Explaining Inefficiency in Nonparametric Production Models: the State of the Art

The performance of economic producers is often affected by external or environmental factors that, unlike the inputs and the outputs, are not under the control of the Decision Making Units (DMUs). These factors can be included in the model as exogenous variables and can help explaining the efficiency differentials, as well as improving managerial policy of the evaluated units. A fully nonparametric methodology which includes external variables in the frontier model and defines conditional DEA and FDH efficiency scores is now available for investigating the impact of external-environmental factors on the performance. In this paper we offer a state of the art review of the literature that has been proposed to include environmental variables in nonparametric and robust (to outliers) frontier models and to analyze and interpret the conditional efficiency scores, capturing their impact on the attainable set and/or on the distribution of the inefficiency scores. This paper develops and complements Badin et al. (2011) approach by suggesting a procedure which allows to make local inference and provide confidence intervals for the impact of the external factors on the process. We advocate for the nonparametric conditional methodology which avoids the restrictive "separability" assumption required by the two-stage approaches in order to provide meaningful results. An illustration with real data on mutual funds shows the usefulness of the proposed approach.