Industrial Policy for the Manufacturing Revolution: Perspectives on Digital Globalisation 1786430312, 9781786430311

Table of contents :
Front Matter
Copyright
Contents
1 Introduction: globalisation and the manufacturing revolution
2 The first manufacturingrevolutions: not just technological change
3 The fourth industrial revolution
4 New modes of interacting on markets: online platforms
5 A concrete experiment of industrial policy for the manufacturing revolution
6 Conclusions: industrial policy for the manufacturing revolution
References
Index

Citation preview

Industrial Policy for the Manufacturing Revolution

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Industrial Policy for the Manufacturing Revolution Perspectives on Digital Globalisation

Patrizio Bianchi and

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

Cheltenham, UK • Northampton, MA, USA

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© Patrizio Bianchi and Sandrine Labory 2018 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: 2018931770 This book is available electronically in the Economics subject collection DOI 10.4337/9781786430328

ISBN 978 1 78643 031 1 (cased) ISBN 978 1 78643 032 8 (eBook)

02

Typeset by Servis Filmsetting Ltd, Stockport, Cheshire

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Contents 1 Introduction: globalisation and the manufacturing revolution1 2 The first manufacturing revolutions: not just technological change 15 3 The fourth industrial revolution 49 4 New modes of interacting on markets: online platforms79 5 A concrete experiment of industrial policy for the manufacturing revolution 104 6 Conclusions: industrial policy for the manufacturing revolution149 References157 Index169

vii

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1. Introduction: globalisation and the manufacturing revolution The twenty-first century is becoming an era of profound societal and economic change. Globalisation has reduced its pace after the financial crisis, at least in terms of global trade of physical goods and services. However, industries are pursuing the structural changes implied by the rising extent of the market brought about by globalisation. Firms sell in global markets and ally with competitors to realise the R&D of specific product components or product varieties, while also creating new forms of market interactions thanks to new digital technologies. They can continuously interact with consumers to identify their needs, propose specific solutions and learn from consumers providing their experience with products on online forums. Global value chains are changing. First, the trend in the last few years has been of reshoring or back shoring – a return of previously outsourced production phases to the home country or to the main production sites. Second, with the hyper connection induced by the fourth industrial revolution (see Chapter 3) offices and divisions even in faraway distance can be managed more efficiently and with lower volumes of flows of parts and goods, which are partly replaced by data flows. Figure 1.1 shows world export of physical goods, in the period 1980 to 2016. Globalisation appears clearly, in the sense of exponential rise of global trade in the 1990s and up to the financial crisis of 2008. This sustained growth came to a halt during the financial crisis and does not appear to be starting again. However, firms continue operating on a global basis. Figure 1.2 shows the transformation of these global operations: they have become essentially digital, in that ‘trade’ appears to increasingly consist of data, and digital globalisation is happening at an extremely rapid pace. Digital globalisation is the result of the fourth industrial revolution, induced by the technological progress that is taking place in many scientific and technological fields, and primarily in the 1

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Industrial policy for the manufacturing revolution

20,000 18,000 Digital globalisation

16,000

Globalisation

14,000 Advanced economies

12,000

Emerging and Developing Asia

10,000 8,000

World

6,000 4,000 2,000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

0

Source:  IMF (2017) Data Mapper, available at http://www.imf.org/external/data​ mapper/datasets, accessed 1 April 2017.

Figure 1.1  Export of goods, annual, billion US $ evolution of ICTs into hyperconnected systems. Internet essentially started to diffuse in the 1990s, and is now pervasive and omnipresent in the everyday working, leisure or family lives of most people on the planet. This book aims at analysing the structural transformations brought about by the fourth industrial revolution – particularly the advent of the digital globalisation and its implications for the structure and dynamics of industry – so as to outline what type of industrial policy or industrial strategies are needed in this era of rapid and substantial transformations. We propose the concept of comprehensive industrial policy, in the sense of policy actions considering the complexity of structural changes, which involve not only industry per se but also institutions (regulation) and social and education policies (to favour the participation of all citizens in the development process). We explain the concept and also provide an example of implementation of such a comprehensive industrial policy, at regional level, in the Emilia-Romagna (ER) region in Italy.

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Introduction: globalisation and the manufacturing revolution

20 18 16 14 12 10 8

3

Segment File sharing Video Audio Web browsing Social networking Software download & update Other encrypted Other

6 4 2 0

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Source:  Ericsson (n.d.) ‘Mobile technology trends: traffic by application category. Mobile video traffic is becoming increasingly dominant’, available at https://www.ericsson. com/en/mobility-report/mobile-traffic-by-application-category, accessed 24 January 2018.

Figure 1.2  Mobile data traffic by application type (monthly exabytes)

1 MANUFACTURING TRANSITION AND INDUSTRIAL POLICY As in previous research on industrial policy (Bianchi and Labory, 2006, 2009, 2011a, 2011b) we define industry as the capacity to organise production in correspondence to market needs, so that industry can be in all sectors, from agricultural to manufacturing and services. In the past, industry studies and industrial economics have tended to confine industry to the manufacturing sector, following the famous division introduced by Fisher in 1935 into the three sectors. However, economic, social and technological developments have made this distinction increasingly outdated, especially in the last few decades where the three sectors have become progressively intertwined. Services and manufacturing are intersecting, since products are bundled with services; the agricultural sector has been deeply transforming and the adoption of new technologies – including the digital ones but not only these – is making this sector not so much ‘primary’ in the sense that the transformation operated on

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the product is substantial (making tomatoes more tasty and more germ-resistant, or other agricultural products that are transformed by bio- and other technologies to make food healthy and even curing diseases). Given this concept of industry we define industrial policy in a broad manner, as a set of actions aimed at favouring structural changes in industries and orientating industrial development in specific directions. Such actions regard innovation, trade, intellectual property rights and antitrust; they also regard labour, because firms cannot upgrade or be created if they do not find the human capital they need for their operations. Looking at providing appropriate human capital means considering social policies, labour contracts and measures to favour the participation in the labour force. It also means considering education and training, because they determine the types of skills people will be able to develop. We argue that industrial policies are particularly needed when the economy experiences deep transformations, such as industrial revolutions. The advent of digital globalisation is primarily driven by the technological progress induced by the fourth industrial revolution, but we believe that industrial revolutions are the result of complex transformations of the economy, the society and culture. Hence the first part of this book is dedicated to the analysis of the fourth industrial revolution, outlining its main features and implications through a comparative analysis with the first industrial revolutions. The comparative and historical analysis of the first industrial revolutions outlines that industrial revolutions represent transitions between manufacturing regimes, that they are complex and require broad industrial policies. The analysis of the fourth industrial revolution outlines the structural changes arising in industries, as described by firm managers, scientists and consultants, as well as the literature on the fourth industrial revolution. It raises the need for new industrial policies, which are discussed in the rest of the book. Major science and technological developments are indeed taking place, in different fields including high power computing, artificial intelligence, robotics, new materials, genomics and nanotechnologies. These developments will have profound impact on industries not only because of technological progress in single scientific fields, but also because they are converging to enable new products and new production processes.

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We outline two aspects of these industrial policy actions for the manufacturing revolution: first, the role of territories in multilevel governance processes; and second, the key role of training and education, which has to be rethought and adapted. In this sense industrial policy has to be comprehensive (keeping together the ecosystem). Bianchi and Labory (2011a) argued that globalisation was, at the turn of the century, inducing substantial structural changes in industry that were calling for industrial policy to promote these changes along desired development paths. Globalisation was intended as the growth of world trade and foreign direct investments, which accelerated in the 1990s following the major political upheaval of the end of communism and the transition of many countries into market economies, as well as the growing importance of emerging economies, the so-called BRIC countries. In fact, industrial policy has returned worldwide as an essential policy in times of deep structural transformation of the economy. In the meantime, however, other disruptive changes have been progressively taking place, in the fields of science and technological developments, leading to what many experts and scholars have come to name the fourth industrial revolution. This industrial revolution is not easy to characterise because it consists of many scientific and technological developments: in genomics, biotechnologies, nanotechnologies, computer and data sciences, artificial intelligence, robotics, sensors and connecting technologies and so on. Many experts argue that what is new in this fourth industrial revolution is both the speed of change and the convergence of scientific fields and technologies in the production of new solutions, new products and new processes. However, as will be claimed in the following chapters of this book, the first three industrial revolutions had the same characteristics. Technological changes take place over a long time period with some sub-periods of acceleration, such as the 1960s during the third industrial revolution; technologies converged even in previous revolutions. The best example of this pattern is probably the automobile, which was developed as a product at the beginning of the twentieth century (during the second industrial revolution), using the new combustion engine, new steel, new rubber and other chemical developments. Some economic historians have characterised industrial r­ evolutions

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by the changing technological paradigm they imply: see Freeman and Lourça (2001) concerning the first three industrial revolutions. However, industrial revolutions are not only about technological developments. They constitute revolutions because they imply changes in manufacturing regimes, determined by socio-economic and political conditions and having in turn large impact on the socio-economic and political conditions. Manufacturing regimes are intended as prevailing production processes, which are methods or technologies of production. The following chapters will show that each industrial revolution is associated with a particular manufacturing regime. It does not mean that all firms in all sectors adopt the same production organisation but there are main features of their production processes that are common; for instance, the division into elementary tasks performed by low-skilled workers under time constraint in the mass production system. The fourth industrial revolution is having a major impact on industry and societies primarily because of what has been called its raw material: data. New technologies are allowing hyperconnection of people on a global basis, but also of people with machines and between machines (the Internet of Things, IoT). Global data flows are booming, raining new regulatory issues: first, a few very large firms are accumulating enormous amounts of data on individuals and base their market power on this (online platforms, see Chapter 4). Besides privacy issues, there are new antitrust issues.

2 GLOBALISATION AFTER THE FINANCIAL CRISIS Real GDP growth has been claimed to be too slow, for too long by the IMF (2016). Figure 1.3 indeed shows the important recession induced by the financial crisis, after which real GDP growth worldwide did not recover the rates prevailing prior to the crisis. World growth was driven by the rapid expansion of emerging and developing countries over the period 2001 to 2008, which is still true after the crisis but with much slower rate. Advanced countries’ growth has tended to stagnate or even reduce. Figures 1.4 and 1.5 add comparisons and show that while all BRIC countries drove global GDP growth in the period prior to the crisis, this is no longer the case, since Brazil and the Russian Federation

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10 8 6 4 2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0 –2 –4

Advanced economies Emerging market and developing economies World

Source:  IMF (2017) Data Mapper, available at http://www.imf.org/external/data​ mapper/datasets, accessed 1 April 2017.

Figure 1.3  Real GDP growth (annual percentage change)

20 15 10 5 0 –5

1990

1995

2000

2005

2010

2015

–10 –15 –20

Brazil India

China, People's Republic of Russian Federation

Source:  IMF (2017) Data Mapper, available at http://www.imf.org/external/data​ mapper/datasets, accessed 1 April 2017.

Figure 1.4  Real GDP growth in BRIC countries, 1990 to 2018

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Industrial policy for the manufacturing revolution

10 8 6 4 2 0

2000

2005

2010

2015

–2 –4 –6

Africa (Region)

Asia and Pacific

North America

South America

Europe

Source:  IMF (2017) Data Mapper, available at http://www.imf.org/external/data​ mapper/datasets, accessed 1 April 2017.​

Figure 1.5  Real GDP growth by world regions, 1990 to 2018 have seen their growth sharply decline. The picture that clearly emerges now is a strong lead of Asia relative to other continents and groups of countries. Europe and North America seem to stabilise around moderate growth rates, at about 2 per cent, while growth in Asia stabilises between 5 per cent and 6 per cent. The worst group of countries in terms of real GDP growth is South America. Figure 1.3 shows that there does not seem to be a recovery of globalisation, in the sense of growing trade flows, after the financial crisis. To the contrary trade flows appear to have remained stable in the last decade, since the volume of exports and imports in the world are about at the same level in 2007 and 2016. This hides a slight decline in advanced countries compensated by a slight increase in emerging and developing Asia. The end of the bipolar world characterised by the dominance of the Western and the Eastern blocks in the 1990s has therefore paved the way to a multipolar world, where new powers were emerging, particularly the BRIC countries, and among them, China. In the twenty-first century and particularly after the financial crisis the

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world is characterised by the continued dominance of the USA, although declining, and the strong rise of Asia, driven by China. Europe is still an important economic block, and the recent compacting of EU countries as a reaction to Brexit is welcome. The 27 countries remaining in the Union are indeed showing increasing collaboration and cooperation in front of the fear of disaggregation induced by the exit of the UK from the Union and the rise of populist movements. After the financial crisis a new phase of the global economy has started, characterised by a moderation of globalisation, since global trade flows are no longer rising at sustained rates, and an increasing importance and leadership of the Asian continent, particularly China. The importance of the Asian block is shown in the following figures. China is the first country in terms of contribution to manufacturing value added, both in levels and in dynamics (fastest growth). The USA and EU28 are second to China in terms of levels

Manufacturing GVA, € bn, current prices

2,500

–2

China 2,000

1,500

EU28 USA

1,000

500

Japan

Canada Australia 0 0 2

South Korea

4

6

8

10

12

Average annual growth rate of manufacturing GVA, 2004–2014, % Source:  European Commission (2016a, p. 106).

Figure 1.6  Manufacturing – gross value added and annual growth rate of gross value added, 2004–2014

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of value added in manufacturing, but the annual growth is much slower than that of China. Chapter 3 provides other data on the strength of manufacturing and the positioning of countries in the fourth industrial revolution, showing that Asian countries and particularly China are strongly investing in R&D and in skills, so that the situation outlined above is likely to strengthen. However, before going further into the fourth industrial revolution another aspect of the restructuring of the global economy has to be outlined: while global flows of goods appear to be stagnating in the last decade, the striking feature is that another aspect of globalisation is exponentially growing, namely global flows of data.

3 GLOBALISATION OF THE EXCHANGE DATA WORLDWIDE Information on the volume of data exchanged from mobile phones, computers and other communicating devices show us that globalisation has not slowed down, but rather it has taken a different form. It has to be noted that despite the substantial development of the use of computers, smartphones, for communication and exchange of data worldwide, statistical offices do not provide statistics on this aspect. We had to look for private sources of data, the reliability of which might not be as strong, but they suggest at least that some more systematic data collection and analysis by national and international statistical offices would be useful. CISCO (2017) provides some statistics on global mobile data since 2011, with some forecasts over the period 2016 to 2021. The result is that mobile data traffic has grown 18-fold over the period 2011 to 2016. Global flows of mobile data are expected to exponentially increase in the future, from seven to 49 exabytes per month. Looking at the different regions, the fastest growth is expected in Asia, which will account for almost half global data traffic by 2021. Hence even in terms of this indicator Asia will consolidate its worldwide lead. In addition, the Middle East and Africa will experience very strong growth in the next five years. The breakdown of global data traffic according to devices is also interesting (Figure 1.8).

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60 50 Exabytes per Month

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Introduction: globalisation and the manufacturing revolution

40 30

Latin America (LATAM)

47% CAGR 2016–2021

Middle East and Africa (MEA) Western Europe (WE)

6% 15%

North America (NA)

10.7%

Asia Pacific (APAC)

8.6%

Central and Eastern Europe (CEE)

13%

20 46.7%

10 0

2016

2017

2018

2019

2020

2021

Source:  CISCO (2017, p. 5).

Figure 1.7  Global mobile data traffic forecast by region Smartphones (38%, 43%)

Phablets (7%, 10%)

M2M (10%, 29%)

Nonsmartphones (41%, 13%)

Tablets (2%, 3%)

PCs (2%, 2%)

Other Portable Devices (0.1%, 0.1%)

8% CAGR 2016–2021

Billions of devices

12 10 8 6 4 2 0

2016

2017

2018

2019

2020

2021

Note:  Figures in parentheses refer to 2016, 2021 device share. Source:  CISCO (2017, p. 6).

Figure 1.8  Global mobile devices and connection growth

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Smartphones are expected to continue being the main source of global data traffic in the next five years, reaching 43 per cent in 2021. However, the interesting trend is that of Machine-to-Machine (M2M) devices, which will constantly grow in the next five years and account for about a third of mobile data traffic by 2021. These devices consist of various wired and wireless technologies that enable different machines of the same type other than smartphones, tablets and wearables to connect with each other so that assets, devices and machines can remotely operate, without human assistance. M2M is in fact the Internet-of-Things (IoT), namely the key feature of the fourth industrial revolution, as will be shown in Chapter 3. Examples include GPS systems in cars, medical applications making patient health record and status more readily available, home and office security and automation, as well as the industrial Internet. Overall, global mobile data traffic is expected to rise sevenfold between 2016 and 2021, and by 2021 almost three quarters of all devices connected to the mobile network will be smart devices. This outlook is coherent with Figure 1.2, showing the growth in global flows of data between 2010 and 2019, data provided by Ericsson (n.d.). It appears therefore that while the flow of physical goods and capital appears to have come to a halt in the last decade, globalisation has not stopped but is taking another form: it is becoming digital (leaving aside financial flows, which are substantial and actually an increasing share of these flows are digital too). The next chapters will provide a reflection on what this ‘digital globalisation’ means for economies and for industrial development policies. The second chapter analyses the characteristics of the first three industrial revolutions, in order to highlight to what extent we are indeed in the middle of a fourth industrial revolution. The importance of innovations and technological progress is stressed, but, moving beyond technological determinism, the cultural, social and political factors favouring industrial revolutions and their retroactions on structural changes are highlighted. The crucial importance of the evolution of education systems during these revolutions is also outlined. The third chapter focuses on the fourth industrial revolution. The technological drivers are examined, and their combined effects together with other factors such as globalisation are examined. The result is that the fourth industrial revolution offers great opportunity

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to resolve the current global societal challenges, such as demographic trends of population growth and population ageing, rapid and wide urbanisation, as well as preservation of ecosystems and climate change. However, this opportunity will be realised only if the large changes induced by the scientific/technical/economic engine are accompanied by appropriate ethical/cultural/social changes, which have been called for, perhaps without a complete awareness, in the search for resilience and sustainability in policymaking worldwide. For this purpose, addressing the complexity is essential, keeping in mind that complexity comes from the Latin word complexus, meaning related, woven together, intertwined. In particular the resolution of this complexity and the needed ethical, cultural and social metamorphosis call for a new role of territories, which is examined in more depth in Chapter 5, with the example of the Italian Emilia-Romagna Region, whose industrial policy one of the authors, Patrizio Bianchi, has contributed to define. While Chapter 3 focuses on the supply side, the fourth chapter analyses the deep transformation of the demand side of markets. The fourth industrial revolution is indeed also a revolution in the interaction between consumers and producers, allowed by the development of platforms, especially online ones. They allow new businesses to be created, such as Uber and Airbnb, and they have deep implications even for incumbent firms in existing industries. The way producers interact with consumers is changing, as well as the nature of products, which services’ content become essential from a strategic point of view. Many manufacturers indeed argue that they are now selling solutions rather than products. Competition intensifies, because even existing businesses can be challenged by new entrants building a new platform, a new variety of the product that serves specific needs and can substantially grow if the new entrant is able to build a new community of consumers around it. Platforms raise new issues of competition policy. We argue that they raise issues of product and services regulation, as well as privacy and market or political power due to the enormous amount of personal data platforms are able to collect and analyse, while consumers are not always aware of the personal information they reveal through their use of these new online markets and apps. Oil is no longer the strategic raw material, data are; firms such as Google, Amazon, Facebook and Apple have been claimed to have the monopolistic dominance that Standard Oil had in the nineteenth century.1

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The fourth industrial revolution has therefore a deep impact on industries, on both the supply and the demand sides. The roots of the structural changes implied in industrial revolutions, namely changes in manufacturing regimes, have to be analysed in order to comprehend this complexity. Each industrial revolution is associated with a particular manufacturing regime, the factory system in the first revolution, mass production in the second, flexible production in the third and mass customisation in the fourth (see also Bianchi and Labory, 2017). These transitions call for comprehensive industrial policy that is analysed and illustrated in Chapter 5. The ER region has indeed shifted from a manufacturing regime based on industrial districts in the 1980s, to a regional innovation system in the 1990s and the first decade of the twenty-first century, to a hub in the mass customised production system since 2010. The industrial policy implemented in the region has been comprehensive, aiming at favouring adaptation and adaptability, by coherently defining actions promoting innovation and adoption of new technologies, adjustment in human capital, and provision of appropriate infrastructure.

NOTE 1. The Economist (2017), ‘The world’s most valuable resource is no longer oil, but data’, 6 May, available at https://www.economist.com/news/leaders/21721656-dataeconomy-demands-new-approach-antitrust-rules-worlds-most-valuable-resource, accessed 25 January 2018.

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2. The first manufacturing revolutions: not just technological change Industrial revolutions imply major discontinuities in the growth and development of economic systems. New products and production processes appear, with pervasive effects. On the demand side, new products may allow new modes of living. For instance, the diffusion of fridges and washing machines transformed domestic life and consumer habits in terms of food shopping, and meant more leisure time for women freed from domestic tasks in the 1950s and 1960s. In addition, new production processes may allow price reductions so that more consumers can afford the goods. An emblematic example in this case is the diffusion of the car in America thanks to the production of the Ford Model T at an affordable price. On the production side, the changing production processes have important implications on the type of jobs and skills required in the labour force. After the first industrial revolution, fewer craftsmen were requested while a new working class emerged, made of workers hired in the factories. As technological progress continued in the nineteenth century, new jobs were created, primarily engineers but also technicians dealing with the maintenance and repair of machines in the factories, managers at different levels of the firms’ organisation, and administrative tasks in the large, vertically integrated company. As a result, the society transformed with new roles for people, different levels of education and different powers. While the noble class had all the power in the feudal system, the bourgeoisie took increasing economic but also political power after the first and second industrial revolutions. The structure of the social system transforms in industrial revolutions and this inevitably implies changes in the culture and the laws produced by specific social systems. Social systems are indeed sets of 15

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individuals that interact, and these interactions imply the formation of a social whole, which produces a language, a culture, a state and its laws, which in turn retroact on individuals because they provide them with the capacity to read, to write, to think and to work, thanks to the culture; through education they obtain the necessary knowledge to move and evolve within the society. For this reason, classical economists viewed the analysis of the division of labour as the starting point to understand the structure of the economy and the society, and their evolution. Thus, Adam Smith argued that ‘the effects of the division of labour, in the general business of society, will be more easily understood by considering in what manner it operates in some particular manufactures’ (Smith, 1776, I, p. 4). The political economy approach to the analysis of industry is characterised by an attention to production organisation and its effects on both productivity and the development of the whole economic system. More specifically, production organisation determines the division of labour and its effects on the specialisation of workers’ and employees’ activities, creating opportunities for them to apply ‘skill, dexterity and judgement’ to their working activities and to learn through their working life, with impact also on their role in societies, their political power and their civic development. Hence a first step in understanding current changes implied by the fourth industrial revolution is to look for lessons from history. This chapter therefore examines previous industrial revolutions to derive their main characteristics and effects, which will then be taken into account in the analysis of the current industrial revolution. Innovations and technological progress implied in industrial revolutions have constituted a main focus and primary characteristic emphasised in such analyses, leading to a sort of technological determinism, namely technological innovations as the main driver of changes, which has to be promoted and which will inevitably induce positive changes in the economy and the society. The next section presents industrial revolutions as changes in technological paradigms. However, industrial revolutions are also determined by long-term evolutions in societies, political systems and culture, and have complex effects on the society, the polity and culture, as will be outlined in subsequent sections.

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The first manufacturing revolutions

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1 INDUSTRIAL REVOLUTIONS AND TECHNOLOGICAL SYSTEMS Industrial revolutions are generally determined by important technological progress, which implies the development of new products and production processes, leading to new industries. The fast growth of these new industries makes them important contributors to economic growth, so that they can be defined as leading industries. Besides their direct impact on growth, leading industries of specific industrial revolutions may also have indirect impact on economic growth, since they generally favour the development of other industries. For instance, information and communication technologies have pervasive impact on all sectors of the economy. Industrial revolutions may also be characterised by their core inputs: for instance, iron for the first industrial revolution, steel and electricity for the second. Different scholars outlined the importance of new technological systems (Gille, 1978) or technological constellations (Freeman and Louça, 2001) as characterising industrial revolutions. According to this view, the appearance and diffusion of innovations is inherently a very uneven process and is sometimes explosive, sometimes gradual. The clustering of innovations may generate ‘technological revolutions’ such as electrification or the diffusion of computers. Many economists and historians of technology have stressed the importance of the systemic feature of technology (Gille, 1978; Hughes, 1983). The innovation and diffusion of new products and new processes are not isolated events but are always and necessarily related to the availability of materials, energy supply, components, skills, infrastructure and so on. As Schumpeter observed, innovations often appear in clusters and are not evenly distributed in time and space. This is reasonable since the invention of a new generic technology often finds applications in different products and sectors, so that a wave of innovations is observed from the specific innovation. Nelson and Winter (1977) described this phenomenon as generalised natural trajectories. Historians have shown the evolution of technological systems across history, from the first tools developed in prehistory to mass production. Gille (1978) argued that the Ancient Greeks in fact created technology. Technologies are intimately linked to life, society and the environment, since they aim at providing material objects for the population and improving living conditions. A specific

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technology is generally a scientific or industrial process, invention or method. In any given period, a particular technological system exists, a set linking the various technologies, and associated to a particular socio-economic regime. This system is not fixed but constantly evolves as some of its elements change and induce the adjustment of the whole. The creation of a technological system generally requires several decades, during which various adaptations are made in order to make the whole coherent. The system of the first industrial revolution was created all through the eighteenth century, while the system of the second industrial revolution was created during the second half of the nineteenth century. Improvements in spinning and weaving arose in Britain all through the eighteenth century, while the automobile was fine-tuned between the end of the nineteenth century and the beginning of the twentieth. Once a technological system is established improvements continue to arise, so that the system is not fixed once and for all. However, in this case evolutions are based on incremental innovations, not on radical innovations that may produce discontinuities. The technological system has important consequences on the economy, because it determines how goods and services are produced, at what cost, hence at what prices can goods and services be sold. It also has important consequences on the society, because it determines the division of labour necessary to produce goods, hence the type of jobs and necessary skills required in the labour force. New technological systems will generate industrial revolutions if they become so pervasive that they have important macroeconomic effects and influence major social and political changes. However, technological change is not the only factor influencing industrial revolutions. The same technological innovations may occur in different countries, but the impact can be substantially differentiated, depending on their industrial base, the availability of skills and their capacity of adaptation. The political system might have an influence too, favouring particular types of applications of the new technology for instance as in a case of a dictatorship strengthening the military apparatus. The uneven nature and differentiated timing of development induced by industrial revolutions also concern regions within a country: some regions will adapt more rapidly than others, depending on their socio-economic characteristics, initial conditions and institutions.

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In fact, historians have stressed the importance of culture and the political system in industrial revolutions. If industrial revolutions can be characterised by the change in technological system, their origins are complex and multidimensional: demographic, social, cultural and political changes combine to create the premises of industrial revolutions. Mokyr (2016) has recently outlined the key importance of the development of a ‘culture of growth’ in Europe in the few centuries preceding the first industrial revolution. This chapter therefore describes the different industrial revolutions, highlighting not only the new technological paradigm they involve but also the main social, cultural and political factors contributing to their outbreak. This is not meant to be a complete overview, which historians have performed much better (Hobsbawm, 1999; more recently Griffin, 2013; Rosen, 2010) but some of the main aspects are pointed out.

2  THE FIRST INDUSTRIAL REVOLUTION The first industrial revolution lasted about 70 years and is generally dated between 1760 and 1830. Some of the industries, which became characteristic of this first revolution, were already growing in the sixteenth and seventeenth centuries (textile for instance). In addition, some cultural and social changes that are often regarded as part of the climate of the industrial revolution started much earlier (for instance, the growing political power of the ‘Commons’ in the UK). However, during the first industrial revolution the rate of growth accelerated, a wave of inventions and innovations occurred, and production organisation shifted from craftsmanship to factory work. The pioneer in the first industrial revolution was Great Britain, where growth accelerated already in the 1780s. The cotton and iron industry were the leading industries of the change. The share of cotton in total value added of industry grew from 2.6 in 1770 to 17 per cent in 1801 (De Simoni, 2016). This was an extraordinarily rapid change in industrial structure. Many inventions succeeded to make this improvement possible, from the invention of the jenny to the water-frame and the mule. The number of patents sealed had been about 80 per year in the 1740–49 period, but increased to nearly 300 in the 1770–79 period and over 600 in 1790–99 (De Simoni, 2016). Many inventions were incremental and,

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as Adam Smith (1776, I, 1, p. 13) observed, were made by workers in the factory, since the new production organisation allowed them to improve their ‘skills, dexterity and judgement’. Productivity rose dramatically: working hours for spinning 100 pounds of cotton reduced by 85 per cent from 1780 to 1795 and by 93 per cent from 1780 to 1830 (Freeman and Louça, 2001). However, still as Adam Smith observed, there were also more radical innovations made by scientists, who were dedicated to research. In the metalworking industries in the eighteenth century, the smelting of iron ore with coke instead of charcoal and Cort’s process for the conversion of pig iron into malleable (wrought) iron by ‘puddling’ represented path-breaking innovations. Together they made possible the huge increase in supply of relatively cheap iron between 1780 and 1840. In addition, some complementary innovations were made. For example, Smeaton invented a water-powered mill made in iron and not wood so that it was much more energy efficient. The falling price of iron enabled its use in many techniques and processes: bridges, ships and buildings could be made with iron in their structure, making them less heavy and more resistant. In fact, the transport infrastructure also drastically changed during the first industrial revolution. Roads were made more robust and resistant thanks to new building methods pioneered by some engineers, such as John Metcalf, Thomas Telford and John McAdam. The diffusion of turnpike roads also contributed to improvements in transport. Water canals were built in order to increase the communication network via rivers, which was essentially used to transport voluminous and heavy goods and raw materials such as stones, wood, bricks and wheat. The British government made huge investments in these infrastructures, helped by the creation of the Bank of England in 1694, which provided regular trade in government bonds. The first industrial revolution implied important and wide changes, not only in the economy, but also in the society and culture. Even the agricultural sector became organised along capitalist lines with tenant farmers employing hired labour and producing for the market. The efficiency of agricultural production improved, allowing the production of higher volumes of food with fewer quantities of labour so that labour was freed from the rural areas and would go to work in factories in cities. Cities therefore experienced rapid growth. Firm size in the first industrial revolution was very small: hardly any firms employed more than a hundred people, and even by the 1840s only

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a very few firms, mainly in the cotton and iron industries, employed over a thousand. During the next decades this number steadily increased, up to the large railway companies and engineering firms of the second industrial revolution. In European countries too, the more successful firms were growing in size and market power.

3  THE SECOND INDUSTRIAL REVOLUTION The second industrial revolution lasted about 60 years and is generally taken to have lasted between 1860–70 and 1914–20. The pathbreaking inventions in energy materials, chemicals and medicine were crucial because they increased the effectiveness of R&D and enabled many smaller inventions. Unlike the first industrial revolution, where new technologies were developed by practical trial and errors without a scientific base, the second industrial revolution was characterised by increasing feedback between technology and science. Technological innovations in the first industrial revolution originated from practical experiments, just as scientific developments were essentially based on practical experiments. For instance, the possibility of stocking electricity was discovered accidentally by Pieter van Musschenbroek in Holland, with the experiment leading to the Leyden jar. Many such experiments were conducted (for instance, Benjamin Franklin discovered electricity conductivity through metals with his experiment with lightning in the eighteenth century, and Alessandro Volta discovered in the end of the eighteenth century the principle of the battery in his experiments with dissimilar metals in common contact with moisture), and scientific research developed afterwards to explain these discoveries. During the nineteenth century, the scientific base started to substantially develop, leading to the possibility for technological developments based on scientific theories. The second industrial revolution has thus two important characteristics. First, it is essentially about the interaction between science and technology, in the sense that technological developments were backed and supported by important improvements in science, namely in the knowledge of natural processes underlying technologies in the various fields, from chemistry to thermodynamics. Mowery and Rosenberg (1989) have characterised the period 1859 to 1873 as the most fruitful and dense in innovations in history. For instance, the automobile is a product developed from

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the convergence of different sciences and technologies: chemicals (oil refinery and fuel production; rubber and tyre production), engineering and electricity, steel and metal working and so on. Second, it is characterised by an important change in the organisation of production, allowing large economies of scale and price reductions, implying rising extent of markets as more and more consumers could afford the goods. The new technology of production combined different technologies: machines, electricity and power generation, chemical innovations and so on. The second half of the nineteenth century is also characterised by rising international trade, triggered by the adoption of free trade principles in most Western countries, following the example of Great Britain, which had fixed tariffs on only about 50 products while all other products were freely imported (De Simoni, 2016). European governments preferred to establish trade agreements, which fixed the tariffs that participating countries would reciprocally apply to the goods they exchanged. The dominant industries of the second industrial revolution were steel, chemicals, electricity and railways. Steel had better properties than iron but remained expensive until Henry Bessemer invented the first low-cost process for steel production in 1856. The steel industry considerably developed afterwards, since steel had many advantages over iron (resistance and strength, light weight, no oxidation) in the composition of many products, including railways, ships and machine tools. The steel industry boomed because iron was replaced by steel in the latter products. Regarding chemicals, it was William Perkin (1838–1907) from Great Britain who made the first important discovery that spurred the development of the modern chemical industry. He discovered the aniline purple, which he called the purple mauveine, obtaining a patent in 1856, at the age of 18. This discovery led to the development of artificial dyes in the chemical industry. The chemical industry also developed during the second industrial revolution in the production of fertilisers and artificial materials. Charles Goodyear (1800–1860) in the USA invented in 1839 the vulcanisation process of rubber that made widespread industrial use of rubber possible. John Wesley Hyatt (1837–1920), also American, invented in 1869 the first synthetic plastic, which he called celluloid. Germany had a strong scientific base in chemistry and the chemical industry also substantially developed in that country.

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From 1815 to 1845, a new constellation of industries, services and technologies rapidly grew, but the society and polity also experienced important turmoil. In fact, the Napoleonic Wars had a negative impact also on the British economy, since France and other European countries reduced their economic activity in favour of the war and therefore their imports from Britain decreased. After these wars other countries in Europe started their industrial revolution and were able to meet their domestic demand, thereby reducing imports particularly from Britain. Economic activity reduced in Britain, causing unemployment and social turmoil. A severe crisis hit Britain in the 1830s. Meanwhile new technologies developed, including infrastructure (railways), new source of power (steam engines) and new machine tools so that other European countries combined together the features of the first and of the second industrial revolution. Railways developed considerably in the second half of the eighteenth century, in Britain but also in Germany and in France, and in the USA. Railways induced an important improvement in the speed and cost of transport, thereby favouring international trade. However, they also had a significant impact on management practices. A smooth and efficient functioning of railways indeed requires elements such as punctuality, forward planning of services, accurate maintenance of tracks and trains, quality control, but also coordinating and controlling the operations of many different locations from a single centre, all features that turned out to be very important for the management of large companies. These new management practices were adopted in many sectors and seemingly favoured firm growth. The capacity to communicate over long distances became crucial for the functioning of railways but also of large companies, and the electric telegraph, invented by Wheatstone in 1837 provided a solution to this communication problem. Numerous other innovations, in signalling equipment, in rolling stocks and in civil engineering for tunnels and embankments, were made by and for the railway companies and their numerous suppliers. O’Brien (1994) highlights that all the investments and innovations brought about by the development of railways had indirect impacts on the rest of the economy, in the form of externalities and spin-offs. For example: canals and later railways made voracious demands for capital over relatively short periods of time which prompted the expansion and

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Industrial policy for the manufacturing revolution improvement of financial intermediaries for the mobilization of domestic and foreign savings. Once established, such institutions continued to meet the needs of other sectors of the economy. (p. 254)

The organisational innovations in the railway sector had impact in other sectors, where the new organisational practices were copied, for instance in the coal, iron and engineering industries. Cotton retained its extraordinary lead in British exports up to the 1920s but between 1830 and 1860 the share of iron increased while that of cotton was falling. Exports of machinery and of coal also grew rapidly in the second half of the nineteenth century, but their importance for the domestic economy was far greater. While coal could be considered as the second core input after iron, the engineering industry was another leading sector. The machine tool industry, namely the production of machines to make machines, particularly developed during the second industrial revolution. According to Musson (1982) it was in fact the British machine tool industry that pioneered mass production. American manufacturing pioneered in light engineering, while British manufacturing pioneered in heavy engineering. The electricity industry was much more closely related to science than the industries of the previous waves. Benjamin Franklin undertook various experiments with electricity in thunderstorms in the 1740s, as did other scientists. In 1800 Volta invented the primary battery. Research in this field continued throughout the nineteenth century. In the 1850s and 1860s the magnetos and dynamos reached a development point where they could be used on a commercial scale for illumination. Innovations were patented and the electricity industry is one of the first industry experiencing patent wars. One example is Thomas Edison’s invention of the incandescent light bulb and the patent war that ensued in the 1880s. Edison registered his patent in 1880. In 1883 the US Patent Office declared Edison’s patent not valid because it was based on an invention by William Sawyer. The patent litigation proceeded for years afterwards and the 1889 Edison patent was declared valid. The electric industry developed at sustained rates in the second half of the nineteenth century and in the twentieth century, with many applications. One was in transport with the development of tramways in cities. Electrification and the diffusion of electric tramways were very rapid in Europe, in the UK, France, Belgium and

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Germany in particular. The two main German electrical companies also contributed to electrification in Italy (Hausman et al., 2008). Chandler (1977) also highlights the speed of change in the USA: by 1890 15 per cent of urban transit lines were using electric-powered streetcars, and by 1904 this had risen to 94 per cent. This brought about important changes in the society and the economy. Offices and firms could be organised differently, because lighting would allow working any time of the day or night; the telephone facilitated the administration of large organisations, but also small firms benefitted. The growth of the new electricity infrastructure required a new regulatory framework, new legislation, new standards, and massive private and public investment. The development of large corporations involved organisational innovations, with bureaucracies, and more substantial role of professional managers, cost accountants and professional engineers. In the factory of the first industrial revolution the organisation was such that the entrepreneur would delegate responsibility for the shop floor management to senior foremen or ‘overseers’, who were often experienced craftworkers. The new system in the second industrial revolution was based on departmental structures controlled by professional managers. The development of a management class was favoured not only by the requirements of companies growing in size, but also by the education system. In both Germany and the US, new institutions of higher education were set up in the 1870s to 1890s, the Technische Hochschulen in Germany and the institutes of technology in the USA, which greatly increased the number and the quality of engineers in the labour force. The new technologies of the late nineteenth century to early twentieth century required specific training outside the firm and prior to the job, while training in Great Britain was still largely based on on-the-job training. Hence industrial revolutions last many years (60 to 70 years in some cases) and are not characterised by constant positive growth rates. Some boom years alternate with crisis years. Crises may be due to the structural adjustment of the economy in the shift from one dominant technical system to another (Freeman and Perez, 1988). However, socio-political events also influence the economy and the evolution during industrial revolutions: there might be wars, new markets may open if countries decide to favour trade.

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The cultural evolution of individuals and societies might also imply important changes, such as the influence of the Lumières ideas on the French Revolution in the late eighteenth century, whereby people called for better living conditions but also a different political regime. Electrification and steel as a new core input are key characteristics of the second industrial revolution, as these two elements came to dominate the world economy from the 1890s to 1914. This upswing phase was followed by a crisis phase in the 1920s and 1930s, which was primarily due to huge losses caused by World War I and also the Versailles Treaty, which imposed huge sanctions on Germany. The crisis continued up to World War II. After the War there was a long boom up to the 1970s, followed by a new crisis of adjustment in the 1980s and 1990s. The long boom in the world economy after World War II (called in France ‘the glorious thirty’), was favoured and in turn favoured a number of changes: Keynesian policies were successful in helping reconstruction and development of economies; social changes were substantial, in that the welfare state expanded, providing health and social benefits to the population; democracies consolidated in the Western world, with extension of voting rights to the whole population and even to women; mass secondary education was established, with important development also in tertiary education. This favoured civil and cultural development, which in turn fed technological and economic improvements. The oil crises of 1973 and 1979 are first and foremost political crises. But the 1970s are also characterised by crises of structural adjustment, since the old dominant model of mass production shows limits: workers ask for better working conditions, young people call for a different society, more peaceful and more equal, like feminist and civil rights movements. The next section argues that the third industrial revolution has information and communication technologies (ICTs) as a leading sector.

4 THE THIRD INDUSTRIAL REVOLUTION: ICTS While the first and second industrial revolutions are coherently dated in the economic history literature, there does not seem to be

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a consensus on the third industrial revolution. Some authors date it from the 1950s to today (De Simoni, 2016), others from the 1960s to the 1990s (Schwab, 2016) or from the 1970s to the 1990s (IndustryResearch Alliance, 2013). For authors indicating an end of the third industrial revolution, the beginning of the twenty-first century marks the start of the fourth industrial revolution. For other authors the third industrial revolution is not yet started, because it should represent the shift to renewable energy and sustainable development (Rifkin, 2011). This reflects the fact that discontinuities are not produced suddenly and abruptly, but result from long-term evolutionary processes whereby different trends interrelate and combine to produce complex effects. There is however agreement on the dominant technology and industry of the third industrial revolution: information and communication technologies (ICTs). In many ways the fourth industrial revolution, that of connected machines and objects through sensors sending data on the cloud, enabling new products and production systems called cyber-physical systems, is the continuation of the third industrial revolution, because ICTs are still dominant and evolving. Biotechnologies and genomics, as well as nanotechnologies and new materials, were born during the third industrial revolution, after World War II, and continued to evolve and converge to produce new technologies that today allow new products and processes. However they are named and classified, they are industrial revolutions driven by important technological changes, or better, new technological systems. The third industrial revolution can therefore be dated between the 1950s and the end of the twentieth century, driven by electronics and ICTs, automobile and airspace, and chemical technologies and industries. The fourth industrial revolution still has ICTs as its core, but allows new major discontinuities that will be shown in the next chapter. After World War II the development of science and technology became indissolubly linked with major impact on industries, allowing transformations in existing industries and the creation of new ones. Metallurgy thus deeply changed, producing new light alloys with copper, manganese, zinc, silicon and magnesium, used in nuclear, aeronautic, spatial and electronic fields. The chemical industry also improved a lot, producing new synthetic and artificial fibres used in textile and other fields. Plastic

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materials also developed substantially. Electricity, generated by power plants running on varied sources such as coal, nuclear, natural gas, hydroelectric and petroleum, became indispensable for all productive activities and everyday life. The automobile industry is almost the symbol of the twentieth century. Production has constantly increased in the period, from about 25 million cars in the mid-1960s (Bianchi, 1984) to more than 87 million in 2013, to 96.1 million in 2016 (ACEA, 2016).1 Most people use cars every day, at least in developed countries, allowing rapid journeys but also problems of congestion and pollution in cities. Aeroplanes have allowed people to increasingly travel over long distances, since planes have been increasingly built and improved, with parallel fall in the price of tickets. Space technologies also improved a lot during the period, allowing man to walk on the moon in the late 1960s, but also allowing the development of the satellite industry, which fed television and telecommunications on the earth. ICTs started to be developed in the 1950s, in terms of science and invention. They consist in different technologies, namely electronics, computer and telecommunications, that have been developed and constantly improved all through the twentieth century and that have converged in the 25 years, thereby making possible major discontinuities in the economy, the society and culture. One of the important innovations leading to the development of computers has been the computer chip. In fact, since their invention in the late 1950s, integrated circuits have been improved rapidly and constantly: Moore’s law has largely held (Brynjolfsson and McAfee, 2014). Moore enunciated his law in 1965, predicting that the capacity of a computer chip would double every year, then in 1975 he predicted that the capacity would double every two years. This paved the way for the development of computers, which, together with the innovations in telecommunications, led to the development of information and communication technologies (ICTs). ICTs in fact originate in the development of electronics all through the twentieth century, generating numerous innovations in radio, radar and television. As in the case of the electric industry, many innovations were made in Europe, but throughout the twentieth century the USA became the leading innovator. Thus, for instance Hertz and Maxwell theorised and demonstrated the existence of electromagnetic waves. The first thermionic valve was invented and patented in 1904 by Sir John Ambrose Fleming, a professor

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at University College London, and at the beginning of the twentieth century the radio industry was dominated by British and German firms. The Italian inventor Guglielmo Marconi established his Wireless Telegraph Company in Britain in 1897 and was the first to demonstrate the feasibility of ship-to-shore radio communications, as well as shore-to-shore and ship-to-ship. The German electrical giants, AEG and Siemens, however closely followed his firm in Britain. AT&T pioneered the use of valves as relays in the ­telecommunication system. The European radio firms however dominated the industry up to the 1920s. The radio industry was perceived as strategic by governments, which supported its development. Thus, the Kaiser convinced AEG and Siemens to establish a joint subsidiary for communications, Telefunken, in 1903. Marconi’s subsidiary in the US was also successful, but the government was increasingly convinced that such a strategic industry could not be left in the hands of a foreign company, so that it bought out Marconi’s subsidiary to create a powerful unified company, RCA (Radio Corporation of America). After World War II all governments supported R&D for electronic components and circuits. It was in the Bell Laboratories of AT&T that civil transistor technology led to the key developments in semiconductors and to the establishment of the US semiconductor industry, mainly by spin-off from the Bell Laboratories. The role of government continued to be important but more in the procurement of new devices. Intel developed the first microprocessor in 1971–2. It had a big impact on the semiconductor and computer industries because it meant that a ‘computer on a chip’ could be manufactured at low cost and large scale. The US dominated the semiconductor industry in the 1970s, 1980s and 1990s, although Japanese firms also became important players in the market. In the US, the government ­supported firms’ R&D activities in the Sematech project. The electronics, telecommunications and computer industries started to converge in the 1960s, leading to the emergence of ICTs. In fact, the intimate relationship with the computer industry had begun as early as the 1940s. The computer industry continuously grew after World War II. The development of minicomputers by DEC in 1963, and later of micro­ anufacturing computers in the 1970s, facilitated the progress of m automation.

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The advent of microcomputers also made computers available at home, to families, because it led to drastic reduction in prices. Automated manufacturing systems were developed as early as the 1960s. It seems that the pioneering firm in this regard was a British firm, Molins, which introduced the Molins System 24 in the 1960s, an automated machine tool system. It was followed by other pioneering flexible manufacturing systems, such as the M250/02 CNC of Auerbach, a German machine tool company, presented in 1972, and consisting in three-axis machining centre, three two-arms changers and one four-arm robot. A wide variety of computer-controlled manufacturing processes diffused over the next decades. The electronic and ICT sectors are characterised by a strong interaction between scientists and engineers as a basis of the innovation process, so that countries with strong scientific institutions, as well as entrepreneurial engineers, have taken the lead. The telecommunication sector has also been developing through successive innovations for a long time (starting with Alexander Graham Bell’s invention of the telephone in 1875), but the 1990s saw the most drastic innovations, making the sector no longer a natural monopoly – to the extent that the essential facility could be ­duplicated – and allowing it to converge with computers to contribute to the new industrial revolution based on ICTs. Parallel to the development of telephone capacity an important field of technological improvement was in fact in the carrying capacity of cables. In the 1990s cables were improving a lot but at the same time new communication infrastructure was developing: wireless communication, which was developing thanks to satellites and digital systems. Voice and data could thus be communicated via different infrastructure. The Internet was originally introduced in the 1960s as an advanced research project agency (ARPA) project, supported by the US Pentagon to ensure that some decentralised communication would remain in operation even in the event of a nuclear war. Today traditional telephone calls continue to be the telecom ­industry’s largest source of revenue, but telecom is less about voice and more about data, namely text, photos and videos, as shown by the data presented in the introduction. High-speed Internet access for computer-based data applications such as broadband information services and interactive entertainment is pervasive. Different

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generations of connecting technologies have been developed one after the other, from GSM (2G) to 4G and even 5G, which is being developed. The telecommunication sector and ICTs continue to be the leading industries in terms of growth, innovation and impact on other industries. Mobile devices and broadband connectivity are increasingly embedded in modern economies and societies. Virtually any industry benefits from telecom innovations and probably the most important impact is now in the Internet factory and the Internet of Things (IoT) (see the next chapter). As a result of technological development (ICTs in particular) the organisation of firms could change. Thus, the period 1970 to 2000 has seen the adoption of new organisational practices and organisational forms by large companies, towards less hierarchical levels and more horizontal communication (Black and Lynch, 2000; Osterman, 1994). This was so important that global value chains could be set up, namely production processes organised on a global scale, with phases realised even in different countries. Without the third industrial revolution based on ICTs, this would certainly not have been possible. The changes in manufacturing regimes, namely production organisation and firm structure, induced by industrial revolutions is examined in more depth in the next section.

5 INDUSTRIAL REVOLUTIONS AND MANUFACTURING REGIMES Different models of production organisation, or manufacturing regimes have dominated specific historical phases of industrialisation. The first model is craft production, characterised by the realisation by one individual of all the phases necessary to produce the good. This was typical before the industrial revolution starting in the late eighteenth century. In that model production could be increased only by adding craftsmen, who could realise the different phases of the production process in parallel. The first industrial revolution results from the introduction of machines in the production processes, inducing the possibility of their division into different tasks and the specialisation of workers on specific tasks that they would carry out sequentially (factory system). The second industrial revolution is associated with a new

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­ anufacturing regime, that prevailed up to the last quarter of the m twentieth century, the mass production system (Bianchi, 1991; Labory, 2002). In that system Ford pushed labour division to the extreme, scientifically dividing the production processes in elementary tasks that low-skilled workers could perform in sequence. The system was extremely rigid in that any change in the product would be very time-consuming and costly. This model allowed the exploitation of economies of scale, with homogenous products produced on very large scale, which could have low price but very little differentiation. Throughout the twentieth century differentiation was introduced in that type of production system by multiplying the divisions of the firm and having different divisions and factories dealing with different product variety (the M-Form introduced by GM in the 1920s, see Chandler, 1982). From the 1980s essentially Japanese producers, especially in the car industry, introduced a new production model that Womack et al. (1990) called the ‘lean’ production system, while Abo (1994) and Shimuzu (1995) preferred to call it the ‘flexible’ production system. Production 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 in tasks, but different varieties of the product shared the same production lines, and differentiation arose at later stages of the production process. In this manner economies of scale were combined with economies of scope. In addition, product changes could be implemented without a complete reorganisation of the production process: the basics of the product remained the same (for instance the car body), so that economies of scale were still generated, while differentiation regarded some subsequences of the production process. Parts across different products were made common, so that different models could be manufactured on the same line of production for some stages. One principle of this production organisation invented by Toyota is ‘kaizen’, namely continuous improvements, whereby workers are encouraged to identify problems and suggest solutions. However, this principle only worked when the production line was ­reorganised into sub-lines, at the beginning of the 1990s (Labory, 2002), ­allowing stocks between lines and thereby reducing time pressure on w ­ orkers who could then dedicate time to problem identification and resolution.

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This modular production process has diffused to different extents in the car industry (Frigant and Jullien, 2014) and other industries. Each manufacturing regime is associated with different skill requirements in the labour force. In the first industrial revolution workers were largely illiterate, providing essentially physical effort to perform their tasks. The mass production system was also based on low-skills and low analytical skills in the labour force. The flexible production system introduced by Japanese producers increasingly encouraged workers even on the shop-floor to develop analytical skills. Lifetime employment characterised these two systems, providing the working class with decent wages and job security.

6 SOCIAL, CULTURAL AND POLITICAL CHANGES EMBEDDING INDUSTRIAL REVOLUTIONS The discussion so far has focused on technological innovations and progress. Each revolution is indeed characterised by a changing technological system. Very succinctly, the first industrial revolution is that of the steam engine, spinning machines, the factory system and iron. The second industrial revolution is that of railways, electricity and steamships, steel and chemicals. The third industrial revolution is that of ICTs, airspace and automobile, petroleum and nuclear energy. Technological changes are fundamental, but they have to combine with other conditions in order to produce industrial revolutions: the social, political and cultural contexts, expressed through the institutional framework, also matters. Otherwise it would be impossible to explain why industrial revolutions start in particular countries and not in others, and why some countries have difficulties in adopting the new technologies, products and production processes. Thus, many authors have shown the long-term evolutionary processes leading to the ­first two industrial revolutions, and leading particular countries to take the lead in them (for instance, Mokyr, 2016; Hobsbawm, 1999). It is widely recognised that the early abolition of serfdom in Britain contributed to the industrial revolution because it freed workers from rural areas that could go and work in the factories located in cities. It also induced a search for improvement in agricultural p ­ roductivity.

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The political system in Britain also favoured the first industrial revolution, since it gave power even to the bourgeoisie and not only to nobles, in the parliamentary monarchy. In 1689, the Bill of Rights was approved in the UK, establishing a parliamentary and constitutional monarchy in the country. The bourgeoisie could thus favour its interests with laws regarding trade in particular. As stressed by Supple (1963, p. 14) many factors contributed to making Britain a pioneer in the first industrial revolution: The development of enterprise, her access to rich sources of supply and large overseas markets within the framework of a dominant trading system, the accumulation of capital, the core of industrial techniques, her geographical position and the relative ease of transportation in an island economy with abundant rivers, a scientific and pragmatic heritage, a stable political and relatively flexible social system, an ideology favourable to business and innovation.

Extraordinary scientific development occurred in the period, starting much earlier. In the UK, the Royal Society was created in 1660 to promote scientific excellence for the good of society. In 1687, Newton published his Philosophiae Naturalis Principia Mathematica, which laid the foundations of modern physics. In the same century Francis Bacon reflected and defined a scientific methodology. As stressed by Acemoglu and Robinson (2012), the development of inclusive institutions was a key driver of the pioneering role of the UK in the first industrial revolution. They argue that institutions are key in enabling economic growth, through the effects they have on innovation and entrepreneurship. In the sixteenth and seventeenth centuries, most countries had exclusive institutions, whereby wealth was concentrated in the noble class while most people were poor and dependent on nobles for their survival. This started to change in the UK with the growing power provided to the parliament and its broader membership. Monopolies were ended and entrepreneurship encouraged. This was essential to start a dynamic of business creation. Adam Smith’s book The Wealth of Nations (1776) exemplified the political and cultural foundations of the British industrial revolution. He showed the benefits of having entrepreneurs looking for profits and hiring workers in factories; he criticised the maintenance of local monopolies and restrictions on trade. He also underlined the benefits of access to decent living conditions for all people, including workers, allowing civil development to the benefits of the whole country.

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Entitlements were thus extended, first to the bourgeois class, which could set up factories and move up the social scale. Workers were predominantly illiterate but they could work in factories, although in poor working conditions. Much later on during the nineteenth century entitlements would be extended to the whole population, by obligatory schooling up to the age of 11 (see section 7). People with low skills but a minimum of knowledge (elementary school level) subsequently provided the labour force for the factories in the mass production system. Later in the nineteenth century secondary and tertiary education developed and started to provide the managers and engineers that the industries needed (especially in countries like the USA, France and Germany). These levels of education started to be open to lower classes, since they were free up to the age of 14 or 15 and then grants were allocated to poorer but talented pupils. The content of education also changed, away from the ancient languages which were the main topics taught at universities in Europe up to that time, to mathematics and sciences, as well as modern languages and philosophy. As we will argue in more detail in the next section, this evolution of education systems was essential in feeding the adjustment and development processes spurred by the technological progress of the industrial revolutions. Besides the UK, other European countries also saw their feudal systems declining in the eighteenth century. These systems were based on personal and patrimonial relationships between the sovereign and their vassals, and between the vassals and the peasants. The vassals promised loyalty to the sovereign in exchange for protection and assignment of a feud, where in turn the vassals would guarantee protection and assistance to peasants in exchange for their labour, tax payments, services and participation in wars. This system has been called the Ancien Régime to describe the society and the institutions prevailing before the first French Revolution of 1789. The end of this regime paved the way to more democratic systems, supported by the idea elaborated and diffused by the Enlightenment that men should have equal rights. Meanwhile a demographic change occurred in Europe from about the middle of the eighteenth century. The European population started to substantially grow in number in that century: while it had been about 73 million in 1350, 89 million in 1650 (not a huge difference), it grew to 140 million in 1750 (Slicher van Bath, 1972, p. 109). The reasons were the reduction in child mortality, better hygiene and

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above all better access to food. Medicine became more organised and new methods were created, such as the vaccination against smallpox by Jenner in 1796. Food became more easily available thanks to the agricultural revolution, which induced substantial improvements in agricultural production and productivity. For instance, the continuous rotation method was successfully experimented in the Netherlands and in the UK, substituting the fallow land with forage cultivation, allowing the lands to be fertilised while providing food for animals; in addition, the enclosures system led to the constitution of large land proprieties where landowners would hire farmers who would receive a wage for their work. Meanwhile the poorest farmers would leave rural areas to cities where they would work in factories. As a consequence, the agricultural revolution contributed to the industrial revolution in a number of ways: it freed labour force for the nascent industries; it provided food for the growing cities; it allowed remaining farmers to have sufficient resources to buy the new machines produced in industries, allowing further increases in agricultural productivity; profits generated in farms was invested in industries and vice versa. The demographic expansion continued in the nineteenth century. Thus the world population is estimated to have increased by 40 per cent between 1850 and 1914 (from 1.3 to 1.8 billion), while the European population experienced higher growth rate (67 per cent, from 275 to 460 million).2 The same factors favouring the population growth prior and during the first industrial revolution contributed to this trend: the improvement in hygienic conditions, with in particular the building of aqueducts and drainage systems in towns, bringing water to houses; the continued progress in medicine (in particular with the discoveries of Louis Pasteur), which helped the fight against infectious diseases thanks to vaccination, to appropriate hygiene and medicines. The diffusion of primary schooling also made the population better educated and more aware of these practices. The evolution continued throughout the nineteenth century. Regarding demographic trends, the improvement in hygiene and medicine allowed a further reduction in mortality, particularly child mortality since the mortality rate in the first year of life reduced from about 250 per thousand in the middle of the eighteenth century to 120–130 per thousand in the middle of the nineteenth century. In addition, during the transition from the first to the second industrial revolution a new urbanisation spread, spurred by people going to

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work in the factories located in cities. The UK was ahead in that trend since about 50 per cent of the population lived in cities around 1850 (De Simoni, 2016). Improvements in the transport systems continued with the introduction of railways and steamships, contributing to make transport cheaper and more rapid, even for heavy and voluminous goods. In the UK, these improvements led to an increase in the extent of the market, both national and international. The increase in the internal market was driven by the rise in consumption, thanks to the rising per capita income, which almost triplicated during the eighteenth century. The standards of living in the UK were the highest in Europe and British families had access to more abundant and varied food, appropriate clothing and houses. However, the increase in the extent of the market was above all international. The external trade of the country duplicated in the second half of the eighteenth century. The UK exported wool, which accounted for about half its exports in 1750, and re-exported to continental Europe the commodities it imported from its colonies. However, the mix of traded products rapidly changed so that for instance exports of wool substantially reduced in favour of cotton that was produced by the rapidly growing industry. Britain was leader in the first industrial revolution. However, the British supremacy came to an end between 1870 and 1913 when American manufacturing started to lead in terms of productivity and growth. Like the British supremacy in the first industrial revolution, the US supremacy in the second industrial revolution resulted from a set of favourable economic, social, cultural, political and technoscientific elements. The support for science and technical inventions was important, as had been stressed by Alexis de Tocqueville in 1836. The education and professional standards of engineers also had a positive effect on industrial development. The country benefitted from the large market and the abundance of land. The development of railways was essential to industrial development, because it allowed more rapid transport at lower costs and therefore allowed the development of many other businesses. About 2,000 miles of track were added each year on average to the US rail network in the 1850s and the 1860s. Complementary to the railway industry were the coal and iron industries, and then the steel industry. One institution that delayed growth in the US was the slave

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economy. In fact, it was in the period that followed the victory of the North in the Civil War that the US achieved higher growth rates than Britain. The North had much more sustained development than the South even after this war. From a policy point of view, governments all over Europe adopted laissez-faire policies and free trade after the 1848 revolutions. Business interests became predominant in most European countries and the landowning aristocracy was generally obliged to share power, if not to surrender it. Serfdom and aristocratic privileges persisted however in Eastern Europe and Russia, where no industrial and economic take-off took place. Such exclusive institutions were also dominant in many countries in Asia and Latin America, which were left behind (Acemoglu and Robinson, 2012). However, the crisis of the 1870s to 1890s put an end to the liberal ideology. The crisis years were also a period where the discontent of the working class grew. The Paris Commune of 1871 was a unique explosion but it caused great alarm and stimulated socialist movements all over Europe. The depression of the 1880s might be partly due to the structural changes in industries, with the crisis or relative decline of the industries of the first industrial revolution (particularly coal and iron) and the rise of new industries (electricity and steel), requiring new skills and resources. These cultural, social and economic changes were also accompanied by important changes in education systems, which we argue to be fundamental to support and smooth the changes induced by industrial revolutions.

7 MAKING APPROPRIATE SKILLS AVAILABLE FOR THE INDUSTRIAL REVOLUTION: EVOLUTION OF EDUCATION SYSTEMS IN THE UK, FRANCE, GERMANY AND THE USA Economic and social development are intimately linked to the education of the population and the labour force. Europe, and specifically the UK, was the pioneer of the first industrial revolution because political change induced the creation of inclusive institutions (Acemoglu and Robinson, 2012) but also because education

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was more widespread and adapted to economic changes. The same is true of the USA, which became the leader from the second industrial revolution onwards. Education attainments in the population of Northern American States were higher than those of European countries, and mass public education was adopted earlier than in other European countries. Not surprisingly, the second industrial revolution in the USA and economic development were more sustained in the Northern States, where education was more diffused in the population and was improved to provide the skills needed in the developing industries. We have argued in the previous sections that while the first industrial revolution was characterised by technological developments without any scientific base, an important feature of the second and subsequent industrial revolutions is the growing interaction between science and technology, up to their integration in the fourth industrial revolution. The scientific base available in a country therefore became increasingly important and countries with strong universities and scientific research, such as Germany at the end of the nineteenth and early twentieth centuries, and the USA in the twentieth century, took leadership. This is reflected in the educational background of great inventors of the first industrial revolutions. All had the opportunity to receive some education. Thus James Watt, born in 1736 in Scotland, who substantially improved the first steam engine developed by Thomas Newcomen, was educated in his early years by his mother, and subsequently went to the engineering school Greenock in Scotland. He also went to the University of Glasgow, where he opened a manufacturing laboratory. Newcomen was a lay preacher and teaching elder at the Baptist Church, besides his activity as ironmonger in Dartmouth, Devon, England. He was born in 1664 and invented the steam engine in 1712. Richard Arkwright (1732–1792) was born in Preston, Lancashire and received elementary teaching from his cousin, before moving to apprenticeship and working life as a barber and wig-maker. He invented a waterproof dye for the use of periwigs in the shop he opened after his apprenticeship. He became interested in spinning and carding in the 1760s, and worked on it with John Kay, a clockmaker, before inventing a spinning frame. Charles Tennant (1768–1838) discovered bleaching powder (patent in 1799) and founded an industrial dynasty. He received schooling

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both at home and at a parish school, and then an apprenticeship. He had no training in chemistry but invented the bleaching powder in practical experimentations. In the US, Eli Whitney who invented the cotton gin was the son of a prosperous farmer. He graduated from the Yale Leicester Academy. Samuel Morse was the son of a pastor (of Calvinist faith) and attended the Phillips Academy before moving to Yale College to study religious philosophy, mathematics and the science of horses. He attended lectures on electricity. He made a living out of painting, drawing portraits, but continued studies in the meantime. From the second industrial revolution onwards, invention and technological progress became increasingly science-based, and more and more inventors had wider educational backgrounds and scientific preparation. The following sections review the evolution of education systems in the UK, Germany, France and the USA over the nineteenth and twentieth centuries. This analysis is carried out along broad lines, outlining some important and interesting aspects, while more exhaustive and precise analyses are left to historians. 7.1  Evolution of Education in the UK As explained by Gillard (2011), improvements in education in England came in the sixteenth century under the impetus of King Henry VIII and the English Reformation. The refounded schools (former grammar schools that had belonged to the Church) constituted the basis of education in England until the eighteenth century. The Bible was translated from Latin into the vernacular, and Thomas Cromwell, Henry VIII’s Vicar-General and Chief Adviser ordered that copies of the Bible were to be placed in every parish church. The availability of the Bible encouraged many to learn to read (although the Parliament had passed an Act that prohibited artisans, husbandmen, labourers, servants and almost all women to read and discuss the Bible), and also to start to think and reflect on the nature of the society and the church. However, the English Renaissance did not lead to a substantial reform of curriculum, which remained focused on Latin and Greek language teaching. Education was extended to the laity though. In Italy, Spain, Portugal and Flanders the situation was worse since ideas were stopped by the Inquisition movement, while in France

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and Germany the same arose due to wars between the upholders of monasticism and the friends of free thought. Williams (1961, p. 133) argues that there were three clear trends in education in the period: the diffusion of vernacular teaching, the lack of adaptation of institutions to a changing economy and culture and the shift of most schools from sponsorship by a national institution to private benefaction. In England the education of younger children was developed: the number of schools increased and there was a ‘bewildering variety of forms, ranging from instruction by priests to private adventure schools, often as a sideline to shopkeeping and trade’ (Williams, 1961, p. 133). New schools were also created, the ‘writing schools’, teaching writing and reading of English, as well as accounting, thereby providing appropriate skills to the growth of trade. In the seventeenth and eighteenth centuries the concept of universal education appeared, proposed by Comenius (1592–1670), a Czech teacher, scientist, educator and writer. He argued in favour of the education of children even of a young age with surprisingly innovative didactic methods, based on play. Comenius was invited by the House of Commons in 1640 to participate in the creation of an agency for the promotion of learning, with the idea of universal books and education for both boys and girls. Debates took place in England at that time on the nature and purpose of education. The idea of inducing pupils to learn by themselves rather than being taught, which sounds modern, was also developed. Comenius left England in 1642 due to the Civil War but Samuel Hartlib carried out the plan with the support of Oliver Cromwell. After the restoration of Charles II in 1660 however these progressive ideas on education were abandoned, and the inertia of grammar schools remained. Families were increasingly asking for practical education that, instead of teaching Greek and Latin, would better prepare their children for the developing economy, including subjects such as mathematics, the emerging sciences and modern languages. However, in the eighteenth century the education system in England remained the same, apart from grammar schools in larger cities, which provided education to the children of merchants and tradesmen. Their social base enlarged and their programme widened to include mathematics and the natural sciences. In addition, new vocational academies were created, preparing students for

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­ rofessions, teaching law and medicine, commerce, engineering, the p arts and the armed services. These ‘nonconformist’ academies served the lower classes while the upper classes received education from the universities, which were influenced by the educational developments in Scotland, Holland, Germany and the Protestant cantons of Switzerland. The industrial revolution put increasing pressure for a national education system, which took a long time to be established, partly because of reluctance to mass education that could question the established order and power of the elites. Workers employed in the cities’ factories were poor and did not have access to any education. In 1802 an Act was adopted that obliged employers to provide instruction in reading, writing and arithmetic during the first years of apprenticeship. At the beginning of the nineteenth century some proposals were made to establish higher-grade elementary schools and secondary schools to meet the needs of the class immediately above the very poor. Democratisation continued in the UK in the nineteenth century, with for instance the People Act of 1832, which gave a million men the right to vote. The large social, political and economic transformation implied by the industrial revolution revealed the inadequacy of the education system, and numerous reports were produced on this issue. Schools of industry were created to provide the poor with manual training and elementary instruction. The first school of industry opened in the Lake District in Kendal in 1799. It seems that training specific to the needs of the new expanding industries was left to the responsibility of the private sector. Thus, Mechanics’ Institutes were opened in Scotland and England. They were financed by industrialists and provided instruction to adults and training to engineers in industry. In addition, new boarding schools were created from the 1840s to provide education to the middle classes that could not afford more expensive public schools. Hence in the nineteenth century education was extended both to the poor and to the middle classes not by the government and ruling elites, which were against mass education, but by the bourgeoisie, which had interest in finding appropriate skills to expand its factories. This system strongly divided according to social classes stands in contrast with the situation of the USA, which created a public school system based on common education for all its citizens in 1830. The

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system was funded by public taxation, like in a number of other nations in Europe. In the UK, elementary school attendance was made compulsory in 1880 and free in 1891 only. So while the organised working classes increasingly demanded education, it was only in the end of the nineteenth century that public education for all was established in the country. The strong class division also had an effect on entrepreneurship, since the industrialists becoming rich would aspire to enter the class of gentry rather than feed an economic growth process. 7.2  Education in Germany Public education started earlier in Germany relative to the UK, since Frederick the Great of Prussia mandated, in 1763, regular school attendance from the ages of five to 13 or 14. Schools were predominantly linked to churches throughout the nineteenth century. Historically, Lutheranism had a strong influence on German culture, including its education. Martin Luther advocated compulsory schooling so that all people would independently be able to read and interpret the Bible. This concept became a model for schools throughout Germany. German public schools generally had religious education provided by the churches in cooperation with the state. During the eighteenth century, the Kingdom of Prussia was among the first countries in the world to introduce free and generally compulsory primary education, consisting of an eight-year course of basic education, in the Volksschule. It provided not only the skills needed in an early industrialised world (reading, writing, and arithmetic), but also a strict education in ethics, duty, discipline and obedience. Children of affluent parents often went on to attend preparatory private schools for an additional four years, but the general population had virtually no access to secondary education. The gymnasium, a secondary school preparing boys for university admission, saw its teaching activities extended to include the preparation of civil servants. By 1900 the gymnasium had three specialisations, in the classical languages, modern languages or mathematics and science. Girls were not admitted to gymnasium until 1908 and universities until 1910. As early as 1920 school attendance up to the age of 18 was made compulsory. At the beginning of the nineteenth century, Wilhelm von Humboldt, a Prussian administrator, influenced by political events

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in France and by German philosophers, became convinced that the development of the State and the society required rational and knowledgeable citizens. Hence, he stressed the importance of general knowledge, Wissenschaft, and freethinking at universities, in contrast to the eighteenth century system of academies and vocational schools, which were focused on specific teaching to train bureaucrats. The new universities proposed by von Humboldt (1920) would teach the ‘pure idea of science’ rather than specific training. Teaching at universities became intimately linked to research, in that university teachers would not only transmit knowledge but also primarily develop knowledge together with the students. As a result, academic research substantially developed in Germany. From the second half of the nineteenth century research at German universities was increasingly oriented towards the needs of both the State (military developments) and business (for instance, chemistry). 7.3  Education in France The French Revolution of 1789 provided the foundations of the modern French education system: the ideas of education for all, provided in a public framework and conveying Republican values to people, with free access. However, it was only at the end of the nineteenth century that these ideas were completely implemented, due to a lack of public resources during the Revolution, and many political upheavals during the century. The Napoleonic era led to an extension of secondary education (the lycées) in order to train the elite for the new state. Compulsory elementary education was established in the third republic, with the idea of conveying Republican values, in the ‘laicité’, namely separated from religion. Until World War II there were in France two different school systems, the primary education cycle with the possibility for the best pupils to go to higher education on the one hand, and the system of secondary education with its own elementary classes on the other. Schools existed prior to the French Revolution and were closely linked to churches, since Charlemagne proclaimed the importance of education in 789. Education was reserved to the noble and high bourgeois classes. Enlightenment fed new considerations on education. However, although enlightened writers stressed the importance of education,

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they did not favour its democratisation. Voltaire thus recommended limiting education to the King and to the elite. Diderot argued in favour of the education of the people, like Condorcet, who developed in 1792 a project for public education based on the principles of equality, secularity (laicité) and freedom. The member of parliament Joseph Charlier proposed in 1793 the idea of compulsory elementary education, secular and free. Two years later it was organised but lost its compulsory character. Secondary education was focused upon afterwards, with the creation of ‘écoles centrales’, while universities were replaced by professional schools of law and medicine in 1794. In the same year the École Polytechnique was created, aimed at training in sciences and technologies. It is important to stress that the latter engineering school was not created from scratch but followed an institutional development which started earlier in the eighteenth century. The Ponts et Chaussées School was created in 1747 and is the oldest civil engineering school in the world, aimed at training engineers for the development of infrastructure in the country. In the same vein Louis XVI established in 1783 the Écoles des Mines, aimed at training engineers and managers for the French royal mines. The schools still exist today, and have trained generations of engineers in France. Progressively in the twentieth century they have increased the scientific research part of their activities as well as teaching. The École Polytechnique was originally named Central School for Public Works, created by the Revolution government in order to upgrade and improve transport and other infrastructure in the country, while universities had been closed. The École was militarised under Napoleon but has also increasingly emphasised research in the twentieth century. The first lycées (high schools) were open under the Consulat, in 1802, while secondary schools (colleges) were rebuilt. The principle of state monopoly over education was also proclaimed and enforced. Mass education was slowly introduced. Thus in 1816 a law proclaimed the principle of education for all people, in the restored monarchical regime. Cities had to ensure access to school for all children via the churches’ offices. In 1850, the Falloux Law promoted free education: any citizen could open a school if he had the required degrees. Religious education was cancelled from public instruction in 1881. Women had access to elementary schooling, and also to lycées from 1867. The real democratisation of education was the result of the laws

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proposed by Jules Ferry in the 1880s, making education secular, compulsory and free. Children aged six to 13 had to go to school, but could leave earlier if they obtained a specific certificate (certificat d’études primaires). Education became a social lift for children of lower social classes who could access education. From then on education in France carried a strong societal value, based on the meritocratic principle. 7.4  Education in the USA The predominantly Protestant religion in colonial New England in the seventeenth century induced a widespread education of individuals. Since religion prescribed that each individual should read the Bible, the teaching of reading and writing was diffused. The Boston Latin School was founded in 1635 and is both the first public school and oldest existing school in the United States (Goldin, 1999). By the time of the American Revolution (1775–87) primary school attendance had dropped in New England. However, after the Revolution the importance of democracy was stressed and gave a new impetus to diffuse education. Compulsory and free schooling was established, and by 1870 all states had implemented it at elementary level. The US had some of the highest level of literacy in the world in that period. It appears to be generally accepted that literacy rates were very high in the Northern US states relative to European countries in the eighteenth and early nineteenth centuries. Southern States did not support mass education and literacy rates were much lower. By the mid-nineteenth century, the role of the schools in New England had expanded to such an extent that they took over many of the educational tasks traditionally handled by parents. All the New England colonies required towns to set up schools, and many did so. In 1642 the Massachusetts Bay Colony made education compulsory; other New England colonies followed this example. Similar statutes were adopted in other colonies in the 1640s and 1650s. The schools were all male and all white, with few facilities for girls. In the eighteenth century, ‘common schools’ were established; students of all ages were under the control of one teacher in one room. Although they were publicly supplied at the local (town) level, they were not free. The larger towns in New England opened grammar schools, the

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forerunner of the modern high school. By the 1780s, most had been replaced by private academies. In the South schooling was closely linked to the Catholic Church. Rich planters hired tutors for the education of their children or sent them to private schools, and sometimes to study in Europe. After the American Revolution, children were often sent to the Northern colleges. The earliest continually operating school for girls in the United States is the Catholic Ursuline Academy in New Orleans, founded in 1727. The Academy graduated the first female pharmacist, and the first woman to write a book of literary merit. Higher education was largely oriented toward training men as ministers before 1800. Doctors and lawyers were trained in local apprentice systems. For instance, the Harvard College was founded in 1636, and at first focused on training young men for the ministry, but not exclusively, since many alumni went into law, medicine, government or business. Progressively higher education turned away from training ministers to other disciplines and professions.

8 CONCLUSIONS Overall, industrial revolutions induce a substantial change in the economic system. Their main characteristics can be argued to be a change in manufacturing regime, namely prevailing production system, made possible by the introduction of new technologies. Innovations are numerous and interrelated, thereby constituting new technological systems, often based on new raw materials and energy source, such as electricity in the second industrial revolution and oil in the third. Industrial revolutions are determined by different social, political, economic and scientific evolutions that combine and co-evolve, influencing each other over long time periods. All these trends in turn have a deep impact on the economic and social spheres. Considering the characteristics of industrial revolutions outlined in this chapter, namely new technological paradigms or systems, as well as complex complementary changes in the social, political and cultural spheres, over long periods, the next chapter examines the fourth industrial revolution.

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NOTES 1. ACEA European Automobile Manufacturers Association (2016), ‘Key figures’, available at http://www.acea.be/statistics/tag/category/key-figures, accessed 12 February 2018. 2. De Simoni, 2016.

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3.  The fourth industrial revolution As in previous industrial revolutions, the fourth industrial revolution is characterised first by impressive technological developments in various scientific disciplines, such as biotechnologies, genomics, new materials, robotics, nanotechnologies and renewable energy; and second, by new products and processes, new industrial sectors that develop as a result of the convergence of technological developments, of which the NBIC technologies (nanotechnologies, biotechnologies, information technologies and cognitive sciences) are an example. A report of the US National Science Foundation of 2003 puts it like this: ‘if the cognitive scientist can think it, the Nano people can build it, the bio people can implement it, and the IT people can monitor and control it’ (Roco and Bainbridge, 2003, p. 13). The NBIC technologies can bring improvements in work efficiency and learning, in individual sensory and cognitive capabilities, in individual and group creativity, as well as path-breaking advances in health care; brain to brain interaction, human-machine interfaces including neuromorphic engineering, enhancing the effectiveness of communication techniques. Examples of new products or new processes include the sequencing of human genome, which has opened new opportunities not only in the health sector. These ‘omics’ technologies allow deeper understanding of the mechanisms with which genes, proteins and enzymes function and therefore more targeted treatments of diseases. Synthetic biology is surpassing the capacity to synthesise artificial cells. Metabolic engineering controls the networks of cell reactions of some bacteria, allowing a new approach to chemical synthesis, including biofuels. Nanotechnology is used in various fields of science such as organic chemistry, molecular biology, energy, environment science, semiconductor physics, food safety and so on. It allows the creation of new materials and devices with a vast range of applications, such as in nanomedicine, nanoelectronics, biomaterials energy p ­ roduction 49

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and consumer products. Industrial applications are numerous, ranging from developing new materials to direct control of matter on the atomic scale, improvements in electrical conductors to nanostructured solar cells for energy generation. Examples include the Silver Nano platform for using silver nanoparticles as an antibacterial agent, nanoparticle-based transparent sunscreens, carbon fibre strengthening using silica nanoparticles and carbon nanotubes for stain-resistant textiles. However, the key element of the fourth industrial revolution in terms of its implications on production processes and products, hence manufacturing regimes, seemingly is hyperconnectivity allowed by the new and upgraded ICTs. They determine the essential infrastructure for economic and social activities today, namely ­ Networks, Connectivity and Digitalisation, as well as Big Data (NCD&BD), since they have both direct and indirect impact on industry, the economy, the society and culture. Their direct impact is the development of new products, from computers to smartphones, smart glasses and so on. Their indirect impact on industry is through smart manufacturing, since they are allowing new production systems, with high automation, as well as new market places, with platforms (see next chapter). In addition, NCD&BD appear to be the most transversal technologies with applications in or combinations with many other fields leading to new developments: for instance, nanotechnology and computing at the basis of quantum computing; NCD&BD and genome sequencing allowing new prevention and curing methods in medicine; NCD&BD and cognitive sciences favouring the development of artificial intelligence. Scientific and technological developments of the fourth industrial revolution build on the development of the previous revolution: computers developed in the third industrial revolution are constantly improved and become capable of collecting and treating enormous amounts of data, thanks to quantum computing; the discoveries of Boyer and Cohen which spurred the development of biotechnologies still represent the basis of genome sequencing and its applications. Nanotechnology emerged in the 1980s following experimental advances such as the invention of the scanning tunnelling microscope in 1981 and the discovery of fullerenes in 1985. The particular feature of this fourth industrial revolution is thus the strong link with the previous revolution, and the acceleration of structural changes implied. In addition, we argue in this chapter that

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the fourth industrial revolution is characterised by real integration of science and production, and not just interaction as in the previous industrial revolutions.

1 THE DIFFERENT ‘ENABLING’ TECHNOLOGIES One of the main features of the fourth industrial revolution is hyperconnection, which is possible thanks to the technological progress and inventions made in the last decades in the field of information and communication technologies. The Internet has opened the possibility for the transfer and communication of data at all levels of society and the economy. The amount of data transferred has progressively but constantly increased, and innovations have made this possible. The connected devices at our disposal have multiplied: while at the beginning of the 1990s we could connect to the Internet mainly using our personal computer, now we can connect via our smartphone, our watch, our car and so on. Sensors that connect, send and receive data are everywhere, in our phones, in our cars, home, appliances and so on. The Internet of Things (IoT) means that any object has the potential to transmit and receive data, from cars and farm equipment to watches and appliances, even clothing: Internet-connected cars, smart controls in houses, smart manufacturing and so on, mean that everything is connected. McKinsey estimates that by 2025, IOT applications will have an economic impact of $900 billion to $2.3 trillion in manufacturing alone (McKinsey, 2015). The increasing connection of people and things thanks to the Internet and sensors generates a huge amount of data that have to be stored, treated and analysed. Big data is the capacity to process large amounts of data in real time, helped by various scientific developments such as quantum computing. 1.1 Robotics Development in robotics and artificial intelligence are offering new opportunities in many fields including health (assistance to the elderly and the disabled), services (robots assisting in house cleaning, restaurant services and so on) as well as in production (as most

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simple tasks in manufacturing are increasingly being performed by robots). A wide range of industries, such as automotive, electronics and food and beverages, is increasingly using robots, whose flexibility, responsiveness and sensing are constantly improving. Robots can fulfil tasks previously performed by humans, especially repetitive ones, or those performed in unhealthy atmospheres or with other risks. Robots can work without any interruption, day and night, and do not need any rest. Used in factories they allow an increase in productivity, especially since their costs are decreasing. In addition, robots can perform tasks without the risks of human handling in factories requiring clean and aseptic environments such as food and beverage preparation and packaging, or in the production of disposable surgical instruments. Thus for instance the Italian food business Barilla is using robots in its packaging phase, and has recently inaugurated a completely automated warehouse. Robots can therefore substantially improve firms’ productivity without looking for low labour costs. The availability of robotic programmers and robotic infrastructure is therefore becoming an important attraction factor for companies in territories. R&D spending in robotics is increasing dramatically; according to BCG (2015) spending on robots worldwide is expected to increase from $15 billion in 2010 to $67 billion by 2025. The international supply of industrial robots has continuously increased in the last ten years (Figure 3.1), apart from a reduction at the beginning of the financial crisis. The major producers of robots are China, Japan, the US, South Korea and Germany, which account for 70 per cent of robot sales in the world. In Europe, Italy is the second largest robot market after Germany, and is the 7th largest market worldwide. Asia is expected to be the biggest industrial robot market in the future (Figure 3.2). The sectors which are acquiring most robots are the automotive, electrical and electronics. Robotic applications have evolved over time. In the past robots were mainly used to perform repetitive tasks such as material handling and processing, welding and soldering, as well as assembly, which required speed and strength but little precision. However, with the inclusion of sensors and their connection to big data, robots are increasingly able to realise precision tasks, as well as ‘perceiving’ and

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350 300 250 200 150 100 50 0

2008

2009

2010

2011

2012

2013

2014

2015

2016

Source:  International Federation of Robotics, 2017.1

Figure 3.1  World annual supply of industrial robots, 1000s 2,500 2,000 1,500

America Asia Europe

1,000 500 0

2015

2016

2017

2020

Source:  International Federation of Robotics, 2017 (2017 and 2020 data are estimates).

Figure 3.2  Estimated total stocks of industrial robots

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16% Automotive

3% 44%

7%

Electrical/electronics Metal Chemical, rubber and plastics Food industry

9%

Others

21% Source:  International Federation of Robotics, 2017.

Figure 3.3  Total sales of industrial robots by industrial sector, 2014 adjusting to unexpected circumstances. In the Netherlands, Philips uses 128 robots to make razors, in an almost completely automated factory where only nine humans perform quality checks. In Taiwan, iPhone manufacturer Foxconn is planning to automate 30 per cent of its factories by 2020, aiming for factories with only minimal labour in the functions of production, logistics, testing and inspection processes.2 Robots also have important applications in logistics. Thus Amazon, the world’s largest online retailer, created Amazon Robotics in 2003, a subsidiary based in the US aimed at developing automation of its warehouses and logistics system. It also acquired Kiva Systems in 2012, a company manufacturing warehouse robots. Instead of humans working long hours moving, storing and despatching products, these robots are small, fast and flexible and constantly moving merchandise from shelves and into the packing and shipping areas. However, robots can also generate rigidities on the financial side. If demand goes down companies cannot lay off robots like they do for human capital. Workers who lose their jobs can be retrained to find new jobs in case of a crisis in the industry. Robots cannot and involve more specific assets than human capital: robots are generally developed specifically for certain tasks in given industries, with imperfect reprogramming and reuse in other production processes.

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In this sense they represent sunk costs. In addition, debt payments on financial capital equipment such as robots are due each month if the equipment is not liquidated. Extensive use of robots is therefore not likely in markets with high seasonal variations, or with products at maturity and declining stages of the product life cycle. The use of robots is not limited to manufacturing. They can also provide services such as personal care and elderly or disabled assistance. In Japan, where population ageing has been increasing for a long time, companies are developing robots particularly in health and person care. Thus Toyota has invented a line of robots for nursing aid called Robina, while Honda has developed ASIMO (the Advanced Step in Innovative Mobility Robot), a humanoid able to interpret emotions, movements and conversation. In the US, President Obama launched the National Robotics Initiative in 2011, to develop robots for industrial automation, elder assistance and military applications. 1.2  Artificial Intelligence Developments in artificial intelligence (AI) allow the interface between humans and machines to deeply change. AI is an example of converging scientific fields and technologies, since it lies at the crossroads of IT, mathematics, cognitive sciences, neurobiology and philosophy. The use of sensors in everyday objects and the collection of large amounts of data on objects and people, together with improvements in advanced statistical techniques and in computing capacity are enabling advances in machines’ learning and perception. AI techniques have many applications. Examples are numerous and include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google), online assistants (such as Siri), image recognition in photographs, spam filtering and targeting online advertisements (Ross, 2016; Kelly, 2016). The field of human–robot interaction (HRI) is related to the interaction modalities between the user and the robot, aiming at developing robots that may ‘perceive’. This is what Cognitive HRI is about, analysing interaction modes between the user and the robot, using textual interfaces, voice and / or gestures. The interface may be more or less intelligent in the sense that the robot may be constrained by a

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fixed set of commands or it may interpret a string written in natural language or a sequence of gestures performed by the operator. 1.3 Genomics The sequencing of human genome has opened new opportunities in many sectors, and primarily in health. Genomics allows deeper understanding of the mechanisms with which genes, proteins and enzymes function and therefore enable the development of more targeted treatments of diseases, directly treating infected cells. Organ transplantations improve, and genomics coupled with the other new technologies allow to deliver medical treatment from the best hospitals to any areas, even the most remote and poor. In just a decade the cost of human genome sequencing has dropped from $100 million in 2001 to $10,000 in 2011, and it is about $1,000 now (National Human Genome Research Institute, see Wetterstrand, 2017).3 In addition, the capacity to analyse the Big Data of genome sequences of humans is improving, thanks to supercomputers. As a consequence, new diagnostics, therapies and drugs developed out of genetics can be developed and commercialised. For example it has been shown that cancer is caused by DNA mutations and about 150 genes have been identified as determining the development of the disease. Human genome sequencing opens the possibility to prevent cancer by identifying the presence of such cancer genes and eliminating them.4 Synthetic biology is surpassing the capacity to synthetise artificial cells. Metabolic engineering controls the networks of cell reactions of some bacteria, allowing a new approach to chemical synthesis, including biofuels. China has emerged as a leader in genomics research. The Beijing Genomic Institute is now the largest genomics research centre in the world. Since 1998, the share of China’s economy devoted to research has tripled. China’s share of global R&D increased from about 2 per cent to 14.5 per cent. R&D funding and scientific articles are the fundamental resources to develop new drugs and treatments, leading to commercial applications. This sector is emblematic in terms of science basis, and fundamental research performed in collaboration between universities or other research centres and firms has constantly increased over the last few decades (Gittelman, 2005; D’Amore et al., 2013).

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1.4 Nanotechnology Nanosciences and nanotechnologies are expected to provide many new applications in new products and new production processes, used in combination with different research fields and application. ICTs and electronics, molecular electronics, nanolithography, extremely thin films and transistors are progressing rapidly and leading to the development of miniaturised supercomputers, the socalled ‘smart dust’. Ultrafast semiconductors and microprocessors are already being generated, as well as for instance low voltage and high brightness displays. In the field of energy and environment, the use of nanotechnologies is expected to contribute to the development of renewable energy. There will be a new generation of highly efficient photovoltaics, nanocomposites for stronger and lighter wind energy rotor blades; a new class of nanomembranes for carbon capture at fossil fuel power plants. Energy savings could be made using nanomaterials in power distribution and transmission, as well as nanosensors in the decentralised management of renewable energy grids, but also in the creation of smart glass and electrochromic windows capable of maximising the use of solar power to heat buildings, while energy storage and fuel production are likely to be improved using nanotechnology. Safe water purification, filtration and desalination through cheap and portable nanotechnology systems are also expected to provide solutions to provide clean and drinkable water in developing countries. In the field of health care, nanomedicine, the application of nanotechnology to human health care, offers numerous opportunities, such as the improvement of medical diagnosis and therapy, the regeneration of tissues and organs. In addition, the costs of such applications are reducing fast so that these new treatments will soon become affordable. The use of nanotechnologies in textiles is allowing the avoidance of certain chemicals in fabrics, as well as UV blocking, flame retardancy and health monitoring. The impact of antibacterial textiles with embedded nanoparticles is expected to be large especially in hospitals and care homes, where cross-contamination of bacteria can be dangerous, because the effective control of bacteria populations in those environments will lead to reduced infection rates. Nanotechnologies are thus expected to impact virtually any sector.

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1.5  New Materials Materials research is a multidisciplinary science developing manmade materials (for example, biomaterials, metals, polymers) for different industrial sectors (for example, metallurgy, chemistry) in multiple applications (for example, energy, health, transport). Materials profit from a wide range of scientific disciplines, such as chemistry, physics, biology and engineering, as well as from all available technologies and multidisciplinary approaches, like nanotechnology and biotechnology. The development of new materials is therefore a key area of convergence of scientific fields and discoveries. Applications are numerous, from health (for example, artificial bone for prosthetic implant or self-assembling nanofibres for nerve repair), to civil engineering (creating more robust concrete for streets and roads, more resistant in time, and contributing to climate change prevention), manufacturing (for example, conducting polymers for ‘printable’ electronic devices) and so on. 1.6  Big Data and Data Analytics AI is making rapid and large advances thanks to big data analytics in supercomputers that are able to collect and treat enormous amounts of data. Learning in artificial intelligence uses algorithms treating large amounts of data; this is beyond human capacity but algorithms are not able to think beyond frames, to provide new hypotheses or be creative as the human brain is. However, the application of big data analytics is much broader than this. ‘Big data’ refers to the spectacular growth of the digital universe, as the society, organisations, and people are increasingly interconnected and ‘always on’. The large amount of data generated by this hyperconnection of people and things offers new opportunities because of the benefits of the collection, observation and analysis of the big data. Two types of big data can be distinguished: scientific and social. Scientific data is collected through scientific experiments and observations. The advances in data science have supplemented the methodology of developing theories and models that are then checked with empirics with a methodology of analysing big scientific data to discover new patterns and hence theories.

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Social data refer to the large amounts of information obtained from the everyday use of smartphones, cars and other connected devices. A prominent problem with the use of social big data relates to their being collected essentially by private companies, while users are often not aware of the personal data they release. This issue of privacy and misuse of this social big data have been increasingly debated both in the US (Federal Trade Commission and the White House)5 and in the EU6 (see Chapter 4 for more considerations on this issue). Data science results from the convergence of different scientific disciplines: database technology and data mining, machine learning and artificial intelligence, complex system theory and network science, statistics and statistical physics, information retrieval and text mining, natural language processing and applied mathematics. The potential applications are numerous: government and governance can be improved, since policy decisions can be based on the insights of big data analytics, and participatory processes can be implemented. Official statistics could be improved by both providing new methods to better collect data and extending the data collected, thereby enriching existing indicators, and perhaps providing more precise evidence on digital globalisation as discussed in Chapter 1. Data science also has important implications for industries, both in existing ones and in opportunities for creating new ones, as will be further discussed in Chapter 4. 1.7 Overall These new technologies, used in isolation or combined, can lead to the development of new products and processes that help solve some of the ‘societal challenges’ outlined by the UN, such as poverty and access to medical treatments, population ageing, urbanisation and, last but not least, climate change and other effects of environmental pollution. New processes boost productivity and responsiveness to markets by making it possible for manufacturers to quickly and easily modify designs and reconfigure production lines according to customer demands. As a consequence, manufacturers can realise a wider set of products with the same production processes. Factories of the future will combine economies of scale (cost reductions resulting from large volumes) with economies of scope, namely high differentiation up to product personalisation

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(mass customisation): single and/or interconnected machines can produce a large variety of goods to meet the specifications required by individual consumers. This is analysed in more detail in the next section.

2 SMART MANUFACTURING: NEW PRODUCTION PROCESSES The effects of the technological developments outlined in the previous section on manufacturing processes are important, and are at the basis of the fourth industrial revolution. The main technologies included in smart manufacturing are as follows (see BCG, 2015). 2.1  Autonomous Robots In the smart factory, the use of sensors and standardised interfaces create control systems to which industrial robots are related, enabling highly automated processes with much fewer workers. 2.2  Integrated Computational Materials Engineering (ICME) Integrated computational materials engineering derive computer models of products and production processes, which can be tested even before their physical creation. Trials and errors decrease in the product development process, saving time and reducing costs. 2.3  Digital Manufacturing The whole manufacturing process can be simulated before being put in place, so that building a factory is less costly and time-consuming. It is also more effective, since phases and steps can be tested before being put together. Production processes also become more easily replicable, since the defined layout and single phases can be repeated in different places adapting to the local conditions and through the same simulation system.

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2.4  Industrial Internet and Flexible Automation Manufacturing hardware can be linked together so that machines can communicate with one another and automatically adjust production based on data generated by sensors. 2.5  Additive Manufacturing Additive manufacturing processes can create three-dimensional objects based on digital models by successively depositing thin layers of materials. Such processes are starting to be used in a number of industries, including aerospace and automobile industries, in order to make parts or basic items. Industries will increasingly use 3D printing in the future to realise basic consumer items requiring simple materials. For the time being it is essentially used for prototyping, where it has allowed substantial reductions in both the timing of realisation and the costs of creation of prototypes, thereby reducing barriers to entry in some markets or niches even for smaller enterprises. Additive manufacturing may have important effects on market competition if it further develops and enables the realisation of final products. Smaller firms realising niche products will find it easier to manufacture their products, even in small batches, in any global market, if they can print them even in remote places. The technology may develop as much as making it possible for consumers to print their own products, inducing industrial goods companies to revise their business models. Advanced manufacturing allows the improvements of methods, processes and knowledge in manufacturing. It is characterised by cyber-physical systems (CPS) and dynamic data processes that use massive amounts of data to drive smart machines. A number of drivers, such as falling prices and rising performance of underlying technologies (both hardware and software), are inducing the adoption of the new advanced manufacturing technologies. In 2015, the Boston Consulting Group reported that 72 per cent of manufacturing executives at companies with sales of at least $1 billion in the US declared that they intended to invest in additional automation and advanced manufacturing technologies in the next five years. Three quarters of these executives expect advanced manufacturing technologies to raise the company’s productivity and create more localised production. More than half of these executives expect

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advanced manufacturing to make production in the home country more cost-effective than production in low-labour cost countries. Smart manufacturing allows the personalisation of products, namely production following each consumer’s specific needs; small batches of production are possible at low cost, but also rapid adjustments to the product thanks to the flexible production processes; and time to market substantially reduces thanks to more effective and rapid development of prototypes. Smart manufacturing also produces in a more eco-friendly way since the consumption of raw materials as well as waste are minimised and energy-efficiency maximised. Production processes including robots are more flexible because the networks of robots able to communicate with one another can collect, treat and transfer information very quickly and can allow adjustment of the production lines in real time. Even if phases of the production process are realised by external suppliers, in outsourcing processes, production flows can easily and rapidly be adjusted thanks to communication in the supplier network through the Industrial Internet. Global value chains have represented a growing trend in many industries, whereby phases of production are offshored, implying exports of unfinished goods or parts and components. With smart manufacturing and 3D printing value chains are likely to remain global, in that goods are sold on the global market, but they will become digital: rather than exporting and importing unfinished goods, parts or components the products or part specification will be digitally communicated or traded to the firm’s division in different regions of the world, or to outside suppliers, thereby eliminating or substantially reducing the need for physical transport of parts and goods, especially those over long distances. Overall advanced manufacturing technologies allow mass customisation at low cost. Products can be extremely personalised just changing the specifications of the manufacturing process where machines transfer the data on the product to be manufactured sequentially one to another. Flexible automation and additive manufacturing make efficient production in the local market feasible. It is likely that these new technologies will lead to the possibility to manufacture products close to consumers, which is an advantage in a context where transport costs are expected to substantially rise in the future. The above-mentioned technological developments will have a

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profound impact on products and production processes. Mass customisation means that economies of scale are perfectly combined with economies of scope. An R&D centre of a multinational company may develop a new product and its production process, which can be replicated in all the manufacturing facilities (with automated systems and 3D printing) and produced for the whole global market. The smart manufacturing system simultaneously enables product customisation to the specific needs of each specific consumer. The consumer sends his or her requests ­regarding the product to the factory, where the smart manufacturing system automatically adjusts bits and parts to produce the specific variety of the product, which the consumer asked for. Hence what we are experiencing is a real change in manufacturing regime, in the sense of prevailing production system (Bianchi and Labory, 2017): from mass production (second industrial revolution) to flexible production (third industrial revolution) and mass customisation (fourth industrial revolution).

3 THE IMPACT ON DEINDUSTRIALISATION AND GLOBAL VALUE CHAINS Smart manufacturing and the other technological and scientific developments of the fourth industrial revolution have deep implications on industry. Global value chains have already been reorganised, with reshoring and investment in new production technologies. Offshoring – the delocalisation of production phases to low-cost countries – is no longer an optimal strategy: as argued by Jeffrey Immelt, CEO of General Electric in 2012, outsourcing is ‘quickly becoming mostly outdated as a business model for GE Appliances’ (Immelt, 2012). There are different reasons for this, including the rising wages in low-labour cost countries such as China, and the quality problems encountered in delocalised production. However, a major reason is the fourth industrial revolution and the new production technologies it brings, as will be argued in this section. Deindustrialisation was worrying in many countries, which saw their manufacturing sectors shrink. This phenomenon is generally measured by data on the share of manufacturing in total employment. This indicator has continuously reduced in developed countries since

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3,000 2,500 United States

2,000

Italy Japan United Kingdom

1,500

France Germany

1,000

China

500

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

Source:  World Bank (2016) available at http://data.worldbank.org/indicator/ NV.IND.MANF.CD?page=2 (downloaded on 20 April 2017).

Figure 3.4  Manufacturing value added, current billion $ the 1970s, shifting from more than 35 per cent in Germany in 1970 to about 17 per cent in 2012; in Japan, the shift is from 26 per cent in 1970 to 16 per cent in 2012; in the USA, from 22 per cent to less than 10 per cent in 2012 (OECD, 2015). However, although employment in manufacturing has continuously declined, output has continued to rise. As a consequence, productivity has steadily increased. China has become the first manufacturing country in the world in 2013, with about a quarter of the value added produced in manufacturing in the world. Meanwhile the share of the world manufacturing value added of OECD countries has decreased from 85 per cent in 1970 to 55 per cent in 2013 (Figure 3.4). In many countries, the relative share of GDP generated by service activities over manufacturing has continuously increased in the last decades (Figure 3.5). The interpretation has been that economies were ‘experiencing tertiarisation’, moving economic activities to the service sector, including the financial, health, knowledge and business services sectors. The worry generated by this trend originated in the fact that the reduction in manufacturing activities was paralleled by large

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35 30 25

China Germany

20

France United Kingdom Italy

15

Japan United States

10 5

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

Source:  The World Bank (2016) available at http://data.worldbank.org/indicator/ NV.IND.MANF.CD?page=2 (downloaded on 20 April 2017).

Figure 3.5  Manufacturing value added as a percentage of GDP offshoring of manufacturing firms, which were in fact shifting their production facilities to lower-cost countries. Manufacturing value added as a percentage of GDP tends to reduce in all countries, except Germany. Among the countries indicated in the figures the UK is the one with sharpest fall and it reaches lowest level in 2014. Manufacturing value added as a percentage of GDP is, in 2014, lower in the USA, France and the UK relative to Italy, Japan and China. One reason for the trend observed in the former three countries might be related to financialisation, namely the relative growth of the financial sector (Bianchi and Labory, 2016). Business investment in R&D is an indicator of research performed by firms but also, at least partly, of investment in new technologies linked to the fourth industrial revolution. In fact, Figure 3.6 shows the growth of business investment in R&D. Figure 3.7 shows that public R&D spending stagnates in OECD countries after the financial crisis, while private expenditures soar. This might be evidence of a new impetus to the integration of science and production brought about by the financial crisis.

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4 3.5 France

3

Germany 2.5

Italy Korea

2

United Kingdom United States

1.5

China (People's Republic of)

1 0.5

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

0

Source:  OECD, 2017.

Figure 3.6  Business expenditure on R&D, as a percentage of GDP 16,000 14,000 12,000 10,000

Higher education R&D funded by industry

8,000

Government R&D funded by industry

6,000 4,000 2,000

13

10

20

20

05

20

00

20

95

19

90

19

85

19

19

81

0

Source:  OECD (2016).

Figure 3.7  Public research funding by industry, OECD, US$ million PPP at constant prices, 1981 to 2013

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120 100 OTHER G20 COUNTRIES

80

OTHER COUNTRIES SOUTH KOREA USA

60

JAPAN CHINA

40

EU28

20 0

2004

2014

Source:  Data in European Commission (2016a, p. 120).

Figure 3.8  Share of world patent applications The leadership of China in terms of innovation is illustrated by Figure 3.8, showing the share of world patent applications in 2004 and 2014. China’s share increases from 4.6 per cent to about a third of world patent applications (30.4 per cent), mainly at the expense of Japan (33.1 to 16.9 per cent), while the shares of the EU28 and of the USA slightly reduce. However, the fourth industrial revolution has been going on for some time and all the technological developments it includes – especially in terms of smart manufacturing – provide explanations to these trends. First, as production processes become increasingly automated, the number of workers in the manufacturing sector inevitably reduces. Even China has recently seen a reduction in the numbers employed in the manufacturing sector. Second, the technological developments implied by the fourth industrial revolution make the service part of manufacturing firms’ activities relatively more important: service-type occupations in the manufacturing sector are for instance in the R&D, marketing and sales, customer support functions. When products and production systems can be realised by ICME systems and smart factories, the main important phases where firms create value is not in the production, but in the R&D activities, where products are created to fit consumers’ needs,

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and production processes are designed and created on computers. The increase in business R&D observed in many countries (Figure 3.6) is consistent with this interpretation. Value is created also in the customer support phase, in the constant interaction with users of the firm’s products that hyperconnected systems and devices allow. This will be further examined in the next chapters. The second important implication of smart manufacturing and more generally, the fourth industrial revolution, regards global value chains. It naturally follows from the above considerations regarding the distribution of value creation in the production system. When value creation is concentrated at pre- and post-manufacturing phases, value chains are not global, but specific to the territories where R&D and marketing departments are located. Especially when parts, components and products do not have to be transported over distance, because data are exchanged between the firm’s divisions in different parts of the world and products are produced near markets, in digital factories or using (perhaps in a more distant future) 3D printing. Baldwin (2006) mentioned trade becoming ‘trade in tasks’ rather than ‘trade in goods’, but now we increasingly have ‘trade in data’, as shown by Figures 1.1 and 1.2 in the introduction of this book. Baldwin (2006) defined production internationalisation as ‘unbundling’, whereby phases of the production process are separated, bundled differently and realised by different firms. Baldwin distinguishes two unbundlings. The first occurred between the late nineteenth century and the late twentieth century, and necessitated the end of a process wherein it was necessary to produce goods close to consumers, thereby allowing the spatial separation of factories and consumers. Trade was consequently primarily of finished goods, and international competition was primarily between firms and sectors in different countries. The second unbundling occurred from the last quarter of the twentieth century and entailed the end of the necessity to perform phases of the production process near each other, allowing the spatial separation between firms’ division and offices, due to falling transport costs and the IT revolution. According to Baldwin (2006), this unbundling occurred first in Asia because the distance between low cost countries and high costs ones was lower. Thus Japanese firms started unbundling to other Asian low-cost countries in the 1980s. In contrast, European firms intensified production internationalisation especially after the transition of Central and Eastern European

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countries to market economies, realising phases of their production processes in the new EU member countries. Robotics and automation are already reducing the competitive advantage that low-labour cost countries had in the 1990s and early 2000s, attracting foreign firms looking for offshoring locations. In addition, unless clean and efficient transport is substantially developed in the future, pressure to minimise transport needs are consistent with digital rather than physical global value chains. Meanwhile smart manufacturing will allow product personalisation or mass customisation using flows of data rather than flows of parts and goods. The smart factory can indeed rapidly design and manufacture customised products in any location close to consumers. In addition, additive manufacturing reinforces this trend of production close to the market, since it is enough to have the 3D printer in different markets to sell the goods, which can be designed and developed in a manufacturing and R&D centre elsewhere in the world. In these centres the capacity to collect and treat information and communicate with customers will be crucial. Production organisation is already experiencing important changes. First, companies focus on high phases of the production process, namely pre- and post-manufacturing. Manufacturing is increasingly performed by robots in smart factories, that can be located anywhere, provided there is access to energy, high capacity Internet and materials. Territories able to pool and develop key resources for pre- and postmanufacturing phases will attract firms, which will be willing to locate their most value-creating activities in these areas if they have access to infrastructure. Also important will be high and rapid communication, as well as low energy costs, and also innovative capacity, with highly qualified human capital and appropriate research facilities, namely hubs of knowledge creation, consisting in dense networks of universities, research centres and other stakeholders (as will be discussed in the next chapters). Offshoring will no longer be necessary because robots will substitute low-cost labour in developing countries, and additive manufacturing will make the printing of products in any place, provided that there is a 3D printer the product can be made. ‘Remote manufacturing’ is possible, whereby a product is printed miles away from the factory floor, at a local repair shop or in a private house. Manufacturers are already making huge investments in smart

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manufacturing. For instance, as reported by the BCG (2015), Ford is using ICME to reduce the time and cost of developing aluminium castings for engines. The traditional method consists of designing the engine on the computer, building a prototype that is tested, so that the engine is then adapted on the computer, a new prototype is built and tested and new adjustments are made until the prototype passes all the tests. With ICME tests on the proposed engine, model can be performed on the computer, without any need for prototypes (only virtual ones), so that they save a lot of time and money. Ford invested $15 million in an ICME investment, which involved 15 of its own engineers and ten university researchers. Another example is the insertion of low-rhenium single-crystal alloy turbine blades in aircraft engines by GE Aviation (TMS, 2013). In the EU, companies are also adopting ICME, with positive impact on innovation, jobs and performance (Goldbeck Consulting, 2016). In fact, manufacturing cost differentials between countries are changing, to the advantage of some industrialised countries that a few years ago were ‘victims’ of deindustrialisation due to shifting production to low labour cost countries such as China. Countries preparing for the fourth industrial revolution are investing in R&D capacity. Innovation in China has been steadily expanding, as shown by Figures 3.9 and 3.10, showing respectively business R&D expenditure and world shares of patent applications. Gross expenditure on R&D, including private and public, as a percentage of GDP, has also constantly increased in the last 20 years, but the steepest growth is that of South Korea. The USA was the world leader up to 2004, when Asian countries started to surpass it in terms of this indicator. Germany also has had a higher R&D expenditure as a percentage of GDP than the USA since 2011, more or less since the launch of the Industrie 4.0 programme. Chinese patent applications have soared in the last ten years, to reach much higher levels than other OECD and Asian countries, and even the USA. Skills are important because in the new manufacturing system the phases more intensive in knowledge communication and creation are the post- (marketing and commercialisation) and above all the pre-manufacturing phases (R&D, prototype). This is where human capital is needed in the digitalised process. These phases are likely to be localised in territories with dense availability of skills, at the individual but also the collective level. The

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4.5 4 3.5

China Germany

3

France United Kingdom

2.5

Italy

2

Korea, Rep. United States

1.5 1 0.5

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0

Source:  World Bank (n.d.).

Figure 3.9  Gross expenditure on R&D as a percentage of GDP latter is that of a locally shared knowledge base, made of appropriate schools and higher education institutions, working in concert with research centres and also business and other institutions. The delay of education institution in adapting to the new requirements of the labour market has led different companies to set up their own education programmes, often in collaboration with colleges or universities. Thus for instance Toyota has set up its Advanced Manufacturing Technicians programme, aiming to train technicians for the factories using advanced manufacturing. In addition, it is widely acknowledged that new skills are required in the population both in their working and daily life. For instance, the European Commission stresses the importance of digital skills and finds that 23 per cent of the EU28 population has low digital skills, and an additional 21 per cent never uses the Internet (European Commission, 2016c). In addition, transversal skills, such as critical thinking, creativity and communication are highlighted as increasingly important in the transforming economy. In the EU, these skills are not sufficiently diffused in the population, since 40 per

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1,200,000

1,000,000

China

800,000

Germany France United Kingdom

600,000

European Union Korea, Rep.

400,000

United States

200,000

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

0

Source:  World Bank online data, www.worldbank.org/indicators.

Figure 3.10  Patent applications, residents, 1985 to 2015 cent of employers have difficulties in finding people with appropriate skills, and they should be taught and developed more strongly in education systems (European Commission, 2016c). As already stressed an important feature regarding business R&D concerns the networks that companies establish with outside organisations in order to increase innovation. In particular, university– industry links have been constantly increasing (see the review by Perkmann et al., 2013). However, external links are set up with other organisations, such as private companies, start-ups, private research centres, users’ organisations such as hospitals in the biomedical sector and so on. Chesbrough (2003) argues that this can be stylised in the open innovation business model. In fact, firms establish networks with suppliers, skills training institutions, research centres and universities, primarily at the local level around factories and other facilities but also coordinated at a global level. With hyperconnection and big data any firm can set up

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such global network or ecosystem, whatever their size. One example is the online high fashion shop Yoox (see Chapter 4). The Porter model of the value chain does not hold any longer: value is created in multidirectional networks, by feeding and exploiting the ecosystem. In these ecosystems, the most important flows are data flows and big data analytics becomes a key asset for firms. This has important implications for industrial policy, since policymakers aiming at favouring structural changes and industrial development must provide the conditions for these ecosystems to set up and consolidate. This means providing infrastructure, for high speed and high capacity connections, as well as capacity for big data storage and analytics. It also means favouring the development of appropriate skills in the labour force, appropriate human capital, by education and training of the local population and attraction of skilled labour where needed. The institutions must adapt to favour the coherence and consistency of the local ecosystem. An example at regional level is provided in Chapter 5.

4 THE REAL REVOLUTION: DIGITALISATION AND HYPERCONNECTION The previous section stressed that the research and development phase of production becomes a strategic phase in the fourth industrial revolution. However, digitalisation and hyperconnection also, and perhaps primarily, have implications on the interaction between consumers and producers, between demand and supply. In the previous industrial ages companies manufactured products that were subsequently sold on markets. The interaction between consumers and producers arose after the product had been made, and companies developed marketing and advertising strategies to convince potential buyers of the superiority of their products. Market surveys could be carried out to identify potential needs, potential preferences of consumers regarding the product’s characteristics, that would then be integrated in R&D to develop products more in line with consumer tastes. In the digitalised age this sequence has changed: interaction can occur at any time, before or during product development and manufacturing. Consumers interact with producers sending data on their preferred characteristics of the product, or on the specific product they would

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like to find. This interaction does not only arise individually but also collectively: consumers can share experience with the product on the company’s forum web pages, leading to identify the best and the worst characteristics, possible defects, as well as possible improvements. Consumers in this sense become producers themselves, or developers, vendors and marketers. Companies do not need to create customer support departments: customers meet and interact directly with the producer on these web pages, share experience of the product with other customers, exchange instructions on the use of the product and so on. This is especially true for services and software, but also for material, tangible products. Regarding software and services, the interaction is clear and strong, since for instance the whole content of Facebook, YouTube or Twitter is not created by these companies’ staff, but by users. Regarding material products, such as cars, phones, fridges, but also food, consumers are now part of the production process, because they can ask for specific characteristics which the smart manufacturing system is able to produce, and they can interact with other customers on platforms. Material products remain tangible, but their value increasingly comes from the service they provide to the consumer, meeting specific needs or tastes. In the last years the increasing bundling of products and services in the manufacturing sector was stressed as an important driver of structural changes in industries. However, what this discussion suggests is that services are integrated into the product, or better, the service part of a product acquisition becomes paramount on the market. When I buy a car, I buy the material product but also and mainly the service that car will provide me with: being able to move faster between places, to transport heavy objects, drive children to school and so on. The competitive advantage of producers primarily derives from their ability to provide the appropriate service through their product, through the knowledge embedded in the product. A clear example of this growing importance of the service content of products is in the book industry. Big data, digitalisation and hyperconnection have led to the absolute reducing importance of the material product: the book made of pages, which is quite heavy to carry around. Many readers now buy their book online, which they read on their tablet or Kindle. The same service, namely relaxing in leisure time by reading a novel, no longer entails a material product, which in the past was bought and collected on shelves at home. When

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the service content of the product grows in importance, what is key is no longer the ownership of the product, it is access to the product (a book, but also a car, a pair of shoes and so on). This has deep implications for production processes. First in terms of production time: in the industrial age, companies tried to save time to increase productivity and efficiency. In the digital age, companies try to save each of their customers’ time, by constantly interacting with them and answering their requests in real time. Companies now sell solutions rather than products. This was stressed by an IBM manager describing the substantial business transformation the IBM company operated in the last years. However, we think this applies, to different extent, to all products. The intangible part of products and production processes become the key source of competitive advantage. We have already analysed the growing importance of intangible assets in companies’ activities and in competitive context (Bianchi and Labory, 2004). We identified globalisation as the main reason for it, because it implies more intense competition on markets, which companies face by rising the knowledge content of products, frequently renewing and upgrading them, implying in turn a rising importance of the intangible-intensive phases of production processes, namely pre- and post-manufacturing phases (respectively, R&D and marketing/distribution). However, globalisation was only one reason: the ongoing fourth industrial revolution was another, which we identified as the consolidation of the information society or knowledge-based economy. In this context, companies have to build and maintain communities of consumers and users: the bigger the community the more the product or simply access to the product is sold, but firms have to implement strategies to keep the community stable or growing. For this purpose, upgrades, new issues, new versions of the product have to be constantly proposed; but also the brand has to be ‘cultivated’ to keep consumers in the community. Apple is a company that has worked a lot on this community aspect of its business: it has its own operating system (iOS), less diffused than Android but more exclusive; a culture of quality, security and innovation was developed through the years, particularly through the personality cult of Steve Jobs. The fourth industrial revolution is therefore a change in the extent of the market made possible by the NCD&BD technologies. Producers can directly and frequently interact with users or

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c­onsumers, both individually and collectively, in the forum where consumers share experience with the product and can make suggestions for improvements. The production process changes, no longer a linear process where phases are sequentially performed but an instant process, whereby the smart manufacturing system captures the requests of consumers and provide answers in real time. This means, referring to the analysis of production organisation made by the classical economist Adam Smith in The Wealth of Nations (1776), that the ‘work done’ (effective stock of products) no longer matters, no stocks of products are necessary, since products are specifically and immediately made for consumers. The whole competitive advantage is based on the ‘work to be done’, namely the capacity to provide solutions to consumers, by building an adequate smart factory system and appropriately managing data flows. The extent of the market becomes a strategic choice to a certain extent, by choosing a product or product range, and building a community of consumers who adhere to it and are kept loyal to the firm thanks to the proposal of updates, versions and targeted advertisement made possible by big data analytics. Big data are the strategic assets of companies: in the online community they collect data on consumers, on their preferences, past choices and purchases, which become even richer if these data can be matched with data on other choices made by these consumers, in their travels, leisure activities, purchase of other products. The companies that collect such rich sets of big data, such as Google or Amazon, namely pure platform businesses, have large market power, not on a single market but on all markets that they serve. Thus for instance Amazon has a big community of consumers, who buy not only its books, but also any other types of products. Google, via the data on the searches performed by individuals on its search engine collects an enormous amount of data on many aspects and characteristics of these individuals: the books they like, the cinema they go to, the work they do and so on. In this context, the Porter value chain is outdated; so is, to some extent, the global value chains’ model: first, the concept of ‘chain’ is not adequate since production does not flow linearly but is performed in networks or better, ecosystems. Second, global value chains transform and are all based on data, data collection, analytics and management. Firms’ competitive advantage and capacity to

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generate value essentially comes from their ability to maintain and develop communities of consumers, or have access to big data on individuals, as well as their capacity to set up smart factories able to respond to consumers’ requests in real time. The role of territories in the fourth industrial revolution also lies there: a territory providing the appropriate infrastructure for the building of smart factories will attract businesses and will grow. This means having broadband, fast and high-capacity communication infrastructure; strong research and scientific capacity, in public and private research centres and universities, with which business can integrate to build the capacity to answer consumers’ needs in real time; human capital, namely data scientists, engineers in various fields, as well as technicians, that can set up and work in the smart factories. Chapter 5 shows the example of the Emilia-Romagna region, which has implemented an industrial policy in recent years aimed at making the regional territory a hub in the digitalised and globalised world. The next chapter analyses in more detail the transformation of the extent of the market induced by the fourth industrial revolution. This completes the outlook of structural changes implied by the revolution, pointing to a real transition between manufacturing regimes. These radical structural changes are the fundamental reasons for the necessity of industrial policy in the manufacturing revolution. Chapter 5 will then suggest what industrial policy could look like in order to favour structural changes and specific development paths for industries and hence the economy and the society.

NOTES 1. International Federation of Robotics (2017), ‘Executive summary world robotics 2017 industrial robots’, available at https://ifr.org/free-downloads/; accessed 3 February 2018. 2. N.Statt (2016), ‘iPhone manufacturer Foxconn plans to replace almost every human worker with robots China’s iPhone factories are being automated’, The Verge, 30 December, available at http://www.theverge.com/2016/12/30/14128870/foxconnrobots-automation-apple-iphone-china-manufacturing (accessed 15 March 2017). 3. K.A. Wetterstrand (2017), ‘DNA sequencing costs: Data from the NHGRI Genome Sequencing Program (GSP)’, available at: www.genome.gov/sequencing​ costsdata (accessed 12 March 2017). 4. Ross (2016, Chapter 2). 5. Federal Trade Commission (2014), ‘Big data: A tool for inclusion or exclusion?’, 15 September, available at http://www.ftc.gov/news-events/events-­calendar/2014/09/

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big-data-tool-inclusion-or-exclusion (accessed 20 July 2017); The White House (2014), ‘Big data: Seizing opportunities, preserving values’, May 2014, available at http​ ://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_ report_5.1.14_final_print.pdf (accessed 20 July 2017). 6. The General Data Protection Supervisor (2015); EU Directive 2016/680 ‘on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision’ 2008/977/JHA. See for instance the activities and publications of the European Data Protection Supervisors (https://edps.europa.eu/).

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4. New modes of interacting on markets: online platforms A primary and important change induced by the fourth industrial revolution is represented by the new relationship between producers and consumers in markets. This does not, yet at least, completely replace the old modes but the trend is of growing importance. As mentioned at the end of Chapter 3, these new modes are related to the hyperconnection and connectivity allowed by the new technologies, implying constant interaction of producers and consumers on a large scale. Firms both in new industries but also in existing ones are transforming their modes of interaction with consumers, constituting online forums, hence platforms where consumers interact and share experiences, product information and instructions, and make suggestions for improvement to producers. Customer support departments are no longer necessary, since consumers find all the necessary information on these platforms, which are also sources of information and ideas for marketing campaigns and product innovations. Platforms are places where two or more user types can directly interact with each other, facilitated and observed by the platform operator. They can be physical platforms, such as dating clubs and stock exchanges, or markets in cities and villages; or online platforms, such as Amazon and eBay, but also Airbnb or Uber. Both types have the main advantage of reducing transaction costs, thanks to the network effects they induce: both direct network effects, namely users gain as the number of users on their side increases. For instance, the possibility of sharing experience with goods, or the search engine of Google which improves as the number of search increases, and indirect network effects, arising when users on one side gain from the increasing number of users on the other side, such as higher variety of products. Hence platforms aim to bring together as many users as they can, up to some points where they might be problems of congestion, 79

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especially in physical platforms like clubs or shopping malls where too many users cause queues and difficulty of interactions. Online platforms have boomed in the last decade. In 2000, 7 per cent of the world population used the Internet, while 43 per cent did so in 2015 (more than 3 billion, two thirds of which are in developed countries); 80 per cent of these people use the Internet to access information on goods and services and most use online marketplaces (Oxera, 2015). Online platforms have had important implications for firms existing in industries, by putting pressure on prices. Consumers can confront prices more easily and also get more information on products. In addition, small firms have been able to enter markets by selling their products on platforms and getting a share of the market of established firms. For instance, Yoox is a new business created by an Italian entrepreneur, selling high quality clothing online (see Section 1). Uber has disrupted the taxi industry, Airbnb is challenging hotels and online ticket booking is disrupting established physical travel or cultural ticket agencies. The main advantage of online platforms is their ability to match users of the different sides by means of personal and business data they collect, exploiting economies of scope in large data sets. Datadriven matching includes paid ads and search engines that provide search rankings to the users. Search rankings generally put more popular products at the top of the rankings, leading to the problem of ‘super star economics’, whereby popular products are always proposed in a lock-in process, while new products or new experiences are not favoured. Search rankings might also be biased in favour of particular commercial interests, primarily that of the platform operator but also that more of those willing and able to pay for the advertising provided. The huge and constant collection of user data raises questions of privacy and consumer protection. Consumers release private information without always being aware of it. In addition, the use made by platform operators of their data is not transparent and could be against users’ interest without their possibility of intervention. This gives rise to the ‘privacy paradox’, whereby individuals ask for privacy but are unable to assess the costs and benefits of releasing their personal data, given that they do not know how their data will be subsequently used. This problem has been discussed in regulatory terms (see Section 4).

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In addition, big data on users’ private information affects market power. Analytics on these big data allows platform operators to influence consumers’ choice and better target advertising, thereby attracting advertisers and gaining market power in the market for ads. This is further examined in this chapter.

1  YOOX AND THE NEW IO Yoox is a company created in 2000 by a young entrepreneur of the Emilia-Romagna region in Italy, Federico Marchetti. He created an online shop of luxury fashion, through an Internet site and agreements with some of the famous luxury brands, such as Dolce & Gabbana, Armani and Diesel, starting with sale of end-of-season and remainder clothing. Yoox has constantly grown in recent years, not only in Italy but also worldwide, providing easy and rapid access to a selection of clothes from the famous brands. The number of brands has grown over the years, and Yoox now sells most high fashion brand names. In 2000, high fashion producers were sceptical about online retail, but they agreed to sell out-of-season and remainder clothing through Yoox. The deal was that they would not lose anything: Yoox would sell out-of-season clothing, which would not have a negative impact on consumer buying in luxury brand boutiques, at a price sufficient for both Yoox and luxury brands to make a profit (higher than wholesale but less than retail). Luxury brands would get a share of the price without any cost; Yoox would get another share and would bear all the costs of the online shopping. Yoox was a start-up, obtaining financing at the beginning from a venture capitalist. Fifteen years later, it had 800 employees and was a multinational reaching 100 countries. The company has grown enormously by focusing on a stage of the value chain: distribution. It has become an important intermediary between producers of high fashion clothing and consumers. It is dedicated to improving the logistical aspect of the business: the management of online stores, the handling and shipping of products, digital production, payments and customer care. It has been so successful that online high fashion retail is growing at strong rates. The company has innovated in the luxury fashion industry by using big data analytics: it collects a wealth of data on consumers

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via their use of the website, from how long their visit last, to how long it takes them to buy, what type of products, how frequently are they on the Yoox’s site and so on. It is using this data to anticipate consumers’ visits or suggest new shopping. Yoox has recently proposed to offer direct sales channels for these brands, distinct from the off-season and remainder clothing sold on the main channels. Yoox uses its competence in online retailing and its logistics network to make these direct channels interesting for the brands. Consequently, it can put together data on consumers from the different channels in order to improve its services. Yoox has to continue innovating in order to stay in the market: luxury brands might be willing to establish their own Internet sites, so as to guarantee exclusivity. In addition big online retail powers like Alibaba and Baidu in China and Amazon in the rest of the world might decide to go into this business. Consumers of luxury goods are unlikely to be shoppers on Amazon, but consumers using Yoox might, unless luxury brands prevent these big powers from selling their products. Yoox went on the stock exchange in December 2009, and later operated a merger with the British firm Net-a-Porter, an online platform selling luxury in-season fashion, in 2015, becoming the Yoox Net-a-Porter (YNAP) Group, with 3,901 employees, €1.7 billion net revenue and €2.5 billion enterprise value. YNAP thus covers off-season fashion (Yoox), as well as in-season, and is now expanding online flagship stores of the brands it has agreements with. The group reaches a global market, including America, Europe, Asia and the Middle East. Since data traffic will increasingly be mobile (see CISCO data reported in Chapter 1), the group is investing in dedicated apps, using its big data on consumers to improve its services and its consumer reach. The group also benefits from joint management of stocks and delivery, in the ‘Omni-stock’ programme, which develops intelligent stock allocation and fulfilment decisions across the group’s global logistics network to maximise return on inventory. The Yoox story provides different insights into structural changes currently arising in industries. First, a new strategic market intermediary has emerged, namely online platforms where consumers can shop and have goods delivered to their home. Yoox’s creator had the visionary expectation that online platforms would become important distribution channels and entered the business by choosing a market niche not yet covered by the large and growing platforms such as

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Amazon. Consumers of luxury goods prefer exclusivity to shopping in big retail stores. They go to boutiques where they pay high prices but they also receive a special service besides exclusive fashion pieces. Marchetti expected that such differentiation could also be proposed on online platforms. He started with off-season clothing to differentiate from the normal boutique, and found a market. In addition, Yoox established an efficient logistical network, so as to be able to deliver orders rapidly and efficiently. Delivery services were also enriched by the possibility to order different clothes and easily return those that did not fit or were not satisfactory. Second, this new market, online platforms, is potentially highly profitable since its reach is truly global. In fact, Yoox started in 2000 and 15 years later had enterprise value (after the merger with NAP) of €2.5 billion. Potential gains are therefore enormous. Third, entry barriers are low so competition is high. Differentiation and innovation are key in order to find a market. Yoox has been a first mover in its business and still benefits from first mover advantage. A characteristic of online platforms is that they generally are winnertake-all markets, so that first movers can take huge advantages. Fourth, the Yoox case shows that the use of big data on consumers can confer large market power: luxury brands are increasingly relying on Yoox for their online sales, since the online store is even starting to develop flagship stores for these brands. The reason is that Yoox is accumulating big data on consumers and is developing big data analytics capacity that is key in managing online stores. One of Yoox’s strategies is to improve its app for smartphones, which are indeed expected to be the main support of data flows and Internet use in the future. The transformation of the mobile phone industry is examined in the next section.

2 TRANSFORMATION OF AN INDUSTRY: FROM MOBILE PHONES TO SMARTPHONES Online platforms expanded with the development of the Internet on PCs. The development and rapid diffusion of smartphones contributed to the boom and ubiquity of these platforms, and the history of the smartphone industry is interesting because it shows how disruptive an innovation can be to incumbent firms in a market, if they are unable to adapt.

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As explained by Moazed and Johnson (2016), Nokia was a successful company selling mobile phones with a specific operating system, Symbian, up to 2011. It was considered the company that defined the mobile phone product and market. In 1991, the world’s first GSM call was made by the then Finnish prime minister, Harri Holkeri, using Nokia equipment. The next year, the company’s first hand-held GSM phone Nokia 1011 was launched, and the company took the lead subsequently in the mobile phone market. It had large market share and was driving the development and the growth of its home country, Finland. However, in 2011 the company got into trouble: Symbian was outdated, the company was still focused on hardware rather than software, in contrast to market trends and the company’s performance started to decline. In the shift from mobile phones to smartphones Nokia presumably did not realise the importance of software and completely missed the market. More precisely, in the new market the key strategic element had become the platform. BlackBerry, like Nokia, failed in the switch from mobile phone to smartphones, which advantage to consumers is the numerous apps they provide access to, making the smartphone an instrument in many activities, from navigator to connection to shops and other service providers, such as airlines, hotel booking and so on. BlackBerry was considered one of the most prominent smartphone sellers in the world, with secure communications. Its main market consisted in business people that could communicate safely using their BlackBerry devices. From 2013 to 2016 however, the number of BlackBerry subscribers fell from about 85 to 23 million.1 The leaders who took over the smartphone market have been Apple, which launched the iPhone in 2007 with its iOS operating system, and Samsung, which adopted the Android system in 2008. These businesses have become very important in today’s economy. In 2016, the companies with highest market capitalisation in the US were Apple and Google (Alphabet Inc.). Apple has become a platform business: through iOS, iTunes and App Store it connects buyers and sellers of any kind of digital goods. Most smartphones in the world (about 80 per cent) have the Android operating system. Google developed this system probably because it realised that as mobile phones became Internet-connected (smartphones), they would become competitors to its search engine. In fact, Google developed this innovation by acquiring it: Android was purchased

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for an estimated $50 million in 2005, and is now of much higher value, since it is essential in driving activity on search and email that Google can then monetise. Google does not earn profit from the operating system itself but from the advertising revenue derived from the searches, Gmail and other Google applications that are tied to Android. The strategy implemented by Google in order to launch Android and conquer the market is quite interesting, and looks like the game Risiko! where the objective of players is to gather armies and armadas of tanks in order to conquer as many territories in the world as possible (or some specific territories). It launched on 5 November 2007 the Open Handset Alliance (OHA), an alliance with over 30 companies that committed to use Android (Samsung, Motorola, HTC and so on). At the beginning, Google only signed agreement with Motorola and HTC. However, HTC and Motorola did not turn out to be leaders in the smartphone market. As a consequence, in 2010, Google signed an agreement with Samsung, which introduced its Galaxy S smartphone with Android as operating system. The Samsung’s Galaxy S III Android became the best-selling smartphone in 2012. Now OHA has 84 technology and mobile companies: telecom operators (Telecom Italia, Buygues, China Telecommunications and so on); handset manufacturers (such as Acer, Dell, Huawei, LG and Samsung); semiconductor companies and software (app) developers. In other words, Google assembled an armada to conquer the market, and Android developed substantially because it was used on many new smartphones produced (not only on Samsung phones) and it was open to any developers willing to offer products and services on it (while Symbian and BlackBerry were not). App Store has been a big success: it was launched on 10 July 2008; on 31 December 2008, it had 500 million downloads; on 31 December 2015, it had 100 billion downloads. Google launched its Android market (now called Google Play Store) in March 2009: it had 50 billion downloads by the end of 2015 (Moazed and Johnson, 2016, p. 12). In China, the main app stores are Taobao (Alibaba) and Baidu. Platform companies are expected to grow in size and in number in the next years. Thus in 2015 only 2 per cent of companies in the S&P 500 were platform businesses, which earn 7 per cent of the net income generated by the S&P 500 companies; in 2040, the expectation is that 14 per cent of companies in the S&P500 will be platforms, which will

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generate about 50 per cent of the net income of all the companies in the S&P 500 classification (Moazed and Johnson, 2016, p. 83).

3 ONLINE PLATFORMS: DEFINITION AND CHARACTERISTICS A platform is a business that connects two or more dependent groups in a way that benefits all sides, facilitating exchange of goods, services or information. They generally create new forms of market interactions: Uber connects passengers and drivers, Airbnb connects travellers and homeowners. There are different types of platforms, according to what is transacted: services, products, monetary payments (payment platforms such as PayPal), investment, social networking. Apple chose to develop an exclusive community, to which consumers loyal to the product adhere because of the quality and security guarantee provided by the reputation of the company (for innovation but also for privacy: quite symptomatic is the case of Apple refusing in 2016 to reveal its code to the FBI, which would have used it to access information on the smartphone of a potential terrorist). Consumers gain from platforms. They can more directly confront product characteristics and prices; they get informed by consumers who have had experience with the product, and can in turn recommend products to other consumers. The information asymmetry between consumers and producers reduces; search and switching costs also decrease thanks to the easier access to information. However, platforms create another information asymmetry, related to the use of the private data released by the consumer. Platforms are key to any business nowadays: they market business (advertising), read content (Facebook), download apps and sell goods online (Amazon, eBay and so on). These businesses are peculiar in that they do not own any product but base their value creation on providing a market place, a digital infrastructure for connecting producers and consumers. 3.1  Ease of Entry and Winner-Takes-All Platform businesses have low fixed costs: they do not have production capacity, they have few employees. In 2017, Walmart had 2.3

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million employees,2 Amazon had reached 350,000 employees,3 and Alibaba has 50,000 employees.4 In terms of revenue, Amazon earned about €116 billion, Alibaba earned about €20 billion, and Walmart, €400 billion. Google (now Alphabet) has about 72,000 employees in 2016 and $90 billion revenue. Given the ease of entry into the market (no fixed costs), many entrepreneurs have tried to launch platform businesses. However, the difficulty in order to get the market is to reach a critical mass, which is necessary before creating value. For instance, the Uber service would not be interesting if it had only a few drivers in its network (many people would not find the driver they need). Hence platform companies implement aggressive strategies at the beginning in order to reach their critical mass. In addition, competition at the market creation phase is generally of a winner-take-all nature: the company, which succeeds in building critical mass often takes all the market. For these reasons, competition at the market creation phase is extremely strong. Alibaba won the Chinese market over eBay by maintaining low transaction prices for three years on its marketplace (called Taobao), while eBay was charging a percentage fee on each completed transaction. In addition, Alibaba also allowed the possibility for buyers and sellers to chat together before completing the transaction on its site, which was very appreciated in China. As a result, eBay exited the Chinese market in 2006. Alibaba however did not have a search engine. Its users had to search for products on the Baidu search engine (Alibaba’s rival) and then be connected to Alibaba for transaction. Alibaba therefore bought Yahoo China in order to compete against Baidu, which replied by creating its own marketplace, Youa, to directly compete with Taobao. Alibaba has become the main platform for product search in China: it accounts for about 75 per cent of online sales.5 Other examples of winner-take-all results include smartphone operating systems. Thus, in February 2017, Android had 67 per cent of the world operating system market, while iOS had about 29.6 per cent. Both Android and iOS account for about 96 per cent of the world operating system market.6 Airbnb provides an example of strategies for audience building and reaching critical mass. This company was created in 2010 (start-up), when the leader in the US market was Craigslist. Airbnb

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i­mplemented a specific strategy to enter the market: it created an unofficial integration with Craigslist, in the sense that users putting an ad for their home on Airbnb could also immediately put it on Craigslist thanks to a simple click. Viewers looking at the ad on Craigslist would automatically be sent back to Airbnb. In this way, Airbnb was able to divert many users away from Craigslist. Airbnb also sent spam to Craigslist posters, so that people posting on Craigslist would immediately receive a message suggesting they also post on Airbnb. This constituted a predatory – but not illegal – strategy to enter the market (Moazed and Johnson, 2016). Platforms are having a big impact on existing businesses. One clear example is that of the book industry, which has redefined its boundaries and products, as well as production processes. For instance, Wikipedia has completely transformed the business of encyclopaedia: a decentralised network of individuals has substituted the business of encyclopaedia editing houses. The Encyclopaedia Britannica ended its print edition in 2012 as a result. Business value chain has changed: some phases have disappeared (for instance, printing for encyclopaedia); others have become strategic (logistics and distribution through e-commerce). 3.2  Big Data Analytics as Key Strategy for All Firms The competition among platforms and the growth of the platform industry is contributing to industrial development and economic growth not only directly, but also indirectly since platforms have effects on all businesses. As stressed by management consultants (for example, IBM), even old businesses can be disrupted and their market position strongly threatened by the entry of new businesses that use platforms to penetrate their markets. A clear example is Uber, which has disrupted the taxi business by becoming a new rival on the market, offering cheaper and better services, or Airbnb, disrupting the hotel business. The problem for incumbent firms is that new businesses, so-called ‘Digital invaders’, may unexpectedly arise and have dramatic effects on their activities: they may target a key part of the value chain, or a specific market segment (for example, luxury fashion in the case of Yoox). These invaders might be large platforms, like in the case for instance of Google or Amazon going into the grocery business; Amazon acquired the Whole Foods retail store in August 2017.

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They might also be small firms, such as those disrupting the banking industry by offering new and easily accessible services, such as Nutmeg for investment services in the US or the stock brokerage app Robinhood. New entrants might also come from the convergence of previously separated businesses. The combination of consumer electronics and health care in digital exercise-tracker Fitbit is an example, an American company headquartered in San Francisco, California, and created in 2007. Fitbit is known for its products of the same name, which are activity trackers, wireless-enabled wearable technology devices that measure data such as the number of steps walked, heart rate, quality of sleep, steps climbed, and other personal metrics involved in fitness. The first of these was the Fitbit Tracker. Another example is the agrochemical company Monsanto moving into precision farming, offering an app to farmers to help them monitor their production from sowing to harvesting. The competitive threat has increased in many industries and firms are implementing strategies to avoid disruptions by new invaders, such as maintaining a community of consumers with an online forum or platform, preparing to offer new solutions to consumers, innovating products and services and raising the service value of products. 3.3  Network Effects In economics platforms are defined as two-sided markets (Rochet and Tirole, 2003, 2006) or multi-sided markets. The main focus of economic analysis is network effects, which generate externalities: the user gets more (or less) than what he pays for. As a result, prices do not equal marginal costs. In order to build audience, namely attract a critical mass of users that make network effects attractive, platforms may set prices below marginal costs or even negative prices. Caillaud and Jullien (2003) find that multi-sided market operators may leverage network effects in order to expand their market shares. Access prices and transaction fees may be manipulated in order to maximise the attractiveness of the platform for all the users. The combination of network effects and potentially predatory pricing in platforms has drawn the attention of economists and antitrust authorities, because some platform operators have succeeded in reaching large market shares in short time. Network effects

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and economies of scale make market concentration more likely. Armstrong (2006), Rochet and Tirole (2003, 2006) for instance study pricing behaviour in two-sided markets. Pricing on the two sides may not reflect social value and pricing strategies may negatively affect some users, while others are not. Overall, whether platform behaviour is welfare reducing has to be assessed on a case-by-case basis (Evans and Schmalensee, 2007). Another implication of network effects is that it might be more efficient to maximise these effects, when all users coordinate on that platform, leading to the existence of only a few and big platforms. The dimension of platforms has been shown to depend on four main factors (Evans and Schmalensee, 2007). Besides network effects, economies of scale such as the cost of setting up the platform (for instance, the networks for authorising and settling transactions for cardholders and merchants in the case of payment card systems), congestion and search optimisation (congestion may limit the size of platforms) and platform differentiation and multi-homing. Platforms can differentiate by choosing particular levels of quality, with consumers choosing the higher or lower quality of platform depending on the income and relative demand for quality (for example, upscale and downscale malls). Horizontal differentiation can result in customers choosing to join and use several platforms (‘multi-homing’), when switching costs between platforms are not too high. Payment cards are an example of multi-homing on both sides: most merchants accept credit and debit cards from several systems, while many cardholders carry multiple cards. Haucap and Stühmeier (2015) argue that the risk of monopolisation by online platform is limited. For example, MySpace was the leading social media platform in the mid-2000s but has now almost disappeared. However, it has been substituted by a new monopoly, that of Facebook. Consumers use different social networks (besides Facebook, Instagram, Snapchat, Messenger, Twitter, WhatsApp). Network effects make markets more concentrated. Nokia’s Symbian operating system has now been replaced by an oligopoly dominated by Android and iOS. New platforms can emerge outside dominant platforms and drive the old platforms almost out of the market (Alibaba in China driving eBay out). A new platform may also emerge within a platform, as in the case of a game or any app developed for a platform that becomes popular and starts its own platform.

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When users can use different platforms or networks at the same time, the strength of network effects on any one platform will be weaker. An industry where users can switch easily between networks often can support more platforms, even as the industry matures. However, the best platforms use specific strategies to avoid users switching platforms: software tools that make transactions easier on the platform, reputation scores on eBay, Airbnb or Taobao; personalisation aspects such as recommendation features. Monetary subsidies are also used, such as price discount if a user invites a new user to join the platform. Platforms are actually in high competition: users can move, switch platforms easily, much easier than switching products in the old monopolies, which had built excess capacity. For instance, Amazon has created the eBook market in the US, but Google and Apple have rapidly and strongly contested it. Platforms are in a way contestable markets: entry is easy and any entrant can come in and try to attract customers by a better featured marketplace (or other platform). Uber was created in San Francisco in 2009. At creation, it needed to establish a liquid marketplace, namely a market where there is enough overlap between demand and supply that most transactions can clear quickly, and demand and supply are balanced. Excess demand would imply delay to get the service (so people would prefer a normal taxi instead of Uber). Excess supply would drive prices down and producers would not earn enough. Uber implemented a ‘surge pricing’ to get supply and demand balanced. When demand grew a lot, prices were increased a lot so that many drivers would show up and demand would go down. The economic literature concludes that there is no antitrust problem in platform competition. However, a key issue appears to be the market power conferred by the collection of big data on users. This is further discussed in Section 4. 3.4  Dynamics of Innovation Large platform businesses also have an important impact on innovation. Besides their numerous engineers and researchers who perform R&D in-house, large platforms have been extremely active in the last decade in acquiring innovation, through acquisition of innovative start-ups. Thus Alphabet (formerly Google) has made a number of notable acquisitions over the years, which have allowed Google to

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expand into new industries. One of the most important and beneficial acquisitions has been Android in 2005, which is now installed on more than 80 per cent of the world’s smartphones. Android is a key source of revenue for Google, helping drive activity on search and email that Google can then monetise. Google does not earn profit from the operating system itself but from the searches, Gmail and other Google applications that are tied to the system, from which the company collects large amounts of personal data on users that are used for targeted advertising. Google has made numerous acquisitions to increase innovation. Thus it bought Keyhole, Whereto and Zipdash in 2004, three mapping start-ups that contribute to create and improve Google Maps. The mobile GPS navigation app Waze is another acquisition Google made to improve its Google Maps software. Today, Google Maps has more than 1 billion users and serves Google in several ways: it generates revenue through the sale of location-based ads, it is an essential element in the development of Google’s self-driving cars and it adds to the ubiquity of Google’s services. The company has also made acquisitions in different technological fields, for instance the AI company DeepMind, which specialises in creating machine-learning algorithms for simulations, e-commerce and games, acquired in 2014. Similarly to Google, Facebook is investing to take control over sectors adjacent to its core product (WhatsApp) or differentiated (Oculus VR). Microsoft, Yahoo and Amazon are doing the same, since they bought, respectively, Minecraft developer Mojang, Tumblr and video game streaming site Twitch. When making high tech acquisitions, the GAFA companies also buy the talents besides the innovation. A TIME analysis of start-up founders’ LinkedIn profiles7 concluded that about two-thirds of the start-up founders that accepted jobs at Google between 2006 and 2014 are still with the company, a similar retention rate to those of Yahoo and Apple. Amazon has retained about 55 per cent of its founders over that time period, while Microsoft’s rate is below 45 per cent and Facebook, 75 per cent. Platforms also facilitate innovation, in different ways. One way is to open up to third-party applications. Third-party developers have been proposing many new ideas and new projects, accessing the platform’s interface, suggesting innovation in complementary

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products, creating an innovation ecosystem, where the firm does not have to search for innovators but rather innovators (developers) seek the platform and try to connect to it. The dynamics of innovation in the business of online platform is therefore two-sided: on the one hand, improving search engines is better for consumers who can more easily find the product they are looking for; on the other hand, they improve the collection and use of their private information which might make them more dependent and vulnerable.

4  BIG DATA AND MARKET POWER There are two issues related to the collection of big data on users: a regulatory issue, linked to privacy problems, and a competition issue, related to the monopolisation practices induced by this collection. 4.1  Key Regulatory Issue: Use of Data in Platforms In the EU, the debate on the liability of platforms or online intermediaries is addressed in the 2000 Electronic Commerce Directive,8 which states that Internet intermediary service providers should not be held liable for the data they transmit, store or host as long as they act in a strictly passive manner. The key issue is therefore the extent to which platforms are passive or active, which is difficult to identify because platforms generally collect data on the content that is offered and feeds back into a matching algorithm that facilitates exchange and reduces search costs. Platform information analytics and user content are complementary products that are necessary ingredients in an exchange. Platforms have some forms of auto-regulation, such as consumer review scores, which are important to give an idea of the quality of products and services, and to attract additional consumers. Consumers provide such reputation scores because they know that if everybody provides theirs the superior product or service will emerge. However, ratings are not perfect instruments: there might be underprovision of ratings, implying their lack of accuracy: Dellarocas and Wood (2007) show that on eBay buyers and sellers with mediocre experience review fewer than 3 per cent of the time. The same good or service can also obtain very different ratings on different platforms:

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for instance, Mayzlin et al. (2014) show that the share of five-star ratings is 31 per cent on TripAdvisor and 44 per cent on Expedia. The potential problem in these services is that they are not subject to product regulation. For instance, who is liable on Airbnb if the home is damaged? Ratings after the use of the service is important to drive misusers out of the market and of the platform, but misuse might generate high costs for some users. Sachs (2009) thus argues that the current situation regarding data and platform businesses can be compared to the beginning of mass consumption markets, where products were not regulated and consumers started to buy goods without any insurance of their quality. This author suggests that a reflection on privacy and security is required and would lead to a new type of product regulation, adapted to the case. Traditional firms can collect information about their consumers’ behaviour and their relationships with suppliers and clients. Platforms can collect data and aggregate them across all firms and consumers on the platform, and derive more information on consumers, so that there are economies of scope from putting together big data from different origins. Private data might be used for price discrimination: Mikians et al. (2013) find evidence of price discrimination based on consumer data, as well as discrimination in search results. Search results and offered prices will vary according to the national location of consumers; for instance, the price of the same hotel room searched on a specific online hotel booking platform can be much different according to the country where the consumer is located. The Mystery Shopping Survey carried out by the European Commission confirms such price discrimination (European Commission, 2016b). Surveys have also found that consumers are concerned about privacy protection and the use of their personal data. However, they simultaneously choose to reveal their personal information on digital technologies, probably because of the benefits derived from online shopping relative to unknown costs in terms of privacy. Online shopping or information research indeed allows consumers to more easily confront products and prices, look at detailed information on product characteristics and recommend products to other consumers. This behaviour has been defined as the ‘privacy paradox’. In the EU, this problem has been addressed by regulation imposing individuals’ consent for data collection, for instance asking users

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whether they accept cookies or when they have to read and consent to detailed privacy rules before proceeding to the use of a site or to transactions. Adjerid et al. (2013) show in different experiments that even simple privacy notices do not consistently impact disclosure behaviour, and may in fact be used to nudge individuals to disclose variable amounts of personal information. An issue relates to the fact that platform businesses are global, while regulatory authorities operate over limited territories. An online shop based in the USA may sell products manufactured in South America to European consumers: which technical standards and regulatory regime apply? An online worker in India can perform a task via a US task-outsourcing platform for a firm based in Europe: what is the relevant labour regulation? 4.2  Market Power The collection of big data on users’ private information provides market power. Amazon or Google derive their market power from their ability to target users with ads because of their dominant control of databases of user personal data. Google for instance provides its services free to consumers: its search engine, Gmail, YouTube videos, Android and so on. The company can increase users’ data by increasing the variety of services offered: Google’s search engine was launched in 1998, Gmail in 2004, and it acquired YouTube in 2006. However, the profit these services provide to Google results from the advertising they allow. The real clients of Google are advertisers, who are attracted by Google’s services because it has many users, from whom it collects large amounts of data, implying a better profiling and hence the possibility to better target ads. The more a platform knows about users, the more ads can be targeted, the market can be segmented, with specific prices, promotions and so on, according to users’ types. Advertisers place bids in auctions based on specific keywords for each specific users’ group. Advertisers pay each time a consumer clicks on the ad: this is the cost-per-click price. Google gets higher prices on each click than its rival because it is dominant on the search advertising market: according to Newman (2014), Google has 78 per cent of US search advertising revenue and 85 per cent of global search advertising revenue. Google can thus accumulate large amounts of data on users,

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which implies both better profiling of each individual users and better knowledge of the behaviour of similar users, making it possible to anticipate their interests. The improvement in the capacity of analysis of big data is therefore important, and Google has invested effort in R&D to improve this capacity, for instance to design better algorithms (as outlined in Section 3.3). Connecting technologies today allow some private companies to accumulate enormous quantities of personal data. According to Kelly (2016), each day more than 3.3 billion requests are made on 30,000 billion pages of Google; about 350 million pictures and 4.5 billion likes are distributed on Facebook; 3 billion people exchange 144 billion emails a day. If all communications and writings made by humans from the dawn of humanity to today were digitalised, about 5 billion gigabits would be necessary. Today this volume of information is generated in just two days. Collecting big data on users is therefore a key strategy, and Google has been convicted of restricting the portability of its data to competitors’ ad platforms.9 The main challenge of big data is to make sense of this huge quantity of raw data. This is done using algorithms, namely mathematical instructions given to the computer so that it could select, treat and represent information. Thus searches on Google are more and more personalised, since algorithms statistically analyse the big data and allow the search engine to anticipate individuals’ expectations and desires. It does so on the basis of both previous choices that are registered in the system, and the choices that similar individuals have made in the past. Specific tools, such as cookies, are inserted in Internet browsers and sites in order to make these computations possible. Lou Montulli, engineer at Netscape, invented them in 1994, as a specific software inserted in the users’ browser in order to remember the Internet (IP) address of his/her computer. This spy software has turned out to be very important for advertisers and big platforms in order to get users’ information. Cookies make it possible to recognise the user each time he or she connects to the Internet, facilitating navigation (since for instance passwords are remembered), but at the same time allowing it to collect more private information on past navigations, leading to the creation of detailed users’ profiles by putting together information on navigation and socio-demographic information.

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Some unknown firms such as Acxiom, BlueKai, eXelate, Rapleaf and so on, compete on the market for advertising on the web. They have imposed the technique of third party cookie, meaning that when a user goes on a site, the advertising appearing on that site will also appear on the following sites that the user will visit. In the EU, the 2002 ePrivacy Directive (Directive 2002/58/EC) imposes the obligation of asking for users’ consent in the use of cookies. However, the user, when permitting cookies, may not be aware that cookies spy his navigations on other sites too. In addition, the recommendation instruments used for books (Amazon), films (Netflix) or music (Deezer, Spotify) are based on a ‘collaborative filtering’ technique: the user is offered a wider range of products on the basis of the products that other users have acquired together with the product he is interested in. However, information on users gets richer and richer: cookies allow platforms to find out whether a music piece has frequently been listened to; how long a user has taken to read an eBook; where the user has gone for the weekend or on holiday (with localisation via GPS); what has been acquired with the credit card and so on, all these signals can be used to analyse the user. The problem of this model is twofold. First, recommendations made to users are based on past choices but do not leave space for novelty and new experiences. Second, recommendations are based on the behaviour of similar individuals, so that if the user follows them this leads to a convergence of behaviour towards the average. Computers and robots can substitute humans in their mechanic, functional and statistical activities. However, computers and robots will never be able to have analytical and creative competencies, which are the competencies that people must develop in the current context. AI is substantially developing nowadays and is based on the statistical analysis of big data. The translating machine is an example. The machine does not translate, it does a statistical estimation of the best translation of words and confronts it with all the translations it has in memory. The computer must include the highest possible amount of texts and their translations in order to ‘learn’. The machine does not ‘understand’ anything it is doing but it estimates the most probable statistical correspondence on the basis of the huge amount of available data. This translation system has improved a lot together with the capacity of computers to memorise and analyse big data. There is also information asymmetry between firms and the

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­ latforms they use to market their products. Data are valuable assets p for firms, and platforms can collect substantial data from the consumers of the different firms offering products on their platforms. This may distort competition, if the platform uses these data to its advantage. The observed data-driven mergers (for instance, Facebook and WhatsApp) might represent attempts to have major control on data, stopping competitors from accessing their own data or ensuring access to competitors’ data through this operation. Free services offered on one side of the platform are seen as pro-competitive, but they also collect data on users, that may be subsequently used to limit competition. Big data collection on individual users implies that information gets concentrated in private profit-maximising organisations. Google earns about 80 per cent of search advertising revenue in the US.10 However, this revenue is generated by selling data that consumers freely release to Google via their search, their use of Gmail and other of the company’s products. With such a dominant position it is likely that Google is able to impose high prices on advertisers willing to use its platform. Google platform is multi-sided, in that Google offers a service to users (Gmail, search) and monetises these services thanks to another transaction, with another side, namely advertisers. Since Google has a large market share advertisers will prefer putting their ad on its platform, with the result that Google is able to ask for high prices to advertisers. Alternative search engines or new entrants will have a price disadvantage; they will have to ask low prices to attract advertisers while they’re building an audience. This constitutes a barrier to entry. Consumer lock-in is likely because network effects combined with quality effects of reaching a certain level of users in the search engine lead to high consumer loyalty. The quality of search results indeed increases as the number of users increases, since it allows collecting more data on search and consumer characteristics. As the quality of search reaches a certain level, consumer loyalty becomes very strong since consumers will only search on the engine that generally gives them useful results. Google has been accused of illegally wiretapping, gathering data about Internet users and showing them related ads.11 The accusations have been made over several years in various lawsuits, asking whether Google went too far in collecting user data in Gmail and Street View, its mapping project. Evidence appears to have been found of

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Google’s track of cookies on Safari users even when Safari was set to block cookies. These cookies, which were invisible to users, could then be controlled by Google and used to store additional information about the users’ behaviour, including information like the users’ Google login information. Consumer profiling used to target ads can be a problem especially for more naive and vulnerable consumers, such as younger ones. Google owns enormous data on individuals located anywhere in the world. Is this too much in the hands of just one company? Should different companies in competition share data? Allowing access to these data to different companies is likely not to resolve the privacy problem, because different companies will end up using the data for ads and to influence their behaviour. Should these data be made public? Should the government have access to these data in order to control the strategies of private firms and protect consumers? Badly intentioned governments could also use these data to reduce the freedom of people. Large online platforms use the data and the contribution of their users, generating value out of it, while the users are not necessarily aware of the value of their contributions. They probably underestimate the value of the personal data they share with these platforms. Models of asymmetric information on markets in economics have shown that firms with higher information than the consumers can exploit their power in product markets by differentiating prices and conditions among individuals with different search costs. Using data on consumer location companies can discriminate prices, as shown by the evidence mentioned above. Various researchers have found evidence of price discrimination in online shopping (Hannak et al., 2014). There is also evidence of obfuscation strategies to create obstacles to price confrontation on the Internet (Ellison and Ellison, 2009). Esteves and Resende (2011) show that information asymmetries make price discrimination likely and this reduces consumer surplus. Ethical problems are also important. Thus in August 2011 Google agreed to pay a $500 million civil forfeiture to the federal government as part of a settlement penalising the company for illegally and knowingly allowing illegal pharmacies to advertise on its site.12 Some antitrust action might therefore be necessary. For instance, greater transparency might be required on how Google monetises users’ data, such as regular reports on the cost-per-click or other

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payments to Google based on user activities. Such information might induce users to more frequently shift between search engines, using the search engines that offer better deals, or even withholding their data after realising the pervasive use by the online platforms. Online platforms should also recognise their power and their responsibility generated from the collection of these big data on individuals. Regulators should closely oversee how online platforms collect and use users’ data. Another problem regards information to users on online platforms. Many platforms are releasing news to users, who are reducing their reading of newspapers and access to TV news as a result. However, this information is not always reliable. Major newspapers have developed apps and their printed version has substantially reduced. Their major problem is that individuals increasingly rely on major platforms, essentially the GAFA, together with Twitter, to get information. News and ads are sent to individuals using these platforms. The problem is twofold: first, news is sent in its gross form, not processed and analysed by professionals such as journalists; second, the reliability of information sources is not always ensured. Major newspapers have reacted with two strategies. First, they stress the accuracy and reliance of their information sources. The French newspaper Le Monde is even developing an app able to check the reliance of information on the tablet or computer that comes from various programmes. In a way, newspapers move to the higher quality segment of their market by ensuring high quality news, not only guaranteeing sources but also providing analyses of new information. Second, newspapers have allied to face the competition from online platforms. Large online platforms rely on the big data they collect to provide each individual with targeted ads but also information. Single newspapers collect data on their users but the size of their platform is much smaller than those of the GAFA (or GAFAM, including Microsoft). Hence they have started to establish alliances to put together their big data on users. For instance, in France, the Gravity alliance gathers the groups Les Echos, Lagardère Active (with magazines Elle, Paris Match, as well as radio stations such as Europe 1), SoLocal and SFR Média (with weekly newspaper L’Express and daily newspapers such as Libération).

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5  BIG DATA AS COLLECTIVE ASSET Platforms crucially rely on big data and the Cloud to function. Businesses using platforms or related to platforms in some ways also rely on these technologies. However, big data require storage and analytics, which in turn require crucial infrastructure: high performance computing, big data and high-speed networks (digital infrastructures or e-infrastructures), as well as human capital, namely individuals adequately trained and with the necessary skills to perform big data analytics. Private firms are the main organisations collecting and using big data in order to improve their performance. However, big data also have the characteristics of collective assets. They might be used to improve public services, such as health or education. They essentially consist in private individual information, and allow understanding of an individual’s tastes but also characteristics, as well as the roots of certain social trends. Their mastering can potentially provide large market and political power. Especially vulnerable individuals can be influenced and manipulated. Some thoughts might therefore be made about the fact that private firms, not public organisations, own these data. The Italian government has signed an agreement in 2016 with IBM, whereby the data of the national health system will be provided to IBM, which will analyse them thanks to its big data analytics capacity and propose new health solutions and even personalised treatments on this basis. The gains are clear for the Italian population, but are we sure it is a good idea to have all data on the health of the Italian population in the hands of a private company? It might sell these data or their analysis to insurance companies for instance, which may increase the price of policies as a result. This suggests that a better policy would have been to raise the capacity of public organisations in Italy to collect and analyse the big health data. However, evidence looking at Italian universities and research centres show that this capacity already exists. The government of the Emilia-Romagna region made a different industrial policy choice in this regard, as shown in Chapter 5. If left to firms, big data have the characteristics of impure public good: they are non rival, in that access by some individuals does not reduce the amount left for other individuals, but they are excludable, in that private companies can forbid access to their data to other

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entities. Data science can provide many benefits for the society, such as the use of health data to design better treatments or improve prevention, or the identification of some social problems thanks to data analytics that lead to the design of specific policy addressing the problem. The use of big data is potentially welfare enhancing. Open access to the results of data science might be a way of guaranteeing their proper use. The issue is whether big data should be made public, freely accessible. Misuse must be guaranteed in case of free access. The difficulty of data analytics and the high skills required to perform it induces that only certain type of individuals will be able to use them. A deep reflection is required on these issues.

NOTES   1. A. Monaghan (2016), ‘Blackberry to stop making phones’, The Guardian, 28 September, available at https://www.theguardian.com/technology/2016/sep/28/ blackberry-to-stop-making-phones-handsets (accessed 14 July 2017).   2. Corporate.walmart.com (accessed 19 August 2017).  3. T. Sopher (2017) ‘Amazon surpasses 350,000 employees, up 43% from last year’, 27 April, available at https://www.geekwire.com/2017/amazon-surpasses350000-employees-43-last-year/ (accessed 13 February 2018).  4. Statista (2017) ‘Number of full-time employees at Alibaba, 2012 to 2017’, available at https://www.statista.com/statistics/226794/number-of-employees-atalibabacom/ (accessed 19 August 2017).  5. P. Liu et al. (2017), ‘The accelerating disruption of China’s economy’, Fortune, 26 June, available at http://fortune.com/2017/06/26/china-alibaba-jack-ma-retailecommerce-e-commerce-new/ (accessed 14 July 2017).   6. According to Netmarketshare (2017), ‘Operating system market share’, available at https://www.netmarketshare.com/operating-system-market-share.aspx​?qprid​ =8&qpcustomd=1 (accessed on 14 March 2017).  7. V. Luckerson (2015), ‘How Google perfected the Silicon Valley acquisition’, TIME, 21 April, available at http://time.com/3815612/silicon-valley-acquisition/ (accessed 19 August 2017).  8. Directive 2000/31/EC of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the internal market.   9. Google promised the Federal Trade Commission to reduce its restrictions on data portability to competitors’ platforms in a January 2013 Settlement in the US, Google Inc. (2012) ‘Letter to Jon Leibowitz: Google Inc., File No. 111-0163’, 27 December, available at https://www.ftc.gov/system/files/documents/closing_let​ ters/google-inc./130103googleletterchairmanleibowitz.pdf (accessed 19 August 2017). 10. According to www.emarketer.com and www.recode.net. Unfortunately, there are no official statistics on this. 11. H. Blodget (2012), ‘BUSTED: Google caught secretly hacking Apple software

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to track Apple iPhone and Mac users’, Business Insider, 17 February, available at http://www.businessinsider.com/google-tracking-apple-users-2012-2?IR=T (acce​s​sed​on 27 March 2017). 12. T. Catan and A. Efrati (2011), ‘New heat for Google CEO’, The Wall Street Journal, 27 March, available at https://www.wsj.com/articles/SB1000142405311 1904787404576532692988751366 (accessed on 26 March 2017).

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5. A concrete experiment of industrial policy for the manufacturing revolution Previous chapters have reviewed industrial revolution and discussed to what extent a fourth industrial revolution is taking place. Implied structural changes for industry are substantial, as production organisation (products and processes) is changing and a new intermediary has taken growing importance, namely platforms, which have an impact on market competition. Regulatory and antitrust issues have been raised in the last chapter. More broadly, these deep structural changes call for new infrastructure, support to the development and adoption of new technologies, developing research and technology transfer capacity. In addition, new skills are required, which public education and training policy have to provide, as they progressively did in the first industrial revolutions (Chapter 2). Bianchi and Labory (2011a) outlined the importance of industrial policy in times of structural changes in industry, proposing a framework for its design. The proposed policy was comprehensive, in the sense of joint definition and implementation of policy for industry and social policy. This chapter examines an experiment in the implementation of such a comprehensive industrial policy. The case study is that of the Emilia-Romagna (ER) region in Italy, which has undertaken important structural changes, particularly in two periods, from industrial districts to innovation system in the 1980s and 1990s, and now in the adaptation to the industrial revolution. The chapter reviews the main elements of industrial policy implemented in both periods, and argues that it has the characteristics of a comprehensive industrial policy, which are defined and explained in Section 1. This type of policy has contributed to the capacity of the region and its industrial system to adapt in the long-term, namely to its resilience. We therefore conclude that if a comprehensive industrial policy is useful 104

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to favour and promote adaptation to the manufacturing revolution, its aim appears to be resilience. The resilience of the ER region is also shown in its emergency and reconstruction management after a natural catastrophe arising in 2012.

1 SUNDIAL: IMPORTANT DIMENSIONS OF A COMPREHENSIVE INDUSTRIAL POLICY Bianchi and Labory (2011a) defined four main pillars that policy action should follow in order to promote industrial development: entitlements, innovation, provisions and territory. Entitlements are socially defined means of access (Dahrendorf, 2008), namely not only the capability of buying goods and finding a job, but also non-economic commodities such as the right to vote and the right to be educated: there are civil rights (basic elements of the rule of law, equality before the law), social rights (universal right to real income) and political rights (suffrage, freedom of association, freedom of speech). These are necessary for industrial development, especially in times of important structural changes. Individuals must have access to education and training, to have the skills required by the new production structures, or to participate in the innovation process; entrepreneurship also requires economic and social systems with sufficient entitlements. Entitlements are therefore a key factor of innovation and upgrading (in the sense of adaptation to the changing competitive context, renewing products and processes, commercialisation channels and so on), in addition to an appropriate knowledge base and knowledge absorption capacity. The latter aspect is what we call the innovation dimension. Policy actions aimed at increasing or updating the knowledge base, such as the promotion of university–industry relationships, or other R&D programmes, are part of this ‘innovation’ dimension. The dimension of ‘provisions’ is more straightforward and concerns the resources for development, from access to raw materials and low-cost energy sources, to infrastructure more generally. As stressed in the previous chapters, information technology, connection and big data infrastructures are particularly important in the fourth industrial revolution. The ER region has thus massively invested in these infrastructures in the last decade, as part of its industrial policy. This will be further discussed in the next sections.

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Industrial policy for the manufacturing revolution Innovation: I Human resources policies

Innovation policies

Entitlements: E

Provisions: P

Social policies

Territorial policies Territory: T

Source:  Bianchi and Labory (2011a).

Figure 5.1  Four levers of industrial development The territory is also a key dimension, in the sense of institutions and their governance, at different levels of government, namely local, regional and national, and also supranational. All the dimensions are important and comprise different determinants and factors that should be assessed and combined in an appropriate manner with respect to the socio-economic and political conditions of the given territory. We therefore summarise our holistic view of industrial policy building a framework based on four pillars (Figure 5.1). Entitlements determine the rights or capabilities of individuals to take part in development as well as in productive and competitive processes; provisions determine the resources available for these processes to develop, including infrastructures and public goods. Innovation is the capacity to create and maintain learning mechanisms that can be applied to production processes, hence an element that directly contributes to the dynamics of the development process. The territory indicates institutions, and how much they are democratic, inclusive, so that combined with entitlements they produce social cohesion. All four levers or gears to industrial development can be

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r­ epresented as​in Figure 5.1. Sustainable industrial policy should aim at extending all four levers, which are complementary and not substitute, by combining policy actions specific to the socio-economic characteristics of the territory. Sustainable industrial policy also requires coherent actions at the different levels of government, namely regional, national and supranational. The areas covered by the sundial represent different policy focus. The north-eastern area corresponds to innovation policies; the south-eastern area is that of territorial policy, providing resources (infrastructure and capital) in the territory; the south-western area is that of social policies, ensuring entitlements; and the north-western area is that of human resources policies, enhancing knowledge and competencies (the innovation axe) in order to raise the capability of individuals to take part in production processes. A holistic approach means considering the whole of which the  ­ particular policy problem is part, in a dynamic way. The whole  forms a system determined by different forces that orientate its development. If certain forces produce undesirable results, such as social exclusion, instruments must be implemented to change these forces and guide development on the desirable path. This representation of industrial policy highlights that policies require adequate politics and coherent polities, namely visions of the future and social structures that are able to sustain its growth through time. Some cases of rapid growth paths have occurred in the past, but generally miracles are also rapidly exhausted, because an unbalanced growth generates negative effects. The difficulty of the UK in promoting industrial development and productivity growth, as well as the unbalanced development of the country (Martin, 2015) and the result of the 2016 referendum inducing Brexit, is largely due to the government’s lack of attention to entitlements since the 1980s, as inequalities and poverty have constantly increased in the period (Cribb et al., 2017). This is also outlined by Rodriguès-Pose (2017). Industrial development, especially in times of industrial revolution, is extremely complex and the sundial helps to orientate this definition by outlining a number of important dimensions of this complexity. As argued by Simon (1962), as long as the parts of a system behave coherently with the whole we can focus analysis on the overall system, and see how it evolves, without looking at the single parts.

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However, if the system substantially changes in its structure, it might be that some parts will disappear, others will combine or interact in novel ways, and yet others will be left apart. If the system in question is the economy, we might only care about the evolution of the overall system (macroeconomy). However, if some parts are left behind or worse off due to structural changes, some issues might arise: social conflict, social movements against the politicians and decisionmakers are likely to arise. Politicians care about unemployment, because they know that if their policies favour growth but rising inequalities they will not be re-elected, unless the rising inequalities will become clear in a more distant future, beyond the next electoral deadline. However, economies are not only systems, they are also hierarchical systems – ‘the complex system being composed of subsystems that, in turn, have their own subsystems, and so on’ (Simon, 1962, p. 468). Subsystems are firms, families, cities, regions and nations. The polity defines the area in which a specific institutional framework applies. Generally, economies are assessed at national level, and national politicians implement economic policies to favour the development of their national economies. However, there are also regions and cities, where policies are implemented too. In case of disruptive change such as an industrial revolution we argue that territories have a role to play in active industrial policy in order to participate in the transformation in a favourable manner. The question for policy is to identify possible development paths on the basis of the current situation of the regional system and its historical evolution, which identifies its distinctive competencies, the current resources and entitlements. Focusing on single firms or industries is likely not to be effective since the regional system is made of different parts and the synergic effects they produce together. Some effects only emerge at system-wide level (Metcalfe et al., 2006), and regional industrial policy should aim at potentiating these system-wide effects. The case of the EmiliaRomagna region is precisely one where regional industrial policy has attempted to potentiate these system-wide effects, as shown in the next section.

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2 INDUSTRIAL POLICY IN THE EMILIAROMAGNA REGION SINCE THE 1980s The Emilia-Romagna (ER) region is an interesting case because this region is to a certain extent a model of application of our sundial, both in the past and today. The ER region has been a reference for development based on the consolidation of a civil society: Brusco (1982), Brusco and Sabel (1981), Putnam et al. (1993) have shown how industrial development (especially of SME systems and districts) was also based on social characteristics and values. Today the ER region is becoming an example of industrial development policies aimed at making the region a knowledge-based economy and society and has been defined a regional innovation system (Cooke, 2001). The ER region is among the most dynamic of the EU. A higher share of companies per inhabitant characterises the regional industrial sector, compared to the national level; the region has 927 active businesses per 10,000 inhabitants while the overall national share is 849 businesses per 10,000 inhabitants. The regional economic system is increasingly focused on international markets and it features a high rate of entrepreneurship, a strong manufacturing sector, a high level of innovation and GDP per capita higher than the Italian and European average. The economic system as a whole consists in 412,000 companies and includes sectors with strong interrelations among sub-sectors along the value chain, and about 50,000 companies belongs to the manufacturing value chain. It is worth mentioning the most significant sectors in terms of personnel and turnover: Mechanical Engineering and Automotive, Agro-food, Housing and Construction, Fashion, Health and Wellness, Culture and Creativity, Tourism. Many of these sectors contribute to the ‘related variety’1 of the industrial structure because they have impact on all other sectors: mechanical engineering produces engines and machines for many different sectors, and culture and creativity is also transversal. Emilia-Romagna also has the highest innovation capability at national level, alongside a few other regions, according to the Regional Innovation Scoreboard (European Commission, 2014). There are 27,000 employees in R&D sectors, while total investment in R&D amounted to €2.29 million in 2012. The share of R&D personnel in the active population is higher than the Italian and European averages. Concerning patent applications to the European

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Patent Office (EPO) per million of inhabitants, the region is above the EU28 average: 127,4 in Emilia-Romagna, 62,2 in Italy and 112,7 the EU28 average in 2011. In addition, the ER regional industrial system has tended to be more focused on intermediate goods rather than final ones. In particular, mechanical engineering has been an important sector, providing machinery for various industries, not only in the region but also in the rest of Italy and abroad. The competitive mechanical engineering industry has favoured the development of other industries in the region, such as motor vehicle production, production of agricultural machines, as well as machinery for ceramics production, for shoe making, but also more high-tech sectors such as biomedical. The region has therefore been characterised by variety, since no strong specialisation in specific sectors existed, but also related variety, since the competitiveness of the mechanical engineering sector has allowed other related sectors to increase their competitiveness. As shown by Bianchi and Labory (2011b), economic development in ER is not based on attempting to desperately maintain old systems such as industrial districts. The regional government authorities identified the limits of the industrial development model based on industrial districts in the late 80s. While the national government was implementing policies specific to industrial districts, providing regions with new competencies in terms of industrial policies for industrial districts (Law 317/1991), the ER region was already stressing that they only represented one type of a diversity of local productive systems which policy should help adapting. The ER region therefore argued in favour of policies aimed at wider types of local production systems and SME systems, which was adopted by the national government in the Bersani Law of 1998 (n. 114/1998). The Italian institutional framework has experienced important changes in the last decades with an increasing decentralisation. Law 112 of 31/03/1998 implements the Bassanini Law 59/1997, which effectively delegates the definition and implementation of industrial policy to regions. In 1999 the constitutional law was reformed, allowing more statutory autonomy to regions, which can decide on both their form of government and their relationships with local governments. The constitutional law of 2001 (n. 3) completes the reform of Title V of the constitution by extending the competencies of the region in terms of legislation, particularly in the field of development policies, including industrial policy.

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The regional industrial policy has promoted related diversification, particularly supporting sectors that might have an impact on the upgrading and restructuring of traditional ones, especially new technological sectors: new materials and machinery for the textile and ceramics industries, robotics and nanotechnologies for sectors such as mechanical engineering, biomedical and packaging, but also the creative and cultural sectors that have a positive impact on the upgrading and development of other sectors. In fact, the ER regional government has been able to build consensus and implement industrial policies in partnership with local actors as far back as the 1970s (Bellini, 1989). One instrument was ERVET (Ente Regionale per la Valorizzazione Economica del Territorio), in effect a regional development agency (RDA), created as a stateowned enterprise in 1973, in order to provide analysis and support to the definition of the regional policies through a dialogue with regional stakeholders. In the 1980s and 1990s, the main instruments used were real services to firms, business services aiming at favouring their restructuring (professional training, the use of IT, provision of infrastructure and so on). Social policies have been strong since the 1980s (see for instance, Law n. 27/1989 for the family), aimed at securing home, health and child assistance for families and working mothers, together with education to provide the regional labour market with adequate skills. Hence participation in the labour force and skill enhancement have been the two priorities of this social policy. Consequently, industrial policy in the ER region has been characterised, in terms of governance, by democracy and inclusiveness: stakeholders were involved in the policy process, from the problem identification to the policy definition and implementation. Another illustration of this aspect is the creation of the ASTER consortium in the early 90s, composed of the region, together with regional universities, other research organisations, chambers of commerce and business associations, which played an important role in the transformation of the region into a regional innovation system by acting as a facilitator, a coordinator of the innovation network (Bianchi and Labory, 2011b). The region’s industrial policy therefore was comprehensive, covering all the dimensions of the sundial: social policy (real business services, support to working families, etc.), territorial policy (infrastructure, region as an innovation system and so on), innovation

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(technological transfer, start-ups, support to R&D collaboration between firms, especially SMEs and so on) and human capital (education and training). This ‘comprehensiveness’ has continued in recent years, and in more explicit terms, as shown in the next section.

3 INDUSTRIAL POLICY IN THE ER REGION SINCE 2015: EXPLICITLY FOLLOWING THE SUNDIAL From 2011, industrial policy definition and implementation in the ER region has increasingly been focused on the sundial. The new industrial policy defined after the financial crisis and the 2012 earthquake even explicitly follows the pillars of the sundial. The main regional law that explains this approach and its main actions in details is the Labour Pact adopted in 2015. This Pact is examined in the next subsection (3.1), followed by the policy actions for each dimension of the sundial, namely social policies (which is in the Labour Pact two, E-T), human resources policy (Section 3.2., E-I), innovation policy (Section 3.3., I-P) and structural policy (for infrastructure and institutions Section 3.4., on P-T). The new industrial policy was adopted by the new regional government that took office in 2014, and where Patrizio Bianchi not only remained minister for education and labour, but also took charge of the coordination of the Ministry, being responsible for the joint management of all European funds. The region had been governed by the old left-wing party for decades, and the candidate who won the 2014 election was that of the reformist branch of the left, a follower of the then Prime Minister Matteo Renzi. In these elections, only 37 per cent of the population did vote, an extremely low rate for the region’s standards. The previous president of the region had to resign because he was accused of corruption, and would be shortly afterwards absolved, but the elections were called in a climate of general distrust for the political class in Italy. The new government decided to continue stressing the priorities of social cohesion, by providing employment opportunities thanks to economic development, by ensuring appropriate education, and support to vulnerable, ill or elder population. Transparency of the policy process and legality were also stressed to rebuild trust in the

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population for the government. These priorities had also been that of the policies for emergency and reconstruction that had been implemented since the 2012 earthquake. The long tradition of social participation and involvement was stressed and renewed in order to define the new industrial development policy. The outcome was the adoption of the Labour Pact in July 2015, a real industrial development policy because it contains not only direct employment policy but also and primarily a set of actions aimed at supporting the industrial and economic development of the region. Its aim was to provide jobs, thanks to access to education and civic development together with job creation in a dynamic economy, hence decent standards of living. The new industrial policy was also accompanied by an institutional reform, aimed at reorganising and simplifying the regional administration. The region had been organised in 12 Ministries, each with its own Directorate General, following the structure of the national administration. After the reform the regional government has now four Directorate Generals: Welfare; Knowledge and Innovation (including former ministries of culture, university, education, labour, enterprise innovation and internationalisation, as well as tourism; Territory (Transport and Mobility, Environment and Civil Protection); and the DG on the coordination of policies and policy assessment and planning. 3.1 The Labour Pact as the Main Document of the ER Comprehensive and Integrated Industrial Policy (Social Policies, E-T) At the end of 2014 the regional government decided to give priority to employment, in a vision of long-run development based on skills, competitiveness and inclusion. This decision was shared in the Labour Pact, signed by the region, local authorities, the trade unions, business organisations, the third-sector forum, the universities and the Regional Education Office. This legislative pact has been meant to guide the region’s actions and all public and private investment in work and growth. Signed on 20 July 2015, the pact is a strategic plan for the future of the region, based on an interpretation of the local context and the regional, national and international situations, a careful survey of resources, and the sharing not only of objectives, but also of the

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conditions required to generate inclusive growth and hence resilience in a deeply changing socio-economic context. The regional government has made such decisions on the basis of a strong belief that the territorial dimension is crucial for the implementation of policies to foster structural changes and ensure good socio-economic conditions that favour higher competitiveness for local operators on a global scale. Emilia-Romagna has long-standing experience of inclusive governance. Since 2015 the regional administration has placed employment at the forefront of its strategy for ‘smart, inclusive and sustainable’ regional development, aiming at making Emilia-Romagna a high-value added region – a global crossroads of competences, investments and high-value production. This strategy is embodied in the document called ‘Patto per il Lavoro’ (Labour Pact or Pact for Employment and Growth) – a Common Agenda developed by the Regional Administration, engaging all the major economic and social players in Emilia-Romagna. The Pact is a shared long-term vision for regional development, including a common understanding of the problems affecting the economy and promoting a joint approach to trigger investment and growth and to secure more and better jobs. It belongs to a new generation of public policies that ground their rationale in a multilevel inclusive and comprehensive governance model to enhance the territory, production, people and employment in the region. This policy framework provides strong legitimacy to the policies enacted by the regional administration and ensures that every action is directed towards maximising the positive impact on jobs and growth in Emilia-Romagna. By signing the Pact, stakeholders committed to contribute to this regional Agenda and to monitor its implementation on a six-month basis. About €15 billion has been allocated to the Pact. Through the periodical monitoring by the stakeholders, the regional administration ensures higher transparency and accountability to its policy actions, showing where the money is spent and what outcomes have been generated. The Pact contains a series of interventions designed to leverage the strategic assets of the region (for example, the excellence of the regional educational system, the innovative and export-oriented manufacturing enterprises, the cultural heritage and territory of Emilia-Romagna). These interventions follow the pillars of the sundial (Figure 5.3).

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Institutions* Trade Unions

Universities

Employers' and Business Associations

Patto per il Lavoro

Chambers of Commerce

Third Sector Organisations

Others Banking Associations

Note:  *This category includes: the Regional Administration, the Regional Representation of the Italian Ministry for Education and local administrations (Municipalities and Provinces of the Emilia-Romagna Region). Source:  ER Region, 2016a.

Figure 5.2  Parties involved in the development and implementation of the regional pact for employment and growth There are six main lines of intervention: 1. People and Employment: the Pact encompasses the creation of a Regional Agency for Employment; the strengthening of the education–training–employment system; new investment in higher education strategic competences for specialisation, internationalisation, social innovation and other emerging needs coming from the market; implementation of EU Youth Employment programmes.

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Industrial policy for the manufacturing revolution RULE OF LAW

INNOVATION

Development, Business and Employment

ENTITLEMENTS

PROVISIONS

Community and Employment

RULE OF LAW

RULE OF LAW

People and Employment

Territory and Employment

TERRITORY RULE OF LAW

Source:  ER Region, 2016a.

Figure 5.3  Policy framework of ‘Patto per il Lavoro’ 2. Community and Employment: welfare is crucial to all stakeholders in Emilia-Romagna. The Pact defines a series of measures to provide better services to its citizens and to promote a more inclusive social system, the reduction of inequality, the achievement of gainful employment, and targeted actions for the third sector. 3. Development, Business and Employment: this line of intervention integrates actions to foster a strong, open, sustainable regional economy; actions for an entrepreneurial and dynamic society; and actions for an inclusive society based on equal opportunities. Interventions included in this section aim at strengthening the competitiveness of the regional system of production, more innovative enterprises and a higher and better qualified labour force. 4. Territory and Employment: the Pact focuses on how to best attract high value investment for the territory of Emilia-Romagna. It defines interventions to address the regional h ­ ydrogeological instability, support the circular economy, develop infrastructures

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for better mobility and foster urban regeneration. The foreseen actions aim at creating synergies between public and private investments, thus to increase the available resources for the territory and maximise the impact for jobs and growth. 5. Rule of Law and Employment: all parties bounded by the Pact to create strategies to promote the strengthening of the rule of law in all sectors of the regional economy and society at large. In particular, the Pact aims at countering any form of illegal action from usury, tax evasion, urbanisation abuses, lack of labour rights protection or employer abuses and any other illegal activity. The interventions are directed to improve the living and working conditions through legality. 6. Simplification and Employment: the Pact praises the role of an efficient public administration, which interacts with companies and citizens in a simple, user-friendly and digital way. It comprises therefore a series of interventions to cut time-consuming bureaucratic procedures and systemic inefficiencies through institutional reforms, greater transparency and simplification. The Labour Pact has been implemented through various actions. First, the Multiannual Plan for Highly Qualified Labour for Industrial Research and Innovation approved by the Regional Council in October 2015 and to which €22 million has been allocated already in 2017. The plan sets measures to facilitate the employment of highly qualified labour (for example, PhD researchers and students) in local enterprises and other investments in human capital favouring industrial research, innovation and internationalisation of the regional enterprises. The plan aims at matching the needs expressed by companies with the offer of competences in the labour market. For this reason, the target of the plan has been defined by the regional administration in consultation with local social and economic players. It resulted in targeted interventions on human capital for the digital economy (digital humanities, social science, e-commerce, industry 4.0, science and big data), smart specialisation, enterprise transformation and research infrastructure. Another highlight of the Pact for employment and growth has been the ability of the regional government to mobilise private resources and establish partnership with socio-economic players on its territory in order to strengthen the regional ecosystem of innovation and in particular in the area of big data and analytics.

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Emilia-Romagna now holds over 70 per cent of the total national numerical super-calculus and its high-level big data infrastructure has received investments to explore applications for medicine, physics and engineering (see Section 3.4). 3.2  Human Resources Policy (E-I) Important structural changes started at the turn of the new century, in the year 2000, with globalisation and disruptive scientific and technological innovations. The regional government decided to rethink and redefine its human policy so as to adapt to the new socio-economic context implied by the fourth industrial revolution. The educational system was reformed as a result, with a stress on technical and professional training. The ER region has about 4.5 million inhabitants, of whom 600,000 are younger than 14 years old, 2.8 million are of working age (aged 15 to 64) and 1 million are over 65. The regional GDP is about €150 billion, of which a third is exported. The region comprises 545 primary and secondary schools, 545,000 students and 43,000 teachers, 5,900 teachers who support disabled students, as well as 156 accredited training centres including 5,500 teachers and almost 100,000 students. As already mentioned the region has four universities, in Bologna, Ferrara, Modena and Parma, as well as some divisions of the Milan Polytechnic and of the Catholic University of Milan. Overall these tertiary education institutions have about 150,000 students and 5,000 professors and researchers. R&D capacity is therefore important, all the more so as the main national research institutes have divisions in the region, and the region has created ten technopoles in recent years (in the various areas of specialisation of public and industrial research). The reform of technical and professional education in the ER region Emilia-Romagna has created an educational and training infrastructure for development called ER Education and Research EmiliaRomagna (Figure 5.4). The infrastructure plays a key role in the promotion of skilled jobs and sustainable growth. Inclusive because they are accessible to everyone, the various branches of the infrastructure have the same overall objectives, based on complementary specialisations, the integration of educational institutions, collabora-

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tion with enterprise, and synergy between European, national and regional resources. The new education system, called ER Education and Research Emilia-Romagna, comprises four branches: Vocational Education and Training, the Polytechnic Network, Higher Education and Research and Employment and Skills. Each of these branches has specific educational objectives and is designed to guarantee the right of individuals to participate in the growth processes, to develop expectations and potential, and to strengthen their technical, critical and relational skills required to grow, work and compete in an increasingly internationalised world. The objective of the educational and training infrastructure is to provide people and the region with strategic knowledge aimed at specialisation, internationalisation and social, organisational and economic innovation, so as to favour industrial development. Achieving this goal involves a number of priorities: combined planning of European, national and regional resources; greater integration between schools, training institutions, universities and research organisations; and an increased role for enterprises in

Figure 5.4  The scheme of the reform of ER Education

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educational processes so as to meet requirements and generate innovative skills by trying out a twofold model of regional training, also through agreements with the various industries. In addition to these priorities cutting across the planning of all actions, an Active Labour Network is being created. Together with the Regional Employment Agency, it is becoming the mainstay of a new generation of active policies able to meet the needs of individuals and businesses. Since 2000 the competitive context has acquired a global dimension, which, together with the fourth industrial revolution, has created a greater market for high value-added goods and services. It has contributed to the rise of new organisational models, which require closer interrelations between industry, research, skills and the big-data infrastructures supporting their long-term goals and development. The reform has been carried out essentially using European Social Funds. The Emilia-Romagna region has elaborated a programme strategy for the 2014 to 2020 period, and received €786 million of European, national and regional funds. The strategy underlying the Regional ESF Operational Programme (OP) was shared with the institutions and social partners of the region, following two priorities. First, guarantee that all citizens have an equal right to acquire innovative, wide-ranging knowledge and skills, and the right to grow and work expressing their full potential. Second, increasingly meet the skill requirements for business innovation and upgrading. The Operational Programme is developed in the framework of the ESF thematic objectives, which the region has defined according to the priorities in the local context (see Box 5.1 for details). The first objective is to create jobs and sustain the professional mobility of workers, especially those on the fringes of the labour market (that is, the long-term unemployed and young people not in education, employment or training) and with a specific effort aimed at guaranteeing equal opportunities for women. The second objective is the social inclusion of the disabled, as well as combatting poverty and preventing discrimination. Schooling, training and assisting job entry are priority tools to contrast marginalisation and to achieve social inclusion. The third objective is to invest in education and training, while guaranteeing equal access, preventing school dropout and enhancing skills by means of continuous learning. Two learning systems are

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BOX 5.1 THE ER REGIONAL OPERATIONAL PROGRAMME, 2014–20 Objective: social inclusion Objective: education and training Operational Programme European Social Fund 2014/2020 Resources € (%) Thematic priority 8 – Axis 1 – EMPLOYMENT Priority 1. Access to employment for job seekers and inactive people 147,808,787 (18.8) Priority 2. Sustainable integration into the labour market of young people (YEI), in particular those not in employment, education or training 257,103,810 (32.7) Priority 4. Equality between men and women 11,800,000 (1.5) Priority 5. Adaptation of workers, enterprises and entrepreneurs to change 55,037,513 (7.0) Priority 7. Modernisation of labour market institutions 18,870,000 (2.4) TOTAL 490,620,110 (62.4) Thematic priority 9 – Axis 2 – SOCIAL INCLUSION Priority 1. Active inclusion 143,883,783 (18.3) Priority 4. Enhancing access to affordable, sustainable and high quality services 13,366,255 (1.7) TOTAL 157,250,038 (20.0) Thematic priority 10 – Axis 3 – EDUCATION AND TRAINING Priority 1. Reducing and preventing early school leaving 29,091,256 (3.7) Priority 2. Improving the quality and efficiency of, and access to, tertiary and equivalent education 33,808,758 (4.3) Priority 4. Improving the labour market relevance of education and training systems 45,602,510 (5.8) TOTAL 108,502,524 (13.8) Thematic priority 11 – Axis 4 – INSTITUTIONAL AND ADMINISTRATIVE CAPACITY Priority 2. Capacity building for all stakeholders delivering education, lifelong learning, training and employment and social policies 1,572,500 (0.2) Axis 5 – TECHNICAL ASSISTANCE 28,305,010 (3.6) Total Operational Programme 2014/2020 786,250,182 (100) Source:  Regional administration, data collected by the authors.

promoted to encourage employment after completing education: traineeships and apprenticeships. Projects financed with ESF resources are selected by the region through a transparent public procedure. Inclusive growth means increasing entitlements so that all citizens can participate in economic activity and have a decent living ­standard.

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For this reason, the first branch of ER Education and Research Emilia-Romagna is the regional Vocational Education and Training system, offering access to training to early school-leavers, at the end of compulsory secondary education, enabling them to embark on a three-year programme to obtain a vocational qualification. The Italian national legislation establishes that the school system – consisting of vocational institutes, technical institutes and high schools – may only award higher education diplomas at the end of a five-year programme. To offer differentiated educational programmes in line with the requirements, the specific features and the nature of the local economic and production system, the ER region adopted Regional Law n. 5/2011, creating the Vocational Education and Training system. It is centred on work-based three-year courses jointly designed and implemented by training providers, accredited by the region, and by vocational institutes. The system entered into force in the academic year 2011–12, with a status equivalent to senior high school education, and is fully part of the second stage of the Italian secondary education system. Characterised by a high level of methodological and didactic experimentation and interaction with local businesses, the three-year programme is designed to provide not only the technical and professional skills required by the labour market but also linguistic, mathematical, scientific, technological, social-historical and economic skills indispensable in empowering young people to construct their own future as citizens. After the first introductory year enabling students to consolidate basic skills through individual pathways, they can decide whether to further their studies at a vocational institute or an accredited training institute, and thus choose the most suitable route to a vocational qualification. This twofold opportunity offered to young people is also a way of combating school dropout and giving Vocational Education and Training a strategic role in the growth of the region. The Vocational Education and Training system is the first branch of the ER Education and Research infrastructure not only because it is designed for young people leaving first-level secondary schooling, but also because it is aimed at translating compulsory education into an effective right. The system does not reduce educational objectives to guarantee this right, but emphasises different learning models for inclusion without discrimination, thus extending opportunities and

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prospects for young people. The three-year courses offer the possibility to choose from 23 vocational qualifications. Global competition, the reorganisation of production processes and the explosion of social networking technologies have led to a deep structural change in economies. In this context, the systematic capacity to generate and transfer new skills has become crucial. Emilia-Romagna thus chose to invest in the education of specialised professional figures trained in operational, critical and relational skills. Required for innovation, such skills can also contribute to the processes of developing, raising standards and digitalising the industries of strategic importance for the growth of the region and the country. The above objectives are pursued primarily through the Polytechnic Network, a crucial branch in the regional educational infrastructure for the purposes of developing scientific, technological, technical and vocational skills. The region designed the Polytechnic Network programmes starting from the priorities identified in the Regional Smart Specialisation Strategy. This strategy aims to guarantee networks of skills meeting the requirements of the leading and emerging sectors of the regional economy. At the same time, it also consolidates the driving forces of change and supports innovation and upgrading of services and the environmental sustainability of production systems. The Polytechnic Network offers various opportunities – including the courses of the foundations managing the Higher Technical Institutes and those of Higher Technical Education and Training – based on the comparison, synergy and integration of varied and complimentary learning worlds and experiences. The seven HTI Foundations of the ER region are technical colleges that offer highly specialised, two-year, post-school diploma programmes to educate and train expert technicians. Once they enter the various strategic sectors of the economic and production system, these highly skilled technicians will have the capacity to innovate and support their chosen businesses in the production system through high performance, environmental awareness and the use of ICTbased production with new technologies. Higher Technical Education and Training courses produce specialised technicians capable of managing the organisational and production processes in businesses also associated with technological innovation and market internationalisation. The programmes lasting

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800 hours are designed to meet the labour market demand for technical, professional, scientific, technological, legal, economic, organisational, communicational and relational skills, which are particularly difficult to find in small and medium-sized enterprises. Students are awarded a certificate of advanced technical specialisation, recognised nationwide, after completing the Higher Technical Education and Training programmes. In addition to those courses, the region devises educational programmes to meet the specific needs of the various areas (cities, rural areas and areas affected by the 2012 earthquake) and to respond to the requirements of the industrial sectors, in SME systems or individual businesses with a high potential for growth and job creation. The Polytechnic Network is a crucial investment, which the region intends to consolidate through a greater interaction with the regional policies for innovation and research. This will provide the region with the indispensable skills to understand, anticipate and guide the deep structural changes in the production systems, as well as providing a solid foundation for growth. In the coming years, competition increasingly will be based on the capacity of a region to attract businesses, human capital and innovative, high value-added projects. The possibility of an economy to reposition at global level will be the result of investments in knowledge, research and innovation plus its capacity to spread and transfer the benefits to institutions, businesses and society. Together with all the stakeholders of the regional society, the regional government has set an objective reflecting the potential, the specialisations and the excellent skills that the region already possesses. That objective is to occupy a leading position in the new production systems that are emerging worldwide. This means manufacturing linked to highly specialised services, capable of combining environmental sustainability, the production of knowledge and the development and transfer of research results, while crossing over cultural and creative skills with technological expertise to transform ideas into high added-value products. Hence the ER regional government is deeply convinced that priority must be given to higher education and research in order to adapt to the manufacturing (and socio-economic) transition induced by the fourth industrial revolution. In 2016 the region approved a three-year plan, ‘Advanced skills for research, technology transfer and entrepreneurship’, in order to strengthen the relevant part of

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the regional educational and training infrastructure by integrating it with the regional policies for innovation and industrial research. This document brings together the resources of the European Structural and Investment Funds (ESF, ERDF and EAFRD) in terms of priorities, objectives, procedures and implementation schedules. The plan is one of the first instruments in the new generation of policies for integrated development that the region committed to launch by signing the Labour Pact. The complementarity and synergy of the European Funds – ESF, ERDF and EAFRD – are meant to guarantee a regional entrepreneurial growth trend within a highly integrated and dynamic regional innovation ecosystem, based on the interaction between the research world and the production world. This is intended to attract investments, entrepreneurial initiatives and creative talents capable of jointly promoting innovation in technological spin-offs, start-ups and mature industries. The three-year plan will promote and finance various actions. These include bursaries and other financial aids to help people entering into higher education; grants for research projects in line with the Horizon 2020 strategic objective to support the conversion of new scientific knowledge into products and innovative services to meet social challenges; research grants and doctorate scholarships consistent with the entrepreneurial traditions of the region and the paths indicated by Smart Specialisation Strategy; actions to accompany processes creating new enterprises and the growth and internationalisation of newly created businesses. The long-lasting financial crisis has induced an increase in the average time it takes for young people to enter the labour market and has also increased the number of adult job seekers. Therefore, the fourth branch of ER Education and Research completes the regional plan with active policies aimed at guiding people in the transition from school to work and from one job to another, enabling workers to upgrade their skills and sustaining the creation of new businesses. The region funds personalised pathways implemented in collaboration with businesses. These are made up of various actions, such as guidance, classroom and workplace learning, and assistance in starting a job, in order to support people seeking work and to supply skills meeting the requirements of the economic and production systems. These opportunities are also structured as a response to situations of corporate or sectoral crisis, such as the building industry

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crisis, in order to combat long-term unemployment of workers and to encourage their possible relocation. Education aimed at enabling people to enter the workforce is a priority tool in strengthening entitlements for growth’s inclusiveness, because it allows to contrast inequality and poverty, which the crisis has not only increased but also diversified. Guidance for personalised career pathways, classroom and workplace learning, and assistance in starting a job are some of the actions designed for more vulnerable people, including the disabled. The region therefore assigned the integrated care of these categories to social, health and labour services in Law n. 14/2015. The Employment and Skills branch also includes traineeships and apprenticeships. Traineeships are crucial in encouraging the acquisition of skills through direct learning in a real work situation and in reducing the time required for entry or re-entry into the labour market. On these grounds, the region passed Law n. 7/2013 establishing new rules for this instrument with four different types of training. The first type concerns young people leaving school, university or vocational training. The second has the objective of enabling the unemployed, unoccupied and laid-off or redundant workers to enter or re-enter the workforce. The third concerns people with disabilities, asylum-seekers, people entitled to international or humanitarian protection and people requiring social protection. The fourth type, introduced with Regional Law n. 14/2015, aims at encouraging social inclusion, independence and rehabilitation of highly vulnerable people, who are excluded from the labour market and cared for by the social or health services. The region has chosen apprenticeship contracts, on the grounds of their considerable educational value, as the best way of encouraging high-qualified entry for young people into the labour market. There are three different forms: apprenticeships for qualifications, apprenticeships for vocational purposes and higher education and research apprenticeships. The region supports apprenticeship contracts enabling young people to obtain a qualification while working: from a vocational qualification to a diploma, a degree, a master’s degree and a PhD. There is a twofold benefit: young people are assisted in gaining further qualifications, even at the highest academic level, and strategic skills for innovation are made available to SMEs. Continuous or lifelong learning concerns all the courses offered to employees, to the self-employed and to entrepreneurs, aimed at

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supporting individual professional development and creating new businesses. Other goals include promoting safety in the workplace, improving the competitive profile of businesses and supporting productive and organisational innovation in the regional economic system. Last but not least, the region has recently undertaken new policy actions in favour of the integration between science, education and production. The region has recently adopted two important actions in order to prepare for the manufacturing revolution: first, the creation of a new joint education institution called Motor Vehicle University of Emilia-Romagna (MUNER) in order to provide appropriate skills for the integration of science and production that characterises the fourth industrial revolution (see Chapter 3). Second, the Big Data Project in order to provide the region and the country with the raw material of the information age, as well as the capacity to analyse and exploit it. The first action is analysed below while the second is examined in the next sub-section (3.4). MUNER In 2016, the regional government started discussion with the automotive producers located in the region and the four regional universities. Globalisation has induced a large increase in the luxury and sports car segment, of which a number of producers are located in the region, namely Ferrari, Maserati (of the FCA group), Lamborghini, Ducati (now in the VW-Audi group). All these firms nowadays have global value chains or global productive ecosystems, and they are all introducing the new process technologies of the fourth industrial revolution, namely smart manufacturing, ICME systems, robots and so on. The limit these firms have outlined however regards the availability of human resources with sufficient and adequate skills. The regional government has therefore started a reflection on how to ensure the availability of adequate human capital in the region, as an important element for the possibility of the territory to become a hub of competencies in the globalised and digitalised world attracting businesses and ensuring its socio-economic development. The regional government has therefore gathered these (rival) firms and the universities in order to start a dialogue on the necessary steps to take in order for the region to adapt. The result has been the definition of two new master’s programmes involving the various

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companies (although competitors) and the regional universities, but also open to the rest of the world. The major international brands of automotive are thus calling for talented young engineers with a passion for innovation in two- and four-wheeled vehicles to develop the future of the industry, through two new master’s degrees (held in English) in a hub of learning excellence. The master’s programmes have been set up with the collaboration of the four regional universities and ten world renowned companies: Automobili Lamborghini, Dallara, Ducati, Ferrari, Haas, Hpe Coxa, Magneti Marelli, Maserati and Toro Rosso. The two programmes are in: 1. Advanced Automotive Engineering – aimed at providing knowledge and skills related to the design of motor vehicles and motorcycles that are high-performance and suitable for competitions. From hybrid and electric motor propellers to production systems with typical features of the new manufacturing 4.0 environment; and 2. Advanced Automotive Electronic Engineering – aimed at training electronic engineers with a professional profile suited for designing, developing and producing the main subsystems that make up road motor vehicles, with a special focus on the premium and motorsport segments of the market. The aim is to attract the best young talents both in the region, in the rest of Italy and from abroad. 3.3 Measures Supporting Innovation and the Transfer of Knowledge and Competencies to Industry (I-P) The current industrial policy of the region is based on the region’s most important sectors in terms of specialisation, namely mechanical engineering, food, building, health industry and cultural-creative industries. A competence map of the region in these various sectors has been carried out, in order to identify the need for support and the potential synergies and complementarities across sectors. As stressed in the previous section, education and training policies are also closely integrated with industrial development policies. One example is the biomedical cluster of Mirandola. This cluster is characterised by the presence of large multinationals, which stayed and even increased their productive capacity even after the 2012 earthquake (Labory and Facchini, 2014). The regional industrial policy provides support to the development of this biomedical cluster by sustaining innovation, technological transfer and t­ raining.

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For instance, the Democenter-Sipe is a centre for technological transfer which brings together institutions, business associations, banks and more than 60 firms of the cluster. It is part of the Rete Regional Alta Tecnologia (high-tech regional network), which is based on the campus of the engineering faculty of the University of Modena, located near the cluster, and favours the collaboration between firms and university. It promotes innovation in existing firms but also new innovative firms supporting the creation of spinoffs from universities. The ‘Tecnopolo’ or technopole, which belongs to the regional high-tech network, and acts together with Aster to favour collaboration and innovation in the sectors of life sciences, mechanical engineering and new materials, ICT and design, also favours the creation of new firms from university innovation. The regional government has also created a biomedical technical institute in life technologies, located at the heart of the cluster, in Mirandola, and is in charge of higher education to provide specific competencies required in the cluster. The policy process is organised so as to define and implement actions, and regularly monitor and evaluate their effects so as to adjust actions if necessary. Thus the three-year plan ‘Regional Territorial Strategy of the Region’ (‘Piano Territoriale Regionale dell’Emilia-Romagna’) defined in 2010 (ER Region, 2010) was followed by a new plan in 2014. Regarding innovation and industrial development, Law n. 14 of 2014 aims at promoting investment in the region, both by existing regional firms and by firms located outside the region, from the rest of Italy and abroad. The actions defined in the plans are always implemented in a multilevel governance framework, in agreement and coherently with the different levels of government, not only local but also national and European. For instance, the ER region attracted an FDI by Audi for the production of SUV’s for the Lamborghini brand, after talks involving the regional Labour, Industry and Innovation and Agriculture DGs, as well as municipalities, the national government and agencies. The region has attracted FDI in recent years from other international groups, such as Philip Morris and Louis Vuitton as well as VW-Audi. Managers of these large companies stress that they are attracted by the commitment and the expectations for stability conferred by the regional government’s long-term vision and participative policymaking process.

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3.4 Resources and Infrastructure for Development, as well as Inclusive and Adaptive Institutions (P-T) The Emilia-Romagna region, in full agreement with the Italian National Government, is substantially investing in research and technology infrastructure. Primarily, the attention to research and innovation comes from a widespread interdisciplinary and dynamic research system (four public universities among the oldest in Europe, including the University of Bologna, the University of Modena and Reggio, the University of Parma and the University of Ferrara); prominent research infrastructures and facilities like the CINECA – the national consortium among all the Italian universities for supercomputing – and the CNAF – the national computing centre of the National Institute of Nuclear Physics, and Tier 1 of the European network of research in physics – and the territorial branches of all national research centres, well integrated into a very dynamic industrial landscape mainly composed by world leaders in automation, luxury automotive and chemical producers. During the last ten years great attention has been paid to: ●

Building on sharing and exploiting existing results, knowledge, capacities, and research and innovation initiatives and frameworks. ● Fostering cooperation between the public and the private sectors to maximise the leverage effects of research investments, both commercially and with respect to public policy at regional, national and EU levels. ● Promoting joint actions including coordination, planning and programming of relevant research and innovation activities. ● Supporting researchers’ careers, training and mobility, and the development of skills in relevant sectors to ensure the necessary highly qualified workforce needed to underpin a prosperous and sustainable growth. Regional policymakers consulted universities, firm managers and other stakeholders and realised that competencies and potential facilities regarding big data were substantial in the region. Hence they put together a project to propose Bologna as a host of the Data Centre of the European Centre for Medium-Range Weather

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Forecasts (ECMWF). Such a project would allow the region to become a key actor at European level regarding big data collection and processing, a strategic activity in the fourth industrial revolution. The Italian government backed the proposal and provided funding for some of the necessary facilities. On 22 June 2017, the ECMWF Board formally accepted to establish its Data Centre in the Bologna location. The research facilities of CINECA and CNAF will be relocated in the same area as the newly established National Weather Forecast Centre, but also the National Excellence Centre for Research and Innovation on Industry 4.0, sponsored by the National Ministry of Industrial Development. Finally, a number of private-based research centres will be based in the Big Data Technopole. The latter will be one of the largest big data infrastructures in Europe, competing for excellence at world level. High performance computing, big data and high-speed networks (digital infrastructures or e-infrastructures) are the technological pillars of the modern society and economy. In all developed countries they support productive sectors, scientific and industrial research, education and health care, and are increasingly strategic for intelligence, cyber security and risk management. The recent 2015 Communication of the European Commission to the European Parliament (COM(2015) 912 Final), ‘A Digital Single Market Strategy for Europe’, identifies the priority of maximising the growth potential of the digital economy through ‘investment in ICT infrastructures and technologies such as Cloud computing and Big Data, and research and innovation to boost industrial competiveness as well as better public services, inclusiveness and skills’ (European Commission, 2015, p. 4). In Italy, the research infrastructures INFN (National Institute of Nuclear Physics), CNR (National Research Centre), GARR (national network for high speed connection in education institutions) and CINECA have already developed big data, high performance computing and high performance national network e-infrastructures to support major research and academic communities. These efforts are coordinated at EU level and, in the majority of cases, connected to worldwide initiatives. The Bologna big data centre will make it possible to create a more powerful local open science platform to support not only the institutional scientific communities covered by these and other ­agencies,

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but also to stimulate regional growth through the exploitation of scientific data and knowledge made openly available. The entire European economic structure with many industries and SMEs in particular may highly benefit from such an advanced and integrated e-infrastructure. In the ER region, existing industries could benefit from the support of local and national ICT enterprises that, according to the latest OECD classification, are over 9,200 in the regional territory. The OECD classification collects ICT companies mainly in six clusters: the Emilia-Romagna breakdown is depicted in Figure 5.5. In the last few years, the regional government supported the introInter-University Consortium for supercomputing

Consiglio Nazionale delle Ricerche

www.cineca.it

www.cnr.it

Centro EuroMediterraneo per i cambiamenti climatici Agenzia nazionale per le Nuove Tecnologie, www.cmcc.it I’energia e lo sviluppo economico sostenibile

www.enea.it

Istituto Nazionale di Astrofisica

Istituto Nazionale di Geofisica e Vulcanologia

Istituto Nazionale di Fisica Nucleare

CINECA CMCC

www.inaf.it

www.infn.it

ENEA

CNR

INFN

INAF

www.ingv.it

Istituto Ortopedico Rizzoli

www.ior.it

INGV

IOR

LEPIDA Regional in house providing company for ICT service and infrastructures

UNIBO

UNIFE

UNIMORE ASTER

www.lepida.it

UNIPR

Università degli studi di Ferrara

www.unife.it

Emilia-Romagna innovation agency

www.aster.it

Università degli studi di Parma

Alma Mater Studiorum Università di Bologna

www.unibo.it Università degli studi di Modena e Reggio Emilia

www.unimore.it

www.unipr.it

Source:  ER Region (2016b).

Figure 5.5  Main stakeholders and clusters of the Big Data Project

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duction of digital technologies with two tenders published in 2008 and in 2014. €42 million was made available in two calls: €22 million in the first one and €20 million in the second one. Approximately, 1,800 projects received a grant, achieving a total amount of planned investment of about €76.7 million. The HPC facility hosted by CINECA is currently based on a Tier 0 System (350,000 cores for a peak performance in excess of 18 Petaflops) and a Tier 1 Big Data System (Linux cluster for high performance computing data analytics – HPDA), making CINECA one of the top HPC supercomputing centres in the world. The CINECA HPC facility enables a wide range of scientific research through open access granted by independent international peer reviewed processes, particularly PRACE (Partnership for Advanced Computing in Europe)2 scientists having an affiliation in a European entity; ISCRA (Italian Super Computer Resources Allocation) European scientists having an affiliation in an Italian entity and LISA (Gravitational Waves Observatory) Italian and European scientists having an affiliation with an entity located in Lombardy region. CINECA also provides operational computing service for weather forecasting for National Civil Protection under the supervision of Emilia-Romagna agency of the national climate and environment agency. Moreover, CINECA is part of the European digital infrastructure for many ESFRI (European Strategy Forum on Research Infrastructures). At national level, CINECA signed a partnership agreement with the Telethon Foundation for the national repository of genomic data and with ISTAT (Italian Central Statistics Office) for web crawling and cognitive computing research and development. CINECA has entered formal partnerships for added value services and R&D activities with ENI (the Italian oil and gas company) and UNIPOL (a large Italian insurance company). The HTC facility is hosted in Bologna by CNAF, which is one of the local structures of the INFN National Centre. CNAF has been responsible for the primary task of setting up and running the so-called Tier 1 data centre for the Large Hadron Collider (LHC) experiments at CERN in Geneva. It now hosts computing for many other experiments ranging from high-energy physics to astroparticles. CNAF participated as a primary contributor in the development of Grid middleware and in the operation of the Italian Grid infrastructure. This facility is operating in the framework of a national INFN HTC infrastructure consisting in the CNAF Tier 1 and 10 smaller

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facilities, called Tier 2, placed over the whole Italian territory. CNAF HTC Tier 1 Data Centre operates about 1,000 computing servers providing about 15,000 computing cores allowing the concurrent run of 20,000 processing tasks. All the computing resources are centrally managed by a single batch system (LSF by IBM) and dynamically allocated through a fair-share mechanism, which allows full ­exploitation of the available CPU production capacity for about 95 per cent of the time. CNAF operates a very large storage infrastructure based on industry standards, for connections (all disk servers and disk enclosures are interconnected through a dedicated Storage Area Network) and for data access (data are hosted on parallel file systems, typically one per major experiment). This solution allows the implementation of a completely redundant data access system. The users’ community of the CNAF Tier 1 facility is primarily composed of research groups from the INFN and most Italian universities (including regional ones), working on nuclear, subnuclear, astroparticles and theoretical physics. In the next five years the HTC computing resources of INFN CNAF will be increased by about a factor of five in order to match the computing and storage requirements of the foreseen experiments. Some of them have been co-funded by the Emilia-Romagna region for the Technopole’s labs under the EU2007-2013 ERDF program. Computing facilities also include state-of-the-art platforms and software tools for data analysis. In addition, the Emilia-Romagna Regional Government has entrusted Lepida SpA, the company created by the ER regional authorities to design and implement ICT infrastructure in cities and localities of the region, with the design, implementation and provision of four geographically distributed data centres, for the use of public administrations. The data centres are ‘Tier 3’ certified and offer advanced computing services, storage and backup regarding disaster recovery and energy management. ICT is a domain – an independent layer – and also a core component of big data platforms. This must be taken into account to deliver solutions that ensure extensibility, efficiency and reusability. A consistent policy for training and education is a mandatory need for professional data scientists with two objectives: first, to support and understand how to make the available data accessible to a wider cultural and scientific disciplinary spectrum; second, to manage the multiple protocol and policy of access and to support the research infrastructure and the industry to handle complex data set. The ICT

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domain covers the entire data value chain, for any type of content, both digital-by-design and digitalised ones (by dematerialisation processes). The data value chain embraces both business data and user-generated contents that are created in an ATAWAD (anytime, anyway, any devices) seamless way; data coming from societal domains like administrative data, broadcast, web multimedia content, social networks, educational/cultural/social archives, as well as financial and production data or coming from scientific experiments and numerical simulation of physical phenomena. The research has a direct impact on the ICT industry, both for the large international and European players and – especially in Emilia-Romagna – for innovative start-ups and cultural and creative industries. The big data value market in hardware, software and ICT services is a fastgrowing multibillion euro business. The exploitation of big data offers many new potential solutions and innovations. It uses new technologies such as Data Management, Data Storage and Data Processing; Data and Business Analytics and Business Intelligence; Multimedia Data Processing; Data protections solutions dealing with privacy and security; Data visualisation tools as well as Data Transmission and Communication, in ultrafast and broadband networks and in mobile devices and access terminals. Examples of new solutions and innovations regard the health sector. In the last few years, hospitals, universities and research centres started fruitful and data-productive analyses at European, national and regional levels, such as ‘omics’ studies and multidimensional imaging scans producing large volumes of data and knowledge. This has led both to better identify common conditions and to disclose new developments for treating rare diseases steering health care towards personalised and ‘precision’ treatments. In addition, a more accurate and defined management of large scale information and local data exchange and integration will lead physicians, medical doctors and researchers to improve treatments in a cost-effective manner. To this important development in infrastructure for development must be added the institutional reform carried out by the region, which is also aimed at providing the conditions for business and industrial development, and mentioned at the beginning of this section. Well-functioning, transparent and democratic institutions and policymaking process are essential for business to find a­ ppropriate

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conditions for development, because this type of institution guarantees appropriate policies to find the resources and catalysts for development, as well as not losing time and money on administrative and regulatory procedures.

4 RESILIENCE AS AN AIM OF INDUSTRIAL POLICY FOR THE MANUFACTURING REVOLUTION The industrial policy defined and implemented in the ER region since the 1980s has therefore aimed to extend the four pillars of the sundial explained in the first section of this chapter. In that period, the region experienced important structural changes: first, the shift of emphasis of the industrial system from industrial districts in traditional sectors to a dynamic industrial system inserted in a regional innovation system; and second, the adaptation to globalisation and now the fourth industrial revolution. This industrial policy aims at increasing the regional capacity to adapt to changes in the competitive context. Industrial policy for the manufacturing revolution, and more generally for territory experiencing important structural changes, seemingly must aim at resilience. 4.1  The Concept of Resilience The concept of regional resilience has recently been widely discussed in the literature, defined as the capacity of a region to recover from shocks and to develop new growth paths. This concept has become increasingly used because it highlights both the process of development rather than development itself, and the continuity of actions needed when the development process is complex and uncertain. The concept of resilience has initially been used in studies of the evolution of ecological systems and in climate change/adaptation policies. Climate change raises the probability of disasters such as floods and extreme weather conditions and climate adaptation policies aim at the resilience of the local system or territory, by providing it with a capacity to adapt to potential disasters. The resilience concept is also relevant in the analysis of economic development and growth. Globalisation and the high integration of the world economy has made changes in the competitive context

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much more frequent, particularly at regional level: products and production processes have to be changed more frequently to face global competition, new technologies must be adopted, market position can be challenged any moment from competitors even from very far away countries. In the context of local economies what is becoming important for development is not growth itself but the capacity to adapt to changes by being able either to maintain existing growth paths or to embark on new ones, and hence carry out the manufacturing transition. For this purpose, constructing regional advantages through the valorisation of specific knowledge assets at regional level to create competitive advantages in the globalised world has been stressed (Asheim, 2006). In other words, constructing regional advantages creates a capacity for adaptation and ultimately favours the resilience of the region. The factors for resilience have been outlined in the literature: structural conditions, industrial variety in particular (Boschma, 2015; Neffke et al., 2011); the need for an appropriate institutional framework, as well as the presence of networks (Boschma, 2015). Bristow and Healy (2014a, b) outline the role of human agency in regional resilience and governance. However, the literature also points out that little is yet known about how regions diversify into new growth paths. In particular, the long-term adaptive capacity of regions appears to be largely unexplored (Martin, 2012; Boschma, 2015). Some studies have shown some aspects (studies of change in declining regions), but the relative role of inherited industrial and economic structure, as well as the social and political aspects, are not clear. There is therefore a need to analyse the socio-economic conditions for resilience in more depth. In addition, the concept of resilience itself has raised debates among scholars. For instance, Hassink (2010) has discussed its fuzziness, particularly regarding the duality of adaptation and adaptability. Resilience has indeed been shown to require adaptation and adaptability, meaning, respectively, capacity to return to pre-shock situation, remaining on the same development path and capacity to embark on new growth paths. The latter concept therefore involves taking advantage of the shock not only to rebuild as before, but also to improve the situation. Regarding regional resilience in terms of development capacity, both adaptation and adaptability have been contrasted and it has

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been argued that there is a trade-off between the two. The reason is that while adaptation requires increased specialisation, adaptability requires redundancy, as well as unspecific and uncommitted resources. Adaptation refers to the short-term reaction to shock or to any factor raising a necessity of change, while adaptability regards the long-term capacity for reaction and adaptation. In the short-term, resilience involves the capacity to maintain a specific evolutionary trajectory. A shock may induce a departure from a specific evolutionary path, and adaptation means the capacity to rapidly return to that particular path. In the long-term however adaptability also includes the capacity to develop new evolutionary paths. Grabher (1993) made the distinction between adaptation and adaptability arguing that adaptation requires increased specialisation, while adaptability requires redundancy, unspecific and uncommitted resources, so that there is a trade-off between adaptation and adaptability. The idea is that adaptation requires a strong cohesion of a system, which is so strong as to support short-term shocks. Adaptability in contrast requires ‘loose and weak couplings between social agents in place, that enhance the overall responsiveness of the system to unforeseen changes’ (Pike et al., 2010, p. 62). Grabher (1993) and Grabher and Stark (1997) emphasised this tension between adaptation and adaptability due in particular to the need for strong institutions to ensure short-term resilience but this strength is argued to be what generally impedes the choice of new growth paths. The ER region has demonstrated both adaptation and adaptability: adaptation since it rapidly recovered from an earthquake in 2012 (see below), and adaptability, as shown by structural changes realised in the industrial system since the 1980s. We argue that both a strong social cohesion and appropriate governance (participative institutions) have contributed to this resilience and generally appear to be important factors of the resolution of the above-mentioned trade-off. Resilience is indeed about building a capacity for reaction and recovery. Short-term resilience (adaptation) requires leadership in policymaking, knowledge of the strengths and weaknesses of the territory, so as to be able to take actions that favour positive evolutions while reducing vulnerabilities; for this purpose, territorial stakeholders must be related in networks that are involved in the governance process, so as to better collect information on needs

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and preferences and to rapidly mobilise the stakeholders for a better implementation of policies. The government and its policies may not be necessary if local stakeholders are able to meet and mobilise to take appropriate actions for the local system to adapt and evolve. 4.2 Factors of Regional Resilience: Structure, Agency and Governance Regarding socio-economic characteristics or structural factors for resilience, the literature has highlighted the importance of the regional industrial system’s structure. A key element for resilience in this respect is the degree to which it is characterised by variety as well as related variety (Neffke et al., 2011). Variety in terms of industrial sectors – industrial diversification – increases adaptation, namely the resilience to short-term shocks, since the potential damage of a sector-specific shock is much lower (there are many other sectors present in the region that will not be affected by the shock) than when the region is specialised in a specific sector. The probability of sector-specific shock is higher in a diversified region (there are many sectors) but the potential damage is much lower (the damaged sector is one among many others). In the long-term, namely in terms of adaptability, a specialised region has less capacity for adaptation since it has only one sector from which the new sector can branch out. The regional knowledge base is limited to the sector in which it is specialised, so that there are few recombination options available. Diversified regions have more possibilities for recombination options and a larger available knowledge base, but new combinations and new sectors will not develop unless there are potential overlaps and combinations between the different knowledge bases. In other words, related variety is necessary to create the potential for learning and for the creation of new sectors from existing knowledge bases. Unrelated variety may also lead to adaptability but only if radical innovations occur, with technological breakthrough (Castaldi et al., 2014). These new combinations however will not emerge if agents holding different knowledge bases do not meet and communicate. Knowledge networks are thus important in determining the capacity of regions to develop new growth paths. These networks may exist in the region, if its underlying society is characterised by a capacity to explore and create these links. In case of systemic failure however, the government

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may decide to favour the creation of links: the literature on innovation systems has widely highlighted the importance of the creation and promotion of links between stakeholders involved in innovation processes to promote the meeting of different knowledge bases in order to create new combinations and new knowledge. Adaptation necessarily implies knowledge creation, since the shock or changing conditions create new knowledge inputs that have to be met by new actions. Building capacity indeed requires analysis, anticipation and learning which only agents, namely people, are able to do. Adaptation requires the collection of information of the changing environment, be it the arrival of new products, new technologies or new competitors, or the occurrence of a shock such as an economic crisis or a natural disaster. The collected information must be analysed in order to derive the consequences of the changes for activities and to identify possible actions to adapt, namely to preserve one’s activities or to develop new activities if the old ones cannot be maintained. As stressed by Bristow and Healy (2014a, b), the role of people, namely individuals and their single or collective actions, is therefore important in determining resilience. Social networks allow easier communication which is important when a shock occurs and requires rapid collection of information and rapid collective decision to implement policies to mitigate negative effects. The role of institutions is also important, namely the laws, norms and cultural attitudes that frame and guide collective action, because agents act in specific social networks and institutional frameworks that enable or constrain particular types of actions. In our view it is the interplay between the various levels of governance that is essential in determining resilience. In other words, resilience must be associated with well-functioning multilevel governance systems. The concept of multilevel governance was defined in political science studies first by Marks (1993) in the context of the European Union decision-making framework. The concept assumes that decision-making competencies and power are not exclusively held by national states, but also by subnational and supranational levels. In multilevel governance, decision-making is made in networks rather than hierarchies. It is therefore characterised by democratic processes, where non-state actors are involved. Multilevel governance has been praised as bringing a number of advantages. First, the dispersion of governance across multiple

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jurisdictions has been shown to be more efficient than central state monopoly in a number of cases (Hooghe and Marks, 2001). For instance, it has been shown that in order to internalise externalities, governance must operate at different scales: global warming for instance implies that externalities arise at world level and public goods should be defined at that level. Second, more decentralised jurisdictions can better reflect the heterogeneity of preferences and needs of citizens. Third, multilevel governance may facilitate credible policy commitments (Majone, 1998). Fourth, it allows for competition between jurisdictions and may facilitate innovation and experimentation. Authority and resources normally flow from the national government while knowledge about the situation, the available local resources, the requirements and needs is higher at the local level. The ER industrial policy has been implemented coherently in a multilevel governance framework. The latter includes not only higher levels, but also lower ones. The regional government has included local stakeholders in regional policy decision-making, and also favoured local initiatives. The region considers itself as a federation of territories, so that local systems are autonomous but highly coordinated at regional level. Industrial policy has been defined in the region in a bottom-up approach, leaving the local systems to define their needs and priorities. An example of such approach concerns industrial districts, for which the region chose not to define specific intervention: a national law provided criteria for the definition of industrial districts (in 1991 and updated in 1999), which many regions followed to define their districts and implement specific policies towards them. The ER region instead did not define its districts but left the local system to organise and define prospects if they wanted to. Industrial policy measures have always included not only specific measures to favour the competitiveness of industry, such as R&D subsidies and other support to firms, but also social measures such as those promoting the participation of workers in the labour force, as well as training and education. The development of new industrial sectors in particular is impossible without an appropriately skilled labour force. Action in favour of training and education is therefore important. However, such action also has an impact on the society and therefore industrial policy has had an integrated character, in the sense of combining

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social and economic objectives. This has contributed to the social sustainability of the ER industrial policy and, as we will argue below, to the resilience of the region. The region has thus built upon an important stock of social capital. Since the 1980s the importance of social capital in the region has been stressed in many studies, particularly those concerning industrial districts (Putnam et al., 1993; Pyke and Sengenberger, 1992; Brusco, 1982). Measuring social capital is difficult. However, a number of elements show the high level in the region. First, the strong and networked cooperative sector in the region is one element. The cooperative sector represents about 30 to 40 per cent of GDP of the region (ER Region, 2013). In 2013, 1.4 per cent of the regional enterprises were cooperatives, representing 15 per cent of the total number of employees in the region. Cooperatives also accounted for about 16 per cent of the total revenue of firms in the region (2013). Cooperatives are enterprises which produce goods and services on behalf of their members. Their primary objective is not profit but satisfying their members’ needs such as employment, cheaper goods, housing and so on. The ER region has been characterised by another element of social capital: a strong participation in the political life, being members of political parties or other association, taking part in meetings of the political parties or other associations, in debates and so on. A 2013 national Central Statistical Office study (ISTAT, 2013) shows that although in a decreasing trend everywhere in Italy and also in the ER region, while 55 per cent of Italians never took part in any political activity in 2007 (60 per cent in 2012), these figures were 45.6 per cent in 2007 and 53.9 per cent in the ER region in 2007 and 2012 respectively. The regional industrial system is still characterised nowadays by a strong presence of SMEs, artisanal and cooperatives. Industrial districts no longer characterise the industrial system,3 but many SMEs are organised in groups (formal groups characterised by shared ownership but where each firm keeps legal autonomy) and in cooperatives. While groups allow the small firms to exploit economies of scale in some functions, such as production, product development and marketing, the diffusion of cooperatives in the region is an expression of its high social capital.

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4.3  Adaptation: Disaster Resilience in the Emilia-Romagna Region An earthquake affected the ER region in May 2012, with two stronger seisms arising, one on 20 May with magnitude 5.9 on the Richter scale and one on 29 May with 5.8 magnitude. The earthquakes affected an area between the cities of Reggio Emilia, Modena, Bologna and Ferrara, the core of the industrial system of the region, since the affected areas represented about 2 per cent of the national GDP, 48,000 firms and about 190,000 employees. The number of casualties was limited but the main characteristic of this disaster is its impact on the productive sector. The industries concentrated in this area are food industry and biomedical in particular. The area comprises the worldwide excellence biomedical cluster in Mirandola, employing about a third of all employees in this sector in Italy. In this cluster, characterised by the presence of foreign multinational leaders in the sector, about 90 per cent of firms were damaged by the earthquake. The affected areas concerned a population of about 550,000 people, and an industrial core of the region and the country. The seisms resulted in 27 deaths, while damages were estimated at about €12 billion in the ER region. This natural catastrophe was totally unexpected: the region had been considered a very low seismic risk, even a non-seismic area. The affected municipalities and the regional government therefore had to rapidly adapt to manage the emergency and recover. The ER regional leaders learnt from previous disasters in Italy and decided rapid action – no involvement at the national level to avoid both delays in decision-making and inadequate priority-setting. They put the society as a priority: the focus was on reconstruction of industry and schools, two important elements of community and social cohesion. A committee for emergency governance was immediately created, consisting not of outside experts but of local and regional government authorities. The president of the region was nominated as head of the committee (rather than nominating an external commissioner as in the L’Aquila case) and mayors of the places affected by the earthquakes (54 towns were affected), together with presidents of the counties (provinces) were designated as committee members. The region took advantage of its reputation for high quality governance and made rapid decisions to signal its determination to avoid another recovery failure like in the case of the L’Aquila e­ arthquake

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in 2009. Anti-corruption measures were also taken to signal its reliability and ‘professionalism’, and protect against infiltration by criminal organisations in reconstruction projects. This was important with regard to industry. Managers of damaged factories decided to stay in the region and reconstruct their businesses there because of the reliability of the institutional framework of the region. Regarding gathering of financial and material resources for reconstruction, the region took advantage of the multilevel governance framework. It immediately appealed to the national government as well as to European institutions. Various financial channels and instruments at various government levels have been used to realise the recovery programme. In addition to some funds from the national government, the European structural funds allocated to the region were partly reprogrammed to be used for reconstruction. In addition, European structural funds allocated to other Italian regions were partly reallocated and Centre-North Italian regions provided 4 per cent of their European resources to the recovery process of the ER region. Measures were immediately taken to preserve one of the main priorities of reconstruction, namely the industrial centre. Funds were allocated to support the reconstruction of damaged factories and business activities. In the biomedical sector, calls were made to the public health system to complete delayed payments to the suppliers located in the cluster; and support to R&D activities was enhanced. The regional government indeed feared that foreign multinationals would leave the region, which had become unsecure. However, no such escape occurred and the regional industry affected by the earthquake rapidly restarted production and research activities. In the Mirandola biomedical district for instance most firms restarted activities within one to three months. The micro-firms encountered more difficulties in general to reconstruct and restart. In addition, disasters create the risk of dismantling the local social cohesion: communities may be too affected to be able to effectively react. Hence in ER the regional government decided to prioritise school reconstruction (as well as industry) because schooling can be considered as one of the pillars of social cohesion, as well as a signal to local population that the regional government was committed to rapidly return to normal life. Participation in the labour force is easier for workers when their children can go to school as normal.

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The school programme was adopted on 5 July 2012. This programme had three important features. First, a clear objective, that of reopening all the schools in the region by 17 September 2012; second, involvement and consensus with the local authorities and population; third, clear and transparent rules for the reconstruction activities. Reconstruction was realised through two tenders allocating funds for reconstruction. Some important special rules were decided to ensure transparency and effectiveness. First, a firm could not apply to more than one call, and could apply to rebuild not more than two schools. This was adopted as a rule in order to allow the participation of SMEs in reconstruction as well as avoiding infiltration by criminal organisations. In addition, this rule increased competition so that the best available technologies would be proposed, minimising costs and allowing rebuilding with anti-seismic features as well as energy efficiency. The region became a laboratory for the most recent technologies for reconstruction and anti-seismic systems. While undoubtedly the magnitude of the natural disaster, the amount of resources, tangible and intangible capital and endowments determine the success of emergency and recovery policies after natural disasters or other shocks, the political leadership and governance of the emergency also influence the outcome. In particular, the case of the ER region shows that the capacity of learning and adaptation of the policymakers, taking lessons from past experiences in their choices, has been crucial. In addition, the regional government took a strong leadership in order to coordinate local efforts and mobilise resources for reconstruction in the multilevel governance system. Such a governance system was so successful that many firms, in particular multinational firms in the biomedical sector in the Mirandola cluster, at the epicentre of the earthquakes, decided to take advantage of the need for reconstruction to increase productive capacity and ended 2012 with increased revenue relative to previous years, despite the crisis and despite the earthquake (Labory and Facchini, 2014).

5 CONCLUSIONS The industrial policy implemented in the ER region since the 1980s therefore represents a sort of experiment in times of substantial structural changes, such as a manufacturing revolution.

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The ER regional policymakers think in terms of a system, recognising the necessity to build a polycentric regional system of cities or a system of local production.4 The region has been able to improve both economic growth and social cohesion, pursuing these two objectives simultaneously on the basis of a regional development model that includes the whole territory, through a polycentric web made of (industrial, environmental, cultural and service) specialisations and cooperation between the poles. In the ER region, inclusive industrial policy means a participative policymaking process. As stressed by Acemoglu and Robinson (2012), institutions supporting development can be either inclusive or exclusive: the former ensure the involvement of all social parties in the policymaking process so as to ensure social dynamic processes are activated together with economic evolutionary processes, so that long-term growth can be sustained. The Labour Pact of the ER region is the main element of the region’s integrated and comprehensive industrial policy, aimed at resilience in the face of deep structural changes such as the fourth industrial revolution, which create numerous opportunities but also risk and uncertainty. Regional socio-economic stakeholders were involved in the policy process so as to stabilise their expectations in the sense of building trust about the commitment of regional authorities to build an environment favourable to inclusive growth. This was especially important in a period of high uncertainty of the international economy and contributed to building a shared vision of the regional territory as a hub of competencies and particularly R&D capacity that could ensure regional resilience. This approach integrated all the instruments and policies in order to create innovative ecosystems able not only to undertake structural changes, but also to do this rapidly, which is important for a territory not to end up lagging behind. Thus the regional industrial policy aimed to strengthen local SME systems and to attract international companies, to reinforce the links between universities and industrial companies and to create closer links with the other regions in Europe and in the rest of the world that have complementarities with the ER region. Agreements were signed in Europe first with the German Hessen region, then with the French Aquitaine and the Polish Wielkopolska regions. The region was also active in promoting the Vanguard Initiative, which gathers the 30 most advanced regions of the EU in

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order to promote industrial innovation in the community. The ER region has also become the leader of all programmes concerning the Balkan and Adriatic–Ionian regions, in order to act as a crossroads of knowledge and actions in different European contexts. In the rest of the world the region has a special relationship with California in the USA, Guangdong in China and Gauteng in South Africa, with which it shares a common vision on global challenges such as climate change and sustainable and inclusive development. The ER region has therefore represented a laboratory for an industrial policy approach with the following characteristics: ●

Structural (with respect to the economic dynamics). Participative (with respect to how to involve all the stakeholders, institutions, companies, universities and research centres). ● Comprehensive (with respect to the capacity to integrate all the instruments). ● Aimed at generating the conditions for an innovative ecosystem supporting the economic and technological transformation. ● Also aimed at creating a resilient society capable of regeneration. ●

The sundial proposed by Bianchi and Labory (2011a) has been implemented and thus demonstrated a strong operative capacity, able to sustain complex processes of participation and long-term planning. Industrial policy in the era of digital globalisation (globalisation and the fourth industrial revolution) is therefore a vision of structural changes, of manufacturing transition and its impact on the economy and the society. This policy takes social aspects into account in order to orientate and support changes (hence it is comprehensive in the sense of keeping all parts together), which also require the participation and the mobilisation of all actors. This experience also shows that in the era of globalisation and industrial revolution it is no longer possible to implement regional policies in the sense of subsets of national (or supranational where it applies) policies. Instead, they have to be defined in a coherent multilevel governance framework, with all horizontal levels (social stakeholders) but also vertical levels (localities, regional, national and supranational level, which is European in the EU but should also be global in many cases, such as regulation of online platforms or climate change and environmental protection issues). Smart specialisation is

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important but it has to be combined with smart complementarity, in the sense of reciprocal adjustment between individuals and entities with various specialisations but able to interact up to integration. The new European policies, especially regional (cohesion) ones, have to focus on smart specialisation but also interregional complementary integration. In this sense, industrial policy is increasingly a literal policy, in that it has the capacity to propose a long-term vision of development, which keeps all social parts together in the difficult transition between manufacturing regimes. It is important that the innovation capacity of some individuals and organisations does not become a temptation for monopolisation of the society in order to avoid ever-growing disparities, social inertia and specific interests that impede change. As stressed by the poet Antonio Machado, our path is not preconstituted and when we look back we can see our past footsteps on which we will never go back, being guided by the conscience of our history and of ourselves and our capacity for a vision of our future. In this phase of globalisation where past parameters, such as the central role of national governments, no longer seem to guarantee a regulated navigation, industrial policy becomes our capacity to provide and strengthen territorial identity and a future perspective to individuals, conscious that people are our main resource.

NOTES 1. See Section 4 for detailed definition of this concept. 2. PRACE is an international not-for-profit association located in Brussels, with 24-member countries all creating a pan-European supercomputing infrastructure. It provides access to computing and data management resources and services for large-scale scientific and engineering applications at the highest performance level. The computer systems and their operations accessible through PRACE are provided by 5 PRACE members (BSC representing Spain, CINECA representing Italy, CSCS representing Switzerland, GCS representing Germany and GENCI representing France). 3. There are 13 industrial districts in the region, out of the 141 identified for the whole country by the 2011 Industrial Census, defined as territorial concentration of particular sectors and employees, not as systems of SMEs characterised by a division of labour between firms to produce a particular product (which were the industrial districts on which attention focused in the 1980s). 4. See www.regione.emilia-romagna.it and ER Region (2007).

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6. Conclusions: industrial policy for the manufacturing revolution This book argues that industrial revolutions cannot be read only through the lenses of technological change. Other important aspects are the nexus between market and production dynamics, and also the social and political conditions that enable some individuals to use these technologies to participate in economic activities and/or introduce or adapt innovation. Public authorities have a role to play in this context by implementing actions aimed at promoting the capacity of operators and citizens not only to resist but also better to anticipate structural changes induced by these dynamic interactions. The analysis of this book has two major implications regarding industrial policy for the manufacturing revolution: first, territories have a responsibility to promote the transition between manufacturing regimes, by implementing comprehensive industrial policy as in the case of the Emilia-Romagna region. Second, the manufacturing revolution raises important institutional issues regarding the rules of the competitive game. More precisely the fourth industrial revolution raises new antitrust and regulatory issues, essentially related to the emergence of online platforms, as shown in Chapter 4.

1 INDUSTRIAL POLICY AS A COMPREHENSIVE PROCESS Industrial policy has been widely debated in the last two decades. The debate initially focused on whether industrial policy was good or bad. However, there has been growing consensus, at least among policymakers and in a part of economists, that industrial policy is required to face the globalisation challenges (Chang, 2010; Cimoli et al. 2009; Rodrik, 2008; Hausmann et al. 2008a; UNIDO, 2011; IDB, 2014; European Commission, 2014). The different contributions of 149

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the International Handbook on Industrial Policy (Bianchi and Labory, 2006) also highlighted the complexity of industrial policy, which is made of interventions to favour innovation, competition, SMEs and territorial development. In recent years and especially since the financial crisis the key issues of what industrial policy is and how it should be implemented have been increasingly raised, suggesting a number of important components of industrial policy: 1. Industrial policy should be implemented as a process, including stakeholders (particularly business) in the definition phase in order to better provide adequate measures (Rodrik, 2008; Bianchi and Labory, 2011a, 2017). 2. Industrial policy should be based on existing assets of the economy and primarily aim at both upgrading existing ones and providing the conditions for the development of new ones; new sectors cannot be built out of nothing, but skills and innovative capacity have to be strengthened (Cimoli et al., 2009; Hausmann et al., 2008a; O’Sullivan et al., 2013). 3. Careful analysis of the strengths and weaknesses of the industrial sectors has to be carried out in order to define appropriate and coherent industrial policy (IDB, 2014; Bianchi and Labory, 2017). 4. Resources are essential: financial, infrastructure, education and training. 5. Entitlements are also key: they allow the development of capabilities, which have been stressed as a key focus of industrial policy (Cimoli et al., 2009, Bianchi and Labory, 2011a). 6. Governance is also important: participative to define appropriate policy, also to mobilise towards its aims and means, monitoring, evaluation and learning. Adequate governance also allows reduction of government failures such as vested interests and capture of policymakers (Hausmann et al., 2008a; Chang, 2010; Lall, 2006). 7. Industrial policy must be comprehensive, namely based on a vision of industrial development, with a choice of development path and on a capacity to coherently embark on that path, in a sustainable manner (Bianchi and Labory, 2011a). The new industrial policy favours learning and self-discovery of private sector actors. Influential scholars (Rodrik, 2008; Hausmann

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et al., 2008a; Cimoli et al., 2009) highlighted that the issue for each country is to acquire comparative advantages over a broader range of activities, not just concentrate on what one does best. Diversification of the productive structure allows discovery of which new activities have low enough cost to be profitable. Such discovery process involves fixed costs (acquisition and processing of information, resources, education and training) which the private sector alone will not bear. Policymaking as a process, involving stakeholders and providing incentives, also avoids rent seeking and capture. Policy choices are better made in environments with strong institutional capacity, availability of information and private–public sector dialogue. Industrial development is a process generated by the capabilities of governments, private sector firms and other agents. Policy implementation is an endogenous process of experimentation and learning. These considerations have been taken into account by international organisations in order to propose methods and support for industrial policy definition and implementation for developing countries (for instance UNIDO, 2011). Two major new approaches to industrial policy seem to have been proposed in order to guide the formulation of these policies. One is the new structuralist approach (Lin, 2011), which proposes to adapt policy to country specificities, focusing on creating comparative advantages for industrial development and growth. A country’s endowment structure determines its comparative advantages, which industrial policy should aim at strengthening. The economy should grow in a manner that is consistent with its comparative advantages. For this purpose, industrial policy should ensure that markets are competitive so that they provide the right price signals, information to firms on new products and new technologies and both hard and soft infrastructure, namely transport, communication as well as skills. According to Lin (2011), industrial policy should identify new industries in which the country may have latent comparative advantages and provides the conditions for the emergence of industries with these advantages. The other approach is centred on capabilities (Cimoli et al., 2009), and is about ‘industrial policies seen as intrinsic fundamental ingredients of all development processes’ (Cimoli et al., 2009, p. 1), supporting the development of knowledge and capabilities necessary for industrial development and upgrading.

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The second approach (Cimoli et al., 2009) also highlights the importance of education policy for development: in particular Amsden (1989) showed that Korea after World War II was supported by government interventions aimed at orientating investments and innovation in specific sectors but also at improving human resources. This is what appears to have been missed in Latin America (Cimoli et al., 2009). According to the second approach, industrial policy should be formulated with the aim of building capabilities and avoiding inertia and rent seeking. Regarding the latter point it has been stressed that industrial development requires competition (Aghion et al., 2015). These two approaches do not stand in contrast but in fact appear to be complementary in their consideration of structural changes: the new structuralist approach highlights technical aspects of production and structural changes (comparative advantages), while the second approach stresses the importance of capabilities. However, both technical knowledge and capabilities are elements of productive structures, which evolve when the production system is upgraded. Structural change is multidimensional. It means adoption of new technologies, new production systems, which imply also new skills required in the labour force. Hence the adaptability of the labour force, namely its capability to learn new skills, is an essential factor for structural changes. New knowledge and competencies may be required, implying the need for vocational training and also adaptation of the education system so that young people might get the preparation for new types of jobs. New sectors might emerge, implying the reform of institutions that provide new regulation (product standards, protection of intellectual property rights, contract law and so on). Structural changes thus have an effect on the society and require institutional changes. This process might create systemic failures in that institutions and productive sectors change at different speeds. One particular problem is the very long timing needed to adapt skills provided in educational systems. The latter might be changed but it takes a generation to take full effect: only pupils having performed their whole schooling in the new system will have full preparation for the new skills. In addition, teachers have to be trained to be able to transmit new knowledge and skills to their students. Structural changes arising in specific industries might have effects on other industries (due to complementarities) and on the whole eco-

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nomic system. Industrial policy must therefore be based on an analysis of productive processes, but also on analyses of the interactions between different productive processes. Favouring structural changes in one sector may have positive or negative impact on other sectors; it may also impact the labour market, changing the skill required in the labour force, as well as wages. The analysis of trajectories of structural change must therefore be performed to choose specific trajectories and adopt/define sets of measures/actions to favour the evolution of the economy towards these trajectories. The complexity of structural changes involved in industrial revolutions therefore call for comprehensive industrial policy, as argued in the next section.

2 INDUSTRIAL REVOLUTIONS AND TRANSITIONS IN PRODUCTION SYSTEMS The analysis of industrial revolutions carried out in Chapters 2 and 3 showed that each of these revolutions is also characterised by a particular production process that prevails in most industries. The first industrial revolution is characterised by the shift from craft production to the factory system, the second industrial revolution induces the transition to mass production and the third to flexible production. The fourth industrial revolution is also inducing a shift to a new manufacturing regime, which can be called mass customisation, using the smart manufacturing technologies, as argued in Chapter 3. We argue in another study (Bianchi and Labory, 2017) that industrial revolutions are primarily transitions in manufacturing regimes that call for comprehensive industrial policies. This book provides an illustration of such policies, with the experience of the Emilia-Romagna region. This experience shows that industrial policy for the adaptation of the industrial system to changing competitive context and particularly to industrial revolutions can be effectively performed at regional level. At least this level has a role to play in resilience, ideally in coherence with actions at other levels of government. This experience also shows that industrial policy for the manufacturing revolution is a set of converging actions, that involves all local actors, in order to increase the resilience of local communities, which today are agents operating in open and competitive contexts.

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The sundial defined by Bianchi and Labory (2011a) highlights the importance of considering different dimensions of development, innovation and territory, and what is called the conflict of modernity, namely the opposition between citizens’ rights and available resources. It reflects a holistic, integrated, comprehensive approach where welfare, human capital, innovation and territorial policies are jointly and coherently designed and implemented so as to create an environment favourable to adaptation and innovation, at both individual and collective levels. The ER region has adopted this policy approach, with a governance process based on participation and democracy, because only this involvement of stakeholders can promote the realisation of a long-term transformation process based on increasing innovation and social cohesion capacity. The regional institutions paid attention to the importance of maintaining the ‘conscience of places’ (Becattini, 2015), namely the knowledge, culture, experiences and traditions that determine the direction of development undertaken by a community and its territory. In the globalised and digitalised world there is no predefined and universal recipe for development, but each territory must build its own development path, with legitimate and responsible institutions. Besides this industrial policy action for the resilience of territories, this book suggests that, beyond the illusion that markets can autoregulate, the fourth industrial revolution also raises new regulatory and antitrust issues related to the accumulation of large amounts of private data on individuals, conferring huge market and also political power to the private companies that own them. These companies operate on a global basis far beyond the dimension of single states.

3 HYPERCONNECTIVITY AND THE NEED FOR NEW INSTITUTIONS Industrial revolutions can be characterised by a specific dominant technology, such as steam, electricity or the Internet, but it is important to historically examine the conditions that enable their development. For instance, the first industrial revolution was prepared well in advance by the Glorious Revolution of the seventeenth century, which established a parliamentary monarchy where the Commons, namely the bourgeoisie, obtained some political power that it used

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to influence regulation in favour of business. Technological developments induced social and political changes, which in turn induced further scientific and cultural developments leading to the industrial revolution. The second industrial revolution and the growth of the leadership of the USA was also long prepared by the American Revolution, the specific institutions developed by Protestant immigrants in the Northern States (especially education institutions), and political upheavals leading to the consolidation of nation states with the difficult consolidation of the French Republic, the unification of Italy, Germany and Japan, Opium Wars in China and the Russian Revolution. The third industrial revolution is associated with the Cold War and the bipolar world opposing two powers, the USA and the USSR, each with its allied states, inducing a technological race between the two blocks, essentially to develop war capacity but the research generated spillovers and knowledge transfer to other industrial sectors. The fourth industrial revolution is taking place in a new world order, after the collapse of the USSR and the socialist block, the end of the bipolar world and the rise of new powers, especially China. In the transition to this new manufacturing regime Germany, Korea and China have already made industrial policy choices that have anticipated the next phase of development, while the USA has lost supremacy, reacted with delay and is now even using protectionist threats. However, globalisation continues, not so much as increasing flows of goods and services across countries but as a global exchange of data. Online platforms examined in Chapter 4, not only online marketplaces but also social networks, are eliminating frontiers and between all consumers in the world. In this sense, the globalisation of the economy is continuing, in the form of what can be called ‘digital globalisation’. In this global economy all government levels are local, in that no national level can control the whole market. We believe that the key factor of the new industrial revolution is what we can define the ‘hyperconnectivity paradox’, that is to say the possibility of person-to-person, person-to-machine and now machine-to-machine networking. It gives the opportunity to respond to new emerging needs and therefore to create new markets and at the same time to generate new models of production organisation, based on global networks of producers. In old classical terms (Smith,

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1776), we can say that hyperconnectivity is able to enlarge the extent of the market and at the same time to regenerate the division of labour at global level. Nevertheless, this hyperconnectivity is also generating an enormous mass of relations such that the new market leaders are those organisations, which are able to manage, order and organise these flows of relations and this accumulation of data. These platforms also manage the private data of all the people and the companies that enter into the interaction game at global level, cumulating a market power that does not find limits in the national antitrust regulations. Consumer defence, company safety and also national security are issues that become more and more relevant in the new perspective of what antitrust means in the age of globalisation. Finally, big data is emerging as a new strategic industry. Big data management is so pervasive for all human activities that it is taking the same importance that oil had in the second half of the twentieth century. We believe that development is related to individuals’ capacity to act with equal rights and equal opportunities, using all available technologies. In the era of digital globalisation, where all public authorities become ‘local’, that is, highly constrained by the behaviour of other territories, the role of the state becomes more complex, aimed at generating the conditions in which all citizens – the members of the polis, of the community – can take part in collective development, using competencies, knowledge and infrastructures useful to organise production and distribute wealth. Meanwhile the concepts of production, competition, monopoly, but also the ideas of the society we want for ourselves and for our children, have to be reconsidered. In the era of digital globalisation industrial policy has again the deep meaning of a collective action aimed at orientating development towards specific paths, transforming productive structures and also the social communities of the territory, so that development can be smart, inclusive and equal.

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Index 3D printing 61–2, 68–9 Abo, T. 32 Acemoglu, D. 34, 146 Acxiom 97 adaptability 139 adaptation 139–40, 143–5 additive manufacturing 61–3, 69 Adjerid, I. 95 advertising 73, 81, 85–6, 92, 95–100 AEG 29 agricultural revolution 36 agriculture 3–4, 20, 33, 36, 110 Airbnb 13, 79–80, 86–8, 91, 94 algorithms 58, 92–3, 96 Alibaba 82, 85, 87, 90 Alphabet 84, 87, 91–2 see also Google Amazon 13, 54, 76, 79, 82, 86–9, 91–2, 95, 97 Amazon Robotics 54 American Revolution 46–7, 155 Amsden, A.H. 152 Ancien Régime 35 Android 75, 84–5, 87, 90, 92, 95 antitrust 4, 6, 89, 91, 99–100, 104, 149, 154, 156 Apple 13, 75, 84, 86, 91–2 Arkwright, Richard 39 Armani 81 Armstrong, M. 90 artificial intelligence 4–5, 50–51, 55–6, 58–9, 92, 97 ASIMO 55 AT&T 29 Audi 129 Auerbach 30 automation 12, 29–30, 50, 52, 54–5, 60–62, 69, 130

automobiles see cars autonomous vehicles 55 back shoring see reshoring Bacon, Francis 34 Baidu 82, 85, 87 Baldwin, R. 68 Bank of England 20 Barilla 52 barriers to entry 61, 83, 98 see also ease of entry BCG 52, 70 Belgium 24 Bell, Alexander Graham 30 Bell Laboratories 29 Bessemer, Henry 22 Bianchi, Patrizio 5, 13, 104–5, 110, 112, 147, 154 big data 51–2, 58–9, 72–4, 76–7, 81–3, 88–9, 96–7, 105, 117, 120, 127, 130–32, 135, 156 analytics see data analytics as collective asset 101–2 and market power 93–100 biofuels 49, 56 biomedical sector 72, 110–11, 128–9, 143–5 biotechnologies 5, 27, 49, 58 BlackBerry 84–5 BlueKai 97 book industry 74–5, 88 Boston Consulting Group 61 Boyer, Herbert 50 Brazil 6–8 see also BRIC countries Brexit 9, 107 BRIC countries 5–8 see also individual countries Bristow, G. 137, 140 169

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broadband 30–31, 77, 135 Brusco, S. 109 Caillaud, B. 89 capitalism 20 cars 5, 12, 15, 18, 21–2, 27–8, 33, 51–2, 54, 59, 61, 74, 110, 127–9 Chandler, A. 25 Charlemagne 44 Charles II 41 Charlier, Joseph 45 chemical industry 5, 21–2, 27, 33, 40, 44, 49, 54, 56–8, 89, 130 Chesbrough, H.W. 72 China 8–10, 52, 56, 63–5, 67, 70–72, 82, 85, 90, 147, 155 see also BRIC countries CISCO 10–11, 82 climate change 13, 58–9, 136, 141, 147 cognitive sciences 50, 55 Cohen, Stanley N. 50 Cold War 155 combustion engine 5 Comenius, John Amos 41 communism 5 comprehensive industrial policy 2, 14, 104–8, 146, 149, 153 computer and data sciences 5 computer chip 28–9 Condorcet, Nicolas de 45 connecting technologies 5 cookies 96–7, 99 cost-per-click 95, 100 Cromwell, Oliver 41 Cromwell, Thomas 40 culture 15–16, 19–20, 26, 28, 33–8, 41, 109, 111, 113–14, 135, 140 cyber-physical systems (CPS) 61 data analytics 58–9, 73, 76, 81–3, 88–9, 96–7, 101, 117, 127, 134 de Tocqueville, Alexis 37 DEC 29 DeepMind 92 Deezer 97 deindustrialisation 63–73

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Dellarocas, C. 93 Diderot, Denis 45 Diesel 81 digital globalisation 1, 4, 10–14, 59, 147, 155–6 digital manufacturing 60 digitalisation 73–7, 96, 123, 127, 135, 154 division of labour 15–16, 18, 31–2 Dolce & Gabanna 81 drones 55 Ducati 127–8 ease of entry 86–8 see also barriers to entry eBay 79, 86, 90–91, 93 economies of scale 22, 32, 59, 63, 80, 90, 94, 142 economies of scope 32, 59, 63, 80, 94 Edison, Thomas 24 education and training 4–5, 15–16, 25–6, 35–47, 71–2, 101, 104–5, 111–12, 114–15, 118–27, 131, 141, 150–52 in France 44–6 in Germany 43–4 in the UK 40–43 in the USA 46–7 electricity 17, 21–6, 28–9, 33, 38, 40, 47, 50, 52, 54, 128, 154 Electronic Commerce Directive 93 Emilia-Romagna region 2, 13–14, 77, 81, 101, 104–5, 108–49, 153 earthquakes 112–13, 128, 138, 143–5 enabling technologies 51–60 see also individual technologies ENI 133 entitlement 105–6 entrepreneurship 34, 43, 105, 109, 116, 124 entry barriers see barriers to entry ePrivacy Directive 97 Ericsson 3, 12 Esteves, R-B. 99 European Centre for Medium-

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Index Range Weather Forecasts (ECMWF) 130–31 European Commission 71, 99, 131 European Patent Office (EPO) 109–10 European Union 9–10, 59, 67, 69–72, 93–5, 97, 109–10, 115, 130, 134, 140, 146–7 eXelate 97 Expedia 94 Facebook 13, 74, 86, 90, 92, 96, 98 factory system 14–15, 19–20, 25, 31, 33–7, 42, 153 Ferrari 127–8 Ferry, Jules 46 financial crisis 1, 6–10, 52, 65, 112, 125, 145, 150 financialisation 65 Finland 84 first mover advantage 83 Fisher, A.G.B. 3 Fitbit 89 Fleming, John Ambrose 28–9 flexible production 14, 33, 61–3, 153 Ford 15, 32, 70 foreign direct investment 5, 129 fourth industrial revolution 1–2, 4–6, 10, 12–14, 16, 27, 39, 49–51, 79, 104–5, 120, 124, 127, 136, 146–7, 149, 153–6 digitalisation and hyperconnection 73–7 see also hyperconnectivity enabling technologies 51–60 impact on deindustrialisation and global value chains 63–73 and smart manufacturing see smart manufacturing Foxconn 54 France 23–4, 26, 35, 40–41, 44, 64–5, 71–2, 100, 146 education and training 44–6 Franklin, Benjamin 21, 24 free trade 38 Freeman, C. 6 French Revolution 26, 35, 44

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GAFA companies see also individual companies GAFA/GAFAM companies 92, 100 GDP 6–8, 64–6, 70, 109, 142–3 GE Aviation 70 genomics 4–5, 27, 49–50, 56 Germany 22–3, 25–6, 29–30, 35, 39, 41–2, 52, 64–5, 70–72, 146, 155 education and training 43–4 Gillard, D. 40 Gille, B. 17 globalisation 1, 5, 75, 77, 118, 127, 136–7, 147–9, 154–6 after the financial crisis 6–10 digital 1–2, 4, 10–14, 59, 147, 155–6 of the exchange data worldwide 10–14 Gmail 85, 92, 95, 98 Goodyear, Charles 22 Google 13, 55, 76, 79, 84–5, 87–8, 91–2, 95–6, 98–100 see also Alphabet Google Maps 92 Google Street View 98 GPS 12, 97 Great Britain see UK Haas 128 Hartlib, Samuel 41 Hassink, R. 137 Haucap, J. 90 health care 49, 51, 56–8, 89, 101, 111, 128, 131, 135, 144 Healy, A. 137, 140 Henry VIII 40 Hertz, Heinrich 28 high power computing 4 Holkeri 84 Honda 55 Horizon 2020 125 Hpe Coxa 128 HTC 85 human capital 4, 14, 54, 69–70, 73, 77, 101, 112, 117, 124, 127, 154 human–robot interaction 55–6 see also robotics

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Humboldt, Wilhelm von 43–4 Hyatt, John Wesley 22 hyperconnectivity 2, 6, 50–51, 58, 68, 72–7, 79, 154–6 IBM 75, 88, 101, 134 ICTs 2, 17, 26–31, 33, 51, 57, 123, 129, 131–2, 134–5 image recognition 55 IMF 2, 6–8 Immelt, Jeffrey 63 India 95 see also BRIC countries Industrial Internet 12, 61–2 industrial policy as a comprehensive process 149–53 industrial policy, definition of 4 industrial revolutions 4–6, 12, 14–16, 47, 73, 104, 107–8, 147, 153 1 first industrial revolution 14–15, 18–21, 25–6, 31, 33–4, 36–9, 42, 153–5 2 second industrial revolution 5, 14–15, 18, 21–6, 31–3, 36–7, 39–40, 47, 63, 153, 155 3 third industrial revolution 5, 14, 26–31, 33, 47, 50, 63, 153, 155 4 fourth industrial revolution see fourth industrial revolution changes embedding 33–8 and education systems 38–47 and manufacturing regimes 31–3 and technological systems 17–19 and transitions in production systems 153–4 Industrie 4.0 programme 70 information and communication technologies see ICTs innovation 4, 12, 14, 16–25, 28, 30–31, 33–4, 41, 47, 51, 55, 67, 69–70, 72, 75, 79, 81–4, 86, 89, 91–3, 104–7, 109, 111–20, 123–32, 135–6, 139–41, 146–50, 152, 154 Instagram 90

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integrated computational materials engineering (ICME) 60, 67, 70, 127 Intel 29 intellectual property rights 4, 152 Internet of Things 6, 12, 31, 51 iOS 75, 84, 87, 90 iPhones 54, 84 see also smartphones Italy 2, 13, 25, 29, 40, 52, 64–5, 71, 81, 101, 104, 109–10, 128–30, 143, 155 see also Emilia-Romagna region Japan 29, 32–3, 52, 55, 64–5, 67–8, 155 Jenner, Edward 36 Jobs, Steve 75 Johnson, N.L. 84 Jullien, B. 89 kaizen 32 Kay, John 39 Kelly, K. 96 Keyhole 92 Kindle 74 Labory, Sandrine 5, 104–5, 110, 147, 154 Labour Pact 112–36 laissez-faire policies 38 Lamborghini 127–9 Large Hadron Collider (LHC) 133 lean production system see flexible production Lin, J. 151 LinkedIn 92 living conditions 17, 26, 34, 37, 113, 121 Louis Vuitton 129 Louis XVI 45 Lourça, F. 6 Luther, Martin 43 Lutheranism 43 Machado, Antonio 148 Machine-to-Machine devices 12

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Index management practices 23, 25 manufacturing regimes 4, 6, 14, 30–33, 47, 50, 77, 148–9, 153 see also individual regimes manufacturing revolution 5, 77, 105, 127, 136, 145, 149 see also fourth industrial revolution; industrial revolutions Marchetti, Federico 81, 83 Marconi, Guglielmo 29 market economies 5 market power 93–100 marketing 73–5, 79, 142 Marks, G. 140 Maserati 127–8 mass customisation 14, 60, 62–3, 69, 74–5, 153 mass production 6, 14, 17, 24, 26, 32–3, 35, 63, 153 Maxwell, James Clark 28 Mayzlin, D. 94 McAdam, John 20 McKinsey 51 Metcalf, John 20 microcomputers 29–30 Microsoft 92 Mikians, J. 99 Minecraft 92 Moazed, A. 84 Mojang 92 Mokyr, J. 19 Molins 30 Monsanto 89 Montulli, Lou 96 Moore, Gordon 28 Moore’s law 28 Morse, Samuel 40 Motor Vehicle University of Emilia-Romagna (MUNER) 127–8 Motorola 85 Mowery, D. 21 multi-homing 90 Musson, A.E. 24 MySpace 90

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nanotechnologies 4–5, 27, 49–50, 57–8, 111 National Robotics Initiative 55 NBIC technologies 49 see also individual elements NCD&BD 50, 75–6 see also individual elements Nelson, R. 17 Net-a-Porter 82–3 Netflix 97 Netherlands 36, 42, 54 Netscape 96 network effects 89–91 new materials 4, 27, 49, 58 Newcomen, Thomas 39 Newman, N. 95 news 100 newspapers 100 Newton, Isaac 34 Nokia 84, 90 Nutmeg 89 Obama, Barack 55 O’Brien, P. 23–4 Oculus VR 92 offshoring 63, 65, 69 oil 13, 26, 47 Open Handset Alliance (OHA) 85 Opium Wars 155 Pasteur, Louis 36 patents 19, 22, 24, 28, 39, 67, 70, 72, 109–10 Patto per il Lavoro see Labour Pact PayPal 86 Perkin, William 22 personal data 13, 59, 78, 80, 92, 94–6, 99–100 see also privacy Philip Morris 129 Philips 54 plastics 22, 27–8, 54 platforms 6, 50, 74, 76, 79–81, 83–6, 97–8, 104, 155 see also individual platforms definition and characteristics 86–93

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Poland 146 pollution 28, 59 population ageing 13, 55, 59 population growth 13 Porter model 73, 76 Portugal 40 poverty 59, 107, 120, 126 price discrimination 94, 99 privacy 6, 13, 59, 80, 86, 93–4, 98–100 see also personal data privacy paradox 94–5 product personalisation see mass customisation Putnam, R. 109 quantum computing 50 R&D 1, 10, 21, 29, 52, 56, 63, 65–70, 72–3, 75, 91, 96, 105, 109, 112, 118, 133, 141, 144, 146 Rapleaf 97 RCA 29 regional development agencies 111 renewable energy 27, 49–50, 57 rent seeking 151–2 Renzi, Matteo 112 Resende, J. 99 reshoring 1, 63 resilience 136–45 concept of 136–9 factors of 139–42 Robina 55 Robinhood 89 Robinson, J. 34, 146 robotics 4–5, 49, 51–6, 60, 69, 97, 127 Rochet, J-C. 90 Rodriguès-Pose, A. 107 Rosenberg, N. 21 Russia 6–8, 38, 155 see also BRIC countries Russian Revolution 155 Sabel, C. 109 Sachs, B.R. 94

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Safari 99 Samsung 84–5 sanctions 26 Sawyer, William 24 schools see education and training Schumpeter, Joseph 17 search rankings 80 Sematech project 29 semiconductors 29, 49, 57, 85 sensors 5 serfdom 33, 38 Shimuzu, K. 32 Siemens 29 Simon, H. 107 Siri 55 skills development 10, 38–47, 71–2, 104 see also education and training slave economy 37–8 smart factories 60, 67, 69, 76–7 smart manufacturing 50–51, 60–63, 67–70, 74, 76, 127, 153 smart specialisation 117, 123, 125, 147–8 smartphones 10, 12, 50–51, 59, 83–7, 92 Smeaton, John 20 SMEs 109–10, 112, 124, 126, 132, 142, 145–6, 150 Smith, Adam 16, 20, 34, 76 Snapchat 90 social data 59 socialism 38 South Africa 147 South Korea 52, 67, 70–72, 155 Spain 40 spam filtering 55 Spotify 97 Standard Oil 13 standards of living see living conditions start-ups 72, 81, 87, 91–2, 112, 125, 135 steam engines 23, 33, 39 Stühmeier, T. 90 sundial 105–9, 111–12, 114, 136, 147

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Index supercomputers 56–8, 130 Supple, B.E. 34 Switzerland 42 Symbian 84–5, 90 tablets 11–12, 74, 100 Taiwan 54 Taobao 85, 87, 91 tariffs 22 technological transfer 112, 128–9 telecommunications sector 28–31 Telefunken 29 Telford, Thomas 20 Tennant, Charles 39–40 Tirole, J. 90 Toyota 32, 55, 71 training see education and training TripAdvisor 94 Tumblr 92 Twitch 92 Twitter 74, 90, 100 Uber 13, 79–80, 86–8, 91 UK 9, 19–20, 22–5, 30, 33–9, 43, 64–5, 71–2, 107 education and training 40–43 unbundling 68–9 unemployment 23, 108, 120, 126 UNIPOL 133 United Nations 59 urbanisation 13, 36–7, 59, 117 US National Science Foundation 49 US Patent Office 24 USA 9–10, 22–4, 28–30, 35, 37–40, 42, 52, 54–5, 59, 61, 64–5, 67, 70–72, 82, 84, 87, 89, 91, 95, 98, 147, 155 education and training 46–7

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vaccination 36 value added 9–10, 64–5, 114, 120, 124, 133 value chains 76–7, 81, 88, 109, 135 global 1, 31, 62–73, 127 value creation 67–9, 86–7 van Musschenbroek, Pieter 21 Vanguard Initiative 146–7 Versailles Treaty 26 Volta, Alessandro 21, 24 Voltaire 45 voting 26, 42, 112 Walmart 86–7 Watt, James 39 Waze 92 welfare state 26 WhatsApp 90, 92, 98 Wheatstone, Charles 23 Whereto 92 Whitney, Eli 40 Whole Foods 88–9 Wikipedia 88 Williams, R. 41 winner takes all 86–8 Winter, S. 17 Wireless Telegraph Company 29 Womack, J.P. 32 Wood, C.A. 93 working conditions 26, 35, 117 World Bank 71–2 World War I 26 World War II 26–7, 29, 152 Yahoo 92 Yoox 73, 80, 88 YouTube 74, 95 Zipdash 92

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