SENSITIVITY AND STABILITY OF EFFICIENCY CLASSIFICATIONS IN DATA ENVELOPMENT ANALYSIS

A new technique for assessing the sensitivity and stability of efficie ncy classifications in Data Envelopment Analysis (DEA) is presented. H ere developed for the ratio (CCR) model, this technique extends easily to other DEA variants. An organization's input-outut vector serves as the center for a cell within which the organization's classification remains unchanged under perturbations of the data. For the l(1), l(inf inity) and generalized l(infinity) norms, the radius of the maximal ce ll can be computed using linear programming formulations. This radius can be interpreted as a measure of the classification's stability, esp ecially with respect to errors in the data.