Cumulative Innovation and Dynamic R&D Spillovers
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Cumulative Innovation and Dynamic R&D Spillovers David Colino ∗ MIT December 5, 2016 JOB MARKET PAPER (Click here for the most recent version) http://economics.mit.edu/grad/dcolino Abstract While much theoretical attention has focused on the important role of dynamic knowledge spillovers for economic growth, such spillovers have been difficult to empirically measure. Using a panel of US firms and a network of corporate patent citations, this paper estimates the dynamic spillovers of corporate R&D on firm productivity, value, and innovation activity. Causal effects are estimated with an instrumental variables strategy that exploits the persistence of the network as well as variation in tax incentives. The positive effect of dynamic spillovers on firm productivity is economically important, and at least as large as that of own R&D investments. Dynamic spillovers accrue mainly for so-called "complex" technologies that build cumulatively on multiple components, they exhibit little depreciation over time, are larger for established firms than for VC-backed startups, and do not decrease in magnitude with geographic distance. Around 40% of dynamic spillovers are re-absorbed by the original innovator, and {accounting for other spillovers{ these estimates suggest that the social returns to R&D are about three times as large as the private returns. ∗Department of Economics, Massachusetts Institute of Technology. Email: [email protected]. I am very grateful to Glenn Ellison and Heidi Williams for their guidance and advice throughout this project. I also thank Daron Acemoglu, Pierre Azoulay, Alex Bartik, Jean-Noel Barrot, Enrico Cantoni, Nicolas Caramp, Caroline Fry, Josh Krieger, Elena Manresa, Brendan Price, Pascual Restrepo, Antoinette Schoar, Scott Stern, John Van Reenen, and participants in the MIT IO, Finance and Applied Micro lunches for their helpful comments and suggestions. All remaining errors are my own. 1 Introduction Innovation and technological progress are at the core of long-term economic growth. The endogenous growth literature attributes steady-state growth to the knowledge spillovers resulting from innovative activity.1 As a result of their central importance, a substantial literature has attempted to estimate knowledge spillovers accruing between firms.2 These measurements have traditionally focused on contemporaneous spillovers resulting among others from complementarities in the research and de- velopment (R&D) efforts of different firms.3 However, innovation is often cumulative in nature and the endogenous growth literature in particular depicts knowledge spillovers as intertemporal or dy- namic, accruing when past ideas become the new foundation on which to build further innovation. We might thus expect a large share of knowledge spillovers to occur dynamically through the impact of a given technology on subsequent future innovation. By not fully accounting for this dynamic dimension, existing measurements of R&D knowledge spillovers may underestimate the wedge be- tween the social and private rates of return to R&D. The contribution of this paper is to provide more robust estimates of the gap between social and private returns to R&D by flexibly estimating dynamic knowledge spillovers of US corporate R&D. Since Jaffe(1986)'s seminal paper, the R&D spillover literature has assumed a rigid inter-temporal structure of knowledge spillovers in which both the private value of knowledge and its spillovers are assumed to depreciate at the same constant rate, which marks the progressive obsolescence of past knowledge as it is replaced by new innovations.4 In other words, R&D expenses build a knowledge stock that depreciates over time, in the same way that capital investments contribute to a capital stock, and this knowledge stock is responsible for both private value creation and contemporaneous knowledge spillovers on others. However, as pointed out by Griliches(1979) the social depreciation rate of innovation is likely to be lower than the private one, and depreciation rates may not be the same across innovative streams, as a result of cumulative processes in which specific past knowledge is used as a foundation upon which to build future innovation. Knowledge spillovers are thus likely to entail a complex dynamic or inter-temporal structure, and not accounting for dynamism is likely to lead to underestimating spillovers. Moreover, this bias can be especially acute for technologies that rely more on long and cumulative innovation processes. In what follows, and in order to make a sharp distinction between the traditional knowledge spillover measures and the more flexible spillovers I introduce, I will denote the former as static knowledge spillovers and the latter as dynamic knowledge spillovers.5 1See e.g. Romer(1990), Aghion and Howitt(1992), and Jones(1995). 2See Griliches(1979), Jaffe(1986), Bloom et al.(2013), and Manresa(2016). 3For example, Bloom et al.(2013) model knowledge spillovers as occurring through encounters between scientists or engineers of different firms. These encounters could be in person through meeting at conferences or local coffee shops, or they could be virtual through online exchanges of ideas. 4For more literature using this same obsolescence structure, see Bloom et al.(2013), Manresa(2016), and Schnitzer and Watzinger(2015). 5Although static spillovers of R&D expenditures still entail some dynamic component through building a longer- lived firm-specific knowledge capital, this slight abuse of denomination is appropriate if one considers that knowledge 1 In this paper, I estimate dynamic knowledge spillovers by constructing measures of cumulative knowledge proximity between US publicly-listed firms. My empirical estimates suggest that the R&D (carried out in the past and largely by other firms) upon which a given firm builds increases its current productivity by at least as much as current own R&D. I find that dynamic spillovers complement the traditional static knowledge and business stealing spillovers, as they were not picked up by these measures.6 However, the share of spillovers that are dynamic versus static is largely heterogeneous across technologies, with dynamic spillovers being more prevalent among industries and technologies characterized by their complexity, that is by commercializable products or processes being comprised of numerous separately patentable elements.7 Dynamic spillovers persistently accrue over a long period of time, are stronger when originating in R&D carried out by established firms rather than VC-backed startups, and do not decrease in magnitude with geographic distance, conditional on citation flows being observed. Including all the spillover measures results in estimates of the social returns to corporate R&D being about three times as large as the private ones. The following example illustrates my empirical estimation of R&D spillovers between organiza- tions. Motorola, IBM, and Apple are R&D-intensive companies that in the 1980s and 1990s are close in technological space (as revealed by both their patenting in similar technological areas and the large cross-citation patterns between them). However, only IBM and Apple compete in the PC market, with little product market competition between the other two pairs. Knowledge spillovers will therefore accrue between all pairwise relationships, but business stealing spillovers will only occur between IBM and Apple, so we can estimate both types of spillovers by comparing the dif- ferent pairwise combinations. Moreover, traditional knowledge spillover measures assume that, for example, in 1996 Motorola and Apple are affected by IBM's 1996 stock of R&D, which includes past R&D spending depreciated at a constant rate.8 However, in 1996 Motorola was investing heavily in research on copper interconnect technology within the semiconductor chip industry and learn- ing from IBM's previously developed technology.9 In particular, Motorola developed and filed two patents that year building upon a 1985 IBM patent that proved to be a foundational innovation for copper interconnect.10 It is therefore hard to believe that knowledge IBM developed in 1985, upon which Motorola built in 1996, should be discounted by 83% in compounded terms when looking for knowledge spillovers between the two firms. My dynamic spillover measure takes this into account, spillovers stem from the knowledge capital itself rather than the R&D spending flow. 6Static knowledge spillovers have been extensively studied by papers such as Griliches(1979), Griliches(1992), and Jaffe(1986). More recently, Bloom et al.(2013) separately measure both types of static spillovers and document that the knowledge spillovers dominate the business stealing effects, resulting in a social rate of return to R&D up to three times as large as the private returns. 7These technologies inherently involve cumulative innovation processes. See Levin et al.(1987), Cohen et al.(2000), Hall et al.(2005), and Galasso and Schankerman(2015). 8The most commonly used rate of depreciation in the literature is 15% annually. See Griliches(1979), Jaffe(1986), Hall et al.(2005), and Bloom et al.(2013). 9See Lim(2009) for more information about the copper interconnect technology, IBM's technological advantage, and its competitors' strategies on absorptive capacity. 10Patent 4789648: Method for producing coplanar multilevel metalinsulator films on a substrate and for forming patterned conductive lines simultaneously with stud vias. 2 and using patent citation patterns, selects IBM's 1985 knowledge stock as a candidate for knowledge