Outlier detection in data envelopment analysis: An analysis of jackknifing

This paper analyzes the resampling technique of jackknifing and its capability of detecting outliers in data envelopment analysis. It is well recognized that measured efficiency is sensitive to outliers; recent research has employed resampling techniques to estimate standard deviations in an attempt to handle outliers. Using jackknifing, each observation other than the decision making unit under analysis is deleted from the sample once and the resulting linear program is solved, leading to a distribution of efficiency estimates. From this distribution, standard deviations and confidence intervals are derived. Two types of outliers can be distinguished conceptually: those belonging to the production possibility set that are efficient, and those that do not belong but appear to due to statistical noise. This paper argues that calculation of the standard deviation is not meaningful because it is not possible to distinguish empirically between the two types of outliers.