Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique

Abstract

Abstract This paper introduces a novel method to incorporate categorical non-discretionary variables in Data Envelopment Analysis (DEA) models. While solutions to this problem have been introduced before, they have rarely been employed in applied work. We surmise that existing solution concepts pose problems for applied researchers and develop a simple and straightforward alternative based on indicator variables. We thereby provide a flexible tool for models with categorical variables that–unlike the approaches mentioned above–can be solved with standard DEA software irrespective of scale assumptions even if no option for non-discretionary variables is available. Furthermore, there is no need to split the data and run multiple DEA, one for each data set generated. The model is extensible to categorical discretionary variables and in addition to non-hierarchical data.