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The purpose of this paper is to analyze the efficiency performance of the Gulf Cooperation Countries (GCC) banking sector. The primary focus is to assess whether market power, risk taking activities, and regulations have significant effects on GCC banks’ efficiency performance.

The estimation and inference has been implemented using a double bootstrap procedure that simultaneously corrects for bias and validates inference on the influence of covariates. In the first stage, efficiency scores are estimated with data envelopment analysis (DEA). In the second stage, variation in the resulting efficiency scores is explained using a truncated regression model with inference based on a semi-parametric bootstrap routine.

The authors found compelling evidence that efficiency is not independent of the market structure, the bank's risk taking activities, and the regulatory environment. In particular, the Lerner Index provides evidence that market power decreases efficiency. The capital adequacy, the supervisory power and the market discipline were all found to improve efficiency. Additionally, when the risk is measured by the Z-Score or even by the ratio of non-performing loans to total loans, it adversely affects efficiency.

The results of the current study have important implications for regulators and supervisors. Promoting banks’ competitive environment in the GCC countries through reducing the information barriers to entry, encouraging bank privatization, and lowering the activities restrictions can potentially improve operational efficiency of banks. Also enhancing banks’ diversification activities and risk management techniques may have the advantage of increasing operational efficiency. Furthermore, improvements in the regulatory conditions that enhance banking supervision and monitoring would also improve efficiency.

The main contributions of the paper are threefold: first, to the knowledge, this study is the first to employ by far the most comprehensive data set of GCC banks investigated to date. Second, the analysis focusses on the influence of a wide set of factors, most of them was not covered before in related economic literature on bank efficiency of the GCC countries. Third, the methodological innovation involves applying a double bootstrap procedure proposed by Simar and Wilson (2007).

There are many types of the ADCs implemented in the mobile and wireless devices. Most of these devices are battery operated and operational at low input voltage. SAR ADC is popular for its low power operations and simple architecture. Scientists are still working to make its working faster under the same low power area. There are many SAR-ADC implemented in the past two decades, but still, there is a big room for dual SAR-ADC.

The authors are presenting a dual SAR-ADC with a smaller number of components and blocks. The proposed ultra-low-power circuit of the SAR-ADC consists of four major blocks, which include Bee-bootstrap, Spider-Latch dual comparator, dual SAR-logic and dual digital to analog converter. The authors have used the 90-nm CMOS library for the construction of the design.

The power breaks down of the comparator are dramatically improved from 0.006 to 0.003 uW. The ultimate design has 5 MHz operating frequency with 25 KS/s sampling frequency. The supply voltage is 1.2 V with 35.724 uW power consumption. Signal-to-noise and distortion ratio and spurious-free dynamic range are 65 and 84 dB, respectively. The Walden's figure of merits calculated 7.08 fj/step.

The authors are proposing two-in-one circuit for SAR-ADC named as “dual SAR-ADC”, which obeys the basic equation of duality, derived and proved under the heading of proposed solution. It shows a clear difference between the performance of two circuit-based ADC and one dual circuit ADC. The number of components is reduced by sharing the work load of some key components.

This paper aims to examine whether board-related characteristics matter for cost efficiency in banking sector.

This study uses a sample of publicly traded US commercial banks and savings institutions to estimate a relationship between cost efficiency measured by stochastic frontier analysis and a set of board-related characteristics for the period 2007-2013.

An inverted U-shape relation is found between board size and efficiency. Thus, there is a trade-off between costs and benefits of larger boards. Optimal board size is higher for banks with more complex operations. This study also observed an inverted U-shape relation between board independence and cost efficiency. The banks where the Chairman also executes the CEO responsibility show lower efficiency. However, a higher proportion of independent board members in banks with unitary leadership structure may mitigate the conflict of interest and lower efficiency stemming from CEO duality.

This study’s evidence supports the Basel Committee on Banking Supervision emphasis on advising a board composition that provides for a sufficient degree of director independence.

The results are relevant for banks and their external and internal stakeholders. Banks may adjust their current board characteristics to increase the board effectiveness. Externally, potential investors can evaluate the quality of corporate governance of banks before making investment decisions. The empirical findings can also be useful for regulators imposing corporate governance codes in banking.

To the best of the authors’ knowledge, this is the first paper to provide empirical evidence on the impact of board characteristics on bank efficiency for a wide panel of US banks. Additionally, a comprehensive set of board-related variables is used.

Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

This paper aims to estimate the technical efficiency (TE) and its determinants in the handloom micro-enterprises of Assam (India) using the double-bootstrap data envelopment analysis (DEA) technique.

The study uses a random sample of 340 handloom micro-entrepreneurs from the three districts of Assam in India. The double-bootstrap DEA was used to calculate the TE and its determinants.

The findings reveal that handloom enterprises are only 60% technically efficient, suggesting room for improvement. The bootstrap truncated regression results demonstrate that the handloom firms’ TE is influenced by both entrepreneur-specific and firm-specific factors.

The implication lies in the fact that the management of a firm may figure out how much it can reduce its input utilization to produce the existing amount of output so that it can move along the TE ladder. Moreover, it can crosscheck the factors to weed out inefficiency.

This paper has made two significant contributions to the extant literature. Firstly, it fills the gap by way of accounting the TE of handloom micro-enterprises, which has so far been neglected. Secondly, it used the bootstrap approach, which otherwise is very rare in the discourse on the Indian manufacturing industry, let alone in the micro, small and medium scale enterprises sector.

This 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.

The 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.

The 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.

The 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.

This 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.

The aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.

The authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.

Most uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.

The results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.

This paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.

This paper examines the technical efficiency (TE) of Indian commercial banks during 1998–2015.

This study uses mathematical programming-based data envelopment analysis (DEA) methodology to measure technical efficiency of Indian banks. Further, Simar and Wilson (2007) double bootstrap procedure is applied to examine the determinants of efficiency of the Indian banks, by examining the effects of various bank specific and other contextual variables.

The results indicate substantial upward bias in the conventional efficiency estimates of the Indian commercial banks. Needless to note, such upward bias is consistent with the theoretical postulates. The bootstrapped regression results show that increasing capital adequacy ratio is positively associated with bank efficiency. The popular belief that non-performing assets have a dampening effect on performance of banks is validated. Among others, ownership category is observed to be an important determining factor of bank efficiency. Specifically, state-owned banks (SOBs) are relatively lagging behind the foreign banks. Moreover, larger banks are observed to have a significantly higher level of efficiency, therefore, recent official policy initiatives toward consolidation of SOBs are validated.

As this study uses Simar and Wilson (2007) bootstrap approach, it enables the authors to have an estimate of the extent of bias in the traditional DEA TE scores. It also helps us drawing consistent inferences by rectifying the problem of serial correlation in the conventional second stage regression in this regard.

The purpose of this paper is to examine the operational efficiency and effects of market concentration and diversification on the efficiency of Chinese and Indian banks in the 1997-2011 period.

This study employs the two-stage bootstrap procedure of Simar and Wilson (2007) to obtain valid inferences on the efficiency scores and the efficiency determinants.

Using data set for each country separately, the authors found that the bias-corrected cost efficiency displays an upward trend in Chinese and Indian banks. This trend is consistent with profit efficiency among Chinese banks, but the trend is unclear in Indian banks. Market concentration is negatively related to cost and profit efficiencies of Chinese banks. However, market concentration is positively associated with cost efficiency, but unrelated to profit efficiency of Indian banks. In Chinese banks, diversification of revenue, earning assets and non-lending earning assets are associated with increasing profit efficiency, but their effects to cost efficiency are not clear. In Indian banks, diversification of earning assets increases profit efficiency while there are cost efficiency losses from diversification of revenue and earning assets.

Bank regulators and supervisors in China should consider establishing policies to reduce market concentration and encourage diversification of revenue, earning assets and non-lending earning assets, while increasing concentration and diversification of earning assets should be encouraged in Indian banks.

To the best of the authors’ knowledge, this is the first study employing the double bootstrap procedure proposed by Simar and Wilson (2007) which can address the problem of the two-stage data envelopment analysis or SFA estimator in the efficiency literature on Chinese and Indian banks that efficiency scores obtained in the first stage are inter-dependent, and hence violating the basic assumption in regression analysis in the second stage.

The Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance.

In the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables.

Efficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency.

Construction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs.

Previous studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants.