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Estimation of the CES Production Function with Biased Technical Change: A Control Function Approach

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  • Xi CHEN
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    Abstract

    In this paper, I extend the Olley-Pakes (1996) estimation method to the CES production function with biased technical change. The new semi-parametric approach allows consistent estimation of the degree of returns to scale, the elasticity of substitution, and the bias in technical change. Identification of these parameters is achieved under the assumption that the data generating process reflects not only technologies but also optimizing behavior of producers. Using data from U.S. manufacturing industries over the period 1958-2005, I find strong evidence that industries are characterized by a production technology with the elasticity of substitution below one and with significant biased technical progress.

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    File URL: http://www.beta-umr7522.fr/productions/publications/2012/2012-20.pdf
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    Bibliographic Info

    Paper provided by Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg in its series Working Papers of BETA with number 2012-20.

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    Date of creation: 2012
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    Handle: RePEc:ulp:sbbeta:2012-20

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    Related research

    Keywords: Semi-parametric estimation; Panel data analysis; Returns to scale; Elasticity of substitution; Factor-augmenting technical change; Industry-level studies.;

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    References

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    1. 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.
    2. Rainer Klump & Peter McAdam & Alpo Willman, 2007. "Factor Substitution and Factor-Augmenting Technical Progress in the United States: A Normalized Supply-Side System Approach," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 183-192, February.
    3. Konings, Jozef & Vandenbussche, Hylke, 2008. "Heterogeneous Responses of Firms to Trade Protection," CEPR Discussion Papers 6724, C.E.P.R. Discussion Papers.
    4. Whitney Newey, 1999. "Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models," Working papers 99-02, Massachusetts Institute of Technology (MIT), Department of Economics.
    5. Eric J. Bartelsman & Wayne Gray, 1996. "The NBER Manufacturing Productivity Database," NBER Technical Working Papers 0205, National Bureau of Economic Research, Inc.
    6. Ilke Van Beveren, 2012. "Total Factor Productivity Estimation: A Practical Review," Journal of Economic Surveys, Wiley Blackwell, vol. 26(1), pages 98-128, 02.
    7. Daron Acemoglu, 2003. "Labor- And Capital-Augmenting Technical Change," Journal of the European Economic Association, MIT Press, vol. 1(1), pages 1-37, 03.
    8. Robert S. Chirinko, 2008. "ó: The Long And Short Of It," CESifo Working Paper Series 2234, CESifo Group Munich.
    9. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    10. Berndt, Ernst R, 1976. "Reconciling Alternative Estimates of the Elasticity of Substitution," The Review of Economics and Statistics, MIT Press, vol. 58(1), pages 59-68, February.
    11. J Malley & V A Muscatelli., . "Business Cycles and Productivity Growth: Are Temporary Downturns Productive or Wasteful?," Working Papers 9605, Business School - Economics, University of Glasgow.
    12. Thursby, Jerry G & Lovell, C A Knox, 1978. "An Investigation of the Kmenta Approximation to the CES Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 19(2), pages 363-77, June.
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    Cited by:
    1. Xi Chen & Bertrand M. Koebel, 2013. "Fixed cost, variable cost, markups and returns to scale," Working Papers of BETA 2013-13, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.

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