Efficiency assessment of microfinance institutions: using DEA with weighted Russell directional distance model

Abstract

PurposeThe study aims to focus on data envelopment analysis for assessing the microfinance institutions (MFIs) efficiency over the footings of its undesirable output, i.e. non-performing loans (NPLs). The attention is not only to evaluate the efficiency but also to identify the variable wise inefficiencies incorporating the quality of the portfolio.Design/methodology/approachThe paper assessed MFI efficiency using three different methods of treatment of undesirable output to portray the significant difference. It also has used an advanced methodological model, i.e. weighted Russell directional distance model (WRDDM), under the non-radial assumption that allowed us to find the variable-wise inefficiency contribution. The study also investigated the efficiency differences concerning ownership, including all sizes of MFIs.FindingsThe study findings evidence the fall in efficiency score as NPL integrated, and it is found to be statistically significant. In the context of inefficiency assessment, among all input and output variables, total employees and operating expenses, portfolio quality inefficiencies are the leading causes of MFI inefficiencies. Undesirable output inefficiency accounts for almost one-third part of the total inefficiencies and remaining due to input inefficiencies. It is significant to draw attention that there is no improvement in undesirable output inefficiency. By contrast, input inefficiencies retained gains for two years and gradually showed a decreasing trend throughout 2015–2017.Research limitations/implicationsThe authors have used balanced panel data of 72 Indian MFIs for five years' period from 2013–2017 whose complete data were available in the Microfinance Information Exchange.Practical implicationsThe paper has focused on identifying the inefficiencies that are needed to be focused on to attain efficiency. It could provide vital information to the managers, policymakers in identifying the causes of inefficiencies, which is crucial to improve for long-term sustainability. It will be a roadmap for benchmarking, strategy building and policy-making processes.Social implicationsThe findings of the study help in finding the benchmarking information for the inefficient decision-making units to identify the target units that need particular attention to focus. These practices could give a positive outcome, not only for institutions but also for the MFI clients.Originality/valueThe study provides an insight in to variable-wise inefficiency measurement using advanced model WRDDM in Indian context MFIs.