on the personalization and high segmentation of services and market-oriented products. Servitization prioritizes the consumer, adjusting production to more perceptive degrees of personalization than those used in the mass segmentation model. In this scenario, when first-degree price discrimination better exploits the benefits of flexible and personalized price strategies, servitization can be a bridge connecting dynamic technology and strategy upgrades with lower costs. Figure 14.1 presents the proposed

Figure 14.1

Framework for integrating operations and price strategies

240  Handbook of industrial development framework, showing an evolution of the strategies in industrial development and how operations strategy can be connected with price strategies.

4 PROPOSITIONS The aim of this section is to further elaborate on our model by comparing historical production models and analyzing their dominance according to their degree of knowledge and integration in relation to demand. Initially, mass production and mass customization are compared, followed by mass customization and servitization. Since its inception, mass production has been the model used by business and industry for constant growth (Wang et al., 2017), a model that responded to the wider market needs of globalized demand. It has evolved with technological advances focusing on an infrastructure that is capable of maximizing profits whilst simplifying the value chain, and those forming part of it, throughout its performance (Qi et al., 2020; Sabel and Zeitlin, 1985). However, this model leads to sacrifices in quality and the perception of a homogeneous market, where competition between actors is reconciled in order to find differential value in second-degree price discrimination, which is based on volume and availability. Such competition in challenging scenarios leads to price wars whose differentiating value lies in discounts and its relationship with volume (Wang et al., 2020). Historically, mass production has given rise to a revolution in how different products and materials associated with a portfolio are produced and distributed, and is always directed towards a single objective: responding to market demand (Sabel and Zeitlin, 1985). However, when advances in technology and the growing information and intelligence capabilities of firms are analyzed, substantial differences between mass production and the benefits of segmentation and personalization become evident in wider markets thanks to diversity and a differentiated product portfolio (Jenkinson, 2009; Stole, 2007). Additionally, production in the segmentation model produces smaller business portfolios since it is more efficient due to frequent trading with fewer demands. An example can be seen in technology firms such as Apple, whose 1997 portfolio consisted of approximately 350 products, which later adopted a production model based on demand segmentation according to geo-referencing demographic and behavioral variables. This enabled regular consumers to be separated from expert consumers in more detailed market niches, resulting in just ten products in its portfolio in the same year. This led to a significant increase in revenue thanks to a better understanding of demand and an approach that brings about supply simplification by means of segmentation-based production models. In addition, the chance to innovate and develop products for new markets increases due to the fact that strategic efforts have focused on product innovation on a wider scale, unlike mass production. In relation to this behavior, the following proposition is put forward: P1: In a system where mass production and segmentation coexist, segmentation will, on average, outperform mass production if the firm understands demand. The mass customization production model enables specific market needs to be understood beyond the information provided by demand. This allows strategies associated with third-degree price discrimination to benefit from segmentation, such as pricing according to recurrence, geographic location, demography, as well as other strategies (Fogliatto, Da

Consumer goods: from mass consumption to servitization  241 Silveira and Borenstein, 2012). Its strength lies in its high degree of differentiation between consumers in the same market. Today, there are various definitions of customization depending on marketing angle focus, cost efficiency and design solutions. One of the mass customization model’s many characteristics is that it is a marketing and manufacturing technique combining the flexibility and personalization of custom-made products with low unit costs associated with mass production (Jenkinson, 2009; Qi et al., 2020; Stole, 2007). Segmentation and customization-based products and strategies can be broken down into three categories: (1) mass personification where products are mass produced but can be modified by the business to meet the consumer preferences identified via existing data on an individual; (2) mass customization or products that are mass produced where consumers are offered limited customization options; and (3) customer requests are tailored from beginning to end in the creation of a unique product. Recent studies show a relationship between the segmentation and servitization production models, fueled by strategies such as customization and personalization (Benedettini, Neely and Swink, 2015; Cortiñas et al., 2019; Donio, Massari and Passiante, 2006; Stole, 2007). However, the results show that product innovation capability directly improves servitization. Although the direct effect of mass customization capability on servitization is not significant, it improves servitization indirectly by means of product innovation capability (Sousa and da Silveira, 2019). Segmentation models still focus on the product only according to personalization offerings and highly segmented market demands, thereby developing a specialized competitive offering. Although third-degree price discrimination strategies lead to effective segmentation, industry’s intense focus on making the product’s business models profitable creates barriers and limits such strategies. Hence, servitization of the production model is required, where the focus is on the product–service relationship (Rabetino et al., 2021; Vandermerwe and Rada, 1988). Manufacturers face intense competition in global markets due to product commoditization, and modern manufacturing extends beyond tangible goods production (Opazo-Basáez et al., 2020; Sousa and da Silveira, 2019). Service-oriented business models are currently seen as essential to industrial success. Therefore, integrating intangible services and tangible products has become a popular strategy for manufacturers to differentiate and gain a competitive edge. The fact that the servitization model benefits from digital technologies is an essential factor that can lead to improvement in operational efficiency due to customization associated with the product–service relationship (Vendrell-Herrero, Bustinza and Opazo-Basáez, 2021). Business models, known as platforms, offer different personalized or highly segmented products or services in order to engage consumers. Cases such as Uber and BlaBlaCar provide an example of operational effectiveness segmented by consumer needs, which may be the same consumer that has different needs associated with an equivalent service (Ranjbari, Morales-Alonso and Carrasco-Gallego, 2018). Therefore, one of the main advantages identified in servitization is its ability to integrate not only product and service innovation, but also business growth strategies. Servitization combines operations strategies with price strategies, paving the way for growth in line with market and consumer demands. Previous research separated these strategic theories; however, the context in which servitization has been implemented has shown that both strategic models can be developed simultaneously. This context gives rise to the second proposition:

242  Handbook of industrial development P2: In a system where segmentation and servitization coexist, servitization will, on average, outperform segmentation if the firm jointly deploys operations and price strategies.

5

DISCUSSIONS AND CONCLUSIONS

5.1

Academic Implications

This chapter puts forward a proposal to merge strategy and production management in the streams of literature by using a historical approach. To this end, the framework proposed combines dominant production models (e.g., craft, mass, segmentation and servitization) with price discrimination strategies (e.g., from first-degree to third-degree price discrimination). Moreover, servitization has in itself become a theory, a concept within the historical context of consumer goods, and now services, production (Rabetino et al., 2021). This research reveals that servitization is proving to be a return to craft/customized production, enabling first-degree price discrimination with cost-efficient production models. Mass production and segmentation use different forms of price discrimination to interact with demand, and achieve considerable cost reduction but lose consumer-based viewpoints in their decision-making (Stole, 2007). The path has now been cleared for its theoretical development and has aroused the academic community’s interest in production and its different models. It has allowed new theoretical grounds to be posited that broaden its horizons. The discussion surrounding servitization and other production models has given rise to constant debates on service monetization strategies, increasingly dynamic segmentation and hybrid business models, and has led to a re-examination of what is considered traditional mass consumption. Although many of these models persist due to the development of strategies in digital, technological and global ecosystems, it is essential to recognize that the inclusion of services has brought about an increase and merging of flows that were previously seen in parallel rather than intertwined. The vision of mass consumption and how it is to be transformed into consumer demand for services has driven the ecosystem, industry and companies to seek new strategies that stand out in an increasingly segmented and global market. 5.2

Managerial Implications

Although production models and price discrimination strategies have been widely studied, the acceptance of new models has proven difficult over the years. The framework lends itself to both theories being merged. Moreover, the observation of servitization and its implication in industry as a model to produce products and services can be approached from different strategic fronts, not simply from the supply viewpoint. Servitization and how it benefits industry in a globalized and dynamic market enables new competitive strategies that add explicit value and encourage business growth in traditional markets in ways not previously approached from a holistic, consumer market point of view (Raddats et al., 2019). Furthermore, the research community’s vision could be broadened to include other dimensions, providing insights into current phenomena and historical events, such as the impact of new technologies, increasingly digitalized markets and supply chains that are mindful of sustainability and accessibility challenges facing local and global consumption.

Consumer goods: from mass consumption to servitization  243 5.3

Industrial Policy Implications

Servitization has opened up a relationship between increasingly personalized, flexible and dynamic services and products combining high innovation, technology and digitalization. However, recent research has revealed certain sluggishness in the advancement of policies that contribute to business growth in highly industrialized regions (Labory and Bianchi, 2021). This chapter provides insight into the evolution of both the production models and growth strategies facing the market and demand. Industrial policy can benefit since servitization, by strengthening highly industrialized regions, facilitates the study of industry-oriented public policy and its relationship with the consumer in a dynamic context permeated by technology and digitalization (Vendrell-Herrero and Wilson, 2017). Industrial policymakers should stimulate regional servitization capacities to develop and transform industrial areas into highly competitive industries in dynamic markets (Bianchi and Labory, 2006). By addressing this implication, the framework herein can benefit the current discussion on industrial policy by acknowledging the challenges and risks identified as external elements that make manufacturing growth difficult (Buckley et al., 2020). This study also contributes to the discussion on market regulation of industrial policy that provides protection when implementing servitized business models seeking practical orientation towards the market (Lafuente, Vaillant and Vendrell-Herrero, 2019). Such regulation, which includes operations and price strategies, business models, competition, and market, will broaden current discussion in the academic community. Servitization is at the center of policymakers (Hojnik, 2016), and the creation of new regulations can benefit the industrial development of firms that had already chosen the servitization production model. Nevertheless, industries must be accompanied by a vision that recognizes its historical value, reviewing lessons learned and documenting the industrial history through the servitization lenses (Brax, 2005). While some sectors may fear and attempt to disregard servitization, it is unlikely that such attempts will yield substantial results (Bailey, Glasmeier and Tomlinson, 2019; Labory and Bianchi, 2021). It seems more constructive to embrace it as a developer working to the benefit of industrial development and economics in society. 5.4

Avenues for Further Research

Although the study presents a summarized and accepted vision of widely investigated concepts, a detailed study of current dynamic phenomena in medium-sized and small production enterprises is required. Additionally, it is essential to note that service monetization is still a subject of debate by academics and business. Phenomena such as de-servitization or the study of the impact of price-oriented strategies on business value chains are overwhelmed by the use of data unassociated with business growth. Data should relate to market evolution, as seen from the viewpoint of disciplines that have a substantial impact on the design and development of new strategies, and solid connections with product and service consumption. The framework proposed is a starting point to understanding the consumer impact and analyze the significance of production models and strategies in industrial development. A review of price discrimination strategies using market behavior variables would provide a predictive approach to demand, enabling businesses to concentrate their efforts on innovation. It is hoped that this study facilitates linkages between seemingly distinct perspectives and sources of knowledge. Future researchers are urged to join forces across disciplines to shed

244  Handbook of industrial development light on the nature of the transitional processes that guide goods consumption increasingly toward service consumption.

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Consumer goods: from mass consumption to servitization  247 Vendrell-Herrero, F., Parry, G., Opazo-Basáez, M. and Sanchez-Montesinos, F.J. (2018). Does business model experimentation in dynamic contexts enhance value capture? International Journal of Business Environment, 10(1), 14–34. Vendrell-Herrero, F. and Wilson, J.R. (2017). Servitization for territorial competitiveness: taxonomy and research agenda. Competitiveness Review, 27(1), 2–11. Visnjic Kastalli, I. and Van Looy, B. (2013). Servitization: disentangling the impact of service business model innovation on manufacturing firm performance. Journal of Operations Management, 31(4), 169–80. Wang, X., Xie, C., Gao, X. and Wang, S. (2020). Price war countermeasures of cloud manufacturing service platform based on game theory. Industrial Engineering Journal, 23(1), 53–8 [in Chinese]. Wang, Y., Ma, H.-S., Yang, J.-H. and Wang, K.-S. (2017). Industry 4.0: a way from mass customization to mass personalization production. Advances in Manufacturing, 5(4), 311–20. Zabihi, H., Habib, F. and Mirsaeedie, L. (2013). Definitions, concepts and new directions in industrialized building systems (IBS). KSCE Journal of Civil Engineering, 17(6), 1199–205.

15. The car industry as a laboratory of transformations induced by industrial development David Bailey, Dan Coffey, Lisa De Propris and Carole Thornley

1 INTRODUCTION This chapter explores the car industry as a ‘laboratory of experiments’ at times of disruptive technological change. Beginning with a brief précis of historical transformations, it moves on to an overview of the current debate on Industry 4.0 broadly defined and discusses the impact of new technologies on the nature of manufacturing, on the reorganization of old industrial spaces, as well as the emergence of new ones. Such industrial transformation will be specifically discussed in relation to the automotive sector, which has been historically at the forefront of technological innovation and adoption, given that few other mass consumption goods embody such a complexity and variety of technologies. We will discuss how automotive is a testing ground not only for the adoption of automation and artificial intelligence (AI) to increase production efficiency and flexibility in new cyber-physical systems of production, but also on the fundamental transformations including the green transition and new business models in manufacturing and broader mobility. In addition, for readers specifically interested in the potential scope of transformations in a critical industry, this chapter is relevant to current debates on regional development in economic geography, which hinges on three main concepts: embeddedness, cumulativeness and resilience. We will discuss what opportunities and challenges new technologies will bring for regional economies characterized by the presence of manufacturing industrial systems.

2

TRAJECTORIES OF INNOVATION AND THE SUSTAINABILITY CRISIS

The history of the global car industry is marked by landmark innovations, incremental developments, and false, if occasionally dramatic, claims. In this section we offer a brief precis to pinpoint aspects of genuine change that appear particularly relevant to the impasse that now confronts the industry in the form of global warming. In doing so, we emphasize the car industry in relation to its product architectures, and the challenge posed by the need to transition a growth-oriented polluter into a sustainable industry. We touch on the importance of state–business relations, and the international state-order.

248

The car industry as a laboratory of transformations  249 2.1

Product Architectures, Mass Production and Corporate Forms

Even a very short review of key stages in the early evolution of the car industry as we know it today must consider multiple elements. Of these, product architecture is the most basic, although tracing exactly when commercially viable product architectures were first pulled together remains a matter for debate, France is generally accepted as the home of undertakings realistically aimed at making and selling cars for profit. Étienne Lenoir of Paris patented an engine in 1860, and combined this with a carriage. Rhys (1972, p. 3), like many commentators, proposes this as the earliest modern car. Fuels were an obvious interest point for inventors: Lenoir chose coal gas. Launch of the first commercial petrol-driven internal combustion engine car (which burns combustible fuel inside the engine, with air as an oxidizer, to release energy to drive the car) is often dated as 1890 and the efforts of another French firm, Panhard et Levassor – albeit using a Gottlieb Daimler design developed in the first half of the 1880s. Karl Benz, another German inventor, made similar breakthroughs around the same time (for further introductory details to all of the above see Rhys, 1972, pp. 3–4). There were, of course, earlier experiments. Priority of earlier steam vehicles is usually debated as between Nicolas-Joseph Cugnot in France, whose field tests began in 1769, albeit with a vehicle intended for a military purpose, and Robert Trevithick in England, who successfully road-tested with passengers in 1801 and 1803. Later, steam and electric would overlap the petrol-fuelled internal combustion engine for a time, but with the latter eventually becoming firmly ensconced as the dominant car technology. The manufacturing mode was artisanal and skilled. Womack, Jones and Roos (1990, pp. 21–3), drawing on Laux (1976), describe a Panhard et Levassor operation employing different contractors, sometimes at the company’s own site and sometimes in separate workshops. Machines lacked a consistent and agreed gauge, so that parts were only approximately specified, and occasionally warped, and had to be fitted together by skilled workers who could make all the necessary adjustments. This allowed latitude for customer requests, but was unsuited to large-scale production. In fact, Womack et al. (ibid, p. 23) identify a system wherein every car produced was essentially a ‘prototype’ built for one. Standardization of parts, by means of a consistent gauge, and improved harder-edged tooling, was a big step. The next landmark development came at Ford Motor Company in America. Interchangeable parts, improved machining processes, and effective designs for petrol-fuelled internal combustion engine vehicles were joined by production on a very large scale. Ford was not the only car maker to work out that costs could be cut and production capacities raised by sequencing activities in the latter stages of car manufacture and assembly in a progressive manner, positioning parts and processes accordingly. Gartman (1979, p. 197) selects Ransom E. Olds, whose factory earlier built the Oldsmobile Model R in the opening years of the 20th century on what many have decided was the original mass production car assembly line, as the first American mover, albeit still with heavily manual work transfers. The experiments at Ford aimed to eliminate this manual component, culminating in the introduction of a chain-driven assembly line at the Highland Park factory on 14 January, 1914, followed soon after by a mechanical line, conventionally dated to 30 April that same year (prior to this, a mechanically driven assembly line had been used for a fly-wheel magneto). Gartman’s account, drawing on a number of standard historical sources, sets out to show how work rates intensified while workers’ autonomies were reduced and the higher-level skills required for assembly work eliminated. The results of this process are interpreted within the framework of Braverman’s (1974) thesis on degra-

250  Handbook of industrial development dation of work. A subsequent re-imagining of the same experiments and data is attempted in Williams, Haslam and Johal (1992).1 A word is needed at this juncture on a confusion that crept into social science commentaries from around the middle part of the 1980s. Academic social scientists, often displaying little knowledge of the actual evolution of the industry, began to reference the era following on from Ford – under the label of ‘Fordism’ – as though it entailed a production system that was unable to do more than replicate prodigious quantities of same-specification products. What actually ended was the era of permanent ‘prototyping’ of products; what began was an era of end-product variety based on producer-specified combinatorial possibilities, whereby sometimes significantly differentiated end products can be assembled by combining different choice specifications based on arrays of part families and colours. Even in the Henry Ford case, while the Ford Model T, whose design dated back to 1908, was converted to a single colour for mass production, it nonetheless remained available in a number of body styles with two- and four-seat variants (see, for example, Womack et al., 1990, p. 37). The ‘essence’ of mass production, wrote Peter Drucker, some 40 years after cars first rolled off the end of Ford’s mechanically integrated assembly line, is ‘greater diversity of production than any method ever devised’, through part-combination possibilities (Drucker, 1955, pp. 85–7). By then, factory observers were already claiming ‘astronomical’ combinatorial possibilities for the end products of an American car line, ‘each car…preceded or followed by a car of completely different type, instead of a “run” of similar models’ (Walker, Guest and Turner, 1956, pp. 7–8). This issue is further discussed, with alternative viewpoints on the reasons for the confusion, in Lyddon (1996), Coffey (2006) (especially Chapter 2) and Coffey and Thornley (2010). It is commonplace for these developments – workable designs, harder-edged tools, standardization of parts, transfer machines and mechanically integrated lines – to be pooled together as the historical basis of mass production in the car industry. The dominant product architecture of the car in the 20th century was completed with the all-steel car body, culminating in a monocoque (single-shell) design, where body and chassis are no longer separate units but rather an integrated structure. The Budd Company built its first all-steel car body in 1913, shortly after its founding, and by the 1930s had become a supplier of ‘unibodies’ for mass-produced American cars. Rhys (2005) goes as far as to suggest that the Budd Company’s historical contribution rivals that of Ford Motor Company. A separate major step in the formative decades of the industry again took place in America, with the emergence of an organizational form capable of handling separate product lines while separating strategic oversight from day-to-day management of operations. The breakthrough here came not with Ford but with General Motors in the 1920s. Along with chemicals giant Du Pont, General Motors is in fact credited with organizational changes that saw the emergence of the multi-divisional form mode of business organization – driven in General Motors’ case by the challenge of organizing merged brands, ranging from entry-price Chevrolets to luxury-brand Cadillacs. The standard historical account is the one provided in Sloan (1963) (see also Chandler 1977), which has become a mainstay of analysis in the organization literature, including economic analysis. An important element in this was the desire to better exploit income disparities amongst customers. Looking at the automotive industry as it exists today, it is impossible to do justice in a short summary to the main stages of how it swelled across the planet and became global.2 Rather than attempt this, suffice it to say that transnational organizational forms came early: Ford

The car industry as a laboratory of transformations  251 established an agent quickly, before moving into Britain as a direct producer even before his company’s experiments in mass production began. State action has played a very major role too, including actions undertaken in the course of two World Wars, and also the experience of colonialism: the middle histories of the car industries of Western Europe and Japan, and its formative history in India, could be written with these themes to the fore, well ahead of conceptualization of the transnational organization. Imperialism, like the struggle for national independence, has quite generally been very significant: one could contrast the experience of Latin America with that of the Asia Pacific.3 Some industries have waned – the Australian car industry is an interesting case – while others wax. A major dispute exists around whether and how the automotive industry became a testing ground for new organizational practices developed in Japan. By the middle part of the 1980s, there was already substantial interest in Western circles in a ‘just-in-time’ business model that had become associated with Toyota, partly through Toyota’s publicity efforts. This was subsequently labelled a lean production system in Womack et al. (1990) – a West-invented term first coined by John F. Krafcik, then a researcher for the International Motor Vehicle Program (IMVP) headquartered at MIT. For proponents, efficiency-seeking strategies and cost minimization drove novel process innovation at a time of technological maturity in the car industry, amidst fierce competition (problems with maturity being a principal theme of a prior influential study by W.J. Abernathy, 1978). The concepts then typically associated include: leaner (more productive) manufacturing, reduced lead times and operating costs thanks to low inventories, key components availability as needed, and superior product quality. A starting point for this literature is Ohno (1978), and the more developed treatment in Monden (1998), plus Womack and Jones (2003), the best-selling follow-on to the already highly successful Womack et al. (1990). And while the body of writing here is now quite massive, Liker (2004) is representative.4 Much of the first generation of enthusiastic commentary on Toyota proposed that its system would come to spell the end of globalized production and supply-chain networks. However, the Japanese automaker has since also been argued to have paved the way for a division of labour that distilled the principle of mass manufacturing and global production into the most ‘efficient’ – that is, efficient in the sense of corporations’ cost-reduction strategies – way of organizing a segmented and globalized production process. Against this, there are other viewpoints. The original framing of the studies used to advance the case for a novel lean production is challenged, for example, in Williams et al. (1994), who see group-think around Toyota as an example of cult behaviour. Assessing claims in America about superior Japanese car quality, Eberts and Eberts (1995) conclude that an anthropological approach is needed to explain the divergence between beliefs and data. Schonberger (2001), by no means antipathetic to claims about Japanese production efficiency, finds that as Toyota extended its supply chains, its inventory ratios stopped looking special. Coffey (2006) argues that the sins of omission and commission are so great as to imply the collective construction of a ‘production fantasy’, one principally of interest because of the insight provided into social and cultural stresses in the West (a short summary of elements is presented in Coffey and Thornley, 2010). Freyssenet and Jetin (2009) dismiss the ‘theoreticians’ who ‘waxed lyrical’ about ‘a so-called “Japanese model” that in actual fact never existed’, ignoring the ‘real dynamics of the era’ (ibid, p. 10).5 There is debate too about the structure of the global car industry as this impinges on corporate strategies within. Some point to price forms of competition within a given context of segmented market demands, reflecting different consumer needs as well as abilities to pay;

252  Handbook of industrial development while others prefer to emphasize it as an arena characterized by rivalrous oligopoly and an ensuing preference for non-price forms of competition, one aspect of which is to reinforce market segmentation. Be this as it may, and taking on board the variety of responses to the emergence of Toyota as a large industry player (one of a pack of large players, the latest of which is the merger-based Stellantis), and the extraordinarily persistent literature that this has generated, the crucial point is this: to what extent did any of this challenge the dominance of the petrol-fuelled internal combustion engine? Paradoxically, there is much more consensus in the literature that Toyota advanced faster with alternatives when compared to the likes of Ford or General Motors – the Toyota Prius for a while became a Hollywood celebrity choice of hybrid-electric car. But for the main part, and even in Japan, production remained overwhelmingly mounted around worldwide expansion in sales of petroleum-based internal combustion technologies. Car makers were still willing to deliver the same ‘species’ of engine as 100 years earlier, under the pretence that it was desirable for the market for petrol cars to further expand, and accepting the existing technology although its environmental impacts were certainly known – expanding demand for petrol cars as assiduously as if this were still an emerging industry. Such developments have, in turn, reflected movements in the international state order. And where cars, and their major component parts, are not actually manufactured, car use has nonetheless expanded, growing around the globe, although the important proviso must still be made that a majority of the earth’s human population do not use cars. In the bigger scheme of things, it may be a moot point whether the auto has led the world in the development of globalized supply chains, or has been one amongst many on the same path. 2.2

The Crisis of the Internal Combustion Engine (ICE) Car

The early American mass production model was enabled by a ready supply of fuel stuff: it was Henry Ford’s good fortune that his enterprise came in the wake of a Texas oil boom. While the face of the global oil industry has changed dramatically since, the symbiotic relationship between cars and oil continues. Sperling and Gordon (2009, pp. 3–5) emphasize the extent of this, with motor vehicles at large accounting for a sizeable part of the subsequent surge in global oil consumption – motor vehicles in this connection being defined as including all civilian road-use vehicles, with trucks and buses on one side, scooters on the other, although a full list of automotive technologies would also have to include specialist motorized equipment of the sorts found in sectors as diverse as agriculture, construction and military. Reviewing the ultimately short-lived check on the car industry caused by oil shocks in the 1970s, they take a sceptical view of ‘peak oil’ as an end point for the industry, but are likewise critical of the environmental impact of the expanding use of unconventional oils derived from Canadian tar sands (bitumen), the American shale industry, Venezuela’s largely untapped reserves of heavy oil, and gasification or liquefaction of coal (ibid., pp. 114–30). Unfortunately for the car industry, mass production of fossil-fuel-powered internal combustion engine (hereafter ICE) technologies is no longer tenable. The crisis of the conventional fossil-fuel-powered car is viewed as terminal. Although efficiency has been improved by advances in technical design, so that less fuel is burned per vehicle tonne per mile travelled, such efficiencies have been swamped by growth in the number of vehicles, tendencies towards vehicle weight gain (bigger cars), and the fact that every litre of petrol burned still releases circa 2.3kg of CO2 into the atmosphere.

The car industry as a laboratory of transformations  253 The automotive industry is again at the forefront of a pivotal change that is affecting all manufacturing. The technological revolution ushered in by a wave of new technologies is currently shifting the techno-economic paradigm underpinning our economies and society. Some would agree that the current Fourth Industrial Revolution (or Industry 4.0) encompasses three groups of technologies: (1) green and renewable technologies; (2) digital technologies (ICT and mobile technologies, additive manufacturing or 3D printing, AI, cloud computing, big data analytics, Internet of Things, advanced robotics, sensoring, space technology and drones); and (3) new materials (biotech, nanotech, neuro-technologies) (Bailey and De Propris, 2019). Each and every one of them has the potential to address the existential threats that the manufacturing sector currently faces and to power the system change that is captured by digital and green transformations. First, science-driven digital technologies are pushed through, causing a disruptive system change that involves production spaces as well as the existence and access to markets. The digitalization and robotization of production processes is the cornerstone of Industry 4.0 (De Propris and Bailey, 2021) as it reconfigures the division of labour between tasks with a greater presence of and connectivity between robots. The adoption of the full spectrum of the new technologies associated with Industry 4.0 – from AI and the Internet of Things to space technology – is expected to optimise an organization of production that is in part still reliant on scale and global value chains, but that is now also able to deliver customization and flexibility. The organization of production in cyber-physical systems within the factory with a hyperbolic level of synchronous connectivity between factories of the same value chain, allows for further efficiency gains. The deployment of Industry 4.0 technologies to reap productivity gains has been widely criticized (ibid.). Digitalization offers, however, the opportunity to redefine from its roots the concept of market. Second, climate change and sustainability require greater efforts in delivering demand-driven new technologies that will also amount to a system change involving, crucially, energy, new materials and consumption. The green revolution is necessary to reset economic growth without compromising the ecology of our planet. Decarbonizing production and consumption, adopting a circular economy approach, banning imperishable materials (such as plastic), and rethinking the energy systems, are just a few of the big challenges facing manufacturing industries. The rationale for prioritizing a sustainable agenda was once formulated as a solution to ‘peak oil’ – that is, renewable energies were presented as a cost-effective alternative to oil and gas – but there was little understanding of the systemic implications of an energy shift (Rifkin, 2015). The language has significantly changed and the green agenda is now a jigsaw of options and priorities that ranges from energy sources, storage to energy use, from consumption to waste, from buying to sharing and networking. Kaplinski (2021) argues that a green agenda has to break the circle of taking (resources from the planet), making, using (overconsumption) and wasting that has characterized the mass production economy. The strategic weaving of digital and sustainable technologies will lead industries to undertake twinned transition (Morisson and Pattinson, 2021), which, as we will argue, is again experimented with first in the automotive industry. This will be discussed in the next section.

3

FROM ICE TO ACE

It is becoming increasingly clear that automotive original equipment manufacturers (OEMs) and the auto value chain can benefit from digitalization and by embracing Industry 4.0, with

254  Handbook of industrial development possible gains in productivity, quality, flexibility and shorter lead times to market (KPMG/ SMMT, 2017). Consumers will also benefit in terms of being able to drive more personalized, higher-quality and connected vehicles (ibid.). In this regard, some of the relevant technologies impacting on production within the auto industry include (ibid.): ● connected devices and sensors allowing physical systems to be replicated in digital form and visualized in real time; ● wider use of predictive analytics, cognitive computing and AI that can make decisions and predictions based on real-time data (this will be augmented by ‘deep learning’ algorithms); ● widespread adoption of mobile, touchscreen and virtual reality, enabling more intuitive physical–digital interaction; ● the development of new flexible systems of production, technologies such as 3D printing and intelligent robotics; and ● connected factories using cloud-based data underpinned by advances in cybersecurity technologies and blockchain, with a sharing of information and new applications across the value chain. More broadly, the auto industry is set for more change over the next 10–20 years than in the last 100 years, as a range of forces combine to transform the industry (Simoudis, 2015). Five key technologies are shifting the industry from the ‘ICE age’ to the development of autonomous, connected and electrified (ACE) vehicles (Simoudis, 2017). The first is the widespread availability of data given the use of sensors built into next-generation autos, as well as from specialized data providers, notably digital mapping companies. Second, computing power and storage is rapidly reducing cost and becoming more available. Third, broadband Internet coverage is rapidly expanding through Wi-Fi, 4G and 5G connectivity. Fourth, as will be explored below, new-technology batteries are driving down costs and recharging times and driving up range. Fifth, ‘third-wave’ AI technologies combined with big data enable the development of autonomous systems (ibid.). These will combine to shift the sector towards ACE vehicles (albeit with these technologies adopted over different timescales), with implications for the business models of major auto firms that will shift towards providing mobility services rather than ‘selling’ cars. To contextualize this last point, for some time, there has been interest in a business-model shift, away from first manufacturing and then ‘selling’ cars. The United Nations Environment Programme (UNEP, 2002) favours a service model, where manufacturers assume life-time responsibility for a product rather than relinquishing responsibility at a point of final sale, selling use rights to access rather than selling the product: a review for the car industry is usefully provided in Ceschin and Vezzoli (2010). Coffey and Thornley (2012, 2013) highlight the relevance to this discussion of a relatively obscure branch of economics that deals with the topic of ‘nurtured’ competition.6 Once cars are sold, and property rights transferred, they can be resold by their new owners, so that car makers have to compete not only with each other but also with a second-hand market. One implication of a thoroughgoing shift to a service- and lease-based model would be the phasing out of this kind of competition, thereby changing in a subtle way the entire economics of the industry. This largely remains hypothetical, although enthusiasm for service models has risen amongst commentators in and around the industry – not as a temporary lease arrangement followed by sale, but as the core business model.

The car industry as a laboratory of transformations  255 We will now explore what ‘ACE’ actually means for the automotive industry before considering the implications for a shift to mobility services. 3.1 Electrification Stricter emission regulations, lower battery costs (and better range), more widely available charging infrastructure, and increasing consumer acceptance will combine to create the ‘landscape’ push for more use of electrified vehicles over the next two decades (hybrid, plug-in hybrid, battery electric, and fuel cell vehicles) (Berkeley et al., 2017). The net speed of adoption will be determined by the interaction of consumer pull (a key factor here being the total cost of ownership) and regulatory push, which will vary strongly at the regional and local level (Gau et al., 2016) as cities and regions and nations regulate in different ways, as well as the scale of barriers to take-up (consumer perception, financial, technological; Berkeley et al., 2017). ‘Bans’ on ICE vehicles have been introduced in some European countries – for example, with the UK banning ICE-only vehicles from 2030, and hybrids (not yet clearly defined) from 2035. Given this, hybrid cars are likely to retain a major role in the short to medium term as an interim technology; on this some firms are better placed than others. Pure electric vehicles (battery EVs or BEVs) are becoming more viable and competitive, but the speed of their adoption will vary strongly (ibid.) across the world (Pejcic, Bailey and Pegoraro, 2018). By 2030, the share of electrified vehicles could range from 10 to 50 per cent of new-vehicle sales (Gau et al., 2016) globally. Morgan Stanley recently increased their estimate of the market share of EVs for 2025 to 10–15% and the Dutch investment bank ING foresees the European car market potentially being fully electric by 2035 (Bailey, 2017), which is, of course, being reinforced by ICE bans. It states that BEVs are on the way to a ‘breakthrough’ by 2024 as barriers to their adoption – think charging infrastructure, range anxiety and pricing – fall, especially as electric batteries become cheaper and better (ibid.). Adoption rates are likely to be the highest in developed dense cities with tough emission regulations and consumer incentives (such as tax breaks, EV shared car clubs, special parking and driving privileges, discounted electricity pricing and so on – Berkeley et al., 2017). Usage will be lower in small towns and rural areas with lower levels of charging infrastructure and higher dependency on driving range (Gau et al., 2016). Over time, however, through improvements in battery technology (hence range) and reductions in cost, those local differences will become less pronounced, and ACE vehicles will gain more market share from conventional (ICE) vehicles. Because of high battery prices, BEVs are expected to cost up to a quarter more to manufacture than equivalent ICE vehicles until 2020 but to reach price parity by 2025 (Bloomberg New Energy Finance, 2017). That gap is narrowing quickly as battery prices are falling rapidly, so this barrier will become less of an issue over the next decade. Researchers such as Nykvist and Nilsson (2015) and Nilsson and Nykvist (2016) have for some time highlighted rapidly falling battery prices; BNEF (2016) stressed that lithium-ion battery costs have dropped by 65 per cent from 2010, reaching $350 per kWh in 2015. They currently stand at around $150 per kWh. When the cost of battery packs falls below $100 per kWh, EVs are likely to achieve cost parity with equivalent ICE cars, suggesting a ‘tipping point’ in the mid-2020s. Battery costs are likely to fall further after that as new chemistries come into play (BNEF, 2017).

256  Handbook of industrial development While battery costs will become less of factor going forward, there remains a need for ongoing policy support to incentivize consumers in the short to medium term and a longer-run holistic policy approach to overcome other barriers identified by Berkeley et al. (2017) – this is in line with Nilsson and Nykvist’s (2016) call for ‘strong governance measures over the coming 5–10 years’ (p. 1368) to deliver a breakthrough scenario. At the same time, it is important to note that electrified vehicles include a large portion of hybrid electrics, which means that even beyond 2030, ICE technology will remain relevant. 3.2

Connected Cars and Autonomous Driving

Fully autonomous vehicles are unlikely to be available until well into the 2030s, despite much early hype. Meanwhile, more advanced driver-assistance systems (ADASs) will develop over time (KPMG/SMMT, 2015). These will play a role in preparing regulators, consumers and firms for cars taking over control from drivers in the medium term. The introduction of ADASs suggests that key challenges impeding take-up are pricing, consumer understanding and safety/security issues. On technological readiness, technology players and start-ups will likely also play an important role (Gau et al., 2016). Regulation and consumer acceptance may represent additional hurdles for autonomous vehicles (Gowling WLG, 2016). However, if and once these challenges are addressed, autonomous vehicles may offer strong value for consumers (low costs, the ability to work while commuting, or the convenience of using social media or watching movies while travelling). Once technological and regulatory issues have been resolved, up to 15 per cent of new cars sold in 2030 could be fully autonomous (Gau et al., 2016). Such prospects are stimulating tie-ups and new entrants into the auto industry and its value chain. Automotive firms are teaming up with taxi firms and ride-hailing firms, seeing driverless taxis as part of the future. Volvo announced a partnership with Uber in 2016 where the two firms will invest US$300 million to develop self-driving vehicles that will be made by Volvo and then purchased by Uber (Bailey, 2016). Uber has also bought Otto, a maker of driverless truck technology. Suppliers and software firms are also lining up to supply ADASs, which are set to take off, with some analysts suggesting that the market will grow from around $6 billion now to some $25 billion by 2020, and over $50 billion by 2025 (ibid.). Such developments led Morgan Stanley to suggest back in 2015 that autonomous cars could go on sale within a decade, at first costing about $10 000 more than conventional cars. KPMG put the difference at just under £5000 (ibid.). This seems optimistic given the complexities of achieving full (level 5) autonomy, as Wolmar (2018) details (and see below). Nevertheless, major social benefits are going to be realized at levels 3 and 4 of autonomy. 3.3

Diverse Mobility Services and Shared Vehicles

Consumers today use their cars as all-purpose vehicles, whether they are commuting alone to work or taking the whole family on holiday. In the future, they may want the flexibility to choose the best service solution for a specific purpose, on demand and via their smartphones (Gau et al., 2016). Changing consumer preferences, tightening regulation and technological breakthroughs will add up in the long run to a fundamental shift in ‘mobility behaviour’ especially in dense

The car industry as a laboratory of transformations  257 urban environments that could proactively discourage private-car use. As a result, the traditional business model of car sales will be complemented by a range of diverse, on-demand mobility solutions (Walker and Johnson, 2016). Consumers’ new habits of using tailored solutions for each purpose will lead to new segments of specialized vehicles designed for very specific needs. For example, as Gau et al. (2016) note, the market for a car specifically designed and manufactured for e-hailing services – that is, a vehicle designed for high utilization, robustness, high mileage and passenger comfort – would already run into millions of units today, and this is just the start. As a result of this shift to diverse mobility solutions, up to one out of ten new cars sold by the late 2030s could be a shared vehicle (ibid.), which could reduce sales of private-use vehicles. This could mean that more than 30 per cent of miles driven in new cars sold could be from shared mobility. On this trajectory, one out of three new cars sold could potentially be a shared vehicle as soon as 2050 (ibid.). In a similar vein, research by the Rocky Mountain Institute (Walker and Johnson, 2016) in the US suggests that automated mobility services could capture two-thirds of the entire US mobility market. It argues that ‘the rise of automated mobility services could be one of the most interesting and complex disruptions of the modern era’ (p. 4), with the potential for a new mobility system to emerge in the next few years that is ‘superior to our existing system in almost every way’ (p. 5). If correct, there are some huge implications for car makers. What has been termed ‘peak car ownership’ in, say, the United States, could occur as early as 2030 and could fall rapidly thereafter. There will be winners and losers depending on how quickly OEMs embrace new business models for mobility services; auto firms and the value chain that provide mobility services and autonomous vehicles could reap substantial profits. There is, of course, a speculative element in this, but, as already observed, the economics of the sector would change at a basic level as nurtured forms of competition phase out. A shift to mobility as a service, along with new entrants, will inevitably force traditional car manufacturers to compete on multiple fronts. Mobility providers (Uber, for example), tech giants (such as Apple, Google), and specialty OEMs (Tesla, for instance) will bring disruptive competition (Gau et al., 2016). Traditional automotive players that are under continuous pressure to reduce costs, improve fuel efficiency, reduce emissions, and become more capital-efficient will feel a squeeze, potentially leading to consolidation or new forms of partnerships among firms. 3.4

Possible Implications for the Value Chain?

Software competence is increasingly becoming one of the most important differentiating factors for the industry – for example, in ADAS/active safety, connectivity and infotainment. Longer term, cars will be increasingly integrated into the connected world, and automakers will have to consider positioning themselves in the ‘new mobility ecosystem’ – effectively a reconfigured value chain: see Figure 15.1, which is from Simoudis (2015). Today, personal mobility services are offered by transportation network companies (TNCs), such as Uber, Lyft and Grab. Such TNCs use technology to coordinate rides by matching drivers with riders, setting prices and enabling payment, managing rides to ensure timeliness, and managing ratings (Simoudis, 2017). TNCs can easily and at low cost adjust the supply of drivers to match the demand for rides. This is at the heart of their business model by not owning the vehicles used for rides. However, if on-demand mobility services (e.g.,

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Source:

Figure reproduced with permission from Evangelos Simoudis (see Simoudis, 2015).

Figure 15.1

Towards a new auto value chain

ride-hailing) become more widespread, with ACE vehicles adopted for such use, intermediaries offering personal mobility services could effectively become fleet operators, similar in nature to firms operating in sectors such as car rental, logistics, or even airlines (ibid.). There could be different categories of TNCs: some coordinating rides in vehicles owned by other individuals in selected cities; others using a hybrid (human/digital) model (i.e., a mix of operating under today’s model and by managing their fleet of autonomous vehicles); and others operating only fleets of autonomous vehicles. Such trends may result in the emergence of a new ‘fleet-based on-demand personal mobility’ value chain, consisting of a number of components, which will share data across the value chain: ● Vehicle design and manufacturing (existing automaker, outsourced automotive manufacturer, supplier or fleet operator, operating more on an open innovation model; Amison and Bailey, 2014). ● Operating platform (existing automaker, tier 1 supplier or new entrant like Waymo, Renovo or Drive.ai). ● User experience platform provider (controlling the passenger’s mobility experience, including the in-cabin experience, including hardware, software and data. ● Data services provider: content such as entertainment, traffic, mapping or weather, consumed by ACE platforms or passengers in ACE vehicles.

The car industry as a laboratory of transformations  259 ● Fleet creation: fleet operators could specify design and buy/lease from a specific vehicle manufacturer or lease vehicles from a ‘fleet creation company’, as in the airline industry. Fleet creation involves financing and insurance. ● Fleet operator: firms operating and managing the fleet of ACE vehicles offering on-demand mobility services. These could extend to integrating on-demand with public transport and to ‘global distribution system’ firms (as in the airline industry) offering reservations to on-demand mobility services. ● Fleet service and maintenance provider: servicing, maintaining and supporting fleets – specialists may provide this service. Clearly there is major uncertainty as to how this may develop. Given such possibilities, auto firms are looking outside the firm and beyond their own capabilities to access information and build partnerships with specialist providers – some inside and some outside of what has traditionally been seen as the ‘auto industry’. At the same time, issues like the shortage of semi-conductors and the shift towards the use of critical components such as e-drives and batteries means that OEMs are looking again at the ‘make or buy’ question and in some cases internalizing component production for control reasons. Moreover, car makers will have think about whether, and if so how to position themselves in new ecosystems while facing disruptive innovation and new entrants. A possible shift towards providing mobility service, for example, could play out across different market segments in different ways. Premium players like BMW may find it easier to build on its experimentation with car-sharing initiatives such as ‘Drive Now’ and offer a premium on demand mobility service than a mass market brand; in the latter case, the service provision may be more likely offered by a mobility service firm such as Uber, Lyft or Grab. While tech firms such as Tesla, Google and Apple generate huge media interest regarding the auto sector, they represent the tip of the iceberg (Gau et al., 2016). New players are likely to enter, especially cash-rich, high-tech companies and start-ups (ibid.). Such new entrants from outside the traditional auto industry attract more interest both from consumers and governments (i.e., generating interest in ‘new mobility’ solutions and lobbying for favourable regulation of new technologies – ibid.). Similarly, some Chinese car manufacturers, with impressive sales growth recently, are likely to play an important role globally, encouraged by the Chinese government, which has backed an ‘EV100’ strategy of creating 100 EV manufacturers in the country.

4

DIESELGATE: A CRISIS THAT SAVED THE GERMAN AUTO INDUSTRY?7

The German auto system can be viewed as an industrial system on a path of regional obsolescence for a period of time until the ‘dieselgate’ scandal of late 2015 forced an abrupt transformation, with it now playing catch-up in BEV technology. The system had by the 1990s established a dominant position in the premium sector, with a reputation built on engineering and reliability. Yet, that was not a position necessarily built on technological and organizational leadership in products. In many regards, German automakers were followers, often lagging Japanese rivals. On the disputatious topic of ‘lean production’, there are mixed views: Womack and Jones (2003), like Womack et al. (1990), actively contrast German manufactur-

260  Handbook of industrial development ing traditions with what they encapsulate as the essence of Toyota-style production, and there is much argument as to whether the likes of VW has ever really felt a need to copy Toyota. Less uncertainly, while Toyota launched its first hybrid model in 1997, VW did not launch a hybrid car until 2010. Where the German auto industry did lead was in the design and build of ICE cars, which suited larger premium models. With the vast bulk of assemblers’ sunk costs related to existing ICE technologies, such firms saw investment in BEVs as highly disruptive. Manufacturers were reluctant to shift investment into BEV development, and retained an expectation that ICE vehicles would continue to become more efficient, with an institutional lock-in for conventional ICE vehicles. While this applied across much of the European industry (Van Bree, Verbong and Kramer, 2010), it was especially marked in this case in diesel technology. Six to seven years ago, leaders in VW in particular still saw BEVs as inferior despite Tesla already eroding German brands’ premium market shares. By 2015, the German system was lagging in terms of patents in key new technologies of electric mobility and with a supply chain firmly locked into ICE technology. It was also investing little in autonomous driving or mobility services, even though these were already seen as key elements of the future auto value chain. The system at this point was investing heavily in ‘Industry 4.0’ technologies in terms of smart factories but not in radically new BEVs as products. It should be noted that some Japanese and American automakers had gone down different technological routes in trying to reduce greenhouse gas emissions. Toyota had invested heavily in petrol hybrids, while Tesla and GM in the US, and the Renault-Nissan alliance in Europe and Japan, had invested heavily in BEVs. The German auto system, in contrast, had effectively placed heavy technological ‘bets’ on what was increasingly seen as an obsolescent ICE (especially diesel) technology. This ICE regime lock-in was the result of a mix of factors, including large firm investment decisions, low fuel prices, the high willingness of customers to pay promoted by high levels of advertising expenditure, and not enough landscape pressure to force change (Clausen, 2018). In late 2015, Volkswagen was found to have cheated emissions tests by installing illegal manipulation software in 11 million diesel cars. The resulting fallout undermined consumer confidence and accelerated a rapid consumer shift away from diesels. What became known as ‘Dieselgate’ forced the German auto system off a path locked into obsolescent ICE technologies. Indeed, combined with stricter new environmental and testing standards at the European level, this acted as a ‘wake-up call’ for Volkswagen, Daimler, and to a lesser extent BMW, spurring them to refocus on future technologies – especially BEVs – and alternative business models. Post Dieselgate, the German auto system has been playing catch-up, investing heavily in BEVs as well as mapping, car sharing, connectivity, autonomous vehicles and the infrastructure required for such technologies. Incumbent German OEMs are now amongst the heaviest investors globally in BEVs, with VW investing tens of billions of euros in BEV technologies and infrastructure, aiming to sell 25 per cent of its own brand vehicles as BEVs by 2035. Heavy investment in its BEV platform has led Ford to partner with it to achieve scale economies in assembly. Germany has also attracted the disrupter firm Tesla to build batteries and BEVs in a new ‘gigafactory’ being built near Berlin. Overall, Dieselgate arguably ‘saved’ the German auto system (Kerier, 2018) by abruptly kicking it off what we have termed a ‘regional obsolescence path’ (De Propris and Bailey, 2021). Nevertheless, its cumulated embedded knowledge in auto manufacturing has helped it

The car industry as a laboratory of transformations  261 ‘jump’ from the ICE regime to the BEV one relatively smoothly with government support and to become attractive to emerging players, such as BEV pioneer Tesla (ibid.).

5

WINNERS AND LOSERS OF THE NEW ACE

However, there is much greater awareness now than there was even ten years ago of the downsides of battery-electric technologies, and the quest for critical materials like cobalt, lithium and nickel to sustain battery production. Conditions of de facto enslavement, child labour, sexual abuse, and medieval working conditions (injury including uranium poisoning as well as broken bodies) have been exposed in the Democratic Republic of Congo (DNC), which sources most of the world’s cobalt. Environmental degradation, including pollution as well as diversion of scarce water resources by intrusive and land-hungry mining and extraction methods, have become points of contention in countries like Chile and Bolivia – in the latter case substantial reserves of lithium resources have been considered an explosive political factor – while Chinese dominance in the field of battery technology and resourcing is a geopolitical stress point with America. As with oil pipelines built through the territorial reserves of the latter country’s indigenous population, objections are now being mounted to plans to source lithium from ancestral American-Indian lands.8 There is no evidence at this stage that proven reserves can in any event be expanded enough to accommodate anything like a total replacement of extant world motor vehicle fleets, even for cars and light vehicles (which do not face the same technical challenges as heavy and specialist vehicles), even before factoring in the implications of year-on-year growth in production, currently stymied only because of a COVID-19 pandemic. An oddly illuminating illustration is the estimate expressed in Busch et al. (2014) that what at the time were relatively modest proposals in Britain to expand its electric car sector were enough when taken just on their own to absorb all of world’s projected net increase in the critical supply of the lithium essential for battery manufacture. While it is naturally tempting for analyses looking at just one national context – Britain, say – not to think too much about the implications of a world-wide surge in BEV production for the supply of critical materials, this is a live issue (Coffey and Thornley, 2021). Microchip shortages, currently blocking general car production, may prove a presentiment. It is therefore important from the viewpoint of industrial strategy discussion to acknowledge that strongly differing positions are held on whether global fleets of cars and other motorized vehicles can be fully replaced by battery-electric alternatives without some accompanying (and possibly severe) reduction in the size of these fleets, let alone on whether past growth patterns for the world industry can realistically continue. Boiling this dispute down into two separate propositions, on the one side there is a view that a complete transition will be possible that allows and even invigorates growth in car fleets, and on the other that transition is likely to be partial rather than complete and its relative success dependent upon curtailing if not reversing said growth. Sperling and Gordon (2009), an intelligent study, is a good example from the second camp. Much mainstream media coverage tends (largely unconsciously) to promote views from the first camp, usually counterbalanced, if at all, by advocacy of continuity in the status quo ante.

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6

REFLECTIONS: THE CAR INDUSTRY AS A LABORATORY FOR TRANSFORMATIVE INNOVATIONS

In thinking about landmark innovations and dominant product designs, sight should not be lost of a running stream of innovations that have characterized the industry since its inception as a practical undertaking, in products as well as manufacture. Without departing from the basic rubric of a petrol (or diesel) fuelled ICE housed in an integrated steel body, what Chesborough and Teece (1996) would characterize as autonomous innovations have appeared constantly – autonomous meaning innovations in one or more parts of a technologically evolving multi-part product architecture that can be codified and communicated to design engineers and others working independently on other parts of the same architecture, to be incorporated as such into the totality of the architecture in a practicable way without loss of overall coherence.9 Similarly, and without departing from the conventional norms of mass production for such vehicles, the industry’s history is one of a steady progression of process innovations, even in the later stages of car manufacture and assembly, such as, for example, with robot welding. Where there is more room for conjecture and debate is whether the organization of the industry tends – outside periods of exceptional crisis: to wit, the current waves of experimentation with alternative vehicle technologies, led by battery electric, in response to the climate crisis – to suppress radical innovation, or what Chesborough and Teece call systemic innovation, wherein unfamiliar and still be fully assimilated developments lack a ready-made professional culture of compartmentalized and communicable pick-and-mix innovations. In saying this, the battery-electric car is not a recent invention, while work on a commercial mass production electric car has been going on now for several decades. A different if temporally overlapping development has come with the advance of technologies enabling so-called autonomous and connected vehicles. The Institute of Transport Engineers in America highlights multiple areas of innovation: ‘advanced wireless communications, on-board computer processing, advanced vehicle-sensors, GPS navigation, smart infrastructure, and others’ (ITE, 2021, n.p.). The collation and transmission of real-time information via a wireless network is one key feature, a distinction typically being drawn between vehicle-to-infrastructure connectivity (V2I), vehicle-to-vehicle connectivity (V2V) and vehicle-to-device connectivity (V2D). The value of the autonomous/automated component is how it advances on the cognitive limitations, as well as propensities for distraction, of the human driver, so that one practical outcome hoped for is an improvement in road safety and a reduction in injuries and fatalities. The possibility of cars carrying people but dispensing with human agency in the driving process has received much attention – along with a plethora of still unresolved issues regarding legal liabilities and difficult questions of criminal and civil tort as well as insurance (see earlier). While it is tempting to see this parallel development as supportive of sustainability, there are ambiguities: for example, improving road congestion via connected vehicles could, by mitigating some of the irritations of packed roads, encourage further enlarging of fleets; the same is true of the boast that car-parking with autonomy will become less of a headache. Similarly, ‘taking the driver’ out of taxis would dramatically reduce costs per mile and could encourage a switch from walking/cycling and public transport to driverless taxis, in turn increasing traffic congestion rather than reducing it. Avoiding the latter will require regulation and perhaps even minimum road pricing for driverless taxis. Indeed, multiple areas require policy work and support – including legal liabilities, data management, data-sharing and cyber-security, and cross-border interoperability. However, claims

The car industry as a laboratory of transformations  263 concerning economic benefits for income and jobs that will accrue still require scrutiny (see above). The larger car players, often with firm backing of national governments, are powerful political-entities in their own right, with considerable lobbying powers. The global car industry as it presently exists involves production on a massive scale concentrated amongst a relatively modest number of business units individually producing on a very large scale. It is thoroughly internationalized, although in some countries more than others. Their competitive relations, as Rhys (2005) observes, fall into the realm of rivalrous oligopoly. One mistake must be avoided: the most significant reason for the dominance of larger car makers, state-support aside, lies in their ability to leverage major distribution and retail networks; major supply networks; and resources for research and development, which includes access to tacit subsidies provided via universities; as well as the absolute capital requirements needed to install and maintain plant and equipment. Technical manufacturing economies of scale are not the most important factor. Moreover, employment in the industry is dominated by post-production services and pre-production supplies, enormous logistical functions encompassed in each including shipping and trucking. And all of this has, until recently, supported dominant fossil-fuel-based product architecture.

ACKNOWLEDGEMENT Part of the research in this article was supported by the EU Horizon 2020 project MAKERS, which was a Research and Innovation Staff Exchange under the Marie Skłodowska-Curie Actions [grant agreement number 691192]; and by the Economic and Social Research Council (ESRC) through a UK in a Changing Europe Senior Fellowship [award reference number ES/ T000848/1].

NOTES 1. As with all the other subject areas touched on in this brief overview, the body of writing on the Ford factory experiments is enormous, and our illustrative choices are necessarily selective. In this instance, readers with a particular interest in seeing how different writers orient towards the same subject could also, for example, compare Gartman’s ‘degradation of work’ perspective with the differently styled summary in Womack et al. (1990, pp. 26–30), which in turn draws on Hounshell (1984) and Lewchuck (1987), who on the question of the original Ford experiments deploy the same historical reference points. 2. The best collective resource now available for a running overview is provided by the successive edited volumes produced by GERPISA (‘the international research network for the automobile’), examples of which include Freyssenet (2009), Jetin (2015a, 2015b) and Covarrubias and Ramirez (2019), and the cross-disciplinary journal International Journal of Automotive Technology and Management. 3. Imperialism, as a category, subsumes colonialism, but is not synonymous with it. 4. Kaplinksi (2021) offers a very recent comment. 5. Others interested in lean production as a corporate onslaught on workers have been divided over questions of substance on the original claims made for Toyota, with some accepting them and others viewing ‘lean’ as cover-all propaganda: see Pulignano et al. (2008) and Stewart et al. (2009). Bungsche ([2016] 2020) describes the breakdown of the wages and working conditions

264  Handbook of industrial development for the ‘life’ employees of the large Japanese corporations within instances including Toyota, for corporate-motivated reasons. 6. The original economist of note here is Ronald Coase. 7. This case study is taken from De Propris and Bailey (2021). 8. A short popularly written historical view is set out in Coffey (2021). On the dispute currently mounting in America over domestic lithium extraction, see Flin (2021). 9. The easiest way to get a sense of a constant stream of inventions and innovations in the car industry is to type the name of a major model – ideally one with a lineage – into a search engine. Because of the large number of car enthusiasts, many with an interest, and frequently an expertise, in which technical experiments have been associated with which car and when, histories are often very detailed.

REFERENCES Abernathy, W.J. (1978), The Productivity Dilemma, Baltimore, MD: John Hopkins University Press. Amison, P. and Bailey, D. (2014), ‘Phoenix industries and open innovation? The Midlands advanced automotive manufacturing and engineering industry’, Cambridge Journal of Regions, Economy and Society, 7(3), 397–411. Bailey, D. (2016, 30 August), ‘Follow that (driverless) cab!’, Birmingham Post, accessed 22 August 2022 at http://​www​.birminghampost​.co​.uk/​business/​business​-opinion/​follow​-that​-driverless​-cab​ -11817752. Bailey, D. (2017, 19 July), ‘Electric vehicle market sparks into life as ICE age comes to end?’, Birmingham Post, accessed 22 August 2022 at http://​www​.birminghampost​.co​.uk/​business/​business​ -opinion/​electric​-vehicle​-market​-sparks​-life​-13354818. Bailey, D. and De Propris, L. (2019), ‘Industry 4.0, regional disparities and transformative industrial policy’, in P. Tomlinson, M. Barzotto and C. Corradini et al. (eds), Revitalising Lagging Regions: Smart Specialisation and Industry 4.0, London: Taylor & Francis, pp. 67–78. Berkeley, N., Bailey, D., Jones, A. and Jarvis, D. (2017), ‘Assessing the transition towards battery electric vehicles: a multi-level perspective on drivers of, and barriers to, take up’, Transportation Research Part A: Policy and Practice, 106, 320–32. Bloomberg New Energy Finance (BNEF) (2016), ‘Electric vehicles to be 35% of global new car sales by 2040’, accessed 22 August 2022 at https://​about​.bnef​.com/​blog/​electric​-vehicles​-to​-be​-35​-of​-global​ -new​-car​-sales​-by​-2040/​. Bloomberg New Energy Finance (BNEF) (2017, 23 June), ‘Electric cars to reach price parity by 2025’, accessed 2 August 2022 at https://​about​.bnef​.com/​blog/​electric​-cars​-reach​-price​-parity​-2025/​. Braverman, H. (1974), Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century, New York and London: Monthly Review Press. Bungsche, H. ([2016] 2020), ‘A system abandoned: twenty years of management, corporate governance and labour market reforms in Japan’, in J. Begley, D. Coffey, T. Donnelly and C. Thornley (eds), Global Economic Crisis and Local Economic Development: International Cases and Policy Responses, London and New York: Routledge, pp. 120–50. Busch, J., Dawson, D. and Purnell, P. et al. (2014), ‘Accounting for critical materials in sustainable energy provision: maintaining systemic resilience’, in A. Brown and M. Robertson (eds), Economic Evaluation of Infrastructure Provision: Concepts, Approaches, Methods. iBUILD/Leeds Report, October, pp. 75–86, accessed at 20 January 2022 at http://​sure​-infrastructure​.leeds​.ac​.uk/​ibuild/​wp​ -content/​uploads/​sites/​5/​2014/​01/​9940​_iBuild​_report​_print​_version​-WEB​.pdf. Ceschin, F. and Vezzoli, C. (2010), ‘The role of public policy in stimulating radical environmental impact reduction in the automotive sector: the need to focus on product-service system innovation’, International Journal of Automotive Technology and Management, 10(2/3), 321–41. Chandler, A.D. (1977), The Visible Hand: The Managerial Revolution in American Business, Cambridge, MA: Harvard University Press. Chesborough, H.W. and Teece, D.J. (1996), ‘When virtual is virtuous: organizing for innovation’, Harvard Business Review, January–February, 65–72.

The car industry as a laboratory of transformations  265 Clausen, J. (2018), Roadmap E-Mobility Germany: Objectives, Chances, Risks, Necessary Measures and Policy Initiatives, Berlin: Adelphi/Borderstep/IZT. Coffey, D. (2006), The Myth of Japanese Efficiency: The World Car Industry in a Globalizing Age, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Coffey, D. (2021), ‘On the road again’, History Magazine, March. Coffey, D. and Thornley, C. (2010), ‘Legitimating precarious employment: aspects of the post-Fordism and lean production debates’, in C. Thornley, S. Jefferys and B. Appay (eds), Globalization and Precarious Forms of Production and Employment: Challenges for Workers and Unions, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 40–61. Coffey, D. and Thornley, C. (2012), ‘Low carbon mobility versus private car ownership: towards a new business vision for the automotive world?’, Local Economy, 27(7), 732–48. Coffey, D. and Thornley, C. (2013), ‘Nurtured competition and optimal vehicle life: a missing theme in public policy formulation for alternative vehicle technologies?’, International Journal of Automotive Technology and Management, 13(2), 134–54. Coffey, D. and Thornley, C. (2021), ‘Sustainability dilemmas and Britain’s national industrial ambitions: Brexit, electric cars, and a petrol and diesel engine ban’, in C. Berry, J. Froud and T. Barker (eds), The Political Economy of Industrial Strategy in the UK, Newcastle upon Tyne: Agenda Publishing Limited. Covarrubias, A. and Ramirez, S.P. (eds) (2019), New Frontiers of the Automobile Industry: Exploring Geographies, Technology, Institutions and Organizational Challenges, London and New York: Palgrave Macmillan. De Propris, L. and Bailey, D. (2021), ‘Pathways of regional transformation and Industry 4.0’, Regional Studies, 55(10–11), 1617–29. Drucker, P. (1955), The Practice of Management, London: Heinemann. Eberts, R. and Eberts, C. (1995), The Myths of Japanese Quality, Upper Saddle River, NJ: Prentice Hall. Flin, B. (2021, 2 December), ‘America’s dirty divide: “Like putting a lithium mine on Arlington cemetery”: the fight to save sacred land in Nevada’, The Guardian, accessed 22 August 2022 at https://​www​ .theguardian​.com/​us​-news/​2021/​dec/​02/​thacker​-pass​-lithium​-mine​-fight​-save​-sacred​-land​-nevada. Freyssenet, M. (2009), The Second Automobile Revolution: Trajectories of the World Carmakers in the 21st Century, London and New York: Palgrave Macmillan. Freyssenet, M. and Jetin, B. (2009), ‘Big Three: le piège de la “libéralisation” salariale et financière se referme’, La Lettre de GERPISA, January–March. Gartman, D. (1979). ‘Origins of the assembly line and capitalist control of work at Ford’, in A. Zimbalist (ed.), Case Studies on the Labor Process, New York and London: Monthly Review Press, pp. 193–205. Gau, P., Kaas, H.-W., Mohr, D. and Wee, D. (2016), ‘Disruptive trends that will transform the auto industry’, McKinsey & Company, accessed 22 August 2022 at https://​www​.mckinsey​.com/​industries/​ automotive​-and​-assembly/​our​-insights/​disruptive​-trends​-that​-will​-transform​-the​-auto​-industry. Gowling WLG (2016), Are You Data Driven? What’s Around the Corner? UK Autodrive White Paper, accessed 22 August 2022 at https://​gowlingwlg​.com/​getmedia/​00546f3a​-9074​-47f8​-b50b​ -fcd048e89095/​162405​-are​-you​-data​-driven​.pdf​.xml. Hounshell, D. (1984), From the American System to Mass Production, 1800–1932, Baltimore, MD: Johns Hopkins Press. Institute of Transport Engineers (ITE) (2021), ‘Technical resources: connected/automated vehicles’, accessed 30 November 2021 at https://​www​.ite​.org/​technical​-resources/​topics/​connected​-automated​ -vehicles/​. Jetin, B. (2015a), Global Automobile Demand Volume One: Major Trends in Mature Economies, London and New York: Palgrave Macmillan. Jetin, B. (2015b), Global Automobile Demand Volume Two: Major Trends in Emerging Economies, London and New York: Palgrave Macmillan. Kaplinski, R. (2021), Sustainable Futures, Cambridge, UK: Polity Press. Kerier, W. (2018, 19 September), ‘How Dieselgate saved Germany’s car industry’, The Verve. KPMG/SMMT (2015), Connected and Autonomous Vehicles – The UK Economic Opportunity, accessed 22 August 2022 at https://​home​.kpmg​.com/​content/​dam/​kpmg/​pdf/​2015/​04/​connected​-and​ -autonomous​-vehicles​.pdf.

266  Handbook of industrial development KPMG/SMMT (2017), The Digitalisation of the UK Automotive Industry, accessed 22 August 2022 at https://​www​.smmt​.co​.uk/​wp​-content/​uploads/​sites/​2/​smmt​_the​-digitalisation​-of​-the​-uk​-auto​ -industry​_kpmg​-apr​-2017​.pdf. Laux, J. (1976), In First Gear: The French Auto Industry to 1914, Liverpool: Liverpool University Press. Lewchuck, W. (1987), American Technology and the British Vehicle Industry, Cambridge, UK: Cambridge University Press. Liker, J.F. (2004), The Toyota Way, New York, McGraw Hill. Lyddon, D. (1996), ‘The myth of mass production and the mass production of myth’, Historical Studies in Industrial Relations, 3(1), 77–105. Monden, Y. (1998), Toyota Production System: An Integrated Approach to Just-in-Time (3rd edition), Norcross, GA: Engineering and Management Press. Morisson, A. and Pattinson, M. (2021), Clusters: Driving the Green and Digital Twin Transitions. Policy Brief from the Policy Learning Platform on Research and Innovation, Lille: Interreg Europe Policy Learning Platform. Nilsson, M. and Nykvist, B. (2016), ‘Governing the electric vehicle transition – near term interventions to support a green energy economy’, Applied Energy, 179, 1360–71. Nykvist, B. and Nilsson, M. (2015), ‘The EV paradox – a multilevel study of why Stockholm is not a leader in electric vehicles’, Environmental Innovation and Societal Transitions, 14, 26–44. Ohno, T. (1978), Toyota Production System: Beyond Large Scale Production, Cambridge, MA: Productivity Press. Pejcic, D., Bailey, D. and Pegoraro, D. (2018), ‘Paper on a case study of value chain upgrading’, MAKERS, accessed 22 August 2022 at http://​www​.makers​-rise​.org/​wp​-content/​uploads/​2018/​02/​D5​ .2​-Paper​-on​-a​-case​-study​-of​-value​-chain​-upgrading​-protected​.pdf. Pulignano, V., Stewart, P., Danford, A. and Richardson, A.M. (eds) (2008), Flexibility at Work: Developments in the International Automobile Industry, London and New York: Palgrave Macmillan. Rhys, D.G. (1972), The Motor Industry: An Economic Survey, London: Butterworth. Rhys, D.G. (2005) ‘Competition in the auto sector: the impact of the interface between supply and demand’, International Journal of Automotive Technology and Management, 5(1), 261–83. Rifkin, J. (2015), The Third Industrial Revolution: How Lateral Power is Transforming Energy, the Economy, and the World, London: Palgrave Macmillan. Schonberger, R.J. (2001), Let’s Fix It!, New York: Free Press. Simoudis, E (2015, 6 April), ‘The innovation-driven disruption of the automotive value chain (part 1)’, Synapse Partners [blog], accessed 22 August 2022 at https://​corporate​-innovation​.co/​2015/​04/​06/​the​ -innovation​-driven​-disruption​-of​-the​-automotive​-value​-chain​-part​-1/​. Simoudis, E. (2017, 25 April), ‘A new value chain for next-generation personal mobility’, Synapse Partners [blog], accessed 29 May 2018 at https://​corporate​-innovation​.co/​2017/​04/​25/​a​-new​-value​ -chain​-for​-next​-generation​-mobility/​. Sloan, A.P. (1963), My Years With General Motors, New York: Doubleday. Sperling, D. and Gordon, D. (2009), Two Billion Cars: Driving Towards Sustainability, Oxford: Oxford University Press. Stewart, P., Richardson, M. and Danford, A. et al. (2009), We Sell Our Time No More: Workers’ Struggles Against Lean Production in the British Car Industry, London and New York: Pluto Press. United Nations Environment Programme (UNEP) (2002), Product-service Systems and Sustainability: Opportunities for Sustainable Solutions, Paris: UNEP. Van Bree, B., Verbong, G.P.J. and Kramer, G.J. (2010), ‘A multi-level perspective on the introduction of hydrogen and battery-electric vehicles’, Technological Forecasting and Social Change, 77(4), 529–40. Walker, C., Guest, R. and Turner, A. (1956), The Foreman on the Assembly Line, Cambridge MA: Harvard University Press. Walker, J. and Johnson, C. (2016), Peak Car Ownership: The Market Opportunity of Electric Automated Mobility Services, Rocky Mountain Institute, accessed 24 March 2018 at https://​www​.rmi​.org/​wp​ -content/​uploads/​2017/​03/​Mobility​_PeakCarOwnership​_Report2017​.pdf. Williams, K., Haslam, C. and Johal, S. (1992), ‘Ford versus Fordism: the beginning of mass production?’, Work, Employment and Society, 6(4), 517–55.

The car industry as a laboratory of transformations  267 Williams, K., Haslam, C. and Williams, J. et al. (1994), Cars: Analysis, History, Cases, Oxford: Berghahn Books. Wolmar, C. (2018), Driverless Cars: On a Road to Nowhere? London: London Publishing Partnership. Womack, J.P. and Jones, D.T. (2003), Lean Thinking: Banish Waste and Create Wealth in Your Corporation (2nd edition), New York: Free Press. Womack, J.P., Jones, D.T. and Roos, D. (1990), The Machine That Changed the World: The Story of Lean Production, New York: Rawson Associates (Macmillan).

16. The propulsive role of the space industry in industrial development: evaluating the case of spaceports Leslie Budd and Davide Villani

1 INTRODUCTION The NASA Mars Perseverance Rover landing in early 2021 provoked general excitement and increased public support for space exploration. Later in the same year, the sight of global billionaires masquerading as astronauts generated opprobrium that appeared to suggest that the space industry only benefits multinational corporations and high-net-worth individuals. As usual, the truth exists along a continuum. In the last 20 years, the European Space Agency (ESA) has contributed significant funding to the International Space Station (ISS), leveraging advances in space science whose multidisciplinary impacts have led to the identification, evaluation and potential measurement of a wide range of socioeconomic benefits (NASA, 2019). Consequently, space is becoming an increasingly important industrial sector whose development is a key component in industrial strategies that are based on the concept and practice of Industry 4.0 (I4.0). In 2020, the size of the global space economy amounted to US$423.8 billion, growing 2.2 per cent from 2018. Commercial revenues climbed to US$336.89 billion in 2019, up 6.3 per cent on the previous year and growing at a compound annual growth rate (CAGR) of 5.5 per cent. By 2040, it is estimated to total US$1.5 trillion in nominal terms (Space Foundation, 2020). Of this total, the EU accounted for 16 per cent, with the UK contributing about 6 per cent (ESA, 2021; London Economics, 2019). The distribution between space products and services and space infrastructure and support industries, shown in Figure 16.1 below, grew 1.7 per cent and 16.1 per cent, respectively, from 2018 (Space Foundation, 2020). The global satellite communications industry totalled US$62.1 billion in 2019 and is expected to grow at a 9.2 per cent CAGR from 2020 to 2027. The escalating demand for small satellites for Earth observation services in various industries such as oil and gas, energy, agriculture and defence across the globe is the primary factor driving market growth (Paladini, 2019). These estimates may include related activities given the spillovers into and from related industries. The space industry tends to be divided into upstream and downstream activities: ● upstream: activities that lead to the development of space infrastructure, including R&D, production of satellites and launchers and the deployment of such infrastructure; ● downstream: activities that employ data and knowledge that are derived from the space for Earth-related objectives as well as the products and services that support them.

268

The propulsive role of the space industry in industrial development  269 There are also a number of tiered segments that constitute the supply chains of the space economy: ● primes: design and assembly of complete spacecraft systems, which are delivered to governmental and/or commercial users; ● tier 1: actors for the design, assembly and manufacture of major sub-systems, such as satellite structures, propulsion sub-systems and payloads; ● tier 2: manufacturers of equipment to be assembled in major sub-systems; ● tier 3 and 4: producers of components and sub-assemblies, specializing in the production of specific electronic, electrical and electromechanical components and materials. Furthermore, the space industry underpins a wider and deeper space economy within which it acts as a propulsive industry stimulating activity-complex agglomeration economies in city-regions (George, 2019). Examples in Europe include Bremen, Rome, Toulouse and Turin, among others. At the level of formulating and implementing industrial strategies, the European Union and ESA published a joint statement and adopted a resolution entitled ‘Towards a United Space in Europe’ as the strategy for Europe’s space industry. Space 4.0 is its application of I4.0 for this increasingly important sector whose development cross-cuts old and new technologies and their application, especially new services (Bohlmann and Petrovici, 2019; ESA, 2018). In Australia, the Department of Industry, Science, Energy and Resources and the Australian Space Agency published its industry strategy for the space sector, whose strategic vision is: ‘A globally responsible and respected space sector that lifts the broader economy, and inspires and improves the lives of Australians’ (Australian Space Agency, 2019, n.p.). Similarly, the 2017 UK Industrial Strategy (Department for Business, Energy & Industrial Strategy, 2017) had the space sector as its first case study, and built upon the Space Growth Action Plan of 2013 (Space IGS, 2013). One can argue that NASA’s role in the US exemplifies the important contribution of the space sector to industrial strategy. Membership of ESA represents a means to develop space-based industrial strategies through access to bidding for contracts, global supply chains and innovation networks. Through the ISS programme, newer and smaller states (for example, Slovenia, who joined in 2011) can also link to ISS’s other partner space agencies (Canada, Japan, Russia, US). This chapter explores the industrial development of the space economy in the context of evaluating its trajectory of Space 4.0 by providing different theoretical and conceptual insights, drawing upon input–output (I–O) analysis and Bourdieu’s economic capitals in particular. The chapter also uses the context of the fast-growing satellite industry as the background against which to analyse the socioeconomic benefits of Prestwick Spaceport base at the international airport in Scotland of the same name. I–O analysis draws upon the World I–O Tables, and Scotland in particular, to calculate income and employment multipliers, its supporting industry linkages with the satellite sector and one of the proposed spaceports in Prestwick, Scotland. Bourdieu’s (1984) economic capitals are used as an evaluative framework that can be applied to assess and potentially measure qualitative benefits to economy and society.

270  Handbook of industrial development

2

GENESIS OF SPACE 4.0

Space 4.0 was promulgated in a joint statement issued by the European Union and the ESA in 2016 (European Commission, 2016). ESA subsequently produced its own version, Space 4.0i, which sets out its own strategic priorities focusing on exploration missions and how they would be operationalized. Space 4.0 in general represents a paradigmatic shift in the role of space exploration, with greater interaction between governments, the private sector and society than hitherto. Space 4.0i is closely tied with ESA’s European Space Exploration Envelope Programme (E3P), adopted in 2016, which consists of four Cornerstones covering Earth, Lunar and Mars missions (ESA, 2016, 2018). The main space programmes of the EU are: ● Copernicus: Earth observation missions; ● European Geostationary Navigation Overlay Service (EGNOS): regional-based global navigation satellite systems; and ● Galileo: Europe’s global navigation satellite system. There is a high level of cooperation between the EU and ESA with regard to the space economy and industry. The EU relies heavily on ESA’s technical excellence and a large part of the EU space budget is delegated to ESA, to the extent that the EU is today among the largest contributors. Article 189 of the Treaty on the Functioning of the European Union, which builds upon the Lisbon Treaty of 2009, calls for the EU to establish any appropriate relations with ESA (European Union, 2012). Table 16.1 sets out the relative functions of the ESA and EU with respect to the European space economy. Space 4.0 has become the general term for the joint EU and ESA strategies and builds upon the achievements and developments of space exploration and its economy, as set out in the last column in Table 16.1 (Bohlmann and Petrovici, 2019). Space 4.0 follows on from, but currently tends to lag, I4.0 developments, but in doing so transforms the nature and definition of the space economy. The design principles of I4.0 correlate to regionally based industrial strategies and policies that are becoming an important agency in contemporary economic development across the global economy (De Propris, 2017). One overlooked sector that has disruptive regional potential within an I4.0 environment is the space economy – one that is growing in size and importance. I4.0-related technologies for space exploration include: ● ● ● ● ● ● ●

additive manufacturing/3D printing; artificial intelligence (AI) and machine learning; augmented and virtual reality; (big) data analytics, simulations and digital twins; intelligent robots and cobots; smart manufacturing and the smart factory; smart sensors and the Internet of Things.

The distribution of these I4.0 technologies across the sectors and regions varies according to capacities and capabilities that also rest upon the history of their constituent industries and the legacy of their infrastructure and knowledge formation. For example, space city-regions places like Bremen, Glasgow, Shetland, Toulouse and Turin were locales for shipbuilding, aeronautics, as well as oil and gas production in the past. Consequently, there is a degree of regional

The propulsive role of the space industry in industrial development  271 Table 16.1

Functions of the European Space Agency and the European Union

 

ESA

EU

Focus

Space activities (research, science, commercial support,

Wide ranging – full range of issues set out in

exploration, research and development)

the Treaty

To deliver a strong European space industry. Focus on

Many – as set out in the Treaties and various

the peaceful uses of space

policies. Space established as an explicit EU

Objectives

competence in the Lisbon Treaty Budget arrangements

Small core of mandatory elements (basic research,

Programmes are funded from within the EU

facilities, salaries etc.) with a wide range of optional

budget. EU member states cannot decide to stop

programmes designed to meet the needs of member

funding a particular programme

states. Very flexible Membership

22 European states, all of which are EU member states

27 European states with accession states

except for Switzerland and Norway. Additional EU member states on course to join Procurement

Legislation

Specialist rules – invested funds are earmarked for

Free and open procurement. Some space

industry from the investing member state. This reflects

procurements are limited to EU member states

the focus on growing industrial capability in Europe

on security grounds

Cannot establish law

Extensive body of law

economic resilience based upon this industrial path dependency in these places that link the histories of these industries to the evolution of the space economy. In 2012, the Organisation for Economic Co-operation and Development (OECD) proposed this definition of the space economy: ‘The full range of activities and the use of resources that create value and benefits to human beings in the course of exploring, researching, understanding, managing, and utilising space’ (OECD, 2012, p. 19, citing NASA). For the EU, and ESA in particular, fulfilling their strategy of ‘Towards Space 4.0 for a United Space in Europe’ rests upon further integrating I4.0 and Space 4.0. Two recent examples of utilizing I4.0 technologies aboard the ISS (part of Cornerstone 1 of E3P) are AI and 3D printing. In 2018, the world’s first, autonomous astronaut in flight using AI was launched on the ISS. There is also a 3D printer aboard, used in a range of scientific experiments, which also produces beneficial applications for a range of end uses on Earth. Increasingly, there is a more symbiotic relationship between I4.0 and Space 4.0 as space exploration contributes to addressing global challenges and technological progress. As Bohlmann and Petrovici (2019, p. 7) note: ‘In a reciprocal use, Industry 4.0 benefits strongly from various features of space activities, ranging from system approaches, connectivity, extreme reliability and remote operations in harsh environments’. As a consequence, the space economy is being transformed, with the following outcomes from its development: The Space Economy is growing and evolving, together with the development and profound transformation of the space sector and the further integration of space into society and economy. Today, the deployed space infrastructure makes the development of new services possible, which in turn enables new applications, in sectors such as meteorology, energy, telecommunications, insurance, transport, maritime, aviation and urban development leading to additional economic and societal benefits. The space sector is not only a growth sector itself, but is the vital enabler of growth in other sectors. (ESA, 2019, n.p.)

272  Handbook of industrial development It is apparent that Space 4.0 encompasses a wider range of outcomes than conventional I4.0 given the latter’s stronger manufacturing basis. These include new services that contribute to the development of servitization1 within the space economy and sectors as a whole, enabled by the adoption of I4.0 technologies in the first instance. The UK National Space Strategy, published in late 2021, set out the UK government’s strategic direction in the light of a changing relationship with its European neighbours due to Brexit (Department for Business, Energy & Industrial Strategy, 2021). The strategy includes the following three sets of objectives: ● growing existing strengths; ● leadership in high-growth areas; and ● emerging sectors. The composition of the objectives is set out in Figure 16.1, whilst Figure 16.2 sets out the key nodes of the UK National Space Strategy, of which spaceports are crucial. To explore these issues, the case of Prestwick is reviewed within the context of the growing importance of the space industry and the small satellite sector to the Scottish economy. It is also of increasing importance to the development of an I4.0 ecosystem at the heart of Scotland’s space economy (Benitez, Ayala and Frank, 2008; Scottish Manufacturing Advisory Service, 2020).

Figure 16.1

Component of the objectives of the UK National Space Strategy

The propulsive role of the space industry in industrial development  273

Figure 16.2

3

Key nodes in the UK National Space Strategy

THE INDUSTRIAL DEVELOPMENT OF SPACEPORTS

The essential driver of building spaceports is the significant growth of small satellite launches both globally and within the UK. The value of the global small satellite markets was estimated at $4.70 billion for 2021 and projected to grow to $10.75 billion in 2028 at a CAGR of 12.6 per cent during the 2021–28 period (Fortune Business Insights, 2021). Typically, ground-based spaceports are constructed in geographically favourable locations. Favourable locations are usually near the Equator, with eastward, near-eastward launch direction and away from centres of population. These factors are more closely associated with vertical launch operations so any rocket debris will fall in remote locations (Roberts, 2019). Although all points along the Earth’s rotational axis have the same horizontal velocity, some spaceports can take advantage of higher launch velocities by being situated at lower latitudes. For example, a satellite launched from Northern Scotland will require more fuel than the same one launched from Northern Brazil. Thus, there are a number of positive externalities from the optimal geographical location of spaceports, including technological and environmental capital (see Section 4 below). The question of location brings us to the question of whether to build horizontal or vertical launch spaceports. The former have a number of locational advantages in usually being sites at disused or existing airports, with associated aerospace infrastructure and direct transport links to economically active places. The growth of spaceport activity also acts as a Schumpeterian propulsive industry at the heart of activity-complex agglomeration economies.2 Horizontal launches consist of a conventional large aircraft taking off from an airport with the satellite launcher slung under the fuselage. The subsequent launch needs less energy and increases environmental capital due to the satellites being released away from land.

274  Handbook of industrial development In 2018, it was estimated that satellite services supporting wider industrial activities across the UK (non-financial business) economy contributed £361 billion to UK gross domestic product (GDP), equivalent to 16.9 per cent of total GDP (ONS, 2018). The distribution of satellite services to the wider non-financial economy at the time consisted of: ● ● ● ●

global navigation satellite systems (GNSS) services: £314 billion (14.7 per cent of GDP); meteorological services: £211 billion (9.8 per cent of GDP); communications services: £101 billion (4.7 per cent of GDP); and Earth observation services: £100 billion (4.7 per cent of GDP).

Since 2018, the sub-region centred on Glasgow, Scotland has become Europe’s largest manufacturer of satellites below 250kg in weight (London Economics, 2020). As the third largest region of the UK space economy, Scotland is an obvious location for the creation and development of spaceports to facilitate the continued growth of satellite launch services. The contribution of the Scottish space industry to the UK is significant and growing. As can be seen in Figure 16.3, space manufacturing contributes most to the Scotland space economy. Table 16.2 sets out the relative contribution of space industry segments to Scotland/UK gross value added (GVA) and Scotland’s space manufacturing to the UK’s total.

Figure 16.3

Scottish space industry GVA by industry segment (£ million, 2017/18)

The propulsive role of the space industry in industrial development  275 Table 16.2

Relative contribution of GVA of space industry segments (£ million, 2017/18)

Industry Segment Space manufacturing Space operations

Scotland

UK

529

1653

5

500

Space applications

328

3656

Ancillary services

19

211

Source:

London Economics (2020).

The business case for the building of spaceports commissioned by the United Kingdom Space Agency (UKSA) concludes: ● The total value of the UK small satellite market for 2021–30 is estimated $5.5 billion. ● Seventy per cent of the estimated number of satellites (17 374) are in the less than 250kg category for the forecast period. ● The existing launch capacity of growing at 5 per cent annually is only capable of meeting 35 per cent of demand (Frost & Sullivan, 2018). It is apparent that spaceports can almost be viewed as an intermediate good given the rapid growth of the satellite industry, both globally and regionally, facilitated by Space 4.0 technologies, innovation and processes. As part of this contemporary industrial development, with their close links to the aerospace sector, spaceports also create important internal and external economies of scale and scope. As stated above, they also generate agglomeration economies as spaceports become propulsive locational nodes of space city-regions. The Prestwick Spaceport Case The Prestwick Spaceport is located at the international airport of the same name with direct transport to links to Glasgow some 60km to the north-east. First built in the 1930s, it served as a base for the US Air Force during World War II. Now owned by the Scottish Government, Prestwick currently accounts for half the employment in the space sector. It is also home to major aerospace companies that help support the growth of a space industry cluster. Consequently, in 2020, the local aerospace and space sector programme received £80 million from the UK government as part of the Ayrshire Growth Deal totalling £250 million. In September 2021, Prestwick Airport and South Ayrshire Council secured a launch partner to help realize plans for a spaceport. A Memorandum of Understanding (MoU) was signed with Astraius, the UK-based small and medium-sized satellite launch services company, which will bring horizontal launch technology to the project. The advantage of Prestwick as a spaceport is its industrial path dependency of having grown from an international airport to being at the heart of an aerospace cluster. The industrial development of small satellite manufacturing and associated launch services and their rapid growth makes the creation and location of spaceports a crucial component of the sector’s infrastructure and associated supply chains. It can be argued that the industrial development of spaceports at various sites in Scotland and the growth in satellite manufacturing and launch services, represent outcomes of Space 4.0 industrial strategy. In this context, a range of socioeconomic benefits beyond the direct impact on science, technology and engineering are created for the regional and wider economy. The challenge is to identify, evaluate and potentially measure these benefits.

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4

AN IMPACT EVALUATION FRAMEWORK (IEF) FOR THE SPACE SECTOR

The development of I4.0 has become an increasingly important discourse within innovation and regional studies as well as policy circles (Barzotto et al., 2020; Cooke, 2021; De Propris, 2017; De Propris and Bailey, 2021; Reischauer, 2018; Sony, 2020; Thompson, 2015). The essential challenge is the evidential base of the longer-term impact and territorial distribution of this form of modern manufacturing. In other words, some kind of evaluative framework to assess socioeconomic benefits beyond the direct application of I4.0 manufacturing technologies is needed. As noted by Cooke: By contract Scherrer (2019) takes a more sanguine techno-economic paradigm (Perez, 1983) in which technical change is seen as embedded in previous innovation waves, embodied in socioeconomic evolution and spatially uneven in its impacts. Accordingly Industry 4.0 is not uniformly ‘pervasive’ but unevenly developed with favoured spatial locations (and social classes) geographically sporadic. (Cooke, 2021, p. 1638)

Like Space 4.0, I4.0 stimulates beneficial agglomeration economies and creates derived demand for linked and downstream economic activities. Space 4.0 builds upon I4.0 manufacturing technologies but encompasses a wider range of activities enabled by scientific discoveries, technological applications and increased innovation externalities aboard satellites whether robotic or human types. The evolution of Space 4.0 as discourse goes beyond I4.0 and includes creating evaluation frameworks to identify, evaluate and potentially measure beneficial socioeconomic outcomes. Within Space 4.0i, ESA established a Benefits Management Office to create an evaluation framework for ESA’s E3P to identify and evaluate a number of potentially key benefit categories, both upstream and downstream, direct and indirect. They defined their approach to benefit evaluation and measurement as follows: The diverse dimensions and characteristics of E3P benefits require a variety of approaches to measurement of benefits which together create a rich picture allowing adequate valuation and effective monitoring of benefit realisation. Measures should be quantitative where possible, i.e. expressed in numerical terms (financial or other). When not possible, descriptive terms can also be used as a qualitative measurement. (ESA, 2018, p. 12)

In late 2018, ESA appointed an Open University research team to create an IEF to evaluate the socioeconomic benefits of E3P. The project name ‘Benefits of the ESA Exploration Roadmap in Socioeconomics’ (BEERS) adopts a multi-criteria analysis (MCA) methodological framework that is stage based and uses a range of criteria combining quantitative and qualitative approaches. It moves beyond conventional studies that focus on quantitative outputs in the form of multiplier effects or cost–benefit measures of changes in economic output, employment and changes in fiscal resources. These studies acknowledge that there are a number of benefits for which quantitative approaches are not appropriate, but do not attempt to measure the impact of these benefits. These non-quantitatively measured benefits can be categorized as spillovers and externalities and in the case of the E3P include, inter alia, cultural events, space architecture and engineering design, networks of mission organizational stakeholders, health and environmental management outcomes of mission experiments.

The propulsive role of the space industry in industrial development  277 The stages of BEERS are: 1. Critical analysis of existing studies of economic impact of space programmes based upon cost–benefits analysis (CBA) and estimation multiplier effects. Limitations of these studies is recognition of the difficulty of evaluating and measuring externalities and spillovers whose indicators are qualitatively based. 2. More comprehensive estimations of the economic impact of space programmes on output, employment and fiscal outcomes on associated industries using World Input–Output Tables. These impacts take the form of multipliers – that is, for every €1 of expenditure on space programmes, a multiple of output, employment, taxation and public expenditure is generated. 3. Logic models are widely used in programme evaluation and can be used for a range of purposes. They can be used to represent the most important relationships between project activities and the kinds of outputs, outcomes and impacts that are anticipated. These models also provide the basis for undertaking scenario analysis of the expected outcomes of space programmes. 4. Capitals are a set of resources that are generated for a community as a result of externalities/and spillovers from economic activities. In the case of space programmes, these outcomes are described above. These forms of economic capital cannot be easily quantified so a combination of qualitatively based indicators are used to identify, evaluate and potentially measure different capitals. They may include database searches to establish frequency counts in the public domain: stakeholder analysis, surveys, number of space-related activities, total social media mentions and so on. 5. Data visualization methods provide an economy of exposition of complex relationships that are derived from different data sets and their outputs. For policy makers, they provide a visual shorthand for assessing the impact of space programmes. An overview of BEERS is displayed in Figure 16.4. Stages 2 and 4 provide a useful basis for constructing an IEF for identifying, assessing and potentially measuring the socioeconomic benefits of spaceports – in this case Prestwick Spaceport. Input–Output (I–O) Analysis The I–O approach provides a comprehensive treatment of the economy as a whole, encompassing all of its industrial sectors. Use of the Leontief multiplier3 provides a vehicle for capturing the impact of an extra unit of final demand (investment in space-related activities) on every industrial sector. Second, I–O analysis is ‘objective’ in its approach, since it uses standardized I–O tables, with coherent and consistent industrial classifications. The third strength highlighted by Hof et al. (2012) is that I–O analysis provides a clear ranking between policy initiatives, which is vital for policymakers, with precise definitions of direct, indirect and induced effects. I–O analysis has undergone something of a renaissance in recent years. A key development has been the construction of the World Input–Output Database (WIOD), which includes national tables with interlinking trade flows (Dietzenbacher et al., 2013). I–O tables are available for all 22 member states of ESA. This database can potentially be adapted at low cost. Furthermore, trade flow linkages provide the basis for studying the increasingly complex

278  Handbook of industrial development

Figure 16.4

Overview of BEERS methodological framework

global supply chains in which modern production is organized. As scholars of globalization, I–O analysts are even described by Baldwin (2013) as ‘rock stars’. Though I–O analysis is still used to calculate backward and forward linkages, as in its early formulations, it also provides the basis for considering supply chains across European countries for expenditure on space missions. One of the classical applications of I–O analysis is the so-called multipliers analysis. Multiplier analysis quantifies the effect of the expenditure in one sector on the other sectors of the economy. In this way, it is possible to capture not only the effect of a shock (such as the investment project) in the industries directly affected by the shock, but also the indirect impact that this expenditure has on the other sectors of the economy. There are different types of multipliers, all of them enabling the estimation of the direct and indirect impacts of an economic shock, of which the following two are most frequently used: ● Simple output multiplier ‘for sector j is defined as the total value of production in all sectors of the economy that is necessary to satisfy a dollar’s worth of final demand for sector j’s output’ (Miller and Blair, 2009, p. 245). In other words, the simple output multiplier estimates the total effect (direct + indirect) of the expenditure of one extra pound in a certain sector. ● Employment multiplier involves the study of the impact of a shock not in monetary terms, but in terms of employment generation. This one estimates the amount of employment that is directly and indirectly generated following an initial shock in the expenditure. The fundamental intuition behind the employment multiplier is that the generation of output is associated with the creation of a certain amount of employment. As for the output multiplier, the impact is both direct (in the industry affected by the shock) and indirect (i.e., in the indus-

The propulsive role of the space industry in industrial development  279 tries that provide intermediate goods). This type of multiplier is therefore very important in policy terms. The economic benefits are not confined to output generation but are also possible sources of employment. The application of I–O analysis to the space sector is based on a particular theoretical perspective, which in its demand-driven formulation does not tend to take into account price flexibility. However, as argued in OECD (2012, p. 80), ‘[n]ot many space products and services are fully commercial, as most are strategic in nature and not freely traded’. This type of analysis provides a relevant focus on the economic impact that derives from investment in the space industry and its industrial development, but on the other hand, it is sometimes not able to identify the specificities of each space project. This is because by using only one type of industry multiplier (i.e., that of the ‘Other Transportation Industry’) they are not able to differentiate between different industries. While it is true that a consistent part of space project expenditure will be channelled towards the space industry itself, there are some projects that may involve other industries too. For example, the construction of a new spaceport will involve different industries from those in the development and assembly of a space rover. It is safe to envisage that while the first will greatly involve the construction industry, the latter will involve more research and development and the space industry itself. Moreover, most of the existing studies only provide estimation of the domestic multipliers, without considering that contemporary economies are highly connected, which implies that the initial shock will have an impact that is not confined to the domestic economy, but also affects other economies. It may be argued that policymakers will be most interested in the quantity effects of space expenditure: the impact on output in different parts of the supply chain and on employment and income in different sectors and countries. As shown in the OECD Handbook (OCED, 2012), space expenditure is allocated to a number of sectors. The key Standard Industrial Classification (SIC) is 30.3 ‘Air and Spacecraft and Related Machinery’, but as the title of this category suggests, it includes both air and spacecraft. There is no separate sector available just for spacecraft. In any case, space expenditure is distributed across a number of sectors. However, several studies have used I–O tables to assess the impacts of space expenditure, which is increasingly the case for spaceports around the world (MOSSADAMS, 2020). These are significant challenges for the evolution of Space 4.0 as a driver for contemporary industrial development in the space sector and economy. Scotland pays particular attention to the production of I–O tables, whose most recent year of publication is 2017. The degree of detail in the Scottish I–O tables is quite rich, as it includes 99 industries. Contrary to WIOD tables, these tables are not multi-regional, which implies that they do not provide information for foreign intermediate transactions but only focus on the relation of production in the domestic economy (in this case, Scotland). This implies that it is not possible to provide a detailed analysis of the geographical impact of the Scottish space activity in other countries. Nevertheless, it is necessary to highlight that the domestic focus of the Scottish tables allows a proper analysis of the direct and indirect effects of investment projects on the Scottish economy. At the same time, although it is not possible to have a foreign country-specific impact of the spillover effects of the initial investment, it is possible to estimate the total foreign spillover derived from a given domestic investment. This would be the estimation of the impact on total imports generated from the initial expenditure.

280  Handbook of industrial development Considering the data availability of I–O tables, there are two possible applications that could be realized using different sources of data: ● Scottish domestic I–O tables: impact on the Scottish economy is the most relevant area of interest to consider for investment projects developed in Scotland. It can be estimated using domestic I–O data developed by the Scottish institutions. The spillovers in foreign economies may be estimated only at the aggregate level. ● WIOD tables: these provide analysis of the foreign impact of domestic expenditure. Given the lack of detailed multi-regional I–O tables that include Scotland, it is not possible to estimate foreign multipliers. One option may be the estimation of sectoral foreign multipliers for the UK and, under the assumption that British multipliers are representative of the Scottish economy, provide a cautious estimation of the foreign impact of the domestic expenditure. The flexibility of analysis of the framework presented here allows applications of similar studies in different contexts. The development of investment projects in Scotland, including the Prestwick Spaceport, is relevant to analysing the impact on the Scottish economy of economic activities linked to the space industry. More importantly, as highlighted above, I–O analysis can capture the effects not only on those industries that are directly affected by the initial investment, but also on the indirect requirements on the rest of the productive structure via the impact that other industries have on the space economy. It is thus apparent that the theoretical trajectory and its applications can make a significant contribution to analysing the industrial development of newer sectors like space. It also has increasing utility as a method for analysing and evaluating direct and indirect socioeconomic benefits of spaceports in different regions – for example, Prestwick Spaceport. Bourdieu’s Economic Capitals as Community Resources The French anthropologist and sociologist, Pierre Bourdieu, argued that people from different social positions differ from one another with regard to their possession of three forms of capital: social, cultural and economic (Bourdieu, 1984). He criticizes the focus on monetary exchange and defines capital as ‘accumulated labour (in its materialized form or its “incorporated” embodied form)’ (Bourdieu, 1986, p. 241). Furthermore economic capital refers to material assets that are ‘immediately and directly convertible into money and may be institutionalized in the form of property rights’ (ibid., p. 242). Economic capital includes all kinds of material resources (for example, financial resources, land or property ownership) that could be used to acquire or maintain individuals’ social position and welfare. The interaction between Bourdieu’s three forms of capital opens up the possibility of creating other forms of associated capitals to identify, evaluate and potentially measure drivers of socioeconomic change. For the purpose of evaluating the impact of these changes, these forms of capital can be classified as community capitals or resources that have been increasingly used in studies evaluating public policy programmes (Butler Flora and Flora, 2008). Within a community or society, the stock of capitals can be enhanced (or diminished) over time, including as a result of policies and initiatives that serve to change the social, political and economic context. An IEF assesses impact by seeking to quantify the changes in availability of capitals experienced by individuals and wider communities as a result of public provision or underwriting of programmes to enhance the socioeconomic welfare of citizens – for example,

The propulsive role of the space industry in industrial development  281 Table 16.3

Benefit categories and potential outcome of ESA E3P

Direct Benefits

Indirect Benefits

Spillover/Externality Benefits

Downstream Benefits

Societal inspiration from space

Improved socioeconomic

Health and medicine

Clean drinking water

flight and exploration

prosperity

Transportation

Improved agriculture and

Creation of scientific knowledge

Health discoveries

Public safety

food distribution

and its general applicability

Sustainable environment

Consumer, home and recreation Telemedicine and wireless

The technical competence of

Greater security and safety

Environmental and agricultural networks

nations and regions is improved

Wider and deeper human

resources

Environmental monitoring

and enhanced

experience

Computer technology

and management

The capacity to work more

Enhanced understanding of

Industrial productivity

Disaster warning and relief

productively and efficiently in

humankind’s role in larger

Educational resources

space

universe

Energy storage

New markets for space products

Hazard reduction

and services are created Greater collaboration through strengthened international space explorations

spaceports. In the case of the ESA-funded BEERS project, capitals were selected as being relevant to a strategic context in which the socioeconomic benefits of E3P were to be identified and evaluated – in particular, those benefits that were not easily identified and evaluated using more quantitative methods. Table 16.3 provides an overview of different benefit categories and potential outcomes of the ESA’s E3P. These benefit categories and potential outcomes are illustrative of the role of the space industry in industrial and socioeconomic development both within and external to a Space 4.0 environment. These equally apply to other programmes within the space economy and spaceports in this case. A first cut of the categories set out in Table 16.3 suggest that the methods underpinning Stage 4 of the BEERS methodological framework are relevant to identifying, evaluating and potentially measuring indirect and spillover/externality categories as well as some downstream benefits – in other words, the capitals approach. It is also apparent that there will be a crossover been quantitative and qualitative measures given the nature of the space industry. The capitals shown in Figure 16.4 for the BEERS project were chosen on the basis of their appropriateness for evaluating ESA’s E3P missions. Table 16.4 lists a set of capitals and related generic evaluation indicators that appear relevant to identifying, evaluating and potentially measure socioeconomic benefits of Prestwick Spaceport. In particular, for a range of indicators, qualitative measures are more appropriate. Although a generic set of capitals can be defined, as can be seen from Figure 16.4 and Table 16.4, the choice of sets will depend on their appropriateness to each case. The utility of this approach is that it provides an analysis of socioeconomic benefits using qualitative indicators beyond combinational multipliers for output, employment and fiscal resources identified using I–O analysis. The complementary nature of the two methods allows a more comprehensive evaluation of the industrial development of the space economy and sector underpinned by Space 4.0 industrial strategy.

Organizational

Infrastructural

Financial

Environmental

Educational/human

Growth in design economy activities, including architecture and engineering related to space and aerospace, underpinned by increase in cultural studies Increase in public perception of international status and standing

status in society

Symbolic is the resources that an individual may access to promote their honour,

prestige or recognition, and serves as the value that individuals hold within

Programmes for training and development Provision of formal school and university education Training and continuous professional development

to promote societal aims

The accumulation of the knowledge, skills, expertise, competences and other

attributes of people, including aspects of physical and mental well-being

Monitoring of positive and negative impacts Mitigation of negative impacts

socioeconomic activity. It also comprises negative values such as pollution,

contamination and climate change

New financial intermediaries and instruments

the distribution of income and wealth for societal benefit

systems

these projects

Networked (e.g., ad hoc) organizations

institutions and networks

Stakeholder engagement

Formal agreements and ad hoc cooperation initiatives

Effective and sustainable organizations

The processes, systems and structures within and between organizations, and

Growth of related foreign direct investment (FDI) in locality

Increase in scale and accessibility ICT-based and interoperable communications

systems to increase productivity and support the management of the workflow of

investment projects to realize socioeconomic benefits. It also includes interoperable and consumer facilities)

Increase in volume and value of supporting infrastructure (transport links, producer

Public funding for investment projects

Financing commercial applications

generation of national income and output, that contribute to economic growth and

Comprises non-digital and digital means that act as the building blocks of

Financial markets and market activity

Resources that fund activities, create investment opportunities and result in the

Positive actions to implement environmental-friendly changes

Positive and negative impacts on environment

The natural resources, and benefits, essential for ecological sustainability and

Personal health, fulfilment and attitudes

Growth in skills, expertise, competences and other attributes

Dissemination of knowledge

The provision and structure of resources that advance and disseminate knowledge

a culture

Generation and dissemination of materials and resources

of artistic and intellectual tradition that can provide the means to advance their

Cultural is accumulated stock of knowledge available to citizens about the products Engagement and communication activities

Potential Generic Evaluation Indicators

Definition/Description

Cultural/symbolic

Community capitals and potential impact indicators for Prestwick Spaceport

Capital

Table 16.4

282  Handbook of industrial development

Greater public perception of status and reputation of locality

or parties and other stakeholders, such as constituents. Political capital can be

Technological

Social

Growth in number and scale of collaborative public–private partnerships

built through relationships, trust, goodwill and influence between politicians

Network events

any form

Product innovations and applications

Social cohesion and shared values

for

Conceptual changes

needs of society. This takes different forms depending on the sector it is developed Process innovations and applications

The scientific innovation and practical application of knowledge to address the

Change in expert networks

gain as a result of mutual and recognized relationships that are institutionalized in Strategic partnerships

Formal and informal networks (at any scale)

The sum of the resources that networks of individuals, groups and organizations

accomplish other political goals

understood as a type of currency used to mobilize voters, achieve policy reform, or Increase in tax base and matching rise in public services

Increase in number of FDI enquiries to local/regional authorities

Used in political theory to conceptualize the accumulation of resources and power

Political

Potential Generic Evaluation Indicators

Definition/Description

Capital

The propulsive role of the space industry in industrial development  283

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5

CONCLUDING DISCUSSION

The recent publicity concerning robotic missions and space tourism tends to overlook the industrial development of the space sector and the deeper and wider socioeconomic benefits it creates. The embrace of I4.0 discourse and its underlying technologies in the form of Space 4.0 industrial strategies have contributed significantly to the development of the space economy. Space 4.0 tends to underpin the creation of a greater set of socioeconomic benefits than mainstream I4.0 manufacturing: direct and indirect, upstream and downstream, as well as spillovers and externalities due to the range of segments created by the industry. One of the fastest-growing segments of the space industry is small satellite manufacturing and launches facilitated by the building of spaceports. Beyond acting as launch infrastructure and intermediate goods for the satellite industry segment, spaceports act as a multiple agency for realizing Space 4.0 in general – in particular, localized space clusters and an accompanying set of internal and external economies of scale and scope. Like I4.0, the essential challenge for Space 4.0 is to establish an IEF to identify and evaluate a potentially wider range of socioeconomic benefits beyond the direct application of I4.0-type technologies. Drawing on the case of the ESA-funded BEERS project undertaken by The Open University research team provides a useful framework for assessing the case of the Prestwick Spaceport. The latter case is analysed within the context of the industrial development of the Scottish space economy and sector, especially the fast-growing small satellite segment. In doing so, it is hoped that this chapter will contribute to a growing research agenda and open up further investigations of Space 4.0.

NOTES 1. Servitization has been defined as the innovation of organizations’ ‘capabilities and processes to better create mutual value through a shift from selling product to selling product-service systems’. Two other definitions accompany this: (1) the idea of a product-service system: ‘an integrated product and service offering that delivers value in use’; and (2) a ‘servitized organisation which designs, builds and delivers an integrated product and service offering that delivers value in use’. See https://​andyneely​.blogspot​.com/​2013/​11/​what​-is​-servitization​.html, accessed 23 August 2022. 2. Activity economies are defined as economies that emerge from the joint location of dissimilar activities that have substantial trading links with one another. In the case of manufacturing, such economies typically occur within industrial complexes, involving structures of a vertical or convergent nature. It is common to find these regions and urban localities in which there is a dominant Schumpeterian industry, in this case spaceports (Parr, 2002). 3. Also known as the Leontief inverse matrix, which shows the coefficients (economic multipliers) that measure the successive effects on the economy as a result of the initial increase in production of an economic activity branch. In other words, if an increase in production initially requires higher demand for intermediate consumption for it to be carried out, the intermediate consumption is in turn produced by other branches through the use of new intermediate consumption, and so on. This is what is known as the spillover effect, which arises between the various activity branches of an economy.

The propulsive role of the space industry in industrial development  285

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286  Handbook of industrial development London Economics (2019). Size & Health of the Space Industry 2018. London: London Economics. London Economics (2020). The Scottish Space Cluster: Now and in the Future. London: London Economics. Miller, R.E. and Blair, P.D. (2009). Input–Output Analysis. Cambridge, UK: Cambridge University Press. MOSSADAMS (2020). SPACEPORT AMERICA: Economic & Fiscal Impact Analyses. Seattle, WA: MOSSADAMS. National Aeronautics and Space Administration (NASA) (2019). International Space Station: Benefits for Humanity (3rd edition). Washington, DC: NASA. Office for National Statistics (2018). Annual Business Survey. London: ONS. Organisation for Economic Co-operation and Development (OECD) (2012). OECD Handbook on Measuring the Space Economy. Paris: OECD Publishing. Paladini, S. (2019). The New Frontiers of Space. London: Macmillan. Parr, J.B. (2002). Agglomerations economies: ambiguities and confusions. Environment and Planning A, 34(4), 717–31. Perez, C. (1983). Structural change and assimilation of new technologies in the economic and social systems. Futures, 15(5), 357–75. Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting & Social Change, 132, 26–33. Roberts, T. (2019). Spaceports of the World. Washington, DC: Center for Strategic and International Studies. Scherrer, W. (2019). Surfing the long wave: changing patterns of innovation in a long-term perspective. In U. Hilpert (ed.), Diversities of Innovation. London: Routledge, pp. 49–68. Scottish Manufacturing Advisory Service (SMAS) (2020). Bringing Scottish Manufacturing to the 4. Glasgow: SMAS and Scottish Enterprise. Accessed 24 August 2022 at https://​ www​.scottish​ -enterprise​.com/​media/​3843/​smas​-bringing​-scottish​-manufacturing​-to​-the​-4​-compressed​.pdf. Sony (2020). Pros and cons of implementing Industry 4.0 for the organizations: a review and synthesis of evidence. Production & Manufacturing Research, 8(1), 244–72. Space Foundation (2020). Global space economy grows in 2019 to $423.8 billion, the Space Report 2020 Q2 analysis shows. Accessed 4 March 2021 at https://​spacefoundation​.org/​2020/​07/​30/​global​-space​ -economy​-grows​-in​-2019​-to​-423​-8​-billion​-the​-space​-report​-2020​-q2​-analysis​-shows/​. Space IGS (2013). Space Innovation and Growth Strategy 2014–2013: Space Growth Action Plan. London: Department for Business, Innovation & Skills (BIS). Thompson, S. (2015, 11 September). Is this the start of a fourth industrial revolution? World Economic Forum. Accessed 20 March 2020 at https://​www​.weforum​.org/​agenda/​2015/​09/​fourth​-industrial​ -revolution/​.

17. The energy sector: an industrial perspective on energy transitions Tuukka Mäkitie and Markus Steen

1 INTRODUCTION There needs to be fundamental changes in the world’s energy systems to avoid catastrophic climate change. In brief, energy production and consumption need to stop emitting fossil-fuel-based greenhouse gases into the atmosphere and instead transition towards alternative solutions. This is a complex process that we discuss through the perspective of socio-technical systems change (Markard, Raven and Truffer, 2012), or, in short, the ‘energy transition’ (Markard, 2018). While incremental improvements such as increased energy efficiency are also needed, at the core of this transition is the radical shift from fossil fuels to renewable energy sources. Moreover, development and deployment of negative carbon technologies such as carbon capture and storage (CCS) are needed to cut emissions in, for instance, cement production. Decarbonization needs to occur everywhere and in practically all sectors of our economies. However, the conditions for energy transitions differ immensely across sectors, regions and countries. Curiously, emission reduction strategies and political commitments to energy transitions have by and large been developed ‘separately from economically-oriented industrial strategies’ (Busch, Foxon and Taylor, 2018, p. 114). Energy transition research has also primarily focused on production technologies (e.g., wind power) and energy use in ‘downstream sectors’ (e.g., transport). By contrast, industry dynamics and the many ‘upstream sectors’ involved in the provision, development and manufacturing of various raw materials, components and services have received far less attention (Andersen et al., 2020). There is thus a need to further explore the intersection between energy transitions and industrial development. The aim of this chapter is to provide a socio-technical perspective on industrial development in the context of energy transitions, both in energy-producing and energy-using sectors. To do so, we draw on the sustainability transition literature (Markard et al., 2012). This field of research offers various perspectives of the phenomenon of socio-technical systems change. What these perspectives share is that they conceptualize and explain broad socio-technical change through the interplay between various social and technological factors, including agency, existing and emerging technologies, policies, institutions, infrastructure and social practices. The sustainability transition literature thus acknowledges the complex and systemic nature of energy transitions. Crucially, such transition processes depend on and have major implications for industrial development. We illustrate this ‘industrial perspective on sustainability transitions’ with insights from the sustainability transitions literature and with empirical examples from two cases: transformations in offshore energy extraction/production and maritime transport in Norway. The remainder of this chapter has four further sections. In Section 2, we provide a brief background to the energy transition. In Section 3, we review two key frameworks in the sus287

288  Handbook of industrial development tainability transitions literature: the multi-level perspective and the technological innovation system approach. Moreover, we present recent elaborations regarding an industrial perspective on sustainability transitions. In Section 4, we provide two brief empirical illustrations of energy transition processes. Finally, in Section 5, we conclude by discussing promising future research avenues.

2

THE ENERGY TRANSITION – ENTERING A NEW PHASE?

The energy transition is a monumental task. In 1950, the direct primary energy consumption globally was 27 972 TWh, of which 20 139 TWh (roughly 67 percent) was provided by natural gas, oil and coal (fossil fuels). Traditional biomass (i.e., wood, agricultural byproducts and dung burned for cooking and heating purposes) accounted for 7500 TWh, implying that a meagre 1 percent was provided by ‘modern’ renewable energy, which in this context refers to all renewable energy except for traditional biomass-based energy. By 2019, global energy consumption has seen an almost six-fold increase to 158 839 TWh. Most of this growth is in fossil fuels, and only 8 percent (or 10 967 TWh) was provided by nuclear energy and modern renewables (e.g., solar PV) combined.1 To meet the climate mitigation target of the Paris Agreement, all sectors of the economy will be influenced directly or indirectly, as fossil fuels need to be substituted with low- and zero-carbon energy solutions. Apart from the shift from fossil fuels to renewables and the need for end-of-pipe solutions such as CCS, the energy transition implies significant changes in energy system architectures. More specifically, this concerns a change from largely centralized power production based on a few energy sources and large-scale solutions towards also including more decentralized (and off-grid) production based on many different energy sources, as well as the need for storage and (smart) grid management technologies. Obviously, this applies to those parts of the world where an energy (power) infrastructure already exists. Currently, 13 percent of the world population does not have access to ‘modern’ energy resources, this referring principally to electricity.2 Therefore, and as mirrored in SDG7, an important objective for the world community moving forward is to ‘ensure access to affordable, reliable, sustainable and modern energy for all.’3 By extension, the development of energy systems where those do not already exist needs to happen in sustainable ways. Given this background, this chapter will focus on the energy transition and industrial transformation in the ‘Global North.’ Moreover, in addition to replacing fossil fuels with renewables, there is also the need for massive energy efficiency improvements and most likely also for technologies that can contribute to removing carbon from the atmosphere. Important energy and material efficiency improvements can furthermore only be gained by transforming from our current-day linear economies towards circular economies (see Chapter 18 by Mazzanti and Zecca in this Handbook). As suggested in the introduction, the energy transition is a slow and challenging process. Fossil fuels remain dominant as the ‘energy staple’ of the global economy and will continue to do so for decades even with a sharp increase in the deployment of renewable energy. There are, however, strong indications that we are entering a new phase of accelerated change (Markard, 2018). This momentum is created by more progressive mitigation policies, and the co-evolution of technology development, policies, and the industrial capacity to produce and deliver new technologies at scale.

The energy sector: an industrial perspective on energy transitions  289 The increasing deployment of renewable energy technologies such as solar PV and wind power, as well as the ramp-up in the adoption of electric vehicles in certain markets, can be regarded as both cause and effect of the development of economies of scale and significant learning effects. The costs of solar PV and wind energy have been drastically reduced over the last two decades, and is now price-competitive with established energy solutions in parts of the world. While a typical wind energy turbine in 1990 was 0.5 MW, the largest wind turbines currently deployed (offshore) are now in the range of 9.5 MW, while turbines in the 14–16 MW range are expected in the coming years. Offshore wind power farms developed in Europe, of which the Hornsea Two (1.4 GW) in the UK is the largest, now constitute some of the world’s biggest infrastructure projects. Not only are these projects very large, but they are also highly complex and involve a multitude of different specialized and multi-industry firms supplying various components and services throughout the different life phases of these energy projects. The upscaling of industrial capacity to provide raw materials, components and services for renewable energy will need to be significantly accelerated in the coming years. Similarly, technological development and diffusion of solutions for alternative energy distribution and consumption in various energy end-use sectors need to greatly accelerate. As energy is integral to all sectors of the economy, and many sectors are involved in energy value chains (e.g., forestry, agriculture, mining, metals, electronics, ICT), the energy transition will have pervasive if not paradigmatic effects. A pertinent question is thus, from both a policy and industry point of view, to what extent energy transition processes require that new solutions are developed from scratch, or whether decarbonization can be aided by the repurposing and reutilization of existing infrastructures, knowledge, manufacturing capacity and other resources. From a value chain perspective, the need for transformation is contingent on the type of energy technology and (industry) characteristics of the sectors involved in the various parts of the value chain. For example, replacing fossil fuels with biofuels primarily demands changes in the production and distribution segments of the value chain, while the need for adaptation in end-user segments (e.g., transport) can be relatively minor. For other low- or zero-carbon solutions, the reverse is the case. Provided that power generation and grid infrastructure is in place, the introduction of battery-electric energy solutions mainly requires significant changes in downstream value chain segments, charging infrastructure and other system interface technologies. Finally, for some of the energy solutions, such as hydrogen, significant innovation and investment is needed throughout the entire value chain (Mäkitie, Hanson et al., 2022). While technologies such as wind power, solar PV and electrical vehicles are now maturing, progress has been slower in ‘hard-to-abate’ sectors such as deep-sea shipping, long-range aviation or the energy-intensive processing industries. This not only involves mere technical feasibility, but also factors such as long investment cycles in many sectors, no premiums on ‘green operations’ and the need for ‘global’ coordination to facilitate infrastructure development. To summarize, energy transitions are complex long-term processes that not only offer a technical challenge, but also a social one due to various factors, including path dependence in old technologies and practices, and the need for market creation, legitimation and resource mobilization around new technologies, and the development of new institutions and behavioral patterns. In the following we discuss key perspectives from the sustainability transitions literature to further conceptualize such transformation and radical innovation processes.

290  Handbook of industrial development

3

THEORETICAL PERSPECTIVES ON ENERGY TRANSITIONS AND INDUSTRIAL TRANSFORMATION

The research field of sustainability transitions has emerged over the last 15–20 years and made significant contributions to our understanding of the drivers and barriers for change processes in the socio-technical systems (discussed as sectors in this chapter) that deliver key societal services such as energy and transport (Markard et al., 2012). This interdisciplinary field emerged from innovation studies, evolutionary economics, science and technology studies, while it is increasingly also influenced by other fields such as sociology, political science and economic geography. As a point of departure, the sustainability transitions research field recognizes that established sectors have developed over long periods of time, whereby technologies, markets, infrastructures, practices, institutions and cultural meanings have co-evolved into coherent functional systems. Transitions are thus multi-dimensional and difficult to achieve due to path dependencies and different types of lock-in (Klitkou et al., 2015). Furthermore, transitions are open-ended, often with significant uncertainties, such as, for instance, which new technologies may prevail in the long run. They are therefore imbued with multiple and often competing expectations and visions, involving various types of actors. With this uncertainty also comes considerable risk, which may deter private actors from investing (sufficiently) in new technologies. There is therefore consensus within this research field that policy plays a key role in facilitating and enabling transitions by supporting niche technologies (from R&D to implementation) until they are competitive. On the other side of the coin, there is increasing recognition that policy also needs to contribute to the destabilization of existing unsustainable patterns and features in sectors; however, this is naturally associated with political problems and resistance from defenders (often industrial incumbents) of the status quo (Kivimaa and Kern, 2016). Needless to say, transition processes often involve power struggles. Often this is articulated as a battle between the incumbent firms and actors associated with established technologies and sectors on the one side, and actors involved in, for example, the development of renewable energy technologies on the other (Hockerts and Wüstenhagen, 2010). However, power struggles in transitions also involve many other types of actors, including environmental non-governmental organizations (NGOs) and the general public, as witnessed in the resistance to some renewable energy projects, or rising fossil fuel prices. 3.1

The Multi-level Perspective

The ways in which transitions unfold has most clearly been articulated in the so-called multi-level perspective (MLP), conceptualized by Geels (2002). According to the MLP, the specific dynamics through which transition processes unfold are contingent on the interplay between developments at landscape, regime and niche levels, with these levels being understood as representing different degrees of institutional structuration. Put simply, transitions require sufficient pressure on the ‘regime’ to change, and this pressure may be exerted by, for example, increasing public and political attention to environmental issues or rising fuel prices (i.e., ‘landscape’ factors), while there also needs to be technological alternatives (‘niche’ technologies) that can supplement or replace ‘regime’ technologies for a transition to occur.

The energy sector: an industrial perspective on energy transitions  291 The key concept in the MLP is the socio-technical regime. The regime is understood as an interrelated and highly institutionalized structure made up of a heterogeneous network of incumbent actors, comprising established products and technologies, infrastructure, user practices, expectations, norms and regulations (Smith, Stirling and Berkhout, 2005). The socio-technical regime concept extends Nelson and Winter’s (1982) conceptualization of technological regimes4 by adding various informal and formal rules that also serve to stabilize regimes. This reflects an important argument in the MLP – namely, that many different types of actors and social networks are involved in reproducing, maintaining and transforming sectors, making transitions – the shift from one regime configuration to another – complex and long-term processes (Geels, 2011). Niches are the ‘protected spaces’ in which new technologies can emerge and develop until they are able to compete with existing technologies on performance or price. Niches are protected spaces in the sense that they offer opportunities for technology development and implementation ‘free’ from the constraints of market selection, performance standards and the infrastructural rigidities of established systems. Given the systemic perspective inherent to the MLP, niche technologies most often require some (small or large) degree of change and adaptation in sectors for them to ‘work’. For example, the upscaling of renewable energy production requires massive investments in grid infrastructure, new storage systems, and grid management technology (Andersen and Markard, 2020). New (niche) technologies have been shown to have lengthy emergence phases, normally spanning several decades (Bento and Wilson, 2016). This underscores the importance of long-term policy support, not least to provide actors involved in innovation with some certainty that there is reason to believe in life after (potentially) crossing the ‘valley of death’ between R&D and commercialization. Based on the MLP, different types of transition pathways (e.g., substitution, reconfiguration, transformation) have been articulated (Geels et al., 2016). In relation to industrial development and transformation, these are important in that they point to different types and degrees of system change (disruption or stability), which have significant implications for the industries involved. 3.2

Technological Innovation Systems

Another prominent approach is the technological innovation system (TIS) framework. While the MLP seeks to provide a holistic view of transition processes, the TIS framework outlines a systemic view on the social structures related to the development of a specific technology, and the systemic processes and agency leading to technological innovation. The TIS framework thus supports the analysis of key innovation dynamics related to the emergence and development of (niche) technologies. While the TIS framework was not initially developed with decarbonization topics in mind, it has emerged as a key framework in the analysis of energy transitions (Bergek, 2019). The TIS approach is particularly geared towards studying the development and deployment of new technologies as well as the institutional and organizational changes that run parallel to enable them (Bergek et al., 2008; Hekkert et al., 2007). A TIS is defined as ‘network(s) of agents interacting in a specific technology area under a particular institutional infrastructure for the purpose of generating, diffusing and utilizing technology’ (Carlsson and Stankiewicz, 1991, p. 111). A TIS is thus defined around a specific focal technology or product, and has two main analytical components. First, a TIS constitutes a structure of dynamic networks of actors

292  Handbook of industrial development Table 17.1

Functions of technological innovation systems

TIS Function

Description

Knowledge development and

Development and diffusion of knowledge regarding the technology over time. Considers

diffusion

both the depth and breadth of knowledge

Influence on the guidance of search

Inducing and pressuring factors for actors to enter the TIS, and mechanisms influencing the direction of innovation in terms of competing technologies, applications, markets, etc.

Entrepreneurial experimentation

Reduction of uncertainty through experimentation with new technologies, applications and markets

Market formation

Opening of (niche) markets, articulation and creation of demand

Legitimation

Formation of social acceptance, and compliance with prevailing institutions in industry and society

Resource mobilization

Mobilization and creation of human and financial capital, and formation of infrastructure and other complementary assets

Development of positive externalities Development of free utilities, such as specialized component suppliers

Source:

Bergek et al. (2008).

and institutions related to the generation, diffusion and use of a given technology. Second, innovation in a TIS is driven by key processes or innovation ‘functions’ (Bergek et al., 2008; Hekkert et al., 2007). These are emergent ‘sub-processes’ of the overall innovation process, and include, for instance, knowledge development and diffusion, market formation, resource mobilization, legitimation, and entrepreneurial experimentation (Bergek, 2019). Table 17.1 provides a full overview of functions. Functions evolve over time through the agency of actors. Moreover, feedback loops (both positive and negative) within and between functions may emerge, further driving (or hindering) the innovation process of a technology. For instance, knowledge development may lead to heightened expectations around a technology, which then may lead to further resource mobilization (e.g., funding for R&D projects), which then again may further drive knowledge development (Suurs and Hekkert, 2009). A typical TIS analysis would assess the performance of the TIS through an analysis of the structure and functions, and identify the inducement and blocking mechanisms for innovation (Bergek et al., 2008). Although new technologies may have many benefits (lower operational costs, less pollution etc.), they often struggle to develop beyond a nascent phase. Not only actors and markets but also institutions and networks can obstruct TIS formation. Emerging TISs often face challenges that can be identified as system weaknesses. TIS analysis may thus identify such bottlenecks and inform policymaking regarding action, which may help to foster further innovation in the technology. TISs are also influenced by their wider context (Bergek et al., 2015). First, TISs have geographic underpinnings, typically with a special anchoring to certain locations in the world (for instance, wind power in Denmark), but the TISs’ structures and functions also have international and multi-scalar features (Binz and Truffer, 2017). Second, TISs are embedded in political contexts, which is particularly relevant for politically contested technologies such as zero-emission technologies (Kern, 2015). The priorities and changes in the political context of a novel technology may thus have importance for innovation (Normann, 2015), and various stakeholders may seek to lobby for or against more conducive policies for a specific technology (Jacobsson and Lauber, 2006). Third, a TIS may be affected by other TISs through, for example, synergetic and competitive relationships (Sandén and Hillman, 2011). For instance, deployment of intermittent renewable energy technologies such as solar PV and wind energy may benefit from energy storage technologies such as batteries, while electric vehicles may

The energy sector: an industrial perspective on energy transitions  293 compete against each other and other types of alternative fuel vehicles, for example, in terms of resources and investments in infrastructure (Markard and Hoffmann, 2016). Fourth, a TIS may be affected by its sectoral context either in the focal sector of transition (e.g., the transport sector) or in the upstream sectors of a technology (e.g., raw materials and components). We elaborate on this topic in the next section (Mäkitie, Hanson et al., 2022). 3.3

The Industrial Perspective on Sustainability Transitions

The above-discussed frameworks have commonly been used to address sectoral reconfigurations (transitions) and radical innovations in sectors such as energy, mobility and food. Consequently, these perspectives have typically paid less explicit attention to the upstream value chains of technologies (such as of renewable energy technologies) central to transitions (Andersen et al., 2020). Accounting for the changes and developments across technology value chains is, however, not only important for understanding transitions themselves, but also for gaining insights into the industrial development opportunities that transitions offer. In other words, transitions affect the different sectors that provide inputs (e.g., raw materials, components and services related to energy technologies) and outputs (e.g., energy production and use) for novel technologies. While the upscaling capability of value chains to meet the growing demand for novel technologies is crucial for transitions, the value chains of old technologies may face decline and collapse in demand. Indeed, while phasing out unsustainable technologies may lead to loss of jobs (e.g., in fossil fuel production), new technologies create economic opportunities and work. Hence, to understand the industrial underpinnings of transitions, and account for the political acceptability and feasibility of transition-related policymaking and the ‘justness’ of transitions, it is therefore necessary to account for what we call ‘the industrial perspective on sustainability transitions.’ Especially in relation to energy transitions, the recent literature has started to focus on industrial topics in greater detail. Andersen and Markard (2020) proposed viewing technology value chains as a set of interacting technologies consisting of components and sub-components provided by various industrial sectors, thus highlighting the inter-industrial nature of radical innovation. For instance, Stephan and colleagues (2017) showed how the TIS around lithium-ion batteries was impacted by innovative activities in various sectors, such as the chemical and electronics sectors. An additional example is the findings of Malhotra, Schmidt and Huenteler (2019), who argue that learning-by-interacting across the sectors in a technology value chain can be highly important for innovation in complex energy technologies. Hence, these contributions show that a holistic view on the industrial features of radical energy innovation can help to identify enabling factors for the development and upscaling of new energy technologies. The availability of raw materials, capabilities in manufacturing, and the build-up of necessary infrastructure for novel energy technologies and alternative fuels are naturally key to achieving an energy transition, and also in realizing the economic opportunities of energy transitions. For instance, the scalability of biofuel production and the conflicts between other uses and values related to biomass (e.g., food production, biodiversity issues) have often hindered biofuel technologies (Sutherland, Peter and Zagata, 2015). Meanwhile, the availability of critical materials and related industries have affected the development of solar PV production in Germany and Norway (Hanson, 2018; Quitzow, 2015). In China, the emphasis on price and limited attention to quality and maintenance of wind turbines have hindered the exports of Chinese wind power turbine producers (Gosens and Lu, 2014). Finally, the vast upscaling

294  Handbook of industrial development of intermittent renewable energy technologies is interdependent with the build up of power transmission and storage capacity (Andersen, 2014). In other words, the diffusion of energy technologies is closely dependent on the features and performance of the different sectors in its value chain (Mäkitie, Hanson et al., 2022). At the firm level, early transitions literature highlighted the role of newcomer actors in pushing forward transitions and radical innovations, while established firms and other incumbents have been portrayed as passive or hindering transitions (Turnheim and Sovacool, 2020). However, this view is incomplete. For instance, Swedish scholars have shown how established firms in the automotive and gas turbine industries have played in a key role in developing radical innovations (Bergek et al., 2013; Berggren, Magnusson and Sushandoyo, 2015), while in Norway, established energy companies have been early entrants in various novel energy technologies (Steen and Weaver, 2017). The engagement and diversification of established industrial players may thus provide various types of resources (such as knowledge, production capacity, human and financial capital) to the development and diffusion of novel technologies (Mäkitie et al., 2018). Such reorientation strategies may indeed become necessary for established firms who may face a decline in their old (unsustainable) markets (Penna and Geels, 2015). Such industrial perspectives are highly relevant for policy. A better understanding of how energy transitions impact, and are impacted by, various industrial sectors provides insights into how policy may foster energy transitions and the formation of green jobs. Green industrial policy may seek to capitalize on the industrial opportunities created by, for example, novel energy technologies but also advance the decarbonization of the energy system (Busch et al., 2018). Facilitating transitions may also require the destabilization of the hegemony of unsustainable technologies through, for example, taxes and reduction of public support (Kivimaa and Kern, 2016). Such instruments explicitly addressing a decline in non-desirable technologies have adverse effects for industries in the value chains of such technologies, making them often politically challenging to implement. Questions related to how the gains and losses related to energy transitions and how the competences and capabilities around unsustainable technologies can be ‘redeployed’ into more sustainable technologies and practices thus become of high relevance for the political feasibility and justice topics related to transition policy (Healy and Barry, 2017; Skjølsvold and Coenen, 2021). In sum, the industrial perspective on sustainability transitions provides insights into the role and implications of various industrial sectors on the development of radical energy innovations, and consequently on energy transitions. In the next section, we provide two brief empirical examples on this topic.

4

EMPIRICAL ILLUSTRATIONS

4.1

From Fossils to Renewable Energy Generation: Offshore Energy in Norway

Since striking oil in the late 1960s, a strong and technologically advanced offshore oil and gas (O&G) industry has developed in Norway. In 2021, this industry is economically still the most important in the country, providing plenty of well-paid jobs across the value chain of production, and vast state revenue through taxation of O&G income. However, due to fluctuations in the O&G market, limited recent oil discoveries, and growing uncertainty regarding the future

The energy sector: an industrial perspective on energy transitions  295 of oil extraction in Norway (due to climate change concerns), firms in the O&G industry have increasingly explored diversification into new markets (Mäkitie, Steen et al., 2020; Normann, 2015; Steen and Weaver, 2017). One technology that has attracted much attention among these firms is offshore wind power (OWP). In MLP terms, O&G, as one of the dominant energy sources in the world, are at the core of the current energy regime. However, over recent decades, climate change concerns have created landscape pressure on the current energy regime, opening a window of opportunity for niche energy technologies such as OWP. A typical MLP interpretation would thus often provide a dichotomous view between incumbent and emerging energy production (Geels, 2014; Hess, 2016), or in our case, O&G and OWP. However, when studied from an industrial perspective, a more diverse picture becomes prevalent. In Norway, various O&G industry companies have diversified into this new technology, leading to, for example, a strengthened knowledge base in OWP technology (Steen and Weaver, 2017). This has especially been the case for floating wind power, where O&G companies have been key entrepreneurial agents in developing this yet emerging technology (Mäkitie, 2020). From a TIS perspective, O&G industry firms have thus supported the OWP innovation in Norway in terms of, for example, knowledge development in the form of offshore technologies along the OWP value chain (subsea technologies, cabling, offshore operations, etc.), entrepreneurial experimentation through exploration of floating wind technologies, and resource mobilization of financial and human capital as well as infrastructure such as offshore bases (Mäkitie et al., 2018). However, these positive effects have been limited by the lukewarm commitment of these actors to OWP. Many of the O&G industry firms engaged in OWP only when the core O&G market entered a decline period, and subsequently diminished their engagement as the demand in the O&G market picked up again, leaving OWP only in the status of an auxiliary market for such firms (Hansen and Steen, 2015; Mäkitie et al., 2019). OWP thus offers novel opportunities for firms and the workforce in the O&G industry in Norway – if firms are willing to pursue them. As the O&G market can be expected to eventually decline, such opportunities may become particularly important for regions where O&G has been a key employer over the last decades. Indeed, OWP may offer new development opportunities for regions with related industrial resources (Steen and Karlsen, 2014), which may become important in seeking to achieve socially just energy transitions (Afewerki and Karlsen, 2021). However, O&G industry firms with general-purpose and ‘fungible’ technological knowledge (e.g., engineering competences) are more likely to diversify to new markets than those with market-specific and specialized knowledge (e.g., related to oil exploration) (Mäkitie, Steen et al., 2020). Related diversification does thus not act as a panacea for all regions and other ways to achieve just energy transitions must also be explored. Seen overall, the Norwegian O&G and OWP case illustrates the relevance of an industrial perspective on energy transitions, as it not only allows a better understanding of how innovation processes in novel technologies may be affected by local industrial contexts, but also opens up perspectives for policymaking in seeking to improve the acceptability of transition policies through novel economic opportunities and job creation.

296  Handbook of industrial development 4.2

From Fossils to Renewable Energy Consumption: Maritime Transport in Norway

The shipping industry is another important industry in Norway. Maritime transport along the Norwegian coast is part of the key infrastructure that allows for movement of goods and people, while several of Norway’s most important sectors are ocean-related (O&G, fishing, aquaculture). Norwegian shipowners also have important positions in certain deep-sea shipping segments, and the Norwegian maritime supplier industry is furthermore highly advanced and export oriented (Mellbye, Helseth and Jakobsen, 2018). Maritime transport is generally considered a ‘hard-to-abate’ sector, alongside energy-intensive processing industries and heavy-duty road transport. The transition challenges faced by such sectors follows from high capital intensity, low profit margins, and international competition (Dewald and Achternbosch, 2016; Hansen and Coenen, 2017). Indeed, shipping has in general been slow to introduce low-carbon fuels (Bows-Larkin, 2015), also as a result of its global functional integration, its commercial and operational characteristics, and lacking global environmental governance (Lister, Poulsen and Ponte, 2015). Regardless, change towards the use of more sustainable energy solutions is underway in certain parts of the global shipping industry (Poulsen, Ponte and Sornn-Friese, 2018), but even more so in certain parts of short-sea and coastal shipping, especially in Norway. In MLP terms, maritime transport is a sector that provides crucial societal services. Most ships run on fossil fuels, as they have for more than a century (Pettit et al., 2018). While the regime of maritime transport has been slow to start decarbonizing, mounting landscape pressure to reduce carbon emissions in this sector is slowly beginning to have an impact. As a result, various niche technologies that can improve the environmental footprint in maritime transport are being explored. This is highly visible in Norway, which is a frontrunner globally in sustainable energy solutions for shipping (Jakobsen and Helseth, 2021). Experimentation with niche technologies has mainly occurred in specific market segments, such as for ferries that operate along the coast and for supply vessels to the offshore O&G industry (Bergek et al., 2021). Here, battery-electric energy solutions have been adopted at remarkable speed over the last few years (since 2015), resulting from both public and private procurement strategies that emphasized emissions, but also because these segments were appropriate for this technology that can be implemented in both hybrid and pure form (Bach et al., 2020). Other low- and zero-carbon energy solutions, such as biofuels and hydrogen, are struggling with value chain and legitimacy issues (Steen et al., 2019), while shipowners’ perceptions of adopting these technologies are also imbued with uncertainties (Mäkitie, Steen et al., 2022). From an industrial perspective, an interesting observation is that many pioneering firms in developing ‘green solutions’ for shipping are established maritime equipment suppliers that also develop and sell marine combustion engines, such as Wärtsilä. Depending on the type of low- or zero-carbon energy solution, however, the involvement of different types of actors differs considerably, not least because of the differences between a value chain based on, for instance, liquefied biogas versus a value chain for battery-electric solutions. It follows that TIS function dynamics (see Table 17.2) have also differed considerably for niche technologies, depending to a large extent on the engagement of different types of actors to develop the different technologies. A striking feature with this analysis is that biofuel innovation systems are found to have weak performance. This is remarkable because biofuels are interchangeable with fossil fuels (i.e., marine diesel and liquefied natural gas) and thus potentially benefit

The energy sector: an industrial perspective on energy transitions  297 Table 17.2  

Comparison of TIS functions for biodiesel, liquefied biogas (LBG), battery-electric, and hydrogen in the context of Norwegian coastal shipping Direction

Entrepreneurial

Market

Development and of Search

Experimentation

Formation

Knowledge

Legitimation

Resource

Positive

Mobilization

Externalities

Diffusion Biodiesel

W

W

W

W

W

W

W

LBG

W

I

W

W

W

W

W

I

S

S

S

S

S

I

I

I

I

W

I

I

W

Batteryelectric Hydrogen

Note: Source:

W = weak; I = intermediate; S = strong. Adapted from Steen et al. (2019).

from existing technology on vessels and infrastructure for storage and distribution (Bach et al., 2021). Put differently, from the maritime sector point of view, biofuels would be far less disruptive than hydrogen, yet they are challenged by low legitimacy levels among maritime industry actors in part due to uncertainties regarding actual emission benefits as well as competition with food production. While there is certainly some contestation among maritime industry actors over the need for decarbonization in general (as well as for particular technology options to achieve this) in the context of coastal shipping, there is generally an agreement that carbon emissions need to be reduced. This is particularly the case among technology suppliers eyeing new market opportunities, but also among some shipowners expecting that being early movers in ‘going green’ will improve their market positions, given that national and international environmental regulations will strengthen in the years to come.

5 CONCLUSION This chapter has provided an overview of socio-technical perspectives on energy transitions that help to conceptualize and understand the complex social and technological processes underlying large-scale and radical energy transformations, such as the one from fossil fuels to renewable energy. We have particularly focused on the industrial transformation processes that are part and parcel of sustainability transitions (Andersen et al., 2020), with two empirical case examplars from Norway. We argue that an industrial perspective on transitions is useful for researchers, policymakers and other practitioners in at least three ways. First, it allows for better understanding of the industrial development necessary for (rapid) upscaling of radically new energy technologies crucial in the struggle to amend global carbon emissions. Second, it provides more explicit insights into how policy may be able to identify and target crucial bottlenecks in the industrial development around novel energy technologies, and thus induce both decarbonization and novel industrial development. Third, an industrial perspective on sustainability transitions combines perspectives on (1) the creation of novel economic opportunities and jobs; and (2) on the declining opportunities and employment in unsustainable industries, thus offering insights regarding possible means to foster ‘just transitions.’

298  Handbook of industrial development Research on the industrial side of energy transitions is still emerging. More attention to such topics is therefore needed. Overall, there are few studies elaborating on the inter-industrial features of energy transitions. For instance, we know yet little of the possible complementary developments within and across the value chains of different technologies, which can be important for achieving an accelerated diffusion of novel energy technologies. Moreover, to contribute to just transitions, further research should explore policy approaches that may contribute to the sustainable reorientation of industrial structures at national and regional level, combining purposeful phase-out of unsustainable technologies and industrial development around new technologies (Mäkitie, Hanson et al., 2022). Finally, most literature has focused on industrial development in developed countries. Further research should elaborate on industrial perspectives on sustainability transitions in the context of the Global South, including how energy transitions may contribute to the economic development in such contexts, but also on the possible adverse effects through negative social and environmental impacts, such as in the extraction of rare earth minerals and other natural resources.

ACKNOWLEDGMENT This book chapter was funded by the Research Council of Norway through the Centre for the Study of Innovation Policy for Industrial Transformation, Sustainability and Digitalization (grant number 295021).

NOTES 1.

These figures are drawn from Our World in Data. Accessed 24 August 2022 at https://​ourworldindata​ .org/​charts​#energy. 2. As of 2019, approximately 3 billion people relied on wood, coal, charcoal or animal waste (i.e., traditional biomass-based energy) for cooking and heating, which has highly detrimental health effects, notably on women and children in developing countries. See https://​trackingsdg7​.esmap​ .org/​data/​files/​download​-documents/​2019​-Tracking​%20SDG7​-Full​%20Report​.pdf. Accessed 24 August 2022. 3. Sustainable Development Goal 7: Affordable and clean energy. See https://​sdgs​.un​.org/​goals/​goal7. Accessed 24 August 2022. 4. This refers to shared cognitive routines or search heuristics that guide technological development within a community of engineers.

REFERENCES Afewerki, S. and Karlsen, A. 2021. Policy mixes for just sustainable development in regions specialized in carbon-intensive industries: the case of two Norwegian Petro-maritime regions. European Planning Studies, https://​doi​.org/​10​.1080/​09654313​.2021​.1941786. Andersen, A.D. 2014. No transition without transmission: HVDC electricity infrastructure as an enabler for renewable energy? Environmental Innovation and Societal Transitions, 13, 75–95. Andersen, A.D. and Markard, J. 2020. Multi-technology interaction in socio-technical transitions: how recent dynamics in HVDC technology can inform transition theories. Technological Forecasting and Social Change, 151, Article 119802.

The energy sector: an industrial perspective on energy transitions  299 Andersen, A.D., Steen, M. and Mäkitie, T. et al. 2020. The role of inter-sectoral dynamics in sustainability transitions: a comment on the transitions research agenda. Environmental Innovation and Societal Transitions, 34, 348–51. Bach, H., Bergek, A. and Bjørgum, Ø. et al. 2020. Implementing maritime battery-electric and hydrogen solutions: a technological innovation systems analysis. Transportation Research Part D: Transport and Environment, 87, Article 102492. Bach, H., Mäkitie, T., Hansen, T. and Steen, M. 2021. Blending new and old in sustainability transitions: technological alignment between fossil fuels and biofuels in Norwegian coastal shipping. Energy Research & Social Science, 74, Article 101957. Bento, N. and Wilson, C. 2016. Measuring the duration of formative phases for energy technologies. Environmental Innovation and Societal Transitions, 21, 95–112. Bergek, A. 2019. Technological innovation systems: a review of recent findings and suggestions for future research. In F. Boons and A. McMeekin, A. (eds), Handbook of Sustainable Innovation. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 200–218. Bergek, A., Berggren, C., Magnusson, T. and Hobday, M. 2013. Technological discontinuities and the challenge for incumbent firms: destruction, disruption or creative accumulation? Research Policy, 42, 1210–24. Bergek, A., Bjørgum, Ø. and Hansen, T. et al. 2021. Sustainability transitions in coastal shipping: the role of regime segmentation. Transportation Research Interdisciplinary Perspectives, 12, Article 100497. Bergek, A., Hekkert, M. and, Jacobsson, S. et al. 2015. Technological innovation systems in contexts: conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51–64. Bergek, A., Jacobsson, S. and Carlsson, B. 2008. Analyzing the functional dynamics of technological innovation systems: a scheme of analysis. Research Policy, 37, 407–29. Berggren, C., Magnusson, T. and Sushandoyo, D. 2015. Transition pathways revisited: established firms as multi-level actors in the heavy vehicle industry. Research Policy, 44, 1017–28. Binz, C. and Truffer, B. 2017. Global innovation systems – a conceptual framework for innovation dynamics in transnational contexts. Research Policy, 46, 1284–98. Bows-Larkin, A. 2015. All adrift: aviation, shipping, and climate change policy. Climate Policy, 15, 681–702. Busch, J., Foxon, T.J. and Taylor, P.G. 2018. Designing industrial strategy for a low carbon transformation. Environmental Innovation and Societal Transitions, 29, 114–25. Carlsson, B. and Stankiewicz, R. 1991. On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1, 93–118. Dewald, U. and Achternbosch, M. 2016. Why more sustainable cements failed so far? Disruptive innovations and their barriers in a basic industry. Environmental Innovation and Societal Transitions, 19, 15–30. Geels, F.W. 2002. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy, 31, 1257–74. Geels, F.W. 2011. The multi-level perspective on sustainability transitions: responses to seven criticisms. Environmental Innovation and Societal Transitions, 1, 24–40. Geels, F.W. 2014. Regime resistance against low-carbon transitions: introducing politics and power into the multi-level perspective. Theory, Culture & Society, 31, 21–40. Geels, F.W., Kern, F. and Fuchs, G. et al. 2016. The enactment of socio-technical transition pathways: a reformulated typology and a comparative multi-level analysis of the German and UK low-carbon electricity transitions (1990–2014). Research Policy, 45, 896–913. Gosens, J. and Lu, Y. 2014. Prospects for global market expansion of China’s wind turbine manufacturing industry. Energy Policy, 67, 301–18. Hansen, G.H. and Steen, M. 2015. Offshore oil and gas firms’ involvement in offshore wind: technological frames and undercurrents. Environmental Innovation and Societal Transitions, 17, 1–14. Hansen, T. and Coenen, L. 2017. Unpacking resource mobilisation by incumbents for biorefineries: the role of micro-level factors for technological innovation system weaknesses. Technology Analysis & Strategic Management, 29, 500–513.

300  Handbook of industrial development Hanson, J. 2018. Established industries as foundations for emerging technological innovation systems: the case of solar photovoltaics in Norway. Environmental Innovation and Societal Transitions, 26, 64–77. Healy, N. and Barry, J. 2017. Politicizing energy justice and energy system transitions: fossil fuel divestment and a ‘just transition.’ Energy Policy, 108, 451–9. Hekkert, M.P., Suurs, R.A.A. and Negro, S.O. et al. 2007. Functions of innovation systems: a new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–32. Hess, D.J. 2016. The politics of niche-regime conflicts: distributed solar energy in the United States. Environmental Innovation and Societal Transitions, 19, 42–50. Hockerts, K. and Wüstenhagen, R. 2010. Greening Goliaths versus emerging Davids – theorizing about the role of incumbents and new entrants in sustainable entrepreneurship. Journal of Business Venturing, 25, 481–92. Jacobsson, S. and Lauber, V. 2006. The politics and policy of energy system transformation – explaining the German diffusion of renewable energy technology. Energy Policy, 34, 256–76. Jakobsen, E.W. and Helseth, A. 2021. Strategier for Grønn Maritim Eksport. Oslo: Menon Economics. Kern, F. 2015. Engaging with the politics, agency and structures in the technological innovation systems approach. Environmental Innovation and Societal Transitions, 16, 67–69. Kivimaa, P. and Kern, F. 2016. Creative destruction or mere niche support? Innovation policy mixes for sustainability transitions. Research Policy, 45, 205–17. Klitkou, A., Bolwig, S., Hansen, T. and Wessberg, N. 2015. The role of lock-in mechanisms in transition processes: the case of energy for road transport. Environmental Innovation and Societal Transitions, 16, 22–37. Lister, J., Poulsen, R.T. and Ponte, S. 2015. Orchestrating transnational environmental governance in maritime shipping. Global Environmental Change – Human and Policy Dimensions, 34, 185–95. Mäkitie, T. 2020. Corporate entrepreneurship and sustainability transitions: resource redeployment of oil and gas industry firms in floating wind power. Technology Analysis and Strategic Management, 32, 474–88. Mäkitie, T., Andersen, A.D. and Hanson, J. et al. 2018. Established sectors expediting clean technology industries? The Norwegian oil and gas sector’s influence on offshore wind power. Journal of Cleaner Production, 177, 813–23. Mäkitie, T., Hanson, J. and Steen, M. et al. 2022. Complementarity formation mechanisms in technology value chains. Research Policy, 51(7), Article 104559. Mäkitie, T., Normann, H.E., Thune, T.M. and Sraml Gonzalez, J. 2019. The green flings: Norwegian oil and gas industry’s engagement in offshore wind power. Energy Policy, 127, 269–79. Mäkitie, T., Steen, M. and Sæther, E.A. et al. 2022. Norwegian ship-owners’ adoption of alternative fuels. Energy Policy, 163, Article 112869. Mäkitie, T., Steen, M. and Thune, T. et al. 2020. Greener and Smarter? Transformations in Five Norwegian Industrial Sectors. Trondheim: SINTEF. Malhotra, A., Schmidt, T.S. and Huenteler, J. 2019. The role of inter-sectoral learning in knowledge development and diffusion: case studies on three clean energy technologies. Technological Forecasting and Social Change, 146, 464–87. Markard, J. 2018. The next phase of the energy transition and its implications for research and policy. Nature Energy, 3, 628–33. Markard, J. and Hoffmann, V.H. 2016. Analysis of complementarities: framework and examples from the energy transition. Technological Forecasting and Social Change, 111, 63–75. Markard, J., Raven, R. and Truffer, B. 2012. Sustainability transitions: an emerging field of research and its prospects. Research Policy, 41, 955–67. Mellbye, C.S., Helseth, A.M. and Jakobsen, E.W. 2018. Maritim Verdiskapingsbok 2018. Oslo: Menon Economics. Nelson, R.R. and Winter, S.G. 1982. An Evolutionary Theory of Economic Change. Cambridge, MA: Harvard University Press. Normann, H.E. 2015. The role of politics in sustainable transitions: the rise and decline of offshore wind in Norway. Environmental Innovation and Societal Transitions, 15, 180–93.

The energy sector: an industrial perspective on energy transitions  301 Penna, C.C.R. and Geels, F.W. 2015. Climate change and the slow reorientation of the American car industry (1979–2012): an application and extension of the Dialectic Issue Lifecycle (DILC) Model. Research Policy, 44, 1029–48. Pettit, S., Wells, P., Haider, J. and Abouarghoub, W. 2018. Revisiting history: can shipping achieve a second socio-technical transition for carbon emissions reduction? Transportation Research Part D: Transport and Environment, 58, 292–307. Poulsen, R.T., Ponte, S. and Sornn-Friese, H. 2018. Environmental upgrading in global value chains: the potential and limitations of ports in the greening of maritime transport. Geoforum, 89, 83–95. Quitzow, R. 2015. Dynamics of a policy-driven market: the co-evolution of technological innovation systems for solar photovoltaics in China and Germany. Environmental Innovation and Societal Transitions, 17, 126–48. Sandén, B.A. and Hillman, K.M. 2011. A framework for analysis of multi-mode interaction among technologies with examples from the history of alternative transport fuels in Sweden. Research Policy, 40, 403–14. Skjølsvold, T.M. and Coenen, L. 2021. Are rapid and inclusive energy and climate transitions oxymorons? Towards principles of responsible acceleration. Energy Research & Social Science, 79, Article 102164. Smith, A., Stirling, A. and Berkhout, F. 2005. The governance of sustainable socio-technical transitions. Research Policy, 34, 1491–510. Steen, M., Bach, H. and Bjørgum, Ø. et al. 2019. Greening the Fleet: A Technological Innovation System (TIS) Analysis of Hydrogen, Battery Electric, Liquefied Biogas, and Biodiesel in the Maritime Sector. Trondheim: SINTEF. Steen, M. and Karlsen, A. 2014. Path creation in a single-industry town: the case of Verdal and Windcluster Mid-Norway. Norsk Geografisk Tidsskrift – Norwegian Journal of Geography, 68, 133–43. Steen, M. and Weaver, T. 2017. Incumbents’ diversification and cross-sectorial energy industry dynamics. Research Policy, 46, 1071–86. Stephan, A., Schmidt, T.S., Bening, C.R. and Hoffmann, V.H. 2017. The sectoral configuration of technological innovation systems: patterns of knowledge development and diffusion in the lithium-ion battery technology in Japan. Research Policy, 46, 709–23. Sutherland, L.-A., Peter, S. and Zagata, L. 2015. Conceptualising multi-regime interactions: the role of the agriculture sector in renewable energy transitions. Research Policy, 44, 1543–54. Suurs, R.A.A. and Hekkert, M.P. 2009. Cumulative causation in the formation of a technological innovation system: the case of biofuels in the Netherlands. Technological Forecasting and Social Change, 76, 1003–20. Turnheim, B. and Sovacool, B.K. 2020. Forever stuck in old ways? Pluralising incumbencies in sustainability transitions. Environmental Innovation and Societal Transitions, 35, 180–84.

18. Industry, innovations and transition to the green and circular economy Massimiliano Mazzanti and Emy Zecca

1 INTRODUCTION 1.1

Sustainability Transitions and Strategies

Sustainability is a vital mission of the European Union (EU). The adoption of the European Green Deal in 2019 has placed the broad concept of sustainability (European Environment Agency [EEA], 2019) at the top of its policy agenda, representing the crucial scope of its overall political vision. The EU goal of climate neutrality by 2050 foresees an economy with net-zero greenhouse gas emissions. Moreover, the EU Biodiversity Strategy Vision 2050 requires biodiversity and the ecosystem services it provides to be protected, valued and appropriately restored for their intrinsic value and essential contribution to human well-being and economic prosperity, while preventing natural disasters caused by human activities. Stated EU targets for 2030 (2030 Climate & Energy Framework) and especially for 2050 (European Strategic Long-term Vision for a Prosperous, Modern, Competitive and Climate Neutral Economy; and Environment Action Programme 2050 Vision)1 provide cross-cutting challenges for substantial economic and social transformations to a new vision in which sustainability represents the concept underlying all development activity. How to reach these goals is characterized by an open debate in which different stakeholders are involved, considering a balance of different interests, and working towards a path of sustainable growth. Indeed, it has become clear that achieving ambitious objectives such as climate neutrality by 2050, environmental and biodiversity protection can only be achieved through a systemic rethinking of the economic system as a whole. Environmental – including the crucial circular economy (CE) strategy – and climate policies alone are not sufficient to sensibly pursue the new objectives. Only a much broader policy action – encompassing its economic, fiscal, industrial, labour, innovation and social policy aspects – can sensibly tackle such challenges. This is not surprising given that shifting our modern economies from fossil fuels to deep decarbonization, and the broader effort of making human prosperity compatible with the planetary boundaries, represents one of the major socioeconomic revolutions ever seen in human history. Meeting this objective requires synergies between policy actions that aim to support innovation pathways towards convergence between countries and regions supporting the ‘Just Transition’ to a green economy (GE) and requiring a well-designed mix of regulations over innovation, industrial and environmental dimensions. As the Ellen MacArthur Foundation (EMF) (2015) argued, the CE may be viewed as a business and policy strategy that targets the redesign of production and consumption through pervasive technological and behavioural changes that revolve around new materials, products and processes.

302

Industry, innovations and transition to the green and circular economy  303 1.2

The Circular Economy Transition: Innovations and Socioeconomic Performances

The transition to a CE is driven by the coevolution of different transitions occurring at different geographical and sectoral dimensions. An evolving understanding of the scale and character of global sustainability challenges implies a shift of reasoning from linear cause–effect principles to a multicausal and systemic view, with a subsequent rethinking of both policies and innovations. ‘Sectoral systems of innovation’ and ‘national systems of innovation’, as developed in the field of innovation economics, are relevant concepts, given the possible pervasiveness of CE changes across industrial and consumption systems (Fondazione ENI Enrico Mattei [FEEM], 2019). Social scientists have actually scrutinized the market and regulatory drivers of eco-innovation (EI)2 since the late 1990s, with attention given to static and dynamic issues (Kemp, 1997). Green techno-organizational development is a key force that can compensate for scale and population effects to obtain the achievement of a low-carbon, circular, sustainable economy. Over the past decade, research has intensified to specifically examine the various forces that underlie EI and inventions in firms (Horbach, 2008). Among others, recent analyses (see Barbieri et al., 2016 for a survey) have focused on the complementarity of EI, human resource practices and organizational change (Antonioli, Mancinelli and Mazzanti, 2013), as well as on financial barriers (Ghisetti et al., 2017), global-local drivers and foreign ownership (Cainelli et al., 2012), geographically localized spillovers, which may first affect EI adoption and then productivity (Antonioli and Della Torre, 2016; Cainelli, De Marchi and Grandinetti, 2015) and specific policies (Borghesi, Cainelli and Mazzanti, 2015; Calel and Dechezleprêtre, 2016). Specific sectors have often been the focus of analysis (Aghion et al., 2016). Analyses have predominantly examined EI drivers in the first phase of this literature development (Horbach, Rammer and Rennings, 2012). For example, despite some analyses of EI performance effects, the analyses of Lanoie et al. (2011), who exploited Organisation for Economic Co-operation and Development (OECD) surveys, demonstrated that the socioeconomic effects of EI have been relatively overlooked. In addition, country-based evidence has prevailed; for example, Martin, De Preux and Wagner (2014) analysed carbon tax effects on revenue, employment and energy intensity in UK manufacturing plants. Furthermore, Ghisetti and Rennings (2014) investigated the link between EI adoption and profitability in Germany, and Marin and Lotti (2017) analysed the productivity effects of EIs on Italian manufacturing firms. Finally, Gagliardi, Marin and Miriello (2016) studied the link between green patents and employment in Italy, and Cecere and Mazzanti (2017) analysed the correlation between EI adoption and green job creation in SMEs in the EU by using Eurobarometer surveys. EI strategies (EEA, 2019) are key factors of the GE and CE transition (FEEM, 2019, 2020; Marin, Marzucchi and Zoboli, 2015). As de Jesus et al. (2019) argued, CE refers to a systemic innovative strategy that emphasizes the role of being innovative in order to be circular. Additionally, the link between CE and EI requires a deep analysis accounting for different heterogeneous dimensions, such as design, productive processes and governance. The heterogeneity of these aspects and their possible combinations can play a strong proactive role in fostering the transition. In this setting, EI has the chance to enhance new business opportunities and strategies, thus helping to generate a change in the whole economic system (Chioatto, Zecco and D’Amato, 2020). The introduction of EI in a circular context translates into the practical application of circular business models (CBMs) that encompass environmental,

304  Handbook of industrial development economic and social sustainability dimensions (Managi and Kumar, 2018), with a specific role for technology and innovation in inclusive and sustainable industrial development (UNIDO, 2016). EI strategies and their effects on socioeconomic performances can also vary across regions, with these effects being characterized by different economic and social and institutional endowments, and, additionally, models of capitalism across the EU (Hall and Soskice, 2001). Different models of capitalism (Hall and Soskice, 2001) exist within the EU and may generate different economic and environmental performances. The models can be defined and aggregated at different levels of governance and institutional relevance (Brusco, 1982). Thus, it is very relevant to observe and analyse innovation data along European, national and also regional dimensions.

2

ECO-INNOVATIONS FOR SUSTAINABILITY: EMPIRICAL EVIDENCE

This section presents some empirical evidence to offer insights into the evolution of the GE and CE in the EU and in Italy, with emphasis on the role of innovations. 2.1

The Macroeconomic Setting

Figure 18.1 illustrates the decrease in greenhouse gas (GHG) emissions in the EU (from 1990 to 2019), showing the stable, important path the EU has taken, thanks to key policies (e.g., the EU Emissions Trading System) and relevant EI performances, induced by policies and firms’ strategic choices to anticipate future demands (Borghesi et al., 2015; Cainelli, D’Amato and Mazzanti, 2020).

Source:

Own elaboration based on EEA data.

Figure 18.1

Percentage decrease in GHG emissions since 1990

In this broad context of GE transition, driven mainly by the crucial decarbonization pathway, the CE represents a revolution affecting the economic and social world, which on the one hand is the consequence of a sustainable transformation pathway already undertaken, and on the other is the engine of a change that is yet to come. The radical change of the economic system considers different definitions of CE, overtaking the old concept of CE based on waste management (see Figure 18.4 below) to a newer one based on innovation. In the last decade,

Industry, innovations and transition to the green and circular economy  305 we observed an increase in ‘material circularity’ indicators3 (Figure 18.2), mainly stimulated by the European waste policy (Mazzanti and Zoboli, 2009), but today this represents just one part of the concept of CE.

Source:

Own elaboration based on Eurostat data.

Figure 18.2

Material circularity rate in the EU27 (%)

The new concept of CE, in fact, tries to extend the circular approach to all sectors by considering as a solution not only increasing material circularity (such as through the sharing economy and reuse), but also through innovation, creating an integrated system of solutions capable of shifting business models to a more circular vision. Definitions of CE are still fuzzy (Haas et al., 2015); the literature contains many similar definitions, which are all potentially worth considering, although not useful for real progress in the debate. Within this scenario, therefore, it became necessary to observe what happens at the core of the economy at industrial level with a micro-lens, and analyse the synergy between different dynamics occurring in companies that are faced with this challenging transition. The economic literature describes the CE as an industrial economy that relies on the ‘restorative capacity of natural resources’ (Bastein et al., 2013, p. 4). In this context, innovation represents a fundamental lever for CE, especially for the ‘new concept’ mentioned above characterized by intensive innovation activity. FEEM (2019) outlines the connections between circularity, decarbonization and bioeconomy, and the important role of innovation and policies, as illustrated by Figure 18.3. Figure 18.4 captures the change in the innovation path by analysing patent data about waste management and climate mitigation. As shown, the trend of patent data in the EU27 has completely shifted in favour of climate change mitigation solutions. Leaving aside the decrease from 2011 to 2015 due to data problems, and to the expected decrease of renewable energy incentives, the trend of waste management data in the EU27 appears flat. In particular, if we look at CE-oriented technologies related to reuse and recycling, we note a variation over time and how innovation efforts are devoted to the relevant circular areas of plastics recycling that represents the conventional sector related to CE (Figure 18.5). It is worth noting that the transition path to CE follows different routes, implying technological, organizational and social innovation adoption and diffusion, since the transition

306  Handbook of industrial development

Source:

FEEM (2019).

Figure 18.3

Source:

Integrated circularity, bioeconomy and decarbonization strategy through innovation and policy

Own elaboration based on OECD data.

Figure 18.4

Number of waste management and climate change mitigation patents

Industry, innovations and transition to the green and circular economy  307

Source:

Own elaboration based on OECD data.

Figure 18.5

Number of reuse and recycling sector patents

path towards CE cannot be separated from intensive innovation and EI activity (Cainelli et al., 2020). As de Jesus et al. (2019) argued, CE refers to a systemic innovative strategy that emphasizes the role of being innovative to be circular. As visible from the literature, the circular revolution (as defined above) implies a complete upheaval of the system in each sector and in the system as a whole, and it cannot ignore the presence of radical forms of innovation, without which the ‘systemic change’ cannot take place. Achieving structural changes requires strong synergies between political decision-makers and firms – the real actors of the new system. The industrial dimension of CE, which gains it strength from the policy interventions, represents the ground floor of this change in which different dynamics occur at different levels. 2.2

Firms’ Eco-innovation Strategies: Empirical Evidence from Europe and Italy Pre- and During COVID

2.2.1 Mapping European eco-innovations A complementary level of analysis of the transition is to consider firms’ behaviour when they make their strategic choices. Following a transition path for companies implies the interplay of different trajectories. On one hand, they must modify the product, process and organizational structure, but on the other, they must quantify these changes in order to be radical enough in

308  Handbook of industrial development line with circular-oriented strategy. In light of these considerations, companies need to adapt their business models to the new paradigm, acting as protagonists of the real application of the new economy. Therefore, knowledge of firms’ choices becomes crucial. Through this analysis we will begin to understand the reasons behind companies’ strategic decisions, considering innovative choices as key points in their life cycle. In particular, we focus on the role played by small and medium-sized enterprises (SMEs), which are key to this revolution. SMEs are commonly defined as reactive, flexible and innovative organizations (Lichtenthaler, 2016; Terzioski, 2010) and these types of firms usually operate in very competitive markets, and hence the introduction of innovation and/or new business models enables them to stand out from the competition by improving their results and business performance both in the short and medium term (Love and Roper, 2015). They must achieve a balance between financial, human and material resources on the one hand, and on the other with the social and economic environment in which they operate. Lack of financial resources and lack of time are often mentioned as factors that prevent SMEs from developing a sustainable strategy, but, nevertheless, investing in sustainability could lead to competitive advantage (Burlea-Schiopoiu and Mihai, 2019). For all these reasons, SMEs are considered the leading actors of the circular revolution as they play a proactive role in the new economy process (FEEM, 2019, 2020; Marin et al., 2015). In the European setting, the Community Innovation Survey4 (Wave 2014) provides interesting information on the adoption of CE-related technologies by European companies. Unfortunately, considering the EI section of the survey, it is impossible to compare the results of the years before and after 2014 (2012 and 2016), but we have made an up-to-date analysis of Italy.

Note: Source:

Portugal = 63.4% Unfortunately not visible. Own elaboration based on CIS data.

Figure 18.6

Percentage of innovative CIS enterprises generating environmental benefits

Industry, innovations and transition to the green and circular economy  309 Figure 18.6 provides a panoramic view of Europe with regard to the percentage of the total number of innovative enterprises generating environmental benefits.5 As can be seen, Germany, Portugal and other northern countries are the most innovative, and the same is apparent if we look at different size classes of enterprises based on number of employees (10–49 on the right; 50–249 on the left) (Figure 18.7). Looking in greater depth, we analyse the percentage of enterprises that reduced their energy use or CO2 ‘footprint’ differentiated by size class. It is unsurprising that companies’ commitment to energy reduction increases with company size, but we can see that it is never by more than 50 per cent (reaching a maximum value of 44.3 per cent in Finland), with significant variation between countries (Figure 18.8). Furthermore, it is interesting to compare this value with information related to innovation aimed at reducing material and water use (Figure 18.9). In the majority of countries (with the notable exception of Italy) innovation adoption rates to reduce energy/climate footprint have been higher than those for material/water intensity reduction. However, despite some exceptions, the rates analysed do not vary significantly from each other, which gives us important information about the high degree of complementarity in the adoption of these strategies by companies. It is important to stress that at firm level, the circular-oriented strategy represents a part of the overall EI strategy framework. On this assumption, the role that policy interventions play in directing firms’ innovative activities becomes even clearer. If EI (at any level) is a strategy to increase the competitiveness of firms, the advantage over compliance becomes a key issue that can influence a change in firms’ business models. Business model innovation in the domain of CE represents an important value-added, referring to changes of single or multiple components in the business model, which guarantee a novel way to create, deliver and capture value while ensuring companies’ survival and growth (Bocken, Schuit and Kraaijenhagen, 2018). What differentiates the implementation of CBMs from purely innovative ones is the high degree of uncertainty, complexity and challenges. For these reasons, some of the recent literature (Evans et al., 2017; Linder and Williander, 2017; Salvador et al., 2020; Tura et al., 2019) has focused on the barriers and drivers that prompt the implementation of business models that are not only innovative, but also become circular if they imply the introduction of innovations aimed at closing the loop in the new economic paradigm.6 Therefore, it is possible to identify a set of innovations that not only have environmental benefits, but also fit into the circular domain and give us a picture of the circular choices of companies. Figure 18.10 presents the rate of adoption of four different circular innovations aimed at saving material recycling and reducing toxicity of materials by substitution within the enterprise. There is a very high variation across countries in the EU, highlighting the presence of different European models, but what emerges by analysing the overall trend is a dominance of innovation to reduce materials/water intensity with respect to innovations aimed at recycling (for internal use or for sale) and substituting materials for environmental performance purposes, the latter being the least important area of innovation in many EU countries; dematerialization seems to be a relevant choice in the European setting. Looking at the product life cycle, dematerialization represents a business model that relates to the inputs used for production.7 Furthermore, Figure 18.11 shows two different innovation practices in the last phase of the product life cycle, facilitating recycling in downstream sectors and extending product life, which create benefits ‘outside’ the enterprise by favouring circularity in subsequent phases of the chain (production in other sectors and consumption). In general, the rates are relatively low compared with innovations aimed at producing environmentally circular benefits ‘inside’

Percentage of innovative CIS enterprises generating environmental benefits by class size

Left: 50–249. Right: 10–49. Own elaboration based on CIS data.

Figure 18.7

Note: Source:

310  Handbook of industrial development

Industry, innovations and transition to the green and circular economy  311

Source:

Own elaboration based on CIS data.

Figure 18.8

Source:

Percentage of CIS SMEs reducing energy use or CO2 footprint by innovating

Own elaboration based on CIS data.

Figure 18.9

Percentage of CIS SMEs that reduced material or water use per unit of output by innovating

the enterprise. However, with some notable exceptions (Portugal, Greece, Croatia), increasing the durability of the product represents an important strategic choice for companies and plays a crucial role in the fight against planned obsolescence.8 2.2.2 SMEs’ EIs: multi-dimensions of firms’ adoption Although there is considerable heterogeneity among SMEs across different sectors, their responses and their capacities to take up a ‘green solution’ seem lively despite the barriers

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Source:

Own elaboration based on CIS data.

Figure 18.10 Percentage of four types of ‘internal’ CE innovations introduced by CIS enterprises, all sizes

that can make the circular transition path difficult. First, the manager is usually also the owner of the company and thus has significant power in the strategic decisions of the firm. As such, some managers may have a positive attitude towards green business while others may not. Second, the upfront costs of any type of investment and the anticipated pay-back period are particularly important for SMEs, which are generally more sensitive to additional financial costs resulting from green business activities compared with large enterprises (Oakdene Hollins, 2011; Rademaekers, Asaad and Berg, 2011). Moreover, aside from the direct financial costs, there are also indirect ‘hidden’ costs such as the time and human resources that businesses need to devote to make environmental improvements (Revell and Blackburn, 2004; Yacob and Moorthy, 2012). In many cases, these indirect costs constitute a critical obstacle to the implementation of ‘green’ innovation due to SMEs’ shortage of time and human capital. Given these significant barriers, the policy target should be to improve access to credit and adequate sources of finance could be essential for SMEs seeking to improve their sustainability performance and/or introduce innovations. This could also increase companies’ perception of a positive relationship between environmental benefit and profit: convincing companies that greener solutions could increase competitive advantage may increase the willingness of managers to take the risk. However, some of the economic literature has identified factors that might help firms overcome these issues. According to Del Río et al. (2016), investing in EIs could increase the prestige of companies compared with their competitors, gaining the endorsement of environmentally conscious consumers. Furthermore, CE represents an opportunity to achieve the sustainability of the company in the long run as it guarantees the availability and accessibility of resources in the future (EMF, 2015; Moore and Manring, 2009).

Industry, innovations and transition to the green and circular economy  313

Source:

Own elaboration based on CIS data.

Figure 18.11 Percentage of different types of ‘internal’ CE innovations for the last phase of the product life cycle introduced by enterprises, all sizes The analysis of SMEs’ strategic choices can help in the design of a coherent mix of regulations over innovation, industrial and environmental dimensions. To this end, we exploit information retrieved by two original surveys on 4600 Italian firms, conducted as a panel in 2017–18 and 2019–20 by the University of Ferrara (CERCIS Research Centre on Circular Economy Innovation and SMEs of the Department of Economics and Management).9 The sample is largely composed of SMEs (respectively representing about 98 per cent for both waves) belonging to the manufacturing sector, which is largely characterized by medium- and low-tech enterprises (Figure 18.12). Looking at the two-year periods analysed (the second being within the first COVID-19 year, 2020), we note a drop in the number of companies investing in R&D as a proportion of the total number of companies, from around 31 per cent to around 28 per cent, respectively. Certainly, the emergency crisis generated by COVID-19 has had an overwhelming influence on the choices made by companies that had to face a totally unforeseen event and the delay in government financial interventions. It is also interesting to analyse the size of the companies that were particularly affected by the phenomenon. Figure 18.13 shows a greater percentage drop for large companies, while SMEs seem to have reacted homogeneously, with a drop of 2 per cent and 4 per cent, respectively. Overall, the first short-term impact of the pandemic crisis (2020 is observed here as part of the 2019–20 period) appears to lower innovation adoption, but some resilience is present in the industrial system, which has admittedly suffered less than services during lockdowns.

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Source:

Own elaboration based on CERCIS data.

Figure 18.12 Percentages of enterprises that have introduced different types of ‘internal’ CE innovations, all sizes

Source:

Own elaboration based on CERCIS data.

Figure 18.13 Percentage of enterprises investing in R&D, by size As discussed above, analysing R&D and innovation adoption ‘investment’ trends is an essential step in understanding circular-oriented innovation choices. In this regard, the survey originally provides data on ten of the most important circular innovations that allow us to better understand whether and in what direction Italian companies are changing their business models (Figure 18.14). As expected, the graph shows a general decline in all innovative initiatives from one period to the next, with a certain degree of homogeneity across green innovations, from waste, to

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Source:

Own elaboration based on CERCIS data.

Figure 18.14 Percentage of enterprises introducing different types of circular innovations energy and climate-related ones. It is interesting to note, however, that some of them have declined less than others – eco-design choices, for example. In this case, companies have shown a greater focus on business models that entail the creation of new products capable of minimizing the use of raw materials and maximizing the useful life of the product. In line with the results examined above for European companies, extending product durability is one of the most widely implemented CBMs. The shares drop but remain around consistent values, showing a real transition phase, where maturity is not achieved but a significant number of firms and innovations are covered. Further research will be needed to assess the impact on innovations and then performances in the 2021–23 phase, a rebirth period where creation and destruction will convey evidence of the shock, but where European Green Deal investments also support the European economy. Finally, considering the geographical distribution of the circular innovations adopted by firms in Italy, we can see from Figure 18.15 that companies in central and southern regions – which have historically been characterized by lower levels of development than northern regions – also show good innovative performance. Southern regions also seem to have faced the economic crisis better. In fact, the drop in the percentage of innovations seems to have been mostly absorbed by those regions considered the Italian ‘engine’ – the industrial north. From one period to the next, regions such as Veneto, Piedmont and Emilia-Romagna seem to have experienced a decrease in intensity in the percentage of circular innovations introduced. This

316  Handbook of industrial development has opened up possibilities for further research to assess the overall sustainability performance of firms, sectors and territories in the crucial 2020–30 decade.

Source:

Own elaboration based on CERCIS data.

Figure 18.15 Geographical distribution of the circular innovations adopted by Italian enterprises

3 CONCLUSIONS The chapter presents a picture of EIs in the EU, focusing on dynamic features, regional issues and the role of SMEs. The picture is obviously variegated, showing some heterogeneity across countries and regions, with signs of system resilience even through the pandemic crisis. Innovation is crucial to the sustainability transition. The achievement of a decarbonized, resource- and energy-efficient economy strictly depends on the generation and global diffusion of technological innovations (UNIDO, 2016, 2018). While technological progress has mostly been incremental over time, at times, technological improvements have been revolutionary, transforming the technological and skill structure of the economies (EEA, 2014). The major technological drivers of this age and the past three decades are the intensification of information and communications technologies, the rise of the Internet of Things, and the development of automation and robotics. GE techno-organizational development can be seen as another innovative step in ‘shifting outward the production possibilities frontier for some generalised aggregate of potential human wants’ (Griliches, 1990, p. 1669) where both smooth changes and discontinuities are present and where market and policy-oriented explanations of the innovative process are necessary. Indeed, GE trajectory characterizes itself as a new socio-technical paradigm with some incremental patterns and some strong discontinuities (Mazzanti and Musolesi, 2020). On EI issues, the EEA (2019) comments on the recent empirical evidence: ‘Overall, the EU trends in R&D spending by governments, patents in the various realms of EI, and data on the uptake of EI by firms show that the strength of motivation and the pace of investment seems

Industry, innovations and transition to the green and circular economy  317 to be far from that required by a GE transition. These point to the need for further triggers and drivers of eco-innovation’ (p. 45). Innovation intensity for sustainability is thus moving through an intermediate phase. New and more radical innovations are certainly necessary, as well as enhanced and broader diffusion of existing EIs. On the other hand, the picture at the beginning of the European Green Deal era is not gloomy, since European countries and firms, even SMEs, show significant green strategies that can be further stimulated by European, national and regional plans over the next decades. Whether we will put the Green Deal at the centre is a political decision that can have medium- to long-term impacts. It is crucial to focus on innovation and knowledge beyond the mere technological realm. This means extending the Green Deal and Just Transition, formulating a broad Well-being Deal, with the environment, education, health or human development at the centre. Investing in knowledge creation thus means strictly connecting and integrating techno-organizational innovation adoption and training for upgrading skills as a pillar of integrated environmental, labour and industrial policies.

NOTES 1. In reality, there is no sign of absolute decoupling, but only of relative decoupling (i.e., a situation where GHG emissions grow less than proportionately to real gross domestic product [GDP]) (Hickel and Kallis, 2020; Parrique et al., 2019; Wiedmann et al., 2020). As can be seen from Figure 18.1, from 1990 to 2019, the EU’s net GHG emissions have decreased by 25 per cent. 2. Eco-innovation is the main reference term in this chapter. The terms sustainable innovation and environmental innovation are also used. Given that there is no unique terminology, we refer to the definitions in the literature (Barbieri et al., 2016) and to two EU projects, including the Measuring Eco-innovation (MEI) project: (see https://​www​.oecd​.org/​env/​consumption​-innovation/​43960830​ .pdf; accessed 26 August 2022) and the green.eu project and EU Horizon 2020 programme, which published the new Maastricht Manual on Measuring Eco-Innovation by Kemp et al. (2019). 3. Main sources included https://​ec​.europa​.eu/​eurostat/​web/​circular​-economy and https://​stats​.oecd​ .org/​: material resources. Accessed 25 August 2020. 4. See https://​ec​.europa​.eu/​eurostat/​web/​microdata/​community​-innovation​-survey. Accessed 25 August 2022. 5. Countries not present on the maps have missing values. 6. For more information, see Lewandowski (2016); Merli, Preziosi and Acampora (2018); Nußholz (2017); Pieroni, McAloone and Pigosso (2019); Rosa-Schleich et al. (2019). 7. As Chioatto et al. (2020) have shown, it is possible to categorize different CBMs related to the different phases of the product life cycle. In particular, it is possible to identify EIs related to the input, use and output stages of the product life cycle. 8. Planned obsolescence is a business strategy in which the obsolescence (the process of becoming obsolete – that is, unfashionable or no longer usable) of a product is planned and built into it from its conception by the manufacturer. This is done so that, in the future, the consumer feels a need to purchase new products and services that the manufacturer introduces as replacements for the old ones. For more information, see Kem-Laurin Kramer (2012) in User Experience in the Age of Sustainability. 9. The aim is to create a longitudinal dataset to improve analyses of EI drivers and effects (Cainelli et al., 2020; Horbach, 2008; Horbach and Rammer, 2020). Even if panel data cannot completely offset endogeneity issues, which remains to be addressed, a panel consisting of two periods helps to deal with unobservable factors behind innovation (e.g., firm culture, idiosyncratic factors). The CERCIS dataset also provides georeferenced data at the municipal level, which may allow improvements with respect to previous studies (e.g., Cainelli, D’Amato and Mazzanti, 2015).

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REFERENCES Aghion, P., Dechezleprêtre, A. and Hemous, D. et al. 2016. Carbon taxes, path dependency, and directed technical change: evidence from the auto industry. Journal of Political Economy, 124(1), 1–51. Antonioli, D. and Della Torre, E. 2016. Innovation adoption and training activities in SMEs. International Journal of Human Resources Management, 27(3), 311–37. Antonioli, D., Mancinelli, S. and Mazzanti, M. 2013. Is environmental innovation embedded within high-performance organisational changes? The role of human resource management and complementarity in green business strategies. Research Policy, 42(4), 975–88. Barbieri, N., Ghisetti, C. and Gilli, M. et al. 2016, A survey of the literature on environmental innovation based on main path analysis. Journal of Economic Surveys, 30(3), 596–623. Bastein, T., Roelofs, E., Rietveld, E. and Hoogendoorn, A. 2013. Opportunities for a Circular Economy in the Netherlands. Delft: Netherlands Organization for Applied Scientific Research (TNO). Bocken, N.M.P, Schuit, C.S.C. and Kraaijenhagen, C. 2018. Experimenting with a circular business model: lessons from eight cases. Environmental Innovation and Societal Transition, 28, 79–95. Borghesi, S., Cainelli, G. and Mazzanti, M. 2015. Linking emission trading to environmental innovation: evidence from the Italian manufacturing industry. Research Policy, 44(3), 669–83. Brusco, S. 1982. The Emilian model: productive decentralisation and social integration. Cambridge Journal of Economics, 6(2), 167–84. Burlea-Schiopoiu, A. and Mihai, L.S. 2019. An integrated framework on the sustainability of SMEs. Sustainability, 11, Article 6026. Cainelli, G., D’Amato, A. and Mazzanti, M. 2015. Adoption of waste-reducing technology in manufacturing: regional factors and policy issues. Resource and Energy Economics, 41, 185–201. Cainelli, G., D’Amato, A. and Mazzanti, M. 2020. Resource efficient eco-innovations for a circular economy: evidence from EU firms. Research Policy, 49(1), Article 103827. Cainelli, G., De Marchi, V. and Grandinetti, R. 2015. Does the development of environmental innovation require different resources? Evidence from Spanish manufacturing firms. Journal of Cleaner Production, 94, 211–20. Cainelli, G., Mazzanti, M. and Montresor, S. 2012. Environmental innovations, local networks and internationalization. Industry and Innovation, 19(8), 697–734. Calel, R. and Dechezleprêtre, A. 2016. Environmental policy and directed technological change: evidence from the European carbon market. The Review of Economics and Statistics, 98(1), 173–91. Cecere, G. and Mazzanti, M. 2017. Green jobs and eco-innovations in European SMEs. Resource and Energy Economics, 49, 86–98. Chioatto, E., Zecca, E. and D’Amato, A. (2020). Which innovations for a circular business model? A product life-cycle approach. FEEM Working Paper, No. 29. de Jesus, A., Antunes, P., Santos, R. and Mendonça, S. 2019. Eco-innovation pathways to a circular economy: envisioning priorities through a Delphi approach. Journal of Cleaner Production, 228, 1494–513. Del Río, P., Carrillo-Hermosilla, J., Könnölä, T. and Bleda, M. 2016. Resources, capabilities and competences for eco-innovation. Technological and Economic Development of Economy, 22, 274–92. Ellen MacArthur Foundation (EMF). 2015. Growth Within: A Circular Economy Vision for a Competitive Europe. Accessed 25 August 2022 at https://​www​.mckinsey​.com/​business​-functions/​sustainability/​ our​-insights/​growth​-within​-a​-circular​-economy​-vision​-for​-a​-competitive​-europe. European Environment Agency (EEA). 2014. Resource-Efficient Green Economy and EU Policies: EEA Report, No. 2-2014. Copenhagen: EEA. European Environment Agency (EEA). 2019. The Sustainability Transition in Europe in an Age of Demographic and Technological Change: An Exploration of Implications for Fiscal and Financial Strategies. Copenhagen: EEA. Evans, S., Vladimirova, D. and Holgado, M. et al. 2017. Business model innovation for sustainability: towards a unified perspective for creation of sustainable business models. Business Strategy and the Environment, 26(5), 597–608. Fondazione ENI Enrico Mattei (FEEM). 2019. Circular Economy: Connecting Research, Industry, and Policy. A Background Report for Initiatives Design. Milan: FEEM.

Industry, innovations and transition to the green and circular economy  319 Fondazione ENI Enrico Mattei (FEEM). 2020. Energy and the Circular Economy: Filling the Gap Through New Business Models within the EGD. Milan: FEEM. Gagliardi, L., Marin, G. and Miriello, C. 2016. The greener the better: job creation and environmentally-friendly technological change. Industrial and Corporate Change, 25(5), 779–807. Gagliardi, L. and Percoco, M. 2016. The impact of European Cohesion Policy in urban and rural regions. Regional Studies, 51(6), 1–12. Ghisetti, C., Mancinelli, S., Mazzanti, M. and Zoli, M. 2017. Financial barriers and environmental innovations: evidence from EU manufacturing firms. Climate Policy, 17(S1), S131–S147. Ghisetti, C. and Rennings, K. 2014. Environmental innovations and profitability: how does it pay to be green? An empirical analysis on the German innovation survey. Journal of Cleaner Production, 75, 106–17. Griliches, Z. 1990. Patent statistics as economic indicators: a survey. Journal of Economic Literature, 28(4), 1661–707. Haas, H., Yin, L. Wang, Y. and Chen, C. 2015. What is LiFi? Journal of Lightwave Technology, 34(6), 1533–44. Hall, P. and Soskice, D. 2001. An introduction to varieties of capitalism. In P. Hall and D. Soskice (eds), Varieties of Capitalism: The Institutional Foundations of Comparative Advantage. Oxford: Oxford University Press, pp. 1–68. Hickel, J. and Kallis, G. 2020. Is green growth possible? New Political Economy, 25(4), 469–86. Horbach, J. 2008. Determinants of environmental innovation – new evidence from German panel data sources. Research Policy, 37(1), 163–73. Horbach, J. and Rammer, C. 2020. Circular economy innovations, growth and employment at the firm level: empirical evidence from Germany. Journal of Industrial Ecology, 24, 615–25. Horbach, J., Rammer, C. and Rennings, K. 2012. Determinants of eco-innovations by type of environmental impact: the role of regulatory push/pull, technology push and market pull. Ecological Economics, 78, 112–22. Kemp, R. 1997. Environmental Policy and Technical Change. Cheltenham, UK and Lyme, NH, USA: Edward Elgar Publishing. Kemp, R., Arundel, A. and Rammer, C. et al. 2019. Maastricht Manual on Measuring Eco-Innovation for a Green Economy. Accessed 26 August 2022 at https://​www​.inno4sd​.net/​uploads/​originals/​1/​ inno4sd​-pub​-mgd​-02​-2019​-fnl​-maastrich​-manual​-ecoinnovation​.pdf. Kramer, K.-L. 2012. User Experience in the Age of Sustainability: A Practitioner’s Blueprint. Waltham, MA: Morgan Kauffmann/Elsevier. Lanoie, P., Laurent-Lucchetti, J., Johnstone, N. and Ambec, S. 2011. Environmental policy, innovation and performance: new insights on the Porter hypothesis. Journal of Economics & Management Strategy, 20(3), 803–42. Lewandowski, R. 2016. Economic sectors of strategic importance to the national security: a case of Poland. Equilibrium: Quarterly Journal of Economics and Economic Policy, 11(3), 473–98. Lichtenthaler, U. 2016. Toward an innovation-based perspective on company performance. Management Decision, 54(1), 66–87. Linder, M. and Williander, M. 2017. Circular business models innovation: inherent uncertainties. Business Strategy and the Environment, 26(2), 182–96. Love, J.H. and Roper, S. 2015. SME innovation, exporting and growth: a review of existing evidence. International Small Business Journal, 33(1), 28–48. Managi, S. and Kumar, P. 2018. Inclusive Wealth Report 2018: Measuring Progress Towards Sustainability. London: Routledge. Marin, G. and Lotti, F. 2017. Productivity effects of eco-innovations using data on eco-patents. Industrial and Corporate Change, 26(1), 125–48. Marin, G., Marzucchi, A. and Zoboli, R. 2015. SMEs and barriers to eco-innovation in the EU: exploring different firm profiles. Journal of Evolutionary Economics, 25(3), 671–705. Martin, R., De Preux, L.B. and Wagner, U.J. 2014. The impact of a carbon tax on manufacturing: evidence from microdata. Journal of Public Economics, 117, 1–14. Mazzanti, M. and Musolesi, A. 2020. A semiparametric analysis of green inventions and environmental policies. SEEDS Working Paper, No. 0920. Sustainability Environmental Economics and Dynamic Studies (SEEDS).

320  Handbook of industrial development Mazzanti, M. and Zoboli, R. 2009. Municipal waste Kuznets curves: evidence on socio-economic drivers and policy effectiveness from the EU. Environmental and Resource Economics, 44(2), 203–30. Merli, R., Preziosi, M. and Acampora, A. 2018. How do scholars approach the circular economy? A systematic literature review. Journal of Cleaner Production, 178, 703–22. Moore, S.B. and Manring, S.L. (2009). Strategy development in small and medium sized enterprises for sustainability and increased value creation. Journal of Cleaner Production, 17(2), 276–82. Nußholz, J.L.K. 2017. Circular business models: defining a concept and framing an emerging research field. Sustainability, 9(10), Article 1810. Oakdene Hollins. 2011. The Further Benefits of Business Resource Efficiency. Research report for the UK Department for Environment, Food & Rural Affairs (DEFRA). Parrique, T., Barth, J. and Briens, F. et al. 2019. Decoupling Debunked: Evidence and Arguments Against Green Growth as a Sole Strategy for Sustainability. Brussels: European Environmental Bureau. Pieroni, M.P.P., McAloone, T.C. and Pigosso, D.C.A. 2019. Business model innovation for circular economy and sustainability: a review of approaches. Journal of Cleaner Production, 215, 198–216. Rademaekers, K., Asaad, S.S.Z. and Berg, J. 2011. Study on the Competitiveness of the European Companies and Resource Efficiency. ECORYS, Teknologisk Institut, Cambridge Econometrics, CESifo and Idea Consult. Report for the European Commission, DG Enterprise and Industry. Revell, A. and Blackburn, R. 2004. SMEs and their response to environmental issues in the UK. Kingston Business School Occasional Paper Series, No. 57. Rosa-Schleich, J., Loos, J., Mußhoff, O. and Tscharntke, T. 2019. Ecological–economic trade-offs of diversified farming systems – a review. Ecological Economics, 160, 251–63. Salvador, R., Barros, M.V. and Mendes da Luz, L. et al. 2020. Circular business models: current aspects that influence implementation and unaddressed subjects. Journal of Cleaner Production, 250, Article 119555. Terziovski, M. (2010). Innovation practice and its performance implications in small and medium enterprises in the manufacturing sector: a resource based view. Strategic Management Journal, 31, 892–902. Tura, N., Hanski, J. and Ahola, T. et al. 2019. Unlocking circular business: a framework of barriers and drivers. Journal of Cleaner Production, 212, 90–98. United Nations Industrial Development Organization (UNIDO). 2016. Industrial Development Report 2016: The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development. Vienna: UNIDO. United Nations Industrial Development Organization (UNIDO). 2018. Industrial Development Report 2018: Demand for Manufacturing – Driving Inclusive and Sustainable Industrial Development. Vienna: UNIDO. Wiedmann, T., Lenzen, M., Keyßer, L.T. and Steinberger, J.K. 2020. Scientists’ warning on affluence. Nature Communications, 11, Article 3107. Yacob, P. and Moorthy, K. 2012. Green practices: perception of Malaysian SME owners/managers. International Journal of Academic Research in Economics and Management Sciences, 1(3), 103–11.

PART IV THE ROLE OF THE STATE IN INDUSTRIAL DEVELOPMENT

19. Industrial policy beyond market failure: structural dynamics, innovation and economic governance for industrial development David Bailey, Sandrine Labory and Philip R. Tomlinson

1 INTRODUCTION Industrial policy is essentially deliberate state intervention aimed at enhancing industrial development in the broad sense (Pitelis, 2015).1 It can sometimes be orientated towards specific sectors, particularly sectors considered as strategic because they provide a key input (e.g., steel) and/or because they have a wider impact on the economy by nurturing new technologies with potential knowledge spillovers (and/or production synergies) in many other industries (for instance, high-tech sectors). In this regard, industrial policy (IP) has co-existed with the development of industrial capitalism and especially in follower countries that caught up from the pioneers during the first (UK) and second (USA) industrial revolutions (Bianchi and Labory, 2020). In the modern era, the immediate post-World War II period might be considered a ‘golden age’ for IP, with governments largely embracing a Keynesian macroeconomic framework, combined with appropriate market interventions at regional, national and international levels (Bianchi and Labory, 2011, 2021). Japan was a classic example, with its Ministry for International Trade and Industry (MITI) playing a critical role in the successful reconstruction of its economy (Johnson, 1982). Policy itself can been classified as either (1) vertical (or selective) when policy instruments are geared towards supporting specific firms, sectors and/or regions (e.g., selective trade tariffs, specific state aids/subsidies); or (2) horizontal where policy is non-discriminatory and seeks to facilitate an enabling and competitive market environment (e.g., generic skills policies, R&D tax credits) (Bartlett, 2014). While the period 1945–79 was largely dominated by vertical policies and governments promoting a ‘national champions’ approach (i.e., favouring particular national firms) (Bianchi and Labory, 2006; Coates, 2015), the neoliberal turn in the early 1980s led to a greater emphasis on horizontal interventions (Bianchi, Cowling and Sugden, 1994; Cowling, Oughton and Sugden, 1999; Warwick, 2013) – this being most visible in the development of the European Union’s (EU) single market. More recently, the EU’s smart specialization programme and the revival of place-based initiatives (e.g., regional policy) indicates a new emphasis on vertically oriented interventions (Bailey and Tomlinson, 2017). Over the last two decades, there has been a significant growth in the academic literature on IP. This research spans several fields, but most notably from economics, sociology, political science and regional studies (see Bulfone, 2022). In part, this interest reflects wide discontent with the dominant neoliberal model, and the relative comparative success of alternative (more interventionist) frameworks, as illustrated by the BRICs countries (Brazil, Russia, India and China) and previously acclaimed practices of countries such as Japan, South Korea and Germany (Chang, 2002). Indeed, Lin and Stiglitz 322

Industrial policy beyond market failure  323 (2013) talk of an ‘industrial policy revolution’ after the Global Financial Crisis (2008) in the sense of a wider acceptance of its relevance and necessity in order to rebalance economies scarred by the crisis. In addition, significant global transformations have taken place in industries, driven by several factors, including globalization and the rise of new powers in the global market (particularly China); pervasive innovation and technological change, leading to a new ‘industrial revolution’, Industry 4.0; and new or more acute global challenges, such as climate change and the threat to biodiversity, as well as population growth and growing inequalities. These drivers have created the need for new products and production processes (e.g., ‘greener’ ones) that innovation and new technologies have partly made possible by transforming industrial structure and processes. In emerging and advanced countries, the state has reacted to these transformations by adopting a wide range of industrial, manufacturing and technological policies, using new instruments and actions – in some cases, these have been recommended by scholars prior to implementation and analysed ex post. This chapter argues that what emerges from theoretical reflections and real cases analysed in the literature is that orientating industrial development towards specific trajectories is possible by using a wide array of instruments at micro, meso and macro levels (Andreoni, Chang and Scazzieri, 2018; Peneder, 2017). A key issue in defining IP and its implementation is therefore the coherence of actions across policy fields and across policy levels (Bianchi and Labory, 2011, 2018), as well as the economic governance of the evolution of the industrial and economic system (Cowling and Tomlinson, 2011). A key feature that has also emerged in the last two decades of reflection on and practice of IP is that this policy is dynamic, and concerned with the evolution of industries, embedded in industrial systems or ecosystems, and wider socioeconomic systems. These systems are characterized by interdependencies and co-evolution of different actors and institutions. As a result, IP must be holistic, based on visions and expectations. Coherence of the various policy fields involved is also important, across policy levels; coherence also with regard to the actions taken by the various actors of the industrial ecosystem, which should be involved in the policy process in order for them to be mobilized towards the policy goals (Rodrik, 2004, 2008). Coordination is key to ensuring coherence, and for this purpose, economic governance structures should be democratic and sustainable, ensuring diffused economic power across the industrial and economic system (Bailey, Cowling and Tomlinson, 2015). This chapter is structured as follows. Section 2 examines the shift of focus of IP away from market failures, and Section 3 shows that a dynamic approach centred on structural changes is necessary when drivers for such transformation are so numerous and imperative today. Section 4 argues that while innovation is a key engine of industrial development, IP focused exclusively on innovation is incomplete and leaves out some elements of the socioeconomic systems in which industries are embedded. Section 5 argues that production should be the unit of analysis of industrial development, not resource allocation. Section 6 shows that this perspective allows the inclusion of issues of competition and economic governance in the design and implementation of IP. Section 7 briefly concludes.

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2

FROM MARKET FAILURES TO THE INDUSTRIAL POLICY REVOLUTION

After the long debate about the role of IP, the literature on IP has primarily been empirical, analysing IPs carried out by various countries in different time periods and deriving policy recommendations for developing countries (Amsden, 1989; Cimoli, Dosi and Stiglitz, 2009; Lall, 2006; Wade, 1990), as well as mature industrial economies (Andreoni, 2016; Bailey et al., 2015; Berger, 2013; Bianchi and Labory, 2011, 2018; Block and Keller, 2009; Cowling and Tomlinson, 2000, 2011). Theoretical developments on IP have long remained mainly focused on the proposition of policy rationales anchored around the idea of market failures. At a basic level, the market failure argument relies on a departure from perfect competition (and the efficiency conditions therein). The main issue here is the absence of perfect information that opens the door to state intervention. Narratives include those that focus on suboptimal private sector R&D, which arises due to the inability of private actors to fully appropriate the returns on innovations (due to the public diffusion of knowledge), and, more generally, the risks and uncertainty associated with the innovation process (Stoneman and Vickers, 1988). Yet the ‘market failure’ approach has two major drawbacks. First, it conveys a static view of industry: market failures arise once and (presumably) disappear with corrective policy intervention. Second, it tends to lead to a fragmentation of industrial development policies: competition policy, innovation policy, regulation and trade policy are all treated separately, yet they have an overall impact on industrial development. According to the market failure paradigm, a holistic, wide-ranging and coordinated IP is not required – essentially because market failures are addressed by specific policies without any need for a consideration of their combined impact. However, a number of contributions, in particular those building on (1) evolutionary economics (Dosi, 1988; Lee, 2013; Nelson and Winter, 1982); (2) classical development economics and structuralist schools (Andreoni and Chang, 2017; Andreoni and Scazzieri, 2014; Chang, 1994; Hirschman, 1958; Lin and Chang, 2009); (3) Marshallian analysis of industrial clusters and policies (Becattini, Bellandi and De Propris, 2009; Bianchi and Bellini, 1991); and (4) innovation economics and societal challenges (e.g., Mazzucato, 2013a, 2013b), have challenged the mainstream ‘market failure’ paradigm along different research lines. These include analyses of systemic and network failures, strategic failures, coordination problems and structural dynamics, as well as state mission-oriented investments. Rodrik (2004, 2008) also strongly argued in favour of approaches going beyond the correction of market failures. Another barrier to IP has been the notion of government failures, which can arise when bureaucrats and policymakers pursue their self-interest and can be ‘captured’ by interest groups and lobbies. In the presence of information asymmetries – which imply that the behaviour of policymakers cannot be perfectly monitored – adopted policies can be wrong from a public welfare viewpoint. The implication is that it is better to leave the market to work freely or keep intervention to a minimum.2 However, looking at the experience of most countries that have reached a high level of industrial development – both industrialized and newly ‘emerged’ economies – it is very difficult to find a case where governments have not implemented some form of IP, in the sense of shaping and promoting industrial development (Bailey, Coffey and Tomlinson, 2007; Bianchi and Labory, 2006, 2020; Lall, 2006; O’Sullivan et al., 2013; Rodrik, 2004; Warwick, 2013).

Industrial policy beyond market failure  325

3

RELENTLESS DRIVERS OF STRUCTURAL CHANGES IN THE 21ST CENTURY

After a long hiatus and having been much maligned during the neoliberal era, the new ‘industrial policy revolution’ means it is again fashionable to talk of IP. Indeed, even ‘right-wing’ governments have not been afraid to embrace the term (though the more encompassing ‘industrial strategy’ is usually favoured). For instance, in 2017, the UK’s Conservative government, led by Theresa May, published an industrial strategy outlining a key role for the state in rebalancing the economy (see Department of Business, Energy & Industrial Strategy, 2017).3 Yet, in reality, governments were never entirely absent during the neoliberal era, their role being largely focused upon facilitating market deregulation, while – especially in the USA – IP was subtly targeted towards maintaining the ‘military industrial complex’ (Kitson, 2005). More generally, industries around the world have experienced significant transformations generated by different drivers, also mentioned in the introduction. Among these, the rise of new industrial powers, China in particular, implied a restructuring of global production systems and changes in global trading patterns. Production processes were internationalized, in the form of global value chains (GVCs) (or global production networks), whereby different phases of production processes are realized throughout various countries in the global economy. Less value-adding phases such as assembly were typically shifted to low-labour-cost countries in the ‘developing’ world. China was an important destination of this offshoring, which has been used to develop autonomous manufacturing capacity thanks to IP implemented by the Chinese government. In response, Western countries have become increasingly concerned about de-industrialization – namely, the shrinking of manufacturing activities in the value-added produced in the country. In addition, technological change has been accelerating and intensifying, leading to what is now commonly called the ‘Fourth Industrial Revolution’ or Industry 4.0 (I4.0). It consists of scientific discoveries and innovations in various fields, including genomics, nanotechnologies, ICTs and big data, digitalization, robotics and artificial intelligence, which all play an enabling role across services and manufacturing. These disruptive technologies are expected not only to change the organization of production inside and between firms, but also fundamentally transform the workplace, the physical environment as well as lifestyles. For example, digital platforms and enhanced data exchange can improve efficiency across the private and public sectors (Bianchi and Labory, 2018), while also having the potential to reduce some of the disadvantages associated with being located in remote and lagging regions. It may also open up new opportunities for producer–consumer collaboration, creating entirely new business ventures or transforming existing ones via big data analytics and the new platforms. However, to realize these opportunities, these economies will require significant investment in new digital infrastructure and broadband speeds. Over the last decade, the emergence of ‘digital divides’ – in terms of capacity, digital capabilities and broadband speeds – has undermined the ability of some countries (such as the UK) to ‘level up’ its industrial base and rebalance its economy (Bailey and De Propris, 2019). In many ‘left behind’ regions, knowledge and application of new I4.0 technologies is so far relatively limited. I4.0 is a transformative process, not only for the economy but also for culture and wider society. Not being able to embark upon the transformations required will entrench regional inequities. The new technologies offer the potential for new collaborations and innovation activities between firms in different (and often unrelated) sectors. Policy can seek to nurture and facil-

326  Handbook of industrial development itate such collaborations between industries using traditional production techniques and new I4.0 technologies. Here, policy platforms for enabling learning between firms across different sectors and knowledge bases has been particularly effective (Asheim, Boschma and Cooke, 2011). In addition, there may also be a prominent role for public procurement to deliberately foster such productive collaborations (Uyarra et al., 2020). Such processes would also benefit from support for entrepreneurship programmes, staff exchanges, research collaboration and enhanced labour mobility between sectors. Here, transformative and holistic IPs are required to address the failures and obstacles of the whole regional ecosystem (rather than specific components); such policies are typically multidimensional and not purely focused on instruments specific to particular sectors (Bianchi and Labory, 2019a, 2019b). The UK government’s ‘Made Smarter’ programme has made a start in this regard but policy needs to go much further.4 All the above elements show that IP is now viewed as essential by policymakers and experts because it is a dynamic policy that takes effect over the long term to favour structural changes along specific development paths. Hence, while the ‘industrial policy revolution’ means that IP has returned to mainstream economic debate, it has taken a new form, particularly through its focus on structural changes and on dynamics. In fact, the Global Financial Crisis has shown the difficulty the economic system has in adapting to the structural changes brought about by globalization and I4.0. There was not one market failure but several market failures, and, more fundamentally, a structural adjustment problem of the system that had to be addressed (Bianchi and Labory, 2011). In such a context, two options are available. First, IP can focus on specific sectors, considered strategic for the overall growth of the economy. This is IP of the past, which has been very selective and often accompanied by direct intervention of governments in markets, sometimes as far as becoming a producer, through state-owned companies. Second, IP shifts attention to structural changes and all activities that favour value creation and capture (Pitelis, 2006). This means providing the conditions for the competitiveness of industries by providing new infrastructure (energy, communication), education (skills for the new activities) and a capacity for innovation by enhancing the knowledge base through public R&D, for instance. This is the approach to IP adopted in the 1990s, which favoured horizontal measures (applied across all sectors) over vertical ones (sector-specific). It was no longer called IP but competitiveness or enterprise policy. This change in approach is important because what it really means is that IP becomes dynamic and structural. The provision of the conditions for change means adopting a long-term vision where specific changes are promoted over time. Industrial policy focused on competitiveness is thus dynamic in that it favours the dynamic adaptation of industry to the changing competitive context. In addition, this adaptation requires the upgrading of existing products and production processes, as well as the creation of new products and production processes. Production and organizations change, new jobs are created requiring new skills, new infrastructure might also be required. The structure of industries might change, implying structural changes in the whole economy – and society – in which industries are embedded. The focus of IP has thus shifted to structural changes, for which a dynamic and systemic approach is required. Industry is not an element of the economic system that can be isolated, so that policies can be specifically defined for it, independently of other policies. Industry depends on the society (labour and its skills, knowledge and capabilities), especially at the local and regional levels (see also Chapter 10 by Bellandi et al. in this Handbook). Industry

Industrial policy beyond market failure  327 also comprises different sectors that are interdependent: sectors use the output of other sectors as input to their activities (see Chapter 23 by Cardinale and Scazzieri). Even services are increasingly integrated with manufacturing (Hauge and Chang, 2019). Appropriate policy for industrial development must therefore consider the industrial system embedded in the wider socioeconomic system, primarily at regional level (see Chapter 12 by Marques and Morgan, and Chapter 8 by Sunley and Martin). Firms search for novelties using open innovation strategies – namely, setting up relationships with external actors, in the region and beyond. On the production side, the organization of production in GVCs also makes industrial systems geographically wider, and territories must develop specific assets and distinctive competences in order to attract businesses. In fact, the literature on IP has increasingly stressed the importance of systemic and holistic approaches to IP (Bailey, Pitelis and Tomlinson, 2020; Bianchi and Labory, 2011, 2018), and attention to industrial development paths (see Sunley and Martin in Chapter 8). From a theoretical perspective, the dynamic and systemic nature of IP has been supported by evolutionary theory, although this has implied a prevalence of innovation policy over other aspects of industrial development (Section 4). Structural approaches to economic and industrial dynamics put production at the heart of economic analysis and offer a promising theoretical basis for IP (Section 5). We now turn to these aspects, showing that the two approaches are not opposed but complementary.

4

INDUSTRIAL POLICY AND INNOVATION POLICY

Evolutionary economics is essentially concerned with economic change and transformation, driven by novelty and innovation. The field is inspired by Schumpeter (1934, 1942), who saw this as a primary question for economists. The seminal work of Nelson and Winter (1982) views technological change as being the driver of continual evolution in the economic system. Analysis of the evolution of the economic system follows a biological analogy and is based upon learning and discovery on the one hand, and selection mechanisms on the other. Institutions or technology are not taken as given; rather, the focus is on how they emerge and develop: novelty and innovation, as well as competitive selection are key concepts. Economies are seen as complex systems, whose evolution generates emergent properties and trajectories (Dosi and Virgillito, 2021) that can be observed. Evolutionary economics has been prominent in various policy debates, particularly concerning policies for innovation and technological change. When a dynamic view of industrial transformation is adopted, innovation becomes the key focus. Evolutionary economics does not see innovation as primarily a process of discovery, but as a process of learning that has been studied within firms, sectors, regions and national systems. It has led to the proposal of new instruments of innovation policy directed at networking, clustering and personnel mobility for knowledge transfer and creation. An important concept of this new innovation paradigm focusing on knowledge, learning and interaction among innovation actors has been defined in the concept of innovation systems (Freeman and Perez, 1988; Lundvall, 1992; Nelson, 1993). The process of innovation thus came to be seen as both path dependent, locationally specific and institutionally shaped. Such systems were essentially studied at the national level in the 1990s, and latterly at the regional level (Cooke, Heidenreich and Braczyk, 2004). Evolutionary economics hence stresses the importance of the analysis of the dynamics of the

328  Handbook of industrial development whole economic system, essentially driven by knowledge creation – innovation – and shaping the evolution of the economy through systemic interdependencies. This field has substantially developed and gained importance in policymaking through its focus on innovation processes and innovation systems. To some extent, during the 1990s and early 2000s, IP was largely subsumed by innovation policy (Mytelka and Smith, 2002). The dominance of this view has led to a focus on innovation policy as the main vehicle for industrial development since the early 1990s. Policy interventions supporting the upgrading and expansion of industries and/or the nurturing and development of networks were justified by the imperative to promote innovation. The evolutionary approach to economic development became the dominant paradigm of innovation and hitherto IP, due to the important insights and new policy instruments it brought. While evolutionary economics has a much broader scope, including, in particular, the consideration of the economy as a system (where innovation is a key engine of evolution), so potentially many insights on a broad range of policies, the focus of scholars has largely been on innovation policy. The policymaking side has also supported this focus, seeing innovation policy as a main action to support the competitiveness of industries, largely through horizontal measures, but also specific ones in the promotion of innovative clusters. In the EU, for instance, innovation policy has been the main policy advocated for industry since the mid-1990s, and this was cemented in the Lisbon Treaty and the later smart specialization programme (Barzotto et al., 2019, 2020; Mytelka and Smith, 2002; Soete, 2007). In practice, emphasis has been put on high-tech sectors and general-purpose technologies. This is useful, but innovation is much broader than that, as also stressed in the evolutionary literature, since it comprises formal and informal knowledge exchange and creation, product, process and organizational innovations also arising in low-tech sectors. The dynamic view of economic systems and the focus on evolutionary patterns has brought many insights, both for economic theory and for policymaking in practice. However, the purist view of evolution as a largely Darwinian process has left out some important aspects of economic systems. In particular, the intentionality of agents in the evolution of economic systems is largely neglected. As noted by Dosi and Virgillito (2021, p. 354), ‘the properties of the whole are generally emergent properties, that is collective properties stemming from the local interaction among multiple agents, which however cannot be attributed to the intentionality of any agents or collection of them’. Applied to industrial systems (or ecosystems), this means that no trajectory can be favoured; the only policy is to favour evolution through innovation policy. For Penrose (1952, pp. 808, 819), the biological analogy should not undermine the ‘conscious willed decision of human beings’ or ‘treat innovations as chance mutation’. In fact, ‘firms not only alter the environmental conditions necessary for the success of their actions, but, even more important, they know that they can alter them and that the environment is not independent of their own activities’ (Penrose, 1959, p. 42). Firms’ environmental conditions primarily concern market competition – namely, demand on the one hand and rivals on the other. Firms design and implement strategies aimed at gaining market shares and increasing profit, or better – creating and capturing value (Bailey, Pitelis and Tomlinson, 2018, 2020). These strategies shape the evolution of industries and of the whole economic system in which they are embedded. In fact, a dynamic approach to industrial development should focus on how strategies and resulting innovation capabilities combine to determine evolutionary trajectories. For this purpose, it is essential to consider production, as stressed by the classical economists (Smith, 1776, and later, Marshall, 1919). Implementing innovation policy without considering sectors,

Industrial policy beyond market failure  329 their productive characteristics and linkages, is likely to lead to failures, such as setting up the proverbial ‘cathedral in the desert’ – namely, R&D capacity in universities or research centres but with little connection to (local) businesses that could use these facilities. The EU’s ‘Research and Innovation Strategies for Smart Specialisation (RIS3)’ programme over the period 2014–20 encountered this problem at least at the beginning: all European regions were to adopt new frontier technologies in their regional sectors, possibly pursuing innovation-led projects with the highest growth and commercial potential. However, this turned out to be very difficult for less developed regions that were characterized by declining industries, low-tech bases and weak social and business networks, and where there were few new opportunities arising.5 In these cases, the priority should be on developing productive and innovation capabilities and supporting both internal and external network development. This has been recognized subsequently (Barzotto et al., 2020; Foray, 2018). Considering the historical and context-specific dynamics of production transformation allows policymakers to address the specific challenges countries face in benefitting from technological change and global production (Andreoni et al., 2018). In fact, the dynamics of production within market competition are key elements to address in the definition and implementation of IP.

5

STRUCTURAL APPROACHES TO ECONOMIC DYNAMICS: THE IMPORTANCE OF PRODUCTION

While it is correct to point out that classical economists stressed the importance of innovation in economic dynamics, it is also important to stress that they considered industry and industrial development as the engine of economic development, and industrial application of innovations (or innovation) within industrial systems as a key driver. This was emphasized, for instance, by Adam Smith in the Wealth of Nations (1776), as well as Schumpeter (1934). For this reason, IP is about production, and more specifically about production dynamics. Hence, the definition of appropriate IP requires entering the ‘black box’ of the production system and disentangling the policy options that are structurally available within it (Andreoni, 2014; Andreoni and Scazzieri, 2014; Rosenberg, 1982). Hence, a detailed investigation of the production processes and capabilities and of the transformations that IP measures can introduce in specific processes or sectors is necessary (Andreoni and Chang, 2017; Bianchi and Labory, 2019a, 2019b). For this purpose, Andreoni (2018) suggests an analysis of industrial ecosystems as a basis for the definition of IPs. Industrial development is essentially a process of productive transformation, led by the expansion of collective capabilities (Andreoni and Chang, 2017). In this vein, an analysis of production processes in relation to the market is key, together with the learning processes implied by structural changes. Industrial policy does not add to other policies (trade policy, competition policy, training and education policy, energy policy, and so on); rather, it integrates all policies, in order to dynamically orientate industrial development towards specific paths. Peneder (2017) argues that, as a consequence, IP must be complex, taking the system’s interdependencies into account and defining coherent instruments at the micro, meso and macro levels. Structural change indeed not just concerns the single firm or industry but the whole economic system in which firms and industries are embedded, which consists of interdependencies. Firms locate in specific territories where they can find appropriate resources and capabilities, which are determined not only by regional policies but also by

330  Handbook of industrial development the wider institutional framework at national level and beyond. Industrial policy is thus about creating competitive advantages by providing, altering and re-combining available resources and capabilities. At the core of modern IP is value creation and capture at micro, meso and macro levels (Bailey et al., 2018, 2020). Following Pitelis (2009), we interpret ‘value’ in terms of the perceived worthiness of an activity/product/service to an economic agent, which in the case of firms aiming to sell products refers to a potential and/or target user. Value creation refers to the additional value engendered through such productive activities. There has been less focus on value capture, which relates to the extent to which firms can realize or capture the monetary value from their productive activities in the market. Yet, without value capture, the firm’s commercial activities are not viable. At the firm level, value creation and capture are often co-determined and co-evolve around cost reduction and/or product-enhancing activities, and the ability to exploit these in the market. The concepts can also be extended to regional (and national) levels, with a focus upon building constructed regional advantage within regional ecosystems through supporting business and knowledge networks, regional anchor tenants and ‘place positioning’ or ‘place branding’. The latter has been particularly successful, allowing ‘place brands’ as diverse as Staffordshire ceramics and Iberian ham to compete in global markets. Industrial policy can play a critical role in these processes by supporting cross-sectoral networks, establishing public anchor tenants to drive new cluster initiatives (see Dimos, Fai and Tomlinson, 2021) and supporting local place leadership (see Bailey et al., 2018, 2020). Overall, the literature on IP has increasingly focused on the analysis of the nature, the drivers and constraints of production and value creation processes, as well as their governance, based on individual and collective learning and capability-building, together with their combined effects on structural dynamics and economic performance. This is what Pasinetti has long stressed in his study of the structural dynamics of economic systems, where the dynamics of production technology, the division and specialization of labour, generating constant learning and innovation, are the roots of structural change (Pasinetti, 1981, 1993). Following this approach, Cardinale and Scazzieri in Chapter 23 examine the systemic character of the transformations that industrial development introduces in networks of interdependent production activities and show important changes in manufacturing technology generate different trajectories of industrial development and different patterns of increasing returns.

6

COMPETITIVE DYNAMICS AND ECONOMIC GOVERNANCE

Structural changes also involve transformation of economic governance structures, which determine the capability of actors in economic systems to participate in strategic decision-making processes, depending on the distribution of economic power that these structures entail (Cowling, 1982; Cowling and Sugden, 1999; Cowling and Tomlinson, 2011). Concerns about the abuse of market power are long-standing in economics and date back to Smith (1776). Many of these concerns are embedded in theories of industrial organization and regulation. However, they can also compromise the efficacy of IP. Cowling and Sugden (1999) argue that the essence of modern corporations is they are essentially controlled by a few elite corporate hierarchies, who will make strategic decisions over the orientation of their firms (i.e., on the

Industrial policy beyond market failure  331 location of investment, employment and production). Yet, these strategic decisions are often taken in the interests of corporate elites (e.g., the relocation of a transnational firm’s production plant to enhance profitability) to the detriment of others (i.e., those employed at the plant threatened with closure). This undermines the public interest, and in effect constitutes a ‘strategic failure’. Not only is this process undemocratic, but it can lead to uncertain and unstable development paths, especially in (local, regional and national) economies that are dominated by corporate giants (Cowling and Sugden, 1999). Numerous examples abound at macro, regional and sectoral levels. For example, Cowling and Tomlinson (2000, 2002, 2011) document the Japanese case, where IP historically favoured the development of Japan’s large corporate firms and business groups, often at the expense of the country’s smaller keiretsu firms. From the 1990s onwards, the large corporates began to shift their operations overseas to the detriment of Japan’s domestic regional industries and supply chains, raising the spectre of a ‘hollowing out’ of Japanese industry (see also Bailey, 2003a). Similarly, in the West Midlands of the UK, an over-reliance on the automotive industry (for investment and employment) meant the region was especially vulnerable to the strategic decisions of the industry’s key players. This contributed to a degree of ‘regional lock-in’ and ‘technological isomorphism’ that inhibited diversity and growth (Bailey, 2003b). In finance, Branston, Tomlinson and Wilson (2012) explore how strategic decisions relating to changes in the ownership and governance of several leading UK building societies – from one of local mutual ownership to public limited company status – during the late 1990s, contributed to a less diverse market structure and a more risk-taking financial sector. These former building societies – Northern Rock, Bradford & Bingley and HBOS – which previously focused upon serving their local communities, were now at the forefront of a new risk-taking culture. Following demutualization, they began to rely heavily on wholesale funding and focused extensively upon higher-risk (sub-prime) mortgage lending, prone to default. These former building societies were in the ‘eye of the storm’ as the Global Financial Crisis hit the UK in 2008 and had to be bailed out by the state. Finally, Branston et al. (2016) highlight how the privatization of UK energy markets led to ‘strategic failure’ as the market became dominated by the ‘Big Six’ suppliers. Regulation and attempts to introduce ‘competition’ were insufficient to suppress excess profits (for the Big Six) and high prices (that were quick to rise when wholesale costs rose, but slower to be lowered as they fell). Moreover, they noted that the security of the nation’s energy supply was severely compromised by the industry’s over-reliance on foreign imports and an inability to maintain sufficient spare capacity in generation and gas storage (ibid., p. 201). Recent turbulence in global energy markets, with high gas prices and threatened shortages have cruelly exposed the UK’s weak position (FT reporters, 2021). Given the market power and influence of modern transnational corporations, the economic governance issue ought then to be a focal issue in the IP debate (Cowling and Tomlinson, 2011). However, addressing ‘strategic failures’ is a long-term, multifaceted and challenging process. In essence, IP needs to consider providing the foundations for a more democratic, pluralistic and inclusive set of economic governance structures. Branston et al. (2016) argue that this involves providing opportunities for relevant stakeholders (i.e., ‘publics’ affected by the strategic decisions of corporations) to articulate their ‘Hirshmanian voice’ (Hirschman, 1970) in strategic decision-making processes. Examples here include public utility sectors being brought under some form of mutual ownership – for example, the UK’s Network Rail and Welsh Water, where professional managers are held to account by a small group of public

332  Handbook of industrial development members appointed to represent and pursue the wider public interest rather than private shareholder interests (ibid., p. 202). At a regional level, the earlier versions of the Italian industrial districts, and their diffuse nature (as popularized by Brusco, 1982, Becattini, 1990 and others; see also Chapter 10 by Bellandi et al. in this Handbook), may offer inspiration for local stakeholders to pursue their own development paths. Similarly, governance issues in GVCs, and ensuring regional businesses do not become vulnerable or ‘locked in’ to the strategic choices of the leading GVC players is also critical. A long term, countervailing response is to encourage regions to develop their own autonomous, productive and innovative capabilities that are hard to dislodge (Bailey et al., 2020; Labory and Bianchi, 2021). Actions regarding governance structures may also address financial issues and the functioning of the financial sector. For instance, financial markets have supported the extraordinary growth of the Big Techs – namely, the large digital platforms such as Google, Apple, Facebook, Amazon and Microsoft – the GAFAM. Over the last two decades, the GAFAM have acquired innovative firms and competitors in adjacent and related markets. They are now akin to ‘natural monopolies’ and exhibit enormous power in both global markets and political theatres. This has undermined the Internet’s original democratic base, and addressing this power is increasingly becoming a concern of national and international policymakers.6

7 CONCLUSIONS The rationale for modern IP now goes beyond merely addressing market failures. New interdisciplinary perspectives on IP have also emerged from beyond the narrow confines of neoclassical economics. In terms of implementation, IP has also moved on from the old statist approach of supporting ‘national champions’, and state bailouts for failing firms. These are welcome developments. Today, advocates of IP emphasize the potential dynamism of IP achieved through long-term public–private partnerships and a focus on promoting innovation and new commercial opportunities. This entrepreneurial focus is evident in both the work of Rodrik (2004) and Mazzucato (2013b), and, as noted earlier, in the implementation of the EU’s smart specialization strategy. Consequently, IP has become synonymous with innovation policy. On the surface, this appears appropriate given the vast technological changes and transformational opportunities that are occurring in the global economy. Yet, and as argued in this chapter, there is a danger that an over-focus on specific policy silos, such as innovation policy, will suffer the same fate as the original market failure approach, and ignore the wider issues at play. This is especially true with regard to the impact of new I4.0 technologies on industrial structures and competitive dynamics. Such structural changes affect production and economic governance, with significant implications for public interest (Branston et al., 2016). If IP is to deliver industrial development in the wider public interest, then a more holistic perspective and wide-ranging approach is required, which cuts across isolated policy silos and offers a more encompassing and coordinated policy framework – at regional, national and supranational levels (the EU being an obvious vehicle for the latter). Such a framework will embrace a stakeholder approach that promotes inclusive and sustainable growth and technological upgrading, where business and people are embedded within competitive places, while simultaneously being outward-looking in the global economy. In our view, this approach is critical if IP is to help the economy and society meet the vast challenges of the 21st century and promote future industrial development.

Industrial policy beyond market failure  333

NOTES 1.

Pitelis (2015, p. 18) offers an encompassing definition, seeing industrial policy as ‘a set of measures taken by a government that aim to influence the performance of firms, sectors, industries, and clusters towards a desired objective as well as the financial, human and organizational resources, and organizational and contingency arrangements made in order to implement this objective’. 2. To quote Becker (1985), ‘The best industrial policy is none at all’! 3. It was abandoned by the Johnson administration in 2021. 4. See https://​www​.birmingham​.ac​.uk/​research/​perspective/​industry​-4​-and​-what​-to​-expect​-for​-indust ry-policy.aspx. Accessed 27 August 2022. 5. Indeed, the early RIS3 programme promoted a ‘Matthew effect’ in which the greater entrepreneurial/technological capabilities and business networks of dynamic and leading regions were in a better position to identify new innovation opportunities and benefit from the programme. ‘For to everyone who has, more will be given, and he will have abundance; but from him who has not, even what he has will be taken away’ (Matthew 25:29) (see Merton, 1968). 6. For example, the European Commission has proposed a Digital Services Act (DSA) and Digital Markets Act (DMA) to create a single supra-national regulatory framework with enforcement mechanisms. In the UK, the Competition and Markets Authority is looking at a Digital Markets Unit.

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Industrial policy beyond market failure  335 Chang, H.J. 2002. Kicking Away the Ladder: Development Strategy in Historical Perspective. London: Anthem Press. Cimoli, M., Dosi, G. and Stiglitz, J. (eds). 2009. Industrial Policy and Development: The Political Economy of Capabilities Accumulation. Oxford: Oxford University Press. Coates, D. 2015. Industrial policy: international experiences. In D. Bailey, K. Cowling and P.R. Tomlinson (eds), New Perspectives on Industrial Policy for a Modern Britain (pp. 41–59). Oxford: Oxford University Press. Cooke, P., Heidenreich, M. and Braczyk, J. 2004. Regional Innovation Systems. London: Routledge. Cowling, K. 1982. Monopoly Capitalism. London: Palgrave Macmillan. Cowling, K., Oughton, C. and Sugden, R. 1999. A reorientation of industrial policy? Horizontal policies and targeting. In K. Cowling (ed.), Industrial Policies in Europe: Theoretical Perspectives and Practical Proposals (pp. 17–32). London: Routledge. Cowling, K. and Sugden, R. 1999. The wealth of localities, regions and nations: developing multinational economies. New Political Economy, 4(3), 361–78. Cowling, K. and Tomlinson, P.R. 2000. The Japanese crisis – a case of strategic failure? Economic Journal, 101(464), 358–81. Cowling, K. and Tomlinson, P.R. 2002. Revisiting the roots of Japan’s economic stagnation: the role of the Japanese corporation. International Review of Applied Economics, 16(4), 373–90. Cowling, K. and Tomlinson, P.R. 2011. Post the ‘Washington Consensus’: economic governance and industrial strategies for the twenty-first century. Cambridge Journal of Economics, 35, 831–52. Department of Business, Energy & Industrial Strategy. 2017. Industrial Strategy: Building a Britain Fit for the Future. Accessed 27 August 2022 at https://​assets​.publishing​.service​.gov​.uk/​government/​ uploads/​system/​uploads/​attachment​_data/​file/​664563/​industrial​-strategy​-white​-paper​-web​-ready​ -version​.pdf. Dimos, C., Fai, F.M. and Tomlinson, P.R. 2021. The attractiveness of university and corporate anchor tenants in the conception of a new cluster. Regional Studies, 55(8), 1473–86. Dosi, G. 1988. Sources, procedures and microeconomic effects of innovation. Journal of Economic Literature, 26(3), 1120–71. Dosi, G. and Virgillito, M.E. 2021. In order to stand up you must keep cycling: change and coordination in complex evolving economies. Structural Change and Economic Dynamics, 56, 363–54. Foray, D. 2018. Smart specialisation strategies and industrial modernisation in European regions – theory and practice. Cambridge Journal of Economics, 42, 1505–20. Freeman, C. and Perez, C. 1988. Structural crises of adjustment: business cycles and investment behaviour. In G. Dosi, C.R. Freeman, R.R. Nelson and L. Soete (eds), Technical Change and Economic Theory (pp. 38–66). London: Burns & Oates. FT reporters. 2021. UK scrambles to contain gas price crisis. Financial Times, 17 September. Accessed 27 August 2022 at https://​www​.ft​.com/​content/​22497cb0​-aaf3​-4afa​-87e1​-e66b67814e48. Hauge, J. and Chang, H.-J. 2019. The role of manufacturing versus services in economic development. In P. Bianchi, C.R. Duran and S. Labory (eds), Transforming Industrial Policy in the Digital Age: Production, Territories and Structural Change (pp. 12–36). Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Hirschman, A. 1958. The Strategy of Economic Development. New Haven, CT: Yale University Press. Hirschman, A. 1970. Exit, Voice and Loyalty: Responses to Decline in Firms, Organizations and States. Cambridge, MA: Harvard University Press. Johnson, C. 1982. MITI and the Japanese miracle: the growth of industrial policy, 1925–1975. Stanford, CA: Stanford University Press. Kitson, M. 2005. The American economic model and European economic policy. Regional Studies, 39(7), 987–1001. Labory, S. and Bianchi, P. 2021. Regional industrial policy in times of big disruption: building dynamic capabilities in regions. Regional Studies, 55(10–11), 1829–38. Lall, S. 2006. Industrial policy in developing countries: what can we learn from East Asia? In P. Bianchi and S. Labory (eds), International Handbook of Industrial Policy (pp. 79–97). Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Lee, K. 2013. Schumpeterian Analysis of Economic Catch-up. Cambridge, UK: Cambridge University Press.

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20. Stages of industrial development and appropriate industrial policy Murat Yülek and K. Ali Akkemik

1 INTRODUCTION It has long been established that structural transformation through industrialization – that is, shift of resources from primary sector and traditional industries towards manufacturing, higher value-added industries in particular – is key to economic development (Ocampo, Rada and Taylor, 2009; Szirmai and Verspagen, 2015). The recent experience of East Asian countries that avoided both poverty and middle-income traps through industrialization is illustrative of this (Hutchison and Das, 2016; Ito, 2017). Industrialization can be suitably defined as a process of capacity building of the industrial layer (industrial firms, industrial entrepreneurs, industrial finance and industrial white- and blue-collar labor) in soft (skill accumulation and technical progress) and hard (physical suband super-structure) elements. Skill requirements rise as the country progresses through more advanced stages of industrialization (Yülek, 2018, pp. 197–206). It is customary to analyze structural changes in an economy through changes in the sectoral composition of output (value-added) and employment (Jorgenson and Timmer, 2011). At the macroeconomic level, structural change in output can be assessed using the value-added shares of broadly defined sectors in gross domestic product (GDP) – namely, primary (agriculture and mining, A), secondary (manufacturing and construction, M), and tertiary (services, S). During the early stages of industrialization, we expect A to fall, and beyond some point M > A. In the successful industrialization cases of East Asia, M > S has been achieved rather rapidly, followed by S > M. Structural change within the manufacturing sector – that is, across manufacturing industries – has also been instrumental in successful industrialization via transforming the structure of manufacturing towards technologically more sophisticated, high-value-added industries that are characterized by high income elasticity and/or potential for further technological progress. In their industrial policies, the governments in East Asia have promoted the development of these industries – for example, machines, electronics, automotive, and computers (Akkemik, 2009). On the other hand, recently, manufacturing technologies have increasingly integrated and bundled certain services into manufacturing processes, and services have gained more importance than before, leading to ‘deindustrialization’ (Hauge and Chang, 2019). It is reasonable to expect that after reaching a peak, the employment share of M (manufacturing) would decline, exhibiting an inverse U-shaped curve over time. Yülek (2018, pp. 112–17) noted that the employment share of M has been on a steady trend of decline in developed economies such as the UK and the USA since the 1970s. Using long-run data for 1970–2010, Felipe, Mehta and Rhee (2019) showed that in advanced countries, the employment share of M was at least 18 percent during their past high industrialization stages. They 338

Stages of industrial development and appropriate industrial policy  339 argued that in developing countries, M peaked below that level recently and, hence, rising to the status of an industrialized economy has become more difficult. Deindustrialization is one of the most debated issues in the related literature. Dasgupta and Singh (2007) and Rodrik (2016) showed that deindustrialization is a concern for both advanced and developing economies. In premature deindustrialization (PD), the economy reaches maturity of S > M too rapidly. A number of recent studies identified PD in some developing countries such as Brazil, India, Malaysia, and Turkey. Rodrik (2016) emphasizes demand-side and technological reasons for PD: shift of the consumers’ preferences towards services on the demand side, and rapid productivity growth in manufacturing compared to the other sectors in the economy, thanks to labor-saving technological progress, which eventually leads to a decline in the share of manufacturing employment even though its share in output does not change or increase. PD is a major impediment for economic development because it disables the manufacturing sector’s dynamic contribution to economic growth of raising overall productivity by attracting an unskilled workforce and putting them to productive use and enhancing high-technology exports. Kruse et al. (2021) found that East Asian economies deindustrialized during the period 1990–2018 and their economies have become more service oriented (see Hauge and Chang, 2019). In this chapter, we emphasize that industrial policy, which may take different forms across countries, is critical to achieving sustained long-run economic growth and enhancing national welfare to avoid falling into the middle-income trap. Successful industrialization experiences, though limited in number (most recently, that of the East Asian countries), have demonstrated that industrial policy may contribute significantly to economic development and help (Yülek, 2018). We define ‘industrial policy’ as the set of public policies aiming at the realization of structural change towards industries with higher value-added by way of channeling available productive resources, public and private, for the promotion of these industries. Incentives are put in place for firms to invest in selected industries. The mix of policies included in the industrial policy set include specific trade, competition, science and technology, education, and innovation policies designed for the promotion of the targeted industries. Therefore, the policy instruments employed in industrial policies are diverse. An important objective of industrial policy is to ensure building a sustainable industrial layer through the active involvement of the state using a number of means to overcome the notorious problem of market failures.1 Here, we define the industrial layer as the broad industrial ecosystem primarily consisting of the industrial firms, industrial labor and managers, industrial entrepreneurs, and industrial finance (Yülek, 2018). To be effective, the industrial policy set should address the entirety of the status of the industrial layer and should not just focus on the industrial firms. In addition, the parameters defining the industrial layer is dependent on the stage of industrialization of the country in question. Hence, the set of industrial policies is not ‘stage-proof’. At a less specific level, economists have been underlining that a ‘one-size-fits-all’ policy cannot appropriately address the uniqueness of country specifications.2 We argue that the relevant policies and the respective policy instruments included in the industrial policy set should follow a sequence based on these stages of industrialization. In particular, we argue that ‘general’ industrial policies are more appropriate in the initial stages of industrialization to build a strong industrial layer that would be the basis for further high-level industrialization. From that point, to successfully leap to the third stage, the government should shift to focused/sectoral/specific industrial policies. Subsequently, to leap to the fourth

340  Handbook of industrial development stage, science and innovation policies should be concentrated on. Thus, we specify appropriate industrial policies for developing economies in conjunction with the stage of industrialization. For that, we develop a theoretical framework regarding the stages of industrial development and attempt to categorize countries by proposing a simple index using relevant country-based data. The structure of the remainder of the chapter is as follows. In the next section, we review the stylized stages of industrial development. In Section 3, we compare the stages of industrialization of countries by developing a specific index of industrialization. In the fourth section, we discuss the appropriate set of industrial policies based on our stage-based categorization of countries. Specifically, we discuss the kind of policies (specific industrial policy, general industrial policy, science and technology policy, innovation policy, etc.) as appropriate in each stage of economic development and provide examples from some countries. We also underline the important link between education policy and industrial policy, especially where industrial policy is concerned, with the formation and accumulation of human capital. Section 5 concludes the chapter.

2

THE STAGES OF INDUSTRIAL DEVELOPMENT

Based on Yülek (2018), in this section we develop a theoretical framework to evaluate the staging of industrial development by considering appropriate technology policies, somewhat akin to Rostowian stages of development. We follow the streamlined process of stages of industrialization developed by Yülek (2018), which comprise four consecutive stages (see Figure 20.1). The first stage – Stage I (Figure 20.1) – is an initial stage of capital deepening. It consists of importing machinery to transform from artisanal production to relatively large-scale industrial production in factories; or, from traditional production methods to modern industrialization. Production technology develops and labor productivity jumps. Following the start of factory operations, learning-by-doing processes help generate a second wave of increase in labor productivity (Stage IIa) as skill accumulation of labor builds up and they use machinery and equipment more efficiently. This stage of technology adoption can be defined as the use (i.e., not the development) of new and more efficient mappings between quantities of inputs and outputs. Better training of the workforce is considered to increase the speed of adoption of the technology embedded in the machinery, leading to further productivity gains for a given level of capital stock. Skill build-up leads to better aftersales services in the country. Acquiring these skills is a further stage in development and such skills would increase the overall productivity gains from the imported machinery by, for example, reducing downtimes or maintenance/repair costs (Stage IIb). At the same time, it reduces negative impact of the dependence on supplier (Yülek, 2018). The next possible stage in the industrialization process is ‘imitation’ (Stage III). If this stage is ever reached by a country, firms reverse-engineer some of the imported machinery or products and build similar or slightly different ones. This is a new sector for the country. For example, starting with firms producing textiles, now the country has firms manufacturing textile machinery. Countries including the United States of America, South Korea, Japan and Russia have experienced this stage at different times. South Korea’s nuclear power program is a good example of the achievement of Stage III. Sung and Hong (1999, pp. 306, 314) define Korea’s nuclear power program, which started in 1956, as an ‘imitative catching-up process,’

Stages of industrial development and appropriate industrial policy  341

Source:

Yülek (2018).

Figure 20.1

Stages of industrialization

with a view to developing ‘the absorptive capacity of foreign technology.’ In 1956, South Korea was a low-income country with low levels of exports that were made up of primary products; by the 1990s, it had completely localized the nuclear power generation technology and turned it into an export item. This made South Korea one of the very few countries in the world with nuclear generation technology. In the case of Japan, the government-led (Agency for Industrial Science and Technology) technological support frameworks to assist private firms’ technological development and then the ‘private laboratory boom’ of 1960s (Yülek and Han, 2017, pp. 34–5) can be considered to lead Stage III and then Stage IV (development of new products and equipment). As an example, Matsushita Electric Company (MEI) – pioneering industrial firm in Japan established in 1918 – can be cited. MEI quickly became a pillar of spectacular Japanese private industrial development in the post-war era. In the 1950s, the MEI management realized that dependence on foreign production technology involved strategic risks for the growth process of the firm (Matsushita, 1988, pp. 264–7). That led to the launching of MEI’s Central R&D Laboratory in 1953. That and the following MEI R&D laboratories developed MEI’s new electrical home appliance products and production equipment in the next four decades (Kotter, 2016, pp. 164–5).

342  Handbook of industrial development Table 20.1

Description of data

Indicator

Code

Data Used

Industrial sophistication

HCI

The share of medium and hi-tech industries in manufacturing value-added (%)

Employment

EMP

The share of manufacturing in total employment (%)

Educational attainment

EDU

Percentage share of population with at least bachelor’s degree and above (%)

Technological progress

R&D

R&D expenditures per GDP (%)

Trade

HITECH

The share of hi-tech exports in total exports (%)

Stage IV requires highly skilled human resources in R&D and innovation. Countries that have reached this stage have firms at the state-of-the-art of the commercialization process. To compete globally, they need to develop new products, which is costly but at the same time provides them with a certain period of pricing power. Successful countries such as those in East Asia have realized uninterrupted structural changes and resource allocation in the past, as well as accelerated technological progress to rise to higher stages. The gaps between industrialization levels of countries is reflected in our classification of stages.

3

COMPARISON OF THE INDUSTRIALIZATION LEVELS OF COUNTRIES

In this section, we develop a simple framework to evaluate the level of industrial development of countries based on our theory of stages of industrialization in the previous section and by constructing a simple composite industrialization index. Specifically, we attempt to locate countries on the spectrum of the stages of industrialization using the index values. 3.1

Construction of the Industrialization Index

Since industrialization is a multifaceted process, we evaluate industrialization using data regarding these different facets: (1) industrial sophistication; (2) technological progress; (3) structure of trade; (4) employment; and (5) human development. We choose appropriate representative indicators and compute a composite index. Table 20.1 lists the indicators and the data used. We then categorize countries in our sample based on their index scores. In what follows, we elaborate on the construction of the index. The first indicator we use measures industrial sophistication, which also indicates structural change within the manufacturing sector. While various institutions provide data about the structure of industrial value-added by sub-industries, disaggregated data that categorize industries as heavy or hi-tech are not readily available for a panel of countries. An available indicator is the share of medium and hi-tech industries in manufacturing value-added in the World Bank World Development Indicators. We use this indicator (HCI) to measure industrial sophistication. The second indicator we employ is a measure of the structural changes in the allocation of productive resources. Here, we focus on the allocation of labor and use two indicators for this purpose. First, we use the share of the manufacturing sector in total employment (EMP) to evaluate the allocation of labor towards industrial activities. While (premature) deindustrialization leads to a decreasing share of manufacturing in total employment in the economy,

Stages of industrial development and appropriate industrial policy  343 for developing economies in their initial stages of industrialization this share is expected to increase over time. Second, we are interested not only in the allocation of labor but also its quality, which we measure by educational attainment (EDU). Specifically, we use the share of persons with an educational attainment of at least a bachelor’s degree in population aged 25 years and above. It is also important to include a measure of technological progress in our index. The indicator we use to measure technological progress is R&D expenditures as a percentage of GDP. While alternative measures such as technicians and researchers per million population are available, this indicator directly measures the resources directed for technology development. Finally, international trade has been an important component of industrial development policies, in developing countries in particular. Subsequently, we use the share of medium and hi-tech exports in total manufacturing exports (HITECH). This indicator is expected to be higher for countries with higher levels of industrialization and technological capacity. Data sources are the International Labour Organization (ILO), United Nations Industrial Development Organization (UNIDO), and the World Development Indicators Dataset of the World Bank. We use the data for the year 2018. For some indicators and some countries, with unavailable data, we used the data for the latest year available. Our dataset consists of 119 countries. For convenience, we have excluded small island countries heavily dependent on certain services such as tourism or natural resources – for example, the Bahamas, Cabo Verde, Maldives, Mauritius. We develop a simple composite index to determine the level of industrial development and industrialization for the sample countries. Since we use a set of indicators that are measured in different scales, we normalize the data and create an index by taking the simple average of the indicators used. We first normalize each of the six indicators that we denote as the vector X. There are three widely used alternative normalization techniques to choose from. Among these alternatives, we use the standardized normalization approach, which is also known as the z-score normalization as  X  X  / Ã, where X is the mean and à is the standard deviation. Values below the mean are negative and those above the mean are positive. This normalization index is very effective in handling outliers.3 The industrialization index for 2018 is computed as the simple averages of the five normalized indicators. We do not specifically use any weighting for the indicators used. While it is possible to use some kind of weighting, insofar as industrialization is concerned, we abstain from judging the relative importance of each component in weights; simple average implies that each indicator has the same weight. 3.2

Results and Interpretation

We plot index scores in a scatter diagram in Figure 20.2 with normalized per capita GDP levels (at current market prices in US dollars). In addition, due to the price differentials across countries, Figure 20.3 shows the same relationship using per capita GDP measured in purchasing power parity (PPP) and constant 2010 US dollars. The countries with high income levels are also those with high index scores, as expected. A positive correlation between the index value and GDP is clear in Figures 20.2 and 20.3. An interesting outlier is China with a relatively low GDP per capita but a higher index value. We return to China and briefly discuss industrial policies in this country below. Norway, Switzerland and Ireland are also outlying in the upper

344  Handbook of industrial development corner with high per capita GDP. However, the index scores of Norway and Ireland do not deviate much from the rest of the high-income countries, while Switzerland has both a high income and high index score.

Figure 20.2

Industrialization and nominal per capita real GDP

The locations of the countries in Figures 20.2 and 20.3 are quite similar and allow us to cluster countries based on their industrialization index scores and the level of development, which we measure by per capita GDP. The advanced countries of Europe and America gather at the top-right quadrant in both Figures 20.2 and 20.3. Korea and Taiwan lie out to the right due to their lower per capita GDP levels, but they also belong to this group. Another group of countries gathers around the center-right. These countries (e.g., Argentina, Brazil, Malaysia, Mexico, Thailand, and Turkey) are generally viewed as developing countries at the middle-income level, some of which are stuck at the middle-income level for a long time. They have positive index scores – that is, they are performing relatively better compared to the average but the prospect of attaining high-income level has not yet materialized. Those countries located at the lower part of this group (e.g., India, the Philippines, and Vietnam) even have lower-than-average per capita income levels. Finally, a large number of countries gather at the lower-left region. These countries have lower levels of industrialization and per capita income. Most of these countries are in the African continent. It is important to note that this group is much larger in reality since many low-income and Sub-Saharan African economies are not included due to lack of data. Next, we turn to index scores calculated for 2018 and attempt a stratification to assess stages of industrialization of the countries. For this purpose, we use a scoring approach and specify certain cutoff values for the index scores. In Table 20.2, we propose a method to categorize

Stages of industrial development and appropriate industrial policy  345

Figure 20.3

Industrialization and purchasing power parity-based per capita real GDP

Table 20.2

Threshold levels of the index by stages (lower bounds for the indicators)

Indicator HCI (%)

Stages IV

III

II

I

25

10

5

…

R&D (%)

2

1

0.5

…

HITECH (%)

25

15

5

…

EMP (%)

10

25

10

…

EDU (%)

25

10

5

…

0.836

0.193

–0.702

< –0.702

Index value (min)

the economies in our sample according to our stages of industrialization. For this purpose, we set lower bounds for each constituting indicator of the index and compute the corresponding index values. Our choice of the threshold levels for each indicator reflects our expectation for each stage. The cutoff values of index in Table 20.2 serve as the lower bounds for each stage. For instance, the countries with an index value between 0.112 and 0.836 fall in Stage III, and those below –0.702 in Stage I. The full list of countries by stages are presented in the Appendix along with their index scores and per capita GDP levels. We also examine the correlation between per capita income and the level of industrialization (as measured by the index) by countries grouped into stages of industrialization in Table 20.3. While the correlation is large (0.742) for the overall sample, within-group correlation coefficients exhibit diversity. The correlation is positive but less powerful in relatively less advanced stages of industrialization – Stages I, II, and III. We argue that industrial policy is effective in raising incomes and productivity in these stages to varying degrees, seemingly

346  Handbook of industrial development Table 20.3 Countries In…

Correlation between per capita GDP and the index by stages Correlation Coefficient

Stage IV

–0.395

Stage III

0.338

Stage II

0.607

Stage I

0.275

Overall sample

0.742

more strongly in Stage II. On the other hand, for the most advanced group of countries in Stage IV, the correlation turns to negative. We interpret this interesting finding as the evidence that the effectiveness of traditional and focused industrial policies vanishes for these advanced and highly industrialized countries, and science and innovation policies are more appropriate for effectiveness.

4

APPROPRIATE INDUSTRIAL POLICIES IN DIFFERENT STAGES OF INDUSTRIALIZATION

We have argued above that it is critical to know at what stage a country is at in its industrialization path. The argument is that policy recommendations for industrialization will be different for countries in different stages. In what follows, we discuss the appropriate industrial policies in conjunction with the stages of industrial development. 4.1

Stages of Industrialization and Sequencing of Industrial and Science, Technology and Innovation (STI) Policies

Following the triumph of the neoliberal ideology from the 1970s, which was endorsed by the Washington Consensus in the 1990s, industrial policy was degraded by mainstream economists as a priority for developing economies. However, the role the government can play in economic development attracted attention after the Global Financial Crisis of 2008 and with the recent rise of China to the status of a major industrial powerhouse via ambitious industrial policies of the government. Subsequently, industrial policy returned to the policy agenda in not only developing but also advanced economies after the crisis. Recently, with the advent of the Fourth Industrial Revolution (IR4) and the digitalization trends along with increasing use of artificial intelligence (AI) and progress in smart manufacturing, interest in industrial policies has further increased. This is most evident from industrial policy documents. As Labrunie, Penna and Kupfer (2020) have shown, there is a resurgence in industrial policies in major economies of the world (namely, China, Germany, Japan, the UK, and the US).4 Choosing and implementing the right industrial policies by considering the stage of industrialization is key to the success of industrial development. The impact of industrial policy is long-lasting not only at the macro level but also at the micro level. In empirical studies, active industrial policy is found to also have affected firms’ long-run performance positively. For instance, Choi and Levchenko (2021) showed that the net effect of the ‘Heavy/Chemical Industrial Drive’ in Korea during the 1970s, which served as a natural experiment through which the impact of industrial policy in firms’ performance could be evaluated, resulted in net benefits for the targeted firms. They have also found that net welfare gain from industrial

Stages of industrial development and appropriate industrial policy  347 policies in Korea was as high as 16 percent. Akkemik (2009) showed that industrial policies in Singapore from the mid-1960s onwards resulted in substantial welfare gains in the long run. Science, technology, and innovation (STI) policies are an essential component of industrial policies. Sequencing of STI and industrial policies is vitally important for industrialization. Yülek (2018, pp. 232–5) argues that the border between STI policies and industrial policies is not clear. STI policies include policies to support basic science, technology (which are ‘horizontal’ policies), and innovation (which is mostly in the form of governmental financial support or mentoring arrangements) to entrepreneurs (mostly new ones) and firms with a new idea or project. Types of general industrial policies may coincide greatly with technology or innovation policies. On the other hand, (horizontally) targeting technologies overlap with sector or product targeting. For example, supporting imaging technologies overlap with supporting electronics companies. For the technological capabilities at the national level (i.e., at the public and private sectors) to flourish, development of an industrial layer is a prerequisite (ibid., p. 183). In other words, a certain amount of capacity building in the industrial sector will increase efficiency and effectiveness of STI policies; STI policies applied in an economy without a strong industrial layer are likely to be ineffective and cost-inefficient. This is because in the non-manufacturing sectors, innovation effort is less productive compared to manufacturing sectors. Once a sufficiently strong industrial layer is formed and the country is advanced to Stage IV, the positive correlation between income (growth) and industrialization vanishes and even turns to negative. Once this stage is reached, therefore, industrial policy becomes ineffective. Instead, science and innovation policies should substitute for the industrial policy (see Figures 20.4 and 20.5). Countries in Stages I and II generally join the global value chains as suppliers of low-cost inputs and those in Stage IV compete in innovation. Lee and Lee (2019) confirm that a sequential approach to technology policies is necessary. While knowledge localization and concentration are necessary in early technology policies that target technologies with short cycles, originality gains importance at later stages in the form of innovation policies especially to be prepared to compete at the world technology frontier. They show specifically that the national innovation systems in the UK and Germany demonstrate a high level of originality and France has a comparably lower level of originality, while Italy and Korea have the lowest level.

Source:

Yülek (2018).

Figure 20.4

Stages of industrialization and sequencing of industrial and STI policies

348  Handbook of industrial development

Source:

Yülek (2018).

Figure 20.5 4.2

Industrial and STI policies

Constraints: State Capacity and Education Policy

A critical issue for the success of industrial policies is ‘state capacity’ (Akkemik and Yülek, 2020a). It is not only direct interventions, as in the case of East Asian industrial policies in the past, that can be instrumental, but also the government’s intermediary role through, for example, development banks and other development-related institutions. In many cases, governments have even taken the initiative in the development of new technologies. Mazzucato (2013) showed that governments in advanced economies, including the US, used the power of the state to interfere with the markets by creating a so-called ‘entrepreneurial state.’ These governments used massive amounts of public funds to finance high-risk projects on innovative technologies (e.g., the WWW, GIS, search engines, etc.) delegated to private firms. Notably, the objectives of industrial policies in the developed countries recently include strengthening the innovation systems, sustainable development with a clean environment, and coping with the problems brought about by the aging of society. An interesting case for the importance of state capacity in recent industrial policies is China, where the ‘developmental state’ ideology is maintained and the government intervenes through extensive use of public funds for industrial policies. The Chinese government briefly experimented with an economic management style that allowed market dominance after the accession to the World Trade Organization (WTO) in 2001, but eventually resorted to government-led industrialization following the 2008–09 global crisis, during which the state intervened heavily, and successfully, to rescue the economy from a deep recession (Brandt and Rawski, 2020). In particular, the economic management of Xi Jinping is more prone to

Stages of industrial development and appropriate industrial policy  349 state control of the economy. In 2003, the central government in China shifted its vision of technology upgrading from imports of technology to the development of indigenous technologies through industry-specific interventionist techno-industrial policies (Chen and Naughton, 2016). Since then, the government has maintained its interventionist-activist position in industrial and technology policies. The recent ‘Made in China 2025’ policy is a major step in this direction.5 Brandt and Rawski (2020) argue that the ‘Made in China 2025’ program resembles the industrialization plans of the 1950s in China, as both are top-down plans, ignore the market, and focus on quantitative targets. We argue above that a certain level of capacity building is necessary for transition from Stage II to Stage III and then to Stage IV.6 An important issue, which is also closely related to state capacity, during this transition is ensuring the development and active use of the necessary amount of human capital. One way to facilitate this is designing and implementing appropriate education policies. Yülek (2018, pp. 211–15) emphasizes the public good character of education and its importance for industrialization. Our emphasis on capacity building as the process through which industrialization and industrial policies evolve leads us to argue that an effective education system that creates the necessary human capital and human resources for industrialization is a must for their success. The successful industrialization trajectories of East Asian economies coincided with enhanced education systems, most notably tertiary education. Another case in point is the contribution of the improved German education system to industrialization from the 18th century onward (ibid., 212–14). The strong positive correlation between education and economic development is evident in Figure 20.6, which plots in a scatter diagram the association between our education variable (EDU), which we measure as the share of population with at least a bachelor’s degree, and per capita GDP. Both these vari-

Figure 20.6

Share of population with at least a bachelor’s degree, and per capita GDP

350  Handbook of industrial development ables are normalized. There is a strong resemblance for the list of countries in the upper-right corner of Figures 20.2 and 20.3 and those in Figure 20.6. In other words, those countries with relatively higher per capita GDP and higher industrialization index scores also perform well in terms of their performance in EDU. EDU captures the contribution of education and human capital in general terms. Previous economic research has broadly suggested that there is a positive relationship between economic development and educational inputs. This finding is corroborated by this study as well; industrial development is positively impacted by education. In the successful industrialization process of countries such as Germany, South Korea or Sweden, educational reforms (especially vocational training) have been critical (Yülek, 2018). However, this result does not mean that any type of education is as effective on long-term growth as the other; in the way forward, further studies are required to measure the impact of different types of education on development and industrialization (Brown, Lauder and Cheung, 2020). 4.3

Changing Industrial Policies and the Challenges of IR4

It is important to note the importance of the recent technological changes that will affect the future of industrial policies when discussing appropriate industrial policies. The Fourth Industrial Revolution, or IR4 as commonly known, has transformed the developed nations’ economies drastically. While IR4 was pronounced first in the beginning of the 2010s following the publication of the report on the strategy to promote hi-tech industries (named Industry 4.0) by the German federal government in 2011, its impact has been felt much faster than the previous three industrial revolutions. The most important characteristics of IR4 are the extensive use of automation, AI, machine learning, robotics, and highly sophisticated digital technologies. Although IR4 has started in developed economies, it is also transforming developing economies to varying degrees. The impact of IR4 on developing economies runs mostly through global linkages. The widespread use of digital technologies has reduced the costs of production across the globe. Access to the Internet has made it possible for developing country workers to join global production networks without physically changing location. Notably, a growing number of such jobs are services jobs rather than manufacturing. Baldwin and Forslid (2020) analyzed, in detail, the changing nature of globalization and its impact on both manufacturing and services during IR4. We find their analysis useful for the purpose of our study and in reminding us of some important issues regarding IR4 in conjunction with the industrial policies in the developing world. As Baldwin and Forslid argued, IR4 has also changed the classification of tradable and non-tradable goods and services. While services were deemed as non-tradable and manufactured goods as tradable, IR4, automation, and digital technologies have made services tradable. As such, comparative advantages in international trade can be affected because IR4 can substantially reduce costs of trade. This is because most services are tradable through the Internet and transport costs are negligible. The availability of the IR4 technologies may cause some sectors to gain comparative advantage over competitors. This is especially important for developing countries, which may benefit from such shifts in comparative advantages over advanced countries. Baldwin and Forslid (2020) also argue that the extent to which production costs and trade costs are reduced by IR4 are different. Their reasoning is as follows. The reduction of labor costs in manufacturing is much larger than in services. This is because IR4 focuses mostly

Stages of industrial development and appropriate industrial policy  351 on smart manufacturing, which reduces the labor costs in total costs by replacing labor with digital technologies. This reduction in the share of labor in total costs is more significant in manufacturing than services because automation focuses on manufacturing processes.7 Since the developing countries are more labor abundant, and labor-intensive exports make up a larger part of their exports, such a reduction in labor costs would help boost competitiveness in the exports of manufactures. On the other hand, the reduction in trade costs are much larger in services than in manufacturing. This is because trade in manufactures is still involved with large transport costs. Since more services have become tradable during the course of IR4, one may also expect an increase in the share of services in total international trade. The net economic effect of IR4 should then be the sum of the effects on trade and on labor costs. Industrial policies emphasizing IR4 lead to enhanced comparative advantages basically in digital technology-intensive services and the cost of manufacturing is reduced as a result of enhanced automation.

5 CONCLUSION In this chapter, we develop a stage-based theoretical framework depicting the industrialization path for countries and develop an industrialization index that can be used to rank countries or classify them into stages of industrialization. We argue and conclude that, for effectiveness, industrial policies must be dependent on industrialization stage. Our results indicate that (1) overall, there is a positive relationship between level of industrialization and per capita income; and (2) when within-stage relationships are examined, in relatively less advanced stages of industrialization (Stages I, II and III) the relationship is positive but less powerful. That is, at these stages, industrial policy is effective in raising incomes and productivity; however, (3) for the most advanced group (Stage IV) of countries, the relationship turns to negative. We interpret this to mean that the effectiveness of traditional and focused industrial and innovation policy vanishes for these advanced and highly industrialized countries and science policy is warranted. Appropriate industrial policies depend on the stage of industrial development. Specifically, while in early stages specific and industrial policies are relevant, science and technology policies and innovation policies gain importance at higher stages of industrial development. The successful industrialization trajectories of East Asian economies coincided with enhanced education systems, most notably the tertiary education. State capacity is highly instrumental for the success of these policies, as exemplified by the East Asian economies in the past, and more recently in China. Industrial policies can be a viable option for countries facing the problem of aging, and also to mitigate climate change. There are strong reasons to believe that there is still room for government involvement to achieve environmental targets. Without a capable state to regulate economic activities for this purpose, it seems that leaving it all to private agents in the economy will not be the optimal choice. Therefore, during these changing times, we believe there is a need for industrial policy. It does not have to be of a developmental type but one with a strong commitment to sustainable development.

352  Handbook of industrial development

NOTES 1. While traditional industrial policies in the past have focused on market failure, more recent industrial policies have moved beyond market failure and market imperfection arguments. For instance, recent industrial and innovation policies target the creation of new markets using public funds (Mazzucato and Semieniuk, 2017). See also Chapter 19 by Bailey, Labory and Tomlinson in this Handbook for a discussion on the economic impact of industrial policies beyond the correction of market failures. 2. See, for example, Rodrik (2007). 3. Two other alternative methods are min-max normalization and mean normalization. The relevant formulas are  X  X min  /  X max  X min  and  X  X  /  X max  X min  , respectively. In the case of min-max normalization, while rescaling each indicator and normalizing the minimum value for each indicator to 0 and the maximum value to 1 seems convenient, this approach is well known to be inefficient in treating outliers. Mean normalization is similar to standard normalization but the denominator is replaced by the sample range. 4. For an inclusive industrial policy proposal for Canada, see the report by Mendelson and Zohn (2021), which calls for a more active involvement of the Canadian government for a strategic industrial policy. 5. For details about ‘Made in China 2025’, see Akkemik and Yülek (2020b) Also see Chapter 22 in this Handbook. 6. For more detailed information about institutional capacity building, see UNDP (2015); World Bank (1997). 7. Hauge and Chang (2019) argue that the new services developed during IR4 mainly serve a manufacturing core and are linked to manufacturing, making manufacturing and services inseparable. They also emphasize that the recent manufacturing processes include a significant amount of service component embedded in them, but innovation mainly targets the manufactured product or the manufacturing process.

REFERENCES Akkemik, K.A. (2009). Industrial Development in East Asia: A Comparative Look at Japan, Korea, Taiwan and Singapore. Singapore: World Scientific. Akkemik, K.A. and M. Yülek (2020a). Imitation, innovation, and state capacity: what do East Asian industrial policies imply? Istanbul University Journal of Sociology, 40(2), 701–22. Akkemik, K.A. and M. Yülek (2020b). ‘Made in China 2025’ and the recent industrial policy in China. In S.T. Otsubo and C. Otchia (eds), Designing Integrated Industrial Policies: Industrial Promotion for Inclusive Development under Globalization. London: Routledge, pp. 335–62. Baldwin, R. and R. Forslid (2020). Globotics and development: when manufacturing is jobless and services are tradable. NBER Working Paper, No. 26731. National Bureau of Economic Research. Brandt, L. and T.G. Rawski (2020). China’s great boom as a historical process. Institute of Labor Economics (IZA) Discussion Paper, No. 13940. Brown, P., H. Lauder and S.-Y. Cheung (2020). The Death of Human Capital? Its Failed Promise and How to Renew It in an Age of Disruption. New York: Oxford University Press. Chen, L. and B. Naughton (2016). An institutionalized policy-making mechanism: China’s return to techno-industrial policy. Research Policy, 45(10), 2138–52. Choi, J. and A.A. Levchenko (2021). The long-term effects of industrial policy. Paper presented at the 16th Meeting of the Asia Pacific Trade Seminars (APTS 2021), University of Tokyo, June 26–27. Dasgupta, S. and A. Singh (2007). Manufacturing, services and premature deindustrialization in developing countries: a Kaldorian analysis. In G. Mavrotas and G. Shorrocks (eds), Advancing Development. London: Palgrave Macmillan, pp. 435–54. Felipe, J., A. Mehta and C. Rhee (2019). Manufacturing matters… but it’s the jobs that count. Cambridge Journal of Economics, 43(1), 139–68.

Stages of industrial development and appropriate industrial policy  353 Hauge, J. and H.J. Chang (2019). The role of manufacturing versus services in economic development. In P. Bianchi, C.R. Durán and S. Labory (eds), Transforming Industrial Policy for the Digital Age: Production, Territories and Structural Change. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 12–36. Hutchinson, F.E. and S.B. Das (2016). Asia and the middle-income trap: an overview. In F.E. Hutchinson and S.B. Das (eds), Asia and the Middle-income Trap. London: Routledge, pp. 17–36. Ito, T. (2017). Growth convergence and the middle-income trap. Asian Development Review, 34(1), 1–27. Jorgenson, D.W. and M.P. Timmer (2011). Structural change in advanced nations: a new set of stylised facts. Scandinavian Journal of Economics, 113, 1–29. Kotter, J.P. (2016). Matsushita Liderliği [Matsushita Leadership]. Istanbul: Sistem Yayıncılık [in Turkish]. Kruse, H., E. Mensah, K. Sen and G. de Vries (2021). A manufacturing renaissance? Industrialization trends in the developing world. UNU Wider Working Paper, No. 2021/28. Labrunie, M.L., C.C.R. Penna and D. Kupfer (2020). The resurgence of industrial policies in the age of advanced manufacturing: an international comparison of industrial policy documents. Revista Brasileira de Inovação, 19, Article e0200020. Lee, K. and J. Lee (2019). The National Innovation System (NIS) and readiness for the fourth industrial revolution: South Korea compared with four European countries. In P. Bianchi, C.R. Durán and S. Labory (eds), Transforming Industrial Policy for the Digital Age: Production, Territories and Structural Change. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 68–81. Matsushita, K. (1988). Quest for Prosperity: The Life of a Japanese Industrialist. Tokyo: PHP Institute. Mazzucato, M. (2013). The Entrepreneurial State: Debunking Public vs. Private Myths. London: Anthem Press. Mazzucato, M. and G. Semieniuk (2017). Public financing of innovation: new questions. Oxford Review of Economic Policy, 33(1), 24–48. Mendelsohn, M. and N. Zohn (2021). No Country of San Franciscos: An Inclusive Industrial Policy for Canada. Report prepared for the Canadian Inclusive Economy Initiative. Ocampo, J.A., C. Rada and L. Taylor (2009). Growth and Policy in Developing Countries. New York: Columbia University Press. Rodrik, D. (2007). World too complex for one-size-fits-all models. Post-Autistic Economics Review, 44, 73–4. Rodrik, D. (2016). Premature deindustrialization. Journal of Economic Growth, 21(1), 1–33. Sung, C.S. and S.K. Hong (1999). Development process of nuclear power industry in a developing country: Korean experience and implications. Technovation, 19, 305–16. Szirmai, A. and B. Verspagen (2015). Manufacturing and economic growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 34, 46–59. United Nations Development Programme (UNDP) (2015). Capacity Development: A UNDP Primer. New York: UNDP. World Bank (1997). World Development Report 1997: The State in a Changing World. New York: Oxford University Press. Yülek, M.A. (2018). How Nations Succeed. New York: Palgrave Macmillan. Yülek, M.A. and H.Y. Han (2017). Industrial, Science, Technology and Innovation Policies in South Korea and Japan. Frankfurt: Peter Lang AG.

354  Handbook of industrial development

APPENDIX Table 20A.1 Country

Industrialization index Code

Index

GDP

Taiwan

TWN

2.324

48 088

South Korea

KOR

1.969

28 158

China

CHN

1.649

7 807

Japan

JPN

1.378

48 766

Israel

ISR

1.353

34 750

Switzerland

CHE

1.276

79 235

Hong Kong

HKG

1.211

38 699

Singapore

SGP

1.058

59 073

Czechia

CZE

1.037

23 801

Germany

DEU

1.028

47 314

United States

USA

0.961

54 833

France

FRA

0.949

43 720

Austria

AUT

0.932

50 052

Sweden

SWE

0.922

57 911

United Kingdom

GBR

0.879

43 324

Denmark

DNK

0.865

64 272

Norway

NOR

0.851

92 120

Finland

FIN

0.842

48 880

Hungary

HUN

0.823

16 793

Malaysia

MYS

0.800

12 131

Netherlands

NLD

0.800

54 894

Belgium

BEL

0.782

47 036

Canada

CAN

0.782

51 466

Russia

RUS

0.776

11 844

Australia

AUS

0.760

56 832

Slovakia

SVK

0.705

20 551

Slovenia

SVN

0.699

26 760

Poland

POL

0.698

16 649

Ireland

IRL

0.681

76 663

Philippines

PHL

0.650

3 191

Estonia

EST

0.587

19 933

Stage IV

Stage III

Iceland

ISL

0.557

51 593

Spain

ESP

0.496

32 949

Lithuania

LTU

0.424

17 742

Latvia

LVA

0.403

16 263

Bulgaria

BGR

0.382

8 675

Vietnam

VNM

0.370

1 964

Cyprus

CYP

0.330

31 507

Iran

IRN

0.316

6 440

Italy

ITA

0.304

35 485

Thailand

THA

0.239

6 370

Mexico

MEX

0.238

10 386

Stages of industrial development and appropriate industrial policy  355 Country

Code

Index

GDP

New Zealand

NZL

0.194

38 054

  Stage II Kazakhstan

KAZ

0.189

11 166

Brazil

BRA

0.187

11 080

Portugal

PRT

0.185

24 085

Belarus

BLR

0.180

6 586

Romania

ROU

0.166

11 541

Greece

GRC

0.118

23 547

Turkey

TUR

0.110

15 190

Croatia

HRV

0.042

15 971

Ukraine

UKR

0.040

3 106

India

IND

0.030

2 086

Argentina

ARG

0.021

10 050

Tunisia

TUN

0.021

4 408

Oman

OMN

–0.011

15 797

Bosnia & Herzegovina

BIH

–0.066

6 097

Serbia

SRB

–0.069

6 898

Costa Rica

CRI

–0.114

9 937

Chile

CHL

–0.123

15 112

Uzbekistan

UZB

–0.139

2 374

Armenia

ARM

–0.230

4 407

Moldova

MDA

–0.242

3 525

Georgia

GEO

–0.250

4 734

Egypt

EGY

–0.256

2 909

South Africa

ZAF

–0.280

7 432

Morocco

MAR

–0.283

3 361

Algeria

DZA

–0.289

4 754

Indonesia

IDN

–0.307

4 285

Lao PDR

LAO

–0.330

1 786

Kyrgyz Republic

KGZ

–0.341

1 091

Mongolia

MNG

–0.342

4 211

Iraq

IRQ

–0.347

5 475

Cuba

CUB

–0.351

6 817

Pakistan

PAK

–0.358

1 198

Azerbaijan

AZE

–0.366

5 801

Uruguay

URY

–0.372

14 617

Colombia

COL

–0.376

7 694

Jamaica

JAM

–0.407

4 855

Jordan

JOR

–0.408

3 310

Panama

PAN

–0.411

11 755

Bolivia

BOL

–0.421

2 560

Paraguay

PRY

–0.433

5 380

El Salvador

SLV

–0.482

3 507

Sri Lanka

LKA

–0.486

3 946

Ecuador

ECU

–0.506

5 181

Bangladesh

BGD

–0.550

1 203

Albania

ALB

–0.555

5 075

Guatemala

GTM

–0.576

3 339

Ghana

GHA

–0.589

1 808

Cambodia

KHM

–0.594

1 203

356  Handbook of industrial development Country

Code

Index

GDP

Peru

PER

–0.617

6 454

Botswana

BWA

–0.618

8 033

Honduras

HND

–0.631

2 219

Eswatini

SWZ

–0.650

4 762

Congo, Rep.

COG

–0.656

2 304

Nigeria

NGA

–0.681

2 383

Ethiopia

ETH

–0.681

571

Central African Republic

CAF

–0.689

379

1 573

  Stage I Myanmar

MMR

–0.704

Nepal

NPL

–0.748

818

Papua New Guinea

PNG

–0.755

2 398

Tajikistan

TJK

–0.757

1 073

Senegal

SEN

–0.793

1 547

Cameroon

CMR

–0.803

1 502

Gabon

GAB

–0.812

9 051

Namibia

NAM

–0.828

5 942

Côte d’Ivoire

CIV

–0.847

1 668

Gambia

GMB

–0.859

791

Rwanda

RWA

–0.872

845

Kenya

KEN

–0.891

1 201

Tanzania

TZA

–0.928

959

Mozambique

MOZ

–0.941

593

Zambia

ZMB

–0.947

1 678

Burundi

BDI

–0.951

211

Niger

NER

–0.981

552

Angola

AGO

–1.011

3 234

Zimbabwe

ZWE

–1.015

1 306

Uganda

UGA

–1.017

934

Malawi

MWI

–1.037

515

Madagascar

MDG

–1.087

490

Note:

GDP refers to GDP per capita in US dollars at current prices in 2018 or latest year available.

21. Platform oligopolies, anti-trust policy and sustainable development Eleni E.N. Piteli and Christos Pitelis

1 INTRODUCTION This chapter reviews debates on monopoly and competition theory and policy to inform contemporary public anti-trust/competition policy. We claim that extant anti-trust policy is not designed to address today’s realities of platform-based global oligopolies and that a new approach is required that accounts for their particularities and fosters sustainable global value creation and co-creation and development. Several aspects of the business models of such firms and their ability to expand cross-border through market-based (as opposed to foreign direct) investments can have adverse effects on sustainable development. The new anti-trust policy requires addressing constraints to sustainability and fostering innovation-promoting workable and healthy competition and co-opetition. It requires equitable international coordination that seeks to level the playing field between firms, nations and peoples and foster innovation and local, place-based development through diversity, pluralism and respect for localities and their cultures. Peoples and policymakers need to address the challenge of regulatory capture and corruption and to review the whole gamut of available options to establish fair, workable and innovation-enhancing competition. This can include internalizing negative externalities of platform oligopolies, determining the full prices charged for their services, discouraging anti-competitive acquisitions, fostering new firm creation and growth and breaking monopolies when required. It is important to do so without undermining the value-creating aspects of the operations of platform oligopolies, their incentives and those of others, to introduce value-creating innovations. This requires a balancing act, and smart yet common-sense policies. The next section discusses alternative perspectives on anti-trust policy in economics and management theory. The third section looks at the role of internationalization, international trade, and sustainable development. The fourth section explores the international practice of anti-trust in the context of wider industrial and international competitiveness policies. The fifth section looks at the new landscape and the nature and characteristics of platform-based firms, provides a new conceptual framework and explores its implications for a contemporary anti-trust/competition policy. The last section offers a summary, limitations and concluding remarks.

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2

ECONOMIC THEORY AND ANTI-TRUST POLICY: THE TEXTBOOK MICROECONOMICS/INDUSTRIAL ORGANIZATION VIEW

The textbook economics perspective on anti-trust/competition policy in microeconomics and industrial organization (IO) regards competition as a type of industry structure, which can be perfect or imperfect. Perfect competition entails numerous firms that produce very similar (homogeneous) products operating in conditions where there is full and symmetrically distributed knowledge about firm and industry conditions (demand and cost conditions, in particular), and free mobility of resources, notably the lack of barriers to entry and exit of firms in the industry. Under such conditions, firms cannot influence prices. These are determined by the free interplay of supply and demand in the industry. In these circumstances, any departure of prices from average costs will attract new entrants, hence equilibrium prices charged will just cover average costs and firms generate normal profits. The opposite of perfect competition is monopoly – namely, a single firm in the industry with blockaded entry. A monopolist that maximizes profits charges a higher price than perfectly competitive firms by restricting output. This is bad for consumers, and it engenders a misallocation of resources because of the restricted output compared with the higher perfectly competitive ideal. The economic problem in this view is to maximize the welfare of its consumers by allocating the economy’s scarce resources efficiently. Monopoly entails market failure due to imperfections of the industry structure – the so-called structural market failure. When such structural market failures exist, the government could be called to step in to solve them through public, such as anti-trust, policies. This might involve reining in activities such as horizontal integration through the acquisition of competitors, and practices such as collusion over prices that facilitate the acquisition and exercise of monopoly power, and when necessary, introducing competition by breaking up monopolies. To gauge the degree of monopoly in real-life industries, economists usually employ indices of concentration. If an industry is highly concentrated, this indicates the potential for reduced competition, collusion, barriers to entry and hence high prices. However, the direction of causality between concentration and higher profits is questionable in that it is likely that more efficient firms can grow larger, hence be more profitable and increase concentration. The crucial issue, accordingly, is not concentration as such but rather the degree of collusion over prices and the strength of barriers to entry. Collusion is normally illegal in most countries. It is not easy to document, especially when it is not overt (i.e., ‘tacit’ collusion). Many studies on barriers to entry have confirmed their existence and importance (Scherer and Ross, 1990). On the other hand, and at least in part, barriers to mobility could also serve as an inducement to innovation (Penrose, [1959] 2009). This complicates the picture further. There are many attempts to measure the welfare losses from monopoly at a point in time and it is widely believed that such losses are positive, hence that monopoly is a problem that needs to be dealt with (Cowling and Mueller, 1978). In such cases, the government could step in to ensure perfectly competitive markets by encouraging mobility, discouraging mergers and acquisitions, punishing collusive pricing and other restrictive prices and even breaking up large firms. If this could be done across the economy, hence establishing economy-wide perfect competition, consumer welfare would be maximized. However, when this is not the case and there exists perfectly competitive structures in one industry but not in others, the result need not improve overall welfare. This is referred to as the ‘second-best’ problem. Given

Platform oligopolies, anti-trust policy and sustainable development  359 that the real world is always at best second best, the textbook microeconomics/IO view leaves something to be desired (Pitelis, 1994). There are several criticisms of this microeconomics/IO (also known as neoclassical) economics view. First, in real life, the two opposites of monopoly and perfect competition are widely recognized as unrealistic, with the most prevalent form of industry structure being some sort of ‘imperfect competition’, such as ‘monopolistic’, ‘oligopolistic’ or ‘big business’ competition. In ‘oligopolistic’ industries, there is interdependence between a small number of rather large firms. When one firm acts, the other is affected, and it needs to react. This raises the question of assessing the potential impact of imperfect, oligopolistic or big business competition on the efficiency of resource allocation both from a static point of view, which is the focus of the neoclassical approach, and intertemporally. The two need not coincide. Intertemporal efficiency in resource allocation entails shifting the productivity frontier through innovation and hence raises the question of which type of market structure is best for this purpose. Perfect competition is not a good candidate because it can blunt the incentive to innovate. The latter is high (even if temporary) profits that perfect competition cannot provide. The same applies for the case of contestable markets – namely, oligopolistic industries where, however, potential or real entry lead to competitive prices. Such (low) prices are unlikely to be an adequate incentive to innovate (Baumol, 1991). Measures of welfare losses also fail to account for any efficiency-related differences between perfectly competitive firms and firms with a degree of monopoly power. These include differences in production and transaction costs. Oliver Williamson (1975), for example, has argued that monopolies may have lower cost curves than those of perfectly competitive firms. This efficiency gain should be traded off against any static losses (the Williamson trade-off). Moreover, oligopolistic market structures may be more prone to innovation. This can result in dynamic productivity benefits, which must also be taken into account (Baumol, 1991; Penrose, [1959] 2009). Firms can increase their size and hence, ceteris paribus, industry concentration through the internalization of market activities. Firms may take over their suppliers or distributors or undertake such activities themselves rather than deal with them at arm’s length. Reasons why firms could thus ‘integrate’ include the pursuit of market power, the reduction in the forces of competition (Porter, 1980), the reduction of production costs through efficiency gains and so forth. But they could also do so to reduce high market transaction costs (Coase, 1937). Williamson (1975) and other economists argued that reducing market transaction costs is an important reason for the existence, growth, boundaries and size of firms. When increasing firm size results from transaction cost reductions, such efficiency savings should be considered by regulatory bodies. A vertical acquisition, motivated by efficiency, and not market power motives, for instance, should be treated more leniently by anti-trust authorities. In the real world, it is unlikely that cost-efficiency motives will be the exclusive determinant of firm size and/or that markets will be perfectly competitive or contestable. Importantly, the textbook microeconomics approach has a rather impoverished view of innovation. This is limiting, not least because innovation is a key determinant of intertemporal efficiency (Baumol, 1991; Pitelis, 2009a). Still, such views remain influential in anti-trust circles, and they cannot be ignored. For instance, the focus on prices remains a key gauge of monopoly to date (Petit and Teece, 2021).

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3

RESOURCE, CAPABILITIES AND EVOLUTIONARY VIEW

An alternative to the textbook microeconomics approach is the resource, capabilities and evolutionary (RCE) view. This is informed by both economics/political economy and by management theory. In this view, the economic problem is not just about the efficient allocation of scarce resources at any given point in time; rather, it is also about the intertemporal creation and capture of value and wealth. While efficient resource allocation can help engender resource creation, there are more and arguably better ways to create resources over time, notably through entrepreneurship and innovation. The lineage of this perspective goes back to classical political economists like Adam Smith (1776) and Karl Marx (1959), and includes influential economists such as Joseph Schumpeter ([1942] 1987), Edith Penrose ([1959] 2009) and Nelson and Winter (1982). The classical economists such as Smith and Marx focused on wealth creation and regarded competition as a process that helped regulate prices. Smith described the productivity gains through specialization, division of labour, teamwork and the generation of knowledge, skills and inventions within firms before going on to explore the marvels of the invisible hand of the market. Marx proposed that there existed a dialectical relation between monopoly and competition whereby competition led to monopoly, and monopoly could maintain itself through competition. Marx also explored competition within the factory between employers and employees (conflict) and in society at large (class struggle). Joseph Schumpeter ([1942] 1987) described competition as a process of creative destruction through innovations. He attributed the differential performance between firms on entrepreneurship and innovativeness. He saw concentration as the result of successful entrepreneurship and innovativeness not market power. Monopoly in his view was transient and a reward for innovativeness and successful entrepreneurship. Penrose ([1959] 2009) regarded firms as bundles of resources, the interaction between which generates knowledge, which in turn enhances productivity and releases ‘excess’ resources. These are resources above those currently necessary to function at full capacity. They are an incentive for management to grow and to innovate in that they can be used at almost zero marginal cost. This is because, typically, resources such as machines and workers need to be paid for even if they are used at less than full capacity. Superiority in innovative capacity leads to growth, hence to concentration. The latter, however, can also be maintained through monopolistic and other restrictive practices. Competition is between mostly big businesses and can at the same time be ‘God and the devil’ (Penrose, [1959] 2009). This is because it drives innovativeness, yet it is through restricting it that monopoly profit can be maintained. In Nelson and Winter’s (1982) evolutionary theory, through their operations, firms develop ‘routines’ that encapsulate the firm’s unique package of knowledge, skills and competencies, and allow them to function in an evolving environment with a degree of path-dependent institutionalization. Firms can pass on their ‘routines’ to the evolving organization without relying on continuous re-design and decision making, except for more strategic matters that can involve the need to develop new routines. George Richardson (1972) drew on Penrose and observed that firms compete but also cooperate. Such cooperation is not just in the form of price collusion, but can also include joint ventures, strategic alliances and several other modalities. Cooperation lies between market and hierarchy and occurs when firms’ activities are complementary but dissimilar in that they require different capabilities. Its co-existence with competition had gradually given rise to the currently popular term co-opetition (Pitelis

Platform oligopolies, anti-trust policy and sustainable development  361 and Teece, 2010). Co-opetition is a key characteristic of firm clusters and business ecosystems (Pitelis, 2012; Pitelis and Teece, 2010; Porter, 1990). Another important aspect is that of location and agglomeration economies in particular locations. The new economic geography has focused on the latter (see Audretsch, 1998; Krugman, 1991). Another additional and rather overlooked dimension on competition relates to its strength. This is also linked to location. For example, Porter claimed that local competition is more potent than distant competition, such as from abroad. For a period of time in the 1990s, cluster policy that combines the elements of co-opetition, co-location and agglomeration economies was viewed as a new industrial policy (Porter, 1998). The ‘new industrial policy’ approach in mainstream economics maintained a focus on the comparative allocative efficiency properties of different industry structures, but it also looked at gap filling, missing linkages and more pervasive market failures, and a basis for public industrial and technology policies to improve firm and industry competitiveness (see Audretsch, 1998). As such, it has affinity with the RCE view. The same is true of other approaches in economics and management such as the ‘systems of innovation’ and the ‘varieties of capitalism’ approaches (see Freeman, 1995; Lundvall, 2007; Nelson, 1995). They all stress the complementary nature of institutions and emphasize value creation through innovation. As such, they are related and/or complementary to the RCE view. Implications from the RCE and related views include the following. First, the focus on intertemporal value and wealth creation suggests a broader welfare criterion than just prices and the consumer surplus. Second, superior resources, routines and capabilities provide an efficiency-based reason for concentration. Third, when competition is seen as a dynamic process, one needs to account for the comparative impact of big business competition and monopoly regarding perfect competition, on innovation and value creation. Fourth, the co-existence of competition with cooperation (or ‘co-opetition’) in clusters and business ecosystems implies the need to consider the potential productivity benefits of inter-firm cooperation when devising anti-trust/competition policies. In closing, it is apparent that the focus of the RCE and related approaches on evolution and innovation, and their ‘systemic’ (as opposed to market-only) perspective, paints a more complex and nuanced picture of competition and economic reality as a whole and hence of requisite anti-trust policy than the textbook microeconomics approach.

4

INTERNATIONALIZATION AND SUSTAINABLE DEVELOPMENT

Discussions on anti-trust assume a closed economy, often implicitly. In an era of internationalization, this cannot be sustained. International trade, the activities of multinational enterprises (MNEs) and geopolitical considerations can shape domestic perceptions about the nature, scope and impact of anti-trust. So do perceptions and theories of growth and (sustainable) development. Yet these issues are rarely considered in discussions about anti-trust. We seek to address this limitation below. Interest in and theories of economic development date back to Adam Smith’s (1776) Wealth of Nations and have more recently developed into an exciting field of enquiry (Rodrik, 2008). The textbook macroeconomics theory usually linked development to the growth of per capita income. More recently, scholars have pointed to more qualitative aspects of development,

362  Handbook of industrial development such as the development of individual and social capabilities and positive freedoms (Sen, 1999). These scholars proposed measures like the Human Development Index (HDI), which includes longevity, education and ‘command over resources to enjoy a decent standard of living’ (UNDP, 1990, p. 1). The Inequality-adjusted HDI (IHDI) also considers inequalities within the various components of HDI, like in education, in life expectancy, and in income (Hicks, 1997). Several scholars have also discussed the importance of learning capability for catching-up and economic development (Hausmann and Rodrik, 2003). In this context, a ‘self-discovery’-based approach to development emphasizes the importance of understanding what a country is good at and building upon it. It regards entrepreneurial ‘discoveries’ as essential and it stresses the role of institutions and governments in fostering industrial growth and transformation (Piteli, 2017). Economic growth need not be sustainable, and it is unlikely to be when it goes hand in hand with economic and social inequalities and/or a degradation of the environment. The United Nations Sustainable Development Goals have gradually become influential and a reminder that development that is not sustainable socially, economically and environmentally is hardly worthy to be called development. Some distinguish between growth and development on this basis, in that growth entails mostly quantitative aspects while development also entails qualitative ones. In the international business (IB) scholarship, the focus on FDI and economic development was mostly examined in the context of theories of international competitiveness and catching-up (Furman, Porter and Stern, 2002; Pitelis, 2009b). These authors claimed that theories of international competitiveness and catching-up can be split up into several categories. These are the neoclassical economic theory-based approach, the Japanese practice-based one, the ‘systems or innovations’ view, and Michael Porter’s ‘diamond’. Despite their overlap, these frameworks have been developed separately to help explain differences in the international competitiveness and economic development of nations (Furman et al., 2002; Piteli, 2017; Pitelis, 2009b). They combine a theory of economic growth with a theory of international trade and have important implications for internationalization, FDI and anti-trust. Mainstream neoclassical economic growth theories stressed exogenous at first, and more recently, endogenous factors that affect growth. In Solow’s (1956) exogenous growth approach, capital accumulation and technological change were seen as lying outside the economic system (hence exogenous). In contrast, the more recent endogenous growth theory has treated technical change, human capital, agglomerations and increasing returns to scale as internal (endogenous) to the economic system (Romer, 1986; Solow, 2000). When combined with the comparative advantage-based free trade theories of David Ricardo (1817), and/or the Heckscher, Ohlin, Samuelson (HES) model (Samuelson, 1962), neoclassical macroeconomics models predict convergence and catching-up between nations (Pitelis, 2009b). Subsequent strategic trade theories have questioned the predictions of the HES model when there is imperfect competition, increasing returns to scale, spillovers, and first-mover advantages (Krugman, 1987, 1989). In such cases, provided there are no government failures or retaliation by other countries, strategic trade policies that involve targeting particular sectors and firms can be beneficial to a nation that can leverage them effectively and without attracting retaliation by other countries (Krugman, 1992). Earlier post-Keynesian scholars such as Kaldor (1972), and subsequently neo-institutional scholars such as North (1994), criticized the ability of the neoclassical macroeconomic model to explain development, in part because of its

Platform oligopolies, anti-trust policy and sustainable development  363 restrictive and mostly static focus on efficient allocation of scarce resources and markets and its inability to understand the creative role of markets. Other approaches to economic development include the ‘national systems of innovation’, varieties of capitalism, agglomeration, and cluster-business ecosystem-related literatures (Freeman, 1995; Krugman, 1991; Lundvall, 2007; Nelson, 1995). As noted in the previous section, these are more system based as opposed to market based. A key tenet of the systems-based views is that intertemporal efficiency and value creation can be achieved through innovation, which is best fostered through complementarities between markets, firms, governments, non-governmental organizations (NGOs) and the promotion of social capital (Freeman, 1995). In the post-World War II reconstruction period, the Japanese government followed its signature approach to competitiveness and catching up, apparently more pragmatic than one informed by neoclassical economic theory. This focused on restructuring the economy toward achieving an acquired or competitive advantage instead of just leveraging existing comparative ones (Shapiro and Taylor, 1990; Wade, 1990). It entailed a wider industrial strategy and a supporting competition/cooperation, trade and FDI policy. We elaborate further on this in our discussion of international practice below. Suffice to note here that the Japanese approach bears close affinity to the RCE view in its focus on a systemic approach, big business competition and cooperation, and constructed advantages (Best, 1990). It has also been emulated and adapted extensively by other East Asian economies, not least by China (see below). Additionally, despite its apparently a-theoretical focus, the Japanese approach to competitiveness has subsequently received conceptual support by several developments, such as strategic trade, endogenous growth, varieties of capitalism and cluster/ecosystem/place-based approaches (Pitelis, 2009b). Porter (1990) sought to fill some gaps in the theories through his ‘diamond’ framework. In his model, a country’s national competitive advantage depends on the co-existence of appropriate factor conditions, demand conditions, firm and sectoral structure and strategy, and related and supporting industries. Important is the consideration of the role of business strategy and the linkages that help create clusters (Furman et al., 2002). Clusters are characterized by both competition and cooperation. The relative economic underperformance of some countries has informed the idea of FDI-assisted development (Gerschenkron, 1962) and the belief that the diffusion of FDI-related technology spillovers is positively correlated to the technology gap between home and host countries (Findlay, 1978). According to the contagion idea of Arrow (1971), moreover, personal contact between the owner of knowledge (the MNE) and those who want to acquire it (domestic firms) is an efficient means of fostering imitation (Narula and Driffield, 2012). In the neoclassical macroeconomics approach to international trade, FDI is one of the vehicles through which factors and resources are transferred from where they are abundant to where they are scarcer, hence fostering catching-up (Stiglitz, 2001). A vehicle through which FDI can help foster development and catching-up is through knowledge diffusion. This can take place through imitation of processes or organizational innovations, as well as improved allocative efficiency and increased competition, which can incentivize domestic firms to innovate (Wang and Blomström, 1992). IB scholars recognize that FDI is not a mere transfer of capital. Rather, it transfers tangible and intangible resources and inward foreign investment can help transfer knowledge from the parent to the affiliate company and potentially to the

364  Handbook of industrial development local economy (Driffield, Love and Menghinello, 2010; Piteli, 2017). The knowledge transfer can be bidirectional too from catching up with richer countries. Instead, in the Japanese approach, FDI was tolerated only to the extent that technology transfer could be achieved in more market-based modalities such as through licensing (Shapiro and Taylor, 1990). In the systems approach, FDI is seen as part of the system – it may help reinforce already existing linkages, but when it is footloose and not embedded in the local economy its significance declines (Freeman, 1995). In the ‘diamond’, FDI operates through the four key determinants. Dunning (1993) and Rugman and Verbeke (1993) have considered FDI as a moderator of the determinants of the ‘diamond’. There has also been extensive work on the interrelationship between FDI, clusters and ecosystems (Freeman, 1995; Pitelis, Sugden and Wilson, 2006; Pitelis and Teece, 2010). The anticipated positive impact of FDI on development depends in part on the conceptual framework and the policies and actions of domestic firms and governments. Scholars like Hymer (1970) had pointed to positive and negative features in FDI-driven development and catching-up. These included dependent and uneven development (Dunning and Pitelis, 2008). More recent work has pointed to the need to leverage migrant remittances alongside FDI. Migrant remittances are quantitatively higher than even FDI and possess comparative advantages and disadvantages with regard to FDI that call for better understanding of their role in sustainable development (Piteli, Buckley and Kafouros, 2019), including their impact on competition, cooperation and small firm creation (Piteli, Kafouros and Pitelis, 2021). The potential effects of non-FDI-based cross-border entry are less explored. To the extent that firms can keep control without FDI (as predicted by Hymer), it is possible that the impact on the host country will retain some of the disadvantages of FDI-driven development, without, however, some of the potential advantages. This may remove regional policymakers’ leverage over decisions by MNEs. These have important implications on anti-trust and wider industrial, regional and competitiveness policies of nations with different interests and degrees of development.

5

INTERNATIONAL PRACTICE

Anti-trust is often discussed in the context of wider industrial and competitiveness policies. Despite its limitations, the textbook microeconomics view has dominated industrial, anti-trust and competition policy thinking in the Western world for decades and it remains influential. The theory of anti-trust legislation in the US, like the Sherman Act, and the original Articles 85 and 86 of the Treaty of Rome in Europe, were directly informed and influenced by their focus on industry structures, prices and the consumer surplus. Practice has varied from theory and between countries and over time. As argued elsewhere (Pitelis, 1984), early European industrial and competition policy, for example, could be described as ad hoc, discontinuous and inconsistent. An example was the ‘national champions’ or ‘picking winners’ policy, which several European countries pursued in the 1960s and 1970s. This involved identifying potential industries and firms that could become internationally competitive (winners), and employing measures like subsidies and tax breaks to support them. It entailed a lenient and often encouraging attitude towards mergers and acquisitions to help create national champions, and in some cases, the nationalization of utilities and other ‘strategic’ industries. It was hoped that such firms and industries could compete successfully with foreign rivals, raising export sur-

Platform oligopolies, anti-trust policy and sustainable development  365 pluses and national competitiveness. This helped exacerbate structural market failures and was also inconsistent with the theoretical focus on the alleged advantages of ‘perfect competition’. The policy was also pursued at a Europe-wide level in the search for pan-European companies that could out-compete large American multinationals. In some cases, such policies blunted incentives for the protected firms to compete, and gradually it gave rise to ‘problematic enterprises’ or ‘lame ducks’. In time, this motivated a drive towards deregulation and privatization, alongside a switch of focus to small firms and entrepreneurship. This entailed a discontinuity of policies, from a focus on helping create large firms to the support of small firms, entrepreneurship and higher reliance on market forces. As we have noted earlier, the approach of Japan, and the ‘Tigers’ of East Asia, was different. Industrial policy was led by the Ministry of International Trade and Industry (MITI) and involved intervention by the government aimed at creating new competitive advantages for firms and in certain sectors. These were chosen based on being high value-added, with high income elasticity of demand and gradually knowledge intensive. MITI provided financial and other support and guidance. It regulated the degree of competition to be neither too little nor too fierce, aiming at an ‘optimum’ amount of firms/degree of competition. It protected selected sectors and firms from foreign competition, while facilitating at the same time ‘technology transfer’ through the promotion of licensing of technology by foreign firms. It also paid attention to the benefits of cooperation and the promotion of small and medium-sized enterprises (SMEs) (Best, 1990). Overall, the Japanese approach entailed an attempt to create new competitive advantages through big business competition, fostering a long-term-oriented strategy of growth and market share acquisition, support for small firms, and novel methods of doing business. The Japanese approach to competition could be described as domestically focused big business co-opetition. This was successful in the early years of catching-up but was later partly abandoned and partly unable to adapt, open up gradually and take advantage of the benefits of competition. Nevertheless, it has helped inform subsequent policies by other Far Eastern countries and China. The East Asian Tigers followed a similar approach, although some of them, especially smaller ones like Singapore, pursued ‘technology transfer’ through inward investment policies too (Pitelis, 1984). The performance of the East Asian Tigers had been impressive. Several commentators attributed their success to their industrial policy, alongside other characteristics, such as the focus on education, relatively equitable distribution of incomes, high savings ratios, and a competent public sector both in cooperation and at arm’s length with the business. This is a key characteristic that did not characterize the Western national champions policy, at least not to the same degree. There are also opposing views on this that continue to vary (see Pitelis, 2001). Attributing the success of the Far Eastern economies to their approach to interventionist industrial and competitiveness policy, when similar policies by some Western governments in the past have not been as effective, implies misconceived policies by the latter, or higher incompetence. As we have noted, this may well be the case, but there is also another potential argument. In contrast to the West, the Japanese and Far Eastern approach had favoured resource creation through innovation-promoting big business competition and public policy, not just through the efficient resource allocation. This approach is aligned to the RCE focus on production and organization (Best, 1990), and could be a differentia specifica of the Far Eastern approach.

366  Handbook of industrial development The Chinese industrial policy is arguably an adaptation and enrichment of previous East Asian experiences. For Rodrik (2005), it included institutional innovations with Chinese characteristics, such as Town and Village Enterprises (TVEs), which were seen as fit for purpose and which had worked up to a point. For Lin (2011), the difference in the Chinese approach lay in its gradualist comparative advantage-friendly approach. Despite different views on the extent to which the policy was comparative advantage-following or -defying (Lin and Chang, 2009), and on whether this was sustainable, it had impressive results at the economic growth level. Few commentators would today question that the state capitalist interventionist industrial policy approach by China has been an important constituent factor of this performance (Cowling and Tomlinson, 2011; Lin and Chang, 2009; Rodrik, 2008). In the EU, an interesting aspect in the 2000s was a shift to an arguably RCE-flavoured approach (see Pitelis, 1998, 2001). Location became important more recently in the context of the currently popular smart specialization strategies (3S) and place-based views. The basic premise of the 3S approach is the need for regions and nations to prioritize public sector support for ‘activities’ in technologies, fields or domains at the regional level that have the potential for ‘entrepreneurial discovery’, knowledge spillovers, innovation, scale, agglomeration and commercial exploitation (Foray, 2015). It places emphasis on the ‘entrepreneurial discovery’ process and (in the context of) a spatial dimension. The place-based industrial policy (such as 3S) seeks to appreciate the nature and dynamics of the regional ecosystem, from which new opportunities and entrepreneurial discoveries can arise, nurture and leverage these to the region’s advantage. Appreciation also entails the possibility to shape and co-create the regional ecosystem that includes a skilled labour pool, an agglomeration of firms, universities and public research organizations and related and supporting institutions and organizations. All these can help foster knowledge and engender spillovers. Proximity within an ecosystem matters to firms’ competitive advantage, but it is the relational embeddedness of firms and other actors within regional networks that helps create and diffuse new knowledge, facilitate innovation (Capello and Faggian, 2005; Maskell and Malmberg, 1999) and to co-create value with an eye to capturing as much of it as possible (Bailey, Pitelis and Tomlinson, 2020; Pitelis, 2012). Identifying, selecting and prioritizing cases that can be supported implies a more vertical and activist industrial policy perspective (Foray, 2013). Identifying appropriate opportunities requires strategic collaboration between regionally based private- and public-sector actors. This can involve sharing of information around potential opportunities, their critical evaluation, as well as learning in a context of ‘embedded autonomy’ (Bailey and Tomlinson, 2017). The diversity of players is said to render policy practices less amenable to ‘regulatory capture’ by powerful firms. Mutual monitoring and checks and balances, collaboration, consultation and ‘brain storming’, can help mitigate the problem of government failure that had plagued earlier more vertical ‘picking winners’-based approaches to industrial policy and strategy. In all, the 3S approach is tailored towards building on a region’s existing industrial commons, in contrast to more spatially blind policy solutions (Bailey, Cowling and Tomlinson, 2015). This brings it closer to the clusters, business ecosystems and the systems of innovation views (Bailey et al., 2020; McCann and Ortega-Argilés, 2015; Pitelis, 2012). It also has close affinity with the RCE view and related frameworks such as the national innovation system (NIS), regional and sectoral systems of innovation (Malerba, 2005), the varieties of capitalism (Hall and Soskice, 2001) and aspects of the Far Eastern approach. In addition, the 3S view is aligned closer to contemporary thinking about modern industrial policy as a ‘process of discovery’

Platform oligopolies, anti-trust policy and sustainable development  367 (Rodrik, 2004, 2008), whereby the private and the public sector can co-learn and engage in strategic coordination. The Far Eastern and the 3S and place-based approaches focused mostly on industrial not anti-trust policies as such. The anti-trust policies are more implicit and related to their assumptions and implications for competition. Basically, this was big business co-opetition. There are interesting anti-trust implications that emanate from these, which, however, remained underexplored. Additionally, they have not sought to deal with the specificities of big business competition and cooperation in the context of the new landscape of the currently ubiquitous platform oligopolies. These are limitations that we seek to address below.

6

THE NEW LANDSCAPE: THE PLATFORM-BASED BIG TECH, AND A NEW PERSPECTIVE ON ANTI-TRUST

6.1

The New Landscape

In the past 20 years or so, we have witnessed the emergence of mostly platform-based Big Tech companies, the so-called ‘sharing economy’ and several exotically named start-ups like unicorns, decacorns and hectocorns (valued at US$1, US$10 and US$100 billion, respectively). There are several types of Big Tech companies. Best known in the West are the infamous FAANGS (Facebook, Apple, Amazon, Netflix, Google). Other Big Tech giants include Microsoft, Big Tech Chinese and Russian counterparts, variants, and developments such as Tencent, Alibaba, Yandex and so on. The acronyms are changing as the names of the (holding) companies do (for instance, Alphabet in the case of Google and Meta in the case of Facebook) and other newcomers emerge. For instance, the first hectacorn became ByteDance, the parent company of Tik Tok. Big Tech companies typically employ a business model that involves them acting as ‘network orchestrators’ of a network of participants who co-create value through participation, collaboration and/or resource sharing. This is built around a transaction and/or technological platform that they control. The platform is characterized by scalability, fungibility (multiple possible applications), hence economies of scope. It is a proprietary asset – that is, controlled by the focal firm. The business model involves the capture of value that is co-created by taking advantage of network effects and complementary resources and capabilities of ecosystem players that are cultivated and orchestrated by the focal firm. The fungibility of the platform facilitates apparently unrelated diversifications like Amazon’s move from books to organic foods, distribution (e.g., parcel delivery) and many other products and services. The fungibility of the platform and the economies of scope that it entails can help facilitate economy-wide concentration, and create major Big Tech monopolies like the infamous FAANGS. In turn, this helps create a supporting ecosystem, including competitors. Like most firms, to capture value, Big Tech and the sharing economy firms need to co-create new value that they can then try to capture. Value can be co-created through the leverage of resources and complementarities between all economic and business actors, buyers, suppliers and even competitors (Pitelis and Teece, 2018). Co-creation helps increase the overall value and allows firms that are well positioned to capture more value than the total value they have helped co-create. Such differential value capture takes place when leakages towards focal firms from the value created by competitors exceed those of value created by them that

368  Handbook of industrial development are leaked to competitors. Accordingly, value co-creation becomes critical. However, for co-created value to be captured, it is important to build a proprietary appropriability strategy and apparatus. The platform itself is often a key part of this. Other key aspects include secrecy about the platform and business model, barriers to entry, and the leverage of an extensive array of other strategies that are highlighted in the strategic management literature, including building inimitable resources and capabilities, relatively impregnable bases (Penrose, [1959] 2009) and branding (Pitelis, 2009a). Value co-creation facilitates value capture by virtue of the fact that the very process of creating value can provide the value creator with knowledge, capabilities and first-mover advantages (Chandler, 1992). Because of its extensive potential scope of application, the existence of a platform entails intrinsic excess capacity that can act as a barrier to entry (Spence, 1977) and opportunity to expand and help alleviate conflict (Cyert and March, 1963; Pitelis, 2007). All these facilitate the leverage of scale, scope and the acquisition of market share and hence to monopoly as well as to ecosystem co-creation and to new competition. Value can be co-created through the mobilization of all socioeconomic, tangible and intangible resources – namely, capital, land, labour and knowledge/organization (Marshall [1910] 2013). The platform-based ‘sharing’ economy accordingly profits from value co-creation that leverages the resources and capabilities of its own as well as those of third parties. This entails a process of gradually socializing the value co-creation potential of socioeconomic resources (Pitelis, 1987), while maintaining control through the orchestrating function and proprietary control over a platform. A key attribute of many a Big Tech company relates to knowledge/organization and involves the harvesting and leveraging of data to provide targeted advertising, promotion and influencing services. Like all information (Shapiro and Varian, 1998), data is a key resource. Its use to target, influence and even manipulate provides a service to producers who wish to market their products and services and are willing to pay for the targeting, promotion and influencing service. In turn, the revenues from advertising help keep prices low, and regulators, still informed by anti-trust ideas that justify intervention only in cases of high consumer prices, at bay (Petit and Teece, 2021). It also helps increase compliance, sometimes through increased surveillance (Zuboff, 2019). It is acknowledged that the conflict between capital and labour that was emphasized by classical economists, helped incentivize labour-saving technological progress (Rosenberg, 1992). It can also incentivize organizational change and business model innovations that can involve the outsourcing of labour (Pitelis, 2009b). The outsourcing of labour is a significant business model innovation in that it turns former, extant or potential employees into self-employed small-scale suppliers. Labour-saving technologies and the outsourcing of labour are key aspects of the business model of platform-based Big Tech companies and a key source of competitive advantage. For instance, in the case of Uber’s hail-riding business, drivers are defined as self-employed entrepreneurs or ‘valuable partners’. This shrinks the ‘firm’ to the core of the employees who are strictly essential for the business. In turn, this makes competition by rivals who do not adopt similar models harder. This helps acquire market share and monopolize markets but also attracts imitators and other competitors. An important rather underexplored aspect of Big Tech, the sharing economy and unicorns relates to the role of finance and the overall financial ecosystem. That includes venture capitalists, angel investors, sovereign wealth funds, private equity firms and so on (Anthopoulos, Pitelis and Liakou, 2016). The ability of start-ups to get funding from the financial ecosystem

Platform oligopolies, anti-trust policy and sustainable development  369 helps facilitate speedy expansion without a short-term binding profit constraint. Arguably, moreover, this gradually helps change the key aim of such firms from profit maximization at any given point in time to the speedy acquisition of market share and the growth of the valuations of their shares. High-valued unicorns, decacorns and so on can be sustained through increasing market valuations based on the expectation of future profitability predicated on projections about scale, scope and eventual market dominance and high profitability achieved at high speed. The high speed of expansion in turn entails short, medium or even long-term losses. For example, in 2019, ten-year-old Uber lost US$3 billion, yet was valued at over US$60 billion at its initial public offering (IPO). Such losses can only be sustained through continuous financing through the financial ecosystem. A major implication of platform-based Big Tech is that cross-border expansion and hence the gradual monopolization of international markets need no longer require foreign direct investment (FDI). Despite its limitations as a development tool, FDI entails investment on the ground. Instead, internationalization through a platform entails transfer of co-created resources back to the firm (and in part to its home country) with less commitment to the host country and region. That changes almost entirely the nature of competition. Given the scale of requisite funding, local competitors often require comparable advanced financial ecosystems and/or government support. This helps transform competition between firms into competition between states. It brings state capitalism and geopolitics centre stage. As we have already noted, the business model of many platform-based Big Tech firms allows them to profit in a way that permits them to keep prices low. That has important implications on anti-trust. Influenced by the microeconomics/IO approach we discussed earlier, regulators can often focus on prices as a gauge of (perfect) competition. This is naive and outdated (Petit and Teece, 2021) but helps Big Tech stay under the radar. A new approach to anti-trust should consider all matters. In addition, as we have already suggested, the new anti-trust requires it to be placed within a wider context – that of global sustainable value creation (Mahoney, McGahan and Pitelis, 2009). We follow these points and their implications below. 6.2

A New Conceptual Framework for Anti-trust and Platform-based Oligopolies

Despite progress, both perspectives discussed in Section 2 suffer from limitations. First, they regard efficient resource allocation and/or innovation, respectively, as the near exclusive determinants of value creation. Second, they under-conceptualize the issue of the sustainability of the value creation process at the system-wide level. Third and related, they do not explore the link between value capture and the sustainability of value co-creation. Fourth, the particularities of the new landscape of platform-based oligopolies receive limited albeit growing attention but are not placed within the wider context of policies towards worldwide sustainable development. In what follows, we try to fill these gaps, and in so doing to offer new insights on anti-trust that are aligned to contemporary conditions. In a recent paper, Petit and Teece (2021) argued that anti-trust/competition policy matters can be seen as being separable from wider societal concerns. The authors stated that issues relating to democratic threats, the control of content, or free speech are analytically separable from monopoly power-related challenges and that anti-trust/competition policy is concerned with the latter, not the former. We find such a claim baffling, for four reasons. First, it was rarely if ever the case that societal concerns were not part of anti-trust; in fact, even the text-

370  Handbook of industrial development book microeconomics focus on prices is meant to be about (consumer) welfare. Second, as argued by Zingales (2017), economic power can lead to political power. Third, in the case of Big Tech, it is arguable that negative externalities such as the promotion of fake news is part and parcel of the organizational business model. The case for separability is even more tenuous in cases where the promotion of content that is deleterious to society confers a commercial advantage in terms of advertising sales and hence revenues and profits. Fourth, the focus of the said paper on dynamic competition is firmly based on the argument that the latter fosters intertemporal efficiency, hence economic welfare. Based on the above, we claim that the aim and scope of anti-trust should be designing and implementing policies towards competition and cooperation (co-opetition) that help foster sustainable economic development. Sustainable development entails intertemporal value and wealth creation and co-creation under conditions that permit its continuation ad infinitum. Environmental, economic and social degradation are inimical to sustainability, intra- and importantly inter-generationally. Accordingly, anti-trust policy should be firmly based on the theory of the impact of competition on sustainable value creation and co-creation. Theories on the nature of value comprise the classical economics perspective of Adam Smith, David Ricardo and Karl Marx, and the ‘neoclassical’ marginalist notion of ‘value’ of William Stanley Jevons, Karl Menger and others (see Dobb, 1973). The former attributes ‘value’ to the cost of production and more specifically to the socially necessary labour power expended to produce a commodity (‘labour theory of value’). The latter considers that value is the perceived ‘utility’ provided by a good to an economic agent. ‘Utility’, in turn, is affected by ‘scarcity’. The determinants of value and wealth creation were the key theme of Adam Smith. In his classic book on the wealth of nations, Smith (1776) attributed wealth creation in market economies to both the ‘visible hand’ of the firm and the ‘invisible hand’ of the market. Smith analysed a pin factory in Scotland and observed that specialization, the division of labour, teamwork and invention within it and such factories engender productivity and create value and wealth. The marvels of the ‘visible hand’ are realized through his famous ‘invisible hand’ – namely, the free interplay of demand and supply by economic agents in pursuit of their own interests. The invisible hand helps provide information, incentives, coordination, and to realize through exchange the value created at the level of production. Competition between firms helps establish what Smith called ‘natural’ prices. Restrictive practices by ‘people of the same trade’, however, could endanger this outcome and call for restraint and/or government intervention/regulation. In this classical tradition, international wealth creation and convergence could be furthered through international trade based on ‘comparative advantage’ – namely, each country specializing and trading in products and services where they had the lowest comparative disadvantage. This outcome, however, was predicated on the heroic and unrealistic assumption of the absence of increasing returns to scale. As we have already noted, in the textbook microeconomics tradition, the focus shifted from value creation in production and realization in markets to exchange relationships, subjective value and efficiency in allocating scarce resources. The aim and scope of economics became one of ‘economizing’ through rational choices between ends and scarce means that had alternative uses (Robbins, 1932). The efficient allocation of scarce resources entails both a static and an intertemporal dimension. Neoclassical economic theory has shown that the former can be achieved through perfectly competitive markets. However, intertemporal efficiency depends on innovation. Unlike static efficiency, perfect competition or perfect contestability

Platform oligopolies, anti-trust policy and sustainable development  371 need not lead to intertemporal efficiency. This is because they remove the incentive to innovate, which according to Schumpeter ([1942] 1987) was the (transient) ‘excess profits’ that they help provide. As already noted, for Baumol (1991), echoing Penrose ([1959] 2009) and for the RCE perspective more generally, the best type of market structure from the point of view of intertemporal efficiency is big business competition, not perfect competition. The ubiquitous presence of increasing returns, originally pointed to by Young (1928), suggests that big business competition is the norm. Despite the above, neoclassical economics and economists continued to argue that perfectly competitive markets and free trade can lead to sustainable value/wealth creation. This is reflected in Washington Consensus and post-Washington Consensus-type views (see Bailey et al., 2006). A problem with this reasoning is that it fails to discuss the role of innovation as a determinant of intertemporal value creation and to appreciate that wealth creation and economic performance include both a value creation and a value appropriation/capture element. It also fails to acknowledge that value capture can impact negatively on the sustainability of value creation and co-creation. The RCE approach improves upon the neoclassical economics one by focusing on entrepreneurship and innovation. However, it shares the other limitations of the neoclassical view we discussed above. It does not consider determinants of value creation other than innovation and it downplays the potentially important role of value capture in its link to value creation. Below we seek to synthesize and extend the resource allocation and resource creation views with an eye to informing a new contemporary anti-trust policy. As we have already noted, in market-assisted organizational economies (Simon, 1991), value is created primarily at the level of production and it is then realized in exchange by selling commodities in markets for a profit. Scarcity affects value, but so does the cost of production. The efficient use of scarce resources, most notably time, can be instrumental in increasing productivity. The (infra)structure of the firm (its organization, management, systems and routines), its strategy and governance, its technology and innovativeness, the quantity-quality and relations of its human (managers, entrepreneurs, labour) and non-human resources, as well as its ability to take advantage of unit cost economies (such as economies of scale, scope, learning, growth, transaction costs and external economies), are important co-determinants of productivity (Pitelis, 1998). They are affected by the external environment. This comprises the meso environment, the macro environment and the international environment. The meso environment includes the industry structure and conduct, and the consequent industry-wide ‘degree of monopoly’. The degree of monopoly serves to realize value by determining the price/cost margin of the industry (see Cowling, 1982). The meso environment also includes locational aspects and the regional milieu, including a region’s ‘social capital’ (see Putnam, 1993). The four determinants at the firm level are scalable to the meso level. Together and in their interrelationship with the ‘external meso environment’, determine productivity and value at the industry, sectoral and regional levels. Surrounding the micro and meso environments is the macro environment. This includes the macroeconomic policy mix and the nature and level of effective demand. It also includes the institutional context and the ‘governance mix’ – namely, the mixture of market, hierarchy and cooperation/hybrids in economic governance. The institutional environment provides sanctions and rewards, culture and attitudes and the overall ‘rules of the game’ of economic activity (North, 1981). The governance mix determines the degree of efficiency and effectiveness of the mode through which the economy operates. It represents the wider context within

372  Handbook of industrial development which firms, industries and ecosystems function, and helps determines the overall ‘size of the market’, hence the value that can be realized at any point in time. The determinants of value are also influenced by the international context. This is the sum of each nation’s determinants of value, their synergies, and the organizations and institutions of supranational governance. These impact upon the size of the global market, and the overall ability of the world to create value and wealth. The firm in market-assisted organizational economies plays an important role in value creation. Another important ‘actor’ is the government. It influences the institutional and macroeconomic context through laws, regulations and ‘leadership’. It can impact the meso environment through its anti-trust/competition, industrial and regulation policies and upon the macro environment through its macroeconomic policies. It can also impart upon the determinants of value creation through several other actions and policies such as education and health, the provision of national infrastructure, its innovation and cluster policies and its policies on ‘social capital’. Both the neoclassical economic and the RCE views pay limited attention to the fact that value creation need not automatically imply value capture and that in some cases the capture of value can impact upon the value creation and co-creation process. To capture value, firms but also individuals and nations can pursue several strategies. These can include both efficiency-enhancing and monopolistic, restrictive and collusive practices by firms and strategic trade policies by nations or blocks of nations such as the EU. The pursuit of value capture by one agent can impact another agent’s ability to further their objectives. This can undermine the sustainability of the value creation and co-creation process. It can be regarded as an ‘agency’ issue, which, however, is much more complex and wider than the traditional agencies considered by neoclassical economic scholars such as those between owners and shareholders and/or shareholders and managers. This entails multiple agencies, which, moreover, are structured hierarchically, between firms, nations and the world as a whole, and their various sub-units. By way of example, if one looks at the top management team (TMT) of a corporation as the ‘agent’ and the corporation as an entity that comprises all its stakeholders as the ‘principal’, it is conceivable that the pursuit of personal interests by the former can compromise those of the latter. This can be the case when the former pursues strategies that favour short-term, share valuation growth and personal compensation packages and perks, beyond those necessary to provide adequate incentives to pursue the interest of the corporation. Another potential layer is the corporation as the agent and the government as the principal. Firms can capture wealth as ‘rent’ through monopolistic and restrictive practices. A high degree of market power, however, can reduce incentives for innovation, productivity and value creation. In this context, government intervention can be necessary. Sustainable value creation requires anti-trust and competition and regulation policies that thwart the abuse of monopoly power, while allowing for an innovation-inducing ‘degree of monopoly’. This is a balancing act that requires smart government and smart policy. This can be in short supply. Good governance is a public good that like other public goods can be undersupplied. At a third layer, nations can be seen as the agents. They can try to capture value through strategic trade and other nation-centred policies that can harm the process of global value co-creation. The aim of the ‘global community’, seen as the ‘principal’, should be to incentivize governments to adopt policies that enhance global productivity and value/wealth creation. For instance, governments of developed economies should refrain from policies that restrain trade. However, the international community should tolerate similar policies by developing

Platform oligopolies, anti-trust policy and sustainable development  373 countries when these foster infant firms and industries that can eventually bring about market and business and ecosystem creation, as well as higher competition and improved innovation and productivity. In the absence of perfect and full global knowledge and a benevolent and enlightened global monitor with power to implement and impose, as well as the undesirability of such a concentrated power, pluralism and diversity become the key to sustainability. Several organizations and institutions such as the family, the church, NGOs, cooperative firms and state-owned enterprises (SOEs), in their interactions, can help foster the ability of firms to improve productivity and value/wealth creation. The specialization and division of labour of alternative institutions and organizations based on their respective comparative capabilities in production, exchange, legitimacy, ideology and culture can help identify the institutional and organizational configurations and conducts that foster productivity and value. Competition and cooperation, self-interest and altruism, big businesses, and smaller cooperating firms (such as in clusters and business ecosystems), capitalist and cooperative, as well as state-owned firms, in their interaction and appropriate mix, can help impact positively productivity/value enhancement. The appropriate mix is a learning process and can be found through experimentation (Rosenberg, 1992). As we have already noted, the sustainability of value creation entails environmental, economic and social aspects. Excessive inequities in distribution, the abuse of the environment, and the exodus of educated human resources from some countries can thwart a country’s ability to create value. The nature, degree and location of competition can impact all these. In such cases, governments can employ market prices and other policies to ensure that the negative externalities caused by economic agents because of reduced or unfair competition are internalized by the said agents. We appreciate that the proposed scope of anti-trust can appear to some to be very wide. This, however, is precisely because of the importance of competition, which Penrose ([1959] 2009) had defined as ‘God and the devil’ – the former because of its benefits, the latter because many companies feel that it is through its restriction that they can capture more value. An approximate way of bringing about sustainable value creation-fostering competition is through the free interplay, pluralism and diversity of institutions, organizations, individuals, ideas, cultures, religions, norms, customs and civilizations. This is because each of these actors, organizations and institutions can serve as a ‘steward’ and/or ‘monitor’ of each other and hence also as a guard against any restrictive practices and abuse of power. At the same time, however, it is important that the process is ‘managed’, ‘guided’ and ‘moulded’ through informed, competent and empowered agency and in a way that marries democracy and economic value creation. This brings to centre stage the issue of ‘power structures’, regulatory capture and conflicts of interest and supranational ‘governance’ to the discussion of anti-trust. The key question is about the types of power structures and governance that best foster sustainable value creation. Corporate, public and supranational governance contribute to sustainable value and wealth creation when both internal and external controls are in place. These include intranational and international incentives and sanctions. They entail the elimination of corruption at all levels – intra-firm, intra-country, between host governments and multinationals, and internationally. And they presuppose a degree of trust, social capital and morality. An exclusive focus on self-interest and profit can be the strongest foe of sustainability.

374  Handbook of industrial development As we have already argued, competition and cooperation (co-opetition) impact upon the determinants of value creation. At the same time, they can help create firm-specific advantages that can be used to restrict competition. Co-opetition policy derives from the need to take advantage of the beneficial effects of competition and cooperation on productivity and value creation. Inter-firm cooperation strategies, such as firm clusters and business ecosystems can enhance productivity and value and should be encouraged. Forms of cooperation that do not enhance value, such as price collusion, should be vigorously discouraged. The same applies to other restrictive business practices. Mergers and acquisitions can enhance value but can also increase market power, which can eventually stifle incentives for innovation, productivity and value creation. Pluralism and diversity should be encouraged because they provide information, benchmarks and comparators. Institutional and regulatory changes that foster productivity enhancing culture are of the essence. Anti-trust and competition policies should also be compatible with wider macroeconomic and other policies, and supported by a facilitatory institutional context (North, 1981). Institutions, culture and ideology all play a role. Governments can be a catalyst of institutional change because they possess a legal monopoly of force and the ability to legislate and regulate. A facilitatory institutional framework is critical for competition and hence for anti-trust policy. The neoclassical economics ‘market failure’ theory of the state takes the institutional context as given. The possibility to vary it implies the possibility for a proactive role for the state. It suggests that governments should intervene not only when markets fail but, rather, legislate and regulate proactively, albeit not unnecessarily, so that markets, firms and the state itself are created and fail less, hence contributing to value co-creation. This also involves guarding against government failures, conflicts of interest and regulatory capture. While pluralism and diversity through mutual monitoring help, they are not a panacea. A degree of enlightenment, long-term thinking, self-restraint and altruism, especially towards the future generations, are also of the essence. To summarize, we have argued that anti-trust policy should be placed within a broader conceptual framework that accounts for the role of competition and cooperation on sustainable value creation. In our framework, a firm anti-trust policy that discourages the acquisition and exploitation of market dominance is maintained, strengthened and extended to account for embedded and sustainability-thwarting ‘power structures’ by individuals, nations and groups of nations. Our discussion of the role of competition and anti-trust policy on value creation and capture, the role of embedded power structures, conflicts of interest and the hierarchy of agencies goes further than extant views. It places centre stage sustainable value co-creation and its foes. It also places centre stage diversity, stewardship and mutual monitoring alongside self-restraint. Placing anti-trust within a wide context is important but it still fails to account for the specificities and particularities of platform-based competition and cooperation. This introduces several important changes. First intra- and international oligopoly can be achieved at a much faster rate and without the necessary use of traditional FDI. Platforms can employ more market-based internationalization strategies to expand cross-border. This entails less commitment and investment to localities. It also renders regulation harder, hence the perceived need by policymakers to build regional and national alternatives. This in turn can bring about protectionism in the form of national Big Tech champions as the chosen alternatives of some states, such as China and Russia. Protectionism can undermine competition and innovation and is comparatively inferior in terms of global value co-creation. It can also serve to transfer

Platform oligopolies, anti-trust policy and sustainable development  375 extant and co-created resources from worse-off to better-off countries, firms and localities, just as strategic trade policies do. The business model of many a platform firm is based on profiting from advertising, not a price charged for their services. This helps create several negative externalities that are currently in vogue. They include intrusions of privacy, attention seeking, fake news, addiction and other psychic costs to users. The leverage and even share and sale of personal data is an externality that brings revenue to firms that helps them keep the apparent prices low. But the real prices that account for the negative externalities can very high. In this context, a focus on price is highly misplaced (Khan, 2019). Regulation and levelling the playing field should entail the internalization by the firms of the negative externalities. This does not happen or is delayed partly because of the opacity of the business model and operations, partly because of their market and political power, hence lobbying and regulatory capture, and partly because of rents generated by such firms for their country of origin. The leverage of the tax system to their advantage also allows such firms to contribute rather tiny parts of their often huge revenues. There are numerous discussions and specific proposals on how to deal with platform-based monopolization. Here we focus on the general principles. These remain mostly self-restraint, rules, regulation, competition/co-opetition and level playing field. In theory, the externalities caused by platform-based Big Tech can be priced. And so can the implicit or shadow prices consumers pay through the provision and use of their data. These can help estimate true, full or shadow prices and hence assess the degree of their monopoly power and any abuse. A minimum threshold of taxation should be applied nationally, and minimums agreed internationally as to revenues, and/or based on best available estimates of profits measured in comparable ways to other firms and considering information on profitability claims made to extant and potential shareholders, not merely on accounting profits that can benefit from transfer prices and tax avoidance. Negative externalities should be internalized, for instance, through taxation or outright bans, depending on their severity. The use of tax savings to fund anti-competitive acquisitions and anti-competitive acquisitions more generally, should be deterred. Such activities include so-called ‘shoot-out’ ones (when the aim is to limit the rise of a potential competitor) and when there are apparently more synergies to be leveraged if the target was acquired by a competitor. The latter is prima facie evidence that the acquisition aims not at benefiting the two parties but at damaging a third one, the acquisition of the target by which the third party might create more synergies with it and hence more intense competition for the acquirer. Conflicts of interest that are embedded in the business model should be within the scope of anti-trust policy. For instance, a business model that is based on advertising messages, alongside evidence that negative messages attract more attention, hence promoted or tolerated despite their pernicious externalities, should be prima facie evidence that any failure to police and remove them serves a commercial purpose, hence goes beyond mere negligence. Negligence and failure to exercise duty of care can be aspects to consider. Issues of built-in obsolescence should be looked at carefully and potentially outlawed because of their deleterious impact on the environment. In many cases, fair and proportional fines for abuses and/or compensation for any deleterious effects that are provable from the promotion of fake news could be enough to deter any such practices. Current such moves by the EU, Russia and a number of developing countries are cases in point. The introduction of regulation and often its mere threat can also serve a disciplinary function. In certain cases, the breaking up of monopolies can be necessary in

376  Handbook of industrial development promoting competition, innovation and value co-creation, as has been the case throughout. New firm creation and growth should be encouraged. A culture of rewarding good corporate citizens on the part of the public should be encouraged and supported. Subject to dealing with regulatory capture concerns, an international competition and regulation agency along the lines of the WTO might be able to address in part the issues of embedded power structures, alongside the hierarchy of agencies, by providing rules and a forum for debate, mediation and conflict resolution. But they are not a panacea and are also subject to potential capture. None of the above should ignore the high value that is also generated via platform firms. Many a sceptic struggles not to employ the services of Big Tech, have benefited, and continue to benefit from their existence. It is important not to punish success but to reward it in a way that allows more success by others, who should in turn also be rewarded. This is a balancing act that requires skill and smart policies as well as a degree of mutual understanding and trust. As a rule of thumb, the activities by Big Tech firms that are aimed at exploration should be supported and rewarded. Those that aim to exploit should be treated with much more scepticism. An argument can be made for life cycle approach to regulation of the activities of Big Tech. At the early exploration stages, regulation can be lenient. At the later exploitation phase, less so. An advantage of this approach is that because it applies to activities, not firms or sectors, it can imply tough measures in an activity that focuses on exploitation (such as the new i-next, which is hardly different from i-before and/or to a new drug that is no different from the old one) rather than to a new innovative activity that helps co-create new markets and value. In several cases, the market and self-restraint can do the trick. But assuming Big Tech is somehow different and better than earlier monopolies is naive. At the very least, this needs arguing and demonstrating. Identifying fierce oligopolistic competition is not enough, and nor is the argument that comparisons based on non-existing ideal worlds are fruitless. We possess sufficient theory and evidence in favour of the benefits of competition and new firm creation to feel confident in stating that the onus of proof for the claim that policies to foster competition are not required should lie with proponents of such claims. In his book, The Road to Serfdom, Hayek (1944) argued that plans are no good, but planning for competition is of the essence. Planning for and ensuring fair and workable competition and cooperation, alongside the other policies suggested in this chapter, is a key to sustainable global value creation.

7

CONCLUSIONS, LIMITATIONS, NEW RESEARCH AVENUES

Anti-trust and regulation/policies should seek to foster the impact of co-opetition on worldwide sustainable development through innovation. It entails thwarting anti-competitive practices and fostering new firm creation and growth, alongside new market and business ecosystem creation and co-creation (Pitelis and Teece, 2010). Business, public and supranational entities should refrain and be discouraged from pursuing restrictive practices and ‘strategic trade’ policies. Pluralism and diversity, through the co-creation and the empowerment of the ‘polity’, should be encouraged to engender mutual stewardship and monitoring. This, in practical terms, aims at the elimination of ‘regulatory capture’, rent seeking and corruption by all actors, especially those with power to exercise it. Enlightened self-interest, pluralism and diversity, business networks and regional ecosystems can help engender ‘mutual stewardship’

Platform oligopolies, anti-trust policy and sustainable development  377 and serve as an approximation to governance for sustainable development (Pitelis, 2013). But they are not a panacea. Platform-based monopolies should be subjected to the same rules, regulations and competition as all other firms. The true prices charged for their services should be calculated, and their negative externalities internalized, nationally and internationally. Conflicts of interest that are embedded within the business model cannot be ignored. Negligence and duty of care considerations should be part of the debate. The internalization of external diseconomies should be a key benchmark to assess whether and to what extent value is added. In many cases, once environmental, economic and social externalities are considered, net value-added may be very small or negative. In other cases, it may not. In the latter case, the value-adding innovations and activities of platforms and other firms should be encouraged and rewarded. Smart anti-trust and cooperation policies are of the essence to achieve that. This also requires international coordination and could lead to and be led by an accountable genuinely independent and monitored international anti-trust/regulatory agency with the remit of fostering global sustainable value creation. Our suggestions depart from extant neoclassical theory and its focus on static competition, and focus instead on dynamic competition, cooperation and innovation. This places our contribution firmly within the camp of the RCE value creation perspective, but it goes further. This is because we consider and account for the limitations of the said perspective and importantly because we place our suggestions within a broader conceptual framework – that of the impact of co-opetition on global sustainable value creation (Mahoney et al., 2009). A limitation of our chapter is that it has painted a rather broad picture of the issues pertaining to Big Tech. Besides space limitations, this was because we wanted to focus on what we see as key issues and offer a broader perspective. More detailed accounts can be found in Petit and Teece (2021) and Pitelis (2022). The key limitation of this chapter, however, is that anti-trust requires limited government failure. Unfortunately, this cannot be assumed. Governments like markets fail and the two types of failures can be linked, for instance, when there is regulatory capture and/or when misguided policies cause market failures. The possibility of international organizational failure is very distinct too and causes serious challenges to the implementability of our suggested framework. Ultimately, strengthening the commons through bottom-up but also top-bottom-up policies and actions is a key prerequisite for effective anti-trust. Exploring the conditions for this to become possible is a key future research avenue. Early scholars have considered cooperatives (Berti and Pitelis, 2022), and/or clusters and business ecosystems (Pitelis et al., 2006), alongside capitalist firms as a means of a more pluralistic governance structure. Going forward, we should also consider how the commons can be strengthened and the political arena become more contestable without becoming capturable, accountable and arm’s length. Simple rules like no politician getting a private sector job for let’s say five years after entering politics can go a long way. There is much to be researched there that also calls for interdisciplinary work.

ACKNOWLEDGEMENTS We are grateful to numerous colleagues for comments and discussion and to Sam Li for research assistance. Errors are ours.

378  Handbook of industrial development

REFERENCES Anthopoulos, I., Pitelis, C. and Liakou, C. 2016. The nature, performance and economic impact of sovereign wealth fund. Working Paper Series, No. 135. European Commission FESSUD project. Arrow, K.J. 1971. Essays in the Theory of Risk-Bearing. Amsterdam: North-Holland. Audretsch, D.B. 1998. Agglomeration and the location of innovative activity. Oxford Review of Economic Policy, 14(2), 18–30. Bailey, D., Cowling, K. and Tomlinson, P.R. 2015. An Industrial Strategy for UK Cities. Oxford: Oxford University Press. Bailey, D., De Propris, L., Sugden, R. and Wilson, J. 2006. Public policy for economic competitiveness: an analytical framework and a research agenda. International Review of Applied Economics, 20(5), 555–72. Bailey, D., Pitelis, C.N. and Tomlinson, P.R. 2020. Strategic management and regional industrial strategy: cross-fertilization to mutual advantage. Regional Studies, 54(5), 647–59. Bailey, D. and Tomlinson, P.R. 2017. Back to the future? UK industrial policy after the great financial crisis. In P. Arestis and M. Sawyer (eds), Economic Policies Since the Global Financial Crisis. London: Palgrave Macmillan, pp. vii–xvii. Baumol, W.J. 1991. Perfect Markets and Easy Virtue Business. Oxford: Blackwell. Berti, M. and Pitelis, C.N. 2022. Open team production, the new cooperative firm, and hybrid advantage. Academy of Management Review, 47(2), https://​doi​.org/​10​.5465/​amr​.2019​.0416. Best, M.H. 1990. The New Competition: Institutions of Industrial Restructuring. Cambridge, MA: Harvard University Press. Capello, R. and Faggian, A. 2005. Collective learning and relational capital in local innovation processes. Regional Studies, 39(1), 75–87. Chandler, A.D. 1992. Organizational capabilities and the economic history of the industrial enterprise. Journal of Economic Perspectives, 6(3), 79–100. Coase, R.H. 1937. The nature of the firm. Economica, 4(16), 386–405. Cowling, K. 1982. Monopoly Capitalism. London: Palgrave Macmillan. Cowling, K. and Mueller, D.C. 1978. The social costs of monopoly power. The Economic Journal, 88(352), 727–48. Cowling, K. and Tomlinson, P.R. 2011. Post the ‘Washington Consensus’: economic governance and industrial strategies for the twenty-first century. Cambridge Journal of Economics, 35(5), 831–52. Cyert, R.M. and March, J.G. 1963. A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall. Dobb, M.H. 1973. Theories of Value and Distribution since Adam Smith: Ideology and Economic Theory. Cambridge, UK: Cambridge University Press. Driffield, N., Love, J.H. and Menghinello, S. 2010. The multinational enterprise as a source of international knowledge flows: direct evidence from Italy. Journal of International Business Studies, 41(2), 350–59. Dunning, J.H. 1993. Internationalizing Porter’s diamond. MIR: Management International Review, 33(2), 7–15. Dunning, J.H. and Pitelis, C.N. 2008. Stephen Hymer’s contribution to international business scholarship: an assessment and extension. Journal of International Business Studies, 39(1), 167–76. Findlay, R. 1978. Relative backwardness, direct foreign investment, and the transfer of technology: a simple dynamic model. The Quarterly Journal of Economics, 92(1), 1–16. Foray, D. 2013. The economic fundamentals of smart specialisation. Economía, 83(2), 55–82. Foray, D. 2015. Smart Specialisation: Opportunities and Challenges for Regional Innovation Policy. London: Routledge. Freeman, C. 1995. The ‘National System of Innovation’ in historical perspective. Cambridge Journal of Economics, 19, 5–24. Furman, J.L., Porter, M.E. and Stern, S. 2002. The determinants of national innovative capacity. Research Policy, 31(6), 899–933. Gerschenkron, A. 1962. Economic Backwardness in Historical Perspective: A Book of Essays. Cambridge, MA: Harvard University Press. Hall, P.A. and Soskice, D. 2001. Varieties of Capitalism: The Institutional Foundations of Comparative Advantage. Oxford: Oxford University Press.

Platform oligopolies, anti-trust policy and sustainable development  379 Hausmann, R. and Rodrik, D. 2003. Economic development as self-discovery. Journal of Development Economics, 72(2), 603–33. Hayek, F.A. 1944. The Road to Serfdom. New York: Routledge. Hicks, D.A. 1997. The Inequality-adjusted Human Development Index: a constructive proposal. World Development, 8(25), 1283–98. Hymer, S.H. 1970. The efficiency (contradictions) of multinational corporations. The American Economic Review, 60(2), 441–8. Kaldor, N. 1972. The irrelevance of equilibrium economics. The Economic Journal, 82(328), 1237–55. Khan, L.M. 2019. The separation of platforms and commerce. Columbia Law Review, 119(4), 973–1098. Krugman, P. 1987. Is free trade passé? Journal of Economic Perspectives, 1(2), 131–44. Krugman, P. 1989. Economic integration in Europe: some conceptual issues. In A. Jacquemin and A. Sapir (eds), The European Internal Market: Trade and Competition. Oxford: Oxford University Press, pp. 117–40. Krugman, P. 1991. Increasing returns and economic geography. The Journal of Political Economy, 99(3), 483–99. Krugman, P. 1992. Does the new trade theory require a new trade policy? World Economy, 15(4), 423–42. Lin, J.Y. 2011. Demystifying the Chinese Economy. Cambridge, UK: Cambridge University Press. Lin, J. and Chang, H.-J. 2009. Should industrial policy in developing countries conform to comparative advantage or defy it? A debate between Justin Lin and Ha-Joon Chang. Development Policy Review, 27(5), 483–502. Lundvall, B.Å. 2007. National innovation systems – analytical concept and development tool. Industry and Innovation, 14(1), 95–119. Mahoney, J.T., McGahan, A.M. and Pitelis, C.N. 2009. Perspective – the interdependence of private and public interests. Organization Science, 20(6), 1034–52. Malerba, F. 2005. Sectoral systems of innovation: a framework for linking innovation to the knowledge base, structure and dynamics of sectors. Economics of Innovation and New Technology, 14(1–2), 63–82. Marshall, A. [1910] 2013. Principles of Economics (Palgrave Classics in Economics). London: Palgrave Macmillan. Marx, K. 1959. Capital. London: Lawrence & Wishart. Maskell, P. and Malmberg, A. 1999. Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23(2), 167–85. McCann, P. and Ortega-Argilés, R. 2015. Smart specialization, regional growth and applications to European Union cohesion policy. Regional Studies, 49(8), 1291–302. Narula, R. and Driffield, N. 2012. Does FDI cause development? The ambiguity of the evidence and why it matters. The European Journal of Development Research, 24(1), 1–7. Nelson, R.R. 1995. Co-evolution of industry structure, technology and supporting institutions, and the making of comparative advantage. International Journal of the Economics of Business, 2(2), 171–84. Nelson, R.R. and Winter, S.G. 1982. An Evolutionary Theory of Economic Change. Cambridge, MA: Harvard University Press. North, D.C. 1981. Structure and Change in Economic History. New York: W.W. Norton & Company. North, D.C. 1994. Economic performance through time. The American Economic Review, 84(3), 359–68. Penrose, E.T. [1959] 2009. The Theory of the Growth of the Firm (4th edition) (with an introduction by C. Pitelis). Oxford: Oxford University Press. Petit, N. and Teece, D.J. 2021. Innovating big tech firms and competition policy: favoring dynamic over static competition. Industrial and Corporate Change, 30(5), 1168–98. Piteli, E.E.N. 2017. Foreign direct investment (FDI) and economic development. In M. Augier and D.J. Teece (eds), The Palgrave Encyclopedia of Strategic Management. London: Palgrave Macmillan, pp. 583–9. Piteli, E.E.N., Buckley, P.J. and Kafouros, M. 2019. Do remittances to emerging countries improve their economic development? Understanding the contingent role of culture. Journal of International Management, 25(4), Article 100675.

380  Handbook of industrial development Piteli, E.E.N., Kafouros, M. and Pitelis, C.N. 2021. Follow the people and the money: effects of inward FDI on migrant remittances and the contingent role of new firm creation and institutional infrastructure in emerging economies. Journal of World Business, 56(2), Article 101178. Pitelis, C.N. 1984. Corporate control, social choice and financial capital accumulation. PhD thesis, University of Warwick. Pitelis, C.N. 1987. Corporate Capital: Control, Ownership, Saving and Crisis: Cambridge, UK: University of Cambridge Press. Pitelis, C.N. 1994. Industrial strategy: for Britain, in Europe and the world. Journal of Economic Studies, 21(5), 3–92. Pitelis, C.N. 1998. Productivity, competitiveness and convergence in the European economy: supply-side considerations. Contributions to Political Economy, 17(1), 1–20. Pitelis, C.N. 2001. Industrial strategy. In M. Warner (ed.), International Encyclopedia of Business & Management. London: Routledge, pp. 2026–44. Pitelis, C.N. 2007. A behavioral resource-based view of the firm: the synergy of Cyert and March (1963) and Penrose (1959). Organization Science, 18(3), 478–90. Pitelis, C.N. 2009a. The co-evolution of organizational value capture, value creation and sustainable advantage. Organization Studies, 30(10), 1115–39. Pitelis, C.N. 2009b. The sustainable competitive advantage and catching-up of nations: FDI, clusters and the liability (asset) of smallness. Management International Review, 1(49), 95–120. Pitelis, C.N. 2012. Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, 21(6), 1359–88. Pitelis, C.N. 2013. Towards a more ‘ethically correct’ governance for economic sustainability. Journal of Business Ethics, 3(118), 655–65. Pitelis, C.N. 2022. Why unicorns exist? On Penrose and Hymer and prediction. Strategic Management Review, forthcoming. Pitelis, C.N., Sugden, R. and Wilson, J.R. 2006. Clusters and Globalisation: The Development of Urban and Regional Economies. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Pitelis, C.N. and Teece, D.J. 2010. Cross-border market co-creation, dynamic capabilities and the entrepreneurial theory of the multinational enterprise. Industrial and Corporate Change, 19(4), 1247–70. Pitelis, C.N. and Teece, D.J. 2018. The new MNE: ‘orchestration’ theory as envelope of ‘internalisation’ theory. Management International Review, 58(4), 523–39. Porter, M.E. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press. Porter, M.E. 1990. The Competitive Advantage of Nations. New York: Free Press. Porter, M.E. 1998. On Competition. Boston, MA: Harvard Business Press. Putnam, R.D. 1993. Making Democracy Work: Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press. Ricardo, D. 1817. Principles of Political Economy and Taxation. London: G. Bell and Sons. Richardson, G.B. 1972. The organisation of industry. The Economic Journal, 82(327), 883–96. Robbins, L. 1932. An Essay on the Nature and Significance of Economic Science. London: Macmillan. Rodrik, D. 2004. Industrial policy for the 21st century. HKS Working Paper, No. RWP04-047. Harvard Kennedy School. Rodrik, D. 2005. Growth strategies. In P. Aghion and S. Durlauf (eds), Handbook of Economic Growth, Volume 1B. Amsterdam: North-Holland, pp. 968–1014. Rodrik, D. 2008. One Economics, Many Recipes. Princeton, NJ: Princeton University Press. Romer, P.M. 1986. Increasing returns and long-run growth. The Journal of Political Economy, 94(5), 1002–37. Rosenberg, N. 1992. Economic experiments. Industrial and Corporate Change, 1(1), 181–203. Rugman, A.M. and Verbeke, A. 1993. Foreign subsidiaries and multinational strategic management: an extension and correction of Porter’s single diamond framework. Management International Review, 33(2), 71–84. Samuelson, P.A. 1962. The gains from international trade once again. The Economic Journal, 72(288), 820–29. Scherer, F.M. and Ross, D. 1990. Industrial Market Structure and Economic Performance. Boston, MA: Houghton Mifflin.

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22. States of innovation: how the state shapes production transformation Antonio Andreoni and Rainer Kattel

1 INTRODUCTION Historically, industrialization and state formation have been linked by a mutually constitutive relationship. Industrialization – a process of continuous change in the productive structure of the economy and extent of the market – has been shaped by the state (or lack thereof) via a range of policies – in today’s terminology, industrial and innovation policies. The formation of state institutions, governance and bureaucracy structures have played a key role. By designing, implementing and enforcing state policies, these structures have constructed and mediated the continuously evolving relationship between state, industry and markets. Equally, industrialization has shaped the political economy of the state, its internal structural formation and policymaking. Thus, state and industry are linked by a mutually constitutive, historically path-dependent and dynamic relationship (Andreoni and Chang, 2017). The majority of today’s industrialized nations took their first steps up the industrialization ladder in the early nineteenth century. Today’s industrialized countries have been using industrial policy more or less consistently since then (in some cases even earlier; Reinert, 2008). Britain was the first and only early industrializer, as it had started its industrial development in late eighteenth century. In a few decades, Britain had acquired a dominant position, given its aggressive imperialism and the limitations imposed on the policy space of other nations – that is, unequal treaties (Chang, 2002). The industrialization pathways of so-called late industrializers can be traced back to the mid-nineteenth century. Germany, the United States, France and Japan, with some delays, joined, and indeed leapfrogged Britain with a series of technological and industrial innovations – for example, in heavy industries such as chemicals (Perez, 2001). These technological advancements and innovations were coupled with the development of new institutions in areas like banking. For instance, in 1853, Japan was forced to open up its economy and, as a result, its feudal political system collapsed. The so-called Meiji Restoration of 1868 began a modernization phase for the country, followed by a fast process of early industrialization, which made Japan one of the so-called ‘Big Five’ nations by the end of the World War I (Ohno, 2013). Different from the other early industrializers, however, Japan regained its policy space only in 1911 with the end of unequal treaties. The recent industrializers (or late developers in Amsden’s 1989 terminology) – such as South Korea, China, Brazil and Malaysia – include several of today’s middle- and upper-middle income countries that started a sustained industrialization journey only during the second half of the twentieth century. They industrialized during the last phase of the global policy regime established after World War II, governed, among other institutions, by the General Agreement on Tariffs and Trade (GATT). However, while Brazil has made use of industrial policy intermittently since then – with a significant retreat during the 1980s and 1990s – South Korea and 382

States of innovation: how the state shapes production transformation  383 China in particular have continuously relied on and upgraded their industrial policies since the 1970s. Following in their footsteps, a group of emerging industrializers includes a number of recently graduated middle-income countries – Vietnam and Indonesia, for example – and low-income countries, especially in Africa – Ethiopia being perhaps the strongest case. In this chapter, we discuss three historical forms of the state – ‘developmental’, ‘entrepreneurial’ and ‘innovation-driven’ state – and focus on the evolution of this state–industrialization relationship. Comparative historical cases – that is, Germany, US and China – are presented to flesh out different configurations of ‘states of innovation’, as well as evolution in policy framing, instruments and challenges.

2

INDUSTRIALIZATION AS STRUCTURAL TRANSFORMATION: CO-EVOLVING DYNAMICS OF CHANGE

Industrialization is a structural transformation process involving changes in the sectoral composition of the economy (Kuznets, 1973). A country’s economy is composed of different sectors, each including several sub-sectors. Sectors (and sub-sectors as their components) are linked to each other by a set of interdependent input–output relationships determining a country’s unique economic structure (Andreoni and Scazzieri, 2014; Pasinetti, 2007). Other types of structural interdependencies such as technological linkages also link different sectors and sub-sectors of the economy. Industrialization is thus about a change in the sectoral composition of the economy – measured in terms of value addition or employment, or both (Reinert, 1995) – but it is also about evolving changes in the structural interdependencies linking sectors of the economy (Andreoni and Chang, 2019; Hirschman, 1977). The industrialization journey of the different groups of countries – early, late, recent and emerging – presents multiple differences across groups, but also a number of similarities between groups – differences because, depending on when they started their industrialization journey, they had a different policy space delineated by varied international political economy and rules (Wade, 2003), but also faced a dominant industrial paradigm (Perez, 2001) that was different; similarities because they had to go through a similar sequence of industrialization steps, and because each faced similar types of industrialization challenges in transforming their economies. Moreover, they all faced similar types of state capacity formation and industrial policy governance challenges in driving industrialization at early, intermediate and more advanced stages of development. Finally, all countries have gone through initial pre-industrial phases in which state building, resource mobilization and macroeconomic stabilization in an open economy were critical in preparing industrialization. It is also important to note that many countries attempt industrialization but fail to do so, or experience premature deindustrialization or partial industrialization (Andreoni, Mondliwa et al., 2021; Reinert and Kattel, 2004). Countries in the former Soviet Union and Latin America serve as the most recent examples of such halted and indeed backwards dynamics. While the reasons for such dynamics are often unique, there are common challenges around constrained policy space provided by the so-called neoliberal Washington Consensus, weak policy implementation capacities despite emulating and copying policies from advanced economies, and outright state formation failures.

384  Handbook of industrial development Building on Andreoni and Tregenna (2020) we identify five different types of industrialization challenges that are common across the experience of late, recent and emerging industrializers. These challenges are related to different steps in the industrialization ladder, from initial forms of integration into the regional and global economy to more significant transformation of the domestic production-technology base and, with it, the capability to compete in innovation. First, all countries face the challenge of breaking into the global economy, especially at early stages of their industrialization when access to technologies and external demand are paramount. This has become an increasingly important challenge. In their analysis of the shifting patterns of manufacturing internationally, Haraguchi, Cheng and Smeets (2017) found that the global industrial sectors have become increasingly concentrated. The G7 countries no longer command the same high share of global manufacturing as was previously the case, yet their share remains high, and the new successful entrants – China in particular – have gained significant market shares. These countries have erected several entry barriers, including developing global-scale economies, international and domestic institutions and capabilities for technological development and innovation. The emergence of major national champions and multinational companies operating globally has also introduced new forms of direct and indirect (via global supply chains) competition in middle-income countries’ domestic markets. Such competitive environments can lead to asymmetrical integration into global markets, whereby only a specific, typically low-value-added segment of a value chain emerges in a developing economy without wider domestic linkages and impact on employment and wage (Reinert and Kattel, 2004). This persistent concentration and compression in global manufacturing – both at the country and sectoral levels – have made it very difficult for the other countries to break into low, medium- and high-tech activities, respectively. Second, integration into regional and global value chains (R&GVCs) has been seen as a pathway for industrialization. By linking up into GVCs, business enterprises have the opportunity to move to more profitable and/or technologically sophisticated capital and skills-intensive economic activities – higher value-creation potential – and capture the value created from them. Companies can specialize in specific production tasks or components, preferably ‘high-value niches’, while avoiding the build-up of entire vertically integrated industrial sectors or blocks of industries (Milberg and Winkler, 2013). The idea of a selective form of specialization in tasks, driven by capturing value opportunities, might encourage companies to upgrade incrementally towards activities such as research and development (R&D), design, and downstream post-sale services. First-tier suppliers and original equipment manufacturers (OEMs) in low- and middle-income countries, however, face multiple challenges in linking up to R&GVCs, especially moving into more technologically sophisticated segments. First, focusing on the production of low-value-added parts and components does not automatically lead to the upgrading of domestic technological capabilities, especially given the endogenous asymmetries characterizing GVCs (Chang and Andreoni, 2020) and the higher capability threshold that companies must reach to engage with digital production technologies (Andreoni, Chang and Labrunie, 2021). Moreover, in a number of cases, middle-income countries that have attempted to integrate globally have also ended up ‘delinking domestically’ and hollowing out the domestic manufacturing sector. Third, in contrast, by linking up to international companies and system integrators while ‘linking back’ to local producers and local supply chains – local production system development – domestic companies can capture international demand and learn from exporting. South Korea and Taiwan between 1970 and 1990, and China in the 1980s and 1990s, all started their

States of innovation: how the state shapes production transformation  385 industrialization by linking (backwards) to global supply chains and adding value (forwards) in electronics and other industries, starting in particular from those characterized by short technology cycles (Lee, 2013). With the expansion of the local production system, more opportunities for backward integration also open up, as domestic companies start importing more intermediate goods while diversifying their export baskets. Over the last two decades, a very small number of middle-income countries have been successful in linking up while linking back. That is, only a few of them have managed to involve OEMs, and first-, secondand third-tier domestically located companies in value-addition processes. There are several reasons why very few countries managed to overcome the linking-back challenge. These include the need to diversify the productive capabilities base of the economy, develop a wide range of technical, production and organizational competences and build several specialized institutions, including technology and research centres, universities and development banks. Fourth, to link up and back successfully, countries that have reached a middle-income status must address a fundamental problem of technological upgrading. And they must do that fast enough to overcome the so-called ‘Red Queen effect’ – that is, the fact that ‘middle-income countries have to move to innovation-based growth more quickly, just to stay in the same place, let alone move up’ (Kang and Paus, 2019, p. 653). Sectoral value chains are based on specific combinations of complementary technological capabilities – that is, technology platforms – required to execute tasks in the different stages of the chain. Technology platforms underpin the production processes of closely related industrial sectors, as well as different product-value segments within the same industrial sector. Keeping pace with technical change effectively might be challenged by the existence of investment gaps along different stages of technological development – the so-called ‘middle-income technology trap’ (Andreoni and Tregenna, 2020). For example, firms in middle-income countries might not be able to leverage a well-funded and diversified domestic science base that provides access to generic technologies. Companies are also unable or unwilling to make significant investments in basic research, as the capital long-term commitment is prohibitive, or the long-term investment is too risky. The fact that the industrial base in these countries has limited diversification and technological depth also means that the scaling up of the new product or technology must rely on external inputs. Finally, those middle-income countries that have managed to reach a sufficient level of global integration, as well as to build a domestic production system with firms capable of absorbing and investing in technologies, are ready to engage in sustained processes of industrial innovation. Competing for innovation at the global frontier is particularly challenging, especially under the most recent industrial paradigm of the digital economy (Andreoni, Chang and Labrunie, 2021). The ‘digital capability threshold’ that companies must reach to engage in digital innovations and industrialize them is particularly high, especially in technology domains such as artificial intelligence, data science, robotization and so forth. Moreover, the digital economy presents new entry barriers in the form of network economies and global concentration in specific industries – especially digital platforms – and endogenous asymmetries along value chains (Sturgeon, 2017). Industrialization is a transformative process shaping countries’ economic structure, as well as impacting on their social and institutional fabric (Abramovitz, 1989; Andreoni and Chang, 2017; Kuznets, 1973). Industrial policies have been a major driver of industrialization in all successful industrialization experiences, both across Europe and North America as well as recent and emerging cases across Eastern and Southern Asia. Industrial policies have been

386  Handbook of industrial development used to develop social capability, to provide market forces directionality, to spur technological and organizational innovation, and to create new markets and institutions. As a result of these industrial policies aimed at production transformation and learning, ‘the state transformed the process of economic development and, in turn, was transformed by it’ (Amsden 1991, p. 286). This observation by Alice Amsden highlights the existence of co-evolving and mutually constituted dynamics of change.

3

STATES OF INNOVATION: VARIETY OF MODELS, FORMS AND FUNCTIONS

In development and industrial policy debates, state formation is often understood through the lenses of state capacity, discussing its nature, formation and change. This literature is distinctly situated in the tradition of Max Weber’s theories of the state and bureaucracy. The key vantage point for authors such as Peter Evans, Theda Scokpol and others in this tradition is the idea of autonomy. The capacity of the state to act on its goals is seen in the ability to stave off or at least navigate pressure from various groups and forces in society (Skocpol, 1985). This understanding of autonomy is best expressed in Weber’s idea of bureaucracy based on political neutrality and expertise (Weber [1921] 2002). The main elements of the state capacity in this tradition are: sovereign integrity as control over state’s territory; loyal and skilled officials; raising and deploying financial resources (including changes to taxes and earmarked funding, and ability to borrow); area-specific skills; and adaptability of state capacities (Cingolani, 2018; Evans, Rueschemeyer and Skocpol, 1985). The latter are explicitly seen as key elements of the state capacity. For instance, Weiss (1998) calls this the transformative capacity of the state. In the last decade, such Weberian notions of capacity have been complemented by what can be called the Schumpeterian alternative. Above all, Breznitz has shown that some of the key innovation agencies in the US, Finland, Sweden, Israel, Ireland and Singapore were not central Weberian agencies with ‘embedded autonomy’ as assumed by developmental state discussions by Evans and others referred to above (see, e.g., Evans, 1995) but rather (at least initially) peripheral agencies (Breznitz and Ornston, 2013; Breznitz, Ornston and Samford, 2018). These agencies were crucial sources of policy innovations necessary for promoting rapid innovation-based competition through explorations in innovation policy, driven partially by continuous, radical experimentation in their core mission and by the existence of sufficient managerial capacities (or slack) (Karo and Kattel, 2014). These agencies’ peripheral status was a vital component of their success. It reduced the likelihood of political interference and opened up space for policy experimentation and the formation of new public–private interactions. Thus, the Schumpeterian alternative to the core Weberian state capacity discussion argues that the adaptive and dynamic traits of state capacity can be engendered by initially peripheral and essentially non-Weberian organizations. As Kattel, Drechsler and Karo (2019) have shown, such ‘central–decentral’ dynamics can be explained through Weber’s theory of authority – in particular, through the interplay between charismatic and legal-rational forms of authority. Thus, from the Weberian standpoint, Schumpeterian charismatic organizational forms will in time be ‘rationalized’, socialized into existing legal-rational forms. Hence, we can call the more traditional central Weberian agencies Weber type I, and more recent Schumpeterian additions as Weber type II organizations (ibid.).

States of innovation: how the state shapes production transformation  387 In terms of developmental trajectories and stages discussed above, we can argue that earlier stages have historically required Weber type I organizations as there is a need to manage known risks and implement efficiently established policy solutions in stable and expert-driven organizations– for example, patient domestic capital, long-term infrastructure and human capital investments. On the other hand, moving closer to the technological frontier means that uncertainty is increasing and hence the requirement for more experimental and adaptable organizations of Weber type II. Importantly for our argument in this chapter, these organizational types co-exist and cooperate within a governance landscape, and also organizations can evolve from one type to the other. For instance, a country needs an expert-driven and stability-focused central bank but also a financial regulator able to create a sandbox for fintech companies. Or, next to basic and applied research agencies, countries need more experimental and open-ended forms of public–private collaborations. In the last three decades, we have seen two key sets of reforms take place that add additional layers to state capacity discussion – first, new public management (NPM) reforms that gathered momentum in the 1980s (Drechsler, 2005; Hood, 1991) and both expanded and limited the idea of autonomy so central to the Weberian view of state capacity. The NPM reforms, first, aimed to increase managerial autonomy of public agencies and thus enabled, for instance, privatization of state-owned companies and supported the creation of arm’s-length agencies. These practices opened up the public sector for an influx of private sector managerial practices such as strategic management (Lapuente and Walle, 2020; Ongaro and Ferlie, 2020) and digital transformation practices such as agile management (Dunleavy et al., 2006). However, second, the NPM reforms also brought about a focus on short-term efficiencies in the form of performance management practices based on measurement of inputs and outputs, benchmarking and an overall stronger drive for governance indicators (Dooren, Bouckaert and Halligan, 2015; Drechsler, 2019; Kattel et al., 2014). In development theory and practice, NPM-inspired reforms focused on the market-failure-based approaches and distinctly diminished the idea of state autonomy, and with it the concept of state capacity shrank around efficiency gains through liberalization and macroeconomic stability. Second, the backlash to the NPM reforms has to led to the emergence of a set of new theories that can be brought under the umbrella of the neo-Weberian state. Introduced by Pollitt and Bouckaert in 2011, the neo-Weberian state emphasizes the importance of public organizations in providing public services and at the same time recognizes the need for more citizen engagement (co-creation and co-production) in the design and delivery of public services (Drechsler and Kattel, 2009; Pollitt and Bouckaert, 2011). Dunleavy et al. have taken a step further and argue that at least some of these changes in public administrations are related to the transformation of societies by the digital revolution (Dunleavy et al., 2006; Margetts and Dunleavy, 2013). In this view, digital technologies enable and drive a deeper transformation in public administrations and services. This is reflected in the adaptation in the public sector of new working practices from (strategic) design and agile software development practices, randomized control trials and experiments from private and third sectors. As recent studies have shown, such practices are mostly taken up by new, Weber type II organizations that are often (initially) peripheral public organizations in the form of public sector design, digital and innovation labs (Tõnurist, Kattel and Lember, 2017). These working practices focus on agile processes such as prototyping and experimentation, relying on epistemological frameworks from action research and ethnography rather than economics or public policy analysis. Such practices have also considerably widened the idea of innovation from a science, technology

388  Handbook of industrial development and industrial realm to that of public services, ways of working and co-creation practices. There are, indeed, an increasing number of public innovation agencies that attempt to combine features of Weber type I and type II organizations. For instance, Swedish innovation agency Vinnova has developed strong capabilities in applying service design practices to innovation policy design while maintaining its focus on managing long-term industrial partnerships. We can argue that such organizations aim to be both dynamic and resilient by design, and we can call these neo-Weberian agencies or, in keeping with the typology developed above, Weber type III organizations (Kattel, 2022). Figure 22.1 summarizes the discussion so far; it maps key industrialization challenges and state capacity formation against the different steps of a stylized industrialization ladder. It also adds two further layers to the framework – policy space and industrial paradigm – to reflect changes in the global industrialization context. Specific reference is also made to a selection of representative countries for each group of late, recent and emerging industrializers. This multi-layered framework points to the fact that while all countries had to face similar industrialization and state formation challenges in climbing their industrialization ladder, they had different policy space and developed under different industrial paradigms (bottom two layers of the framework). As a result, recent and emerging industrializers could not use the same industrial policy instruments used by early industrializers. Moreover, under different industrial paradigms, certain policy instruments turn out to be more (or less) effective than others. One key dimension determining a country’s policy space is its space in trade policy – that is, its ability to use tariffs strategically to sustain the process of industrial learning (Chang and Andreoni, 2020). Since the Uruguay Round started in 1986 and completed in 1994 and then with the establishment of the World Trade Organization, the global policy space has been shrinking as a result of bilateral trade agreements and the introduction of a more comprehensive set of regulations on investments, intellectual property rights (IPRs) and other sectors of the economy that were uncovered before. Of course, while the policy space matters a lot, it is also how countries strategically engaged with global regulations that makes a difference. In some cases, countries self-inflicted ‘too early, too fast’ integration into the global economy, while others have not used the available industrial policy instruments and institutions, despite the fact that they were feasible under the existing global regime. The third layer is the dominant industrial paradigm a country faced when it took its first steps on the global industrialization ladder. Building on the seminal work of Joseph Schumpeter and later Carlota Perez (2001), by industrial paradigm we refer to both the techno-economic and organizational modes of production that are dominant in a certain period. Whittaker et al. (2020), for example, define the experience of countries who industrialized after the 1970s in a ‘network development era’ dominated by GVCs as a ‘compressed development’ experience. Compressed developers such as our recent and emerging industrializers, they argue, faced opportunities and challenges that are fundamentally different from those faced by early industrializers such as Germany, Japan and the United States. At the interface of these two layers – policy space and industrial paradigm – another key consideration is nature of financing (Burlamaqui and Kregel, 2005). In past decades, many developing countries have experienced increased vulnerability to financial flows via the footloose nature of foreign direct and portfolio investments, and through increased foreign ownership of domestic banks. Such financialization of industrialization attempts tends to worsen the terms of trade for poorer countries (raising costs of imports and lowering costs of exports) and to reinforce lock-in into lower-value-added activities and increase financing of consumption,

States of innovation: how the state shapes production transformation  389

Figure 22.1

The industrialization ladder multi-layered framework

real estate and retail sectors rather than industrialization. Thus, the nature and ownership of development finance is a key variable in state and industrialization co-evolution. Thus, we can summarize our argument in (so far) three successful ideal-typical states of innovation that capture the evolving nature of how state formation and industrialization co-evolve: developmental state; entrepreneurial state; and innovation-challenge-led state. This is summarized in Table 22.1. At early stages of industrialization, the developmental state is centralized and hierarchical; however, the capacity of the state in implementing a wide range of selective industrial policies is weak due to limited long-term expertise in civil service, limited autonomy

390  Handbook of industrial development Table 22.1  

Three states of innovation Developmental State

Entrepreneurial State

Innovation-challenge-driven State

Industrialization

Breaking into

Linking back

Keeping pace

challenges

Linking up

Keeping pace

Competing for innovation

Linking back

Competing for innovation

Industrial policy

Structural coordination

Market failures

Market shaping

Rationales

Dependency & dualism

System of innovation

Public-purpose innovation

Technology backwardness

Technology race

Sustainability challenges

Market failures

National security

Economic resilience

Industrial policy

Supply side

Supply side

Demand side

Approach

Production & technology

Technology & innovation

Challenges & innovation

focused

focused

focused

Firm-level capabilities

Innovation system

Cross-sectoral society

Industrial policy

Trade policy

Trade policy

Public procurement

Instruments

Subsidy policy

Technology policy

Technology policy

Technology policy

Innovation policy

Innovation policy

Development finance

Long-term finance

Long-term finance

Hierarchical

Heroic

Experimental

Centralized

Distributed

Networked and multi-layered

State model

State capacity and institutions Autonomy and expertise Weber type I

Investment and system building Challenge focused Weber type I and II

Weber type III

and state legitimacy in conflict management (Chang and Andreoni, 2020; Harrison, 2020). State capacity formation and legitimation presupposes the emergence of a strong developmental state and the emergence of a fully-fledged entrepreneurial state increasingly focused on technology innovation and systems, also enabled by a distributed mix of Weber I and Weber II agencies and institutions (Mazzucato, 2013). The most advanced state of innovation is the one encapsulated by an innovation-challenge-driven state whose mandate becomes explicitly and directly cross-sectoral society. Weber II type state institutions are organized within networked and multi-layered governance structure, experimenting with a challenge-focused approach. This adaptation of state capacity and industrial policy approaches, rationales and instruments also reflects the evolving industrialization and broader societal challenges.

4

CASE STUDIES

4.1

Germany: From a Developmental State to an Innovation-challenge-driven State

Germany’s industrialization and use of a wide range of industrial policy dates back to the eighteenth century. Under Frederick William I (r. 1713–40) and Frederick the Great (r. 1740–86), the Prussian state provided monopoly rights, trade protection, export subsidies, capital investments and skilled workers from abroad to develop a number of emerging (at that time) industries, including textiles and metals. Starting with the early nineteenth century, the Prussian state invested significantly in infrastructures and educational reforms, especially technical schools and universities. With the increasing growth of the private sector, during the

States of innovation: how the state shapes production transformation  391 second half of the nineteenth century, the German state moved from a directive to a guiding role (Chang, 2002). The use of tariffs as a form of ‘infant industry protection’ remained relatively mild until 1879 in comparison to Britain and the United States, although a German customs union under Prussia’s leadership was already established in 1834. The last two decades of the nineteenth century witnessed a significant tariff increase aimed at cementing a political coalition between the landlords and the industrialists – known as the ‘marriage of iron and rye’. Under Otto von Bismarck, Chancellor of Germany, tariffs were, however, used in a selective manner, targeting heavy industries such as steel and iron. Cartel policies were also used. With the erosion of state capacity during the Second Reich (1870–1914), the state became relatively less involved in industrial development, although it still played an important role through its tariff policy and cartel policy. With the reconstruction of the German state and economy after World War II, industrial policy returned to the centre of economic policy in Germany. The German developmental state model: the building of an industrial ecosystem During the first two decades after the World War II, Germany’s recovery was driven by those industries in which the country had a long-standing competitive advantage and it was sustained by the high demand of investment goods from the rest of Europe. Between 1950 and 1970, investments remained high at 22–24 per cent of national income, while exports rose from 9 to 19 per cent of national income. The German model (Modell Deutschland), as Helmut Schmidt called it in the 1970s, was developed during this period thanks to an articulated package of industrial policies operating both at the national and regional (Länder and municipalities) levels. These policies built the foundations of today’s German industrial ecosystem and are still central to its competitive global success. In the early decades of the post-World War II period, the German industrial policy focused on five main axes: industrial restructuring and public ownership; regulation of the labour market; the development of an integrated vocational training system; creation of a basic science and industrial research infrastructure; and public support for industrial finance. During the 1950s, the German government built a number of public or quasi-public special-purpose banks, whose functioning and mandate adapted over the years with the changing needs of industries. For example, the Bank for Reconstruction (KfW), founded in 1947, increasingly moved away from direct lending and became a long-term refinance bank specialized in lending to banks strongly linked with industrial companies. The Mittelstand companies – that is, firms with the number of employees between 100 and 500 – were mainly served by the German Bank for Settlements (AG) as well as by a strong network of public saving banks and credit cooperatives, linked by a ‘three-tier’ organizational structure, which allowed them to overcome scale disadvantages by aggregating credit demands (as well as savings) at the upper tiers (regional or national) while remaining strongly embedded in the local community. From the mid-1960s until the mid-1970s, Germany’s investments in basic science and industrial research tended to be sectoral and technology targeted. In 1962, the Ministry for Atomic Questions was converted into the Ministry of Research and Technology (BTFM). Three major industrial strategies were implemented. The first was on data processing and computer hardware development, which channelled resources mainly to Siemens. The second was on nuclear power, focusing on fast breeder reactors. Third, both the federal and land governments heavily supported civil aircraft projects through subsidies and organized ‘rationalization’ and concentration, which led to the creation of the MBB group, later one of

392  Handbook of industrial development the main partners in the Airbus consortium. Since the mid-1970s, the German government increasingly developed its public R&D infrastructure built around two publicly funded networks of institutes, the Fraunhofer Society and the Max Planck Society. Fraunhofer Institutes were explicitly aimed at filling the gap between basic science and company-based industrial research and at overcoming the disadvantages and scale bottlenecks faced by Mittelstand companies. Fraunhofer Institutes undertake collaborative manufacturing research and address technological challenges for the entire industrial system (big and small companies, public sector included). Institutes are required to balance their own budgets, which requires them to generate contract research (Andreoni, 2016). The German model has gone through important changes since the 1980s, which accelerated with reunification. In 1982, Helmut Kohl began to reduce the role of the government by cutting public expenditure and taxes as well as partially deregulating the labour market and promoting privatizations. With reunification, the government adopted a dual system of industrial policy: continuity of the industrial policy for West Germany and policies directed towards East Germany. The industrial policy measures in East Germany focused on the creation and development of new small and medium-sized enterprises (SMEs) (both in manufacturing and services), infrastructural investments, and the privatization and rationalization of state-owned enterprises (SOEs) (the public agency in charge was Treuhand Gesellschaft). In West Germany, industrial policy has remained very much focused on existing Mittelstand companies and their innovative capacity, especially those large medium-sized companies (up to 1000 employees), known as ‘hidden champions’, many of which dominate global niches, with 40–90 per cent of the global market shares. The German innovation-challenge-driven state model: competing for green innovation Together with this process of decentralization of industrial relations, early 2000 to 2005 was characterized by an increasing emphasis on environmental sustainability, energy efficiency and renewable energy (e.g., German Renewable Energy Act). KfW played a central role as an institutional arm of the government in the implementation of its green innovation challenge and related industrial policy. Germany entered into the renewable energy industry as a follower, Denmark already being established as the country with first-mover advantage, and in an industry with a massive sunk cost advantage of incumbent technologies. In its early days, renewable energies were costly and the market was not willing to channel significant resources into the sector. The Federal Government relied on both demand- and supply-side instruments. In the late 1990s, the launch of a feed-in tariff scheme created domestic demand and subsidized domestic transition from fossil fuel to renewable energy. From the supply side, KfW was used to channel subsidized long-term finance to promote investment in the industry, and later export promotion especially across developing countries. KfW’s special renewable energy programme entails long-term credit with subsidized interest rates and other favourable conditions. It is estimated that about 80 per cent of wind energy plant development and 40 per cent of the total renewable energy development in Germany have been financed by KfW if we include co-financing projects (see Figure 22.2). By 2014, three out of the top ten wind turbine manufacturers were German companies: Enercon, Siemens and Nordex. In 2011, KfW became the most important promoter of renewable energies worldwide, together with the World Bank. This global influence was made possible thanks again to the IPEX export finance programme, including direct provision export and project finance. IPEX

States of innovation: how the state shapes production transformation  393 can provide this aid at favourable conditions both in its capacity as an official export credit agency and through its market window. KfW Entwicklungsbank and German Investment Corporation support IPEX’s direct promotion of German firms by financing renewable projects in developing countries and increase global demand for this new industry (Naqvi, Henow and Chang, 2018).

Source:

Naqvi et al. (2018).

Figure 22.2

Installed renewables capacity in Germany and major programmes

Germany’s use of these state subsidies was compliant with the EU regulatory framework on state aid, as the energy renewables were covered under the General Block Exemption Regulations. Other EU countries, but also other global powers like US and China, have adopted similar finance schemes and regulatory exceptions to gain leadership in this new industry since the mid-2000s. Among them, China has gained significant production capacity in solar panels. Globally, in 2017, cumulative solar PV capacity reached almost 398 GW and generated over 460 TWh, representing around 2 per cent of global power output. Utility-scale projects account for just over 60 per cent of total PV installed capacity, with the rest in distributed applications (residential, commercial and off-grid). 4.2

The United States: From a Developmental State to an Entrepreneurial Networked State of Missions

From its early days, the US was a pioneer of industrial policy (Chang, 2002; Reinert, 2008). The infant industry argument was invented by the first American finance minister (Treasury Secretary), Alexander Hamilton, in his 1791 Report on the Subject of Manufactures. The

394  Handbook of industrial development report was, contrary to what many believe, not narrowly focused on tariff protection but discussed a whole range of (general and selective) industrial policy measures, including targeted subsidies, infrastructural development, financial development (the banking system, the government bond market), and the promotion of innovation through the development of the patent system. Between 1816 and the end of World War II, the US had one of the highest average tariff rates on manufacturing imports in the world. Given that the country enjoyed an exceptionally high degree of ‘natural’ protection due to high transportation costs at least until the 1870s, US industries were the most protected in the world until 1945. In 1890, agricultural products accounted for almost 75 per cent of total US exports, with cotton and grain products making up close to 50 per cent of the agricultural export total (Ferleger and Lazonick, 1993). To overcome this situation, during this period, the US government promoted productive transformation via an extensive range of agricultural research through the granting of government land to agricultural colleges and the establishment of government research institutes to diffuse scientific and managerial advances. In the second half of the nineteenth century, the government expanded public educational investments and promoted and invested directly in the development of the transportation infrastructure. The US networked model of an entrepreneurial state The industrial policy ‘networked model’ the US adopted in the second half of the twentieth century was established during the wartime period between the 1940s and 1950s. In 1942, the War Production Board (WPB) was constituted to meet the production targets and public procurement required during World War II. The production system was subject to the WPB governance and coordination but remained a private-based system. At the core of the strategy developed by Simon Kuznets and spelled out in the Victory Plan, the WPB focused on measurement (standardization and interchangeable parts), coordination (mainly through procurement) and transformation of the production system (ramping up production and development of scaling-up capabilities). Under the lead of Vannevar Bush, the Office of Scientific Research and Development became the critical node of a networked interorganizational system for science and technology R&D (Best, 2019). The integration of mass production and technological innovation was achieved through a networked entrepreneurial state made up of several public agencies and schemes, many of which steered the US economy in new sectors while creating new markets (Mazzucato, 2013). This included state agencies (e.g., the Advanced Research Projects Agency [ARPA, now DARPA, with its emphasis on defence] of the Pentagon; the National Institutes of Health [NIH]; the National Science Foundation [NSF]; the National Institute for Standards and Technology [NIST]; the Departments of Energy and Agriculture; the National Aeronautics and Space Administration [NASA]); and industries, universities, national laboratories and other research institutes. During the 1950s and 1960s, many of these institutions were strongly focused on translating cutting-edge technological research, much of which was generated through massive public funding of R&D (especially in defence and health), into commercial use. Throughout the Cold War period, the US implemented a comprehensive industrial policy package, including long-term procurement contracts, subsidies, investment guarantees and strategic bailouts. During this period, industrial policy in the US was conducted under other names – defence policy, health policy, agricultural policy and so on – prompting the eminent American economic sociologist Fred Block to talk of a ‘hidden developmental state’ (Block

States of innovation: how the state shapes production transformation  395 and Keller, 2011). Between the 1950s and 1980s, the share of government funding in total R&D in the supposedly free-market US accounted for, depending on the year, between 47 per cent and 65 per cent, as against around 20 per cent in Japan and Korea and less than 40 per cent in several European countries (e.g., Belgium, Finland, Germany, Sweden). These public R&D investments were pivotal in the development of key technologies, especially ‘defence’ (computer, semiconductors, aircraft, Internet) and ‘health’ (drugs, genetic engineering). More recently, R&D funding has taken the form of grants, deferral of liability, tax provisions and exemption. Some of today’s most successful industrial policy measures in the US have been introduced and continuously supported over several years. This is the case of two programmes run by the Small Business Administration – namely, the Small Business Investment Company (SBIC) and the Small Business Innovation Research and Technology Transfer (SBIR/STTR). These programmes combine loans, R&D grants and pre-commercial public procurement to support businesses engaged in the development and scaling-up of technological systems or components (Andreoni, 2016). The US innovation-challenge-driven state model: mission-oriented innovation The 2007/08 financial crisis, and the subsequent sharp manufacturing loss and employment crisis, opened up a new industrial policy cycle with an unprecedented one-time US$787 billion stimulus package – that is, the American Recovery and Reinvestment Act (ARRA) of 2009. The Obama administration first addressed the dramatic shortage of science, technology, engineering and mathematics graduates and skilled workers via almost US$100 billion in federal investment coupled with state-level initiatives. The health sector (and its industries) received another massive boost of more than US$100 billion, while an ambitious infrastructural programme was launched to address communication, energy and transportation infrastructure. Finally, the possibility of inducing a techno-paradigmatic shift in the energy sector was taken up as a new pathway for systemic structural change and sustained growth. Clean-energy initiatives, mixing loan guarantees for renewable energies, electricity transmission projects and smart grids, as well as grants for batteries and advanced materials were financed. Additionally, the Advanced Research Projects Agency–Energy (ARPA-E) coordinated a new mission-oriented research venture in energy. Historically, in the US, the public sector has played a key role in ensuring that each stage of the innovation chains are adequately funded, especially those stages in which private sector companies might be unwilling to pump resources. Technological development follows different stages and can be measured in different ways. Traditionally, scholars have distinguished five stages of technology development – research, concept/invention, early-stage technology development, product development and production/commercialization. As shown in Figure 22.3, the US government has been particularly successful in developing public agencies specialized and focused on all stages of the innovation chain by deploying different financial and non-financial instruments. As pointed out by Mazzucato (2017, p. 20), ‘Such organizations have been “mission driven” in that they have directed their actions based on the need to solve big problems, and in the process actively created new technological landscapes, rather than just fix existing ones’. This model is so successful for several reasons. First, the amount of resources for R&D is extraordinary, and often exceeds private investments along the innovation chain. For example, from 1936 to 2016, cumulative R&D expenditure by NIH has amounted to more than US$900 billion (in 2015 dollars), and since 2004 has exceeded US$30 billion per year. However, what

396  Handbook of industrial development

Source:

Mazzucato (2017, p. 20).

Figure 22.3

Mission-oriented finance along the entire innovation chain

makes this model so successful is not simply the large amounts of resources that actors like the NIH pumps into research, but also the way in which agencies are distributed along the innovation chain, and how coordination is achieved to maximize returns on public investments with a balance between directive and bottom-up interactions. Third, within these mission-oriented agencies, investments are ‘direct’ and tend to crowd-in private investments more than indirect tax incentives. Finally, by adopting this model, the government can potentially introduce a number of returns-generating mechanisms for its investments, including retaining equity or royalties, retaining a golden share of the IPRs, using income-contingent loans, or capping the prices (which the tax payer pays) of those products that emanate, as drugs do, from public funds (Mazzucato, 2017). 4.3

China: From a Developmental to an Innovation-challenge-driven State

Since the late 1970s, industrial policy has been an integral part of China’s five-year planning. Many initiatives and policy measures, especially in the early period, were inspired by the successful experiences of Japan and Korea and focused on breaking into the global economy by linking up to value chains. The 6th Five-Year Plan (1981 to 1985) marked a more outward-oriented approach, focusing on importing technologies and developing endogenous technological and innovation capabilities. Thus, since the 1980s, China started using several policy instruments to link back and develop linkages with the local production system and keep pace with technological change. To achieve these complementary sets of goals, China adopted a selective approach to industrial policy. Strategic industries, or ‘pillar industries’, were identified based on their importance to China’s national security and economy and growth potential. Each targeted sector received a package of complementary industrial policy measures, including tariff and non-tariff barriers, import quotas, local content requirements, licensing

States of innovation: how the state shapes production transformation  397 systems, tax exemptions, subsidized land and subsidized loans from state-owned policy banks. Firms from prioritized industries benefited from subsidized loans from development banks, such as the Export-Import (Exim) Bank of China, the Agricultural Development Bank of China (ADBC), and the China Development Bank (CDB). The overall financial infrastructure was also given a pro-industrial development orientation by law. SOEs played a critical role in coordinating processes of industrial upgrading and restructuring, in some cases limiting domestic competition to achieve economies of scale and overcome entry barriers. SOEs benefited from incentives and preferential loan terms. Finally, foreign direct investment (FDI) policies were widely used by China in linking up to GVCs while creating the conditions for the development of domestic production linkages. Targeted industries typically involved high-end manufacturing, new and advanced technologies, energy efficiency and environmental protection. The automobile and semiconductor industries, for example, were guaranteed market protection in exchange for technology transfer, while increases in companies’ production scales were reached through government-led mergers and acquisitions (Lo and Wu, 2014). The market liberalization agenda in the second part of the 1990s brought about various changes in Chinese industrial policy efforts. Agriculture, infrastructure, construction and services were included in the list of pillar industries. The 10th Five-Year Plan (2001 to 2005) marked renewed systemic industrial and technology policy efforts. Several other policy measures have been introduced since 2005 as part of subsequent Five-Year Plans. The policy model has increasingly relied on the involvement of provinces and municipalities. As a result of this accelerated process of structural change and the new industrial policy approach, China has entered a path of indigenous innovation (zizhu chuangxin). Berger (2013, p. 145) shows that, until 2005, there was limited evidence of domestic innovation capabilities. Thereafter, companies in high-tech sectors developed enhanced capabilities (increasingly mastering the scale-up of complex system products and processes, translating into advanced product design and advanced manufacturing, and reducing the time to the market). Companies have also developed redesign, reverse and re-engineering competencies. Thus, these companies are increasingly able to produce products with ‘Japanese [good enough] quality at Chinese prices’. Since the 1980s, China has adopted several technologies and R&D-financing policies to keep pace with technological change. In 1986, the National High-Tech Development Plan introduced the first articulated national technology strategy targeting biotechnology, space, information technology, laser technology, automation, energy and new materials. This technology plan was updated over time to include emerging technologies, such as telecommunications (1992) and marine technology (1996). The Torch Programme was initiated in 1988. It promoted (1) high-tech cluster development around Science and Technology Industrial Parks (STIPs), Software Parks, and Productivity Promotion Centres (Innovation Clusters); (2) high-tech business start-up services (Technology Business Incubators); and (3) financial services for innovation (InnoFund and the Venture Guiding Fund). Indeed, China relied on a full range of financial and non-financial incentives to catch up technologically and develop innovation capabilities. The Chinese innovation-challenge-driven state: Made in China 2025 In 2015, the Chinese government launched an ambitious ten-year strategy – Made in China (MIC) 2025 – aimed at transforming the economy along the pathway started in the 1990s towards becoming a high-tech innovative industry powerhouse. The strategy is articulated in two different phases, and the target year 2025 refers only to the so-called ‘foundations’

398  Handbook of industrial development phase. During this first phase, the following ten strategic sectors were targeted including: next-generation IT, high-end numerical control machinery and robotics; aerospace and aviation equipment; maritime engineering equipment and high-tech shipping; advanced rail equipment; energy-saving and new-energy vehicles; electric power equipment; agricultural machinery and equipment; new materials; biopharmaceuticals; and high-performance medical devices. The second phase, from 2025 to 2049, aims at upgrading the whole Chinese economy and reducing unbalanced transformation across provinces or sectors – in particular, by attainable high levels of automation and vertical integration. The third phase is finally focused on horizontal integration of the industries and broader uplifting of productivity. By 2049, China aims to belong to the top innovation-driven economies in the world. The implementation of such large schemes calls for significant institutional restructuring and effective governance schemes. This is why MIC 2025 emphasizes the importance of collaborations between universities and research organizations, and industry. Innovation alliances and demonstration centres are among the main tools MIC 2025 is using to drive innovation, diffuse new technologies, and to develop joint standards between science and industry. At the subnational level, eight cities and five city clusters are acting as pilots for implementation of the policies. China has launched 19 provincial manufacturing innovation centres and 109 smart manufacturing pilot programmes, including the National Power Battery Innovation Center in Beijing, the National Innovation Institute of Additive Manufacturing in Xi’an, and the National Information and Optoelectronics Innovation Center in Wuhan. From a state capacity and policy governance perspective, the State Council acts as the main coordinating organization, while China’s Ministry of Industry and Information Technology (MIIT) is directly responsible for implementation (Figure 22.4).

Sources: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ); European Union Chamber of Commerce in China (2017).

Figure 22.4

Made in China 2025 governance system

States of innovation: how the state shapes production transformation  399 Different from previous strategies, MIC has broadened its sectoral and technological focus, including traditional industries and services, alongside previously targeted renewables, alternative fuels, artificial intelligence, cybersecurity services, integrated circuits, network equipment and software, biotechnology, energy-efficient and environmental technologies, and high-end manufacturing. The MIC strategy focuses on reducing reliance on foreign technology and improving along several critical technology/innovation dimensions – that is, innovation capacity, efficiency, quality of industrial infrastructure, quality of outputs and degree of digitalization. In particular, technological innovation and smart manufacturing have been targeted. Green technologies are also seen as central to addressing the mobility challenge transition, and large investments and incentives in energy- and material-efficient production have been introduced. For example, in April 2018, the dual credit policy scheme to incentivize transition to electric cars was launched. Alongside this scheme, the Chinese government has built the largest charging stations networks for electric vehicles reaching more than 210 000 charging points with a fast rate of growth of new 6000 charging stations per month. Such transformative modernization of the economy will create an enormous demand for advanced manufacturing technologies. The Chinese government aims to fill this gap with major funding support and strategic market engagement.

5

CONCLUDING REMARKS

Industrial policy is central to the structural and innovative transformation of the economy. Historically, no country has managed to achieve these goals without the state playing an active role in shaping markets and industries. Industrial innovation is, however, both the outcome and a driver of state formation. They are linked by a complex co-evolving dynamics of change involving the transformation of the productive structure as much as the formation of state capacity. In stepping up the industrialization ladder, countries are transformed by these co-evolving dynamics (or lack thereof) that unfold under different industrial paradigm and global policy space regimes. In this chapter we introduce a multi-layered framework to contextualize industrial innovation policies and conduct a comparative political economy of industrialization. We distilled three main states of innovation and for each of them – developmental state, entrepreneurial state and innovation-challenge-driven state – we provide a stylized analysis of its main features. The analysis of these features is conducted through three historical case studies of Germany, US and China. Specifically, we selectively review the evolution on industrial policy instruments, approaches and state institutions and how these countries have shifted from one state of innovation to another over time. The framework and illustrative case studies have highlighted a number of emerging trends across countries – in particular, the increasing shift from a traditional form of developmental and entrepreneurial state towards a more innovation-challenge-driven state. This reflects the need to address cross-sectoral society challenges whose solutions require coordinated efforts and experimentation across several agencies and institutions. This shift has also entailed an expansion of the policy toolkit and instruments that the government can use and align to address these challenges. Another emerging trend is the focus on the energy and sustainability transition as one of the key challenges across early, late and recent industrializers. Finally, changes in state forms and functions is encapsulated by the shift towards Weber II type agen-

400  Handbook of industrial development cies and institutions, as well as their integration into multi-layered and networked governance structures.

REFERENCES Abramovitz, M. (1989). Catching up, forging ahead, and falling behind. Journal of Economic History, 46(2), 385–406. Amsden, A. (1989). Asia’s Next Giant. New York: Oxford University Press. Amsden, A. (1991). Diffusion of development. American Economic Review, 81, 282–6. Andreoni, A. (2016). Varieties of industrial policy. In A. Noman and J. Stiglitz (eds), Efficiency, Finance and Varieties of Industrial Policy. New York: Columbia University Press, pp. 245–305. Andreoni, A. and Chang, H.-J. (2017). Bringing production transformation and jobs creation back to development. Cambridge Journal of Regions, Economy and Society, 10(1), 173–87. Andreoni, A. and Chang, H.-J. (2019). The political economy of industrial policy. Structural Change and Economic Dynamics, 48, 136–50. Andreoni, A., Chang, H.-J. and Labrunie, M. (2021). Natura non facit saltus: challenges and opportunities for digital industrialisation across developing countries. European Journal of Development Research, 33, 330–70. Andreoni, A., Mondliwa, P., Roberts, S. and Tregenna, F. (eds) (2021). Structural Transformation in South Africa: The Challenges of Inclusive Industrial Development in Middle-Income Countries. Oxford: Oxford University Press. Andreoni, A. and Scazzieri, R. (2014). Triggers of change. Cambridge Journal of Economics, 38(6), 1391–408. Andreoni, A. and Tregenna, F. (2020). Escaping the middle-income technology trap. Structural Change and Economic Dynamics, 54, 324–40. Berger, S. (2013). Making in America: From Innovation to Market. Cambridge, MA: MIT Press. Best, M. (2019). How Growth Really Happens. Princeton, NJ: Princeton University Press. Block, F. and Keller, M. (2011). State of Innovation. The US Government’s Role in Technology Development. New York: Routledge. Breznitz, D. and Ornston, D. (2013). The revolutionary power of peripheral agencies. Comparative Political Studies, 46(10), 1219–45. Breznitz, D., Ornston, D. and Samford, S. (2018). Mission critical: the ends, means, and design of innovation agencies. Industrial and Corporate Change, 27(5), 883–96. Burlamaqui, L. and Kregel, J. (2005). Innovation, competition and financial vulnerability in economic development. Revista de Economia Política, 25(2), http://​doi​.org/​10​.1590/​S0101​-31572005000200002. Chang, H.-J. (2002). Kicking Away the Ladder: Development Strategy in Historical Perspective. London: Anthem Press. Chang, H.-J. and Andreoni, A. (2020). Industrial policy in the 21st century. Development and Change, 51(2), 324–51. Cingolani, L. (2018). The role of state capacity in development studies. Journal of Development Perspectives, 2(1–2), 88–114. Dooren, W.V., Bouckaert, G. and Halligan, J. (2015). Performance Management in the Public Sector. London: Routledge. Drechsler, W. (2005). The rise and demise of the new public management. Post-autistic Economics Review, 33(14), 17–28. Drechsler, W. (2019). Kings and indicators: options for governing without numbers. In M.J. Prutsch (ed.), Science, Numbers and Politics. Cham, Switzerland: Springer, pp. 227–62. Drechsler, W. and Kattel, R. (2009). Towards the neo-Weberian state? Perhaps, but certainly adieu, NPM! NISPAcee Journal of Public Administration and Policy, 1(2), 95–9. Dunleavy, P., Margetts, H., Bastow, S. and Tinkler, J. (2006). Digital Era Governance: IT Corporations, the State, and e-Government. Oxford: Oxford University Press.

States of innovation: how the state shapes production transformation  401 European Union Chamber of Commerce in China (2017). China Manufacturing 2025: Putting Industrial Policy Ahead of Market Forces. Accessed 13 September 2022 at http://​docs​.dpaq​.de/​12007​ -european​_chamber​_cm2025​-en​.pdf. Evans, P. (1995). Embedded Autonomy. Princeton, NJ: Princeton University Press. Evans, P.B., Rueschemeyer, D. and Skocpol, T. (eds) (1985). Bringing the State Back In. Cambridge, UK: Cambridge University Press. Ferleger, L. and Lazonick, W. (1993). The managerial revolution and the developmental state: the case of US agriculture. Business and Economic History, 22(2), 67–98. Haraguchi, N., Cheng, C.F.C. and Smeets, E. (2017). The importance of manufacturing in economic development: has this changed? World Development, 93, 293–315. Harrison, G. (2020). Developmentalism. Oxford: Oxford University Press. Hirschman, A. (1977). A generalized linkage approach to economic development with special reference to staples. Economic Development and Cultural Change, 25, 67–97. Hood, C. (1991). A public management for all seasons? Public Administration, 69(1), 3–19. Kang, N. and Paus, E. (2019). The political economy of the middle income trap. Journal of Development Studies, 56(4), 651–6. Karo, E. and Kattel, R. (2014). Public management, policy capacity, innovation and development. Revista de Economia Política, 34, 80–102. Kattel, R. (2022). Dynamic capabilities of the public sector: towards a new synthesis. UCL Institute for Innovation and Public Purpose (IIPP) Working Paper Series: IIPP WP 2022/07. Kattel, R., Cepilovs, A. and Drechsler, W. et al. (2014). Can we measure public sector innovation? A literature review. LIPSE Project Working Paper, No. 2. Kattel, R., Drechsler, W. and Karo, E. (2019). Innovation bureaucracies: how agile stability creates the entrepreneurial state. UCL Institute for Innovation and Public Purpose Working Paper Series: IIPP WP 2019-12. Kuznets, S. (1973). Modern economic growth. American Economic Review, 63, 247–58. Lapuente, V. and de Walle, S.V. (2020). The effects of new public management on the quality of public services. Governance, 33(3), 461–75. Lee, K. (2013). Schumpeterian Analysis of Economic Catch-up. Cambridge, UK: Cambridge University Press. Lo, D. and Wu, M. (2014). The state and industrial policy in Chinese economic development. In J.M. Salazar-Xirinachs, I. Nubler and R. Kozul-Wright (eds), Transforming Economies. Geneva: International Labour Organization, pp. 307–26. Margetts, H. and Dunleavy, P. (2013). The second wave of digital-era governance. Philosophical Transactions of the Royal Society, 371(1987), https://​doi​.org/​10​.1098/​rsta​.2012​.0382. Mazzucato, M. (2013). The Entrepreneurial State: Debunking Public vs. Private Sector Myths. London: Anthem Press. Mazzucato, M. (2017). Mission-oriented innovation policy: challenges and opportunities. UCL Institute for Innovation and Public Purpose (IIPP) Working Paper Series: IIPP WP 2017-01. Milberg, W. and Winkler, D. (2013). Outsourcing Economics. Cambridge, UK: Cambridge University Press. Naqvi, N., Henow, A. and Chang, H.-J. (2018). Kicking away the financial ladder? German development banking under economic globalisation. Review of International Political Economy, 25(5), 672–98. Ohno, K. (2013). Learning to Industrialize. London: Routledge. Ongaro, E. and Ferlie, E. (2020). Strategic management in public organizations. The American Review of Public Administration, 50(4–5), 360–74. Pasinetti, L.L. (2007). Keynes and the Cambridge Keynesians. Cambridge, UK: Cambridge University Press. Perez, C. (2001). Technological Revolutions and Financial Capital. Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing. Pollitt, C. and Bouckaert, G. (2011). Public Management Reform: A Comparative Analysis. Oxford: Oxford University Press. Reinert, E.S. (1995). Competitiveness and its predecessors – a 500-year cross-national perspective. Structural Change and Economic Dynamics, 6(1), 23–42.

402  Handbook of industrial development Reinert, E.S. (2008). How Rich Countries Got Rich…and Why Poor Countries Stay Poor. New York: Carroll & Graf. Reinert, E.S. and Kattel, R. (2004). The qualitative shift in European integration: towards permanent wage pressures and a ‘Latin-Americanization’ of Europe? PRAXIS Working Papers, No. 17. Tallinn University of Technology, PRAXIS Center for Policy Studies. Skocpol, T. (1985). Bringing the state back in: strategies of analysis in current research. In D. Rueschemeyer, P.B. Evans and T. Skocpol (eds), Bringing the State Back In. Cambridge, UK: Cambridge University Press, pp. 3–38. Sturgeon, T. (2017). The ‘new’ digital economy and development. UNCTAD Technical Notes on ICT for Development, No. 8. Tõnurist, P., Kattel, R. and Lember, V. (2017). Innovation labs in the public sector: what they are and what they do? Public Management Review, 19(10), 1455–79. Wade, R. (2003). What strategies are viable for developing countries today? The World Trade Organization and the shrinking of ‘development space’. Review of International Political Economy, 10(4), 621–44. Weber, M. ([1921] 2002). Wirtschaft und Gesellschaft (5th edition). Tübingen: Mohr Siebeck, pp. xxxiii, 948. Weiss, L. (1998). The Myth of the Powerless State. Ithaca, NY: Cornell University Press. Whittaker, D.H., Sturgeon, T., Okita, T. and Zhu, T. (2020). Compressed Development. Oxford: Oxford University Press.

23. Industrial development and the growth process: a structural framework Ivano Cardinale and Roberto Scazzieri

1 INTRODUCTION The nexus between economic growth and the development of manufactures has been a central theme of political economy since the early modern age. In that connection, the relationship between increasing employment in manufacturing and a more than proportional output increase (increasing returns) is often seen as fundamental in explaining the dynamics of the wealth of nations. The aim of this chapter is to examine in that light the relationship between industrial development and the growth process. Section 2 provides a historical background to the political economy of manufacturing, focussing on the line of thinking that joins Antonio Serra’s discovery of the virtuous circle (cumulative causation) induced by the dynamic relationship between manufacturing and trade; Adam Smith’s analysis of the connection between increasing productivity, division of labour and the extent of the market; Melchiorre Gioja’s and Charles Babbage’s discussion of proportionality requirements in manufacturing; and Friedrich List’s exploration of the range of policy options associated with different stages of industrial development. Section 3 outlines an analytical framework for examining the relationship between industrial revolutions and patterns of interdependence between production processes. This section addresses the connectivity side of division of labour and analyses the architectures of interdependence associated with different patterns for the analytical splitting and integrating of processes. Section 4 examines manufacturing regimes as alternative forms of manufacturing organization and explores the relationship between the material interdependence of production processes under a given pattern of division of labour and the coordination of processes made possible by specific forms of manufacturing organization. The final section reflects on how the chapter can provide the building blocks for a political economy analysis of the transition between manufacturing regimes, which would make it possible to study how material conditions are intertwined with the interests and actions of relevant coalitions of actors within the productive system.

2

MANUFACTURING AND ECONOMIC GROWTH: SERRA, SMITH, BABBAGE AND LIST

Manufacturing enters systematic economic discussion with the early modern literature on the conditions for the sustainability and expansion of a sovereign polity. In connection with this, Giovanni Botero’s works on the ‘greatness of cities’ and on ‘reason of state’ were fundamental in calling attention to the multiple ways in which sustainability and expansion conditions could be achieved. A characteristic feature of Botero’s thinking is consideration of the relative importance of concentration versus diffusion in allowing the sustainability and expansion of 403

404  Handbook of industrial development a polity. A central theme of Botero’s study, Causes of the Greatness and Magnificence of Cities (Botero [1588] 2012), is the relationship between the sustainability conditions to be satisfied in a polity capable of maintaining itself over time and the ‘attractive power’ allowing a polity to expand beyond the limits set by the extent of its territory and its internal endowment of resources. It is in this connection that manufacturing takes centre stage, for manufacturing is a means by which a polity can extend its power of attraction beyond natural constraints, allowing it to attract resources (essentially staples) from abroad. This theme is again prominent in Botero’s subsequent work, Reason of State, which stresses manufacturing as an effective means to ‘increase people and resources’ (Botero [1589] 2017, p. 134) and calls attention to the need to achieve a balance between internal and external supply of staples as the best way to meet the conditions for sustainability and expansion. Antonio Serra’s Short Treatise, written a few decades after Botero’s works, is the first systematic attempt to delve into the ‘mechanism’ explaining the central role of manufacturing in economic growth. Serra’s analysis starts with the distinction between fundamental and contingent factors in explaining the dynamics of the wealth of nations: [The causes of national wealth] may be subdivided into two kinds: proper accidents and common accidents. Accidents are proper when they occur, or may occur, in one particular kingdom and not in others; and they are common when they occur, or may occur, in any kingdom. Of the proper accidents which can make a kingdom abound in gold and silver, there are two main ones. The first is a domestic agricultural surplus, which occurs when the commodities produced by the kingdom exceed the amount required for the needs and comfort of the inhabitants… This accident is proper because it does not, and cannot, occur in every kingdom. It is more important in our Kingdom than in any other part of Italy, as is well known. The second proper accident is geographical position with respect to other kingdoms and parts of the world. This must be numbered among the proper accidents because it is a powerful occasion for, and almost a cause of, vigorous trade, both with other parts of the world and within the kingdom itself, and this trade causes an abundance of gold and silver… In this proper accident [the city of Venice] holds the first place, not only in Italy but in Asia and Europe: whereas the Kingdom of Naples is more deficient in this accident than any other region… The principal common accidents are four in number: a multiplicity of manufacturing activities, an enterprising population, extensive trade, and effective government. These accidents may be termed common because they are possible in any kingdom. If all four of them should occur in one place, there is no doubt that, even if there were no domestic agricultural surplus and everything had to be imported, they would still make that place abound in gold and silver even if the country had no mines of those metals. (Serra [1613] 2011, p. 119)

Serra’s identification of increasing returns as the most important factor explaining the relative economic performance of economic systems closely follows his distinction between ‘common factors’ (accidenti communi) and ‘particular factors’ (accidenti propri). The mechanism generating increasing returns belongs to the working of common factors, as it is based on cumulative causation primarily involving ‘quantity of industry’ and ‘extensive trading operations’. In Serra’s words, increasing returns presuppose a technical condition characteristic of manufacturing, in which production can be increased ‘at a proportionately lower cost’ (ibid., p. 121). At the same time, increasing returns can become a decisive factor in triggering economic growth as a result of a cumulative process, in which trade and manufactures support each other along a dynamic trajectory: [Venice] is…aided by its multiplicity of manufacturing activities, an accident which attracts a large number of people to the city. Here the determining factor is not the multiplicity of manufacturing activities alone, for if that were the case we would have to attribute the cause to that accident, but

Industrial development and the growth process: a structural framework  405 a combination of two accidents, each of which lends force to the other. For the number of people attracted by the extensive trade and the geographical position is increased still further by the number of businesses, and the number of businesses is increased by the extensive trade, which is itself increased by the number of people who come to the city. (Ibid., p. 127)

In short, Serra identifies increasing returns as a central element explaining the growth performance of a manufacturing economy. However, he is careful to avoid any kind of technological or organizational determinism since increasing returns trigger the growth process through mutual interaction with extensive trade, which in turn is facilitated by Venice’s ‘geographical position’ (a ‘proper accident’) and also, presumably, by successful policy measures (‘effective government’). Serra’s distinction between proper accidents and common accidents is at the root of an approach to policymaking that acknowledges the central role of artefacts (artifici) in making increasing returns possible, but also brings to light the additional conditions and actions that turn that possibility into an actual process of cumulative expansion. Adam Smith’s theory of manufacturing takes the splitting and specialization of production tasks to the centre stage of the mechanism generating increasing returns trajectories: The greatest improvements in the productive powers of labour, and the greater part of the skill, dexterity, and judgement with which it is anywhere directed, or applied, seem to have been the effects of the division of labour… This great increase of the quantity of work, which, in consequence of the division of labour, the same number of people are capable of performing, is owing to three different circumstances: first, to the increase of dexterity in every particular workman; secondly, to the saving of the time which is commonly lost in passing from one species of work to another; and lastly, to the invention of a great number of machines which facilitate and abridge labour, and enable one man to do the work of many. (Smith [1776] 1976, I.i.1, p. 13 and I.i.5, p. 17)

The above ‘trio of advantages’ (Edgeworth, 1911, p. 554) explains why ‘the division of labour, so far as it can be introduced, occasions, in every art, a proportionable increase of the productive powers of labour’, so that ‘the separation of different trades and employments from one another, seems to have taken place, in consequence of this advantage’ (Smith [1776] 1976, I.i.4, p. 15). However, the splitting and specialization of tasks cannot be carried out to the same degree in all fields of production: The spinner is almost always a distinct person from the weaver; but the ploughman, the harrower, the sower of the seed, and the reaper of the corn, are often the same. The occasions for those different sorts of labour returning with the different seasons of the year, it is impossible that one man should be constantly employed in any one of them. This impossibility of making so complete and entire a separation of all the different branches of labour employed in agriculture, is perhaps the reason why the improvement of the productive powers of labour in this art, does not always keep pace with their improvement in manufactures. (Ibid., p. 16)

Smith’s emphasis on the link between division of labour and manufacturing opens the way to further investigation into the conditions for effective division and specialization of tasks in a manufacturing enterprise. Charles Babbage’s Economy and Machinery and Manufactures (Babbage, 1835), building on a previous contribution by Melchiorre Gioja (Gioja, 1815–17), developed Smith’s analysis of division of labour in manufacturing by identifying proportionality requirements between productive factors as a condition triggering the splitting and specialization of tasks as well as limiting the scope of division of labour at any given scale of production:

406  Handbook of industrial development [T]he master manufacturer, by dividing the work to be executed into different processes, each requiring different degrees of skill or of force, can purchase exactly that precise quantity of both which is necessary for each process; whereas, if the whole work were executed by one workman, that person must possess sufficient skill to perform the most difficult, and sufficient strength to execute the most laborious, of the operations into which the art is divided. (Babbage, 1835, pp. 175–6)

As the above passage shows, the asymmetries between components of the production process bring to light an advantage of division of labour that Smith had overlooked. At the same time, precisely the proportionality advantage that Babbage and Gioja had identified introduces a technical and organizational constraint on the splitting and specialization of tasks: When the number of processes into which it is most advantageous to divide [a production process], and the number of individuals to be employed in it, are ascertained, then all factories which do not employ a direct multiple of this latter number, will produce the article at a greater cost. (Ibid., p. 212)

In other words, the internal differences within the production process create a condition that makes the splitting and specialization of tasks potentially advantageous (this we may call the First Babbage Law). On the other hand, this splitting and specialization must follow a proportionality criterion that requires a minimum scale to be satisfied and introduces a constraint requiring further scale expansion to follow the criterion of increase by integer multiples of that minimum scale (this we may call the Second Babbage Law). The two Babbage laws deeply transform Adam Smith’s treatment of the division of labour by introducing a change with regard to the relationship between division and labour and the extent of the market. Smith maintains that: [a]s it is the power of exchanging that gives occasion to the division of labour, so the extent of this division must always be limited by the extent of that power, or, in other words, by the extent of the market. When the market is very small, no person can have any encouragement to dedicate himself entirely to one employment, for want of the power to exchange all that surplus part of the produce of his own labour, which is over and above his own consumption, for such parts of the produce of other men’s labour as he has occasion for. (Smith [1776] 1976, I.iii.1, p. 31)

Smith views division of labour as a technical and societal process that, under conditions of unhindered decomposability of the production process,1 is only constrained by the ‘extent of the market’ as identified with the ‘power to exchange’ any significant quantity of a specialized product. A similar argument applies to Smith’s view of the internal differentiation and specialization of tasks within manufacturing workshops. Different from Smith, Babbage brings to light opportunities and constraints for the differentiation and specialization of manufacturing tasks that arise from within the internal structure of the manufacturing process. These opportunities and constraints reflect the two Babbage laws and generate a relationship between increasing scale and the technical organization of production that is at variance with Smith’s view of the relationship between division of labour and the extent of the market. In particular, the Second Babbage Law brings to light that a continuously expanding scale of production (triggered by a continuously increasing ‘extent of the market’) is not always consistent with a constant or increasing differentiation and specialization of productive tasks. For this to be possible, the scale of production has to increase by integer multiples of the minimum scale, allowing the ‘received’ pattern of division of labour (by the Second Babbage Law), and for a more advanced division of labour to be feasible, the scale of production has to reach the

Industrial development and the growth process: a structural framework  407 minimum level compatible with it (by the First Babbage Law). In short, Babbage’s view of division of labour in manufacturing begins a structural approach that looks at increasing returns as a process characterized by jumps and discontinuities, and in which different patterns of scale expansion may set a manufacturing process along entirely different trajectories of increasing returns (Scazzieri, 1993, Chapters 5 and 8; Scazzieri, 2014). Division of labour brings to light not only the advantages of differentiation and specialization but also the need for coordinating the specialized activities within an interrelated whole. Friedrich List identified the necessary conditions for effective manufacturing by considering the interdependencies between manufacturing, agriculture and other activities within a national economy at a given stage of development. List’s view is that a ‘greater part of the productive power consists in the intellectual and social conditions of the individuals, which I call capital of mind’ (List [1827] 1996, p. 63), and that manufacturing has the specific advantage of making productive power increasingly independent of the availability of natural resources (what he called ‘capital of nature’). However, he was also convinced that attempts to promote manufacturing under conditions of inadequate development of the ‘capital of mind’ were doomed to failure, and that manufacturing itself should proceed ‘by steps’ – that is, by considering each economy’s stage of development as signalled by the viable type of interdependence between manufacturing and other activities: A new country like this [the US] increases its productive powers by only fostering those manufactories which employ a number of labourers, and consume great quantities of agricultural produce and raw materials; which can be supported by machinery and by a great internal consumption (like chemical produce, woollen, cotton, hardware, iron, earthenware, etc. manufactories), and which are not easy to be smuggled. In fostering finer articles with equal care, they would injure the development of the productive powers. (Ibid., p. 79)

List also maintained that this constraint on the type of manufacturing that a given economy can afford at a particular stage of its development is only temporary, due to the likely transformation of the internal market for more refined products: These articles of comfort and luxury, if imported cheaper than we can manufacture them, get in use among all labouring classes, and act as a stimulus in exciting the productive powers of the nation. Its consumption becomes by and by more important, and by and by time will arrive when these articles, with a moderate encouragement, will be manufactured too within our limits. (Ibid.)

In short, List’s analysis of manufacturing moves from the extent of the market that makes given patterns of specialization possible (Smith), and from the opportunities and constraints of technical division of labour within each workshop (Babbage), to the viability conditions for the manufacturing sector within the set of interdependencies of an economic system at a given stage of its historical trajectory. This shift from individual manufactures or the manufacturing sector to the whole economic system makes List’s analysis close to that of Serra, with whom he shares the attention for specific conditions (accidenti propri) – that is, for the opportunities and constraints characterizing a particular economy at a given stage of development (see also Kaldor, 1966, 1967).

408  Handbook of industrial development

3

DIVISION OF LABOUR AND PRODUCTION NETWORKS

The long-standing recognition of manufacturing as a central feature of economic growth goes hand in hand with shifting emphasis on which features of manufacturing are central at different stages and contexts of development. List’s attention to the differences in the manufacturing prospects of different economies depending on the type of interdependencies between productive sectors signals a shift to the meso level of analysis in assessing the ‘productive powers’ of a given economy under given historical and institutional conditions. Patterns of interdependence are in fact central to the relationship between increasing returns and the division of labour. However, division of labour may be considered in its dual aspect of (1) differentiation between sectors producing different goods (such as cutting tools, textiles, domestic appliances); and (2) differentiation between operations (tasks) carried out within productive establishments (such as cutting, throwing, melting and so forth). Serra and Smith consider both aspects, even if Serra privileges the sectoral point of view (that is, the division of labour triggered by differentiation between goods produced), while Smith privileges the operational point of view (that is, the division of labour triggered by differentiation between the tasks carried out in the establishment). The two paths of differentiation are closely intertwined, since specialized artefacts are often associated with the introduction of new and more specific ways of performing productive operations, which may in turn require specialized capabilities and task-specific tools and machines. The relationship between the degree of specialization by task and the degree of specialization by product brings to light the connection between the transformation of productive tasks and the dynamics of interdependence between product flows, since changes in the pattern of task differentiation is closely associated with the need to achieve a degree of coordination between specialized product flows (Ames and Rosenberg, 1965). This means that changing division of labour always involves changes in the network of product flows, which can be considered as the ‘objective’ counterpart of transformations in capabilities and productive tasks. Networks of product flows may take different forms depending on whether they follow one or the other of the two patterns below. In one case, changes in specialization generate ‘one-line’ (vertical) supply chains leading from labour, primary resources, and productive equipment to particular finished goods, so that the different product flows contributing to any given finished good can be considered to be part of the same vertically integrated sector (Pasinetti, 1973). This case can be represented by an economic system consisting of specialized vertically integrated sectors producing goods C1, C2, …, Cn by means of supply chains that include the corresponding quantities of direct and indirect labour li (i = 1, 2, …, n) as well as the corresponding stocks of material inputs s(i) (i = 1, 2, …, n) (see Landesmann and Scazzieri, 1990, p. 117). In the other case, the pattern of product specialization generates a ‘circular’ supply network producing intermediate products that are themselves required as inputs necessary to the maintenance (and possible expansion) of the network itself (Leontief [1928] 1991; Pasinetti, 1977). This case can be represented by an economic system consisting of sectors Si (i = 1, 2, 3) specialized in producing goods C1, C2, C3, respectively, by means of fully interdependent supply chains, so that flows of specialized products connect each activity with any other activity within the same network (Landesmann and Scazzieri, 1990, p. 110). Specialization by product may take place both in vertical and in circular production networks. For example, in a vertical network we may have an increase in the range of final prod-

Industrial development and the growth process: a structural framework  409 ucts from set {C1, C2, …, Cn} to set {C1, C2, …, Cn, Cn+1, Cn+2, …, Cn+s}. In this case, increasing specialization by final product may trigger a further upstream specialization in the types of labour and productive equipment needed to deliver the expanded collection of final products. On the other hand, in a circular network we may have an increase in the range of sectors from set {S1, S2, S3} to a set such as{S1, S2, S3, S4, S5, S6}. In this case, specialization takes the form of an increasing range of intermediate products, which may in turn trigger further upstream or downstream specialization with an increasing range of intermediate products entering the network of interdependent supply chains. Vertical production networks bring to light the possibility of a widening division of labour, which expands the range of finished goods produced, and which may or may not involve further upstream specialization of labour capabilities and productive equipment. Circular production networks bring to light the possibility of a deepening division of labour, which expands the range of intermediate products within the network, and which may or may not involve further upstream and downstream upstream specialization of labour capabilities and productive equipment. Widening division of labour may be represented by considering the representation of production technology outlined in John Hicks’s Capital and Time (Hicks, 1973). Hicks considers a production system in which no produced good enters the production of itself, thereby introducing a fully vertical structure. Technology matrix AH represents this arrangement of productive activities (what we may call a ‘Hicks technology’): AH =

0 a12 (23.1) l2

l1

In matrix AH no ‘downstream’ process can deliver a good that is needed as an input in an ‘upstream’ process (Magnan de Bornier, 1990, p. 129). This means that good 1 (say, ‘machine tools’) does not enter its own production (thus a11 = 0), but enters the production of good 2 (say, ‘corn’). On the other hand, labour (here considered as an original input) enters the production of both good 1 and good 2. A Hicks technology allows a simplified representation of widening division of labour in a vertical production network by adding more ‘vertical’ supply chains, as shown in matrix AH' below: AH  

0 a12 l1 l2

0 l3

a34  0 l4  ln

anm (23.2) lm

The shift from technology AH to technology AH' brings to light a process of widening specialization: the ‘labour fund’ of the economy is split into more (and, potentially, more and more) specialized labour funds distinguished by which good is produced, while no good enters as an input in the production of itself. As mentioned above, we cannot exclude the appearance of labour funds specialized in producing the intermediates required in sectors making new finished goods, but these new intermediates do not enter the supply chains of other goods. This means that, in this case, increasing specialization does not entail any increase in the ‘depth’ of technology. Deepening division of labour may be represented by considering a representation of production technology presented in several contributions by Wassily Leontief (Leontief [1928] 1991;

410  Handbook of industrial development 1941). Leontief considers a production system in which produced goods enter the production of themselves, thereby introducing a fully circular structure. Technology matrix AL represents this arrangement of productive activities (what we may call a ‘Leontief technology’): AL =

a11 a21

a12 (23.3) a22

In matrix AL ‘downstream’ processes deliver goods that are needed as inputs in ‘upstream’ processes and vice versa. This means that good 1 (‘machine tools’) enters both its own production (thus a11 ≠ 0), and the production of good 2 (‘corn’). At the same time, good 2 (‘corn’) enters both its own production (thus a22 ≠ 0) and the production of good 1 (‘machine tools’). On the other hand, labour is no longer considered as an original input and labour quantities are substituted by the ‘necessary consumption’ included in the intermediate input coefficients aij (i =1, 2). A Leontief technology allows a simplified representation of deepening division of labour in a circular production network by adding the supply-and-demand chains for more intermediate products, as shown in matrix AL' below: a11  I  0 a12  I  A  0 a11  II  a12  II   a21  I  a21  II  a22 L'

(23.4)

Matrix AL' shows an increase of specialization due to the ‘splitting’ of the sector producing good 1 into two distinct sectors, respectively denoted by superscripts (I) and (II).2 The shift from technology AL to technology AL' brings to light a process of widening specialization: the ‘circular flow’ of the economy is split into more (and, potentially, more and more) specialized circular flows distinguished by which intermediate inputs enter the production of themselves and of other goods in the economy. Here, the introduction of specialized intermediate inputs may trigger further specialization by product both along the upstream and the downstream route. In this case, increasing specialization would entail an increase in the ‘depth’ of technology. Circular and vertical features are often combined in production networks, which may lead to more complex patterns of interdependence and specialization. The technology matrix B below brings to light vertical relationships within a production network of the circular type: a11 B= 0 l1

a12 0 l2

0 a23 (23.5) l3

In matrix B, each coefficient aij denotes the quantity of good i entering the production of each unit of good j, while each coefficient li denotes the quantity of labour entering the production of each unit of j. The matrix shows a hierarchical arrangement of production processes, whereby the sector producing commodity 1 (say, ‘machine tools’) delivers outputs both to itself and to the sector producing commodity 2 (say, ‘tractors’), while the sector producing commodity 2 delivers its output only to the sector producing commodity 3 (say, ‘corn’). Labour is needed as

Industrial development and the growth process: a structural framework  411 an input in all three sectors (Lowe, 1976). The structure of matrix B brings to light the potential interplay between widening and deepening division of labour. This is shown by transforming matrix B into matrix B', which describes a production network characterized by an increase of finished goods (a higher number of columns) and by an increase of intermediates (a higher number of rows). Matrix B' includes two additional sectors producing, respectively, good 4 and good 5, which are used as intermediates for producing, respectively, both goods 4 and 5, and good 5 only: a11 0 B' =

a12

0

0

a22 0

a23 a33

0 0

0 0

0 0

a44

0 0 (23.6) a45

0

a55

l1

l2

l3

l4

l5

0 0 a34

0

Matrix B' brings to light a situation in which division of labour is of the widening type (higher number of finished goods) and of the deepening type (higher number of intermediates) at the same time. A comparison between matrices B and B' shows a pattern of specialization by product associated with an increase in the layers of intermediate goods in the production network. Technology B' involves a production network structurally equivalent to the network associated with technology B, since any additional subsystem of specialized products, starting a 0 with subsystem 23 follows the pattern of utilization of intermediate inputs that characa33 a34 a11 a12 . Specialization by product may also lead to production networks, 0 a22 such as B'', which are structurally different from B: terizes subsystem

B'' =

a11 0 a31

a12 a22 a32

0 a23 a33

0 0

0 0

0 0

a44

0 0 a35 (23.7) a45

0

a55

l1

l2

l3

l4

l5

0 0 a34

In B'', product 3 is an intermediate input in all sectors of the economy from sector 1 to sector 5. This means that technology B'' not only increases the number of intermediates relative to technology B, but also increases the systemic interdependence of productive sectors, making all sectors dependent on the supply of a particular intermediate product (good 3). In particular, the shift from B to B'' involves the change from a ‘loose’ to a ‘centric’ structure (Figure 23.1). The above case shows that a change in the degree of specialization by product may bring about significant changes in the internal structure of the production network. For example, increasing specialization may turn a loose network, such as the one shown in matrix B', into

412  Handbook of industrial development

Figure 23.1

From a ‘loose’ to a ‘centric’ production network

a ‘centric’ and hierarchically organized network such as the one associated with matrix B'', in which the sector specialized in supplying intermediate good 3 to all other sectors becomes essential to the whole economy. Changes in connectivity are a characteristic feature of industrial economies once they have attained a sufficiently developed stage of organizational complexity. As Allyn Young noted: First, the mechanism of increasing returns is not to be discerned adequately by observing the effects of variations in the size of an individual firm or of a particular industry, for the progressive division and specialisation of industries is an essential part of the process by which increasing returns are realised. What is required is that industrial operations be seen as an interrelated whole. Second, the securing of increasing returns depends upon the progressive division of labour, and the principal economies of the division of labour, in its modern forms, are the economies which are to be had by using labour in roundabout or indirect ways. Third, the division of labour depends upon the extent of the market, but the extent of the market also depends upon the division of labour. In this circumstance lies the possibility of economic progress, apart from the progress which comes as a result of the new knowledge which men are able to gain, whether in the pursuit of their economic or of their non-economic interests. (Young, 1928, pp. 539–40)

The relationship between transformations in connectivity and changing specialization by product brings to light a change in the working of increasing returns as we move from the individual establishment or productive sector to the network of productive activities in the economic system. The relationship between division of labour and extent of the market (which is central to Smith’s treatment of increasing returns within the individual establishment) has far reaching consequences for the network of production units in the whole economy. This means that Smith’s original emphasis on the extent of the market ‘available’ to individual workshops must shift to the way in which the aggregate extent of the market is distributed across different production units. As for Babbage laws, the proportionality condition between production capabilities (First Babbage Law), which Babbage identified for individual establishments,

Industrial development and the growth process: a structural framework  413 now becomes a condition to be satisfied by the network of interdependent production units in the economy. At the same time, the integer multiples condition on the expansion of the scale of production (Second Babbage Law) also becomes a condition to be satisfied in the production network. This brings to light a constraint on the proportional rates of expansion of different specialized activities once division of labour moves from the differentiation of tasks within establishments to the specialization by product in the industrial system considered ‘as an interrelated whole’ (ibid.). In conclusion, the transformations of connectivity triggered by changes in the specialization by task and by product involve a transformation of the original Smith and Babbage laws. The domain of those laws shifts from individual production units to networks of production activities, which involves: (1) the availability, for each production unit, of an extent of the market sufficient to achieve and maintain the corresponding degree of specialization (Smith Law); (2) the attainment, by each production unit, of a minimum size compatible with the First Babbage Law; and (3) a trajectory of scale expansion for specialized products compatible with the Second Babbage Law (expansion of establishment size by integer multiples of the minimum size identified by the First Babbage Law). The relationship between the differentiation of productive functions and the scale dimensions of productive activity (such as the scale of productive activity carried within production units at different levels of aggregation, the size of individual establishments, the output levels of individual products) provides a unifying framework for the analysis of increasing returns both within production units and within networks of production units. However, both the Smith Law and the two Babbage laws become increasingly complex as we move from lower to higher levels of aggregation. At the core of this transformation of the increasing returns process is the need to shift goods across production units. This process is Janus-faced. On one side, we may conjecture that establishments producing a single product, or a limited range of products, may more easily meet the First Babbage Law due to the limited range of capabilities to be coordinated within the production process. On the other side, specialization by product may or may not facilitate the fulfilment of Smith Law (extent of the market needed to support the given degree of specialization) and of the Second Babbage Law (‘discrete’ expansion trajectory by integer multiples of the minimum size identified by the First Babbage Law). In that connection, changing specialization by product is likely to bring about different outcomes depending on: (1) the scale of activity achieved by the production network as a whole; and (2) the way in which the aggregate scale of the production network is distributed across the different production units within the network.

4

MANUFACTURING REGIMES AND PATTERNS OF INTERDEPENDENCE: A DYNAMIC INTERPLAY

Division of labour generates patterns of interdependence within the production domain, and interdependence takes different forms depending on which dimensions of the production process are most directly involved. This in turn depends on technology in use and organizational form. Patterns of interdependence in manufacturing provide an important example of the interplay between technology and organization and bring to light the manifold routes by which that interplay shapes division of labour in a production network. At the core of the mutual influence between technology and organization is the relationship between the tasks that human or non-human agents perform in the production process

414  Handbook of industrial development to achieve certain transformations and the general functions that those human or non-human agents are capable to perform independently of the specific tasks in whose execution they are used (Cardinale and Scazzieri, 2011). The distinction between tasks and functions is inherent to the definition of a productive task. Tasks are ‘completed operation[s] usually performed without interruption on some particular object’, which are ‘not further divisible (at least for the purposes of the analysis at hand)’ (Scazzieri, 1993, p. 84). The above definition involves both a general action or ‘power’ capable of executing particular operations in the production process and the specific operation(s) executed on a particular object in order to bring about a certain transformation or set of transformations. For example, a task description such as ‘cutting a steel bar of type X’ entails both the component denoting a general (and inherently open-ended) action (‘cutting’) as well as the component designating the specific task in whose execution that action is performed (‘cutting a steel bar of type X’). Task definitions therefore involve a distinction between functions, considered as abilities, dispositions or states of readiness presupposed in the performance of any given productive operation, and the operations executed when those abilities, dispositions or states of readiness are activated to achieve a certain transformation. The distinction between functions and tasks is central to understanding the relationship between technology and organization and what that relationship involves for patterns of interdependence in an industrial economy. In fact, the interplay between technology and organization leads to different manufacturing regimes depending on the way in which functions are associated with operations in each production process. This mode of association is central in determining the prevailing patterns of interdependence in a manufacturing regime. Machinery is the most important criterion for the division and aggregation of processes during the First Industrial Revolution and its aftermath (Bianchi and Labory, 2019; Hicks, 1969). This involves a break from craft production, in which human abilities provide the basis for division of labour, and leads to growing emphasis on machine tools, standardization of components, and interchangeable-parts production (Best, 2018). Machinery drives the division and specialization of processes both within and across productive establishments. Within establishments, machinery induces the arrangement of processes according to the flow principles that characterize factory production and allow the continuous utilization of productive equipment (Best, 2018; Georgescu-Roegen, 1970, 1990; Landesmann, 1986; Landesmann and Scazzieri, 1996; Scazzieri, 1993). Across establishments, production by machinery induces the production of machines as specialized products as well as the production of standardized components, which may in turn become specialized products (Best, 2018; Rosenberg, 1963; Young, 1928). Increasing utilization of intermediate products is a characterizing feature of machinery-based manufacturing and a distinctive feature of division of labour in it: ‘the principal economies of the division of labour, in its modern forms, are the economies which are to be had by using labour in roundabout or indirect ways’ (Young, 1928, p. 539). At the same time, the twin processes of interchangeable-parts engineering and standardization of product components makes intermediate inputs at least potentially available to a plurality of industrial uses. Division of labour across production units under the factory system involves the formation of production networks in which the vertical supply chains leading from primary and intermediate inputs to finished products are ‘broken’ by the introduction of horizontal linkages due to standardized intermediate inputs used in a variety of different supply chains. An example is shown by matrix B'' above in which intermediate product 3 holds the production network together by entering

Industrial development and the growth process: a structural framework  415 the fabrication of all goods in the economy (including itself). The process of increasing returns depends on whether the conditions expressed by the Smith and Babbage laws are satisfied at the different levels of aggregation of the production units. To assess whether increasing connectivity in a production network is compatible with increasing returns, one needs to check whether the scale conditions for product specialization are met at each level of aggregation (these scale conditions correspond to Smith’s ‘extent of the market’ for specialized products). The network structure also brings to light the need to assess whether the existing pattern of product specialization allows each specialized process to use ‘that precise quantity’ of ‘skill or of force’ that is needed for the working of each process (First Babbage Law). Finally, one should ask whether the pattern of specialization shown by network structure makes it possible to achieve for each specialized process the scale expansion by integer multiples identified by the Second Babbage Law. Materials provide the central organizing principle for the division and specialization of processes in the manufacturing regime of flexible production, which originated in post-World War II Japan and expanded to the whole manufacturing world in the following decades (Abo, 1994). In this manufacturing regime, ‘[p]roduction systems became flexible in that they were able to produce partially differentiated products on a large scale, with the possibility to reduce the time to market and therefore more rapidly react to changes in demand or in competitors’ strategies. Production processes were still divided into tasks, but different varieties of the product shared the same production lines, and differentiation arose at later stages of the production process’ (Bianchi and Labory, 2019, p. 26). In the flexible manufacturing regime, each manufacturing unit consists of a ‘system’ of interdependent machines (as in the classical factory), but machines are versatile (multi-skilled) agents capable of moving from one task to another (within a pre-programmed range of feasible tasks). This rearrangement of the relationship between functions and operations within the productive establishment allows the detachment of the organization of productive equipment from the execution of specific job-specification programmes. In this way, different lines of material transformation leading to different product specifications can be activated within the establishment depending on changes in final demand (Landesmann and Scazzieri, 1996, pp. 283–9). Materials-in-process drive the division and specialization of processes both within and across productive establishments. Within establishments, the different flows of sequentially related transformation stages for materials-in-process bring to light the need to achieve ‘a proper sequencing and bundling of tasks in an environment of changing production programmes’, so that the ‘multi-skilled fund-input elements [versatile machinery] are relatively well utilised in all their capacity dimensions’ (Landesmann and Scazzieri, 1996, p. 284). Across establishments, flexible manufacturing is associated with the ‘just-in-time’ arrangement of production flows between production units according to the following principle: ‘[p]roduce and deliver goods just in time to be sold, sub-assemblies just in time to be assembled into finished goods, fabricated parts just in time into sub-assemblies, and purchased materials just in time to be transformed into fabricated parts’ (Schonberger, 1986, p. 16). The versatility of the materials-in-process flows within individual establishments brings to light the need for coordination between mutually substitutable flows as a condition to ensure the feasibility of the production network. This condition has far-reaching consequences for the division of labour across production units and involves the formation of production networks in which each vertical supply chain shares early fabrication stages with other supply chains and only requires specific fabrication stages when the different production processes get close

416  Handbook of industrial development to completion. This situation is represented in matrix F(I, II), which shows ‘shared’ fabrication stages between the supply chains I and II associated, respectively, with the finished products m and m+1:

F  I , II 

0 a21   am 1,1 am1

a12 a22  am 1, 2 am 2

 a1m  I  a1, m 1  II   0 0      am 1, m  I  0  0 am , m 1  II 

(23.8)

In the production network F(I, II) the supply chains leading to goods m and m+1 share the fabrication stages associated with the utilization of input 1 but are otherwise associated with separate stages of transformation of the materials-in-process. This situation may entail significant changes of productive interdependencies relative to those characterizing the manufacturing regime centred on machinery (the factory system). For example, the increasing utilization of specialized intermediate inputs associated with component standardization and interchangeable parts manufacturing may be partially offset by the synchronization requirements characterizing the combination of flexible machinery and just-in-time delivery of components along any supply chain in which the flexible manufacturing system (FMS) is adopted. For example, the making of input 1, which is needed in the FMS supply chains of goods m and m+1 (respectively, supply chains I and II in matrix F(I, II)) could be combined in a single process executing the fabrication stages common to the supply chains for goods m and m+1 independently of the existence of specialized producers who may supply input 1 to other supply chains in the economy. The dual character of the manufacturing system based on substitutable materials-in-process flows should be noted. For the integration between fabrication stages common to several supply chains is associated with the separation between fabrication stages that are different from one supply chain to another. This involves the coexistence between aggregation and disaggregation of fabrication stages depending on the specific position of each fabrication stage within the network of transformation processes for work-in-progress materials. Under flexible manufacturing and just-in time production, the mechanism of increasing returns is subject to aggregation and disaggregation dynamics that are not always consistent with the Smith and Babbage laws. Smith’s ‘extent of the market’ condition may work against the division and specialization of processes when the combined need for product diversification and synchronization of fabrication stages leads to the integration of stages common to several supply chains. In a similar way, the two Babbage laws may or may not be satisfied depending on the interplay between division and integration of fabrication stages. The existence of stages of transformation of materials-in-process common to different supply chains may sometimes be compatible with the division of labour between productive agents (human agents and machinery) identified by the First Babbage Law, but in different circumstances it may also work against it. In the latter case, it is also likely that the pattern of scale variation of the different fabrication stages would be inconsistent with the variation by integer multiples required by the Second Babbage law. Productive functions are the central organizing principle for the division and specialization of processes in the manufacturing regime of mass customization, which developed in recent

Industrial development and the growth process: a structural framework  417 years and in which ‘the product is able to “communicate” with machines in order to “tell them” what to do. Machines can exchange information and modify their own “behaviour” on the basis of the received inputs, to memorize instructions and therefore learn from the digital interaction’ (Bianchi and Labory, 2019, p. 27). This manufacturing regime is different from machinery-based mass production since productive agents (including machinery) are multi-skilled, and the job-specification programmes to which they are assigned may vary depending on customer requirements. The assignment of productive agents to tasks is not determined ex ante so that agents’ capabilities to perform general (and open-ended) actions, such as ‘cutting’, is prioritized relative to the execution of those actions as operations (tasks) required in specific fabrication stages. Mass customization is also different from the FMS since the fabrication stages of the materials-in-process are no longer required to be sequentially coordinated with one another along partially common supply chains. Under mass customization, the continuous (or semi-continuous) utilization of productive agents requires agents’ responsiveness to consumers’ and users’ needs by adapting their abilities to the job specifications required at particular transformation stages of the materials-in-process. In short, mass customization, differently from classical mass production, does not require that fabrication stages be adapted to the pre-programmed job-specification programmes of the existing productive equipment; and differently from the FMS, it does not require that fabrication stages be coordinated with one another to ensure the availability of materials-in-process at critical points along the supply chains for a variety of finished products. Division of labour under mass customization moves beyond the specialization by product (mass production) and the specialization by supply chain (FMS) and introduces a pattern of division of labour centred on the distinction between general productive functions – that is, on the differentiation between abilities to perform general actions independently of the way in which those actions are executed according to particular job-specification programmes. This paradigm of division of labour involves a shift away from Adam Smith’s emphasis on division of labour within the workshop, which is based on the distinction between the operations (tasks) performed in particular production processes. Division of labour in the mass-customization regime shifts from tasks to functions and brings to light the pools of job-specification programmes associated with the decomposition of capabilities by general functions rather than by job-specific tasks. This pattern of specialization and integration triggers the emergence of production networks that are no longer based on specialization by product (mass production) or specialization by supply chain (FMS). In this case, the emergence of production networks may reflect distinctions and aggregations of capabilities that do not coincide with the patterns of division of labour that involve finished products (specialization by product) or materials-in-process (specialization by supply chain). This may lead to production networks whose internal hierarchy shows the coexistence of fragmented manufacturing of materials-in-process with the integrated organization of the capabilities to perform productive functions central to the working and maintenance over time of a system of interdependent activities.

418  Handbook of industrial development Matrix MC shows a mass-customization network in which a general capability (here represented by productive input 1) generates the hierarchical structure supporting the whole production network: a11 0 0 MC   0 0

a12 a22 0  0 0

a13 a23 a33  0 0

a14 0 a34  0 0

 a1, m  2  0  0    am  2, m  2  0

a1, m 1 0 0  am  2, m 1 am 1, m 1

a1m 0 0 .  0

(23.9)

am

A characterizing feature of the production network in MC is that the output from activity 1 is needed for the execution of all other activities, while each other activity provides inputs only to itself and to the activity that comes next in the hierarchy. This organization of activities reflects a production network in which connections are defined by the range of application of capabilities rather than by the interdependence between products or by the sequential dependencies between fabrication stages within supply chains. In this situation, a production network such as MC can be ‘rewired’ into a significantly different network such as MC': a11 0 0 MC '  0  0 0

a12 a22 a32 0  0 0

a13 0 a33 a43  0 0

a14 a24 0 a44  0 0

 a1,m  2  0  0  0    am  2 ,m  2  0

a1,m 1 0 0 0  am  2 ,m 1 am 1,m 1

a1m 0 0 0 (23.10)  0 am

In production network MC', function 1 is required to perform all other productive functions in the economy, while all functions from function 2 to function m–1 are only a prerequisite for performing a limited number of other functions (function 2 is required to perform functions 2 and 4; function 3 is required to perform functions 2 and 3, …, function m–1 is required to perform functions m–1 and m). This pattern of interdependence suggests that specialization by product is no longer of central importance. This is shown by comparing production networks MC and MC': apart from function 1 all other functions switch from one pattern of integration to another. At the same time, function 1 is required as a condition for performing all other functions from function 2 to function m. Flexibility in associating functions with one another reduces the importance of division of labour by task since alternative patterns of functional integration are feasible depending on which combinations of functions are required when switching from one product specification to another. On the other hand, production networks MC and MC' show an invariant hierarchical structure, which persists in spite of the changes in pattern of functional integration between MC and MC' due to the higher hierarchical position of function 1 in both production networks. This persistent hierarchy brings to light the role

Industrial development and the growth process: a structural framework  419 of relative invariance (here associated with a degree of hub centrality) under conditions of versatile functions and flexible connectivity. In the mass-customization regime, the interdependencies between production activities are likely to generate production networks in which the specialization and integration of processes may bring about increasing returns trajectories different from those associated with mass production and flexible manufacturing regimes. This is because under mass customization, the disaggregation and aggregation of activities follow the dividing lines between versatile functions rather than the separation between specific tasks. Similarly, the dividing lines between fabrication stages of the materials-in-process also follow the separation between productive functions (or sets of productive functions) rather than the distinction between finished products. This means that Smith’s Law connecting the extent of the market with division of labour now applies to the division of productive functions rather than to the division of productive tasks. As a result, there may be circumstances in which an increasing scale of the production process (identified with a measure of the aggregate scale of the activities carried out in the productive establishment) makes it possible to switch to a more effective organization of production characterized by increasing conjunction (rather than separation) of tasks. On the other hand, there may also be circumstances in which an increasing scale of production requires the separation of certain functions from other functions with which they had been previously combined. At the origin of the open-ended character of the division of labour under mass customization is the structural condition regulating division of labour in the case of versatile functions and flexible connectivity. The relevant scale dimension is associated here with the degree to which the productive functions available in the establishment can be active in performing tasks belonging to several different sequences of fabrication stages. The continuous, or semi-continuous, utilization of productive functions presupposes a proportionality condition between the different fabrication stages carried out in the establishment (Scazzieri, 1983, 1993).3 However, this proportionality cannot be achieved at any level of the quantities of the different products made in the establishment. For this reason, an increasing extent of the market for a given establishment (identified with the set of productive functions available in it) may sometimes be associated with a decreasing degree of specialization (in the sense of an increasing range of the tasks carried out by its productive functions) and sometimes with an increasing degree of specialization (in the sense of an increasing specialization of functions in the performance of tasks). In the former case, the increasing extent of the market allows the switch to a range of proportions between production lines compatible with the performance of a broader range of tasks by the same set of functions, or even with the performance of a broader range of tasks by a different and ‘more general’ set of functions. In the latter case, the increasing extent of the market is associated with the switch to a range of proportions between production lines that allows the performance of a narrower range of tasks by the same set of functions, or even the performance of a narrower range of tasks by a different set of more specialized functions. Conditions of mass customization also directly affect Babbage’s two laws connecting the internal organization of the establishment with the scale of the operations carried out within it. In general, the situation presupposed by the First Babbage Law is inverted. For ‘the master manufacturer’, rather than purchasing ‘the degrees of skill or of force’ that are ‘necessary for each process’ (Babbage, 1835, pp. 175–6), is likely to adjust the composition of the process itself (which would often be a mix of different production lines) to the pool of productive func-

420  Handbook of industrial development tions available in the establishment and to the job-specification programme (assignments of functions to tasks) required by the different production lines active in that establishment. This means that a Babbage proportionality condition would still be at work, but that the assignment of productive functions to tasks would be determined together with the internal structure of each production process (that is, together with the mix of production lines active in the establishment). Following this reformulation of the First Babbage Law, also the Second Babbage Law takes a different form, since increasing returns may alternatively require a greater or a lower degree of specialization in the assignment of functions to tasks, depending on which mix of production lines is carried out in the establishment. For this reason, the condition on scale expansion by integer multiples, which characterizes the Second Babbage Law (ibid., p. 212), becomes a proportionality condition on the different production lines that can be simultaneously active within each establishment to allow a satisfactory utilization of the pool of productive functions available in the establishment. The form taken by Smith and Babbage laws under conditions of mass customization at establishment level has far-reaching implications for increasing returns trajectories at the level of the production network. This is because scale technology expansion may induce an increasing returns trajectory along which the integration of tasks (de-specialization of processes) may coexist with the separation between tasks (specialization of processes). In short, increasing division of labour ceases to be a distinguishing feature of increasing returns. The relative importance of the specialization and integration of processes becomes context-dependent and reflects the distribution of productive functions (capabilities) across productive units in the economy. Phases of increasing specialization of functions by task may alternate with phases of increasing integration of tasks under functions, depending on the features of the pools of capabilities in each production network and their transformation over time. In general, mass customization leads to a split between the scale of specific lines of production (the sequences of fabrication stages for specific materials in process) and the size of productive units (the pools of productive functions determining which fabrication stages can be carried out in each establishment). This means that the continuous expansion of particular lines of production (leading to specific final products) may be incompatible with the proportionality condition between lines of production for the individual establishment (Second Babbage Law for the mass-customization regime) and may induce the splitting of versatile establishments into specialized productive units. On the other hand, a more discontinuous expansion of specific production lines may be compatible with the above proportionality condition as it may facilitate adjusting the mix of production lines to the pool of productive functions available in the establishment. In this case, changes in activity levels of different production lines (leading to changes in the quantity produced of specific goods) may allow the integration of tasks under functions within a versatile establishment independently of the particular job-specification programmes in which these functions are involved.

5

CONCLUSION: TOWARDS A STRUCTURAL POLITICAL ECONOMY OF INDUSTRY

As we have seen, economic analysis from Serra to Young and Kaldor suggests an important causal link from the growth of manufacturing to the macroeconomic growth rate (Section 2). It also suggests that increasing returns are at the root of that link (Section 3). Increasing returns

Industrial development and the growth process: a structural framework  421 in turn derive from the dynamics of division of labour, which involve not only growing complexity but also the transition between different manufacturing regimes (Section 4). A key direction of further research lies in fleshing out how the material conditions that are involved in the transition between manufacturing regimes underlying increasing returns are intertwined with the actions of the relevant actors. A useful lens can be provided by the structural political economy approach, whose key steps consist in identifying the interdependencies of the production system under analysis, the relevant actors and their interests, and the opportunities afforded and constraints imposed by those interdependencies (Cardinale, 2015, 2018a, 2018b, 2019, 2022; Cardinale and Landesmann, 2017, 2020; Cardinale and Scazzieri, 2019, 2020). In fact, the shift from one manufacturing regime to another involves significant changes in the patterns of interdependence between tasks, between processes, between firms, and between sectors constructed by aggregating firms according to different criteria (such as by industry or through vertical integration). The foregoing analysis has highlighted how changes at one level potentially have an impact on changes at other levels. Each system of interdependencies, at each level of aggregation, has its own conditions of viability – that is, the conditions that allow the system to maintain itself over time. Of particular interest in the structural economic analysis literature have been those at the industry level, where the conditions that must be satisfied by each production network to allow its viability have been explicitly formulated and formalized (Hawkins and Simon, 1949; Pasinetti, 1977). For our purposes, it is crucial that a system can be viable under different proportions between the outputs of different industries (Hawkins and Simon, 1949) and under different price systems (Steenge and Van den Berg, 2001). This implies that viability is compatible with conflicts of interests – for example, over value-added or over quantities produced (Cardinale, 2018a, 2018b). Each form of conflict brings to the fore different potential coalitions of actors who carry specific interests. For example, the relevant actors could be groupings of firms belonging to different industries or different vertically integrated sectors. In turn, such vertically integrated sectors could be built on the basis of final demand or on the use of some inputs, such as scarce resources or key infrastructure. Each coalition of actors is likely to have different interests, which may conflict with those of other coalitions, but the viability of the system of interdependencies imposes constraints on actors’ pursuit of their own interests, because if viability is compromised, then those actors’ pursuit of their objectives could be jeopardized. Besides proportions and the price system, an important dimension of the coexistence between viability conditions and possibilities for conflict in the changes in division of labour underlying increasing returns lies in the asymmetries and hierarchies for what concerns the importance of certain activities for the viability of the production network. For example, it can be conjectured that in a classical mass-production regime, activities producing ‘general’ intermediate inputs (such as certain machine tools) play a central role for viability, while in the FMS this central role shifts (at least partly) to the activities processing fabrication stages common to a plurality of supply chains. In the mass-customization regime, the focus shifts to activities most directly associated with ‘general’ productive functions – that is, functions that are least dependent on specific tasks and job-specification programmes. It was shown above that structural dynamics may take place both when there are changes in proportions between activities for any given technology and when there are transformations of technology in use. Each technology in turn is associated with conditions under which an increasing returns trajectory may or may not be sustained by changes in the scale of the

422  Handbook of industrial development production network. Coalitions of actors and their pursuit of interests within boundaries that guarantee viability are relevant for proportions, as discussed above, but also because they can promote the development of certain technologies and industrial specializations while blocking others (Bianchi and Miller, 1996). A political-economy reading along the lines discussed above can therefore prove to be an essential building block to fully understand the dynamics of production systems and its effects on the macroeconomy.

NOTES 1. As we have seen, Smith acknowledges that in agriculture, natural timing constraints limit the decomposability of the production process and the scope for division of labour. 2. This way of representing a process of differentiation in the supply of intermediates in a circular production economy is due to Alberto Quadrio Curzio, who considers ‘two processes’, such as a1(I) and a1(II), which ‘are now jointed’ in the production of a commodity 1 ‘which is required by the whole economic system’ (Quadrio Curzio, 1996, p. 112). This network structure requires the introduction of ‘“splitting or split supply coefficients” [the alpha coefficients of matrix AL'] that identify the extent to which processes [I and II], respectively, supply [commodity 1] to every other process of the economic system’ (ibid.). 3. This proportionality condition reflects the ‘job-shop’ structure that is common to the mass-customization regime and some pre-industrial forms of craft production. In this type of production organization, the links between productive tasks are the immediate consequence of the integration of tasks under functions and are not based on work pace. As a result, ‘the time taken by any task is flexible, and may be adjusted according to the operations needed in the shop. It follows that productive organization determines the order, but not the lengths, of the various tasks’ (Scazzieri, 1993, pp. 113–14, original emphasis; see also Scazzieri, 1983, pp. 601–2). This relationship between productive functions and tasks has far-reaching consequences since, in this case, ‘a necessary condition for continuous utilization’ would be that ‘the proportions among the sets of [elementary processes] having different precedence patterns stay fixed when process scale is varied’ (Scazzieri, 1993, pp. 249–50). Historically, the need to satisfy this proportionality requirement under conditions of increasing demand for particular commodities led ‘either to the “splitting off” of new handicrafts that are also based on the job-shop pattern or to the introduction of the straight-line establishment [as in the mass production or FMS regimes]’ (Scazzieri, 1993, p. 250; see also Bücher [1893] 1968, p. 171).

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Industrial development and the growth process: a structural framework  423 Botero, G. ([1589] 2017) The Reason of State [Della ragion di stato], edited by R. Bireley, Cambridge, UK: Cambridge University Press. Bücher, C. ([1893] 1968), Industrial Evolution, New York: Augustus M. Kelley. Cardinale, I. (2015), ‘Towards a structural political economy of resources’, in M. Baranzini, C. Rotondi and R. Scazzieri (eds), Resources, Production and Structural Dynamics, Cambridge, UK: Cambridge University Press, pp. 198–210. Cardinale, I. (2018a), ‘A bridge over troubled water: a structural political economy of vertical integration’, Structural Change and Economic Dynamics, 46, 172–9. Cardinale, I. (2018b), ‘Structural political economy’, in I. Cardinale and R. Scazzieri (eds), The Palgrave Handbook of Political Economy, London: Palgrave Macmillan, pp. 769–84. Cardinale, I. (2019), ‘Vulnerability, resilience and “systemic interest”: a connectivity approach’, Networks and Spatial Economics, https://​doi​.org/​10​.1007/​s11067​-019​-09462​-9. Cardinale, I. (2022), ‘On means and ends in structural economic analysis: broadening the field of enquiry’, Structural Change and Economic Dynamics, 61, 450–57. Cardinale, I. and Landesmann, M.A. (2017), ‘Exploring sectoral conflicts of interest in the Eurozone: a structural political economy approach’, in I. Cardinale, D. Coffman and R. Scazzieri (eds), The Political Economy of the Eurozone, Cambridge, UK: Cambridge University Press, pp. 284–336. Cardinale, I. and Landesmann, M.A. (2020), ‘Generalising the political economy of structural change: a structural political economy approach’, Structural Change and Economic Dynamics, https://​doi​.org/​ 10​.1016/​j​.strueco​.2020​.07​.001. Cardinale, I. and Scazzieri, R. (2011), ‘Production and dynamics: perspectives on innovative choice’, paper presented at the Conference ‘Innovation, Economic Change and Policies: An Out of Equilibrium Perspective’, 18 November, University of Rome La Sapienza, Italy. Cardinale, I. and Scazzieri, R. (2019), ‘Explaining structural change: actions and transformations’, Structural Change and Economic Dynamics, 51, 393–404. Cardinale, I. and Scazzieri, R. (2020), ‘Interdipendenze produttive, interessi e condizioni sistemiche: elementi per un’economia politica delle strutture industriali’, L’industria: Review of Industrial Economics and Policy, 41(1), 21–50. Edgeworth, F.Y. (1911). ‘Contributions to the theory of railway rates – I and II’, The Economic Journal, 12, 346–70 and 551–71. Georgescu-Roegen, N. (1970), ‘The economics of production’ (Richard T. Ely Lecture), American Economic Review, 60, 1–9. Georgescu-Roegen, N. (1990), ‘Production process and dynamic economics’, in M. Baranzini and R. Scazzieri (eds), The Economic Theory of Structure and Change, Cambridge, UK: Cambridge University Press, pp. 198–226. Gioja, M. (1815–17), Nuovo prospetto delle scienze economiche, Milan: Pirotta. Hawkins, D. and Simon, H.A. (1949), ‘Note: some conditions of macroeconomic stability’, Econometrica, 17, 245–8. Hicks, J. (1969), A Theory of Economic History, Oxford: Clarendon Press. Hicks, J. (1973), Capital and Time. A Neo-Austrian Theory, Oxford: Clarendon Press. Kaldor, N. (1966), Causes of the Slow Rate of Economic Growth of the United Kingdom: An Inaugural Lecture, Cambridge, UK: Cambridge University Press. Kaldor, N. (1967), Strategic Factors in Economic Development, Ithaca, NY: Cornell University Press. Landesmann, M. (1986), ‘Conceptions of technology and the production process’, in M. Baranzini and R. Scazzieri (eds), Foundations of Economics: Structures of Inquiry and Economic Theory, Oxford: Blackwell, pp. 281–310. Landesmann, M. and Scazzieri, R. (1990), ‘Specification of structure and economic dynamics’, in M. Baranzini and R. Scazzieri (eds), The Economic Theory of Structure and Change, Cambridge, UK: Cambridge University Press, pp. 95–121. Landesmann, M. and Scazzieri, R. (1996), ‘Forms of production organisation: the case of manufacturing processes’, in M. Landesmann and R. Scazzieri (eds), Production and Economic Dynamics, Cambridge, UK: Cambridge University Press, pp. 252–303. Leontief, W. ([1928] 1991), ‘The economy as a circular flow’, Structural Change and Economic Dynamics, 2(1), 181–212 [originally, ‘Die Wirtschaft als Kreislauf’, Archiv für Sozialwissenschaft und Sozialpolitik, 60, 577–623].

424  Handbook of industrial development Leontief, W. (1941), The Structure of the American Economy, 1919–1929, New York: Oxford University Press. List, F. ([1827] 1996), Grundriß der amerikanischen politischen Ökonomie in zwölf Briefen an Charles J. Ingersol/Outlines of American Political Economy in Twelve Letters to Charles J. Ingersoll, Wiesbaden: Dr. Böttiger Verlags-GmbH. Lowe, A. (1976), The Path of Economic Growth, Cambridge, UK: Cambridge University Press. Magnan de Bornier, J. (1990), ‘Vertical integration, growth and sequential change’, in M. Baranzini and R. Scazzieri (eds), The Economic Theory of Structure and Change, Cambridge, UK: Cambridge University Press, pp. 122–43. Pasinetti, L.L. (1973), ‘The notion of vertical integration in economic analysis’, Metroeconomica, 25(1), 1–29. Pasinetti, L.L. (1977), Lectures on the Theory of Production, New York: Columbia University Press. Quadrio Curzio, A. (1996), ‘Production and efficiency with global technologies’, in M. Landesmann and R. Scazzieri (eds), Production and Economic Dynamics, Cambridge, UK: Cambridge University Press, pp. 105–26. Rosenberg, N. (1963), ‘Technological change in the machine tool industry, 1840–1910’, The Journal of Economic History, 23(4), 414–43. Scazzieri, R. (1983), ‘The production process: general characteristics and taxonomy’, Rivista internazionale di scienze economiche e commerciali, 30, 597–611. Scazzieri, R. (1993), A Theory of Production: Tasks, Processes, and Technical Practices, Oxford: Clarendon Press. Scazzieri, R. (2014), ‘A structural theory of increasing returns’, Structural Change and Economic Dynamics, 2, 75–88. Schonberger, R.J. (1986), World Class Manufacturing: The Lessons of Simplicity Applied, New York: Free Press. Serra, A. ([1613] 2011), A Short Treatise on the Wealth and Poverty of Nations [Breve trattato delle cause che possono far abbondare li regni d’oro e argento dove non sono miniere, con applicazione al Regno di Napoli, Naples, Scorriggio], translated by Jonathan Hunt, edited and with an introduction by Sophus A. Reinert, London: Anthem Press. Smith, A. ([1776] 1976), An Inquiry into the Nature and Causes of the Wealth of Nations, general editors R.H. Campbell and A.S. Skinner, textual editor W.B. Todd, Oxford: Oxford University Press. Steenge, A.E. and Van den Berg, R. (2001), ‘Generalising the Tableau économique: Isnard’s Système des richesses’, International Journal of Applied Economics and Econometrics, 9(2), 121–46. Young, A. (1928). ‘Increasing returns and economic progress’, The Economic Journal, 38, 527–42.

Index

abruptness/acceleration of change 55 advanced capitalist democracies (ADCs) 79 advanced capitalist societies 95–7 advanced driver-assistance systems (ADASs) 256 African Continental Free Trade Area (AfCFTA) 55, 70 African industrial development see African industrial revolution (AIR) African industrial revolution (AIR) 54–72 abruptness/acceleration of change 55 African Tsunami 60 arable land pressure 64–6 backbone services 69 demographic dividend 59–60, 64–6 dependency ratio 63, 64 Drucker and 57 employment crunch 59 features of 56–7 foreign investments 71 industrial policy 69–70 industriousness 56 industrious revolution 58–9 Made in Africa theme 70–71 manufactured value added 70 overview of 54–5 phases of 57–8 regional value chains 68–9 urbanization 66–8 working age population 60–63 Agricultural Adjustment Law (1933) 30–31 Agricultural Basic Law (1961) 31 Akkemik, K. Ali 14 Amazon 219–23 Amazon Marketplace 222 Amazon Prime 220 American Recovery and Reinvestment Act (ARRA) 395 Amsden, Alice 386 Andreoni, Antonio 15 anti-trust policy 357–77 conceptual framework 369–76 economic theory 358–9 monopoly 358 neoclassical 358–9, 362 perfect competition 358, 365 structural market failures 358 tacit collusion 358 industrial organization perspectives 358–9 internationalization 361–4

Japanese approach 363–5 self-discovery-based approach 362 international practice 364–7 embedded autonomy 366 place-based industrial policy 366 limitations 376–7 microeconomic perspectives 358–9 overview of 357 platform-based Big Tech companies 367–9 research avenues 376–7 resource, capabilities and evolutionary perspectives 360–61 sustainable development 361–4 arable land pressure 64–6 artificial intelligence (AI) 154, 158 autonomous, connected and electrified (ACE) vehicles 254 connected cars and autonomous driving 256 electrification 255–6 fleet-based on-demand personal mobility 258–9 mobility services and shared vehicles 256–7 value chain implications 257–9 Babbage, Charles 403, 405 backbone services 69 backward linkage 24–5, 31, 278, 385 backwardness, theory of 77 Bagó, Sebastián 46 Bailey, David 13, 14 Balestro, Moisés 9 Barzotto, Mariachiara 11 Bearson, Dafna 12 Becattini, Giacomo 166 Bellandi, Marco 11 Benefits of the ESA Exploration Roadmap in Socioeconomics (BEERS) methodological framework 278 multi-criteria analysis 276 stages of 277 Best, Michael 78 between-country inequality 27–30 Bianchi, Patrizio 10 big data phenomenon 57 Biggeri, Mario 10 Block, Fred 394 Border Industrialization Program (BIP) 47, 48 Boschma, Ron 11 Bosma, Ulbe 9

425

426  Handbook of industrial development Botero, Giovanni 403 Bourdieu, Pierre 13, 280 Brazilian National Development Bank (BNDES) 42, 43 British Industrial Revolution (BIR) 21, 55–6 Brownlow, Graham 10 Budd, Leslie 13 Bush, Vannevar 394 Camacho, Ávila 44 Capital and Time (Hicks) 409 capital-intensive industries 23, 45, 121 capital-intensive production 9, 28, 31 capitalism 24, 76, 80, 112, 142 consumer 100 employment relationships as 96 fictional expectations 47–8 flexible 10, 90–95, 97, 100–101 models of 304 socioeconomic systems 39 varieties of 91, 143, 361, 363, 366 welfare state 10, 90–95, 100, 101 Cardinale, Ivano 15 car industry 248–63 autonomous, connected and electrified (ACE) vehicles 254 connected cars and autonomous driving 256 electrification 255–6 mobility services and shared vehicles 256–7 value chain implications 257–9 Dieselgate scandal 259–61 German auto system 259–61 innovation trajectories 248–53 corporate forms 249–52 mass production 249–52 product architectures 249–52 internal combustion engine (ICE) climate change and sustainability 253 crisis 252–3 Fourth Industrial Revolution 253 technologies impacting on production 254 laboratory for transformative innovations 262–3 sustainability crisis 248–53 Carlton, Camille 12 Causes of the Greatness and Magnificence of Cities (Botero) 404 Cecchetti, Maria Chiara 11 central–decentral dynamics 386 China industrial policy 111–13 conditionality 112

experimentation 112 market socialist economy 111 Open Door policy 111 Soviet model 111 states of innovation developmental state model 396–7 innovation-challenge-driven state model 397–9 market liberalization agenda 397 Chitonge, Horman 9 circular business models (CBMs) 303–4 circular economy (CE) innovations 303–4 internal innovations 312–14 restorative capacity of natural resources 305 socioeconomic performances 303–4 systemic innovative strategy 307 systemic rethinking 302 circular innovations geographical distribution of 315–16 types of 314, 315 circular production networks 409 classic/canonical model of path dependence 137 Coffey, Dan 13 collaboration for innovation 182–94 diminishing and negative returns 186–7 EU policy initiatives 191–3 firm and industrial studies 185–6 overview of 182 regional collaboration 187–90 resource-based view 183–4 small and medium-sized firms 184–5 supply chains and networks 190–91 transaction cost perspectives 183 commodity frontiers 9, 18, 23–7, 31 backward and forward linkages 24–5 cotton textile industry 25–7 definition of 25 global economic chains 24–5 global economic structure 24 political, economic and military factors 24 Community Renewal Fund 208–9 comparative advantage theory 39 competitive dynamics 330–32 competitive strategies 233 consumer capitalism 100 consumer goods see servitization cooperation–innovation nexus empirical studies 187–8 industrial districts and clusters 187 links between regional actors and universities 189–90 regional-level studies 187–8 co-opetition 360–61, 374 coordinated market economy (CME) 81, 91

Index  427 Corradini, Carlo 11 corruption, good governance 203–5 cotton textile industry 25–7 COVID-19 pandemic precarious employment 97–100 transformation of work 90, 97–100 craft production 235–7 Crafts, Nicholas 75, 80 critical junctures 37, 38, 47, 51, 142 cumulative causation 75, 78 debt crisis 47–8 Deese, Brian 115 deindustrialization 24, 30, 37–8, 76, 79, 115, 116, 338, 339 premature 50, 85, 339, 342, 383 demographic dividend (DD) 59–60, 64–6 accounting effects 60 arable land pressure 64–6 behavioural effects 60 dependency ratio 63, 64 De Propris, Lisa 13 developmental state model China 396–7 Germany 391–2 development banking 42–3 devolved governments 209 Dieselgate scandal 259–61 digital capability threshold 385 digital online platforms 215–27 Amazon 219–23 characteristics 217 economic geography 216–17 Google Maps 223–7 at macro level 215 at meso level 215 at micro level 215 overview of 215–26 spatial relations 215 digital revolution 57 Digital Single Market 8 Di Tommaso, Marco R. 10 Dodge, Martin 216 Drakeford, Mark 210 Drucker, Peter 57, 250 early industrializer 382 eco-innovation (EI) 14 climate change mitigation patents 306 Community Innovation Survey 308 environmental benefits 308–10 firms’ behaviours 307–16 footprints 309, 311 greenhouse gas (GHG) emissions 304

integrated circularity/bioeconomy/ decarbonization 305, 306 macroeconomic settings 304–7 market and regulatory drivers of 303 material circularity rate 305 reuse and recycling sector patents 307 small and medium-sized enterprises 308, 311–15 waste management patents 306 ecological transition 7 Economic and Monetary Union (EMU) 202 Economic Commission for Latin America (ECLA) 41 Economic Commission for Latin America and the Caribbean (ECLAC) 41 ‘The Economic Development of Latin America and its Principal Problems’ (Prebisch) 41 economic divergence 19 economic dynamics 329–30 economic governance 330–32 Economy and Machinery and Manufactures (Babbage) 405 educational attainment 343 Ellen MacArthur Foundation (EMF) 302 embedded autonomy 366 emerging industrializer 383 employment industrialization index 342–3 multiplier 278 non-standard 97 precarious 95–101 relationships as capitalism 96 work and change 91–2 energy transitions 13–14, 287–98 challenges 288–9 empirical illustrations fossils to renewable energy consumption 296–7 fossils to renewable energy generation 294–5 overview of 287–8 socio-technical perspectives 287 theoretical perspectives 290–94 multi-level perspective 290–91 sustainability transitions 293–4 technological innovation system 291–3 entrepreneurship innovative 141 institutional 141 environmental degradation 261 European Green Deal 91, 97–100, 117, 302, 315, 317 European Space Agency (ESA) 13, 268, 271 European Union industrial policy 116–19 features 116

428  Handbook of industrial development New Industrial Strategy 117, 118 Smart Specialisation Strategies for Sustainability (S4+) approach 119 Smart Specialisation Strategy (S3) 118–19 vision and goals 117 whole-of-government approach 118 European Union Structural Funds 207, 208 Evans, Peter 386 evolutionary economics 327 external disequilibrium 41 extra-regional collaboration see regional collaboration

sanitary and phytosanitary (SPS) standards settings 206 socio-economic outcomes 202 United Kingdom Internal Market Act 208–10 Valencia, evolution of 203–5 Google Maps (GM) 223–7 governance mix 371 Government of Wales Act (2006) 210 Great Depression 44, 51, 113 green economy (GE) 302 green innovation 392–3 Green New Deal 10

Fai, Felicia 11 Ferrannini, Andrea 10 first-degree price discrimination 234, 235 flexibility theory 93 flexible capitalism 10, 90–95, 97, 100–101 flexible specialization approach 93 Ford, Henry 250, 252 foreign direct investment (FDI) 45, 71, 84, 111, 169, 357, 369, 397 Forging Ahead, Falling Behind and Fighting Back (Crafts) 80 forward linkage 24–5, 31, 278

Hamilton, Alexander 393 heterogeneous firms’ populations 171–3 Hicks, John 409 horizontal IP 322 How Growth Really Happens (Best) 78 human development see sustainable human development (SHD) Human Development Index (HDI) 362

General Agreement on Tariffs and Trade (GATT) 382 general-purpose technologies 142–3 Germany auto system 259–61 Renewable Energy Act 392 states of innovation developmental state model 391–2 innovation-challenge-driven state model 392–3 installed renewables capacity 393 Gioja, Melchiorre 403, 405 Global Entrepreneurship Monitor 58 Global Financial Crisis 323, 326, 331 global income convergence 29–30 global inequality 27–31 between-country inequality 27–30 within-country inequality 30–31 global value chains (GVCs) 9, 48–51, 68, 325 good governance 7, 12, 200–210 challenges of 201–3 corruption 203–5 devolved governments 209 fragile territories 202 multi-scalar framework 201 overview of 200 post-Brexit regional policy 207–8 redemption 203–5 reserved powers model 206

impact evaluation framework (IEF), space industry 276–83 import substitution industrialization (ISI) strategies 9, 37 India industrialization 82–4 constitutional settlement 84 multinational enterprises 84 Nehru-Mahalanobis model 83, 84 industrial policy 119–21 challenges 121 inclusive growth 119 manufacturing growth 120–21 new free trade agreements 120 Statement on Industrial Policy, 1991 119–20 individual capitalist’s interest 38 industrial development in Africa see African industrial revolution (AIR) in automobiles see car industry in China see China definition of 2 in Germany see Germany in India see India in Latin America see Latin American industrial development in online platforms see digital online platforms political economy approach 5 production system see local production systems (LPSs) secular stagnation 2

Index  429 socioeconomic systems 3 structural framework see industrial structural framework in US see United States industrial development policy 3–4, 343 definition of 5 market failures 324 socioeconomic systems 5, 7–8, 323, 327 wealth of nations 4 industrial districts contemporary 173–6 case study selection 173 selected case studies 175 theoretical and targeted sampling approach 173 cooperation 187 new forms of 168 industrialization between-country inequality 27–30 commodity frontiers 23–7 definition of 18, 338 global income convergence 29–30 global inequality 27–31 idealist approach 21, 22 incentive approach 21, 22 ladder multi-layered framework 389 state formation 15 as structural transformation 383–6 digital capability threshold 385 middle-income technology trap 385 types of challenges 384–6 within-country inequality 30–31 industrialization index 354–6 correlation between per capita GDP and index 346 educational attainment 343 employment 342–3 international trade 343 sophistication 342 technological progress 343 threshold levels 345 z-score normalization 343 industrialization paths 75–85 advanced capitalist democracies 79 coordinated market economy 81 cumulative causation 75, 78 economic transformative experiences 78 industrial policy 76–80 institutional legacies, early start 80–82 Kaldor’s analysis 78 Keynesian model 78 liberal market economy 81 long-run Indian industrialization 82–4 Nirvana fallacy 76 path dependence 75–6

rhythm of development 78 Rostow’s take-off analysis 76–7 industrialization stages 14, 338–51 capital deepening 340 challenges 350–51 description of data 342 education policy 348–50 framework 340–41 imitation 340–41 industrial policies 346–8, 350–51 new products and equipment 341 nominal per capita real GDP 344 purchasing power parity-based per capita real GDP 345 science/technology/innovation (STI) policies 346–8 state capacity policy 348–50 technology adoption 340 industrial layers 339 Industrial Licensing Act (1951) 84 industrial path development 6, 133–46 adaptive cycle perspective 143 advantages and value of 133 definitional challenges 136 empirical definition of 135 firm capabilities 140 general-purpose technologies 142–3 history-friendly models 146 innovative entrepreneurship 141 institutional entrepreneurship 141 neo-Schumpeterian approaches 134–5 path branching 138 path creation 138, 141–2 path extension 137 path importation 138 path interaction 142–4 path plasticity 138 path upgrading 138 place-based industrial paths 134–6 place-based path creation 141 place dependence 144–6 place leadership 141 regional industrial paths 139–40 relatedness 139 strict/canonical path dependence 135 unrelatedness 139 industrial poles 169 industrial policy (IP) 3–4 African industrial revolution 69–70 in China 111–13 conditionality 112 experimentation 112 market socialist economy 111 Open Door policy 111 Soviet model 111

430  Handbook of industrial development definition of 339 in European Union 116–19 features 116 New Industrial Strategy 117, 118 Smart Specialisation Strategies for Sustainability (S4+) approach 119 Smart Specialisation Strategy (S3) 118–19 vision and goals 117 whole-of-government approach 118 governance see good governance horizontal 322 implications, servitization 243 in India 119–21 challenges 121 inclusive growth 119 manufacturing growth 120–21 new free trade agreements 120 Statement on Industrial Policy, 1991 119–20 industrialization paths 76–80 industrialization stages 346–8, 350–51 market failures 322–32 competitive dynamics 330–32 drawbacks 324 dynamic and systemic nature 325–7 economic dynamics 329–30 economic governance 330–32 evolutionary economics 327 Global Financial Crisis 323, 326, 331 innovation policy 327–9 structural changes 325–7 technological isomorphism 331 value creation 330 post-Brexit regional policy 207–8 sustainable human development 107–10 capability approach 109 good jobs economy 108 human development paradigm 109 United Kingdom Internal Market Act 208–10 in United States 113–16 American Jobs Plan 114 Buy American acts/campaigns 114 climate change 115 Executive Order on Promoting Competition in the American Economy 114 Infrastructure Investment and Jobs Act 114 Made in America Office 114 Made in America Tax Plan 115 United States Innovation and Competition Act 114–15 vertical 322

Industrial Revolution British 21, 55–6 causes of 19–21 economic divergence 19 first 2, 4–5, 18–19, 25, 28, 30, 31, 166, 414 fourth 2, 28, 57, 106, 143, 253, 325, 346, 350–51 third 6 industrial sophistication 342 industrial structural framework 403–22 division of labour 408–13 manufacturing and economic growth 403–7 dynamics of wealth of nations 404 relative economic performance 404 splitting and specialization of tasks 405–6 sustainability conditions 404 manufacturing regimes and patterns of interdependence 413–20 flexible manufacturing system 416 flexible production 415 functions vs. tasks 414 intermediate products 414–15 machinery 414 mass customization 416–19 mutually substitutable flows 415 production functions 416 proportionality conditions 420 range of proportions 419 task definitions 414 political economy analysis 420–22 production networks 408–13 industrial transformation 290–94 multi-level perspective 290–91 sustainability transitions 293–4 technological innovation system 291–3 industriousness 4, 8, 9, 56, 64, 66 Inequality-adjusted Human Development Index (IHDI) 362 innovation-challenge-driven state model China 397–9 Germany 392–3 United States 395–6 innovation/industrial dynamics/regional inequalities artificial intelligence 154, 158 future research 157–9 intra/inter-regional inequalities 154–7 evolutionary thinking 155 regional path dependencies 155 routine-biased technological change hypothesis 156 skill-biased technological change hypothesis 156 structural change 155

Index  431 superstar firms 157 wage inequality vs. innovation 156 literature review 152–4 regional diversification 152–3 regional dynamics vs. industrial dynamics 152 innovative entrepreneurship 141 institutional entrepreneurship 141 institutional legacies, early start 80–82 internal combustion engine (ICE) 252–3 climate change and sustainability 253 crisis 252–3 Fourth Industrial Revolution 253 technologies impacting on production 254 International Labour Organization (ILO) 122, 343 International Monetary Fund 22 international trade 343 Internet bubble 216 intra/inter-regional inequalities 154–7 evolutionary thinking 155 regional path dependencies 155 routine-biased technological change hypothesis 156 skill-biased technological change hypothesis 156 structural change 155 superstar firms 157 wage inequality vs. innovation 156 Jacobs, Jane 144 Japanese approach 363–5 Jevons, William Stanley 370 Jin, Gao 27 Jinping, Xi 112, 348 Jintao, Hu 112 Justo, Agustín 44 just transitions 14, 293, 297, 298 Kattel, Rainer 15 Kenney, Martin 12 Khan, Lina 219 Kitchin, Rob 216 Kohl, Helmut 392 Krafcik, John F. 251 Kubitschek, Juscelino 42 Labory, Sandrine 11, 14 Land Acquisition Act (1894) 83 late industrializer 382 Latin American Free Trade Association (LAFTA) 41, 45 Latin American industrial development 9, 37–52 capitalist economic development 40 capitalist fictional expectations 47–8 comparative advantage theory 39

critical junctures 37, 38 debt crisis 47–8 development banking 42–3 external and intraregional merchandise trade 50 fictional expectations and learning process 43–7 global value chains paradigm 9, 48–51 import substitution industrialization strategies 9, 37 individual capitalist’s interest 38 nation-making process 38 regional economic commissions 40–41 regional value chains 50 Ricardian comparative advantage 39 Leontief, Wassily 409 Levelling Up Fund 208, 209 liberal market economy (LME) 81, 91 Lisbon Treaty (2009) 270 List, Friedrich 403, 407 local production systems (LPSs) 11, 165–77 argumentation 165 heterogeneous firms’ populations 171–3 industrial organizations 171–3 market and cognitive sides 166 models and types of 172 traditional/emerging specializations 172 industrial poles 169 industrial regions 168 institutional and governance support 167 multidimensional concept 166–71 natural resources 168 place-based development 169–70 place-blind forces of development 169–70 productive development policies 170–71 socio-cultural relationships 166–7 sources and screening methods 181 territorial development policies 170–71 territorial structure 166–7 Made in Africa: Industrial Policy in Ethiopia (Oqubay) 70 Made in Africa: Learning to Compete in Industry 70 Made in China (MIC) 2025 397–9 Mäkitie, Tuukka 13 management revolution 57 maritime transport 296–7 market failures 322–32 competitive dynamics 330–32 drawbacks 324 dynamic and systemic nature 325–7 economic dynamics 329–30 economic governance 330–32 evolutionary economics 327

432  Handbook of industrial development Global Financial Crisis 323, 326, 331 innovation policy 327–9 structural changes 325–7 technological isomorphism 331 value creation 330 Marques, Pedro 12 Marshallian industrial districts (MIDs) 165–7 Martin, Ron 10 Marx, Karl 360, 370 mass customization 235, 237–8, 240–41 mass personification 241 mass production 235, 237–8, 240–41 material incentives 21–3 Mazzanti, Massimiliano 14 Medici vicious circle 85 Mehrotra, Santosh 10 Meiji Restoration (1868) 382 Menger, Karl 370 middle-income technology trap 385 mission-oriented innovation 395–6 monopoly 358 Monroy-Osorio, Juan Carlos 12 Montevideo Treaty 41 Morgan, Kevin 12 multinational enterprises (MNEs) 84, 169, 361 multiplier analysis 278 Nafinsa, development banking 42–3 national systems of innovation 303 Nehru-Mahalanobis model 83, 84 neoclassical economic approach 23, 358–9, 362 Netherlands Trade Union Confederation (FNV) 99 new public management (NPM) 387 non-standard employment 97 oil and gas (O&G) industry fossils to renewable energy consumption 296–7 fossils to renewable energy generation 294–5 Okonjo-Iweala, Ngozi 71 Olson, Mancur 10, 75 Opazo-Basáez, Marco 12 open innovation 182–3, 185, 190 operations strategies 235–6 Oqubay, Arkebe 70 Organisation for Economic Co-operation and Development (OECD) 56, 106, 190, 271, 303 original equipment manufacturers (OEMs) 253–4 Pardy, Martina 11 path branching 138 path creation 138, 141–2 place-based 141

path dependence 75–6 classic/canonical model of 137 strict/canonical 135 path extension 137 path importation 138 path interaction 142–4 path plasticity 138 path upgrading 138 Penrose, Edith 360 Perez, Carlota 388 perfect competition 358, 365 Petralia, Sergio 11 Piteli, Eleni E.N. 15 Pitelis, Christos 15 place-based industrial paths 134–6 place-based industrial policy 366 place-based path creation 141 place dependence 144–6 place leadership 4, 141 platform-based oligopolies 369–76 political economy approach 5 Porter, Michael 362 post-Brexit regional policy 207–8 power resource theory 92 Prebisch, Raúl 41 precarious employment advanced capitalist societies 95–7 under COVID-19 pandemic 97–101 premature deindustrialization 339 premature deindustrialization (PD) 50, 85, 339, 342, 383 Prestwick Spaceport BEERS methodology 276–8 benefit categories and potential outcomes 281 Bourdieu’s economic capitals 280–81 community capitals and impact indicators 282–3 European Space Exploration Envelope Programme (E3P) 276, 281 impact evaluation framework 276–83 industrial development of 273–5 input–output analysis 277–80 multiplier analysis 278 satellite service distribution 274 space industry segments 274–5 Standard Industrial Classification 279 price discrimination strategies 233–5 degrees of 235 description of 234 first-degree 234, 235 second-degree 234, 235 third-degree 234, 235 price strategies see price discrimination strategies productivity revolution 57

Index  433 protectionism 45, 47, 374–5 Pulignano, Valeria 10 Reason of State (Botero) 404 recent industrializer 382 regional collaboration 187–90 empirical studies 187–8 EU policy initiatives 191–3 links between regional actors and universities 189–90 regional diversification 152–3 regional dynamics vs. industrial dynamics 152 regional industrial development 6 regional industrial paths 139–40 regional inequalities see intra/inter-regional inequalities regional path dependencies 155 regional value chains (RVCs) 50, 68–9 relatedness 139 Report on the Subject of Manufactures (Hamilton) 393 Research and Innovation for Smart Specialisations Strategies (RIS3) 182, 191–3, 329 reserved powers model 206 resource-based collaboration 183–4 Ricardian comparative advantage 39 Ricardo, David 362, 370 Richardson, George 360 The Road to Serfdom (Hayek) 376 Rostow’s take-off analysis 76–7 Rostow, W. W. 76 routine-biased technological change (RBTC) hypothesis 156 Ruiz Durán, Clemente 9 Santini, Erica 11 Scazzieri, Roberto 15 Schumpeter, Joseph 360, 388 Scokpol, Theda 386 second-degree price discrimination 234, 235 sectoral systems of innovation 303 segmentation strategies 236–41 self-discovery-based approach 362 Sen, Amartya 108 Serra, Antonio 403, 404 servitization 232–44 academic implications 242 competitive strategies 233 connected working benefits 238–40 craft production 235–7 definition of 7 future research 243–4 industrial policy implications 243 integrative strategies framework 237–40

managerial implications 242 mass customization 235, 237–8, 240–41 mass production 235, 237–8, 240–41 operations strategies 235–6 overview of 232–3 price strategies 233–5 segmentation strategies 236–41 value of 238–40 Short Treatise (Serra) 404 Signé, Landry 55 simple output multiplier 278 Singer, Hans W. 41 skill-biased technological change (SBTC) hypothesis 156 small to medium-sized firms (SMEs) 11, 42, 165 collaboration 184–5 eco-innovations 308, 311–15 multi-dimensions of firms’ adoption 311–15 resource-constrained 184 sources and screening methods 181 specialized 166, 173, 174 Smith, Adam 3, 5, 108, 329, 360, 361, 370, 403, 405, 406, 417 social Europe 10, 101 socioeconomic systems capitalism 39 industrial development 3 industrial development policy 5, 7–8, 323, 327 political economy approach 5 Space 4.0 270–72 space industry 13 downstream activities 268 Prestwick Spaceport BEERS methodology 276–8 benefit categories and potential outcomes 281 Bourdieu’s economic capitals 280–81 community capitals and impact indicators 282–3 European Space Exploration Envelope Programme 276, 281 impact evaluation framework 276–83 industrial development of 273–5 input–output analysis 277–80 multiplier analysis 278 satellite service distribution 274 space industry segments 274–5 Standard Industrial Classification 279 tiered segments 269 UK Industrial Strategy, 2017 269 UK National Space Strategy 272, 273 upstream activities 268 spatial implications, platform economy see digital online platforms

434  Handbook of industrial development spatial knowledge 226 Special Economic Zone (SEZ) Act (2005) 83 states of innovation 386–90 central–decentral dynamics 386 China developmental state model 396–7 innovation-challenge-driven state model 397–9 market liberalization agenda 397 Germany developmental state model 391–2 innovation-challenge-driven state model 392–3 installed renewables capacity 393 industrial paradigm 388 new public management 387 transformative capacity 386 United States innovation-challenge-driven state model 395–6 mission-oriented innovation 395–6 networked model of an entrepreneurial state 394–5 Weberian notions of capacity 386 Weber type I/II/III organizations 387–8 Steen, Markus 13 strategic coupling 6 strict/canonical path dependence 135 structuralism 41 structural market failures 358 structural unemployment 41 Sunley, Peter 10 sustainability transitions 13 industrial perspectives 293–4 and strategies 302 sustainable human development (SHD) 106–25 government failures 125 industrial development processes 121–4 collective aggregations 122 collective efficiency 122 healthy competitive environment 122 healthy cooperative environment 122–3 high road/low road/dirt road 122–4 productive coalitions 124 two-synergies strategic route 124 industrial policy 107–21 capability approach 109 in China 111–13 in European Union 116–19 good jobs economy 108 human development paradigm 109 in India 119–21 in United States 113–16 normative societal vision 125 overview of 106–7

pillars of 109 equity 109 participation and empowerment 109 productivity 109 sustainability 109 sustainable industrial development 8 tacit collusion 358 technological innovation system (TIS) 291–3 technological isomorphism 331 theory of backwardness 77 third-degree price discrimination 234, 235 Thornley, Carole 13 Tomlinson, Philip R. 11, 14 total factor productivity (TFP) 19, 124 tout court development 8 transaction cost economics (TCE) 183 transformation of work 90–101 COVID-19 pandemic 90, 97–100 employment and change 91–2 flexibility theory 93 flexible capitalism 90, 92–4 power resource theory 92 precarious employment 95–7 social order 90 varieties of capitalism (VoC) approach 91–2 welfare state capitalism 90, 92–4 transportation network companies (TNCs) 257–8 2030 Agenda for Sustainable Development 106–8, 112 UK Industrial Strategy (2017) 269 UK National Space Strategy 272, 273 UK Shared Prosperity Fund (SPF) 207 UN Framework Convention on Climate Change 100 United Kingdom Internal Market Act (UKIMA) 208–10 United Nations Conference on Trade and Development (UNCTAD) 41 United Nations Environment Programme (UNEP) 254 United Nations Industrial Development Organization (UNIDO) 343 United Nations Sustainable Development Goals 3, 106 United States industrial policy 113–16 American Jobs Plan 114 Buy American acts/campaigns 114 climate change 115 Executive Order on Promoting Competition in the American Economy 114

Index  435 Infrastructure Investment and Jobs Act 114 Made in America Office 114 Made in America Tax Plan 115 United States Innovation and Competition Act 114–15 states of innovation innovation-challenge-driven state model 395–6 mission-oriented innovation 395–6 networked model of an entrepreneurial state 394–5 United States–Mexico–Canada Agreement (USMCA) 114 unrelatedness 139 urbanization 66–8 US New Deal 44

wage inequality 156, 158 Wang, Meimei 9 War Production Board (WPB) 394 Washington Consensus 22, 346, 371, 383 The Wealth of Nations (Smith) 3, 329, 361 Weberian notions of capacity 386 Weber, Max 386 Weber type I/II/III organizations 387–8 welfare state capitalism 10, 90–95, 100, 101 William I, Frederick 390 Williamson, Oliver 359 within-country inequality 30–31 working age population (WAP) 60–63 World Bank 22, 46, 342, 343, 392 World War I 44, 113, 381 World War II 40, 43, 44, 57, 275, 322, 363, 382, 391, 394, 415

value co-creation 368–9, 374 value creation 330 van Leeuwen, Bas 9 varieties of capitalism (VoC) approach 91, 143, 361, 363, 366 Vendrell-Herrero, Ferran 12 vertical IP 322 vertical production networks 409 Villani, Davide 13 von Bismarck, Otto 391

Xiaoping, Deng 111 Young, Allyn 412 Yülek, Murat 14 Zecca, Emy 14 z-score normalization 343 Zuckerberg, Mark 217 Zysman, John 12