Balancing Fairness and Efficiency: Performance Evaluation with Disadvantaged Units in Non-homogeneous Environments

Balancing fairness and efficiency has become an emerging issue in today's society. In this paper we propose a balanced benchmarking methodology to address the fairness issue in performance evaluation. The methodology used to create performance measures is data envelopment analysis (DEA), a tool designed to evaluate the relative efficiencies of comparable decision-making units (DMUs); i.e. all DMUs use the same inputs and outputs and experience the same general operating conditions. In many applications, however, the DMUs may experience non-homogenous operating conditions or environments. An example might be a set of manufacturing plants where some have been upgraded and others not. Such settings can necessitate modifying the DEA structure such as to make allowance for different environmental conditions. Such a model is developed herein to create a level playing field for performance evaluation in two different settings: a setting involving hybrid and conventional (non-hybrid) vehicles; and another setting involving bank branches located in poverty and non-poverty regions. Our model and empirical tests contribute not only to the advance of balanced benchmarking methodologies, but also to the practice of incorporating fairness in performance evaluation across multiple products and organizations. (C) 2020 Elsevier B.V. All rights reserved.