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Learning by Doing and Aggregate Fluctuations

  • Russell Cooper
  • Alok Johri

A major unresolved issue in business cycle theory is the construction of an endogenous propagation mechanism capable of capturing the amount of persistence displayed in the data. In this paper we explore the quantitative implications of one propagation mechanism: learning by doing. Estimation of the parameters characterizing learning by doing is based both on aggregate 2-digit data and plant level observations in the US. The estimated learning by doing function is then integrated into a stochastic growth model in which fluctuations are driven by technology shocks. We conclude that learning by doing can be a powerful mechanism for generating endogenous persistence.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 6898.

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Date of creation: Jan 1999
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Publication status: published as Cooper, Russell and Alok Johri. "Learning-By-Doing And Aggregate Fluctuations," Journal of Monetary Economics, 2002, v49(8,Nov), 1539-1566.
Handle: RePEc:nbr:nberwo:6898
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  1. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 1994. "The effects of monetary policy shocks: evidence from the Flow of Funds," Working Paper Series, Macroeconomic Issues 94-2, Federal Reserve Bank of Chicago.
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  3. A. Johri & M-A. Letendre, 2001. "Labour Market Dynamics in RBC Models," Department of Economics Working Papers 2001-03, McMaster University.
  4. Susumu Imai & Michael P. Keane, 2004. "Intertemporal Labor Supply and Human Capital Accumulation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 601-641, 05.
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  7. Russell Cooper & John Haltiwanger, 1993. "Evidence on Macroeconomic Complementarities," NBER Working Papers 4577, National Bureau of Economic Research, Inc.
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  9. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
  10. Susanto Basu & John G. Fernald, 1995. "Are Apparent Productive Spillovers a Figment of Specification Error?," NBER Working Papers 5073, National Bureau of Economic Research, Inc.
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  12. Christiano, Lawrence J & Eichenbaum, Martin, 1992. "Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations," American Economic Review, American Economic Association, vol. 82(3), pages 430-50, June.
  13. Hall, Robert E, 1997. "Macroeconomic Fluctuations and the Allocation of Time," Journal of Labor Economics, University of Chicago Press, vol. 15(1), pages S223-50, January.
  14. Rotemberg, Julio J & Woodford, Michael, 1996. "Real-Business-Cycle Models and the Forecastable Movements in Output, Hours, and Consumption," American Economic Review, American Economic Association, vol. 86(1), pages 71-89, March.
  15. Basu, S., 1993. "Procyclical Productivity: Overhead Inputs or Cyclical Utilization," Papers 93-25, Michigan - Center for Research on Economic & Social Theory.
  16. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 1995. "Capital utilization and returns to scale," Working Paper Series, Macroeconomic Issues 95-5, Federal Reserve Bank of Chicago.
  17. Cooper, Russell W. & Johri, Alok, 1997. "Dynamic complementarities: A quantitative analysis," Journal of Monetary Economics, Elsevier, vol. 40(1), pages 97-119, September.
  18. Russell Cooper & Andrew John, 1988. "Coordinating Coordination Failures in Keynesian Models," The Quarterly Journal of Economics, Oxford University Press, vol. 103(3), pages 441-463.
  19. 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.
  20. 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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