R&D decisions are not made in a vacuum: my firm both benefits from information about new technologies discovered by others, and is harmed when other firms create new products that steal from my firm’s existing product lines. Almost every workhorse model in innovation is concerned with these effects, but measuring them empirically, and understanding how they interact, is difficult. Bloom, Schankerman and van Reenen have a new paper with a simple but clever idea for understanding these two effects (and it will be no surprise to readers given how often I discuss their work that I think these three are doing some of the world’s best applied micro work these days).
First, note that firms may be in the same technology area but not in the same product area; Intel and Motorola work on similar technologies, but compete on very few products. In a simple model, firms first choose R&D, knowledge is produced, and then firms compete on the product market. The qualitative results of this model are as you might expect: firms in a technology space with many other firms will be more productive due to spillovers, and may or may not actually perform more R&D depending on the nature of diminishing returns in the knowledge production function. Product market rivalry is always bad for profits, does not affect productivity, and increases R&D only if research across firms is a strategic complement; this strategic complementarity could be something like a patent race model, where if firms I compete with are working hard trying to invent the Next Big Thing, then I am incentivized to do even more R&D so I can invent first.
On the empirical side, we need a measure of “product market similarity” and “technological similarity”. Let there be M product classes and N patent classes, and construct vectors for each firm of their share of sales across product classes and share of R&D across patent classes. There are many measures of the similarity of a vector, of course, including a well-known measure in innovation from Jaffe. Bloom et al, after my heart, note that we really ought use measures that have proper axiomatic microfoundations; though they do show the properties of a variety of measures of similarity, they don’t actually show the existence (or impossibility) of their optimal measure of similarity. This sounds like a quick job for a good microtheorist.
With similarity measures, all that’s left to do is run regressions of technological and product market similarity, as well as all sorts of fixed effects, on outcomes like R&D performed, productivity (measured using patents or out of a Cobb-Douglas equation) and market value (via the Griliches-style Tobin’s Q). These guys know their econometrics, so I’m omitting many details here, but I should mention that they do use the idea from Wilson’s 2009 ReSTAT of basically random changes in state R&D tax laws as an IV for the cost of R&D; this is a great technique, and very well implemented by Wilson, but getting these state-level R&D costs is really challenging and I can easily imagine a future where the idea is abused by naive implementation.
The results are actually pretty interesting. Qualitatively, the empirical results look quite like the theory, and in particular, the impact of technological similarity looks really important; having lots of firms working on similar technologies but working in different industries is really good for your firm’s productivity and profits. Looking at a handful of high-tech sectors, Bloom et al estimate that the marginal social return on R&D is on the order of 40 percentage points higher than the marginal private return of R&D, implying (with some huge caveats) that R&D in the United States might be something like 3 times smaller than it ought to be. This estimate is actually quite similar to what researchers using other methods have estimated. Interestingly, since bigger firms tend to work in more dense parts of the technology space, they tend to generate more spillovers, hence the common policy prescription of giving smaller firms higher R&D tax credits may be a mistake.
Two caveats. As far as I can tell, the model does not allow a role for absorptive capacity, where firm’s ability to integrate outside knowledge is endogenous to their existing R&D stock. Second, the estimated marginal private rate of return on R&D is something like 20 percent for the average firm; many other papers have estimated very high private benefits from research, but I have a hard time interpreting these estimates. If there really are 20% rates of return lying around, why aren’t firms cranking up their research? At least anecdotally, you hear complaints from industries like pharma about low returns from R&D. Third, there are some suggestive comments near the end about how government subsidies might be used to increase R&D given these huge social returns. I would be really cautious here, since there is quite a bit of evidence that government-sponsored R&D generates a much lower private and social rate of return that the other forms of R&D.
Final July 2013 Econometrica version (IDEAS version). Thumbs up to Nick Bloom for making the final version freely available on his website. The paper has an exhaustive appendix with technical details, as well as all of the data freely available for you to play with.