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 efficient set is relatively large and the average efficiency is generally high. The high average efficiency is the result of assuming that the units in the efficient set are 100% efficient. If this assumption is not valid, this results in an overestimation of the efficiencies, which will be larger for a smaller number of units. Samples of various sizes are used to find the related bias in the efficiency estimation. The samples are drawn from a large scale application of DEA to bank branch efficiency. The effects of different assumptions as to returns to scale and the number of inputs and outputs are investigated.