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Assessing the effects of measurement errors on the estimation of production functions

  • Carmine Ornaghi

    (Economics Division, School of Social Sciences, University of Southampton, Southampton, UK)

This article explores to what extent the poor results that are often found when estimating parameters of production functions can be attributed to measurement errors, due to the use of common price deflators across firms. Because of the lack of detailed micro-economic data, econometricians have to rely on industry-wide deflators when computing outputs and intermediate inputs. A unique feature of the longitudinal data used in this paper is that it reports firm-level prices. This allows for a comparative assessment of production function parameters where the outputs and intermediate inputs are computed using both firm-specific prices and industry-wide deflators. The empirical results presented in this paper show that the use of common deflators across firms leads to lower scale estimates, mainly because of a large downward bias in the estimated coefficients for labour. Copyright © 2006 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.887
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File URL: http://qed.econ.queensu.ca:80/jae/2006-v21.6/
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 21 (2006)
Issue (Month): 6 ()
Pages: 879-891

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Handle: RePEc:jae:japmet:v:21:y:2006:i:6:p:879-891
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  1. George S Olley & Ariel Pakes, 1992. "The Dynamics Of Productivity In The Telecommunications Equipment Industry," Working Papers 92-2, Center for Economic Studies, U.S. Census Bureau.
  2. Klette, T.J., 1992. "The Inconsistency of Common Scale Estimators when Output Prices are unobserved and Endogenous," Harvard Institute of Economic Research Working Papers 1586, Harvard - Institute of Economic Research.
  3. 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.
  4. César Alonso-Borrego & Rocío Sánchez-Mangas, 2001. "Gmm Estimation Of A Production Function With Panel Data: An Application To Spanish Manufacturing Firms," Statistics and Econometrics Working Papers ws015527, Universidad Carlos III, Departamento de Estadística y Econometría.
  5. Mark Bils & Peter J. Klenow, 2004. "Some Evidence on the Importance of Sticky Prices," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 947-985, October.
  6. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
  7. Hall, Robert E, 1988. "The Relation between Price and Marginal Cost in U.S. Industry," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 921-47, October.
  8. Richard Blundell & Steve Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
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