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.

Efficiency of financial cooperatives. A structured review of the literature

“…On the other hand, risks are also considered in the various variables used for the estimation of global, social, and economic efficiency (San-Jose, Retolaza, Torres Pruñonosa, 2014; Sousa de Abreu et al, 2018; San-Jose, Retolaza, Torres Pruñonosa, 2020), as well as scale and technical efficiency (Chongrun and Kai, 2017). In addition, the NPL variable is considered in the efficiency of microfinance institutions (Nashihin and Harahap, 2014;Mohini M and Vilvanathan, 2020).…”

“…In addition, the Malmquist Index (MI) is employed to look for productivity change in a productive system over time (Chongrun and Kai, 2017;Martins, Steiner, Wilhem, Steiner Neto and dos Santos, 2018). Finally, the Weighted Russell Directional Distance model (WRDDM) to find the inefficiency scores of the variables (Mohini and Vilvanathan, 2020). These functions allow for a simultaneous increase in output along with a reduction in input while measuring technical efficiency.…”

“…The credit union approach took place in the 19th century in Germany as a response to the hardships suffered by people derived from the economic changes that the country was going through at that time. Cooperatives driven by the pursuit of common welfare became a global phenomenon and have been increasingly popular in many countries (McKillop and Wilson, 2011), to address the great challenge of eradicating poverty and improving the quality of life of the poor (Mohini and Vilvanathan, 2020).…”

An integrated framework for analysing performance indicators of Indian microfinance institutions: a multi-stakeholder perspective

“…Furthermore, they conclude that the impact of governance on MFI performance is largely dependent on the legal structure of MFIs. Past literature on MFI's performance were based on secondary data obtained from MFI's report and data published by different organisations like MIX Market (Mohini and Vilvanathan, 2020;Khan and Shireen 2020;Churchill, 2019a, Lebovics et al, 2016, Annim, 2012. Studies related to the Impact assessment of microfinance intervention used primary data through survey and randomised control trials (RCT) (Banerjee et al, 2015;Kaboski and Townsend, 2012).…”

The influence of SYRIZA-ANEL Greek health policies on hospital efficiency

“…Data Envelopment Analysis (DEA) is the prevailing method for assessing the efficiency of the healthcare system [ 20 , 21 ]. The DEA method was originally developed by Farell [ 22 ] to measure the change to efficiency of related decision making units [ 23 – 25 ]. Charnes et al [ 26 ] then developed the CCR model which is currently used to measure technical efficiency [ 27 , 28 ].…”