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..
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 55 (2007)
Issue (Month): 3 (09)
|Contact details of provider:|| Web page: http://www.blackwellpublishing.com/journal.asp?ref=0022-1821|
|Order Information:||Web: http://www.blackwellpublishing.com/subs.asp?ref=0022-1821|
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.:
- Blundell, R. & Bond, S., 1995.
"Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,"
104, Economics Group, Nuffield College, University of Oxford.
- 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.
- Richard Blundell & Steve Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
- 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.
- 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 & Steve Bond, 1999. "GMM estimation with persistent panel data: an application to production functions," IFS Working Papers W99/04, 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.
When requesting a correction, please mention this item's handle: RePEc:bla:jindec:v:55:y:2007:i:3:p:529-569. 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: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.