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The Sensitivity of Productivity Estimates

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  • Van Biesebroeck, Johannes

Abstract

Researchers interested in estimating productivity can choose from an array of methodologies, each with its strengths and weaknesses. This study compares productivity estimates and evaluates the extent to which the conclusions of three important productivity debates in the economic development literature are sensitive to the choice of estimation method. Five widely used techniques are considered, two nonparametric and three parametric: index numbers, data envelopment analysis, instrumental variables estimation, stochastic frontiers, and semiparametric estimation. Using data on manufacturing firms in two developing countries, Colombia and Zimbabwe, we find that the different methods produce surprisingly similar productivity estimates when the measures are compared directly, even though the estimated input elasticities vary widely. Furthermore, the methods reach the same conclusions on two of the debates, supporting endogenous growth effects and showing that firm-level productivity changes are an important contributor to aggregate productivity growth. On the third debate, only with the parametric productivity measures is there evidence of learning by exporting.

Suggested Citation

  • Van Biesebroeck, Johannes, 2008. "The Sensitivity of Productivity Estimates," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 311-328.
  • Handle: RePEc:bes:jnlbes:v:26:y:2008:p:311-328
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    References listed on IDEAS

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    1. Bernard, Andrew B. & Bradford Jensen, J., 1999. "Exceptional exporter performance: cause, effect, or both?," Journal of International Economics, Elsevier, vol. 47(1), pages 1-25, February.
    2. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, Oxford University Press, vol. 116(2), pages 563-606.
    3. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    5. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    6. Aw, Bee Yan & Chen, Xiaomin & Roberts, Mark J., 2001. "Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing," Journal of Development Economics, Elsevier, vol. 66(1), pages 51-86, October.
    7. Erik Brynjolfsson & Lorin M. Hitt, 2003. "Computing Productivity: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 793-808, November.
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    More about this item

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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