Data Envelopment Analysis (DEA) allows healthcare scholars to measure productivity in a holistic manner. It combines a production unit's multiple outputs and multiple inputs into a single measure of its overall performance relative to other units in the sample being analyzed. It accomplishes this task by aggregating a unit's weighted outputs and dividing the output sum by the unit's aggregated weighted inputs, choosing output and input weights that maximize its output/input ratio when the same weights are applied to other units in the sample. Conventional DEA assumes that inputs and outputs are used in different proportions by the units in the sample. So, for the sample as a whole, inputs have been substituted for each other and outputs have been transformed into each other. Variables are assigned different weights based on their marginal rates of substitution and marginal rates of transformation. If in truth inputs have not been substituted nor outputs transformed, then there will be no marginal rates and therefore no valid basis for differential weights. This paper explains how to statistically test for the presence of substitutions among inputs and transformations among outputs. Then, it applies these tests to the input and output data from three healthcare DEA articles, in order to identify the effects on DEA scores when input substitutions and output transformations are absent in the sample data. It finds that DEA scores are badly biased when substitution and transformation are absent and conventional DEA models are used.
['Darold T. Barnum', 'Matthew T. Johnson', 'John M. Gleason']
Importance of Statistical Evidence in Estimating Valid DEA Scores