Working Paper: NBER ID: w10303
Authors: Johannes Van Biesebroeck
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.
Keywords: No keywords provided
JEL Codes: D24; C13; C14; C15; C43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| measurement errors (C20) | productivity estimates (E23) |
| errors in independent variables (C29) | productivity estimates (E23) |
| errors in dependent variable (C29) | consistency in least squares estimates (C51) |
| index numbers (C43) | sensitivity to measurement errors (C20) |
| data envelopment analysis (C51) | sensitivity to measurement errors (C20) |
| instrumental variables (C36) | robustness to measurement errors (C20) |
| stochastic frontiers (C51) | robustness to measurement errors (C20) |
| semiparametric methods (C14) | robustness to measurement errors (C20) |