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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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  • Daniel Wilson, 2003. "Embodying Embodiment in a Structural, Macroeconomic Input-Output Model," Economic Systems Research, Taylor & Francis Journals, vol. 15(3), pages 371-398.
  • Handle: RePEc:taf:ecsysr:v:15:y:2003:i:3:p:371-398 DOI: 10.1080/0953531032000111817
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    References listed on IDEAS

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    7. Wilson, Daniel J., 2000. "Estimating Returns to Scale: Lo, Still No Balance," Journal of Macroeconomics, Elsevier, pages 285-314.
    8. Daniel J. Wilson, 2002. "Is Embodied Technology the Result of Upstream R&D? Industry-Level Evidence," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 5(2), pages 285-317, April.
    9. Miguel A. Ferreira, 2005. "Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework," Journal of Financial Econometrics, Society for Financial Econometrics, pages 126-168.
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    13. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
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    Cited by:

    1. Randy A. Becker & John Haltiwanger & Ron S. Jarmin & Shawn D. Klimek & Daniel J. Wilson, 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.
    2. Bormotov, Michael, 2009. "Economic cycles: historical evidence, classification and explication," MPRA Paper 19616, University Library of Munich, Germany.
    3. Mulas-Granados, Carlos & Sanz, Ismael, 2008. "The dispersion of technology and income in Europe: Evolution and mutual relationship across regions," Research Policy, Elsevier, pages 836-848.
    4. 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.

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