Robustness of productivity estimates
By: Johannes van Biesebroeck.
Contributors: National Bureau of Economic Research.
Series: NBER working paper series ;; working paper 10303; Working paper series (National Bureau of Economic Research : Online) ;; working paper no. 10303.
Publisher: National Bureau of Economic Research
Published: 2004
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Subjects: Industrial productivity -- Measurement.
Description: "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"--National Bureau of Economic Research web site.
Notes: Also available in print.
Includes bibliographical references.
Title from PDF file as viewed on 1/26/2005.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.