How to measure the impact of environmental factors in a nonparametric production model

The measurement of technical efficiency allows managers and policy makers to enhance existing differentials and potential improvements across a sample of analyzed units. The next step involves relating the obtained efficiency estimates to some external or environmental factors which may influence the production process, affect the performances and explain the efficiency differentials. Recently introduced conditional efficiency measures (,  and ), including conditional FDH, conditional DEA, conditional order-m and conditional order-α, have rapidly developed into a useful tool to explore the impact of exogenous factors on the performance of Decision Making Units in a nonparametric framework. This paper contributes in a twofold fashion. It first extends previous studies by showing that a careful analysis of both full and partial conditional measures allows the disentangling of the impact of environmental factors on the production process in its two components: impact on the attainable set and/or impact on the distribution of the efficiency scores. The authors investigate these interrelationships, both from an individual and a global perspective. Second, this paper examines the impact of environmental factors on the production process in a new two-stage type approach but using conditional measures to avoid the flaws of the traditional two-stage analysis. This novel approach also provides a measure of inefficiency whitened from the main effect of the environmental factors allowing a ranking of units according to their managerial efficiency, even when facing heterogeneous environmental conditions. The paper includes an illustration on simulated samples and a real data set from the banking industry.