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Technology Diffusion and Aggregate Dynamics

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Author Info

  • David Andolfatto

    () (Department of Economics, University of Waterloo and CREFE/UQAM)

  • Glenn M. MacDonald

    () (W. E. Simon Graduate School of Business Administration and Department of Economics, University of Rochester; Economics Research Center / NORC)

Abstract

This paper develops and analyzes a macroeconomic model in which aggregate growth and fluctuations arise from the discovery and diffusion of new technologies; there are no exogenous aggregate shocks. The temporal behavior of aggregates is driven by individuals' efforts to innovate and/or make use of others' innovations. Parameters describing preferences, production possibilities and learning technologies are estimated using post-war U.S. data. The model delivers predicted aggregates that grow and fluctuate much like the data. The key feature of post-war growth are explained by new technologies that differ in terms of the magnitude of their improvement over existing methods and the difficulty of acquiring them. The model implies a negative trend in technological dispersion, and that the generally lower growth witnessed during the last two decades is the result of new technologies offering comparatively minor or less broadly-applicable improvements. Data on the growing and fluctuating share of engineering Ph.D.s support the model's technological interpretation of the growth facts, and data on patent applications and adult schooling are consistent with the notion that newer technologies are more specific and proprietary. Ce papier développe et analyse un modèle macroéconomique dans lequel croissance et fluctuations sont le résultat de la découverte et de la diffusion de nouvelles technologies; il n'y a pas de chocs agrégés exogènes. Le comportement dans le temps des agrégats est mû par l'effort d'innovation des individus ou par l'effort d'utilisation des innovations faites par des autres. Les paramètres décrivant les préférences, les possibilités de production et les technologies d'apprentissage sont estimés au moyen de données américaines d'après-guerre. Le modèle livre des agrégats qui croissent et fluctuent en grande partie comme dans les données. Les faits saillants concernant la croissance d'après-guerre sont expliqués par de nouvelles technologies qui diffèrent en termes d'ampleur d'amélioration par rapport aux méthodes existantes et de difficulté d'acquisition. Le modèle implique une tendance négative de la dispersion technologique. De plus, il montre que la croissance généralement plus faible des deux dernières décennies est le résultat de nouvelles technologies offrant des améliorations comparativement faibles ou moins bien applicables. Les données sur la part croissante et variable de Ph.D. en ingénieurie corroborent l'interprétation technologique des faits saillants de la croissance par le modèle, et les données sur les demandes de brevets et l'éducation des adultes sont conformes avec la notion que les technologies les plus récentes sont plus spécifiques et exclusives.

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Bibliographic Info

Paper provided by CREFE, Université du Québec à Montréal in its series Cahiers de recherche CREFE / CREFE Working Papers with number 58.

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Length: 60 pages
Date of creation: Jan 1998
Date of revision:
Publication status: Forthcoming, Review of Economic Dynamics
Handle: RePEc:cre:crefwp:58

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Keywords: Growth; Technology; Fluctuations;

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References

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  1. Trajtenberg, M. & Bresnahan, T.F., 1992. "General Purpose Technologies: "Engines of Growth"," Papers 16-92, Tel Aviv.
  2. Andreas Hornstein & Per Krusell, 1996. "Can Technology Improvements Cause Productivity Slowdowns?," NBER Chapters, in: NBER Macroeconomics Annual 1996, Volume 11, pages 209-276 National Bureau of Economic Research, Inc.
  3. Jovanovic, B. & Macdonald, G.M., 1988. "Competitive Diffusion," RCER Working Papers 160, University of Rochester - Center for Economic Research (RCER).
  4. 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.
  5. Bental, Benjamin & Peled, Dan, 1996. "The Accumulation of Wealth and the Cyclical Generation of New Technologies: A Search Theoretic Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(3), pages 687-718, August.
  6. Aghion, P. & Howitt, P., 1990. "A Model Of Growth Through Creative Destruction," DELTA Working Papers 90-12, DELTA (Ecole normale supérieure).
  7. Jovanovic, Boyan & Rob, Rafael, 1987. "Long Waves and Short Waves: Growth Through Intensive and Extensive Search," Working Papers 87-35, C.V. Starr Center for Applied Economics, New York University.
  8. Lippi, Marco & Reichlin, Lucrezia, 1993. "Diffusion of Technical Change and the Decomposition of Output into Trend and Cycle," CEPR Discussion Papers 775, C.E.P.R. Discussion Papers.
  9. Andolfatto, D. & MacDonald, G.M., 1995. "Endogeneous Technological Change, Growth, and Aggregate Functions," Working Papers 9504, University of Waterloo, Department of Economics.
  10. Finn E. Kydland, 1993. "Business cycles and aggregate labor-market fluctuations," Working Paper 9312, Federal Reserve Bank of Cleveland.
  11. Jovanovic, Boyan & Lach, Saul, 1997. "Product Innovation and the Business Cycle," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(1), pages 3-22, February.
  12. 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.
  13. Jeffrey Campbell, 1998. "Entry, Exit, Embodied Technology, and Business Cycles," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 1(2), pages 371-408, April.
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