Purpose: The measurement of sustainability for microfinance institutions (MFIs) has been a serious problem for both practitioners and researchers over the last few decades. A multicriteria decision-making approach is used to develop an index that measures the sustainability of microfinance institutions based on the double bottom line.

Methodology: The sustainability score of MFIs operating in Pakistan for the year 2006-2015 is measured using the technique for order preference by similarity to ideal solution (TOPSIS). During the assessment, equal weights are assigned to all indicators of sustainability. Additionally, a hypothetical organization was assigned the industry threshold to generate composite scores using TOPSIS. Later, sustainability levels of individual MFIs were compared with this industry threshold.

Findings: Microfinance institutions that attain higher financial sustainability and positive outreach are ranked high. The result shows that the threshold sustainability level of the microfinance sector in Pakistan from 2006-2015 was 23.52, 26.31, 23.80, 45.83, 45.83, 66.67, 77.77, 91.60, and 88.88 percent respectively. Although the sustainability level in 2015 decreases with respect to 2014, still the overall growth of the sector is remarkable. Practical implications: The results obtained from TOPSIS for evaluating the sustainability of MFIs under the double bottom line highlight its practical applicability. MFIs are under immense pressure by regulatory bodies, investors, donors, and financial experts to achieve sustainability. This index would help MFIs to track progress and improve their sustainability.

Novelty/Originality: This study is the first of its kind to determine the sustainability of MFI by using all the four indicators of sustainability, including financial self-sufficiency, operational self-sufficiency, depth of outreach and breadth of outreach. Existing sustainability indicators does not provide the threshold level of sustainability. Instead, they provide a ranking of MFIs from top to bottom only. This study is novel to identify whether MFIs have met or failed to achieve sustainability by providing the threshold level.


  1. Afful-Dadzie, A., E. Afful-Dadzie and C. Turkson, 2016. A topsis extension framework for re-conceptualizing sustainability measurement. Kybernetes, 45(1): 70-86.https://doi.org/10.1108/K-04-2015-0106
  2. Arrow, K.J., P. Dasgupta, L.H. Goulder, K.J. Mumford and K. Oleson, 2012. Sustainability and the measurement of wealth. Environment and development economics, 17(3): 317-353.https://doi.org/10.1017/S1355770X12000137
  3. Bhanot, D., V. Bapat and J. Connelly, 2015. Sustainability index of micro finance institutions (mfis) and contributory factors. International Journal of Social Economics, 42(4).https://doi.org/10.1108/IJSE-01-2014-0001
  4. Bilbao-Terol, A., M. Arenas-Parra, V. Cañal-Fernández and J. Antomil-Ibias, 2014. Using topsis for assessing the sustainability of government bond funds. Omega, 49: 1-17.https://doi.org/10.1016/j.omega.2014.04.005
  5. Böhringer, C. and P.E. Jochem, 2007. Measuring the immeasurable-a survey of sustainability indices. Ecological economics, 63(1): 1-8.https://doi.org/10.1016/j.ecolecon.2007.03.008
  6. Boran, F.E., S. Genç, M. Kurt and D. Akay, 2009. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with topsis method. Expert Systems with Applications, 36(8): 11363-11368.https://doi.org/10.1016/j.eswa.2009.03.039
  7. Cervelló-Royo, R., F. Guijarro and V. Martinez-Gomez, 2017. Social performance considered within the global performance of microfinance institutions: A new approach. Operational Research: 1-19.https://doi.org/10.1007/s12351-017-0360-3
  8. CGAP, 2003. Microfinance consensus guidelines.
  9. CGAP, 2004. Key principles of microfinance. CGAP, WASHINGTON.
  10. Christen, R.P., 1995. Maximizing the outreach of microenterprise finance: An analysis of successful microfinance programs. Center for Development Information and Evaluation, US Agency for International Development.
  11. Deng, H., C.-H. Yeh and R.J. Willis, 2000. Inter-company comparison using modified topsis with objective weights. Computers & Operations Research, 27(10): 963-973.https://doi.org/10.1016/S0305-0548(99)00069-6
  12. Gutiérrez-Nieto, B., C. Serrano-Cinca and C.M. Molinero, 2009. Social efficiency in microfinance institutions. J Oper Res Soc, 60(1): 104-119.https://doi.org/10.1057/palgrave.jors.2602527
  13. Hermes, N., R. Lensink and A. Meesters, 2011. Outreach and efficiency of microfinance institutions. World Development, 39(6): 938-948.https://doi.org/10.1016/j.worlddev.2009.10.018
  14. Hwang, C.-L. and K. Yoon, 1981. Methods for multiple attribute decision making. In: Multiple attribute decision making. Springer: pp: 58-191.https://doi.org/10.1007/978-3-642-48318-9_3
  15. Lancker, E. and P. Nijkamp, 2000. A policy scenario analysis of sustainable agricultural development options: A case study for nepal. Impact Assessment and Project Appraisal, 18(2): 111-124.https://doi.org/10.3152/147154600781767493
  16. Louis, P. and B. Baesens, 2013. Do for-profit microfinance institutions achieve better financial efficiency and social impact? A generalised estimating equations panel data approach. J Dev Effect, 5(3): 359-380.https://doi.org/10.1080/19439342.2013.822015
  17. Mia, M.A., S. Nasrin and Z. Cheng, 2015. Quality, quantity and financial sustainability of microfinance: Does resource allocation matter? Quality & Quantity: 1-14.https://doi.org/10.1007/s11135-015-0205-1
  18. Nanayakkara, G., 2012. Measuring the performance of microfinancing institutions: A new approach. South Asia Economic Journal, 13(1): 85-104.https://doi.org/10.1177/139156141101300105
  19. Okumu, L.J., 2007. The microfinance industry in uganda: Sustainability, outreach and regulation. Stellenbosch: University of Stellenbosch.
  20. Rai, A.K. and S. Rai, 2012. Factors affecting financial sustainability of microfinance institutions. Journal of Economics and Sustainable Development, 3(6): 1-9.
  21. Rauf, S.A. and T. Mahmood, 2009. Growth and performance of microfinance in pakistan. Pakistan Economic and Social Review: 99-122.
  22. Rismayadi, B. and M. Maemunah, 2018. Creative economy to increase community revenue based on tourism object, medalsari village, pangkalan district karawang regency. Journal of Accounting, Business and Finance Research, 3(1): 28-35.https://doi.org/10.20448/2002.31.28.35
  23. Riyanti, M.T., 2018. Development of learning devices commercial graphic based planning project. International Journal of Education, Training and Learning, 2(1): 1-6.https://doi.org/10.33094/6.2017.2018.21.1.6
  24. Rosli, A. and T.I. Siong, 2018. Determinants of customer satisfaction towards services provided by agencies in urban transformation centre (utc). International Journal of Economics, Business and Management Studies, 5(1): 9-15.https://doi.org/10.20448/802.51.9.15
  25. Rotova, N.A., 2018. Development of independence among future primary school teachers by applying interactive learning methods. Journal of Education and e-Learning Research, 5(2): 118-121.https://doi.org/10.20448/journal.509.2018.52.118.121
  26. Saad, S., I. Umer and F. Ahmed, 2018. An empirical evidence of over reaction hypothesis on karachi stock exchange (kse). Asian Economic and Financial Review, 8(4): 449-465.https://doi.org/10.18488/journal.aefr.2018.84.449.465
  27. Saeed, N. and A.I. Kayani, 2018. Role of college principals in promoting quality of education in district kotli aj&k. Asian Journal of Contemporary Education, 2(2): 149-158.https://doi.org/10.18488/journal.137.2018.22.149.158
  28. Samaila, M., O.C. Uzochukwu and M. Ishaq, 2018. Organizational politics and workplace conflict in selected tertiary institutions in edo state, nigeria. International Journal of Emerging Trends in Social Sciences, 4(1): 26-41.https://doi.org/10.20448/2001.41.26.41
  29. Shih, H.-S., H.-J. Shyur and E.S. Lee, 2007. An extension of topsis for group decision making. Mathematical and Computer Modelling 45(7-8): 801-813.https://doi.org/10.1016/j.mcm.2006.03.023
  30. Wanke, P., M.A.K. Azad and C. Barros, 2016. Predicting efficiency in malaysian islamic banks: A two-stage topsis and neural networks approach. Research in International Business and Finance, 36: 485-498.https://doi.org/10.1016/j.ribaf.2015.10.002
  31. Yaron, J., 1994. What makes rural finance institutions successful? The World Bank Research Observer, 9(1): 49-70.https://doi.org/10.1093/wbro/9.1.49