Ict-driven Economic and Financial Development: Analyses of European Countries 0128137983, 9780128137987

Table of contents :
Cover
ICT-DRIVEN ECONOMIC
AND FINANCIAL
DEVELOPMENT:

Analyses of European Countries
Copyright
Acknowledgements
1
Introduction
Cutting-edge technologies-Setting the context
The goals
Structure and contents
References
2
The fifth technological revolution: context and background
Technology and technological change in the historical perspective
Technology, economy, society: A few words on techno-economic paradigms
ICT as GPT
Final note
References
Further reading
3
The digital revolution for development: Identifying the channels of impact
Technological change and ICT as opportunity windows
ICT for socio-economic growth and development
ICT, financial markets, financial innovation
References
Further reading
4
ICT development and diffusion: Evidence for Europe
The context
Sample and empirical data
ICT deployment in Europe: An overview
ICT diffusion paths, dynamics, and future scenarios
Developing ICT diffusion patterns
Fixed-to-mobile technological substitution?
Do prices matter?
Final note
References
Further reading
5
ICT and socio-economic development dynamics
The context and data explanation
General picture
Economic development: Towards a structural shift?
The evidence
Social development patterns: Paving the road ahead
Gaps growing-Gaps narrowing?
References
Further reading
6
ICT for financial development: Shaping the new landscape
Introductory
ICT and financial development
Financial market evolution trajectories
How ICT impacts on financial markets
ICT and financial innovations
References
Further reading
7
Conclusions
The questions
The answers
What are the dynamics of the process of diffusion of ICT across European countries and how are ICT diffusion patte ...
How does ICT deployment impact the process of social and economic development and convergence?
How does growing access to ICT contribute to the state development of financial markets?
Appendix
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Future ICT development scenarios
Appendix F
Appendix G
Appendix H
Appendix I
Appendix J
Economic and social convergence
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
W
Back Cover

Citation preview

ICT-DRIVEN ECONOMIC AND FINANCIAL DEVELOPMENT

ICT-DRIVEN ECONOMIC AND FINANCIAL DEVELOPMENT Analyses of European Countries

EWA LECHMAN ADAM MARSZK

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-813798-7 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisition Editor: Scott Bentley Editorial Project Manager: Susan Ikeda Production Project Manager: Mohana Natarajan Cover Designer: Mark Rogers Typeset by SPi Global, India

Acknowledgements This research is part of Project No. DEC-2015/19/B/HS4/03220 financed by the National Science Centre, Poland.

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CHAPTER ONE

Introduction Contents 1.1 Cutting-edge technologies—Setting the context 1.2 The goals 1.3 Structure and contents References

1 3 4 6

Technology is widely considered the main source of economic progress (…). The myth of Prometheus is nothing if not a cautionary tale of those uncontrollable effects of technology. —Mokyr, Vickers, and Ziebarth (2015)

1.1 Cutting-edge technologies—Setting the context Technological change has a unique ability to induce short-term disruptions, which usually unveil long-term benefits (Mokyr et al., 2015). ‘Technology is a vitally important aspect of the human condition. Technologies feed, clothe, and provide shelter for us; they transport, entertain, and heal us; they provide the bases of wealth and of leisure; they also pollute and kill. For good or ill, they are woven inextricably into the fabric of our lives, from birth to death, at home, in school, in paid work. Rich or poor, employed or nonemployed, woman or man, “black” or “white”, north or south—all of our lives are intertwined with technologies, from simple tools to large technical systems’ (Donald & Wajcman, 1986, p. 2). Despite the fact that the relationship between technology diffusion and socioeconomic development is usually hard to quantify and trace empirically, clearly, society, economy, and technology are fundamentally inseparable. In that sense, profound contextualisation of social and technological change becomes a prerequisite to understand the dynamics of society’s shaping by technology. Technological change often brings disruptive changes, which has far-reaching consequences usually unveiled in a long-run perspective. ‘The Great Escape’ of Deaton (2013) may never come if societies do not ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00001-4

© 2019 Elsevier Inc. All rights reserved.

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get assimilated and put into work technological progress embodying humans’ knowledge and ideas. The first industrial revolution, a pathbreaking event, enforced the global economic takeoff, and since the beginning of the 18th century, the world’s gross output started to grow at an unprecedented fast pace. Examining, even very superficially, the historical economic statistics provided in Maddison (2007), it is quite evident that, in terms of per capita income, the world has grown enormously after the 17th century. In 1500, the average per capita income of the developed world was at about 704 U$a; in 1700, it was 907; in 1820, 1132; and in 1998, it reached 21,470, which shows that within barely 180 years the average per capita income increased about 19 times. Whatever the case may be, technology has been central to the dynamics of the economy in the past two centuries (Mokyr, 2005, p. 1113). Since early seventies of the 20th century, the world has witnessed a process of rapid diffusion of new information and communication technologies (ICTs)—and undeniably, this process has enforced remarkable changes and structural shifts going far beyond the economic sphere of life. ICTs become fast available worldwide and, as already stated, the rate of diffusion was extremely high (Comin, Hobijn, & Rovito, 2006). The adoption of ICTs allows for a rapid growth of social networks (Castells, Fernandez-Ardevol, Qiu, & Sey, 2009), which generates economic advantages, such as, for instance, economies of scale (Economides, 1996; Katz & Shapiro, 1985), providing solid foundations for long-term economic growth and development. A dynamic growth of socio-economic networks is predominantly facilitated by the type of technological solution offered by ICTs. These are, for instance, wireless networks, which enable direct connections among society members, allowing for information and knowledge flows, regardless of the physical location of the agents, diminishing their economic and technological marginalisation. Cairncross (2001) writes about the death of distance showing that the geographic distance is no longer a barrier for various types of economic activities, as ICTs become widely adopted and used. A broad adoption of ICTs enables a fast and low-cost flow of knowledge and information (Quah, 2001; Venables, 2001). ICTs are claimed to be general-purpose technologies (GPTs) (Bresnahan & Trajtenberg, 1995), which means that they are technologies that enforce significant structural changes and pervasively impact society and economy. Bresnahan and Trajtenberg (1995) underline

a

1990 International dollars.

Introduction

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that a broad adoption of GPTs fosters radical changes of social norms and structures, which leads to the transformation of social systems and ways of doing business, heavily impacting the growth of productivity (Helpman, 1998) while providing a solid background for long-term economic growth and development. Moreover, ICTs have been profoundly reshaping global economic landscape, providing, inter alia, solid foundations for a rapid expansion of financial innovations worldwide. Undeniably, a fast spread of such instruments has been facilitated by the growing penetration of ICTs, which enables unrestricted and unbounded flows of information and knowledge among geographically separated actors. Many claim that differences among countries today in their economic growth dynamics and long-term trajectories are, to a large extent, determined by differences in their technological advancements and state of assimilation of new technological solutions. Society- and economy-wide adoption of new ICTs paves road ahead for changes in production, development of new cheaper goods and services, enhancement of international trade flows, and mobilisation of financial and human capital resources, which in the long-time perspective constitutes an important factor of international competitiveness of nations. Still, the potential benefits of ICT development are various, but hard to quantify. ICTs drive management efficiency and increase productivity, show the emergence of new export opportunities, and empower distance education. Some countries consider ICTs to be a major incentive to promote their social and economic development. By means of ICTs, they can quickly bridge the economic gap with world leaders and get huge returns on creating more wealth and jobs for highly skilled workers. But, does technological progress bringing critical changes into social and economic structures and life also allows for closing the gaps? Or, reversely, is uneven speed of technology diffusion about to enforce cross-country divergence and aggravate inequalities in terms of wealth? This work intends to challenge these questions, although the answers are partial and imperfect.

1.2 The goals This book is designed to extensively portray issues associated with the process of diffusion of ICTs in Europe, identifying potential channels of ICTs that impact on socio-economic and financial market development; and above all, it demonstrates changes in development patterns across European economies that have been subjected and determined by the

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emergence and country-wide application of ICTs. Put differently, we aim to identify how ICTs shape social, economic, and financial market development patterns and the dynamics of change and structural shifts across European economies. To ensure a logic flow of this research, we set three major questions to be answered, as follows: • What is the dynamics of ICT diffusion across European countries and how ICT diffusion patterns are shaped? • How ICT deployment impacts the process of social and economic development and convergence? • How growing access to ICTs contributes to the state development of financial markets? The analysis is restricted to the period between 1990 and 2017. This is the first book presenting Europe-wide empirical evidence on the relationships between ICT diffusion and its socio-economic consequences, which may be traced within the last 37-year period. It is broad in scope and well contextualises the process of structural changes in Europe, trying to identify the major breakthrough changes.

1.3 Structure and contents This book contains seven logically structured chapters. This Chapter is the introduction itself. Chapter 2 offers a discussion on technology, technological progress, and the successive technological revolutions of modern history. It briefly demonstrates how technology and technological progress are defined; it defines the role of technological change in creating the wealth of nations and presents it as a driving force of global shifts observed during ages of economic development of the world. In addition, this chapter explains the main ideas behind the concept of techno-economic paradigms and identifies the major channels through which technology, society, and economy are impacting one another. Next, it introduces the term ‘Fifth Technological Revolution’, also labelled the ‘Digital Revolution’, and explains why new ICTs are treated as GPTs. It underscores the major points underlying the advantages of ICTs over ‘old’ technological solutions. Next Chapter 3 continues the discussion on the role new ICTs play in the process of enhancing the process of social, economic, and financial developments. This chapter examines various aspects of the relationship of ICTs with social and economic development: why ICTs are treated as

Introduction

5

opportunity windows; why they are a prerequisite for socio-economic development; and how they can affect the performance of financial markets. It briefly discusses the channels through which ICTs can affect social, financial, and economic development. Chapters 4, 5, and 6 are entirely empirical. First Chapter 4 unveils the pattern of ICT diffusion in 32 European countries between 1990 and 2017. It provides a detailed analysis of country-specific curves, using four core indicators of ICT diffusion: mobile-cellular telephony, fixed broadband networks, active mobile broadband networks, and Internet users. In this chapter, we highlight the unique features of the process in each of the sample economies, illustrating the dynamics, country-specific patterns, and future development scenarios. It also examines changing cross-country inequalities in access to and use of new ICTs. To enrich the picture, this chapter also briefly examines the emerging process of technological substitution that demonstrates a gradual switch from ‘old’ to ‘new’ technology. All the data used to study the process of ICT diffusion are entirely derived from the World Telecommunication/ICT Indicators database 2017 and 2018. Chapter 5, using data on 32 European countries between 1990 and 2017, shows the process of social and economic development. With this aim we have selected a bundle of 15 different economic variables (out of which 6 are ICT trade-related), and 9 different social variables. Such an approach allows capturing changes and shifts observable across economies in the overall social and economic welfare. It additionally sheds light on various aspects of social and economic spheres of life that, as we claim, may be both directly and indirectly impacted by the technological change generated by a wider adoption and usage of new ICTs. This chapter unveils development trends and shows structural shifts across European economies. It aims to examine whether ICT deployment may be treated as an important factor driving the overall social and economic welfare. It additionally tests the process of social and economic convergence, aiming to uncover if increasing access to and use of ICTs diminishes cross-country disparities. Chapter 6 continues the empirical study and presents results of the analysis of links between the diffusion of ICTs and financial development in European countries in 1990–2016; the sample is identical as in Chapter 5. The analysis is divided into three parts. Its first part covers financial development at large—the development of banking sector and insurance industry (as well as some additional elements, i.e. savings and electronic payments). The second part demonstrates the results for financial markets, with a particular focus on stock markets and a brief examination of bond markets.

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In the final, third part, the main area of interest is financial innovations, in particular, exchange-traded funds (ETFs); additionally, the significance of ICTs for mutual funds is also studied. The structure of the analysis is similar in all three parts. First, in each case we provide an introductory analysis of changes in the levels of various aspects of financial development based on timelines, and then a cursory analysis of the distribution of variables based on density functions. Second, we examine the results of the nonparametric analysis (local polynomial regressions) for pairs of variables—in each case we juxtapose one of the ICT indicators to a select non-ICT variable; in some cases, the analysis is supplemented by correlation coefficients. Finally, we interpret the estimates of panel models with a single explanatory variable (one of the ICT indicators) in which the respective indicators of financial development are used as dependent variables. The utilised data sources are World Telecommunication/ICT Indicators, the IMF Financial Development Index, and the Global Financial Development Database; additional databases are used for the analysis of certain aspects of financial development (e.g. development of ETFs). In order to be able to formulate general conclusions, we concentrate mostly on the results based on panel data rather than country-specific evidence. Finally, Chapter 7 briefly discusses conclusions derived from empirical parts of this work.

References Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies ‘Engines of growth’? Journal of Econometrics, 65(1), 83–108. Cairncross, F. (2001). The death of distance: 2.0: How the communications revolution will change our lives Texere. Castells, M., Fernandez-Ardevol, M., Qiu, J. L., & Sey, A. (2009). Mobile communication and society: A global perspective. MIT Press. Comin, D., Hobijn, B., & Rovito, E. (2006). Five facts you need to know about technology diffusion. National Bureau of Economic Research (No. w11928). Deaton, A. (2013). The great escape: Health, wealth, and the origins of inequality. Princeton University Press. Donald, M., & Wajcman, J. (1986). Introductory essay: The social shaping of technology. idem (Eds.) (pp. 2–25). New York: McGraw Hill. Economides, N. (1996). The economics of networks. International Journal of Industrial Organization, 14(6), 673–699. Helpman, E. (Ed.), (1998). General purpose technologies and economic growth: MIT press. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440.

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Maddison, A. (2007). Contours of the world economy 1-2030 AD: Essays in macro-economic history. Oxford University Press. Mokyr, J. (2005). Long-term economic growth and the history of technology. In vol. 1. Handbook of economic growth (pp. 1113–1180): Elsevier. Mokyr, J., Vickers, C., & Ziebarth, N. L. (2015). The history of technological anxiety and the future of economic growth: Is this time different? The Journal of Economic Perspectives, 29(3), 31–50. Quah, D. (2001). ICT clusters in development: Theory and evidence. EIB Papers, 6(1), 85–100. Venables, A. J. (2001). Geography and international inequalities: The impact of new technologies. Journal of Industry, Competition and Trade, 1(2), 135–159.

CHAPTER TWO

The fifth technological revolution: context and background Contents 2.1 Technology and technological change in the historical perspective 2.2 Technology, economy, society: A few words on techno-economic paradigms 2.3 ICT as GPT 2.3.1 Final note References Further reading

9 23 35 42 42 51

2.1 Technology and technological change in the historical perspective The Industrial Revolution created a critical juncture that affected almost every country. nations (…) not only allowed but (…) encouraged commerce, industrialization and entrepreneurship grew rapidly. Many, such as the Ottoman Empire (…), lagged behind as they blocked or (…) did nothing to encourage the spread of industry. —Landes (2003)

Technological change and human progress are historically inseparable. Throughout the ages, we observe a continuous interplay between technology, technological change, and socio-economic development (Galor & Tsiddon, 1997). Undeniably, the dynamics of technological change and economic growth are mutually conditioned, as technology and all the knowledge embedded in it profoundly affects the way societies and economies work (Inglehart & Welzel, 2005; Nelson & Phelps, 1966). The argument set forth by Saviotti (1997) makes it clear that technological progress contributes to economic change not only qualitatively but also quantitatively, stimulating the emergence of new products and services and boosting the demand for them. Mokyr, Vickers, and Ziebarth (2015) noted that ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00002-6

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‘technology is widely considered as main the source of economic progress’ (p. 31), while Franko (2018) emphasised that economic growth pulls societies out of material poverty, the low-productivity trap, and enables them to climb up the development ladder. In Platteau (2000), we learn that institutions and societies as such, with their norms and attitudes, culture, and value systems, also heavily condition the acquisition of economically efficient technologies; put differently, technological change translates into social development and economic wealth only when it is accepted by individuals in the society (Drucker, 2017). Castells and Cardoso (2006) contend that technology often becomes the main driving force in societal transformation, even though technology alone is not a sufficient condition for it. These works have established a number of generalisations concerning technological change and socio-economic development; they direct our attention to more extensive research on how and why technology determines shifts in economies, redefines the way in which people communicate, and ushers in different types of network that in the long run become driving forces of both economies and societies (Hakansson, 2015; Metcalfe, 2018; Rosenberg, 1969). And while a precise definition or quantification is difficult, it is widely acknowledged among scholars that technology and technological knowledge are fundamental elements in economic, social, institutional progress (Rosenberg, 1994), although it must be borne in mind that the impact of technology is usually neither direct nor easy to measure (David, 1999; Triplett, 1999). Before we start putting the puzzle together, let us attempt, at least in part, to capture the generic meaning of technology and technological change. Technology and technological change are inherently characterised by complexity, interdependency, and multidimensionality (Lechman, 2017). In fact, the literature offers a solid if varied body of definitions of technology, which, although differing, do interestingly share one common element, namely, knowledge, even though direct connotations are not always easily recognised. For instance, in Singer and Williams (1954), we read that technology may be defined as ‘how things are made or done’ (I:vii). This short and simple, albeit indirect, definition directs our attention to knowledge; the word ‘how’ implies that human action, skills, and knowledge are all involved in the creation of technology. Campbell, Wang, Hsu, Duffy, and Wolf (2010) perceive technology in a similar vein, through the lens of developed tools, crafts, and techniques that, if adopted by the society, serve to control the production process and possibly the environment. Comin, Hobijn, and

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Rovito (2006), in their definition of technology, stress the importance of knowledge in developing new technologies, which allow people to use new methods and technical processes. This straightforward conception of technology can also be found in Olsen and Engen (2007), who stress that technology is frequently perceived exclusively in terms of tools, machines, or devices for use in the production of goods. And in Gomulka (2006) we read that technology may be simply defined as a bundle of techniques that are used to produce goods. The view of Olsen and Engen (2007) is to some extent consistent with the proposition of Pinch and Bijker (1984) and Bijker, Hughes, and Pinch (1987) that technology may be understood as both artefact and knowledge. Clearly, even when technology is narrowly defined and viewed only from the standpoint of new techniques and tools for material production, it always embodies human knowledge. Dosi (1982) emphasises that tacit knowledge demonstrated through technological change can help to solve both practical and theoretical problems that arise in the production process, and in this sense, technological progress is the demonstration of human knowhow and skills. Qualitatively similar arguments for the thesis that technology embodies human knowledge are also found in Wilson and Heeks (2000) or Collins (1990), all arguing that technology is a specific kind of activity designed for people’s application of their entire stock of knowledge. In J. Mokyr’s influential Gifts of Athena: Historical Origins of the Knowledge Economy (Mokyr, 2002) we read that ‘technology is knowledge, even if not all knowledge is technology’ (p. 2). An analogous understanding of technology is to be found in the conceptual works of Law (1991) and Bijker and Law (1992), while Arrow (1962), Dosi (1988), and Pavitt (1999) claim that in addition to human knowledge, technology as such and technological progress encompass another important component, namely, information. In their view, technological change is an outcome, the end result of accumulated knowledge and information, but at the same time technology also facilitates flows of knowledge and information among social and economic actors. This dual role of technology and technological change, in society and the economy, is essential. That is, technology is not only a desired result of knowledge but also itself a force for change in the socio-economic environment, inducing further propagation of both technology and knowledge. The concept of technology as knowledge (Mokyr, 2002, 2013) carries far-reaching implications. First, it defines technology as a ‘product’ of the human brain, thought, and intelligence, embodying the knowledge

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accumulated through the ages. Second, it means that technology can serve as a tool to propagate and transmit this knowledge across societies. Needless to say, technology and technological change have always been at the centre of human interests. The modern era can be termed the era of technology-led economic growth. In the past two centuries, per capita output has increased dramatically, and the increase has happened in a sustained manner, as never before in history. The universal consensus associates the beginning of these phenomenally rapid economic advances with the Industrial Revolution. Angus Deaton, in his book The Great Escape: health, wealth, and the origins of inequality (Deaton, 2013), writes: ‘The desire to escape is always there. (…) New knowledge, new inventions, and new ways of doing things are the key to progress’, and ‘Economists think of eras of innovation as powering up waves of “creative destruction”. New methods sweep away old methods, destroying the lives and livelihoods of those who were dependent on the old order’ (p. 9, 10). This passage suggests just how powerfully technology and technological change can impact society and the economy; how disruptive and profound they can be in destroying the status quo and driving the emergence of a new social, institutional, and economic order. Therefore, it is easy to conclude that technology is useful for social and economic development, but if it is to truly transform economies and societies, it must be widely accepted, adopted, and used by individuals and firms. The unbounded diffusion of technology is critical. If technologies and knowledge do not come into widespread use, their impact on society and economy remains negligible. In pre-industrial times the rate of economic growth was regularly negligible or even nil (Cipolla, 2004; De Vries, 1994). Deane (1979) and Hartwell (2017) argue that in pre-industrial society the increase in material wealth was painfully slow and easily reversible; there was no fundamental upward trend in economic activity. For centuries, living standards and average material well-being rose little if at all, while the population was growing dramatically ( Jones, 2001). This is not to say that pre-industrial economies were completely without technological advance. In fact, even medieval European societies made path-breaking inventions (Mokyr, 2005a, 2005b) and produced a wide variety of goods and services. The Middle Ages saw the invention of paper, mechanical clocks, and gunpowder, to cite just a few examples. Inventions such as navigational instruments and innovations such as Arabic numerals were relatively broadly adopted among these societies (Aiyar, Dalgaard, & Moav, 2008; Crone, 2015). All of these constituted a kind of technological progress that societies could benefit from, but before

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about 1750, most people were too poorly educated and knew too little to convert this technological progress into long-term growth in wealth (Bell, 1976). In a way, technological advances in pre-modern societies remained uncodified and informal; they were rarely diffused throughout the society. Knowledge was mainly tacit and hard to transmit. As noted by Mokyr (2005b), individuals in pre-industrial societies were not in a position to lay the intellectual groundwork for technological progress; indeed, the impact of what inventions there were on material well-being remained barely detectable. ‘The quality of life failed to improve in any (…) observable dimension. (…) nor the variety of material consumption improved. (…) For the majority of the English as late as 1813 conditions were no better than for their naked ancestors of the African savannah’ (Clark, 2008, p. 1). In order to work effectively, knowledge and technology must be shared among individuals. Those pre-industrial societies were locked in the Malthusian trap (Nelson, 1956; Steinmann, Prskawetz, & Feichtinger, 1998), and any gains in income, thanks to technical advances, were immediately swallowed up by population growth (Galor & Weil, 1999; Wood, 1998). Still, the pre-1750 period did produce several episodes of economic growth, which, according to economic historians, was enhanced by institutional change. The period of so-called ‘Smithian growth’ (Barkai, 1969; Kelly, 1997) consisted of some economic growth that enhanced the increase in economic output that was generated by commercial progress but not by technological change. Improvements in the quality of institutions (North, 1990; Shleifer & Vishny, 1991; Baumol, 2002; Greif, 2003) made it possible to take advantage of the economies of scale that were emerging in trade, which sparked competition among market agents, in turn stimulating efficiency gains and better resource allocation. Apparently, sound institutions, trust, the introduction of money, and credit institutions were solid foundations for economic growth even in the absence of rapid, deep-going technological advance. Evidently, economic growth before 1750 was primarily based on Smithian and Northian effects, namely, the benefits of trade and efficient allocation of resources. ‘The wealth of Imperial Rome and the flourishing of the medieval Italian and Flemish cities (…) were based (…) on commercial progress, (…) woolen cloth production in Flanders or the production of glass in Venice’ (Mokyr, 2005a, p. 1119). Those pre-industrial times have been called a ‘consumer revolution’ (Breen, 1988), which unquestionably produced a significant rise in income prior to 1750 (Weatherill, 2002). Some economic historians suggest that the peak of the ‘consumer revolution’ can be dated to between 1680 and 1720, and

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contend that without this significant boost in consumer demand the historical success of the First Technological (Industrial) Revolution would remain inexplicable. Mokyr (2005a, 2015b) writes, ‘On the eve of the Industrial Revolution, large parts of Europe and some parts of Asia were enjoying a standard of living that had not been experienced ever before, in terms of the quantity, quality, and variety of consumption’ (p. 1118). This created the groundwork for the Technological Revolution of 18th-century England. On the eve of the Industrial Revolution, England seemed to be relatively well prepared for what was about to arrive. The Glorious Revolution of 1688, Parliament’s Bill of Rights in 1689 (Landes, 2003), and the institution of the Bank of England in 1694 as the source of funds for industrial development effectively paved the way for the Industrial Revolution. The profound transformation of English institutions towards greater pluralism, the strengthening of property rights, the economic reforms passed by the Parliament to promote manufacturing, and the first ‘financial revolution’ gave solid background for the emergence of inclusive economic institutions. As Landes (2003) emphasises, ‘also significantly, after 1688 the state began to rely more on talent and less on political appointees, and developed a powerful infrastructure to run the country’ (p. 197). The Manchester Act of 1736 stimulated the nascent cotton manufacturing industry, with far-reaching social and economic effects throughout the rest of the 18th century; technological advance in textiles played a dominant role in the First Technological Revolution, profoundly reshaping social and economic structures. ‘The Industrial Revolution was manifested in every aspect of the English economy. There were major improvements in transportation, metallurgy, and steam power’ (Landes, 2003, p. 197). In similar vein, Peter Stearns writes, ‘The industrial revolution was a global process from the first. It resulted from changes that had been occurring in global economic relations. (…) It has changed the world. (…) Focused on new methods (…) for producing goods, industrialization has altered where people live, how they play, and how they define political issues—even, many historians would argue, how they have sex’ (2018, p. 1). It is hardly possible to date the First Technological Revolution or First Technological Wave exactly, but this turning point in the world economic history is conventionally placed somewhere between the 1770s and the 1840s. This revolution brought radical changes in the social, institutional, and economic spheres, eventually lifting the masses out of material misery and creating decent living standards. A series of minor and major inventions,

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introduced initially in the cotton textile industry, allowed the transformation of domestic manual work (cottage industry) into factory production. The textile industry grew dramatically; mechanisation and the massive application of machinery produced exponential gains in productivity. In Teich and Porter (1996), we read that ‘the cotton industry presented the most dramatic example of rapid transition from traditional (…) and loosely organized (…) system to rationally managed (…) factory system using large-scale machinery’ (p. 17). However, the textile revolution triggered by the spinning jenny was not the sole achievement of the First Industrial Revolution. Much also changed in iron-producing techniques, and the steam engine was invented. These two last inventions were of seminal importance for a further profound mechanisation of production; even more important, they ushered in totally new industries, bringing not only quantitative changes but also structural shifts in the English economy. Across England there emerged waterways, canals, turnpike roads that engendered novel industrial networks and greatly increased the country’s economic potential. This first period of extraordinary innovation and economic transformation was followed by a Second Industrial Revolution between the 1830s and 1870s. This era, also dubbed the ‘Age of Steam and Railways’ (Briggs, 1982; Crump, 2007), was a period during which the full potential of the main innovations of the First Industrial Revolution—the steam engine and steam-powered railways— were fully unleashed. The Second Industrial Revolution unfettered the full potential of the productivity of labour and capital. Economic growth took off. According to Jones (2013), by 1861 only 21% of the English labour force was engaged in agriculture. Steam engines, the ramifying canals and waterways, the explosive expansion of railways, the postal and telegraph services, great international ports and depots all helped the formation of new networks, which made for increasing interconnectedness, but also interdependency, among the social and economic actors. Obviously, those networks quickly became the driving force of economic growth, facilitating the expansion of trade and cooperation between firms. Unexpectedly, however, all this led to uncontrolled capital accumulation and the advent of large corporations. In the ensuing decades, the formation of trusts and the tendency towards monopoly became a crucial issue. Since the late 18th century, the English economy had gradually grown not only in material wealth but also in complexity, with profound structural transformations. The question is whether it was during the First and the Second Technological Revolutions that modern economic growth actually

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began (Clark, 1996; Kuznets & Murphy, 1966). Arguably, the period from the 1750s to the 1870s was the time when economies started to record regularly higher growth rates than in the past; when technology began to have ever-increasing weight in the generation of material wealth; and when economic growth accelerated radically. As Maddison (2007a) calculates, the estimated average annual growth of 0.15%–0.20% in Western Europe between 1000 and 1500 AD, marked, moreover, by high volatility and frequent setbacks, was supplanted in the later 19th century by much steadier, and faster, growth of 1.5% annually (Galor & Weil, 2000). While, of course, the First Technological Revolution cannot be taken as the starting point in economic history, it undeniably constituted a watershed separating Malthusian and post-Malthusian socio-economic regimes, impossible without a sharp acceleration in the pace of technological advance and consequently economic growth (Galor & Weil, 2000). With the industrial revolutions, the prime factor in the expansion of the economy came to be technical progress and hence productivity leaps rather than population growth. The causal links between technology, economy, and demography cannot be neglected. As the First Industrial Revolution dawned, the Malthusian world came to an end; the expansion of output no longer depended strictly on population growth and access to arable land but was increasingly technology- and productivity-driven. The 1880s saw the advent of the ‘Age of Steel, Electricity and Heavy Engineering’—now also known as the Third Technological Revolution (Rifkin, 2011). This relatively brief epoch—historians tend to define it as running from the 1880s to World War I—was characterised by the very widespread adoption in a broad range of industries of cheap steel, copper, cables, and other electrical equipment (Stine, 1979). These sparked the take-off in civil engineering and chemicals and a rapid expansion of communication and transportation networks. Railway, telephone and telegraph, and electricity networks spread economy-wide, establishing solid foundations for an extensive development of various sectors. Giant corporations arose, along with trusts and cartels (Greenwood, 1997). The British economy faced a serious threat of massive monopolisation. With the first decade of the 20th century, the West underwent its Fourth Technological Revolution: the ‘Age of Oil, Automobile and Mass Production’ dawned as all the inventions of past technological revolutions now actually ‘started to work’. Cheap oil, other petroleum fuels, and synthetic petrochemicals were used massively not only by industry but also by consumers. Networks of railways, roads, ports, and airports allowed further

The fifth technological revolution: context and background

17

development, heightening demand, on the one hand, and boosting production and trade, on the other. Electricity now began to be used not only in industry but also domestically. Telecommunication networks flourished. Inventions made in earlier technological revolutions played a major role as the drivers of structural change in the society and the economy. They reshaped and extended market boundaries and opened up new opportunities. Such fundamental changes came, thanks mainly to the diffusion of those innovations, progressive mass access to the knowledge embodied in them. Economies started to be more efficient, more productive, and more complex. The ‘Industrial Enlightenment’ (Mokyr, 2005a) gave rise to new manufacturing and service industries, networks, the explosive growth of mass production, the intensification trade flows, and so on, contributing significantly to the creation of wealth. The history of technological revolutions demonstrates that, to be useful to the society and the economy, knowledge and technology must spread and be sustained, and ‘as far as future technological progress and economic growth are concerned, not even the sky is the limit’ (Mokyr, 2005a, p. 1180). The Fourth Technological Revolution appeared to be on the wane by the early 1970s, as totally new and pervasive innovations saw daylight. The Fifth Technological Revolution was now reality. Societies and economies observed radical path-breaking inventions that completely reshaped social and economic life. This new ‘Age of Information and Telecommunications’ offers cheap microelectronics, computer hardware and software, and telecommunication tools. The Fifth Technological Revolution has forged a new digital world: high-speed optical fibre allowing for fast data transmission, and mobile telecommunication instruments that are flexible and multipurpose (Freeman, Louc¸a˜, & Louc¸a˜, 2001; Perez, 2003). Since the 1970s, digital technologies have invaded societies and economies, so this is often labelled the Digital Revolution (Brynjolfsson & McAfee, 2014; Helbing, 2015; Perez, 2010). The emergence of the ‘digital economy’ (Brynjolfsson, Diewert, Eggers, Fox, & Gannamaneni, 2018; Tapscott, 1996) is driven by massive and unbounded flows of information and knowledge, making the physical location of agents practically irrelevant, and at nearly zero cost. Digitalised information and knowledge become a strategic resource, the main factor in comparative advantage. Digital technologies have created new types of network in which growth is enhanced by externalities (Katz & Shapiro, 1985, 1986); these networks are gradually becoming a decisive element in the way organisations, economies, and societies function.

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Historically, it is evident that the impact of technological change on economic growth and development is fundamental. Through the ages, technology has been economically important. Technology and knowledge have been decisive to nations’ economic power, their share in global production, and thus their material wealth. Obviously, before the First Industrial Revolution the world was locked in the Malthusian trap; economic systems were a sort of ‘zero-sum game’ in which a gain in wealth by one individual came only at the expense of another. If someone was to win, someone else had to lose. As we know from the historical statistics calculated by Maddison (2007b), between years 1 and 1500 AD, the average per capita income hardly changed at all. People were equally poor for hundreds of years; average per capita income growth for a millennium and a half was a bare 0.13% per annum, and from 1500 to 1820, it was still just 0.15%. In pre-industrial times economic growth was very slow, spasmodic, easily reversed; and per capita income growth was indecently low. In fact, until the end of the 18th century there was simply no increase in per capita output. The Malthusian trap meant that the expansion of the economy depended strictly on the growth of population. Still, even then there were multiple inventions, but owing to the lack of mass education and the limited diffusion of innovations, their impact on society and economy remained almost imperceptible. Obviously, positive feedback from ‘pre-industrial technological progress’ was observable but only in the short run. Over the long-term horizon, the short-run benefits vanished, swallowed up by population growth. In effect, people lived permanently at a bare subsistence level. In pre-1750 economies even the productivity improvements generated by technological change never led to permanent improvements in living standards. Fig. 2.1 illustrates the fact that prior to the industrial revolution, improvements in material well-being were practically unheard of. The left-hand graph in Fig. 2.1 shows the dramatically unequal regional distribution of economic power—approximated by share of global GDP—of regions before the First Technological Revolution. Note that Western Europe and its Offshoots, before AD 1000, generated something like 10% of total world’s gross output. In that year, according to Maddison (2007b), Western Europe, Eastern Europe, and the Western Offshoots, together with the areas that would eventually make up the USSR, generated less than 15% of gross world production, while Asia accounted for 68%, including 23% in China and 28% in India. For the next 500 years these shares changed only marginally, with some relative gain for Europe and Russia as compared with the overall world economy.

.35 .3

1.0e+07

.25 8.0e+06 .2 6.0e+06 .15 4.0e+06 .1 2.0e+06

.05

0

The fifth technological revolution: context and background

Share of global GDP by region, period 1–2008.

GDP total by region, period 0–2008. 1.2e+07

0 0

200

400

600

800

1000 1200 1400 1600 1800 2000

Western Europe

East Europe

Former USSR

Western offshoots

Latin America

China

India

Asia

Africa

0

200

400

600

800

1000 1200 1400 1600 1800 2000

Western Europe

Eastern Europe

Western offshots

Latin America

Former USSR China

India

Asia

Africa

Fig. 2.1 Regional GDP and regional share of global GDP, AD 1-2008. (Based on data derived from Maddison (2007b). The world economy volume 1: A millennial perspective volume 2: Historical statistics. Academic Foundation; GDP expressed in 1990 International Geary-Khamis dollars.) 19

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ICT-driven economic and financial development

Even a cursory look at historical world population statistics (Maddison, 2007b) shows the indisputably close relationship between population and production. In 1000, Asia had 66% of world population and generated 68% of total world production; in Western Europe, the comparable numbers were 9.6% and 9.0%. Until 1700 we detect further, but neither rapid nor radical, shifts in relative economic power (see the calculations provided in Table 2.1). Undeniably, the gain in Europe’s share of global output was the effect of the growing importance of innovations and the ‘consumer revolution’ in the 16th and early 17th centuries. Szpak (2001) and Sanyal (2014) label this period ‘pre-capitalism’ or ‘early capitalism’. This period saw a gradual loss in the economic power of Asia, including China and India, while Europe steadily improved its global position. The new economic system that was gradually emerging, with farreaching social and economic change that overwhelmed significant parts of the European continent, laid down solid foundations for the First Industrial Revolution. Acemoglu and Robinson (2012) call this technological breakthrough the ‘turning point’ in world economic history, and in fact there is no question that it utterly disrupted the pre-existing economic order, the economic status quo, which had remained untouched for centuries. It spelt the definitive end of the Malthusian epoch of slow, populationdependent, low-productivity growth and, as is argued in Hobsbawm (2010), ushered in the modern age of technology-led economic development. There was now a solid basis for leaps in productivity and—in the long-run horizon—rising incomes and personal welfare. The First Technological Revolution ignited the ‘global economic take-off’ or ‘take-off into self-sustained’ growth, permanently transforming the world economy and social structure. From that moment on, a country’s economic output was no longer strictly subject to its population size but became productivity-driven. Growth gained dynamism, stability, and irreversibility. Fig. 2.2 (left-hand graph) draws the new world contours shaped by the Industrial Revolution, the phenomenon labelled ‘global shift’ (Clark & Wo´jcik, 2018; Dicken, 2007), which determined long-term radical changes in regional shares of global output, their relative economic power. The numerical evidence summarised in Table 2.1 shows the changes in the dynamics of economic growth in world regions in the first two millennia of the Common Era (1-2008), highlighting the pervasive shift in economic output at the beginning of the Industrial Era. The period between 1700 and 1900 was characterised by fast economic growth, especially in the Western Offshoots, Western and Eastern Europe, and Russia. The regions of Asia, Africa, and Latin America developed at a significantly slower

1 1000 1500 1600 1700 1820 1870 1900 1913 1940 1950 1960 1980 1990 2000 2008

– 0.76 4.04 1.48 1.24 1.96 2.31 1.84 1.34 1.48 1.04 1.06 1.02 1.01 1.04 1.01

– 1.33 2.58 1.39 1.23 2.19 2.01 2.04 1.32 1.37 1.00 1.06 1.01 1.29 1.04 1.04

– 1.82 2.98 1.35 1.42 2.33 2.22 1.84 1.51 1.81 1.21 1.10 1.00 0.98 1.08 1.05

– 1.67 1.50 0.82 0.91 16.21 8.26 3.11 1.68 1.79 1.56 1.03 1.00 1.02 1.04 1.00

– 2.04 1.60 0.52 1.69 2.35 1.83 2.63 1.68 2.08 1.65 1.07 1.05 1.01 1.04 1.04

– 1.03 2.25 1.55 0.86 2.76 0.83 1.15 1.11 – – 0.97 1.03 1.04 1.09 1.07

– 1.00 1.79 1.23 1.22 1.23 1.21 1.26 1.20 1.30 0.84 1.07 1.07 1.05 1.04 1.07

– 1.52 1.59 1.20 1.20 1.29 1.39 1.67 1.40 3.58 0.62 1.08 1.03 1.07 1.05 1.02

– 1.71 1.41 1.21 1.10 1.21 1.45 1.46 1.20 1.98 1.29 1.04 1.05 1.01 1.04 1.06

– 1.15 2.05 1.33 1.12 1.87 1.60 1.78 1.39 1.65 1.19 1.05 1.02 1.02 1.05 1.03

The fifth technological revolution: context and background

Table 2.1 Dynamics of change of GDP total by region, AD 1-2008 Year Western Europe Eastern Europe Former USSR Western Offshoots Latin America China India Other Asia Africa World

Note: GDP expressed in 1990 International Geary-Khamis dollars; dynamics calculated as chain index. Based on data derived from Maddison (2007b). The world economy volume 1: A millennial perspective volume 2: Historical statistics. Academic Foundation.

21

22

Shares of global GDP by region, period 1–1870.

Shares of global GDP by region, period 1820–1960. –4

–3

–3

–2

–2

–1

–1

0

0 0

500 Western Europe Western offshots India

1000 Eastern Europe Latin America Asia

1500

2000 Former USSR China Africa

1800

1850 Western Europe Western offshots India

1900 Eastern Europe Latin America Asia

1950 Former USSR China Africa

Fig. 2.2 Share of global GDP by region, AD 1-1870 and 1820–1960. (Based on data derived from Maddison (2007b). The world economy volume 1: A millennial perspective volume 2: Historical statistics. Academic Foundation; GDP expressed in 1990 International Geary-Khamis dollars.)

ICT-driven economic and financial development

–4

The fifth technological revolution: context and background

23

pace, lagging behind and losing economic power. The onset of the First Industrial Revolution produced rapid and profound changes in the global economic landscape, the economic contours of the world. Starting in the 19th century we observe gradual losses in economic power in China and India, while Western Europe benefited from the technical advance brought by the Industrial Revolution (Fig. 2.2 traces the changes in regional shares of global output since 1800). Western Europe forged ahead, while Asia and the rest of the world lagged. The fast-growing role of the Western Offshoots (Australia, Canada, New Zealand, the United States) is plain to see. These areas’ share of global output soared from 2% in 1820 to 10% in 1870 and 18% in 1900. In the decades that followed, the entire Western world developed dynamically, gaining uncontested world economic dominance. In 1960 the West accounted for 56% of global output, China and India just 9%. This shows how overwhelming the technological breakthrough can be for an economy and how technological change reshapes world economic contours. Undeniably, industrialisation and the series of technological revolutions it brought about constituted one of the major forces shaping the past and present world.

2.2 Technology, economy, society: A few words on techno-economic paradigms As Morgan (1980) observes, the concept of paradigm introduced by Thomas Kuhn (2012) can be understood and interpreted in three main ways. First, a paradigm defines a complete view of reality, a way of seeing and perceiving things and their interrelations. Second, the paradigm may relate to certain schools of thought, possibly social or economic, that are connected with specific scientific achievements. Third, the paradigm may relate to some specific tools employed in the process of solving scientific puzzles. Always, however, whichever of these is the case, the paradigm in itself denotes, meta-theoretically or philosophically, a particular view of reality. This ‘worldview’ may encompass different schools of thought, each having its own, broadly accepted, way of studying and interpreting reality. In a sense, the paradigm reflects a network of various schools, hence different views, perspectives, and approaches to reality. Lehnert (1984) writes that the paradigm provides researchers with ‘topics, tools, methodologies, and premises’ (p. 22). Still, paradigms are not fixed; they can change in the course of history (Koschmann, 1996); they may be extended, adjusted, refined, and redefined ‘under new and stringent conditions’ (Kuhn, 2012, p. 23).

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As Kuhn (1981, 2012) observes, with time, inventions and scientific progress give rise to new paradigms supplanting the old. This may stem simply from the force of logic, the intensity of fundamental changes, and experience. As is emphasised by Eckberg and Hill Jr (1979), paradigms may be perceived as a way of analysing economic development, a complex and long-term process driven by interdependent elements. Evidently, the first way of viewing paradigms is definitely the broadest one, and probably the most suitable for understanding the complex nature of relationships between society, economy, and technology that are our main concern in this work. Technology, society, and economy constitute a complex system whose elements are fundamentally inseparable (Rosenberg, 1982), linked together by two-way causality (Lechman, 2015). They are inherently highly interdependent, reciprocally forging a complex system characterised by dynamism and the ability to change (Grubler, 1998). This ‘socioeconomic-technological system’ is usually prone to external shocks that upset its internal equilibrium. This system demonstrates a certain reactivity when it is thrown off balance, moving towards a new equilibrium. These unique, balanced systems evolve the trajectory of change subject to the external environment, the legal and institutional framework, religion, the current state of economic development, and many other factors, which are often proved to be hard to identify or capture (Mowery & Rosenberg, 1991; Gr€ ubler, 2003). Undoubtedly, technology makes societies and economies progress, move ahead, and climb up the development ladder. But societies obviously remain complex systems, encompassing individuals sharing common values, norms, and attitudes (Krohn, Layton Jr., & Weingart, 2012). An ‘open-minded society’ appears to be a fundamental prerequisite for making technology actually work to produce economic well-being. Only societies that are internally flexible, adaptable to a changing environment, and willing to assimilate new ideas and knowledge can truly convert technological progress into wealth for their members (Castells, 1997; MacKenzie & Wajcman, 1999). As Section 2.1 notes, technology encompasses technological progress, different methods, and knowledge of how things work (Comin et al., 2006; Lechman, 2015). In Fagerberg, Srholec, and Verspagen (2010) we read that technology constitutes a knowledge set that if put to work maximises economic efficiency and the productivity of capital and labour. The fundamental works of such scholars as Layton Jr. (1974), Rosenberg (1972, 1976, 1982), Stoneman (2001), Gomulka (2006), Pavitt (1999), and Stoneman and Battisti (2010) make it clear that knowledge, hence technology, constitutes an important element of socio-economic systems.

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25

In the same vein we read in Perez and Soete (1988) that technological progress is a disruptive process that alters social and economic structures and stimulates the emergence of a new status quo. Technology and technological change do not just bring change or inventions to economy and society; they enrich and shape socio-economic systems, enhancing their responsiveness and adaptability to further technological change. This demonstrates the interrelatedness of society, economy, and technology, driving home the point that none of these elements exists in isolation. Although intuitively we know that society, economy, and technology are linked by multidirectional causality, the literature is often dominated by the view that it is technology that drives change in societies and economies; that technology itself, having the power to change things, is what shifts societies, and hence economies, into higher states of development. Such a way of thinking and defining the role of technology in society and economy can also be found in Schumpeter, who contended that technology and technological change are at the centre of modern economic growth (Schumpeter, 1934, 1939). Schumpeter’s pioneering works supported the hypothesis that technology and technological progress as such should be treated as exogenous factors of economic growth and development. His thesis of parallel changes in the state of development of technology and in economic activity added up to the observation that long-run economic expansion has coincided with periods of wide diffusion of innovations. Schumpeter associated these two elements with the emergence, due to technological change, of novel, market-leading products and services, or even entire industries and essential infrastructures, which inevitably led to structural changes in the economy. Schumpeter’s original view was later modified and endogenised by the neo-Schumpeterian school, for which technological change becomes an essential but endogenous factor in economic growth and development (Magnusson, 1994; Hanusch & Pyka, 2006). A core factor in the importance of technological change as a driver of socio-economic shifts is the process of technology diffusion. Technology diffusion is dynamic, time-related, involving the transfer of knowledge, information, innovations, new ideas, and concepts through large, usually heterogonous societies and economies (Gray, 1973; Lechman, 2015; Rogers, 2010). Stoneman (2001) suggests that the diffusion of technology introduces economic innovations that are gradually adopted and put into use by individuals and companies. Saviotti (1996a, 1996b, 2002) holds that the spread of a new technology brings desirable new solutions to the market, which if utilised widely enough can generate profound structural shifts in

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ICT-driven economic and financial development

production, consumption, and more. This thesis is supported by Keller (2004) and Ward and Pede (2013), who make the point that individuals and whole societies make decisions either to adopt or to reject new technological solutions first under uncertainty and then through cost-benefit analysis. And if even risk-averse people decide in favour of an innovation, this can happen only on the condition that the new solution is expected to bring greater benefits than the existing technology. If the group of innovators and early adopters (Rogers, 2010) likes new, more than old, technologies and so favours replacing current with innovative technology, a ‘domino effect’ (Abrahamson & Rosenkopf, 1993; Valente, 1996) may set in, effectively perpetuating a further rapid spread of innovations. Conceptually, the ‘domino effect’ is closely related to the notion of positive network externalities, or the ‘network effect’ (Katz & Shapiro, 1985, 1986)—that is, the demonstrable benefits generated by the growing number of users. This ‘bandwagon effect’ (Katz & Shapiro, 1994) significantly strengthens the process of technology diffusion in heterogeneous societies, which is associated with specific behaviour by consumers, permanent status-seeking regarding access to technological novelties (Lechman, 2017). Cabral (2006) writes that ‘network effects, that is, the case when adoption benefits are increasing in the number of adopters, (…) suppose that each potential user derives a benefit from communicating with (…) others. Such benefits can only be gained if the other users are also hooked up in the network’ (p. 2). If positive externalities are triggered, the diffusion of technology accelerates; sometimes the process is marked by uncertainty and random fluctuation, but usually it generates disorder in well-established social and economic systems. However, while technology diffusion may be marked by discontinuities, abrupt ups-and-downs (Ehrnberg, 1995), over a longer period, the spread of technology is fairly well approximated by the sigmoid (S-shaped) pattern (Kwasnicki, 2013; Nakicenovic, 1991; Rogers, 2010). This technology diffusion curve represents a quite simple account of several phases in diffusion (see Fig. 2.3). The process of diffusion is slow at first, the rate of technology adoption barely detectable and easily reversible. However, once a critical threshold, saturation is reached (Marwell & Oliver, 1993; Marwell, Oliver, & Prahl, 1988), the process speeds up radically, growth now going exponential. During the second stage of diffusion, the number of people adopting the new technology increases massively, and the saturation rate soars. Finally, as saturation becomes nearly complete in the phase of maturity, diffusion diminishes to a final stage of slowing diffusion as the society heads towards full saturation.

27

The fifth technological revolution: context and background

Carrying capacity (k)

S a t u r a t i o n

Technological take-off (somewhere here)

Threshold saturation

Length of pre-take-off stage

Critical mass (somewhere here) – one year ahead of the technological take-off

Pre-take-off stage Technological take-off

Post-take-off stage

Time (t)

Fig. 2.3 Sigmoid technology diffusion curve. Conceptualisation. (Adapted from Lechman, E. (2017). The diffusion of information and communication technologies. Routledge.)

As Fig. 2.3 illustrates, technology diffusion is non-linear. As Arnulf Gr€ ubler (2003) observes, ‘the patterns of temporal diffusion do not vary across countries, cultures, and artifacts; slow growth at the beginning, followed by accelerating and then decelerating growth, culminating in saturation or a full niche’ (p. 14). Yet while the sigmoid pattern appears to be the rule, so the basic diffusion curve is largely invariant, the timing and the pace of diffusion may vary greatly depending on the individual society, economy, and technology involved (Comin et al., 2006). As noted at the beginning of this section, technological change is not a merely technical phenomenon but rather is deeply involved in complex socio-economic systems. Moreover, path-breaking technological solutions do not emerge randomly or in isolation from existing technologies; they are path-dependent and emerge in specific social, institutional, and economic environments. Contextualising the process of technological change is crucial to understanding an array of changes and shifts that are usually both society- and economy-wide. Extensive exploration and conceptualisation of the interaction between the emergence of a new technological solution and the established pattern of socio-economic development is undeniably needed. By its very nature, new technology tends to be disruptive, bringing Schumpeterian ‘creative destruction’ (Schumpeter, 1962) to societies and

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economies. Whether the process of substitution of new for old technological solutions is smooth or instead marked by abrupt shifts and surges, it has farreaching socio-economic implications. Major society- and economy-wide transformations, as long-run effects of technological change, may take the form of radical shifts, restructuring, and reorientation in a whole range of fields. Developed in the 1970s and 1980s, the intellectual concepts of technological and techno-economic paradigms made a fresh, stimulating contribution to the debate on the interdependency between technology, society, and economy. These concepts significantly influenced the theoretical frameworks for analysing the relationships between technology and socio-economic systems, paving the way to the transition from static to dynamic analysis (Von Tunzelmann, Malerba, Nightingale, & Metcalfe, 2008). Even more importantly, the concept of techno-economic paradigm combines formal economic modelling with historical inquiry; it links various ideas and notions and provides a broad perspective and context, allowing for more profound and insightful interpretation of past and present events. Techno-economic paradigms capture the multidimensionality and interconnectedness of technological revolution, society, and economy. They are an elegant way of conceptualising the array of interactions between the process of technological change and social and economic development. These paradigms can be also seen as a unique idea for exploring the intimate interdependence and causal relationships among elements of a given system; they offer a conceptual framework for analysing the relationships between technological change and socio-economic development. The concept of techno-economic paradigm, initially proposed by Carlota Perez (1986) and then enhanced and modified by Freeman (1986), Freeman and Perez (1988), and Perez (2003, 2009), is closely related to the idea of technological paradigms developed by Dosi (1982). Fundamentally, both technological and techno-economic paradigms rely on Kuhn’s concept of scientific paradigm (Kuhn, 1962), which denotes how the world is perceived and defines the key problems to be solved. Kuhn’s thesis is that an old paradigm will be superseded by a new one when it no longer appears to offer adequate solutions to the problems encountered. He further argues that a paradigm shift reflects radical changes in current concepts and ways of perceiving and explaining reality.a Dosi (1982, 1988) argues that, as proposed by Kuhn, a

The Kuhnian concept of paradigms has been extensively used in social science, especially sociology and economics; see, for instance, Blaug (1975), Folbre (1986), Ramstad (1989), Argyrous (1992), Palley (2005) and Coates (2005), to cite just a few.

The fifth technological revolution: context and background

29

a scientific paradigm may be ‘approximately defined as an “outlook” which defines the relevant problems, a “model” and a “pattern” of inquiry’ (p. 152). In this vein, Dosi defines the ‘technological paradigm as “model” and a “pattern” of solution of selected technological problems based on selected principles derived from natural sciences and on selected material technologies’ (1982, p. 152).b In Dosi and Nelson (1994) and Dosi, Teece, and Chytry (1998), we read that technological paradigms are strictly related to knowledge for problem-solving in specific fields. Before Dosi, Johnston (1970) gave a different definition of technological paradigm as a bundle of principles that are widely accepted on certain technological grounds. In the same vein, Gibbons and Johnston (1974) suggest that technological development is periodical in nature, which to some extent coincides with Kuhn’s vision of the revolutionary nature of science, and hence technology. Arguably, technological paradigms define a set of needs that can be served in a given techno-economic context, and in this sense, technological change and economic development are linked and condition each other. As is argued in Van den Ende and Dolfsma (2005) and Sinclair-Desgagne (2000), new technological paradigms arise from the advance of science and the accumulation of technological knowledge. These authors in fact advocate the very radical hypothesis that technological knowledge is the main if not the only factor in the emergence of new technological paradigms. This coincides with the view of Dosi (1988) that fundamental advances in science and in closely related ‘general’ technologies form a solid background for new technological paradigms. Technological paradigms may also be seen as homogenous spheres of technology, which are socially and economically contextualised, demarcating certain fields of research aimed at invention. By convention, a technological paradigm provides a unique framework for research, and when it materialises, research often provokes severe discontinuities along the technological trajectory.c Those discontinuities, caused by novel technological paradigms, are usually associated with radical innovations to the socio-economic system and profound economy-wide change. The process of change is driven exclusively by the diffusion of inventions developed within the new technological paradigm, but it may also stem from b

In Nelson and Winter (1977a, 1977b, 1982) we find the term ‘technological regime’, which, however, coincides with Dosi’s ‘technological paradigm’. c A technological trajectory is defined as a pattern of problem-solving within a given technological paradigm (Dosi, 1982). Dosi (1988) also defines the technological trajectory, initially proposed by Nelson and Winter (1977a, 1977b), as development along the specific paths of the current technological paradigm.

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a gradual switchover from one technology to another. Such changes, if implemented effectively, will enhance productivity and generate benefits in both social and economic terms, but they may also provoke temporary turbulence, instability, and uncertainty. The concept of techno-economic paradigm proposed by Perez (1986), while fundamentally related to Dosi’s idea of technological paradigm, is at a higher level of generality. Whereas the technological paradigm in its generic sense is quite narrowly defined, the techno-economic paradigm (or meta-paradigm) is a ‘synthetic definition of macro-level systems of production, innovation, governance and social relations’ as suggested by Freeman and Perez (1988) and endorsed by Cimoli and Dosi (1995, p. 255). Freeman and Perez (1988) also propose to label change in the techno-economic paradigm as ‘technological revolution’, which encompasses both radical and incremental innovations. Perez (1983) argues that technology and economy are inseparably connected, so the two phenomena must not be explored separately. Technology shapes the economy and vice versa, so the techno-economic paradigm constitutes a perfectly integrated approach to the analysis and comprehension of the relationship between economy and technological change. Techno-economic paradigms allow generalisation but also contextualisation of the process of technological change, which is usually strongly dynamic and highly pervasive. These pervasive effects then become perceptible throughout the economy and society, and all the easier to identify and quantify as new technologies are diffused in a country. A series of works by Perez (1983, 2002, 2003, 2007) make it clear that the concept of techno-economic paradigm goes far beyond the purely technical perception of technological change to emphasise that technology reshapes economic systems, economic and social structures, norms, and attitudes. Along these lines, Green, Hull, McMeekin, and Walsh (1999) define the quantum leap in potential productivity as a key inherent feature of the concept of techno-economic paradigm. Broadly conceived, a techno-economic paradigm unveils the interaction between technological change and socio-economic development. Such a paradigm is a ‘set of best practice principles for efficiency (…) applicable to all (…) industries and serving to overcome maturity and increase productivity across the whole economy through more efficient equipment, better organisational models and much wider market reach’ (Perez, 2009, p. 781). The techno-economic paradigm is ‘a quantum jump in potential productivity’ and ‘an overarching logic for the technology system of a period’ (Perez, 2007, p. 229).

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The key to laying down solid foundations for understanding the concept of techno-economic paradigms is to know how they arise and how technoeconomic systems are shaped and evolve. Technological change depends heavily on the emergence of innovations. As already noted, this is not a random process but is shaped and predetermined by the entire context, including the institutional and economic environment, laws and regulations, social norms, attitudes towards innovation, and, most importantly, the technological solutions already in being. All these factors create a set of interactions, expressing the relationships between technology, society, and economy. Schumpeter (1939) calls them ‘clusters’; Freeman (1982, 1992) and Freeman and Soete (1997) contend that this type of interconnectedness creates a ‘technology system’. New ‘technology systems’, once they have appeared within the ‘techno-economic space’, have a powerful, long-term impact on ways of doing business, shaping a given country’s social and economic contours. The initial concept of ‘technology system’ has been broadened and further conceptualised by such scholars as Freeman (1987, 1995) and Lundvall (1988, 2007), as ‘national system of innovation’. Lundvall proposes to define the ‘national system of innovation’ in revolutionary terms, insofar as this concept would allow the identification of systems that ‘create diversity, reproduce routines and select firms, products and routines’ (Lundvall, 2007, p. 14). In the same vein, Lundvall and Johnson (1994) claim that national systems of innovation transform the structure of production, technology, and institutions, generating significant externalities and competitive advantages for all agents. Just as single innovations gradually accrete to form systems of innovation, national systems of innovation eventually interconnect to form a single system and give rise to a technological revolution. Along these lines, Perez writes that ‘on first approximation a technological revolution can be defined as a set of interrelated radical breakthroughs, forming a major constellation of interdependent technologies’ (2009, p. 5). Technological revolution is reinforced by radical innovations,d which spread and overwhelm the society and the economy. Radical innovations initiate new paths in technology; they are embodied in truly new products and processes. Radical innovations may arise at any point in time; as a rule they supplant an ‘old’ technology and give birth to a new industry. Grinin d

By contrast, incremental innovations are improvements and adjustments to existing products and/or processes. Although they do generate productivity shifts and thus contribute positively to economic growth, they do not provoke the kind of radical and revolutionary changes that radical innovation does.

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and Korotayev (2015) consider the industrial revolution as ‘a process of active development of technology, especially designed to save labor in different areas’ (p. 52). Undeniably, technological revolution provides a positive impulse to the creation of wealth throughout an economy; it provides a wide array of new infrastructure; it allows for organisational improvements and thus enforces productivity shifts. Technological breakthroughs do not bring only strictly technical solutions, meaning that their influence is not purely technological. Technological revolutions may also entail organisational innovation, possibly inducing significant social, economic, and institutional change. Technological revolutions are gradually assimilated by the economic and social system, generating surges of development that are followed by transformation in the social, institutional, and economic spheres. Technological revolutions spread; they extend across societies and economies to trigger ‘great surges of development’ (Perez, 2002). ‘Each (…) revolution has driven a great surge of development that takes a half century or more to spread unevenly across the economy’ (Perez, 2007). Every such ‘great surge’, as a time-related process, shows certain regularities and encompasses two consecutive periods (phases): installation and deployment. The first phase (installation) is sometimes likened to Schumpeter’s ‘creative destruction’, which simply means the battle between new and old ideas and concepts (technologies). Schumpeter describes ‘the process of industrial mutation (…) that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one’ (Schumpeter, 1943, p. 83). To some extent installation constitutes an experimental period, during which new technologies try to invade the market and are either accepted or rejected. These times of creative destruction are extremely turbulent, unstable periods during which the old regime is gradually expelled from the market, while the new regime and the new technology break abruptly in, pervasively invading the social, organisational, financial, and institutional frameworks. The installation period is marked by a rapid diffusion of innovation, its assimilation, and then adoption by a constantly growing number of new users. During this phase, new industrial processes, new modes of production and infrastructure, new ways of doing business, even new products and inputs are widely articulated. The installation period creates a new common sense throughout the society and the economy; it also most often generates huge inequalities both within and between societies and nations. In other words, the world becomes more highly differentiated and polarised owing to sequentially emerging technological revolutions.

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The installation period is followed by the deployment phase, which historians call a ‘golden age’. The deployment period is an age of prosperity, with a massive utilisation of country-wide innovations introduced during the installation phase. The deployment phase is the period that fully unveils the gains and benefits offered by the new socio-economic paradigm, and the enormous inequalities of the previous phase are at least partially reduced. This phase also brings significant gains in productivity, and it can be argued that this is when the full potential of certain technological revolutions is successfully converted into individual wealth. The growth of wealth is thus enhanced by the technological revolution, in the course of which social, economic, and institutional innovations have been ‘installed’ and assimilated. Carlota Perez (see, e.g. Perez, 2003, 2007, and Perez, 2010) associates technological revolution with what she calls a ‘great surge of development’, which stretches over at least 50 years and consists of two distinct phases: the installation period and the deployment period. She sees the installation period as the time during which the new infrastructure develops, thanks to technological change and is installed (albeit unevenly) within and across countries. The subsequent deployment period unleashes the full potential of the newly installed technologies and gives rise to synergy between old and new industries, which inevitably leads to productivity jumps, material gains, and the establishment of a new techno-economic regime—a new paradigm. The deployment period is the time of full synergy among the society, economy, and technology, when positive externalities arise. Eventually, once the market is almost saturated by innovations, the stage of maturity is reached. These long economic cycles or waves usually last for around 50–60 years (see Grinin & Grinin, 2016; Grinin, Grinin, & Korotayev, 2017; Linstone & Devezas, 2012), up until a full exploitation of the potential of new technology, with its conversion into increasing total factor productivity and increasing socio-economic well-being.e So far, scholars (Freeman & Soete, 1997; Perez, 2010) have identified five major techno-economic paradigms, corresponding to the five technological revolutions in the world’s economy since the early 18th century (see Table 2.2). e

To a certain extent, this view coincides with Kondratiev’s concept of long waves and its Schumpeterian interpretation on the role of technological progress in long-term growth. Both Kondratiev and Schumpeter attribute long economic cycles of 50–60 years to the diffusion of technological progress. As successive technologies spread out along logistic patterns, they gradually realize their potential for productivity growth, but this potential is not fully exhibited until they are broadly adopted by society, which allows for significant gains in per capita income. A similar approach is taken by G€ oransson and S€ oderberg (2005).

Historical period

Conventional name of surge

Major invention/sectors of influence

‘Canal mania’ ‘Railway mania’

‘London-funded global market infrastructure build-up’ ‘The roaring twenties’

‘Great British leap’ ‘The Victorian Boom’ ‘Belle Epoque’ in Europe, ‘Progressive Era’ in USA ‘Post-War Golden Age’

‘Emerging markets, ‘Sustainable Global dotcom and Knowledge Internet mania’ Society Age’?

Adapted from Freeman, C., & Soete, L. (1997). The economics of industrial revolution. London: Pinter; Perez, C. (2010). Technological revolutions and techno-economic paradigms. Cambridge Journal of Economics, 34(1), 185–202.

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1770s–1840s Early mechanisation/ Turnpike roads, canals, textiles/small and local Industrial Revolution enterprises, increases in individual wealth 1830s–1890s Steam power and railway/ Steam engine, railway transportation networks, worldwide shipping/emergence of small-firm age of steam and competition, emergence of large firms and joint railways stock companies Electrical engineering, chemical industries, steel 1880s–1940s Electrical and heavy shipping, heavy armament/emergence of giant engineering/age of companies, cartels, trusts, aggressive mergers steel and heavy and acquisitions, enforcement of anti-trust law engineering 1930s–1980s Fordist mass production/ Automobile and aircraft transportation, synthetic materials, development of consumer durable/ age of oil, autos and emergence of oligopolistic competition and mass production multinationals, boost in foreign direct investment, trade and production integration Development of computers, software, 1970s to …? Information and telecommunication, digital technologies/ communication emergence of large communication and technologies/The ICT production networks, technology-based Revolution entrepreneurial wave, regional ICT clusters

Deployment Installation period— period—‘Golden ‘Gilded Age’ Bubbles Ages’

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Table 2.2 Technological revolutions and great surges of development. Historical perspective

The fifth technological revolution: context and background

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The Fifth Technological Revolution—the ICT Revolution —began in the 1970s, when Intel’s first microprocessor was produced in Santa Clara, California. This path-breaking invention ushered in the Digital Era and created a new techno-economic paradigm, which for nearly half a century now has been continuously transforming the way societies and economies are organised and work.

2.3 ICT as GPT In any given ‘era’ there typically exist a handful of technologies that play a farreaching role in fostering technical change in a wide range of user sectors (…) bringing about sustained and pervasive productivity gains. The steam engine during the first industrial revolution, electricity in the early part of this century and microelectronics in the past two decades are widely thought to have played such a role. —Elhanan Helpman and Manuel Trajrenberg

At the outset, we can observe that general purpose technologies (GPTs) are distinguished by three fundamental elements: scope for improvement—these are technologies for which the process of technological change and diffusion is associated with time, space, and function in the society and economy; range and variety of use—they are widely used for a significant number of different purposes; and spillovers—these technologies bring a whole series of unprecedented opportunities for profitable investment in a large set of products and services, organisations, and processes (Lipsey, Carlaw, & Bekar, 2005). Some non-GPT technologies have some of these features, but only general purpose technologies have them all. GPTs are path-breaking innovations, one of the fundamental factors in long-run technological progress and deep-going structural and qualitative shifts in economies and societies (Bresnahan, 2010; Coccia, 2017; Sahal, 1981). Rosenberg and Trajtenberg (2004) call them ‘epochal innovations’, which came in the course of our successive technological revolutions and demonstrated their capacity to radically reshape the contours of the world economy. As Helpman (1998) observes, general purpose technologies induce overwhelming changes in various sectors of the economy and foster the creation and marketing of new products, services, and processes. GPTs are characterised by pervasiveness, i.e. their ability to generate structural transformations throughout the entire society and economy. Freeman and

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Soete (1987) argue that these special technologies have the power to force change even in the socio-economic paradigm, thus supporting economic growth and development (Bresnahan & Trajtenberg, 1995; Helpman, 1998; Ruttan, 2008). Conceptually, GPTs can be defined as revolutionary transformations of the patterns (trajectories) of technological change (Bresnahan, 2010). In Calvano (2007) we read that GPTs as path-breaking inventions often drive ‘destructive creation’, bringing in technology that matters for societies and economies currently and making the products of ‘past technological revolutions’ outdated, obsolete. As Lipsey, Bekar, and Carlaw (1998) and Coccia (2005, 2010) observe, GPTs are introduced widely throughout the society and economy and impact pervasively various forms of economic activity and ways of doing business. That is, they generate overwhelming, permanent change in economic structures. Coccia (2005) emphasises that by comparison with non-GPTs, these technologies have a much more powerful, intensive impact on the society and economy, going far beyond that of more ‘standard’ innovations, useful as the latter may be. Just to repeat, ‘GPTs are characterized by pervasiveness, inherent potential for technical improvements, and “innovational complementarities”, giving rise to increasing returns-to-scale, such as steam engine, the electric motor, and semiconductors’ (Coccia, 2017, p. 292). Like Lipsey et al. (2005), Jovanovic and Rousseau identify three key characteristics of GPTs, namely, pervasiveness—the impact spreads over all the sectors of the economy and the innovations are assimilated by a significant portion of social and economic agents; improvements— GPTs are continuously improved, drastically lowering the costs of adoption and use; and innovation spawning—widespread adoption facilitates the invention and production of new products and processes (Bresnahan & Trajtenberg, 1995). As is underlined in Helpman and Trajtenberg (1994), in the long run, the adoption of GPTs results in faster economic output growth. GPTs are the ‘engine of growth’. These authors note that ‘as [a] better GPT becomes available, it gets adopted by an increasing number of user sectors and it fosters complementary advances that raise the attractiveness of its adoption’ (Helpman & Trajtenberg, 1994, p. 1). This concords with the thesis of Lipsey et al. (2005) that the emergence of GPTs generates unique externalities that make the new technologies still more attractive and thus enhance economic transformation. The emergence of such externalities is strictly bound up with, or dependent on, the broad diffusion of technological innovations, their adoption by individual market actors, and their effective deployment in the economy. Importantly, in the case of GPTs, these

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externalities are usually dynamic. Dynamic externalities (Boldrin, 1992; Henderson, 1997) may arise when the continuous adaptation of a given technology by market agents generates further technological progress, which in the long run increases the total utility of that technology, i.e. the economic and social benefits that it brings. Effective adoption of a new technology, which is obviously the precondition for dynamic externalities, is a multistage process, and especially in its early stages, it is marked by great uncertainty. In other words, uncertainty is inherent in the diffusion of any new technology, especially in the initial stages. Stoneman and Battisti (2010) point out that the areas of uncertainty are multiple, involving such matters as the legal framework, which may impede the diffusion of innovations, the cost of adoption, and the sunk cost of existing technology. Other major sources of uncertainty cited by scholars as possible impediments to innovation are limited access to information and the inherent slowness of interpersonal communication channels (Rogers, 2010); social attitudes toward risk-taking; and social ability to assimilate innovations, agents’ decision-making process under uncertainty, and risk aversion (there is clearly a trade-off between risk and potential benefits). Whenever the process of introducing new technology begins, in order to be effective, it must be pervasive and overwhelming, driven by low cost and readily available inputs. If this is the case, then diffusion is reinforced and generates scale economies, hence additional opportunities for the adoption of the new solution, which, in effect, further accelerates the diffusion of technology. This view on the broad adaptability and usefulness of GPTs coincides with that of other authors (Bresnahan & Trajtenberg, 1995; Coccia, 2015; Gambardella & McGahan, 2010; Jovanovic & Rousseau, 2005) who underscore that as GPTs spread throughout the society and economy they significantly impact economic growth and development. However, as Helpman and Trajtenberg (1994, 1996) note, there is a ‘time to sow’ and a ‘time to reap’; that is, productivity jumps, increases in GDP, real wages, and personal wealth do not come immediately upon the arrival of a new technology. An analogous position is that of Stearns (2018), namely that during the early stages, the economic effects of technological revolutions may be hard to perceive, appreciate, and measure. To see whether technological advances engender solid and profound social and economic transformations, the long timeframe is indispensable. Technology as such, with all its embedded knowledge, unquestionably gives society the chance to climb the development ladder, even though the tangible effects are sometimes hard to

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capture. In this vein, Nagy K. Hanna (2010) contends that ‘economic history, the cumulative learning and transformation process involved in using ICT, and the pace of this wave of technological change suggest that a “wait and see” attitude would keep many developing countries out of a technological revolution no less profound than the last industrial revolution’ (p. 29). A look at history makes it clear that certain general purpose technologies have specific spatial-temporal dynamics. The GPTs already in being first compete with, then supplant ‘old’ technologies and finally are themselves replaced by the emergence of new GPTs. This dynamic, temporal interchangeability relates closely to the wave configuration of technological progress and technological change (Greenwood & Jovanovic, 1999). As was argued in Section 2.1, since the 18th century the world has gone through five technological revolutions, marking turning points in the economic history of the world as a whole and in the relative economic power of certain regions and nations. The onset of the First Technological Wave—the First Industrial Revolution (Deane, 1979)—in Britain gave rise to the mechanisation of the cotton industry, improvements in water mills, and the refinement of turnpike roads and canals (Lechman, 2017). In the 1700s these were radical innovations, powering steady increases in the productivity of labour and capital, rising overall wealth and improved living conditions. There is a good case that some of the innovations made during the First Industrial Wave were GPTs, in that their introduction to the society, organisations, and entire economies was the starting point for the long-term structural transformations that gave rise to the modern economic growth (Akamatsu, 1962; Kuznets, 1973; Mokyr, 2010). The Second Technological Wave beginning in 1829 (Perez, 2010; Wertime, 1962) witnessed the massive development of railways, postal and telegraph services, ports, and sailing ships. All of this was facilitated by new GPTs invading the markets. This new generation of GPTs dramatically increased the importance of networks and communication (Allen, 2009; Mokyr, 1998). The period spanning the years from 1875 to 1908 has been called the Third Technological Revolution, i.e. the third wave of novel GPTs, such as steel and electricity to name just two. The GPTs that emerged during this period, such as global railway and telegraph services (Stearns, 2018) and the ceaseless development of telephone services (Beniger, 2009; Faulhaber, 1995), now described as ‘old information and communication technologies’, were crucial to the way in which societies and economies function, even now in the 21st century. Those inventions came to be of common use, and there is no denying their enormous impact on social and economic life.

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The Fourth Technological Wave came in 1908 (Christopher & Louca, 2001; Coleman, 1956) with additional path-breaking GPTs, allowing for the dynamic development of roads, automobiles (Ford-style assembly line production plants), ports, and airports (Freeman et al., 2001; Stern & Kander, 2012). Their spatial-temporal impact was fundamental, as this wave made people and goods far more ‘movable and transferable’, although a significant amount of time was still required to transport them from place to place. In the early 20th century, a crucial general purpose technology was electricity, which was installed in a steadily increasing number of households, while such means of communication as telephone, telegraph, and telegram were coming into more common use among the general public. These changes sparked a further development of various networks, which since then have become the prime engines of economic development, radically transforming social structures, norms, and attitudes (Rosenberg et al., 2008). Finally, the 1970s saw the birth of the Fifth Technological Wave (Perez, 2010) or ‘Digital Revolution’ (Abdelgawad & Wheeler, 2009; Dreyer, Hirschorn, Thrall, Mehta, & PACS, 2006). This last breakthrough brought such novel GPTs as information and telecommunication tools, microelectronics, computers, software, and various forms of digital communication, including the Internet (Freeman et al., 2001). The Fifth Technological Revolution gave rise to an extraordinary range of path-breaking inventions, resulting in the radical restructuring of economic and social life. The ICT Revolution, or the Information and Communication Age, changed the way people communicate (Cairncross, 2001), interact, do business; and in this sense, it changed the society itself. The new ICTs are digital technologies that can convert the real world of information and knowledge into binary numerical systems (forms). Significantly, the widespread dissemination of ICTs makes possible the progressive transition from analog to digital technology (Toumazou, Hughes, Battersby, & Battersby, 1993). Information and Communication Technologies, broadly defined, represent an extension of the old class of Information Technologies (IT). But with reference to ICTs the primary focus is on the various media that permit interpersonal communication. Going by the definition of the World Bank (2014), ICTs encompass hardware, software, networks, and media for the collection, storage, processing, transmission, and presentation of information (e.g. voice or data) and related services.f Functionally, they f

See the ICT Glossary Guide (100 ICT Concepts) at http://web. worldbank.org (accessed September 2017).

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constitute a unique set of activities that facilitate electronic storage, processing, transmission, retrieval, and display of all sorts of information (Rodriguez & Wilson, 2000). Hargittai (1999) defined ICTs essentially as perceived through the lens of the Internet. His position is that the Internet, i.e. the worldwide network of both computers and users, was a great ‘invention’, enabling people to acquire vast amounts of information as the third millennium was getting under way. In the same vein, Kiiski and Pohjola (2002) claimed that the ICTs have unlimited possibilities for delivering information, regardless of the physical location of agents, and also facilitate interpersonal interactions that forge new networks. In addition, they contend that the Internet makes possible the emergence of a ‘virtual’ market for the sale and purchase of goods and services. In the broad sense, ICTs are technologies that, by electronic means, serve people by sharing, distributing, and storing all sorts of information and knowledge, facilitate market transactions, and allow millions, if not billions, of people to ‘keep in touch’ regardless of geographical barriers. ICTs are often perceived in terms of their functions, applicability, and usability. As a rule, great emphasis is placed on their role in sustaining various spheres of socio-economic activity. As already stated, Information and Communication Technologies are the technologies that gave rise to the Fifth Technological Revolution— the Digital Revolution, which introduced multiple, radical social and economic innovations. The emergence and worldwide diffusion of ICTs gave birth to totally new products and services, entirely new industries, and business models. Additionally, new types of networks materialised and generated disruptive effects, overwhelming societies and economies, institutions, organisations, and entire economic systems. ICTs are disruptive technologies (Latzer, 2009); they deliver radical and transformational change to the markets, modifying the landscape. The effects cut across all sectors of the economy. ICTs are said to be pervasive (ubiquitous) technologies that are permanently accessible and network-connected; they enrich interactions among entities and provide effectiveness, efficiency, and empowerment (Lechman, 2017). This is why ICTs are called general purpose technologies, and as such, they demonstrate enormous and overwhelming potential to impact on organisational and socio-economic systems. As argued by Jovanovic and Rousseau (2005), by bringing cutting-edge technological advancements, ICTs invade societies and economies; hence in the long run, they boost the productivity of capital and labour, which unleashes gains in terms of material wealth. ICTs are endogenously diffused throughout society, and to a large extent, they not only change economic structures but also strengthen economic growth and the dynamics of development.

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ICTs are characterised by technological dynamism, which means constant efforts to increase the efficiency of the new technology. Information and communication technologies, as GPTs, pave the way ahead and foster new opportunities. In Helpman and Trajtenberg (1996) we find the thesis that ‘as GPTs appear (…) there is a spell of growth, with rising output, real wages, and profits’ (p. 4). In another work, Bresnahan and Trajtenberg (1995) isolate several specific features of GPTs, which ICTs evidently share. ICTs as GPTs are described as ‘enabling technologies’, which means that they contribute significantly to the functioning of socio-economic systems and open up new paths for gradual improvements, taking the form of productivity gains. Continuous advances in technological development drive the emergence of innovational complementarities, which further strengthen the productivity gains—a whole series of positive externalities arises. The widespread introduction and deployment of ICTs by individuals but also by whole organisations and countries augments the emergence of novel downstream inventions and innovations, which would not be achieved without those ICTs. Finally, Bresnahan and Trajtenberg (1995) note an interesting feature of GPTs as such—they have no close substitutes, which obviously distinguishes them from other widely used technologies. Meanwhile, Coccia (2010) stresses that various GPTs, including ICTs, have a unique ‘ability’ to remove or overcome barriers to wider technological and economic development, with a major impact on social welfare. As argued earlier, information and communication technologies are recognised as today’s general purpose technologies. They are ‘enabling technologies’ that offer practically unbounded opportunities through their adoption in multiple social, institutional, and economic spheres. The impact of ICTs in reshaping social and economic development is considered pervasive and, in the long-term perspective, likely to induce structural and organisational changes that will result in enormous leaps in productivity. Hanna (2010) calls the information and communication revolution probably the most pervasive in recent human history. He considers that the timing of the Fifth Technological Revolution was due chiefly to decentralisation and integration, network structures, adaptability, knowledge as capital, and economies of scope (Hanna, 2010, p.32). According to Freeman et al. (2001), Perez (2010), and Conceic¸a˜o, Heitor, and Lundvall (2003), the development of ICTs constitutes an emerging techno-economic paradigm, also called the digital or ICT paradigm. It is evident that in many ways the Digital Revolution differs from previous technological revolutions. The prime and most essential element is that revolutionary changes are now incomparably faster and more pervasive than those generated

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by past revolutions. Moreover, technological changes today are generally embodied in goods and services for a mass market.

2.3.1 Final note Any attempt to encapsulate the whole stock of ideas, contexts, and knowledge underpinning a ‘socio-technological system’ in a single ‘box’ is bound to fail. The effort to reduce the complexity of connections between the society and technology is almost certain to result in oversimplification. In examining the nature and characteristic features of this dynamic system, it is essential to bear in mind that technology does not merely bring changes to society as it is. Technology is not passive. Above all, technology shapes the society. On the one hand, it enriches society, while enabling the society to respond actively to change, new ideas, and innovations and so gives the society the power to shape future technological developments, on the other. Today, as noted, ICTs are receiving growing attention; the new information and communication technologies are perceived as tools (enablers) whose unique features foster economic growth and development. Arguably ICTs can help countries around the world to combat underdevelopment and technological backwardness. By improving economic performance and enhancing the ability to compete on global markets, ICTs provide the means for bringing idle labour power into use and increasing social capital (Lipsey et al., 2005). What seems to be of seminal importance is that ICTs create effective and cheap means for the transmission of information, opening up new possibilities for economic activity on a larger scale (Coccia, 2018). The nexus between the deployment of new technology and the attainment of certain ‘development goals’ has been recognised. It is based on shared objectives—namely the efficient, scalable, affordable, and pervasive delivery of goods and services and information flows between people, governments, and firms. And while the exploration of links between technology adoption and economic advancements is not straightforward, and is usually hard to quantify and isolate, there is no denying that ICTs create new ‘windows of opportunity’ (Perez, 2003; Perez & Soete, 1988).

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Further reading Acemoglu, D., Johnson, S., & Robinson, J. (2005). The rise of Europe: Atlantic trade, institutional change, and economic growth. The American Economic Review, 95(3), 546–579. Lechman, E. (2017). The diffusion of information and communication technologies. Routledge. Maddison, A. (1995). Monitoring the world economy, 1820–1992 (p. 238). Paris: Development Centre of the Organisation for Economic Co-operation and Development. Maddison, A. (2001). Monitoring the World economy: A millennial perspective. Paris: OECD. Weber, M. (1981). General economic history. New York: Routledge.

CHAPTER THREE

The digital revolution for development: Identifying the channels of impact Contents 3.1 Technological change and ICT as opportunity windows 3.2 ICT for socio-economic growth and development 3.3 ICT, financial markets, financial innovation References Further reading

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3.1 Technological change and ICT as opportunity windows New technologies of a revolutionary nature open up new opportunities for learning and catching-up —Carlota Perez

Technological change is a significant factor in economic, social, institutional, and political life. Technology engenders jumps in productivity, enabling organisations and individuals to work more effectively. Technological progress and innovation pave the way for the creation of wealth. Yet these benefits are subject to the effective diffusion of technology and to the society’s ability to adopt and deploy innovations (Keller, 1996; Nieto & Quevedo, 2005). These two aspects are of crucial importance to an analysis of technology’s impact on socio-economic development. Diffusion usually proceeds in a nonlinear mode, and its evolution is characterised by certain regularities; it is not instantaneous but rather tends to be distributed across time. Path-breaking innovations usually generate some sort of disorder, heightening the initial uncertainty of individuals and whole societies, altering the nature of relationships and possibly generating chaos and turbulence. The ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00003-8

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trajectory of diffusion may be marked by random fluctuations, but in general, one can expect it to trigger disorder within established social, organisational, and economic systems. Yet this initial disorder, destroying the status quo, is essential if technological change is to bring cutting-edge changes to the society and the economy (Castells, 2014). This constitutes an argument for the hypothesis that technology and economy are not equilibrium systems (Lechman, 2017; Romer, 1990) but may be volatile, often entailing abrupt ups and downs, and thus in permanent disequilibrium (Anderson & Tushman, 1990). This disequilibrium is a special feature of a dynamic techno-economic system, and should be seen as one of the chief benefits of techno-economic dynamism, stimulating profound social and economic transformation. Undeniably, societies and economies develop once they have adopted and deployed technological advances, although the impact of technology is neither direct nor immediate. As Kindleberger (1995) argues, socio-economic systems and technological advance are interdependent; these relationships between society, economy, and technology are linked by twoway causality. On the one hand, technological progress generates socioeconomic change, but at the same time the pace at which new technologies are adopted is essentially predetermined by the capabilities and current performance of economic actors. As noted by J. Mokyr, for centuries, technological innovation ‘revolutionized the structure of firms and households, it altered the way people look and feel, how long they live, how many children they have, and how they spend their time’ (Mokyr, 2002, p. 2). This argument further bolsters the thesis that technological progress and the novelties it brings are deeply rooted in the broad socio-economic context (Amin & Goddard, 2018; Fox, 1996; Mokyr & Scherer, 1990), and that all societies, whatever their stage of development, have to face emerging technology and the discontinuities that it generates, with far-reaching consequences for dynamic performance (Bresnahan & Trajtenberg, 1995). From the social and economic perspective, the digital revolution is clearly a trail-blazing event in the history of world development. This Fifth Technological Wave is not only of seminal importance for the core-inventor countries, which develop digital solutions; even more important, ICTs are being rapidly deployed in economically and technologically peripheral countries as well. The information revolution, happily, has offered new opportunities to societies and economies worldwide, regardless of their material status and other conditions. This is a radical difference from previous technological breakthroughs, and as Hanna (2003, 2010) argues, the digital revolution is a real technological ‘tsunami’ compared with past waves of technology.

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The linkage between technological change and new opportunities that increase overall wealth is especially appreciable in economically and technologically backward countries. The developing world—countries that lag behind especially in per capita income—has clearly been at least partially bypassed by previous technological waves, deprived of the potential benefits and gains. That exclusion from access to technological novelties obviously had very powerful repercussions. Of course, we do not contend that the developing countries were deprived of any and all technological progress. Certainly, some progress reached them; but its diffusion, access to its use, tended to be extremely limited. This is evident if we consider the situation from the standpoint of poorly developed backbone infrastructure (low electrification rates or access to railway networks, negligible access to means of communication). Many economically deprived countries have never had the opportunity to adopt and effectively deploy the creations of previous technological revolutions, and they have ‘traditionally’ lagged behind in the adoption of ‘modern’ technology. This lag has obviously undermined their ability to develop rapidly or advance in overall well-being. The secular inability to access technological progress meant that major barriers to development were never overcome but instead persisted. In a way, societies in the economically backward countries have never had an opportunity to ‘consume’ the technological inventions and innovations of the last 200 years. These societies have been unable to use technological progress as a driving force for socio-economic development. This unfavourable situation stemmed first from difficulty in exploiting past technological revolutions, which required financial resources and relatively well-developed hard infrastructure country-wide, and second from the fact that realising their potential required much more knowledge, skills, and absorptive capacities for the technologies to be deployed and then used effectively to induce scalable and long-term economic benefits. In short, the economically backward countries were never real beneficiaries of past technological revolutions. Luckily, with the digital revolution the permanent deprivation of the poorer countries may come to an end (Aker & Mbiti, 2010), mainly because ICTs are different in some significant ways from past inventions. That is why many claim that the impact of ICTs on the social and economic spheres may be particularly dramatic. The astonishingly rapid adoption and use of various ICT tools all around the world has generated a good deal of speculation and optimism concerning its effect on socio-economic development in the long run.

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The literature cites at least four elements (aspects) that make up the substantial advantages and importance of ICTs compared with ‘old’ technologies. The first is that ICT gives rise to a series of types of network (Castells, Fernandez-Ardevol, Qiu, & Sey, 2009; Shapiro & Varian, 1999, 2013; Valente, 1995, 1996; van den Berg, Arentze, & Timmermans, 2013). Shapiro and Varian (1999) argue that the chief difference between the ‘old’ and the ‘new’ economies is that the former were driven mainly by scale economies and the latter by network economies. Similarly, Servon (2008) argued that ICT has fundamentally reshaped societies and shifted many countries from the ‘industrial age to a network age’. Following Katz and Shapiro (1985) and Economides (1996), network effects consist in the greater utility that derives from the use of a given good or service when accompanied by an increasing number of other users. ‘Network effects’ explain the value of potential connectivity, which tends to grow exponentially in heterogeneous societies. In economic terms this means that the growing number of links potentially translates into real income (i.e. higher GDP per capita). The second aspect is the ease of creating different forms of network, insofar as the adoption of ICT itself depends on the nature of these technologies. Currently, ICT offers a wide array of services that are based on wireless solutions, which enables connectivity, data, and voice transfer regardless of geographical location. ICT may thus be perceived as technologies that give people freedom from isolation and virtual exclusion, possibly shifting various activities to remote regions. Physical distance itself does not restrict the possibility of accessing ICT, and with it the ability to communicate and acquire information. Bearing this in mind, ICT may be defined as inclusive technologies that make possible the ‘death of distance’ (Cairncross, 2001). Similar arguments have been made by Quah (2001), Venables (2001), Redding and Venables (2002), and Wresch and Fraser (2012), all emphasising the special role of ICTs for various aspects of socio-economic development. Third, ICT enables and enhances massive flows of information between individuals and over entire societies. What is essential here is that the sharing of information and knowledge is extremely rapid and negligible in cost, and so becomes available even in the low-income societies traditionally denied access to various forms of technology. Up to a point, the widespread deployment of ICT allows for the gradual eradication of various forms of exclusion from access to knowledge and information. From a broader perspective, unbounded flows of knowledge and information have immense implications

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for socio-economic development and growth. The key is that ICT provides a solid background for making knowledge work, thus helping to transform knowledge into long-term economic and social gains. Fourth, and finally, ICTs are recognised as GPTs; that is, their prime characteristic is generality of purpose (Bresnahan & Trajtenberg, 1995). For this reason, they have a pervasive influence on the society; they affect a wide range of economic sectors, social structures, and institutions, creating new frameworks within which all actors operate and interact. Many authors have argued that the deployment of ICT has now become sufficiently extensive that no individual remains unaffected by the information revolution. The unbounded diffusion of new technologies in recent times is possible because ICTs are unique. Due to their specific features, they are readily installed and distributed among agents. ICTs are diffused worldwide and may be adopted even in geographically remote, underserved, and infrastructurally disadvantaged areas. In her seminal Death of Distance, Frances Cairncross argues that due to the spread of this new type of technology, communication and market activities have become distance-free, and this ‘death of distance’ is the most evident manifestation of the astonishingly rapid worldwide change (Lechman, 2016). Cairncross writes, ‘wireless communication (…) is killing location, putting the world in our pocket’ (Cairncross, 2001, p. 2). As a rule, ICT can be acquired quite cheaply, so even low-income societies can afford it. The marginal cost of an additional user of ICT tools is close to zero (Bugamelli & Pagano, 2004), which is why increasing the number of users requires no additional investment in backbone infrastructure. All of this makes ICT tools relatively easily affordable for people living in material deprivation. New technologies are easily imitable and deliverable (Lechman & Marszk, 2015); ICTs can be readily adapted for low-skilled, poorly educated, even illiterate people. ICTs are intended as ‘for all’ technologies (Avgerou & Walsham, 2017), in that their dissemination is not bound by financial, societal, linguistic, educational, or geographical barriers. Crucially, ICT is the prime driver in the development of worldwide networks linking physically separated agents (Castells et al., 2009; Valente, 1996), and it can be argued that modern economic growth and development are in fact determined by the emerging network economies (Shapiro & Varian, 1999). Katz and Shapiro (1985) and Economides (1996) stress the importance of the ‘network effects’ (Grajek, 2003; Katz & Shapiro, 1986, 1994), which reveal the potential of increasing connectivity and the ever-growing utility of certain networks as the number of users increases.

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All of the elements listed above are decisive to throwing open the opportunity windows. They justify treating ICT as a highly favourable technoeconomic paradigm compared with those of historical technological revolutions, particularly with regard to the special characteristics of economically backward countries and their multiple constraints. These are the opportunity windows (see, e.g. Gr€ ubler, 1991; Perez & Soete, 1988; Tyre & Orlikowski, 1994) through which societies and economies can effectively benefit from ICTs. The prime attribute of ICT is that it facilitates practically universal and unlimited interpersonal communication and quick, easy, and cheap access to information and knowledge, which are the critical prerequisites for a solid foundation for long-term socio-economic development. All these opportunities offered by ICT are in fact intimately bound up with the limitless flows of information and knowledge that it fosters (Hanna, Guy, & Arnold, 1995). ICT may directly affect socio-economic development by mobilising resources and strengthening market activity. In fact, this is a crucial point: ICT offers opportunities for a greater deployment of resources to market activities. Through better access to financial markets (e.g. e-finance and mobile finance solutions), ICT fosters the mobilisation of savings and provides opportunities for its conversion into investment, with long-term benefits for market activity and economic growth. In addition, ICT enhances mobilisation of the labour force, which has multidimensional consequences. First, growing participation in the formal labour markets provides the possibility of obtaining a regular income, lifting people out of subsistence living, gradually alleviating poverty, reducing people’s vulnerability and exposure to the risk of external shocks. Unquestionably, engagement in formal labour markets, both through growing employment and through the establishment of small businesses, creates economic gains and allows for the gradual eradication of various forms of socio-economic deprivation. Most likely, increasing labour force participation is the first and most important step in exploiting the potential of ICT in developing countries. In fact, ICT enables timely access to information, helping to overcome one of the fundamental barriers to the effective functioning of the market, namely information asymmetries. Appropriately combined, these two elements—increased labour force participation and removal of constraints on information access—foster increases in the number of transactions, enable participation in global trading markets, drive down transaction costs, and ensure worldwide visibility, all of which offers good prospects for longterm economic growth and development.

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Indirectly, ICTs affect socio-economic development though better access to education and knowledge, better healthcare (mainly through e-health and telemedicine applications) or so-called e-government. All of these significantly stimulate increases in skills and human capital, contribute to social cohesion, enhance empowerment of all social groups (including indigenous peoples), and ensure transparency and political inclusion. Obviously, the effect of ICT on, say, education and healthcare systems is qualitative in nature; the real gains appear gradually in social and economic advances. In summary, ICT opens the opportunity windows by breaking down the barriers that had blocked societies from engaging in various social and economic activities and offering instead all of these previously denied opportunities. This is the potential of ICT from the perspective of both the economically backward and the advanced countries. Obviously, the adoption of ICT and the channels through which it impacts socio-economic systems are not limited to the foregoing, especially for highly developed countries. Evidently, ICT applications, modes of use, and channels of impact differ dramatically between developing and advanced economies. Even so, for the economically backward countries, ICTs are initially suitable for rather ‘basic’ activities that do not require massive knowledge and financial resources, before moving on to more sophisticated applications and channels affecting socio-economic systems. These windows of opportunity do not open unconditionally, and realisation of the full potential of ICT is far from automatic. Favourable legal and institutional environments and competitive telecommunication markets are obviously important to the adoption and widespread use of ICTs. A whole series of preexisting factors help or hinder the widespread implementation of ICTs. Some authors cite as indispensable prerequisites such factors as basic infrastructure, legal reforms for telecommunication market competition, and inflows of foreign direct investment. Further, effective ICT deployment requires continuous learning and growing social capabilities, so that the potential of the technology can be realised. ICT needs to be promoted and supported to facilitate economic application and social adoption. If and only if the fundamental prerequisites for the acquisition and spread of ICT are ensured will ICT be disseminated and implemented progressively in multiple fields, generating social and economic gains and transforming ‘information-poor’ into ‘information-rich’ societies. Technological development is evidently a key factor with enormous potential to foster economic growth at macro and micro levels. Social and economic development comes when social actors can make

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technological advances and incorporate them into everyday life, but also when the advances are adopted and broadly used for production as well, which eventually translates into increasing social welfare. At the same time, however, it would also appear that to some extent economy and society guide technological change and the emergence of innovations, whose introduction is closely correlated with economy and society, following economic and social relationships. Societies that efficiently disseminate technology, innovations, knowledge, and information to all spheres of life have great opportunities to create new areas of employment, production, and other market activities. Finally, as is observed in Cassiolato, Pagola, and Lastres (2009), ‘technical change is a requisite for development, and technical upgrading is part of the developmental strategy. Taking advantage of the windows of opportunity requires searching and following a specific developmental path’ (p. 56).

3.2 ICT for socio-economic growth and development It is at least plausible to argue that modern economic growth has resulted largely from the investment in a particular type of capital, ‘knowledge capital’

—Gregory Clark

The sources of economic growth and development and of social progress have changed in the course of history (Rioja & Valev, 2004). Technology developed and advanced unceasingly for ages. Fagerberg and Verspagen contend that ‘technology is a key factor shaping economic growth’ (Fagerberg & Verspagen, 2002, p. 1294). Companies and individuals are continuously learning to use technology more efficiently, which results in productivity gains, quantum jumps in growth rates, international cooperation, on the one hand, and competition, on the other, which stimulates further advances in the quality of production. Though there is, of course, no denying the complexity and multi-dependency of modern economic growth, it can be said that since the onset of the First Technological Wave, the growth of per capita income has had two main sources: first, the increase in the stock of capital per capita; and second, more efficient use of available land, labour, and capital. However, as argued by Clark in Seligson, Passe-Smith, and Seligson (1998), ‘advances in the measured efficiency of economies don’t just drop out of the sky, but result from

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(…) better techniques. Thus the same measured amount of capital, labour and land may produce more output than before’ (p. 113). Moreover, according to Mokyr (2002), technological changes are often revolutionary in their nature, meaning that they are disruptive and sometimes abrupt, causing deep-going and long-lasting changes in the social and economic status quo and becoming the primary engine of economic growth and development (Landes, 2003). The importance of knowledge and thus technology for social and economic development is widely acknowledged. Classical economists such as Smith, Ricardo, and Malthus (Eagly, 1974) hypothesised a close relationship between economic growth and population. Let us repeat that in the 18th century, growth was driven mainly by the number of people at work, most of them being in agriculture. It was hard, time-consuming, and lowproductivity labour. However, the rapid technological progress of the late 18th and the 19th centuries gave rise to quite a different concept and perception of the process of economic growth and its relationship with technological change. The neoclassical growth theorists emphasised the role of technological progress in generating per capita growth and the expansion of a society’s wealth. Thorsten Veblen was one of the first to perceive technology as the ‘great equalizer’, with the power to offer economically effective solutions to the problem of producing the goods and services that a society desired (Brette, 2003; Veblen, 1906; Veblen, Hobson, & Cain, 1994), whereas Joseph Schumpeter is broadly recognised as the first economist to define the role of technological advances in enhancing per capita growth. Veblen was also one of the first to examine the role of technological change in economic development and the catching-up process. Veblen argued that cross-country technology transfers enable poor countries to achieve sustainable economic growth and development and hence, over the long haul, to catch up with the more highly developed economies. Early neoclassical models and concepts (Meade, 2013), such as that of Robert Solow (1956), treat technological progress as exogenous and emphasise its crucial role in fostering long-run economic growth. The importance of technology in shaping economic performance may be traced not only in Schumpeter (1934, 1947) but also in the work of Nicholas Kaldor (1957). Other significant theoretical and empirical contributions on the links between technology and economic development have come from such authors as Uzawa (1965), Nelson and Phelps (1966), and Shell (1967). All these scholars stressed on the role of technological change and of permanent technological innovation as key determinants of upward shifts in labour

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force skills and abilities, which in turn drive up national income growth rates. In addition, a significant body of literature has contributed to endogenous growth models; see, for instance, the seminal works of Robert Lucas (1988), Paul Romer (1990), Grossman and Helpman (1991), and Aghion and Howitt (1992), all emphasising technology and technological change as critical factors in growth and development. Another strand of economic theory combines the foregoing ideas with the hypothesis of catch-up by developing countries. The idea of implementing technology in broad development theories in this sense was undertaken in the works of Alexander Gerschenkron, Ronald Findlay (1978), and Moses Abramovitz (1986), to name just a few. Gerschenkron (1962) raised the argument that developing and economically lagging countries operate mainly below the world technology frontier, but by imitating developed technologies, they have a chance to catch up with the more advanced economies. The need for ‘technological congruence’ (Antonelli, 2016), meaning appropriate technology to enter the development path, has also been stressed in the works of Abramowitz (1994). Technology and innovation should potentially foster catching-up by poorer countries mainly by allowing improvements in education, diffusion of knowledge, and shifts in labour productivity; see inter alia the works of Castellacci (2006, 2008, 2011), Ben-David (1998), Kumar and Russell (2002), and Herr and Ruoff (2018). Apart from the works cited above, there is a vast body of contemporary theoretical and empirical literature on the role of technology in economic growth and development. This strand of research comprises influential works by Romer (1990, 1993, 1994), Hewitt and Wield (1992), Mankiw, Phelps, and Romer (1995), Savvides and Zachariadis (2005), Antonelli (2011), Nelson (2011), Fukuda-Parr and Lopes (2013), or, more recently, Stanley et al. (2018), Aydalot and Keeble (2018), Malecki (2018), and Lacasa, Jindra, Radosevic, and Shubbak (2019). However, technology acquisition and absorption is not automatic. Baumol (1986), Perez and Soete (1988), and Verspagen (1991) have argued that a country’s ability to adopt new technologies is predetermined by multiple specific features. While a society’s ability to assess and assimilate technological novelties depends on ‘intellectual’ capital (Soete & Verspagen, 1993) as well as institutional, governmental, and cultural conditions, there is also empirical evidence that the chief factor in successful adoption and dissemination of technological progress is the education and skill of the labour force (Baumol, 1989; Srivastava, Gnyawali, & Hatfield, 2015; Zahra & George, 2002). Countries with a poorly educated or unskilled labour pool

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are unlikely to be able to harness the potential of new technology and so will miss the opportunity to catch up with richer countries, instead remaining economically disadvantaged. Similar arguments are made by Gregory Clark: ‘Poor countries have remained poor because they cannot absorb the technologies of the advanced countries’ (Clark, 1987, p. 141). The underlying explanation is set forth by such authors as Hirschman (1958), Rosenberg (1976), and Easterlin (1981), who argued that poorer countries are simply not prepared to absorb technologies from developed countries because high-quality institutions are scarce and their labour pool is poorly educated and lacking in managerial skills. Moreover, the effective outcome of technological change can differ substantially from country to country, as it will engender economic gains only where it is accepted and assimilated by the society. That is, the adoption of new technologies so they can contribute to growth is anything but automatic. Instead, the acceptance of new technology, its pervasiveness, and the speed of its dissemination all depend on complex social, institutional, and economic forces. As argued by Keller (1996) and Kostopoulos, Papalexandris, Papachroni, and Ioannou (2011), the emergence of sustained benefits from technology is determined essentially by a society’s absorptive capacity, i.e. its ability to deploy and use the technology. Whether or not a society can rapidly adopt new technologies depends essentially on the institutional environment, social attitudes, norms and values, and a wide array of economic and institutional incentives (Rosenberg, 1982, 1994; Rosenberg & Birdzell Jr., 2008). Certain societies may be hampered by poor education, low-quality human capital, cultural constraints, an unfavourable institutional and legal environment, or simply by geographical disadvantages. Any of these handicaps can severely impede the deployment of new technologies. By comparison, better educated societies with little risk aversion and high propensity to adopt novelties assimilate new technologies relatively quickly and easily. Put another way, the rate of diffusion and deployment of technologies depends on the absorptive capacity of the society (see, e.g. Baumol, 1986; Perez & Soete, 1988; Cohen & Levinthal, 1990; Verspagen, 1991; Criscuolo & Narula, 2008). There is some evidence that the education level and skills of the labour force are in fact the most important factors in a country’s ability to adopt and effectively use new technology (Baumol, 1989; Benhabib & Spiegel, 2005). Countries that lag significantly behind in these areas may never be able to realise the full potential of technological change. Various aspects of the way in which societies progress technologically and succeed in exploiting the full potential of new technologies are discussed in the works of such

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scholars as Kim and Lee (2004) and Jensen, Johnson, Lorenz, and Lundvall (2007), who write about the technological capabilities of societies. Other researchers writing on this topic include Lundvall (2010), Lall (1992) and Nelson (1993), who underline the role of innovation systems in the adoption of technology. Regarding the benefits produced by technological change, it is important to note that the effective impact of a technology on the economy or society is not always apparent immediately. The gains in quality of life tend not to be fully appreciable until a significant amount of time has elapsed. As noted by Mokyr (2005), the period of the industrial revolution itself was not, as such, one of rapid economic growth. The benefits generated by technological changes did not become clear right away. Instead, the early industrial societies waited almost 100 years for economic growth to speed up. In support of Mokyr’s supposition, Clark (2008) observes that in Britain from the 1760s to the 1860s, the signs of rapid growth in per capita income were scanty indeed. The substantial time lag before the gains due to technological progress show up in the national accounts and in per capita income growth is now known as ‘Solow’s productivity paradox’ (David, 1990; Triplett, 1999). Solow himself wrote in 1987, ‘You can see the computer age everywhere but in the productivity statistics’, explaining that a rapid technological change produces only slow gains in total productivity (David, 1990). Intuitively, this productivity paradox may be explained, at least in part, by the inevitable gradualness of diffusion, adoption, and deployment of new technology within a socio-economic system. To repeat, new technologies are necessarily installed and embodied within the society and economy in two phases (see Cvetanovic, Despotovic, & Mladenovic, 2012; Perez, 1985, 2002). The first period is installation; the second, deployment. During the installation period, the new technology spreads through the society and economy. Diffusion may be slow at first, but once a critical mass of users emerges, it accelerates and technological change spreads. The installation phase is critical, the prerequisite for wider deployment and social adaptation to technological change, which in turn induces structural shifts and economy-wide reorganisation. When the installation phase gives way to broad deployment, the new technology is adopted by the vast majority, and the change becomes disruptive, inducing significant changes in productivity. By the end of the deployment period, the benefits of change should begin to appear in growth statistics. To a certain extent, this view coincides with the Kondratiev’s concept of long-waves (Kondrat’ev, 1984; Silverberg

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& Lehnert, 1993) and its Schumpeterian interpretation regarding the role of technological progress in long-term growth (Rosenberg & Frischtak, 1984). Both Kondratiev and Schumpeter attribute the emergence of long business cycles (approximately 50–60 years long) to the diffusion of technological progress. As successive technologies diffuse along logistic patterns, they gradually unveil their potential growth in productivity. The full potential of newly emerged technological changes, however, is exhibited once they are broadly adopted by the society, which allows for gain generation such as growth in per capita income. A similar approach can be also found in the work of G€ oransson and S€ oderberg (2005). Undeniably, ICTs can play a critical role in the development process by broadening access to information and all types of knowledge, which empowers people and heightens participation in socio-economic life (Mansell, 1999, 2001; Mansell, Avgerou, Quah, & Silverstone, 2009; Wilson, 2004). The very substantial economic impact of ICTs may be further reinforced by means of forging positive links between market agents, providing opportunities for more flexible work and producing new contacts, which results in increased economic activity, potential gains in productivity and the efficiency of firms, and lower costs. Cost savings are closely associated with the reduction in information asymmetries, which generate transaction costs, uncertainty, and possibly market failures (Wolf, 2001). ICTs can help to eradicate information asymmetries and so make resource allocation more efficient. Rectifying these asymmetries improves access to economic activity for a multitude of agents, fostering labour market participation by previously excluded social groups. Above all, ICTs offer connectivity and the transfer of knowledge and information, regardless of the physical distance. The ‘death of distance’ (Cairncross, 2001; Redding & Venables, 2004) becomes a fact, in many situations dispensing with face-to-face contact. Moreover, ICT gives developing countries new tools for fighting rural and urban poverty (Cecchini & Scott, 2003; Forestier, Grace, & Kenny, 2002; Graham, 2002), by improving economic performance and the ability to compete on global markets. It also provides a means for turning idle labour force to advantage and increasing social capital (Chong, 2011). Most importantly, the impact of ICTs goes far beyond the ICT sector itself to transform social and economic life, playing an enabling and unlocking role for economic growth and development (ITU, 2012). There is a causal link between the adoption of ICT and a country’s ability to achieve a pattern of long-term

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economic development, which should eventually enable backward countries to catch up with the most advanced economies. New ICTs have spread worldwide, overcoming classical barriers to adoption such as low literacy rates, underdeveloped infrastructure, lack of access to electricity, extreme poverty, authoritarian regimes, and weak institutions (Lechman, 2016). Although Internet access remains restricted in some of the low-income countries, many of them are improving rapidly on this front, facilitated mainly by the development of wireless networks. These promising trends open up prospects for the future, because new ICTs are widely seen as offering an opportunity for poor countries to embark on a path of stable socio-economic development (Gruber & Koutroumpis, 2011; Hanna, 2003; Torero & Von Braun, 2006; Unwin, 2009). The rapid diffusion of ICT worldwide has triggered interest in their role in promoting economic development and raised the question of whether or not societies and economies can harness their full potential (Elliott, 2012; Heeks, 1999) to catalyse economic growth and development. These issues are receiving growing attention from scholars around the world (see, e.g. Heeks, 2010; Hanson & Narula, 2013; Khavul & Bruton, 2013), as the near-ubiquitous spread of ICTs offers unprecedented opportunities for economic take-off (Desai & Potter, 2013). Most importantly, the extraordinary impact of ICT on the level of development of a country will be confirmed only when ICT is effectively converted into human development and progress. To date, a definite, precise answer on how the new technology actually promotes socio-economic development remains elusive. However, if countries are unable to harness the potential of ICT owing to multiple constraints and poor socio-economic and institutional conditions, then the international development gap can only widen, producing more severe disparities. Following Hanna (2003), David (2001), and Perez (2001), one cannot fail to recognise that countries that are permanently inactive in adopting new ICTs and make little effort to improve their situation may miss out on the opportunities and benefits of ICTs, which would result in digital and economic marginalisation. A more optimistic scenario is that ICTs will provide a path to development and growth so that the developing countries are able to climb the ladder and achieve a stable pattern of development. Ultimately, however, in discussing the potential impact of technological progress, we must not forget that ICTs, similar to GPTs, do not necessarily induce gains in productivity and economic growth immediately upon their emergence (David, 1990). Put simply, it takes time to unlock the potential of

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ICTs and transform it into socio-economic development, productivity gains, and higher per capita income. This will come only considerably after the moment of technological progress. Helpman and Trajtenberg (1994) and Helpman (1998) suggest that the full incorporation and deployment of all GPTs (thus including ICTs) takes time, and their role in fostering economic development may be appreciated only in the long-term perspective. They call the initial phase of diffusion of GPTs the ‘time to sow’, when resources need to be ‘diverted to the development of complementary inputs to take advantage of the new GPT’ (Helpman & Trajtenberg, 1994, p. 85). The second stage is accordingly the ‘time to reap’, when a growth in total productivity and per capita output becomes evident. Modern economic growth is attributed to three factors that appear to be crucial: capital accumulation, technological progress, and available labour force. While capital accumulation and investment are widely recognised as the fundamental factor in economic growth, only with the help of technology can there be more output for the same quantity of input. Technological advances also allow more effective use of labour, as people become more educated and acquire vocational skills. This, in the long-run perspective, offers substantial benefits in terms of the productivity of capital and labour. All these factors are likely to result in greater social and economic wealth, higher living standards, and improved quality of life.

3.3 ICT, financial markets, financial innovation Following the general discussion of the socio-economic impact of ICTs, we now focus on the specific role of these new technologies in the financial system. Specifically, we examine the impact of ICTs on two aspects of the financial sector: financial markets and financial innovations. The section begins with a brief overview of the relationship between ICTs and the financial system at large, focusing on the issues of financial development (other attributes of the financial system discussed are financial access and inclusion) as well as financial globalisation and integration. Next we outline the main consequences for the banking industry, which represents one of the most important sectors of the contemporary economy. The remainder of the section is given over to a discussion on financial market changes that have stemmed from the diffusion of ICT, and an examination of the role of ICTs in the launch and spread of financial innovations, in particular those in financial markets. The final issue explored involves one of the most intensively

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discussed areas of overlap between new technology and finance—various types of fintech, including the use of big data and blockchain in the financial industry. Before discussing the deployment of ICT in financial markets, it is worth recalling that many of the effects in the markets for equities, fixed income instruments, derivatives, etc., are inextricably intertwined with developments in the financial system as a whole. To begin with, therefore, we first consider the effects of ICT on financial development, considered from various perspectives (note that the converse relationship, i.e. the impact of financial development on the spread of ICTs, has also been examined and confirmed; see, e.g. Yartey, 2008). The potential impact of ICT on the financial sector needs to be framed broadly and discussed in the context of transformation of this industry in the 20th century, with the exponential growth in both the number and value of transactions within the global financial system, often at great distances and without any physical transfer of assets, which are dematerialised and thus stored only in the form of electronic records (Garretsen, Kitson, & Martin, 2009). More generally, one can say that, to a large extent, the global financial system is now ‘virtual’. One of the most notable, essential instances of the use of ICT in the financial system is cross-border electronic money transfers, which are now much faster and more economical than in the past. The way ICT influences various financial products and services depends on its effect on information flows. Financial systems, in fact, in view of the crucial importance of information to their functioning, are considered ‘information systems’ (Ocampo, 2018; Stigler, 1961), and the consequences of improvements in information exchange are accordingly vast and profound. For one thing, the increasingly widespread introduction of ICT facilitates the creation of networks linking entities within the financial system; in the longer time horizon, one consequence is the attenuation of information asymmetries (unequal access to information) and the more rapid spread of information (Asongu & Moulin, 2016; Mishkin & Strahan, 1999). As Shamim (2007, p. 352) puts it, ‘new technologies lower processing costs for suppliers and information costs for consumers’. The second broad channel for the impact of ICT is increasing participation in the financial system (see the discussion of the relationship between ICT and financial access and inclusion), due to such factors as more economical financial services, thanks to ICT (owing, among other things, to the possibility of reducing the number of physical bank branches; Pradhan, Arvin, Nair, Bennett, & Bahmani, 2017), or that these services now better

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address customer needs and are better aligned with their competences. In a different analytical framework, Shahrokhi (2008) provides a detailed discussion of e-finance (various types of financial services provided with the application of ICT), emphasising the following effects: reduced transaction costs, disintermediation and electronic reintermediation, customised solutions and integrated services, and financial portals. Fostering ICT may also be a part of government policy for the financial sector (Wang, 2018). Godsiff, Mulligan, and Gurguc (2014) list four key areas of impact of ICT on financial services: improving productivity (especially in banking), facilitating the entry of new financial service providers, increasing cross-over with other sectors of the economy, and fostering radically new financial services and supply methods. The penetration of ICT may also be expected to affect financial access, or inclusion. As Asongu and Acha-Anyi (2017) conclude, ICT has the potential to overcome barriers to financial access by mitigating information asymmetries, consolidating market participation, lowering marketing costs, and easing constraints on doing business. From a different perspective, ICT can be expected to heighten competition between the informal and the formal financial sector (Asongu, 2013). In order to maximise the potential benefits, however, the introduction of ICT must take account of multiple, complex factors, such as the financial literacy of the population (Berger & Nakata, 2013; Masiero & Ravishankar, 2018). Moreover, ICT-driven development of financial services aimed at overcoming financial exclusion should provide for a proper balance between structure and flexibility, and should not limit the choices of end-users (Bisht & Mishra, 2016). These stipulations apply also to single financial institutions and their relationships with clients, as shown by Mulligan and Gordon (2002). According to Gabor and Brooks (2017), the key attribute of ICT-enabled financial inclusion is the ‘commodification’ of the new class of financial consumers, and in particular their personal data. He et al. (2017) argue that ICT (they focus on Internet access, in particular through mobile connections) has given customers of financial institutions access to a broad range of financial services. The benefits of ICT for financial development and financial inclusion have been verified by a number of empirical studies, the vast majority of which confirm the positive role of new technologies. In one of the first studies devoted to this topic, Shamim (2007) examined 61 countries in the period 1990–2002, confirming the positive impact of ICT on the development of financial sector in most of the surveyed countries, although in a few the relationship was reversed or bidirectional. Moreover, ICT proved to

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strengthen the positive influence of financial development on economic growth. A study of 44 African countries from 1988 to 2007 by Andrianaivo and Kpodar (2011) found that ICT (in particular mobile phones) made a positive contribution to the economic growth, owing in part to a positive effect on financial inclusion. Two studies of the impact of ICT on financial development in the Middle East and North Africa also found a positive relationship (Falahaty & Jusoh, 2013; Sassi & Goaied, 2013). Pradhan, Arvin, and Norman (2015) demonstrated causal links between ICT infrastructure and financial development in Asia from 2001 to 2012. Asongu and Moulin (2016) examined 53 African countries between 2004 and 2011, focusing on information asymmetries in the context of financial access. Using data from private credit bureaus and public credit registries, they found only a limited impact of ICT on financial access. Taking into consideration particular types of ICT and their relationship with financial development, Pradhan, Arvin, Bahmani, & Bennett, 2017), using data from 22 Arab League countries for 2001–2013, showed that broadband Internet connections contribute to financial development (to be exact, they confirmed feedback between these two processes in the short run). In a similar study, for the Next-11 countries in the half century from 1961 to 2012, Pradhan, Arvin, Nair, et al. (2017) found both bidirectional and unidirectional causality between the adoption of ICT and financial development. Asongu and Acha-Anyi (2017) verified the role of ICT in 53 African countries in 2004–2011 and confirmed its positive effect on financial access (through financial formalisation). Pradhan, Arvin, Hall, and Bennett (2018) examined the relationship between the diffusion of mobile phones and financial development in the G-20 countries between 1990 and 2014, confirming unidirectional or bidirectional Granger causality in some instances. Apart from the possible effects of ICT on financial development or variously defined concepts of financial access or inclusion, system-wide effects are also found, in relation to the integration of financial systems and, in a closely related process, financial globalisation. Increasing penetration of ICT on a global scale (not limited to the most advanced economies; indeed, in some cases the emerging countries have been world leaders in ICT adoption) is perceived as one of the key accelerators of financial integration and globalisation, as geography plays an ever less significant role in flows, products, and services of financial institutions (Cerny, 1994; Garretsen et al., 2009; O’Brien, 1992). It should be remembered, though, that some countries, owing to an insufficient adoption of ICT, may lag behind and be

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unable to benefit from the emerging possibilities (Pozzi, Di Matteo, & Aste, 2013). However, these processes may also have adverse side-effects, such as increased market volatility as fluctuations and shocks are transferred through the global financial network (Cont & Bouchaud, 2000; Garretsen et al., 2009). Another negative consequence of ICT may be the financialisation of nonfinancial markets, especially commodity markets (Diaz-Rainey & Ibikunle, 2012), due to easier and cheaper access to the relevant financial instruments even for retail investors, who were previously absent from such markets. The function of ICT for financialisation of the economy was verified empirically by Drummer, Feuerriegel, and Neumann (2017), who concluded that advances in ICT have led to the development of online credit marketplaces. The areas of potential impact of ICT on banking are multiple—indeed, banking is certainly one part of the financial system most substantially affected, with new financial services and products as well as new ways of supplying preexisting ones (e.g. online bank accounts and electronic payments). As noted by Diaz-Rainey, Ibikunle, and Mention (2015), companies that provide financial services (above all, banks) have higher ICT expenditures, on average, than companies in other sectors. As our concern here is with financial markets, we discuss exclusively some selected issues related to banking. A brief list of the most important ICT-enabled banking services necessarily includes: online and mobile banking, electronic payments, and mobile services for various groups of customers, both retail and corporate (Diaz-Rainey et al., 2015). However, even taking into consideration the more established types of services, there is no doubt that increasing penetration of new technologies has enhanced access and cut costs, for instance, of ATMs and credit cards. Muthinja and Chipeta (2018) link the changes in mobile telecommunications and data processing technology to the development of mobile banking; the main contribution of the new technologies consists in the various money transfer possibilities (between mobile phones, between online and mobile bank accounts, and so on). Interestingly, many of the lead countries in mobile banking are not advanced but rather emerging and developing economies in Asia or Africa—Kenya for one (Muthinja & Chipeta, 2018). This suggests the possibility of leap-frogging in banking and other financial services. In these countries the popularity of the brick-and-mortar bank branch has remained limited, but the most up-to-date solutions, particularly those based on mobile technology, have been adopted rapidly and on a large scale. Diaz-Rainey et al. (2015) hold that the spread of ICT in the banking system helps to counteract the complexity of task-sharing, thus

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making it feasible, for instance, for a bank to operate with relatively few branches, physically distant from one another. This could curb labour and other costs for banks. Digitalisation in banking can generate new business models and value chains, insofar as it alters the conventional orientation towards the branch system (Drasch, Schweizer, & Urbach, 2018). According to Asongu and Nwachukwu (2017), ICT can foster development of the banking industry (and, consequently, increase the availability of credit to households and firms) by improving the capacity of financial institutions to evaluate customer risk profiles. Asongu and Biekpe (2018) contend that ICT reduces market power and its possible abuse by local elites and leading banks, as they no longer enjoy information monopoly. A study of two African countries by Karakara and Osabuohien (2019) shows that increasing ICT access for households in the developing countries can be associated with increased use of banking services, and that the connection appears to be the effect of increased financial literacy. Tchamyou, Erreygers, and Cassimon (2018) affirm that ICT can foster access to formal financial services by improving the store of value within the banking sector, improving interbank communications and transactions, and easing access to bank accounts through digital banking. Mobile banking has shown the potential to affect various aspects of the economy, and in particular to help foster inclusive development (Asongu & Asongu, 2018; Asongu & Nwachukwu, 2018a; Asongu & Odhiambo, 2019; Tchamyou et al., 2018) and inclusive human development (Asongu & Nwachukwu, 2018b). The linkage between ICT adoption and banking development (gauged here by the provision of bank credit to enterprises) is also observed at the micro level: as demonstrated by Pellegrina, Frazzoni, Rotondi, and Vezzulli (2017), small companies that adopt ICT more extensively are offered increasing amounts of credit, thanks, among other factors, to the better quality of information they transmit to banks and to the consideration of ICT adoption by banks as a signal of innovative readiness. Modern technologies may also be used in procurement finance, as discussed in Nicoletti (2018). Finally, however, the impact of ICT in banking, and in particular on the financial standing of banks, has not been unambiguously positive. The spread of new technologies has facilitated the provision of services once provided exclusively by banks by new types of companies, sometimes quite distant from the financial sector but part of the broader ‘fintech’ industry (a notable example is the variety of online and mobile payment services offered by non-bank companies). Jaksˇic and Marinc (2019) discuss the potential threats to traditional banks deriving from the new technologies

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and fintech companies, concluding that banks should concentrate on relationship banking rather than transaction banking. Moreover, even the gains in financial inclusion due to ICT may have drawbacks. Diniz, Birochi, and Pozzebon (2012) examined branchless banking in the Amazon region of Brazil and concluded that, while there was a positive effect on socio-economic development, ICT-based banking aggravated the indebtedness of the low-income population, exacerbated asymmetries of power, and reproduced practices of social exclusion. Another component of the contemporary financial system that has been strongly affected by the accessibility of ICT, obviously, is the financial markets. In what follows we outline the most important linkages between ICT and financial markets, the main areas of impact, and the positive and negative consequences, with a brief review of empirical research on this matter. However, it is worth underscoring that Internet and similar technologies are only the latest in the series of telecommunication services used by financial market participants. Hermans and De Wit (2004) showed, citing the Dutch securities market, that other older types of communication services have affected financial markets since at least the mid-19th century. At the end of the last century, when some of today’s predominant ICTs were still only nascent, Mishkin and Strahan (1999) formulated forwardlooking conclusions concerning their impact on the financial system. They clearly foresaw that a narrow, geographically based approach to defining the markets for financial products would be increasingly obsolete as those markets grew larger and more contestable. Probably the most important attribute of the financial markets affected by the spread of ICT is infrastructure, i.e. the mechanism for trading, processing, and settlement of transactions in commodities, currencies, and securities, plus such ancillary services as data aggregation and exchange (Essendorfer, Diaz-Rainey, & Falta, 2015). Without adequate physical infrastructure, the expansion of financial markets is impossible (McMahon, 2004). The old paper-based transactions imposed significant limits on trading capacity, whereas today’s ICT-based infrastructure fosters incomparably greater efficiency, security, and speed in the clearing and settlement of financial transactions (Zagorchev, Vasconcellos, & Bae, 2011). One of the key trends, described by Hendershott and Madhavan (2015) as ‘voice to electronic’, is the declining role of once-predominant physical trading venues, now increasingly supplanted by electronic systems; trading venues may be fully electronic-based (in the vast majority of cases) or at least strongly supported by ICT-based systems. These systems may be accessed

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and used by a large number of entities that can effect transactions through the intermediation of centralised institutions (e.g. central securities depositories). The progressive introduction of new technologies has essentially transformed financial markets into a network of interconnected electronic systems (McMahon, 2004), no longer limited by physical location. In other words, to cite O’Hara and Ye (2011), they have become one single virtual market, but with many available trading venues. Kluber (2017, p. 189) goes even further, asserting that stock exchanges have become ‘first and foremost an IT service provider’. The world’s first electronic stock market was NASDAQ (Brummer, 2015); the US financial markets may be regarded as having pioneered the use of new technologies—indeed, the computerisation of the US stock market got underway as far back as the 1960s. According to McMahon (2004, p. 78), the main technologies supporting the development of the infrastructure of global financial markets over the subsequent decades were semiconductors, communication satellites, computers, and computer networks. The transformation of trading systems entails substantial consequences, above all the increased volume and reduced cost of transactions. Another effect is the development of competition in this part of the financial industry—in some respects, entry barriers have been lowered with the declining position of traditional, floor-based exchanges and the strengthening of more technologically sophisticated trading venues (digital exchanges) based on electronic trading, settlement, etc. (Lee, 2010). One of the most striking examples is derivatives exchanges (see the discussion on options exchanges in Ernkvist, 2015); one of the effects has been the introduction of new types of financial instrument. Moreover, the new technologies have contributed to the cost and revenue efficiency of stock exchanges (Hasan, Malkam€aki, & Schmiedel, 2003) and economies of scale (Li & Marinc, 2018). The introduction of electronic trading systems (and ancillary systems) has greatly enlarged the role of computers, not only in the execution and settlement of transactions but also, increasingly, in actual trading decisions, now frequently determined by algorithms (Brummer, 2015). Algorithmic trading can be defined as ‘automated electronic trading subject to quantitative rules and user-specified benchmarks and constraints’ (Madhavan, Treynor, & Wagner, 2007, p. 670); automated trading is a broader term, as it refers to all types of nonmanual trading; the fast growth of the turnover generated by algorithmic trading has been labelled the ‘algorithmic revolution’ (Madhavan et al., 2007). Over the years the evergreater recourse to various forms of automated trading has produced such

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phenomena as, for one, high-frequency trading (we describe it in the context of financial innovations). Another consequence of ICT penetration in the financial markets is the emergence of trading venues other than traditional stock exchanges, labelled by Brummer (2015) as ‘private capital markets’, most notably electronic communication networks (ECNs).1 ECNs are computer-based (automated) order-driven equity trading systems that operate continuously and provide price discovery (Madhavan et al., 2007). They contribute to the development of financial markets by expanding liquidity and narrowing spreads (one factor in trading costs). One of the reasons for the growth of ECNs and other private capital markets is the shift of large transactions of institutional investors into non-exchange trading systems, so that such operations do not have such a powerful effect on the markets, and in particular on the prices of securities (Brummer, 2015). As the competition to execute transactions has intensified (Angel, Harris, & Spatt, 2011), investors have been able to identify and utilise the trading systems that best serve their needs. On a more basic level, the adoption of ICT may be perceived as a crucial factor in the dematerialisation of securities (Kauffman, Liu, & Ma, 2015), with clear benefits for market participants, such as lower transaction costs and enormously quicker trading, as the need for physical exchange of securities has been eliminated. More generally, the benefits of the growth in electronic trading, depository, and settlement systems consist in improvement of the pricing mechanism, expansion of liquidity, and decreased trading costs (Hendershott, Jones, & Menkveld, 2011; Schmiedel, Malkam€aki, & Tarkka, 2006). Similar to financial systems at large, financial markets can also be seen as information markets. Information exchange is the backbone of financial markets, and ICT has produced epoch-making breakthroughs in this field—enormous amounts of information are now readily available through a variety of channels (personal computers, mobile phones, etc.). That is, the new technology has reduced the cost of data transfer, greatly diminished delays, and increased the scope and accessibility of information (Miller & Skinner, 2015; Zagorchev et al., 2011), reducing information asymmetries (Gajewski & Li, 2015). The importance of ICT for information flows is stressed by Shiller (2012), who observed that for stock markets this was the primary effect of its introduction, even more important than the impact 1

The other types (or alternative names) of such venues include ‘broker crossing networks’ and ‘multilateral trading facilities’ (Diaz-Rainey et al., 2015).

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on the speed of transactions. A similar assessment of the importance of immediate access to data is found in Madhavan (2012). Ngassam and Gani (2003) explain that the popularity of stock investing has been boosted by access to cheap, user-friendly ICT: mobile phones, Internet, and personal computers have replaced newspapers or other sources of stock information. They facilitate data access even in remote locations and have thus attenuated information deficits. ICT has also produced a massive expansion of information storage capacity (McMahon, 2004). The contribution of ICT to the development of financial markets may also be indirect. According to Lee, Alford, Cresson, and Gardner (2017), the spread of ICT, by stimulating economic growth, is likely to boost the stock market as well, regarded as the barometer of the economy. These authors contend that the more ICT-oriented the economy is, the greater the contribution of the growing access to and utilisation of new technologies to the economic growth and, indirectly, to the long-term development of financial markets (owing, e.g. to the attenuation of the risks associated with lack of information). The consequences of pervasive diffusion of ICT in financial markets should also be viewed in international perspective, not only at national level (for a country-level analysis, see, e.g. Essendorfer et al., 2015; Preece, 2012, who showed the significance of ICT for the fragmented US equity market). The increased introduction of ICT in many countries, at all levels of economic development, has created such new opportunities as the listing and trading of securities on venues in geographically distant countries; obviously, some assets were cross-listed in the past (even the quite distant past), but operations between markets were burdened by substantial delays, and securities transfers were much more problematic (Schmiedel et al., 2006). Another problem inherent in cross-listing that has been attenuated by the ICT-based infrastructure of financial markets is foreign exchange risk. Easing the barriers to cross-listing (or, more broadly, the barriers to cross-border investment) may play a significant role in less developed countries and contribute to their integration into the financial markets of other countries, especially the economically advanced. It may lead to greater international integration of financial markets (see the discussion of the integration of capital markets, thanks to cross-border settlement systems, in Panourgias, 2015). Apart from the mostly beneficial consequences listed above, ICT may also have some adverse effects on financial markets, such as fragmentation of liquidity (stock exchanges lose their dominant position in securities trading, which can hamper a proper evaluation of liquidity; see Preece, 2012),

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heightened investor short-termism (Singh, 1997), and aggravated volatility (Ilyina & Samaniego, 2011). One of the most worrisome problem areas is the emergence of ‘dark pools’, i.e. trading venues with no pre-trade transparency, which are likely to exacerbate fragmentation, decrease price transparency, and undermine market efficiency (Diaz-Rainey et al., 2015). The disruptive potential of ICT may also undercut the effectiveness of capital market regulation and supervision (Brummer, 2015; Diaz-Rainey & Ibikunle, 2012), engendering threats to investor protection and market integrity. Another danger is that of shifting investors’ preferences towards the short term, which may impede one of the capital market’s fundamental functions, namely, raising capital for companies through stock or bond issuance. The virtualisation of capital markets (i.e. the diminishing role of physical exchanges) may also result in the decline of some second-tier financial centres (Engelen & Grote, 2009). Studies on the impact that ICT penetration has on financial markets are still relatively few in number, and most of them focus on stock markets, owing to the clearly identifiable links with ICT, as noted above, and to the availability of data. For the most part this research highlights the positive (or, at worst, neutral) impact of new technologies. These studies can be divided into two broad groups: country-specific analysis and panel studies. The latter group is much bigger (only three country studies were identified), which should not come as a surprise, given that national studies can scarcely serve to formulate generalisations. As far as country-specific studies are concerned, Ashraf and Joarder (2009) conclude that ICT boosted stock exchange turnover in Bangladesh in the mid-2000s. Bhunia (2011) confirmed that the adoption of ICT was a positive factor in the Indian market as well. For two other emerging economies in 1995–2014, Nigeria and South Africa, Okwu (2015) found that ICT was among the main determinants of stock market development, owing, above all, to enhanced transaction capacity. The second strand of research is highly heterogeneous, as panel studies differ both in the countries and in the time periods considered (although the diversity in the indicators used as regards stock markets or ICT is much lower). Ngassam and Gani (2003) investigated the impact of ICT in a sample of high-income and emerging economies, concluding that the key technological factors in stock market development are personal computers and the Internet, while mobile phones proved to be insignificant. Their findings suggest that some of the emerging economies have successfully exploited the opportunities generated by ICT and leap-frogged from severely

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underdeveloped stock markets to markets based on fully digitised communication networks. Zagorchev et al. (2011) studied 86 developing and developed countries between 1991 and 2006 and confirmed the positive impact of Internet on financial markets, in particular stock market trading. They also found a positive relationship between mobile phones and the size and liquidity of financial markets. Hossein, Fatemeh, and Seyed (2013), in an examination of leading national capital markets (60 countries) up to 2011, concluded that the introduction of ICT had positive effects on most aspects of stock market development. Janke, Packova, and Pridavok (2015) studied the linkages between ICT and stock markets from a different perspective, checking the impact of ICT adoption on share prices in four countries in Central and Eastern Europe; their results offer some support for a positive influence. Lee et al. (2017) analysed ICT adoption (using the three major indicators, i.e. Internet users, fixed broadband subscriptions, and mobile phones) and stock exchange capitalisation in 81 countries between 1998 and 2014, finding that the expansion of ICT is positively associated with the size of stock market. Lee, Tsai, Chen, and Lio (2019), on stock market data in 71 countries from 2002 to 2014, concluded that markets are more efficient in countries where ICT is more widely diffused (thanks chiefly to the reduction of noise in market signals). The results of a study of 14 countries (mostly emerging economies) by Pradhan, Mallik, Bagchi, and Sharma (2018) demonstrate both unidirectional and bidirectional relations between adoption of ICT and development of stock markets. Let us now turn to the linkage between new technologies and financial innovation, with the caveat that the discussion will be quite concise, insofar as most of the potential effects on financial innovation are in fact closely associated with the consequences for the entire financial system or financial markets (and, to a lesser extent, with consequences specific to banking). We therefore discuss only some of the issues that are most important in the context of a further empirical study, i.e. the general impact of ICT on financial innovation and some noteworthy examples of recent financial innovation, in particular exchangetraded funds (ETFs), which are examined in the empirical study. The impact of ICT in fostering financial innovation is by no means a new topic. Nearly three decades ago, Sharpe (1991) posited that technological advances (in particular computing and communication) constituted a major factor in innovative activity in the financial industry. ICT’s ability to influence the financial system is shown by the potential of new technologies to foster the development of new financial products (Sassi & Goaied, 2013), which make ICT one of the key determinants in the launch of financial innovations. From

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another standpoint, as Muthinja and Chipeta (2018) note, technological development can be seen as one of the crucial drivers of financial innovation at firm level. Moreover, these authors argue, ICT (considered as an element of the infrastructure, again at firm level) constitutes the platform for the expansion of financial innovations, in that a large endowment of ICT infrastructure is associated with a high degree of financial innovation. On the macroeconomic level, these authors found that financial innovations are determined to a considerable extent by ICT, owing to such factors as network externalities (meaning that an increasing number of persons adopting a financial innovation increases its value for each one). Their empirical results on commercial banks in Kenya, 2004–2013, indicate that ICT infrastructure at the micro level is more important for financial innovations than ICT infrastructure at the macro level. According to Kauffman et al. (2015), the influence of ICT on financial innovations operates both on the supply side (as new opportunities for financial corporations emerge) and the demand side (owing to the pressure from financial regulators and clients). Some of the many innovative financial products and services launched in the past few years are truly noteworthy. One such is ETFs, which can be succinctly defined as investment funds whose units are listed and traded on various venues (both conventional and over-the-counter markets), in the same way as ordinary shares or bonds. What distinguishes ETFs from conventional investment funds (mainly mutual funds) is essentially their unique mechanisms of creation and redemption (for more on this, see Abner, 2016; Agapova, 2011; Gastineau, 2010; Hill, Nadig, & Hougan, 2015; Lettau & Madhavan, 2018; Madhavan, 2016). The vast majority of ETFs are passive investment products. That is, they offer returns that track some selected benchmark as closely as possible rather than seeking to outperform it. The benchmarks are usually well-established financial market indexes, mainly stock market indexes. While the exact tracking methods of ETF managers to maintain the declared exposure may differ somewhat, in general, a proper management of an ETF requires a large number of trades. One reason for this is the composition of their portfolios—if these correspond to the composition of the benchmark (i.e. the index portfolio), then they may consist of a very large number of stocks and other securities. The penetration of ICT, therefore, may be seen as one of the elementary processes contributing to the introduction of ETFs and the development of the ETF market. According to Lettau and Madhavan (2018), for instance, in the past it was simply too costly to manage the portfolios required for a passive investment strategy, limiting the attractiveness of this approach. With

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the introduction of electronic trading systems, however, the situation was radically altered. The positive effect of ICT on the diffusion of ETFs is confirmed in two studies. Lechman and Marszk (2015) examined the impact of ICT adoption on the growth of ETF assets in Brazil, Japan, Mexico, South Korea, and the United States in the period 2002–2012. For all five countries the role of ICT was confirmed to be positive (albeit with considerable differences in the strength of the relationship). A more recent analysis of the ETF market in 32 countries (both emerging and advanced) for 2004–2014, based on the volume of trading in ETFs, also shows that ICT penetration may be associated with more highly developed markets for innovative investment funds (Marszk & Lechman, 2018). In a related work, Khodayari and Sanoubar (2016) studied the development (asset growth) of the mutual fund industry in eight developing and emerging economies in 1999–2014. Their analysis showed that two ICT indicators, in particular, were related with mutual fund assets: the number of mobile phones and of fixed broadband subscriptions. However, two other indicators—the number of Internet users and the ratio of ICT expenditure to GDP—were found to have had a negative and statistically significant impact. The authors hypothesised that Internet users, while numerous, may have lacked sufficient knowledge about financial markets and institutions, while the second negative relationship may be attributed to an insufficient investment in the organisational infrastructure necessary to support ICT. Another important issue in modern financial systems is high-frequency trading (HFT). This represents the clearest possible example of ICTenabled financial innovation, insofar as its development sprang directly from the introduction of more and more sophisticated electronic trading systems. HFT requires extremely rapid systems of communication and order processing (Harris, 2013). Its main attributes are the application of advanced computer software allowing for high-speed decision-making and communication with the exchange’s trading system, the submission of a large number of orders, and the short lifespan of trading algorithms used (Kauffman et al., 2015). High-frequency traders utilise computer systems to ‘monitor market data, identify opportunities to make profitable trades, and submit large orders’ (Kauffman et al., 2015, p. 339). The impact of HFT on the financial markets is ambiguous (for an in-depth discussion, see Menkveld, 2016). Empirical studies indicate that the most important benefits include heightened liquidity, lower trading costs, and greater price efficiency (Brogaard et al., 2018). Nonetheless, the swift expansion of HFT may also

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entail a series of threats for the financial markets, including greater volatility and market fragmentation (Diaz-Rainey et al., 2015). It is also problematic from the viewpoint of financial regulators. One of the most intensely scrutinised financial events of recent years relates directly to HFT: the ‘flash crash’ of the US securities market in 2010. In contrast with the reservations that have been voiced over the possible adverse effects on liquidity during the periods of market turmoil, Brogaard et al. (2018) reach the opposite conclusion, namely that HFT can increase liquidity. The other fundamental issue concerns the optimal speed of trading on the securities markets (in other words, whether HFT and other ultra-rapid trading methods are in fact necessary or useful). Fricke and Gerig (2018), studying a typical S&P500 stock, found evidence that the optimal interval is a few seconds; in other words, today’s extreme speeds are not justified. However, the potential impact of ICT on financial innovation is not limited to the two examples discussed above. To varying degrees the new technologies have facilitated the launch and development of such innovative financial products as credit derivatives (e.g. credit default swaps), fixedincome securities based on mortgages or other types of loan (e.g. mortgagebacked securities), and green digital finance (Zhang, Zhou, Lin, Li, & Xu, 2018). The final part of this section focuses on the hotly debated issue of fintech, the part of the economy where new technologies and the financial industry overlap most noticeably. But the discussion on fintech is hampered by severe definitional ambiguity: in fact, we have no common, broadly accepted understanding of the concept. An extensive account of the various meanings of fintech concluded by suggesting this broad definition: ‘a new financial industry that applies technology to improve financial activities’ (Schueffel, 2016, p. 45). Nicoletti (2017, p. 12) defines fintech as ‘initiatives, with an innovative and disruptive business model, which leverage ICT in the area of financial services’. The classification of Dorfleitner, Hornuf, Schmitt, and Weber (2017) distinguishes four segments of the fintech industry: payments, financing, asset management, and other (insurance etc.). For a more detailed classification, see the report by Deloitte (2017). Some of the main business-toconsumer services offered by fintech firms are alternative payment methods, digital lending, crowdfunding, crowdinvesting, crowdlending, robo advising, and social trading (the list is by no means exhaustive). Fintech companies also operate in the business-to-business sector, providing services connected with banking infrastructure, transaction security, payment infrastructure, investment management, and insurance infrastructure. Yet

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another field of activity involves services for financial regulators, dubbed ‘regtech’ (Anagnostopoulos, 2018). In all these categories, however, as Nicoletti (2017) notes, the development of the fintech industry has been substantially influenced by the diffusion of ICT, which has engendered new solutions on both the production and the distribution side: new decision-making tools, the capacity to create and manage large databases, digital distribution channels, and flexible customer services. The impetus to the development of the fintech industry imparted by the introduction of ICT is confirmed empirically by Haddad and Hornuf (2016) in a study of fintech start-ups. In many respects, fintech differs substantially from earlier types of digitally backed financial services. It is more customer-centric, highly focused on online services, less centralised; the industry operates at lower profit margins owing to more intense competition, and it consists to a large extent of startups rather than established financial institutions (Alt, Beck, & Smits, 2018). We mentioned ETFs as an instance of influential financial innovation. The diffusion of ETFs is also affected by certain types of fintech, above all, ‘robo-advisory’ services. Essentially, this reflects the widespread use of ETF units as basic instruments in the investment services offered (for more on this topic, see Madhavan, 2016). Robo-advising involves computer programs that gather information about customers and use it to provide investment advice (Vives, 2017); the advice offered to clients is tailored to their personal and financial situation and their stated investment aims. The process whereby the advice is generated is governed principally by intelligent algorithms (Gomber, Koch, & Siering, 2017). Another crucial development in the financial industry, albeit not strictly limited to fintech services and companies, is big data, understood here, in line with Madhavan (2016), as the application of advanced methods to analyse vast amounts of data gathered from a variety of sources. Big data techniques serve to track the patterns of prices and behaviour and, ultimately, will lead to changes in the financial industry, such as the introduction of highly personalised services and improved credit rating systems. Another application is personalised marketing. There are also a whole series of big data applications linked to the financial markets—big data is regarded as a significant factor in their dynamics and efficiency (Shen & Chen, 2018). The technological solutions in this sphere are artificial intelligence and machine learning. In many cases fintech payment services use blockchain technology, i.e. algorithmic and cryptographic methods to generate packages of data that are

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linked through a digital chain (Natarajan, Krause, & Gradstein, 2017). More generally, the possible applications of distributed ledger and blockchain technology in the financial industry include cryptocurrencies, cross-border payments, collateral registries, and securities market infrastructure. Apart from big data, blockchain and the like, another important area of fintech that has undeniably been made possible by ICT is cloud computing for financial services (Yablonsky, 2017). Given its relatively brief history, fintech’s impact on the financial system is still difficult to evaluate. Some efforts, based on the limited evidence available, have been made. According to He et al. (2017), four crucial effects of fintech on the financial system can be distinguished: changes in entry barriers (usually easing them, but in some cases the development of fintech may stiffen such barriers, say, by imposing higher technological requirements), decreased significance of traditional financial institutions (superseded by various types of networks for, say, payment processing), blurred boundaries between different types of financial markets or institutions (banks and nonbank companies, say), and, finally, the emergence of new (often complex) challenges for financial authorities (owing to the provision of financial services by previously unregulated companies from outside the financial industry). To complicate this issue even further, the impact of fintech must not be considered solely from the standpoint of the financial system; rather, it can affect entire economies and societies. For example, robo-advice may generate new opportunities for pension systems (OECD, 2017). More important still, fintech may constitute another leap-frogging opportunity for the developing or emerging economies, enabling them, for instance, to overcome the handicap of traditional banking networks by adopting payment and other services provided by fintech companies. Another example might be the supply of credit by fintech companies in lieu of less competitive bank loans (Claessens, Frost, Turner, & Zhu, 2018; Jagtiani & Lemieux, 2018; Leong, Tan, Xiao, Tan, & Sun, 2017). As Cumming and Schwienbacher (2018) show, fintech investments tend to be higher in countries with no major financial centres. These changes may have profound consequences on socio-economic development. The expansion of financial services provided by fintech may also have consequences that are not necessarily positive for the rest of the financial system ( Jagtiani & John, 2018). According to Nicoletti (2017), the three main downsides to fintech are new risk exposures, problems for proper regulation, and challenges to traditional financial institutions. Elsinger et al. (2018) state

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that fintech produces more direct matching of savers with investors, at the expense of traditional banking intermediation (though obviously in many areas, fintech companies are complementary to banks, as in the case of business-to-business services where the two are often partners; Li, Spigt, & Swinkels, 2017; Blomstrom, 2018). Moreover, privacy and security issues constitute a significant challenge for fintech companies and, perhaps more importantly, for their clients (Gai, Qiu, & Sun, 2018).

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Ocampo, J. A. (2018). International asymmetries and the design of the international financial system 1. In Critical issues in international financial reform (pp. 45–74): Routledge. OECD (2017). Robo-advice for pensions. O’Hara, M., & Ye, M. (2011). Is market fragmentation harming market quality? Journal of Financial Economics, 100(3), 459–474. Okwu, A. T. (2015). ICT adoption and financial markets: A study of the leading stock exchange markets in Africa. Journal of Accounting and Management, 2, 53–76. Panourgias, N. S. (2015). Capital markets integration: A sociotechnical study of the development of a cross-border securities settlement system. Technological Forecasting and Social Change, 99, 317–338. Pellegrina, L. D., Frazzoni, S., Rotondi, Z., & Vezzulli, A. (2017). Does ICT adoption improve access to credit for small enterprises? Small Business Economics, 48(3), 657–679. Perez, C. (1985). Microelectronics, long waves and world structural change: New perspectives for developing countries. World Development, 13(3), 441–463. Perez, C. (2002). Technological revolutions and financial capital: The dynamics of bubbles and golden ages. Edward Elgar Publishing. Perez, C., & Soete, L. (1988). Catching up in technology: Entry barriers and windows of opportunity. In G. Dosi, C. Freeman, R. Nelson, G. Silverberg, & L. Soete (Eds.), Technical Change and Economic Theory (pp. 458–479). Pisa, Italy: Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies. Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to invest in the peripheries. Scientific Reports, 3, 1665. Pradhan, R. P., Arvin, M., Nair, M., Bennett, S., & Bahmani, S. (2017). ICT-financegrowth nexus: Empirical evidence from the next-11 countries. Cuadernos de Economia, 40(113), 115–134. Pradhan, R. P., Arvin, M. B., Bahmani, S., & Bennett, S. E. (2017). Broadband penetration, financial development, and economic growth nexus: Evidence from the Arab League countries. Macroeconomics and Finance in Emerging Market Economies, 10(2), 151–171. Pradhan, R. P., Arvin, M. B., Hall, J. H., & Bennett, S. E. (2018). Mobile telephony, economic growth, financial development, foreign direct investment, and imports of ICT goods: The case of the G-20 countries. Economia e Politica Industriale, 45, 279–310. Pradhan, R. P., Arvin, M. B., & Norman, N. R. (2015). The dynamics of information and communications technologies infrastructure, economic growth, and financial development: Evidence from Asian countries. Technology in Society, 42, 135–149. Pradhan, R. P., Mallik, G., Bagchi, T. P., & Sharma, M. (2018). Information communications technology penetration and stock markets–growth nexus: From cross country panel evidence. International Journal of Services, Technology and Management, 24(4), 307. Preece, R. G. (2012). Dark pools, internationalisation and equity market quality. Charlottesville, VA: Chartered Financial Analysts Institute. Quah, D. (2001). ICT clusters in development: Theory and evidence. EIB Papers, 6(1), 85–100. Redding, S., & Venables, A. J. (2002). The economics of isolation and distance. Nordic Journal of Political Economy, 28, 93–108. Redding, S., & Venables, A. J. (2004). Economic geography and international inequality. Journal of International Economics, 62(1), 53–82. Rioja, F., & Valev, N. (2004). Finance and the sources of growth at various stages of economic development. Economic Inquiry, 42(1), 127–140. Romer, P. (1990). Endogenous technological change. Journal of Political Economy, 98(Oct), S71–S102. Romer, P. (1993). Idea gaps and object gaps in economic development. Journal of Monetary Economics, 32(3), 543–573.

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Romer, P. M. (1994). The origins of endogenous growth. The Journal of Economic Perspectives, 3–22. Rosenberg, N. (1976). Perspectives on technology. CUP Archive. Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge University Press. Rosenberg, N. (1994). Exploring the black box: Technology, economics, and history. Cambridge University Press. Rosenberg, N., & Birdzell, L. E., Jr. (2008). How the West grew rich: The economic transformation of the industrial world. Basic books. Rosenberg, N., & Frischtak, C. R. (1984). Technological innovation and long waves. Cambridge Journal of Economics, 8(1), 7–24. Sassi, S., & Goaied, M. (2013). Financial development, ICT diffusion and economic growth: Lessons from MENA region. Telecommunications Policy, 37(4–5), 252–261. Savvides, A., & Zachariadis, M. (2005). International technology diffusion and the growth of TFP in the manufacturing sector of developing economies. Review of Development Economics, 9(4), 482–501. Schmiedel, H., Malkam€aki, M., & Tarkka, J. (2006). Economies of scale and technological development in securities depository and settlement systems. Journal of Banking & Finance, 30(6), 1783–1806. Schueffel, P. (2016). Taming the beast: A scientific definition of Fintech. Journal of Innovation Management, 4(4), 32–54. Schumpeter, J. A. (1934). Theory of economic development. Transaction Publishers. Schumpeter, J. A. (1947). The creative responses in economic history. The Journal of Economic History, 7, 149–159. Seligson, M. A., Passe-Smith, J. T., & Seligson, M. A. (Eds.), (1998). Development and underdevelopment: The political economy of global inequality. Boulder, CO: Lynne Rienner Publishers. Servon, L. J. (2008). Bridging the digital divide: Technology, community and public policy. John Wiley & Sons. Shahrokhi, M. (2008). E-finance: Status, innovations, resources and future challenges. Managerial Finance, 34(6), 365–398. Shamim, F. (2007). The ICT environment, financial sector and economic growth: A crosscountry analysis. Journal of Economics Studies, 34(4), 352–370. Shapiro, C., & Varian, H. (1999). Information rules: A strategic guide to the network economy. Boston: Harvard Business School Press. Shapiro, C., & Varian, H. R. (2013). Information rules: A strategic guide to the network economy. Harvard Business Press. Sharpe, W. (1991). Capital asset prices with and without negative holdings. The Journal of Finance, 46(2), 489–509. Shell, K. (1967). A model of innovative activity and capital accumulation. In K. Shell (Ed.), Essays on the theory of optimal economic growth (pp. 67–85). Cambridge: MIT Press. Shen, D., & Chen, S. H. (2018). Big data finance and financial markets. In Big data in computational social science and humanities (pp. 235–248). Springer. Shiller, R. J. (2012). Finance and the good society. Princeton University Press. Silverberg, G., & Lehnert, D. (1993). Long waves and ‘evolutionary chaos’ in a simple Schumpeterian model of embodied technical change. Structural Change and Economic Dynamics, 4(1), 9–37. Singh, A. (1997). Financial liberalization, stock markets and economic development. The Econometrics Journal, 107(442), 771–782. Soete, L., & Verspagen, B. (1993). Technology and growth: The complex dynamics of catching-up, falling behind and taking overṣ. In A. Szirmai (Ed.), Explaining economic growth: Elsevier.

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Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70, 65–94. Srivastava, M. K., Gnyawali, D. R., & Hatfield, D. E. (2015). Behavioral implications of absorptive capacity: The role of technological effort and technological capability in leveraging alliance network technological resources. Technological Forecasting and Social Change, 92, 346–358. Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213–225. Tchamyou, V. S., Erreygers, G., & Cassimon, D. (2018). Inequality, ICT and financial access in Africa. Technological Forecasting and Social Change, 139, 169–184. Torero, M., & Von Braun, J. (Eds.), (2006). Information and communication technologies for development and poverty reduction: The potential of telecommunications. International Food Policy Research Institute. Triplett, J. E. (1999). The Solow productivity paradox: What do computers do to productivity? The Canadian Journal of Economics/Revue canadienne d’Economique, 32(2), 309–334. Tyre, M. J., & Orlikowski, W. J. (1994). Windows of opportunity: Temporal patterns of technological adaptation in organizations. Organization Science, 5(1), 98–118. Unwin, P. T. H. (Ed.), (2009). ICT4D: Information and communication technology for development: Cambridge University Press. Uzawa, H. (1965). Optimum technical change in an aggregate model of economic growth. International Economic Review, 6, 18–31. Valente, T. W. (1995). Network models of the diffusion of innovations. Vol. 2. Cresskill, NJ: Hampton Press. No. 2. Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89. van den Berg, P., Arentze, T., & Timmermans, H. (2013). A path analysis of social networks, telecommunication and social activity–travel patterns. Transportation Research Part C: Emerging Technologies, 26, 256–268. Veblen, T. (1906). The place of science in modern civilization. The American Journal of Sociology, 11(5), 585–609. Veblen, T., Hobson, J. A., & Cain, P. (1994). The collected works of Thorstein Veblen. London: Routledge. Venables, A. J. (2001). Geography and international inequalities: The impact of new technologies. Journal of Industry, Competition and Trade, 1(2), 135–159. Verspagen, B. (1991). A new empirical approach to catching up or falling behind. Structural Change and Economic Dynamics, 2(2), 359–380. Vives, X. (2017). The impact of FinTech on banking. European Economy, 2, 97–105. Wang, J. (2018). From aperture satellite to “Internet finance”: Institutionalization of ICTs in China’s financial sector since 1991. Telecommunications Policy, 42, 566–574. Wolf, S. (2001). Determinants and impact of ICT use for African SMEs: Implications for rural South Africa. Paper prepared for TIPS Forum Center for Development Research (ZEF Bonn). Wresch, W., & Fraser, S. (2012). ICT–enabled market freedoms and their impacts in developing countries: Opportunities, frustrations, and surprises. Information Technology for Development, 18(1), 76–86. Yablonsky, S. A. (2017). Multidimensional cloud-enabled innovations for financial services. International Journal of Business Excellence, 11(4), 464–486. Yartey, C. A. (2008). Financial development, the structure of capital markets, and the global digital divide. Information Economics and Policy, 20(2), 208–227. Zagorchev, A. G., Vasconcellos, G., & Bae, Y. (2011). The long-run relation among financial development, technology and GDP: A panel cointegration study. Applied Financial Economics, 21(14), 1021–1034.

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Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. The Academy of Management Review, 27(2), 185–203. Zhang, Y., Zhou, X., Lin, X., Li, X., & Xu, Y. (2018). Research and innovation of green digital finance model. In DEStech Transactions on Social Science Education and Human Science.

Further reading Archibugi, D., & Pietrobelli, C. (2003). The globalisation of technology and its implications for developing countries: Windows of opportunity or further burden? Technological Forecasting and Social Change, 70(9), 861–883. Boschma, R. A., & Van der Knaap, G. A. (1999). New high-tech industries and windows of locational opportunity: The role of labour markets and knowledge institutions during the industrial era. Geografiska Annaler: Series B, Human Geography, 81(2), 73–89. Jaffe, A. B., & Trajtenberg, M. (2002). Patents, citations, and innovations: A window on the knowledge economy. MIT Press. Kurzweil, R. (1999). The age of spiritual machines: When computers exceed human intelligence. New York: Viking. Lee, K., Lim, C., & Song, W. (2005). Emerging digital technology as a window of opportunity and technological leapfrogging: Catch-up in digital TV by the Korean firms. International Journal of Technology Management, 29(1–2), 40–63. Lee, K., & Malerba, F. (2017). Catch-up cycles and changes in industrial leadership: Windows of opportunity and responses of firms and countries in the evolution of sectoral systems. Research Policy, 46(2), 338–351. Perez, C. (2003). Technological change and opportunities for development as a moving target. In Trade and development: Directions for the 21st century (p. 100). Perez, C. (2011). Finance and technical change: A long-term view. African Journal of Science, Technology, Innovation and Development, 3(1), 10–35.

CHAPTER FOUR

ICT development and diffusion: Evidence for Europe Contents 4.1 4.2 4.3 4.4

The context Sample and empirical data ICT deployment in Europe: An overview ICT diffusion paths, dynamics, and future scenarios 4.4.1 Developing ICT diffusion patterns 4.4.2 Fixed-to-mobile technological substitution? 4.4.3 Do prices matter? 4.4.4 Final note References Further reading

97 100 103 111 112 129 131 139 140 142

4.1 The context Since the 1970s, not only Europe but also the entire world has been experiencing an astonishingly rapid diffusion of new information and communication technologies (ICTs). Needless to say, this far-reaching development has produced profound changes and structural shifts far beyond the strictly economic sphere. As noted by Comin and Hobijn (2011), the diffusion of ICT differs radically from the process of diffusion of ‘old’ technologies. It is much faster, and it seems that the new technologies spread worldwide regardless of geophysical conditions; they are acquired immediately even by relatively less educated and materially poor people. The exponential growth of ICTs, as well as the explosively rising demand for all the innovative technological solutions available—above all, Internet and its applications, has offered unprecedented opportunities to all the members of the society. These changes are revolutionary, and ICT has begun to trigger structural shifts both between and within societies and economies. ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00004-X

© 2019 Elsevier Inc. All rights reserved.

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According to the International Telecommunication Union, in all the countries of the world, without exception, there has been a steady increase in access to and use of ICTs. All the core ICT indicators except landline telephony, which is recognised as an ‘old’ ICT tool, show constant growth over the last two decades. According to ITU estimates, in 2018 more than half of the world’s population was online, meaning that they enjoyed access to and could benefit from the World Wide Web. In developed countries, the average Internet penetration rate was over 80% in 2018, while in less developed economies, only 20% of the population enjoyed access. In those poorer countries there is still work to be done, but the worldwide uptrend in Internet usage undeniably represents an important step towards a more inclusive global information society. Although, through the early 2000s, there was relatively little diffusion of fixed broadband networks, which were the main technology for accessing the Internet, since 2007 the rapid development of mobile broadband technology has come to constitute an advantageous tool for Internet access. This has resulted in significant increases in access to the World Wide Web by individuals at home. However, everywhere in the world, mobile telephony has dominated. In 2018, even in the group of 47 less developed countries, cell phone penetration reached an average of more than 70%, which means that even in materially deprived countries, individuals can benefit from the possibilities offered by the digital revolution. A brief general look at the current state of ICT development clearly shows that since 2005, Europe has outperformed the other world regions. According to the ITU, in 2018 the average European citizen appeared to be benefiting fully from the opportunities offered by the Fifth Technological Revolution. During the last two decades the great majority of European residents have gained reliable and affordable access to a bundle of ICT tools, thanks to rapidly falling prices. Effective telecommunications policies, national e-strategies, and free market competition since the early 1980s greatly facilitated a practically unbounded spread of ICTs across the continent. The striking jumps in the core ICT indicators between 2005 and 2018 alone speak for themselves (Fig. 4.1). Of course, already at the beginning of this period, the European countries enjoyed a relatively high access to mobile telephony (on average 89% of the population), but over the years, mobile phone penetration rose to nearly 120 per 100 inhabitants. In a word, by 2018, the European society had reached full saturation of ICTs. And while it might seem that Europe achieved astonishing results, it is worth noting that mobile phone technology actually spread fairly evenly across the globe. According to ITU statistics, in 2018 even Africa showed an average mobile phone penetration rate

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Fixed and mobile telephony

Internet usage 100

120

80 100

60 80 40

60 20

0

40

2005 2005

2010

2015 FTL

MCS

2020

2010 FBS House_comp

2015 AMS House_Internet

2020 IU

Fig. 4.1 Core ICT indicators in Europe, 2005–2018. Note: On y-axis—share of population having access to and/or using a given ICT. FTL, fixed telephony; MCS, mobile-cellular telephony; FBS, fixed broadband networks; AMS, active mobile broadband networks; IU, Internet users (percentage of individuals); House_comp, households with computers; House_Internet, households with access to the Internet.

of over 75%. Apparently, soaring demand for mobile-cellular telephony is one reason why landline telephones are gradually losing popularity. Between 2005 and 2018, average fixed telephony use dropped by nearly 10 percentage points, from 43% to 34%. This striking substitution of mobile telephony for fixed telephony is clearly visible not only in the most developed countries but also in less technologically advanced economies (see, e.g. the evidence in Lechman, 2016, 2017). A glance at the trends in fixed and mobile broadband infrastructure for Internet use indicates the shift that has taken place since 2010.1 However, fixed broadband networks require a relatively well-developed backbone infrastructure, so their diffusion has been less than universal and some poorly developed and isolated regions remain deprived of ICT. Happily, however, since 2007 not only Europe but the whole world has seen an exponential expansion of mobile broadband networks. Over a span of just 10 years, mobile broadband solutions were acquired by practically all the countries of Europe and the Americas. In 2018, in the former Soviet republics, Asian and Pacific countries, and the Arab states, mobile broadband penetration rates jumped to 79, 68, and 62, respectively, per 100 inhabitants. Africa had the lowest adoption of mobile broadband networks, averaging under 30 per 100 inhabitants, but 1

Before 2010, access to Internet was mainly via dial-up connections—a low-speed network (below 256 kbps). Since 2010, with the emergence of broadband infrastructure with high-speed networks accessed via a modem, DSL, or optical fibre, low-speed solutions have been almost totally superseded.

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a good share of African economies are trying to catch up rapidly. The fast growth in access to both fixed and mobile broadband networks resulted in a significant rise in the share of the European society using the Internet. Between 2005 and 2018 the share of individuals using the Internet, irrespective of the type of network, doubled to around 80%. This brief summary of the overall global and European uptrends in ICT coverage and use indicates the very substantial shifts in access to and use of the new information and communication technologies. In what follows, we set out the empirical evidence, focusing on the unique characteristics of ICT diffusion in the European economies on which this project centres. We trace national diffusion patterns, illustrating both general trends and more specific estimates on the spread of ICTs in different countries. This shows the current state and trend over time of adoption and use of new technologies in the European economies. Our study covers the period between 1990 and 2017.

4.2 Sample and empirical data Our research concentrates on the unique features of the process of ICT diffusion in the 32 European countries (see Table 4.1). Although this group of economies ranks relatively high in material wealth and per capita income, this seeming homogeneity might be somewhat spurious. In fact, the group is internally heterogeneous, which makes studying these countries a challenging task. They differ greatly not only in per capita income but also in the level of social development and economic welfare, as well as in political regimes and institutional and legal frameworks. They also vary in terms of culture, social norms, and attitudes, not to mention geopolitical location, Table 4.1 Sample countries

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany

Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland

Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

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population density, and other factors. All these differences matter, heavily affecting each country’s social, economic, and technological landscape. Further, these help to shape national characteristics and predetermine a country’s ability to assimilate and effectively use the new technology. In short, treating all these countries as one homogenous group would result in a loss of an enormous amount of information and make it impossible to bring out various unique national features. Accordingly we disaggregated the data to perform country-specific analysis. This approach reveals country-specific ICT diffusion paths, estimating the dynamics of the process. The time span for our empirical analysis was affected by the availability of ICT data, and we have elected to take the period starting 1990 and running to 2017. Although in many cases national statistics on ICTs do trace back to the early 1980s, we have excluded those earlier years because in a large majority of cases, ICT development was almost negligible during 1980–1989, and there are multiple breaks in the time series. Including those years, in short, would not produce any additional conclusions concerning specific ICT diffusion patterns and dynamics. All the statistical data, including ICT penetration rates and ICT prices, come from the World Telecommunication/ICT Indicators database 2018 (22th Edition/June), in short WTI 2018. The data for WTI 2018 (and earlier editions) are collected annually from national ministries, telecommunication authorities, and statistical agencies. All our data on ICT access and use are based on common international statistical standards, which means they are relatively little plagued by scarcity and inconsistency and ensures cross-country comparability. To examine national achievements in terms of access to and use of new information and communication technologies, we selected four core ICT indicators: • Mobile cell telephone subscriptions per 100 inhabitants (MCS): the number of subscriptions to a public mobile telephone service that gives access to the PSTN (Public Switched Telephone Network) using mobile-cellular technology. The indicator is the number of postpaid subscriptions plus the number of active prepaid accounts that have been used during the last three months (definition provided in WTI 2018). • Active mobile broadband subscriptions per 100 inhabitants (AMS): the sum of standard plus dedicated mobile broadband subscriptions to the public Internet—we count actual, not potential, subscribers (definition provided in WTI 2018). • Fixed broadband subscriptions per 100 inhabitants (FBS): the number of landline subscriptions giving high-speed access to the public Internet (a

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TCP/IP connection). This count is irrespective of the method of payment; it excludes subscriptions with access to data communications (including the Internet) via mobile-cellular networks (definition provided in WTI 2018). • Internet users as a share of the population (IU): the proportion of individuals using the Internet (possibly including both survey data and estimates); based on the results of national household surveys (definition provided in WTI 2018). For more detailed and technical definitions of the four indicators, see the explanations in Appendix A. also seek to trace the unique process of switching from ‘old’ technology (voice communication) to ‘new’ techniques, using the data on landline telephone subscriptions per 100 inhabitants (for the definition, see Appendix A). The mobile-cellular, active mobile broadband, and fixed broadband indicators basically reflect the state of development of ICT infrastructure and the possibility for consumers to access these new technologies; the Internet users indicator, instead, is restricted to access to and use of the Internet network by individuals and households through whatever channel—fixed narrowband, fixed broadband, mobile. The basic data on ICT performance should be used cautiously and interpreted carefully. Despite the simplicity of the indicators, in-depth analysis clearly discloses the areas where countries are doing well and those where there is still room for improvement. Even a cursory analysis of these statistics can indicate which countries are forging ahead and which are lagging behind in certain regards. In reading and interpreting the ICT statistics, it is important to remember that in every country, the following inequality always holds: ‘inhabitants > people with access > users > subscribers’. That is, the number of legal subscribers accounts for the fewest people; the number of users is larger than the number of subscribers; and people simply with access outnumber the users. ‘Subscribers’ are the legal consumers of a given technology, those who are obliged to pay for it; ‘users’ are those who can use a technology but not necessarily pay for it. Only in the case of mobile phone subscribers is this simple relationship untrue, as in many countries the number of subscriptions per 100 inhabitants is greater than 100, which suggests that ‘inhabitants < subscribers < (potential) users’. Additionally, we want to relate the process of ICT deployment with the costs of access to and using the basic ICT tools and services. We selected seven costs that impinge on the availability of ICT, namely, the monthly mobile phone subscription charge (in USD), the cost of a 3-minute local call (peak rate, in USD), the price plan for 1 GB of postpaid data transfer (in USD),

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the price plan for 500 MB of postpaid data transfer (in USD), the price plan for 1 GB of prepaid data transfer (in USD), the price plan for 500 MB of prepaid data transfer (in USD), and the monthly fixed broadband Internet subscription (in USD). As above, all data are from World Telecommunications/ICT Indicators database 2018 (22th Edition/June)—WTI 2018.

4.3 ICT deployment in Europe: An overview Here we explore the key trends of ICT access and use in the 32 European economies in our sample. The sample period is restricted to the years from 1990 to 2017. The preliminary results, given below, serve to focus on the issues related to the changing availability and use of ICT. They consist of basic descriptive statistics on the penetration rates of fixed telephony (FTL), mobile-cellular telephony (MCS), fixed broadband (FBS), active mobile broadband (AMS), and, finally, overall Internet usage (IU), i.e. the share of population that uses Internet regardless of the access channel. In addition to this general overview of changing ICT deployment, the descriptive statistics also show changes in ICT distribution and crosscountry inequalities. Table 4.2 summarises the descriptive statistics of the core ICT indicators for our 32 countries in 1990–2017. Additional graphical evidence is provided in Fig. 4.2, with density curves2 for the various ICT indicators in selected years; in Fig. 4.3, which draws averaged diffusion lines for ICT; and in Fig. 4.4, where changes in the Gini coefficients of the indicators are displayed. Additionally, changes in cross-country inequalities in ICT access and use are graphed using Lorenz curves3 (see Appendix C). 2

The density curves are plotted via non-parametric estimation of the probability density function: f ðxÞ ¼ dxd F (x), where F(x) explains the continuous distribution of random variable X. Kernel density estimator results were useful, allowing relaxation of restrictive assumptions on the possible shape of f(x); thus, it is flexible. The density curves that were generated by the kernel density estimator are continuous and show an ‘empirical’ distribution of variables. To estimate f(x), we use its discrete derivative, a Pn Xi x 0 1 special case of the kernel estimator taking a general form f ‘ ðxÞ ¼ nh , where k(u) is a kernel i¼1 k h Ð∞ 0 function that satisfies ∞ k(u)du ¼ 1. f (x) shows the percentage of observations located near x. If many 0 observations are located near x, then f‘ (x) is large. 3 The Lorenz curve is a conventional graphical representation of distributions, such as income or wealth. Formally, let x stand for income and F(x) for its distribution, which explains the proportion of individuals who have incomes less than or equal to x. The first moment distribution function can be defined as F1(x), which gives the fraction of total income earned by individuals with incomes less than or equal to x. Accordingly, the Lorenz curve expresses the relationship between F(x) and F1(x). The area below the Lorenz curve is commonly used to calculate the value of the Gini index (Gini ! 1 minus twice the area below the Lorenz curve). The generalised Lorenz curves are commonly labelled ‘concentration curves’ and are widely used in economic analyses to consider different aspects of distribution.

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Table 4.2 ICT core indicators. Descriptive statistics, 1990–2017 No. of Indicator/year obs.

Mean

MCS_1990 MCS_2000 MCS_2010 MCS_2017 FTL_1990 FTL_2000 FTL_2010 FTL_2017 FBS_2000 FBS_2005 FBS_2010 FBS_2017 AMS_2010 AMS_2014 AMS_2017 IU_1990 IU_2000 IU_2010 IU_2017

1.6 45.6 117.1 123.2 32.1 45.2 39.7 32.6 0.74 11.5 24.4 31.9 33.5 70.2 91.8 0.24 20.6 68.5 81.6

19 32 32 32 32 32 32 31 19 32 32 32 31 32 31 13 32 32 27

SD

Min. value

Max. value

Gini coefficienta

Atkinson coefficient (ε 5 0.5)b

1.8 24.8 19.2 15.7 17.1 16.4 14.6 14.3 0.81 8.6 8.6 8.5 21.1 24.9 22.9 0.25 15.4 16.6 9.9

0.005 0.95 62.4 90.4 1.21 4.9 11.3 6.8 0.01 0.008 3.6 10.2 2.9 33.7 49.2 0.001 0.11 32.3 61.3

5.4 76.2 156.8 170.8 68.3 73.1 65.4 59.5 2.8 27.9 38.1 45.4 84.3 138.1 153.7 0.71 52.0 93.4 97.1

0.58 0.31 0.08 0.06 0.31 0.20 0.20 0.24 0.54 0.42 0.19 0.14 0.33 0.18 0.13 0.53 0.42 0.13 0.06

0.32 0.10 0.006 0.003 0.08 0.04 0.03 0.05 0.26 0.17 0.03 0.02 0.09 0.02 0.01 0.28 0.15 0.01 0.003

a In generic form, the Gini coefficient traditionally measures the inequality of income distribution, 0 corresponding to perfect distributional equality, and 1 to total inequality. For a given population to which ¼ 1, …, n, if(yi  yi+1), are attributed to, the general formula for the Gini coefficient is: the values of yi, iP  n

¼ 1n

n + 12

ðn + 1iÞyi P n

i¼1

y i¼1 i

(Gini, 1912). Graphically, the value of the Gini coefficient measures

twice the area between the Lorenz curve, which shows the cumulative distribution of a variable in the sample, and the line representing total equal distribution. Here, instead of income, we use our core ICT indicators. b The Atkinson inequality 1970), with inequality aversion parameter ε included, has a " measure(Atkinson,  # 1 Xn yi 1ε 1 1ε , where y stands for average individual income in the samgeneral form: Aε ¼ 1  i¼1 y n ple, yi for the income of an individual, n the number of individuals in the sample. Here, instead of income, we use our core ICT indicators.

The table in Appendix B summarises the statistics for four ICT indicators in each country in 1990 (or the earliest year available) and 2017. The results (Table 4.2, Fig. 4.2) show just how dynamic and disruptive were the changes in access to and use of ICT in the European economies between 1990 and 2017. In addition to the core ICT indicators, we have included elementary data on fixed telephony penetration rates, to show the trend of switching from fixed telephony to mobile telephony, thanks to the digital revolution. Fig. 4.2, which displays the average diffusion

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AMS vs FBS and IU (averaged values)

MCS vs FTL 150

100

80

100 60

40 50

20

0

0 1990

2000

2010 MCS

2020

1990

2000

FTL

2010

FBS

2020

AMS

IU

Fig. 4.2 Average fixed telephony, mobile-cellular telephony, fixed broadband networks, active mobile broadband networks, and Internet user penetration rates. Period 1990–2017.

.04

.04

.03

.03

.02

.02

.01

.01 0

0 0

50

100

150

200

0

10

20

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30

40

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60

80

100

FBS

.025 .04 .02 .03 .015 .02

.01

.01

.005 0

0 0

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patterns for mobile telephony (solid line) and fixed telephony (dashed line), shows this process clearly: mobile phone penetration rises, while that of landline telephones declines slightly. Although there is hardly any change in the latter between 1990 and 2017, if we divide this into two sub-periods (1990–2000 and 2000–2017) we see an increase in the first sub-period, with FTL peaking in 2000 at 45.16 per hundred inhabitants and then gradually declining from then on (see data in Appendix B). Evidently, a slight growth in fixed telephone use between 1990 and 2000 was associated with the still low accessibility of mobile phone technology, just being introduced to the market. Then, as cell phones became more available and affordable, fixed telephone penetration rates started to fall. That is, we have a visual representation of the process of switching from ‘old’ to ‘new’ telephony. A closer look at the MCS diffusion path in Fig. 4.2 brings out a few striking characteristics. One may distinguish three characteristic phases of diffusion. First, we have an early (initial) stage during which ICT diffusion is modest, and growth is spasmodic and easily reversible. In this stage we find no significant or rapid shifts in ICT adoption. All the sample countries were ‘locked’ in this stage until the mid-1990s. If a longer period is included— say, back to 1980—it becomes clear that no significant increases in access to mobile phone technology are found between 1980 and around 1995.

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This decade-and-a-half thus represents the initial stage of slow diffusion, with no abrupt shifts. Apparently, this interval was needed for a certain number of people to assimilate the technological innovation. These innovators and early adopters are the ones who acquired and used the new telecommunication tool. This was clearly the precondition for the subsequent exponential spread of mobile phone service. The second stage, that of rapid diffusion, began in 1996 or 1997, with a take-off into an exponential growth in the number of new users. The third stage is the stabilisation or saturation phase, when the rate of diffusion slows down. In our sample, this began around 2010. Over the entire 28-year period, the average mobile-cellular telephony penetration rate (MCS) rose dramatically throughout Europe, from just 1.6 per 100 inhabitants in 1990 to 123.2 in 2017. That is, the deployment of mobile phones was negligible in almost all the economies examined in 1990, with exceptions of Finland, Norway, and Sweden, with MCS of 5.16, 4.63, and 5.34, respectively. In most of the other countries, the MCS rate was less than 1 per 100 inhabitants. This situation changed dramatically starting in the middle of 1990s, however, with a ‘take off’ in MCS to very rapid diffusion across Europe. The result was that in 2010, after 15 years of a continuous rapid growth in mobile-cellular telephony, the average MCS penetration rate was 117.1, rising modestly thereafter to 123.2 in 2017. Only one country, Moldova, lagged significantly behind in 2010, and even there the rate was 62 per 100 inhabitants; almost everywhere else (save for Albania and France) the 100 mark had been surpassed. Finally, in 2017 Moldova was still the laggard, with an MCS of ‘only’ 90. The leaders in MCS penetration rates were Austria at 170.8, Lithuania at 150.8, and Italy at 141.3. These fast advances are also evident in the changing distribution of the MCS indicator in the top left graph of Fig. 4.3. In 2000, we observe high levels of dispersion and significant cross-country inequalities, although the average MCS penetration rate tended to be quite low; and the density line for that year is highly platykurtic, offering additional evidence of enormous cross-country inequalities in access to mobile phone technology. By 2010, however, the MCS distribution is strongly leptokurtic, indicating effectively diminishing inequality. That is, as cell phones gained popularity, not only did the use of this telecommunication tool flourish, but international disparities in diffusion were enormously diminished. Just compare the changes in the Gini and Atkinson coefficients and coefficient of variation between 1990 and 2017 (Fig. 4.4). The downtrend is unmistakable; the Gini coefficient falls from 0.58 in 1990 to just 0.06 in 2017, and the Atkinson coefficient from

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0.32 to 0.003 (see also the Lorenz curves in Appendix C): essentially, national disparities were practically eradicated. Regarding the key trends in fixed broadband network deployment, Table 4.2 and Fig. 4.2 suggest that the advance between 2000 and 2017 was less dynamic than for mobile-cellular telephony. In 2000 the data on fixed broadband penetration were available for only 19 of our 32 countries, and the average FBS deployment was astonishingly low—0.74 per 100 inhabitants. The top countries were Sweden, Austria, and the Netherlands, where the FBS penetration rates came to 2.8, 2.3, and 1.6, respectively. Two other countries, Belgium and Denmark, had FBS penetration above 1 per 100 inhabitants, and all the rest were far below 1. By 2010 the average adoption rate had soared to 24.4 per 100 inhabitants (see Table 4.2), and it continued to rise over the subsequent 7 years to reach 31.9. This preliminary numerical evidence very clearly indicates that in the developed countries of Europe, the growth in fixed broadband subscriptions is slowing. In fact, these countries are now approaching saturation levels (recall that fixed networks are ordinarily shared by all the members of a household, so penetration rates are unlikely ever to go over 50%). In 2017, the rates in the highest ranked countries were already between 40 and 45 subscriptions per 100 inhabitants (see Switzerland, Denmark, France just to cite a few), compared with 31.9 for all the 32 countries. The slow growth in fixed broadband deployment between 2010 and 2017 is also shown in the international distribution plotted in Fig. 4.3. The shape and location of the density line drawn for the 2005 data differ significantly from the density curves for 2010 and 2017, which suggests significant shifts both in FBS deployment and distribution. But the difference between 2010 and 2017 is negligible, indicating that no great change in cross-country distribution and inequalities occurred in the interim. This conclusion is strongly supported by the results on inequality (the Gini and Atkinson coefficients) given in Table 4.2. Over these 7 years, the Gini coefficient declined by a mere 0.05 and the Atkinson coefficient by 0.01. Apparently fixed broadband deployment was not all that impressively fast, presumably owing largely to insufficiently developed backbone infrastructure in some European regions. Additionally, acquisition and use costs are relatively high (see the summary evidence on ICT prices in Section 4.4): these ICT tools are less affordable. In fact, since it emerged some 15 years ago, mobile broadband technology has grown into one of the most important channels of interpersonal communication, data, knowledge, and information transmission. The ITU (2018) data show that since 2007 not only Europe but the whole world is happily experiencing a rapid

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diffusion of mobile broadband Internet connections. Mobile solutions would appear to be of greater importance even for companies, not only for consumers. Mobile broadband access allows for a high degree of flexibility that fixed broadband cannot match. Mobile high-speed connections are essential for many people and organisations to function in the modern world. As a consequence, a larger portion of the society tends to opt for mobile access, possibly in addition to fixed connections. In regions where fixed broadband is relatively unavailable or unaffordable, mobile broadband connections can be the only feasible access to the World Wide Web. The descriptive statistics reported in Table 4.2 and the graphical evidence displayed in Figs. 4.2 and 4.3 show that, as we presumed, mobile broadband networks since their emergence have expanded at an astonishing pace. Data on active mobile broadband penetration rates go back to 2007, but in that year the statistics were available for just 8 of our 32 sample economies, and the average was relatively low—5.7 per 100 inhabitants. By 2010 the rate had jumped to 33.5; Finland led the way at a rate of 84.3 and Moldova brought up the rear at 2.9. Over the subsequent 7 years the average active mobile broadband penetration rate tripled to 91.8 per 100 inhabitants. In 2017 the highest rates were in Finland (153 per 100 inhabitants), Estonia (133), and Denmark (129); the lowest were in Hungary (49 per 100), Moldova (59), and Greece (63). Fig. 4.2 visualises the process of diffusion and the quickly shifting mobile broadband penetration rates in Europe from 2000 to 2017. The shape of the AMS diffusion curve speaks for itself—it does not resemble the ‘classical’ (sigmoid) technology diffusion trajectory, in that one cannot identify three characteristic phases. In fact, no early (initial) stage of slow diffusion is discernible; that is, immediately upon the introduction of mobile broadband technology, societies entered the phase of exponential growth. And at least up to 2017 no saturation phase is to be found; i.e. diffusion continues at a fast pace and has not yet slowed. As for changes in cross-country distribution (see Fig. 4.3), the AMS density curves for 2010, 2014, and 2017 show no significant change in shape—since 2010 they have been regularly leptokurtic, but their location supports the hypothesis of rapid deployment of this ICT in the European society. Further support for this reading comes from the dramatic diminution in cross-country disparities. The Gini coefficient dropped from 0.33 to 0.13, and the Atkinson coefficient from 0.09 to 0.01 (see also the respective Lorenz curves in Appendix C). However, the trend lines in Fig. 4.4 for changes in national inequalities (proxied by standard deviation, coefficient of variation, and the Gini and Atkinson indexes)

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show that during the initial phase, cross-country disparities increased rapidly. The fact is that in 2007 only eight countries had mobile broadband connections available, whereas by 2010 these were present almost everywhere (excepting only Albania, where the AMS data start in 2011). Presumably this drastic alteration of sample composition generated the initial rise in inequality. Another interesting point is the increase in the standard deviation of mobile broadband deployment during the entire period of analysis (see Fig. 4.3), while the standard deviation of the other ICT indicators decreases. However, this may be induced by growing absolute inequalities, meaning that the absolute disparities in AMS penetration rates between the ‘top’ and ‘bottom’ countries increase over time. This was not the case for fixed broadband or Internet user penetration rates; in the case of mobile phone technology, a slight increase in the standard deviation is noted at the end of the period. Obviously, the spread of fixed and mobile broadband networks enabled growth in Internet usage by individuals. Internet user penetration rates have risen steadily since 1990, as shown in Fig. 4.2. The IU diffusion curve closely resembles the classic S-shaped trajectory. After an initial period of slow growth, Internet penetration rates took off in the late 1990s and continued to increase exponentially to approach saturation levels. Since 2013 IU growth has slowed as societies have reached practically full saturation. The descriptive statistics in Table 4.2 support this interpretation. Steadily rising average Internet penetration rates and falling cross-country disparities demonstrate that regardless of region, between 1990 and 2017, the Europeans gained essentially unlimited opportunities to use Internet networks. In countries or regions where fixed broadband deployment was insufficient, this shortcoming was perfectly overcome by mobile broadband solutions offering Internet access even in remote areas. In 2017 the average Internet penetration rate was 81.6%, irrespective of the particular ICT tool used or the speed of data transfer. This thesis of nearly equal access to Internet is strongly supported by the evidence on cross-country inequality (see Table 4.2, Fig. 4.4, and the respective Lorenz curves in Appendix C). The Gini coefficient fell from 0.53 in 1990 to 0.06 in 2017, while the Atkinson coefficient dropped from 0.28 to 0.003. Fig. 4.4 also shows rapid declines in the coefficient of variation, as with mobile phone technology. Needless to say, in the 32 European countries, the rapid diffusion of ICT was reshaping the landscape. In general, between 1990 and 2017 the European countries studied here experienced a rapid growth in ICT access and use. We see enormous shifts in access to mobile-cellular telephony, mobile

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broadband networks, and Internet use. However, although average ICT growth rates were high, in some countries ICT deployment was still relatively low in 2017. The cross-country distribution and inequalities as regards the core indicators evince significant changes (compare Figs. 4.3 and 4.4). In Fig. 4.3, the separate density curves for the core indicators individually confirm the rapid, radical shifts in access to and use of basic ICTs, while the trends in cross-country disparities demonstrate a gradual eradication of digital disparities. Although the statistics suggest that the expansion of fixed broadband networks was insufficient, between 1990 and 2017, to meet the high demand for ‘staying connected’, that need—for individuals and organisations alike—has been successfully served by a rapid expansion of mobile networks. One gets the impression that in developed economies such as those of Europe, ICT is spreading fast, unhindered by any sort of constraint or limit.

4.4 ICT diffusion paths, dynamics, and future scenarios This section describes the way in which ICT expanded in the 32 European countries selected for this study. It presents a normative view of ICT diffusion trajectories and also reports the national dynamics of the process. The empirical evidence covers the core ICT indicators per hundred inhabitants: mobile-cellular telephony subscriptions (MCS), fixed broadband subscriptions (FBS), active mobile broadband subscriptions (AMS), and Internet users (IU). To test the hypothesis that this process was endogenous, we take a closer look at the process in the single economies. We disaggregate the evidence, developing technology-specific diffusion patterns for individual countries to reveal regularities in ICT diffusion trajectories between 1990 and 2017 (omitting the period prior to 1989 owing to very low ICT penetration rates and significant breaks in the time series). The national ICT profiles offer a clear picture of diffusion trajectories and allow for extensive and well-justified conclusions. Further, they disclose unique national characteristics of ICT diffusion while also bringing out similarities and regularities. We also trace the fixed telephony indicator, the ‘old’ communication technology, and thus capture the gradual process of ‘fixed-to-mobile technological substitution’ (Lechman, 2016). All the data come exclusively from the World Telecommunication/ICT Indicators database 2018 (22nd Edition). To approximate country-specific ICT diffusion trends (S-shaped path), we use a logistic growth model formalised as:

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NICT , i, y ðt Þ ¼

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where NICT,i,y(t) specifies the saturation of a given ICT in country i and year y, and κ stands for the upper asymptote (growth limit), i.e. the system’s (country’s) ‘carrying capacity’. The parameter α represents the speed of diffusion, and β indicates the midpoint (Tm), the inflection point of the logistic curve, when the logistic pattern reaches 0.5κ. To facilitate interpretation of these parameters and provide a bit of ‘economic logic’, one may replace α with a ‘specific duration’ parameter, defined as Δt ¼ lnðα81Þ, which approximates the time needed for x to grow from 10% to 90% of κ. The midpoint (β) specifies the point in time at which the logistic growth starts to level off. The first part of this section describes the results of our analysis of mobilecellular telephony, fixed and active-mobile broadband networks, and Internet user diffusion patterns between 1990 and 2017, in each of the 32 countries. Figs. 4.4 and 4.5 display the country-specific diffusion trajectories. The value of this graphical evidence is enhanced by logistic growth estimates; the tables in Appendix D summarise the estimated logistic growth models for each core ICT indicator, which explain the unique features of the process in each economy. In each picture in Fig. 4.4 (which visualises mobile-cellular telephony diffusion), we added country-specific paths approximating changes in fixed telephony penetration rates,4 which enriches the whole picture by demonstrating the unique process of ‘fixed-to-mobile telephony substitution’, a term proposed by Lechman (2016) to define the unique, gradual switch from the old to the new technology (from fixed to mobile phones). For both theoretical and empirical work on the process of technological substitution, see, among others, Gruber (2001), Sung and Lee (2002), Banerjee and Ros (2004), Garbacz and Thompson (2005, 2007), Gunasekaran and Harmantzis (2007), Briglauer, Schwarz, and Zulehner (2011), Grzybowski (2014), Ward and Zheng (2012), and Srinuan, Srinuan, and Bohlin (2012).

4.4.1 Developing ICT diffusion patterns The first observation is that the leaps in mobile-cellular telephony access and adoption are drastic. In all the 32 countries considered, the shift to mobilecellular technology was radical between 1990 and 2017, and except for Moldova, all achieved a level of saturation far exceeding 100%. That is, 4

The period of analysis is restricted to 1990–2017, even though these time series can be retrieved back to the 1960s.

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an average European had more than one mobile phone subscription. The prime example is Austria, where the MCS penetration rate soared from a mere 0.9 per 100 inhabitants in 1990 to 170 in 2017. Three countries, namely Lithuania, Estonia, and Italy, also recorded dramatic increases in mobile phone density, with respective MCS penetration rates in 2017 of 150.8, 145, and 141 per 100 inhabitants. The graph (Fig. 4.5A–D) suggests that Lithuania, Estonia, and Italy have also entered the stabilisation phase on the S-time path towards full saturation. All these top four countries had negligible mobile-cellular penetration rates in 1990. In 2017, for the entire group of 32 countries, the average penetration rate was 123 per 100 inhabitants (see Table 4.1), with 15 of them above this average. And even in the five ‘bottom’ countries—i.e. France, Belgium, Croatia, Spain, and Moldova—the rate was above or near 100. Interestingly, at the beginning of our period, in 1990, when mobile phone technology was just emerging, all the economies were equally ‘ICT-poor’, lacking access to this form of communication. Fortunately, however, in the later part of 1990s, the prices of mobile-cellular telephony began to drop significantly, and this was an effective driver of fast-growing demand (see below). In nearly all the countries, the deployment of mobile phones expanded exponentially, resulting in unbounded access in all these societies and drastically reducing international inequalities. Logistic growth estimates, summarised in the first table in Appendix D, reveal other characteristics of mobile-cellular telephony diffusion in highincome economies between 1990 and 2017. In 30 cases the estimated κ parameters indicating a ceiling (saturation, growth limit) is higher than 100, suggesting that these countries have achieved ‘full’ saturation. The highest κ values are those of Austria, at κ ¼ 161.4, and Italy and Lithuania, both κ ¼ 154.4. On the heels of the three leaders, we find Poland (142.1), Estonia (142.9), Finland (148.4), Cyprus (136), and Switzerland (133.1). The ceiling was below 100 in just two cases—Moldova (96.3) and France (98.9). Considering the midpoint (denoted by β), which indicates the year when the spread of technology is half-completed (κ value of 0.5), we may note that it ranges from 1998 (Iceland and Norway) to 2007 (Moldova). That is, during the course of a decade, all the economies reached 50% of their estimated saturation with mobile-cellular telephony. The countries that passed this midpoint in the MCS rate in 1999 were Ireland and Sweden; in 2000 came Belgium, Denmark, Finland, Greece, Italy, the Netherlands, Slovenia, and the United Kingdom. The laggards were Moldova (midpoint in 2007) and Albania (2008). Finally, we estimated

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two other parameters—diffusion rate (α) and specific duration (ΔT), defined as the time required to go from 10% to 90% of saturation. These two parameters differ significantly among the sample economies. Note that the highest value of ΔT was registered by Finland, at 15.8 years. The next four longest specific durations were for Austria (14.7 years), Sweden (13.5), Moldova and Switzerland (13.3 years each). The shortest was for Lithuania at 5.7 years, followed by Slovenia (6.1 years) and Bulgaria and the Czech Republic (6.2 years each). Obviously the rate of diffusion and specific duration are closely related, in that the speed of deployment directly determines the length of the period. Thus, the highest diffusion rates are found in Lithuania (76% per year), Slovenia (71% per year), and the Czech Republic (70%); the lowest in Finland (27%), Austria (29%), and Sweden (35%). The values of parameters α and the number of years of specific duration are negatively correlated; that is, a faster pace of diffusion ensures less time to ‘full’ saturation. Note that the speed of diffusion effectively determines the shape of the trajectory, by determining the steepness of the diffusion curve. Consequently, specific duration, midpoint, and finally ceiling (upper asymptote) are predetermined by the value of α. This examination of the 32 economies indicates that mobile-cellular technology spread quickly and broadly enough to be accessed and used by nearly all members of the society. By 2017 the great majority of countries were almost at ‘full’ saturation by 2017; none lacked access to this ICT. Fig. 4.5A–D further indicates that the mobile-cellular telephony diffusion paths for our sample economies are quite similar in shape, all conforming well to the theoretical sigmoid pattern generated by the logistic growth model. For the individual countries, the MCS diffusion path shows three characteristic phases: early (pre-take-off ), rapid growth, and stabilisation. In Belgium, Bulgaria, Finland, Germany, Ireland, Lithuania, and Poland we observe slight declines in the penetration rate at the end of the period, but this is not necessarily associated with actual decreases in the number of legal mobile phone subscribers and may instead reflect statistical corrections made periodically by national statistical institutes. In the great majority of cases, mobile-cellular telephony diffusion paths are characterised by a relatively short pre-take-off stage and a very steep growth curve. Recall, however, that in this study the curves only start in 1990. If the pre-1990 period were included, the early (initial) phase would be significantly longer, especially in countries where ICT was developing (compare Lechman, 2017). In each country the early diffusion phase of slow growth and low MCS

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penetration rates ends with the unique take-off. A visual inspection of the curves puts this take-off generally in the mid-1990s. This appears to hold for all 32 economies. Once the MCS take-off begins, diffusion goes exponential and the MCS rates soar. Finally, as the domestic mobile phone market approaches saturation, diffusion slows down and reaches the phase of maturity. Again, the graphical evidence shows that all the countries reached the maturity phase around 2010. As expected, in countries where ICTs are actually developed, the diffusion curve is relatively ‘flatter’ (the case of Denmark, Finland, Norway, and Sweden), i.e. the rate of diffusion is lower (see the numerical evidence in Appendix D) than in countries that simply import and assimilate technological novelties. It is perfectly natural for the diffusion of new technologies to be slower where they are invented and first, gradually, introduced. The countries that are not ‘inventors’ but merely have adopted and are using them, ironically, may benefit from their relative technological backwardness, catching up rapidly and raising their level of ICT access and use. Moreover, all the single-country MCS diffusion trajectories display very considerable regularities, suggesting that the spread of mobilecellular telephony is to a great extent endogenously driven. Our data support the hypothesis that the national dissemination of ICT is independent of per capita income, institutional or political regime, level of social development, norms and attitudes, and geography. Now let us take a brief look at the process of diffusion of fixed and mobile broadband networks in our sample countries. We also consider changes in Internet use, proxied by the Internet user penetration rate. By definition, Internet use is directly conditional on the access to technological infrastructure enabling connection with the web. In the case of fixed broadband networks, the period of data availability is limited to 1998–2017; for mobile broadband, to 2007–2017 only. The time series for Internet users dates back to 1990, so in this case, the analysis covers 1990–2017. The Internet penetration rate data are available for a longer period because societies were accessing Internet even before 1997 via fixed narrowband solutions, with low-speed data transfer. Significantly, however, these low-speed solutions never gained much popularity, and as soon as fixed broadband networks emerged in the late 1990s, people started to abandon narrowband. Analogue technologies were being supplanted by high-speed optical fibre connections, which enabled a further expansion of unrestricted access. Needless to say, fixed broadband networks are advantageous compared with narrowband technology, but for a large part of the society, these still represent a sort of ‘luxury’ good. Fixed (wired) networks require well-developed backbone

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infrastructure and financial resources, which many regions still lack. These fundamental prerequisites are a major impediment to high-speed fixed networks, preventing the disadvantaged regions from forging ahead technologically and then economically. Mobile (wireless) broadband technology began to spread worldwide in 2007, offering a better solution for access to the web. Mobile broadband technology overcomes ‘traditional’ geophysical barriers and thus becomes available ‘for all’. In just a decade since its inception, this communication channel has gained enormous worldwide success. Mobile broadband technology has special importance for rural, remote, and technologically underserved areas, where most of the populace has always lacked any technology connecting them with the rest of the world. Mobile broadband is cheaper than wired infrastructure, readily installed even in unfavourable geographical environments. Essentially, it has redefined Internet access, sharply boosted penetration rates, and empowered individuals, offering multiple opportunities for transformation. Its surging penetration rates in rural and geographically isolated areas are remarkable. The mobile broadband technology is the most dynamic telecommunication market segment. According to ITU estimates, between 2007 and 2017, its average global penetration rate jumped twelvefold and it continues to grow at a fast pace. Fig. 4.6.A–D illustrates the national diffusion trajectories of FBS, AMS, and IU during our study period. The tables in Appendix D show logistic growth model estimates for the three indicators separately for each country. Needless to say, Internet changed the ways of communicating, ways of doing business, and the working of markets. The new forms of network dramatically facilitate global knowledge and information flows, i.e. information and knowledge sharing. They make economies and businesses more internationally oriented, allowing for radical productivity gains and thus growth in wealth. Internet helps to eradicate information asymmetries, making national economies more efficient. The enormous growth of Internet in the recent decades is illustrated in the national FBS, AMS, and IU diffusion patterns; Fig. 4.6.A–D shows the main trends in Internet accessibility as represented by fixed broadband subscriptions (FBS) and active mobile broadband subscriptions (AMS). This graphic evidence is then compared with the increasing number of Internet users (IU). Essentially, the series of graphs demonstrate that all the countries surveyed here are marked by parallel accelerations in access to and use of the Internet. Obviously, there are cross-country differences, especially in FBS and AMS penetration rates in 2017, but regardless of the state of development of these two ICT tools, it is clear that all countries are rapidly extending their possibilities for Internet access. It is no surprise that

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the massive deployment of both fixed and mobile broadband networks produced a surge in the share of the population using the web. Fig. 4.6.A–D shows the curves for fixed and mobile broadband diffusion from 1990 to 2017. This picture, showing the trends in these two innovations, is completed by country-specific paths displaying the jump in Internet access and use. A brief examination of fixed broadband diffusion and trajectories indicates that changes in FBS penetration rates were relatively moderate compared, for instance, with the surging deployment of mobilecellular telephony. This does not mean that fixed broadband did not reach a significant share of the population, but even by 2017, its penetration rates were relatively low, at an average of 32 per 100 inhabitants (see Table 4.2), certainly not impressive for such highly developed economies. The leaders in this technology were Switzerland, Denmark, France, the Netherlands, and Norway, with rates somewhat above 40; Albania, Moldova, and Poland brought up the rear. It is worth recalling, here, that fixed broadband is an ICT tool generally limited to one subscription per household; that is, the penetration rate as defined here certainly underestimates the share of the population with a fixed broadband access. The country-wise FBS diffusion curves generally conform quite well to the S-curve pattern, even though no country reports a ‘full’ (100%) saturation. The graphical evidence in Fig. 4.5 shows that all the countries went through the early (slow) diffusion phase along the trajectory and then took off into the exponential growth phase. But this rapid growth stopped at about 30 per 100 inhabitants. Examining the FBS logistic growth estimates in the table in Appendix D, we can see that the estimated ceiling (upper asymptote) ranges from 11.3 in Albania to 43.3 in Switzerland, which would suggest that these two countries have already attained ‘full’ deployment of fixed broadband networks. These estimates further suggest that we are unlikely to see any significant increases in fixed broadband network penetration in the foreseeable future. Next, the midpoints indicting the year when FBS diffusion was halfway to full vary from 2003 in Iceland and Sweden to 2012 in Albania, i.e. a difference of 9 years. As to the estimated intrinsic growth rates (α), these range from 37% per year in Malta and 38% in Albania to 89% in Finland and 95% in Latvia. Obviously the differences in the speed of diffusion directly impact the specific duration (ΔT), i.e. the number of years required to advance from 10% to 90% of saturation. For fixed broadband networks, the shortest specific durations were in Finland and Latvia, at 4.9 and 4.6 years, respectively. The longest were in Malta and Albania, at 11.6 and 11.3 years.

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The relatively moderate pace of deployment of fixed broadband networks in Europe should not come as a great surprise given the more powerful diffusion of mobile broadband networks and the surge in active mobile broadband subscriptions. Unquestionably, since 2007, the market demand driven by individual needs for this new ICT service has increased enormously and rapidly conquered the telecommunications market. According to our estimates, in 2010 the average mobile broadband penetration rate was 33.5 subscriptions per 100 inhabitants (Table 4.2), but over the subsequent 4 years, it jumped to 70.2, and by 2017 it had reached 91.8. These basic statistics clearly indicate that mobile broadband has been the fastest growing ICT so far. In just 10 years (the first ITU statistics on mobile broadband access date to 2007), this sophisticated and highly advantageous technology, allowing interpersonal communication and data transfer regardless of physical location, simply overwhelmed national and global telecommunications markets. In 2017 the top five countries with respect to active mobile broadband subscription were Finland (153.7 per 100 inhabitants), Estonia (133.3), Denmark (129), Sweden (122.5), and Latvia (117.8). Unfortunately, even in 2017 there were still a few laggard countries: Hungary, with only 49.2 subscriptions per 100 inhabitants, and Moldova with 59.9. The data show that the European diffusion of mobile broadband networks offering high-speed connections was astonishingly rapid in the course of the decade, significantly improving individuals’ access to the Internet. And while the gap in active mobile broadband penetration rates remained significant in absolute terms, in relative terms, inequality was drastically reduced (see Section 4.3). Country-specific AMS diffusion trajectories, plotted in Fig. 4.6.A–D, demonstrate several interesting features. First, in the great majority of cases, mobile broadband diffusion did not follow the typical sigmoid trajectory. Share trends differ significantly from the paths of fixed broadband or mobile-cellular technology. Second, the pre-take-off stage is discernible in just a few cases: only in Cyprus, the Czech Republic, France, Latvia, Moldova, and Poland. And even there, the pre-take-off stage is extremely short, compared with mobile-cellular telephony. In all the other countries, no initial stage of slow diffusion is to be seen (or at least it is so short that it is barely detectable). That is, in the majority of European economies, no early diffusion phase is found: rather, mobile broadband diffusion commences immediately with the high-growth stage, as in such countries as Albania, Austria, Estonia, Finland, Italy, and the Netherlands, to name a few. This unique pattern, unprecedented in the studies of technology diffusion, tells us that as soon as this technology was introduced to a national

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telecommunications market, effective demand jumped. A third interesting point is that in most of these economies, the shape of mobile broadband diffusion trajectories does not suggest that they are heading towards saturation yet, even though several countries have achieved 100% penetration rates. Some signs of levelling-off may be discerned in Finland, Norway, Sweden, Switzerland, and the United Kingdom, but the other 27 countries were still, in 2017, in the stage of exponential growth along an S-shaped trajectory, with no signs of reaching maturity. Country-wise AMS logistic growth estimates entirely support our conclusions on mobile broadband expansion in Europe. The estimated ceilings generally confirm the data on active mobile broadband subscriptions, except for four countries where the model returns evident overestimates (see the table in Appendix D). Compared with mobile-cellular telephony and fixed broadband, the mobile broadband parameters indicate diffusion rates (α) that are significantly higher. For instance, in Moldova, the estimated intrinsic growth rate was 268% per year; and in Croatia, 121%. In a few other countries, although lower than in Moldova and Croatia, the velocity of diffusion was still quite high compared with fixed broadband (in particular, Belgium, Denmark, and Estonia; see Appendix D). In examining the spread of mobile broadband technology, it should be borne in mind that our sample period is quite short; so, to some extent, the estimates may be biased. Undeniably, since its very inception, mobile broadband technology has been rapidly invading the telecommunications markets. Clearly it represents a ‘better option’ for accessing the Internet than fixed (wired) broadband, whose diffusion even in high-income economies has not been so overwhelmingly fast. The general picture of fixed and mobile broadband network diffusion in upper-middle-income economies also displays several unique characteristics. For instance, the fixed and mobile broadband diffusion trajectories in each country suggest another striking observation. Assume that, in a given country, the diffusion of Internet access can be decomposed into subprocesses. To rephrase, we might say that the process can be broken down into two growth ‘impulses’: first, the growth of the fixed broadband network, then that of the mobile broadband network. In this case we have a ‘component logistic curve’ with two clearly distinguishable phases of growth—in our case, fixed and then mobile broadband growth impulses. This ‘new’ diffusion curve can be approximated by the sum of two discrete wavelets. In these countries, we see that the rise of mobile broadband technology actually impedes a further expansion of fixed broadband; the former takes off rapidly, conquering the telecommunications market, as the growth

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of fixed networks slows down sharply. In a few countries—Albania, Cyprus, Ireland, Norway, Poland, Sweden, and the United Kingdom—the component logistic curve is visually disrupted, given that, in the very first year for which AMS data are available, mobile broadband already exceeded fixed broadband penetration rates. This analysis of the diffusion of fixed and mobile broadband networks in Europe demonstrates that the accessibility of ICT has been in a continuous flux, in all the countries considered. In many cases, countries have made a very substantial effort to ensure broad, unrestricted assimilation of ICT tools by individuals and organisations. These shifts have reshaped national telecommunications market structures, giving more space for mobile technology. The great merit of this dramatic surge in ICT adoption has been to create sound foundations for a broader use of the Internet, at the same time as creating new opportunities, particularly for societies suffering from relative technological backwardness. One gets the impression that the increasing accessibility of ICT infrastructure for Internet connectivity has greatly strengthened the use of various digital technologies. Technically speaking, ensuring physical access to computers may be a touchstone for technological opportunities, but also, inevitably, leads to higher individual Internet user rates. Our research on Internet penetration rates (IU) shows the familiar S-shaped pattern in a large majority of European countries. The statistics unequivocally show that Internet penetration rates soared between 1990 and 2017, an explosive growth that has fuelled optimistic expectations for increasing connectivity and the prospect of eradication of the enormous cross-country disparities that still exist in the future. In 2017, the average Internet penetration rate was 81.6% in these European economies, so we can agree that these societies enjoy practically unbounded access to the Internet and effectively take advantage of it. Even so, some economies are still lagging behind in relative terms. Sharp increases in Internet assimilation rates are critical, especially in the technologically and economically backward countries, as newly deployed ICT infrastructure creates new types of communication networks and advances in economic and social life. Unquestionably, wider connectivity improves the overall quality of socio-economic life. Internet is certainly the most important technology globally. However, there still remain significant differences between rural and urban areas in access to and use of this new ICT. Rural, remote, underserved regions permanently suffer from technological isolation, insufficiently developed backbone infrastructure, limited access to information, and slow adoption of technological innovations. These shortcomings generate

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rural-urban disparities, even though these inequalities are usually far less severe in Europe than in less economically and technologically advanced countries. The differences in access to and use of digital technologies are not exclusively rural-urban. Inequalities in the level of education are even more evident. Not surprisingly, people who have completed only primary or lower secondary school have far less access to the Internet. This depends on low income; further, these groups more often live in rural areas. Conversely, the university-educated are generally much better ‘equipped’ with the digital infrastructure (ITU, 2017a, 2017b). Nevertheless, the dynamic expansion of low-cost wireless broadband technology means better prospects for the future, with reasonable hopes that the persistent disparities both between and within countries can be overcome.

4.4.2 Fixed-to-mobile technological substitution? This broadly documented description in the diffusion of mobile-cellular and mobile broadband technology raises the question of the extent to which the ‘old’ ICT of telephone landlines is being supplanted by the new mobilecellular communication modes. The same question arises vis-a`-vis the deployment of mobile vs fixed broadband networks. Certainly the rapid growth in the adoption of new solutions suggests that they may well be substitutes, and not complements, for the old technologies. Examining the empirical evidence on the pattern of development of mobile and wire (fixed) broadband networks in the European economies, the fundamental conclusion is that mobile broadband technologies have far outdistanced fixed broadband technologies. As we have seen, mobile broadband has expanded faster and, in 2017, recorded significantly higher penetration rates than fixed broadband, albeit clearly with some cross-country diversity. Soaring demand for wireless (mobile) telecommunication services, such as cell phones, has triggered an unprecedented process of ‘fixed-to-mobile technological substitution’. Here this means a progressive switch from fixed (wired) broadband to mobile (wireless) broadband technology for Internet access. Thanks to the possibility of rapid, low-cost deployment, mobile broadband networks are invading national telecommunications markets, while fixed broadband market shares are diminishing. And in poorly developed and geographically isolated regions, mobile cellular technology is not only the best but practically the only option for access to the outside world. But the lack of access to fixed infrastructure, which impels societies to adopt the wireless solution, is the key factor in another significant development, namely,

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technological leapfrogging. Here, this consists in omitting the ‘fixed infrastructure stage of development’ and moving directly to the ‘mobile infrastructure stage’. The processes of ‘fixed-to-mobile technological substitution’ and technological leapfrogging (Davison, Vogel, Harris, & Jones, 2000; James, 2009; Sharif, 1989; Steinmueller, 2001) generate profound and usually persistent structural shifts in national telecommunications markets. The market shares of mobile technologies are systematically rising, at the expense of fixed infrastructure solutions. In Figs. 4.5A–D and 4.6A–D we plotted country-specific mobilecellular telephony, fixed broadband, and mobile broadband diffusion curves in the 32 European countries between 1990 and 2017. Generally speaking, we can identify three major tendencies. On the graphs for mobile-cellular telephony, we added lines approximating changes in fixed telephony penetration rates. What we already know is that the deployment of fixed broadband networks has been far less dynamic than that of mobile broadband. Second, in 2017, the average fixed broadband penetration rates were significantly lower than that of mobile broadband. The same applies to fixed vs mobile telephony. Third, the share of mobile broadband technologies in the telecommunications market has risen sharply, while wired networks have been losing ground. As to telephony, the general pattern of ‘fixedto-mobile technological substitution’ is highly similar in all the countries analysed. From 1990 onward, fixed telephony has continued to be deployed, but the diffusion was rather slow. The average penetration rates rose systematically until 2000, but then began to decline as mobile phones gained popularity. In some countries, the substitution process has been quite slow. The individual country graphs in Fig. 4.5A–D show that in several cases the penetration rates of fixed telephony changed almost insignificantly. This was so, for instance, in Cyprus, France, Greece, Italy, Romania, and the United Kingdom. In the second group of countries, the transition from old to new ICT was more dynamic and radical, as is clear from the rapidly falling penetration rates of fixed telephony in Finland, the Netherlands, Norway, Sweden, and Switzerland. In some cases we observe increases in fixed telephony penetration rates as well, suggesting that fixed and mobile telephony in these countries are seen as complements rather than as substitutes. This is the case of Austria, Croatia (increase in FTL by 16 percentage points), Hungary, and Portugal (increase in FTL by 22 points in both countries). In fact, in 18 of the 32 countries we find some growth in fixed telephony use. This suggests that in Europe, with its relatively well-developed infrastructure, fixed and mobile telephony are perceived as complementary communication

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channels. Society tends to prefer the two technologies interchangeably, instead of dropping the old one in favour of the new. Sweeping radical fixed-to-mobile phone substitution is discernible in only five countries, namely, Finland, Sweden, Norway, Denmark, and Switzerland, where fixed telephony penetration rates fell by 46.6, 40.1, 36.2, 31.5, and 15.7 percentage points, respectively. Notably, all five countries are mountainous, making mobile technology clearly superior to fixed telephony. The switch from fixed to mobile broadband networks differs from that of telephony infrastructure. Still, one gets the impression that fixed and mobile broadband networks are treated as complementary technological solutions for Internet access. The lines representing national FBS diffusion rates show that in all the 32 European countries, fixed broadband penetration rates rose between 1998 and 2017. At the same time, however, the deployment of mobile broadband networks was far more dynamic and abrupt. In effect, the market shares shifted significantly from fixed to mobile broadband networks. There is no question that mobile broadband technology has invaded telecommunications markets everywhere since 2007, and that the rapidly growing demand for it has resulted in drastic declines in the dynamics of diffusion of wired telecommunications networks. The change in FBS penetration rates reveals that the deployment of fixed networks has slowed significantly. The progressive changeover from fixed to mobile broadband networks, in terms of the market share, is clear.

4.4.3 Do prices matter? The data testify to the rapid and dynamic diffusion of ICTs in all the countries studied, producing extremely high penetration rates, especially for mobile-cellular telephony and active mobile broadband networks. None of these European countries got stuck in a ‘low-level trap’. Naturally, the broad and rapid diffusion of mobile phones depended on multiple factors. First, during these years, mobile telephony became relatively accessible and affordable for the majority of the population, thanks mainly to the parallel institution of both pre- and postpaid systems, which made ICT tools and services accessible to all, including people lacking permanent employment. Second, the broad adoption of mobile telephony overcomes one of the fundamental shortcomings of fixed telephony, namely the long time required to procure a fixed telephone line. Third, a rapid infrastructural development was crucially powered by market liberalisation, which enhanced competition and drove the prices of mobile telephony down.

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This was possible because of the introduction of certain policies in favour of telecommunications. Thus, telecommunications markets became progressively more open, facilitating imports of innovative solutions and expanded access to and use of mobile telephony. All of the foregoing were key drivers of relatively low-cost, robust mobile telephony and Internet networks, ensuring wider access to ICT tools and services. Now let us take a closer look at ICT price developments, so as to frame the growing deployment in the context of costs of access and use. We accordingly chose seven costs that affect the availability of ICTs, namely, monthly mobile-cellular subscription charges (in USD), mobile-cellular price for a 3-minute local call (on peak, in USD), price plan for 1 GB of data transfer (postpaid, in USD), price plan for 500 MB of data transfer (postpaid, in USD), price plan for 1 GB of data transfer (prepaid, in USD), price plan for 500 MB of data transfer (prepaid, in USD), and monthly fixed broadband Internet subscription (in USD). As earlier, all the data are derived from the WTI 2018. Fig. 4.7 shows the prices of mobile-cellular subscriptions from 1990 to 2006 and monthly fixed broadband subscription charges from 2003 to 2017. Fig. 4.7 shows changes in the prices of data transfer both pre- and postpaid, both only for the 5-year period, 2012–2017. When inspecting the paths of monthly subscription charges in mobilecellular telephony access (see Fig. 4.7), it shows that these prices fell drastically between 1990 and 2017, albeit with significant variation across countries, with sharp, multiple ups and downs in some cases. For instance, in Slovakia, the price of a monthly mobile-cellular subscription was almost Mobile cellular monthly subscription charge (US$)

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$85.00 in 1990, but by 2005, it had plunged to $6.70. Similarly, dramatic decreases are observed in countries such as Belgium, the Czech Republic, France, Germany, Ireland, the Netherlands, and Spain. Elsewhere, the price declines were not always that significant; see, for instance, the cases of Hungary (from $33 to $15), Lithuania (from $36 to $28), Norway (from $23 to $17), Slovenia (from $30 to $22), and the United Kingdom (from $50 to $25). Finally, in a third group of countries, including Bulgaria, Croatia, Iceland, Italy, and Sweden, these subscription charges did not change significantly between 1990 and 2006, though there were considerable differences between countries. Switzerland had the highest cost for a mobile-cellular subscription throughout the period—in 2006, it was $35. The average price5 of a monthly subscription declined from $39.80 in 1990 to $21.10 in 2006, with a minimum of $12.80 in 2001. And excluding Switzerland, the average would be barely above $6.00. The data on the cost of monthly fixed broadband subscriptions are available from 2003 to 2016. The tendencies resemble those of mobile telephones. The average fixed broadband subscription cost was just over $40 a month in 2003 and declined to $27 by 2016. The differences between economies remain very substantial indeed. And while the cost of access to fixed broadband networks fell drastically in the great majority of countries, in some they actually increased, namely Belgium, Germany, the Netherlands, the United Kingdom, Denmark, Sweden, Malta, Ireland, and Switzerland. In the latter two countries, the cost of a fixed broadband subscription increased by as much as $14. Surprisingly, however, the rising cost of access did not stop the increasing deployment of fixed broadband. The steepest declines in monthly fixed broadband subscription charges were registered in Austria (where they came down by $27), Albania ($24), Cyprus ($23), Estonia ($19), Moldova ($12?), and Slovakia ($11). Elsewhere these charges were held relatively stable, albeit with some minor ups and downs. Even though landline networks are more reliable and allow faster data transfer and greater download capacity, the emergence of mobile broadband effectively impeded the further diffusion of fixed broadband networks, as noted above. In fact, even reductions in subscription charges for fixed lines failed to impart great impetus on demand. For regular users, mobile solutions appeared to be more advantageous. They are portable, they can connect with broadband in places where fixed networks are not available, and you do not need to lease a line to use it (all you need is a compatible device). 5

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Above all, mobile price schedules are more flexible; some companies offer monthly rolling or pay-as-you-go contracts, while fixed broadband is almost always on at least a 12-month contract. Thus, consumers showed increasing willingness to pay for mobile broadband networks, and the soaring demand for high-speed data transfer was one of the main driving factors to increase the propensity to spend on mobile contracts. As we have seen, rapidly expanding mobile broadband allowed for a more ‘inclusive Internet’ that is widely available and affordable. The numerical evidence shows that a large and growing proportion of Internet use is via mobile devices. In ITU (2017a, 2017b), we find ‘that 71% of all Internet consumption took place via mobile in 2016 and three-quarters of all Internet use was via mobile by 2017, with a growing number of consumers around the world accessing the web on smartphones and tablets’ (p. 18). Examining Fig. 4.8, we observe that between 2012 and 2017 the cost of data transfer via Internet differed significantly from country to country. The lowest average prices were for 500 MB data transfer in prepaid systems, at $9.00 in 2017, while in postpaid plans, the same amount came to over $15.00. Interestingly, in postpaid plans, 1 GB turned out to cost less than for 500 MB, just $15.00. It is also worth noting that in a good number of countries, both in pre- and postpaid plans, the cost of data transfer was relatively stable over the period. In only a few countries—for instance, France, Ireland, Italy, and Sweden—do we find significant declines. And in some

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countries, the price of data transfer in mobile services increased; this is the case of Switzerland and Denmark. Although in some cases there were upward shifts in the cost of access to and use of ICT, in general, declining prices intuitively signal significant improvements in affordability, which coincides with the exponential rise in the use of ICT services in Europe between 1990 and 2017. Logically, the prices of ICT tools and services should be inversely correlated with ICT penetration rates. Figs. 4.9 and 4.10 graph statistical relationships between ICT penetration rates and the prices of ICT access and use. Fig. 4.9 plots mobile-cellular telephony penetration rates against monthly subscription charges and the peak-hour price of a 3-minute call. It also plots the statistical relationship between fixed broadband penetration rates and the price of monthly fixed broadband subscriptions. Statistically, the strongest negative relationship is that between MCS penetration rates and MCS charges, with a correlation coefficient of 0.52. The graph, in this case, suggests that falling prices of monthly mobile-cellular subscriptions boost mobile telephony use.

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Moreover, Fig. 4.9 also suggests another highly interesting observation: for MCS charges ranging from nil to $20, the state of mobile telephony deployment differs enormously between countries. In fact, for quite comparable prices, we find radically different MCS penetration rates, ranging from near zero to 100 per 100 inhabitants. This suggests that the propensity to use mobile-cellular telephony ‘exceeds’ the ‘charge burden’. In other words, people want to join the unique network that mobile phones generate, as they see multiple benefits. Qualitatively similar conclusions are implied by an analysis of the statistical association between MCS penetration rates and the price of a 3-minute call. Here too the correlation is negative, as expected, but the coefficient is much weaker at 0.23. Again, even for quite similar prices for 3-minute calls, national MCS penetration rates differ greatly. These observations offer support for the thesis that mobile telephony is the sort of technological novelty that brings new opportunities to societies, so that deployment grows regardless of price. This is all the more evident from the bottom graph in Fig. 4.8, which shows the relationship for fixed broadband subscriptions. Clearly, these two variables are strongly positively associated, with a correlation coefficient of 0.48. Apparently, rising prices did not deter either organisations or individuals from increasing fixed broadband deployment. This surprising observation is again confirmed by the graphical evidence summarised in Fig. 4.10. Fig. 4.10 plots Internet user vs fixed broadband and mobile broadband penetration rates, and vs prices of data transfer both in pre- and postpaid plans. As expected, the IU rates (the share of society that uses the Internet, regardless of the device) are closely correlated with FBS and AMS penetration rates, with coefficients of 0.81 with FBS and 0.58 with AMS. These strong positive correlations are depicted in the graphs of Fig. 4.9. Turning to the association between Internet use and data transfer prices, however, the picture becomes less clear-cut. In only one case—IU and 1 GB prepaid plan price—is the correlation negative, with a coefficient of 0.35. In the other three cases, the statistical association is positive although weak (R2 approximately 0.20). For instance, the graph plotting IU against 500 MB prepaid prices shows that the IU penetration rates associated with data transfer prices of around $10.00 are highly variable. The remaining graphs of IU against prices show similar results. The numerical evidence suggests one key observation, namely, the significance of the network effect (Cabral, 1990, 2006; Economides, 1996; Katz & Shapiro, 1985) in driving and strengthening the diffusion of

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emerging technologies. The data support the proposition that new ICT solutions generate enormous benefits for their users, which boosts the demand for these even if prices are rising. The inverse demand curve in the case of FBS and monthly subscription charges may be the best example of this unique relationship. The network externalities, or network effects, are actually analogous to what we call the ‘imitation effect’ (Bass, 1969), or the ‘word-of-mouth’ effect (Geroski, 2000; Lee, Kim, & Cho, 2010). Cabral (2006) writes, ‘the network effects, that is, the case when adoption benefits are increasing in the number of adopters (…) suppose that each potential user derives a benefit from communicating with (…) others’ (p. 2). Put differently, the value of a new technology for an individual depends very significantly on how many others have already adopted it. Network externalities, which are strictly related to the process of technology diffusion and emerge as positive feedback from random contacts among economic agents, effectively strengthen the rapid growth of the network itself (Lechman, 2016; Valente, 1996). In other words, the network effect—positive feedback— triggers the sustainable proliferation of new users (Markus, 1987), hence ordinarily an exponential rise in their number. This is obviously especially true of communication technologies, or other technologies allowing the spread of knowledge and information. As observed by Katz and Shapiro (1985) and David (1985), and then repeated in Shapiro and Varian (1998), Shy (2001), and Keser, Suleymanova, and Wey (2012), the positive social and economic outcomes associated with society- and economy-wide application of new technologies depend, above all, on the number of people who decide to acquire and use them—new users. If the number is high enough, this effectively drives further increases in new users, and one has the network effect, or positive feedback. It may be claimed that the network effect is tantamount to imitative behaviour, which runs throughout the process of diffusion of new technologies. Positive feedback arises when people tend to communicate with one another and transmit, through a variety of channels, a stock of knowledge on the advantages and benefits of the new technology. Rogers (1983) sees diffusion as endogenous and demonstrates that a rapid spread of technological novelties among the members of a society is driven by networking activities and not strongly conditioned by the prices of improvements in the quality and utility of products and/or services. Still, the process of diffusion may be impeded by certain factors, such as legal and institutional constraints, spatial hindrances, risk aversion, information gaps and asymmetries, or simply the chaotic behaviour of individuals.

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Where such impediments prevail, policy should seek to stimulate the diffusion of technologies, so that challenge-response type of socio-economic behaviour by agents serves to foster the growth of the network. If such ‘bottlenecks’ are overcome, the number of new users of a technology will take off into exponential growth, so the society and economy can ‘reap the fruits’ of new technologies.

4.4.4 Final note Our examination of the patterns of ICT diffusion in Europe in 1990–2017 found growth in practically all the 32 countries studied. Naturally, the annual growth rates differ among countries, but it is clear that, on average, by 2017 these European societies had mostly reached nearly full ICT saturation. That is, we now have conditions of practically unbounded access to and use of ICT, with all its multiple benefits. Concerning mobile-cellular telephony, many countries showed penetration rates in excess of 100 per 100 inhabitants. In fact, the overwhelming expansion of this communication mode resulted in a gradual process of fixed-to-mobile telephony substitution. The growth in fixed broadband deployment was not that impressive, however, given that we have seen an astonishing expansion of mobile broadband technology for Internet access since 2007. The graphical evidence presented in Section 4.2 certainly indicates suggests that mobile broadband became an ideal complement in fixed broadband infrastructure. Mobile broadband subscriptions increased dramatically and the European societies enjoyed virtually unlimited access to this ICT tool by 2017. Fast-growing, nearly unlimited fixed and mobile broadband networks triggered quantum jumps in Internet user penetration rates. In effect, during the period of our study (less than 30 years), organisations and individual consumers in Europe became almost ‘fully saturated’ with the new information and communication technologies. The tables in Appendix E summarise forecasts regarding the future scenarios of ICT development. Given that fixed broadband has been steadily supplanted by mobile broadband infrastructure since 2007, we make no predictions in this regard. Our development scenarios call for the continuation of European efforts for higher ICT penetration rates. Needless to say, universal access to mobile broadband technology will not be a ‘luxury good’ in Europe, and no area will remain unconnected in the foreseeable future. Our empirical results imply several important considerations on the diffusion of new technologies. The deployment rates of mobile (wireless)

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technologies have been astonishingly high in Europe, which suggests that the diffusion of new information and communication technologies is endogenous by nature and that once a critical mass of new users (innovators) is attained, it proceeds at exponential rates. Moreover, the pattern of diffusion of new mobile technologies confirms the hypothesis of network effects (externalities), which are recognised as one of the prime drivers of universal dissemination of new technologies. From the economic and social points of view, the global diffusion of wireless solutions is essential for physically isolated, underserved, and infrastructurally backward regions. Mobile communications technology offers people in remote areas the possibility of connection. As many observers have pointed out, mobile technology is the first and probably the only means of access to the Internet for large numbers of people in these underdeveloped areas. Interestingly, the paths of growing access to mobile-cellular telephony and to Internet technology (fixed and mobile broadband networks) have been relatively similar in the economies studied here. Does this perhaps suggest that new information and communication technologies can be diffused throughout various societies regardless of per capita income, institutional and legal frameworks, social norms and attitudes, or geographical conditions? If so, we can say that the diffusion of ICT is endogenously driven and, to some extent, self-perpetuating—a self-sustaining process. In our sample countries, we witnessed the enormous ability of societies to assimilate and adopt new technologies, which will unquestionably have far-reaching economic and social consequences. Putting in people’s hands an instrument that enables them to ‘stay connected’ with the outside world is revolutionary, not only because it intensifies and multiplies personal contacts but, above all, because it fosters the emergence of totally new forms of networks. The new networks will inevitably impact the way people live and economies operate. In short, although mobile telephony has gained popularity at different paces in different countries, it must be recognised as an extremely successful technology that has provoked radical transformations in telecommunications markets and, at the same time, revolutionised the way people communicate and has thus powered enormous social changes.

References Atkinson, A. B. (1970). On the measurement of inequality. Journal of Economic Theory, 2(3), 244–263. Banerjee, A., & Ros, A. J. (2004). Patterns in global fixed and mobile telecommunications development: A cluster analysis. Telecommunications Policy, 28(2), 107–132.

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Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227. Briglauer, W., Schwarz, A., & Zulehner, C. (2011). Is fixed-mobile substitution strong enough to de-regulate fixed voice telephony? Evidence from the Austrian markets. Journal of Regulatory Economics, 39(1), 50–67. Cabral, L. M. (1990). On the adoption of innovations with ‘network’ externalities. Mathematical Social Sciences, 19(3), 299–308. Cabral, L. M. (2006). 15. Equilibrium, epidemic and catastrophe: Diffusion of innovations with network effects New frontiers in the economics of innovation and new technology: Essays in honour of Paul A. David, 427. Comin, D., & Hobijn, B. (2011). Technology diffusion and postwar growth. NBER Macroeconomics Annual, 25(1), 209–246. David, P. A. (1985). Clio and the economics of QWERTY. The American Economic Review, 75(2), 332–337. Davison, R., Vogel, D., Harris, R., & Jones, N. (2000). Technology leapfrogging in developing countries–an inevitable luxury? The Electronic Journal of Information Systems in Developing Countries, 1(1), 1–10. Economides, N. (1996). The economics of networks. International Journal of Industrial Organization, 14(6), 673–699. Garbacz, C., & Thompson, H. G., Jr. (2005). Universal telecommunication service: A world perspective. Information Economics and Policy, 17(4), 495–512. Garbacz, C., & Thompson, H. G., Jr. (2007). Demand for telecommunication services in developing countries. Telecommunications Policy, 31(5), 276–289. Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4), 603–625. Gini, C. (1912). Variabilita` e mutabilita`. In E. Pizetti & T. Salvemini (Eds.), Reprinted in Memorie di metodologica statistica. Rome: Libreria Eredi Virgilio Veschi. Gruber, H. (2001). Competition and innovation: The diffusion of mobile telecommunications in Central and Eastern Europe. Information Economics and policy, 13(1), 19–34. Grzybowski, L. (2014). Fixed-to-mobile substitution in the European Union. Telecommunications Policy, 38(7), 601–612. Gunasekaran, V., & Harmantzis, F. C. (2007). Emerging wireless technologies for developing countries. Technology in Society, 29(1), 23–42. ITU. (2017a). Measuring the information society 2017. Switzerland: International Telecommunication Union. ITU. (2017b). The state of broadband 2017: Broadband catalyzing sustainable development. Broadband commission for sustainable development. Switzerland: ITU/UNESCO. ITU. (2018). Measuring the information society 2018. Switzerland: International Telecommunication Union. James, J. (2009). Leapfrogging in mobile telephony: A measure for comparing country performance. Technological Forecasting and Social Change, 76(7), 991–998. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424–440. Keser, C., Suleymanova, I., & Wey, C. (2012). Technology adoption in markets with network effects: Theory and experimental evidence. Information Economics and Policy, 24(3), 262–276. Lechman, E. (2016). ICT diffusion in developing countries. (Springer International Pub.). Lechman, E. (2017). The diffusion of information and communication technologies. Routledge. Lee, M., Kim, K., & Cho, Y. (2010). A study on the relationship between technology diffusion and new product diffusion. Technological Forecasting and Social Change, 77(5), 796–802. Markus, M. L. (1987). Toward a “critical mass” theory of interactive media universal access, interdependence and diffusion. Communication Research, 14(5), 491–511.

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Rogers, E. M. (1983). Diffusion of innovations. New York: The Free Press (A Division of Macmillan Publishing Co.). Shapiro, C., & Varian, H. (1998). Information rules: A strategic guide to the. [Network Economy]. Sharif, M. N. (1989). Technological leapfrogging: Implications for developing countries. Technological Forecasting and Social Change, 36(1–2), 201–208. Shy, O. (2001). The economics of network industries. Cambridge University Press. Srinuan, P., Srinuan, C., & Bohlin, E. (2012). Fixed and mobile broadband substitution in Sweden. Telecommunications Policy, 36(3), 237–251. Steinmueller, W. E. (2001). ICTs and the possibilities for leapfrogging by developing countries. International Labour Review, 140(2), 193–210. Sung, N., & Lee, Y. H. (2002). Substitution between mobile and fixed telephones in Korea. Review of Industrial Organization, 20(4), 367–374. Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69–89. Ward, M. R., & Zheng, S. (2012). Mobile and fixed substitution for telephone service in China. Telecommunications Policy, 36(4), 301–310.

Further reading Bass, F. M. (1980). The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations. Journal of Business, S51–S67.

CHAPTER FIVE

ICT and socio-economic development dynamics Contents 5.1 The context and data explanation 5.2 General picture 5.3 Economic development: Towards a structural shift? 5.3.1 The evidence 5.4 Social development patterns: Paving the road ahead 5.5 Gaps growing—Gaps narrowing? References Further reading

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5.1 The context and data explanation Since early 1980s all world regions have been rapidly and profoundly transforming due to an explosive growth of new information and communication technologies. Boosting demand for novel technological solutions offering cheap, fast, and unrestricted dissemination of knowledge and information has been gradually reshaping social and economic structures, thereby enforcing the emergence of new networks and facilitating immediate communication regardless of the physical location of agents. Available time series on changing ICT penetration rates suggests that all the European economies have been included in this overwhelming process. A rapidly growing interest in the adoption and broader usage for new technologies offering ‘connection with outside world’, mainly due to low-cost, distributable, and easily adaptable wireless solutions, has disruptively reshaped the world landscape. A fast spread of ICT has opened new windows of opportunities for technological catching-up, leapfrogging other countries technologically, or simply escaping from permanent, often historically conditioned, technological marginalisation. Still, as raised in different discussions, the role of ICT in enhancing social and economic development is undeniable. Although the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00005-1

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fact that the impact of new technologies on the socio-economic aspects of life may be either direct or indirect, either short term or rather unveiled in the long-run time horizon, there are quite many channels through which this impact may be demonstrated. Needless to say, the seizing benefits of the adoption of ICT remain one of the greatest challenges of both developed and developing countries. New technologies offer unbounded opportunities to develop, regardless of physical location, gender, or other prerequisites. ICT may have a substantial impact on the economic welfare of people, performance of companies and whole nations, as well as on different spheres of social life. Numerous studies seem to confirm this; see, for instance, Avgerou (2003), Sein and Harindranath (2004), Cortes and Navarro (2011), Cruz-Jesus, Oliveira, Bacao, and Irani (2017), Niebel (2018), Stanley, Doucouliagos, and Steel (2018), Bhandari (2019), or Haftu (2019). In what follows we intend to address, at least partially, the questions on the impacts of new information and communication technologies in the social and economic performance of the European countries. We try to capture these impacts by running the analysis from two different perspectives: economic and social. With these aims, we selected a bundle of 15 economic variables and 9 social variables. Regarding economica variables, we chose the following: • GDP per capita, PPP (GDP)b; • GDP per person employed (GDP_empl); • contribution to the national output from three main sectors: agricultural sector (Agr_VA), industrial sector (Ind_VA), and service sector (Serv_VA); • employment structure by three main sectors: agricultural sector (Agr_empl), industrial sector (Ind_empl), and service sector (Serv_empl); • labour force participation rate for ages 15–24 (LF_15_24); • High-technology exports as a share of total manufactured exports (HT_exp); • ICT goods exports as a share of total goods exports (ICT_good_exp); and ICT goods imports as a share of total goods imports (ICT_good_imp); • ICT service exports as a share of total service exports (ICT_serv_exp); • communications, computer, etc., as a share of total service exports (Comp_serv_exp) and as a share of total service imports (Comp_serv_imp). a

For definitions of variables, see Appendix F. For abbreviations, see also Appendix H.

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As for the socialc variables, we decided to concentrate on the following: • school enrolment, tertiary as a share of gross (School); • female labour force as a share of total labour force (LE_female); • female labour force participation rate for ages 15–24 as a share of total female labour force at this age (LF_female_15_24); • contributing family workers as a share of total employment (Family_tot); • female contributing family workers as a share of total female employment (Family_female); • vulnerable employment as a share of total employment (Vulner_tot); • female vulnerable employment as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Vulner_tot); • female waged and salaried workers as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Wage_tot); • female waged and salaried workers as a share of total female employment (Wage_female). By convention, our empirical sample consists of 32 European countries, and the period of analysis covers the years between 1990 and 2017. All economic data used in this research are exclusively extracted from the World Development Indicators 2018 database. In what follows we aim to, first, examine in-time changes in each of the variables listed above and, second, to determine their statistical association with two core ICT indicators: mobile cellular subscriptions (MCS) and Internet user penetration rates (IU). By this we aim to show how the European countries were changing their economic and social performance between the years 1990 and 2017, as these years mark the period of extraordinary rapid expansion of new ICTs. Drawing this general picture of socioeconomic shifts in Europe, hopefully, shall allow concluding whether and to what extent these changes are accompanied by fast deployment of ICT. In this case, we expect to show that ICT’s growing adoption gives a strong impulse for growth in economic and social welfare approximated by a shift in, inter alia, national output per employee, the share of high-tech exports in total exported goods, or female engagement in labour market activities. On the other hand, we expect that positive changes in ICT’s increasing usage may be demonstrated indirectly through, for instance, drops in female labour force participation rate for ages 15–24, or female family workers as a share of c

For definitions of variables, see Appendix G.

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total female employment. In the final part of this chapter, we are examining the process of economic and social convergence that we expect to take place across the European countries. We challenge the process of convergence unconditionally, and our intention is to unveil if the process of rapid adoption of ICT is accompanied by gradually diminishing cross-country disparities with respect to both economic and social performance. In this case, we expect to uncover that the process of ICT diffusion enhances dropping economic and social inequalities among the European economies. Obviously, this astonishing rapid growth in ICT deployment observed over the last decades makes us think of the pervasive impact of ICT on the economic growth et alia, despite the fact that social performance is not merely detectable, but obvious. The absence of clear and easily identifiable impacts of new technologies on the economic and social performance has already been claimed in various researches and is widely recognised as the ‘productivity paradox’ (Brynjolfsson, 1993; David, 1989; Willcocks & Lester, 1999). Notably, even if technologies grow exponentially, the latter is not immediately or directly ‘converted’ into economic growth. Probably this is also the case with new information and communication technologies. We are fully aware that this picture is only partial, and concluding on the existence of casual relationships between the examined variables would be an overestimation in many cases. Obviously, detected correlations can be spurious, and the results of the research may be ambiguous.

5.2 General picture As shown in Chapter 4, the rate of technological progress that we observed across the European economies, in terms of increasing adoption of ICT, has been impressive over the last three decades. Facing these phenomenally explosive shifts in the adoption and usage of ICT can effectively stimulate the identification of its macroeconomic and social consequences, and potentially trace the extent to which the adoption of ICT affects, for instance, economic growth. Here, we briefly discuss the changes in economic and social performance, observed between 1990 and 2017, across the 32 European countries that come within the scope of this research. Figs. 5.1–5.3 show changes in the average values of each economic and social variable selected for the study. Our first general observation is that, during the period 1990–2017, there were significant fluctuations in the great majority of indicators. Fig. 5.1 reveals the main shifts in macroeconomic aggregate indicators, such as gross domestic output per head

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(GDPpc) and gross domestic output per person employed (GDP_empl), which shows a shift in the overall economic welfare of the European countries. Visibly, the growth of GDP per capita grew dynamically, especially since the mid-1990s, dropped slightly after the 2008 economic crisis, and has grown again since 2010. Surely a rapid growth of per capita income in the early 1990s has been associated with the collapse of the ‘Berlin Wall’, which began a new economic era for Central-Eastern European countries. Between 1990 and 2017, the GDP per capita increased by more than 10,000 USD. Similarly, strong improvements are observed when considering the national output per person employed. Basic numbers suggest that this grew by more than 50%, between 1990 and 2017, which supports the hypothesis of strong productivity shifts during this period. Enormous improvements in the general material well-being of the European countries were accompanied by massive and visible changes in the structure of national economies. That is to say that, between 1990 and 2017, we observe dynamic changes in the input from three main economic sectors—agriculture, industry, and services—to the national economies. On average, since 1990 onward in Europe, we observe continuous drops in the share of national output from agricultural and industrial sectors, whereas the total of value added services gains in importance. Analogous trends are demonstrated when visualising changes in employment by sectors. Again, there are radical drops in agriculture (from 14% to 7%) and industries (from 33% to 23%), accompanied by a growth in employment in the service sector of 15% during the period examined. Apparently, such radical changes in the structure of national economies, both in terms of valued added and employment, can be an effect of a rapid technological progress that the European countries underwent between 1990 and 2017. Growing importance in creating the national output of service sector can, unquestionably, be closely related and determined by the increasing deployment and usage of new ICTs. ICT has enabled the emergence of new types of products and services and further and more intensive mechanisation of production and, over the long term, drives structural changes in the national economies and domestic labour markets. As ICT is broadly claimed to be one of the ‘prime movers’ of access to education, this positive effect of the indirect impact of ICT on the labour market can be demonstrated through the decreasing share of the population aged between 15 and 24 who are actively engaged in the labour market. As displayed in Fig. 5.1, the participation of people aged 15–24 in the labour force (LF_15_24) dropped from 53% in 1990 to 42% in 2017. Such a change suggests that, on average, pupils leave the educational system late to enter the

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labour market. This view is supported by elementary data on the gross school tertiary-level enrolment rates; see Fig. 5.3. Next, Fig. 5.2 briefly explains trade-related macroeconomic indicators, which are potentially affected by technological progress. More specifically, we refer to high-technology exports expressed as a share of total exported goods (HT_exp) and to five other variables demonstrating export and import intensity with regard to ICT goods and services. As for the hightechnology exports, it can be seen that the path is highly unstable, marked by multiple ups and downs between 1990 and 2017. In the mid-1990s, after an abrupt shift in HT_exp, the general trend seems to be rather downward. Available time series for ICT goods’ exports and imports extend back to 2000. Between 2000 and 2017, massive drops in the values of these two indicators are revealed, which might suggest a diminishing role of ICT goods’ exports and imports in the total of ICT goods traded internationally. However, this surprising change is one of the effects of the globally recognised process of moving ICT production to Asian countries. The emergence of the so-called ‘Factory Asia’ (Ito & Vezina, 2016; Kam, 2017) at the beginning of 20th century is, inter alia, demonstrated through a falling share of ICT exports and imports with regard to high-income economies. When looking at the average changes in the exports of ICT services (explained as a share of total service exports), there has been an upward trend since the 1990s. Similar observations are valid for variables explaining communication and telecommunication services, both exported and imported (as a share of total service exports/imports). In the case of Comp_serv_imp, the growing patterns are relatively stable, and between 1990 and 2017 we observed a growth of total service imports, going from 30% to 45%. As for Comp_serv_exp, the path is less stable, with a dramatic drop in the late 1990s; however, since 1997, when average Comp_serv_exp was just 16% of total service exports, its share grew to 43% in 2017. Finally, let us move briefly to shifts observed on the social ground—see graphs in Fig. 5.3. In this case, we selected nine indicators that demonstrate the rather indirect impact of technological change on the social spheres of life, associated here mostly with female engagement in economic activities. As raised in many studies, a higher participation in the labour force is very likely to be the first and most important step in exploiting the full potential of ICTs. Its deployment permits a timely access to information, helping to overcome one of the fundamental barriers to the effective functioning of the market, namely, information asymmetries. Combined appropriately, these two elements—shifting labour force participation and removing

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constraints on information access—lead to increases in the number of transactions, enable participation in global markets, reduce transaction costs, and ensure worldwide visibility, all of which, in the longer run, offer better prospects for economic growth and development. One of the most serious problems for economically backward countries is low female participation in the formal market economy, i.e. in the job market and in entrepreneurial activities. Women’s relatively low rate of economic activity may be a direct effect of poor education, poor skills, and illiteracy. Women in less developed and tradition-oriented regions are often deprived of access to the financial system; they have no permanent income from contracted work. In traditional societies, a whole series of social or religious norms and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Women are exposed to poverty more often than are men, and they are often denied basic rights. In fact, a significant share of women still constitute an unused labour force, which may significantly impede national growth and development. Additionally, women who want to engage in economic activity face various gender-specific constraints, such as barriers to education and lack of a combination of relevant education, professional skills, and work experience, which may be a severe handicap not only in seeking decently paid jobs but also in forming businesses of their own. Such claims may be traced in Lindio-McGovern and Wallimann (2016) and Sachs (2018), where the authors argue that the problem of marginal engagement of women in formal economic activities is especially severe in the rural areas. In Klasen, Lechtenfeld, and Povel (2015) and Klasen (2018), we find more arguments and explanations of female exclusion in the labour market. He shows that social values and norms, the structure of national economies, and also state political regimes in the developing countries can effectively hinder women’s participation in value creation. Ortiz Rodrı´guez and Pillai (2019) present evidence in the same vein. Fig. 5.3 summarises the general average tendencies in school enrolment in the tertiary level and female engagement in formal labour market activities. First, we observe an impressive shift in gross school enrolment at the tertiary level. Between 1990 and 2017 this grew by 40%—from 29% to 69%. This radical change in access to education, both for men and women, observed across Europe during the last three decades has created a structural shift in, inter alia, the labour market. These shifts are, probably, driven partially by a greater accessibility to the education system and, to some extent, by technological progress, which enforces multiple changes across the whole economic and social systems. Throughout this research, we claim that the impact of ICT on the social spheres of life associated with economic activities may, in the first place,

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be demonstrated through women’s participation in the labour market. To stay in line with the latter, along with increasing school enrolment, we examine shifts in female participation in the labour force (as a share of total; LF_female), participation in the labour force of females aged 15–24 (as a share of total; LF_female_15_24), and three indicators indicating the share of women falling into the categories of ‘contributing family workers’ and ‘vulnerable employment’, on the one hand, and ‘wage workers’, on the other. We argue that improving the overall economic performance along with the implementation of technological progress in economic and social systems shall have positive effects in terms of reducing female (as well as total) vulnerable employment and reducing the share of women working as contributing, usually unpaid, family workers; henceforth, the share of women being employed as waged and salaried workers shall increase. Such structural changes in labour markets would, evidently, be one of the most important manifestations of the positive impact of ICT on the overall socio-economic development, even though this impact may be hard to capture in numbers. Our preliminary evidence, summarised in Fig. 5.3, demonstrates clearly a strong drop in female labour participation (ages 15–24), from 49% to 38%, between 1990 and 2017. Moreover, we observe relatively substantial decreases both in the share of women working as contributing family workers and in the share of women who fall into vulnerable employment. These positive shifts are accompanied by a growth in the share of women being employed as waged and salaried workers. In what follows, we present and discuss the results of a more detailed analysis intended to uncover associations between growing adoption and usage of ICT and socio-economic development in the 32 European countries between 1990 and 2017.

5.3 Economic development: Towards a structural shift? As described briefly in Section 5.2, across the European countries, during 1990–2017, there have been massive changes in the levels of social and economic development. The general picture we described suggests that all the 32 economies examined between these years have experienced a rapid economic growth, which is demonstrated clearly through shifts in gross per capita income and final production value per person employed, to cite just two examples. Notably, based on the average values of consecutive variables, we also observed radical structural changes in the national output; the gradually declining share of value added in the agricultural and industrial

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sectors was compensated by a growth in the share of value created in services. Analogously, general tendencies were observed in employment in the three main sectors of national economies: significant drops in employment in the agricultural and industrial sectors but fast increases of employment in the services sector. Moreover, our general conclusion, based on a brief analysis of trade-related indicators, demonstrates important changes in this respect. International trade patterns showing ICT goods and services exports and imports are unstable but, at a time, they show dynamically changing situations with this respect. This picture of fast-changing structures of economies coincides with the debate on the potential benefits of new ICTs and the changes that it can bring to the societies and economies. This debate concentrates mainly on the productivity, economic growth, and overall welfare gains that the newly emerging digital economy may offer. Moreover, something that is relatively less intensively discussed, the broader adoption and usage of ICT may potentially create several structural shifts in the national economies. Obviously, these shifts are long-term processes that are dependent on various factors, and our supposition that some of them are generated by technological progress might be spurious. Still, there is not wide consensus over whether standard quantitative measures can fully demonstrate the impact of ICT on the economic performance of countries. Examining the relationships between the process of ICT diffusion and economic development is a challenging task. This is not only because countries included in empirical samples are relatively heterogeneous but also, and mostly, because these relationships are complex in their nature and are influenced by multiple external factors that may be even hard to identify. Statistical analysis and econometric modelling techniques are used conventionally to trace the relationships between variables. Still, some unique features of countries are often hard to capture and include in econometric models. Some elements and determinants that shape the process of economic development and either foster or hinder the impact of ICT on economic processes may be empirically intractable, but their influence is still massive. In what follows, we develop a quite intuitive framework analysing major economic areas that, potentially, may be affected by a rapid ICT expansion over the societies and economies that all the European countries have experienced during the last three decades. Our intention is, at least partially, to broaden our knowledge, draw a general picture of how the contours of the European economies were changing between 1990 and 2017, and associate those changes and structural shifts with the process of diffusion of new ICTs.

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5.3.1 The evidence The following empirical evidence confronts the process of ICT diffusion vs economic development across the 32 European economies, between 1990 and 2017. Put differently, we trace the statistical relationships between ICT deployment and the process of economic development and investigate whether the impact of ICT has been either positive and strong, or, conversely, negligible. To this end, we use two core ICT indicators: mobilecellular telephony subscription rates (MCS) and Internet users penetration rates (IU), which we claim are good proxies of access to and use of ICT.d Economic development is approximated by a bundle of arbitrary variables (see Section 5.2), which, we believe, allow us to demonstrate the types of changes and structural shifts that have been observed during the last three decades in countries that are within the scope of this research. The results of our analysis are demonstrated in Figs. 5.4–5.7, which display the statistical associations between mobile-cellular telephony and Internet users penetration rates and selected economic variables. Intentionally, we develop separate graphs for each pair of variables, which allows for a detailed analysis of the issue. Our graphical evidence is then enriched by panel regression estimates; the results of these analyses are summarised in Tables 5.1 and 5.2. Inspecting the empirical findings reveals that certain regularities can be identified with respect to the relationships examined. As presented briefly in Section 5.2, many European countries experienced a rapid economic development, on the one hand, and significant structural shifts regarding, for instance, value creation and/or employment structure of the main economic sectors, agricultural, industrial, and services, on the other. Obviously, the average annual speed of these changes differs across the countries. Central-Eastern European countries, the economies of which were boosted in 1990, experienced much faster growth than did wellestablished Western European countries. For instance, in Albania, Latvia, Lithuania, and Poland, the average annual GDP per capita growth ratese between 1990 and 2017 were 3.4%, 3.9%, 4.1%, and 3.5%, respectively. During an analogous period, countries such as France and the United Kingdom grew at a slower rate, hardly reaching 1% per annum. Similarly, dynamic changes were observed with respect to the gross national output per person employed; the calculated correlation between GDPpc and GDP_empl is almost 0.95. Rapid economic advances, measured in gross d

In Chapter 4, we described extensively the process of ICT diffusion, providing country-wise evidence. Authors’ calculations based on data derived from the World Development Indicators 2018.

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Table 5.1 Mobile cellular subscriptions vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl

MCS Rho R2 (within) F (prob > F) No. of obs.

0.07 [0.00] 0.96 0.69 1772 [0.00] 833

20.15 [0.00] 0.93 0.63 1332 [0.00] 799

20.03 [0.00] 0.72 0.21 202 [0.00] 799

0.02 [0.00] 0.82 0.45 620 [0.00] 794

20.11 [0.00] 0.94 0.57 1108 [0.00] 846

20.02 [0.00] 0.81 0.26 284 [0.00] 846

0.03 [0.00] 0.92 0.64 1436 [0.00] 845

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20.03 [0.00] 0.89 0.41 586 [0.00] 865

0.09 [0.00] 0.78 0.17 165 [0.00] 830

0.02 [0.04] 0.86 0.00 0.42 [0.51] 544

20.16 [0.02] 0.77 0.09 52.1 [0.00] 544

0.24 [0.01] 0.55 0.41 477.6 [0.00] 721

0.04 [0.00] 0.55 0.07 52 [0.00] 779

0.04 [0.00] 0.64 0.09 78 [0.00] 766

Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.

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0.08 [0.00] 0.95 0.65 1516 [0.00] 838

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0.07 [0.00] 0.95 0.63 1330 [0.00] 824

0.06 [0.00] 0.95 0.62 1311 [0.00] 809

20.14 [0.00] 0.93 0.65 1450 [0.00] 789

20.02 [0.00] 0.78 0.23 226 [0.00] 789

0.02 [0.00] 0.84 0.41 544 [0.00] 786

20.11 [0.00] 0.95 0.62 1297 [0.00] 824

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0.08 [0.00] 0.78 0.14 129 [0.00] 812

20.08 [0.03] 0.87 0.01 7.5 [0.00] 544

20.15 [0.01] 0.79 0.14 84 [0.00] 544

0.23 [0.00] 0.61 0.46 575 [0.00] 703

0.03 [0.00] 0.55 0.03 26.9 [0.00] 762

0.03 [0.00] 0.63 0.10 79.8 [0.00] 749

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Table 5.2 Internet user penetration rates vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl

Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.

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per capita income, were accompanied by structural shifts in terms of the contribution of agriculture, industry, and service sectors to the GDP. Massive increases in GDP created in the service sector were observed, for instance, in Albania (from 16% to 47% of gross value added), Malta (from 35% to 75% of gross value added), and Moldova (from 33% to 56% of gross value added). In other countries, these changes were not that radical, but the trend was positive in each country examined. On the other hand, a rapidly falling role of the agricultural sector was traced in each country. Again, analogous structural shifts were observed with respect to employment in consecutive economic sectors—a rapidly dropping share of people being employed in agricultural and industrial activities, and an increasing share of employees in the services sector. Fig. 5.8 represents this type of structural changes in terms of employment across sectors. Correlation coefficients for Agr_empl vs Serv_empl and Agr_empl vs Ind_empl are (0.83) and (0.56f), which directs our attention towards the fact that radical flows of the labour force are observed across the sectors. Obviously, this general view does not necessarily show that the labour force previously employed in the agricultural sector moves directly to the service sector, effectively ‘jumping over’ industry. Labour force leaving the agricultural sector moves towards both industry and services, but, unquestionably, a broader deployment of new ICT opens multiple opportunities to operate in the service sector. These opportunities are opened not only in the ICT service sector itself, despite the fact that the ICT service sector is recognised globally as a key driving sector of economies, but also in other types of services that need ICT to work effectively. It is needless to say that ICT positively impacts firm performance, and this impact is even stronger when accompanied either by other types of investments or by organisational changes in companies. These investments and other expenditures on skills improvements or, inter alia, firm market reorientation and external market expansion, together with ICT deployment, may lead to a kind of synergism that boosts economic performance. Those synergies will lead inevitably to the development of new products and services and their introduction to the market. Moreover, changes in production and the service sector, from the long-run perspective, will generate profound changes in the labour market, and, above all, in the structure of labour demand and supply. Those effects are, to some extent, also traceable at the macro level.

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Graphical evidence summarised in Figs. 5.4–5.7 demonstrates the statistical relationships between the two core ICT indicators and select macroeconomic variables that approximate the economic performance of countries. Not surprisingly, mobile-cellular telephony penetration rates plotted vs GDPpc, GDP_empl, Serv_VA, and Serv_empl show a positive statistical relationship. Statistically, the strongest association is demonstrated in the case of changes in the share of labour force employed in the service sector (correlation coefficient is 0.41). This result confirms our general intuition that a rapid expansion of ICT and a broader adoption and extensive deployment of new technologies across both social and business activities constitutes a strong stimulus for moving from an agriculture- and industry-oriented economy towards a service-based economy. Respective graph in Fig. 5.6 displaying analogous evidence, confronting Internet users vs economic variables as in Fig. 5.4, supports this supposition. The shift in the share of labour force being employed in the service sector is strongly and positively correlated with growing IU. In this case, the correlation coefficient is even higher, at 0.57, and it represents the strongest positive statistical relationship among the others examined and presented in Figs. 5.4–5.7. The growing role of ICT in enhancing structural changes in the national and global economies is demonstrated effectively not only through changes in the structure of production and employment across the main economic sectors but also through changes in the composition of the labour force. As economies and societies head towards full saturation with new ICT, it becomes quite natural that ICTs proliferate not only social life, but, above all, in the way of running business, structure of consumption patterns and habits, and thus structure of production, just to cite a few examples. Obviously, a broader deployment of ICT offers new opportunities to intensify business activities, internationalise companies, and enhance the penetration of new markets through e-platforms, et alia; and as raised by some scholars, ICT usage increases storability and tradability (e.g. Boden & Miles, 2000), thereby positively affecting the emergence of a service-based economy. It is needless to emphasise that the expansion of ICT generates changes in demand for different skill levels. As raised by many, technological progress in the field of ICT drives massive robotisation and automatisation of production processes. This is then reflected in growing job polarisation and a divide between the low-skilled and the high-skilled labour force. Low-skilled and non-routine manual jobs decline, but the demand for high-skilled professionals who can do non-routine cognitive jobs grows. Technological progress drives the demand for new types of jobs and skills, which allows the

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potential of ICT to be exploited fully. In that context, reorientation towards a service-based economy seems to be one of the manifestations of the growing importance of technologies offered by the Digital Revolution. Referring back to the evidence in Fig. 5.4, conversely to what was observed with respect to the service sector’s contribution to gross value addition and the share of labour force working in the service sector, the statistical relationships revealed between mobile-cellular telephony vs employment in the agricultural and industrial sectors are negative. Analogous observations are reported when confronting MCS vs Ind_VA and Agr_VA. In each of the countries examined, except Albania, between 1990 and 2017, the share of employment in the industrial sector showed negative average annual growth rates. The highest average annual growth rates were observed in Cyprus (2.0% per annum), Malta (2.7% per annum), Spain, and the United Kingdom (1.9% per annum for both). Average annual drops in the share of labour force working in the agricultural sector were even more radical in Germany (4.3% per annum), Estonia (5.9% per annum), the Slovak Republic (4.4% per annum), and also in many other countries (approximately 3.5% per annum). Fig. 5.6, which offers a graphical explanation of the relationships between IU and economic variables, supports our previous findings. In this case, the statistical associations between IU vs Serv_VA and IU vs Serv_empl are also positive; the correlation coefficients are 0.33 and 0.57, respectively. When considering IU vs GDP per capita and IU vs GDP per person employed, the picture that emerges shows clearly that there is a close statistical relationship between these variables. During 1990–2017, in the European countries, gross per capita input and gross input per person employed was accompanied by growing ICT usage. Obviously, economic growth was driven by many factors, but, as claimed in many studies, in that period, the growth of ICT investments, ICT per capita, and ICT-driven international trade, in addition to changing consumption patterns and consumer preferences, effectively enhanced—although not always directly—economic growth and productivity shifts (Latif et al., 2018; Niebel, 2018). Additionally, a broader deployment and usage of new technological solutions contributed to the emergence of knowledge-based and technology-based economic structures, which, by definition, are characterised by a relatively higher productivity and efficiency than are non-technology-intensive sectors (Singh, Dı´az Andrade, & Techatassanasoontorn, 2018). The positive role of ICT in boosting economic growth has been recognised not only the area of dynamically increasing investments, but also in growing role of ICT manufacturing sector that

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contributes to aggregate productivity shifts (Pradhan, Arvin, & Norman, 2015). A dynamic development and broader implementation of ICT in the various economic sectors has been linked partially to the liberalisation of national telecommunication markets, which resulted in rapidly growing competition and reduction in the prices of access to and use of ICT tools and services. On the other hand, due to the unlimited opportunities and benefits offered by ICT, economic players began to adapt new technologies rapidly into daily usage (Luo & Bu, 2016). That greater use of ICT contributed to the emergence of network effects and lowered transaction costs, which improved the overall efficiency of the economy (Arthur, 2018; Edquist, Goodridge, Haskel, Li, & Lindquist, 2018). Today, many economic organisations use ICT not only in the production process but also in the services sector, such as, inter alia, finance services, retail trade, and insurance. The overall use of capital and labour has been much improved due to ICT usage, and growths of national per capita output are one of its major manifestations. Figs. 5.5 and 5.7 provide additional evidence of the statistical relationships between MCS/IU and international trade-related variables across the European economies. In this case, the period of analysis covers our standard period between 1990 and 2017 for high technology exports (HT_exp), ICT service exports (ICT_serv_exp), communications, and computer services exports and imports (Comp_serv_exp/Comp_serv_imp), although there are significant breaks in the time series. As the available time series for ICT goods exports and imports (ICT_good_exp/ICT_good_imp) go back only to 2000, the period of analysis is significantly shorter in this case. In Section 5.2, Fig. 5.2 demonstrates changes in the average values of international trade-related variables and allows conclusions to be drawn on relatively unstable development patterns marked by multiple ups and downs in short time intervals and, above all, on significant drops in ICT goods exports and importsg observed since 2000. At first glance, the evidence summarised in Fig. 5.5 does not suggest the existence of strong and positive relationships between mobile telephony accessibility and consecutive international traderelated variables, as might be expected. The statistical relationship between MCS vs HT_exp, ICT_good_exp, and ICT_good_imp seems to be negligible, and correlation coefficients are 0.06, (0.05), and (0.11), respectively. However, when we view these results in light of the general tendencies observed across the European countries in regard to the radically falling role of ICT goods exports in total goods exports and ICT goods g

Calculated as a share of total value of services export, BoP.

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imports in total goods imports, with the fast-growing deployment of ICT, this zero relationship becomes obvious. Note that, between 2000 and 2017, the share of ICT goods exports in the total value of exports of goods fell from 10% to 5%, whereas the share of ICT goods imports in the total value of imports of goods fell from barely 12% to below 7%. Next, when we look at the high-tech goods export pattern (see Fig. 5.2), we see that, on average, this value is stable over time, despite the HT_exp pattern showing in-time instability. However, for Internet users, analogous relationships are similarly weak (see Fig. 5.7). As mentioned in Section 5.2, the falling ‘contribution’ of ICT goods exports and imports to the total value of exported and imported goods is the ostensible signal of the dynamic ‘moving to Asia’ of ICT goods production. A radically different situation is reported when considering ICT variables vs ICT_serv_exp, Comp_serv_exp, and Comp_serv_imp. In these cases, the relationship is positive; the strongest relationship is revealed for MCS/IU vs ICT_serv_exp, where the correlation coefficients are 0.37 and 0.41, respectively. This boosting of ICT services sector and its role in the international trade is driven mostly by the profound impact of the Digital Transformation of various spheres of life. The use of IT platforms and cloud solutions is growing, and, according to various sources, the demand for IoT platforms is characterised by the most dynamic shifts. Moreover, Web solutions for managing organisations, e-commerce, et alia, and platforms to manage relationships with consumers, social platforms, Data Centre services, and e-governance solutions, and many other ICT implementations effectively drive the demand for ICT services. Finally, our graphical evidence is enriched by panel regression estimates summarised in Tables 5.1 and 5.2. We define two separate panels. In the first panel (estimates are presented in Table 5.1), MCS is the treated regressor, and all the economic variables examined above are considered as regressands. In the second panel (estimates are presented in Table 5.2), IU is defined as a regressor, and all the economic variables examined above are dependent variables. Both panels are balanced strongly. By convention, the empirical sample covers 32 European countries, and the time span of analysis is set as 1990–2017. Bearing in mind the results presented graphically, uncovering the statistical association between the two core ICT variables and economic variables, we expect qualitatively analogous outcomes from the estimated panels. Panel analysis results summarised in Table 5.1 demonstrate how strongly the growing deployment of mobile-cellular telephony impacted the macroeconomic performance of the European countries. However, Table 5.2 shows analogous results in examining the impact

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of increasing usage of the Internet by the members of the society. In general, all estimates confirm the type of ‘linkages’ between variables previously reported in Figs. 5.4–5.7. Even more interesting is the observation that, as reported in Table 5.1 and 5.2, the estimated coefficients that MCS and IU variables hold are barely the same for the respective panel modelsh; for instance, when estimating the impact of ICT on GDP per capita, MCS holds the coefficient 0.08, whereas IU holds the coefficient (0.07). When looking at the results of consecutive estimated coefficients standing by MCS and IU, we find similar analogies. The latter suggest that the potential impact of both MCS and IU on the examined economic variables is equally strong (or weak). When concentrating exclusively on the impact of Internet penetration rates on the economic performance, we see that growing Internet usage demonstrates, relatively, the strongest positive associations with ICT_serv_exp; in this case, the estimated coefficient is 0.23. Hence, the growth of IU at 1% may potentially enhance the growth of ICT_serv_exp at 0.23%. Tracing the potential impact of IU on remaining trade-related variables, we observe that, in the case of high-technology manufacturing exports, ICT goods exports, and computers services exports and imports, the estimated coefficients are close to zero, although still statistically significant. That might suggest that no clearly traceable and direct relationships between these variables may be identified. The coefficients that IU holds when estimating its impact on ICT goods import are statistically significant and negative. Its estimated value would suggest that a 1% IU increase generates a drop of 0.15% in ICT_goods_imp. Clearly, this result should not be interpreted in a straightforward way. In fact, drawing the conclusion that a shift in Internet usage in a country determines falling imports of ICT goods is at odds with general logic. Observed across the European economies, a drop in ICT_goods_imp expressed as a share of the total value of imported goods does not show a diminishing role of international trade in ICT goods. This ‘strange’ effect is rather associated with the changing structure of goods imported into the European countries. According to UNCTAD (2017) data, during the last three decades, international trade flows in CT goods have grown dramatically, driven by multiple factors, the most important of which seem to be the WTO Information Technology Agreement, boosting bilateral trade arrangements, dynamic technological changes, consumer demand for technological novelties (both tools and services), liberalisation of national telecommunication h

Note that MCS and IU are highly correlated. The calculated correlation coefficient is 0.92.

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markets, and the emergence of new technology-led business models. However, as noted in UNCTAD (2017) ‘for the first time since 2009, global imports of ICT goods declined in 2015 by 3.6 per cent in current prices, to just over $2 trillion. Most of this decline was due to lower imports from developed economies in Asia and Europe, which fell by 11 per cent and 7 per cent, respectively, and also to the decline in imports of computers and peripherals as well as consumer electronic equipment’ (p. 49). Despite the fact that a huge share of the trade in ICT goods, including finished and intermediate goods, is hosted between Europe, Asia, and the United States, the UNCTAD (2017) data reveal that, in 2015, developing Asian countries in which huge manufacturing facilities are located account for nearly 50% of the global ICT goods imports, and China alone accounts for 20% of the latter. Our estimates also show significant and relatively strong negative associations for IU vs valued addition created in the agricultural sector (Agric_VA)—the estimated coefficient is (0.14)—and for the share of the labour force employed in agricultural activities (Agric_empl)—estimated coefficient is (0.11). These results support the evidence revealed previously in this section. A direct interpretation of the estimated coefficients would show that, for instance, a 1% growth of IU induces a drop of 0.14% in valued addition created in the agricultural sector. However, it is rather obvious that ICT itself does not drive a decrease in valued addition in the agricultural sector as such, but enhances economic activities in other sectors. Henceforth, this impact should be treated as indirect. Technological advances that allow for the emergence of new types of products and services, professional skill development, firm reorientations, and many other things effectively generate structural shifts that are seen throughout the economy. Labour force and financial capital move to dynamically developing sectors, offering new opportunities and bringing benefits and profits. In that sense, technological progress, and, here, ICT-driven changes in particular, have far-reaching implications, for, inter alia, computerisation of jobs, automatisation of production, and the emergence of new types of plants where all the work is done by robots. From the longer-term perspective, these changes are transmitted into structural shifts; the labour force moves from low-technology to technology-intensive sectors, and manufacturing and services in which production is strictly ICT-dependent play a growing and pivotal role in the economic systems. In the long term, these shifts are converted into economic welfare.

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5.4 Social development patterns: Paving the road ahead Many countries face the problem of relatively poor participation of women in the formal market economy, i.e. in the job market and in entrepreneurial activities. The low rate of female economic activity may be identified as one of the negative effects of difficult access to the education system, poorly developed professional skills, and high illiteracy. It happens that women are massively deprived of access to financial systems, and they have no permanent income from contracted work. In rural areas and/or less developed European economies, inhabited by more traditional societies, the existing social, religious norms, and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Even in high-income and well-developed countries, where female population seems to be treated ‘equally’ to men in terms of free access to labour market, the basic national statistics speak in support of the supposition on the existence of a relatively huge gender wage gap. Undeniably, women belonging to traditional societies face deprivation from unrestricted access to the labour market and, thus, constitute an unused labour force, which impedes economic growth and development. On the other hand, female population is often engaged in informal home-based businesses, occupying traditional activities. These, however, require less seed capital and professional experience, yielding lower returns and benefits. Women without access to the formal labour market tend to run home-based businesses in traditional and sometimes in informal sectors, characterised by low effective demand, low profits, and high exposure to risks and external shocks. They are highly vulnerable inside workers, suffering from permanent material and institutional exclusion (Klasen et al., 2015). Across the countries, we observe a gradually increasing number of women running their own businesses; however, it should be noted that in many economies the main enhancement for women to set up a new business is necessity-, and not opportunity, driven. Facing the absence of alternative ways of supplementing household income, entrepreneurship or self-employment is the only viable option. Across less advanced regions, restricted access to the technology and difficulty in financial market participation (financial exclusion) are large barriers for women seeking to escape vulnerable, low-paid, and indecent employment (Benerı´a, Berik, & Floro, 2015). Still, pretty often, women’s labour and entrepreneurial activities are the ‘untapped resource’, and ICTs, if used properly, can unlock the potential of female population, mainly by making it easier to overcome

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various gender-specific constraints on entrepreneurial behaviour. A broader deployment of ICT may have either direct or indirect effects on social development by mobilising resources and reinforcing market activities. ICT may foster the mobilisation of savings and offer opportunities to convert these savings into investment; ICT may facilitate a greater mobilisation of the labour force. All these have far-reaching consequences. Increasing active engagement in the formal labour markets constitutes a solid base for earning a regular income, which relieves people living in material deprivation out of the subsistence economy. Having a regular salaried work effectively reduces vulnerability to risks and external shocks, which bring a danger of falling into poverty. A greater engagement in labour markets, both through salaried employment and through small business start-ups, produces economic gains and wealth in the long-run perspective. A higher participation in the labour force is very likely to be the first, most important step for developing countries to exploit the full potential of ICT. Additionally, a broader usage of ICT allows timely access to various types of information, helping to combat one of the fundamental impediments to the effective functioning of the market, that is, information asymmetries. A growing labour force participation and the eradication of multiple barriers on information access shall drive an increase in the number of market transactions, boost the presence in global markets, and reduce transaction costs. Indirectly, unbounded access to the ICT drives socio-economic development through a better access to education and knowledge, more effective functioning of healthcare systems, and many other ways where new technologies may support the functioning of different organisations and mechanisms. That the positive effects of ICTs on, inter alia, education and healthcare systems are qualitative in nature will be unveiled in the long-time horizon, but surely the positive gains emerge progressively in the form of social and economic advance. Capturing the effects of the implementation of new ICTs on social development in numbers remains a very challenging task. This is not only because the availability of long and complete time series in this case is limited but also, and above all, because social development is an extremely complex process, preconditioned by multiple unquantifiable elements, such as culture, religion, social norms and attitudes, tradition, and history. The process of social development is characterised by long-time inertia. Changes are usually slow, and only detectable over the long term. In what follows, we present empirical evidence intended to reveal, at least partially, the relationships between growing ICT deployment and social development. By convention, we concentrate on the 32 European

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economies between 1990 and 2017. ICT development is represented by the two core indicators: mobile-cellular telephony and IU. As for the social development, we concentrate on select elements of socially related economic aspects, which we suppose may be, at least to some extent, affected by the Digital Revolution; we intended to use those indicators, by which changes potentially may be driven by technological progress. In particular, we concentrate on female-related indicators,i as these aspects are often raised in the context of the ‘Opportunity Windows’ that ICT may bring to the overall socio-economic development. Figs. 5.9 and 5.10 present graphical evidence of the statistical relationships of MCS vs social variables and IU vs social variables, respectively. Examining the relationships between consecutive pairs of variables suggests a relatively strong statistical association between ICT and school enrolment (tertiary, as a share of gross). Clearly, the growth of both MCS and IU is accompanied by a fast shift in school enrolments across the European economies. The basic statistics on tertiary school enrolment show rapid changes in this respect that are easily observable since 1990. In some of the countries analysed, the average annual growth rate was extraordinarily high; for instance, Albania (7.6% per annum), Cyprus (7.3% per annum), and Greece and Romania, with analogously high dynamics. The correlation coefficients for MCS vs School and IU vs School are 0.76 and 0.73, respectively. Of course, it is hard to agree that the impact of ICT diffusion on tertiary school enrolment is direct. It should be borne in mind that a high correlation in this respect may be spurious. Growing tertiary school enrolment is, above all, an effect of long-term state education policies. However, it is needless to explain that a positive impact of increasing tertiary school enrolment may, although again indirectly, positively influence female labour market participation. Next, the two analysed indicators are LF_female and LF_female_15_24. In-time dynamics of female labour force participation rates (a share of total labour force) are comparably slow. Significant inertia and time lags characterise the process of changing (herein, growing) women’s share in the total labour force. Across the countries examined, the highest average annual dynamics were observed in Malta (1.2% per annum; a change from 28% to 38%) and Spain (1.05% per annum; a change from 34% to 46%). In the remaining economies, these changes were significantly slower (e.g. Hungary and Slovenia—0.02% per annum), and, in some cases, even negative (Bulgaria, the Czech Republic, Moldova, Poland, Romania, the Slovak i

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Fig. 5.9 MCS vs social variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on x-axis—MCS; on y-axis— variables expressed as a share of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.

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Fig. 5.10 IU vs social variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on x-axis—IU; on y-axis—variables expressed as a share of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.

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Republic, and Sweden). Still, as demonstrated in Figs. 5.9 and 5.10, statistical associations between MCS/IU and LF_female are relatively strong; the correlation coefficients are (0.26) and (0.33), respectively. The situation is different when the LF_female_15_24 indicator is considered; the correlation coefficients for MCS vs LF_female_15_24 and IU vs LF_female_15_24 are 0.11 and 0.13, respectively. Initially, we would expect that both growing ICT deployment and increasing scholarisation rates would contribute effectively to dynamically reduce the engagement of young women (between 15 and 24 years) in labour markets. However, when looking at preliminary descriptive evidence summarised in Section 5.2, the time trend (see Fig. 5.3) for averaged values shows a massive drop in LF_female_15_24 by more than 10%. This evidence suggests that, in the European countries, between 1990 and 2017, a significant share of women, instead of entering the labour market between the ages of 15 and 24, stayed in the education system, at university (tertiary) level. This structural break plays a crucial role in the society; well-educated people may foster innovation, which increases economic development and growth over the long term, contributing to the overall welfare of societies. The other side of the story is that, as predicted by many, in the forthcoming years, there will obviously be a significant growth in demand for highly skilled labour forces. Technological progress, especially a broader adoption and usage of digital technologies, generates changes in labour markets—jobs are increasingly flexible and complex. People working in knowledge- and technology-intensive sectors must be able to deal with complex and fast-changing information and provide technological solutions; they must become ‘autonomous and smart workers’. Evidently, technological progress effectively reshapes economies and labour markets, but it also drives the demand for skilled and specialised workers. All these elements, mentioned briefly above, induce people to continue in education instead of joining the labour market. That change is especially visible with respect to female engagement in labour market activities. The behaviour patterns of female labour markets have changed significantly with higher educational attainments and better access to the education system. Faced with a rapid ICT development, labour market pressure for highly skilled workers who can exploit the potential offered by new technologies, and social policies directed towards a higher educational attainment, there have been massive drops in the rates of participation by those aged 15–24 in the labour force. Next, we considered changes in contributing family workers (total/ female), vulnerable employment (total/female), and waged and salaried workers (total/female). We expected that, with increasing ICT deployment and usage, the first two variables might fall, whereas the share in total

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employment of waged and salaried workers might rise. Fig. 5.3 in Section 5.2 reveals a relatively significant drop in all the indicators cited above; however, it is important to emphasise that falls in Family_female and Vulner_female are more radical than are changes referred to the total population. Analogously, increases in upward trends in Wage_female are greater than those observed for Wage_tot. On inspecting the graphical evidence in Fig. 5.9, it may be noted that IU is negatively correlated both with contributing family workers (total/female) and with vulnerable employment (total/female). The correlation coefficients for consecutive pairs of variables are slightly higher for female-related variables. Apparently, there is one outlying country for which both contributing family workers and vulnerable employment shares in total are significantly higher; on the other hand, waged and salaried workers make up a very low share of total employment. This country is Albania, where, for instance, vulnerable female employment increased from 48% in 1990 to 57% in 2017, reaching a peak of 78% in 1999. In the remaining countries in the group, these variables were significantly lower during the whole period analysed. If we exclude Albania from the sample, the correlation coefficients for IU vs Family_female and IU vs Vulner_female are (0.42) and (0.41), respectively. Apparently, drops in contributing family workers and female vulnerable employment are a direct consequence of women moving towards waged and salaried work. These conclusions are supported by panel regression results summarised in Tables 5.3 and 5.4. Similar to the empirical evidence provided in Table 5.3 MCS vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 MCS Rho R2 (within) F (prob > F) No. of obs.

MCS Rho R2 (within) F (prob > F) No. of obs.

School

LF_female

LF_female_15_24 Family_female Family_tot

0.14 [0.00] 0.61 0.74 2228 [0.00] 793

0.008 [0.00] 0.83 0.21 213 [0.00] 865

20.04 [0.00] 0.89 0.38 514 [0.00] 865

20.18 [0.01] 0.81 0.27 304 [0.00] 845

20.15 [0.00] 0.83 0.28 327 [0.00] 846

Vulner_female

Vulner_tot

Wage_female

Wage_tot

20.04 [0.00] 0.91 0.16 154 [0.00] 846

20.02 [0.00] 0.89 0.08 73.9 [0.00] 846

0.01 [0.00] 0.94 0.22 235 [0.00] 846

0.008 [0.00] 0.95 0.16 164 [0.00] 846

Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.

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Table 5.4 IU vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 IU Rho R2 (within) F (prob > F) No. of obs.

IU Rho R2 (within) F (prob > F) No. of obs.

School

LF_female

LF_female_15_24 Family_female Family_tot

0.13 [0.00] 0.63 0.74 2189 [0.00] 771

0.009 [0.00] 0.84 0.28 322 [0.00] 837

20.03 [0.00] 0.89 0.33 401 [0.00] 837

20.21 [0.00] 0.83 0.36 443 [0.00] 821

20.18 [0.00] 0.86 0.37 468 [0.00] 824

Vulner_female

Vulner_tot

Wage_female

Wage_tot

20.06 [0.00] 0.91 0.27 292 [0.00] 824

20.03 [0.00] 0.90 0.17 171 [0.00] 824

0.01 [0.00] 0.95 0.31 358 [0.00] 824

0.01 [0.00] 0.96 0.26 288 [0.00] 824

Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.

Section 5.3, in this case, the estimated parameters held by MCS and IU are very close for the respective model specifications. For instance, when estimating the impact of ICT on school enrolment, the parameters standing by MCS and IU are (0.14) and (0.13), respectively. Apparently, all the estimated parameters are statistically significant and hold the expected sign. As might be expected, the highest estimated parameters are demonstrated when examining the impact of ICT on contributing family workers: both total (Family_tot) and female (Family_female). The highest parameter (0.21) is held by IU when estimating its impact on changes in Family_female. The potential impact of IU on the size of vulnerable employment is significantly weaker; however, it is still significant. The International Labour Organisation defines vulnerable employment as ‘the sum of the employment status groups of own account workers and contributing family workers. They are less likely to have formal work arrangements, and are therefore more likely to lack decent working conditions, adequate social security (…). Vulnerable employment is often characterised by inadequate earnings, low productivity and difficult conditions of work that undermine workers’ fundamental rights’ (ILO, 2010). Obviously, the problem of vulnerable employment, and a high share of the labour force being ‘employed’ as contributing family workers, is not evenly distributed across the European countries and regions. In high-income, well-developed economies, the problem is marginal. However, in more backward regions, both in terms of overall economics and of social and institutional development,

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the problem does exist. The latter is closely associated with a lack of adequate labour market legal frameworks, established social habits and norms, poor education, and—in pure economic terms—a lack of opportunities to find a regular job. Regions suffering from a massive vulnerable employment usually are those where the shadow economy is extensive, and a significant share of the national output is being produced outside of the formal economy. The problem of female vulnerable employment is urgent, as national statistics tend to be significantly higher for women than men in this case. This is the case not only for Albania but also for Romania, Moldova, Greece, and Italy. ICTs as enabling technologies may play a pivotal role in women’s economic empowerment, mainly by opening the ‘opportunity windows’ to different forms of economic and social activities. The ongoing digital revolution will, inevitably, have a positive influence on gender equality. This may happen, potentially, in two different ways. First, technological change, and the economic structural changes that it brings, radically changes the composition of jobs and the skills that are required to perform those jobs. Enabled by the digital technologies, the growing work automation may potentially enhance women’s labour market inclusion by restructuring the demand for typical women’s jobs differently from that for typical men’s jobs. What is observed across branches is that, on the one hand, robots and algorithms simply substitute for different jobs, but, on the other hand, they complement jobs in occupations such as management, research, engineering, legal services, and many others. These shifts bring new opportunities not only to women, of course, but also to the whole labour force. Next, digital technologies enable ‘home-based’ jobs in the area of trade or services, which constitutes an important alternative for women willing to leave ‘vulnerable sectors’ and be engaged in formal market activities. Digital technologies allow the elimination of multiple barriers to full-female economic activity. To a large extent, ICT offers opportunities for one to become fully active financially, entrepreneurially, and economically (Gardey, 2015; Goldin, 2006). Newly emerged services, due to the implementation of ICT solutions, may effectively help women to access new markets, work flexibly and distantly, acquire and interact with customers, receive training and provide mentoring, improve their financial autonomy, and gain access to finance for their ventures. Apparently, the Digital Revolution seems to favour the female labour force, since women face, on average, a lower risk of being replaced by machines than do men. Women’s often superior social skills represent a comparative advantage in the digital age, and this is

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particularly so when social skills are complemented with higher education and advanced digital literacy (Vazquez & Winkler, 2017).

5.5 Gaps growing—Gaps narrowing? This last section is dedicated entirely to the examination of the process of convergence among the 32 European countries between 1990 and 2017. We aim to verify the hypotheses on the existence of economic and social convergence across the countries. To this end, we use three different empirical approaches allowing us to determine whether economic and social development gaps are diminishing across the European countries (hence, the process of convergence is reported) or, vice versa, cross-country gaps are growing (hence, the process of divergence is reported). More specifically, we calculate the standard deviation (SD), the Gini coefficient (classical inequality measure), and coefficients of variation (CV) to verify the hypothesis on σ-convergence, and based on the neoclassical growth theory, we estimate regression models to verify the hypothesis on β-convergence. To enrich the whole picture, for individual variables, we draw density functions to examine changes in the distribution of variables during the period examined (see Appendix I). Figs. 5.11 and 5.12 show the process of economic σ-convergence and changes in economic cross-country inequalities, and Fig. 5.13 shows social σ-convergence and changes in cross-country social inequalities. To provide an exhaustive picture, we examine each variable separately. A brief look at the consecutive graphs in Fig. 5.10 enables several specific tendencies in cross-country inequalities to be observed. The first interesting observation is that, with respect to the gross per capita income and gross income per person employed, drops in cross-country disparities are noted; however, in both cases, drops in the Gini coefficient and the coefficients of variations are relatively weak. For GDP per capita, the Gini coefficient falls by only 0.02 (from 0.40 in 1990 to 0.38 in 2017); however, there are significant rises in the mid-1990s. Notably, despite dropping Gini coefficients and coefficients of variations, we observe massive increases in standard deviations (from approximately 10,000 in 1990 to almost 14,000 in 2017). The latter suggests that, despite the fall in cross-country inequalities in relative terms, in absolute terms, the GDP per capita gaps are growing. Analogous tendencies are detected for GDP_empl and also for Ind_VA and Ind_empl. Looking at inequality changes in regard to Serv_VA and Serv_empl, we observe that, in these cases, the drops in cross-country disparities are massive. The Serv_VA

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Fig. 5.12 Economic (trade-related) variables. σ-convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; short-dash line—coefficient of variation.

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ICT-Driven economic and financial development

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coefficient of variation fell from 0.31 to 0.10 in 2017; for Serv_empl, the fall was from 0.25 to 0.14.j In these two cases, we also note diminishing standard deviations, which may confirm our supposition of the gradual eradication of cross-country differences as all the European countries head rapidly towards service-based economies (see also graphs in Appendix I). These results are consistent with what is reported for the agricultural sector in terms of the gross value addition created there. Notably, since 1990, the role of the agricultural sector has been declining in Europe in terms of its total contribution to countries’ GDPs, and that tendency is also demonstrated in falling crosscountry inequalities in this respect. A rapid fall in the coefficients of variation and Gini coefficients for Agr_VA and Agr_empl supports this view. The European economies are becoming more and more similar in terms of the role that the agricultural sector plays in creating their national wealth. Unexpectedly, changes in LF_15_24 show growing cross-country disparities. Massive increases in all the three considered inequality measures clearly show increasing gaps in this respect, both in relative and absolute terms. Growing inequalities in LF_15_24 may be caused by significant intercountry disparities in the dynamics of change in this regard. Notably, some countries move fast ahead and fast diminish the labour force participation rate for those aged 15–24 (as a share of total), whereas others demonstrate negligible dynamics in this process. In effect, countries differ more in this respect in 2017 than was observed in 1990. Turning to a brief analysis of trade-related economic variables (see Fig. 5.12), we observe that, in the cases of three variables (HT_exp, ICT_good_exp, and ICT_imp_exp) falls in cross-country gaps are striking. The coefficients of variation for the respective variables dropped radically; from 1.3 to 0.8 for ICT_good_exp and from 0.7 to 0.4 for ICT_good_imp. A similar tendency, although decreases are less dynamic, is reported for Comp_serv_exp. For the remaining two indicators, ICT_serv_exp and Comp_serv_exp, despite an abrupt shift in 1997, declines in inequalities are observable, although less radical than in the case of other economic variables. The graphical evidence summarised in Fig. 5.13 shows changes in crosscountry social inequalities. With regard to LF_female and LF_female_15_24, general tendencies and direction of changes are clearly identifiable, however, in the cases of the remaining variables, the picture is scattered and unclear and does not allow rigid conclusions to be drawn. In the case of LF_female and j

Authors’ calculations.

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LF_female_15_24, the Gini coefficient, coefficients of variation, and standard deviations calculated for the respective variables move in the same direction; although gradual decreases in cross-country disparities are manifested for LF_female, the opposite is the case for LF_female_15_24. Our preliminary evidence on the process of economic and social convergence seems to be slightly confusing, especially in the cases of the indicators examined. On the one hand, we observe fast-dropping cross-country inequalities, expressed as the Gini coefficient, whereas, on the other hand, the coefficients of variation seem to go in the opposite direction. Hence, to re-examine the existence of the process of σ-convergence, we provide additional empirical evidence on economic and social β-convergence. Figs. 5.14–5.16 plot the average annual growth rates vs the level of variable in the initial year of analysis. By definition, the process of β-convergence is demonstrated when this relationship is negative, which shows that initially poorer countries tend to grow faster compared to initially richer countries. Inevitably, this shall lead to a gradual eradication of cross-country disparities. The graphical evidence on economic and social β-convergence is next enriched by regression estimates—see Appendix J. Figs. 5.14 and 5.15 display the scatterplot for the average annual rates of growth of the respective economic variables vs their initial level in either 1990 or the earliest year available. Both suggest unquestionably that, among the group of European countries analysed between 1990 and 2017, the process of economic β-convergence is unambiguous with regard to all the variables examined. In all the cases, the statistical relationship is negative, which initially enables confirmation of the existence of the process of convergence. According to these data, we may conclude that, among these 32 European countries, economic gaps (disparities) were diminishing gradually between 1990 and 2017. This may support our initial supposition that a rapid ICT deployment enhances and accelerates the growth of relatively more backward regions. As expected, the process of gap elimination seems to be the fastest in the cases of Serv_VA and Serv_empl. This coincides with our initial evidence, as provided at the beginning of this section. A relatively fast process of convergence is also revealed for GDP per capita, HT_exp, and all the remaining trade-related variables. This graphical evidence demonstrates evidently how dynamic changes in this respect were. Hence, the period between 1990 and 2017 is revealed not only as a period of rapid shifts in economic material wealth—see gross per capita income and growth in per employee final production—but also as a period of dynamically diminishing cross-country disparities. Obviously, these economic disparities

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Fig. 5.14 Economic β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.

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Fig. 5.15 Economic β-convergence (trade-related variables). Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.

ICT-Driven economic and financial development

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Fig. 5.16 Social β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.

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were not eradicated totally, but significantly higher average annual growth rates observed for the relatively poorer countries pave the way for a less differentiated Europe, at least in economic terms. We claim, that, despite the positive impact of a broader deployment of new technologies being neither direct nor immediate in the cases of the variables examined in Fig. 5.14, the impact of ICT is more direct and obvious in the cases of ICT trade-related indicators (see Fig. 5.15). A fast diffusion of new technologies enabled all these economies to intensify their international trade activities in this respect. The growing domestic demand for ICT tools and services, boosting the demand from firms for ICT solutions, evidently was the driving factor of export-import flows. Our graphical evidence on economic β-convergence is then enriched by panel regression estimates summarised in Appendix J. As in previous cases, we examine each variable separately. The sample composition and period of analysis are analogous, as in the analysis above. In 10 out of the 15 cases examined, the estimated regression coefficients hold the expected negative sign and are statistically significant. Both OLS and robust regression estimates returned analogous (in qualitative terms) results, with the only exception being HT_exp, which confirms the relative stability of these estimates. The highest parameters were obtained for Ind_VA (2.6), Serv_VA (2.83), ICT_good_imp (3.68), Comp_serv_exp (3.08), and Comp_serv_imp (3.45), which shows that the process of β-convergence is fastest in this regard. The estimated parameters allow the calculation of specific half-time, hence the time needed to diminish cross-country inequalities by half. The shortest half-times are 14.4 years for ICT_good_imp and 14.8 years for Comp_serv_imp. Next, there is 15.8 years for Comp_serv_exp, 16.5years for Serv_VA, and 17.3years for Ind_VA. This means that, for instance, for ICT_good_imp, only 14.4 years are needed to diminish cross-country disparities by 50%. For GDP per capita and GDP_empl these half-times are approximately 30 years. These results allow for one very important observation; between 1990 and 2017 economic disparities among the European countries have declined massively. Finally, we take a look at the process of social β-convergence. The graphical evidence in Fig. 5.16 also suggests the existence of the process of social convergence among the European economies, with reported falling inequalities in this regard. However, for some of examined indicators— FL_female_15_24, Family_tot, and Family_female—the statistical relationship between average annual growth rates and the initial value of a given variable is negative, although not that ‘impressive’, as for School, FL_female,

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or Vulnet_tot. The table in Appendix J summarises regression estimates from which we may deduce more precisely the speed of the process of social βconvergence. Considering OLS estimates, in only four out of nine cases, the parameters of estimates are negative (as expected) and statistically significant. These cases are School, LF_female, Vulner_tot, and Vulner_female. The highest parameters are for School (2.91) and LF_female (2.67), which automatically implies the shortest half-times—16.2 and 17.1 years, respectively. However, these results are not surprising. Profound changes in the European education system encouraged more girls to stay at school, which, on the one hand, increased the tertiary enrolment rate, but, on the other hand, inspired women to get more actively involved in labour market activities. For the remaining social indicators, the process of social β-convergence was not confirmed by OLS estimates. Although the returned parameters were negative (with the only exception being LF_female_15_24), they were statistically insignificant. When we ran robust regressions, all parameters of the estimates, except for LF_female_15_24, were statistically significant again, which suggests that the examination of the process of social βconvergence does not give unambiguous and robust results. However, it should be noted that the process of declining cross-country disparities in the level of social development is, by its nature, much slower than is the process of economic convergence. Social convergence requires, to a large extent, changes in social norms, attitudes, and expectations. It is also preconditioned by economic shifts, legal regulations, and state policies. Social changes are always slow and characterised by high in-time inertia. The primary goal of this chapter was to identify whether the process of ICT diffusion and deployment of new technologies enhances two important processes across the European countries. We aimed to determine whether ICT may be claimed as the driving force of socio-economic development and whether ICT changes are accompanied by declining cross-country disparities in respect of social and economic development levels. To meet these goals, we selected a bundle of social and economic indicators that approximate, at least to some extent, the overall socio-economic welfare. Our general findings provide support for the hypothesis that the growing deployment of ICT is associated positively with the process of growing socio-economic development and also contributes to various structural shifts and declining inter-country inequalities. It seems likely that this ICT contribution is either direct or immediate. Nevertheless, our empirical evidence detects the statistical relationships exclusively, and one must remember that these correlations may be simply spurious. A strict identification of the ICT

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impact channels is a challenging task, and the processes of social and economic development and their determinants are hard to capture in numbers. No equation can show fully and profoundly the complexity and multidimensionality of the impact of ICT on the economic sphere of life. The causality between ICT diffusion and socio-economic development seems to be obvious but hard to quantify. Finally, countries have been carrying out rapid ICT deployment only since 1990; hence, there is limited availability of time series to facilitate a more profound analysis of the relationships between ICT and socio-economic development. This is a serious limitation that our results may lack robustness; severe time lags may emerge between the root causes, i.e. ICT, and the outcomes, i.e. leveraging the overall socioeconomic welfare.

References Arthur, W. B. (2018). Self-reinforcing mechanisms in economics. In The economy as an evolving complex system (pp. 9–31): CRC Press. Avgerou, C. (2003). The link between ICT and economic growth in the discourse of development. In Organizational information systems in the context of globalization (pp. 373–386). Boston, MA: Springer. Benerı´a, L., Berik, G., & Floro, M. (2015). Gender, development and globalization: Economics as if all people mattered. Routledge. Bhandari, A. (2019). Gender inequality in mobile technology access: The role of economic and social development. Information, Communication & Society, 1–17. Boden, M., & Miles, I. (Eds.), (2000). Services and the knowledge-based economy: Psychology Press. Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66–77. Cortes, E. A., & Navarro, J. L. A. (2011). Do ICT influence economic growth and human development in European Union countries? International Advances in Economic Research, 17(1), 28–44. Cruz-Jesus, F., Oliveira, T., Bacao, F., & Irani, Z. (2017). Assessing the pattern between economic and digital development of countries. Information Systems Frontiers, 19(4), 835–854. David, P. A. (1989). The dynamo and the computer: An historical perspective on the modern productivity paradox. The American Economic Review, 80(2), 355–361. Edquist, H., Goodridge, P., Haskel, J., Li, X., & Lindquist, E. (2018). How important are mobile broadband networks for the global economic development? Information Economics and Policy, 45, 16–29. Gardey, D. (2015). Gender-technology relations in the various ages of information societies. In Connecting women (pp. 157–169). Cham: Springer. Goldin, C. (2006). The quiet revolution that transformed women’s employment, education, and family. The American Economic Review, 96(2), 1–21. Haftu, G. G. (2019). Information communications technology and economic growth in SubSaharan Africa: A panel data approach. Telecommunications Policy, 43(1), 88–99. ILOGlobal employment trends, January 2010, 2010, International Labour Office, Geneva. Ito, T., & Vezina, P. L. (2016). Production fragmentation, upstreamness, and value added: Evidence from Factory Asia 1990–2005. Journal of the Japanese and International Economies, 42, 1–9.

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Kam, A. (2017). Dynamics of trade in value-added in ‘Factory Asia’. Journal of Contemporary Asia, 47(5), 704–727. Klasen, S. (2018). What explains uneven female labor force participation levels and trends in developing countries? (No. 246). Courant research centre: Poverty, equity and growthdiscussion papers. Klasen, S., Lechtenfeld, T., & Povel, F. (2015). A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam. World Development, 71, 36–53. Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., & Jianqiu, Z. (2018). The dynamics of ICT, foreign direct investment, globalization and economic growth: Panel estimation robust to heterogeneity and cross-sectional dependence. Telematics and Informatics, 35 (2), 318–328. Lindio-McGovern, L., & Wallimann, I. (2016). Globalization and third world women: Exploitation, coping and resistance. Routledge. Luo, Y., & Bu, J. (2016). How valuable is information and communication technology? A study of emerging economy enterprises. Journal of World Business, 51(2), 200–211. Niebel, T. (2018). ICT and economic growth–comparing developing, emerging and developed countries. World Development, 104, 197–211. Ortiz Rodrı´guez, J., & Pillai, V. K. (2019). Advancing support for gender equality among women in Mexico: Significance of labor force participation. International Social Work, 62(1), 172–184. Pradhan, R. P., Arvin, M. B., & Norman, N. R. (2015). The dynamics of information and communications technologies infrastructure, economic growth, and financial development: Evidence from Asian countries. Technology in Society, 42, 135–149. Sachs, C. E. (2018). Gendered fields: Rural women, agriculture, and environment. Routledge. Sein, M. K., & Harindranath, G. (2004). Conceptualizing the ICT artifact: Toward understanding the role of ICT in national development. The Information Society, 20(1), 15–24. Singh, H., Dı´az Andrade, A., & Techatassanasoontorn, A. A. (2018). The practice of ICTenabled development. Information Technology for Development, 24(1), 37–62. Stanley, T. D., Doucouliagos, H., & Steel, P. (2018). Does ICT generate economic growth? A meta-regression analysis. Journal of Economic Surveys, 32(3), 705–726. UNCTAD. (2017). Information economy report 2017. Digitalization, trade and development. Switzerland: United Nations/UNCTAD. Vazquez, E., & Winkler, H. (2017). How is the internet changing labor market arrangements? Evidence from telecommunications reforms in Europe. The World Bank. Willcocks, L. P., & Lester, S. (1999). Beyond the IT productivity paradox. John Wiley & Sons Inc.

Further reading Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., et al. (2017). Women’s empowerment in action: Evidence from a randomized control trial in Africa. World Bank.

CHAPTER SIX

ICT for financial development: Shaping the new landscape Contents 6.1 Introductory 6.2 ICT and financial development 6.3 Financial market evolution trajectories 6.4 How ICT impacts on financial markets 6.5 ICT and financial innovations References Further reading

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6.1 Introductory This chapter presents the results of our analysis of the role of information and communication technology (ICT) in various aspects of financial development in the European countries belonging to the Organisation for Economic Co-operation and Development (OECD). We begin with a few introductory remarks, including an overview of the chapter’s structure, the dataset, and our research methods. Some supplementary technical comments are also offered. In the next section we focus on the impact of ICT on the financial system at large and on selected segments of it. We present both graphical evidence and estimates of the panel models with regard to financial development, the banking sector, the insurance industry, and, from a broader perspective, the gross savings rate. The third section of the chapter considers the evolution of financial markets in the countries examined—we discuss the graphical evidence on the changes that have occurred, above all, in stock markets and briefly analyse selected indicators for bond markets. The fourth section analyses the influence of ICT on the financial markets, drawing conclusions based both on local polynomial regressions and on panel models. The fifth and final section shows the results relating to the linkages between ICT and financial innovations, in the particular case of exchange-traded funds (ETFs). As a supplement to the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00006-3

© 2019 Elsevier Inc. All rights reserved.

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study of ETFs we discuss the significance of ICT for mutual funds, a type of investment fund that may be regarded as the main, and much better established, alternative to ETFs. The variables used in our analysis are presented in Table 6.1, in the approximate order in which they are referenced in the discussion. They can be divided broadly into two groups—first, our two ICT indicators (FBS and IU); and second, all the remaining variables that can be labelled as ‘financial’, i.e. associated in some way with financial development. The latter group comprises three partly overlapping sub-categories (for the explanation of the abbreviations, see Table 6.1): general indicators of financial development (FD, FI, FM, BD_GDP, Ins_GDP, and SAV_GDP), discussed in Section 6.2; financial market indicators (FM, FMD, FMA, FME, SMC_GDP, SMTV_GDP, SPV, ODP_GDP, and ODPub_GDP), discussed in Sections 6.3 and 6.4; and two measures linked to the financial innovations analysed (ETF_GDP and MFA_GDP), discussed in Section 6.5. The base time period covered is 1990–2016, and the data are annual (both the choice of period and the frequency of data depended on the availability of data, which was insufficient for a longer period or for quarterly or monthly frequency); we do not extend our study to 2017, as not all of the key indicators for that year were available at the time of analysis. Further, for some variables the period is severely shortened in relation to the base period, as the required data were not available. This is the case, for instance, of the assets of ETFs, in that the first such funds were launched in Europe in the early 2000s, and of data on electronic payments (consistent data are available only in some years since 2011). Moreover, ETFs are traded only in some countries—the others are omitted not for lack of data but simply because the ETF market is non-existent (or because of problems in correct attribution of the funds, as discussed below). For the full list of countries, see Table 4.1 in Section 4.2. Luxembourg was excluded as an outlier among European members of the OECD given its unique position as a regional financial hub—a small country with an oversized financial system whose development depended mainly on international factors rather than local adoption of ICT. There are three main data sources for this chapter: World Telecommunication/ICT Indicators (for the two indicators of ICT adoption), the IMF Financial Development Index Database (for the indexes of financial development), and the Global Financial Development Database (from which a number of variables were extracted). The remaining databases used are OECD Insurance Statistics, World Development Indicators, and Thomson Reuter’s Lipper. Note that for the purposes of data consistency we used only data from the international sources with broad geographical and time coverage and with common

Abbreviations

Name of variable

Units

Source(s) of data

Supplementary comments

Category

FBS

Fixed broadband subscriptions

Per 100 inhabitants

–

ICT

IU

Internet users

Per 100 inhabitants

–

ICT

FD

Financial development

Index

World Telecommunication/ICT Indicators World Telecommunication/ ICT Indicators IMF Financial Development Index Database

General

FI

Development of Index financial institutions

IMF Financial Development Index Database

FM

Development of financial markets

Index

IMF Financial Development Index Database

BD_GDP

Bank deposits

% of GDP

Ins_GDP

Total insurance spending Gross savings

% of GDP

Global Financial Development Database OECD Insurance Statistics

Index normalised with values ranging between 0 and 1 (higher values, higher level of financial development); based on the values of sub-indexes FI and FM Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes Index normalised with values ranging between 0 and 1 (higher values, higher level of development); based on the values of three sub-indexes: FMD, FMA, and FME – Value of direct gross premiums

General

Sav_GDP

% of GDP

World Development Indicators

General

General/financial markets

ICT for financial development: Shaping the new landscape

Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development

General

Difference between disposable income and General consumption (indicator known as ‘gross domestic savings’ in the pre-2006 editions of the World Development Indicators)

195

Continued

Abbreviations

Name of variable

Units

Source(s) of data

Supplementary comments

Category

Electr_paym

Electronic payments

% of respondents aged 15+

Global Financial Development Database

General

FMD

Financial market depth Index

IMF Financial Development Index Database

FMA

Financial markets access Index

IMF Financial Development Index Database

FME

Financial market efficiency

IMF Financial Development Index Database

SMC_GDP

Stock market % of GDP capitalisation Stock market total value % of GDP traded Stock price volatility Index

Global Financial Development Database Global Financial Development Database Global Financial Development Database

Electronic payments (made automatically, including online payments) in the past 12 months Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) Index normalised with values ranging between 0 and 1 (higher values, higher level of development) –

Financial markets

–

Financial markets

Outstanding domestic % of GDP private debt securities Outstanding domestic % of GDP public debt securities

Global Financial Development Database

SPV

ODP_GDP

ODPub_GDP

Global Financial Development Database

Financial markets

Financial markets

Financial markets

Average of 360-day volatility of the Financial markets national stock market index (calculated by Bloomberg) – Financial markets –

Financial markets

ICT-Driven Economic and Financial Development

SMTV_GDP

Index

196

Table 6.1 List of variables in the analysis of implications of ICT adoption for financial development—cont’d

Assets of primary-listed % of GDP ETFs

Thomson Reuter’s Lipper

MFA_GDP

Assets of mutual funds

Global Financial Development Database

% of GDP

Sum of the total net assets of ETFs primary- Financial listed in the country. Country of ETF’s innovations primary listing is defined, as in Deutsche Bank (2017), as the location of fund’s primary exchange. This is usually that of first listing and also of the highest turnover in the funds’ shares. Lipper’s classification is used For some countries the values were revised Financial (assets of ETFs were removed in order to innovations ensure no overlapping with ETF_GDP)

Note: For information on methodology and data sources of the databases, see their documentation. The ‘categories’ in the last column are mostly technical and serve to ensure clarity of the analysis.

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ETF_GDP

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methodology; we avoided national sources, as they could lead to errors owing to varying methodology or currency conversion. Another problematic issue was missing observations. Nonetheless, for the variables examined most thoroughly, data were available for the full time period—the breaks cited are for particular countries in some specific periods and do not undermine robust analysis (we explain these issues within either our country-specific or panel data analysis by indicating the data breaks). The most complete datasets were for the ICT variables and the indexes of financial development; the least complete for Electr_paym (only data for 2011 and 2014 are available, and in 2014 for some countries only; there are no data for Iceland), ETF_GDP, and the debt securities indicators (ODP_GDP and ODPub_GDP); for the remaining variables, the problem of missing data is not severe. Another observation bears on the impact of the sizes of economies—our dataset covers such enormously different countries and economies as Estonia or Lithuania, on the one hand, and France or Germany, on the other. To mitigate the effect of size differences (and facilitate between-country comparisons), our variables are expressed as percent of GDP (in all cases the conversions were already made in the original databases, so no further calculations were necessary); obviously, this does not apply to indexes or other similar variables for which such conversions were not essential. One more very specific stipulation concerns the variable ETF_GDP. Given the structure of the European stock exchanges, which in some cases operate in multiple countries or consist of more than one country segment, accurate attribution of certain ETFs to a particular country is sometimes very difficult or impossible. We accordingly decided to consider only the countries for which data on ETF_GDP can be estimated with sufficient reliability. Consequently, even though there are some ETFs classified as primary-listed in Finland, Iceland, Latvia, and Lithuania, these countries were excluded in the analysis of ETF_GDP; in any event, given the extremely low ratio of local ETF assets to GDP in these countries, this decision may be regarded as practically negligible as regards potential distortion of our results. The same applies to Belgium, the Netherlands, and Portugal (which together with France form the Euronext exchange). In order not to omit France, with one of the largest European ETF markets, we assigned all the ETFs listed on Euronext to the French market (which accounts for over 95% of the assets of ETFs primary-listed on Euronext). Finally, due to the organisational structure of the Italian and UK stock exchanges, which are both part of the London Stock Exchange Group, the exact classification of ETFs between Italy and the United Kingdom is also somewhat problematic. However, we decided to leave data on the two countries unchanged, as no sufficiently reliable method of division between the two markets could be devised.

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In our study of the links between ICT and the financial system, we utilise a few well-established research methods. First, we examine the graphical evidence on the relevant variables, in a two-part analysis. The first part focuses on the trajectories of changes at country level (i.e. their timelines) in order to identify the main trends and national differences. The second part examines the density functions in order to assess the distribution of variables, including comparisons between variables. Second, we consider the estimates of local polynomial regressions for the pairs of variables, prepared using panel data—in each case we juxtapose one of the ICT indicators to another selected variable (non-ICT, i.e. financial); this part of the analysis is preceded by a brief introductory examination of correlation coefficients. The analysis bears exclusively on the graphical evidence obtained within the local polynomial regressions, as this can be considered sufficient confirmation of the implicit direction of the causal relationship between ICT and the selected variables; it also allows for some basic comparisons of the strength of the relationships (in other words, formulating basic statements concerning the strength of the impact of ICT). Finally, we use panel models with a single explanatory variable—always one of the ICT indicators, either FBS or IU; as dependent variables we include the indicators of financial development.

6.2 ICT and financial development This section focuses on the links between ICT deployment and various aspects of financial development (apart from financial market development, which is discussed separately in the two subsequent sections). We track variables representing financial development at large, the development of the banking sector and the insurance industry, savings, and electronic payments (for details on the variables, see Section 6.1). This is the set of variables that, for clarity, we labelled as ‘general’ variables, as distinct from two other groups of variables bearing, respectively, on ‘financial markets’ and ‘financial innovations’. First we conduct an introductory graphical analysis based on timelines and density functions, examining changes over time, differences between countries, and the distribution of the variables. Next we analyse further graphical evidence, examining the graphs representing the local polynomial regressions in order to gain basic insights into the direction and strength of the effects of the diffusion of ICT on the financial development variables. Finally, we interpret the estimates of the panel models to extend and conclude the preceding stages of the analysis. The first variable in Fig. 6.1—FD—is the IMF’s most general and comprehensive indicator of financial development; FD (like FI and FM) has a

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range of 0–1, higher values indicating higher levels of development. In most European countries the level of financial development was increasing or at least broadly stable between 1990 and 2016. In some economies, however, the peak was reached around the time of the 2008 global financial crisis and the euro-area sovereign debt crisis. Declines are registered in subsequent years in such countries as Greece, Iceland, the Netherlands, Portugal, and the United Kingdom. And even in countries apparently less affected by the turmoil in the global and European financial systems, some variability is discernible—see, for instance, the brief dip in FD in Switzerland (which overall has been the most financially developed of all the countries analysed and also a global leader, for some time actually ranked first worldwide in FD). The density function (see first part of Fig. 6.2) shows that the vast majority of FD values in 1990–2016 are in the range between 0.3 and 0.8, and mostly above the midpoint of 0.5. Extremely low values were very rare, found exclusively in the post-communist countries in the early 1990s, i.e. at the beginning of their economic and political transformation. Despite

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the substantial gains in financial development in the 1990s and early 2000s (the exception being Slovakia, where FD has remained almost flat at around 0.3), these countries have remained significantly less developed financially than those of Western Europe. This is clearly represented by the two peaks in the density function curve—one for the less advanced countries, and the second, a higher peak, for the more advanced, which are relatively more frequent in our sample. However, even in the group of the ‘old’ countries of the EU there is considerable heterogeneity, with such Southern countries as Greece and Portugal lagging behind at levels of FD closer to the postcommunist countries. Interestingly, this does not apply to Spain, which since the turn of the century has been among the most finally developed in the world, at FD values close to the United Kingdom. Now let us examine the two indicators that make up FD in order to determine some of the reasons behind these disparities. We start our analysis of the components of FD with FI, the index of financial institutions (see the second part of Fig. 6.1 and the first part of Fig. 6.2). Before examining our empirical data it is necessary to refer to the index methodology (for a detailed discussion, see Sahay et al., 2015; Svirydzenka, 2016). The variables used to construct the index refer, above all, to the banking sector (only one of the three sub-indexes takes account also of pension funds, mutual funds, and insurance), so FI can be regarded as a de facto index of banking development. Accordingly we analyse it jointly with another banking variable, namely BD_GDP (which is not used in calculating FI, so these two variables are not strictly overlapping). The variability of FI over time has been rather limited in most countries—in most of the advanced European economies it has been substantially above 0.5 for the entire period. The highest levels of FI are observed in Denmark, France, Italy, Switzerland, and the United Kingdom. Clearly, some post-communist countries entered the 1990s with substantially underdeveloped financial institutions (see, e.g. the very low levels of FI in Latvia or Lithuania). But this group is by no means homogenous, given that in some other post-communist countries, such as the Czech Republic and Hungary, levels of FI were relatively high already in 1990 and have stayed broadly constant. The FI density function shows that values of FI have been slightly less scattered than those of FM and mostly higher than 0.5, with a marginal share of values below 0.3. The ratio of bank deposits to GDP, BD_GDP, has remained very stable in most countries, with insignificant year-to-year changes (see the fourth part of Fig. 6.1). The few exceptions are for countries where significant variations resulted from particular events,

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such as financial crisis (see the fall in BD_GDP in Iceland after 2008) or entry into the euro area (see the increase in Greece in the early 2000s). The highest ratios of bank deposits are scored in Switzerland, which strengthened its position as European leader of the banking industry as uncertainty in the European financial and political system fuelled deposits in the traditional ‘safe harbour’ of Swiss banks. Regionally, on average BD_GDP has been markedly lower in the less developed countries. The difference in BD_GDP between the more and the less advanced European economies is greater than that in FI. This suggests that even though the post-communist countries have gained some ground on the more advanced in terms of efficiency and access to financial institutions, the disparities in the ‘depth’ of financial institutions (measured, say, by credit to the private sector or bank deposits) continue to be sizeable, as the data for countries such as the Czech Republic or Poland demonstrate. The distribution of BD_GDP (see the third part of Fig. 6.2) is relatively symmetrical at an average of about 50% of GDP; the long right tail of the distribution depends, above all, on Switzerland. The second component of FD, and the next variable we analyse, is one of the core indicators of our analysis—FM. The development of the financial markets is discussed in depth in the next section; here we only formulate some basic preliminary conclusions. The first and most striking observation suggested by the third part of Fig. 6.1 is the great variability of FM over time, much greater than that of the other core component of FD (i.e. FI). International differences are also incomparably greater than in the case of FI—we have countries with FM close to 0 over the entire period (Latvia, Lithuania, and Slovakia) and others where it averaged more than 0.7 (e.g. Switzerland). These disparities are clearly evidenced by the distribution of the variable (see the first part of Fig. 6.2), as the values of FM are spread over practically the entire spectrum, from 0.1 to 0.8 (although values greater than 0.9 were extremely rare), with no dominant value. The next variable analysed, Ins_GDP, is the ratio of total expenditure on insurance services to GDP in our sample countries. Like banking, the insurance industry is to some extent comprised within one of the sub-indexes of FI (through the somewhat similar indicators of the value of insurance premiums), but it is worth evaluating separately in order to assess the basic changes over time and international differences. For some countries analysis is hindered by lack of data—see, for example, the short timelines for Latvia, Lithuania, and Slovenia. For insurance, unlike most of the variables mentioned above, international differences are not exclusively between the post-communist countries and the other European OECD members;

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instead there is considerable heterogeneity within each group. However, the difference between the two groups is clear, as the less developed countries lag behind (see the fifth part of Fig. 6.1). For example, the values of Ins_GDP in France average 2 percentage points higher than in Germany, but in both countries they are much higher than in the post-communist countries, even those with the highest levels of insurance spending such as the Czech Republic or Poland. The graph also shows the broad stability of Ins_GDP over time, albeit with some exceptions; nonetheless in most of the sample insurance spending has been slowly increasing. Ireland and the United Kingdom are the only countries where Ins_GDP has exceeded 10% for sustained periods, owing to the strong position of their local insurance companies on a regional scale. This conclusion is confirmed by the probability function of the variable, which indicates that in the vast majority of cases values were just a few percentage points of GDP, with a mean of about 5% (see the third part of Fig. 6.2). Analysis of the graphical evidence for the savings rate, Sav_GDP, leads to some interesting conclusions (see the sixth part of Fig. 6.1). The differences between countries cannot be simply attributed to disparities in economic development or even to the state of their financial systems (see the low levels in the United Kingdom). Rather, the timelines of Sav_GDP are highly country-specific. Significantly, there are only a few countries in which gross savings clearly increased between 1990 and 2016, above all such postcommunist countries as Latvia and Lithuania (but also Sweden). The increase in Ireland was preceded by a significant decline in the aftermath of the 2008 global financial crisis. The probability function of Sav_GDP (see the third part of Fig. 6.2) shows that its distribution was rather symmetrical and most values were at approximately 20%. The analysis of Sav_GDP serves to broaden the study—it would be a definite oversimplification to assume that this variable is shaped exclusively or even predominantly by the adoption of ICT or by the factors in financial development; for more on this topic see the extensive literature on savings, including Leff (1969), Singh (1972), Venieris and Gupta (1986), Gupta (1987), Mason (1988), Collins (1991), Bailliu and Reisen (1998), Ozcan, Gunay, and Ertac (2003), us Swaleheen (2008), Yang, Zhang, and Zhou (2012), Beckmann (2013). In particular, Kolasa and Liberda (2015) identified the key factors that affect the savings rate in OECD countries. The final variable examined at this stage represents one of the potential channels of intermediation between the adoption of ICT and financial development (in particular in the banking sector)—namely, Electr_paym.

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This may be considered as supplementary here, in that while it does not directly represent any of the fundamental aspects of financial development it is nevertheless plausible that an increase in electronic payments is at once an important prerequisite for and a sign of financial development. It also applies to financial inclusion; here we focus on financial development, but the effects on financial access and inclusion should not be disregarded. For more on these topics see Section 3.3 and such publications as Donner and Tellez (2008), Maurer (2012), Klapper and Singer (2014), Slozko and Pelo (2014), Arun and Kamath (2015), and Demirguc-Kunt, Klapper, Singer, Ansar, and Hess (2018). The most serious problem here is sharply limited data availability (exclusively for 2011 and 2014). For these reasons we do not show the timelines for Electr_paym, as they are only marginally informative (at most two observations for each country). The lowest values of Electr_paym both in 2011 and 2014 were in Italy, Hungary, Poland, and Lithuania (all but Italy among the less developed economies in our sample). The highest values are in the highly developed Nordic countries of Finland, Denmark, and Sweden (in 2014 almost 100% of the respondents there reported having utilised electronic payments). Nevertheless, as the probability function shows (see second part of Fig. 6.2), electronic payments are common in Europe—in 2014 in all examined countries the value of Electr_paym exceeded 50%, and in most it was near or above 80%. After this examination of the graphical evidence, we proceed to the core matter of this section, i.e. analysis of the relationships between the diffusion of ICT and the variables representing financial development (based exclusively on the panel data rather than country-specific evidence). We start with the correlation coefficients (not presented in a separate table owing to the large number of variables) and graphical representations of the local polynomial regressions (see Figs. 6.3 and 6.4). Focusing on the estimates of the panel models, we consider the relationship of each general variable with both ICT indicators, i.e. IU and FBS. IU, which is more wide-ranging and covers various types of Internet access, is presented first. FBS, presented afterward, refers to high-speed connections via fixed broadband. The first, fundamental financial development variable we examine in relation to ICT is FD. The graphs for IU and FBS show that the direction and strength of this relationship are difficult to establish. Nevertheless, in both cases the impact of ICT appears to be mainly positive or at least neutral (the only exception is the negative correlation for the highest values of IU, while for the highest values of FBS the correlation is strongly positive). This is confirmed by the correlation coefficients, which are positive both for FD vs IU and for FD vs FBS. Interestingly, for IU the coefficient is much higher

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this issue in interpreting the estimates of the panel models to explain the reasons for this inconsistency. As observed above, Figs. 6.3 and 6.4 show that the relationship between the adoption of ICT and overall financial development is generally positive. To determine which aspects of financial development are most affected by the new technologies, we consider the two sub-indexes, FI and FM. For FI the picture is quite clear—the diffusion of ICT and the development of financial institutions are positively related, in particular in the case of fixed broadband connections. For FM (financial market development), the relationship with the ICT indicators is harder to pinpoint. For Internet broadly defined, i.e. measured with IU, for values of 0 to 40 the correlation is

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positive (and stronger for lower levels of both variables), similar to FD. This might mean that the positive effect of ICT diffusion for financial market development materialises, above all, in the less highly developed European economies (where both IU and FM are mostly lower). In the interval of IU from 40 to 70 the relationship is weakly negative, and for values higher than 70 it can be described as inverted U-shaped: first strongly positive and then substantially negative, precluding clear-cut conclusions. However, as in case of FD, for the highest values of IU the relationship with FM (as well as FI) is negative. This is explained by reference to the financial development index values (either at large or measured by the two sub-indexes) in certain countries, such as Iceland and Norway, which are among the leaders in ICT adoption but have been surpassed by others in financial development. As Fig. 6.4 shows, the relationship between FBS and FM is clearer—for the lower values of FBS it is mixed but for values above about 20 it is strictly positive. The correlation coefficients for all four pairs of variables (FI vs IU, etc.) are positive, between 0.2 and 0.4, which further confirms the main implications of the estimates of the local polynomial regressions. In short, the graphical representations of the local polynomial regressions show that when FD is decomposed into FI and FM, the positive relationship between ICT and overall financial development depends more on the development of financial institutions than of financial markets. Moreover, the graphical evidence again suggests a more definite positive impact of FBS than IU. After these indications from the general indexes of financial development, let us focus on some specific variables linked to this process. First we examine one of the indicators of banking development, i.e. the ratio of bank deposits to GDP, BD_GDP. The correlation coefficients of BD_GDP with both ICT variables are positive and greater than 0.3, implying a positive relationship between the spread of the new technologies and the growth of the banking sector. This is almost unequivocally confirmed by both local polynomial regressions, regardless of which dimension of ICT diffusion is considered (see Figs. 6.3 and 6.4). Referring again to our discussion of FD and its sub-indexes, we can add that ICT adoption may have contributed to financial development through its positive effect on bank deposits. Ins_GDP is our proxy for development of the insurance industry. The results for Ins_GDP are to some extent similar to those for BD_GDP— compare the relevant parts of Figs. 6.3 and 6.4. The correlation coefficient of BD_GDP with Ins_GDP is 0.42, which explains these results. Nevertheless, the relationship between ICT and insurance development is noticeably weaker, as is evinced by the practically almost flat lines in both figures and

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the correlation coefficients of just 0.06 (Ins_GDP vs FBS) and 0.17 (Ins_GDP vs IU). To conclude, banks would appear to be more strongly influenced by ICT diffusion than insurance companies; these relationships are we further verified and compared using panel models. In contrast with the other variables, the relationship between ICT and gross savings (Sav_GDP) is hard to determine. Despite the positive correlation coefficients (0.18 for both ICT indicators), the actual relationship is somewhat more complicated, as is indicated by Figs. 6.3 and 6.4. It can be characterised as U-shaped, especially for IU (for FBS it is mostly positive but with a negative coefficient for the lower values of FBS). So it is not easy to determine the direction and strength of the relationship between savings and increased ICT use in our sample countries. With some stipulations, we can say that the impact of ICT is neutral or weakly positive (taking into account fast Internet connections measured with FBS). The results obtained for our last general variable—Electr_paym—are consistent with expectations, given the basic attributes of this financial service. Both correlation coefficients and the results of the local polynomial regressions make it clear that the relationship between ICT diffusion and electronic payments is strongly positive; the correlation coefficients are 0.83 (Electr_paym vs IU) and 0.71 (Electr_paym vs FBS). Apparently, that is, adoption of ICT is a fundamental precondition for the increasing utilisation of electronic payments in a society. The reverse relationship is implausible, of course, but a positive feedback loop is obviously possible—growing recourse to electronic payments may encourage more people to obtain access to the Internet. This process may also be supported by government policy as a way of attenuating financial exclusion (Ouma, Odongo, & Were, 2017). In any event, the results for Electr_paym should be interpreted with caution, as they are based on a limited dataset (only 46 observations). Tables 6.2 and 6.3 convey the panel model estimates with IU and FBS, respectively, as explanatory variable. All values are in log, although for clarity we refer to base variables (i.e. we refer to ‘IU’ instead of ‘LnIU’). This also applies to Sections 6.4 and 6.5. There is one necessary general stipulation prior to this analysis. In discussing the results for the local polynomial regressions, we observed that they imply somewhat different conclusions from those implied by the correlation coefficients. These differences also obtain with respect to the estimates of the panel models. One of the most important of the various reasons for these discrepancies is that in most of the relationships the observations are clustered in a few intervals—see, for example,

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Table 6.2 IU vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP

LnIU R2 (within) No. of obs. Rho F (prob F) LnIU R2 (within) No. of obs. Rho F (prob > F)

0.07 [0.00] 0.64 644 0.91 1119.4 [0.00]

0.04 [0.00] 0.41 645 0.83 425.3 [0.00]

0.11 [0.00] 0.37 645 0.84 366.2 [0.00]

0.07 [0.00] 0.37 599 0.88 343.9 [0.00]

LnIns_GDP

LnSav_GDP

LnElectr_paym

0.08 [0.00] 0.42 567 0.91 385.1 [0.00]

0.005 [0.00] 0.00 590 0.61 1.46 [0.23]

4.99 [0.62] 0.75 46 0.88 64.8 [0.00]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.

Table 6.3 FBS vs general variables. Fixed effects regressions. 1990–2016, annual data LnFD LnFI LnFM LnBD_GDP

LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)

0.02 [0.00] 0.14 422 0.94 66.8 [0.00]

0.02 [0.00] 0.26 422 0.90 144.9 [0.00]

0.00 [0.00] 0.00 422 0.94 0.07 [0.79]

0.06 [0.00] 0.36 399 0.94 218.8 [0.00]

LnIns_GDP

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0.01 [0.00] 0.01 379 0.94 5.4 [0.02]

0.01 [0.00] 0.02 410 0.75 10.7 [0.00]

3.33 [0.39] 0.77 46 0.92 72.4 [0.00]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.

FD vs IU in Fig. 6.3—with most observations at either very low or very high levels of IU. This suggests that the inconsistencies in the relationships identified by local polynomial regressions may actually be overstated, as is demonstrated by the panel models discussed in the subsequent paragraphs. The positive impact of the diffusion of ICT on overall financial development as proxied by FD is confirmed regardless of the explanatory variable

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chosen. What is important is that the within R2 of the IU model is the third highest of all those estimated (only for the two models with Electr_paym as the dependent variable is it higher), indicating a relatively good fit. For the FBS model, however, it is much lower. Even so, both ICT indicators prove to be statistically significant. Comparing the coefficients of the two ICT variables indicates that the number of Internet users has a stronger impact on financial development than does the number of fixed broadband subscriptions (a conclusion already foreshadowed by the correlation coefficients). However, given the substantial difference in number of observations (much lower in the case of FBS, because this type of service was introduced more recently), this conclusion may be oversimplified. Considered jointly with the higher value of the within R2 in the IU model, it suggests that initial access to the Internet fosters financial development more than such further advancements as better quality of connections. This may depend partly on the still limited use of fixed broadband subscriptions in some European countries, whereas other Internet services (e.g. mobile broadband) have become widely available in all our sample economies. Our results are in line with the findings of previous studies (see Section 3.3). Estimating the models for the sub-indexes of financial development uncovers a somewhat more complicated picture than for financial development in general (FD), at least as far as financial market development is concerned. For both FI and FM the fit is generally worse than for the overall index, as is shown by the within R2. This applies in particular to the model with FBS as explanatory and FM as dependent variable—its within R2 is nearly 0 (see Table 6.3). The only exception is the FBS model for FI, where the within R2 is higher than for the FD model, suggesting that FBS may better explain the development of financial institutions in our sample countries. The coefficients of both IU and FBS are positive and statistically significant in the models with FI as the dependent variable, implying that ICT has a positive effect on the development of financial institutions. These results come as no surprise, as they are consistent with previous empirical studies (see Section 3.3). The channels whereby ICT may impact on banks are multiple, one of the foremost being the spread of mobile banking (Alalwan, Dwivedi, & Rana, 2017; Shaikh & Karjaluoto, 2015; Sharma, Govindaluri, Al-Muharrami, & Tarhini, 2017) or, more broadly, Internet banking (Szopi
nski, 2016; Takieddine & Sun, 2015). Another area of growing importance is bound up with one of the categories of fintech, namely ‘regtech’—use of ICT in the context of financial regulation and oversight (Arner, Barberis, & Buckley, 2016). Regtech lowers the cost of compliance

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with regulatory requirements; it also facilitates entry of start-ups into the highly regulated financial industry (Larsen & Gilani, 2017). And this part of the fintech sector can contribute to the stability of the financial system overall (Arner, Barberis, & Buckley, 2018; Arner, Zetzsche, Buckley, & Barberis, 2017), thus strengthening people’s trust in financial institutions and attracting them to the formal financial system. However, given its relatively brief existence the impact of regtech is still difficult to assess. The results for FM are more complicated—only the coefficient of IU is statistically significant and positive, indicating that this dimension of ICT diffusion is more important for the development of the financial markets, given that no impact, either positive or negative, was found for FBS. We discuss this issue in more detail in Section 6.4, analysing selected aspects of the relationship of ICT with FM. The positive impact of ICT penetration on the development of financial institutions is confirmed by the models using BD_GDP as the indicator of banking development. As could be expected given the correlation coefficients and the estimates of the local polynomial regressions, both IU and FBS are positive and statistically significant, with comparable values of the coefficients and the within R2. Consequently we confirm, using bank deposits, the positive impact of ICT on the primary category of financial institutions. Once again, this result is consistent with the bulk of the previous literature. Furthermore, it shows that in our sample countries the growing deployment of ICT between 1990 and 2016 has not constituted a threat to the banking industry (as by undercutting banks’ position within the financial system in favour of various fintech companies offering similar services). Apart from the general channels of ICT impact on the banking sector, others relating more specifically to changes in deposits can be traced to the benefits for the financial performance and the competitiveness of the deposit banks, as suggested among others by Monyoncho (2015) and Nkiru, Sidi, and Abomeh (2018). The second major type of financial institution considered here is insurance companies (investment funds are discussed in Section 6.5). See the models with Ins_GDP in Tables 6.2 and 6.3. The estimates of the IU model are similar to those for banking but with slightly better fit (the models can be compared directly, since the datasets were almost identical). IU proves to be a statistically significant determinant of the development of the insurance industry. However, this does not go for the other ICT indicator—while it is statistically significant, the extremely low value of within R2 precludes all meaningful interpretation. The importance of ICT for the insurance

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industry can be explained as relating to such factors as the emergence of digital insurance (Nicoletti, 2016) and electronic payments (Nwankwo, Ajemunigbohun, & Iyun, 2015). In addition, the industry is affected by the rise of insurance services provided by fintech companies, or ‘insurtech,’ defined by Ricciardi (2018, p. 8) as ‘… innovation-based companies … that generate value … by disrupting or solving problems across the insurance value chain through the engagement of technology’. The heightened availability of regtech may result in increased insurance spending owing, say, to improvements in customer service processes such as claims management (Cappiello, 2018; Cortis, Debattista, Debono, & Farrell, 2019). Nevertheless, this is still a relatively new part of the financial system. Estimates of the panel models with Sav_GDP provide no information on the effect of ICT deployment on the gross savings rate. This was perhaps to be expected, given the markedly non-linear relationship between Sav_GDP and both IU and FBS (see Figs. 6.3 and 6.4), which impedes the application of our models. Finally, the results for Electr_paym obtained with the panel models strictly confirm the foregoing conclusions arising from both correlation coefficients and local polynomial regressions—namely, the positive influence of ICT on electronic payments (at least their reported use, insofar as the data on Electr_paym are drawn from surveys). The values of the within R2 of both models estimated for Electr_paym are the highest of all those in Tables 6.2 and 6.3, indicating the best fit. A comparison of the coefficients of IU and FBS shows that the popularity of the electronic payments depends more heavily on overall access to the Internet than on the availability of fixed broadband connections. This is readily explained—electronic payments are possible through devices using various types of connection, not necessarily only those with high speed and stability. For example, in many countries financial transactions are conducted to a large extent via mobile devices (Iman, 2018; Li
ebana-Cabanillas, Mun˜oz-Leiva, & Sa´nchez-Ferna´ndez, 2018; Shaikh & Karjaluoto, 2015), including transactions using near-field communication (NFC), i.e. communication between two devices brought into proximity (de Luna, Montoro-Rı´os, & Li
ebana-Cabanillas, 2018; Jeffus, Zeltmann, Griffin, & Chen, 2015). Yet stable, rapid connections are indispensable to construct and operate the infrastructure of electronic payment systems, which means that the role of FBS cannot be disregarded. Further, the more sophisticated technologies impact on the intentions of their users (Jun, Cho, & Park, 2018), as electronic payments can be provided at lower prices than other similar financial services.

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Table 6.4 Results of the analysis of general variables: summary Variable Impact of ICT adoption

FD FI FM BD_GDP Ins_GDP Sav_GDP Electr_paym

Positive Positive Positive (IU)/not identified (FBS) Positive Positive Not identified Positive

Note: For explanation of the variables, see Table 6.1 in Section 6.1.

Our key conclusions on the relationship between ICT diffusion and various aspects of financial development are summarised in Table 6.4. In most cases we found a positive effect, with the sole exception of savings, for which neither the strength nor the direction of the correlation could be established. For the financial market development the results for the FBS variable are inconclusive. To sum up, we determined positive effects of ICT adoption both on financial development overall and on selected aspects of it.

6.3 Financial market evolution trajectories This section begins our analysis of the relationships between the diffusion of ICT and the development of the financial markets. The main results will be presented in Section 6.4. The present section is given over to graphical analysis, with two elements, analogous to the first two parts of Section 6.2 analysing the general variables. In the first part we examine the timelines of the financial variables to trace changes over time (the time period is 1990–2016 or shorter, depending on data availability) and compare levels of financial market development in the European OECD members. The second part comments briefly on their distribution using density functions. The financial market variables are those described in Table 6.1, examined in the same order. We start with the most general indicator of financial market development, i.e. FM, discussed in Section 6.2, with some additional insights. We then analyse the three sub-indexes of FM, accounting respectively for the depth, accessibility and efficiency of the financial markets, followed by a discussion of three variables representing various dimensions of the stock market: namely, capitalisation, turnover, and volatility. Finally, in line with Sahay et al. (2015) and Svirydzenka (2016), we take total outstanding domestic private and public debt securities as proxies for the development of the bond markets. Other financial market segments are omitted

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for methodological reasons and problems of data availability. The discussion is continued in Section 6.4. The first variable (Figs. 6.1 and 6.2) is our core indicator for financial market development—FM, the index published by the IMF, which is in fact one of the two sub-indexes of the broadest index of financial development, FD. FM itself is the resultant of three sub-indexes, representing the depth, accessibility, and efficiency of the financial markets, designated respectively FMD, FMA, and FME. The values of FM are normalised to range from 0 to 1, higher values implying higher levels of development. Most of the component variables of FM are linked to the stock markets, but a few represent the bond markets; efficiency is gauged exclusively by the stock market turnover ratio. The other financial market categories are not included. This means that, de facto, we analyse mainly the development of stock markets and to a lesser extent bond markets. This approach is suitable given the main aim of our analysis, namely determining the impact of ICT diffusion on various aspects of financial development; as we showed in Section 3.3, stock markets are among the parts of the financial system most powerfully affected by the information and communication technologies. Some of the variables used to calculate FM are taken separately: for instance, SMC_GDP, to assess differences in levels of FM over time and between countries. The only financial variable that is entirely separate from FM is SPV—stock price volatility is not covered directly by the index. As we stated in Section 6.2, FM displays high temporal variability, substantially greater than that of FD or FI, which may well imply that this segment of the financial system has experienced much greater fluctuations in development than have financial institutions, whose trajectory has been relatively stable. In most of our sample countries FM increased until the 2008 global financial crisis, which initiated at the beginning of financial market decline or stabilisation. This is evident in the trend lines for countries such as Belgium, the Czech Republic, France, Iceland, Norway, Sweden and the United Kingdom; the decline was particularly severe in the case of Iceland, which was strongly affected by the financial crisis—a comparison of the changes in FI and FM in Iceland shows that the disruptions were much more severe for the financial markets than for financial institutions. In some countries, such as Greece, the decline was aggravated by the euro-area debt crisis. There are also a number of countries where the financial market development trajectories from 1990 to 2016 differed considerably—above all the postcommunist countries (we address this issue in the next paragraph within the comparison of the levels of FM in the analysed countries), but also some

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particular cases, including Germany, Ireland, and Switzerland. In Germany, high levels of FM had already been reached by the late 1990s, and the financial markets did not experience substantial variability over the ensuing years. However, the downtrend after 2008 is clear. In Ireland changes in financial market development have been rather minor—indeed, this is one of the most stable countries in our sample in this regard (even during the global financial crisis declines were not as sharp as in other economies, and were followed by rebounds). However, the country’s FM values have not been among the highest in our sample. In Switzerland, like Germany, levels of FM were very high by the end of the 1990s, with a perceptible downtrend after 2008. Here, however, the distinctive feature is the abrupt decline in 2004–2006 to the lowest levels in the entire period. This can be attributed to the diminution in stock market capitalisation and especially turnover. This issue is addressed as part of the analysis of the stock market variables. After this examination of changes over time, we now focus on international differences in financial market development. What is most striking is the significant disparities in FM, as our sample includes both economies with some of the world’s highest levels of FM (Norway, Switzerland, and the United Kingdom in some years) and countries where it has remained close to 0, indicating seriously underdeveloped financial markets. The latter group consists exclusively of post-communist countries: the least developed financial markets are those of Latvia, Lithuania, and Slovakia, all with FM just over 0.1 at best. Comparing this with the values for various groups of countries (see Svirydzenka, 2016, p. 22), we can see that these three countries had levels of FM not only below the global average but also below the mean for the emerging markets (albeit still much higher than in the low-income and developing countries). That is, these countries’ financial markets were underdeveloped not only by European standards. The main reason is low stock market capitalisation and turnover. In three other post-communist countries, the Czech Republic, Estonia, and Slovenia, there has been some development, but after rapid increases in FM in the early 1990s its levels have declined, indicating that the original development was not sustainable. Naturally, the low ranking of the post-communist countries in this regard is no surprise, given that at the turn of the 1990s their financial markets were tiny or non-existent. Despite this initial disadvantage, two of them—Hungary and Poland—attained levels of FM close to or even above 0.5 (the mean for the advanced markets according to Svirydzenka, 2016), comparable to countries such as Greece and Portugal or, among the more advanced economies, Belgium and Denmark.

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The remaining countries in our sample show average values of FM in the period 1990–2016 between 0.5 and 0.8. With the exception of outliers (Greece on the downside and Switzerland on the upside), the group of advanced economies is rather homogenous in terms of financial market development. To some extent the absence of substantial disparities may be due to integration within the European Union and also within the euro area. In fact, only three of our sample countries are outside the EU. One readily apparent relationship is that between the size of the economy and the level of FM (a larger economy seems to contribute to the development of financial markets). Recall that all our indicators are relative, i.e. expressed in proportion to GDP. The density function of FM confirms the conclusions drawn from analysis of national timelines, namely that the distribution of FM is rather even. Our sample includes countries at various levels of financial market development during the period, and the differences were more significant than for FD or FI. After this analysis of the broad indicator of financial market development, let us now briefly address its three component sub-indexes: FMD, FMA, and FME. As with FM, the values of all three range between 0 and 1, higher values corresponding to higher levels of development of the relevant dimension (for more details on the methodology see Svirydzenka, 2016). FMD stands for the depth of financial markets; it is based on indicators of various aspects of the size of stock and bond markets: capitalisation and turnover of the stock markets (we also analyse these two, SMC_GDP and SMTV_GDP, separately) as well as volume of debt securities issued by financial and non-financial corporations (considered separately, though in our analysis we use ODP_GDP which stands for the sum of these two values). All these variables are in percentage of GDP. For most countries, the FMD timelines resemble those of FM, as regards both changes over time and differences between countries (see first part of Fig. 6.5), so we do not discuss them. However, it is worth noticing that some countries have attained values of FMD close to the maximum limit of 1—Switzerland and the United Kingdom have been close to 1 for most of the 2000s, confirming these nations’ status as global leaders in depth of financial markets. The FMD density function shows, however, that most of the other countries had much lower levels of this variable, below 0.4, and the remainder between 0.4 and 0.9. Levels higher than 0.9 were rare (see first part of Fig. 6.6).

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The next variable, FMA, corresponds to financial market access—it too consists of two component variables: share of total stock market capitalisation accounted for by companies outside the top 10 and total number of corporations that are debt issuers. Unlike FMD, the FMA trajectories for some countries differ considerably from those of FM (see second part of Fig. 6.5). In about half the sample there was no substantial change in FMA between 1990 and 2016 (see, e.g. Belgium, Iceland, Poland, and Sweden); for the other countries there was fairly regular growth, save for Slovenia, which experienced rapid growth up to 2009 and dramatic decline thereafter. Comparing countries, the post-communist countries generally have much lower levels of FMA, except Hungary and Poland, which have attained levels broadly on a par with some of the less advanced ‘old’ EU members. The density function indicates that in most countries FMA values are spread rather evenly between 0.2 and 0.6. Higher levels are uncommon (see second part of Fig. 6.6), but are found in some countries that are global leaders in financial market access by this gauge. For the most part these are smaller economies—see in particular Austria, Ireland, Norway, and Switzerland in Fig. 6.5. In the larger countries FMA was usually much lower. The third and final sub-index of FM, labelled FME, is financial market efficiency. This is a single variable; it is defined as the ratio of stock market turnover to capitalisation and should thus be regarded as a somewhat approximate measure of financial market efficiency. Values of FME have been highly volatile in our sample (see third part of Fig. 6.5), and full interpretation would require country-by-country and year-by-year analysis. Here we limit ourselves to several general remarks. The lowest levels of FME, with no substantial improvement, are found in the smallest postcommunist countries such as Latvia or Lithuania, and the high levels of Estonia and Slovenia were not sustained. Hungary and Poland were comparable to the advanced EU economies. There are a number of countries in which FME was close to 1 for a shorter or longer period (most consistently in Germany, Italy, the Netherlands, and Spain), but it is difficult to discern a common explanation for this apparent efficiency of financial markets. Considerable variability is also evidenced in the distribution of FME—its values are significantly spread out (to some extent similar to the distribution of FM); only values ranging from 0.5 to 0.8 are relatively less common (see third part of Fig. 6.6). The analysis of the three sub-factors in FM thus shows that the results are relatively consistent (albeit with some exceptions), which means that European financial markets tend to develop in similar ways in all three dimensions.

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At this point, we turn specifically to our three stock market variables: SMC_GDP, SMTV_GDP, and SPV. The examination of overall development of the financial markets picked up significant international differences. In what follows we offer some insights by studying first our proxy for stock market size (i.e. capitalisation), then liquidity (turnover, which could also be seen as another size indicator), and finally price volatility. The timelines of SMC_GDP (see fourth part of Fig. 6.5) show that in most of our sample countries in the period considered (1990–2016), capitalisation it has been substantially below 100% of GDP; the density function (see fourth part of Fig. 6.6) suggests the same conclusion, as the majority of observations are well below that level (and most, in fact, below 50). The countries with the largest stock markets relative to the national economy were Finland and Iceland (although only temporarily, for a few years), the Netherlands, Sweden, Switzerland, and the United Kingdom (these four for a sustained period). As for overall financial development, Switzerland was also the European leader in SMC_GDP; in some years it exceeded 250, one of the highest values anywhere in the world. To some extent the decline in SMC_GDP between 2004 and 2006 explains the decrease in FM (although SMTV_GDP was a more important factor). The difference in the capitalisation of the stock markets between the post-communist and the more advanced European economies is clear; one factor is certainly the much shorter history of the equity markets in the former group; these countries had to build stock markets from scratch starting in the 1990s, whereas in the latter group they were already quite advanced. Analysis of SMC_GDP over time shows that national equity markets generally expanded up to 2008, which marked the onset of a downtrend, even in countries such as Switzerland. Generally, SMC_GDP is the most stable stock market indicator, given the measurement methodology based on the market value of all listed companies. Even though equity prices can undergo considerable variations, the stabilising element is their number, which depends, above all, on the activities of issuers (such as initial public offerings). The changes in SMTV_GDP (see fifth part of Fig. 6.5) are relatively similar to those in SMC_GDP—the size and the liquidity of European stock markets are closely related (the correlation coefficient between these two variables is about 0.8). This is not surprising, as both indicators, albeit from different perspectives, are based on the number of equities and their market value. Fig. 6.5 shows, however, that turnover tends to change more from year to year than capitalisation. Compare, for instance, the graphs of SMC_GDP and SMTV_GDP for Spain—the trajectory of capitalisation

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has been rather stable while that of turnover has fluctuated (witness the short-lived record-high levels of turnover on the verge of the 21st century). One of the striking features of Fig. 6.5 is SMTV_GDP for Switzerland, with the very deep decline of trading in 2004–2006 from 144% of GDP in 2003 to 12% in 2005—one of the steepest decreases in our sample and a key factor in the significant decrease of FM in that period. However, this finding should not be read as really indicating the sudden demise of the Swiss equity market—its highly transitory nature and the fact that it was not accompanied by any comparable decline in capitalisation indicate that these results may actually stem from some methodological or measurement issues in the source database. Examination of the turnover data in the World Federation of Exchanges (WFE) database confirms this intuition, as it offers no evidence for the sudden decline—on the contrary, it shows a continuation of the growth that began in the early 1990s. As with capitalisation (SMC_GDP), there is a group of regional leaders in stock market turnover, namely the Netherlands, Spain, Sweden, Switzerland, and the United Kingdom (Finland and Iceland registered short-lived periods of high activity, which then gave way to very low values of SMTV_GDP). Denmark, France, Germany, and Italy constitute the next group, characterised by a notable increase in SMTV_GDP although still lagging behind the leaders. SMTV_GDP was lowest in the post-communist countries, mostly below 10, and not substantially higher in Greece or Portugal. The density function (see fourth part of Fig. 6.6) shows that the heterogeneity of SMTV_GDP in our sample is limited, also in comparison with SMC_GDP—most values are considerably below 50, with rare exceptions—countries where equity turnover exceeded 100% or even 200% of GDP (Spain and Switzerland). The next variable, stock price volatility (SPV), is not a direct indicator of market development but does offer additional insights into the attributes of the European equity markets. For technical reasons SPV is not measured in relation to GDP: unlike capitalisation or turnover, it is an index variable. Timelines of SPV for the European countries (see the sixth part of Fig. 6.5) show that changes in volatility followed a similar trajectory. In most countries SPV was relatively low in the 1990s and increased at the beginning of the new century, in connection with global financial uncertainty due, among other things to the dot.com bubble (and then crash) on some major stock markets or events such as terrorist attack of 11 September 2001. In the years that followed, European stock markets became more tranquil, on average. However, the global financial crisis of 2008 produced a sharp

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increase in SPV in all our sample countries (see the spike in 2009, though some effects were discernible already in 2008). In most countries the period of heightened volatility was relatively brief and within a few years it had subsided to pre-crisis levels. The effects of the euro-area debt crisis on SPV were much more moderate, with another much less marked spike in 2011 in most countries. Generally speaking, the trend since 2009 has been downward, though specific countries were affected more severely by either the global or the euro-area crisis, most notably Iceland and Greece respectively. In Iceland SPV in 2009 was the highest in our entire dataset at close to 100; by comparison, there are only a few cases in which it reached even 50. That is, the Icelandic stock market was affected much more strongly than any other European market by the global financial turmoil; this conclusion is also supported by the data on the decline in capitalisation and turnover. In Greece, stock price volatility continued to grow after 2009, SPV hitting a record high of 45 in 2016. The cause was the euro-area sovereign debt crisis and its severe consequences for the Greek economic and financial system, including the stock markets (capitalisation and turnover also declined). Examining national stock market volatility according to levels of economic development reveals rather minor differences. In the final years of our period stock markets in some of the post-communist countries were actually less volatile than in the more economically advanced countries. In some of the countries that launched stock exchanges in the 1990s, such as Estonia, Latvia and Slovakia, volatility was initially quite high, reflecting the small size and limited liquidity of these new equity markets. Their growth over the years and then gradual integration into the EU (all three countries adopted the single currency) may be regarded as the stabilising factor, as the values of SPV have declined. The SPV density function in the sixth part of Fig. 6.6 shows that the differences both over time and between countries were small, SPV rarely exceeding 40 and in the vast majority of cases holding below 20. Two final financial variables are proxies for bond market development, namely the value of outstanding domestic private debt securities (ODP_GDP) and domestic public debt securities (ODPub_GDP) in relation to GDP. Owing to lack of data, for most countries the timelines for ODP_GDP and ODPub_GDP are much shorter than in the case of stock market size variables. Consequently, the conclusions should be regarded with caution. The charts of ODP_GDP and ODPub_GDP (the seventh and eighth parts of Fig. 6.5, respectively) show that European countries are rather

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heterogeneous both in trends and in average values of outstanding debt securities. Denmark, Iceland, and Ireland have the highest values of private bonds, while Belgium and Italy are the leaders in public debt securities. A look at these two lists shows that high values of ODP_GDP are not necessarily accompanied by comparably high values of ODPub_GDP, indicating that the two segments of the debt market tend to develop independently; this is confirmed by the correlation coefficient between them, which while positive is very low at 0.13. The smallest bond markets, both private and public, are found in the post-communist countries, which suggests the relative underdevelopment of their debt market. This is particularly true as regards the indicator of private bonds, in that a higher volume of public debt securities should not necessarily be seen as unambiguously positive. It could reflect rising public debt and accompanying economic and financial problems. However, ODP_GDP has also been low in Switzerland and the United Kingdom, i.e. the advanced economies with very highly developed financial markets as gauged by the other indicators. Moreover, it is difficult to establish strong trends in the level of either ODP_GDP or ODPub_GDP. For instance, the relative volume of private debt securities has generally declined in such countries as Belgium, Finland and Germany, whereas in Ireland, the Netherlands, Portugal, and Spain ODP_GDP has increased. The value of outstanding public debt securities has declined in Belgium, Denmark, Ireland, Sweden, and Switzerland while increasing in the Czech Republic, France, Germany, and the United Kingdom. Generally, the local debt market has grown in some of the countries that experienced the most severe problems during the global financial crisis and the euro-area debt crisis. One of the reasons for this is government anti-crisis policies, financed in part by public debt issuance. However, notwithstanding the serious problems of Greece, ODPub_GDP has actually been declining there in most recent years, owing essentially to difficulty in accessing the debt markets due to the high risk of default. The density functions of the two bond market variables are similar (see the fifth part of Fig. 6.6). Most observations were substantially lower than 50% of GDP, for private and public securities alike. The functions also show that the public segment of the European bond markets has been larger, on average, than the private segment. Another interesting issue is the linkages between stock and bond market development. No matter which pair of indicators is considered, the correlation coefficients are close to 0—that is, there is no direct relationship between these two segments of the financial system.

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6.4 How ICT impacts on financial markets We now address one of the pivotal issues for this study—the impact of ICT on the financial markets, with a special focus on the development of this part of the financial system. Preliminary graphical evidence consisting in the timelines and density functions of the financial market variables was discussed in Section 6.3. We identified the main trends and international differences for our sample of European members of the OECD. Here we discuss the graphical representations of the local polynomial regressions (supplemented by correlation coefficients) and the estimates of the panel models to identify the main attributes of the relationship between ICT and financial markets. First we take the IMF’s most comprehensive index of financial market development, namely FM (and its three sub-indexes); next we focus on stock market variables; and finally we examine the proxies for bond market development. In each case we consider the results for both of our ICT indicators, namely Internet users (IU) and fixed broadband subscriptions (FBS), in order to pinpoint possible differences between the role of access as such and availability of fast and stable connections. The dataset is identical to that used in the rest of this chapter (for details on the variables see Table 6.1). The relationship between the general index of financial market development, FM, and ICT penetration was studied in Section 6.2. The current section reproduces the relevant graphs and repeats the main conclusions reached in that section. But we also extend the analysis with some additional insights that will serve as the starting point for further analysis of the specific stock and bond market variables. The first part of Fig. 6.7 graphs the local polynomial regression for IU vs FM. Using this evidence alone, it is hard to establish either the direction or the strength of the impact of ICT diffusion (in terms of usership) and the development of financial markets. For the lowest values of IU, from 0 to 40, the relationship appears to be positive, in particular for the lowest levels of both IU and FM. The number of observations clustered in this initial interval is large compared with the higher levels of IU. This can be seen as confirmation of the positive impact of the increasing number of Internet users on the development of the financial markets in the early years of our sample period (in the vast majority of cases these observations refer to the 1990s, given that in subsequent years ICT diffusion increased substantially). For the next interval, namely IU ranging from 40 to 70, the relationship is

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weakly negative, suggesting that at later stages of ICT deployment its role in supporting the development of the financial markets weakens or even turns slightly negative. This surprising result would appear to reflect trends in FM—in most European countries, after very rapid initial increases, the growth of FM has slowed (and in some cases, in connection with the global financial crisis, stagnated or even declined). Considering levels of IU, these results refer to the mid-developed European economies, and mostly in the period around 2008 rather than the final years of our period. At the same time, the values of IU have continued to increase. The third interval, IU ranging from 70 to 90, again implies a significantly positive relationship. This is followed by the fourth interval, values of 90 and up, in which the relationship becomes strongly negative. These latter two intervals refer essentially to the most advanced economies and the last years of the time interval. The seemingly contradictory results here (as for the IU interval of 40–70) reflect the turmoil in their financial markets post-2008. Clearly, for the 2000s, especially in the pre- and post-crisis period, the impact of rising IU on financial market development is harder to determine than for the 1990s. The correlation coefficient between FM and IU is 0.38. That is, taking the entire set of observations we have a positive relationship between the

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Table 6.5 IU vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP

LnIU R2 (within) No. of obs. Rho F (prob > F) LnIU R2 (within) No. of obs. Rho F (prob > F)

0.11 [0.00] 0.37 645 0.84 366.2 [0.00]

0.23 [0.00] 0.69 646 0.87 1444.9 [0.00]

0.11 [0.00] 0.28 646 0.88 248.4 [0.00]

0.05 [0.01] 0.02 646 0.63 16.9 [0.00]

0.18 [0.01] 0.37 584 0.76 333.3 [0.00]

LnSMTV_GDP

LnSPV

LnODP_GDP

LnODPub_GDP

0.28 [0.01] 0.31 586 0.73 242.5 [0.00]

0.03 [0.00] 0.03 582 0.45 19.4 [0.00]

0.08 [0.01] 0.06 457 0.78 32.2 [0.00]

0.04 [0.00] 0.06 487 0.72 30.9 [0.00]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.

two variables. The periods where the correlation is positive seem to outweigh those with no or negative correlations. The panel model estimate for this pair of variables (see the first part of Table 6.5) confirms this conclusion—the coefficient of the single explanatory variable, IU, is positive and statistically significant, with an estimated value of 0.11. Comparing this with the values of the corresponding parameters estimated for FD and FI (see Table 6.2), we find that development of the financial markets is much more strongly affected by growing access to the Internet than is overall financial development or the development of financial institutions. However, the fit of the FM model, as indicated by the within R2, is not as good, especially compared with the FD model, which means that it is less useful for analysing the dependent variable. Still, when compared with the models for the other financial market variables (see Table 6.5) it is the second highest, together with the model for SMC_GDP. After this discussion of the IU indicator, we now turn to our second measure of ICT diffusion, namely fixed broadband subscriptions (FBS). The implications of the first part of Fig. 6.8 largely overlap with those concerning IU on the basis of its local polynomial regression, here too with several different intervals. Again, for the lowest values of FBS (up to 10) the impact of the new technology on FM appears to be positive; for levels of FM in the range of 10–25 it is moderately negative; and in the 25–40 interval it is positive. Unlike the results for IU, however, for FBS the fourth and final

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interval supports the thesis of a strong positive influence on the development of the financial markets. The caveat here is that the number of observations with FBS close to 45 is extremely low, which means that the last stage of the relationship hypothesised refers to just a few countries in the last few years.

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This datum may be regarded as an outlier rather than meaningful evidence. Excluding this fourth interval, the overall impact of FBS on FM is much weaker than that of IU as evidenced by the lower slope of the line in Fig. 6.8. The correlation coefficient between FM and FBS is much lower than for IU, the more general ICT indicator. It comes to 0.22, confirming the conclusion that the effect of FBS on financial market development is weaker than that of IU. Estimates of the panel model with FBS as the explanatory and FM as the dependent variable (see first part of Fig. 6.6) show that this tool should not be used to assess the relationship between the two variables—the model’s within R2 of the model is close to 0, and the coefficient of FBS is also 0 and is statistically insignificant. To conclude, the empirical evidence presented here strongly indicates that the diffusion of ICT has contributed to the development of the financial markets, above all, through growing Internet access and that the increase in the number of fixed broadband subscriptions has been less important. Obviously IU and FBS, important as they may be, are only two dimensions of ICT; and the European financial markets could well have been affected by other elements of ICT not considered in our analysis (although most other, similar technologies are in fact heavily conditioned by those

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considered here, in particular access to the Internet broadly defined). Again we find that the simple possibility of using Internet services is more important to financial market development than the speed and quality of the web connections. In the rest of this section, we seek to identify the channels whereby ICT affects FM, studying its role in the stock and bond markets. Our analysis of the linkages between ICT adoption and development of the financial market can be compared with previous works on this issue. However, these are very few in number, especially compared with studies of the ICT-financial development nexus or those on the banking sector. Because most empirical research concentrates on stock markets (and, albeit to a much lesser extent, on bond markets), we discuss the relevant studies as part of the analysis of the stock and bond market variables. In any event, the positive impact of various types of ICT on the development of other financial market segments has been confirmed: for money markets by Imakubo and Soejima (2010) and van Lelyveld (2014) and for derivatives markets by Daniel (2010), Wilkins and Woodman (2010) Benito (2012), Lannoo and Valiante (2013), and Heath, Kelly, Manning, Markose, and Shaghaghi (2016). We can now briefly discuss the graphical representations of the local polynomial regressions obtained for the three sub-indexes of FM: FMD, FMA, and FME (see the seventh, eighth, and ninth parts of Figs. 6.7 and 6.8). We begin with their relationship with IU. Clearly, the curves for all three sub-indexes somewhat resemble that of the aggregate. The parallel is closest for FMD, while for the other two there are some visible disparities. The relationship between IU and FMA is different, as the part of the curve corresponding to the third and fourth intervals is flatter, as in the case of the other variables. But the rest of the IU-FMA curve is rather similar, if on the whole flatter than the general FM curve (still with a slight upward slope). The great variability of FME makes determination of the relationship between this variable and IU rather difficult—see, for example, the many observations of FME close to 1 (the maximum) over practically the entire range of IU levels, implying that no meaningful conclusion at European level is possible (country-specific analysis seems more robust). Nevertheless, despite some caveats, the estimates of the local polynomial regressions show a mostly positive effect of IU on financial market development in the case of FMD and FMA, while in the case of FME the relationship is hard to determine. The results concerning the influence of the second ICT variable, FBS, on the three aspects of FM are more heterogeneous. For the depth of financial

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markets (FD), the apparent effect of the increasing use of fixed broadband connections is similar to the relationship between IU and FM (at least in terms of its direction), with a clear positive effect of ICT adoption for all levels of FBS above 20. For access to the financial markets (FMA), however, the results are slightly different. The impact of FBS on FMA is difficult to determine on the basis of the local polynomial regressions—the relevant curve is practically flat; the strong uptick for levels of FBS above 40 refers to a very small number of observations and can be considered as an outlier. The results for the third component, financial market efficiency (FME), are even more variable than those for FM generally. As in the case of IU vs FME, the great variability of efficiency results in an inconclusive relationship between FBS and FME, both in direction and in strength: the curve is essentially flat, with some minor ups and downs. In short, the local polynomial estimates offer evidence exclusively of the positive impact of FBS on the depth dimension of FM; for the other two sub-indexes the results are inconclusive. We also estimated panel models for each sub-index, using the ICT indicators as explanatory variables. The results for FMD (see second parts of Tables 6.5 and 6.6) indicate the positive impact of our ICT proxies, both IU and FBS. Moreover, the within R2 of the IU model for depth is the highest of all those estimated, indicating that this model performs best in terms of goodness of fit and robustness. In the case of the FBS vs FMD, Table 6.6 FBS vs financial market variables. Fixed effects regressions. 1990–2016, annual data LnFM LnFMD LnFMA LnFME LnSMC_GDP

LnFBS R2 (within) No. of obs. Rho F (prob > F) LnFBS R2 (within) No. of obs. Rho F (prob > F)

0.00 [0.00] 0.05 [0.00] 0.00 0.15 422 423 0.94 0.95 0.07 [0.79] 71.9 [0.00]

0.02 [0.00] 0.01 423 0.92 7.45 [0.00]

0.03 [0.05] 0.00 423 0.81 3.89 [0.05]

0.01 [0.01] 0.00 380 0.85 0.77 [0.38]

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0.05 [0.02] 0.02 378 0.87 7.62 [0.00]

0.008 [0.01] 0.00 419 0.43 0.57 [0.44]

0.13 [0.02] 0.16 287 0.85 50.6 [0.00]

0.01 [0.00] 0.01 294 0.78 4.0 [0.04]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1.

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the relevant value is considerably lower but still among the highest. In general the coefficients for FMD are much higher than for FM, FMA, and FME, meaning that this is the dimension of the financial markets most powerfully affected by the diffusion of ICT. In the case of FMA (see third parts of Tables 6.5 and 6.6) the very poor fit hinders the interpretation of the FBS model, but the IU model confirms the positive impact of Internet access on this dimension of financial market development; interestingly, the results are very similar to those for FM (except for the within R2). Finally, no meaningful conclusion is possible for FME (see fourth parts of Tables 6.5 and 6.6). To conclude, the panel models constitute further evidence for the positive impact of ICT on the depth of European financial markets. As to financial market access, the positive role of IU stands confirmed, and the results for FBS again prove to be inconclusive. No relationship was established between ICT diffusion and financial market efficiency. To refine the analysis of the indicators of overall financial market development, we focus on one key component, namely stock markets. We begin with the most basic gauge of stock market size—the capitalisation of the listed companies in proportion to GDP (SMC_GDP). The estimates of the local polynomial regressions for this variable (see the second parts of Figs. 6.7 and 6.8) closely resemble the results for FM, as regards IU and FBS alike. For low values of IU (and for extremely low values of FBS) the impact of ICT on stock market capitalisation is weakly positive. Next, for somewhat higher values (IU of 40–70, FBS of 5–20) it is negative, but again weak. In the third interval of IU values, ranging from 70 to 90, the relationship is clearly positive, but in the fourth (values exceeding 90 and in some cases near the maximum of 100, which indicates full saturation in terms of users) it turns strongly negative. For FBS the third interval is the final one, in that above the level of 20 the influence of FBS is almost unambiguously positive, rather moderate initially but very strongly for the highest values. However, as with the results obtained for FBS vs FM, the final ‘uptick’ in FBS vs SMC_GDP can be taken as an outlier, given the extremely low number of observations in that range. Generally, the relationship of SMC_GDP to our two ICT variables is similar. The graphical representation of the local polynomial regressions makes it clear that both the direction and strength of the impact of ICT on the capitalisation of European stock markets have evolved over time and differed between countries. The impact was positive, above all, at the beginning of the sample period (especially in the less advanced economies, which were catching up both in ICT and in stock market size) and in the early 2000s up until the global

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financial crisis of 2008, corresponding to the third interval. For the remaining years the links were generally quite insignificant, as is indicated by the nearly ‘flat’ parts of the relevant curves in Figs. 6.7 and 6.8. Generally, the results for FBS are less clear-cut, and the evidence of positive impact is rather inconclusive, as for FM. However, given the underlying reasons for the declines in SMC_GDP, which depended on various national and global macroeconomic factors, it seems quite improbable that even in these periods the diffusion of ICT could have had a negative impact on the size of European stock markets. Indeed, it is more likely that a positive effect of the new technologies was offset, or more than offset, by the strong negative influence of, say, the withdrawal of investors during the global financial turmoil or the euro-area sovereign debt crisis. Before proceeding to the interpretation of the panel model estimates, let us mention the correlation coefficients between SMC_GDP and the two ICT indicators. For both pairs of variables they are positive—SMC_GDP vs IU is 0.32 and SMC_GDP vs FBS is 0.19—providing some support for positive effects, especially that of Internet use on stock market capitalisation. The panel estimates (see fifth parts of Tables 6.5 and 6.6) closely resemble those for FM, even in terms of goodness of fit (see the almost identical within R2). The coefficient of IU is statistically significant and positive, although that of FBS is near 0 (as is the within R2). The most substantial difference is the considerably higher coefficient of IU in this case than in that of FM (0.18 vs 0.11). That is, the panel estimates confirm the main findings drawn from the local polynomial regressions, namely that increasing access to the Internet has had a positive effect on the size of European stock markets, while the results as regards fixed broadband subscriptions are inconclusive. The positive impact of IU on size appears to be more substantial than on the development of the financial markets overall (obviously, this is a somewhat simplistic conclusion, insofar as the two models are not perfectly comparable; for instance, they differ in number of observations, although they are mostly overlapping). Section 3.3 discussed possible channels whereby ICT may affect the development of the stock markets. Here let us add that from the standpoint of stock market capitalisation there are at least two other potentially important if less straightforward explanations for the positive linkage between ICT diffusion and SMC_GDP. First, ICT companies (a synonym for ‘high-tech’ companies) are relatively riskier and more information-intensive than other companies (Bruinshoofd & De Haan, 2005) which may result in their preference for equity financing (see, inter alia, the seminal study by Carpenter

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and Petersen (2002), who argue that this form of financing, via IPOs, is crucial and leads to substantial growth in size). Further, ICT company investment decisions tend to be more seriously affected by financial crises. As Giebel and Kraft (2018) show, they limit their investment more sharply than other types of enterprise; this tendency may help explain the weakening of the linkage between ICT adoption and increasing stock market capitalisation in Europe in the post-crisis period. Given the increasing size of the ICT sector in most European economies, this can be seen as one of the mechanisms of transmission of ICT adoption to stock market size. A second possible channel, which should be seen in broader perspective, is suggested by Hobijn and Jovanovic (2001). They demonstrate that over the last few decades revolutions in information technology have been a key determinant of stock market capitalisation in proportion to GDP. At first the technological shift leads to a decline in SMC_GDP but this initial decrease is followed by a sharp increase. And this explanation is largely consistent with the previous one, as the authors contend that technological revolutions favour new companies, which have a comparative advantage in adoption of the new technologies. As a technical note, given that SMC_GDP is one of the components of the IMF’s financial market development index, the clear similarity of its relationship with ICT adoption (measured either in terms of IU or in terms of FBS, and demonstrated both by local polynomial regressions and by panel models) and that of FM overall is not highly surprising. Nonetheless, the single element of FM and its importance in shaping the index values should not be overemphasised (for more details on FM and other financial development indexes, see Sahay et al., 2015, and Svirydzenka, 2016). SMC_GDP and FM in our dataset are positively correlated, with a coefficient of 0.69. Most of these conclusions relative to SMC_GDP also hold for SMTV_GDP, i.e. the ratio of stock market trading volume to GDP, which is the other fundamental dimension we analyse here. The relevant graphs for SMTV_GDP (see the third parts of Figs. 6.7 and 6.8) yield observations almost identical to those for SMC_GDP. To begin with, the relationship with ICT adoption as measured by IU again displays a series of stages, with changing sign and strength of the impact of ICT. For levels of IU lower than 70, the relationship is rather weak (see the nearly flat curve in Fig. 6.7). For values ranging from 70 to 90 it is substantially positive, but for the highest levels of ICT adoption, above 90, it is definitely negative. The explanation for the changing direction of the effect is similar to that provided for SMC_GDP. That is, it reflects the changes in the sample countries between

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1990 and 2016, in particular the rise in equity market trading in the years prior to the global financial crisis of 2008 and the subsequent fall. As for our second ICT variable, FBS, there are slight differences between the estimates of the local polynomial regressions. For FBS in the interval of 30–40, its relation with SMTV_GDP is substantially negative; for the two financial market variables analysed earlier, FM and SMC_GDP, the negatively sloped segments of the relevant curves were much less evident (for FM, almost imperceptible). This could be interpreted as evidence of a negative impact of fixed broadband subscriptions on stock market turnover where this technology has achieved a high degree of penetration. Such a reading would be oversimplified, however, because for most countries these data refer to the final years of our sample period, which was a time of rapid FBS diffusion but also, owing to economic and financial turmoil, a decline in activity on the majority of European stock markets. The two processes were largely independent, and it would be misleading to contend that the diffusion of FBS was the main factor (or a factor at all) in the decline of SMTV_GDP. We further address this issue by referring to the estimates of the panel models. The indicator of stock market turnover is positively correlated with both measures of ICT adoption, with correlation coefficients 0.33 for SMTV_GDP vs IU and 0.20 for SMTV_GDP vs FBS. That is, they are marginally higher than those for capitalisation, indicating a slightly stronger relationship with the diffusion of ICT. This conclusion is confirmed by panel estimates (see the sixth parts of Tables 6.5 and 6.6). As with FM and SMC_GDP, with FBS as sole explanatory variable the model yields no meaningful conclusion owing to an extremely low within R2 and the statistically insignificant coefficient for FBS. With IU as independent variable, instead, the model shows the positive influence of greater Internet access on trading volume—the correlation coefficient with IU is 0.28 and statistically significant. This result can be compared with the coefficient of 0.18 for SMC_GDP, using an almost identical dataset (all available data on the stock markets of the European members of the OECD). The implication is that the impact of ICT diffusion has been relatively stronger on turnover than on total capitalisation. To sum up this part of our analysis, once again we find a positive influence of IU on SMTV_GDP, whereas the results for the effect of FBS are inconclusive. Again, this can be read as meaning that mere access to the Internet as such has a more substantial effect on equity turnover than does the availability of high-quality connections. Perhaps this is because it gives

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more consumers and investors access to information on the stock market and online brokerage accounts, whereas the fixed broadband is generally important only for the establishment and management of market infrastructures. This was suggested by Bogan (2008), who demonstrated that a rise in the share of households using Internet led to a significant increase in stock market participation. Section 3.3 gives a more in-depth explanation of the impact of ICT on stock exchange turnover. It is worth adding that there are some other possible channels of transmission between ICT and equity trading. Bank, Larch, and Peter (2011) showed that trading activity (as well as liquidity) is positively affected by search engines, thanks to the attenuation of information asymmetries. Qualitatively similar results were obtained by Zhang, Song, Shen, and Zhang (2016) in a study of market reactions to Internet news that correlates news with turnover. According to Aouadi, Arouri, and Teulon (2013), increases in online searches concerning the entire stock market cause a decrease in liquidity, but searches for specific stocks have the opposite effect. As Shen, Zhang, Xiong, Li, and Zhang (2016) show, online trading period information is a better proxy for the Internet information flow than its non-period counterpart. Tantaopas, Padungsaksawasdi, and Treepongkaruna (2016) confirmed the positive effect on market efficiency of the investors’ attention, as gauged by the volume of online search (but the effects on trading volume were found to be mixed). Zhang, Shen, Zhang, and Xiong (2013) prove that open source information can contribute significantly to market efficiency and the speed of information dissemination. Additionally, stock markets can be affected by developments in the fintech industry, such as the application of blockchain to post-trading activities (Geranio, 2017) or the provision of other exchange services (Haddad & Hornuf, 2019). Our discussion of ICT’s impact on stock market development (as measured by capitalisation and turnover) can be compared with previous empirical studies. In Section 3.3 we briefly recounted the main empirical analyses in this field—most of which found evidence of the positive role of ICT, and in particular of Internet access. Our results are therefore consistent with the conclusions of Ngassam and Gani (2003), Zagorchev, Vasconcellos, and Bae (2011), Hossein, Fatemeh, and Seyed (2013), Lee, Alford, Cresson, and Gardner (2017), and Pradhan, Mallik, Bagchi, and Sharma (2018). Our findings are also in line with some other, indirectly related studies. According to Liang and Guo (2015), the effect of Internet access on stock market participation by Chinese households depends on their social interactions— increased access may actually reduce participation if it undercuts the positive

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effects of social interactions. Zhang and Zhang (2015) showed that the increasing availability of online trading accounts attracted many new investors to the equity markets; but as these investors are to a large extent uninformed, their trades do not affect the price informativeness of market activity. Narayan (2018) checked the impact of technology investments on the stock markets in a sample of Islamic and non-Islamic countries, demonstrating that their profitability is positively linked to investment in technology. It is worth remarking that the reverse relation between stock markets and ICT has also been demonstrated empirically—by Brown, Martinsson, and Petersen (2017), for example, who documented the significance of stock markets for the growth of the high-tech sector and consequently for the technology-led growth of the economy. Analysis of the next variable, price volatility (SPV), supplements the foregoing, in that its movements cannot be interpreted as evidence of changes in the level of stock market development. Volatility in fact represents a separate dimension of the markets. Unlike the analysis of capitalisation and turnover, the results on volatility are quite unambiguous: they tell very strongly against any significant linkage between ICT diffusion and the volatility of the stock market. The graphs of the local polynomial regression estimates are almost flat (see fourth parts of Figs. 6.7 and 6.8), aside from the apparently negative relationship at the highest levels of both IU (over 90) and FBS (over 35). This negative correlation is readily explained by the fact that it reflects the moderation of volatility in the last few years of our sample period, which correspond to the maximum ICT penetration. Further evidence against any significant impact of ICT adoption on SPV comes from the correlation coefficients and panel model estimates. The correlation between either IU or FBS and SPV has always been nearly nil—in both cases the relevant coefficients are about 0.05. The goodness of fit of both panel models with each ICT indicator as single explanatory variable is extremely low, with within R2 values close to 0 (see seventh parts of Tables 6.5 and 6.6). Only in the case of IU was the relevant coefficient statistically significant, and at 0.03 it might imply some weak positive impact of ICT. But given the very poor fit, our analysis offers no significant evidence either for or against the influence of ICT on SPV in Europe. Our findings may be compared with those of previous studies. According to Shen et al. (2016), the Internet information flow can decrease the persistence of volatility. In an interesting study, Jin, Shen, and Zhang (2016) verified the impact of microblogging (which would be impossible without ICT) on several attributes of the Chinese stock market. They found that it is now an

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alternative information source and has increased the speed of information diffusion, thus diminishing price volatility. On the other hand, Dimpfl and Jank (2016) found some evidence that the increasing attention of investors, evidenced by a greater number of online search queries relating to the stock market index, magnifies realised volatility, which means that this Internet service may actually increase SPV. Similar conclusions were reached by Zhang et al. (2016), who studied reactions to online news Sekmen and Hatipoglu (2019) verified the impact of high-frequency trading and algorithmic trading (both impossible without ICT) on the volatility of the Turkish stock market and concluded that these activities had a disruptive impact. The remainder of this section deals with bond markets. We consider two proxies for their level of development: ODP_GDP (outstanding private debt securities over GDP) and ODPub_GDP (public bonds over GDP). In both cases, problems of data availability limit the examination to domestic securities alone. As both variables display the value of securities in being, they may be taken as indicators of the bond market size. Analysis of such other factors as turnover is impossible owing to insufficiently consistent data. We begin this portion of our study with what in most countries is the smaller bond market segment, i.e. private domestic debt securities. The graph of the local polynomial regression for IU vs ODP_GDP (see the fifth part of Fig. 6.7) displays some resemblance to both our indicators of stock market development, SMC_GDP and SMTV_GDP. For levels of IU lower than 70 the curve in Fig. 6.7 is almost flat, indicating no correlation between IU and ODP_GDP. For higher values, however, ranging from 70 to 95, the impact of increased Internet access is perceptibly positive; for the few observations at IU above 95, again the graph shows no identifiable influence. Given the distributions of these two variables, with most of the observations clustered at relatively low levels of both (see Fig. 6.7), the local polynomial regression shows that in the observation period 1990–2016 the impact of IU on the value of outstanding private debt securities in Europe was moderately positive. It is worth pointing out that unlike the financial market variables examined above, ODP_GDP is never negatively affected by IU for any interval of values of the latter. That is, the effects of Internet access on this segment of the bond market are less ambiguous than for the stock market. Interpretation of the results for the second ICT variable, FBS, is a bit more complicated (see the fifth part of Fig. 6.8). As with IU, the relationship between FBS and ODP_GDP appears to be mostly neutral for levels of FBS lower than 30 (for values below 20 it is slightly positive and for those from 20 to 30 it is very weakly negative). For higher levels, between 30 and 40,

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fixed broadband connections appear to exert a strongly positive effect. The substantial downturn at levels of FBS above 40 may be regarded as an outlier, given the extremely small number of observations in this range. Consequently, as in the relationship of IU with ODP_GDP, when the entire range of observations is considered the impact of FBS on the value of outstanding private debt securities has been positive, and more so than for IU. This might be read as implying that, in contrast with stock markets, for European private bond markets fast, high-quality Internet connection is more important than simple access to Internet per se. This presumably reflects the structure of the bond market, whose participants are rarely individuals but mostly institutional investors, which account for the preponderance of transactions. Institutional investors have greater needs and higher expectations for their communication facilities, which may help explain why IU is relatively less significant here than FBS. And even though financial institutions are also major participants in many European stock markets, the importance of retail investors cannot be disregarded. As the analysis set out in the preceding paragraphs shows, for stock exchanges IU reflects the impact of ICT on the development of the stock markets more accurately than FBS. Graphical representations of the linkages between the public bond market variable, ODPub_GDP, and the two indicators of ICT diffusion are quite similar (see sixth parts of Figs. 6.7 and 6.8). For most levels of both IU and FBS the relationship between ICT adoption and outstanding public debt securities is neutral (or weakly negative, as for IU of 40–90 and FBS of 20–40). For the highest levels of the two ICT variables, however, the results are radically different. The relationship between IU and ODPub_GDP suddenly becomes strongly positive as IU approaches 100 (i.e. full saturation), whereas for FBS exceeding 40 the impact on ODPub_GDP appears to turn strongly negative. Here again, however, we are dealing with a strictly limited number of observations. Generally, the local polynomial regressions suggest that the influence of ICT on the size of the public bond market is fairly insignificant. We shall check this conclusion using the panel models, but we can already affirm that the impact of ICT diffusion on the volume of outstanding public as opposed to private debt securities has been negligible. In other words, the changes in ODPub_GDP appear to have been shaped more by other factors, such as decisions by the authorities. Before analysing the correlations and panel models, one additional observation is in order. As noted above, the local polynomial regressions indicate substantial differences between the impact of ICT on ODP_GDP and on ODPub_GDP—compare the relevant parts of Figs. 6.7 and 6.8. This is

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interesting, as it shows that the private and public segments of the bond market have been affected differently by the new technologies. The impact on ODP_GDP has been to some extent similar to those on both stock market indicators (capitalisation and turnover), whereas in the case of ODPub_GDP there are substantial differences. This dissimilarity may be due to the fact that the private bond market, like the stock market, is factored into corporate financing decisions. One may thus suppose that changes in the economic and financial system, such as the effects engendered by the increasing penetration of ICT, will affect corporations’ decisions to undertake various investment projects that can be financed through either bond or stock issuance. Generally, this tendency is found in countries experiencing economic development. Importantly, such countries usually rely less on public debt issuance, thanks to sounder public finances. This means that the effects on the size of the two segments may tend to be opposite. The correlation coefficients with the two ICT variables differ dramatically between the two segments of the bond market. For private bonds the correlation is positive, with coefficients of around 0.30 (ODP_GDP vs IU) and 0.35 (ODP_GDP vs FBS). For public bonds, by contrast, the correlation is negative, with similar coefficients of 0.13 (ODPub_GDP vs IU) and 0.12 (ODPub_GDP vs FBS). Interestingly, ODPub_GDP is the only financial market variable considered in this section that is negatively (if extremely weakly) correlated with the indicators of ICT adoption. The estimates of the panel models for ODP_GDP (see eighth parts of Tables 6.5 and 6.6) confirm the moderately positive impact of ICT: the coefficients for both IU and FBS are positive and statically significant in the relevant models. In keeping with the indications of the local polynomial regressions, the impact of FBS seems perceptibly stronger, as indicated by its much higher coefficient. However, straightforward comparison with the IU model is impossible owing to different datasets; furthermore, the fit of the IU model is extremely poor, which means that it should not be deemed indicative. And while the two models’ coefficients for the public bond market are also positive and statistically significant (see ninth parts of Tables 6.5 and 6.6), the within R2 of both models is close to 0 (especially for FBS), which means that they do not provide meaningful evidence for positive linkages between ICT and ODPub_GDP. Accordingly, we can reiterate the finding of insignificant impact of ICT diffusion on ODPub_GDP. Nevertheless, the divergent values of the correlation coefficients and estimates of the panel models show that for this variable too the results are inconclusive.

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Table 6.7 Results of the analysis of financial market variables: summary Variable Impact of ICT adoption

FM FMD FMA FME SMC_GDP SMTV_GDP SPV ODP_GDP ODPub_GDP

Positive (IU)/inconclusive Positive Positive (IU)/inconclusive Inconclusive/none Positive (IU)/inconclusive Positive (IU)/inconclusive None Positive Inconclusive/none

(FBS) (FBS) (FBS) (FBS)

Note: For explanation of the variables, see Table 6.1 in Section 6.1.

Given the rarity of previous works on the link between ICT diffusion and bond market development, it is difficult to juxtapose our results with earlier findings. However, the conclusions of what studies we have found are generally consistent with our own. Styles and Tennyson (2007) concentrated on one specific segment of the bond market and in a way addressed the issue of the impact of ICT on its development. Studying access to financial information on municipalities through the Internet and its relationship with a number of variables, including the level of debt, they confirmed the positive relationship. Astrauskait_e (2014) verified the impact of ICT adoption on the development of the bond market in Lithuania, but with highly inconclusive results—positive effects associated, above all, with the broadband indicator, suggesting that the diffusion of ICT influences the development of the bond market through its effect on investors rather than issuers. The findings of the current section are summarised in Table 6.7. For most of the financial market variables analysed we found a positive impact of ICT adoption as gauged by at least one of our ICT indicators. But there are exceptions: for stock price volatility we concluded that the role of ICT has been broadly neutral, and for outstanding domestic public debt securities the results were inconclusive (from a different perspective, this implies a neutral relationship). Thus we can say that the two processes—ICT diffusion and public bond market growth—proceeded for the most part independently. The results for the efficiency sub-index of FM, FME, were also inconclusive.

6.5 ICT and financial innovations In this final section we focus on how ICT has affected our chosen category of financial innovation, namely exchange-traded funds (ETFs). We

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gauge the relative size, or level of development, of the national ETFs market as assets over GDP (ETF_GDP); for additional data and methodological comments see Section 6.1. First, we examine the graphical evidence on ETF_GDP: national development trajectories and density function. The first part of the analysis serves to discover the main international differences and trends over time. Second, we analyse the graphs of the local polynomial regressions to detect the main features of the relationships between our indicators of ICT diffusion (IU and FBS) and ETF_GDP. Finally, we use the correlation coefficients and panel model estimates for additional verification of the impact of ICT on the size of the ETF markets. ETFs are not the only type of investment fund we study—we also examine the parallel results on the assets of mutual funds over GDP (MFA_GDP). This serves to supplement the analysis of ETFs, so as to compare the changes in the market for conventional investment funds with the parallel changes in the market for innovative funds and examine the role of ICT as regards both. The core subject of this section, however, is ETFs. ETFs, of course, are not the sole type of financial innovation presumably influenced by the adoption of ICT, but we decided to concentrate on this instrument owing to problems with other possible choices, such as insufficient data (on highfrequency trading, for instance) or difficulties of quantification (in particular with regard to various types of fintech). The period covered is 2000–2016 for ETFs and 1990–2016 for mutual funds. We examine the assets of ETFs in 11 European OECD members (see Fig. 6.9); due to different periods of ETF presence on different national financial markets, for most countries data are not available for the entire period 2000–2016 (indeed only France has data available for the full period of our analysis). It is apparent that European countries are highly heterogeneous in the size of ETF markets (i.e. in ETF_GDP). The leader here is the United Kingdom, with ETF assets exceeding 9% of GDP in 2016; the British ETFs market has experienced substantial growth since the early 2000s. Next comes Switzerland, where ETF assets came to about 8% of GDP in 2016. But the growth of the Swiss ETFs market has been more volatile (see Fig. 6.9). France and Germany also have somewhat well-developed ETF markets—in both countries ETF_GDP has been significantly lower than in the United Kingdom or Switzerland, but still much higher than in any other country analysed here. Like the British, the French and German ETF markets have grown almost constantly, with just a few dips. ETF markets in the remaining countries have been small, although two groups can be distinguished—those with slightly more developed ETF markets and those with extremely low levels of ETF_GDP. The first group consists of

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Fig. 6.9 Development trajectories of the assets of ETFs as % of GDP. 2000–2016, annual country-level data. Note: Raw data used. (continued)

258 ICT-Driven Economic and Financial Development

Fig. 6.9, cont’d

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countries where ETF_GDP has approached or exceeded 0.1%: Italy, Spain, and Sweden. The Italian ETFs market is the smallest of the three but has experienced the most significant growth. Spain and Sweden display considerable variations in ETF_GDP during the period (Fig. 6.9), but the overarching trend for these years has been positive. The second group comprises Greece, Hungary, Norway (the leader in this group, almost on a par with the previous set of countries in some years), and Poland. In all these countries the development trajectories of ETF_GDP have been very unstable, growth repeatedly interrupted by usually sharper declines. Except for Norway the trend in this group has been strictly negative. In short, the timelines for ETF_GDP indicates significant diversity among European ETF markets in terms both of levels of development and of trends. However, taking the full dataset and the density function of ETF_GDP (see first part of Fig. 6.10), it is clear that in the large majority .6

.4

.2

0 0

2

4 6 ETF_GDP

8

10

.025 .02 .015 .01 .005 0 0

50

100

150

MFA_GDP

Fig. 6.10 Density functions of the financial innovation variables, annual data. Note: Raw data used. Ireland excluded. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.

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of cases ETF assets are very low in relation to GDP, below 1.5%. Higher levels are quite rare, attained in just a few countries and mostly in years nearing 2016. Generally, that is, European ETF markets have remained at low levels of development (with significant growth only in the four largest). The possible determinants are multiple, but in our analysis we focus on one factor only, i.e. ICT diffusion. The development trajectories of MFA_GDP, like ETF_GDP, display considerable international differences (see Fig. 6.11). Mutual fund assets have generally been highest in relation to GDP in the large European economies, i.e. France, Germany, and the United Kingdom (exceeding 50% in some years, usually the most recent), and in some smaller but comparatively highly developed economies, including but not limited to Austria, Iceland, and the Netherlands (the leader in our sample, with a value of over 100% in 2016), Sweden, and Switzerland. In most of these countries the size of the national mutual fund market grew over our time period. Note that we have excluded one country, Ireland, which can be regarded as an outlier with mutual fund assets incomparably greater than in other countries (in some years almost 800% of GDP). In fact, Ireland is the domicile of choice of many European investment funds thanks to legal and tax advantages for financial companies (Clarke & White, 2018; Weiler, 2015); as a consequence the values of MFA_GDP simply cannot be taken as an accurate measure of the development of the local market (for similar reasons Ireland is also excluded from our analysis of ETF_GDP). There are also two other groups of countries as indicated by the MFA_GDP timelines. First are such Southern European countries as Greece, Italy, Portugal, and Spain, where MFA_GDP has been lower on average than in the other advanced economies and has actually declined between 1990 and 2016. Both of these features can be explained by their economic situation, in particular in the wake of the 2008 financial crisis and the sovereign debt crisis, which would appear to have undermined investor interest in this category of investment fund. Interestingly, in the case of Italy ETF_GDP and MFA_GDP moved in exactly opposite directions— that is, ETFs were not affected by the general downtrend in investment fund assets. The second group is the post-communist countries, where mutual fund assets have been very low (in some cases, such as Lithuania, practically nil), with no substantial or sustainable growth (even in the leader of this group, Hungary, mutual fund assets are below 20% of GDP; in Poland they have averaged 3.6%).

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Fig. 6.11 Timelines of the assets of mutual funds (as % of GDP). 1990–2016, annual data. Note: Raw data used. Ireland excluded.

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The density function of MFA_GDP (second part of Fig. 6.10) confirms these conclusions—for all countries and years, levels of MFA_GDP higher than 50% were rare; most observations were between 10% and 30%. Comparison of the density functions for ETF_GDP and MFA_GDP shows similar distributions—in the vast majority of cases the values are barely above 0. For comparison, in the United States, the country with the most advanced investment fund industry, ETF_GDP was higher than 13% at the end of 2016 and MFA_GDP has been over 50% since at least 1997. Significantly, an examination of developments in MFA_GDP and ETF_GDP reveals that the market for innovative funds has not developed in all the countries with high levels of mutual fund assets but only, for the most part, in the largest economies. However, the markedly positive correlation coefficient of 0.57 shows that changes in ETF_GDP and MFA_GDP have generally followed similar trajectories. This certainly suggests that a substantial national market for mutual funds (in most cases thanks to the large size of the economy) and a high level of economic development are preconditions for the substantial growth of ETFs. The rest of this section focuses on our fundamental issue—the linkages between ICT and various aspects of financial development, in this case the market for ETFs and for their conventional counterpart, traditional mutual funds. We start with the interpretations of local polynomial regressions. The relationship between IU and ETF_GDP can be described as twostage (see first part of Fig. 6.12). For low and moderate degrees of ICT (measured by IU) the impact of this technology on the assets of ETFs appears to be neutral (see the relatively flat curve for values of IU below 70). This portion of the curve is explained by the insignificant growth of the ETF markets in some countries and the lower rate of increase in ETF_GDP (despite the rapid growth of IU) in countries such as Switzerland initially, compared with the faster pace of change in later years. For higher levels of IU the relationship with ETF_GDP is radically different—it is strongly positive, suggesting that increased access to the Internet may contribute to the development of the ETFs market in Europe. This conclusion applies, above all, to countries such as Germany and the United Kingdom in the final years of our period. There is also a third stage, for the highest values of IU in our sample (95 and over), where the correlation is significantly negative. Nonetheless, this stage may be considered country-specific: in fact it refers to some of the most highly developed European economies but with severely underdeveloped ETF markets (such as Norway). These results distort the generally strong positive relationship found at the highest levels of both ETF_GDP and

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10

150

8

100 ETF_GDP

MFA_GDP

6

4

50

2

0

0 20

40

60 IU

Kernel = epanechnikov, degree = 4, bandwidth = 13.53

80

100

0

20

40

60

80

100

IU Kernel = epanechnikov, degree = 3, bandwidth = 10.27

Fig. 6.12 IU vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—IU; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.

IU—see the clearly upward segment on the right-hand side of the relevant graph. The results for MFA_GDP vs IU (see second part of Fig. 6.12) are highly similar to those for ETF_GDP. The relationship between MFA_GDP and IU is also two-stage (with a minor third stage for the highest levels of IU). For IU ranging from 0 to 70, despite some variability, the relationship can be characterised as neutral. In the second stage, with IU between 70 and 95, the impact of Internet access is unquestionably positive. To conclude, the estimates of the local polynomial regressions indicate a positive influence of high levels of IU penetration on the assets of both categories of investment fund; for low and medium levels, however, no impact is detected. The local polynomial regressions of ETF_GDP vs FBS are more clearcut than those vs IU (see first part of Fig. 6.13). There are again two stages: first, neutral (FBS ranging from 0 to 20) and second, considerably positive (FBS above 20). However, unlike IU, FBS displays no third stage. These results would appear to imply that for most countries FBS has played at most a minor role in supporting the development of the national ETFs market. Still, in the countries with the greatest ICT penetration in terms of fixed broadband subscriptions, accompanied by the highest values of ETF assets,

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10

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100 ETF_GDP

MFA_GDP

6

4 50

2

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0 0

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30 FBS

Kernel = epanechnikov, degree = 0, bandwidth = 2.82

40

50

0

10

20

30

40

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FBS Kernel = epanechnikov, degree = 4, bandwidth = 8.18

Fig. 6.13 FBS vs financial innovation variables. Local polynomial regressions, annual data. Note: On x-axis—FBS; raw data used; Ireland excluded; Kernel-weighed local polynomial smoothing applied; Kernel ¼ epanechnikov. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.

the effects of FBS are definitely positive. Estimates for MFA_GDP are very similar (see second part of Fig. 6.13)—the cut-off point between the first and second stage is again FBS of 20, as in the case of ETF_GDP. However, again the positive relationship refers to relatively few observations, as most are clustered in the interval corresponding to neutral impact. In the final part of our analysis we concentrate on the estimates of the panel models. As before, we begin our examination with the correlation coefficients. ETF_GDP is positively correlated with both measures of ICT diffusion: the coefficient for IU is 0.42 and for FBS, 0.56. Among all the variables considered in this chapter, ETF_GDP is thus one of the most closely correlated with our indicators of ICT adoption (only for Electr_paym are the coefficients higher). For MFA_GDP too, both correlation coefficients are positive, but considerably lower at 0.14 and 0.10 respectively. Estimates of the panel models with each of the ICT indicators as the single explanatory variable confirm the polynomial regressions and correlation coefficients. When the dependent variable is ETF_GDP, the coefficient of IU is positive and statistically significant (see Table 6.8); the same applies to the coefficient of FBS (see Table 6.9). Importantly, both of these models have values of within R2 among the highest of all those tested, which

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Table 6.8 IU vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP

LnIU R2 (within) No. of obs. Rho F (prob > F)

5.15 [0.46] 0.49 134 0.75 121.8 [0.00]

0.37 [0.00] 0.29 452 0.85 179.1 [0.00]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.

Table 6.9 FBS vs financial innovation variables. Fixed effects regressions, annual data LnETF_GDP LnMFA_GDP

LnFBS R2 (within) No. of obs. Rho F (prob > F)

1.88 [0.11] 0.69 134 0.84 271.3 [0.00]

0.09 [0.01] 0.09 408 0.91 42.1 [0.00]

Note: All values are logs; SE below coefficients; in bold—results statistically significant at 5% level; results account for GLS regressions; panel balanced; constant not reported. For explanation of the variables, see Table 6.1 in Section 6.1. Time period is 2000–2016 for ETF_GDP and 1990–2016 for MFA_GDP.

indicates their relatively good fit. As the two models were estimated on an identical dataset, the coefficients can be compared (with the usual caveats). The coefficient of IU is 5.15 and that of FBS is much lower (1.88), which indicates that IU may be making a substantially greater contribution to the expansion of ETF markets in European countries. Similar results are found for MFA_GDP, but the goodness of fit of these two models is far inferior (especially where the independent variable is FBS). Since the dataset for estimating MFA_GDP is much larger than that for ETF_GDP, the two sets of models cannot be reliably compared. Overall, it can be said that our study offers evidence that ICT does have an impact on the size of the markets for both ETFs and traditional mutual funds in our sample of European OECD members. The influence of ICT on investment fund markets should be considered in the framework of the broader effect of the new technologies on financial markets in general (see Section 3.3 and our discussion in Section 6.4). Identification of the exact channels of impact is complicated by lack of data, but we can say that the main mechanism whereby new technologies could have boosted the growth of both ETFs and mutual funds is the positive influence

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of ICT adoption on the development of the stock and bond markets, at least in some dimensions (see Section 6.4). As regards the impact of ICT on ETF markets, however, it appears necessary to take into account also some categories of fintech that could affect the innovative funds: robo advisors and social trading platforms (in particular the former; for more on fintech see Section 3.3). Even though the volume of assets managed by robo advisory platforms in Europe is still small by comparison with the more mature US market (Kaya, 2017; Phoon & Koh, 2017; Vives, 2017), this is a fast-growing segment of the financial industry, facilitated by the new technologies. Significantly, the fundamental investment instrument used by robo advisors is ETF shares. Consequently, the emergence of robo advisors, already discernible to some extent in the last years covered by our analysis (2015 and 2016), is bound to contribute to the development of the ETFs market—and in a broader perspective to financial inclusion—by attracting retail investors (Jung, Glaser, & K€ opplin, 2019; Schwinn & Teo, 2018). Social trading platforms are less important, indeed of negligible significance in most European countries. An important aspect of social trading is that users can copy the investment strategies of trusted traders and apply automated tools to execute transactions (Berger, Wenzel, & Wohlgemuth, 2018; Dorfleitner et al., 2018; Kromidha & Li, 2019), including trades in ETF shares. That is, they constitute yet another instance of an ICT-enabled service that employs ETFs. To finally conclude our analysis of the linkages between ICT deployment and the emergence of financial innovations, it is clear that both general access to the Internet and an increase in fixed broadband subscriptions can contribute to the development of the ETFs market. But the presence and strength of such links appear to be highly country-specific. Other determinants also need to be taken into account. Our results are consistent with the findings of earlier empirical work on the linkages between ICT and investment funds, and in particular studies of its impact on the development of ETF markets (see Lechman & Marszk, 2015; for more details see Section 3.3). Our findings are also analogous to the implications of the analysis by Marszk and Lechman (2019), in a study of a partially overlapping sample of European countries from 2004 to 2016 that confirmed the positive impact of both FBS and IU on the development of ETF markets. Finally, the positive impact of FBS on the assets of mutual funds is in keeping with Khodayari and Sanoubar (2016). In the case of IU, however, our results here are contradictory, finding a negative impact. As a conclusive observation, let us add that throughout the current section we have assumed that ICT diffusion can affect the emergence of

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innovative financial instruments; the possible inverse relationship was not considered. This approach may be motivated by the plausibly low likelihood that ETFs may have a causal impact on the launch and diffusion of ICT— above all, participation in and the scope of ETF markets is incomparably lower than ICT penetration, which is pervasive in all the countries analysed. Nevertheless, the possible impact of ETFs on ICT should not be utterly disregarded, in view of potential indirect channels of transmission, such as easier access to financing for the technology companies thanks to ETFs (e.g. increased stock market liquidity; Madhavan & Sobczyk, 2016). However, such reverse causal links have yet to be studied.

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Further reading Marszk, A., & Lechman, E. (2018). New technologies and diffusion of innovative financial products: Evidence on exchange-traded funds in selected emerging and developed economies. Journal of Macroeconomics, [in press].

CHAPTER SEVEN

Conclusions Contents 7.1 The questions 7.2 The answers 7.2.1 What are the dynamics of the process of diffusion of ICT across European countries and how are ICT diffusion patterns shaped? 7.2.2 How does ICT deployment impact the process of social and economic development and convergence? 7.2.3 How does growing access to ICT contribute to the state development of financial markets?

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7.1 The questions This book sheds light on the process of spreading new information and communication technologies (ICT) and on the general trends of social, economic, and financial development observed across the 32 European economies during the period 1990–2017. Throughout this research, we intended to draw a general picture of the process of ICT diffusion in Europe. We aimed to develop country-specific ICT diffusion patterns and to identify the dynamics of this process. We also tried to trace the potential channels of ICT impact on socio-economic and financial market development and to demonstrate changes in development patterns and across European economies that have been subjected to and determined by the emergence and country-wide application of ICT. The structure of this study was based on addressing three major research questions: • What are the dynamics of the process of diffusion of ICT across the European countries, and how are ICT diffusion patterns shaped? • How does ICT deployment impact the process of social and economic development and convergence?

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© 2019 Elsevier Inc. All rights reserved.

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• How does growing access to ICT contribute to the state development of financial markets? In what follows, our main empirical findings are recapitulated briefly.

7.2 The answers 7.2.1 What are the dynamics of the process of diffusion of ICT across European countries and how are ICT diffusion patterns shaped? Our first research target was to demonstrate that, since the 1970s, the European economies have been experiencing an astonishingly rapid diffusion of new ICTs. During that period, the newly emerged ICTs rapidly became a favoured alternative for ‘old’ communication technologies that, first, did not diffuse everywhere and, second, did not offer the wide bundle of opportunities that ICT brings to the social and economic spheres of life. Unquestionably, an exponential growth of access to and use of ICT and the explosive rising demand for all the innovative technological solutions available, especially the Internet and its applications, have offered unprecedented opportunities for all the members of the society. Using data from the World Telecommunication/ICT Indicators database 2018 (22th Edition/June), we examined changes in individual country’s achievements in four areas: mobile-cellular telephony, fixed broadband adoption, active mobile broadband network adoption, and Internet user penetration rates. Our results clearly show that the process of growing access to and use of ICT was both extremely dynamic and disruptive. The growth in the adoption of mobile-cellular telephony was extraordinarily rapid. In the early 1990s, the rate of MCS usage was negligible—1.6 per 100 inhabitants, whereas, by 2017, the rate had grown to 123.2 per 100 inhabitants, with many countries exceeding this average to a significant extent. Changes in access to fixed broadband networks were not that impressive; however, relatively poor diffusion of this ICT tool was compensated effectively by mobile broadband technological solutions, which, since 2007, are fast evading telecommunications markets. Note that, between 2010 and 2017, the average rates of AMS penetration jumped from 33.5 to almost 99 per 100 inhabitants. As this ICT tool facilitates worldwide communication and because access to the World Wide Web was expanding rapidly, this growth had a positive impact on the share of individuals using the Internet, growing from 0.24% in 1990 to 81.6% in 2017. Another interesting observation is that, due to the enormous

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expansion of mobile-cellular telephony, technological substitution of mobile telephones for fixed telephones proceeded gradually across the European economies. In many countries, we observed that the usage of fixed telephones declined, obviously due to the growth of cheap and unlimited access to mobile-cellular telephony. We conducted a closer analysis of country-wise diffusion patterns with respect to certain ICT indicators. When looking at individual country’s diffusion trajectories, with regard to MCS, three characteristic phases are clearly identifiable: early (pre-take-off), rapid growth, and stabilisation. In the great majority of cases, diffusion paths of mobile-cellular telephony are characterised by a relatively short pre-take-off stage and a very steep growth curve. Once the MCS take-off begins, diffusion increases exponentially and MCS rates slow down. Finally, as the domestic mobile phone market approaches saturation, diffusion slows down and reaches the phase of maturity. We made similar observations for Internet user penetration rates. However, when looking at AMS diffusion patterns, they can be seen to be very specific and unclassical. The early diffusion phase is barely detectable, and most countries enter the exponential diffusion phase directly. In this case, such unique patterns are, of course, determined by the extremely rapid progress of diffusion. A rapid diffusion of ICT led inevitably to a gradual eradication of relative cross-country inequalities in access to and use of ICT. Decreases in Gini and Atkinson coefficients, and also coefficients of variation, were radical, which supports our supposition that, as ICT diffuses both society- and economy-wide, making access to and use of these technologies more ubiquitous, countries differ less and less in this regard over time. Our empirical results imply several important general considerations on the diffusion of new technologies. The deployment rates of mobile technologies have been astonishingly high across Europe, which suggests that the diffusion of new ICTs is endogenous by nature and grows exponentially, irrespective of the features of different countries. As new ICTs can be recognised as successful technology that has provoked radical transformations in telecommunications markets, the question arises: has ICT deployment induced any fundamental changes in terms of social and economic development?

7.2.2 How does ICT deployment impact the process of social and economic development and convergence? Having identified the huge extent and the tempo of ICT diffusion in Europe between 1990 and 2017, we next trace the potential impact of deployment

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of new technologies on socio-economic development. With this aim, we identified long-term development trends, structural shifts, statistical relationships between growing access to and use of ICT, and changes in the level of social and economic development. Finally, we examined the process of social and economic convergence. To achieve these goals, we selected 15 economic indicators (6 of which are ICT trade-related) and 9 social variables, with which we approximate the level of a country’s development and the overall welfare of its society. Our general observations are as follows: First, the general development trends approximated by changes in average gross per capita income and per employee national output are not only positive but also show high dynamics of change in this regard. The latter observation reveals significant shifts in people’s overall material wealth during the last three decades. We also observed that, between 1990 and 2017, the share of national output generated in the service sector shifted massively, and this was accompanied by a growth in employment in this sector of the economy. Conversely, we observed opposite tendencies in respect of industrial and agricultural sectors, both in terms of the value addition created and employment. Such changes suggest important structural shifts in national economies; the European countries are becoming increasingly servicesbased, rather than industry-dependent. Through a brief analysis of ICT trade-related variables, we demonstrated that both export and import patterns are marked by high in-time instability and abrupt ups and downs, and we detected a significantly declining share of exports and imports of ICT goods in the total value of internationally traded goods. As for social variables, they change more slowly, due to their nature. The most massive shifts are reported in tertiary school enrolment and falling engagement in the labour force for women aged between 15 and 24. Additionally, vulnerable (also contributing family workers) employment was falling during the period examined, whereas the share of the labour force employed as wage-earning and salaried workers was increasing. Second, we examined the statistical relationships between ICT (MCS and IU) and all selected social and economic variables. For this purpose, we provided graphical evidence and estimated panel regression models treating ICT as an explanatory variable in each case. The empirical results reveal the existence of strong and, in most cases, statistically significant estimates demonstrating the strength and direction of the impact of ICT on the various aspects of economic and social development. In brief, we showed that ICT, both MCS and IU, is supposed to have a positive impact on gross per capita income and per employee gross income and that ICT seems to stimulate shifts in the structure of national

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economies. The strongest negative impact of ICT (IU) is reported for valued addition created by the agricultural sector, supporting the supposition that with technological progress economies are becoming technology-driven, which leads inevitably to a switch from agricultural, less technologyintensive activities towards technology-intensive industries and technologybased services. We showed that the potential impact of ICT (especially IU) on social development is significant, and the estimated parameters hold the expected sign in each case examined. The strongest positive relationship is reported in the case of the impact of IU on tertiary school enrolment. According to our results, we may also state that increasing employment in ICT affects drops in the share of labour force working as contributing family workers. As claimed by many, ICT may offer multiple job opportunities, alternative for being a family (usually unpaid) worker. Finally, we examined social and economic convergence. Convergence was revealed for each indicator considered, showing that, in relative terms, cross-country disparities in the level of social and economic development are diminishing gradually. In general, our results support the hypothesis that a growing deployment of ICT is associated positively with growing socio-economic development, and also contributes to various structural shifts and reduction in inter-country inequalities. Surely, this ICT contribution is either direct or immediate. Still, our empirical evidence detects statistical relationships exclusively, and one must remember that these correlations may be simply spurious. A strict identification of ICT impact channels is a challenging task, and the processes of social and economic development and their determinants are hard to capture in numbers. No equations are able to show, fully and profoundly enough, the complexity and multidimensionality of the impact of ICT on the economic sphere of life. The causality between ICT diffusion and socio-economic development seems to be obvious but still hard to quantify.

7.2.3 How does growing access to ICT contribute to the state development of financial markets? After addressing ICT diffusion in the context of socio-economic development, we addressed another fundamental issue (yet still rather understudied), i.e. the role of ICT in the various aspects of financial development in the European member-states of the OECD. To this end, we used a dataset containing several financial variables, including the indexes of the International Monetary Fund (IMF) and development indicators of the following segments: the banking sector, the insurance industry, the stock and bond

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markets, and the investment funds market. All variables were either indexes or expressed in relation to the particular country’s GDP. Our analysis was divided into several parts, corresponding to the following examined dimensions of financial development: general financial development, development of financial markets, and development of financial innovations. In each case, we applied similar research methods. First, we analysed the development trajectories and density functions to gain basic insights into the heterogeneity of our sample; second, we conducted non-parametric analysis to identify the direction and strength of the linkages between ICT adoption and the selected financial variables; finally, we used the estimates of the panel models as further evidence in regard to the studied relationships. Throughout our analysis, we concentrated on the evidence based on panel data, the only exception being the country-level examination of the timelines of the considered variables. First, we examined the impact of ICT diffusion on the financial systems overall and on some of their specific segments: the banking and insurance sectors. We considered both the indexes of IFM and some additional variables, such as either values of deposits or insurance spending. Both non-parametric analysis and estimates of the panel models proved a positive contribution of ICT to the general process of financial development, in line with what could be expected based on an overview of theoretical concepts and the results of previous studies. What seems important is the fact that the impact of the overall number of Internet users was proven to be more significant in this context than was the number of fixed broadband subscriptions, thus indicating that gaining access to the Internet (e.g. through mobile devices) is more important than the speed and quality of the connections. We also confirmed the positive impact of ICT with regard to the development of financial institutions in the cases of both banking sector and insurance industry, using various types of indicators, suggesting that a part of the financial system undoubtedly is influenced by new technologies. As an extension of this part of the analysis, we also verified the impact of ICT on gross savings; the results were inconclusive, which indicates that the contribution of ICT to the financial development has not, on average, resulted in changes in savings in the European economies. Additionally, we confirmed the positive contribution of ICT diffusion to the utilisation of electronic payments; the links are unambiguous and may be regarded as one of the possible channels of transmission between ICT and financial development. In the second part of the analysis, we focused on the development of financial markets, attempting to identify the significance of ICT in this process. Our study started with the IMF index of development of financial

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markets and its three sub-indexes, representing the following aspects of financial markets: depth, access, and efficiency. The positive impact of ICT adoption was confirmed unambiguously and exclusively with regard to the depth of financial markets; in the case of access to financial markets, the proof of the importance of Internet access was presented, although the results were mixed for the fixed broadband subscriptions. Finally, no robust conclusions were reached regarding the efficiency of financial markets, implying that this aspect of the financial markets is relatively least affected by the ICT. Next, we examined two segments of financial markets: stock markets and bond markets. We also analysed three dimensions of stock markets: capitalisation, turnover, and volatility. The results obtained for the two former dimensions were similar to those obtained for the financial depth, i.e. the positive influence of ICT diffusion in terms of the number of Internet users was confirmed, indicating that this type of technology has contributed to both the size and value of transactions on the European stock markets. However, no relationship with ICT diffusion could be identified for the latter dimension. The analysis of the two variables representing the development of bond markets led to contradicting conclusions. First, regardless of the ICT indicator considered, new technologies were proven to be related positively to the value of outstanding debt securities issued by private entities. Nonetheless, and second, no such relationship could be established in the case of a much larger segment of public debt securities. To conclude, the effects of ICT adoption with regard to the development of financial markets were demonstrated to be more mixed than in the context of financial institutions. The final issue analysed was the role of ICT in supporting the development of the selected category of financial innovations—exchange-traded funds (ETFs)—which are innovative investment funds. Additionally, we compared this role to the impact of ICT on the major category of investment funds—mutual funds. For both types of investment funds, we found evidence for a positive influence of ICT adoption on the values of their assets, regardless of the ICT indicator considered. The impact of ICT was demonstrated to be relatively stronger in the case of the innovative category, thus providing support for the positive contribution of new technologies to the spread of financial innovations. However, the development of ETF markets seems highly country-specific, and the role of non-ICT factors should not be disregarded. Throughout —our study, we focused on the impact of ICT on financial development and we mostly neglected the other side of this relationship, i.e. the role played by the financial sector (in particular by the

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development of financial markets) in the emergence and diffusion of this type of technological innovations. This remains a rarely covered issue that requires further examination and should be addressed in future studies. Nevertheless, it can be stated that the results of our analysis (e.g. correlation coefficients and nonparametric analysis) provide some support to the view that not only does the ICT contribute to the processes of financial development but also the diffusion of new technologies is, to some extent, spurred by the more developed financial sector. There are several possible interdependencies, such as easier access to financing high-risk investments that facilitate the spread of new technologies, which, in turn, as we demonstrated, can boost financial development. The results of our analysis of the role of ICT in financial development may also have substantial implications for various entities that are parts of the financial system, including financial institutions and regulatory authorities. Confirmed significance of ICT diffusion on the development of almost all parts of the European financial systems means that the business strategies of financial institutions need to take into account the growth of the banking sector in addition to the insurance industry and financial markets inflicted by, above all, increasing access to the Internet. Moreover, another undeniably important trend is the increasing role of fintech companies. Consequently, it is necessary to adapt the range and scope of the services offered to compete not only with their traditional competitors but also with totally new types of ICT-based companies that have also become a part of the financial sector. The resulting network of linkages between various entities that are active within the financial system becomes increasingly more complicated, which leads to new challenges also for the bodies that are responsible for financial supervision. More generally, institutions such as regulatory authorities and policy-makers must take into account the undeniable influence of new technologies on the financial systems. Even though, as we demonstrated, the adoption of ICT appears not to affect the volatility of stock markets (which could be perceived as one of the aspects of financial instability), the other possible effects, such as positive influence on the size and turnover of stock markets, can have some profound consequences, taking into account the fact that the levels of ICT penetration are bound to increase in some of the countries analysed (i.e. those in which these levels have remained relatively lower—usually the less economically advanced economies). However, it remains to be seen whether they follow the patterns observed in the past. In particular, the way in which they will be affected by the overall still-nascent fintech industry is rather unclear.

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The Digital Revolution boosts economic activities. ICT drives the creation of social and economic networks. It connects the previously unconnected and, thus, facilitates flows of not only ideas but also both tangible and intangible goods. Yet it is important to note here that the evidence for the channels through which ICTs impact social and economic life in developing countries is limited, predominantly due to short time series and restricted availability of reliable data. The available time span for empirical studies on the effect of new ICTs on development is no longer than 30 years and is as brief as 20 years in some cases. This is definitely too short a time to reveal whether changes caused by ICTs are profound enough to help countries escape the underdevelopment trap and head toward higher development stages—or vice versa, if the impact of ICT deployment is superficial, spurious, and short term. ICTs, as general-purpose technologies, do not induce gains in productivity and growth immediately after their introduction. In simple terms, it takes time to unleash the potential of ICT and transform this potential into socio-economic development.

Appendix

Appendix A ICT core indicators. Definitions ICT indicator

Definition

Fixed telephone subscriptions

The sum of active number of analogue fixed telephone lines; voice-over IP (VoIP) subscriptions; fixed wireless local loop (WLL) subscriptions; ISDN voice-channel equivalents; and fixed public payphones; this indicator was previously called main telephone lines in operation Mobile-cellular telephone subscriptions The number of subscriptions to a public (including both postpaid and prepaid) mobile-cellular service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile-cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging, and telemetry services Active mobile broadband subscriptions The sum of standard mobile broadband and dedicated mobile broadband subscriptions to the public Internet. It covers actual subscribers, not potential subscribers, even though the latter may have broadband-enabled handsets Continued

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ICT core indicators. Definitions—cont’d ICT indicator

Definition

Fixed broadband subscriptions

Fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to or greater than 256 kbit/s. This includes cable modem, DSL, fibre-tothe-home/building, other fixed (wired) broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations This indicator can include both estimates and survey data corresponding to the proportion of individuals using the Internet, based on results from national household surveys. The number should reflect the total population of the country, or at least individuals 5 years and older. If this number is not available (i.e. target population reflects a more limited age group) an estimate for the entire population should be produced. If this is not possible at this stage, the age group reflected in the number (e.g. population aged 10 +; population aged 15–74) should be indicated in a note. If no survey data are available at all, provide an estimate specifying in detail the methodology that has been applied to calculate the estimate The monthly subscription charge for fixed (wired) broadband Internet service. Fixed (wired) broadband is considered to be any dedicated connection to the Internet at downstream speeds equal to or greater than 256 kbit/s. If several offers are available, preference should be given to the 256 kbit/s connection (USD)

Internet users (%)

Fixed broadband Internet monthly subscription (USD)

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Appendix

ICT core indicators. Definitions—cont’d ICT indicator

Definition

Handset_500MB; prepaid; price of the Price of the plan, in local currency, for a plan (USD) mobile broadband handset-based prepaid tariffs with 500 MB volume of data (USD) USB_1GB; prepaid; price of the plan Price of the plan, in local currency, for a (USD) mobile broadband USB/dongle-based prepaid tariffs with 1 GB volume of data (USD) Handset_500MB; postpaid; price of the Price of the plan, in local currency, for a plan (USD) mobile broadband handset-based postpaid tariffs with 500 MB volume of data (USD) USB_1GB; postpaid; price of the plan Price of the plan, in local currency, for a (USD) mobile broadband USB/dongle-based postpaid tariffs with 1 GB volume of data (USD) Mobile-cellular monthly subscription The monthly subscription charge for charge (USD) mobile-cellular service. Due to the variety of plans available in many countries, it is preferable to use the tariff with the cheapest initiation/connection charge. If prepaid services are used (for those countries that have more prepaid than postpaid subscribers), the monthly subscription charge would be zero. If the plan includes free minutes and/or free SMS, this should be put in a note. Taxes should be included. If not included, it should be specified in a note including the applicable tax rate (USD) Mobile-cellular price of 3-minute local The price of a 3-minute peak rate call call (peak) (USD) from a mobile-cellular prepaid telephone to a mobile-cellular subscriber of the same network. Taxes should be included. If not included, it should be specified in a note including the applicable tax rate (USD) Derived from World Telecommunication/ICT Indicators database 2018.

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Appendix B Mobile-cellular Fixed broadband telephony subscriptions (per (per 100 Fixed telephony 100 inhabitants) (per 100 inhabitants) inhabitants)

Active mobile broadband subscriptions (per 100 inhabitants)

Internet users (share of population, %)

1990

2017

1990

2017

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland

1.2 41.7 39.1 24.6 17.2 32.1 15.7

8.4 43.1 37.2 18.4 33.5 37.3 15.2

0.1 1.0 0.4 0.0 0.0 0.4 0.0

119.4 170.8 104.7 120.4 103.0 138.5 119.0

0.0 4.0 4.5 0.1 0.6 0.3 0.1

10.0 9.7 28.7 11.6 38.3 3.4 24.9 1.9 26.2 3.6 34.8 39.3 28.8 19.4

69.3 86.2 75.1 91.6 79.7 106.4 81.9

0.0 0.1 0.0 0.0 0.1 0.1 0.6

66.3 87.9 87.7 63.4 67.1 80.7 78.7

56.6 20.4 53.4 49.3 40.3 38.5 9.6 51.2

25.1 27.7 6.8 59.5 54.1 46.4 32.2 43.6

2.9 0.0 5.2 0.5 0.3 0.5 0.0 3.9

121.7 145.4 132.3 106.2 129.1 115.9 123.8 122.6

4.4 1.3 2.6 1.0 2.6 0.1 0.3 3.7

43.2 30.9 30.9 43.8 40.5 33.9 30.4 39.9

129.0 133.4 153.8 87.5 79.8 63.4 49.1 113.3

0.1 0.1 0.4 0.1 0.1 0.0 0.0 0.5

97.1 88.1 87.5 80.5 84.4 69.1 76.8 98.2

2008 (or the 2017 earliest available)

2017

1990

2017

19.2 24.2 9.0 18.4 16.8 12.0 3.7 15.7

Appendix

Country

2000 (or the earliest available)

27.5 39.1 23.3 21.1 35.2 10.6 46.4 50.2 8.7 23.9 10.1 13.4 21.0 32.1 68.3 59.1 44.4

38.7 34.9 17.5 16.8 55.8 28.2 38.5 14.0 21.3 46.8 19.8 13.9 34.5 42.5 28.2 43.3 50.1

0.7 0.5 0.0 0.0 0.9 0.0 0.5 4.6 0.0 0.1 0.0 0.0 0.0 0.1 5.4 1.9 1.9

102.9 141.3 126.4 150.9 130.0 90.4 120.5 107.8 130.5 113.9 114.6 130.7 117.5 113.2 125.5 133.2 119.6

0.3 0.7 0.1 0.1 2.3 0.0 2.9 2.0 0.0 1.0 0.0 0.1 0.3 1.1 6.6 1.9 0.6

29.4 27.9 27.0 27.6 42.1 14.4 42.3 40.2 18.5 34.6 24.3 25.8 28.9 31.2 37.7 45.4 39.3

49.5 13.6 10.9 3.6 11.1 0.9 37.8 57.9 42.5 20.4 5.0 10.5 24.2 9.8 65.2 24.9 36.3

102.0 87.9 117.9 79.8 102.5 60.0 90.8 95.1 57.3 68.9 82.9 82.6 70.0 95.5 122.6 99.7 88.1

0.1 0.0 0.8 0.3 1.1 0.0 0.3 0.7 0.1 0.5 0.0 0.1 0.4 0.0 0.6 0.6 0.1

84.5 61.3 81.3 77.6 80.1 71.0 93.2 96.5 76.0 73.8 63.7 81.6 78.9 84.6 96.4 93.7 94.7

Appendix

Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

Based on data derived from World Telecommunication/ICT Indicators Database (22nd Edition) 2018.

287

288

Appendix

Appendix C Lorenz Curves for MCS, FBS, AMS, and IU. Period 1990–2017 (selected years).

Appendix D Mobile-cellular telephony penetration rates. Logistic growth estimates. Period 1990–2017 α (rate of R2 of ΔT κ (upper diffusion/ (specific the asymptote/ Tm curve No. of duration) model obs. ceiling) Country (midpoint) steepness) Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

123.5 161.4 108.3 137.3 110.5 136.5 124.6

2006.3 2002.4 2000.4 2004.1 2002.6 2004.2 2001.1

0.36 0.29 0.53 0.71 0.56 0.38 0.70

12.1 14.7 8.2 6.2 7.7 11.6 6.2

0.99 0.99 0.99 0.99 0.99 0.99 0.99

22 28 28 25 28 28 27

125.6 142.9 148.4 98.8 121.8 111.4 120.2 110.2 105.5 154.4 124.7 154.4 128.6 96.3 119.1 112.3 142.1 115.8 118.3 118.4 105.2 108.8 124.1 133.3 121.2

2000.3 2002.6 2000.8 2000.8 2001.2 2000.6 2001.9 1998.8 1999.7 2000.9 2003.7 2003.2 2003.3 2007.6 2000.6 1998.8 2004.6 1999.9 2004.5 2002.7 2000.1 2000.2 1999.5 2001.6 2000.1

0.35 0.41 0.27 0.42 0.45 0.51 0.51 0.52 0.59 0.41 0.45 0.76 0.33 0.41 0.46 0.38 0.43 0.58 0.61 0.46 0.71 0.57 0.32 0.33 0.48

12.4 10.7 15.8 10.4 9.6 8.7 8.5 8.3 7.4 10.6 9.7 5.7 13.3 10.6 9.5 11.6 10.1 7.5 7.1 9.5 6.1 7.6 13.5 13.3 9.1

0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

28 27 28 28 28 25 28 28 28 28 26 26 27 23 28 28 26 28 25 27 27 28 28 28 28

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

289

Appendix

Fixed broadband network penetration rates. Logistic growth estimates. Period 1998–2017 α (rate of R2 of ΔT diffusion/ κ (upper (specific the curve asymptote/ Tm No. of duration) model obs. ceiling) (midpoint) steepness) Country Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

11.3 27.9 36.3 23.9 24.2 31.1 28.3

2012.2 2005.2 2005.1 2008.8 2007.9 2008.1 2007.4

0.38 0.41 0.42 0.45 0.64 0.59 0.53

11.3 10.6 10.3 9.7 6.8 7.4 8.2

0.99 0.99 0.99 0.99 0.99 0.99 0.99

12 19 20 14 15 17 18

40.8 28.9 30.9 41.7 37.8 32.8 28.4 36.4 26.9 25.1 24.5 27.9 40.1 14.8 40.4 38.3 18.8 37.5 22.4 24.8 27.1 29.6 34.4 43.3 36.7

2004.2 2005.4 2003.9 2006.3 2006.2 2009.4 2007.3 2003.8 2006.5 2005.7 2006.9 2006.9 2007.3 2010.3 2004.3 2004.8 2006.9 2009.6 2008.4 2008.6 2006.2 2006.5 2003.7 2005.2 2005.8

0.61 0.48 0.89 0.48 0.52 0.46 0.48 0.69 0.59 0.52 0.95 0.53 0.37 0.62 0.62 0.64 0.66 0.26 0.47 0.44 0.55 0.41 0.61 0.48 0.52

7.2 9.1 4.9 9.2 8.5 9.4 9.1 6.3 7.4 8.4 4.6 8.2 11.6 7.1 7.0 6.8 6.6 16.8 9.2 9.8 7.9 10.8 7.3 8.9 8.4

0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

18 17 18 20 18 15 18 19 16 18 18 17 18 17 19 18 17 19 17 16 17 18 18 18 18

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

290

Appendix

Active mobile broadband network penetration rates. Logistic growth estimates. Period 2007–2017

Country

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

α (rate of κ (upper diffusion/ asymptote/ Tm curve ceiling) (midpoint) steepness)

R2 of ΔT (specific the No. of duration) model obs.

122.7 2016.4 0.38 89.5 2011.2 0.46 71.8 2012.2 0.81 98.3 2012.1 0.51 74.8 2011.1 1.21 Misspecification returned 96.4 2012.1 0.37

11.5 9.3 5.5 8.6 3.6

0.99 0.99 0.99 0.99 0.99

7 11 10 9 10

11.6

0.99

9

121.8 2010.2 0.75 128.5 2011.7 0.79 155.6 2010.3 0.54 99.1 2011.8 0.37 103.3 2012.9 0.34 141.2 2018.3 0.20 54.1 2012.8 0.52 121.3 2011.6 0.42 208.3 2016.7 0.16 96.1 2011.5 0.48 211.7 2017.1 0.27 79.9 2012.3 0.67 249.5 2018.7 0.26 45.7 2010.7 2.68 110.1 2011.7 0.30 Misspecification returned 57.1 2007.4 0.69 242.6 2021.8 0.18 94.2 2013.4 0.61 97.2 2012.9 0.44 Misspecification returned 95.5 2011.7 0.67 129.6 2008.2 0.37 116.7 2012.4 0.51 90.1 2009.8 0.71

5.8 5.5 8.1 11.9 12.8 21.5 8.5 10.4 26.1 9.1 16.3 6.5 16.8 1.6 14.6

0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

11 8 10 11 11 9 11 10 9 10 11 10 10 10 8

1.9 23.2 7.1 9.9

0.99 0.99 0.99 0.99

7 9 11 11

6.5 11.9 8.5 6.2

0.99 0.99 0.99 0.99

9 10 9 9

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates; in italics—overestimates returned.

291

Appendix

Internet user penetration rates. Logistic growth estimates. Period 1990–2017 α (rate of diffusion/ ΔT R2 of κ (upper curve (specific the No. of asymptote/ Tm (midpoint) steepness) duration) model obs. Country ceiling)

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

Misspecification returned 83.9 2002.4 84.2 2002.7 60.6 2006.6 71.9 2005.8 86.6 2007.4 77.7 2004.6

0.32 0.34 0.36 0.32 0.21 0.37

6.9 13.3 12.6 12.1 20.5 11.8

0.99 0.99 0.99 0.99 0.99 0.99

28 28 26 25 26 25

92.8 83.9 88.5 83.2 84.4 76.3 76.2 96.5 85.2 60.2 77.1 73.3 80.1 100.3 92.2 94.1 69.9 75.9 64.8 77.5 75.6 77.8 92.1 85.6 88.5

0.51 0.36 0.39 0.37 0.46 0.26 0.41 0.42 0.32 0.29 0.46 0.39 0.28 0.28 0.41 0.45 0.38 0.23 0.29 0.48 0.32 0.35 0.47 0.43 0.44

8.6 12.3 11.3 11.8 9.4 17.1 10.7 10.3 13.4 14.8 9.4 10.9 15.5 15.7 10.6 9.6 11.4 18.6 15.1 9.1 13.5 12.4 9.3 10.1 10.1

0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

28 26 28 28 28 26 27 26 27 28 22 22 23 23 28 28 27 27 25 25 25 28 28 28 27

2000.6 2002.6 2000.4 2004.1 2001.7 2007.9 2005.1 1999.7 2004.6 2003.5 2004.4 2005.2 2005.4 2012.3 2000.6 1999.7 2004.7 2006.1 2008.3 2003.1 2003.6 2004.2 1999.7 2000.2 2001.6

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

292

Appendix

Appendix E Future ICT development scenarios Mobile-cellular telephony penetration rates. Upper ceiling (growth limit) fixed for 170 per 100 inhabitants. Logistic growth estimates. Period 1990–2017 Country

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

TmETF i (midpoint)

(rate of αETF i diffusion)

Δ tETF (specific i duration)

R2 of the model

2009.7 Achieved 2006.8 2005.4 2007.8 2006.5 2004.4

0.21

20.5

0.97

0.15 0.36 0.17 0.23 0.21

29.3 12.3 25.6 18.9 20.6

0.85 0.93 0.85 0.96 0.86

2004.4 2004.9 Achieved 2009.1 2005.4 2006.4 2006.2 2005.4 2006.7 2001.7 2007.2 2003.6 2006.8 2013.4 2005.3 2005.2 2006.3 2005.1 2008.2 2007.2 2006.9 2006.5 2004.2 2004.7 2004.5

0.16 0.26

26.6 16.9

0.92 0.97

0.14 0.18 0.15 0.19 0.13 0.13 0.33 0.21 0.61 0.19 0.19 0.17 0.12 0.28 0.16 0.21 0.18 0.15 0.15 0.16 0.19 0.17

33.2 24.4 29.8 23.1 32.8 32.9 13.3 20.7 7.3 22.6 23.1 25.8 34.9 15.4 27.6 20.6 23.3 29.5 29.5 29.7 22.8 25.9

0.87 0.89 0.82 0.80 0.85 0.79 0.97 0.94 0.98 0.94 0.94 0.88 0.85 0.97 0.82 0.89 0.93 0.84 0.84 0.98 0.95 0.87

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

293

Appendix

Active mobile broadband network penetration rates. Upper ceiling (growth limit) fixed for 153 per 100 inhabitants. Logistic growth estimates. Period 2007–2017 Country

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

TmETF i (midpoint)

(rate of αETF i diffusion)

Δ tETF (specific i duration)

R2 of the model

2017.5 2015.2 2016.5 2014.9 2015.4 Achieved 2015.6

0.34 0.25 0.31 0.29 0.28

12.7 17.6 14.1 14.8 15.4

0.98 0.94 0.92 0.92 0.82

0.23

19.2

0.96

2011.5 2012.5 Achieved 2015.2 2015.9 2018.9 2019.3 2013.1 2013.3 2014.7 2014.8 2016.1 2015.5 2018.5 2014.6 Achieved 2022.5 2017.8 2015.9 2015.8 Achieved 2014.5 2009.1 2014.0 2009.8

0.42 0.50

10.5 8.7

0.94 0.95

0.24 0.25 0.19 0.26 0.31 0.21 0.26 0.32 0.32 0.32 0.24 0.19

18.3 17.5 22.1 16.7 14.4 20.6 16.6 13.8 13.8 13.7 17.8 22.1

0.97 0.97 0.95 0.95 0.97 0.95 0.94 0.92 0.93 0.96 0.79 0.96

0.06 0.22 0.38 0.29

70.9 20.1 11.5 14.7

0.69 0.99 0.97 0.98

0.32 0.22 0.35 0.71

13.8 19.8 12.5 6.2

0.94 0.96 0.93 0.97

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

294

Appendix

Internet user penetration rates. Upper ceiling (growth limit) fixed for 97 per 100 inhabitants. Logistic growth estimates. Period 1990–2017 Country

Albania Austria Belgium Bulgaria Croatia Cyprus The Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Malta Moldova The Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden Switzerland The United Kingdom

TmETF i (midpoint)

Achieved 2011.8 2004.1 2004.3 2011.8 2008.8 2007.1 Achieved 2004.2 2001.2 2005.6 2002.9 2010.6 2007.5 Achieved 2005.9 2010.2 2006.6 2008.1 2007.6 2012.3 2000.9 1999.9 2008.5 2009.3 2012.7 2005.4 2006.6 2006.7 2000.1 2001.5 2002.5

(rate of αETF i diffusion)

ΔtETF (specific i duration)

R2 of the model

0.29 0.24 0.25 0.18 0.19 0.23

14.8 18.4 17.5 23.6 23.1 18.8

0.96 0.98 0.97 0.96 0.98 0.97

0.25 0.31 0.36 0.31 0.21 0.24

17.5 14.4 16.5 14.3 21.7 18.2

0.98 0.98 0.97 0.97 0.94 0.97

0.25 0.15 0.25 0.22 0.21 0.28 0.37 0.42 0.21 0.17 0.19 0.26 0.19 0.23 0.42 0.29 0.34

17.3 29.7 17.4 19.8 21.2 15.3 11.9 10.5 21.6 25.0 22.7 17.1 22.3 19.4 10.5 14.9 12.9

0.98 0.93 0.95 0.95 0.97 0.98 0.99 0.99 0.99 0.98 0.97 0.94 0.96 0.97 0.99 0.97 0.98

Note: Three-parameter logistic function applied; estimation method—nonlinear least-square estimates.

Appendix

295

Appendix F Definitions. Economic variables

GDP per capita, PPP (constant 2011 international dollars)

GDP per capita based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. Data are in constant 2011 international dollars GDP per person employed GDP per person employed is gross domestic (constant 2011 PPP dollars) product (GDP) divided by total employment in the economy. PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates. Data are in constant 2011 international dollars Agriculture, forestry, and fishing, Agriculture corresponds to ISIC divisions 1–5 value added (% of GDP) and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4 Industry (including construction), Industry corresponds to ISIC divisions 10–45 value added (% of GDP) and includes manufacturing (ISIC divisions 15–37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4 Services, value added (% of GDP) Services correspond to ISIC divisions 50–99 and include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, healthcare, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4 Continued

296

Appendix

Definitions. Economic variables—cont’d

Employment in agriculture (% of total employment) Employment in industry (% of total employment) Employment in services (% of total employment)

Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. Labour force participation rate for Labour force participation rate for ages 15–24 ages 15–24, total (%) is the proportion of the population ages 15–24 that is economically active: all people who supply labour for the production of goods and services during a specified period High-technology exports (% of High-technology exports are products with manufactured exports) high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery ICT goods exports (% of total Information and communication technology goods exports) goods exports include computers and ICT goods imports (% of total peripheral equipment, communication goods imports) equipment, consumer electronic equipment, electronic components, and other information and technology goods ICT service exports (% of service Information and communication technology exports, BoP) service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions) Communications, computer, etc. Communications, computer, information, (% of service exports, BoP) and other services cover international Communications, computer, etc. telecommunications; computer data; newsrelated service transactions between residents (% of service imports, BoP) and non-residents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere Based on World Bank Development Indicators database 2019.

Appendix

297

Appendix G Definitions. Social and quasi-social variables

School enrolment, tertiary (% gross) Gross enrolment ratio is the ratio of total enrolment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level Labour force, female (% of total Female labour force as a percentage of the labour force) total shows the extent to which women are active in the labour force. Labour force comprises people aged 15 and older who supply labour for the production of goods and services during a specified period Labour force participation rate for Labour force participation rate for ages 15–24 ages 15–24, female (%) is the proportion of the population ages 15–24 that is economically active: all people who supply labour for the production of goods and services during a specified period Contributing family workers, total Contributing family workers are those (% of total employment) workers who hold ‘self-employment jobs’ as Contributing family workers, own-account workers in a market-oriented female (% of female employment) establishment operated by a related person living in the same household Vulnerable employment, total Vulnerable employment is contributing (% of total employment) family workers and own-account workers as a Vulnerable employment, female percentage of total employment (% of female employment) Waged and salaried workers, total Waged and salaried workers (employees) are (% of total employment) those workers who hold the type of jobs defined as ‘paid employment jobs’, where the Waged and salaried workers, female (% of female employment) incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work Based on World Bank Development Indicators database 2019. Note: All labour market-related variables are modelled ILO estimates.

298

Appendix H

Appendix

Abbreviations. Economic and Social Variables

GDP per capita, PPP (constant 2011 international dollars) GDP per person employed (constant 2011 PPP dollars) Agriculture, forestry, and fishing, value added (% of GDP) Industry (including construction), value added (% of GDP) Services, value added (% of GDP) Employment in agriculture (% of total employment) Employment in industry (% of total employment) Employment in services (% of total employment) Labour force participation rate for ages 15–24, total (%) High-technology exports (% of manufactured exports) ICT goods exports (% of total goods exports) ICT goods imports (% of total goods imports) ICT service exports (% of service exports, BoP) Communications, computer, etc. (% of service exports, BoP) Communications, computer, etc. (% of service imports, BoP) School enrolment, tertiary (% gross) Labour force, female (% of total labour force) Labour force participation rate for ages 15–24, female (%) Contributing family workers, total (% of total employment) Contributing family workers, female (% of female employment) Vulnerable employment, total (% of total employment) Vulnerable employment, female (% of female employment) Wage and salaried workers, total (% of total employment) Wage and salaried workers, female (% of female employment)

Appendix

GDPpc GDP_empl Agr_VA Ind_VA Serv_VA Agr_empl Ind_empl Serv_empl LF_15_24 HT_exp ICT_good_exp ICT_good_imp ICT_serv_exp Comp_serv_exp Comp_serv_imp School LF_female LF_female_15_24 Family_tot Family_female Vulner_tot Vulner_female Wage_tot Wage_female

299

300

Appendix I

Appendix

Fig. A Economic variables density representations. Period 1990–2017. Note: all density curves are from raw data; density function ¼ epanechnikov; on x-axis—raw variable values; on y-axis—densities; solid lines are for 1990 or the earliest available; long-dash lines are for 2000; short-dash lines are for 2010; long-line-dot lines are for 2017 or 2016 (if data for 2017 are not available).

Appendix

Fig. B Economic (trade-related) variables density representations. Period 1990–2017. Note: all density curves are from raw data; density function ¼ epanechnikov; on x-axis—raw variable values; on y-axis—densities; solid lines are for 1990 or the earliest available; long-dash lines are for 2000; short-dash lines are for 2010; long-line-dot lines are for 2017 or 2016 (if for 2017 data is not available); for ICT_good_exp and ICT_good_imp variables—solid lines are for 2000; long-dash lines are for 2010 and short-dash lines are for 2016. 301

302 Appendix

Fig. C Social variables density representations. Period 1990–2017. Note: all density curves are from raw data; density function ¼ epanechnikov; on x-axis—raw variable values; on y-axis—densities; solid lines are for 1990 or the earliest available; long-dash lines are for 2000; short-dash lines are for 2010; long-line-dot lines are for 2017 or 2016 (if data for 2017 are not available).

Appendix J Economic and social convergence Appendix

Economic β-convergence. Period 1990–2017 GDP

GDP_empl Agric_VA

Ind_VA

Serv_VA

Agric_empl

Ind_empl

Serv_empl

21.14 0.31 0.48 2% 29.1 32

21.12 0.39 0.43 2.3% 29.5 32

0.35 0.28 0.06 0.9% 73.9 32

22.6 1.06 0.28 4% 17.3 32

22.83 0.15 0.88 4.2% 16.5 32

0.06 0.24 0.00 Misspecification returned 32

0.65 0.54 0.07 1.5% 44.3 32

21.8 0.16 0.91 3% 21.5 32

21.5 0.21 0.00 FL_15_24

21.65 0.17 0.00 HT_exp

0.31 0.27 0.25 ICT_good_exp

21.47 0.67 0.03 ICT_good_imp

22.73 0.17 0.00 ICT_serv_exp

0.13 0.21 0.49 Comp_serv_exp

0.06 21.66 0.43 0.13 0.87 0.00 Comp_serv_imp

0.69 0.52 0.03 Misspecification returned 32

1.42 1.06 0.25 2.7% 25.1 32

22.51 0.72 0.33 3.9% 17.67 32

23.68 0.83 0.45 4% 14.4 32

20.03 0.00 0.49 0.9% 750.4 32

23.08 0.35 0.66 4.3% 15.8 32

23.45 0.67 0.62 4.6% 14.8 32

23.07 0.34 0.00

22.22 0.65 0.00

23.14 0.74 0.00

20.03 0.00 0.00

22.99 0.39 0.00

22.92 0.46 0.00

OLS estimates β-Coefficient Robust SE R2 β-Parameters Half-time (in years) No. of years (obs.)

Robust regression estimates

β-Coefficient SE Prob > F OLS estimates

β-Coefficient Robust SE R2 β-Parameters Half-time (in years) No. of years (obs.)

Robust regression estimates

0.62 0.73 0.41

Note: all estimates at 5% level of significance; for robust regression—biweight iteration ¼ 7 (set as default); constant not reported; in bold—results statistically significant.

303

β-Coefficient SE Prob > F

School

LF_female

LF_female_15_24

Family_female

Family_tot

22.91 0.24 0.76 4.2% 16.2 32

–2.67 0.25 0.84 4% 17.1 32

0.63 0.55 0.02 Misspecification returned 32

0.76 0.86 0.02 1.7% 39.2 32

0.61 0.74 0.02 1.4% 46.6 32

22.78 0.22 0.00 Vulner_female

22.81 0.23 0.00 Vulner_tot

0.71 0.67 0.29 Wage_female

21.62 0.45 0.00 Wage_tot

21.38 0.48 0.00

21.61 0.65 0.33 2.9% 23.1 32

21.41 0.64 0.25 2.7% 25.2 32

0.91 0.97 0.14 2% 34.3 32

0.42 1.06 0.03 1% 63.3 32

21.04 0.39 0.01

20.98 0.37 0.01

22.16 0.21 0.00

21.81 0.29 0.00

304

Social β-convergence. Period 1990–2017 OLS estimates

β-Coefficient Robust SE R2 β-Parameters Half-time (in years) No. of years (obs.) Robust regression estimates

β-Coefficient SE Prob > F OLS estimates

β-Coefficient Robust SE R2 β-Parameters Half-time (in years) No. of years (obs.)

β-Coefficient SE Prob > F

Note: All estimates at 5% level of significance; for robust regression—biweight iteration ¼ 7 (set as default); constant not reported.

Appendix

Robust regression estimates

Index

Note: Page numbers followed by f indicate figures and t indicate tables.

A

D

Active mobile broadband subscriptions (AMS), 101, 103–104, 105f, 120–126, 137 diffusion patterns, 275 national diffusion trajectories, 120–125 network penetration rates, 274–275, 290t, 293t Age of Information and Telecommunications, 17 Age of Steel, Electricity and Heavy Engineering, 16 Algorithmic revolution, 74–75 Algorithmic trading, 74–75, 251–252 Analogous trends, 146–150 Analogue technologies, 119–120

Data processing technology, 71–72 Decision-making process risk aversion, 37 uncertainty, 37 Deployment and social adaptation to technological change, 64–65 Depository and settlement systems, 75 Depth of financial markets (FMD), 223, 244–245, 277–279 Diffusion and socio-economic development, 1–2 Digital and economic marginalisation, 66 Digital banking, 71–72 Digital communication, 39 Digital revolution, 4, 17, 39, 281 economic growth and development, 65–67 financial markets, 67–84 socio-economic growth and development, 56–57 technological change and ICT, 53–60 Digital technologies, 17 Distributed ledger, 82–83 Domino effect, 25–26

B Bandwagon effect, 25–26 Banking development, 72, 207, 214, 218 Big data techniques, 67–68, 82–83 Blockchain technology, 67–68, 82–83 Bond markets, 254–255 density functions, 237 development of, 220–221, 255, 265–266, 277–279 indicators for, 193–194, 252 and stock, 244 structure of, 252–253 variables, 237, 252–253

C Cell phone penetration, 98, 104–108 Claims management, 218–219 Country-specific analysis, 61–62, 77 AMS diffusion trajectories, 126–127 diffusion patterns, ICT indicators, 275 social inequalities, 179–184, 182f Customer service processes, 218–219 Cutting-edge technologies, 1–3

E Early capitalism, 20 Econometric modelling techniques, 153–154 Economic activities female engagement, 151–153 gender-specific constraints, 151–153 and social systems, 151–153 social values and norms, 151–153 Economic and institutional incentives, 62–64 Economic β-convergence, 184, 185–186f, 188, 303t 305

306 Economic cross-country inequalities, 179–183, 181f Economic growth and development, 65–66, 151–153 agricultural and industrial activities, 155–162 capital accumulation and investment, 67 econometric modelling techniques, 153–154 empirical evidence, 155–169 employment, agricultural and industrial and service sectors, 153–162, 163f gross per capita income, 153–154 ICT diffusion and economic development, 153–154 international trade patterns, 153–154 vs. internet user penetration rate, 155, 158f, 161t labour demand and supply structure, 162 MCS/IU and international trade-related variables, 166–167 vs. mobile cellular subscription, 155, 156f, 160t productivity, economic growth and welfare gains, 153–154 radical structural changes, national output, 153–154 statistical analysis, 153–154 statistical relationships, core ICT indicators and macroeconomic variables, 164–165 structural changes, employment across sectors, 155–162, 163f structural shifts, 155 technological progress, 67 value creation and/or employment structure, 155 Economic power, regional distribution, 18, 19f Economic σ-convergence, 179–183, 180f Economic theory, 61–62 Economic (trade-related) variables, 295–296t density representations, 300–301f in-time changes, 146–150, 147f and social variables, 298f Education system, 151–153 e-finance, 68–69

Index

Electronic communication networks (ECNs), 75 Electronic trading systems, 74–75 English economy, 14–15 English labour force, 15 European stock exchanges, 198 European stock markets, 246–247 Exchange-traded funds (ETFs), 5–6, 78–80, 82, 193–194, 198, 255–256, 259–260, 262–265, 279–280 development trajectories, 256–259, 257–258f fundamental investment instrument, 265–266

F Female engagement in economic activities, 151–153 in labour market activities, 145–146, 172–175 Fifth Technological Revolution, 17, 39, 98 path-breaking invention, 35 Fifth Technological Wave, 39, 54 Financial access/inclusion, 69 Financial development banking, 218 density functions, 199–207, 206f Electr_paym, 219 euro-area sovereign debt crisis, 199–207 explanatory variable, 215–216 FBS vs. general variables, 210, 212–213f, 216t financial markets, 208 global financial crisis, 199–207 graphical analysis, timelines and density functions, 199 and ICT adoption, 194, 195–197t, 209–210, 215 index values, 213–214 Ins_GDP, 218–219 insurance services, 208–209, 214–215, 218–219 IU vs general variables, 210, 211–212f, 216t polynomial regressions, 210 Regtech, 217–218 Sav_GDP, 219 variables timelines, 199–207, 200–205f

307

Index

Financial globalisation and integration, 67–68, 70–71 Financial innovation variables, 67–84 density functions, 259–260, 259f vs. FBS, 263–264, 264f, 265t vs. IU, 262–263, 263f, 265t local polynomial regressions, 263–264 Financial market access (FMA), 233, 244–245 Financial market development, 273, 277–281 Financial market efficiency (FME), 233, 244–245 Financial market evolution trajectories accessibility and efficiency, 220–221 data availability, 220–221 density functions, 220–221, 223, 232f graphical analysis, 220–221 low stock market capitalisation and turnover, 222 stock market variables, 221–222 temporal variability, 221–222 timelines of, 223, 224–231f Financial markets, 67–84, 170–171 evolution trajectories (see Financial market evolution trajectories) Financial systems, 170–171 development, 68 Fintech industry, 81–84 Fintech payment services, 82–83 First Technological (Industrial) Revolution, 13–15 economic change, 20–23 global economic take-off, 20–23 First Technological Wave, 38, 60–61 Fixed broadband subscriptions (FBS), 103–104, 105f, 238, 241–243 country-wise diffusion curves, 125 deployment, 108–109 vs. financial markets variables, 241–243, 242–243f, 245t ICT diffusion, 243–244 and mobile broadband network diffusion in Europe, 128–129 in upper-middle-income economies, 127–128 national diffusion trajectories, 120–125 network deployment, 108–111 rates vs. access prices, 135–137, 135f

network penetration rates, 289t stock and bond markets, 244 subscriptions, 101 Fixed-income securities, 81 Fixed telephony (FTL), 103–106, 105f, 113–116f Fixed-to-mobile technological substitution, 129–131 Fourth Technological Revolution, 16–17 Fourth Technological Wave, 39

G General purpose technologies (GPTs), 35–38 computers, 39 digital communication, 39 information and telecommunication tools, 39 microelectronics, 39 Gini coefficient, 108–110 Global financial crisis, 238–240 Global Financial Development Database, 194–198 Global financial system, 68 Global shift, 20–23 Glorious Revolution of 1688, 14

H High-frequency trading (HFT), 80–81

I ICT-enabled financial inclusion, 69–70 IMF Financial Development Index Database, 194–198 IMF’s financial market development index, 248 Imitation effect, 138 Industrial age to a network age, 56 Industrial Enlightenment, 16–17 Industrialisation, 20–23 Industrial Revolution, 9, 12, 14, 18, 20–23, 31–32 Information and communication technologies (ICTs), 39–41, 139, 143 access costs, 132 adoption and banking development, 56, 59, 72 advantages and importance, 56

308 Information and communication technologies (ICTs) (Continued ) aggravated volatility, 76–77 applications, 59 in banking system, 71–73 benefits of, 3 capital market regulation and supervision, 76–77 core indicators, 103–104, 104t, 110–111, 283–285t cross-country inequalities, 103–104, 106f, 109–110 deployment in Europe, 103–111 descriptive statistics, 103–104, 104t diffusion patterns, 112–129 digital technologies, 39 and economic activities (see Economic growth and development) economic application and social adoption, 59 economic growth and development, 2–3 dynamics, 3 and population, 61–62 Europe-wide empirical evidence, 4 financial and human capital resources, 3 for financial development (see Financial development) and financial innovations, 255–267 financial markets, 67–84 fixed-to-mobile technological substitution, 129–131 as GPTs, 57 heightened investor short-termism, 76–77 human development and progress., 66 indicators in Europe, 2005–2018, 98, 99f infrastructure and financial development, 69–70 infrastructure for Internet connectivity, 128–129 internet user penetration rate, 119–120 legal and institutional environments, 59 liquidity fragmentation, 76–77 low-income societies, 56–57 management efficiency and productivity increase, 3

Index

national dissemination, 118–119 national inequalities, 109–110 penetration rates, 101 sample and empirical data, 100–103, 100t social and economic spheres, 55 and social development dynamics (see Social development patterns) society- and economy-wide adoption, 3 socio-economic and financial market development, 3–5, 273 socio-economic growth and development, 56–67 socio-economic networks, 2–3 state development of financial markets, 277–281 stock markets, 77 techno-economic paradigm, 41–42 and technological change, 53–60 time span, empirical analysis, 101 trade-related variables, 275–277 universal and unlimited interpersonal communication, 58 Information asymmetries, 151–153 Information diffusion, 251–252 Information exchange, 75–76 Information markets, 75–76 Innovation systems in adoption of technology, 62–64 Insurance companies, 208–209, 214–215, 218–219 Inter-country inequalities, structural shifts and reduction, 275–277 International Telecommunication Union (ITU), 98–100 Internet penetration rates, 98, 110 vs. access indicators and prices (data transfer), 135–137, 136f Internet users (IU), 102–104, 105f, 238 vs. financial markets variables, 255t fixed effects regressions, 238–240, 241t local polynomial regressions, 238–240, 239–240f national diffusion trajectories, 120–125 penetration rates, 145–146, 290–291t, 294t vs. economic variables, 155, 158f, 161t vs. trade-related variables, 155, 159f

309

Index

vs. social and quasi-social variables, 175–178, 177t vs. social variables, 172, 174f Investment fund markets, 265–266

K Kondratiev’s concept of long-waves, 64–65

L Labour force and financial capital, 169 Labour market participation, 65–66 Landline networks, 104–106, 129–130, 133–134 Landline telephony, old ICT tool, 98 Long-term economic development, 2–3, 55–57, 65–66

M Malthusian trap, 12–13, 16, 18 Market-leading products and services, 25 Mobile banking, 71–72, 217–218 Mobile (wireless) broadband technology, 108–110, 120 access, 109–110 deployment rates, 139–140 network diffusion in Europe, 128–129 information gaps and asymmetries, 138–139 legal and institutional constraints, 138–139 risk aversion, 138–139 spatial hindrances, 138–139 in upper-middle-income economies, 127–128 solutions, 98–100 subscriptions, 139 Mobile cellular subscription (MCS), 145–146 vs. economic variables, 155, 156f, 160t vs. social and quasi-social variables, 175–178, 176t vs. social variables, 172, 173f vs. trade-related variables, 155, 157f Mobile-cellular telephony adoption, growth, 274–275

vs. employment in agricultural and industrial sectors, 165–166 macroeconomic performance of European countries, 167–169 penetration rates, 288t, 292t technological substitution of mobile telephones, 274–275 Mobile-cellular telephony subscriptions, 101, 103–104, 105f, 107–108, 113–116f data prices, 132, 132f, 134f diffusion path, 106–107 early (pre-take-off) phase, 118–119 logistic growth estimates, 117–118 monthly subscription charges, 132–133 penetration rate, 107–108 rapid growth phase, 118–119 rates vs access prices, 135–137, 135f stabilisation phase, 118–119 theoretical sigmoid pattern, 118 Mobile technologies deployment rates, 275 hypothesis of network effects (externalities), 139–140 Mobile telecommunications, 71–72 Mobile telephony, 98–100, 104–106 Mutual fund assets, 260, 261f

N NASDAQ, electronic stock market, 74 National and global telecommunications markets, 126 National diffusion patterns, 100 National stock market volatility, 236 National systems of innovation, 31–32 National telecommunication markets, 165–166 Near-field communication (NFC), 219 Network effects, 57, 138 Network externalities, 138

O Organisation for Economic Co-operation and Development (OECD), 193–198, 208–209, 277–279 Outstanding domestic private debt securities (ODP_GDP), 236–237

310 Outstanding domestic public debt securities (ODPub_GDP), 236–237

P Paper-based transactions, 73 Pervasive diffusion of ICT in financial markets, 76 Pre-capitalism, 20 Pre-industrial technological progress, 18 Pricing mechanism, 75, 132f Private capital markets, 75 Private domestic debt securities, 252 Procurement finance, 72 Public bond market variable, 253

Q Quasi-social variables, 175–178, 176–177t, 297t

R Radical changes, structure of national economies, 150–151 Radical innovations, 31–32 Risk aversion, 37, 62–64, 138–139 Robo-advisory services, 82

S Second Industrial Revolution, 14–15 Second Technological Revolution, 15–16 Second Technological Wave, 38 Smithian growth, 13–14 Social and economic outcomes, 138 Social β-convergence, 184, 187f, 188–189, 304t Social development patterns economic structural changes, 178–179 empirical evidence, 171–172 female labour market participation, 172–175 female-related indicators, 171–172 female-related variables, 175–178 female vulnerable employment, 175–178 ICT implementation, 171 and institutional development, 175–178 vs. IU, 172, 174f market economy, 170–171 vs. MCS, 172, 173f

Index

preliminary descriptive evidence, 172–175 social habits and norms, 175–178 technological change, 178–179 technological progress, 172–175 Social σ-convergence, 179–183, 182f Social variables, 146–150, 149f, 298f density representations, 302f and quasi-social variables, 297t Socio-economic development, 24–25, 64–65 and convergence, ICT deployment, 275–277 dynamics, 153–179 data explanation, 143–146 technological progress, 146–150 Statistical analysis, 153–154 Stock market capitalisation, 247–248 Stock market development, 77 Stock market participation, 249–250 bond market, 254–255 ICT’s impact on, 250–251 price volatility, 251 technology investments, 250–251 Stock market trading, 77–78 volume to GDP, 248–249 Stock market turnover, 249 Stock market variables, 234 Stock price volatility (SPV), 235–236 density function, 236 Swiss equity market, 234–235

T Techno-economic dynamism, 53–54 Techno-economic paradigms, 23–35, 58 technological change and socio-economic development, 30 Technological breakthroughs, 31–32 Technological change, 1–2 diffusion of, 53–54 economic and social life, 53–54 and ICT, 53–60 institutional and political life, 53–54 organisational and economic systems, 53–54 socio-economic development, 55 Technological congruence, 61–62

311

Index

Technological revolution, 13–14, 16–17, 20–23, 27–29, 37–38, 58 and development surges, 33, 34t economic effect, 37–38 organisational innovation, 31–32 Technology acquisition and absorption, 62–64 Technology and innovation, 61–62 Technology and technological change deployment period, 33 economically efficient technologies, acquisition, 9–10 growth of wealth, 33 in historical perspective, 9–23, 34t and human progress, 9–10 institutional and economic environment, 31 laws and regulations, 31 material wealth, 12–13 pre-industrial economies, 12–13 production process, 10–11 propagation of, 10–11 social norms, 31 technology-led economic growth, 12 unbounded diffusion, 12 Technology diffusion, 37, 274–275 economic innovations, 25–26

exploration and conceptualisation, 27–29 network effects, 25–26 phases, 26 Technology-intensive activities/services, 275–277 Technology-intensive industries, 275–277 Technology-led business models, 167–169 Technology system concept, 31 Telecommunication networks, 16–17 Telecommunications market, 126 Third Technological Revolution, 16, 38 Trade-related economic variables, 146–150, 148f Trade-related macroeconomic indicators, 151 Trading system transformation, 74 Trading venues, 75

W World Federation of Exchanges (WFE) database, 234–235 World Telecommunication/ICT Indicators, 5–6, 101, 194–198, 274–275 World Wide Web, 98, 109–110, 274–275 WTO Information Technology Agreement, 167–169