DEA and Directional Distance Functions

In Chapter 4 we undertake an in-depth study of directional distance functions and how they apply to DEA…We begin the chapter with a discussion of the directional vectors and how they influence the DEA scores for inefficient DMUs. We next consider the role of the direction vectors when the goal is to aggregate across DMUs. Here we show that aggregation is facilitated when the DMUs are given the same direction vector, in particular the unit direction.The concluding section addresses the choice of direction in general, and how to endogenize that choice in particular. This leads us to a discussion of directional distance functions and their indication properties, i.e., the subsets of the technology to which the DMUs under evaluation are projected. We also show how the endogenization of the directions are related to slackbased directional distance functions.An appendix including an indication theorem concludes.