Robustness of Productivity Estimates
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.
|Date of creation:||Feb 2004|
|Publication status:||published as Van Biesebroeck, Johannes. "Robustness of Productivity Estimates." Journal of Industrial Economics 55, 3 (September 2005): 529-69.|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Richard Blundell & Stephen Bond, 2000.
"GMM Estimation with persistent panel data: an application to production functions,"
Taylor & Francis Journals, vol. 19(3), pages 321-340.
- 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.
- 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.
- 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.
- Richard Blundell & Stephen Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
- 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. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:10303. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.