ROBUSTNESS OF PRODUCTIVITY ESTIMATES -super-
Researchers interested in estimating productivity can choose from an array of methodologies, each with its strengths and weaknesses. We compare the robustness of five widely used techniques, two non-parametric and three parametric: in order, (a) index numbers, (b) data envelopment analysis (DEA), (c) stochastic frontiers, (d) instrumental variables (GMM) and (e) semiparametric estimation. Using simulated samples of firms, we analyze the sensitivity of alternative methods to the way randomness is introduced in the data generating process. Three experiments are considered, introducing randomness via factor price heterogeneity, measurement error and differences in production technology respectively. When measurement error is small, index numbers are excellent for estimating productivity growth and are among the best for estimating productivity levels. DEA excels when technology is heterogeneous and returns to scale are not constant. When measurement or optimization errors are nonnegligible, parametric approaches are preferred. Ranked by the persistence of the productivity differentials between firms (in decreasing order), one should prefer the stochastic frontiers, GMM, or semiparametric estimation methods. The practical relevance of each experiment for applied researchers is discussed explicitly. Copyright 2007 Blackwell Publishing Ltd..
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Volume (Year): 55 (2007)
Issue (Month): 3 (September)
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- 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.
- R Blundell & Steven Bond, "undated". "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
- Richard Blundell & Stephen Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
- Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
- 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.
- 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.Full references (including those not matched with items on IDEAS)
- Richard Blundell & Stephen Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers W99/04, Institute for Fiscal Studies.
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