Journal of Applied Mathematics and Decision Sciences
Volume 2 (1998), Issue 1, Pages 51-64

Sampling size and efficiency bias in data envelopment analysis

Mohammad R. Alirezaee,1 Murray Howland,2 and Cornelis van de Panne2

1University of Calgary and Teacher Training University, Canada
2University of Calgary, Canada

Copyright © 1998 Mohammad R. Alirezaee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In Data Envelopment Analysis, when the number of decision making units is small, the number of units of the dominant or effcient set is relatively large and the average effciency is generally high. The high average effciency is the result of assuming that the units in the effcient set are 100% effcient. 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 effciency 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.