Sampling size and efficiency bias in data envelopment analysis

In Data Envelopment Analysis when the number of decision making units is small the number of units of the dominant or e cient set is relatively large and the average e ciency is generally high The high average e ciency is the result of assuming that the units in the e cient set are e cient If this assumption is not valid this results in an overestimation of the e ciencies which will be larger for a smaller number of units Samples of various sizes are used to nd the related bias in the e ciency estimation The samples are drawn from a large scale application of DEA to bank branch e ciency The e ects of di erent assumptions as to returns to scale and the number of inputs and outputs are investigated