India is the emerging country with the world’s greatest social banking program, so Indian banks are required to finance the weaker sectors of society that are excluded from the traditional financial system (priority sectors), while also providing mainstream banking services to non-priority sectors. For social banks to promote the ethical–social management of their dual mission and to be successful in today’s business environment, they must be as efficient as possible in both dimensions of their banking activity. Whereas the efficiency of Indian banks in the financial dimension is well understood, to date there has been no research evaluating their double bottom-line of achieving social and financial goals. Our study applies an innovative Network Slack-Based DEA model to evaluate how efficient Indian public banks are when providing credit to priority and non-priority sectors. We also explore the main factors influencing bank efficiency. Results suggest that Indian public banks have performed relatively well in both activities, although social efficiency has been slightly greater than financial efficiency. Moreover, their commitment to priority sector lending has not come into conflict with the profit-seeking objectives of mainstream banking services. As regards determinants of social and financial efficiency, there are countervailing forces played by regional wealth, bank size, branch networks, and rural location. Our findings are therefore useful for stakeholders of Indian public banks as they indicate if these entities have adequately managed their double bottom-line, and hence if they are critical for poverty alleviation and development in India.
Double bottom-line Efficiency Indian social banks Priority and non-priority sectors Ethical–social management Network slack-based DEA model
This is a preview of subscription content, log in to check access.
A part of the research for this paper was completed while Mahinda Wijesiri was a visiting scholar at the Indira Gandhi Institute of Development Research (IGIDR), India. He gratefully acknowledges the funding and support from the International Development Research Center (IDRC), Canada. The authors would like to thank the Section Editor and the anonymous referees for their useful comments. Any remaining errors are solely the responsibility of the authors.
This research was funded by the International Development Research Center (IDRC) of Canada (Grant No.: IDRC 107125; Recipient: Mahinda Wijesiri).
Compliance with Ethical Standards
Conflict of interest
Almudena Martínez-Campillo declares that she has no conflict of interest. Mahinda Wijesiri has received a research grant from the “International Development Research Center (IDRC),” Canada. Peter Wanke declares that he has no conflict of interest.
Research Involving Human Participants or Animals
This article does not contain any studies with human participants or animals performed by any of the authors.
Bagnoli, L., & Megali, C. (2009). Measuring performance in social enterprises. Nonprofit and Voluntary Sector Quarterly, 20(10), 1–17.Google Scholar
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.CrossRefGoogle Scholar
Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212.CrossRefGoogle Scholar
Bhattacharyya, A., Lovell, C. K., & Sahay, P. (1997). The impact of liberalization on the productive efficiency of Indian commercial banks. European Journal of Operational Research, 98(2), 332–345.CrossRefGoogle Scholar
Bhattacharyya, A., & Pal, S. (2013). Financial reforms and technical efficiency in Indian commercial banking: A generalized stochastic frontier analysis. Review of Financial Economics, 22(3), 109–117.CrossRefGoogle Scholar
Burgess, R., & Pande, R. (2005). Do rural banks matter? Evidence from the Indian social banking experiment. The American Economic Review, 95(3), 780–795.CrossRefGoogle Scholar
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis. New York: Springer.Google Scholar
Cornée, S., & Szafarz, A. (2014). Vive la différence: Social banks and reciprocity in the credit marked. Journal of Business Ethics, 125(3), 361–380.CrossRefGoogle Scholar
Crowther, D., & Lauesen, L. M. (2016). Accountability and social responsibility: International perspectives. Bingley: Emerald Group Publishing Limited.CrossRefGoogle Scholar
Das, A., & Ghosh, S. (2006). Financial deregulation and efficiency: An empirical analysis of Indian banks during the post reform period. Review of Financial Economics, 15(3), 193–221.CrossRefGoogle Scholar
Das, A., & Kumbhakar, S. C. (2012). Productivity and efficiency dynamics in Indian banking: An input distance function approach incorporating quality of inputs and outputs. Journal of Applied Econometrics, 27(2), 205–234.CrossRefGoogle Scholar
Ebrahim, A., Battilana, J., & Mair, J. (2014). The governance of social enterprises: Mission drift and accountability challenges in hybrid organizations. Research in Organizational Behavior, 34, 81–100.CrossRefGoogle Scholar
Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.CrossRefGoogle Scholar
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253–290.CrossRefGoogle Scholar
Ferrari, S. L., & Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.CrossRefGoogle Scholar
Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston: Pitman.Google Scholar
Freeman, R. E., Harrison, J. S., Wicks, A. C., Parmar, B. L., & Colle, S. (2010). Stakeholder theory: The state of the art. New York: Cambridge University Press.CrossRefGoogle Scholar
Fujii, H., Managi, S., & Matousek, R. (2014). Indian bank efficiency and productivity changes with undesirable outputs: A disaggregated approach. Journal of Banking & Finance, 38(1), 41–50.CrossRefGoogle Scholar
Fukuyama, H., & Matousek, R. (2017). Innovative applications of O.R. Modelling bank performance: A network DEA approach. European Journal of Operational Research, 259(1), 721–732.CrossRefGoogle Scholar
Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38(5), 239–410.CrossRefGoogle Scholar
Holod, D., & Lewis, H. F. (2011). Resolving the deposit dilemma: A new DEA bank efficiency model. Journal of Banking & Finance, 35(11), 2801–2810.CrossRefGoogle Scholar
Huang, J., Chen, J., & Yin, Z. (2014). A network DEA model with super efficiency and undesirable outputs: An application to bank efficiency in China. Mathematical Problems in Engineering, 9, 1–14.Google Scholar
Lozano, S. (2016). Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector. Omega, 60, 73–84.CrossRefGoogle Scholar
Martínez-Campillo, A., Fernández-Santos, Y. & Sierra-Fernández, M. P. (2016). How well have Social Economy financial institutions performed during the crisis period? Exploring financial and social efficiency in Spanish credit unions. Journal of Business Ethics. https://doi.org/10.1007/s10551-016-3192-9.Google Scholar
Mia, A., & Chandran, V. G. R. (2016). Measuring financial and social outreach productivity of microfinance institutions in Bangladesh. Social Indicators Research, 127(2), 505–527.CrossRefGoogle Scholar
Mohan, R., & Ray, P. (2017): Indian financial sector: Structure, trends and turns. IMF Working Paper, WP/17/7, International Monetary Fund.Google Scholar
Ramus, T., & Vaccaro, A. (2017). Stakeholders matter: How social enterprises address mission drift. Journal of Business Ethics, 143(2), 307–322.CrossRefGoogle Scholar
Sahoo, B., & Tone, K. (2009). Decomposing capacity utilization in Data envelopment analysis: An application to banks in India. European Journal of Operational Research, 195(2), 575–594.CrossRefGoogle Scholar
Smith, J. (2018). Efficiency and ethically responsible management. Journal of Business Ethics, 150(3), 603–618.Google Scholar
Srinivasan, A., & Thampy, T. (2017). The effect of relationships with government-owned banks on cash flow constraints: Evidence from India. Journal of Corporate Finance, 46, 361–373.CrossRefGoogle Scholar
Thorat, A., Vanneman, R., Desai, S., & Dubey, A. (2017). Escaping and falling into poverty in India today. World Development, 93, 413–426.CrossRefGoogle Scholar
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.CrossRefGoogle Scholar
Tzeremes, N. G. (2015). Efficiency dynamics in Indian banking: A conditional directional distance approach. European Journal of Operational Research, 240(3), 807–818.CrossRefGoogle Scholar
Wijesiri, M., Viganò, L. & Meoli, M. (2015). Efficiency of microfinance institutions in Sri Lanka: A two-stage double bootstrap DEA approach. Economic Modelling,47, 74–83.CrossRefGoogle Scholar
Zha, Y., Liang, N., Wu, M., & Bian, Y. (2016). Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach. Omega, 60, 60–72.CrossRefGoogle Scholar
Zhang, P., & Qiu, Z. (2014). Regression analysis of proportional data using simplex distribution. Science China Mathematics, 44(1), 89–104.Google Scholar