PurposeThe study attempts to examine the bias-adjusted financial and operational efficiency estimates of microfinance institutions (MFIs) operating in the Eastern Europe and Central Asia (ECA) region during the financial year 2017–2018. In addition, the study also identifies the responsible factors determining the financial and operational performances of MFIs operating in the ECA region.Design/methodology/approachThe study employs two-stage bootstrap data envelopment analysis (DEA). In the first stage, the authors incorporate the bootstrap procedure in the DEA framework as suggested by Simar and Wilson (2000) to estimate the bias-corrected efficiency scores of 67 sample MFIs. In order to identify the drivers of efficiency level, the study deploys the bootstrap truncated regression model following the Simar and Wilson (2007) guidelines in the second stage of analysis.FindingsThe authors note from the empirical results that MFIs operating in the ECA region are relatively more financially efficient (0.588) than socially efficient (0.496). However, none of the MFIs were found to be operating at best-practice frontier while considering the bias-adjusted efficiency estimates. Further, the results of second stage of analysis confirm that corporate governance, that is, board size has positive and statistically significant impact on MFIs’ performances. In addition, the bad credit quality deteriorates both financial revenue and operational efficiency. Moreover, the MFIs’ size, profit status and debt-to-equity ratio were also found to be statistically significant to determine the operational and financial efficiency of MFIs in the ECA region.Practical implicationsThe study provides the robust efficiency estimates and factors responsible to determine the financial and operational efficiency of MFIs operating in the ECA region. Further, the empirical results of the study provide the inputs and further direction to the policymakers, regulators, practitioners and managers in framing the policy and optimal operating strategies for ECA MFIs industry.Originality/valueThe study extends the DEA analysis by incorporating the bootstrap procedure in DEA model to estimate the bias-adjusted efficiency scores which are more reliable and robust. In addition, bootstrap truncated regression has been applied to identify the drivers of efficiency. Moreover, in the literature there is no single study which has deployed the double bootstrap DEA framework to examine the financial and operational efficiency estimates and its drivers.

This paper examines the financial, social and overall bias-corrected efficiencies of 82 Indian microfinance institutions for the year 2015-2016, and obtains the potential determinants of efficiency scores using double bootstrap procedure by Simar and Wilson (2007). Bias-corrected efficiency scores are used to provide a strict ranking to sampled MFIs. An attempt is made to resolve the unsettled debate on trade-off between the dual goals of the MFIs. The results reveal that Indian MFIs are found more financially efficient than socially efficient in the sampled year. The efficiency differences across the MFIs' operating in central and northern regions and distinct sizes classes of MFIs are statistically significant. The study establishes enough evidence to advocate the absence of trade-off in achieving dual goals in the Indian MFI industry in the year 2015-2016. Finally, size of MFI and equity to assets ratio influences the efficiency levels of Indian MFIs.

The study examines the efficiency differences across the ownership structure of Indian microfinance institutions (MFIs) operating during the year 2005/ 06 to 2017/18 in response to regulatory reforms initiated by the Reserve Bank of India (RBI) in the year 2011. We remove the outliers from the dataset first. Thereafter, we employ the bootstrap data envelopment analysis (DEA) to assess the bias-corrected efficiency scores. To identify the performance determinants, we use bootstrap truncated regression. The empirical results suggest that the performance difference between NBFCs and Non-NBFC MFIs is not statistically significant in the sample period. Further, the study finds that the size and ownership structure of MFI has a positive and statistically significant impact on the efficiency level. Although the coefficient of PAR30 (Portfolio at risk, 30 days) is statistically insignificant, however, the results conclude that the deteriorating credit quality has hindered the efficiency level. The Indian MFI industry needs to focus on the adoption of more innovative technology and partnership with FinTech (financial technology

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PurposeThis paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.Design/methodology/approachThe study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.FindingsThe empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.Practical implicationsThe practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.Originality/valueThis study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.