The Entrepreneurial State Debunking Public vs. Private
Sector Myths Mariana Mazzucato
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 , 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)
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’
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’
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!’
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
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
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
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)
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
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
(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.
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
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
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
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.
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
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
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.
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
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.
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
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
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
‘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
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
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
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 ) 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.
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
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
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.
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
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.
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
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
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
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
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
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.
Chapter 2 TECHNOLOGY, INNOVATION
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 ). 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,
centrally organized and controlled interventionism’ (Polanyi 2001 , 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
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.
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
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 ), 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).
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
(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.
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.
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?
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.
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
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–
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
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
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).
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
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’.