Covid-19 Innovation – Are We on the Right Track?

I never discuss my own research on this website – it’s more fun (for me at the very least!) to dive in to the great results the rest of the economics community produces. So I hope you’ll forgive me for breaking this rule today, as I want to show a few interesting, very time-sensitive results Jorge Lemus, Guillermo Marshall and I have developed about Covid-19 innovation.

Many of us in the innovation economics world have been asked by governments how they should handle R&D right now. The basic problem is clear. There is a pandemic. Stopping this has enormous economic benefits – a vaccine that arrived tomorrow would literally be among the most economically valuable inventions ever made. Treatments which allow normal economic activity are incredibly valuable as well. As always, governments have limited knowledge about who is able to invent what. There is tremendous uncertainty about how various R&D projects will pan out. Should government be running huge prizes for specific inventions? General subsidies for medical R&D? Precommitments to buy certain remedies? Perhaps laissez faire alone is enough to induce this invention? Should patents be stronger, to increase the returns to R&D, or should they be weaker, to encourage the WHO’s global access to remedies?

These are very challenging questions. Let’s instead narrow down to a simpler one: given existing policy, is the rate and direction of Covid-19 R&D worrying in any way? Our basic finding is that the rate of Covid innovation is incredibly rapid, but competitive forces are pushing that research in a very short-term direction. The policy implications are subtle – many ideas that we normally think of as useful for R&D, especially on global health issues, may actually be counterproductive.

Here we compare the rate of therapies somewhere in the pipeline, and the number of academic publications, related to Covid-19 compared to other epidemics like Zika, Ebola, and H1N1, and compared to breast cancer, the most heavily-funded long-run disease. The pipeline data is from BioMedTracker, a standardized commercial research database that independently validates reports of new projects on a given indication. Note two things. First, the rate of Covid research far exceeds the long-term average for breast cancer or the post-epidemic rate of research on Ebola, H1N1, or Zika. Second, this gap grows even larger after the globalization of the pandemic in early March 2020, as indicated by the vertical line in the figures. Covid therapies are entering the pipeline at a rate 15 to 80 times faster than any previous epidemic, with over 4 new therapies entering the commercial pipeline every single day. The number of these therapies in clinical trials within four months of the early December beginning of the epidemic exceeds the entire first-year number of trials for H1N1, Zika, and Ebola combined. This figure tracks through April 22, but the rate of new drugs in the pipelines since that date has continued at nearly the same pace. The expected return on Covid innovation is high enough to induce an incredible amount of entry.

Alas, there is a downside. Let’s split the pipeline into vaccines vs. other drugs, and repurposed drugs vs. novel compounds. The relative share of “short term” solutions – non-vaccines and repurposed drugs – is unusually high. 23 percent of Covid therapies are vaccines, versus at least half for the previous three recent less severe epidemics. Over 60 percent of Covid therapies are repurposed drugs, versus no more than a quarter of those for Ebola, Zika, or H1N1. The short-term share is particularly strong after Covid explodes globally in early March – the rate at which new vaccines enter the pipeline is essentially the same in February and April! Broadly, as the epidemic gets worse, a greater share of R&D goes to projects that can be developed quickly.

How should we interpret this? We need a model to understand what the direction of invention “should be”. Theoretically, let there be firms of different sizes considering paying a fixed cost to enter the market for drug therapies on a particular disease. After entering, these firms choose whether to work on short-term therapies, which can be developed quickly but are not as valuable, or long-term ones, which take time to invent but are quite valuable. Once some firm invents a remedy, the marginal value of other remedies change: for instance, a vaccine is more valuable than a treatment, but the marginal value of the vaccine if a reasonable treatment exists is lower than it would have been otherwise.

Let’s model an epidemic intensifying by a multiplicative increase in the payoff to all inventions related to that disease. This increased payoff for successful invention, holding the number of firms constant, increased each firm’s expected payoff from R&D. The higher expected payoff induces more firms to enter, particularly firms with limited specialized research capacity who otherwise wouldn’t bother with a disease outside their wheelhouse.

Increased entry means a more fractured market for R&D, with many small firms doing research instead of just a few big ones. Assume it would be efficient for most firms to try to invent a vaccine. A small firm – say, one which represents 1% of total research capacity in the industry – will reason as follows. “I can try to invent the vaccine, but the Sanofis and the Modernas of the world are likely to get their vaccine way before I do. However, since their projects will take many months to develop and validate, I can instead try to quickly develop a marginally useful treatment. The existence of that treatment, once invented, lowers the marginal payoff to working on vaccines, but who cares – I am very unlikely to invent a vaccine in any case.” The increased entry driven by huge payoffs to any successful Covid therapies causes entrants to inefficiently race toward lower-value therapies. If enough small firms begin to race in this way, even the large firms that otherwise would have worked on vaccines will give up. And note that this pattern appears empirically: more severe epidemic leads to more entry by small firms, more work on short-term projects, and a decreasing share of long-term projects even from large firms as that competitive racing gets worse.

The policy implication is somewhat worrying. Normally, we worry in global public health that there isn’t enough incentive to work on a disease class at all, since firms worry that poor countries would either be unable to afford, or would expropriate ex-post, any useful invention. Things like the advance market commitments supported by last year’s Nobel winner Michael Kremer, have richer governments precommit to buying successful therapies for diseases like malaria. In the case of Covid, however, the main problem isn’t the rate of innovation, but the direction – the expected financial return on innovation may be too high for short-run, partial solutions, yet too low for vaccines or novel therapies.

So how can society both encourage a ton of Covid-19 R&D so we can escape this pandemic, while also discouraging racing behavior toward minor solutions? We propose three solutions. First, limited antitrust enforcement on research joint ventures can help by causing firms to partially internalize the racing externality they would otherwise have imposed on their joint venture partner. Second, targeted subsidies specifically for vaccines or promising novel therapies reshift the balance back toward the social optimum. It’s amazing how little of this is being done: a recent Guardian roundup of leading vaccine candidates proudly notes that 40 million pounds of support has been given to the Imperial College London team. 40 million in support for a vaccine that, if successful, probably has a net present value of over a trillion pounds! Third, we suggest AMCs can be useful as long as the value of the AMC is based on the ex-ante value of the therapy being developed, rather than the ex-post value – doing so allows firms with ability to work on long-term, high-value solutions to ignore the racing being done by their smaller rivals.

April 2020 Draft: “Innovation During a Crisis: Evidence from Covid-19”. We worked very hard – literally many all nighters – to get an analysis done quickly which nonetheless uses the best available Covid R&D data, and provides policy advice based on rigorous innovation theory. In my work with an entrepreneurship program here at the University of Toronto since we finished the draft, where we consult with very experienced biotech VCs, I’ve actually noticed our proposed mechanism in action – everyone is aware of the competitive nature of Covid research, and the first-order questions are always whether a firm has the capacity to work at high speed, and whether their therapy could be modified into something less significant but quicker to develop. I truly do not believe the externality we suggest is merely a theoretical curiosity.

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How We Create and Destroy Growth: A Nobel for Romer and Nordhaus

Occasionally, the Nobel Committee gives a prize which is unexpected, surprising, yet deft in how it points out underappreciated research. This year, they did no such thing. Both William Nordhaus and Paul Romer have been running favorites for years in my Nobel betting pool with friends at the Federal Reserve. The surprise, if anything, is that the prize went to both men together: Nordhaus is best known for his environmental economics, and Romer for his theory of “endogenous” growth.

On reflection, the connection between their work is obvious. But it is the connection that makes clear how inaccurate many of today’s headlines – “an economic prize for climate change” – really is. Because it is not the climate that both winners build on, but rather a more fundamental economic question: economic growth. Why are some places and times rich and others poor? And what is the impact of these differences? Adam Smith’s “The Wealth of Nations” is formally titled “An Inquiry into the Nature and Causes of the Wealth of Nations”, so these are certainly not new questions in economics. Yet the Classical economists did not have the same conception of economic growth that we have; they largely lived in a world of cycles, of ebbs and flows, with income per capita facing the constraint of agricultural land. Schumpeter, who certainly cared about growth, notes that Smith’s discussion of the “different progress of opulence in different nations” is “dry and uninspired”, perhaps only a “starting point of a sort of economic sociology that was never written.”

As each generation became richer than the one before it – at least in a handful of Western countries and Japan – economists began to search more deeply for the reason. Marx saw capital accumulation as the driver. Schumpeter certainly saw innovation (though not invention, as he always made clear) as important, though he had no formal theory. It was two models that appear during and soon after World War II – that of Harrod-Domar, and Solow-Swan-Tinbergen – which began to make real progress. In Harrod-Domar, economic output is a function of capital Y=f(K), nothing is produced without capital f(0)=0, the economy is constant returns to scale in capital df/dK=c, and the change in capital over time depends on what is saved from output minus what depreciates dK/dt=sY-zK, where z is the rate of depreciation. Put those assumptions together and you will see that growth, dY/dt=sc-z. Since c and z are fixed, the only way to grow is to crank up the savings rate, Soviet style. And no doubt, capital deepening has worked in many places.

Solow-type models push further. They let the economy be a function of “technology” A(t), the capital stock K(t), and labor L(t), where output Y(t)=K^a*(A(t)L(t))^(1-a) – that is, that production is constant returns to scale in capital and labor. Solow assumes capital depends on savings and depreciation as in Harrod-Domar, that labor grows at a constant rate n, and that “technology” grows at constant rate g. Solving this model gets you that the economy grows such that dY/dt=sy-k(n+z+g), and that output is exactly proportional to capital. You can therefore just run a regression: we observe the amount of labor and capital, and Solow shows that there is not enough growth in those factors to explain U.S. growth. Instead, growth seems to be largely driven by change in A(t), what Abramovitz called “the measure of our ignorance” but which we often call “technology” or “total factor productivity”.

Well, who can see that fact, as well as the massive corporate R&D facilities of the post-war era throwing out inventions like the transistor, and not think: surely the factors that drive A(t) are endogenous, meaning “from within”, to the profit-maximizing choices of firms? If firms produce technology, what stops other firms from replicating these ideas, a classic positive externality which would lead the rate of technology in a free market to be too low? And who can see the low level of convergence of poor country incomes to rich, and not think: there must be some barrier to the spread of A(t) around the world, since otherwise the return to capital must be extraordinary in places with access to great technology, really cheap labor, and little existing capital to combine with it. And another question: if technology – productivity itself! – is endogenous, then ought we consider not just the positive externality that spills over to other firms, but also the negative externality of pollution, especially climate change, that new technologies both induce and help fix? Finally, if we know how to incentivize new technology, and how growth harms the environment, what is the best way to mitigate the great environmental problem of our day, climate change, without stopping the wondrous increase in living standards growth keeps providing? It is precisely for helping answer these questions that Romer and Nordhaus won the Nobel.

Romer and Endogenous Growth

Let us start with Paul Romer. You know you have knocked your Ph.D. thesis out of the park when the great economics journalist David Warsh writes an entire book hailing your work as solving the oldest puzzle in economics. The two early Romer papers, published in 1986 and 1990, have each been cited more than 25,000 times, which is an absolutely extraordinary number by the standards of economics.

Romer’s achievement was writing a model where inventors spend money to produce inventions with increasing returns to scale, other firms use those inventions to produce goods, and a competitive Arrow-Debreu equilibrium still exists. If we had such a model, we could investigate what policies a government might wish to pursue if it wanted to induce firms to produce growth-enhancing inventions.

Let’s be more specific. First, innovation is increasing returns to scale because ideas are nonrival. If I double the amount of labor and capital, holding technology fixed, I double output, but if I double technology, labor, and capital, I more than double output. That is, give one person a hammer, and they can build, say, one staircase a day. Give two people two hammers, and they can build two staircases by just performing exactly the same tasks. But give two people two hammers, and teach them a more efficient way to combine nail and wood, and they will be able to build more than two staircases. Second, if capital and labor are constant returns to scale and are paid their marginal product in a competitive equilibrium, then there is no output left to pay inventors anything for their ideas. That is, it is not tough to model in partial equilibrium the idea of nonrival ideas, and indeed the realization that a single invention improves productivity for all is also an old one: as Thomas Jefferson wrote in 1813, “[h]e who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me.” The difficulty is figuring out how to get these positive spillovers yet still have “prices” or some sort of rent for the invention. Otherwise, why would anyone pursue costly invention?

We also need to ensure that growth is not too fast. There is a stock of existing technology in the world. I use that technology to create new innovations which grow the economy. With more people over time and more innovations over time, you may expect the growth rate to be higher in bigger and more technologically advanced societies. It is in part, as Michael Kremer points out in his One Million B.C. paper. Nonetheless, the rate of growth is not asymptotically increasing by any stretch (see, e.g., Ben Jones on this point). Indeed, growth is nearly constant, abstracting from the business cycle, in the United States, despite a big growth in population and the stock of existing technology.

Romer’s first attempt at endogenous growth was based on his thesis and published in the JPE in 1986. Here, he adds “learning by doing” to Solow: technology is a function of the capital stock A(t)=bK(t). As each firm uses capital, they generate learning which spills over to other firms. Even if population is constant, with appropriate assumptions on production functions and capital depreciation, capital, output, and technology grow over time. There is a problem here, however, and one that is common to any model based on learning-by-doing which partially spills over to other firms. As Dasgupta and Stiglitz point out, if there is learning-by-doing which only partially spills over, the industry is a natural monopoly. And even if it starts competitively, as I learn more than you, dynamically I can produce more efficiently, lower my prices, and take market share from you. A decentralized competitive equilibrium with endogenous technological growth is unsustainable!

Back to the drawing board, then. We want firms to intentionally produce technology in a competitive market as they would other goods. We want technology to be nonrival. And we want technology production to lead to growth. Learning-by-doing allows technology to spill over, but would simply lead to a monopoly producer. Pure constant-returns-to-scale competitive production, where technology is just an input like capital produced with a “nonconvexity” – only the initial inventor pays the fixed cost of invention – means that there is no output left to pay for invention once other factors get their marginal product. A natural idea, well known to Arrow 1962 and others, emerges: we need some source of market power for inventors.

Romer’s insight is that inventions are nonrival, yes, but they are also partially excludable, via secrecy, patents, or other means. In his blockbuster 1990 JPE Endogenous Technological Change, he lets inventions be given an infinite patent, but also be partially substitutable by other inventions, constraining price (this is just a Spence-style monopolistic competition model). The more inventions there are, the more efficiently final goods can be made. Future researchers can use present technology as an input to their invention for free. Invention is thus partially excludable in the sense that my exact invention is “protected” from competition, but also spills over to other researchers by making it easier for them to invent other things. Inventions are therefore neither public nor private goods, and also not “club goods” (nonrival but excludable) since inventors cannot exclude future inventors from using their good idea to motivate more invention. Since there is free entry into invention, the infinite stream of monopoly rents from inventions is exactly equal to their opportunity cost.

From the perspective of final goods producers, there are just technologies I can license as inputs, which I then use in a constant returns to scale way to produce goods, as in Solow. Every factor is paid its marginal product, but inventions are sold for more than their marginal cost due to monopolistic excludability from secrecy or patents. The model is general equilibrium, and gives a ton of insight about policy: for instance, if you subsidize capital goods, do you get more or less growth? In Romer (1986), where all growth is learning-by-doing, cheaper capital means more learning means more growth. In Romer (1990), capital subsidies can be counterproductive!

There are some issues to be worked out: the Romer models still have “scale effects” where growth is not constant, roughly true in the modern world, despite changes in population and the stock of technology (see Chad Jones’ 1995 and 1999 papers). The neo-Schumpeterian models of Aghion-Howitt and Grossman-Helpman add the important idea that new inventions don’t just add to the stock of knowledge, but also make old inventions less valuable. And really critically, the idea that institutions and not just economic fundamentals affect growth – meaning laws, culture, and so on – is a massive field of research at present. But it was Romer who first cracked the nut of how to model invention in general equilibrium, and I am unaware of any later model which solves this problem in a more satisfying way.

Nordhaus and the Economic Solution to Pollution

So we have, with Romer, a general equilibrium model for thinking about why people produce new technology. The connection with Nordhaus comes in a problem that is both caused by, and potentially solved by, growth. In 2018, even an ignoramus knows the terms “climate change” and “global warming”. This was not at all the case when William Nordhaus began thinking about how the economy and the environment interrelate in the early 1970s.

Growth as a policy goal was fairly unobjectionable as a policy goal in 1960: indeed, a greater capability of making goods, and of making war, seemed a necessity for both the Free and Soviet worlds. But by the early 1970s, environmental concerns arose. The Club of Rome warned that we were going to run out of resources if we continued to use them so unsustainably: resources are of course finite, and there are therefore “limits to growth”. Beyond just running out of resources, growth could also be harmful because of negative externalities on the environment, particularly the newfangled idea of global warming an MIT report warned about in 1970.

Nordhaus treated those ideas both seriously and skeptically. In a 1974 AER P&P, he notes that technological progress or adequate factor substitution allow us to avoid “limits to growth”. To put it simply, whales are limited in supply, and hence whale oil is as well, yet we light many more rooms than we did in 1870 due to new technologies and substitutes for whale oil. Despite this skepticism, Nordhaus does show concern for the externalities of growth on global warming, giving a back-of-the-envelope calculation that along a projected Solow-type growth path, the amount of carbon in the atmosphere will reach a dangerous 487ppm by 2030, surprisingly close to our current estimates. In a contemporaneous essay with Tobin, and in a review of an environmentalist’s “system dynamics” predictions of future economic collapse, Nordhaus reaches a similar conclusion: substitutable factors mean that running out of resources is not a huge concern, but rather the exact opposite, that we will have access to and use too many polluting resources, should worry us. That is tremendous foresight for someone writing in 1974!

Before turning back to climate change, can we celebrate again the success of economics against the Club of Rome ridiculousness? There were widespread predictions, from very serious people, that growth would not just slow but reverse by the end of the 1980s due to “unsustainable” resource use. Instead, GDP per capita has nearly doubled since 1990, with the most critical change coming for the very poorest. There would have been no greater disaster for the twentieth century than had we attempted to slow the progress and diffusion of technology, in agriculture, manufacturing and services alike, in order to follow the nonsense economics being promulgated by prominent biologists and environmental scientists.

Now, being wrong once is no guarantee of being wrong again, and the environmentalists appear quite right about climate change. So it is again a feather in the cap of Nordhaus to both be skeptical of economic nonsense, and also sound the alarm about true environmental problems where economics has something to contribute. As Nordhaus writes, “to dismiss today’s ecological concerns out of hand would be reckless. Because boys have mistakenly cried “wolf’ in the past does not mean that the woods are safe.”

Just as we can refute Club of Rome worries with serious economics, so too can we study climate change. The economy affects the climate, and the climate effects the economy. What we need an integrated model to assess how economic activity, including growth, affects CO2 production and therefore climate change, allowing us to back out the appropriate Pigouvian carbon tax. This is precisely what Nordhaus did with his two celebrated “Integrated Assessment Models”, which built on his earlier simplified models (e.g., 1975’s Can We Control Carbon Dioxide?). These models have Solow-type endogenous savings, and make precise the tradeoffs of lower economic growth against lower climate change, as well as making clear the critical importance of the social discount rate and the micro-estimates of the cost of adjustment to climate change.

The latter goes well beyond the science of climate change holding the world constant: the Netherlands, in a climate sense, should be underwater, but they use dikes to restraint the ocean. Likewise, the cost of adjusting to an increase in temperature is something to be estimated empirically. Nordhaus takes climate change very seriously, but he is much less concerned about the need for immediate action than the famous Stern report, which takes fairly extreme positions about the discount rate (1000 generations in the future are weighed the same as us, in Stern) and the costs of adjustment.

Consider the following “optimal path” for carbon from Nordhaus’ most recent run of the model, where the blue line is his optimum.

Note that he permits much more carbon than Stern or a policy which mandates temperatures stay below a 2.5 C rise forever. The reason is the costs to growth in the short term are high: the world is still very poor in many places! There was a vitriolic debate following the Stern report about who was correct: whether the appropriate social discount rate is zero or something higher is a quasi-philosophical debate going back to Ramsey (1928). But you can see here how important the calibration is.

There are other minor points of disagreement between Nordhaus and Stern, and my sense is that there has been some, though not full, convergence if their beliefs about optimal policy. But there is no disagreement whatsoever between the economic and environmental community that the appropriate way to estimate the optimal response to climate change is via an explicit model incorporating some sort of endogeneity of economic reaction to climate policy. The power of the model is that we can be extremely clear about what points of disagreement remain, and we can examine the sensitivity of optimal policy to factors like climate “tipping points”.

There is one other issue: in Nordhaus’ IAMs, and in Stern, you limit climate change by imposing cap and trade or carbon taxes. But carbon harms cross borders. How do you stop free riding? Nordhaus, in a 2015 AER, shows theoretically that there is no way to generate optimal climate abatement without sanctions for non-participants, but that relatively small trade penalties work quite well. This is precisely what Emmanuel Macron is currently proposing!

Let’s wrap up by linking Nordhaus even more tightly back to Romer. It should be noted that Nordhaus was very interested in the idea of pure endogenous growth, as distinct from any environmental concerns, from the very start of his career. His thesis was on the topic (leading to a proto-endogenous growth paper in the AER P&P in 1969), and he wrote a skeptical piece in the QJE in 1973 about the then-leading theories of what factors induce certain types of innovation (objections which I think have been fixed by Acemoglu 2002). Like Romer, Nordhaus has long worried that inventors do not receive enough of the return to their invention, and that we measure innovation poorly – see his classic NBER chapter on inventions in lighting, and his attempt to estimate how much of how much of society’s output goes to innovators.

The connection between the very frontier of endogenous growth models, and environmental IAMs, has not gone unnoticed by other scholars. Nordhaus IAMs tend to have limited incorporation of endogenous innovation in dirty or clean sectors. But a fantastic paper by Acemoglu, Aghion, Bursztyn, and Hemous combines endogenous technical change with Nordhaus-type climate modeling to suggest a middle ground between Stern and Nordhaus: use subsidies to get green energy close to the technological frontier, then use taxes once their distortion is relatively limited because a good green substitute exists. Indeed, since this paper first started floating around 8 or so years ago, massive subsidies to green energy sources like solar by many countries have indeed made the “cost” of stopping climate change much lower than if we’d relied solely on taxes, since now production of very low cost solar, and mass market electric cars, is in fact economically viable.

It may indeed be possible to solve climate change – what Stern called “the greatest market failure” man has ever seen – by changing the incentives for green innovation, rather than just by making economic growth more expensive by taxing carbon. Going beyond just solving the problem of climate change, to solving it in a way that minimizes economic harm, is a hell of an accomplishment, and more than worthy of the Nobel prizes Romer and Nordhaus won for showing us this path!

Some Further Reading

In my PhD class on innovation, the handout I give on the very first day introduces Romer’s work and why non-mathematical models of endogenous innovation mislead. Paul Romer himself has a nice essay on climate optimism, and the extent to which endogenous invention matters for how we stop global warming. On why anyone signs climate change abatement agreements, instead of just free riding, see the clever incomplete contracts insight of Battaglini and Harstad. Romer has also been greatly interested in the policy of “high-growth” places, pushing the idea of Charter Cities. Charter Cities involve Hong Kong like exclaves of a developing country where the institutions and legal systems are farmed out to a more stable nation. Totally reasonable, but in fact quite controversial: a charter city proposal in Madagascar led to a coup, and I can easily imagine that the Charter City controversy delayed Romer’s well-deserved Nobel laurel. The New York Times points out that Nordhaus’ brother helped write the Clean Air Act of 1970. Finally, as is always true with the Nobel, the official scientific summary is lucid and deep in its exploration of the two winners’ work.

“Is Knowledge Trapped Inside the Ivory Tower?,” M. Bikard (2013)

Simultaneous discovery, as famously discussed by Merton, is really a fascinating idea. On the one hand, we have famous examples like Bell and Gray sending in patents for a telephone on exactly the same day. On the other hand, when you investigate supposed examples of simultaneous discovery more closely, it is rarely the case that the discoveries are that similar. The legendary Jacob Schmookler described – in a less-than-politically-correct way! – historians who see patterns of simultaneous discovery everywhere as similar to tourists who think “all Chinamen look alike.” There is sufficient sociological evidence today that Schmookler largely seems correct: simultaneous discovery, like “lucky” inventions, are much less common than the man on the street believes (see, e.g., Simon Schaeffer’s article on the famous story of the dragon dream and the invention of benzene for a typical reconstruction of how “lucky” inventions actually happen).

Michaël Bikard thinks we are giving simultaneous discovery too little credit as a tool for investigating important topics in the economics of innovation. Even if simultaneous discovery is uncommon, it still exists. If there were an automated process to generate a large set of simultaneous inventions (on relatively more minor topics than the telephone), there are tons of interesting questions we can answer, since we would have compelling evidence of the same piece of knowledge existing in different places at the same time. For instance, how important are agglomeration economies? Does a biotech invention get developed further if it is invented on Route 128 in Massachusetts instead of in Lithuania?

Bikard has developed an automated process to do this (and that linked paper also provides a nice literature review concerning simultaneous discovery). Just scrape huge number of articles and their citations, look for pairs of papers which were published at almost the same time and cited frequently in the future, and then limit further to articles which have a “Jaccard index” which implies that they are frequently cited together if they are cited at all. Applying this technique to the life sciences, he finds 578 examples of simultaneous discovery; chatting with a randomly selected sample of the researchers, most mentioned the simultaneous discovery without being asked, though at least one claimed his idea had been stolen! 578 is a ton: this is more than double the number that the historical analysis in Merton discovered, and as noted, many of the Merton multiples are not really examples of simultaneous discovery at all.

He then applies this dataset in a second paper, asking whether inventions in academia are used more often (because of the culture of openness) or whether private sector inventions are used more often in follow-up inventions (because the control rights can help even follow-up inventors extract rents). It turns out that private-sector inventors of the identical invention are three times more likely to patent, but even excluding the inventors themselves, the private sector inventions are cited 10-20% more frequently in future patents. The sample size of simultaneous academic-private discovery is small, so this evidence is only suggestive. You might imagine that the private sector inventors are more likely to be colocated near other private sector firms in the same area; we think that noncodified aspects of knowledge flow locally, so it wouldn’t be surprising that the private sector multiple was cited more often in future patents.

Heavy caveats are also needed on the sample. This result certainly doesn’t suggest that, overall, private sector workers are doing more “useful” work than Ivory Tower researchers, since restricting the sample to multiple discoveries limits the potential observations to areas where academia and the private sector are working on the same type of discovery. Certainly, academics and the private sector often work on different types of research, and openness is probably more important in more basic discoveries (where transaction or bargaining costs on follow-up uses are more distortionary). In any case, the method for identifying simultaneous discoveries is quite interesting indeed; if you are empirically minded, there are tons of interesting questions you could investigate with such a dataset.

September 2012 working paper (No IDEAS version). Forthcoming in Management Science.

“Diffusing New Technology Without Dissipating Rents: Some Historical Case Studies of Knowledge Sharing,” J. Bessen & A. Nuvolari (2012)

The most fundamental fact in the economic history of the world is that, from the dawn on mankind until the middle of the 19th century in a small corner of Europe, the material living standards of the average human varied within a very small range: perhaps the wealthiest places, ever, were five times richer than regions on the edge of subsistence. The end of this Malthusian world is generally credited to changes following the Industrial Revolution. The Industrial Revolution is sometimes credited to changes in the nature of invention in England and Holland in the 1700s. If you believe those claims, then understanding what spurred invention from that point to the present is of singular importance.

A traditional story, going back to North and others, is that property rights were very important here. England had patents. England had well-enforced contracts for labor and capital. But, at least as far as patents are concerned, recent evidence suggests they couldn’t have been too critical. Moser showed that only 10% or so of important inventions in the mid-1800s were ever patented in the UK. Bob Allen, who we’ve met before on this site, has inspired a large literature on collective invention, or periods of open-source style sharing of information among industry leaders during critical phases of tinkering with new techniques.

Why would you share, though? Doesn’t this simply dissipate your rents? If you publicize knowledge of a productive process for which you are earning some rent, imitators can just come in and replicate that technology, competing away your profit. And yet, and yet, this doesn’t appear to happen in many historical circumstances. Bessen (he of Bessen and Maskin 2009, one of my favorite recent theoretical papers on innovation) and Nuvolari examine three nineteenth century industries, American steel, Cornish steam engines and New England power weavers. They show that periods of open sharing on invention, free transfer of technology to rivals, industry newsletters detailing new techniques, etc. can predominate for periods a decade and longer. In all three cases, patents are unimportant in this initial stage, though (at least outside of Cornwall) quite frequently used later in the development of the industry. Further, many of the important cost reducing microinventions in these industries came precisely during the period of collective invention.

The paper has no model, but very simply, here is what is going on. Consider a fast growing industry where some factors important for entry are in fixed supply; for example, the engineer Alexander Holley personally helped design eight of the first nine American mills using Bessemer’s technology. Assume all inventions are cost reducing. Holding sales price and demand constant, cost reductions increase industry profit. Sharing your invention ensures that you will not be frozen out of sharing by others. Trying to rely only on your own inventions to gain a cost advantage is not as useful as in standard Bertrand, since the fixed factors for entry in a new industry mean you can’t expand fast enough to meet market demand even if you had the cost advantage. There is little worry about free riding since the inventions are natural by-products of day-to-day problem solving rather than the result of concentrated effort: early product improvement is often an engineering problem, not a scientific one. Why would I assume sales price is roughly constant? Imagine an industry where the new technology is replacing something already being produced by a competitive industry (link steel rail ties replaced iron ties). The early Bessemer-produced ties in America were exactly this story, initially being a tiny fraction of the rail tie market, so the market price for ties was being determined by the older vintage of technology.

Open source invention is nothing unusual, nor is it something new. It has long coexisted with the type of invention for which patents may (only may!) be more suitable vectors for development. Policies that gunk up these periods of collective invention can be really damaging. I will discuss some new research in coming weeks about a common policy that appears to provide exactly this sort of gunk: the strict enforcement of non-compete agreements in certain states.

2012 Working Paper (IDEAS version)