An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment

Abstract

Performance evaluation is an important part in the management of any decision-making unit (DMU) as it identifies sources of managerial inefficiencies and provides a policy for inefficient DMUs to improve their efficiency. The latter is generally affected by environmental variables that are beyond managerial control. Modeling the impact of these environmental variables is a critical issue for both researchers and practitioners. Researchers developed and proposed several methods to deal with this issue in general and in the data envelopment analysis (DEA) literature in particular. However, the available two-stage DEA methods do not account for interdependence between observations and they are of limited use when the number of variables is fairly large. This paper proposes an integrated framework combining DEA, and boosted generalized linear mixed models (GLMMs) that accounts for the interdependence problem when studying the impact of environmental variables on performance. Additionally, the framework carries out variable selection. The framework is illustrated with a sample of 151 commercial banks from Middle East and North African countries.