How to Measure the Impact of Environmental Factors in a Nonparametric Production Model?

The measurement of technical efficiency of decision making units is useful for making comparisons and informing managers and policy makers on existing differentials and potential improvements across a sample of analyzed units. The step further is to relate the obtained efficiency estimates to some external or environmental variables which may influence the production process, affect the performances and explain the efficiency differentials. Conditional efficiency measures (Daraio and Simar, 2005; 2007a), including conditional FDH, conditional DEA, conditional order−m and conditional order−α have been recently introduced and became rapidly a useful tool to explore the impact of external-environmental factors on the performance of Decision Making Units in a nonparametric framework. In this paper, we show that analyzing these conditional efficiency scores we can disentangle the impact of these factors on the production process in its components: impact on the attainable set in the input x output space, and/or impact on the distribution of the inefficiency scores. We extend existing methodological tools to investigate these interrelationships, both from an individual and a global perspective. We emphasize the usefulness of regressing the conditional efficiencies on the explaining factors. The analysis of the residuals provides a measure of efficiency whitened from the main effect of the environmental factors. This allows to rank the firms according to their “managerial” efficiency, even when facing heterogeneous environmental conditions. Our approach is illustrated through simulated samples and with a real data set in the Banking industry.