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Microfinance institutions (MFIs) provide small loans and other financial services to the poor. These institutions are established for helping the poor to raise income levels and to reduce poverty. Recently, MFIs are required to reduce their dependence on grants and subsidies. Consequently, they face conflicting objectives of improving reach and profitability. These can be achieved by improving productivity. This paper aims to investigate productivity change in 21 major MFIs in India which are rated by Credit Rating and Information Services of India Limited in 2014.

This paper attempts to examine total factor productivity change in 21 major Indian MFIs during the period from 2014 to 2016 using Malmquist productivity index. The inputs and outputs are selected considering objectives of outreach and financial sustainability. The authors have categorized MFIs in three categories, namely, large, medium and small, depending on asset size.

It is revealed that large MFIs are able to catch up with industry best practices by improving their systems and processes, but they need to improve scale efficiency. The Reserve Bank of India has recently initiated a policy of granting banking licenses to those financial institutions which have good outreach and are financially strong. It can be used for shortlisting MFIs before granting permission to operate as banks. The method can also be used for benchmarking them for productivity. It can also be replicated in other countries.

In India, MFIs are playing important role in economic development by providing microcredit to the poor. However, very few studies have been undertaken regarding productivity of MFIs in India. The present study intends to fill this gap. It will facilitate benchmarking of MFIs as competitive and sustainable financial institutions catering to the requirements of small borrowers.

This paper attempts to measure the state-wise impact of Prime Minister's Jan Dhan Yojana (PMJDY) in 30 states and 6 union territories of India for the years 2016, 2017 and 2018; and tries to develop a state-wise plan for geographical expansion of outlets optimizing the overall impact of the scheme.

The state-wise impact factor is calculated using demographic penetration of the scheme in rural areas, demographic penetration of the scheme in urban areas, percentage of accounts with Rupay cards and average balance in these accounts. The impact factor is postulated to be a linear function of literacy, per capita GDP, demographic and geographic penetration of banks and the number of poor people. The weights for the sub-parameters are derived through principal component analysis. A generalized linear model with heteroscedasticity and autocorrelation consistency method for estimation of the equation with robust standard errors is used.

It is found that the scheme has been more effective in the states with higher levels of illiteracy which is contrary to the findings of existing studies where illiteracy is identified as a barrier to financial inclusion. A state-wise plan for geographical expansion of outlets is proposed with a view to optimizing the overall impact of the scheme, along with suggestions for improvement.

The data for ATMs and bank mitras are available for some of the years, for some states and hence missing data were estimated using extrapolation or on an average basis. Furthermore, the panel data are available for three years making the period of panel small. These aspects might have affected the efficacy of our estimates.

The paper evaluates the newly launched ambitious program PMJDY by the Government of India (GoI), it will have far reaching impact on financial inclusion.