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Pro-cyclical Solow Residuals without Technology Shocks

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Author Info
Adnrew J. Clarke
Alok Johri

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Abstract

Most Real Business Cycle models have a hard time jointly explaining the twin facts of strongly pro-cyclical Solow residuals and extremely low correlations between wages and hours. We present a model that delivers both these results without using exogenous variation in total factor productivity (technology shocks). The key innovation of the paper is to add learning-by-doing to firms technology. As a result firms optimally vary their prices to control the amount of learning which in turn influences future productivity. We show that exogenous variation in labour wedges (preference shocks) measured from aggregate data deliver around fifty percent of the standard deviation in the efficiency wedge (Solow residual) as well as realistic second moments for key aggregate variables which is in sharp contrast to the model without learning-by-doing.

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Paper provided by McMaster University in its series Department of Economics Working Papers with number 2008-02.

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Length: 41 pages
Date of creation: Feb 2008
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Handle: RePEc:mcm:deptwp:2008-02

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Related research
Keywords: Business cycles Learning-by-Doing Productivity

Find related papers by JEL classification:
E2 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment
E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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  6. Marianne Baxter & Robert G. King, 1991. "Productive externalities and business cycles," Discussion Paper / Institute for Empirical Macroeconomics 53, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  17. Peter N. Ireland, 2004. "Technology Shocks in the New Keynesian Model," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 923-936, 01. [Downloadable!] (restricted)
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  18. Clarke, Andrew J., 2006. "Learning-by-doing and aggregate fluctuations: Does the form of the accumulation technology matter?," Economics Letters, Elsevier, vol. 92(3), pages 434-439, September. [Downloadable!] (restricted)
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  20. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2002. "Accounting for the Great Depression," American Economic Review, American Economic Association, vol. 92(2), pages 22-27, May. [Downloadable!] (restricted)
    Other versions:
  21. 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. [Downloadable!] (restricted)
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  24. Parkin, M., 1988. "A Method For Determining Whether Parameters In Aggregative Models Are Structural," UWO Department of Economics Working Papers 8803, University of Western Ontario, Department of Economics.
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  28. Lilia Maliar & Serguei Maliar, 2004. "Preference shocks from aggregation: time series data evidence," Canadian Journal of Economics, Canadian Economics Association, vol. 37(3), pages 768-781, August. [Downloadable!] (restricted)
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  29. Lang, Larry H P & Stulz, Rene M, 1994. "Tobin's q, Corporate Diversification, and Firm Performance," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1248-80, December. [Downloadable!] (restricted)
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  30. Carol A. Corrado & Charles R. Hulten & Daniel E. Sichel, 2006. "Intangible Capital and Economic Growth," NBER Working Papers 11948, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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