How Important Are Spillovers?

They're more than half the point

Hey everyone; thanks for reading this newsletter. My original goal for this project was for it to be a weekly delivered on Thursdays. But it turns out time is scarce when you have two kids under five at home due to the global pandemic. So delivery will be ad-hoc for awhile.

Like the rest of New Things Under the Sun, this article will be updated as the state of the academic literature evolves; you can read the latest version here.

Knowledge spillovers, in the economics of innovation literature, are when a firm makes use of knowledge discovered by other firms. 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 drew 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 don’t. But if you pick one of the six fields below at random, one of our five indicators at random, and a specific knowledge flow proxy (citation or concept) 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? Azoulay, Graff Zivin, Li, and Sampat (2015) 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 again; this is another specific area. 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. Unlike the previous two papers, it 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!