Incorporating causal modeling into data envelopment analysis for performance evaluation

Abstract

The risk factors in banking have been considered an undesirable carryover variable by the literature. Methodologically, we consider the risk factor using loan loss reserves as a desirable carryover input with dynamic characteristics, which provides a new framework in the dynamic network Data Envelopment Analysis (DEA) modelling. We substantiate our formulation and results using novel techniques for causal modelling to ensure that our dynamic network model admits a causal interpretation. Finally, we empirically examine the impact of risk from various economic sectors on efficiency. Our results show that the inefficiencies were volatile in Chinese banking over the period 2013–2020, and we further find that the state-owned banks experienced the highest levels of inefficiency and volatility. The findings report that credit risk derived from the agricultural sector and the Water Conservancy, Environment and Public Facilities management sector decreases bank efficiency, while credit risk derived from the wholesale and retail sector improves bank efficiency. The results of our innovative causal modelling show that our pioneering modelling on the role of loan loss reserves is valid. In addition, from an empirical perspective, our second-stage analysis regarding the impact of risk derived from different economic sectors on bank efficiency can be applied to other banking systems worldwide because of our successful validation from causal modelling. Our attempt to incorporate causal inference into DEA can be generalized to future studies of using DEA for performance evaluation.