Identifying Technology Spillovers and Product Market Rivalry


  • Nicholas Bloom,

    1. Dept. of Economics, Stanford University, 450 Serra Mall, Stanford, CA 94305, U.S.A., CEPR, and NBER;
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  • Mark Schankerman,

    1. Dept. of Economics, London School of Economics, Houghton Street, London, WC2A 2AE, U.K., and CEPR;
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  • John Van Reenen

    1. Centre for Economic Performance, London School of Economics, Houghton Street, London, WC2A 2AE, U.K., CEPR, NBER, and IZA;
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    • This is a heavily revised version of Bloom, Schankerman, and Van Reenen (2007). We would like to thank Jean-Marc Robin, three anonymous referees, Philippe Aghion, Lanier Benkard, Bronwyn Hall, Elhanan Helpman, Adam Jaffe, Dani Rodrik, Scott Stern, Peter Thompson, Joel Waldfogel, and seminar participants in the AEA, Barcelona, Berkeley, CEPR, Columbia, Harvard, Hebrew University, INSEE, LSE, Michigan, NBER, Northwestern, NYU, San Diego, San Franscico Fed, Stanford, Tel Aviv, Toronto, and Yale for helpful comments. Finance was provided by the ESRC at the Centre for Economic Performance and the NSF.


The impact of R&D on growth through spillovers has been a major topic of economic research over the last thirty years. A central problem in the literature is that firm performance is affected by two countervailing “spillovers” : a positive effect from technology (knowledge) spillovers and a negative business stealing effects from product market rivals. We develop a general framework incorporating these two types of spillovers and implement this model using measures of a firm's position in technology space and productmarket space. Using panel data on U.S. firms, we show that technology spillovers quantitatively dominate, so that the gross social returns to R&D are at least twice as high as the private returns. We identify the causal effect of R&D spillovers by using changes in federal and state tax incentives for R&D. We also find that smaller firms generate lower social returns to R&D because they operate more in technological niches. Finally, we detail the desirable properties of an ideal spillover measure and how existing approaches, including our new Mahalanobis measure, compare to these criteria.