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Revisiting Some Productivity Debates

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  • Johannes Van Biesebroeck

Abstract

Researchers interested in estimating productivity can choose from an array of methodologies, each with its strengths and weaknesses. Methods differ by the assumptions they rely on and imply very different calculations. I compare five widely used techniques: (a) index numbers, (b) data envelopment analysis, and three parametric methods, (c) instrumental variables estimation, (d) stochastic frontiers, and (e) semi-parametric estimation. I compare the estimates directly and evaluate three productivity debates using a panel of manufacturing plants in Colombia. The different methods generate surprisingly similar results. Correlations between alternative productivity estimates are invariably high. All methods confirm that exporters are more productive on average and that only a small portion of the productivity advantage is due to scale economies. Productivity growth is correlated more strongly with export status, frequent investments in capital equipment, and employment of managers than with the use of imported inputs or foreign ownership. On the debate whether aggregate productivity growth is driven by plant-level changes or output share relocation, all methods point to the the importance of plant-level changes, in contrast to results from the U.S.

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  • Johannes Van Biesebroeck, 2003. "Revisiting Some Productivity Debates," NBER Working Papers 10065, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:10065
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    1. 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.
    2. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, Oxford University Press, vol. 116(2), pages 563-606.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    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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