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Do Input Quality and Structural Productivity Estimates Drive Measured Differences in Firm Productivity?

  • Fox, Jeremy T.

    ()

    (University of Chicago)

  • Smeets, Valérie

    ()

    (Department of Economics, Aarhus School of Business)

Firms in the same industry can differ in measured total factor productivity (TFP) by multiples of 3. Griliches (1957) suggests one explanation: the quality of inputs differs across firms. Labor inputs are traditionally measured only as the number of workers. We investigate whether adjusting for the quality of labor inputs substantially decreases measured TFP dispersion. We add labor market history variables such as experience and firm and industry tenure, as well as general human capital measures such as schooling and sex. We also investigate whether an innovative structural estimator for productivity due to Olley and Pakes (1996) substantially decreases measured residual TFP. Combining labor quality and structural estimates of productivity, the one standard deviation difference in residual TFPs in manufacturing drops from 0.70 to 0.67 multiples. Neither the structural productivity measure nor detailed input quality measures explain the very large measured residual TFP dispersion, despite statistically precise coefficient estimates

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File URL: http://www.hha.dk/nat/wper/07-2_foxvas.pdf
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Paper provided by University of Aarhus, Aarhus School of Business, Department of Economics in its series Working Papers with number 07-2.

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Length: 40 pages
Date of creation: 01 Feb 2007
Date of revision:
Handle: RePEc:hhs:aareco:2007_002
Contact details of provider: Postal: The Aarhus School of Business, Prismet, Silkeborgvej 2, DK 8000 Aarhus C, Denmark
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Web page: http://www.asb.dk/departments/nat.aspx

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  1. Eric J. Bartelsman & Mark Doms, 2000. "Understanding productivity: lessons from longitudinal microdata," Finance and Economics Discussion Series 2000-19, Board of Governors of the Federal Reserve System (U.S.).
  2. Judith K. Hellerstein & David Neumark, 2004. "Production Function and Wage Equation Estimation with Heterogeneous Labor: Evidence from a New Matched Employer-Employee Data Set," NBER Working Papers 10325, National Bureau of Economic Research, Inc.
  3. Bloom, Nicholas & Van Reenen, John, 2006. "Measuring and Explaining Management Practices Across Firms and Countries," CEPR Discussion Papers 5581, C.E.P.R. Discussion Papers.
  4. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Wiley Blackwell, vol. 70(2), pages 317-341, 04.
  5. Z, Griliches & Jacques Mairesse, 1997. "Production Functions : The Search for Identification," Working Papers 97-30, Centre de Recherche en Economie et Statistique.
  6. Steve J. Davis & John Haltiwanger, 1991. "Gross job creation, gross job destruction and employment reallocation," Working Paper Series, Macroeconomic Issues 91-5, Federal Reserve Bank of Chicago.
  7. Lucia Foster & John Haltiwanger & Chad Syverson, 2005. "Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?," Working Papers 05-11, Center for Economic Studies, U.S. Census Bureau.
  8. Johannes Van Biesebroeck, 2004. "Robustness of Productivity Estimates," NBER Working Papers 10303, National Bureau of Economic Research, Inc.
  9. G. Steven Olley & Ariel Pakes, 1992. "The Dynamics of Productivity in the Telecommunications Equipment Industry," NBER Working Papers 3977, National Bureau of Economic Research, Inc.
  10. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
  11. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
  12. Finis Welch, 1969. "Linear Synthesis of Skill Distribution," Journal of Human Resources, University of Wisconsin Press, vol. 4(3), pages 311-327.
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