Alternative approaches to include exogenous variables in DEA measures: A comparison using Monte Carlo

The theory for measuring efficiency of producers has developed alternative approaches to correct for the effect of non-discretionary variables in the analysis. A review of different options in the specific literature of Data envelopment analysis (DEA) allows us to identify three main approaches: one-stage, two-stage and multi-stage models. Recently, some of these models have been improved through the development of bootstrap methods making it possible to make inference and to avoid bias in the estimation of efficiency scores. The aim of this paper is to test the performance of these recent models and to compare among them using simulated data from a Monte Carlo experimental design.