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Robustness of Productivity Estimates

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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. Many methodologies are not very robust to measurement error in inputs. This is particularly troublesome, because fundamentally the objective of productivity measurement is to identify output differences that cannot be explained by input differences. Two other sources of error are misspecifications in the deterministic portion of the production technology and erroneous assumptions on the evolution of unobserved productivity. Techniques to control for the endogeneity of productivity in the firm's input choice decision risk exacerbating these problems. I compare the robustness of five widely used techniques: (a) index numbers, (b) data envelopment analysis, and three parametric methods: (c) instrumental variables estimation, (d) stochastic frontiers, and (e) semiparametric estimation. The sensitivity of each method to a variety of measurement and specification errors is evaluated using Monte Carlo simulations.

Suggested Citation

  • Johannes Van Biesebroeck, 2004. "Robustness of Productivity Estimates," NBER Working Papers 10303, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:10303
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    References listed on IDEAS

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    1. Van Biesebroeck, Johannes, 2008. "The Sensitivity of Productivity Estimates," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 311-328.
    2. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    3. 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.
    4. Johannes Van Biesebroeck, 2007. "Robustness Of Productivity Estimates," Journal of Industrial Economics, Wiley Blackwell, vol. 55(3), pages 529-569, September.
    5. 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.
    6. 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.
    7. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
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    More about this item

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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