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Assessing The Effectos Of Measurement Errors On The Estimation Of The Production Function

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  • Carmine Ornaghi

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    Abstract

    This article explores the reasons why GMM estimators of production function parameters are generally found to produce unsatisfactory results. I attribute this finding to the inaccurate construction of the variables used in production function analysis. In particular, I suggest that the problem lies in the use of common price deflators as well as in a capital variable that does not reflect the true flow of capital services because of short-run equilibrium effects. I show that the practice of using industry-wide deflators leads to lower scale estimates, mainly due to a relevant downward bias in the labour coefficient. At the same time, it introduces a large upward bias in estimating the elasticity of output with respect to technological innovation. Moreover, a significant improvement in the estimates of capital coefficients is obtained if the information on the degree of capacity utilization is adequately exploited.

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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Economía in its series Economics Working Papers with number we023314.

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    Date of creation: Feb 2002
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    Handle: RePEc:cte:werepe:we023314

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    1. Russell Cooper & John Haltiwanger & Laura Power, 1995. "Machine Replacement and the Business Cycle: Lumps and Bumps," Papers 0062, Boston University - Industry Studies Programme.
    2. Robert E. Hall, 1986. "The Relation Between Price and Marginal Cost in U.S. Industry," NBER Working Papers 1785, National Bureau of Economic Research, Inc.
    3. Mark Doms & Timothy Dunne, 1994. "Capital Adjustment Patterns in Manufacturing Plants," Working Papers 94-11, Center for Economic Studies, U.S. Census Bureau.
    4. Bronwyn H. Hall & Jacques Mairesse, 1992. "Exploring the Relationship Between R&D and Productivity in French Manufacturing Firms," NBER Working Papers 3956, National Bureau of Economic Research, Inc.
    5. Tor Jakob Klette, 1996. "R&D, Scope Economies, and Plant Performance," RAND Journal of Economics, The RAND Corporation, vol. 27(3), pages 502-522, Autumn.
    6. Richard Blundell & Steve Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers W99/04, Institute for Fiscal Studies.
    7. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," Harvard Institute of Economic Research Working Papers 1719, Harvard - Institute of Economic Research.
    8. Berndt, Ernst R. & Fuss, Melvyn A., 1986. "Productivity measurement with adjustments for variations in capacity utilization and other forms of temporary equilibrium," Journal of Econometrics, Elsevier, vol. 33(1-2), pages 7-29.
    9. 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.
    10. Garcia, Angel & Jaumandreu, Jordi & Rodriguez, Cesar, 2004. "Innovation and jobs: evidence from manufacturing firms," MPRA Paper 1204, University Library of Munich, Germany.
    11. Ornaghi, Carmine, 2006. "Spillovers in product and process innovation: Evidence from manufacturing firms," International Journal of Industrial Organization, Elsevier, vol. 24(2), pages 349-380, March.
    12. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
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    Cited by:
    1. Ornaghi, Carmine, 2006. "Spillovers in product and process innovation: Evidence from manufacturing firms," International Journal of Industrial Organization, Elsevier, vol. 24(2), pages 349-380, March.

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