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Revisiting Some Productivity Debates

  • Johannes Van Biesebroeck

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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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10065.

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Date of creation: Nov 2003
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Publication status: published as Van Biesebroeck, Johannes, 2008. "The Sensitivity of Productivity Estimates," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 311-328.
Handle: RePEc:nbr:nberwo:10065
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  1. Bernard, A., 1997. "Exceptional Exporter Performance: Cause, Effect, or Both?," Working papers 97-21, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, MIT Press, vol. 116(2), pages 563-606, May.
  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. Brynjolfsson, Erik & Hitt, Lorin M., 2004. "Computing Productivity: Firm-Level Evidence," Working papers 4210-01, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  5. 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.
  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. 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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