Robustness of Productivity Estimates
41 Pages Posted: 14 Sep 2007
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) semi-parametric 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 non-negligible, 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 semi-parametric estimation methods. The practical relevance of each experiment for applied researchers is discussed explicitly.
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