Ram Pratap Sinha

Commercial bank efficiency is of paramount importance for the sustenance of the financial sector. In view of this, the study seeks to evaluate and explain the technical efficiency of Indian private and publicly owned banks for a seven-year period (2017–2018 to 2023–2024) using the data envelopment analysis approach. However, the present study does not follow the Markov chain Monte Carlo simulation approach, which has been followed in the extant efficiency literature. Instead, the study uses a Gaussian distribution for deriving the prior and posterior probability distributions. For modelling the weights, we have used the Dirichlet distribution to ensure non-negativity. The study finds two key results. First, no definite trend of superiority/inferiority of publicly owned commercial banks over the private ownership banks is found. Second, efficiency has a significant and positive relationship to asset composition. The impact of bank size, however, was found to be negative.

The current research applies a multi-stage approach for measuring and explaining the efficiency performance of 20 general insurers operating in India for the phase 2012–2013 to 2019–2020. In the first stage, the study adopts non-parametric radial data envelopment analysis (DEA) for point and interval estimation of firm-specific efficiency, scale efficiency, returns to scale (RTS) and scale elasticity. The second stage of the study applies panel data regression for regressing technical and scale efficiency scores on the index of market concentration, insurer age, return on shareholders’ capital and the solvency indicator. The outcome of the next stage indicates that the index of market concentration and insurer age are the two contextual variables which are statistically significant, although their impacts on technical and scale efficiency are negative. The influence of the solvency ratio is significant for scale efficiency only. JEL Classifications: C-23, C-61, D-22, G-22

The extant DEA estimation of efficiency presupposes the existence of a monotone strictly convex production function. The presence of increasing or decreasing returns to scale are two cases of scale inefficiency where the firm are producing at sub-optimal scales. The convex DEA framework is, however, in conflict with the S-shaped production function introduced by Ragnar Frisch. The present study provides an empirical investigation of life insurer efficiency using the cluster-based approach introduced by Tone and Tsutsui. The present study thus estimates and explains the cluster adjusted efficiency scores of Indian life insurers.

Ignoring the presence of non-performing assets makes efficiency measurement inappropriate and incomplete. Thus, the present study considers non-performing assets as an undesirable output and applies the slack-based efficiency model to measure the efficiency of public sector banks in India during 2004–2005 to 2018–2019. A two-metric performance assessment of sample banks is carried out using mean efficiency and the non-performing assets management ratio. This study is extended to investigate determinants of bank efficiency using a fixed effects model and dynamic panel data regression on the contextual variables. Results show that profitability as measured by return on equity (ROE) and priority sector exposure have had no impact on efficiency. However, cost of deposits and capital adequacy ratio have a significant negative impact on the efficiency of public sector banks in India. Most importantly, the study finds a decline in efficiency in recent years, indicating a necessity of serious efforts for revamping these state-owned banks.

The deregulation of Indian life insurance industry completed two decades, from 2000 to 2020. During the post-deregulation phase, competition in the insurance industry increased manifold. The regulatory environment also witnessed radical changes in this phase. Against this backdrop, the present study seeks to assess the impact of various idiosyncratic variables, economic growth, and competition on the efficiency performance of life insurance companies operating in India. Our study is based on observations relating to 22 life insurance companies doing business in Indian market and the period of observation is 2013–2014 to 2020–2021. For efficiency evaluation, we have applied the directional distance function approach. The observed life insurers have been ranked on the basis of their efficiency performance. Finally, the impact of several contextual variables (solvency ratio, asset size, insurer age, Hirschman-Herfindahl index, and gross domestic product [GDP] growth rate) on technical efficiency has been evaluated using panel data and Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The results indicate that the role of asset size and GDP growth rate is crucial in determining life insurer performance.