Meta-Frontier Stochastic Cost and Revenue Efficiency Analysis: An Application to Bank Branches

In many real-world situations, the cost and revenue performance of heterogeneous decision making units (DMUs) should be assessed while uncertain data are presented. The exiting data envelopment analysis (DEA) models have dealt with the economic efficiency of nonhomogenous DMUs without considering random performance measures. In this paper, a stochastic DEA approach is, therefore, proposed to estimate meta-frontier stochastic cost and revenue efficiencies under the convex technology. To illustrate, group cost and revenue efficiencies and meta cost and revenue efficiency scores under convex metatechnology are measured using cost-based and revenue-based chance-constrained DEA models. Furthermore, the deterministic frameworks of approaches are provided. Cost and revenue gap ratios and sources of meta-frontier stochastic cost and revenue inefficiencies are also handled. An empirical study of the banking industry is used to show the applicability and reliability of the proposed technique.

Automated summary

This paper proposes a stochastic DEA approach to assess the cost and revenue performance of heterogeneous decision making units (DMUs) under uncertain data. The cost and revenue efficiencies under convex metatechnology are measured using cost-based and revenue-based chance-constrained DEA models, providing deterministic frameworks of approaches. Cost and revenue gap ratios and sources of meta-frontier stochastic cost and revenue inefficiencies are also addressed. An empirical study of the banking industry validates the applicability and reliability of the proposed technique.