ROBUSTNESS OF PRODUCTIVITY ESTIMATES

Author: VAN BIESEBROECK, JOHANNES

Source: Journal of Industrial Economics, Volume 55, Number 3, September 2007 , pp. 529-569(41)

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Abstract:

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.

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-6451.2007.00322.x

Affiliations: 1: Department of Economics, University of Toronto; and NBER; 140 St. George Street, Suite 707, Toronto, Ontario M5S 3G6, Canada., Email: johannes.vanbiesebroeck@utoronto.ca

Publication date: 2007-09-01