We usually argue that competition - free entry, zero profits, price-taking behavior - is the most efficient outcome for an economy. But that holds given a set of technology. Generating innovations, and hence economic growth, relies on a breakdown of some of those characteristics of competition. Thinking about this really gets to the heart of what drives growth: non-rival ideas combined with some excludability.
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- 19 Jun 2015 Embedded Ideas and Economic Growth
- 13 Jun 2015 What Assumptions Matter for Growth Theory?
- 05 Jun 2015 Market Power versus Price-taking in Economic Growth
- 29 May 2015 More on Mathiness
- 21 May 2015 Mathiness versus Science in Growth Economics
- 28 Jul 2014 Patents, Bargaining, and Innovation
- Romer, P. M. (2015) “Economic Growth.” Available at: Link.
- Romer, P. M. (2015) “Where has all the excludability gone?” Available at: Link.
- Romer, P. M. (2015) “Speeding-up: Theory.” Available at: Link.
- Romer, P. M. (2015) “Nonrival Goods after 25 Years.” Available at: Link.
- Romer, P. M. (2015) “Human Capital and Knowledge.” Available at: Link.
- Romer, P. M. (2015) “Speeding-up and Missed Opportunities: Evidence.” Available at: Link.
- Schmitz, J. A. (2016) “The costs of monopoly.” Available at: Link.
- Aghion, P., Bloom, N., Blundell, R., Griffith, R. and Howitt, P. (2005) “Competition and Innovation: a Inverted-U Relationship,” Quarterly Journal of Economics, 120(2), pp. 701–728.
This paper investigates the relationship between product market competition and innovation. We find strong evidence of an inverted-U relationship using panel data. We develop a model where competition discourages laggard firms from innovating but encourages neck-and-neck firms to innovate. Together with the effect of competition on the equilibrium industry structure, these generate an inverted-U. Two additional predictions of the model-that the average technological distance between leaders and followers increases with competition, and that the inverted-U is steeper when industries are more neck-and-neck-are both supported by the data.
- Jones, C. I. (2002) “Sources of U.S. Economic Growth in a World of Ideas,” American Economic Review, 92(1), pp. 220–239.
Rising educational attainment and research intensity in recent decades suggest that the U.S. economy is far from its steady state. This paper develops a model reconciling these facts with the stability of U.S. growth rates. In the model, long-run growth arises from the worldwide discovery of ideas, which depends on population growth. Nevertheless, constant growth can temporarily proceed at a faster rate, provided research intensity and educational attainment rise steadily over time. Growth accounting reveals that these factors explain 80 percent of recent U.S. growth, with less than 20 percent coming from world population growth.
- Jones, C. I. (1995) “R&D-Based Models of Economics Growth,” Journal of Political Economy, 103, pp. 759–784.
This paper argues that the ’scale effects’ prediction of many recent R&D-based models of growth is inconsistent with the time-series evidence from industrialized economies. A modified version of the Romer model that is consistent with this evidence is proposed, but the extended model alters a key implication usually found in endogenous growth theory. Although growth in the extended model is generated endogenously through R&D, the long-run growth rate depends only on parameters that are usually taken to be exogenous, including the rate of population growth.
- Romer, P. M. (1990) “Endogenous Technological Change,” Journal of Political Economy. The University of Chicago Press, 98(5), pp. pp. S71–S102. Available at: Link.
Growth in this model is driven by technological change that arises from intentional investment decisions made by profit-maximizing agents. The distinguishing feature of the technology as an input is that it is neither a conventional good nor a public good; it is a nonrival, partially excludable good. Because of the nonconvexity introduced by a nonrival good, price-taking competition cannot be supported. Instead, the equilibrium is one with monopolistic competition. The main conclusions are that the stock of human capital determines the rate of growth, that too little human capital is devoted to research in equilibrium, that integration into world markets will increase growth rates, and that having a large population is not sufficient to generate growth.
- Romer, P. M. (1986) “Increasing Returns and Long-Run Growth,” Journal of Political Economy. The University of Chicago Press, 94(5), pp. pp. 1002–1037. Available at: Link.
This paper presents a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity. It is essentially a competitive equilibrium model with endogenous technological change. In contrast to models based on diminishing returns, growth rates can be increasing over time, the effects of small disturbances can be amplified by the actions of private agents, and large countries may always grow faster than small countries. Long-run evidence is offered in support of the empirical relevance of these possibilities.
- Schmitz, J. A., Jr. (2005) “What Determines Productivity? Lessons from the Dramatic Recovery of the U.S. and Canadian Iron Ore Industries Following Their Early 1980s Crisis,” Journal of Political Economy, 113(3), pp. 582–625. Available at: Link.
- Williams, H. L. (2017) How Do Patents Affect Research Investments? NBER Working Papers 23088. National Bureau of Economic Research, Inc. Available at: Link.
While patent systems have been widely used both historically and internationally, there is nonetheless a tremendous amount of controversy over whether patent systems – in practice – improve the alignment between private returns and social contributions. In this paper, I describe three parameters – how the disclosure function affects research investments, how patent strength affects research investments in new technologies, and how patents on existing technologies affect follow-on innovation – needed to inform the question of how patents affect research investments, and review the available evidence which has attempted to empirically estimate these parameters.