Knowledge spillovers, in the economics of innovation literature, are when an inventor or scientist makes use of knowledge discovered by others. The existence of knowledge spillovers is a classic reason why there may be underinvestment in R&D. When a firm decides how much R&D to do, it weighs the costs it bears against the benefits it expects to receive - not the benefits all firms expect to receive.
Of course, just because something could happen in theory doesn’t mean it happens in practice. So how big a deal are spillovers anyway? A couple studies using data on patents suggests spillovers are really important. More knowledge spills over than stays put.
Clancy (me), Heisey, Moschini, and Ji (2019) have a forthcoming paper that looks at this question for the specific case of agriculture. We wanted to see how much agricultural innovation draws on ideas developed outside of agriculture. So we identified all US patents for a variety of agricultural subsectors over 1976-2018 and tried to measure the share of “knowledge flows” from outside agriculture to inside it. We used a few different approaches, each imperfect in different ways, but hopefully adding up to something convincing.
First, we looked at the citations patents make to other patents. In most cases, more than half of the citations go to patents we don’t classify as belonging to agriculture, or to patents belonging to firms whose patent portfolio is mostly non-agricultural.
Second, we looked at the citations patents make to academic journal articles. Again, most of those citations don’t go to journals we classify as agricultural science journals.
Third, we look for “novel concepts” in the text of the abstracts and titles of agricultural patents. For our purposes, a “novel concept” is a string of one-to-three word text that is common after 1996, but absent from agricultural patents before then. (We also manually go through all these concepts to verify they correspond to technological ideas). We then look to see how many of these novel concepts are already out there, in non-agricultural patents. It turns out, most of them are.
As the figure above makes clear, there is some significant variation, even within agriculture: newly patented plants really do mostly rely mostly on agricultural R&D. Veterinary drugs really don’t. But if you pick one of the six fields below at random and one of our five indicators at random, the chance it corresponds to a flow from out of agriculture to inside it is about 65%. That suggests spillovers are the main source of ideas in agriculture!
What about outside of agriculture? Myers and Lanahan (2021) use a quasi-experimental methodology to provide some evidence from the Department of Energy’s Small Business Innovation Research (SBIR) program. Every year, the Department of Energy’s SBIR program solicits proposals related to specific kinds of technology. Small businesses submit proposals for R&D projects related to these Department of Energy priorities, and the best ones get funded. These businesses go on to use the funding to do R&D.
But Myers and Lanahan show that the funding also leads to more innovation (or at least more patents) in technology fields other than the one funded. For data, Myers and Lanahan use the cooperative patent classification system, which divides patents up into thousands of different technology categories, ranging from bridges to artificial intelligence. Each one of the SBIR program’s grant competitions is focused on a different technology, but the SBIR’s interests do not cleanly line up with the patent office’s classification system. So Myers and Lanahan use natural language processing to compute the degree of similarity between the text of patents in each technology category with the text of the SBIR’s solicitation for proposals (a patent and the SBIR program will be counted as more similar if they share more words that are usually not common). That means at the end of the day, for every SBIR request for proposals related to a certain technology, Myers and Lanahan can order each of the thousands of cooperative patent classifications from most to least similar to the SBIR request.
The last piece of the puzzle is Myers and Lanahan’s measure of how much funding is given out for R&D in different technology areas. To get as close to possible to a pure experiment, where money is just randomly given to some technologies and not others, Myers and Lanahan painstakingly construct a set of data on money that comes not directly from the SBIR program, but from state-level matching programs. As Myers and Lanahan argue, the basic idea is that money from these state-level matches (where, say, Iowa agrees to top up the R&D funds won by an Iowa company with additional money from state revenues) is pretty randomly distributed among different kinds of technology. It’s mostly down to luck that, say, winning solar technology companies happen to reside in states with these match programs and, say, wind turbine technology companies do not.
Comparing the patent output of technology classes that are textually “close” to classes that get money to the output of classes that aren’t, they can measure how many patents are generated for every million dollars in funding, both in the technology classes closest to the SBIR program’s intention, and those farther away. The figure below shows how many extra patents come from different patent classes, with classes on the left closer to the SBIR proposal and classes to the right farther away. Adding everything up, they find that for every patent directly induced by R&D funding to a specific firm working on a specific technology, another 3 patents are created by other firms working in other technologies. Spillovers account for the majority of the benefits from the Department of Energy’s SBIR program!
Azoulay, Graff Zivin, Li, and Sampat (2019) also find a big role for knowledge spillovers in medicine. They identify 315,982 life sciences patents granted between 1980 and 2012, plus the citations those patents make to scientific journal articles in PubMed, plus the grant acknowledgements those journal articles make to NIH funded grants. In this way, they can trace out the link between an NIH grant for basic scientific research and a patent.
One nice thing about NIH grants is that they’re assigned to different disease areas. So one thing the authors do in this paper is look to see if grants contribute to patents that belong to different disease areas than what was funded: how often does a grant for cancer research contribute to a patent for diabetes medication (for example)?
To do that, they have to assign patents to different disease areas. Their approach is to use the disease area associated with the plurality of cited publications; i.e., if 35% of cited papers are associated with cancer grants, and no other disease has more than 35% of citations, then the patent is assigned to cancer. By this method, grants are slightly more likely to be cited by a patent from a different disease than they are from a patent associated with the grant’s disease. In short - spillovers in the basic science underlying different diseases are substantial.
But in all three of these cases, we looked at spillovers within a specific domain: agriculture, energy, and life sciences. Can we say anything systematic?
Bloom, Schankerman, and Van Reenen (2013) tries. This paper uses the set of all publicly traded US firms over 1980-2001 in an effort to assess how R&D by one firm affects others. Like Myers and Lanahan (2021), this paper comes in with beliefs about what kinds of firms are likely to have strong spillovers and then checks those beliefs by seeing if they are correlated with predicted outcomes. Essentially, they create measures for spillovers based on (1) the amount of R&D a firm does (more R&D leads to more potential spillovers to all firms) (2) adjusted for each firm by the degree of overlap in the kinds of patents they own (more R&D spills over to firms with similar technologies in their patent portfolios).
Then, with these measures of potential spillovers across different firms, they look to see if those measures are correlated with things in the expected ways: do more spillovers (by this measure) lead to more patents, productivity or profits, for example? It’s actually a lot more complicated than that (I took a shot at explaining how this paper works to non-specialists here). But suffice it to say they have nice estimates of how R&D by one firm affects every other firm in their sample. This lets them estimate the private return on R&D and the social return on R&D.
To see the difference, suppose Apple is deciding whether to spend another dollar on R&D. The increase in Apple’s profits due to that dollar are the private returns to R&D. The increase in Apple’s profits, and Google’s profits, and all other publicly traded firms, is the social return on R&D, as measured in this paper. If there’s a strong link between spillovers and profits, than the social return might be large. If there’s no link between their hypothesized measure of spillovers, than the social return might simply be the private return.
They find the private return on R&D is 21%; but the social return is 55%. Again - more than half the value of R&D comes from it’s impact on other firms!
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Clancy, Matthew, Paul Heisey, Yongjie Ji, and GianCarlo Moschini. 2019. The Roots of Agricultural Innovation: Patent Evidence of Knowledge Spillovers. NBER Working Paper 27011. https://www.nber.org/papers/w27011
Myers, Kyle, and Lauren Lanahan. 2021. Estimating spillovers from publicly funded R&D: Evidence from the US Department of Energy. Working paper.
Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. 2019. Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules. Review of Economic Studies 86(1): 117-152. https://doi.org/10.1093/restud/rdy034
Bloom, Nicholas, Mark Schankerman, and John Van Reenen. 2013. Identifying Technology Spillovers and Product Market Rivalry. Econometrica 81(4): 1347-1393. https://doi.org/10.3982/ECTA9466