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PROCYCLICAL SOLOW RESIDUALS WITHOUT TECHNOLOGY SHOCKS

Published online by Cambridge University Press:  01 June 2009

Andrew J. Clarke  and
Alok Johri
Andrew J. Clarke
Affiliation:
University of Melbourne
Alok Johri*
Affiliation:
McMaster University
*
Address correspondence to: Alok Johri, Department of Economics, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4M4, Canada; e-mail: johria@mcmaster.ca.
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Abstract

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Most real business cycle models have a hard time jointly explaining the twin facts of strongly procyclical 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 labor wedges (preference shocks) measured from aggregate data deliver around 50% 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.

Keywords

Business CyclesLearning-by-DoingProductivity

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 13 , Issue 3 , June 2009 , pp. 366 - 389
Copyright
Copyright © Cambridge University Press 2009

References

REFERENCES

Argote, L., Beckman, S.L., and Epple, D. (1990) The persistence and transfer of learning in industrial settings. Management Science 36 (2), 140–154.Google Scholar
Atkeson, A. and Kehoe, P.J. (2005) Modeling and measuring organization capital. Journal of Political Economy 113 (5), 1026–1053.CrossRefGoogle Scholar
Bahk, B.-H. and Gort, M. (1993) Decomposing learning by doing in new plants. Journal of Political Economy 101 (4), 561–583.CrossRefGoogle Scholar
Barnett, S.A. and Sakellaris, P. (1999) A new look at firm market value, investment, and adjustment costs. Review of Economics and Statistics 81 (2), 250–260.CrossRefGoogle Scholar
Basu, S. (1996) Procyclical productivity: Increasing returns or cyclical utlilization? Quarterly Journal of Economics 111 (3), 719–751.Google Scholar
Basu, S. and Fernald, J.G. (1997) Returns to scale in U.S. production: Estimates and implications. Journal of Political Economy 105 (2), 249–283.CrossRefGoogle Scholar
Baxter, M. and King, R.G. (1991) Productive Externalities and Business Cycles. Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis, Discussion Paper 53.Google Scholar
Benkard, C.L. (2000) Learning and forgetting: the dynamics of aircraft production. American Economic Review 90 (4), 1034–1054.CrossRefGoogle Scholar
Blanchard, O. and Kiyotaki, N. (1987) Monopolistic competition and aggregate demand disturbances. American Economic Review 77 (4), 647–666.Google Scholar
Browning, M., Hansen, L.P., and Heckman, J. (1999) Micro data and general equilibrium models. In Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, Vol. IA, pp. 543–633. Amsterdam: Elsevier Science.Google Scholar
Brynjolfsson, E. and Hitt, L.M. (2000) Beyond computation: information technology, organizational transformation and business performance. Journal of Economic Perspectives 14 (4), 23–48.CrossRefGoogle Scholar
Burnside, C. and Eichenbaum, M. (1996) Factor-hoarding and the propagation of business-cycle shocks. American Economic Review 86 (5), 1154–1174.Google Scholar
Burnside, C., Eichenbaum, M., and Rebelo, S. (1993) Labor hoarding and the business cycle. Journal of Political Economy 101, 245–273.Google Scholar
Chang, Y., Gomes, J.F., and Schorfheide, F. (2002) Learning-by-doing as a propagation mechanism. American Economic Review 92 (5), 1498–1520.Google Scholar
Chang, Y. and Kim, S.-B. (2007) Heterogeneity and aggregation in the labor market: Implications for aggregate preference shifts. American Economic Review 97 (5), 1939–1956.Google Scholar
Chari, V.V., Kehoe, P.J., and McGrattan, E.R. (2002) Accounting for the great depression. American Economic Review 92 (2), 22–27.Google Scholar
Chari, V.V., Kehoe, P.J., and McGrattan, E.R. (2007) Business cycle accounting. Econometrica 75 (3), 781–836.Google Scholar
Christiano, L.J. and Eichenbaum, M. (1992) Current real-business-cycle theories and aggregate labor-market fluctuations. American Economic Review 82 (3), 430–450.Google Scholar
Clarke, A.J. (2006) Learning-by-doing and aggregate fluctuations: Does the form of the accumulation technology matter? Economics Letters 92, 434–439.Google Scholar
Clarke, A.J. (2007) Learning-by-Doing and Productivity Dynamics in Manufacturing Establishments. Manuscript, University of Melbourne.Google Scholar
Cole, H.L. and Ohanian, L.E. (2002) The U.S. and U.K. great depressions through the lens of neoclassical growth theory. American Economic Review 92 (2), 28–32.Google Scholar
Cole, H.L. and Ohanian, L.E. (2004) New deal policies and the persistence of the great depression: A general equilibrium analysis. Journal of Political Economy 112 (4), 779–816.Google Scholar
Cooper, R.W. and Johri, A. (1997) Dynamic complementarities: A quantitative analysis. Journal of Monetary Economics 40, 97–119.CrossRefGoogle Scholar
Cooper, R.W. and Johri, A. (2002) Learning-by-doing and aggregate fluctuations. Journal of Monetary Economics 49, 1539–1566.Google Scholar
Corrado, C.A., Hulten, C.R., and Sichel, D.E. (2006) Intangible capital and economic growth. NBER working paper 11948.Google Scholar
Darr, E.D., Argote, L., and Epple, D. (1995) The acquisition, transfer, and depreciation of knowledge in service organizations: productivity in franchises. Management Science 41 (11), 1750–1762.Google Scholar
Hall, R.E. (1997) Macroeconomic fluctuations and the allocation of time. Journal of Labor Economics 15 (2), S223–S250.Google Scholar
Hall, R.E. (2000) E-capital: The link between the stock market and the labor market in the 1990's. Brookings Papers on Economic Activity 2, 73–102.Google Scholar
Hou, K. and Johri, A. (2007) Costly Investments in Organizational Capital and Learning-by-Doing. Manuscript, McMaster University.Google Scholar
Ireland, P.N. (2004a) Money's role in the monetary business cycle. Journal of Money, Credit and Banking 36 (6), 969–983.Google Scholar
Ireland, P.N. (2004b) Technology shocks in the new keynesian model. Review of Economics and Statistics 86 (4), 923–936.CrossRefGoogle Scholar
Irwin, D.A. and Klenow, P.J. (1994) Learning-by-doing spillovers in the semiconductor industry. Journal of Political Economy 102 (6), 1200–1227.Google Scholar
Jarmin, R.S. (1994) Learning by doing and competition in the early rayon industry. RAND Journal of Economics 25 (3), 441–454.Google Scholar
Johri, A. and Letendre, M.-A. (2007) What do “residuals” from first-order conditons reveal about dge models? Journal of Economic Dynamics and Control 31 (8), 2744–2773.Google Scholar
King, R.G. and Rebelo, S.T. (1999) Resuscitating real business cycles. In Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, Vol. IB, pp. 927–1007. Amsterdam: Elsevier Science.Google Scholar
King, R.G. and Watson, M.W. (2002) System reduction and solution algorithms for singular linear difference systems under rational expectations. Computational Economics 20, 57–86.CrossRefGoogle Scholar
Lang, L. H.P. and Schultz, R.M. (1994) Tobin's q: Corporate diversification and firm performance. Journal of Political Economy 6, 1248–1280.Google Scholar
Lev, B. and Radharkrishnan, S. (2003) The Measurement of Firm-Specific Organizational Captial. NBER working paper 9581.Google Scholar
Maliar, L. and Maliar, S. (2004) Preference shocks from aggregation: time series evidence. Canadian Journal of Economics 37 (3), 768–781.Google Scholar
Mulligan, C.B. (2002) A Century of Labor-Leisure Distortions. NBER working paper 8774.Google Scholar
Parkin, M. (1988) A method for determining whether parameters in aggregative models are structural. Carnegie-Rochester Conference Sreies on Public Policy 29, 215–252.Google Scholar
Rosen, S. (1972) Learning by experience as joint production. Quarterly Journal of Economics 86 (3), 366–382.CrossRefGoogle Scholar
Rotemberg, J.J. and Woodford, M. (1991) Markups and the business cycle. NBER Macroeconomics Annual 63–129.Google Scholar
Rotemberg, J.J. and Woodford, M. (1999) The cyclical behaviour of prices and costs. In Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, Vol. IA, pp. 1051–1135. Amsterdam: Elsevier Science.Google Scholar
Summers, L.H., Bosworth, B.P., Tobin, J., and White, P.M. (1981) Taxation and corporate investment: A q-Theory Approach. Brookings Papers on Economic Activity 1, 67–140.Google Scholar
Thompson, P. (2001) How much did the liberty shipbuilders learn?: New evidence for an old case study. Journal of Political Economy 109 (1), 103–137.CrossRefGoogle Scholar
Thornton, R.A. and Thompson, P. (2001) Learning from experience and learning from others: An exploration of learning and spillovers in wartime shipbuilding. American Economic Review 91 (5), 1350–1369.Google Scholar
Uhlig, H. (2004) Do technology shocks lead to a fall in total hours worked? Journal of the European Economics Association 2, 361–371.CrossRefGoogle Scholar