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The Sensitivity of Productivity Estimates

  • Van Biesebroeck, Johannes

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.

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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 26 (2008)
Issue (Month): ()
Pages: 311-328

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Handle: RePEc:bes:jnlbes:v:26:y:2008:p:311-328
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  1. R Blundell & Steven Bond, . "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
  2. Acemoglu, D. & Zilibotti, F., 1998. "Productivity Differences," Papers 660, Stockholm - International Economic Studies.
  3. Andrew B. Bernard & J. Bradford Jensen, 1997. "Exceptional Exporter Performance: Cause, Effect, or Both?," NBER Working Papers 6272, National Bureau of Economic Research, Inc.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
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