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Embodying embodiment in a structural, macroeconomic input-output model

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  • Daniel J. Wilson

Abstract

This paper describes an attempt to build a regression-based system of labor productivity equations that incorporate the effects of capital-embodied technological change into IDLIFT, a structural, macroeconomic input-output model of the U.S. economy. Builders of regression-based forecasting models have long had difficulty finding labor productivity equations that exhibit the Neoclassical or Solowian property that movements in investment should cause accompanying movements in labor productivity. Theory 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 properly estimate the productivity-investment relationship. Wilson (2001a), by estimating industry-level embodied technological change, has alleviated this difficulty. In this paper, I utilize those estimates to construct capital stocks that are adjusted for technological change which are then used to estimate Neoclassical-type labor 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 behavior of the model and that predicted by Neoclassical production theory.

Suggested Citation

  • Daniel J. Wilson, 2001. "Embodying embodiment in a structural, macroeconomic input-output model," Working Paper Series 2001-18, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:2001-18
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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 & 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 US 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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