Soft Machines – Page 8 – Some personal views on nanotechnology, science and science policy from Richard Jones

If new nuclear doesn’t get built, it will be fossil fuels, not renewables, that fill the gap

The UK’s programme to build a new generation of nuclear power stations is in deep trouble. Last month, Hitachi announced that it is pulling out of a project to build two new nuclear power stations in the UK; Toshiba had already announced last year that it was pulling out of the Moorside project.

The reaction to this news has been largely one of indifference. In one sense this is understandable – my own view is that it represents the inevitable unravelling of an approach to nuclear new build that was monumentally misconceived in the first place, maximising costs to the energy consumer while minimising benefits to UK industry. But many commentators have taken the news to indicate that nuclear power is no longer needed at all, and that we can achieve our goal of decarbonising our energy economy entirely on the basis of renewables like wind and solar. I think this argument is wrong. We should accelerate the deployment of wind and solar, but this is not enough for the scale of the task we face. The brutal fact is that if we don’t deploy new nuclear, it won’t be renewables that fill the gap, but more fossil fuels.

Let’s recall how much energy the UK actually uses, and where it comes from. In 2017, we used just over 2200 TWh. The majority of the energy we use – 1325 TWh – is in the form of directly burnt oil and gas. 730 TWh of energy inputs went in to produce the 350 TWh of electricity we used. Of that 350 TWh, 70 TWh came from nuclear, 61.5 TWh came from wind and solar, and another 6 TWh from hydroelectricity. Right now, our biggest source of low carbon electricity is nuclear energy.

But most of that nuclear power currently comes from the ageing fleet of Advanced Gas Cooled reactors. By 2030, all of our AGRs will be retired, leaving only Sizewell B’s 1.2 GW of capacity. In 2017, the AGRs generated a bit more than 60 TWh – by coincidence, almost exactly the same amount of electricity as the total from wind and solar.

The growth in wind and solar power in the UK in recent years has been tremendous – but there are two things we need to stress. Firstly, taking out the existing nuclear AGR fleet – as has to happen over the next decade – would entirely undo this progress, without nuclear new build. Secondly, in the context of the overall scale of the challenge of decarbonisation, the contribution of both nuclear and renewables to our total energy consumption remains small – currently less than 16%.

One very common response to this issue is to point out that the cost of renewables has now fallen so far that at the margin, it’s cheaper to bring new renewable capacity online than to build new nuclear. But this argument from marginal cost is only valid if you are only interested in marginal changes. If we’re happy with continuing to get around 80% of our energy from fossil fuels, then the marginal cost argument makes sense. But if we’re serious about making real progress towards decarbonisation – and I think the urgency of the climate change issue and the scale of the downside risks means we should be – then what’s important isn’t the marginal cost of low-carbon energy, but the whole system cost of replacing, not a few percent, but close to 100% of our current fossil fuel use.

So how much more wind and solar energy capacity can we realistically expect to be able to build? The obvious point here is that the total amount is limited – the UK is a small, densely populated, and not very sunny island – even in the absence of economic constraints, there are limits to how much of it can be covered in solar cells. And although its position on the fringes of the Atlantic makes it a very favourable location for offshore wind, there are not unlimited areas of the relatively shallow water that current offshore wind technology needs.

Currently, the current portfolio of offshore wind projects amounts to a capacity of 33.2 GW, with one further round of 7 GW planned. According to the most recent information I can find, “Industry says it could deliver 30GW installed by 2030”. If we assume the industry does a bit better than this, and delivers the entire current portfolio, that would produce about 120 TWh a year.

Solar energy produced 11.5 TWh in 2017. The very fast rate of growth that led us to that point has levelled off, due to changes in the subsidy regime. Nonetheless, there’s clearly room for further expansion, both of rooftop solar and grid scale installations. The most aggressive of the National Grid scenarios envisages a tripling of solar by 2030, to 32 TWh.

Thus by 2030, in the best case for renewables, wind and solar produce about 150 TWh of electricity, compared to our current total demand for electricity of 350 TWh. We can reasonably expect demand for electricity, all else equal, to slowly decrease as a result of efficiency measures. Estimating this by the long term rate of reduction of energy demand of 2% a year, we might hope to drive demand down to around 270 TWh by 2030. Where does that leave us? With all the new renewables, together with nuclear generation at its current level, we’d be generating 220 TWh out of 270 TWh. Adding on some biomass generation (currently about 35 TWh, much of which comes from burning environmentally dubious imported wood-chips), 6 TWh of hydroelectricity and some imported French nuclear power, and the job of decarbonising our electricity supply is nearly done. What would we do without the 70 TWh of nuclear power? We’d have to keep our gas-fired power stations running.

But, but, but… most of the energy we use isn’t in the form of electricity – it’s directly burnt gas and oil. So if we are serious about decarbonising the whole energy system, we need to be reducing that massive 1325 TWh of direct fossil fuel consumption. The most obvious way of doing that is by shifting from directly burning oil to using low-carbon electricity. This means that to get anywhere close to deep decarbonisation we are going to need to increase our consumption of electricity substantially – and then increase our capacity for low-carbon generation to match.

This is one driving force for the policy imperative to move away from internal combustion engines to electric vehicles. Despite the rapid growth of electric vehicles, we still use less than 0.2 TWh charging our electric cars. This compares with a total of 4.8 TWh of electricity used for transport, mostly for trains (at this point we should stop and note that we really should electrify all our mainline and suburban train-lines). But these energy totals are dwarfed by the 830 TWh of oil we burn in cars and trucks.

How rapidly can we expect to electrify vehicle transport? This is limited by economics, by the world capacity to produce batteries, by the relatively long lifetime of our vehicle stock, and by the difficulty of electrifying heavy goods vehicles. The most aggressive scenario looked at by the National Grid suggests electric vehicles consuming 20 TWh by 2030, a more than one-hundred-fold increase on today’s figures, representing 44% a year growth compounded. Roughly speaking, 1 TWh of electricity used in an electric vehicle displaces 3.25 TWh of oil – electric motors are much more efficient at energy conversion than internal combustion engines. So even at this aggressive growth rate, electric vehicles will only have displaced 8% of the oil burnt for transport. Full electrification of transport would require more than 250 TWh of new electricity generation, unless we are able to generate substantial new efficiencies.

Last, but not least, what of the 495 TWh of gas we burn directly, to heat our homes and hot water, and to drive industrial processes? A serious programme of home energy efficiency could make some inroads into this, we could make more use of ground source heat pumps, and we could displace some with hydrogen, generated from renewable electricity (which would help overcome the intermittency problem) or (in the future, perhaps) process heat from high temperature nuclear power stations. In any case, if we do decarbonise the domestic and industrial sectors currently dominated by natural gas, several hundred more TWh of electricity will be required.

So achieve the deep decarbonisation we need by 2050, electricity generation will need to be more than doubled. Where could that come from? A further doubling of solar energy from our already optimistic 2030 estimate might take that to 60 TWh. Beyond that, for renewables to make deep inroads we need new technologies. Marine technologies – wave and tide – have potential, but in terms of possible capacity deep offshore wind perhaps offers the biggest prize, with the Scottish Government estimating possible capacities up to 100 GW. But this is a new and untried technology, which will certainly be very much more expensive than current offshore wind. The problem of intermittency also substantially increases the effective cost of renewables at high penetrations, because of the need for large scale energy storage and redundancy. I find it difficult to see how the UK could achieve deep decarbonisation without a further expansion of nuclear power.

Coming back to the near future – keeping decarbonisation on track up to 2030 – we need to bring at least enough new nuclear on stream to replace the lost generation capacity of the AGR fleet, and preferably more, while at the same time accelerating the deployment of renewables. We need to be honest with ourselves about how little of our energy currently comes from low-carbon sources; even with the progress that’s been made deploying renewable electricity, most of our energy still arises from directly burning oil and gas. If we’re serious about decarbonisation, we need the rapid deployment of all low carbon energy sources.

And yet, our current policy for nuclear power is demonstrably failing. How should we do things differently, more quickly and at lower cost, to reboot the UK’s nuclear new build programme? That will be the subject of another post.

Notes on sources.
Current UK energy statistics are from the 2018 edition of the Digest of UK Energy Statistics.
Status of current and planned offshore wind capacity, from Crown Estates consultation.
National Grid future energy scenarios.
Oil displaced by electric vehicles – current estimates based on worldwide data, as reported by Bloomberg New Energy Finance.

How inevitable was the decline of the UK’s Engineering industry?

My last post identified manufacturing as being one of three sectors in the UK which combined material scale relative to the overall size of the economy with a long term record of improving total factor productivity. Yet, as us widely known, manufacturing’s share of the economy has been in long term decline, from 27% in 1970 to 10.6% in 2014. Manufacturing’s share of employment has fallen even further, as a consequence of its above-average rate of improvement in labour productivity. This fall in importance of manufacturing has been a common feature of all developed economies, yet the UK has seen the steepest decline.

This prompts two questions – was this decline inevitable, and does it matter? A recent book by industry veteran Tom Brown – Tragedy and Challenge: an inside view of UK Engineering’s Decline and the Challenge of the Brexit Economy, makes a strong argument that this decline wasn’t inevitable, and that it does matter. It’s a challenge to conventional wisdom, but one that’s rooted in deep experience. Brown is hardly the first to identify as the culprits the banks, fund managers, and private equity houses collectively described as “the City” – but his detailed, textured description of the ways in which these institutions have exerted their malign influence makes a compelling charge sheet against the UK economy’s excessive financialisation.

Brown’s focus is not on the highest performing parts of manufacturing – chemicals, pharmaceuticals and aerospace – but on what he describes as the backbone of the manufacturing sector – medium technology engineering companies, usually operating business-to-business, selling the components of finished products in highly competitive, international supply chains. The book is a combination of autobiography, analysis and polemic. The focus of the book reflects Brown’s own experience managing engineering firms in the UK and Europe, and it’s his own personal reflections that provide a convincing foundation for his wider conclusions.

His analysis rehearses the decline of the UK’s engineering sector, pointing to the wider undesirable consequences of this decline, both at the macro level, in terms of the UK’s overall declining productivity growth and its worsening balance of payments position, and at the micro level. He is particularly concerned by the role of the decline of manufacturing in hollowing out the mid-level of the jobs market, and exacerbating the UK’s regional inequality. He talks about the development of a “caste system of the southern Brahmins, who can’t be expected to leave the oxygen of London, and the northern Untouchables who should consider themselves lucky just to have a job”.

This leads on to his polemic – that the decline of the UK’s engineering firms was not inevitable, and that its consequences have been regrettable, severe, and will be difficult to reverse.

Brown is not blind to the industry’s own failings. Far from it – the autobiographical sections make clear what he saw was wrong with the UK’s engineering industry at the beginning of his career. The quality of management was terrible and industrial relations were dreadful; he’s clear that, in the 1970’s, the unions hastened the industry’s decline. But you get the strong impression that he believes management and unions at the time deserved each other, and a chronic lack of investment in new plant and machinery, and a complete failure to develop the workforce led to a severe loss of competitiveness.

The union problem ended with Thatcher, but the decline continued and accelerated. Like many others, Brown draws an unfavourable comparison between the German and British traditions of engineering management. We hear a lot about the Mittelstand, but it’s really helpful to see in practise what the cultural and practical differences are. For example, Brown writes “German managers tend to be concerned about their people, and far slower to lay off in a downturn. Their training of both management and shop-floor employees is vastly better than the UK… in contrast many UK employees have expected skilled people to be available on demand, and if they fired them then they could rehire at will like the gaffer in the old ship yards”.

For Brown, its no longer the unions that are the problem – it’s the City. It’s fair to say that he takes a dim view of the elevated position of the Financial Services sector since the Big Bang – “Overall the City is a major source of problems – to UK engineering, and to society as a whole. Much that has happened there is crazy, and still is. Many of our brightest and best have been sucked in and become personally corrupted.”

But where his book adds real value is in going beyond the rhetoric to fill out the precise details of exactly how the City serves engineering firms so badly. To Brown, the fund managers and private equity houses that exert control over firms dictate strategies to the firms that are usually pretty much the opposite of what would be required for them to achieve long-term growth. Investment in new plant and equipment is starved due to an emphasis on short-term results, and firms are forced into futile mergers and acquisitions activity, which generate big fees for the advisors but are almost always counterproductive for the long-term sustainability of the firms, because they force them away from developing long-term, focused strategies. These criticisms echo many made by John Kay in his 2012 report, which Brown cites with approval, combined with disappointment that so few of the recommendations have been implemented.

“I do not suffer fools gladly”, says Brown, a comment which sets the tone for his discussion of the fund management industry. While he excoriates fund managers for their lack of diligence and technical expertise, he condemns the lending banks for outright unethical and predatory behaviour, deliberately driving distressed companies into receivership, all the time collecting fees for themselves and their favoured partners, while stiffing the suppliers and trade creditors. The well-publicised malpractice of RBS’s “Global Restructuring Group” offers just one example.

One very helpful section of the book discusses the way Private Equity operates. Brown makes the very important point that not enough people understand the difference between Venture Capital and Private Equity. The former, Brown believes, represents technically sophisticated investors creating genuine new value –
“investing real equity, taking real risks, and creating value, not just transferring it”.

But what too many politicians, and too much of the press fail to realise is that genuine Venture Capital in the UK is a very small sector – in 2014, only £0.3 billion out of a total £4.3 billion invested by BVCA members fell into this category. Most of the investment is Private Equity, in which the investments are in existing assets.

“The PE houses’ basic model is to buy companies as cheaply as possible, seek to “enhance” them, and then sell them for as much as possible in only three years’ time, so it is extremely short-termist. They “invest” money in buying the shares of these companies from the previous owners, but they invest as little as possible into the actual companies themselves; this crucial distinction is often completely misunderstood by the government and the media who applied the PE houses for the billions they are “investing” in British industry… in fact, much more cash is often extracted from these companies in dividends than is ever invested in them”.

To Brown, much Private Equity is simply a vehicle for large scale tax avoidance, through eliding the distinction between debt and equity in “complex structures that just adhere to the letter of the law”. These complex structures of ownership and control lead to a misalignment of risk and reward – when their investments fail, as they often do, the PE houses get some of their investment back as it is secured debt, while trade suppliers, employees and the taxpayer get stiffed.

To be more positive, what does Brown regard as the ingredients for success for an engineering firm? His list includes:

an international outlook, stressing the importance of being in the most competitive markets to understand your customers and the directions of the wider industry;

a long-term vision for growth, stressing innovation, R&D, and investment in latest equipment;

conservative finance, keeping strong balance sheet to avoid being knocked off course by the inevitable ups and downs of the markets, allowing the firm to keep control of its own destiny;

a focus on the quality of people – with managements who understand engineering and are not just from a financial background, and excellent training for the shop floor workers.

The book focuses on manufacturing and engineering, but I suspect many of its lessons have a much wider applicability. People interested in economic growth and industrial strategy necessarily, and rightly, focus on statistics, but this book offers an invaluable additional dimension of ground truth to these discussions.

What drives productivity growth in the UK economy?

How do you get economic growth? Economists have a simple answer – you can put in more labour, by having more people working for longer hours, or you can put in more capital, building more factories or buying more machines, or – and here things get a little more sketchy – you can find ways of innovating, of getting more outputs out of the same inputs. In the framework economists have developed for thinking about economic growth, the latter is called “total factor productivity”, and it is loosely equated with technological progress, taking this in its broadest sense. In the long run it is technological progress that drives improved living standards. Although we may not have a great theoretical handle on where total factor productivity comes from, its empirical study should tell us something important about the sources of our productivity growth. Or, in our current position of stagnation, why productivity growth has slowed down so much.

Of course, the economy is not a uniform thing – some parts of it may be showing very fast technological progress, like the IT industry, while other parts – running restaurants, for example, might show very little real change over the decades. These differences emerge from the sector based statistics that have been collected and analysed for the EU countries by the EU KLEMS Growth and Productivity Accounts database.

Sector percentage of 2015 economy by GVA contribution versus aggregate total factor productivity growth from 1998 to 2015. Data from EU KLEMS Growth and Productivity Accounts database.

Here’s a very simple visualisation of some key results of that data set for the UK. For each sector, the relative importance of the sector to the economy as a whole is plotted on the x-axis, expressed as a percentage of the gross value added of the whole economy. On the y-axis is plotted the total change in total factor productivity over the whole 17 year period covered by the data. This, then, is the factor by which that sector has produced more output than would be expected on the basis of additional labour and capital. This may tell us something about the relative effectiveness of technological progress in driving productivity growth in each of these sectors.

Broadly, one can read this graph as follows: the further right a sector is, the more important it is as a proportion of the whole economy, while the nearer the top a sector is, the more dynamic its performance has been over the 17 years covered by the data. Before a more detailed discussion, we should bear in mind some caveats. What goes into these numbers are the same ingredients as go into the measurement of GDP as a whole, so all the shortcomings of that statistic are potentially issues here.

A great starting point for understanding these issues is Diane Coyle’s book GDP: a brief but affectional history. The first set of issues concern what GDP measures and what it doesn’t measure. Lots of kinds of activity are important for the economy, but they only tend to count in GDP if money changes hands. New technology can shift these balances – if supermarkets replace humans at the checkouts by machines, the groceries still have to be scanned, but now the customer is doing the work for nothing.

Then there are some quite technical issues about how the measurements are done. This includes properly accounting for improvements in quality where technology is advancing very quickly; failing to fully account for the increased information transferred through a typical internet connection will mean that overall inflation will be overestimated, and productivity gains in the ICT will be understated (see e.g. A Comparison of Approaches to Deflating Telecoms Services Output, PDF). For some of the more abstract transactions in the modern economy – particularly in the banking and financial services sector, some big assumptions have to be made about where and how much value is added. For example, the method used to estimate the contribution of financial services – FISIM, for “Financial intermediation services indirectly measured” – has probably materially overstated the contribution of financial services to GDP by not handling risk correctly, as argued in this recent ONS article.

Finally, there’s the big question of whether increases in GDP correspond to increases in welfare. The general answer to this question is, obviously, not necessarily. Unlike some commentators, I don’t take this to mean that we shouldn’t take any notice of GDP – it is an important indicator of the health of an economy and its potential to supply people’s needs. But it does need looking at critically. A glazing company that spent its nights breaking shop windows and its days mending them would be increasing GDP, but not doing much for welfare – this is a ridiculous example, but there’s a continuum between what economist William Baumol called unproductive entrepreneurship, the more extractive varieties of capitalism documented by Acemoglu and Robinson – and outright organised crime.

To return to our plot, we might focus first on three dynamic sectors – information and communications, manufacturing, and professional, scientific, technical and admin services. Between them, these sectors account for a bit more than a quarter of the economy, and have shown significant improvements in total factor productivity over the period. In this sense it’s been ICT, manufacturing and knowledge-based services that have driven the UK economy over this period.

Next we have a massive sector that is important, but not yet dynamic, in the sense of having demonstrated slightly negative total factor productivity growth over the period. This comprises community, personal and social services – notably including education, health and social care. Of course, in service activities like health and social care it’s very easy to mischaracterise as a lowering of productivity a change that actually corresponds to an increase in welfare. On the other hand, I’ve argued elsewhere that we’ve not devoted enough attention to the kinds of technological innovation in health and social care sectors that could deliver genuine productivity increases.

Real estate comprises a sector that is both significant in size, and has shown significant apparent increases in total factor productivity. This is a point at which I think one should question the nature of the value added. A real estate business makes money by taking a commission on property transactions; hence an increase in property prices, given constant transaction volume, leads to an apparent increase in productivity. Yet I’m not convinced that a continuous increase in property prices represents the economy generating real value for people.

Finance and insurance represents a significant part of the economy – 7% – but its overall long term increase in total factor productivity is unimpressive, and probably overstated. The importance of this sector in thinking about the UK economy represents a distortion of our political economy.

The big outlier at the bottom left of the plot is mining and quarrying, whose total factor productivity has dropped by 50% – what isn’t shown is that its share of the economy has substantially fallen over the period too. The biggest contributor to this sector is North Sea oil, whose production peaked around 2000 and which has since been rapidly falling. The drop in total factor productivity does not, of course, mean that technological progress has gone backwards in this sector. Quite the opposite – as the easy oil fields are exhausted, more resource – and better technology – are required to extract what remains. This should remind us of one massive weakness in GDP as a sole measure of economic progress – it doesn’t take account of the balance sheet, of the non-renewable natural resources we use to create that GDP. The North Sea oil has largely gone now and this represents an ongoing headwind to the UK economy that will need more innovation in other sectors to overcome.

This approach is limited by the way the economy needs to be divided up into sectors. Of course, this sectoral breakdown is very coarse – within each sector there are likely to be outliers with very high total productivity growth which dramatically pull up the average of the whole sector. More fundamentally, it’s not obvious that the complex, networked nature of the modern economy is well captured by these rather rigid barriers. Many of the most successful manufacturing enterprises add big value to their products with the services that come attached to them, for example.

We can look into the EU Klems data at a slightly finer grained level; the next plot shows importance and dynamism for the various subsectors of manufacturing. This shows well the wide dispersions within the overall sectors – and of course within each of these subsectors there will be yet more dispersion.

Sub-sector fraction of 2015 economy by GVA contribution versus aggregate total factor productivity growth from 1998 to 2015 for subsectors of manufacturing. Data from EU KLEMS Growth and Productivity Accounts database.

The results are perhaps unsurprising – areas traditionally considered part of high value manufacturing – transport equipment and chemicals, which include aerospace, automotive, pharmaceuticals and speciality chemicals – are found in the top right quadrant, important in terms of their share of the economy, dynamic in terms of high total factor productivity growth. The good total factor productivity performance of textiles is perhaps more surprising, for an area often written off as part of our industrial heritage. It would be interesting to look in more detail at what’s going on here, but I suspect that a big part of it could be the value that can be added by intangibles like branding and design. Total factor productivity is not just about high tech and R&D, important though the latter is.

Clearly this is a very superficial look at a very complicated area. Even within the limitations of the EU Klems data set, I’ve not considered how rates of TFP growth have varied by time – before and after the global financial crisis, for example. Nor have I considered the way shifts between sectors have contributed to overall changes in productivity across the economy – I’ve focused only on rates, not on starting levels. And of course, we’re talking here about history, which isn’t always a good guide to the future, where there will be a whole new set of technological opportunities and competitive challenges. But as we start to get serious about industrial strategy, these are the sorts of questions that we need to be looking into.

Eroom’s law strikes again

“Eroom’s law” is the name given by pharma industry analyst Jack Scannell to the observation that the productivity of research and development in the pharmaceutical industry has been falling exponentially for decades – discussed in my earlier post Productivity: in R&D, healthcare and the whole economy. The name is an ironic play on Moore’s law, the statement that the number of transistors on an integrated circuit increases exponentially.

It’s Moore’s law that has underlain the orders of magnitude increases in computing power we’ve grown used to. But if computing power has been increasing exponentially, what can we say about the productivity of the research and development effort that’s underpinned those increases? It turns out that in the semiconductor industry, too, research and development productivity has been falling exponentially. Eroom’s law describes the R&D effort needed to deliver Moore’s law – and the unsustainability of this situation must surely play a large part in the slow-down in the growth in computing power that we are seeing now.

Falling R&D productivity has been explicitly studied by the economists Nicholas Bloom, Charles Jones, John Van Reenen and Michael Webb, in a paper called “Are ideas getting harder to find?” (PDF). I discussed an earlier version of this paper here – I made some criticisms of the paper, though I think its broad thrust is right. One of the case studies the economists look at is indeed the electronics industry, and there’s one particular problem with their reasoning that I want to focus on here – though fixing this actually makes their overall argument stronger.

The authors estimate the total world R&D effort underlying Moore’s law, and conclude: “The striking fact, shown in Figure 4, is that research effort has risen by a factor of 18 since 1971. This increase occurs while the growth rate of chip density is more or less stable: the constant exponential growth implied by Moore’s Law has been achieved only by a massive increase in the amount of resources devoted to pushing the frontier forward.”

R&D expenditure in the microelectronics industry, showing Intel’s R&D expenditure, and a broader estimate of world microelectronics R&D including semiconductor companies and equipment manufacturers. Data from the “Are Ideas Getting Harder to Find?” dataset on Chad Jones’s website. Inflation corrected using the US GDP deflator.

The growth in R&D effort is illustrated in my first plot, which compares the growth of world R&D expenditure in microelectronics with the growth of computing power. I plot two measures from the Bloom/Jones/van Reenen/Webb data set – the R&D expenditure of Intel, and an estimate of broader world R&D expenditure on integrated circuits, which includes both semiconductor companies and equipment manufacturers (I’ve corrected for inflation using the US GDP deflator). The plot shows an exponential period of increasing R&D expenditure, which levelled off around 2000, to rise again from 2010.

The weakness of their argument, that increasing R&D effort has been needed to maintain the same rate of technological improvement, is that it selects the wrong output measure. No-one is interested in how many transistors there are per chip – what matters to the user, and the wider economy – is that computing power continues to increase exponentially. As I discussed in an earlier post – Technological innovation in the linear age, the fact is that the period of maximum growth in computing power ended in 2004. Moore’s law continued after this time, but the end of Dennard scaling meant that the rate of increase of computing power began to fall. This is illustrated in my second plot. This, after a plot in Hennessy & Patterson’s textbook Computer Architecture: A Quantitative Approach (6th edn) and using their data, shows the relative computing power of microprocessors as a function of their year of introduction. The solid lines illustrate 52% pa growth from 1984 to 2003, 23% pa growth from 2003 – 201, and 9% pa growth from 2011 – 2014.

The growth in processor performance since 1988. Data from figure 1.1 in Computer Architecture: A Quantitative Approach (6th edn) by Hennessy & Patterson.

What’s interesting is that the slowdown in the rate of growth in R&D expenditure around 2000 is followed by a slowdown in the rate of growth of computing power. I’ve attempted a direct correlation between R&D expenditure and rate of increase of computing power in my next plot, which plots the R&D expenditure needed to produce a doubling of computer power as a function of time. This is a bit crude, as I’ve used the actual yearly figures without any smoothing, but it does seem to show a relatively constant increase of 18% per year, both for the total industry and for the Intel only figures.

Eroom’s law at work in the semiconductor industry. Real R&D expenditure needed to produce a doubling of processing power as a function of time.

What is the cause of this exponential fall in R&D productivity? A small part reflects Baumol’s cost disease – R&D is essentially a service business done by skilled people, who command wages that reflect the growth of the whole economy rather than their own output (the Bloom et al paper accounts for this to some extent by deflating R&D expenditure by scientific salary levels rather than inflation). But this is a relatively small effect compared to the more general problem of the diminishing returns to continually improving an already very complex and sophisticated technology.

The consequence seems inescapable – at some point the economic returns of improving the technology will not justify the R&D expenditure needed, and companies will stop making the investments. We seem to be close to that point now, with Intel’s annual R&D spend – $12 billion in 2015 – only a little less than the entire R&D expenditure of the UK government, and the projected cost of doubling processor power from here exceeding $100 billion. The first sign has been the increased concentration of the industry. For the 10 nm node, only four companies remained in the game – Intel, Samsung, the Taiwanese foundry company TSMC, and GlobalFoundries, which acquired the microelectronics capabilities of AMD and IBM. As the 7 nm node is being developed, GlobalFoundries has announced that it too is stepping back from the competition to produce next-generation chips, leaving only 3 companies at the technology frontier.

The end of this remarkable half-century of exponential growth in computing power has arrived – and it’s important that economists studying economic growth come to terms with this. However, this doesn’t mean innovation comes to an end too. All periods of exponential growth in particular technologies must eventually saturate, whether that’s as a result of physical or economic limits. In order for economic growth to continue, what’s important is that entirely new technologies must appear to replace them. The urgent question we face is what new technology is now on the horizon, to drive economic growth from here.

Innovation, regional economic growth, and the UK’s productivity problem

A week ago I gave a talk with this title at a conference organised by the Smart Specialisation Hub. This organisation was set up to help regional authorities in developing their economic plans; given the importance of local industrial strategies in the government’s overall industrial strategy its role becomes all the more important.

Other speakers at the conference represented central government, the UK’s innovation agency InnovateUK, and the Smart Specialisation Hub itself. Representing no-one but myself, I was able to be more provocative in my own talk, which you can download here (PDF, 4.7 MB).

My talk had four sections. Opening with the economic background, I argued that the UK’s stagnation in productivity growth and regional economic inequality has broken our political settlement. Looking at what’s going on in Westminster at the moment, I don’t think this is an exaggeration.

I went on to discuss the implications of the 2.4% R&D target – it’s not ambitious by developed world standards, but will be a stretch from our current position, as I discussed in an earlier blogpost: Reaching the 2.4% R&D intensity target.

Moving on to the regional aspects of research and innovation policy, I argued (as I did in this blog post: Making UK Research and Innovation work for the whole UK) that the UK’s regional concentration of R&D (especially public sector) is extreme and must be corrected. To illustrate this point, I used this version of Tom Forth’s plot splitting out the relative contributions of public and private sector to R&D regionally.

I argued that this plot gives a helpful framework for thinking about the different policy interventions needed in different parts of the country. I summarised this in this quadrant diagram [1].

Finally, I discussed the University of Sheffield’s Advanced Manufacturing Research Centre as an example of the kind of initiative that can help regenerate the economy of a de-industrialised area. Here a focus on translational research & skills at all levels both drives inward investment by international firms at the technology frontier & helps the existing business base upgrade.

I set this story in the context of Shih and Pisano’s notion of the “industrial commons” [2] – a set of resources that supports the collective knowledge, much of it tacit, that drives innovations in products and processes in a successful cluster. A successful industrial commons is rooted in a combination of large anchor companies & institutions, networks of supplying companies, R&D facilities, informal knowledge networks and formal institutions for training and skills. I argue that a focus of regional economic policy should be a conscious attempt to rebuild the “industrial commons” in an industrial sector which allows the opportunities of new technology to be embraced, yet which works with grain of the existing industry and institutional base. The “smart specialisation” framework is a good framework for identifying the right places to look.

1. As a participant later remarked, I’ve omitted the South East from this diagram – it should be in the bottom right quadrant, albeit with less business R&D than East Anglia, though with the benefits more widely spread.

2. See Pisano, G. P., & Shih, W. C. (2009). Restoring American Competitiveness. Harvard Business Review, 87(7-8), 114–125.

The semiconductor industry and economic growth theory

In my last post, I discussed how “econophysics” has been criticised for focusing on exchange, not production – in effect, for not concerning itself with the roots of economic growth in technological innovation. Of course, some of that technological innovation has arisen from physics itself – so here I talk about what economic growth theory might learn from an important episode of technological innovation with its origins in physics – the development of the semiconductor industry.

Economic growth and technological innovation

In my last post, I criticised econophysics for not talking enough about economic growth – but to be fair, it’s not just econophysics that suffers from this problem – mainstream economics doesn’t have a satisfactory theory of economic growth either. And yet economic growth and technological innovation provides an all-pervasive background to our personal economic experience. We expect to be better off than our parents, who were themselves better off than our grandparents. Economics without a theory of growth and innovation is like physics without an arrow of time – a marvellous intellectual construction that misses the most fundamental observation of our lived experience.

Defenders of economics at this point will object that it does have theories of growth, and there are even some excellent textbooks on the subject [1]. Moreover, they might remind us, wasn’t the Nobel Prize for economics awarded this year to Paul Romer, precisely for his contribution to theories of economic growth? This is indeed so. The mainstream approach to economic growth pioneered by Robert Solow regarded technological innovation as something externally imposed, and Romer’s contribution has been to devise a picture of growth in which technological innovation arises naturally from the economic models – the “post-neoclassical endogenous growth theory” that ex-Prime Minister Gordon Brown was so (unfairly) lampooned for invoking.

This body of work has undoubtedly highlighted some very useful concepts, stressing the non-rivalrous nature of ideas and the economic basis for investments in R&D, especially for the day-to-day business of incremental innovation. But it is not a theory in the sense a physicist might understand that – it doesn’t explain past economic growth, so it can’t make predictions about the future.

How the information technology revolution really happened

Perhaps to understand economic growth we need to turn to physics again – this time, to the economic consequences of the innovations that physics provides. Few would disagree that a – perhaps the – major driver of technological innovation, and thus economic growth, over the last fifty years has been the huge progress in information technology, with the exponential growth in the availability of computing power that is summed up by Moore’s law.

The modern era of information technology rests on the solid-state transistor, which was invented by William Shockley at Bell Labs in the late 1940’s (with Brattain and Bardeen – the three received the 1956 Nobel Prize for Physics). In 1956 Shockley left Bell Labs and went to Palo Alto (in what would later be called Silicon Valley) to found a company to commercialise solid-state electronics. However, his key employees in this venture soon left – essentially because he was, by all accounts, a horrible human being – and founded Fairchild Semiconductors in 1957. Key figures amongst those refugees were Gordon Moore – of eponymous law fame – and Robert Noyce. It was Noyce who, in 1960, made the next breakthrough, inventing the silicon integrated circuit, in which a number of transistors and other circuit elements were combined on a single slab of silicon to make a integrated functional device. Jack Kilby, at Texas Instruments, had, more or less at the same time, independently developed an integrated circuit on germanium, for which he was awarded the 2000 Physics Nobel prize (Noyce, having died in 1990, was unable to share this). Integrated circuits didn’t take off immediately, but according to Kilby it was their use in the Apollo mission and the Minuteman ICBM programme that provided a turning point in their acceptance and widespread use[2] – the Minuteman II guidance and control system was the first mass produced computer to rely on integrated circuits.

Moore and Noyce founded the electronics company Intel in 1968, to focus on developing integrated circuits. Moore had already, in 1965, formulated his famous law about the exponential growth with time of the number of transistors per integrated circuit. The next step was to incorporate all the elements of a computer on a single integrated circuit – a single piece of silicon. Intel duly produced the first commercially available microprocessor – the 4004 – in 1971, though this had been (possibly) anticipated by the earlier microprocessor that formed the flight control computer for the F14 Tomcat fighter aircraft. From these origins emerged the microprocessor revolution and personal computers, with its giant wave of derivative innovations, leading up to the current focus on machine learning and AI.

Lessons from Moore’s law for growth economics

What should clear from this very brief account is that classical theories of economic growth cannot account for this wave of innovation. The motivations that drove it were not economic – they arose from a powerful state with enormous resources at its disposal pursuing complex, but entirely non-economic projects – such as the goal of being able to land a nuclear weapon on any point of the earth’s surface with an accuracy of a few hundred meters.

Endogenous growth theories perhaps can give us some insight into the decisions companies made about R&D investment and the wider spillovers that such spending led to. They would need to take account of the complex institutional landscape that gave rise to this innovation. This isn’t simply a distinction between public and private sectors – the original discovery of the transistor was made at Bell Labs – nominally in the private sector, but sustained by monopoly rents arising from government action.

The landscape in which this innovation took place seems much more complex than growth economics, with its array of firms employing undifferentiated labour, capital, all benefiting from some kind of soup of spillovers seems able to handle. Semiconductor fabs are perhaps the most capital intensive plants in the world, with just a handful of bunny-suited individuals tending a clean-room full of machines that individually might be worth tens or even hundreds of millions of dollars. Yet the value of those machines represents, as much as anything physical, the embodied value of the intangible investments in R&D and process know-how.

How are the complex networks of equipment and materials manufacturers coordinated to make sure technological advances in different parts of this system happen at the right time and in the right sequence? These are independent companies operating in a market – but the market alone has not been sufficient to transmit the information needed to keep it coordinated. An enormously important mechanism for this coordination has been the National Technology Roadmap for Semiconductors (later the International Technology Roadmap for Semiconductors), initiated by a US trade body, the Semiconductor Industry Association. This was an important social innovation which allowed companies to compete in meeting collaborative goals; it was supported by the US government by the relaxation of anti-trust law and the foundation of a federally funded organisation to support “pre-competitive” research – SEMATECH.

The involvement of the US government reflected the importance of the idea of competition between nation states in driving technological innovation. Because of the cold war origins of the integrated circuits, the original competition was with the Soviet Union, which created an industry to produce ICs for military use, based around Zelenograd. The degree to which this industry was driven by indigenous innovation as against the acquisition of equipment and know-how from the west isn’t clear to me, but it seems that by the early 1980’s the gap between Soviet and US achievements was widening, contributing to the sense of stagnation of the later Brezhnev years and the drive for economic reform under Gorbachev.

From the 1980’s, the key competitor was Japan, whose electronics industry had been built up in the 1960’s and 70’s driven not by defense, but by consumer products such as transistor radios, calculators and video recorders. In the mid-1970’s the Japanese government’s MITI provided substantial R&D subsidies to support the development of integrated circuits, and by the late 1980’s Japan appeared within sight of achieving dominance, to the dismay of many commentators in the USA.

That didn’t happen, and Intel still remains at the technological frontier. Its main rivals now are Korea’s Samsung and Taiwan’s TSMC. Their success reflects different versions of the East Asian developmental state model; Samsung is Korea’s biggest industrial conglomerate (or chaebol), whose involvement in electronics was heavily sponsored by its government. TSMC was a spin-out from a state-run research institute in Taiwan, ITRI, which grew by licensing US technology and then very effectively driving process improvements.

Could one build an economic theory that encompasses all this complexity? For me, the most coherent account has been Bill Janeway’s description of the way government investment combines with the bubble dynamics that drives venture capitalism, in his book “Doing Capitalism in the Innovation Economy”. Of course, the idea that financial bubbles are important for driving innovation is not new – that’s how the UK got a railway network, after all – but the econophysicist Didier Sornette has extended this to introduce the idea of a “social bubble” driving innovation[3].

This long story suggests that the ambition of economics to “endogenise” innovation is a bad idea, because history tells us that the motivations for some of the most significant innovations weren’t economic. To understand innovation in the past, we don’t just need economics, we need to understand politics, history, sociology … and perhaps even natural science and engineering. The corollary of this is that devising policy solely on the basis of our current theories of economic growth is likely to lead to disappointing outcomes. At a time when the remarkable half-century of exponential growth in computing power seems to be coming to an end, it’s more important than ever to learn the right lessons from history.

[1] I’ve found “Introduction to Modern Economic Growth”, by Daron Acemoglu, particularly useful

[2] Jack Kilby: Nobel Prize lecture, https://www.nobelprize.org/uploads/2018/06/kilby-lecture.pdf

[3] See also that great authority, The Onion “Recession-Plagued Nation Demands New Bubble to Invest In”

The Physics of Economics

This is the first of two posts which began life as a single piece with the title “The Physics of Economics (and the Economics of Physics)”. In the first section, here, I discuss some ways physicists have attempted to contribute to economics. In the second half, I turn to the lessons that economics should learn from the history of a technological innovation with its origin in physics – the semiconductor industry.

Physics and economics are two disciplines which have quite a lot in common – they’re both mathematical in character, many of their practitioners are not short of intellectual self-confidence – and they both have imperialist tendencies towards their neighbouring disciplines. So the interaction between the two fields should be, if nothing else, interesting.

The origins of econophysics

The most concerted attempt by physicists to colonise an area of economics is in the area of the behaviour of financial markets – in the field which calls itself “econophysics”. Actually at its origins, the traffic went both ways – the mathematical theory of random walks that Einstein developed to explain the phenomenon of Brownian motion had been anticipated by the French mathematician Bachelier, who derived the theory to explain the movements of stock markets. Much later, the economic theory that markets are efficient brought this line of thinking back into vogue – it turns out that financial markets can be quite often modelled as simple random walks – but not quite always. The random steps that markets take aren’t drawn from a Gaussian distribution – the distribution has “fat tails”, so rare events – like big market crashes – aren’t anywhere like as rare as simple theories assume.

Empirically, it turns out that the distributions of these rare events can sometimes be described by power laws. In physics power laws are associated with what are known as critical phenomena – behaviours such as the transition from a liquid to a gas or from a magnet to a non-magnet. These phenomena are characterised by a certain universality, in the sense that the quantitative laws – typically power laws – that describe the large scale behaviour of these systems doesn’t strongly depend on the details of the individual interactions between the elementary objects (the atoms and molecules, in the case of magnetism and liquids) whose interaction leads collectively to the larger scale phenomenon we’re interested in.

For “econophysicists” – whose background often has been in the study of critical phenomenon – it is natural to try and situate theories of the movements of financial markets in this tradition, finding analogies with other places where power laws can be found, such as the distribution of earthquake sizes and the behaviour of sand-piles. In terms of physicists’ actual impact on participants in financial markets, though, there’s a paradox. Many physicists have found (often very lucrative) employment as quantitative traders, but the theories that academic physicists have developed to describe these markets haven’t made much impact on the practitioners of financial economics, who have their own models to describe market movements.

Other ideas from physics have made their way into discussions about economics. Much of classical economics depends on ideas like the “representative household” or the “representative firm”. Physicists with a background in statistical mechanics recognise this sort of approach as akin to a “mean field theory”. The idea that a complex system is well represented by its average member is one that can be quite fruitful, but in some important circumstances fails – and fails badly – because the fluctuations around the average become as important as the average itself. This motivates the idea of agent based models, to which physicists bring the hope that even simple “toy” models can bring insight. The Schelling model is one such very simple model that came from economics, but which has a formal similarity with some important models in physics. The study of networks is another place where one learns that the atypical can be disproportionately important.

If markets are about information, then physics should be able to help…

One very attractive emerging application of ideas from physics to economics concerns the place of information. Friedrich Hayek stressed the compelling insight that one can think of a market as a mechanism for aggregating information – but a physicist should understand that information is something that can be quantified, and (via Shannon’s theory) that there are hard limits on how much information can transmitted in a physical system . Jason Smith’s research programme builds on this insight to analyse markets in terms of an information equilibrium[1].

Some criticisms of econophysics

How significant is econophysics? A critique from some (rather heterodox) economists – Worrying trends in econophysics – is now more than a decade old, but still stings (see also this commentary from the time from Cosma Shalizi – Why Oh Why Can’t We Have Better Econophysics? ). Some of the criticism is methodological – and could be mostly summed up by saying, just because you’ve got a straight bit on a log-log plot doesn’t mean you’ve got a power law. Some criticism is about the norms of scholarship – in brief: read the literature and stop congratulating yourselves for reinventing the wheel.

But the most compelling criticism of all is about the choice of problem that econophysics typically takes. Most attention has been focused on the behaviour of financial markets, not least because these provide a wealth of detailed data to analyse. But there’s more to the economy – much, much more – than the financial markets. More generally, the areas of economics that physicists have tended to apply themselves to have been about exchange, not production – studying how a fixed pool of resources can be allocated, not how the size of the pool can be increased.

[1] For a more detailed motivation of this line of reasoning, see this commentary, also from Cosma Shalizi on Francis Spufford’s great book “Red Plenty” – “In Soviet Union, Optimization Problem Solves You”.

The UK’s top six productivity underperformers

The FT has been running a series of articles about the UK’s dreadful recent productivity performance, kicked off with this very helpful summary – Britain’s productivity crisis in eight charts. One important aspect of this was to focus on the (negative) contribution of formerly leading sectors of the economy which have, since the financial crisis, underperformed:

“Computer programming, energy, finance, mining, pharmaceuticals and telecoms — which together account for only one-fifth of the economy — generated three-fifths of the decline in productivity growth.”

The original source of this striking statistic is a paper by Rebecca Riley, Ana Rincon-Aznar and Lea Samek – Below the Aggregate: A Sectoral Account of the UK Productivity Puzzle.

What this should stress is that there’s no single answer to the productivity crisis. We need to look in detail at different industrial sectors, different regions of the UK, and identify the different problems they face before we can work out the appropriate policy responses.

So what can we say about what’s behind the underperformance of each of these six sectors, and what lessons should policy-makers learn in each case? Here are a few preliminary thoughts.

Mining. This is dominated by North Sea Oil. The oil is running out, and won’t be coming back – production peaked in 2000; what oil is left is more expensive and difficult to get out.
Lessons for policy makers: more recognition is needed that the UK’s prosperity in the 90’s and early 2000’s depended as much on the accident of North Sea oil as any particular strength of the policy framework.

Finance. It’s not clear to me how much of the apparent pre-crisis productivity boom was real, but post-crisis increased regulation and greater capital requirements have reduced apparent rates of return in financial services. This is as it should be.
Lessons for policy makers: this sector is the problem, not the solution, so calls to relax regulation should be resisted, and so-called “innovation” that in practise amounts to regulatory arbitrage discouraged.

The end of North Sea oil and the finance bubble cannot be reversed – these are headwinds that the economy has to overcome. We have to find new sources of productivity growth rather than looking back nostalgically at these former glories (for example, there’s a risk that the enthusiasm for fracking and fintech represent just such nostalgia).

Energy. Here, a post-privatisation dysfunctional pseudo-market has prioritised sweating existing assets rather than investing. Meanwhile there’s been an unclear and inconsistent government policy environment; sometimes the government has willed the ends without providing the means (e.g. nuclear new build), elsewhere it has introduced perverse and abrupt changes of tack (e.g. in its support for onshore wind and solar).
Lessons for policy makers: develop a rational, long-term energy strategy that will deliver the necessary decarbonisation of the energy economy. Then stick to it, driving innovation to support the strategy. For more details, read chapter 4 – Decarbonisation of the energy economy – of the Industrial Strategy Commission’s final report.

Computer programming. Here I find myself on less sure ground. Are we seeing the effects of increasing overseas outsourcing and competition, for example to India’s growing IT industry? Are we seeing the effect of more commoditisation of computer programming, with new business models such as “software as a service”?

Telecoms. Again, here I’m less certain of what’s been going on. Are we seeing the effect of lengthening product cycles as the growth in processor power slows? Is this the effect of overseas competition – for example, rapidly growing Chinese firms like Huawei – moving up the value chain? Here it’s also likely that measurement problems – in correctly accounting for improvements in quality – will be most acute.

Pharmaceuticals. As my last blogpost outlined, productivity growth in pharmaceuticals depends on new products being developed through formal R&D, their value being protected by patents. There has been a dramatic, long-term fall in the productivity of pharma R&D, so it is unsurprising that this is now feeding through into reduced labour productivity.
Lessons for policy makers: see the recent NESTA report “The Biomedical Bubble”.

Many of these issues were already discussed in my 2016 SPERI paper Innovation, research and the UK’s productivity crisis. Two years on, the productivity crisis seems even more pressing, and as the FT series illustrates, is receiving more attention from policy makers and economists (though still not enough, in view of its fundamental importance for living standards and fiscal stability). The lesson I would want to stress is that, to make progress, policy makers and economists need to go beyond generalities, and pay more attention to the detailed particulars of individual industries, sectors and regions, and the different way innovation takes place – or hasn’t being taking place – within them.

Productivity: in R&D, healthcare and the whole economy

This is a slightly adapted extract from The Biomedical Bubble: Why UK research and innovation needs a greater diversity of priorities, politics, places and people, my report for NESTA, with James Wilsdon.

Productivity is a measure of the efficiency with which inputs are converted into outputs of value – increasing productivity lets us get more from less. We talk about different kinds of productivity in our report:

● Economic productivity, at the level of the nation, regions and industry sectors, most usefully expressed as labour productivity;
● R&D productivity: the effectiveness with which research and development expenditure translates into new products and processes and thus economic value;
● Healthcare productivity: the effectiveness with which given inputs of money and labour produce improved health outcomes.

The UK’s productivity problem

The performance of the whole national economy is measured by labour productivity – the value of the goods and services (as measured by GDP) produced by an (average) hour of work. Increases in labour productivity arise from a combination of capital investment and technological progress, and are the fundamental drivers of economic growth and increasing living standards.

Labour productivity since 1970. ONS, January 2018 release.

Labour productivity in the UK has stagnated since the global financial crisis of 2007/8 : currently it’s some 15-20% below what would be expected if the pre-crisis trend had continued, the worst performance for at least a century . It’s this stagnation of labour productivity that sets our overall economic environment, leading directly to wage stagnation and a persistently challenging fiscal situation for the government, which has responded with sustained austerity.

The overall labour productivity of the economy is an aggregate; we can decompose it to consider the contribution of different geographical regions or industry sectors. A regional breakdown reveals how geographically unbalanced the UK economy is. London dominates, with labour productivity 33% above the UK average. Of the other regions, only the South East is above the national average. Wales and Northern Ireland are 17% below the UK average, with other regions in the English North and Midlands between 7 and 15% below average.

The pharmaceutical industry’s contribution to overall productivity growth – from leader to laggard

There’s a very wide dispersion of labour productivity across industrial sectors. In understanding their contribution to the overall productivity puzzle, it’s important to consider both the level of labour productivity and the rate of growth. The pharmaceutical industry is particularly important to the UK here – its level of labour productivity is very high, so even though it only constitutes a relatively small part of the overall economy, shifts in its performance can have a material effect on the whole economy.

But recent years have seen a big fall in the rate of growth of labour productivity in the pharmaceutical industry [1]. Between 1999 and 2007, labour productivity in the pharmaceutical industry grew by 9.7% a year – this excellent performance made a material difference to the whole economy, contributing 0.11 percentage points to the total annual labour productivity growth in the pre-crisis economy of 2.8%. But between 2008 and 2015, labour productivity in pharma actually shrank by 11% a year, dragging down labour productivity growth in the whole economy.

The origins of the pharmaceutical industry’s productivity problem – falling R&D productivity

Labour productivity gains arise from the introduction of new, high value, products and improved processes. In the pharmaceutical industry, new products are created by research and development (R&D), with their value being protected by patents.

R&D productivity expresses the efficiency with which R&D produces value through new products and processes. This can be difficult to quantify: a new drug is the product of perhaps 15 years of R&D and for each successful drug produced many candidates fail. One simple measure is the number of new drugs produced for a given value of R&D; as the graph shows, on this measure R&D productivity has fallen substantially over the decades.

Exponentially falling R&D productivity in the pharmaceutical industry worldwide. Number of new molecules approved by FDA (pharma and biotech) per $bn global R&D spending. Plot after Scannell et al [2], with additional post-2012 data courtesy of Jack Scannell.

Falling R&D productivity explains falling labour productivity in pharmaceuticals, with a lag time that expresses the time it takes to develop and test new drugs. This will be exacerbated if the total volume of R&D falls as well, as it has begun to do in recent years.

The recent weak performance of the UK economy can be linked in part to its low overall R&D intensity , and this has been recognised by the government’s commitment to raise this to 2.4% of GDP. As I described in an earlier post – Making UK Research and Innovation work for the whole UK – R&D intensity varies strongly across the country, with these variations being correlated with regional economic performance. The commitment to raise the overall R&D intensity of the UK economy is welcome, but it will only deliver the hoped-for economic benefits if overall R&D productivity across all sectors can be maintained or increased.

Healthcare productivity – the pressure for improvements

The purpose of health-related research and development is not simply economic, however. We hope that research will improve people’s lives, reducing mortality and morbidity.

But we can’t avoid the economic dimension of healthcare either – the pressures on health service budgets are all too obvious in this time of continuing public austerity, so the idea that innovation – technological, social and organisational – can allow us to achieve the same or better healthcare outcomes for less money is compelling.

Healthcare productivity can be estimated by comparing inputs – labour, goods and services and capital expenditure – with some measure of the amount of treatment delivered. This needs to be adjusted for improved quality of care – for example, from improved survival rates, and measures of patient satisfaction. The ONS produces estimates of quality adjusted public service healthcare productivity , which show an average increase of 0.8% a year, between 1995 – 2015.

The context for this continuous improvement in healthcare productivity is an even larger increase in demand for healthcare . For example, between 2003/4 and 2015/16 there was an average annual rise in hospital admissions a year, driven by demographic changes – in particular – a 40% rise in the number of people aged 85 and over.

This demand pressure is likely to continue into the future, so without further increases in healthcare productivity, quality will suffer and costs will rise.

Labour productivity, R&D productivity, healthcare productivity – the vicious circle and how to break out of it

These three aspects of productivity are linked. Falling R&D productivity in pharmaceuticals has led to falling labour productivity in that industry. That in turn has made a material contribution to stagnant labour productivity across the whole economy. On the other hand, stagnant labour productivity in the whole economy has produced a government response of continuing austerity, putting pressure on health service budgets, and increasing the demand for improved healthcare productivity.

How can we break out of this trap? Improving the effectiveness and targeting of our R&D effort has to be central to this. Better R&D productivity will lead to improvements in labour productivity in pharmaceuticals, biotechnology and medical technology across the whole country, leading to sustained, geographically balanced economic growth. And if we do the right R&D to deliver improved healthcare productivity, that will lead to better health outcomes for everyone.

1. R. Riley, A. Rincon-Aznar, L. Samek, Below the Aggregate: A Sectoral Account of the UK Productivity Puzzle, ESCoE Discussion Papaer 2018-6 (May 2018)
https://www.escoe.ac.uk/wp-content/uploads/2018/05/ESCoE-DP-2018-06.pdf

2. Scannell, J. W., Blanckley, A., Boldon, H., & Warrington, B. (2012). Diagnosing the decline in pharmaceutical R&D efficiency, 1–10. http://doi.org/10.1038/nrd3681

William: on The shifting sands of UK Government technology prioritisation
Douglas Robertson: on The shifting sands of UK Government technology prioritisation
Matt Cliffe: on When did the UK’s productivity slowdown begin?
Zelah: on What the UK should – and should not – do about semiconductors
Richard Jones: on “Science Superpower: the UK’s Global Science Strategy beyond Horizon Europe”