Sensitivity analysis in data envelopment analysis

Abstract

Data envelopment analysis (DEA) is a non-parametric technique and therefore hypothesis testing is very difficult. So, to determine the robustness of the efficiency scores obtained by DEA, sensitivity analysis is applied. Sensitivity analysis is used to know how sensitive the solution values and efficiency scores of the DMUs are to the numerical observations. In this paper, we propose a new model of sensitivity analysis in data envelopment analysis (DEA). The proposed new model examines the robustness of DEA efficiency scores by changing the reference set of the decision making units (DMUs). The model is also used for ranking the efficient DMUs and to identify the outliers on the frontier. Super efficiency is also estimated by applying the model as omitting the DMU itself from its reference set. Applying the proposed sensitivity model, this article empirically examines the robustness of the efficiency scores of 15 regions of Uttar Pradesh State Road Transport Corporation (UPSRTC) in India obtained by new slack model of DEA. The results of empirical illustration of sensitivity analysis reveal that the efficiency scores of the regions are robust, i.e., they are not sensitive to the efficient regions.