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It is well known in the Data Envelopment Analysis literature that proper variable selection is necessary for the reliable measurement of efficiency. Omitting production relevant variables and/or including irrelevant variables will lead to biased measurement. It is also known that the sample size needs to be large relative to the number of inputs and outputs to prevent classification of efficiency by default. In some empirical settings the number of potential relevant variables is large. Careful selection of an appropriate set of variables is necessary for reliable efficiency measurement. This paper looks at the issue of input selection and uses simulation analysis to develop statistical procedures to provide guidelines for input selection.
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Stored only when both papers are in the corpus — 93 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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