Sustainability index of micro finance institutions (MFIs) and contributory factors

Abstract

Purpose – The purpose of this paper is to conceptualize the sustainability of micro finance institutions (MFIs) in a holistic manner. The idea is to create an index of sustainability for MFIs which includes financial and outreach aspects of sustainability. Further, it also discerns the factors which contribute to high (low) sustainability scores of MFIs. Design/methodology/approach – Data on Indian MFIs was collected from Microfinance Information Exchange database. Using the technique of order preference by similarity to ideal solution (TOPSIS), an Index of sustainability is built by aggregating multiple indicators (operational self-sufficiency ratio, the average loan balance per borrower and the number of active borrowers) to arrive at composite sustainability score of MFIs. Contributory factors of sustainability were identified using a multiple regression model. Findings – The sustainability score for MFIs ranges from a maximum score of 0.80 to a minimum of 0.26. Gross loan portfolio, No. of borrower per staff member, portfolio at risk>30 days and return on assets, are significant contributors to sustainability scores of Indian MFIs. Practical implications – The index of sustainability is a useful tool to rank the MFIs on a multi-dimensional construct of sustainability. The study also helps to unravel factors that significantly contribute to sustainability of Indian MFIs. Originality/value – This study is novel in its attempt to measure sustainability in a holistic fashion by focussing not just on the financial performance of the MFI but also on outreach dimensions. It is also unique in its approach to adopt a multi criteria decision-making technique of TOPSIS to measure sustainability of Indian MFIs.

Effect of Deposit Mobilization on the Financial Sustainability of Rural Saving and Credit Cooperatives: Evidence from Ethiopia

“…Rhyne [19] and Helms [16] also posited that sustainability was the means to the goal of outreach. This shifted our attention to focus on deposit mobilization, which is seen as a contributing factor towards operational self-sufficiency [20].…”

Re-Evaluating Sustainability of Microfinance Institutions by Using Topsis

“…Column 2 in Table 2 provides a list of criteria, where C1 is financial self-sufficiency, C2 is operational self-sufficiency, C3 is the depth of outreach measured by average loan balance per borrower, and C4 is the breadth of outreach approximated by number of active borrowers, for assessing the sustainability score of MFIs. Among these four criteria's C1, C2, and C4 are designated as benefits or "more is better" criteria, which indicates that MFI is advancing towards a higher level of sustainability when FSS, OSS, and breadth of outreach (measured by the number of active borrowers) increases (Bhanot et al, 2015). On the other hand, the increase in average loan balance per borrower weakens the social role of MFIs.…”

“…Financial self-sufficiency ratio has a minimum standard value of 100 percent which denotes that MFIs are able to cover their cost without depending on subsidized funding and may continue on a going concern (CGAP, 2004). OSS of more than 100 percent denotes that operating revenues of MFIs exceed their operating expenses, and is a standard measure used by Bhanot et al (2015) and CGAP (2004). According to prudential regulations of Microfinance Banks, issued by State Bank of Pakistan, the maximum exposure for both general and microenterprise loan shall not exceed PKR 500,000.…”

“…Moreover, there is a strong disagreement on the financial and operational self-sufficiency measures of sustainability as these measures mainly focus on the financial aspects, ignoring the social dimension of sustainability. The same has been criticized by Bhanot et al (2015), who stated that merely focusing on financial measures and ignoring the outreach measure deviates MFI from its original mission. There is a need to develop the sustainability measure which considers both financial and outreach measures.…”

Implementation of Fuzzy Tsukamoto Algorithm in Determining the Level of Financial Distress in Microfinance Institutions

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[9][10][11][12][13][14][15] © 2018 IJSRST | Volume 4 | Issue 11 | Print ABSTRACT Fuzzy Tsukamoto is one method that is very flexible and tolerant of existing data. Fuzzy Tsukamoto has the advantage of being more intuitive, accepted by many, more suitable for the input received from humans rather than machines.

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