Robustness of Productivity Estimates
Johannes Van Biesebroeck ()
No 10303, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: D24 C13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ino and nep-mic
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Published as Van Biesebroeck, Johannes. "Robustness of Productivity Estimates." Journal of Industrial Economics 55, 3 (September 2005): 529-69.
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Journal Article: ROBUSTNESS OF PRODUCTIVITY ESTIMATES* (2007)
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