Bootstrapped efficiency estimates for a model for groups and hierarchies in data envelopment analysis

We apply some recently introduced bootstrap techniques to derive bias corrected efficiency scores for a model for groups and hierarchies in DEA. The use of the bootstrap makes it possible to overcome some deficiencies of the original formulation of this model, which rests on rescaling individual efficiency scores using average efficiencies calculated from different subsets of the data. These average or structural efficiencies are differently biased and bias varies with sample size when standard DEA techniques are used. Bias correction makes it possible to identify the true differences in efficiency and thus to compare decision-making units (DMUs) belonging to different groups via their rescaled individual efficiency scores on one common basis. Moreover, this type of bias problem is present in other DEA applications. Therefore, the method proposed to deal with it has many potential applications beyond the groups and hierarchies model.