Testing for Aggregation Bias in Efficiency Measurement
C. A. Knox Lovell, Asani Sarkar, Robin C. Sickles
Abstract
C. A. Knox Lovell, Asani Sarkar, Robin C. Sickles
Abstract
It is common practice to aggregate inputs prior to estimating the structure of production technology. It is of interest, therefore to have some idea of the impact of such aggregation on the resulting inferences concerning the structure of production technology. This information is of interest in its own right, and has been the subject of considerable research. However a knowledge of the effect of input aggregation on inferences concerning the structure of technology is valuable for another reason: since productive efficiency is measured relative to an estimated technology, input aggregation also influences one’s inferences concerning the structure of productive efficiency. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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It is common practice to aggregate inputs prior to estimating the structure of production technology. It is of interest, therefore to have some idea of the impact of such aggregation on the resulting inferences concerning the structure of production technology. This information is of interest in its own right, and has been the subject of considerable research. However a knowledge of the effect of input aggregation on inferences concerning the structure of technology is valuable for another reason: since productive efficiency is measured relative to an estimated technology, input aggregation also influences one’s inferences concerning the structure of productive efficiency. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Key concepts: Aggregate (composite), Production (economics), Econometrics, Computer science, Economics, Microeconomics, Nanotechnology, Materials science