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The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

The purpose of this paper is to investigate the input- and output-specific technical inefficiency of Vietnamese prawn-rice rotational crops (PRRC) and to identify the impacts of the socio-economic characteristics of farmers and farms on these technical inefficiencies.

This study first used a Russell-type (input-output) directional distance function to estimate the input- and output-specific technical inefficiency. Second, it applied a bootstrap truncated regression to analyze the factors influencing these technical inefficiencies. Data were gathered through a survey among 94 farmers, from Ben Tre and Kien Giang provinces, the two popular PRRC areas in the Mekong Delta.

Results show that Vietnamese PRRC farmers could reduce the water surface area by 3%, the use of seedlings by 15%, labor by 16%, fertilizers by 26%, and the use of others by 24%, while simultaneously increasing the revenue of farming system by 57% relative to the variable returns to scale (VRS) frontier. Farmers with more years of experience are generally better in managing the use of seedlings and in improvement of revenue. Farmers in Kien Giang province are more efficient in achieving revenue of the PRRC farming system than farmers in Ben Tre province.

Outcomes of this study are useful to identify strategies in minimizing the use of inputs while simultaneously maximizing PRRC production.

This paper relates to the comparison of two mostly different ecological zones, being the dominant production areas of PRRC, in which, Kien Giang represents the western part, while Ben Tre is in the eastern part of the Vietnam’s Mekong Delta. The findings not only expand the current understanding but also suggest various meaningful research questions regarding the development of Vietnamese PRRC under the impacts of climate change. The study also contributes to the literature on examining the input- and output-specific technical inefficiencies and influencing factors.

This paper studies the efficiency of Indian coal-fired thermal power plants (CTPPs) in by-production of electricity and particulates also known as Suspended Particulate Matter (SPM).

A non-radial directional distance function is optimized using data envelopment analysis to enumerate the overall inefficiency of CTPPs and its components in recent times. Further, second-stage regression analysis is conducted to identify factors that affect the inefficiency of plants.

The low inefficiency score for electricity generation suggests that most CTPPs operate close to the good output frontier. A high degree of emissions inefficiency is a challenge for Indian CTPPs. Ever-rising coal use inefficiency is a hindrance to control SPM emissions. The second stage regression analysis concludes that factors like ownership and capacity utilization play vital roles in determining a plant’s inefficiency level. Privately owned CTPPs have performed better in terms of technical inefficiency and emission inefficiency than plants owned by Central and State governments.

To the best of the authors’ knowledge, this study is one of the few published works that benchmark the productive and environmental performance of Indian CTPPs.

Recent advances in stochastic frontier analysis (SFA) suggest two alternative approaches to account for unobserved heterogeneity and to distinguish between persistent and transient inefficiency. The first approach is the generalized true random effects (GTRE) model, and the second approach is an autoregressive inefficiency (ARI) model. This study compares them to highlight whether they capture similar inefficiency aspects.

Using recent methodological advances in SFA, the authors estimate the GTRE and the ARI models using a Monte Carlo experiment and two real datasets from two industries (banking and agriculture).

The authors find that the two models provide quite different results in terms of inefficiency persistence and overall inefficiency (combination of transient and persistent inefficiency), regardless of the dataset considered.

The study findings suggest that researchers should be careful when referring to these two models because they do not capture the same inefficiency aspects, even though they have the same conceptual basis. This work is a warning about the empirical aspects of the persistent and transient efficiency framework, in order to convey a consistent story to the reader on firms' performance.

Even though they are used in a large number of studies, the present paper contributes to the productivity and efficiency literature by providing the first comparison of the GTRE and the ARI models.

The purpose of this paper is to investigate the relation between industrial concentration and technical inefficiency in the Indonesian food and beverages industry using a dynamic performance measure (dynamic technical inefficiency) that accounts for the presence of adjustment costs.

This research uses panel data of 44 subsectors in the Indonesian food and beverages industry for the period 1980-2014. The dynamic input directional distance function is applied to estimate the dynamic technical inefficiency. Further, the Granger causality between industrial concentration and dynamic technical inefficiency is tested using a dynamic panel data model. A bootstrap truncated regression model is finally applied to estimate the relation between industrial concentration and dynamic technical inefficiency based on the results from the Granger causality test.

The results show that the Indonesian food and beverages industry has a high dynamic technical inefficiency. Investigation of the causality of the relation shows that industrial concentration has a positive effect on dynamic technical inefficiency at the subsector level, with no reversed causality. The results suggest that the quiet life hypothesis applies to the Indonesian food and beverages industry.

The literature investigating the relation between industrial concentration and performance relies on static measures of performance, such as technical efficiency. Static measures provide an incorrect metric of the firms’ performance in the presence of adjustment costs associated with investment. Therefore, this research has a contribution in measuring dynamic technical inefficiency that accounts for the presence of the adjustment cost as well as its relation with industrial concentration in the Indonesian food and beverages industry.

The purpose of this paper is to measure the technical inefficiency of dairy farms and subsequently investigate the factors affecting technical inefficiency in the Malaysian dairy industry.

This study uses multi-directional efficiency analysis to measure the technical inefficiency scores on a sample of 200 farm observations and single-bootstrap truncated regression model to define factors affecting technical inefficiency.

Managerial and program inefficiency scores are presented for intensive and semi-intensive production systems. The results reveal marked differences in the inefficiency scores across inputs and between production systems.

Intensive systems generally have lowest managerial and program inefficiency scores in the Malaysian dairy farming sector. Policy makers could use this information to advise dairy farmers to convert their farming system to the intensive system.

The results suggest that the Malaysian Government should redefine its policy for providing farm finance and should target young farmers when designing training and extension programs in order to improve the performance of the dairy sector.

The existing literature on Southeast Asian dairy farming has neither focused on investigating input-specific efficiency nor on comparing managerial and program efficiency. This paper aims to fill this gap.

The purpose of this study is to explore and evaluate potential nonmonotonicity in the determinants of profit efficiency, specifically IT and R&D investments in the Indian commercial banking sector.

The study employs an alternative stochastic profit efficiency framework and introduces nonmonotonic effects by parameterizing the location and scale parameters of the inefficiency component on an unbalanced panel data set of 72 commercial banks in the 2008–2019 period. Marginal effects across quartiles are calculated using a bias-corrected and accelerated bootstrap procedure of 500 simulations. The study disaggregates across ownership and size for gauging the impact of structure on the associations between determinants of profit efficiency.

The study partially rejects the productivity paradox as it discovers a negative association of IT and R&D with profit inefficiency. However, the observed nonmonotonicity of IT is of significance for bank managers, as the study concludes that overinvestment in IT is detrimental to a bank’s profit-maximizing interests. Further, bank size, loan default and credit risk depict a nonmonotonic relationship across the sample with large banks, high NPAs and high credit risk associated with reducing profit efficiency. In addition, higher margins and greater diversification are related positively to efficiency, and banks with cost-heavy structures or having high liquidity risk associated negatively with efficiency.

To the best knowledge of the authors, the study is perhaps the first to acknowledge and incorporate nonmonotonic associations of IT investments amidst other exogenous determinants under a stochastic profit efficiency framework.