Too Much of a Good Thing? The Economics of Investment in R&D
Charles I. Jones
John C. Williams
February 26, 1996, Version 5.00 Empirical research in the micro productivity literature consistently supports the notion that there is too little R&D. However, the methodology of this literature, based on the neoclassical growth model, is challenged by new growth theory, which emphasizes a richer description of the relationship between R&D and productivity. In particular, it allows for incentives that lead to overinvestment in R&D. We incorporate several distortions to R&D into a general equilibrium growth model that provides a framework for the analytical and empirical analysis of the degree of over- or underinvestment in R&D. We derive the relationship between the social rate of return to R&D and the parameters estimated in the productivity literature. Surprisingly, our results indicate that estimates in the productivity literature represent lower bounds on the social rate of return to R&D and that the bias is limited to the overall growth rate of the economy. Additional supporting evidence for underinvestment is provided by the implied equilibrium R&D share from a calibrated version of the theoretical model.
July 6, 1999 Research and development (R&D) is a key determinant of long run productivity and welfare. A central issue is whether a decentralized economy undertakes too little or too much R&D. We develop an endogenous growth model that incorporates parametrically four important distortions to R&D: the surplus appropriability problem, knowledge spillovers, creative destruction, and congestion externalities. We show that our model is consistent with the available evidence on R&D, growth, and markups. Calibrating the model to micro and macro data, we find that the decentralized economy typically underinvests in R&D relative to what is socially optimal. The only exceptions to this conclusion occur when both the congestion externality is extremely strong and the equilibrium real interest rate is very high. These results are robust to reasonable variations in model parameters.
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