Robustness of Productivity Estimates

55 Pages Posted: 17 Feb 2004 Last revised: 28 Aug 2010

See all articles by Johannes Van Biesebroeck

Johannes Van Biesebroeck

K.U.Leuven; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 2 versions of this paper

Date Written: February 2004

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

Van Biesebroeck, Johannes, Robustness of Productivity Estimates (February 2004). NBER Working Paper No. w10303. Available at SSRN: https://ssrn.com/abstract=502889

Johannes Van Biesebroeck (Contact Author)

K.U.Leuven ( email )

Naamsestraat 69
B-3000 Leuven
Belgium
+3216326793 (Phone)
+3216326796 (Fax)

HOME PAGE: http://www.econ.kuleuven.be/public/N07057/

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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