IDEAS home Printed from https://ideas.repec.org/p/fip/fedfwp/2001-18.html
   My bibliography  Save this paper

Embodying embodiment in a structural, macroeconomic input-output model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.frbsf.org/economic-research/files/wp01-18bk.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    2. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    3. Neil R. Ericsson & Jaime Marquez, 1998. "A framework for economic forecasting," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 228-266.
    4. Plutarchos Sakellaris & Daniel J. Wilson, 2004. "Quantifying Embodied Technological Change," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(1), pages 1-26, January.
    5. 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.
    6. Greenwood, Jeremy & Hercowitz, Zvi & Krusell, Per, 1997. "Long-Run Implications of Investment-Specific Technological Change," American Economic Review, American Economic Association, vol. 87(3), pages 342-362, June.
    7. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    8. Plutarchos Sakellaris & Dan Wilson, 2000. "The Production-Side Approach to Estimating Embodied Technological Change," Electronic Working Papers 00-002, University of Maryland, Department of Economics.
    9. Susanto Basu, 1996. "Procyclical Productivity: Increasing Returns or Cyclical Utilization?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(3), pages 719-751.
    10. Hendry, David F, 1997. "The Econometrics of Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 107(444), pages 1330-1357, September.
    11. Gort, Michael & Wall, Richard A., 1998. "Obsolescence, input augmentation, and growth accounting," European Economic Review, Elsevier, vol. 42(9), pages 1653-1665, November.
    12. 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.
    13. Wilson, Daniel J., 2000. "Estimating Returns to Scale: Lo, Still No Balance," Journal of Macroeconomics, Elsevier, vol. 22(2), pages 285-314, April.
    14. repec:umd:umdeco:sakellaris0002 is not listed on IDEAS
    15. repec:bla:econom:v:47:y:1980:i:188:p:387-406 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 US 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, vol. 37(5), pages 836-848, June.
    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 245-274, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jason G. Cummins & Giovanni L. Violante, 2002. "Investment-Specific Technical Change in the US (1947-2000): Measurement and Macroeconomic Consequences," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 5(2), pages 243-284, April.
    2. 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.
    3. Plutarchos Sakellaris & Daniel J. Wilson, 2004. "Quantifying Embodied Technological Change," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(1), pages 1-26, January.
    4. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    5. Sergio Rebelo, 2005. "Real Business Cycle Models: Past, Present, and Future," NBER Working Papers 11401, National Bureau of Economic Research, Inc.
    6. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    7. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    8. Savvidou, Eleni, 2003. "The Relationship Between Skilled Labor and Technical Change," Working Paper Series 2003:27, Uppsala University, Department of Economics.
    9. Mukoyama, Toshihiko, 2008. "Endogenous depreciation, mismeasurement of aggregate capital, and the productivity slowdown," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 513-522, March.
    10. Jonas D. M. Fisher, 2006. "The Dynamic Effects of Neutral and Investment-Specific Technology Shocks," Journal of Political Economy, University of Chicago Press, vol. 114(3), pages 413-451, June.
    11. Faccini, Renato & Ortigueira, Salvador, 2010. "Labor-market volatility in the search-and-matching model: The role of investment-specific technology shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(8), pages 1509-1527, August.
    12. Groth, Christian & Wendner, Ronald, 2011. "Learning by investing, embodiment, and speed of convergence," MPRA Paper 29008, University Library of Munich, Germany.
    13. Weber, Henning, 2015. "Innovation and the Optimal Rate of Inflation," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113087, Verein für Socialpolitik / German Economic Association.
    14. Plutarchos Sakellaris & Dan Wilson, 2000. "The Production-Side Approach to Estimating Embodied Technological Change," Electronic Working Papers 00-002, University of Maryland, Department of Economics.
    15. James Bessen, 2002. "Technology Adoption Costs and Productivity Growth: The Transition to Information Technology," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 5(2), pages 443-469, April.
    16. Groth, Christian & Wendner, Ronald, 2014. "Embodied learning by investing and speed of convergence," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 245-269.
    17. Raouf Boucekkine & Fernando Del Río & Omar Licandro, 2003. "Embodied Technological Change, Learning‐by‐doing and the Productivity Slowdown," Scandinavian Journal of Economics, Wiley Blackwell, vol. 105(1), pages 87-98, March.
    18. Elstner, Steffen & Feld, Lars P. & Schmidt, Christoph M., 2018. "The German productivity paradox: Facts and explanations," Ruhr Economic Papers 767, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    19. Janet L. Yellen, 2005. "The U.S. economic outlook," Speech 5, Federal Reserve Bank of San Francisco.
    20. Kimball, Miles, 2017. "Next generation monetary policy," Journal of Macroeconomics, Elsevier, vol. 54(PA), pages 100-109.

    More about this item

    Keywords

    Technology; Productivity;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedfwp:2001-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Federal Reserve Bank of San Francisco Research Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbsfus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.