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Embodying Embodiment in a Structural, Macroeconomic Input-Output Model

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  • Daniel Wilson

Abstract

In this paper, I develop a regression-based system of labour productivity equations that account for capital-embodied technological change and I incorporate this system into IDLIFT, a structural, macroeconomic input-output model of the US economy. Builders of regression-based forecasting models have long had difficulty finding labour productivity equations that exhibit the "Solowian' property that movements in investment should cause accompanying movements in labour productivity. The production theory developed by Solow and others dictates that this causation is driven by the effect of traditional capital deepening as well as technological change embodied in capital. Lack of measurement of the latter has hampered the ability of researchers to estimate properly the productivity-investment relationship. Recent research by Wilson (2001) has alleviated this difficulty by estimating industry-level embodied technological change. In this paper, I utilize those estimates to construct capital stocks adjusted for technological change and then use these adjusted stocks to estimate Solow-type labour productivity equations. It is shown that replacing IDLIFT's former productivity equations, based on changes in output and time trends, with the new equations, results in a convergence between the dynamic behaviour of the model and that predicted by traditional (Solowian) production theory.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Economic Systems Research.

Volume (Year): 15 (2003)
Issue (Month): 3 ()
Pages: 371-398

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Handle: RePEc:taf:ecsysr:v:15:y:2003:i:3:p:371-398

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Keywords: Equipment-embodied Technological Change; Productivity; Forecasting;

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  1. Susanto Basu & John Fernald & Miles Kimball, 2004. "Are Technology Improvements Contractionary?," NBER Working Papers 10592, National Bureau of Economic Research, Inc.
  2. Plutarchos Sakellaris & Daniel J. Wilson, 2001. "The production-side approach to estimating embodied technological change," Finance and Economics Discussion Series 2001-20, Board of Governors of the Federal Reserve System (U.S.).
  3. Plutarchos Sakellaris & Daniel J. Wilson, 2001. "Quantifying embodied technological change," Working Paper Series 2001-16, Federal Reserve Bank of San Francisco.
  4. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
  5. 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.
  6. Wilson, Daniel J., 2000. "Estimating Returns to Scale: Lo, Still No Balance," Journal of Macroeconomics, Elsevier, vol. 22(2), pages 285-314, April.
  7. Daniel J. Wilson, 2001. "Is embodied technology the result of upstream R&D? industry-level evidence," Working Paper Series 2001-17, Federal Reserve Bank of San Francisco.
  8. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164, September.
  9. Hendry, David F, 1997. "The Econometrics of Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 107(444), pages 1330-57, September.
  10. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
  11. Basu, Susanto, 1996. "Procyclical Productivity: Increasing Returns or Cyclical Utilization?," The Quarterly Journal of Economics, MIT Press, vol. 111(3), pages 719-51, August.
  12. Gort, Michael & Wall, Richard A., 1998. "Obsolescence, input augmentation, and growth accounting," European Economic Review, Elsevier, vol. 42(9), pages 1653-1665, November.
  13. Neil R. Ericsson & Jaime Marquez, 1998. "A framework for economic forecasting," International Finance Discussion Papers 626, Board of Governors of the Federal Reserve System (U.S.).
  14. 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.
  15. repec:umd:umdeco:sakellaris0002 is not listed on IDEAS
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Cited by:
  1. Mulas-Granados, Carlos & Sanz, Ismael, 2008. "The dispersion of technology and income in Europe: Evolution and mutual relationship across regions," Research Policy, Elsevier, vol. 37(5), pages 836-848, June.
  2. Werling Jeffrey & Horst Ronald, 2009. "Macroeconomic and Industry Impacts of 9/11: An Interindustry Macroeconomic Approach," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 15(2), pages 1-32, July.
  3. Randy A. Becker & John Haltiwanger, 2006. "Micro and Macro Data Integration: The Case of Capital," NBER Chapters, in: A New Architecture for the U.S. National Accounts, pages 541-610 National Bureau of Economic Research, Inc.
  4. Bormotov, Michael, 2009. "Economic cycles: historical evidence, classification and explication," MPRA Paper 19616, University Library of Munich, Germany.

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