On FDH context-dependent data envelopment analysis

Data envelopment analysis (DEA) is a nonparametric technique for measuring and evaluating the relative efficiencies of decision making units (DMUs) with multiple inputs and multiple outputs. In the DEA literature, a context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a par- ticular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. Considering the fact that competition and dependency always exist between two real DMUs, the obtained efficiency levels must not be a combination of real DMUs. That is to say, a DMU must only be compared with real DMUs lying at different efficiency levels. This necessitates FDH to be the model chosen for evaluating and comparing a DMU against all efficiency levels. This paper proposes an FDH context-dependent DEA.