Political Science

The Entrepreneurial State Debunking Public vs. Private

Sector Myths Mariana Mazzucato

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As a matter of fact, capitalist economy is not and cannot be stationary. Nor is it merely expanding in a steady manner. It is incessantly being revolutionized from within by new enterprise, i.e., by the intrusion of new commodities or new methods of production or new commercial opportunities into the industrial structure as it exists at any moment.

Joseph Schumpeter (1942 [2003], 13)

The important thing for Government is not to do things which individuals are doing already, and to do them a little better or a little worse; but to do those things which at present are not done at all.

John Maynard Keynes (1926, xxx)

It is a popular error that bureaucracy is less flexible than private enterprise. It may be so in detail, but when large scale adaptations have to be made, central control is far more flexible. It may take two months to get an answer to a letter from a government department, but it takes twenty years for an industry under private enterprise to readjust itself to a fall in demand.

Joan Robinson (1978, 27)

Where were you guys [venture capitalists] in the ’50s and ’60s when all the funding had to be done in the basic science? Most of the discoveries that have fuelled [the industry] were created back then.

Paul Berg, 1980 Nobel Prize in Chemistry winner (quoted in Henderson and Schrage 1984)

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CONTENTS List of Tables and Figures List of Acronyms Acknowledgements Foreword by Carlota Perez

Introduction: Do Something Different A Discursive Battle

Beyond Fixing Failures

From ‘Crowding In’ to ‘Dynamizing In’

Images Matter

Structure of the Book

Chapter 1: From Crisis Ideology to the Division of Innovative Labour And in the Eurozone

State Picking Winners vs. Losers Picking the State

Beyond Market Failures and System Failures

The Bumpy Risk Landscape

Symbiotic vs. Parasitic Innovation ‘Ecosystems’

Financialization

Chapter 2: Technology, Innovation and Growth Technology and Growth

From Market Failures to System Failures

Myths about Drivers of Innovation and Ineffective Innovation Policy

Myth 1: Innovation is about R&D

Myth 2: Small is Beautiful

Myth 3: Venture Capital is Risk Loving

Myth 4: We Live in a Knowledge Economy – Just Look at all the Patents!

Myth 5: Europe’s Problem is all about Commercialization

Myth 6: Business Investment Requires ‘Less Tax and Red Tape’

Chapter 3: Risk-Taking State: From ‘De-risking’ to ‘Bring It On!’

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What Type of Risk?

State Leading in Radical (Risky) Innovation

Pharmaceuticals: Radical vs. ‘Me Too’ Drugs

Biotechnology: Public Leader, Private Laggard

The National Institutes of Health: Creating the Wave vs. Surfing It

Chapter 4: The Us Entrepreneurial State The Defense Advanced Research Projects Agency (DARPA)

The Small Business Innovation Research (SBIR) Programme

Orphan Drugs

The National Nanotechnology Initiative

Chapter 5: The State behind the iPhone The ‘State’ of Apple Innovation

Surfing through the Waves of Technological Advancements

From Apple I to the iPad: The State’s very visible hand How State-funded research made possible Apple’s ‘invention’ of the iPod Giant magnetoresistance (GMR), SPINTRONICS programme and hard disk drives Solid-state chemistry and silicon-based semiconductor devices From capacitive sensing to click-wheels

The Birth of the iPod’s Siblings: The iPhone and iPad

From click-wheels to multi-touch screens Internet and HTTP/HTML GPS and SIRI Battery, display and other technologies

Did the US Government ‘Pick’ the iPod?

Fostering an Indigenous Sector

Chapter 6: Pushing vs. Nudging the Green Industrial Revolution Funding a Green Industrial Revolution

National Approaches to Green Economic Development

China’s ‘green’ 5-year plan UK’s start–stop approach to green initiatives

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United States: An ambiguous approach to green technologies Pros and cons of the US model

Pushing – Not Stalling – Green Development

The Importance of Patient Capital: Public Finance and State Development Banks

Chapter 7: Wind and Solar Power: Government Success Stories and Technology in Crisis

Wind and Solar Power: Growth Powered by Crisis

From the First ‘Wind Rush’ to the Rise of China’s Wind Power Sector

Solar Power Companies and the Origin of Their Technologies

Solar Bankruptcies: Where There’s a Will There’s a Way

Competition, Innovation and Market Size (Who’s Complaining?)

Conclusion: Clean Technology in Crisis

Myth 1: It’s all about R&D Myth 2: Small is beautiful Myth 3: Venture capital is risk loving Building a green innovation ecosystem (symbiotic not parasitic)

Chapter 8: Risks and Rewards: From Rotten Apples to Symbiotic Ecosystems Back to Apple: What Did the US Government Get Back for Its Investments?

Apple’s job-creation myth: Not all jobs are created equally Apple’s love–hate relationship with US tax policies The paradox of miracles in the digital economy: Why does corporate success result in regional economic misery?

Where Are Today’s Bell Labs?

Chapter 9: Socialization of Risk and Privatization of Rewards: Can the Entrepreneurial State Eat Its Cake Too?

The Skewed Reality of Risk and Reward

A New Framework

Direct or Indirect Returns

Chapter 10: Conclusion Appendix Bibliography

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LIST OF ACRONYMS

AEIC American Energy Innovation Council ARPA-E Advanced Research Projects Agency – Energy (US Department of Energy) ARRA American Recovery and Reinvestment Act ATP Advanced Technology Program BIS Department of Business, Innovation and Skills (UK) BNDES Banco Nacional de Desenvolvimento Econômico e Social (Brazilian Development Bank) CBI Confederation of British Industries CBO Congressional Budget Office (UK) CERN European Organization for Nuclear Research, Geneva DARPA Defense Advanced Research Projects Agency (USA) DECC Department of Energy and Climate Change (UK) DEMOS UK think tank DoD US Department of Defense DoE US Department of Energy DRAM Dynamic random-access memory EC European Commission, Brussels EPA Environmental Protection Agency (USA) EPRI Electric Power Research Institute FDA Food and Drug Administration (USA) FINNOV FINNOV EC FP7 project (www.finnov-fp7.eu) FIT Feed-in tariff GDP Gross domestic product GE General Electric GMR Giant magnetoresistance GPS Global positioning system GPT General purpose technology GW Gigawatt GWEC Global Wind Energy Council HM Treasury Her Majesty’s Treasury (UK) IP Intellectual property IPO Initial public offering on stock market IPR Intellectual property rights MIT Massachusetts Institute of Technology MITI Ministry of International Trade and Industry (Japan) MRC Medical Research Council (UK) MW Megawatt NAS National Academy of Sciences (USA)

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NBER National Bureau of Economic Research (USA, non-profit)NESTA National Endowment for Science, Technology and the Arts (UK) NIH National Institutes of Health (USA) NIST National Institute of Standards and Technology (USA) NME New molecular entity NNI National Nanotechnology Initiative (USA) NSF National Science Foundation (USA) NYT New York Times (USA) OECD Organisation for Economic Co-operation and Development OSTP Office of Science and Technology Policy (USA) OTA Office of Technology Assessment (USA) OTP Office of Tax Policy (USA) PhRMA Pharmaceutical Research and Manufacturers of America (trade association) PIRC Public Interest Research Centre (USA, non-profit) PV Photovoltaic R&D Research and development

S&P 500 Standard & Poor’s (S&P) stock market index, based on the market capitalizations of 500leading companies publicly traded in the US SBIC Small Business Investment Company (USA) SBIR Small Business Innovation Research (USA) SITRA Suomen itsenäisyyden juhlarahasto (Finnish Innovation Fund) SMEs Small and medium enterprises SRI Stanford Research Institute (USA, non-profit) SST (American) Supersonic Transport project TFT Thin-film transistor TFP Total factor productivity TW Terawatt VC Venture capital WIPO World Intellectual Property Organization

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ACKNOWLEDGEMENTS The book could not have been written without the intellectual stimulus and hard work of many colleagues and friends.

First and foremost were inspirational exchanges with two of the world’s best economic historians: Carlota Perez and Bill Lazonick. Carlota’s work, and our constant discussions, on the role of the State in different phases of technological revolutions, has challenged me to think hard about the changing role of different types of ‘capital’ – finance and production – over time. And the role of the State in guiding both for productive rather than purely speculative ends. But of course innovation requires some speculation – which Bill has been very careful to distinguish from ‘manipulation’. Bill’s incisive analysis leaves no words untouched, careful for example to distinguish business from the market, what most of us confuse when we use the term ‘private sector’. Bill’s work on the changing structure of capitalist production, and its relationship to labour markets and financial dynamics should be required reading for all students interested in the theory of the firm, and all policymakers interested in reforming finance to make capitalist production more inclusive and sustainable.

I also thank Bill for introducing me to two of his most brilliant master’s students: Oner Tulum and Matt Hopkins, who provided me with the best possible research assistance one can get. Oner applied his surgical methods of studying company reports to get to the bottom of how much the State provided to Apple, both in terms of the underlying technologies as well as early stage financing; Chapter 5 could not have been written without him. And Matt applied his sharp and passionate understanding of clean technology – something he is both academically an expert on but also politically committed to; Chapters 6 and 7 could not have been written without him.

I’m also grateful to Caetano Penna and Caroline Barrow, who provided laborious editorial assistance. Caetano’s background in both heterodox economics (and ‘the Other Canon’ framework) and in innovation studies – and his ground-breaking PhD on the ‘transition’ required in automobiles – made him a unique and stimulating sounding board and proofreader. Caroline, who found herself drowning in the editing and formatting the manuscript immediately after joining the Science and Technology Policy Research Unit (SPRU) at Sussex University, never lost her patience, and even provided interesting insights on the role of the public sector in the arts, from her experience as a professional dancer.

Finally, I am grateful for funding which allowed me to take some time off to write the manuscript. A grant from the Ford Foundation’s Reforming Global Finance initiative, led by Leonardo Burlamaqui, was not only helpful but useful due to Leonardo’s own work on understanding ways in which ‘knowledge governance’ can ‘shape’ markets. It was indeed Leonardo’s work with Ford that inspired the first meetings and work that led to another research project, funded by the Institute for New Economic Thinking

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(INET), in which Randy Wray and I are today banging heads: a project on how to bring together the thinking of Joseph Schumpeter on innovation and Hyman Minsky on finance, to understand the degree to which finance can be turned into a vehicle for creative destruction rather than its current obsession with Ponzi-like destructive creation.

Amongst other friends and colleagues who have provided inspiration through interaction and feedback, I want to mention Fred Block, Michael Jacobs, Paul Nightingale and Andy Stirling, the latter two from SPRU, my new academic home. SPRU, founded by Chris Freeman, is one of the most dynamic environments in which I have worked – a place where innovation is understood to be at the core of capitalist competition, and where rather than mythologizing the process, it is studied ‘critically’ – in both its rate and its direction.

Lastly, over the last two years I have had the fortune to work closely with different policymakers around the world, who rightly yearn to hear ‘different’ voices in economics. In the UK I have found particular inspiration working with Secretary of State David Willetts, Shadow Business Secretary Chuka Umunna, Shadow Science Minister Chi Onwurah (now in the Cabinet Office) and Andrew Adonis. In the European Commission, working with Peter Droell (head of the Innovation Unit of the DG RTD) on how to think about public sector innovation (both ‘within’ and ‘through’) has provided me with motivation to not only talk about the potential ‘entrepreneurial’ role of government but to think concretely about how to build ‘entrepreneurial’ organizations within the public sector.

Of course none of the people listed here bear any responsibility for my own errors, exaggerations, provocations and sometimes too passionate opinions expressed in this book.

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FOREWORD By Carlota Perez

Debunking myths is never easy. Swimming against the tide requires determination, a serious commitment to the truth and massive evidence. That is what Mariana Mazzucato displays in this book, which successfully challenges the widespread idea that the State cannot pick winners, that it is clumsy, bureaucratic and incapable of entrepreneurial risk taking.

Her analysis is not just Keynesian; it is also Schumpeterian. The role of the State is not limited to interventions into the macroeconomy as a ‘market fixer’ or as for the passive financer of public R&D. The State is also seen as entrepreneur, risk taker and market creator. Mazzucato’s argument goes well beyond the role played by government in the countries that recently forged ahead (Japan in the 1980s or South Korea in the 1990s) to focus on the role played by the public sector agencies of the United States – the wealthiest country in the world and an active promoter of ‘free markets’ – in making risky investments behind the Internet and in funding most of the crucial elements behind the ‘stars’ of the information revolution, companies such as Google and Apple. Indeed, an illuminating chapter on Apple computers shows how each of the technologies that make the iPhone so ‘smart’ can be traced back to State investments, from the Internet itself, to the touch-screen display, to the new voice-activated SIRI personal assistant. Mazzucato also analyses the crucial role of the German, Danish and other governments (including China, of course) in recent attempts to develop and diffuse clean energy technologies.

Her key point is that the most radical new technologies in different sectors – from the Internet to pharmaceuticals – trace their funding to a courageous, risk-taking State. Her account of the US government’s investment in the Internet provides evidence for the complex set of actions that make such wide-ranging innovations happen. She highlights the importance of mission-oriented funding and procurement; of the bringing together of multiple agencies; and also of the creation of incentives for multiple sectors and the multiple financing tools deployed to make it happen.

Successful efforts do not stop at basic and applied research but carry out the work of achieving commercialization. Companies like Apple, Compaq, Intel and many others received early stage financing through government funding programmes like the SBIR (Small Business Innovation Research). For example, the infrastructure of the ICT revolution, laying the basis for the Internet, was lavishly funded by the State from its beginning stages until it was installed and fully functional and could be turned over for commercial use. As Mazzucato argues, no private investors or market forces could have done that job on their own.

Her more recent examples concerning investments in ‘green’ technologies show the

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significance of long-term, committed ‘patient’ finance. In the advanced world this funding has been provided by State agencies such as the US ARPA-E (the energy version of DARPA, the Defense Advanced Research Projects Agency, which developed the Internet) or by State investment banks such as KfW in Germany. In the emerging world, funds have come from BNDES, the national development bank of Brazil, or the Chinese Development Bank. In all cases and in all contexts – as Mazzucato convincingly shows – major innovations require time and patience. Private finance has become too short-termist and is increasingly dependent on government labs that engage in high-risk portions of the innovation chain before committing its own funds.

This is another myth that this book debunks: the much celebrated role of venture capital (VC). Mazzucato demonstrates how VC has depended on government for the more expensive and uncertain research, before entering and cashing in when the uncertainty of investing in new innovations have been significantly reduced. She even reveals that the much-vaunted failure of the Obama administration’s support for Solyndra was equally, if not more, a result of venture capitalists withdrawing funding at a critical moment in the company’s development.

In the course of the analysis, Mazzucato manages to establish a strong connection with the literature of ‘industry dynamics’. This is a major contribution. Most of the arguments in favour of State intervention for growth and development forget to mention innovation, taking it as a natural companion of growth, a sort of manna from heaven. What Mazzucato does is to link the government directly to technology, innovation and entrepreneurship, while examining the key issues in the economics of innovation such as R&D and growth, the role of patents, and the role of SMEs and large firms acting as innovators and other related aspects.

Hence, this book appears with perfect timing. The stubborn economic crisis is not likely to be overcome with austerity measures or the expectation that ‘business as usual’ can return by saving the banks. This is a crisis like that of the 1930s, which requires measures as bold and as imaginative as those of the welfare state and Bretton Woods, but geared to the need for sustainable global development lead by today’s knowledge society. It is to be hoped that the politicians in the advanced world will come around to understanding this, and that when they look for guidance they will discover the value of Mazzucato’s ideas and arguments.

It is a good sign that the much shorter and earlier ‘report’ version of the current book was immediately recognized as relevant by the European Union and is being increasingly cited by top policy officials. In the United Kingdom also, the ideas have been highlighted in the media and both ministers and shadow ministers have been including them in their declarations and projects. There has also been growing attention to Mazzucato’s work in other European countries at very high levels. It is to be expected that this complete version, with the path-breaking chapters on green technology and on

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the real story of the iPhone will be received with even greater interest.

There are at least three lessons vital for effective institutionalization of innovation that stem from Mariana Mazzucato’s analysis. There is a need to strengthen the funding sources of public R&D; a need to increase public commitment to ‘green’ technology innovation and direction setting; and a need to update the Keynesian responses to modern economic crises.

If State investment in R&D is a necessary first condition in generating private innovation later, then guaranteeing a steady flow of funds for such purposes is in everybody’s interest. Her account of the Apple story shows that, apart from ‘staying foolish’ as Steve Jobs recommended, what many successful entrepreneurs have done – including him – is to integrate State-funded technological developments into breakthrough products. Given the massive returns generated by their success, shouldn’t entrepreneurs then return some of the rewards to the government, so it can continue taking the big risks that can later be turned into market game-changers? One could indeed hold that the reward is created in new tax revenues. Yet, globalization and information technology have enabled profits to migrate to low tax regions or even within tax havens. It is clear that innovation is needed in the tax system to ensure that high-risk public spending can continue to guarantee future private innovation. Mazzucato’s analysis provides a framework for thinking about ways to reform the current model to achieve that.

The other direction for public sector innovation relates to ‘green’ technology. It is my own conviction that other than saving the planet, the green direction can, if properly supported, save the economy. By transforming consumption and production patterns and revamping existing structures and infrastructures, green technology can generate economic growth and long-term environmental sustainability. ‘Green growth’ can have an impact equivalent to what suburbanization and postwar reconstruction did to unleash the golden age in the West on the basis of the ‘American way of life’. It is impossible for the new millions of consumers being incorporated into the global economy to find wellbeing following the energy- and materials-intensive path exploited in the past. The limits to resources plus the threat of global warming could either become a powerful brake against the globalization process or the most powerful driver of growth, employment and innovation in a generation.

Mazzucato holds that the ‘green revolution’ will depend on proactive governments. She shows, with ample illustration from the experience of the last decades in Europe, the US, China and Brazil, that success along the green direction has followed where clear, committed and stable government support has been available. As in the case of the US with information technology, it is those countries that are willing to accept the high risks and that are determined to support their entrepreneurs that are likely to lead the world markets in green technologies. Market uncertainty is unavoidable in the

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context of innovation, but policy uncertainty – as experienced in the US and UK with respect to all things ‘green’ – is deadly. Her analysis suggests that success is met by those countries that have been able to reach a strong national consensus and can therefore maintain the level of funding and sustained policy support through the ups and downs of the economy.

This brings us to the third lesson: we need the economic insights of both Keynes and Schumpeter. As Keynes rightly argued, government must become the investor of last resort when the private sector freezes. But in the modern knowledge economy it is not enough to invest in infrastructure or to generate demand for the expansion of production. If innovation has always been – as Schumpeter said – the force driving growth in the market economy, it is even more critical in the information age to continue to direct public resources into catalysing innovation. In her book, following the success of the mission-oriented experience of the United States for public R&D and innovation procurement, Mazzucato argues for the government to overcome recession by intensifying innovation efforts. It would now be crucial for governments to combine traditional infrastructures with modern technologies and to become active in the creation of the new markets through directly promoting and preparing the way for radical innovation.

This is one of those books that should be read by everybody: by those in the public sector that hope to solve the major issues of today; by those in the private sector aware that it is better to engage in a positive-sum game; by economists that need to abandon the narrow understanding of market forces promulgated in conventional economics texts; by academics that seek to do more research into these issues; by students that must realize that widely shared ideas are not necessarily true; by the general public frequently asked to view the State as a burden; and by politicians that need to overcome their fear of government action and design the bold policies that can unleash growth and restore wellbeing to all.

Carlota Perez

Author of Technological Revolutions and Financial Capital: The Dynamics of Bubble and Golden Ages Technological University of Tallinn, Estonia;

London School of Economics, University of Cambridge

and University of Sussex, UK

February 2013

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INTRODUCTION DO SOMETHING DIFFERENT

…our disability is discursive: we simply do not know how to talk about things anymore.

Tony Judt (2010, 34)

A Discursive Battle Never more than today is it necessary to question the role of the State in the economy – a burning issue since Adam Smith’s An Inquiry into the Nature and Causes of the Wealth of Nations (Smith, 1776). This is because in most parts of the world we are witnessing a massive withdrawal of the State, one that has been justified in terms of debt reduction and – perhaps more systematically – in terms of rendering the economy more ‘dynamic’, ‘competitive’ and ‘innovative’. Business is accepted as the innovative force, while the State is cast as the inertial one – necessary for the ‘basics’, but too large and heavy to be the dynamic engine.

The book is committed to dismantling this false image. In the same way that Mexico was stolen from California and Texas through the purposeful fabricated image of the ‘lazy Mexican’ under a palm tree (Acuña 1976), the State has been attacked and increasingly dismantled, through images of its bureaucratic, inertial, heavy-handed character. While innovation is not the State’s main role, illustrating its potential innovative and dynamic character – its historical ability, in some countries, to play an entrepreneurial role in society – is perhaps the most effective way to defend its existence, and size, in a proactive way. Indeed, in Ill Fares the Land, Tony Judt (2010) describes that the attack on the welfare state, over the last three decades, has involved a ‘discursive’ battle – changing the ways we talk about it – with words like ‘administration’ rendering the State less important and adventurous. The book seeks to change how we talk about the State, dismantling the ideological stories and images – separating evidence from fiction.

This work is based on a revised and significant expansion of a report I wrote for DEMOS, a UK-based think tank, on The Entrepreneurial State. Unlike a more traditional academic piece of writing – that can take years from start to finish – I wrote the DEMOS work in a style similar to the political pamphlets of the 1800s: quickly, and out of a sense of urgency. I wanted to convince the UK government to change strategy: to not cut State programmes in the name of making the economy ‘more competitive’ and more ‘entrepreneurial’, but to reimagine what the State can and must do to ensure a sustainable post-crisis recovery. Highlighting the active role that the State has played in the ‘hotbeds’ of innovation and entrepreneurship – like Silicon Valley – was the key to

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showing that the State can not only facilitate the knowledge economy, but actively create it with a bold vision and targeted investment.

This expanded version of the DEMOS report (more than double its size) builds on that initial research and pushes it harder, drawing out further implications at the firm and sectoral level. Chapter 5, dedicated entirely to Apple, looks at the whole span of State support that this leading ‘new economy’ company has received. After looking at the role of the State in making the most courageous investments behind the Internet and IT revolution, Chapters 6 and 7 look at the next big thing: ‘green’ technology. Unsurprisingly we find that across the globe the countries leading in the green revolution (solar and wind energy are the paradigmatic examples explored) are those where the State is playing an active role beyond that which is typically attributed to market failure theory. And the public sector organizations involved, such as development banks in Brazil and China, are not just providing countercyclical lending (as Keynes would have asked for), but are even ‘directing’ that lending towards the most innovative parts of the ‘green’ economy. Questions about whether such ‘directionality’ should raise the usual worries about the State’s inability to ‘pick winners’ are confronted head on – demystifying old assumptions. The book also looks more explicitly at the collective group of actors that are required to create innovation-led growth and questions whether the current innovation ‘ecosystem’ is a functional symbiotic one or a dysfunctional parasitic one. Can a nonconfident State even recognize the difference? Chapters 8 and 9 go deeper into this question by asking how we can make sure that the distribution of the returns (rewards) generated from active State investments in innovation are just as social as the risks taken. Indeed, some of the very criticisms that have recently been directed at the banks (socialization of risk, privatization of rewards) appear to be just as relevant in the ‘real’ innovation economy.

The reason I call, both the DEMOS report and the current book, the ‘entrepreneurial’ State is that entrepreneurship – what every policymaker today seems to want to encourage – is not (just) about start-ups, venture capital and ‘garage tinkerers’. It is about the willingness and ability of economic agents to take on risk and real Knightian uncertainty: what is genuinely unknown.1 Attempts at innovation usually fail – otherwise it would not be called ‘innovation’. This is why you have to be a bit ‘crazy’ to engage with innovation… it will often cost you more than it brings back, making traditional cost–benefit analysis stop it from the start. But whereas Steve Jobs talked about this in his charismatic 2005 Stanford lecture on the need for innovators to stay ‘hungry and foolish’ (Jobs 2005), few have admitted how much such foolishness has been ‘seriously’ riding on the wave of State-funded and -directed innovations.

The State… ‘foolishly’ developing innovations? Yes, most of the radical, revolutionary innovations that have fuelled the dynamics of capitalism – from railroads to the Internet, to modern-day nanotechnology and pharmaceuticals – trace the most courageous, early and capitalintensive ‘entrepreneurial’ investments back to the State.

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And, as will be argued fully in Chapter 5, all of the technologies that make Jobs’ iPhone so ‘smart’ were government funded (Internet, GPS, touch-screen display and the recent SIRI voice activated personal assistant). Such radical investments – which embedded extreme uncertainty – did not come about due to the presence of venture capitalists, nor of ‘garage tinkerers’. It was the visible hand of the State which made these innovations happen. Innovation that would not have come about had we waited for the ‘market’ and business to do it alone – or government to simply stand aside and provide the basics.

Beyond Fixing Failures But how have economists talked about this? They have either ignored it or talked about it in terms of the State simply fixing ‘market failures’. Standard economic theory justifies State intervention when the social return on investment is higher than the private return – making it unlikely that a private business will invest. From cleaning up pollution (a negative ‘externality’ not included in companies’ costs) to funding basic research (a ‘public good’ difficult to appropriate). Yet this explains less than one- quarter of the R&D investments made in the USA. Big visionary projects – like putting ‘a man on the moon’, or creating the vision behind the Internet – required much more than the calculation of social and private returns (Mowery 2010).

Such challenges required a vision, a mission, and most of all confidence about what the State’s role in the economy is. As eloquently argued by Keynes in the The End of Laissez Faire (1926, xxx), ‘The important thing for Government is not to do things which individuals are doing already, and to do them a little better or a little worse; but to do those things which at present are not done at all.’ Such a task requires vision and the desire to make things happen in specific spaces – requiring not just bureaucratic skills (though these are critical, as pointed out by Max Weber)2 but real technology-specific and sector-specific expertise. It is only through an exciting vision of the State’s role that such expertise can be recruited, and is then able to map out the landscape in the relevant space. Indeed, a key part of DARPA’s ‘secret’ – the agency that invented and commercialized the Internet within the US Department of Defense (examined in Chapter 4) – has been its ability to attract talent and create excitement around specific missions. And it is no coincidence that a similar agency in today’s US Department of Energy, ARPA-E, is not only leading US green investments, but also having fun on the way (welcoming the trial and error process in energy research rather than fearing it) and attracting great brains in energy research (Grunwald 2012).

While many of the examples in the book come from the US – purposely to show how the country that is often argued to most represent the benefits of the ‘free-market system’ has one of the most interventionist governments when it comes to innovation – modern-day examples are coming more from ‘emerging’ countries. Visionary investments are exemplified today by confident State investment banks in countries like Brazil and China – not only providing countercyclical lending but also directing that

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lending to new uncertain areas that private banks and venture capitalists (VCs) fear. And here too, like in DARPA, expertise, talent and vision matter. In Brazil, it is no coincidence that BNDES, the State investment bank, is run by two individuals whose background is Schumpeterian innovation economics – and it is their team of experts that have allowed the bold risk taking in key new sectors like biotech and cleantech to occur. The bank is today earning record-level returns in productive, rather than purely speculative, investments: in 2010 its return on equity was an astounding 21.2 per cent (reinvested by the Brazilian Treasury in areas like health and education) while that of the World Bank’s equivalent organization, the International Bank for Reconstruction and Development (IBRD), was not even positive (−2.3 per cent). Equally, it is the Chinese Development Bank that is today leading the country’s investments in the green economy (Sanderson and Forsythe 2012). While the usual suspects worry that these public banks ‘crowd out’ private lending (Financial Times 2012), the truth is that these banks are operating in sectors, and particular areas within these sectors, that the private banks fear. It is about the State acting as a force for innovation and change, not only ‘de-risking’ risk-averse private actors, but also boldly leading the way, with a clear and courageous vision – exactly the opposite image of the State that is usually sold.

From ‘Crowding In’ to ‘Dynamizing In’ And this is the punchline: when organized effectively, the State’s hand is firm but not heavy, providing the vision and the dynamic push (as well as some ‘nudges’ – though nudges don’t get you the IT revolution of the past, nor the green revolution today) to make things happen that otherwise would not have. Such actions are meant to increase the courage of private business. This requires understanding the State as neither a ‘meddler’ nor a simple ‘facilitator’ of economic growth. It is a key partner of the private sector – and often a more daring one, willing to take the risks that business won’t. The State cannot and should not bow down easily to interest groups who approach it to seek handouts, rents and unnecessary privileges like tax cuts. It should seek instead for those interest groups to work dynamically with it in its search for growth and technological change.

Understanding the unique nature of the public sector – as more than an inefficient ‘social’ version of the private sector – impacts the nature of the public–private collaborations that emerge, as well as the ‘rewards’ that the State feels justified to reap (an area I focus on in Chapter 9) . An entrepreneurial State does not only ‘de-risk’ the private sector, but envisions the risk space and operates boldly and effectively within it to make things happen. Indeed, when not confident, it is more likely that the State will get ‘captured’ and bow to private interests. When not taking a leading role, the State becomes a poor imitator of private sector behaviours, rather than a real alternative. And the usual criticisms of the State as slow and bureaucratic are more likely in countries that sideline it to play a purely ‘administrative’ role.

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So it is a self-fulfilling prophecy to treat the State as cumbersome, and only able to correct ‘market failures’. Who would want to work in the State sector if that is how it is described? And is it a coincidence that the ‘picking winners’ problem – the fear that the State is unable to make bold decisions on the direction of change – is discussed especially in countries that don’t have an entrepreneurial vision for the State, i.e. countries where the State takes a backseat and is then blamed as soon as it makes a mistake? Major socioeconomic ‘challenges’ such as climate change and ‘ageing’ require an active State, making the need for a better understanding of its role within public– private partnerships more important than ever (Foray et al. 2012).

Images Matter The cover of this book shows a face of a lion and a pussycat. Which one has ‘animal spirits’ (Keynes’s famous expression) and which one is domesticated and ‘lags’ behind due to passivity? Which is the State? Which is business? This might be an exaggerated dichotomy but it is one that needs consideration because, as I will argue, we are continuously fed the image of just the opposite: a roaring business sector and purring bureaucratic State sector. Even Keynes, in discussing the volatility of private business investment, fed this contrast by talking about ‘animal spirits’ as guiding business investment – the image of a roaring lion. But in a secret letter to Roosevelt he also talked about business as ‘domesticated animals’:

Businessmen have a different set of delusions from politicians, and need, therefore, different handling. They are, however, much milder than politicians, at the same time allured and terrified by the glare of publicity, easily persuaded to be ‘patriots’, perplexed, bemused, indeed terrified, yet only too anxious to take a cheerful view, vain perhaps but very unsure of themselves, pathetically responsive to a kind word. You could do anything you liked with them, if you would treat them (even the big ones), not as wolves or tigers, but as domestic animals by nature, even though they have been badly brought up and not trained as you would wish. It is a mistake to think that they are more immoral than politicians. If you work them into the surly, obstinate, terrified mood, of which domestic animals, wrongly handled, are so capable, the nation’s burdens will not get carried to market; and in the end public opinion will veer their way… (Keynes 1938, 607; emphasis added)

This view, of business not as tigers and lions, but as pussycats means that the State is not only important for the usual Keynesian countercyclical reasons – stepping in when demand and investment is too low – but also at any time in the business cycle to play the role of real tigers. Nowhere is this truer than in the world of innovation – where uncertainty is so high. Indeed, the green revolution that is taking off in the world, only happens to coincide with a crisis environment (and in fact the government’s relevant investments reach much farther back in time). But even if today were a boom period, there would not be enough investments being made in radical green technologies were it

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not for the State. Even during a boom most firms and banks would prefer to fund low- risk incremental innovations, waiting for the State to make its mark in more radical areas. But as with all technological revolutions, green technology requires a bold government to take the lead – as this was the case with the Internet, biotech and nanotech.

Providing such leadership, the State makes things happen that otherwise would not have. But whether this role is justified given the characteristics of ‘public good’ and the role of ‘externalities’ (both critical to the market failure argument), or whether it is justified due to a broader understanding of the State as a courageous actor in the economic system makes all the difference. The former understanding leads to discussions about the possibilities of the State ‘crowding out’ (or ‘crowding in’) private investment, creating a narrow view of what the State is and what policy options are acceptable (Friedman 1979). The latter understanding leads to (more) exciting discussions about what the State can do to raise the ‘animal spirits’ of business – to get it to stop hoarding cash and to spend it in new path-breaking areas. This makes a big difference in how one imagines the policy ‘space’. For a start, it makes the State less vulnerable to hype about what the business sector can (and does) do. It is indeed the weakest States that give in (the most) to the rhetoric that what is needed are different types of ‘tax cuts’ and elimination of regulatory ‘red tape’. A confident government recognizes fully that the business sector might ‘talk’ about tax but ‘walks’ to where new technological and market opportunities are – and that this is strongly correlated with areas characterized by major public sector investments. Did Pfizer recently leave Sandwich, Kent (UK) to go to Boston in the US due to the latter’s lower tax and lower regulation? Or was it due to the fact that the public sector National Institutes of Health (NIH) have been spending close to $30.9 billion per year in the USA funding the knowledge base on which private pharmaceutical firms thrive?

In economics, the ‘crowding-out’ hypothesis is used to analyse the possibility that increased State spending reduces private business investment, since both compete for the same pool of savings (through borrowing), which might then result in higher interest rates which reduces the willingness of private firms to borrow, and hence invest. While Keynesian analysis has argued against this possibility during periods of underutilized capacity (Zenghelis 2011), the point here is that even in the boom (when in theory there is full capacity utilization), there are in practice many parts of the risk landscape where private business fears treading and government leads the way. In fact, the spending that led to the Internet occurred mainly during boom times – as was the government spending that lead to the nanotechnology industry (Motoyama et al. 2001).

Thus a proper defence of the State should argue that it not only ‘crowds in’ private investment (by increasing GDP through the multiplier effect) – a correct but limited point made by Keynesians – it does something more. The way that I interpret Judt’s challenge is that we must start using new words to describe the State. Crowding in is a

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concept that – while defending the public sector – is still using as a benchmark the negative: the possibility that government investment crowds out private investment, by competing for the same limited amount of savings. If we want to describe something positive and visionary, a word that is bolder and offensive, not defensive, should be used. Rather than analysing the State’s active role through its correction of ‘market failures’ (emphasized by many ‘progressive’ economists who rightly see many failures), it is necessary to build a theory of the State’s role in shaping and creating markets – more in line with the work of Karl Polanyi (1944) who emphasized how the capitalist ‘market’ has from the start been heavily shaped by State actions. In innovation, the State not only ‘crowds in’ business investment but also ‘dynamizes it in’ – creating the vision, the mission and the plan. This book is committed to explaining the process by which this happens.

The book tries to change the ways we talk about the State, in order to expand our vision of what it can do – it takes on Judt’s ‘discursive’ battle. From an inertial bureaucratic ‘leviathan’ to the very catalyst for new business investment; from market ‘fixer’ to market shaper and creator; from simply ‘de-risking’ the private sector, to welcoming and taking on risk due to the opportunities it presents for future growth. Against all odds.

Structure of the Book The book is structured as follows:

Chapter 1 begins by confronting the popular image of the State as a bureaucratic machine with a different image of the State as lead risk taker. The State is presented as an entrepreneurial agent – taking on the most risky and uncertain investments in the economy. Rather than understanding State risk taking through the usual lens of ‘market failures’ – with the State acting as an inert bandage for areas underserved by the market – the concept of its entrepreneurial risk taking is introduced. The State does not ‘de- risk’ as if it has a ‘magic wand’ that makes risks disappear. It takes on risks, shaping and creating new markets. The fact economists have no words for this role has limited our understanding of the role the State has played in the past – in areas like Silicon Valley – and the role that it can play in the future, in areas like the ‘green revolution’.3

Chapter 2 provides background to the discussion by looking at how economists understand the role of innovation and technology in economic growth. Whereas a generation ago, technological advance was seen as something that was externally given in economic models, there is now extensive literature to show that actually it is the rate – and direction – of innovation that drives the ability for economies to grow. The chapter juxtaposes two very different frameworks for understanding the role of the State in innovation-led growth – both framed in terms of different types of ‘failures’ that the State corrects. The first is the ‘market failure’ approach, in which the State is simply remedying the wedge between private and social returns. The second is the

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‘systems of innovation’ approach, which looks at R&D spending in a more holistic way, as part of a system in which knowledge is not only produced but also diffused throughout an economy. But even in this second approach the State is mainly fixing failures, this time ‘system failures’ – with the conclusion being that it is ‘facilitating’ innovation by ‘creating the conditions’ for it. These frameworks have provided the justification for increased government spending on innovation, while at the same time – due to the lack of attention on the State as lead risk taker – allowed certain myths to survive. These myths describe the relationship between innovation and growth; the role of SMEs; the meaning of patents in the knowledge economy; the degree to which venture capital is risk-loving; and the degree to which investment in innovation is sensitive to tax cuts of different kinds.

Chapter 3 presents a different view, of an entrepreneurial State acting as a lead risk taker and market-shaper. This is not a substitute for the view espoused in the other two frameworks, but a complement, and one that by being ignored has caused policies informed by the ‘failures’ approach to be limited in nature, and often more ‘ideologically’ driven. Examples are provided from the pharmaceutical industry – where the most revolutionary new drugs are produced mainly with public, not private, funds. I also examine the way in which venture capital has ‘surfed the wave’ of State investments in biotechnology.

Chapter 4 exemplifies the key points on the ‘entrepreneurial State’ by focusing on the recent industrial policy history of the US, and shows that despite common perceptions, there the State has been extremely proactive and entrepreneurial in the development and commercialization of new technologies. Entrepreneurship by the State can take on many forms. Four examples – the creation of the Defense Advanced Research Projects Agency (DARPA), the Small Business Innovation Research (SBIR) programme, the Orphan Drug Act of 1983, and recent developments in nanotechnology – are used to illustrate this point. It builds on the notion of the ‘Developmental State’ (Block 2008; Chang 2008; Johnson 1982) pushing it further by focusing on the type of risk that the public sector has been willing to absorb and take on.

While Chapters 3 and 4 look at sectors, Chapter 5 focuses on the history of one particular company – Apple – a company that is often used to laud the power of the market and the genius of the ‘garage tinkerers’ who revolutionize capitalism. A company that is used to illustrate the power of Schumpeterian creative destruction.4 I turn this notion on its head. Apple is far from the ‘market’ example it is often used to depict. It is a company that not only received early stage finance from the government (through the SBIC programme, which is related to the SBIR programme discussed in Chapter 4), but also ‘ingeniously’ made use of publicly funded technology to create ‘smart’ products. In fact, there is not a single key technology behind the iPhone that has not been State-funded. Besides the communication technologies (discussed in Chapter 4), the iPhone is smart because of features such as the Internet, GPS, a touch-screen

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display, and the latest new voice activated personal assistant (SIRI). While Steve Jobs was no doubt an inspiring genius worthy of praise, the fact that the iPhone/iPad empire was built on these State-funded technologies provides a far more accurate tale of technological and economic change than what is offered by mainstream discussions. Given the critical role of the State in enabling companies like Apple, it is especially curious that the debate surrounding Apple’s tax avoidance has failed to make this fact more broadly known. Apple must pay tax not only because it is the right thing to do, but because it is the epitome of a company that requires the public purse to be large and risk-loving enough to continue making the investments that entrepreneurs like Jobs will later capitalize on (Mazzucato 2013b).

Chapter 6 looks at the next ‘big thing’ after the Internet: the green revolution, which is today being led by the State, just like the IT revolution was. In 2012 China announced its plan to produce 1,000 GWs of wind power by 2050. That would be approximately equal to replacing the entire existing US electric infrastructure with wind turbines. Are the US and Europe still able to dream so big? It appears not. In many countries, the State is asked to take a back seat and simply ‘subsidize’ or incentivize investments for the private sector. We thus fail to build visions for the future similar to those that two decades ago resulted in the mass diffusion of the Internet. The chapter looks at which countries in the world are leading with a green vision, and the role of their States – and the ‘patient’ finance supplied by State development banks – in creating the ‘catalytical’ early, and risky, investments necessary to make it happen.

Chapter 7 focuses on the role of the ‘entrepreneurial’ risk-taking State in launching specific clean technologies, in this case wind turbines and solar PV panels. It was State funding and the work of particular State agencies that provided the initial push, early stage high-risk funding and institutional environment that could establish these important technologies. While Chapter 5 emphasized the role of the US entrepreneurial State in leading the IT revolution as well as in establishing the foundations of the biotech industry, this chapter emphasizes the role of countries like Germany, Denmark and China in directing the green revolution as it spreads across more economies.

Chapters 8 and 9 argue that once we accept the role of the State as lead risk taker – beyond the usual ‘market fixing’ or ‘creating conditions’ approach – the question arises as to whether this role is represented in the risk–reward relationship. In so many cases, public investments have become business giveaways, making individuals and their companies rich but providing little (direct or indirect) return to the economy or to the State. This is most evident in the case of pharmaceuticals, where publicly funded drugs end up being too expensive for the taxpayers (who funded them) to purchase. It is also true in the case of IT, where the State’s active risk-taking investments have fuelled private profits, which are then sheltered and fail to pay taxes back to the governments that supported them. Chapter 8 illustrates this point focusing in on Apple. Chapter 9 considers the points more generally, arguing that in a period of major cutbacks to

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reduce budget deficits, it is more critical than ever to engage in a discussion of how the State can ensure that its ‘risk taking’ earns back a direct return, beyond easily avoided taxation. Precisely because State investments are uncertain, there is a high risk that they will fail. But when they are successful, it is naïve and dangerous to allow all the rewards to be privatized. Indeed, criticism of the financial sector for launching the current economic crisis, reaping massive private returns and then socializing risk through unpopular bailouts is a general and unpopular feature of dysfunctional modern capitalism that should not become the norm.

Chapter 10 concludes by reflecting on how the core argument in the book – the State as an active, entrepreneurial, risk-taking agent – is not always a reality, but a possibility too often dismissed. The ‘possibility’ is only realized once key assumptions are overturned. From how we envision the State within its own organizations (encouraging departments in the public sector to be entrepreneurial, including the need to ‘welcome’ rather than fear failure), to the relationship between the State and other actors in the innovation system (e.g. by accepting itself as a more active agent, there will be many instances where the State’s role is less about ‘nudging’ and ‘incentivizing’ and more about ‘pushing’). The State’s ability to push and direct is dependent on the kind of talent and expertise it is able to attract. And the irony is that the latter is more of a problem in countries where the State takes a back seat, only ‘administering’ and not leading with dynamic vision. Unless we challenge the numerous ‘myths’ of economic development, and abandon conventional views of the State’s role in it, we cannot hope to address the structural challenges of the twenty-first century nor produce the technological and organizational change we need for long-term sustainable and equitable growth.

Taken as a whole, the book provides a fuller understanding of the public sector’s centrality to risk-taking activities and radical technological change, essential to promote growth and development. It offers a very different description of the State from that envisaged by present economic policymakers, which tends to deny the State’s leading role in innovation and production. It also challenges conventional industrial policy, which unduly downplays its scope for pioneering and promoting new technologies. In contrast, it describes scenarios where the State has provided the main source of dynamism and innovation in advanced industrial economies, by pointing out that the public sector has been the lead player in what is often referred to as the ‘knowledge economy’ – an economy driven by technological change and knowledge production and diffusion. From the development of aviation, nuclear energy, computers, the Internet, biotechnology, and today’s developments in green technology, it is, and has been, the State – not the private sector – that has kick-started and developed the engine of growth, because of its willingness to take risks in areas where the private sector has been too risk averse. In a political environment where the policy frontiers of the State are now being deliberately rolled back, the contributions of the State need to be

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understood more than ever. Otherwise we miss an opportunity to build greater prosperity in the future by emulating the successful public investments of the past.

What is needed is a fully-fledged understanding of the division of innovative labour in capitalism (described in Chapter 1 below), and the role that both the private and public sector play in creating, producing and diffusing innovations. The book focuses on innovation not because this is the only or most important thing the State can invest in. The State’s role in guaranteeing basic human rights for all citizens – from public healthcare to public education – as well as creating the necessary infrastructure, legal and justice system that allows the economy to function properly are equally if not more important activities. The focus on innovation is due in part to the fact that it is a point of discussion where the State is most frequently attacked for its role. While the role of the private sector has typically been hyped up, the public sector’s role has been hyped down. The State is often being cast as the problem, whether it is investing in new technology or improving market function. A key aspect of the challenge is therefore to rebalance our understanding of how economies really work. Only once that is done can we begin to formulate the kinds of policies that work, rather than reproduce stereotypes and images which serve only ideological ends.

1 ‘Knightian uncertainty’ refers to the ‘immeasurable’ risk, i.e. a risk that cannot be calculated. This economic concept is named after University of Chicago economist Frank Knight (1885–1972), who theorized about risk and uncertainty and their differences in economic terms.

2 Evans and Rauch (1999) show, for instance, that a Weberian-type State bureaucracy that employs meritocratic recruitment and offers predictable, rewarding longterm careers enhances prospects for growth, even when controlling for initial levels of GDP per capita and human capital.

3 Contemporary political economists, such as Chang (2008) and Reinert (2007), who specialize in the history of economic policy do of course talk about the role of the State in promoting a ‘catching-up’ process, or in actively acting countercyclically. Yet these are more in line with a view of the State not as an entrepreneurial risk taker (of first resort) but a more passive entrepreneur of last resort.

4 Joseph Schumpeter (1942 [2003]) referred to ‘creative destruction’ as the process by which innovation changes the status quo, allowing the market shares of firms which introduce new products and processes to grow, and those of the firms that resist change to fall.

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Chapter 1 FROM CRISIS IDEOLOGY

TO THE DIVISION OF INNOVATIVE LABOUR

Governments have always been lousy at picking winners, and they are likely to become more so, as legions of entrepreneurs and tinkerers swap designs online, turn them into products at home and market them globally from a garage. As the revolution rages, governments should stick to the basics: better schools for a skilled workforce, clear rules and a level playing field for enterprises of all kinds. Leave the rest to the revolutionaries.

Economist (2012) Across the globe we are hearing that the State has to be cut back in order to foster a post-crisis recovery. The assumption is that, with the State in the backseat, we unleash the power of entrepreneurship and innovation in the private sector. The media, business and libertarian politicians draw from this convenient contrast, and feed into the dichotomy of a dynamic, innovative and competitive ‘revolutionary’ private sector versus a sluggish, bureaucratic, inertial, ‘meddling’ public sector. The message is repeated so much so that it is accepted by the many as a ‘common sense’ truth, and has even made many believe that the 2007 financial crisis, which soon precipitated into a full blown economic crisis, was caused by public sector debt, rather than the truth.

And the language used has been forceful. In March 2011, UK prime minister David Cameron promised to take on the ‘enemies of enterprise’ working in government, which he defined as the ‘bureaucrats in government departments’ (Wheeler 2011). The rhetoric fits in with the UK government’s broader theme of the Big Society, where responsibility for the delivery of public services is shifted away from the State to individuals operating either on their own or by coming together through the third sector – with the justification that such ‘freedom’ from the State’s influence will reinvigorate such services. The terms used, such as ‘free’ schools (the equivalent of charter schools in the USA) imply that by freeing schools from the heavy hand of the State, they will be both more interesting to students and also run more efficiently.

The increasing percentage of public services, across the globe, that are being ‘outsourced’ to the private sector, is usually done using precisely this ‘efficiency’ argument. Yet a proper look at the real cost savings that such outsourcing provides – especially taking into account the lack of ‘quality control’ and absurd costs that ensue – is almost never carried out. The recent scandal where the security for London’s 2012 Olympics was outsourced to a company called G4S, which then failed due to utter

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incompetence to deliver, meant that the British Army was called in to provide security during the Olympics. While the managers of the company were ‘reprimanded’ the company today is still making profits and outsourcing remains on the rise. Examples where outsourcing is resisted, such as the BBC’s choice to build the Internet platform for its broadcasting, the iPlayer, in-house has meant that it has been able to keep the BBC a dynamic innovative organization, that continues to attract top talent, retaining its high market share in both radio and TV – what public broadcasters in other countries can only dream of.

The view of the State as enemy of enterprise is a point of view found constantly in the respected business press, such as the Economist, which often refers to government as a ‘Hobbesian Leviathan’ which should take the back seat (Economist 2011a). Their prescription for economic growth includes focusing on creating freer markets and creating the right conditions for new ideas to prosper, rather than taking a more activist approach (Economist 2012). And in a recent special issue on the green revolution, the magazine explicitly made the case, as quoted in the beginning of this chapter, that while the government should ‘stick to the basics’, such as funding education and research, the rest should be left to the ‘revolutionaries’, i.e. businesses. Yet as will be argued in Chapters 4–8, this revolutionary spirit is often hard to find in the private sector, with the State having to take on the greatest areas of risk and uncertainty.

When not lobbying the State for specific types of support, established business lobby groups – in areas as diverse as weapons, medicine and oil – have long argued for freedom from the long arm of the State, which they see as stifling their ability to succeed through the imposition of employee rights, tax and regulation. The conservative Adam Smith Institute argues that the number of regulators in the UK should be reduced to enable the British economy to ‘experience a burst of innovation and growth’ (Ambler and Boyfield 2010, 4). In the USA, supporters of the Tea Party movement are united by a desire to limit State budgets and promote free markets. Big pharmaceutical companies, which, as we will see in Chapter 3, are some of the biggest beneficiaries of publicly funded research, constantly argue for less regulation and ‘meddling’ in what they claim is a very innovative industry.

And in the Eurozone And, in the eurozone, it is today argued that all the ills of the ‘peripheral’ EU countries like Portugal and Italy come from having a ‘profligate’ public sector, ignoring the evidence that such countries are characterized more by a stagnant public sector which has not made the kind of strategic investments that the more successful ‘core’ countries, such as Germany, have been making for decades (Mazzucato 2012b).

The power of the ideology is so strong that history is easily fabricated. A remarkable aspect of the financial crisis that began in 2007 was that even though it was blatantly caused by excessive private debt (mainly in the US real estate market), many people

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were later led to believe that the chief culprit was public debt. It is true that public sector debt (Alessandri and Haldane 2009) rose drastically both due to the government-funded bank bailouts and reduced tax receipts that accompanied the ensuing recession in many countries. But it can hardly be argued that the financial crisis, or the resulting economic crisis, was caused by public debt. The key issue was not the amount of public sector spending but the type of spending. Indeed, one of the reasons that Italy’s growth rate has been so low for the last 15 years is not that it has been spending too much but that it has not been spending enough in areas like education, human capital and R&D. So even with a relatively modest pre-crisis deficit (around 4 per cent), its debt/GDP ratio kept rising because the rate of growth of the denominator in this ratio remained close to zero.

While there are of course low-growth countries with large public debts, the question of which causes which is highly debatable. Indeed, the recent controversy over the work of Reinhart and Rogoff (2010) shows just how heated the debate is. What was most shocking, however, from that recent debate was not only the finding that their statistical work (published in what is deemed the top economics journal) was done incorrectly (and recklessly), but how quickly people had believed the core result: that debt above 90 per cent of GDP will necessarily bring down growth. The corollary became the new dogma: austerity will necessarily (and sufficiently) bring back growth. And yet there are many countries with higher debt that have grown in a stable fashion (such as Canada, New Zealand and Australia – all ignored by their results). Even more obvious is the point that what matters is surely not the aggregate size of the public sector, but what it is spending on. Spending on useless paperwork, or kickbacks, is surely not the same thing as spending on making a healthcare system more functional and efficient, or spending on top-quality education or groundbreaking research that can fuel human capital formation and future technologies. Indeed, the variables that economists have found to be important for growth – such as education and research and development – are expensive. The fact that the weakest countries in Europe, with high debt/GDP ratios, have been spending very little in these areas (thus causing the denominator in this ratio to suffer) should not come as a surprise. Yet the austerity recipes that are currently being forced on them will make this problem only worse.

And this is where there is a self-fulfilling prophecy: the more we talk down the State’s role in the economy, the less able we are to up its game and make it a relevant player, and so the less able it is to attract top talent. Is it a coincidence that the US Department of Energy, which is the lead spender on R&D in the US government and one of the lead spenders (per capita) on energy research in the OECD, has been able to attract a Nobel Prize–winning physicist to run it? Or that those countries with much less ambitious plans for government organizations are more susceptible to crony-type promotions and little expertise within ministries? Of course the problem is not simply of ‘expertise’, but the ability to attract it is an indicator of the importance it is given within public agencies in a given country.

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State Picking Winners vs. Losers Picking the State We are constantly told that the State should have a limited role in the economy due to its inability to ‘pick winners’, whether the ‘winners’ are new technologies, economic sectors or specific firms. But what is ignored is that, in many of the cases that the State ‘failed’, it was trying to do something much more difficult than what many private businesses do: either trying to extend the period of glory of a mature industry (the Concorde experiment or the American Supersonic Transport project), or actively trying to launch a new technology sector (the Internet, or the IT revolution).

Operating in such difficult territory makes the probability of failure much higher. Yet by constantly bashing the State’s ability to be an effective and innovative agent in society, not only have we too easily blamed the State for some of its failures, we have also not developed the accurate metrics needed to judge its investments fairly. Public venture capital, for example, is very different from private venture capital. It is willing to invest in areas with much higher risk, while providing greater patience and lower expectations of future returns. By definition this is a more difficult situation. Yet the returns to public versus private venture capital are compared without taking this difference into account.

Ironically, the inability of the State to argue its own position, to explain its role in the winners that have been picked (from the Internet to companies like Apple) has made it easier to criticize it for its occasional failures (e.g. the Supersonic Transport project). Or even worse, it has responded to criticism by becoming vulnerable and timid, easily ‘captured’ by lobbies seeking public resources for private gain, or by pundits that parrot the ‘myths’ about the origins of economic dynamism.

In the late 1970s capital gains taxes fell significantly following lobbying efforts on behalf of the US venture capital industry (Lazonick 2009, 73). The lobbyists argued before the government that venture capitalists had funded both the Internet and the early semiconductor industry, and that without venture capitalists innovation would not happen. Thus the same actors who rode the wave of expensive State investments in what would later become the dot.com revolution, successfully lobbied government to reduce their taxes. In that way the government’s own pockets, so critical for funding innovation, were being emptied by those who had depended on it for their success.

Furthermore, by not being confident of its own role, government has been easily captured by the myths describing where innovation and entrepreneurship come from. Big Pharma tries to convince government that it is subject to too much regulation and red tape, while it is simultaneously dependent on government-funded R&D. Small business associations have convinced governments in many countries that they are underfunded as a category. Yet in many countries, they receive more support than the police force, without providing the jobs or innovation that helps justify such support (Hughes 2008; Storey 2006). Had the State better understood how its own investments

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have led to the emergence of the most successful new companies, like Google, Apple and Compaq, it would perhaps mount a stronger defence against such arguments.

But the State has not had a good marketing/communications department. Imagine how much easier President Barack Obama’s fight for US national healthcare policy would have been if the US population knew the important role that the US government had in funding the most radical new drugs in the industry (discussed in Chapter 3). This is not ‘propaganda’ – it’s raising awareness about history of technology. In health, the State has not ‘meddled’ but created and innovated. Yet the story told, and unfortunately believed, is one of an innovative Big Pharma and a meddling government. Getting the (complex) history right is important for many reasons. Indeed, the high prices charged for drugs, whether they are subsidized by the State or not, are justified by the industry with their alleged ‘high R&D costs’. Uncovering the truth not only helps government policies to be better designed but also can help the ‘market’ system work better.

The emphasis on the State as an entrepreneurial agent is not of course meant to deny the existence of private sector entrepreneurial activity, from the role of young new companies in providing the dynamism behind new sectors (e.g. Google), to the important source of funding from private sources like venture capital. The key problem is that this is the only story that is usually told. Silicon Valley and the emergence of the biotech industry are usually attributed to the geniuses behind the small high-tech firms like Facebook, or the plethora of small biotech companies in Boston (US) or Cambridge (UK). Europe’s ‘lag’ behind the USA is often attributed to its weak venture capital sector. Examples from these high-tech sectors in the USA are often used to argue why we need less State and more market: tipping the balance in favour of the market would allow Europe to produce its own ‘Googles’. But how many people know that the algorithm that led to Google’s success was funded by a public sector National Science Foundation grant (Battelle 2005)? Or that molecular antibodies, which provided the foundation for biotechnology before venture capital moved into the sector, were discovered in public Medical Research Council (MRC) labs in the UK? How many people realize that many of the most innovative young companies in the US were funded not by private venture capital but by public venture capital, such as that provided by the Small Business Innovation Research (SBIR) programme?

Lessons from these experiences are important. They force the debate to go beyond the role of the State in stimulating demand, or the worry of ‘picking winners’. What we have instead is a case for a targeted, proactive, entrepreneurial State, one able to take risks and create a highly networked system of actors that harness the best of the private sector for the national good over a medium- to long-term time horizon. It is the State acting as lead investor and catalyst which sparks the network to act and spread knowledge. The State can and does act as creator, not just facilitator of the knowledge economy.

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Arguing for an entrepreneurial State is not ‘new’ industrial policy because it is in fact what has happened. As Block and Keller (2011, 95) have explained so well, the industrial directives of the State are ‘hidden’ primarily to prevent a backlash from the conservative right. Evidence abounds of the State’s pivotal role in the history of the computer industry, the Internet, the pharmaceutical-biotech industry, nanotech and the emerging green tech sector. In all these cases, the State dared to think – against all odds – about the ‘impossible’: creating a new technological opportunity; making the initial large necessary investments; enabling a decentralized network of actors to carry out the risky research; and then allowing the development and commercialization process to occur in a dynamic way.

Beyond Market Failures and System Failures Economists willing to admit the State has an important role have often argued so using a specific framework called ‘market failure’. From this perspective the fact that markets are ‘imperfect’ is seen as the exception, which means that the State has a role to play – but not a very interesting one. Imperfections can arise for various reasons: the unwillingness of private firms to invest in areas, like basic research, from which they cannot appropriate private profits because the results are a ‘public good’ accessible to all firms (results of basic R&D as a positive externality); the fact that private firms do not factor in the cost of their pollution in setting prices (pollution as a negative externality); or the fact that the risk of certain investments is too high for any one firm to bear them all alone (leading to incomplete markets). Given these different forms of market failure, examples of the expected role of the State would include publicly funded basic research, taxes levied on polluting firms and public funding for infrastructure projects. While this framework is useful, it cannot explain the ‘visionary’ strategic role that government has played in making these investments. Indeed, the discovery of the Internet or the emergence of the nanotechnology industry did not occur because the private sector wanted something but could not find the resources to invest in it. Both happened due to the vision that the government had in an area that had not yet been fathomed by the private sector. Even after these new technologies were introduced by government, the private sector still was too scared to invest. Government even had to support the commercialization of the Internet. And it took years for private venture capitalists to start financing biotech or nanotech companies. It was – in these and many such cases – the State that appeared to have the most aggressive ‘animal spirits’.

There are many counterexamples that would characterize the State as far from an ‘entrepreneurial’ force. Developing new technologies and supporting new industries is not the only important role of the State, after all. But admitting the instances where it has played an entrepreneurial role will help inform policies, which are too often based on the assumption that at most the State’s role is to correct market failures or facilitate innovation for the ‘dynamic’ private sector. The assumptions that all the State has to do is to ‘nudge’ the private sector in the right direction; that tax credits will work because

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business is eager to invest in innovation; that removing obstacles and regulations is necessary; that small firms – simply due to their size – are more flexible and entrepreneurial and should be given direct and indirect support; that the core problem in Europe is simply one of ‘commercialization’ – are all myths. They are myths about where entrepreneurship and innovation come from. They have prevented policies from being as effective as they could be in stimulating the kinds of innovation that businesses would not have attempted on their own.

The Bumpy Risk Landscape As will be explained in more detail in the next chapter, innovation economists from the ‘evolutionary’ tradition (Nelson and Winter 1982) have argued that ‘systems’ of innovation are needed so that new knowledge and innovation can diffuse throughout the economy, and that systems of innovation (sectoral, regional, national) require the presence of dynamic links between the different actors (firms, financial institutions, research/education, public sector funds, intermediary institutions), as well as horizontal links within organizations and institutions (Lundvall 1992; Freeman 1995). What has been ignored even in this debate, however, is the exact role that each actor realistically plays in the ‘bumpy’ and complex risk landscape. Many errors of current innovation policy are due to placing actors in the wrong part of this landscape (both in time and space). For example, it is naïve to expect venture capital to lead in the early and most risky stage of any new economic sector today (such as clean technology). In biotechnology, nanotechnology and the Internet, venture capital arrived 15–20 years after the most important investments were made by public sector funds.

In fact, history shows that those areas of the risk landscape (within sectors at any point in time, or at the start of new sectors) that are defined by high capital intensity and high technological and market risk tend to be avoided by the private sector, and have required great amounts of public sector funding (of different types), as well as public sector vision and leadership to get them off the ground. The State has been behind most technological revolutions and periods of long-run growth. This is why an ‘entrepreneurial State’ is needed to engage in risk taking and the creation of a new vision, rather than just fixing market failures.

Not understanding the role that different actors play makes it easier for government to get ‘captured’ by special interests which portray their role in a rhetorical and ideological way that lacks evidence or reason. While venture capitalists have lobbied hard for lower capital gains taxes (mentioned above), they do not make their investments in new technologies on the basis of tax rates; they make them based on perceived risk, something typically reduced by decades of prior State investment. Without a better understanding of the actors involved in the innovation process, we risk allowing a symbiotic innovation system, in which the State and private sector mutually benefit, to transform into a parasitic one in which the private sector is able to leach benefits from a

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State that it simultaneously refuses to finance.

Symbiotic vs. Parasitic Innovation ‘Ecosystems’ It is now common to talk about innovation ‘systems’ as ‘ecosystems’. Indeed it seems to be on the tongue of many innovation specialists and policymakers. But how can we be sure that the innovation ecosystem is one that results in a symbiotic relationship between the public and private sector rather than a parasitic one? That is, will increased investments by the State in the innovation ecosystem cause the private sector to invest less, and use its retained earnings to fund short-term profits (via practices like ‘share buybacks’), or more, in riskier areas like human capital formation and R&D, to promote long-term growth?

Usually a question like this might be framed in terms of the ‘crowding-out’ concept. Crowding out is a hypothesis in economics that says that the danger of State investment is that it uses up savings that could have been used by the private sector for its own investment plans (Friedman 1979). Keynesians have argued against the idea that State spending crowds out private investment, by emphasizing that this would only hold in a period of full resource utilization, a state that hardly ever occurs. However, the issues raised in this book present a different view: that an entrepreneurial State invests in areas that the private sector would not invest even if it had the resources. And it is the courageous risk-taking visionary role of the State which has been ignored. Business investment is mainly limited not by savings but by its own lack of courage (or Keynesian ‘animal spirits’) – the ‘business as usual’ state of mind. Indeed, firm-level studies have shown that what drives entry behaviour into industries (companies deciding to move into one particular sector) are not existing profits in that sector but projected technological and market opportunities (Dosi et al. 1997). And such opportunities are linked to the amount of State investment in those areas.

But what if that potentially courageous aspect of the private sector is diminished precisely because the public sector fills the gap? Rather than framing the question in terms of ‘crowding out’, I believe we must frame it in such a way that results in building private–public partnerships that are more symbiotic and less parasitic. The problem is not that the State has financed too much innovation, making the private sector less ambitious. It is that policymakers have not been ambitious enough to demand that such support be part of a more collaborative effort in which the private sector also steps up to the challenge. Instead big R&D labs have been closing, and the R of the R&D spend has also been falling, with BERD (business expenditure on R&D) falling in many countries like the UK (Hughes and Mina 2011). While State spending on R&D and business spending tend to be correlated (the former ups the game for the latter), it is important that policymakers be more courageous – not only in agreeing to ‘fund’ sectors but also in demanding that businesses in those sectors increase their own stakes and commitment to innovation. A recent study by MIT claims that the current

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absence in the US of corporate labs like Xerox PARC (which produced the graphical user interface technology that led to both Apple’s and Windows’ operating systems) and Bell Labs – both highly co-financed by government agency budgets – is one of the reasons why the US innovation machine is under threat (MIT 2013).

The problem is also evidenced in industries, like pharmaceuticals, where there is a trend of increasing public sector investments in R&D, while private sector spending is decreasing. According to Lazonick and Tulum (2012), the National Institutes of Health (NIH) have spent more than $300 billion over the last decade ($30.9 billion in 2012 alone), and become more involved in the D component of R&D, meaning they absorb greater costs of drug development (such as through clinical trials), while private pharmaceutical companies1 have been spending less on R&D overall, with many shutting down R&D labs altogether. Of course the total R&D spent may be increasing, because the development (D) part is getting increasingly expensive. But this hides the underlying issue. While some analysts have justified the decreasing expenditure on research in terms of low productivity of R&D (increased expenditures, not matched by increased discoveries), others, like Angell (1984, ex-editor of the New England Journal of Medicine), have been more explicit in blaming Big Pharma for not doing its share. She argues that for decades the most radical new drugs have been coming out of public labs, with private pharma concerned more with ‘me too’ drugs (slight variations of existing drugs) and marketing (see Chapter 3 for more details). And in recent years, CEOs of large pharma companies have admitted that their decision to downsize – or in some cases eliminate – their R&D labs is due to their recognition that in the ‘open’ model of innovation most of their research is obtained by small biotech firms or public labs (Gambardella 1995; China Briefing 2012). Big Pharma’s focus is thus turned to working with such alliances, and ‘integrating’ knowledge produced elsewhere, rather than funding R&D internally.

Financialization One of the greatest problems, which we return to in Chapter 9, has been the way in which such reductions in spending on R&D have coincided with an increasing ‘financialization’ of the private sector. While causality may be hard to prove, it cannot be denied that at the same time that private pharma companies have been reducing the R of R&D, they have been increasing the amount of funds used to repurchase their own shares – a strategy used to boost their stock price, which affects the price of stock options and executive pay linked to such options. For example, in 2011, along with $6.2 billion paid in dividends, Pfizer repurchased $9 billion in stock, equivalent to 90 per cent of its net income and 99 per cent of its R&D expenditures. Amgen, the largest dedicated biopharma company, has repurchased stock in every year since 1992, for a total of $42.2 billion through 2011, including $8.3 billion in 2011. Since 2002 the cost of Amgen’s stock repurchases has surpassed the company’s R&D expenditures in every year except 2004, and for the period 1992–2011 was equal to fully 115 per cent of R&D

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outlays and 113 per cent of net income (Lazonick and Tulum 2011). The fact that top pharma companies are spending a decreasing amount of funds on R&D at the same time that the State is spending more – all while increasing the amount they spend on share buybacks, makes this particular innovation ecosystem much more parasitic than symbiotic. This is not the ‘crowding out’ effect: this is free-riding. Share buyback schemes boost stock prices, benefitting senior executives, managers and investors that hold the majority of company stock. Boosting share prices does not create value (the point of innovation), but facilitates its extraction. Shareholders and executives are thus ‘rewarded’ for riding the innovation wave the State created. In Chapter 9 I look more closely at the problem of value extraction and ask whether and how some of the ‘returns’ from innovation should be returned to the employees and State that are also key contributors and stakeholders in the innovation process.

Unfortunately the same problem seems to be appearing in the emerging clean technology sector. In 2010, the US American Energy Innovation Council (AEIC), an industry association, asked the US government to increase its spending on clean technology by three times to $16 billion annually, with an additional $1 billion given to the Advanced Research Projects Agency – Energy (Lazonick 2011c). On the other hand, companies in the council have together spent $237 billion on stock repurchases between 2001 and 2010. The major directors of the AEIC come from companies with collective 2011 net incomes of $37 billion and R&D expenditures of approximately $16 billion. That they believe their own companies’ enormous resources are inadequate to foster greater clean technology innovation is indicative of the State’s role as the first driver of innovation or of their own aversion to taking on risks – or both.

The problem of share buybacks is not isolated but rampant: in the last decade, S&P 500 companies have spent $3 trillion on share buybacks (Lazonick 2012). The largest repurchasers (especially in oil and pharmaceuticals) claim that this is due to the lack of new opportunities. In fact in many cases the most expensive (e.g. capital-intensive) investments in new opportunities such as medicine and renewable energy (investments with high market and technological risk) are being made by the public sector (GWEC 2012). This raises the question of whether the ‘open innovation’ model is becoming a dysfunctional model. As large companies are increasingly relying on alliances with small companies and the public sector, the indication is that large players invest more in short-run profit gains (through market gimmicks) than long-run investments. I return to this question in Chapters 9 and 10.

Now that ‘new’ industrial policy is back on the agenda, with many nations trying to ‘rebalance’ their economies away from finance and towards ‘real’ economy sectors, it is more important than ever to question exactly what this rebalancing will entail (Mazzucato 2012a). While some have focused on the need for different types of private– public partnerships that can foster innovation and economic growth, what I’m arguing here (and will focus on more in Chapters 8 and 9) is that we need to be more careful to

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build the type of partnerships which increase the stakes of all involved, and which do not lead to similar problems that the financialization of the economy led to: socialization of risk, privatization of rewards.

The work of Rodrik (2004) has been particularly important in highlighting the need to rethink public and private sector interactions, and to focus more on processes rather than policy outcomes. His focus is on the types of exploratory processes that allow the public and private sectors to learn from each other, especially the opportunities and constraints that each face (Rodrik 2004, 3). He takes this to mean that the problem is not which types of tools (R&D tax credits vs. subsidies) or which types of sectors to choose (steel vs. software), but how policy can foster self-discovery processes, which will foster creativity and innovation. While I agree with Rodrick’s general point about the need to foster exploration and trial and error (and this is in fact a core tenet of the ‘evolutionary theory of economic change’, which I review in the next chapter), I believe that the history of technological change teaches us that choosing particular sectors in this process is absolutely crucial. The Internet would never have happened without it being forcefully ‘picked’ by DARPA, and the same holds for nanotechnology which was picked by the NSF and later by the National Nanotech Initiative (both discussed in Chapter 4). And, most importantly, the green revolution will not take off until it is firmly picked and backed by the State (as will be discussed in Chapters 6 and 7).

Coming back to Keynes’s (1926) fundamental point about the essential role of government, what we need to ask is: how can horizontal and vertical tools and policies ‘make things happen’ that would not have otherwise? The problem with R&D tax credits is not that they are specific policy tools, but they have been designed wrongly and do not increase private investments in R&D. Evidence shows that targeting R&D labour rather than R&D income (through credits) is much better for that (Lockshin and Mohnen 2012). And the problems with throwing money at a particular area like life sciences is not that it was ‘picked’ but that it was not first transformed to be less dysfunctional before it was supported. When so many ‘life science’ companies are focusing on their stock price rather than on increasing their side of the R in R&D, simply subsidising their research will only worsen the problem rather than create the type of learning that Rodrik (2004) rightly calls for.

1 From now on ‘pharma’ will refer to pharmaceutical companies, and Big Pharma the top international pharma companies.

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Chapter 2 TECHNOLOGY, INNOVATION

AND GROWTH

You can see the computer age everywhere but in the productivity statistics. Solow (1987, 36)

In a special report on the world economy, the Economist (2010a) stated: A smart innovation agenda, in short, would be quite different from the one that most rich governments seem to favor. It would be more about freeing markets and less about picking winners; more about creating the right conditions for bright ideas to emerge and less about promises like green jobs. But pursuing that kind of policy requires courage and vision – and most of the rich economies are not displaying enough of either.

This view is also espoused by some ‘progressive’ academics, who argue that the State is limited to creating the ‘conditions for innovation’:

…accepting that the state will have a vital role in ensuring that market conditions reach the ‘just right’ balance which will spur innovation and that adequate investment is available for innovators. (Lent and Lockwood 2010, 7)

This is the view that asks little of government other than correcting market failures – such as through investment in basic science, education and infrastructure. The ‘appropriate’ role of the State is not a new debate, but it is one that benefits from a broader understanding of the academic literature on the role of innovation in creating economic growth.

Over two hundred and fifty years ago, when discussing his notion of the ‘Invisible Hand’, Adam Smith argued that capitalist markets left on their own would self-regulate, with the State’s role being limited to that of creating basic infrastructure (schools, hospitals, motorways) and making sure that private property, and ‘trust’ (a moral code) between actors, were nurtured and protected (Smith 1904 [1776]). Smith’s background in politics and philosophy meant that his writings were much more profound than the simple libertarian economics position for which he is usually acknowledged, but there is no escaping that he believed that the magic of capitalism consisted in the ability of the market to organize production and distribution without coercion by the State.

The path-breaking work of Karl Polanyi (who had a doctorate in law but is considered an important economist) has instead shown how the notion of the market as self-regulating is a myth unsupported by the historical origins of markets: ‘The road to the free market was opened and kept open by an enormous increase in continuous,

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centrally organized and controlled interventionism’ (Polanyi 2001 [1944], 144). In his view, it was the State which imposed the conditions that allowed for the emergence of a market-based economy. Polanyi’s work has been revolutionary in showing the myth of the State vs. market distinction: the most capitalist of all markets, i.e. the national market, was forcefully ‘pushed’ into existence by the State. If anything it was the more local and international markets, which have pre-dated capitalism, that have been less tied to the State. But capitalism, the system that is usually thought of being ‘market’ driven, has been strongly embedded in, and shaped by, the State from day one (Evans 1995).

John Maynard Keynes believed that capitalist markets, regardless of their origin, need constant regulation because of the inherent instability of capitalism. Keynes contended that the stability of capitalism was dependent on keeping all of the four categories of spending (aggregate demand) in GDP in balance with one another: business investment (I), government investment (G), consumption spending (C), and net exports (X−M). A key source of extreme volatility was found in private business investment. The reason it is so volatile is that far from being a simple function of interest rates or taxes, 1 it is subject to ‘animal spirits’ – the gut-instinct assumptions made about future growth prospects in an economy or specific sector by investors (Keynes 1934). In his view, this uncertainty constantly creates periods of under- or overinvestment, causing severe fluctuations in the economy that are compounded by the multiplier effect. According to Keynes, unless private investment is balanced by increased government spending, declines in consumption and investment will lead to market crashes and depressions, which were indeed a frequent fact of life before Keynes’s ideas found their way into post–Second World War economic policies.

Keynesians have argued forcefully for the importance of using government spending to boost demand and stabilize the economy. Economists, inspired by the work of Joseph Schumpeter (1883–1950), have gone further, asking that the government also spend on those specific areas that increase a nation’s capacity for innovation (reviewed further below). Support for innovation can take the form of investments made in R&D, infrastructure, labour skills, and in direct and indirect support for specific technologies and companies.

On the left side of the political spectrum, investments into programme areas that increase productivity have been less fashionable than simple spending on welfare state institutions such as education or health. But welfare state institutions cannot survive without a productive economy behind it that generates the profits and tax receipts that can fund such entitlements (Nordhaus and Shellenberger 2011; Atkinson 2011). While progressive redistributional policies are fundamental to ensuring that the results of economic growth are fair, they do not in themselves cause growth. Inequality can hurt growth but equality does not alone foster it. What has been missing from much of the Keynesian left is a growth agenda which creates and simultaneously redistributes the

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riches. Bringing together the lessons of Keynes and Schumpeter can make this happen. This is why the last chapters of this book focus on the need to better understand why innovation and inequality can go hand in hand, and how this requires realigning the risks and rewards of economic growth to put a stop to one of the unfortunate consequences of modern-day capitalism: risks that are socialized and rewards that are privatized, not just in the financial sector but also in manufacturing.

In general, there has been a lack of connection between Keynesian fiscal spending and Schumpeterian investments in innovation. The lack of connection is due in no small part to Keynes advocating ‘useless government’; that is, that State intervention into an economy was based primarily on temporary spending that could occur in any manner (even if it was hiring workers to dig up treasure hidden in an abandoned coal mine)2. Indeed, this is the micro–macro connection that is still missing in modern-day economics. Yet empirically the connection is there. Not only is it true that productive investments generate growth, but that when spending is more ‘directed’ towards, say, the IT revolution in the 1980s and 1990s, and perhaps the green revolution in the years to come, the Keynesian multiplier effect is stronger. As Tassey argues:

…the highest order problem is the long-term inadequacy of productivity enhancing investments (technology, physical, human and organizational capital). Increasing the demand for housing does have a multiplier effect on that industry’s supply chain, but this effect pales compared to the leverage from investment in technology for hardware and software that drive productivity in many industries. Equally important, the jobs created by a technology-driven supply chain are much higher paying – but, they must be sustained over entire technology life cycles. (2012, 31)

Keynes focused on the need for the State to intervene in order to bring stability and prevent crises, certainly a pressing issue in today’s circumstances.3 But in order to understand the dynamics of such investments, it is fundamental to better understand different perspectives on the theory of economic growth first, and then to establish the role of technology and innovation in driving that economic growth.

Technology and Growth While growth and the wealth of nations has been the lead concern of economists since Adam Smith, in the 1950s it was shown by Abramovitz (1956) and Solow (1956) that conventional measures of capital and labour inputs could not account for 90 per cent of economic growth in an advanced industrialized country such as the United States. It was assumed that the unexplained residual must reflect productivity growth, rather than the quantity of factors of production. And still today there is immense debate among economists over which factors are most important in producing growth. This debate is reflected in politics, where different views about growth are espoused with great vehemence, often ignorant of the underlying theoretical assumptions and origins driving those views.

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For years, economists have tried to model growth. Neoclassical economics developed its first growth model in the work of Harrod and Domar (Harrod 1939; Domar 1946), but it was Robert Solow who won the Nobel Prize for his growth ‘theory’. In the Solow growth model, growth is modelled through a production function where output (Y) is a function of the quantity of physical capital (K) and human labour (L), ceteris paribus – other things remaining equal. Included in ‘other things’ was technological change.

Y = F (K, L)

While increases in K and L would cause movements along the production function (curve), exogenous (unexplained) changes in technical change would cause an upward shift in the curve (allowing both K and L to be used more productively). When Solow discovered that 90 per cent of variation in economic output was not explained by capital and labour, he called the residual ‘technical change’. Abramovitz, who knew much more about the social conditions that support technical change than Solow, famously called the residual a ‘measure of our ignorance’ (Abromovitz 1956).

If the underlying model was found to be so deficient that it could not explain 90 per cent of the dependent variable it was describing, then it should have been thrown out and a new model developed. This was indeed what many, such as Joan Robinson (Harcourt 1972) had been arguing for decades. Robinson and others were highly critical of the production function framework. Instead of getting rid of the bad old model, however, technical change was simply added into it. Solow’s theory (1956) became known as ‘exogenous growth theory’ because the variable for technical change was inserted exogenously, as a time trend A (t) (similar to population growth):

Y = A (t) F (K, L)

As economists became more aware of the crucial role that technology plays in economic growth, it became necessary to think more seriously about how to include technology in growth models. This gave rise to ‘endogenous’ or ‘new growth’ theory, which modelled technology as the endogenous outcome of an R&D investment function, as well as investment in human capital formation (Grossman and Helpman 1991). Rather than assuming constant or diminishing marginal returns as in the Solow model (every extra unit of capital employed earned a smaller return), the addition of human capital and technology introduced increasing returns to scale , the engine of growth. Increasing returns, which arise from different types of dynamic behaviour like learning by doing, can help explain why certain firms or countries persistently outperform others – there is no ‘catch-up’ effect.

Although new growth theory provided a rational argument for government investment, it did not lead to it explicitly. This is because new ideas were treated as endogenous to the firm, not as part of the institutional organization required to transform ideas into products. Nevertheless, the increasing emphasis on the relationship between technical change and growth indirectly led government policymakers to focus

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on the importance of investments in technology and human capital to foster growth. The result was innovation-led growth policies to support the knowledge economy, a term used to denote the greater importance of investing in knowledge creation in promoting economic competitiveness (Mason, Bishop and Robinson 2009). Studies that showed a direct relationship between the market value of firms and their innovation performance as measured by R&D spending and patent success supported these policies (Griliches, Hall and Pakes 1991).

From Market Failures to System Failures In their ground-breaking An Evolutionary Theory of Economic Change, Nelson and Winter (1982) argued that the production function framework (exogenous or endogenous) was in fact the wrong way to understand technological change. Building on the work of Joseph Schumpeter (1934, 1942 [2003]), they argued for an ‘evolutionary theory’ of production (and economic change), which delved inside the ‘black box’ of the production function in order to understand how innovation occurs and affects competition and economic growth. In this approach, there is no assumption of ‘representative agents’ (as in standard growth theory) but rather a constant process of differentiation among firms, based on their different abilities to innovate because of different internal routines and competencies. Competition in this perspective is about the coevolution of those processes that create constant differences between firms and the processes of competitive selection that winnow in on those differences, allowing only some firms to survive and grow.

Rather than relying on laws of ‘diminishing returns’, which lead to a unique equilibrium, and assumptions about the ‘average’ firm, this approach focuses on dynamic increasing returns to scale (from the dynamics of learning by doing, as well as the kind of ‘path-dependent’ dynamics described by David 2004), and on different types of processes that lead to persistent differences between firms that do not disappear in the long run. The question is then: which firms survive and grow? Selection does not always lead to ‘survival of the fittest’ both due to the effect of increasing returns (allowing first-mover advantages which then ‘stick’) and also to the effects of policies which might favour certain types of firms over others. It might also be that selection dynamics in product markets and financial markets are at odds (Geroski and Mazzucato 2002b).

But most importantly, in this perspective innovation is firm specific, and highly uncertain. The ‘evolutionary’ and Schumpeterian approach to studying firm behaviour and competition has led to a ‘systems of innovation’ view of policy where what matters is understanding the way in which firms of different type are embedded in a system at sectoral, regional and national levels. In this systems view, it is not the quantity of R&D that matters, but how it is distributed throughout an economy, often reflective of the crucial role of the State in influencing the distribution (Freeman 1995; Lundvall 1992).

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Schumpeterian economists criticize endogenous growth theory because of its assumption that R&D can be modelled as a lottery where a certain amount of R&D investment will create a certain probability for successful innovation. They argue that in fact innovation is an example of true Knightian uncertainty, which cannot be modelled with a normal (or any other) probability distribution that is implicit in endogenous growth theory, where R&D is often modelled using game theory (Reinganum 1984). By highlighting the strong uncertainty underlying technological innovation, as well as the very strong feedback effects that exist between innovation, growth and market structure, Schumpeterians emphasize the ‘systems’ component of technological progress and growth.4 Systems of innovation are defined as ‘the network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies’ (Freeman 1995), or ‘the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge’ (Lundvall 1992, 2).

The emphasis here is not on the stock of R&D but on the circulation of knowledge and its diffusion throughout the economy. Institutional change is not assessed through criteria based on static allocative efficiency, but rather on how it promotes technological and structural change. The perspective is neither macro nor micro, but more meso, where individual firms are seen as part of broader network of firms with whom they cooperate and compete. The system of innovation can be interfirm, regional, national or global. From the meso perspective the network is the unit of analysis (not the firm). The network consists of customers, subcontractors, infrastructure, suppliers, competencies or functions, and the links or relationships between them. The point is that the competencies that generate innovation are part of a collective activity occurring through a network of actors and their links or relationships (Freeman 1995).

The causation that occurs in the steps taken between basic science, to large-scale R&D, to applications, and finally to diffusing innovations is not ‘linear’. Rather, innovation networks are full of feedback loops existing between markets and technology, applications and science. In the linear model, the R&D system is seen as the main source of innovation, reinforcing economists’ use of R&D stats to understand growth. In this more non-linear view, the roles of education, training, design, quality control and effective demand are just as important. Furthermore, it is better able to recognize the serendipity and uncertainty that characterizes the innovation process. It is useful for understanding the rise and fall of different economic powers in history. For example, it explains the rise of Germany as a major economic power in the nineteenth century, as a result of State-fostered technological education and training systems. It also explains the rise of the United States as a major economic power in the twentieth century as a result of the rise of mass production and in-house R&D. The United States and Germany became economic powers for different reasons but what they had in common was attention to developing systems of innovation rather than a narrow focus

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on raising or lowering R&D expenditures.

The general point can be illustrated by contrasting the experience of Japan in the 1970s and 1980s with that of the Soviet Union (Freeman 1995).The rise of Japan is explained as new knowledge flowing through a more horizontal economic structure consisting of the Ministry of International Trade and Industry (MITI), academia and business R&D. In the 1970s Japan was spending 2.5 per cent of its GDP on R&D while the Soviet Union was spending more than 4 per cent. Yet Japan eventually grew much faster than the Soviet Union because R&D funding was spread across a wider variety of economic sectors, not just those focused on the military and space as was the case in the Soviet Union. In Japan, there was a strong integration between R&D, production and technology import activities at the enterprise level, whereas in the Soviet Union there was separation. Crucially, the Soviet Union did not have, or permit, business enterprises to commercialize the technologies developed by the State. Japan had strong user– producer linkages, which were nonexistent in the Soviet system. Japan also encouraged innovation with incentives provided to management and the workforce of companies, rather than focusing mainly on the ministries of science. Johnson (1982) argues that the ‘Japanese miracle’ was in essence the presence of a Developmental State,5 or, the coordination of the Japanese economy through deliberate and targeted industrial policy instituted by MITI. Yet, Lazonick (2008, 27–8) adds that, ‘the contribution of the developmental state in Japan cannot be understood in abstraction from the growth of companies’ (such as Toyota, Sony or Hitachi); aside from the Japanese State’s public support for industry, ‘it was the strategy, organization, and finance, internal’ to Japan’s leading firms that transformed them ‘from entrepreneurial firms into innovative firms’ and that ‘made them successful’ in challenging the competitiveness of the world’s most advanced economies. Equally important were the lessons learned by Japanese people that went abroad to study Western technologies for their companies, and relationships between those companies to US firms. These companies benefitted from the lessons of the US ‘Developmental State’, and then transferred that knowledge to Japanese companies which developed internal routines that could produce Western technologies and eventually surpass them. Japanese conglomerates were among the first foreign companies to license the transistor from AT&T (Bell Labs) in the early 1950s. As a result key connections were made with Western companies such as GE, IBM, HP and Xerox. Particular sectors like electronics were targeted forcefully, and the organizational innovation adopted by Japanese firms embodied a flexible ‘just-in-time’ and ‘total quality’ production system (which was a necessity to avoid unused capacity and waste, and deal with the lack of natural resources in Japan) that was applied to a wide variety of economic sectors with great success.

Table 1 compares the Japanese and Soviet systems of innovation. It is important in this context to highlight that the MITI’s industrial policy was beyond the ‘picking winners’ idea that many opposed to industrial policy cite today. Japan’s approach was

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about coordinating intra-industrial change, inter-sectoral linkages, inter-company linkages and the private–public space in a way that allowed growth to occur in a holistic and targeted manner. The Japanese model, which was an alternative to the more vertical ‘Fordist’ model of production in the US, characterized by rigidity and tense relations between trade unions and management, caused a more solid flow of knowledge and competencies in the economy that provided an advantage to the horizontally structured and flexible Japanese firms. While on opposite ends of the political spectrum, the production model in the USSR and the USA were equally ‘rigid’, allowing the Japanese model to supersede both.

Table 1. Contrasting national systems of innovation: Japan and the USSR in the 1970s Japan USSR High gross domestic expenditure on R&D (GERD)/GNP ratio (2.5%) Very high GERD/GNP ratio (c. 4%)

Very low proportion of military or space R&D (<2% of R&D)

Extremely high proportion of military or space R&D (>70% of R&D)

High proportion of total R&D at enterprise level and company financed (approx. 67%)

Low proportion of total R&D at enterprise level and company financed (<10%)

Strong integration of R&D, production and technology import at enterprise level

Separation of R&D, production and technology import, weak institutional linkages

Strong user–producer and subcontractor network linkages

Weak or nonexistent linkages between marketing, production, and procurement

Strong incentives to innovate at enterprise level that involve management and workforce

Some incentives to innovate made increasingly strong in 1960s and 1970s but offset by other negative disincentives affecting management and workforce

Intensive experience of competition in international markets

Relatively weak exposure to international competition except in arms race

Source: Freeman (1995). Note: Gross domestic expenditures on research and development (GERD) are all monies expended on R&D performed within the country in a given year.

Regional systems of innovation focus on the cultural, geographical and institutional proximity that create and facilitate transactions between different socioeconomic actors. Studies focusing on innovative milieu such as industrial districts and local systems of innovation have demonstrated that conventions and specific socioinstitutional factors in regions affect technological change at a national level. Specific factors might include interactions between local administrations, unions and family-owned companies in, for example, the Italian industrial districts.

The State’s role is not just to create knowledge through national labs and universities, but also to mobilize resources that allow knowledge and innovations to diffuse broadly across sectors of the economy. It does this by rallying existing innovation networks or by facilitating the development of new ones that bring together a diverse group of

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stakeholders. However, having a national system of innovation that is rich in horizontal and vertical networks is not sufficient. The State must also lead the process of industrial development, by developing strategies for technological advance in priority areas.

This version of the State’s role has been accepted in a consensus between multiple countries that are attempting to catch up with most technologically advanced economies. There is a whole literature devoted to the role of the so-called ‘Developmental State’, where the State is active not only in Keynesian demand management but also in leading the process of industrialization. The most typical examples are the East Asian economies, which through planning and active industrial policy were able to ‘catch up’ technologically and economically with the West (Amsden 1989). In states that were late to industrialize, the State itself led the industrialization drive. It took on developmental functions, for example by targeting certain sectors for investment, placed barriers to foreign competition until such time as companies in the targeted sectors were ready to export, and then provided assistance finding new export markets for companies. In Japan, for example, Johnson (1982) illustrates how the MITI worked to coordinate Japanese firms in new international markets. This occurred through investments made in particular technologies (picking winners), and the creation of specific business strategies meant to win particular domestic and international markets. Furthermore, the Japanese State coordinated the finance system through the Bank of Japan as well as through the Fiscal Investment Loan Program (funded by the postal savings system).

Chang (2008) offers similar illustrations for South Korea and other recently emerged economies. China has engaged in a targeted industrialization strategy too, only joining the World Trade Organization once its industries were ready to compete, rather than as part of an International Monetary Fund–backed industrialization strategy. The Chinese strategy showed the weaknesses of the Washington Consensus on development, which denied the State the active role that it played in the development of major industrialized nations such as the United States, Germany and the United Kingdom.

If there is strong evidence that the State can be effective in pursuing targeted catch-up policies by focusing resources on being dominant in certain industrial sectors, why is it not accepted that the State can have a greater role in the development of new technologies and applications beyond simply funding basic science and having an infrastructure to support private sector activity?

Myths about Drivers of Innovation and Ineffective Innovation Policy The fact that economics was putting so much emphasis on innovation in the growth process caused policymakers, since the 1980s, to begin paying much more attention to variables like R&D and patents as a predicator of innovation and therefore of economic growth. For example, the European Union’s Lisbon Agenda (2000) and its current Europe 2020 strategy (EC 2010) set a target for 3 per cent of the EU’s GDP to be

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invested in R&D, along with other policies meant to encourage the flow of knowledge between universities and business – and a stronger link between financial markets and innovative firms of different size.

While countries within the OECD continue to differ greatly in their R&D spending (Figure 1 below), what is interesting is that those European countries that have suffered the most from the financial crisis, which later turned into a sovereign debt crisis, were also countries that have the lowest R&D expenditures. This of course does not mean that it is their low R&D intensity that caused their problems, but it is surely related. In the case of Italy, in fact, its high debt/GDP ratio (120 per cent in 2011) was not due to too much spending but spending in the wrong places. Its deficit for many years was relatively mild, at around 4 per cent. But its lack of investment in productivity- enhancing R&D and human capital development meant that its growth rate remained below the interest rate that it paid on its debt, thus making the numerator of the debt/GDP ratio grow more than the denominator. The fact that EU countries spend so differently on areas that create long-run growth is one of the reasons that they were each affected so differently by the economic crisis. The numerous approaches to growth were a reason that there was such little solidarity when it came time to help each other out. German ‘falks’ feel that German tax money should not be used to bail out the Greeks. However, they err in thinking that the Greeks are spendthrifts. The reforms that are required to make the European project work require not only ‘structural’ reforms (increasing the propensity to pay tax, labour market reform etc.) but also, and especially, increases in public and private sector investment in research and human capital formation that produce innovation. Getting support for such policies is virtually impossible under the current new ‘fiscal compact’, which limits spending by European member states to 3 per cent of GDP without differentiating between the spending that, through innovation and capital investments, can lead to future growth.

While low spending on R&D is a problem throughout much of the European ‘periphery’, it is also true that if a country has lower than average R&D spending, this is not necessarily a problem if the sectors that the country specializes in are not sectors in which innovation occurs necessarily through R&D (Pierrakis 2010). For example, the UK specializes in financial services, construction and creative industries (such as music) – all with relatively low needs for basic R&D. And there are many industries, especially in the service sector, that do no R&D at all. Yet these industries often employ large numbers of knowledge workers to generate, absorb and analyse information. If, all other things equal, these industries represented a smaller proportion of GDP, it would be easier for an economy to reach the 3 per cent target for R&D/GDP (which characterized both the European Commission’s Lisbon Agenda and the current EC 2020 agenda). But would the performance of the economy be superior as a result? It depends on how these industries contribute to the economy. Are these ‘low-tech’ industries providing important services that enhance the value-creating capabilities of other

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industries or the welfare of households as consumers? Or are they, as is often the case in financial services, focused on extracting value from the economy, even if that process undermines the conditions for innovation in other industries (Mazzucato and Lazonick 2010)?

One of the problems that such simple targets encounter is that they divert attention from the vast differences in R&D spending across industries and even across firms within an industry. They can also mask significant differences in the complementary levels of R&D investments made by governments and businesses that are also required to generate superior economic performance.

The National Systems of Innovation perspective described above highlights the important role of intermediary institutions in diffusing the knowledge created by new R&D throughout a system. An even greater problem with R&D-based innovation policies is the lack of understanding of the complementary assets that must be in place at the firm level that make it possible for technological innovations to reach the market, e.g. infrastructure or marketing capabilities.

Figure 1. Gross R&D spending (GERD) as a percentage of GDP in OECD, 1981–2010

Source: OECD (2011).

There have been many myths created around innovation-led growth. These have been based on wrong assumptions about the key drivers of innovation, from R&D, to small firms, venture capital and patents. A brief discussion of these follows. I call them ‘myths’, though they are perhaps more clearly called false assumptions leading to ineffective innovation policy.

Myth 1: Innovation is about R&D The literature on the economics of innovation, from different camps, has often assumed a direct causal link between R&D and innovation, and between innovation and economic growth. While the systems of innovation literature referred to above has

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argued strongly against the linear model of innovation, much innovation policy still targets R&D spending at the firm, industry and national levels. Yet there are very few studies which prove that innovation carried out by large or small firms actually increases their growth performance – that is, the macro models on innovation and growth (whether ‘new growth’ theory models or the ‘Schumpeterian’ models) do not seem to have strong empirical ‘micro foundations’ (Geroski and Mazzucato 2002a). Some company-level studies have found a positive impact of R&D on growth (Geroski, Machin and Toker 1992; 1996, Yasuda 2005) while others found no significant impact (Almus and Nerlinger 1999; Bottazzi et al. 2001; Lööf and Heshmati 2006). Some studies have found even a negative impact of R&D on growth, which is not surprising: if the firms in the sample don’t have the complementary assets needed, R&D becomes only a cost (Brouwer, Kleinknecht and Reijnen 1993; Freel and Robson 2004).

It is thus fundamental to identify the company-specific conditions that must be present to allow spending on R&D to positively affect growth. These conditions will no doubt differ between sectors. Demirel and Mazzucato (2012), for example, find that in the pharmaceutical industry, only those firms that patent five years in a row (the ‘persistent’ patenters) and which engage in alliances achieve any growth from their R&D spending. Innovation policies in this sector must thus target not only R&D but also different attributes of firms. Coad and Rao (2008) found that only the fastest- growing firms reap benefits from their R&D spending (the top 6 per cent identified in NESTA’s 2009 report ‘The Vital 6%’). Mazzucato and Parris (2011) find that the relationship between R&D spending and fast-growing firms only holds in specific periods of the industry life-cycle, when competition is particularly fierce.

Myth 2: Small is Beautiful Finding that the impact of innovation on growth is indeed different for different types of firms has important implications for the commonly held assumption that ‘small firms’ matter (for growth, for innovation and for employment), and hence that many different policies that target SMEs are needed to generate innovation and growth. Hughes (2008) has shown that in the UK SMEs received close to £9 billion in direct and indirect government support, which is more than the police force receives. Is this money well spent? The hype around small firms arises mainly from the confusion between size and growth. The most robust evidence available emphasizes not the role of small firms in the economy but to a greater extent the role of young high-growth firms. NESTA, for example, showed that the firms most important to growth in the UK have been the small number of fast-growing businesses that, between 2002 and 2008, generated the greatest employment increase in the country (NESTA 2011). And while many high-growth firms are small, many small firms are not high growth.6 The bursts of rapid growth that promote innovation and create employment are often staged by firms that have existed for several years and grown incrementally until they reached a take-off stage. This is a major problem since so many government policies focus on tax

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breaks and benefits to SMEs, with the aim of making the economy more innovative and productive.

Although there is much talk about small firms creating jobs, and increasingly a focus of policymakers, this is mainly a myth. While by definition small firms will create jobs, they will in fact also destroy a large number of jobs when they go out of business. Haltiwanger, Jarmin and Miranda (2010) find that there is indeed no systematic relationship between firm size and growth. Most of the effect is from age: young firms (and business start-ups) contribute substantially to both gross and net job creation.

Productivity should be the focus, and small firms are often less productive than large firms. Indeed recent evidence has suggested that some economies that have favoured small firms, such as India, have in fact performed worse. Hsieh and Klenow (2009), for example, suggest that 40–60 per cent of the total factor productivity (TFP) difference between India and the United States is due to misallocation of output to too many small and low-productivity SMEs in India. As most small start-up firms fail, or are incapable of growing beyond the stage of having a sole owner-operator, targeting assistance to them through grants, soft loans or tax breaks will necessarily involve a high degree of waste. While this waste is a necessary gamble in the innovation process (Janeway 2012), it is important to at least guide the funding process with what we know about ‘high growth’ innovative firms rather than some folkloristic notion of the value of SMEs as an aggregate category – which actually means very little.

Bloom and Van Reenen (2006) argue that small firms are less productive than large ones because they are less well managed and subject to provincial family favouritism. Furthermore, small firms have lower average wages, fewer skilled workers, less training, fewer fringe benefits and higher instances of bankruptcy. They argue that the UK has many family firms and a poor record of management in comparison with other countries such as the US and Germany (2006). Among other reasons, this is related to the fact that the tax system is distorted by giving inheritance tax breaks to family firms.

Some have interpreted as a result that it is high growth rather than size that matters, and that the best that government can do is to provide the conditions for growth through policies that foster innovation. Bloom and Van Reenen (2006) argue that instead of having tax breaks and benefits target SMEs, the best way to support small firms is to ‘ensure a level playing field by removing barriers to entry and growth, among firms of all sizes, enforcing competition policy, and strongly resisting the lobbying efforts of larger firms and their agents’. But as we will see in Chapters 3 and 5, often the most innovative firms are precisely those that have benefitted the most from direct public investments of different types, making the association between size and growth much more complex.

The policy implication is that rather than giving handouts to small companies in the hope that they will grow, it is better to give contracts to young companies that have

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already demonstrated ambition. It is more effective to commission the technologies that require innovation than to hand out subsidies in the hope that innovation will follow. In an era where budget deficits are constraining available resources, this approach could yield significant taxpayer savings if, for example, direct transfers to firms that are given just because of the size of a company were ended, such as small business rate relief for smaller companies and inheritance tax relief for family firms (Schmidt 2012).

Myth 3: Venture Capital is Risk Loving If the role of small firms and R&D is overstated by policymakers, a similar hype exists in relation to the potential for venture capital to create growth, particularly in knowledge-based sectors where capital intensity and technological complexity are high.

Venture capital is a type of private equity capital focused on early stage, high- potential growth companies. The funding tends to come either as seed funding or as later-stage growth funding where the objective of venture capitalists is to earn a high return following a successful IPO, merger or acquisition of the company. Venture capital fills a funding void that exists for new firms, which often have trouble gaining credit from traditional financial institutions such as banks. Such firms thus often have to rely on other sorts of funding such as ‘business angels’ (including family and friends), venture capital and private equity. Such alternative funding is most important for new knowledge-based firms trying to enter existing sectors or for new firms trying to form a new sector.

Risk capital is scarce in the seed stage of firm growth because there is a much higher degree of risk in this early phase, when the potential of the new idea and its technological and demand conditions are completely uncertain (see Table 2). The risk in later phases falls dramatically.

Figure 2 shows that the usual place where it is assumed venture capital will enter is the stage of the invention-innovation process (second and third stage above). In reality the real picture is much more nonlinear and full of feedback loops. Many firms die during the transition between a new scientific or engineering discovery and its successful transformation into a commercial application. Thus moving from the second to the third phase shown in Figure 2 is often referred to as the valley of death.

Figure 2 does not illustrate how time after time it has been public rather than privately funded venture capital that has taken the most risks. In the US, government programmes such as the Small Business Innovation Research (SBIR) programme and the Advanced Technology Program (ATP) within the US Department of Commerce have provided 20–25 per cent of total funding for early stage technology firms (Auerswald and Branscomb 2003, 232). Thus, government has played a leading role not only in the early stage research illustrated in Figure 2, but also in the commercial viability stage. Auerswald and Branscomb (2003) claim that government funding for early stage technology firms is equal to the total investments of ‘business angels’ and

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about two to eight times the amount invested by private venture capital.

Table 2. Risk of loss for different stages at which investments are made (%) Point at which investment made Risk of loss Seed stage 66.2% Start-up stage 53.0% Second stage 33.7% Third stage 20.1% Bridge or pre-public stage 20.9%

Source: Pierrakis (2010).

Figure 2. Stages of venture capital investment

Source: Ghosh and Nanda (2010, 6).

Venture capital funds tend to be concentrated in areas of high potential growth, low technological complexity and low capital intensity, since the latter raises the cost significantly. Since there are so many failures in the high-risk stages of growth, venture capital funds tend to have a portfolio of different investments with only the tails (extremes) earning high returns – a very skewed distribution.

Although most venture capital funds are usually structured to have a life of ten years, they tend to prefer to exit much earlier than ten years because of the management fees and the bonuses earned for high returns. Early exits are preferred in order to establish a winning track record and raise a follow-on fund. This creates a situation whereby venture capital funds therefore have a bias towards investing in projects where the commercial viability is established within a 3-to-5-year period (Ghosh and Nanda 2010). Although this is sometimes possible (e.g. Google), it is often not. In the case of an emerging sector like biotech or green tech today, where the underlying knowledge base is still in its early exploratory phase, such a short-term bias is damaging to the scientific exploration process which requires longer time horizons and tolerance of failure. Venture capital has succeeded more in the US when it provided not only committed finance, but managerial expertise and the construction of a viable organization (Lazonick 2002).

The problem has been not only the lack of venture capital investment in the most critical early seed stage, but also its own objectives in the innovation process. This has

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been strongly evidenced in the biotech industry, where an increasing number of researchers have criticized the venture capital model of science, indicating that significant investor speculation has a detrimental effect on the underlying innovation (Coriat, Orsi and Weinstein 2003; Lazonick 2011; Mirowski 2011). The fact that so many venture capital backed biotech companies end up producing nothing, yet make millions for the venture capital firms that sell them on the public market is highly problematic. It creates a need to question the role of venture capital in supporting the development of science and also its effect on the growth process. The increased focus on patenting and venture capital is not the right way to understand how risky and long-term innovations occur. Pisano (2006) in fact claimed that the stock market was never designed to deal with the governance challenges presented by R&D-driven businesses. Mirkowski (2011, 208) describes the venture capital–backed biotech model as:

…commercialized scientific research in the absence of any product lines, heavily dependent upon early-stage venture capital and a later IPO launch, deriving from or displacing academic research, with mergers and acquisitions as the most common terminal state, pitched to facilitate the outsourcing of R&D from large corporations bent upon shedding their previous in-house capacity.

The problem with the model has been that the ‘progressive commercialization of science’ seems to be unproductive, generating few products, and damaging to long-term scientific discoveries and findings over time.

An alternative view is presented in Janeway (2012) who argues that stock market speculation is necessary for innovation. However, what he describes as a semi-natural element of capitalism was instead a result of a hefty political process, of lobbying (Lazonick 2009). NASDAQ was put in place to provide a speculative market on which high-tech start-ups could be funded but also exit quickly. And without NASDAQ, launched in 1971, VC would not have emerged as a well-defined industry in the 1970s. The coevolution of VC and NASDAQ is a result of the policy space being ‘captured’. Another element not emphasized in Janeway, is the degree to which the ‘rewards’ to VC have been disproportional to the risks taken. His own VC company, Warburg Pincus, made millions in a game that he admits was about entering after the State did the hard work. While he says that the period of speculation was necessary, he does not confront the issue of how VC was justified in capturing such high returns. And neither that VC is itself becoming one of its own worst enemies by being such adamant lobbyists for a lower public purse (via lower taxes), which will not be able to fund the future innovations for VC to piggyback on.

Myth 4: We Live in a Knowledge Economy – Just Look at all the Patents! Similarly to the myth that ‘innovation is about R&D’, a misunderstanding exists in relation to the role of patents in innovation and economic growth. For example, when policymakers look at the number of patents in the pharmaceutical industry, they

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presume it is one of the most innovative sectors in the world. This rise in patents does not however reflect a rise in innovation, but a change in patent laws and a rise in the strategic reasons why patents are being used. In ICT there has been a shift in the use of patents from the development and protection of proprietary technologies, resulting from in-house R&D, to cross-licensing in open systems, with the purpose of buying in technology (and the related patents) produced elsewhere (Chesbrough 2003; Grindley and Teece 1997). This has caused the R&D budgets of large companies, such as IBM, to fall at the same time that their patent numbers rose (Lazonick 2009, 88–9). Not recognizing these dynamics cause a focus on the number of patents to be misguided.

The exponential rise in patents, and the increasing lack of relationship this rise has had with actual ‘innovation’ (e.g. new products and processes), has occurred for various reasons. First, the types of inventions that can be patented has widened to include publicly funded research, upstream research tools (rather than only final products and processes), and even ‘discoveries’ (as opposed to inventions) of existing objects of study such as genes. The 1980 Bayh–Dole Act, which allowed publicly funded research to be patented rather than remain in the public domain, encouraged the emergence of the biotechnology industry, as most of the new biotech companies were new spinoffs from university labs receiving heavy State funding. Furthermore, the fact that venture capital often uses patents to signal which companies to invest in means that patents have increased in their strategic value to companies seeking to attract financing. All these factors have caused the number of patents to rise, with most of them being of little worth (e.g. very few citations received from other patents), and with most not resulting in a high number of innovations, e.g. new drugs in pharma (see Figure 5 in Chapter 3). Thus directing too much attention to patents, rather than to specific types of patents, such as those that are highly cited, risks wasting a lot of money (as argued below for the patent box case).

Researchers have argued that many of the recent trends in patents, such as the increase in upstream patents for things like ‘research tools’ have caused the rate of innovation to fall rather than increase as it blocks the ability of science to move forward in an open exploratory way (Mazzoleni and Nelson 1998). The effect has been especially deleterious to the ability of scientists in the developing world to repeat experiments carried out in the developed world. Prevented from replicating results, they cannot build on those experiments with their own developments, thus hurting their ability to ‘catch up’ (Forero-Pineda 2006).

Notwithstanding the fact that most patents are of little value, and that patents play a controversial role in innovation dynamics, different government policies continue to assume that patents have a strong link to ongoing high-tech R&D and must be incentivized to create innovation-led growth. In October 2010, George Osborne (the UK’s chancellor of the exchequer, a role equivalent of the minister of finance or secretary of the treasury in other countries) announced a ‘patent box’ policy beginning

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in 2013, which will reduce the rate of corporation tax on the income derived from patents to 10 per cent. This of course fits with the current government’s belief that investment and innovation can be easily nudged through tax policy. The same policy has recently been introduced in the Netherlands.

The Institute for Fiscal Studies (IFS) has argued against this policy, claiming that the only effect it will have is to reduce government tax revenue (by a large amount) without affecting innovation (Griffith et al. 2010). It is argued that R&D tax credits are enough to address the market failure issue around R&D, and that the patent box policy is instead poorly targeted at research, as the policy targets the income that results from patented technology, not the research or innovation itself. Furthermore, the authors maintain that the patent box policy will also add complexity to the tax system and require expensive policing to ensure that income and costs are being appropriately assigned to patents. They claim that the great uncertainty and time lags behind creating patentable technologies will counteract the incentives. Since international collaborations are increasingly common, there is no guarantee that the extra research that is incentivized will be conducted in the country introducing the policy.

Myth 5: Europe’s Problem is all about Commercialization It is often assumed that Europe’s main disadvantage in innovation as compared to the US is its lack of capability for ‘commercialization’ (see Figure 2) which stems from problems with the ‘transfer’ of knowledge. In fact, EU problems don’t come from poor flow of knowledge from research but from the EU firms’ smaller stock of knowledge. This is due to the great differences in public and private spending on R&D. While in the US R&D/GDP is 2.6 per cent, it is only 1.3 per cent in the UK. In Italy, Greece and Portugal – the countries experiencing the worst effects of the eurozone crisis – R&D/GDP spending is less than 0.5 per cent (Mazzucato 2012b).

If the US is better at innovation, it isn’t because university–industry links are better (they aren’t), or because US universities produce more spinouts (they don’t). It simply reflects more research being done in more institutions, which generates better technical skills in the workforce (Salter et al. 2000). Furthermore, US funding is split between research in universities and early stage technology development in firms. Getting EU universities to do both runs the risk of generating technologies unfit for the market.

Thus there is not a problem of research quality in universities in Europe, nor in the collaboration between industry and universities, which probably occurs more frequently in the UK than the US. Nor is there a problem in universities generating firms, which again occurs more frequently in Europe than in the US (although there are major concerns about the quality of the firms that are generated, Salter et al. 2000; Nightingale 2012). If European firms lack the ability to innovate then technology transfer policies are like pushing a piece of string.

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More generally, in the economics of innovation literature, there is often talk of the ‘European Paradox’ – the conjecture that EU countries play a leading global role in top- level scientific output, but lag behind in the ability to convert this strength into wealth- generating innovations. Dosi, Llerena and Labini (2006) support the points made above by providing evidence that the reason for European weakness is not, as is commonly claimed, the lack of science parks or interaction between education and industry. It is a weaker system of scientific research and the presence of weaker and less innovative companies. Policy implications include less emphasis on ‘networking’ and more on policy measures aimed to strengthen ‘frontier’ research or, put another way, a better division of labour between universities and companies, where universities should focus on high-level research and firms on technology development.

An alternative view – often voiced – is that Europe lacks sufficiently speculative stock markets to induce VC investment (Janeway 2012). While there are surely problems with the European venture capital industry (Bottazzi and Da Rin 2002), and there is perhaps not an equivalent to NASDAQ, this view ignores how the overly speculative US model undermines innovation. The problem is that the ideology surrounding both the role of VC, the role of the stock market and innovation, and the analysis of where innovation comes from, has prevented a ‘healthy balance’ of speculation and investment to be sustainable over time.

Myth 6: Business Investment Requires ‘Less Tax and Red Tape’ While there is a research component in innovation, there is not a linear relationship between research and development, innovation and economic growth. While it is important that the frontiers of science advance and that economies develop the nodes and networks that enable knowledge to be transferred between different organizations and individuals, it does not follow that subsidizing the activity of R&D per se within individual firms is the best use of taxpayers’ money. Although it is common sense that there is a relationship between a decision to engage in R&D and its cost (see Myth 1), qualitative surveys of the effectiveness of the R&D tax credit for both large and small firms provide little evidence that it has positively impacted on the decision to engage in R&D, rather than simply providing a welcome cash transfer to some firms that have already done so.7 There is also a potential problem under the current R&D tax credit system, in many countries, that it does not hold firms accountable as to whether they have conducted new innovation that would not otherwise have taken place, or simply pursued routine forms of product development. In time, therefore, as the entrepreneurial State is built, it would be more effective to use some of the expenditure on R&D tax credits to directly commission the technological advance in question. Recently, the Netherlands has introduced an R&D tax credit that targets not the income from R&D (easily fudged) but R&D workers – and this has been found to be more effective, creating the kind of ‘additionality’ that income-based R&D tax credits don’t

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(Lockshin and Mohnen 2012).

More generally, as Keynes emphasized, business investment (especially innovative investment) is a function of ‘animal spirits’, the gut instinct of investors about future growth prospects. These are impacted to a greater extent not by taxes but by the strength of a nation’s science base, its system of credit creation, and its quality of education and hence human capital. Tax cuts in the 1980s did not produce more investment in innovation; they only affected income distribution (increasing inequality). For this same reason, ‘enterprise zones’ which are focused almost exclusively on tax benefits and weakened regulation are not innovation zones. It would be best to save that money or to invest it in properly run science parks for which there is better evidence that innovation will follow (Massey, Quintas and Wield 1992).

It is important for innovation policy to resist the appeal for tax measures of different kinds – such as the patent box discussed above, or R&D tax credits – unless they are structured in such a way that will lead to investments in innovation that would not have happened anyway, and real evidence confirms it. Most of all, it is essential for policymakers to be wary of companies that complain about ‘tax and red tape’, when it is clear that their own global actions reflect a preference for areas of the world where the State is spending precisely in those areas that create confidence and ‘animal spirits’ regarding future growth possibilities.

This chapter has argued that many of the assumptions that underlie current growth policy should not be taken for granted. Over the last decade or so, policymakers searching for proxies for economic growth have looked to things they can measure such as R&D spending, patents, venture capital investment, and the number of small firms that are assumed to be important for growth. I have attempted to demystify these assumptions and now turn to the largest myth of all: the limited role for government in producing entrepreneurship, innovation and growth.

1 The insensitivity of investment to taxes is the reason that the 1980s-style ‘supply-side’ economics had little effect on investment and hence GDP, and a large effect on income distribution (no ‘trickle-down’ effect).

2 This refers to Keynes’s provocative statement that: ‘If the Treasury were to fill old bottles with bank-notes, bury them at suitable depths in disused coal-mines which are then filled up to the surface with town rubbish, and leave it to private enterprise on well-tried principles of laissez-faire to dig the notes up again (the right to do so being obtained, of course, by tendering for leases of the note-bearing territory), there need be no more unemployment and, with the help of repercussions, the real income of the community, and its capital wealth, would probably become a good deal greater than it actually is’ (1936, 129). Keynes was referring to the fact that in times of underutilized capacity, even such apparently useless actions could get the economic engine going.

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However, the point of this book is to highlight how the State has, even in the boom periods such as the 1990s, provided important directionality in its spending, increasing the animal spirits of the private sector by investing in areas that the private sector fears.

3 Indeed, the application of Keynesian analysis to the theory of economic crises, with a proper understanding of finance in this dynamic, was developed by Hyman Minsky. Minsky (1992) focused on the financial fragility of capitalism by highlighting the way that financial markets cause crises to occur. Financial bubbles followed cycles of credit expansion, and exaggerated growth expectations were followed by retraction, causing bubbles to burst and asset prices to collapse. Like Keynes, he believed that the State had a crucial role in preventing this vicious cycle and stabilizing growth.

4 The emphasis on heterogeneity and multiple equilibria requires this branch of theory to rely less on assumptions of representative agents (the average company) and unique equilibria, so dear to neoclassical economics. Rather than using incremental calculus from Newtonian physics, mathematics from biology (such as distance from mean replicator dynamics) are used, which can explicitly take into account heterogeneity, and the possibility of path dependency and multiple equilibria. See M. Mazzucato, Firm Size, Innovation and Market Structure: The Evolution of Market Concentration and Instability (Northampton, MA: Edward Elgar, 2000).

5 Chalmers Johnson (1982) was one of the first authors to conceptualize the ‘Developmental State’, when he analysed the State-led industrialization of Japan. Johnson argued that, in contrast to a (supposedly) hands-off, regulatory orientation in the US, the Japanese ‘Developmental State’ directly intervened in the economy, with strong planning promoted by a relatively independent State bureaucracy, which also promoted a close business–government relationship, whereby governmental support, protection and discipline resulted in a private elite willing to take on risky enterprises. Subsequent elaborations of the ‘Developmental State’ concept can be found in, among others, Wade (1990), Chang (1993), Evans (1995), Woo-Cumings (1999) and Chang and Evans (2000). Recently, contrary to Johnson’s (1982) original view, Block (2008) showed the existence of an often ‘hidden’ Developmental State in the US, a view similarly espoused by Reinert (2007) and Chang (2008).

6 Not to mention the statistical effect of being small: while a one-person microenterprise that hires an additional employee will display a 100 per cent growth in employment, a 100,000 person enterprise that hires 1,000 employees will show ‘only’ a 1 per cent increase in employment. And yet, it is obvious which of these hypothetical firms contributes more to a decrease in unemployment at the macro-level.

7 See HMRC, An Evaluation of R&D Tax Credits (2010) for an example of this.

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Chapter 3 RISK-TAKING STATE:

FROM ‘DE-RISKING’ TO ‘BRING IT ON!’

During a recent visit to the United States, French President François Mitterrand stopped to tour California’s Silicon Valley, where he hoped to learn more about the ingenuity and entrepreneurial drive that gave birth to so many companies there. Over lunch, Mitterrand listened as Thomas Perkins, a partner in the venture capital fund that started Genentech Inc., extolled the virtues of the risk- taking investors who finance the entrepreneurs. Perkins was cut off by Stanford University Professor Paul Berg, who won a Nobel Prize for work in genetic engineering. He asked, ‘Where were you guys in the ’50s and ’60s when all the funding had to be done in the basic science? Most of the discoveries that have fuelled [the industry] were created back then.’

Henderson and Schrage, in the Washington Post (1984) The debate about what type of research is best conducted by the public or private sector tends to come down to a discussion of two important characteristics of research. The first is the long time horizon necessary (e.g. for ‘basic’ research) followed by the fact that many investments in research contribute to the public good (making it difficult for businesses to appropriate returns). These issues provide the rationale for public sector funding and establish the classic market failure argument for research (Bush 1945).

What is less understood is the fact that public sector funding often ends up doing much more than fixing market failures. By being more willing to engage in the world of Knightian uncertainty, investing in early stage technology development, the public sector can in fact create new products and related markets. Two examples include its role in dreaming up the possibility of the Internet or nanotech when the terms did not even exist. By envisioning new spaces, creating new ‘missions’ (Foray et al. 2012), the State leads the growth process rather than just incentivizing or stabilizing it. And coming back to Judt’s point about the ‘discursive’ battle, this courageous act is poorly reflected by the term ‘de-risking’. The role of the State has been more about taking on risk with courage and vision – not simply taking it away from someone else who then captures the returns. As discussed at the end of Chapter 1, this is about the State investing on a bumpy risk landscape in a dynamic division of innovative labour. In order for us to avoid the myths discussed in Chapter 2, it is essential to map the types of risk we are talking about in better ways. Illustrating these better ways is the subject of this chapter.

What Type of Risk?

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Entrepreneurship, like growth, is one of the least-well-understood topics in economics. What is it? According to the Austrian economist Joseph Schumpeter, an entrepreneur is a person, or group of people, who is willing and able to convert a new idea or invention into a successful innovation. It is not just about setting up a new business (the more common definition), but doing so in a way that produces a new product, or a new process, or a new market for an existing product or process. Entrepreneurship, he wrote, employs ‘the gale of creative destruction’ to replace, in whole or in part, inferior innovations across markets and industries, simultaneously creating new products including new business models, and in so doing destroying the lead of the incumbents (Schumpeter 1949). In this way, creative destruction is largely responsible for the dynamism of industries and long-run economic growth. Each major new technology leads to creative destruction: the steam engine, the railway, electricity, electronics, the car, the computer, the Internet. Each has destroyed as much as they have created but each has also led to increased wealth overall.

For Frank H. Knight (1921) and Peter Drucker (1970), entrepreneurship is about taking risk. The behaviour of the entrepreneur is that of a person willing to put his or her career and financial security on the line and take risks in the name of an idea, spending time as well as capital on an uncertain venture. In fact, entrepreneurial risk taking, like technological change, is not just risky, it is highly ‘uncertain’. Knight (2002, 233) distinguished risk from uncertainty in the following way:

The practical difference between the two categories, risk and uncertainty, is that in the former the distribution of the outcome in a group of instances is known… While in the case of uncertainty that is not true, the reason being in general that it is impossible to form a group of instances, because the situation dealt with is in a high degree unique.

John Maynard Keynes (1937, 213–14) also emphasized these differences:

By ‘uncertain’ knowledge, let me explain, I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty… The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention… About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know!

Technological change is a good example of the truly unique situation. R&D investments that contribute to technological change not only take years to materialize into new products, but most products developed fail. In the pharmaceutical sector, for example, innovation from an R&D project can take up to 17 years from its beginning to its end. It costs about $403 million per drug, and the failure rate is extremely high: only 1 in 10,000 compounds reach the market approval phase, a success rate of just 0.01 per cent.

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When successful, often the search for one product leads to the discovery of a completely different one, in a process characterized by serendipity.1 This of course does not mean that innovation is based on luck, far from it. It is based on long-term strategies and targeted investments. But the returns from those investments are highly uncertain and thus cannot be understood through rational economic theory (as was discussed above, this is one of the critiques that modern day Schumpeterians make of ‘endogenous growth theory’, which models R&D as a game-theoretic choice). Furthermore, the ability to engage in innovation differs greatly between companies and is one of the main reasons that firms are so different from each other, and why it is nearly impossible to find firms distributed ‘normally’ around an ‘optimal-size firm’ (the ‘representative’ agent), a concept so dear to neoclassical microeconomic theory.

Figure 3. Sources of funding for R&D in the USA in 2008

Source: National Science Foundation (2008).

The high risk and serendipitous characteristic of the innovation process is one of the main reasons why profit-maximizing companies will invest less in basic research; they can receive greater and more immediate returns from applied research. Investment in basic research is a typical example of a ‘market failure’: an instance where the market alone would not produce enough basic research so the government must step in. This is why there are few people, on all sides of the political spectrum, who would not agree that it should be (and is) the State that tends to fund most basic research. For the US economy, for example, Figures 3 and 4 show that while government spending on R&D makes up only 26 per cent of total R&D,2 with the private sector making up 67 per cent, the proportion is much higher when basic research is considered in isolation. Public spending accounts for 57 per cent of basic research in the US, with the private sector taking on only 18 per cent.

Figure 4. Sources of funding for basic research R&D in the USA in 2008

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Source: National Science Foundation (2008).

A core difference between the US and Europe is the degree to which public R&D spending is for ‘general advancement’ rather than mission-oriented. Market failure theories of R&D are more useful to understand general ‘advancement of knowledge’ type R&D than that which is ‘mission oriented’. Mission-oriented R&D investment targets a government agency programme or goal that may be found, for example, in defence, space, agriculture, health, energy or industrial-technology programmes. While public R&D spent on general advancement usually makes up less than 50 per cent of total R&D, in 2003/2004 mission-oriented R&D made up more than 60 per cent of public R&D spending in South Korea, the US, the UK, France, Canada, Japan and Germany (Mowery 2010).

Mowery (2010) argues that trying to cut and paste lessons learned from one mission- oriented programme to another is dangerous, as each one has its own specificities (e.g. defence vs. health). To understand programme differences, he argues that the ‘systems of innovation’ approach is much more useful than the market failure approach. It is able to take into consideration how the dynamics of each sector and nation vary, and how each mission is defined by the specific structures, institutions and incentives used to carry it out.

State Leading in Radical (Risky) Innovation A key reason why the concept of market failure is problematic for understanding the role of government in the innovation process is that it ignores a fundamental fact about the history of innovation. Not only has government funded the riskiest research, whether applied or basic, but it has indeed often been the source of the most radical, path-breaking types of innovation. To this extent it has actively created markets, not just fixed them, a topic examined in depth in Chapter 4. By looking at examples of the State’s leading role in the development of Internet- and nanotechnology we will further develop our understanding of the link between R&D and growth, and the public–

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private divide.

Not all innovations lead to economy-wide growth. Economy-wide growth is generally caused by new products or processes that have an impact on a wide variety of sectors in the economy, as was the case with the rise with electricity and computers. These are what macroeconomists call general purpose technologies (GPTs). GPTs are characterized by three core qualities:

• They are pervasive in that they spread into many sectors.

• They improve over time and should keep lowering the cost to their users.

• They make it easier to spawn innovation through the invention and production of new products or processes (Helpman 1998).

Ruttan (2006) argues that large-scale and long-term government investment has been the engine behind almost every GPT in the last century. He analysed the development of six different technology complexes (the US ‘mass production’ system, aviation technologies, space technologies, information technology, Internet technologies and nuclear power) and concluded that government investments have been important in bringing these new technologies into being. He adds that nuclear power would most probably not have been developed at all in the absence of large government investments. In each case successful development of new technology complexes was not just a result of funding and creating the right conditions for innovation. Equally important was envisioning the opportunity space, engaging in the riskiest and most uncertain early research, and overseeing the commercialization process (Ruttan 2006). In Chapter 4 I will show that the same has been the case for the recent development of nanotechnology, which many believe is the next GPT.

Examples of the leading role played by the US government in technology development in fact abound. Lazonick (2013) presents a compelling summary of cases where the US Developmental State played a prominent role, ranging from land freely handed to private companies for the construction of railroads and the financial support of agricultural research in the nineteenth century, through the funding, support and active development of the aeronautical, space and aircraft industries in the twentieth century, to R&D grants and other types of finance for life sciences, nanotechnology and clean energy industries in the twenty-first century.

Abbate’s (1999) extensive research shows how the Internet grew out of the small Defense Department network project (ARPANET) of connecting a dozen research sites in the US into a network linking millions of computers and billions of people. Leslie (2000) argues that while Silicon Valley has been an attractive and influential model for regional development, it has been also difficult to copy it, because almost every advocate of the Silicon Valley model tells a story of ‘freewheeling entrepreneurs and visionary venture capitalists’ and yet misses the crucial factor: the military’s role in

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creating and sustaining it. Leslie shows that ‘Silicon Valley owes its present configuration to patterns of federal spending, corporate strategies, industry–university relationships, and technological innovation shaped by the assumptions and priorities of Cold War defense policy’ (Leslie 2000, 49). Notwithstanding, the Silicon Valley model still lingers in the collective imagination of policymakers as a place where VC created a revolution. The 1999 National Research Council report Funding a Revolution: Government Support for Computing Research is in fact an attempt to recall and acknowledge the major role the US federal government has played in launching and giving momentum to the computer revolution. We look at this further below.

Given the leading developmental role the US government plays in a vast number of sectors, it is no surprise that at a more micro level, Block and Keller (2011b) found that between 1971 and 2006, 77 out of the most important 88 innovations (rated by R&D Magazine’s annual awards) – or 88 per cent – have been fully dependent on federal research support, especially, but not only, in their early phases – and the R&D Magazine’s award excludes ICT innovations.

Figure 5. Classifications of new drugs

These examples are fundamental for understanding the impact of publicly funded research. It is not just about funding blue-sky research but creating visions around important new technologies. To illustrate the general point, I turn now to the specific examples of early stage government investment into the US pharmaceutical and biotechnology sectors.

Pharmaceuticals: Radical vs. ‘Me Too’ Drugs The pharmaceutical industry is interesting because of the new division of innovative labour. Large pharma, small biotech, universities and government labs are all parts of the ecology of the industry. But it is especially government labs and government-backed universities that invest in the research responsible for producing the most radical new drugs – the new molecular entities with priority rating in Figure 5. The ex-editor of the New England Journal of Medicine, Marcia Angell (2004), has argued forcefully that while private pharmaceutical companies justify their exorbitantly high prices by saying they need to cover their high R&D costs, in fact most of the really ‘innovative’ new drugs, i.e. new molecular entities with priority rating, come from publicly funded laboratories. Private pharma has focused more on ‘me too’ drugs (slight variations of

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existing ones) and the development (including clinical trials) and marketing side of the business. It is of course highly ironic, given this sector’s constant bemoaning of ‘stifling’ regulations.

Economists measure productivity by comparing the amount of input into production with the amount of output that emerges. In this sense the large pharmaceutical companies have been fairly unproductive over the last few years in the production of innovations. As Figure 6 shows, there has been an exponential rise in R&D spending by members of the Pharmaceutical Research and Manufacturers of America (PhRMA) with no corresponding increase in the number of new drugs, commonly known as new molecular entities (NMEs). This also holds for patenting: while the number of patents has skyrocketed since the Bayh–Dole Act (1980) allowed publicly funded research to be patented, most of these patents are of little value (Demirel and Mazzucato 2012). When patents are weighted by the amount of citations they receive (the common indicator of ‘important’ patents), the figure is relatively flat, meaning that there are few important patents.

Figure 6. Number of NMEs approved compared with spending by PhRMA members in the USA, 1970–2004

Source: Congressional Budget Office (2006).

Figure 7. Percentages of new drugs by type in the pharmaceutical industry (1993–94)

Source: Angell (2004).

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Of the 1,072 drugs approved by the FDA between 1993 and 2004, only 357 were NMEs rather than just variations of existing ‘me too’ drugs. The number of important ‘priority’ new drugs is even more worrying: only 146 of these had priority rating (NME with ‘P’ rating). In Figure 7 we see that only 14 per cent were seen as important new drugs.

For the sake of the argument being made in this book, what is important is that 75 per cent of the NMEs trace their research not to private companies but to publicly funded National Institutes of Health (NIH) labs in the US. While the State-funded labs have invested in the riskiest phase, the big pharmaceutical companies have preferred to invest in the less risky variations of existing drugs (a drug that simply has a different dosage than a previous version of the same drug).

All a far cry, for example, from the recent quote by UK-based GlaxoSmithKline CEO Andrew Witty: ‘The pharmaceutical industry is hugely innovative… If governments work to support, not stifle innovation, the industry will deliver the next era of revolutionary medicine’ (Economist 2010b). It is the ‘revolutionary’ spirit of the State labs, producing 75 per cent of the radical new drugs, that is allowing Witty and his fellow CEOs to spend most of their time focusing on how to boost their stock prices (e.g. through stock repurchase programmes). Whether this parasitic relationship is sustainable or not is discussed further in the Chapters 8 and 9.

Biotechnology: Public Leader, Private Laggard In the UK, the Medical Research Council (MRC) receives annual ‘grant-in-aid’ funding from Parliament through the Department for Business, Innovation and Skills (BIS). It is government funded, though independent in its choice of which research to support. It works closely with the Department of Health and other UK research councils, industry and other stakeholders to identify and respond to the UK’s health needs. It was MRC research in the 1970s that led to the development of monoclonal antibodies – which, according to the MRC, make up a third of all new drug treatments for many different major diseases such as cancer, arthritis and asthma.

A similar story can be told for the US biopharmaceutical industry. Its growth was not, as is often claimed, rooted in business finance (such as venture capital), but rather emerged and was guided by government investment and spending (Mazzucato and Dosi 2006). In fact, the immense interest of venture capital and big pharmaceutical companies in biotech was paradoxical given the industry’s risky and lengthy process of recouping its investment (Pisano 2006). According to Lazonick and Tulum (2011), the answer to this puzzling paradox is two-fold. First, early investors had the availability of easy exit opportunities through speculative stock market flotations and investors willing to fund initial public offerings (IPOs). Second, significant government support and involvement helped this industry to flourish over the last several decades.

In fact, the development of the biotech industry in the US is a direct product of the

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key role of the government in leading the development of the knowledge base that has thus provided firm success and the overall growth of the biotech industry. As Vallas, Kleinman and Biscotti (2009, 66) eloquently summarize:

…the knowledge economy did not spontaneously emerge from the bottom up, but was prompted by a top-down stealth industrial policy; government and industry leaders simultaneously advocated government intervention to foster the development of the biotechnology industry and argued hypocritically that government should ‘let the free market work’.

As this quote indicates, not only was this knowledge economy guided by government, but, strikingly, it was done as the leaders of industry were on the one hand privately demanding government intervention to facilitate the industry’s development, and on the other hand publicly declaring their support for a free market. It is no wonder given this hypocrisy that so much confusion now exists among policymakers and the general public regarding the role of the government in the economy. Without question some of this confusion is explained by Block (2008), who argues that the US proceeds with ‘hidden’ industrial policy, but clarifies that it is hidden primarily by the fact that it is not discussed as a matter of public debate, by policymakers or by the mainstream media. Block (2008, 15) claims that ‘like the purloined letter, the hidden Developmental State is in plain view. But it has been rendered invisible by the success of the market fundamentalist ideology’ that typically plays out in partisan debate (as also discussed in Chapter 1). Given the efforts of international policymakers in seeking to advance their own economies and in replicating the successes of the US, it is imperative now, more than ever, that the ‘real’ story behind this innovation and economic growth and development be told. If the components of the Developmental State are already visible and in action, why does the logic that defies their value triumph?

Summarizing their findings of the strong role of the government in the development of the biotech industry, Vallas, Kleinman and Biscotti emphasize the significance of ‘massive shifts in federal R&D that were involved’, adding that, ‘it is difficult to avoid the conclusion that the knowledge economy was not born but made’ (2009, 71). Though pharmaceutical companies spend a lot on R&D, supplementing these private investments has been completely dependent on a ‘ready supply of scientific knowledge that has been either funded or actually produced by federal agencies’.

The National Institutes of Health: Creating the Wave vs. Surfing It State support and involvement in biotech span a wide range of forms, the most significant being that the enormous knowledge base which biopharmaceutical companies are dependent on has developed more from government investment than from business. The knowledge base has been developed from the critical investment the government has given to funding basic science. At the forefront lie the National

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Institutes of Health (NIH) and other government programmes which have invested in many of the key scientific achievements that the industry’s success has been built on. Drawing on NIH spending data compiled in Lazonick and Tulum (2011), it is easy to see how crucial this funding was for biotech innovation. From 1978 to 2004, NIH spending on life sciences research totalled $365 billion. Every year from 1970 to 2009, with the exception of a small decline in 2006, NIH funding increased in nominal terms, in contrast to the widely fluctuating funds from venture capital and stock market investments.

Figure 8 below shows that total NIH spending between 1936 and 2011 (in 2011 dollars) was $792 billion. The budget for 2012 alone reached $30.9 billion. Thus, while business continues to lobby for tax cuts and less ‘red tape’, in the end they are greatly dependent on the finance of the tax receipts which they fight against. And indeed, those countries, like the UK, that are increasingly convinced that what drives business are ‘low taxes and low regulation’ are suffering from the flight of many companies, such as Pfizer and Sanofi.

More striking is that in the 35 years since the founding of Genentech as the first biotech company in 1976, the NIH funded the pharmabiotech sector with $624 billion (figure to 2010). As evidenced in this data, Lazonick and Tulum (2011, 9) argue that the US government, through the NIH, and by extension via the US taxpayer, ‘has long been the nation’s (and the world’s) most important investor in knowledge creation in the medical fields’. This knowledge base was ‘indispensable’ and without it, venture capital and public equity funds would not have poured into the industry. They have ‘surfed the wave’ rather than created it.

Through a system of nearly 50,000 competitive grants, the NIH supports more than 325,000 researchers at over 3,000 universities, medical schools and other research institutions in every US state and throughout the world. These grants represent 80 per cent of the agency’s budget with another 10 per cent used to directly employ 6,000 individuals in its own laboratories. The agency’s 26 research centres in Maryland serve a prominent role in the biotech industry – one that is increasing as more centres and institutes continue to develop within the NIH. Beyond these ‘knowledge-creating programs’, traces of government support can also be seen in almost every single major biopharmaceutical product in the US (Vallas, Kleinman and Biscotti 2009). Although many biotech scholars acknowledge the immense government support in the science base, overall they fail to draw the causal relationship between the successful growth of this industry, its attractiveness to investors, and the long-lasting government efforts that develop and sustain the substantial knowledge base found in the US.

Figure 8. National Institutes of Health budgets, 1938–2012

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Source: Office of the Budget, National Institutes of Health (2011), 1176.

So why does venture capital often get so much credit for creating the biotech revolution? The story of private and public investments in biotech is perfectly described by Paul Berg (the 1980 Nobel Prize winner in chemistry) in the quote at the beginning of this chapter. In essence, Berg was aware that the State actively paved the way for future industry development by bringing the courage, vision and funding so lacking in the private sector. Or perhaps more fairly, by investing in new technology until fear- inducing uncertainty was transformed into mere risk.

The point of this chapter was to show that the case for State investment goes beyond ‘blue-sky’ basic research. In fact, it applies to all the different types of ‘risky’ and uncertain research, since the private sector is in many ways less entrepreneurial than the public sector: it shies away from radically new products and processes, leaving the most uncertain investments to be first taken on by the State. So while blue-sky research is necessary for innovation to occur, it is far from sufficient, and indeed the role of the State goes deeper. I continue to examine the breadth and depth of State leadership in producing the knowledge economy in Chapter 4. In Chapter 5 I review the specific case of Apple as an example of a company that has benefitted enormously from both publicly funded blue-sky research as well as State policies that facilitate commercialization.

1 In numerous historical instances scientific theory and explanations have emerged after the technologies they were seeking to explain. The Wright brothers flew before aerodynamics was developed and the steam engine was operational before thermodynamics was understood. Technology often advances slightly ahead of science, and industrial innovation provides problems for academics to solve. See P. Nightingale, ‘Technological Capabilities, Invisible Infrastructure and the Un-social

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Construction of Predictability: The Overlooked Fixed Costs of Useful Research’, Research Policy 33, no. 9 (2004).

2 It is also important to note that in the US, some public R&D funding is awarded with the expectation that it will be matched by business funds, or used to attract other funding, meaning that much of the ‘private’ R&D has been publicly induced.

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Chapter 4 THE US ENTREPRENEURIAL

STATE

…since its founding fathers, the United States has always been torn between two traditions, the activist policies of Alexander Hamilton (1755–1804) and Thomas Jefferson’s (1743–1826) maxim that ‘the government that governs least, governs best’. With time and usual American pragmatism, this rivalry has been resolved by putting the Jeffersonians in charge of the rhetoric and the Hamiltonians in charge of policy.

Erik Reinert (2007, 23)

Despite the perception of the US as the epitome of private sector–led wealth creation, in reality it is the State that has been engaged on a massive scale in entrepreneurial risk taking to spur innovation. In this chapter four key successful examples are given: the roles of DARPA (the US government’s Defense Advance Research Project Agency), SBIR (Small Business Innovation Research), the Orphan Drug Act (the EU passed its own Orphan Drug Act in 2001, imitating the US act passed in 1983) and the National Nanotechnology Initiative. What they share is a proactive approach by the State to shape a market in order to drive innovation. The insight gained is that other than being an entrepreneurial society, a place where it is culturally natural to start and grow a business, the US is also a place where the State plays an entrepreneurial role, by making investments in radical new areas. The State has provided early stage finance where venture capital ran away, while also commissioning high-level innovative private sector activity that would not have happened without public policy goals backing a strategy and vision.

So far I have argued that while the level of technological innovation is integral to the rate of economic growth, there is no linear relationship between R&D spending, the size of companies, the number of patents and the level of innovation in an economy. What does seem to be clear, however, is that a necessary precursor for innovation to occur is to have a highly networked economy, with continuous feedback loops established between different individuals and organizations to enable knowledge to be shared and its boundaries to be pushed back.

This chapter attempts to illustrate that at the frontiers of knowledge, simply having a national system of innovation is not enough. Over time, more impressive results can be achieved when the State is a major player operating within this system. This role does not necessarily have to take place at a national level (although it can) and should not only involve long-term subsidies to certain companies (‘picking winners’). Rather the State, through its various agencies and laboratories, has the potential to disseminate new

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ideas rapidly. It can also be nimble, using its procurement, commissioning and regulatory functions to shape markets and drive technological advance. In this way it acts as a catalyst for change, the spark that lights the fire.

The Defense Advanced Research Projects Agency (DARPA) The role that military engagement has had for economic growth and development does not differentiate US history from other modern countries. But in the US, the experience of technological development necessary to win wars has provided strong lessons to those seeking to improve innovation policy.

The role of the State in the Defense Advanced Research Projects Agency (DARPA) model goes far beyond simply funding basic science. It is about targeting resources in specific areas and directions; it is about opening new windows of opportunities; brokering the interactions between public and private agents involved in technological development, including those between private and public venture capital; and facilitating commercialization (Block 2008; Fuchs 2010).

In contrast to the emphasis placed by market fundamentalists on Franklin D. Roosevelt’s New Deal as the critical turning point in US economic history, Block (2008) argues that the Second World War was a more significant period for the development of innovation policies in the US. It was during the period following the Second World War that the Pentagon worked closely with other national security agencies like the Atomic Energy Commission and the National Aeronautics and Space Agency (NASA). The interagency collaborations led to the development of technologies such as computers, jet planes, civilian nuclear energy, lasers and biotechnology (Block 2008). The way this was done was ‘pioneered’ by the Advanced Research Projects Agency (ARPA), an office created by the Pentagon in 1958. This agency, also commonly referred to as the Defense Advanced Research Projects Agency (DARPA), and consequently the acronym used throughout this book, engaged in developing critical initiatives across a broad range of technologies.1 However, it was the government support for technological advancement in the computer field that led to the establishment of a new paradigm for technology policy.

DARPA was set up to give the US technological superiority in different sectors, mainly (but not only) those related to technology, and has always been aggressively mission oriented. It has a budget of more than $3 billion per year, 240 staff, operates flexibly with few overheads, and is connected to but separated from government. It has successfully recruited high-quality programme managers who are willing to take risks because of their short-term contracts, which last anywhere between four and six years. Its structure is meant to bridge the gap between bluesky academic work, with long time horizons, and the more incremental technological development occurring within the military.

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After a Second World War victory that relied heavily on State-sponsored and – organized technological developments, the federal government was quick to implement the recommendations of Vannevar Bush’s 1945 report, which called for ongoing public support for basic as well as applied scientific research. The relationship between government and science was further strengthened by the Manhattan Project (the major scientific effort led by the US, with the UK and Canada, which led to the invention and use of the atomic bomb in the Second World War), as physicists instructed policymakers on the military implications of new technology. From this point on, it became the government’s business to understand which technologies provided possible applications for military purposes as well as commercial use.

According to Block (2011, 7), during this period an increased number of government workers took on a more direct role in advancing innovation, procuring additional researchers, encouraging researchers to solve specific problems, and requiring that those researchers meet specific objectives. The insight that followed was that this was something government could do for economic and civilian purposes, in addition to the traditional military function.

The launching of Sputnik in 1957 by the Soviets led to an eruption of panic among US policymakers, fearful that they were losing the technological battle. The creation of DARPA in 1958 was a direct result. Before the formation of DARPA the military was the sole controller of all military R&D dollars. Through the formation of DARPA a portion of military spending on R&D was now designated to ‘blue-sky thinking’ – ideas that went beyond the horizon in that they may not produce results for ten or twenty years. As a result of this mandate DARPA was free to focus on advancing innovative technological development with novel strategies. This opened numerous windows for scientists and engineers to propose innovative ideas and receive funding and assistance (Block 2008).

Going way beyond simply funding research, DARPA funded the formation of computer science departments, provided start-up firms with early research support, contributed to semiconductor research and support to human–computer interface research, and oversaw the early stages of the Internet. Many of these critical activities were carried out by its Information Processing Techniques Office, originally established in 1962. Such strategies contributed hugely to the development of the computer industry during the 1960s and 1970s, and many of the technologies later incorporated in the design of the personal computer were developed by DARPA-funded researchers (Abbate 1999).

Another key event during this period was the new innovation environment that emerged after a group of scientists and engineers in 1957 broke away from a firm started by William Shockley (Block 2011). The rebellious group of scientists and engineers, often referred to as the ‘traitorous eight’, went on to form Fairchild

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Semiconductor, a new firm that advanced semiconductor technology and continued ‘a process of economic fission that was constantly spinning off new economic challengers’ (Block and Keller 2011, 12–13). Lazonick (2009) adds that the spinoff culture ultimately began with Fairchild Semiconductor – and the firm owed nearly all of its growth to military procurement. The spinoff business model became viable and popular for technological advancement following the 1957 revolt, yet would not have been possible without State involvement and it functioning as a major early customer. A new paradigm emerged that resulted in innovative ideas moving from labs to market in far greater quantity.

Before this, government officials’ leverage in generating rapid technological advancement was limited, as large defence firms still deflected the pressure and demands for innovation with the tremendous power they wielded. The leverage government officials had in advancing innovative breakthroughs was also limited by the small number of firms with such capabilities. Bonded by a shared interest in avoiding the certain risks that accompanied an uncertain technological path, the firms resisted government pressure for innovation. However, in a new environment with ambitious start-ups, the opportunity for generating real competition among firms presented itself more fully.

Programme officers at DARPA recognized the potential this new innovation environment provided and were able to take advantage of it, focusing at first on new, smaller firms to which they could provide much smaller funds than was possible with the larger defence contractors. These firms recognized the need for ambitious innovation as part of their overall future viability. With small, newer firms engaged in real competition and as the spinoff model became more institutionalized, Block (2008) notes that large firms also had to get on board with this quest for rapid innovative breakthroughs. By taking advantage of this new environment, the government was able to play a leading role in mobilizing innovation among big and small firms, and in university and government laboratories. The dynamic and flexible structure of DARPA in contrast to the more formal and bureaucratic structure of other government programmes allowed it to maximize the increased leverage it now had in generating real competition across the network. Using its funding networks, DARPA increased the flow of knowledge across competing research groups. It facilitated workshops for researchers to gather and share ideas while also learning of the paths identified as ‘dead ends’ by others. DARPA officers engaged in business and technological brokering by linking university researchers to entrepreneurs interested in starting a new firm; connecting start-up firms with venture capitalists; finding a larger company to commercialize the technology; or assisting in procuring a government contract to support the commercialization process.

Pursuing this brokering function, DARPA officers not only developed links among those involved in the network system, but also engaged in efforts to expand the pool of

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scientists and engineers working in specific areas. An example of this is the role DARPA played in the 1960s by funding the establishment of new computer science departments at various universities in the US. By increasing the number of researchers who possessed the necessary and particular expertise, DARPA was able, over an extended period of time, to accelerate technological change in this area. In the area of computer chip fabrication during the 1970s, DARPA assumed the expenses associated with getting a design into a prototype by funding a laboratory affiliated with the University of Southern California. Anyone who possessed a superior design for a new microchip could have the chips fabricated at this laboratory, thus expanding the pool of participants designing faster and better microchips.

The personal computer emerged during this time with Apple introducing the first one in 1976. Following this, the computer industry’s boom in Silicon Valley and the key role of DARPA in the massive growth of personal computing received significant attention, but has since been forgotten by those who claim Silicon Valley is an example of ‘free market’ capitalism. In a recent documentary, Something Ventured, Something Gained, for example, the role of the State is not mentioned once in the 85 minutes spent describing the development of Silicon Valley (Geller and Goldfine 2012).

Also, during the 1970s, the significant developments taking place in biotechnology illustrated to policymakers that the role of DARPA in the computer industry was not a unique or isolated case of success. The decentralized form of industrial policy that played such a crucial role in setting the context for the dramatic expansion of personal computing was also instrumental in accelerating growth and development in biotechnology.

Block (2008, 188) identifies the four key characteristics of the DARPA model:2

• A series of relatively small offices, often staffed with leading scientists and engineers, are given considerable budget autonomy to support promising ideas. These offices are proactive rather than reactive and work to set an agenda for researchers in the field. The goal is to create a scientific community with a presence in universities, the public sector and corporations that focuses on specific technological challenges that have to be overcome.

• Funding is provided to a mix of university-based researchers, start-up firms, established firms and industry consortia. There is no dividing line between ‘basic research’ and ‘applied research’, since the two are deeply intertwined. Moreover, the DARPA personnel are encouraged to cut off funding to groups that were not making progress and reallocate resources to other groups that have more promise.

• Since the goal is to produce usable technological advances, the agency’s mandate extends to helping firms get products to the stage of commercial viability. The agency can provide firms with assistance that goes well beyond research funding.

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• Part of the agency’s task is to use its oversight role to link ideas, resources and people in constructive ways across the different research and development sites.

The main focus is to assist firms in developing new product and process innovations. The key is that the government serves as a leader for firms to imitate, in an approach that is much more ‘hands on’, in that public sector officials are working directly with firms in identifying and pursuing the most promising innovative paths. In so doing, the government is able to attract top minds – exactly the kind of expertise that generates the dynamism that government is often accused of not having. As mentioned in the forward, this is clearly a self-fulfilling prophecy, because a government under constant attack will not dare be confident and dynamic.

I n Chapter 6, we will see how today ARPA-E, the newest agency within the US Department of Energy, is trying to do for ‘green’ what DARPA did for IT.

The Small Business Innovation Research (SBIR) Programme Contrary to conventional wisdom regarding the domination of free market ideology during the Reagan Administration, the US government in the 1980s, in fact, acted to build on the successes of DARPA’s decentralized industrial policy. One of the most significant events during this period was the signing of the Small Business Innovation Development Act by Reagan in 1982, as a consortium between the Small Business Administration and different government agencies like the Department of Defense, Department of Energy and Environmental Protection Agency. The act was based on a National Science Foundation (NSF) pilot programme initiated during the Carter administration. The Small Business Innovation Research (SBIR) programme required government agencies with large research budgets to designate a fraction (originally 1.25 per cent) of their research funding to support small, independent, for-profit firms. As a result, the programme has provided support to a significant number of highly innovative start-up firms (Lerner 1999; Audretsch 2003).

In addition, the network of State and local institutions that worked in partnership with the federal programmes was expanded. An example of this is the development of organizations that were funded by state and local governments to assist entrepreneurs in submitting successful applications to the SBIR programme to secure funds for their projects. The SBIR programme fulfils a unique role in this new innovation system, because it serves as the first place many entrepreneurs involved in technological innovation go to for funding. The programme, which provides more than $2 billion per year in direct support to high-tech firms, has fostered development of new enterprises, and has guided the commercialization of hundreds of new technologies from the laboratory to the market. Given the instrumental role of the SBIR programme and its successes, it is surprising how little attention it receives. Although the UK has, since 2001, attempted to copy its success, it has not been successful yet, as we will see in the

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next chapter.

Figure 9. Number of early stage and seed funding awards, SBIR and venture capital

Source: Block and Keller (2012, 15).

Block (2011, 14) highlights the lack of visibility of the SBIR programme in an effort to illustrate what he describes as ‘a discrepancy between the growing importance of these federal initiatives and the absence of public debate or discussion about them’. As indicated in the introduction of this book and again in the early stages of this chapter, this discrepancy poses an exceptional challenge; for both policymakers and the public who are engaged in economic debates as well as making efforts to address the current economic crises and while also paving the way for the future of innovation and development in the globalized world.

As can be seen in Figure 9, the role of the SBIR programme has not been diminishing, but increasing. Indeed, as venture capital has become increasingly short- termist, focused on pursuing capital gains, and seeking early exit through an IPO, the SBIR programme has had to step up its risk finance (Block and Keller 2012).

Orphan Drugs A year after the SBIR programme was established, a further legislative spur to private sector innovation occurred, this time specific to the biotech industry. The 1983 Orphan Drug Act (ODA) made it possible for small, dedicated biotech firms to carve a sliver from the drug market. The act includes certain tax incentives, clinical as well as R&D subsidies, fast-track drug approval, along with strong intellectual and marketing rights for products developed for treating rare conditions. A rare disease is defined as any disease that affects less than 200,000 people and, given this potentially small market, it was argued that without financial incentives these potential drugs would remain ‘orphans’. The impetus behind this legislation was to advance the investment of pharmaceutical companies in developing these drugs.

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The protection provided by the act enables small firms to improve their technology platforms and scale up their operations, allowing them to advance to the position of becoming a major player in the biopharmaceutical industry. In fact, orphan drugs played an important role for the major biopharmaceutical firms such as Genzyme, Biogen, Amgen and Genentech to become what they are today (Lazonick and Tulum 2011). Since the introduction of the ODA, 2,364 products have been designated as orphan drugs and 370 of these drugs have gained marketing approval (FDA, n.d.).

In addition to all of the conditions outlined by the ODA, Lazonick and Tulum (2011) draw attention to the fact that multiple versions of the same drug can be designated as ‘orphan’. The example of Novartis illustrates this point. In May 2001 the company received marketing approval by the FDA with market exclusivity for its ‘chronic myelogenous leukemia’ drug Gleevec under the ODA. In 2005 over a span of five months, Novartis applied for and was later granted orphan drug designation for five different indications for this same drug. According to the company’s 2010 annual report, in 2010 Gleevec recorded global sales of $4.3 billion, thus confirming the point raised by Lazonick and Tulum (2011), that even when the market size for a drug is small, the revenues can be considerable.

When it comes to the substantial revenues that are generated from drugs designated as ‘orphan’ it is not only small firms that appear to be benefitting. Some of the world’s largest pharmaceutical firms such as Roche, Johnson & Johnson, GlaxoSmithKline and Pfizer, among others, have applied for orphan drug designation for their products. The National Organization for Rare Disorders, a non-profit public organization largely funded by the federal government, has been encouraging large pharmaceutical firms to share their redundant proprietary knowledge with smaller biotech firms through licensing deals, in an effort to develop drugs for orphan indications. Lazonick and Tulum (2011) explain the importance of the Orphan Drug Act by calculating the share of orphan drugs as a percentage of total product revenues for major biopharmaceutical firms. The financial histories of the six leading biopharmaceutical companies reveal a dependence on orphan drugs as a significant portion of the companies’ overall product revenues. In fact, 59 per cent of total product revenues and 61 per cent of the product revenues of the six leading dedicated biopharmaceutical firms come from orphan drug sales. When this calculation also includes the later-generation derivatives of drugs that have orphan status, the figure (calculated for 2008) goes up to 74 per cent of total revenues and 74 per cent of the product revenues for the six leading biopharmaceutical firms. Comparing the timing and growth of revenues for orphan and non-orphan ‘blockbusters’, Lazonick and Tulum (2011) show that orphan drugs are more numerous, their revenue growth began earlier, and many of them have greater 2007 sales (in dollars) than leading non-orphan drugs.

The central role that orphan drugs have played in leading the development of the biotech industry is undeniable, yet this is just one of many critical moves the US

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government made in supporting the biotech industry. It is also evident that Big Pharma plays a significant role in the biopharmaceutical industry, as illustrated in analyses of orphan drugs. Big Pharma and the biotech industry are significantly dependent on one another in this area, and the distinction between Big Pharma and big biopharma has become ‘blurred’. However, the role of government for both these areas was crucial to their development and success. Lazonick and Tulum summarized the government’s role for both during the 2000s:

The US government still serves as an investor in knowledge creation, subsidizer of drug development, protector of drug markets, and, last but not least…purchaser of the drugs that the biopharmaceutical [BP] companies have to sell. The BP industry has become big business because of big government, and…remains highly dependent on big government to sustain its commercial success. (2011, 18)

From this brief overview of these three examples of State-led support for innovation – DARPA, the SBIR programme and creation of a market for orphan drugs with the ODA – a general point can be drawn: the US has spent the last few decades using active interventionist policies to drive private sector innovation in pursuit of broad public policy goals. What all three interventions have in common is that they do not tie the shirt-tails of government to any one firm, yet it still ‘picks winners’; there are no accusations of lame-duck industrial policy here. Instead it is a nimble government that rewards innovation and directs resources over a relatively short time horizon to the companies that show promise, whether through supply-side policies (e.g. DARPA’s information and brokerage support, strategic programmes and vision building) or through demand-side policies and funding for start up interventions (the SBIR programme and orphan drugs). The government has not simply created the ‘conditions for innovation’, but actively funded the early radical research and created the necessary networks between State agencies and the private sector that facilitate commercial development. This is very far from current UK government policy approaches, which assume that the State can simply nudge the private sector into action.

The National Nanotechnology Initiative The entrepreneurial role that the State can play to foster the development of new technologies, which provide the foundation for decades of economic growth, has most recently been seen in the development of nanotechnology in the US. The types of investments and strategic decisions that the State has made have gone beyond simply creating the right infrastructure, funding basic research, and setting rules and regulations (as in a simple ‘systems failure’ approach).

Nanotechnology is very likely to be the next general purpose technology, having a pervasive effect on many different sectors and becoming the foundation of new economic growth. However, while this is commonly accepted now, in the 1990s it was not. Motoyama, Appelbaum and Parker (2011, 109–19) describe in detail how the US

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government has in fact been the lead visionary in dreaming up the possibility of a nanotech revolution – by making the ‘against all odds’ initial investments and by explicitly forming dynamic networks that bring together different public actors (universities, national labs, government agencies) and when available, the private sector, to kick start a major new revolution which many believe will be even more important than the computer revolution. It has even been the first to ‘define’ what nanotechnology is. It did so through the active development of the National Nanotechnology Initiative (NNI). Motoyama, Appelbaum and Parker (2011, 111) describe how it was set up:

The creation and subsequent development of the NNI has been neither a purely bottom-up nor top-down approach: it did not derive from a groundswell of private sector initiative, nor was it the result of strategic decisions by government officials. Rather it resulted from the vision and efforts of a small group of scientists and engineers at the National Science Foundation and the Clinton White House in the late 1990s… It seems clear that Washington selected nanotechnology as the leading front runner, initiated the policy, and invested in its development on a multi-billion dollar scale.

The government’s objective was to find the ‘next new thing’ to replace the Internet. After receiving ‘blank stares’, the key players (civil servants) in Washington convinced the US government to invest in the creation of a new research agenda, to prepare a set of budget options, and to provide a clear division of labour between different government agencies. But it had first to define nanotech. The President’s Committee of Advisors on Science and Technology (PCAST) did so by arguing that the private sector could not expect to lead in developing applications of nanotech that were still 10 to 20 years away from commercial market viability (Motoyama, Appelbaum and Parker 2011, 113):

Industry generally invests only in developing cost-competitive products in the 3 to 5 year time frame. It is difficult for industry management to justify to their shareholders the large investments in long-term, fundamental research needed to make nanotechnology-based products possible. Furthermore, the highly interdisciplinary nature of the needed research is incompatible with many current corporate structures.

This quote is fascinating because of the way it highlights how the private sector is too focused on the short term (mainly, but not only, as a result of the effect the 1980s shareholder revolution has had on long-term business strategy) and that its rigid structures are not conducive to completing the R&D required. Far from being less innovative than the private sector, government has shown itself to be more flexible and dynamic in understanding the connections between different disciplines relevant to the nanotechnology revolution (that draws on physics, chemistry, materials science, biology, medicine, engineering and computer simulation). As Block and Keller (2011a)

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discuss, government actions for cutting-edge new technologies have often had to remain veiled behind a ‘hidden’ industrial policy. The public sector activists driving nanotechnology had to continuously talk about a ‘bottom-up’ approach so that it would not seem to be an instance of ‘picking winners’ or choosing national champions. Though in the end, ‘while most of the policy-making process involved consultation with academics and corporate experts, it is clear that the principal impetus and direction – from background reports to budget scheme – came from the top’ (Motoyama, Appelbaum and Parker 2011, 112). The approach succeeded in convincing Clinton, and then Bush, that investments in nanotechnology would have the potential to ‘spawn the growth of future industrial productivity’, and that ‘the country that leads in discovery and implementation of nanotechnology will have great advantage in the economic and military scene for many decades to come’ (Motoyama, Appelbaum and Parker 2011, 113).

In the end, the US government took action. It not only selected nanotechnology as the sector to back most forcefully (‘picking it’ as a winning sector), but it also proceeded to launch the NNI, review rules and regulations concerning nanotech by studying the various risks involved, and become the largest investor, even beyond what it has done for biotech and the life sciences. Although the strongest action was carried out top down by key senior-level officers in the NSF and the White House, the actual activity behind nanotech was, as in the case of the Internet and computers, heavily decentralized through various State agencies (a total of 13, led by the NSF, but also involving the NIH, the Defense Department and the SBIR programme). Across these different agencies, currently the US government spends approximately $1.8 billion annually on the NNI.

Nanotechnology today does not yet create a major economic impact because of the lack of commercialization of new technologies. Motoyama, Appelbaum and Parker (2011) claim that this is due to the excessive investments made in research relative to the lack of investments in commercialization. They call for a more active government investment in commercialization. However, this raises the question, if government has to do the research, fund major infrastructure investments and also undertake the commercialization effort, what exactly is the role of the private sector?

This chapter has highlighted the important role that government has played in leading innovation and economic growth. Far from stifling innovation and being a drag on the economic system, it has fostered innovation and dynamism in many important modern industries, with the private sector often taking a back seat. Ironically the State has often done so in the US, which in policy circles is often discussed as following a more ‘market’-oriented (liberal) model than Europe. This has not been the case where innovation is concerned.

1 The literature refers to both ARPA and DARPA.

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2 Block uses this to characterize his concept of a ‘developmental network state’ discussed in footnote 5 on page 37.

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Chapter 5 THE STATE BEHIND THE iPHONE

Stay hungry, stay foolish Steve Jobs (2005)

In his now well-known Stanford University commencement address, delivered on 12 June 2005, Steve Jobs, then CEO of Apple Computer and Pixar Animation Studios, encouraged the graduating class to be innovative by ‘pursuing what you love’ and ‘staying foolish’. The speech has been cited worldwide as it epitomizes the culture of the ‘knowledge’ economy, whereby what are deemed important for innovation are not just large R&D labs but also a ‘culture’ of innovation and the ability of key players to change the ‘rules of the game’. By emphasizing the ‘foolish’ part of innovation, Jobs highlights the fact that underlying the success of a company like Apple – at the heart of the Silicon Valley revolution – is not (just) the experience and technical expertise of its staff, but (also) their ability to be a bit ‘crazy’, take risks and give ‘design’ as much importance as hardcore technology. The fact that Jobs dropped out of school, took calligraphy classes and continued to dress all his life like a college student in sneakers is all symbolic of his own style of staying young and ‘foolish’.

While the speech is inspiring, and Jobs has rightly been called a ‘genius’ for the visionary products he conceived and marketed, this story creates a myth about the origin of Apple’s success. Individual genius, attention to design, a love for play, and foolishness were no doubt important characteristics. But without the massive amount of public investment behind the computer and Internet revolutions, such attributes might have led only to the invention of a new toy – not to cutting-edge revolutionary products like the iPad and iPhone which have changed the way that people work and communicate. Like the discussion of venture capital in Chapter 2, whereby venture capital has entered industries like biotechnology only after the State had done the messy groundwork, the genius and ‘foolishness’ of Steve Jobs led to massive profits and success, largely because Apple was able to ride the wave of massive State investments in the ‘revolutionary’ technologies that underpinned the iPhone and iPad: the Internet, GPS, touch-screen displays and communication technologies. Without these publicly funded technologies, there would have been no wave to foolishly surf.

This chapter is dedicated to telling the story of Apple, and in doing so, asks questions that provocatively challenge the ways in which the role of the State and Apple’s success is viewed. In Chapter 8 we ask whether the US public benefited, in terms of employment and tax receipts, from these major risks taken by such an investment of US tax dollars? Or were the profits siphoned off and taxes avoided? Why is the State eagerly blamed for failed investments in ventures like the American Supersonic Transport (SST) project (when it ‘picks losers’), and not praised for successful early

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stage investments in companies like Apple (when it ‘picks winners’)? And why is the State not rewarded for its direct investments in basic and applied research that lead to successful technologies that underpin revolutionary commercial products such as the iPod, the iPhone and the iPad?

The ‘State’ of Apple Innovation Apple has been at the forefront of introducing the world’s most popular electronic products as it continues to navigate the seemingly infinite frontiers of the digital revolution and the consumer electronics industry. The popularity and success of Apple products like the iPod, iPhone and iPad have altered the competitive landscape in mobile computing and communication technologies. In less than a decade the company’s consumer electronic products have helped secure its place among the most valuable companies in the world, making record profits of $26 billion in 2011 for its owners. Apple’s new iOS family of products brought great success to the company, but what remains relatively unknown to the average consumer is that the core technologies embedded in Apple’s innovative products are in fact the results of decades of federal support for innovation. While the products owe their beautiful design and slick integration to the genius of Jobs and his large team, nearly every state-of-the-art technology found in the iPod, iPhone and iPad is an often overlooked and ignored achievement of the research efforts and funding support of the government and military.

Only about a decade ago Apple was best known for its innovative personal computer design and production. Established on 1 April 1976 in Cupertino, California by Steve Jobs, Steve Wozniak and Ronald Wayne, Apple was incorporated in 1977 by Jobs and Wozniak to sell the Apple I personal computer. 1 The company was originally named Apple Computer, Inc. and for 30 years focused on the production of personal computers. On 9 January 2007, the company announced it was removing the ‘Computer’ from its name, reflecting its shift in focus from personal computers to consumer electronics. This same year, Apple launched the iPhone and iPod Touch featuring its new mobile operating system, iOS, which is now used in other Apple products such as the iPad and Apple TV. Drawing on many of the technological capabilities of earlier generations of the iPod, the iPhone (and iPod Touch) featured a revolutionary multi-touch screen with a virtual keyboard as part of its new operating system.

Table 3. Apple’s net sales, income and R&D figures between 1999 and 2011 (US$, millions)

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Note: Apple’s annual net sales, income and R&D figures were obtained from company’s annual SEC 10-K filings.

Figure 10. Apple net sales by region and product (US$, billions)

While Apple achieved notable success during its 30-year history by focusing on personal computers, the success and popularity of its new iOS products has far exceeded any of its former achievements in personal computing.2 In the 5-year period following the launch of the iPhone and iPod Touch in 2007, Apple’s global net sales increased nearly 460 per cent. As Table 3 illustrates, the new iOS product line represented nearly 70 per cent of the overall net sales of Apple in 2011.

The success and popularity of Apple’s new products was quickly reflected in the company’s revenues. In 2011, Apple’s revenue ($76.4 billion) was so big that it surpassed the US government’s operating cash balance ($73.7 billion) according to the

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latest figures from the US Treasury Department available at that time (BBC 2012). This surge in Apple’s revenues was quickly translated into better market valuations and increased popularity of shares of Apple stock listed on the NASDAQ. As shown in Figure 11, Apple’s stock price has increased from $8/ share to $700/share since the iPod was first introduced by Steve Jobs on 23 October 2001. The launch of iOS products in 2007 enabled the company to secure a place among the most valuable companies in the US.3

Figure 11. Apple stock prices between 1990 and 20124

Figure 12. Productive R&D or free lunch?

Source: Retrieved from Schmidt’s article (2012) ‘You Cannot Buy Innovation’, Asymco, 30th January. Note: The author’s calculations are based on the leading smartphone developers’ 5-year average R&D figures between 2006 and 2011.

As indicated by Figure 10 and documented in company financial reports, the rampant growth in product sales following the launch of the iOS family of products paved the

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way for Apple’s successful comeback from its wobbly conditions in the late 1980s. Interestingly, as the company continued to launch one new product after the next with increasing success, the company’s financial reports reveal a steady decline in the global sales/R&D ratios, which indicate the portion of funds allocated to R&D activities in comparison to global product sales was falling over time (see Table 3). It could be argued that this is simply a testament to how unprecedented and exponential growth in product sales was relative to the annual growth of R&D expenditures. It could also be interpreted as the expected outcome of steady investment in R&D efforts. However, when viewed in the context of just how competitive the product markets are for consumer electronic products, these rather unimpressive R&D figures stand out. Long- time Apple analyst Horace Schmidt approaches this issue from a different angle by comparing Apple’s R&D figures against that of the company’s rivals. According to the data compiled by Schmidt (2012) and presented in Figure 12, Apple ranks in the bottom three in terms of the portion of sales allocated for supporting R&D activities among 13 of its top rivals.

Schmidt therefore inquires how Apple manages to get away with such a relatively low rate of R&D (as a percentage of sales ratios) in comparison to its competitors while still outpacing them in product sales. Many Apple experts explain this marginal R&D productivity as the company’s success in implementing effective R&D programmes in a fashion that can only be seen in small technology start-ups. There is no doubt that Apple’s ingenuity in engineering design, combined with Steve Job’s commitment to simplicity, certainly contributed to its efficiency. But, the most crucial facts have been omitted when explaining this figure, which is that Apple concentrates its ingenuity not o n developing new technologies and components, but on integrating them into an innovative architecture: its great in-house innovative product designs are, like that of many ‘smart phone’ producers, based on technologies that are mostly invented somewhere else, often backed by tax dollars. The following section will provide historical background on technologies that enabled the future glory of the company.

Surfing through the Waves of Technological Advancements From its humble beginnings selling personal computer kits to its current place as the leader in the global information and communications industry, Apple has mastered designing and engineering technologies that were first developed and funded by the US government and military. Apple’s capabilities are mainly related to their ability to (a) recognize emerging technologies with great potential, (b) apply complex engineering skills that successfully integrate recognized emerging technologies, and (c) maintain a clear corporate vision prioritizing design-oriented product development for ultimate user satisfaction. It is these capabilities that have enabled Apple to become a global powerhouse in the computer and electronics industry. During this period prior to launching its popular iOS platform products, Apple received enormous direct and/or

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indirect government support derived from three major areas:

1) Direct equity investment during the early stages of venture creation and growth.

2) Access to technologies that resulted from major government research programmes, military initiatives, public procurement contracts, or that were developed by public research institutions, all backed by state or federal dollars.

3) Creation of tax, trade or technology policies that supported US companies such as Apple that allowed them to sustain their innovation efforts during times when national and/or global challenges hindered US companies from staying ahead, or caused them to fall behind in the race for capturing world markets.

Each of these points is elaborated on in the following section, as the histories of key technological capabilities underlying Apple’s success are traced.

From Apple I to the iPad: The State’s very visible hand From the very start, Jobs and Wozniak sought the support of various public and private funding sources in their effort to form and develop Apple. Each believed in the vision in their mind: that enormous value could be captured from the technologies made available mostly as a result of the prior efforts of the State. Venture capital pioneers and Silicon Valley legends such as Don Valentine, founder of Sequoia; Arthur Rock, founder of Arthur Rock & Company; Venrock, the venture capital arm of the Rockefeller Family; and Fairchild and Intel veteran Mike Markkula were among the first angel and equity investors who bought into their vision (Rao and Scaruffi 2011). In addition to the technologies that were going to help Apple revolutionize the computer industry, the company also received cash support from the government to implement its visionary business ideas in the computer industry. Prior to its IPO in 1980, Apple additionally secured $500,000 as an early stage equity investment from Continental Illinois Venture Corp. (CIVC), a Small Business Investment Company (SBIC) licensed by the Small Business Administration (a federal agency created in 1953) to invest in small firms (Slater 1983; Audretsch 1995).

As briefly discussed in Chapter 4, the emergence of personal computing was made possible by the technological breakthroughs achieved through various public–private partnerships established largely by government and military agencies (Markusen et al. 1991; Lazonick 2008; Block 2008; Breakthrough Institute 2010). When Apple was formed to sell the Apple I personal computer kit in 1976, the product’s key technologies were based on public investments made in the computer industry during the 1960 and 1970s. Introduction of silicon during this period revolutionized the semiconductor industry and heralded in the start of a new age when access to affordable personal computers for wider consumer markets was made possible. These breakthroughs were the result of research carried out in various public–private partnerships at labs including those at DARPA, AT&T Bell Labs, Xerox PARC, Shockley and Fairchild, to name a

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few. Silicon Valley quickly became the nation’s ‘computer innovation hub’ and the resulting climate stimulated and nurtured by the government’s leading role in funding and research (both basic and applied) was harnessed by innovative entrepreneurs and private industry in what many observers have called the ‘Internet California Gold Rush’ or the ‘Silicon Gold Rush’ (Kenney 2003; Southwick 1999).

There are 12 major technologies integrated within the iPod, iPhone and iPad that stand out as features that are either ‘enablers’, or that differentiate these products from their rivals in the market. These include semiconductor devices such as (1) microprocessors or central processing units (CPU); (2) dynamic random-access memory (DRAM); as well as (3) micro hard drive storage or hard drive disks (HDD); (4) liquid-crystal displays (LCDs); (5) lithium-polymer (Li-pol) and lithium-ion (Li- ion) batteries; (6) digital signal processing (DSP), based on the advancement in fast Fourier transform (FFT) algorithms; (7) the Internet; (8) the Hypertext Transfer Protocol (HTTP) and Hypertext Markup Language (HTML); (9) and cellular technology and networks – all of which can be considered as the core enabler technologies for products such as the iPod, iPhone and iPad. On the other hand, (10) global positioning systems (GPS), (11) click-wheel navigation and multi-touch screens, (12) and artificial intelligence with a voice-user interface program (a.k.a. Apple’s SIRI) are innovative features that have drastically impacted consumer expectations and user experiences, further enhancing the popularity and success of these products. The following sections take a closer look at the core technologies and features that Apple has managed to ingeniously integrate, initially in the iPod and later in the iPhone and iPad.

How State-funded research made possible Apple’s ‘invention’ of the iPod Shortly after introducing the first generation iPod in 2001, Apple began to create waves of new innovative products (e.g. the iPhone, iPad) that would eventually revolutionize the entire mobile entertainment industry. The iPod, a new portable handheld device, allowed consumers to store thousands of songs without using any cassettes or CDs. In the early 2000s, this new Apple device was gaining popularity among consumers and replacing portable devices such as Sony’s Walkman and Discman in the market. This novel application of existing magnetic storage technology therefore enabled Apple to take on an iconic rival such as Sony, and eventually to rise to the top of the music and entertainment market (Adner 2012). The success of iPod in gaining a competitive market position was important in two major aspects: (1) the success was going to set the stage for Apple’s comeback from years of stagnant, if not declining, growth; and, (2) the popularity of this new product would constitute precedence to a family of new innovative Apple iOS products. While this much is often known and noted, the fact that much of Apple’s success lies in technologies that were developed through government support and -funded research is an often overlooked story to which I now turn.

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Giant magnetoresistance (GMR), SPINTRONICS programme and hard disk drives A rare instance of public recognition of the role played by State-backed technological research in paving the way for Apple products occurred during the 2007 Nobel Prize ceremony. European scientists Albert Fert and Peter Grünberg were awarded the 2007 Nobel Prize in Physics for their work in developing giant magnetoresistance (GMR). The GMR is a quantum mechanical effect observed in thin-film layered structures, for which the main application has been in magnetic field sensors used in hard disk drives (HDD) and other devices. In his ceremony remarks, Börje Johannson (2007), a member of the Royal Swedish Academy of Sciences, explained what the invention of GMR meant for society by attributing the existence of the iPod to this major scientific breakthrough.

Invention and commercialization of the micro hard drive is especially interesting since the technology development process from its origin to its current form illustrates the role of government not only in establishing the science base for innovation, but also in facilitating the advancement of abstract ideas into manufactured and commercially viable products (McCray 2009). What started as two separate and independent academic, State-funded and -supported research projects in physics in Germany and France culminated into one of the most successful technology breakthroughs in recent years, worthy of the Nobel Prize. Following this scientific breakthrough that Dr Fert and Dr Grünberg achieved, other researchers successfully expanded the size of data storage in conventional hard disk drives during the 1980s and 1990s, breaking new ground for future research and technological advancement (Overbye 2007). While the major scientific breakthrough in GMR was accomplished in Europe, the US government played a critical role in the basic research as well as commercialization of this technology. Dr Peter Grünberg’s laboratory was affiliated with Argonne National Laboratory (the US Department of Energy’s largest R&D lab, located in Illinois) and received critical support from the Department of Energy (DoE) prior to his discovery (DoE 2007). Based on these developments in hard disk technology, companies such as IBM and Seagate moved quickly to translate the new knowledge into successful commercial products (McCray 2009). Despite the advances taking place in the hard drive industry at the time, they would experience similar competitive challenges faced by the semiconductor industry in the late 1980s, which I discuss in the following section on semiconductor devices.

In his 2009 study, McCray details how DARPA’s wartime missions to create and sustain an innovation ecosystem for producing superior defence technologies was transformed during peace time by the new mission of transforming those prior investments into technologies supporting economic competitiveness. McCray (2009) documents that the Department of Defense (DoD) initiated the Technology Reinvestment Program (TRP) and allocated $800 million to upgrade the nation’s existing technological capabilities following the Cold War. Through TRP, DARPA

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targeted dual-use technologies that would benefit the military as well as produce commercially viable technologies such as SPINTRONICS.5 McCray (2009) especially documents the increase in scientific research efforts and publications taking place during DARPA’s support for SPINTRONIC during 1990s. McCray (2009, 74) also argues that the role DARPA played in the advancement of this technology was not ‘insignificant’, simply because the programme was initiated during the time when Japanese competition in computer electronics was pushing computer giants such as IBM and Bell Labs to downsize spending on basic research.

Solid-state chemistry and silicon-based semiconductor devices Since the launch of the first iPod, the first major new Apple product has evolved many times and also inspired the design of the future iPad and iPhone. Among the factors that have made the iPod, iPhone and iPad possible today are the small microchips that enable handheld smart devices to process large amounts of information and pass it through memory in a virtual instant. Today, central processing units (CPUs) depend on integrated circuits (ICs) that are considerably smaller in size and feature much larger memory capacity in comparison to the integrated circuits once used for processing needs and first designed by Jack Kilby and Robert Noyce in the 1950s. The invention of new silicon-based ICs led to technological developments in various fields in electronics. The rise of Personal Computers (PCs), cellular technology, the Internet and most of the electronic devices found on the market today utilize these smart, tiny devices. The journey of ICs from Bell Labs, Fairchild Semiconductor and Intel into devices such as iPhone or iPad was aided by procurement by the US Air Force and NASA. As the sole consumers of the first processing units based on this new circuit design, defence contracts helped fund the development of the infant microprocessor industry and those introducing complementary electronic equipment and devices that were simply unaffordable in regular commercial markets. Large-scale demand for microprocessors by the US Air Force was created by the Minuteman II missile programme. NASA’s Apollo mission pushed the technological envelope, requiring significant improvements in the production process of microprocessors and also greater memory capacity. In turn, each of the government agencies helped to drive down the costs of integrated circuits significantly within a matter of years.6

Although the US was the home for early innovation in semiconductors, throughout the 1980s, Japan was developing advanced manufacturing capabilities and competitive memory products at a faster pace.7 Given the significant role of semiconductors in defence technologies, the DoD considered the industry vital to its military capabilities and national security. Growing fears that the manufacturing equipment essential for production of these technologies, now vital to national defence, would be imported from countries like Japan spurred the DoD to act. The result was the Strategic Computing Initiative (SCI) which allocated over $1 billion to support research efforts in advanced computer technologies between 1983 and 1993 (Roland and Shiman 2002).

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Additionally, the manufacturing of highly advanced technologies such as microprocessors had significant economic implications that required collaborative efforts between the government and industry. Recognizing the unique opportunity that semiconductor manufacturing would provide, and fearful of the consequences of lagging behind newly emerging competitors in semiconductor manufacturing such as Japan, the federal government gathered competitive domestic manufacturers and universities together to form a new partnership, the Semiconductor Manufacturing Technology (SEMATECH) consortium.

This move, to advance US-based semiconductor manufacturing technology and capability above and beyond that of the nation’s competitors, was part of an overall effort to promote US economic and technological competitiveness globally. The process of organizing collaborative effort between semiconductor companies through SEMATECH was a challenge for the government. In order to make this partnership more appealing, the US government subsidized SEMATECH R&D with $100 million annually. Over time, the members of the consortium came to recognize the benefits of the R&D partnership fostered by SEMATECH. The extensive knowledge sharing efforts that took place among members of SEMATECH helped them avoid duplicating research efforts and translated into less R&D spending. The advanced performance and affordability of microprocessors and memory chips today are to a great extent the result of years of government intervention and supervision (Irwin and Klenow 1996).

From capacitive sensing to click-wheels As the pioneer of personal computers, Steve Jobs was on his second mission for re- revolutionizing them. His vision for Apple was to prepare the company for the post- computer era, in what he envisioned and often acknowledged in his interviews and media appearances as the new era of the consumer–computer relationship. During an interview at the 2010 D8 conference, Steve Jobs explained his vision of the future for computing by using the analogy of rapid urbanization and its effects on changing consumer views and the need for transportation (Jobs 2010). During his talk, Jobs redefined Apple’s overall strategy as building a family of products around the concept of fragmented computing needs by different uses. Jobs often acknowledged his trust in the data processing 100 The Entrepreneurial State technologies that had enabled Apple to come up with compact portable devices. It was these processing technologies leading to the portable iOS products that eventually replaced desktop computers. To do this, Apple had begun to work on building a periphery of portable iOS devices, with the Mac becoming the ‘digital hub’ that would integrate the entire product family together (Walker 2003).

Despite his strong opposition to tablet computers in the 1980s and 1990s, upon his return to Apple in the late 1990s, Jobs had decided that the time was right to focus once again on tablets. Underlying this shift in perspective was the fact that technology in

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semiconductor devices, batteries and displays had progressed significantly. However, a challenge still remained given the absence of sophisticated technology to successfully replace the stylus pen, a feature that Jobs had long despised and considered an inconvenience (Isaacson 2011, 490). The emergence of more sophisticated applications such as inertia scrolling, finger tracking and gesture-recognition systems for touch- screen-enabled displays presented Jobs and his team with the possibility of moving forward (and far beyond the stylus pen). Jobs and his team thus gathered experts together that could integrate these new technologies. The end results included replacing buttons and roll-balls on devices, developing a new navigation system, and enhancing input techniques on touch-screens. 8

The iPod’s click-wheel component that allowed users to navigate quickly through their music library was part of Apple’s earlier attempts to implement touch-based features with finger scrolling. In addition to the micro hard disk drive for the storage of memory intensive digital records, the finger scrolling click-wheel feature also differentiated the iPod from the majority of other available portable music players. Although the application of finger scrolling was something novel at the time, the technology behind this feature had been around for decades. The click-wheel significantly benefitted from the capacitive sensing technology widely applied in the design of various other products.9 In fact, the click-wheel feature was not the only feature of Apple products that benefitted from capacitive sensing. The iPod Touch, iPhone and iPad’s multi-touch screen also embodies the same principles of finger(s)- operated scrolling on a glass screen.

E. A. Johnson, considered the inventor of capacitive touch-screens, published his first studies in the 1960s while working at Royal Radar Establishment (RRE), a British government agency established for R&D of defence-related technologies (Buxton 2012). One of the first notable developments of the touch-screen was at the European Organization for Nuclear Research (CERN) by Bent Stumpe and his colleague Frank Beck in 1973 (CERN 2010). Samuel Hurst’s invention of resistive touch-screens was another notable breakthrough. Hurst’s invention came right after leaving Oak Ridge National Laboratory (a national research laboratory in Tennessee established in 1943 and the site of the Manhattan Project and first functional nuclear reactor) for two years to teach at the University of Kentucky (Brown et al., n.d.). While at University of Kentucky, Hurst and his colleagues developed the first resistive touch-screens. Upon his return to Oak Ridge, they started a new company in 1971 to commercialize the new technology and produced the first functioning version in 1983 (Brown et al., n.d.). Earlier work on touch-screens in the 1970s and 1980s, such as that conducted by Johnson, Stumpe, Hurst and others has been carried forward in different public and private research labs, yet their work is considered foundational to today’s important multi-touch applications (Buxton 2012). Among various other factors, moving from touchpads with limited functionality to multi-touch screens was a major leap forward

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for Apple in the smartphone race. Along with the other technological advancements they exploited, Apple has not only helped redefine the markets it competes from within but has also defined a different path for growth.

The Birth of the iPod’s Siblings: The iPhone and iPad Apple’s new vision included radical redefinitions of conventional consumer products and was a great success. The introduction of the iPod generated over $22 billion in revenues for Apple. It was the company’s most important global product until the iPhone was introduced in 2007. The cohesion of aesthetic design, system engineering and user experience combined with great marketing helped Apple rapidly penetrate and capture market share in different consumer electronics markets. Apple’s new generation of iPods, iPhones and iPads have been built under the assumption that new consumer needs and preferences can be invented by hybridizing existing technologies developed after decades of government support. As a pioneer of the ‘smartphone’ revolution, Apple led the way in successfully integrating cellular communication, mobile computing and digital entertainment technologies within a single device. The iconic iPhone dramatically altered consumer expectations of what a cellular phone was and can do. With the introduction of the iPad, Apple transformed the portable computer industry that had been dominated for decades by laptops, netbooks and other devices. By offering a slimmer handheld device equipped with a large touch-screen and virtual keyboard, with solid Internet browsing and multimedia capabilities, along with broad compatibility across other Apple products and applications, the iPad virtually created a new niche and captured it at the same time. In less than a decade, Apple singlehandedly came to dominate the consumer electronics industry, a testament to Apple’s ingenuity in consumer-oriented device product design and marketing, as well as their organizational capabilities in managing complex ‘systems integration’ (Lazonick 2011).

From click-wheels to multi-touch screens Development of touch-screen displays recognizing multi-touch gestures was one of the most important technologies integrated into Apple’s devices and for their successful introduction of pocket-sized portable devices such as the iPod. The technology allowed human–machine interaction through a new interface that allowed fingers to navigate the glass surface of LCD displays included with handheld devices. As with the click-wheel feature, the technology behind this ground-breaking new way to interface with electronic devices relied on earlier basic and applied research that had been supported by the State. During the 1990s, touch-screen technology was incorporated into a variety of products by numerous computer developers, including Apple, but the majority of the touch-screen technologies available during these earlier days were only capable of handling single-touch manipulation.10 The introduction of multi-touch scrolling and gestures was developed by Wayne Westerman and John Elias at the University of Delaware (Westerman 1999).

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Wayne Westerman was a doctoral candidate under the supervision of Professor John Elias studying neuromorphic systems at the (publicly funded) University of Delaware, as part of the National Science Foundation (NSF) and Central Intelligence Agency/Director of Central Intelligence (CIA/DCI) Post-Doctoral Fellowship programme (Westerman 1999). Following the completion of Westerman’s PhD, he and Elias commercialized this new technology after founding the FingerWorks company. Their new product, called ‘iGesture Numpad’, enabled many computer users to enter input by applying ‘zero-force’ pressure on an electronic screen with no need of additional devices such as a keyboard or a mouse. The underlying scientific base and patent application for the new finger tracking and gesture identification system was built on the earlier studies on capacitive sensing and touch-screen technologies. FingerWorks’ successful attempt to translate prior touch-screen research into a commercial product was quickly recognized by Apple, which was interested in developing a multi-touch navigation capability on a fully glass LCD display for the new generation iOS products. FingerWorks was acquired by Apple in 2005 prior to the launch of Apple’s first generation iPhone in 2007, and today this new technology lies at the heart of the coveted multi-touch screen featured on Apple’s iOS products. As a result, Westerman and Elias, with funding from government agencies, produced a technology that has revolutionized the multi-billion dollar mobile electronic devices industry. Apple’s highly comprehensive intellectual property portfolio had benefitted, once again, from technology that was originally underwritten by the State.

Internet and HTTP/HTML Although the iPhone appears to be a ‘cool’ gadget with its cutting-edge technology features and hardware components, what makes a phone ‘smart’ is its ability to connect phone users to the virtual world at any point in time. With the artificial intelligence application named SIRI on board, the iPhone appears to be attempting to outsmart its users. After replacing the handset-industry-standard keypads with touch-screens, SIRI is Apple’s attempt to transform input entry and navigation interfaces. As Apple’s ‘smartphone’ continues to evolve into an even smarter device, it is important to recognize and value the underlying and necessary intelligence and technological capabilities that have smart-wired, if you will, this smart device. If hardware, software, memory and the processor were to be the body, soul and brain of a computer, what does the Internet, Hypertext Transfer Protocol (HTTP) or Hypertext Markup Language (HTML) mean to any computer or smart device? Or, what would a computer or smart device be worth in the absence of Internet or without cellular communication capability? Answers to these questions can help us understand the value of the networking capabilities of smart devices. But more importantly, they can help us understand the value of support efforts that the government played in the process of inventing and developing cellular technology, the Internet and satellites.

During the Cold War era, US authorities were concerned about possible nuclear

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attacks and the state of communication networks following the aftermath of possible attacks. Paul Baran, a researcher at RAND – an organization with its origins in the US Air Force’s project for ‘Research and Development’, or RAND for short – recommended a solution that envisioned a distributed network of communication stations as opposed to centralized switching facilities. With a decentralized communication system in place, the command and network system would survive during and after nuclear attacks (Research and Development 2011).11 The technological challenges of devising such a network were overcome thanks to the various teams assembled by DARPA to work on networking stations and the transmission of information. Although DARPA approached AT&T and IBM to build such a network, both companies declined the request believing that such a network was a threat to their business; with the help of the State-owned British Post Office, DARPA successfully networked various stations from the west to east coast (Abbate 1999). From the 1970s through the 1990s, DARPA funded the necessary communication protocol (TCP/IP), operating system (UNIX) and email programs needed for the communication system, while the National Science Foundation (NSF) initiated the development of the first high-speed digital networks in the US (Kenney 2003).

Meanwhile, in the late 1980s, British scientist Tim Berners-Lee was developing the Hypertext Markup Language (HTML), uniform resource locators (URL) and uniform Hypertext Transfer Protocol (HTTP) (Wright 1997). Berners-Lee, with the help of another computer scientist named Robert Cailliau, implemented the first successful HTTP for the computers installed at CERN. Berners-Lee and Cailliau’s 1989 manifesto describing the construction of the World Wide Web eventually became the international standard for computers all over the world to connect. Public funding has played a significant role for the Internet from its conception to its worldwide application. The Internet is now in many ways a foundational technology that has affected the course of world history by allowing users all over the globe to engage in knowledge sharing and commerce using computers and popular smart gadgets such as the iPhone, iPod or iPad.

GPS and SIRI Another great feature that an iPod, iPhone or iPad offers is global positioning system (GPS) integration. GPS was an attempt by the DoD to digitize worldwide geographic positioning to enhance the coordination and accuracy of deployed military assets (Breakthrough Institute 2010). What initially began in the 1970s as a strictly military- use-only technology is now widely available to civilians for various uses. In fact, civilian use of GPS quickly outnumbered military utilization following the release of GPS for public applications in the mid-1990s. Yet, even today, the US Air Force has been at the forefront of developing and maintaining the system, which costs the government an average of $705 million annually.12 An iPhone user can search for a nearby restaurant or an address, based on the NAVSTAR GPS system, which consists of a 24-satellite constellation providing global navigation and timing data for its users.

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This technology, as well as the infrastructure of the system, would have been impossible without the government taking the initiative and making the necessary financial commitment for such a highly complex system.

Apple’s latest iPhone feature is a virtual personal assistant known as SIRI. And, like most of the other key technological features in Apple’s iOS products, SIRI has its roots in federal funding and research. SIRI is an artificial intelligence program consisting of machine learning, natural language processing and a Web search algorithm (Roush 2010). In 2000, DARPA asked the Stanford Research Institute (SRI) to take the lead on a project to develop a sort of ‘virtual office assistant’ to assist military personnel. SRI was put in charge of coordinating the ‘Cognitive Assistant that Learns and Organizes’ (CALO) project which included 20 universities all over the US collaborating to develop the necessary technology base. When the iPhone was launched in 2007, SRI recognized the opportunity for CALO as a smartphone application and then commercialized the technology by forming ‘SIRI’ as a venture-backed start-up in the same year. In 2010, SIRI was acquired by Apple for an amount that is undisclosed by both parties.

Changing industry standards from keypad to touchpad input and adding GPS navigation was a significant achievement when iPod was first introduced. A second game-changer for cell phone, media player and tablet computer developers was the introduction of multi-touch screens and gesture recognition. With SIRI, Apple introduced another radical idea for a device input mechanism that has been integrated within various iOS features and applications. The introduction of SIRI has launched a new round of redefining standards of human–machine interaction and creates a new means of interaction between the user and the machine. Steve Jobs often acknowledged the potential of artificial intelligence and his interest in the future of the technology. During his 2010 interview with Walt Mossberg and Kara Swisher (2010) at the California D8 conference, Jobs had shared his excitement about the recent acquisition of SIRI by Apple, and talked about the great potential the technology offered. Once again, Apple is on the verge of building the future for information and communication industry based on the radically complex ideas and technologies conceived and patiently fostered by the government.

Battery, display and other technologies The story of the liquid-crystal display (LCD) shares great similarities with the hard disk drive, microprocessor and memory chip (among other major technologies) that emerged during the Cold War era: it is rooted in the US military’s need to strengthen its technological capabilities as a matter of national security. Rising competition from the Japanese flat panel display (FPD) industry was a concern for the DoD because the US military’s future demand for the technology could not be met solely by the Japanese suppliers. Given this determination, the DoD began implementing a variety of programmes geared towards strengthening the industry’s competitiveness, including the

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formation of an industry consortium and deployment of new resources for the improvement of manufacturing capabilities and commercial products.

The major breakthrough in LCD technology came about during the 1970s, when the thin-film transistor (TFT) was being developed at the laboratory of Westinghouse under the direction of Peter Brody. The research carried out at Westinghouse was almost entirely funded by the US Army (Hart and Borrus 1992). However, when management at Westinghouse decided to shut down the research, Brody sought out possible funding opportunities elsewhere in the hopes of commercializing this technology independently. In the process of appealing for contracts to ramp up the production of TFT displays, Brody contacted a number of top computer and electronic companies including Apple and others such as Xerox, 3M, IBM, DEC and Compaq. All these major private companies refused to sign on with Brody largely because they doubted his ability to build the manufacturing capability necessary to provide the product at a competitive price compared to his Japanese counterparts (Florida and Browdy 1991, 51). In 1988, after receiving a $7.8 million contract from DARPA, Brody established Magnascreen to develop the TFT-LCD. This advancement in the LCD technology became the basis for the new generation displays for the portable electronic devices such as microcomputers, phones, etc.

Florida and Browdy argued that this pattern of the inability of private actors to build or sustain manufacturing capabilities in various high-technology fields presented a broader problem with the nation’s innovation system:

The loss of this [TFT-LCD] display technology reveals fundamental weaknesses of the U.S. high-technology system. Not only did our large corporations lack the vision and the persistence to turn this invention into a marketable product, but the venture capital financiers, who made possible such high-technology industries as semiconductors and personal computers, failed too. Neither large nor small firms were able to match a dazzling innovation with the manufacturing muscle needed for commercial production. (1991, 43)

In an attempt to retain the manufacturing of TFT-LCDs in the US, the Advanced Display Manufacturers of America Research Consortium (ADMARC) was established by the major display manufacturers with initial funding appearing from the National Institute of Standards and Technology’s (NIST) Advanced Technology Program (ATP) (Florida and Browdy 1991). The industry also received additional assistance from the US government in the form of antidumping tariffs (while at the same time touting the ‘free competition’ line), as well as funds and contracts provided by various military or civilian agencies that supported many start-ups in the US as part of an effort to develop manufacturing capabilities of TFT-LCDs in the 1990s (OTA 1995).

The lithium-ion battery is another example of a US-invented but Japanese-perfected and manufactured-in-volume technology. John B. Goodenough who pioneered the early

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research on lithium-ion battery technology received his main funding support from the Department of Energy (DoE) and National Science Foundation (NSF) in the late 1980s (Henderson 2004; OSTI 2009). Major scientific breakthroughs accomplished at the University of Texas at Austin were quickly commercialized and launched in 1991 by the Japanese electronics giant Sony. In a 2005 working paper for the National Institute of Standards and Technology (NIST), Ralph J. Brodd (2005) identified issues with the advanced battery industry innovation model that were similar to the issues within the TFT-LCD industry. Another major scientific success faded away without greater value being captured in the form of US-based high-volume manufacture. Brodd’s study identifies the factors hindering the volume production of lithium-ion batteries in the US, but particularly placed emphasis on the short-termist approach of US corporations and venture capitalists. Brodd (2005, 22) argued that their short-termism was based upon achieving rapid financial returns (in comparison to their Japanese competitors’ focus on maximizing market share in the long run), which often discouraged them from any interest in building the domestic manufacturing capabilities while encouraging outsourcing of manufacture as an option.

Absence of a battery technology that met the storage capacity needs of increasingly powerful electronic devices posed one of the greatest challenges that the electronics industry faced following the revolution in semiconductor devices. The invention of lithium-ion technology enabled portable devices to become much slimmer and lighter as battery capacity increased relative to size. Once again, the federal government stepped in to assist smaller battery companies through a variety of agencies and programmes that invested in the industry in an effort to develop the necessary manufacturing capabilities (Brodd 2005) – not only for electronic devices but, equally or even more importantly, for ‘zero-emission’ electric vehicles. The US government has been actively involved with the energy industry for decades as part of a broader effort to address economic and social needs, which is extensively discussed in Chapters 6 and 7.

Figure 13. Origins of popular Apple products

Source: Author’s own drawing based on the OSTP diagram ‘Impact on Basic Research on Innovation’ that illustrates the benefits of basic research on innovation (2006, 8).

State-of-the-art iOS products are highly complex electronic devices. Despite the

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