This should worry us. The failure to find effective therapies for widespread and devastating conditions – Alzheimer’s, to take just one example – leads to enormous human suffering. The escalating cost of developing new drugs is ultimately passed on to society through their pricing, leading to strains on national healthcare systems that will become more acute as populations age. As a second-order effect, scientists should be concerned in case the drying up of medical innovation casts doubt on some of the justifications for government spending on fundamental life sciences research. And, of course, a healthy and innovative pharmaceutical industry is itself important for economic growth, particularly here in the UK, where it remains the one truly internationally competitive high technology sector of the economy. So what can be done to speed up innovation in this vital sector?

The problem for the pharmaceutical industry is particularly acute, as the healthy revenues they depend on from existing medicines are threatened by the expiry of their patent protection. One common response has been to reorganise – to try and rationalise the industry through mergers, or to change the way research and development is done. The net effect seems to be the same – to reduce expenditure on R&D. One of the world’s largest drugs companies – Pfizer – is on a three year plan to reduce its R&D expenditure from $9.4 billion to 2010 to $7.8 billion in 2012, with further cuts to come; this has involved the closure of famous laboratories like its site in Sandwich, UK. Unless this genuinely does make R&D more efficient it’s difficult to see that this is much of a long term solution for the companies, and it isn’t clear how it improves the supply of more medicines.

What seems at first to be a more positive response is the idea that fast-moving new biotechnology companies can exploit the new science emerging from universities and develop innovative new therapeutic approaches which, if they show signs of working, can be acquired by big pharmaceutical companies at a later stage. It’s certainly the case that more and more new therapeutic molecules are biological macromolecules rather than the small molecules of traditional medicinal chemistry – antibodies and antibody fragments, other proteins and peptides, and perhaps soon nucleic acids like the si-RNAs. Some of these molecules have already had a significant clinical impact (e.g. herceptin and avastin), but the idea that there has already been a biotechnology revolution is probably overstated, as argued by Michael Hopkins and coworkers in their article The myth of the biotech revolution.

One problem is that the venture capital money small biotech start-ups rely on is hard to get, and this seriously limits the scale of the sector. The total invested by VCs in biotech and pharma in the UK in 2012 was just £38 million – a tiny fraction of the annual total R&D spend of the pharmaceutical sector in the UK, which was £4.85 billion in 2011 (The data sources here are ONS and the British Venture Capital Association). The financial crisis has hit venture capital hard – there’s been more than a four-fold drop in investment in biotech and pharma since 2006.

But there are a couple of other problems too. Although the total sums handled by venture capital and private equity in the UK are large – £5.7 billion in 2012 – the fraction of this that goes into fast growing technology companies is rather small. Investments in financial engineering are more attractive than the risky world of new technology, as I discussed in an earlier post on Bad Capitalism. The £420 million spent financing the early stages or later expansion of companies developing new technologies contrasts with the £3.8 billion spent on management buyouts, management buyins and refinancing deals, often getting their value from the different tax treatment of debt and equity.

And of the sums that are invested in the technology sector, only a small fraction ends up in biotech and pharma. More than five times more – £198 million in 2012 – goes into software and internet companies. This reflects the fact that innovation isn’t uniformly easy – innovation in the digital realm has lower barriers to entry and is cheaper than innovation in the biological realm, as I discussed in this earlier post, Innovation Stagnation. This allocation of money is probably entirely sensible and rational from the point of view of the markets, but it doesn’t help us meet the broader societal needs for new drugs and treatments.

The final response is to rely even more on the state and the non-profit sector for this R&D funding. I say even more to remind us how much state funding already goes in to provide the underpinning advances in life sciences that the pharmaceutical and biotechnology industries depend on. This is dominated in the USA by the huge budget of the National Institutes of Health – $38 billion. But even in the UK, the Medical Research Council spends £550 million, the Wellcome Trust spends another £750 million, and big medical charities put in substantial further sums – £330 million from Cancer Research UK, for example, this largely arising from individual donations and fundraising. The difficulties of the pharma and biotech industries put pressure for more of this state and non-profit spending to be moved downstream, closer to the clinic and the market. The result is an increasing number of calls for translational funding, and schemes such as the £180 million in the Biomedical Catalyst Fund, providing government grants directly to start-up companies. In fact, substantial amounts – perhaps the majority – of the funding directed to start-up companies through venture capital funding in the UK originates from government agencies of one sort or another (the total money raised by venture capital and private equity from government agencies in 2012 – £424 million – actually exceeds the total investment in technology companies, but of course some of the government investment will have been into non-technology areas).

I don’t know what the fundamental problem is here. Is it a question of how research is organised and funded, is it a question of the regulatory environment and the broader issues about how healthcare is organised and paid for, or are we making some fundamentally wrong assumptions about how to think about the biology? One has to be optimistic in the long run that the astonishing progress in the life sciences of the last twenty or thirty years will ultimately yield the benefits we hope for and need. But this story is a salutary reminder that not all innovation is accelerating, and for all our scientific success in creating fundamental understanding of biology, our current system of innovation isn’t as successful as it should be at translating that into better health.

1. The UK’s current growth crisis follows a sustained period of national disinvestment in R&D

Red, left axis. The percentage deviation of real GDP per person from the 1948-1979 trend line, corresponding to 2.57% annual growth. Sources: solid line, 2012 National Accounts. Dotted line, March 2013 estimates from the Office for Budgetary Responsibility.
Blue, right axis. Total R&D intensity, all sectors, as percentage of GDP. Data: Eurostat.

No single measure can capture the overall performance of an economy, but the long term trajectory of real GDP per person in the UK tells an interesting story, with a clear discontinuity in 1979. From 1948 to 1979 this measure grew remarkably steadily; a best fit corresponds to 2.57% annual growth. Since 1979 we have seen two deep recessions, each followed by a period of faster growth that, in each case, didn’t quite make up the lost ground to the pre-1979 trend line, and proved unsustainable. The third recession, following the 2008 financial crisis, has been both deeper and more long lasting than previous recessions. Meanwhile, since 1980, there has been a substantial fall in the overall research intensity of the economy, measured by the fraction of GDP spent on research and development. Without claiming simple causality, my SPERI post looks at the relationship between our current growth crisis and this disinvestment in R&D.

2. Since 1980, the UK has moved from being one of the most R&D intensive economies in the developed world, to one of the least.

Total R&D expenditure as % of GDP. Data: Eurostat

This decline in R&D intensity in the UK has happened at a time when other countries have been increasing investment in research. These increases have been particularly marked in fast growing Asian countries like South Korea and China.

3. The overall decline in the UK’s R&D intensity has been driven primarily by a long- term decline in private sector R&D

Value of R&D performed by sector as % of GDP. Data: Eurostat

Most R&D is performed in the private sector, with other substantial contributions happening in Government laboratories and Universities. R&D in the combined Government and HE sectors in the UK dropped substantially in the 1980′s and then stabilised, but the largest decline has taken place in private sector R&D. One might be tempted to argue that this reflects changes in the sectoral balance of the UK economy, but a more detailed analysis by Alan Hughes and Andrea Mina shows that, even after adjusting for structural differences between countries, the business enterprise component of R&D remains low by international standards.

4. The relative value of R&D performed in the business sector in the UK has been falling since the mid-1980s and has slipped far behind key rivals

Value of R&D performed by the business sector as % of GDP. Source: Eurostat

The contrast between the declining R&D intensity of the UK’s private sector and its growth in competitor nations is marked. My SPERI blogpost The failures of supply side innovation policy explores what might lie behind this.

To begin with, one should understand Drexler’s position by reading his own words. His first publication on the subject was a short paper in the journal Proceedings of the National Academy of Sciences, in 1981, Molecular engineering: An approach to the development of general capabilities for molecular manipulation. This paper demonstrated the possibility of artificial molecular machines by analogy with the protein-based molecular machines of biology, and argued that protein engineering is the natural route by which a second generation of artificial molecular machines, more powerful than their natural precursors could be made.

Drexler’s next publication was perhaps his most influential; this was his 1986 popular science book Engines of Creation: the coming era of nanotechnology. This explored the consequences of the molecular assemblers that he argued could be made from the second generation molecular machines, able to make virtually anything consistent with the basic laws of physics, atom-by-atom, with atomic precision. One consequence would be cell repair machines able to halt and reverse the effects of ageing and disease, leading to indefinite human lifespans.

Engines of Creation was not a technical book, so it did not include much more in the way of detail of how these universal assemblers would be made. This detail was provided in Drexler’s 1992 book, Nanosystems: Molecular Machinery, Manufacturing, and Computation. It’s difficult to imagine a book more different to Engines of Creation than Nanosystems. It’s almost gratuitously dry and technical, a textbook for a yet-to-be-developed technology, based on the principle that “molecular manufacturing applies the principles of mechanical engineering to chemistry”.

My own thinking on nanotechnology – summarised initially in my 2004 book Soft machines: nanotechnology and life – was at the same time inspired by Drexler’s work and a reaction against it. Like Drexler, I was fascinated by the example that cell biology provided of intricate, molecular scale machines. But I was also struck by the insights that the new single molecule biophysics was providing (using the new tools of nanoscience) – insights that stressed that the principles used by the molecular machines were not the principles of mechanical engineering, but a quite alien set of design principles optimised for the peculiar physics of the warm, wet, nanoscale world – the principles of soft nanotechnology.

I dealt with the question of what nanotechnology should learn from biology in this blog post – What biology does and doesn’t prove about nanotechnology – which was a riposte to some heated discussions on the blogs of the time. I came back to this question with a more reflective discussion of the same themes in my column in Nature Nanotechnology -
Right and wrong lessons from biology.

Moving to my criticisms to the vision of nanotechnology presented in Nanosystems, the context can be found in this piece: Molecular nanotechnology, Drexler and Nanosystems – where I stand. In making and doing I argued that matter is not digital, responding to quite extensive discussion of that post in bits and atoms. I outlined some specific technical issues in Six challenges for molecular nanotechnology.

My most widely circulated critique was published in the US magazine IEEE Spectrum – Rupturing the nanotech rapture. By this time Drexler’s vision of radical nanotechnology had become a central part of the belief package of transhumanists and proponents of the secular eschatology of the technological singularity, as most notably and influentially popularised by Ray Kurzweil in his book The Singularity is Near. My article was part of a special issue exploring, mostly from a critical perspective, this idea (misguided as it is, in my opinion). Nanobots, nanomedicine, Kurzweil, Freitas and Merkle was a response to criticisms of the IEEE Spectrum article.

Lately, Drexler has been writing on his own blog Metamodern. From there it is clear that we agree about some things – the importance of the “soft” route to radical nanotechnology in the near future, the achievements and potential of DNA nanotechnology, for example – and remain in disagreement about others. I look forward to discussing these issues with him on Tuesday.

In a speech at the Royal Society last November George Osborne said that, as Chancellor of the Exchequer, it is his job “to focus on the economic benefits of scientific excellence”. He then listed eight key technologies that he challenged the scientific community in Britain to lead the world in, and for which he promised continuing financial support. Among these technologies were synthetic biology, regenerative medicine and agri-science, key examples of what a recent report from the Nuffield Council for Bioethics calls emerging biotechnologies. Picking technology winners is clearly high on the UK science policy agenda, and this kind of list will increasingly inform the science funding choices the government and its agencies, like the research councils, make. So the focus of the Nuffield’s report, on how those choices are made and what kind of ethics should guide them, couldn’t be more timely.

These emerging technologies are not short of promises. According to Osborne, synthetic biology will have an £11 billion market by 2016 producing new medicines, biofuels and food – “they say that synthetic biology will heal us, heat and feed us.” Regenerative medicine is “set to transform current clinical approaches to replacing or regenerating damaged human organs or tissue,” while through new agri-science “we can design better seeds and more productive farm animals”; a doubling of wheat yield in the UK would generate £1.5 billion at the farm gate.

But how do we choose these problems as the ones for which we most need technological solutions? How do we know that these particular emerging technologies offer the best way of delivering these goals while minimizing the risk of unintended and undesirable consequences? And given that, by definition, these technologies are still immature, what’s the best way of ensuring that they really can make the transition from the laboratory into the real world, to yield those promised benefits and fulfill those heady financial predictions?

In a world of limited resources, choosing one approach implicitly means not choosing another, and other potential technological solutions go unexplored. We may neglect potential solutions whose innovations are social rather than technical in nature. For example, food security could be improved by increases in wheat yield, but in the light of estimates that 30-50% of all food produced is wasted (according to this recent report from the IMechE), one should look at ways the food distribution system could be changed too. Technology choices need to underpinned by evidence. This will usually be multidisciplinary in character, social factors are likely to be important, and public engagement may often have an important role to make sure that innovations support widely shared societal goals.

Where do the promises come from, and how much can we rely on them? Some in the scientific community resent the idea that the government would attempt to direct research at all. But the idea of synthetic biology, for example, wasn’t hatched in Whitehall – it’s from the research community itself that these ideas emerge and in which the promises are generated. Some would argue that it is the pressures on the scientific enterprise to deliver economic and other benefits themselves that impose perverse incentives on researchers to make promises about the potential impacts of their research that are too optimistic. I believe that this arises from a misinterpretation of the “impact agenda”, as it is understood by the research councils, but there are real dangers, as I discussed in an earlier piece about The Economy of Promises. Researchers may not be well placed to appreciate the broader societal dimensions of the problems that their technologies might be in a position to solve – here moves to incorporate emerging ideas about “responsible innovation” into research council practice are very welcome. And researchers – particularly in academia – are often not well placed to understand the difficulties of putting new technologies into practice and commercializing them.

It’s a cliché that the UK is excellent at doing fundamental science, but not so good at commercializing it. This has never actually been true, and it’s certainly not true now – universities have never been more active in partnerships with the private sector and in making the most of the intellectual property their researchers produce. But what is true is that the economic environment for bringing potentially valuable innovations to market seems to be particularly difficult at the moment.

Reading about emerging biotechnologies in the mainstream press, it’s the breakthroughs and marvels that catch the eye. But in the financial press the story is as much the apparent failure of anyone to be able to make any money from these marvels. The travails of the big pharmaceutical companies are well known, as the patents on their most lucrative products expire, and the cost and difficulty of developing new drugs escalates, to more than $1 billion per new medicine, according to one recent estimate. The response of the drug companies has been increasingly to rely on innovation in small biotech companies, often spin-outs from universities. But the environment for such spin-outs hasn’t been easy, either; for more than 10 years the venture capital industry, taken overall, has taken in more money from investors that it has paid out (see for example this FT article: Funding woes threaten next tech revolution (£). Meanwhile financially hard-pressed health systems like the NHS increasingly balk at the very high prices pharmaceutical companies demand for new treatments.

For new biotechnologies outside the biomedical arena, there’s an easier regulatory environment. But in many cases these technologies aren’t producing something completely new, they are offering another way – perhaps a more sustainable way – of making a product that already exists. For these technologies, the challenge of displacing the incumbent can be too great, particularly if the price of the existing technology doesn’t fully reflect its total costs to society. For example, it is reported that one of the pioneers of commercial synthetic biology, Amyris, is scaling back its ambitions to produce biodiesel, because, with a production cost of $29 a liter, it has no chance of competing with its fossil fuel competitor.

One company that does look like it may be successful in commercializing a genuinely innovative bionanotechnology product is Oxford Nanopore. Its technique for sequencing single molecules of DNA, if it can deliver on its promises, will offer greater speed for lower cost in a market that has been already developed by others, with relatively low regulatory barriers and secure protection of its intellectual property for long enough to make a good return. These conditions for success don’t apply over much of the pharmaceutical, biomedical or agricultural biotechnology sectors.

Our system isn’t set up to reward innovation and the development of those emerging biotechnologies that would bring widespread public benefit, and this needs to be changed. New biotechnologies which promise environmental benefits won’t be able to compete with unsustainable incumbent technologies unless their environmental impact is correctly priced. For new therapies, we may need to separate the reward for innovation from the price paid for products in order to get the innovation people want.

Technology choice is an ethical issue. When we make the wrong choices – whether consciously, or through not understanding the institutional pressures that make those choices by default – we not only suffer from the downsides of inappropriate technologies, but we don’t get the benefit of the better technologies we didn’t choose. But choosing between technologies is inevitable, so we need to organise our research policies and our broader innovation systems to make sure these new technologies can and do fulfil their promises.

Alas, no. As my graph shows, the decade from 1980 saw a worldwide decline in the fraction of GDP major industrial countries devoted to government funded energy research, development, and demonstration, with only Japan sustaining anything like its earlier intensity of energy research into the 1990s. It was only in the second half of the decade after 2000 that we began to see a recovery, though in the UK and the USA a rapid upturn following the 2007 financial crisis has fallen away again. A rapid post-2000 growth of energy RD&D in Korea is an exception to the general picture. There’s a good discussion of the situation in the USA in a paper by Kamman and Nemet – Reversing the incredible shrinking energy R&D budget. But the largest fall by far was in the UK, where at its low point, the fraction of national resource devoted to energy RD&D fell, in 2003, to an astonishing 0.2% of its value at the 1981 high point.

Government spending on energy research, development and demonstration. Data: International Energy Authority

What’s the story behind these numbers? This can be illustrated by a few key dates. By 1986, a major wind-down of the UK’s program in civil nuclear power was taking place, and the Atomic Energy Authority, the UK government agency responsible for research into civil nuclear power, was made into a “trading fund”, in preparation for privatisation. AEA Technology Ltd was duly floated in 1996, with a rump of the UK Atomic Energy Authority left to decommission nuclear legacy sites and manage the UK’s participation in international fusion research. The privatised AEA Technology attempted to make its way as an energy and environmental consultancy, but the company finally went into liquidation last month, with its assets subsequently being acquired by the engineering company Ricardo. In 1981 civil nuclear power accounted for 68% of the energy R&D budget, so the winding down of this program accounts for a substantial fraction of the loss of energy R&D – but not, as we shall see, all of it.

The definitive story of the UK’s civil nuclear program has yet to be written. That story will include some engineering brilliance, some dismal economics, and an expensive and dangerous legacy of waste and decommissioning, many of whose most undesirable features reflect a deeply entangled relationship with the nuclear weapons program. The run-down of civil nuclear research will have been welcomed by an unlikely alliance of free-marketeers and green campaigners. But now we’re in a situation where even some environmental campaigners are thinking that, if we are to have any hope of limiting climate change, we’ll need nuclear power. From a national perspective this means that this technology will need to be imported. And, because the slow-down in civil nuclear power research has been global, the only technologies that are currently available are essentially incremental upgrades of 1970′s designs.

But even in the 1970′s and 1980′s, nuclear research wasn’t the only energy research going on. In 1983 non-nuclear research and development was £104 million, 34% of the total. By 2001 spending on non-nuclear energy research had declined to £16 million in cash terms (another £15 million was spent on what was left of the nuclear program, including the UK contribution to the Joint European Torus, an international fusion energy research project based in the UK). This is an astonishingly small number, given that the turnover of the UK energy industry at the time accounted for abut 2% of GDP (excluding oil and gas extraction), and given the central importance of energy to a modern economy.

To explain this remarkable decline, we need another date – in 1990 the UK’s electricity industry was privatised, resulting in ten years of corporate reorganisations. The full story is related in Dieter Helm’s book Energy, the State and the Market: British Energy Policy since 1979, but there’s a very readable account in this recent article by James Meek – How we happened to sell off our electricity. In brief, the interests who acquired this national infrastructure found it more profitable to use financial engineering to extract cash from these assets than to invest in them, particularly if those investments – such as research and development – were long term in nature. The government, meanwhile, believed that the magic of a competitive market was the best way of ensuring the long-term security of energy supplies. As the mergers and acquisitions played out, by 2002 most of the UK’s electricity industry was in the hands of vertically integrated European companies like E.on, RWE and EDF (the latter of course being controlled by the French state). In 1994, in the privatised utility sector as a whole (comprising electricity, gas and water supply), £170 million was spent on R&D but by 2005 the total industry spend on R&D in the UK, again across the whole utility sector, was down to £15 million.

By 2005, a recognition was growing in the UK government that the extremely low level of R&D effort in energy – both by government and in industry – might not be a good idea. It is probably fair to associate this change to a single individual, Sir David King, who became the UK Government’s Chief Scientific Advisor in 2000. King was an outspoken and effective advocate about the dangers of climate change, and the need for more research into energy, and as a result there was a significant rise in government R&D spending. A public-private Energy Technology Institute was set up, and the research councils co-operated on a joint energy research program. By 2010 this had led to a significant rise in R&D, but a new government and its austerity policy reversed this, with a 23% cut in the cash budget in 2011.

The direction of the UK government’s current energy policy is, to be polite, not wholly clear. Painfully slow moves are being made to secure some nuclear new build, the government is hanging back from supporting proposals to implement carbon capture and sequestration at scale, current renewables are facing difficulties of politics and cost, and the issue of climate change has been sidelined. Current scenarios anticipate a substantial increase in electricity generation from gas without carbon capture. You could make a case that current renewables are too expensive, and we should use a combination of gas and nuclear as a stop-gap while we wait for new technologies to emerge. But they won’t emerge unless someone does the research and development work to make them happen. The inaction of the last couple of decades mean that we’ve got a huge amount of ground to make up. I don’t yet see the will to develop the capacity we need to do this, and I don’t think the industry structure we’ve got helps.

Statistics on government energy RD&D expenditure from the International Energy Authority, on UK industry R&D from Office of National Statistics Business Enterprise R&D figures.

As recently as 2009, a UK government strategy document (Plastic Electronics: a UK strategy for Success (PDF)) felt able to make the claim that “currently the UK is among the world’s leading players in plastic electronics”. This was arguably true in the academic world, with large, world-leading groups in Cambridge and Imperial and significant activity elsewhere. But by 2009 there were already signs of trouble on the commercial side of plastic electronics in the UK. The original Cambridge spin-out, Cambridge Display Technology, had been taken over by Japan’s Sumitomo in 2007, and while research activity continues in the UK, device development is increasingly going to happen in Japan. The second spin-out from Richard Friend’s group, Plastic Logic, established its first production facility, not in the UK, but near Dresden, in 2006. The focus of Plastic Logic has been on low-cost, printed plastic electronic logic circuits for use as the backplanes of e-ink readers, but its progress since then has been chequered. Having established a substantial Californian presence, it aimed to launch its own e-reader, but this never reached market. It subsequently received a large injection of cash from the Russian state organisation Rusnano, with the intention of establishing production facilities near Moscow. An article in the FT (£) earlier this year reported that it would close its USA operation, abandon plans to manufacture its own e-readers and focus on selling its technology to other companies. Another spin-out, this time from Edinburgh University, Microemissive Displays, went into receivership in 2008. The Manchester based speciality chemicals company Avecia had an R&D activity in plastic electronics, associated with its JV with Hoechst, Covion Organic Semiconductors, but both the materials business and the R&D activity were sold to the German chemical company Merck in 2005. Difficulties in the sector haven’t been confined to the UK – the leading company attempting to commercialise solar cells made from semiconducting polymers was the US company Konarka, which filed for bankruptcy this June, having burned through $170 million (see this Boston Globe article: Why did solar cell company Konarka fail?).

So has anyone made a success of organic electronics? Samsung is one of a number of companies in the far east to bring organic light emitting diode displays to a mass market, as illustrated by the Samsung Galaxy S. But this is a slightly different technology to the plastic electronics that the UK companies were concentrating on. Both depend on organic molecules as the semiconductors, but Samsung and its ilk uses small molecules – quite possibly supplied by Merck – rather than polymers. Similarly one of the leaders in the field of organic solar cells – Heliatek – is at pilot plant stage for roll-to-roll production of small molecule organic photovoltaics at its base in Dresden, in a growing organic electronics cluster in Saxony. The small molecule and polymer variants of organic electronics are based on very similar physics. The advantage of polymers over small molecules is that the latter need to be applied by more expensive, vacuum based techniques than the polymers, which can be applied by printing or coating from solution. But the necessary very high degree of purity in the materials is easier to achieve with small molecules than with polymers. What we are now beginning to see is the prospect of small molecule organic electronics being produced by wet coating techniques, combining the advantages of both methods. In Japan, for example, a JV between Mitsubishi Chemicals and Pioneer is building a factory to produce organic light emitting diode modules for lighting. They will be using small molecule organic light emitting layers, but manufacturing them using a wet coating process (press release here (PDF)), and they aim to have mass production starting in 2014.

What lessons can we learn from this story? Firstly, introducing new technologies is never easy, because the incumbent technologies they aim to replace themselves don’t stand still. When Cambridge Display Technology was founded in 1992, the vision was to produce cheap, thin, large area TV screens. At that time, liquid crystal displays were small and expensive; the first generation LCD factories of the time made displays on glass panels with dimensions 30 x 40 cm. What’s driven the LCD industry since then has been a drive to build new factories every year or two to use bigger and bigger glass panels; we’re now on generation 10, making LCDs on picture window scales – 2.9 x 3.1 m. It’s this that has driven both the availability of very large screens and the very low prices of more standard sizes, undercutting two out of three potential advantages of screens based on organic LEDs. Plastic Logic’s plan to bring an e-reader to market based on a screen with a polymer electronic backplane was sunk by more specific factors – it couldn’t match the functionality of Apple’s iPad, nor did it have the connection to a large library of content that made Amazon’s Kindle a success. A technical advance in one aspect of the device didn’t make it competitive with the integrated user experience offered by its rivals. The story of organic photovoltaics so far is one of the difficulties of a currently less efficient technology attempting to compete on cost when the cost of the more efficient technology – silicon solar cells – has plummeted in conditions of considerable market instability.

Secondly, small, undercapitalised companies without any sustaining income streams from established business, like University spinouts, always have a strategic dilemma – should they get some early income streams from licensing their technologies to bigger, more established companies, or try and find the much larger sums of patient capital needed to establish production facilities, to allow them to capture more of the value of their innovations if they are successful in the market? An oscillation between the poles of this dilemma has characterised the changing strategies both of Cambridge Display Technology and Plastic Logic at various times in their history, as they wrestled with the need to generate returns while their technologies matured more slowly than they had first envisaged. They’ve not always been immune to the fashionable view that manufacturing is simply a function that can outsourced to the lowest cost location. But manufacturing does matter, because (as argued forcefully, for example, in this article by Pisano and Shih (PDF)) process and product innovation are closely intertwined.

This leads us to the third lesson, which concerns the importance of clustering. It’s well known that high technology manufacturing tends to take place in geographical clusters, where manufacturers, materials suppliers, manufacturers of plant and machinery and support one another as they mutually drive manufacturing innovation. The question for a new technology is how to nucleate such a cluster, and I think one important lesson from organic electronics is that sometimes an existing technology cluster can adapt to a new technology if new and existing technologies have enough in common. The things you need to make LCDs – large area clean rooms, clean handling facilities for big sheets of glass, the precise polymer coating technologies that are needed to make colour filters, the technologies to make transparent electrodes, are transferrable to organic electronics, at least in their earliest implementations. It’s possible that in the long run manufacturing technologies for organic electronics may look rather different, with widespread use of roll-to-roll printing methods, but before that happens it is likely that the clusters that have developed around existing display technologies will have achieved too much of an advantage in organic electronics to be easily displaced.

There have been many positive outcomes from the UK’s commercial activity in organic electronics – as research organisations, they’ve moved the technology closer to implementation, as businesses they’ve generated employment and economic benefit, and in financial terms they’ve brought useful returns to the universities and academics involved, and (sometimes) their investors. What they have not done is contributed to a wider transformation of the economy, either by leading to major new investments in manufacturing in the UK, or by growing to the scale of a new Vodafone or a new Google.

Now the UK government is looking to new areas of science-based innovation, like graphene and synthetic biology, to help extract the UK economy from its current problems. The story of plastic electronics should remind us that science is not the same as innovation, that innovation in the material world (as opposed to the digital domain) needs patient, long term, capital, and that it matters where manufacturing takes place. Above all, internationally leading science doesn’t automatically translate into economy transforming innovation.

There’s no question that many big problems could be addressed by new materials. Building cars and aeroplanes from lighter and stronger materials, like carbon fibre composites, will reduce the energy intensity of transport. Electric cars won’t get very far unless we have better batteries – with much higher energy densities at lower cost – and this needs better materials. Better materials will be needed if we are to harness the energy from the sun on a useful scale, through better photovoltaics and artificial photosynthesis. Looking further ahead, what will stop us from exploiting nuclear fusion on the earth through the scale up of fusion reactors such as ITER isn’t so much that we don’t understand enough nuclear physics, or even that we can’t control plasmas, it will be that we don’t have structural materials able to withstand the heat and radiation damage of a commercially useful fusion power reactor.

So what slows down the pace of materials innovation? One factor is that some of the sectors most in need of materials innovation are very highly regulated – for example civil aerospace and medicine. Some ideologues identify this regulation as the problem. I don’t think it would take many passenger aeroplanes falling out of the sky because of an unexpected bug in the way a new material behaves to discredit this point of view, though. A more fundamental problem is that when a new material is discovered, even if it potentially offers much higher performance than existing materials, it often can’t be fully exploited until people have worked out how to manufacture things with it. There’s a nice historical example from Sheffield – when Benjamin Huntsman invented the first process for mass-producing high quality steel around 1750, the local cutlery industries refused at first to take up the new material, because it was harder than traditional steel and more difficult to weld. Nowadays the replacement of aerospace alloys by carbon fiber composites has been slow; the natural anisotropy of composites makes parts harder to design, the most efficient manufacturing processes are quite different to the machining processes familiar for metals, and while glued joints can be very strong quality control can be difficult to guarantee.

The proposed solution from the USA is “integrated computational materials engineering”, as described in a National Research Council report of that name. The idea is to move materials science out of the real world into the virtual world of computer simulation, integrating research relevant for all stages of the use of a material, including processing, manufacturing, use and recycling. So rather than waiting to make the material before trying out how to manufacture something from it, the idea is that you do these things in parallel, on a computer, invoking the ideas of big data and open innovation.

This all sounds very timely and convincing. But there are some very fundamental difficulties that make this much harder than it sounds. Even with the fastest computers, you can’t simulate the behaviour of a piece of metal by modelling what the atoms are doing in it – there’s just too big a spread of relevant length and timescales. If you wanted to study the way different atoms cluster together as you cast an alloy, you need to be concerned with picosecond times and nanometer lengths, but then if you want to see what happens to a turbine blade made of it in an operating jet engine, you’re interested in meter lengths and timescales of days and years (it is the slow changes in dimension and shape of materials in use – their creep – that often limits their lifetime in high temperature situations). What’s needed to bridge this vast range of length and timescales is modelling the system at a hierarchy of different levels. For the small lengths and times you are interested in what the atoms themselves do; for larger scales there are coarser grained phenomena – dislocations and grain boundaries, for example – that you might use as the basis of the simulation, while at even larger scales you can construct continuum mathematical models. The art of doing this right involves connecting all the levels, so that you derive the properties of one level from the behaviour you simulate from the smaller and faster level, right down to the atoms (or indeed to the quantum mechanics that characterises the way atoms interact).

This kind of multiscale modelling isn’t new – in the context of the behaviour of plastics, for example, this was exactly the aim of Masao Doi’s Octa project. To get it right one needs a very good understanding of the physics of the materials at the various length-scales, protocols for taking the results at the lower levels and converting them into the parameters that feed into the higher level models, and above all an extensive program of experimental checking of the predictions at all levels. One key question is how generic this process can be – how big are the classes of materials for which the basic physics looks the same? Within such a class, predictions for different materials would be available by simple changes of parameters, as opposed to having to start from scratch with a completely different set of models. For example, we have good reason to suppose that one set of models work for all single-component, non-crystalline, linear chain polymers, but to extend this to polymer blends and composite materials would introduce very substantial new complexities. Likewise it might be possible to find some general models that could be applied to the class of nanoparticle reinforced alloys. Nonetheless, I’m sceptical that anyone trying to test out how to shape and weld big structures out of an oxide dispersion strengthened steel (these steels, reinforced with 2 nm nanoparticles of yttrium oxide, are candidate materials for fusion and 4th generation fission reactors, due to their creep resistance and resistance to radiation damage) without getting someone to make a big enough batch to try it out.

What’s all this got to do with the genome? Students of British journalism know that if ever you see a headline in the form of a question, you know the answer is no. The idea of the Human Genome Project had a degree of rhetorical force because of the idea that underlying all of life was a digital key – the genome – that if decoded would answer many of the unanswered questions of biology. I don’t think it is possible to argue that there is any sort of convincing analogy in the more general world of materials. Clearly at some level it is true to say that the behaviour of any material is determined by what the atoms are doing, and I’d certainly be the first to agree that computational materials science is well worth devoting some effort to. But the genome reference seems to me to be a rhetorical flourish too far.

Some of the material I talked about is covered in my chapter in the recent book Quantum Engagements: Social Reflections of Nanoscience and Emerging Technologies. A preprint of the chapter can be downloaded here: What has nanotechnology taught us about contemporary technoscience?”

But first, who are these geeks who Henderson thinks should organise? “Geek” was the one name I wasn’t called as a child, as it’s current usage hadn’t then crossed the Atlantic. The word has well-attested northern European roots, with connotations of a fool or a stupid person – in English fairground argot to “geck” someone is to fool them. But it was in the sideshows and carnivals of the USA that “geeks” were largely to be found. There, the dwarfs and giants, the bearded ladies and monkey-boys of the sideshow attractions were the “freaks”, while the “geeks” were physically typical people who did extreme things, often to themselves, to attract custom and attention, such as sticking nails up their noses or biting the heads off chickens. While the “freaks” were part of the aristocracy of the closed world of the showmen and carnies, “geeks” were regarded as inauthentic imitators. So as an insult, “geek” has a double force – as someone who indulges as repetitive, stupid and possibly self-destructive behaviour, and who yet, despite these extreme efforts, is still not wholly accepted in the group to which they aspire to belong. But just as, in the counterculture of the 1960′s, “freak” was reclaimed by those thus insulted as a badge of pride, so we’ve seen “geek” appropriated by those who as youths didn’t fit in, being more interested in science, maths and computers than sport and celebrities.

But if the word had been widely used in this way in my childhood, I’m sure I would have fitted the bill. I still have on my bookshelf the copy of “Mellor’s Modern Inorganic Chemistry” I acquired as a scientifically precocious ten year old. At a boarding school in rural mid-Wales at that time, I shared a surname with more than half the school, but that was pretty much all I had in common with a population of small boys obsessed with trapping small animals, poaching sewin and playing rugby. I did attempt to fit in by ill-advised, poorly executed and unsuccessful experiments to see if it was possible to stun fish using home-made explosives (and I take this opportunity to apologise for any hitherto unexplained pollution spikes in the River Ystwyth around 1971) but I remained an outsider, who would have made an ideal candidate for a geek identity.

And yet, why should we think that geeks really can form a single group, with a homogenous set of interests, from which one could build some kind of identity politics? And even if they do, doesn’t that contradict the broader aim of Henderson’s book, that public life in general ought to be more rational and sensible – surely rationality shouldn’t be restricted to a single interest group? Speaking for myself, perhaps I am a geek, but that’s not where my group identity comes from (not least, because my identity is strongly connected to my sense of being an outsider). One of the ways in which groups come together is through the development of a set of shared beliefs. I sense that we are seeing the emergence of something of a geek belief package to accompany this development of a geek identity. I’m sure I share a great deal of this, but by no means all, and actually I positively resent the idea that seems implicit that to be part of this movement I have to sign up to a few key shibboleths. For example, while I know that the claimed basis for homeopathy is clearly rubbish, I just can’t bring myself to think that the public toleration of homeopathy is the worst thing in the world (see the footnote for further justification of my ambivalence on this). Surely, though, you might argue, I must have in common with other geeks an understanding of and reliance on the scientific method? Well, no; here again we see the problems of trying to construct a single geek identity when science embraces a very wide variety of methodological approaches, styles of explanation and indeed ways of thinking.

For example, in common with other writing from cheer-leaders of the geek movement, Henderson emphasises randomised controlled trials (RCTs) as a pinnacle of the scientific method. Now, I understand why one needs RCTs for experiments in clinical medicine, and I can see the value – much stressed in Henderson’s book – for RCTs to test different interventions in social policy. But for many intellectual traditions in science, RCTs are simply unimportant and irrelevant. Looking back on my own training as an experimental physicist, on the one hand, I would hear quoted Rutherford’s apocryphal words “If your experiment needs statistics, you should have done a better experiment”, while on the other I didn’t entirely escape the blandishments of the Cavendish Laboratory’s nest of evangelical Bayesians. What I took from my training as I made a career at the messier end of condensed matter physics was an obsession for designing clean experiments in complicated systems together with (I hope) a reasonably thoughtful approach to data analysis. To such a physicist as me, doing a randomised controlled trial is in effect an admission that you don’t understand your system well enough to design a clean experiment (and of course I understand entirely why this must be the case in the difficult world of medicine and the even more difficult world of social science).

If identity forms one of the dimensions of politics, another involves power, and the way in which groups and interests that have power can maintain that position or be displaced from it. It is a recurring vice of those with a scientific or technical worldview to narrow the scope of discussions down from the political to the purely technical, and to deny the political dimensions of technical questions; this closing down rather often favours incumbent interest groups. So while the mythology of science often emphasises its revolutionary character, overturning received wisdoms with the power of evidence and critical thinking, history shows that science has often been used to support very conservative positions. The geek hero may be Galileo, fearlessly breaking the intellectual grip of the medieval church, but we shouldn’t forget the social Darwinists of late Victorian England, confidently arguing that the theory of Natural Selection justified the politics of laissez-faire capitalism, nor the racial scientists with their spurious justifications for colonialism. One can summarise conservative thought as a systematic attempt to argue that the way things are is the way things ought to be, and it’s not difficult to see how science can be used in support of these positions. While we should have learnt from Hume that you can’t get an ought from an is, that lesson doesn’t seem to have been learnt by many of those who combine an enthusiasm for evolutionary theory – and particularly evolutionary psychology – with Hayekian free-market liberalism.

In Henderson’s book, these issues come to a head in the chapter on environmentalism. Here it is the green movement that attracts Henderson’s blame for discrediting the science of climate change in the eyes of the public and politicians; because greens see economic growth and new technology as the cause of the problems of environmental degradation and climate change, they are only willing to accept solutions that involve a reduction in consumption in the rich west – “there is no place here for a dispassionate, method-blind approach to mitigating and adapting to climate change by any means necessary”. Now, my personal view of the fundamental position of the deep greens – that our problems will only be solved if a substantially smaller world population lives more simply – is that it is irresponsible and fundamentally inhumane, given their failure to specify how the fraction of humanity deemed surplus to requirements will be identified and removed (or indeed to volunteer to serve as part of that fraction). But I will give them credit for two things – they make explicit their vision of what they want society to look like, which is, after all, the proper business of politics. And they do confront the awkward truth that, given our current existential dependence on fossil fuel energy, the challenge of climate change isn’t something to be overcome by a few technical tweaks.

For Henderson, one of the technical solutions to climate change is nuclear power; he quotes Mark Lynas as saying “the reason why nuclear power is so heavily opposed by the Greens is not because it can’t help to solve climate change, but because it can”. For Henderson, nuclear power, like nanotechnology and biotechnology, are “technical solutions which allow humanity to continue with something approaching business as usual, allowing sustainable growth and economic development without requiring significant changes to consumer and corporate behaviour, or to the capitalist system” . To speak personally again, I do think we should be moving much faster than we are to deploy new nuclear power stations (and for my views on nanotechnology, look around this blog), but nonetheless two aspects of these statements trouble me deeply. Firstly, wanting our current economic system to continue without change is not an apolitical position. On the contrary, it is a profoundly political position in itself, and there’s no reason to suppose it will command universal assent. Secondly, it leaves unexamined the credibility of the original claim that nuclear power actually can make a significant difference to climate change. Currently nuclear power accounts for 5.8% of the world’s energy requirements (12150 Mtoe) with 371 GW of installed capacity. Using the estimates of the World Nuclear Association, to decarbonise the world’s current electricity supply with nuclear now would require about 2,000 GW of capacity. But by 2050 we’d need 10,000 GW, to account for the increase in demand for energy and for an increasing share of energy being supplied through electricity. Even neglecting the need to replace the existing fleet, that amounts to completing one new 1 GW nuclear power station every day and a half for the next 38 years. This is a tall order – it’s roughly a factor of 10 faster than the roll-out of nuclear power in the 1980′s, sustained for a longer period. Perhaps this is not inconsistent with the laws of physics, but looking at progress so far I’m not sure about its compatibility with the laws of politics and economics. My point here is that even geek dreams need to be subjected to some critical examination.

There is a reason why climate change is such a profoundly difficult issue, that so resists attempts to separate the politics from science. If one accepts the science connecting human CO2 emissions with global warming (and in my opinion the scientific case for the effect is quite watertight, even if there is uncertainty about its magnitude and its likely effects on ecosystems and economies), and one fully understands the degree to which our prosperity and way of life depends on burning really substantial quantities of fossil fuels, it is difficult to maintain that things can continue much as they are. Henderson’s ire for those who mix the science and politics of climate change is reserved for the deep greens, who he blames for the resistance of conservatives to climate change science. I think this hugely underestimates the capacity of conservatives to realise for themselves that the existence of anthropogenic climate change poses a deep threat to their conviction that the way things are is largely the way things ought to be. And such conservatives don’t all by any means think of themselves as anti-science. Take, for example, today’s leading spokesman of the “cornucopianist” tendency – Matt Ridley – whose book “The Rational Optimist” explains that free market economics and technological progress will inevitably lead to universal prosperity and the progressive solution of our environmental problems. Ridley is a successful science writer, author of a string of well-regarded books on evolutionary biology, but this has not prevented him from expressing strong scepticism about climate change science. (It’s also not entirely irrelevant to note that he was also Chairman of the UK bank Northern Rock, whose failure during his tenure and subsequent nationalisation initiated the current financial crisis in the UK, and will lead to direct costs to the UK taxpayer estimated at £2 billion. I suspect his dislike of government intervention in the free market doesn’t extend to the idea of limited liability for bank chairmen. Ridley has admitted to failing fully to appreciate the risks his bank was exposed to, but pleads that people more expert than him didn’t appreciate those risks either).

This all brings to mind something George Orwell wrote – “People can only foresee the future when it coincides with their own wishes, and the most grossly obvious facts can be ignored when they are unwelcome”. Neither deep greens nor cornucopians are intrinsically anti-science; they simply predict a future that at some level accords with their wishes – whether that is the collapse of global capitalism or its triumph. Can we escape this trap? Orwell again: “I believe that it is possible to be more objective than most of us are, but that it involves a moral effort. One cannot get away from one’s own subjective feelings, but at least one can know what they are and make allowance for them.” The question is – are geeks necessarily any better than anyone else at this kind of critical thinking? Some would argue that science brings habits of objectivity, of following the data where it leads. There is something to this, but I’m not convinced that history shows this always to be the case. Some part of science is about gathering data without preconceptions, but much of it is about applying, with great focus and single-mindedness, a very tightly defined set of methods and ways of thinking.

To return to nuclear power, Henderson makes some sound arguments about the way the dangers of nuclear power have been overstated, and he’s quite right to make the comparison between the safety record of the nuclear industry and coal mining. The cheap energy that underpins our way of life has come at a considerable human cost – to add another example not mentioned by Henderson, what I believe to be the world’s worst energy-related disaster involved neither nuclear nor coal, but renewable hydroelectricity – 26,000 people were killed by flooding when the Banqiao Hydroelectric Reservoir Dam in Henan, China burst in August 1975. But there’s a danger of missing the point here. One of the lessons we should have learnt from analysing public reactions to issues like genetic modification of food or nuclear power is that the narrow question of risk isn’t the fundamental issue – the issue is one of trust. People do appreciate that technologies have risks and uncertainties attached to them, but they need to trust the institutions – whether these are private companies or government agencies – that control them. The nuclear industry does not have a great record of winning this trust.

There’s one way in which the development of a more coherent geek political bloc has both potential and danger – that is in the question of science funding. The potential arises because government science funding has much less political visibility than it deserves, so anything that raises its profile as a political issue must be a good thing. The danger is that scientists are seen as a special interest group, pleading for more tax-payers money for their own benefit, rather than as a group able to make a compelling case for science spending as a benefit of the nation as a whole. Henderson, for his “geekonomics” chapter, goes back to 2010 and the way the new coalition government, wishing to make substantial cuts in public spending, set the science budget. An unexpectedly fast-growing grass-roots movement initiated by Jenny Rohn – “Science is Vital” – raised the political profile of the decision, and there was general relief that the ultimate flat-cash budget settlement was not as bad as had been feared. I certainly shared this relief – having responsibility for research in a large research-intensive university, and having seen the contingency planning for likely budget cuts in research councils, I had some sleepless nights trying to work out how to minimise the potential terminal career damage to talented young scientists, given the likelihood, in plausible budget scenarios, that research councils would be terminating existing grants and expecting universities to make young researchers redundant before the end of their (already short-term) contracts. I was privileged and proud to be involved in the Royal Society’s study “The Scientific Century”, which assembled the evidence supporting the economic and social case for maintaining or growing government spending on science, and I did my best to make that case in public whenever I could (including the one occasion when I’ve met Mark Henderson in person, when he chaired an event I spoke at at the Cheltenham Science Festival – for my talk see: “Is debt putting British science at risk?”). And yet, what I learnt from that experience was that the arguments are less clear cut and the evidence less solid than one might think.

Even the driest economists concede the case in principle for public funding of science. What’s at issue is how much funding there ought to be, and how it is allocated – in particular how much emphasis you give to basic research as opposed to more applied efforts, how much is directed and how much is left to support the unfettered curiosity of researchers. Elite academic scientists are naturally in favour of more undirected, basic research and Henderson quotes a few of them to this effect. We do need to ask whether a narrow focus on economic measures of the benefit of science is helping us, or whether a broader definition of the public good that science should yield would be healthier. But if we accept that much of the justification for public science spending derives from economic arguments, we need to think about the broader innovation system that this basic research fits into. We need to recognise that our national innovation system has changed substantially in the last few decades (see my last post, The UK’s thirty year experiment in innovation policy), and understand that the biggest impact of those changes hasn’t been in basic, academic research, but in the decline of more applied research in both the government and private sectors. This has a huge potential impact on the ability of basic science to deliver the economic benefits that we are promising. My own view is that we now need to go beyond simple calls to increase science spending to a much wider consideration of how our current political economy helps or hinders the development of the innovation we need (themes I’ve begun exploring in posts such as “Good capitalism, bad capitalism and turning science into economic benefit” and When technologies can’t evolve. Given current economic problems – in the wider world but in the UK especially – we urgently need a much better understanding of the link between science and prosperity.

I’ve written a lot more than I first intended, and I’m conscious that this all reads rather critically. So I should stress again that I’m glad that Henderson has written this book (though I might have preferred a different title), there’s much that I entirely agree with that I haven’t discussed here at all (for example, the chapters on science education, science in the media and science in the courts). The issues that surround the interactions between science and politics aren’t straightforward, and anything that begins a discussion on them is welcome.

On sources
My thanks to my colleague Professor Vanessa Toulmin, curator of the National Fairground Archive at Sheffield, for guidance on the origins of the word “geek”. The Orwell quotation comes from his London Letter to Partisan Review, December 1944, from volume 3 of his collected essays, journalism and letters. Energy statistics are from the International Energy Authority – Key World Energy Statistics. Estimates on the amount of nuclear new build needed to decarbonise world energy supplies are from the World Nuclear Association – World Nuclear Energy Outlook. Ridley’s comments on Northern Rock are from How Darwin would reform Britain’s banks. Sewin is the name given to sea-trout in mid and west Wales. They’re the most delicious of all fishes, whether obtained legally or otherwise.

On homeopathy
Why do I think that tolerating homeopathy isn’t the worst thing in the world, despite the fact that it has no scientific justification and clinical trials reveal it to be no more effective than a placebo? To answer that, first recall that the placebo effect can be very real – people who’s health improves as a result of a placebo often don’t just feel better, they have real and measurable health benefits. This, of course, is why it is so important to use careful randomised controlled trials for clinical testing of new drugs. It seems likely that the placebo effect can be particularly effective for a number of conditions that while not life threatening, are chronic and debilitating. So why doesn’t conventional medicine make more use of the placebo effect? Firstly, because, for the placebo effect to work, patients must believe the treatment is real, and we don’t think it’s ethical for doctors to lie to their patients. And secondly, for it to be most effective, the doctor has to believe in the treatment too. This, of course, is why one has to go to so much trouble to make clinical trials double blind, so patients don’t pick up unconscious cues from their doctors that they are being given non-physiologically active treatments.

Of course, the scheme I described corresponds pretty closely to the situation of homeopathy, with this difference – in the case of homeopathy, this situation has arisen spontaneously, without (I believe) an act of conscious deception. For many people, even without conscious deception this situation remains an affront to the principles of a rational society so great that it cannot be outweighed by any number of patients who might have benefitted from it. I sympathise with this view. But given all the other problems of the world, and given that we’re balancing an abstract principle about society against some real benefits to individuals, I wonder whether in this case it wouldn’t hurt too much to let sleeping dogs lie.

Gross expenditure on research and development as a % of GDP from 1981 to 2010. Data from Eurostat.

The second graph breaks down where R&D takes place. The largest fractional fall has been in research in government establishments, which has dropped by more than 60%. The largest part of this fall took place in the early part of the period, under a series of Conservative governments. This reflects a general drive towards a smaller state, a run-down of defence research, and the privatisation of major, previously research intensive sectors such as energy. However, it is clear that privatisation didn’t lead to a transfer of the associated R&D to the business sector. It is in the business sector that the largest absolute drop in R&D intensity has taken place – from 1.48% of GDP to 1.08%. Cutting government R&D didn’t lead to increases in private sector R&D, contrary to the expectations of free marketeers who think the state “crowds out” private spending. Instead the business climate of the time, with a drive to unlock “shareholder value” in the short-term, squeezed out longer term investments in R&D. Some seek to explain this drop in R&D intensity in terms of a change in the sectoral balance of the UK economy, away from manufacturing and towards financial services, and this is clearly part of the picture. However, I wonder whether this should be thought of not so much as an explanation, but more as a symptom. I’ve discussed in an earlier post the suggestion that “bad capitalism” – for example, speculations in financial and property markets ,with the downside risk being shouldered by the tax-payer – squeezes out genuine innovation.

UK R&D as % of GDP by sector of performance from 1981 to 2010. Data from Eurostat.

The Labour government that came to power in 1997 did worry about the declining R&D intensity of the UK economy, and, in its Science Investment Framework 2004-2014 (PDF), set about trying to reverse the trend. This long-term policy set a target of reaching an overall R&D intensity of 2.5% by 2014, and an increase in R&D intensity in the business sector from to 1.7%. The mechanisms put in place to achieve this included a period of real-terms increase in R&D spending by government, some tax incentives for business R&D, and a new agency for nearer term research in collaboration with business, the Technology Strategy Board. In the event, the increases in government spending on R&D did lead to some increase in the UK’s overall research intensity, but the hoped-for increase in business R&D simply did not happen.

This isn’t predominantly a story about academic science, but it provides a context that’s important to appreciate for some current issues in science policy. Over the last thirty years, the research intensity of the UK’s university sector has increased, from 0.32% of GDP to 0.48% of GDP. This reflects, to some extent, real-term increases in government science budgets, together with the growing success of universities in raising research funds from non UK-government sources. The resulting R&D intensity of the UK HE sector is at the high end of international comparisons (the corresponding figures for Germany, Japan, Korea and the USA are 0.45%, 0.4%, 0.37% and 0.36%). But where the UK is very much an outlier is in the proportion of the country’s research that takes place in universities. This proportion now stands at 26%, which is much higher than international competitors (again, we can compare with Germany, Japan, Korea and the USA, where the proportions are 17%, 12%, 11% and 13%), and much higher now than it has been historically (in 1981 it was 14%). So one way of interpreting the pressure on universities to demonstrate the “impact” of their research, which is such a prominent part of the discourse in UK science policy at the moment, is as a symptom of the disproportionate importance of university research in the overall national R&D picture. But the high proportion of UK R&D carried out in universities is as much a measure of the weakness of the government and corporate applied and strategic research sectors as the strength of its HE research enterprise. The worry, of course, has to be that, given the hollowed-out state of the business and government R&D sectors, where in the past the more applied research needed to convert ideas into new products and services was done, universities won’t be able to meet the expectations being placed on them.

To return to the big picture, I’ve seen surprisingly little discussion of the effects on the UK economy of this dramatic and sustained decrease in research intensity. Aside from the obvious fact that we’re four years into an economic slump with no apparent prospect of rapid recovery, we know that the UK’s productivity growth has been unimpressive, and the lack of new, high tech companies that grow fast to a large scale is frequently commented on – where, people ask, is the UK’s Google? We also know that there are urgent unmet needs that only new innovation can fulfil – in healthcare, in clean energy, for example. Surely now is the time to examine the outcomes of the UK’s thirty year experiment in innovation theory.

Finally, I think it’s worth looking at these statistics again, because they contradict the stories we tell about ourselves as a country. We think of our postwar history as characterised by brilliant invention let down by poor exploitation, whereas the truth is that the UK, in the thirty post-war years, had a substantial and successful applied research and development enterprise. We imagine now that we can make our way in the world as a “knowledge economy”, based on innovation and brain-power. I know that innovation isn’t always the same as research and development, but it seems odd that we should think that innovation can be the speciality of a nation which is substantially less intensive in research and development than its competitors. We should worry instead that we’re in danger of condemning ourselves to being a low innovation, low productivity, low growth economy.