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Neoclassical Growth and the Adoption of Technologies

  • Diego Comin
  • Bart Hobijn

We introduce a growth model of technology diffusion and endogenous Total Factor Productivity (TFP) levels both at the sector and aggregate level. At the aggregate, the model behaves as the Neoclassical growth model. Our goal is for this model to bridge the gap between the theoretical and empirical studies of technology adoption and economic growth. We bridge this gap in three important directions. First of all, we use our model to show how one unified theoretical framework is broadly consistent with the observed dynamics of both economic growth as well as of many different measures of technology adoption, like adoption rates, capital to output ratios, and output ratios. Secondly, we estimate our model using a broad range of technological adoption measures, covering 17 technologies and 21 industrialized countries over the past 180 years. This allows us to show how its predicted adoption patterns fit those observed in the data. Finally, we estimate the disparities in sectoral productivity levels as well as aggregate TFP that can be attributed to the differences in the range of technologies in use across countries. These disparities are almost completely determined by the quality of the worst technology in use, rather than by the quality of the newest technology that has just been adopted or by the number of technologies in use. Further, we find that the TFP component attributable to the range of technologies used is highly correlated with overall sectoral TFP differences across countries, though the variance is smaller.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10733.

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Date of creation: Sep 2004
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Handle: RePEc:nbr:nberwo:10733
Note: EFG ME
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  1. Diego Comin & Bart Hobijn, 2003. "Cross-country technology adoption: making the theories face the facts," Staff Reports 169, Federal Reserve Bank of New York.
  2. John Laitner & Dmitriy Stolyarov, 2003. "Technological Change and the Stock Market," American Economic Review, American Economic Association, vol. 93(4), pages 1240-1267, September.
  3. Bernard, Andrew B & Jones, Charles I, 1996. "Comparing Apples to Oranges: Productivity Convergence and Measurement across Industries and Countries," American Economic Review, American Economic Association, vol. 86(5), pages 1216-38, December.
  4. Greenwood, J. & Hercowitz, Z. & Krusell, P., 1995. "Long-Run Implications of Investment-Specific Technological Change," UWO Department of Economics Working Papers 9510, University of Western Ontario, Department of Economics.
  5. David N. Weil, 1996. "Appropriate Technology and Growth," Working Papers 96-24, Brown University, Department of Economics.
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  8. Francesco Caselli & Wilbur John Coleman, 2001. "Cross-Country Technology Diffusion: The Case of Computers," American Economic Review, American Economic Association, vol. 91(2), pages 328-335, May.
  9. Bernard, A.B. & Jones, C.I., 1993. "Productivity Across Industries and Countries: Time Series Theory and Evidence," Working papers 93-17, Massachusetts Institute of Technology (MIT), Department of Economics.
  10. Gort, Michael & Klepper, Steven, 1982. "Time Paths in the Diffusion of Product Innovations," Economic Journal, Royal Economic Society, vol. 92(367), pages 630-53, September.
  11. Caselli, G & Ventura, J, 1996. "A Representative Consumer Theory of Distribution," Papers 534, Harvard - Institute for International Development.
  12. De Long, J Bradford, 1988. "Productivity Growth, Convergence, and Welfare: Comment," American Economic Review, American Economic Association, vol. 78(5), pages 1138-54, December.
  13. Peter Klenow & Andrés Rodríguez-Clare, 1997. "The Neoclassical Revival in Growth Economics: Has It Gone Too Far?," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 73-114 National Bureau of Economic Research, Inc.
  14. Chari, V V & Hopenhayn, Hugo, 1991. "Vintage Human Capital, Growth, and the Diffusion of New Technology," Journal of Political Economy, University of Chicago Press, vol. 99(6), pages 1142-65, December.
  15. Ana Fernandes, 2004. "What Does the Walrasian Auctioneer Know? Technology Adoption and Financial Innovation," 2004 Meeting Papers 526, Society for Economic Dynamics.
  16. Donald W.K. Andrews & C. John McDermott, 1993. "Nonlinear Econometric Models with Deterministically Trending Variables," Cowles Foundation Discussion Papers 1053, Cowles Foundation for Research in Economics, Yale University.
  17. Bahk, Byong-Hong & Gort, Michael, 1993. "Decomposing Learning by Doing in New Plants," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 561-83, August.
  18. Parente, Stephen L & Prescott, Edward C, 1994. "Barriers to Technology Adoption and Development," Journal of Political Economy, University of Chicago Press, vol. 102(2), pages 298-321, April.
  19. Holmes, Thomas J. & Jr., James A. Schmitz, 2001. "A gain from trade: From unproductive to productive entrepreneurship," Journal of Monetary Economics, Elsevier, vol. 47(2), pages 417-446, April.
  20. Edward C. Prescott, 1997. "Needed: a theory of total factor productivity," Staff Report 242, Federal Reserve Bank of Minneapolis.
  21. Lucas, Robert E, Jr, 1990. "Why Doesn't Capital Flow from Rich to Poor Countries?," American Economic Review, American Economic Association, vol. 80(2), pages 92-96, May.
  22. Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
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