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The purpose of this paper is to study the efficiency level of Islamic banks, the differences between Islamic banks in the MENA region and Southeast Asia and the role of the governance in improving performance.

This paper examines, on the one hand, the performance of Islamic banks by measuring their efficiency through data envelopment analysis (DEA) method and, on the other hand, the determinants of this efficiency emphasizing on the impact of the governance structure through the panel estimation of Islamic banks based on the three proxies of cost efficiency, namely, technical efficiency (TES), pure technique (PTE) and scale efficiency (SES).

The findings indicate that Islamic banks are experiencing an improvement in their efficiency cost. The technical efficiency of Islamic banks is largely explained by the scale efficiency where Islamic banks realize large economies of scale in order to achieve optimal size, especially in Malaysia and the GCC countries. Pure technical efficiency is less important than the efficiency of scale and improvement is necessary regarding the managerial performance. In terms of governance, the results show that the board of directors through its size and independence and the presence of a central Sharia board constitute a robust determinant of the Islamic banks’ efficiency. The ownership structure and the size of the Sharia board do no effect banking efficiency.

The originality of this paper lies mainly on the examination of the effect of the governance structure on the Islamic banks’ efficiency where studies on this issue for Islamic banks are almost inexistent. In addition, the size and the diversity of the Islamic banks’ panel constitute the strong point of this study.

This paper aims to examine technical efficiency and its determinants in Indian textile garments industry in post-agreement on textiles and clothing regime and evaluate the technical efficiency among micro, small and medium enterprises (MSMEs) firms.

This study uses unbalanced panel data for the period 2005–2010 to 2015–2016. The stochastic frontier function is used to estimate technical efficiency and its determinants.

The results show that the overall ecosystem of textile garments’ value chains could be improved to enhance the technical efficiency thereof. The result also reveals that small-scale firms have the highest technical efficiency scores, and medium-scale firms have the least technical efficiency score among all the categories of MSMEs.

The textile garments industry needs to define its innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations.

This study has shown that to offset India’s cost disadvantage in the international markets, there is a need to develop an ecosystem of textile manufacturing and value chains, eliminate the inverted duty structure (where inputs are taxed at a higher rate than the final product) and switch over from shuttle looms toward shuttle-less looms. This would unleash the potential of textile and garments industry and make it globally competitive and technically efficient. Further, there will be an alignment with the ease of doing business with an appropriate mix of policy, technology, institution, infrastructure, information and services.

Using frontier production function takes stochastic context into account for the dynamic character of technical efficiency and its components. Most of the past studies have assessed technical efficiency at the aggregate level using three-digit National Industrial Classification (NIC) or four-digit NIC code. An analysis at higher levels of aggregation masks the variation in technical efficiency. This study used five-digit NIC data to measure the firm-specific technical efficiency of the textile industry. According to the authors’ knowledge, this study is the first of its kind in the Indian textile industry using stochastic frontier approach and panel data. Further, it also looks at the contribution of different determinants in technical efficiency to the firms.

From the perspective of cause and effect, the operational processes of property insurance companies can be considered as historical events. The purpose of this study is to measure the operating efficiency of China's property insurance industry, explore the determinants that affect technical efficiency and outline the path to achieving high-quality development.

We chose 44 Chinese property insurance companies as research objects. The data were obtained from the Chinese Insurance Yearbook and China Statistical Yearbook 2015–2017. First, the data envelopment analysis (DEA) method was used to calculate the technical efficiency of property insurance companies. Then, Tobit regression and quantile regression were adopted to explore the influencing factors of technical efficiency. Finally, the fuzzy-set qualitative comparative analysis (fsQCA) method was employed to summarize the path to improving the operating efficiency of property insurance companies.

The empirical results in the first stage suggested that the operation efficiency of China's property insurance industry was technically inefficient, and the scale efficiency was relatively better than the pure technical efficiency. In the second stage, we observed that the drivers for firm size, reinsurance rate, claim ratio and equity restriction were important determinants of an insurance firm's efficiency.

We also put forward four applicable, targeted and proven ways to improve the technical efficiency of property insurance companies. These configurations are verified by cases of existing property insurance companies, which can provide practical references for the insurance industry.

Our research enriches the insurance literature and efficiency methods, particularly regarding the specific paths of improving the technical efficiency. The relationship between elements and results is analyzed from a systematic perspective, and the research results are not only more consistent with what logic might imply but also more instructive for the improvement of reality.

The purpose of this paper is to provide new evidence about the technical efficiency and its determinants in Spanish textile sector during the period 2002-2009. The empirical results suggest that the effects of trade liberalization have led to higher levels of inefficiency in the Spanish sector, due to the lack of flexibility of firms to adjust to the environment, and perhaps to aggressive competition with fuzzy rules of the game. Controlling for specific factor like age, intensity of capital, salary by worker, regions and market share, the authors have obtained that the interaction between market share and size indicates that as firms have more size are also more inefficient.

In this paper, the stochastic frontier production function is considered, specifically, a panel data version of Battese and Coelli (1995), in which the technical inefficiency is estimated from the stochastic frontier and simultaneously explained by a set of variables. This approach avoids the inconsistency problems of the two-stage approach used in other empirical works when analyzing the inefficiency determinants.

This work provides new evidence about the technical efficiency and its determinants can be due to environmental or firm-specific factors in Spanish textile sector during the period 2002-2009. The authors have estimated the Cobb-Douglass stochastic production frontier following Battese and Coelli (1995) model to analyze an unbalanced panel.

The empirical results suggest that the trend of the inefficiency shows a curvilinear behavior in the form of U (turning point third-quarter of 2004). This result is related to the efficiency analysis through Kernel distributions (in static and dynamic form) confirmed a clear process of divergence. In the period 2002-2005 the efficiency of the firms analyzed maintained higher levels than the 2005-2009 period where there is deterioration. This may be related to the increased competition due to the end of the Multi-Fiber Arrangement in January 2005 and the entry of Chinese products in 2004.

The purpose of this paper is to estimate the influence of inventory investment, wage levels, and age of the firm on retailer technical efficiency.

The methodology is based on the estimation of a stochastic parametric function. To estimate technical efficiency the output supermarket chain sales volume is used, calculated by isolating the retailer price effect on sales revenue.

The empirical analyses applied to panel data show that inventory investment and wage level have a positive impact on technical efficiency.

The limitations of the study include the generalisation of the conclusions to the entire sector, which must be done with due care, since only one of the players in the distribution channel (supermarket chains) has been analysed.

First, the estimation of the efficiency of the different supermarket chains helps the management of producers, since they can identify efficient supermarket chains, which is important for vertical relationships in the distribution channel. Second, the analysis of the determinant factors of efficiency for different chains may be used as external benchmarking.

The focus of the paper is on developing a useful methodology for managers, determining some of the sources of retail efficiency gains. The paper develops and tests a composite set of hypotheses, analysing the influence of some managerially controlled factors on technical efficiency.

Does MFIs agricultural credit influence the determinants of the efficiency of SFF which are socio-economic factors of the farmers but also agricultural endowments of family farms? This paper aims to study the contribution of MFI services on improving the technical efficiency of SFFs in Cameroon.

The stochastic frontier analysis (SFA) model permits the estimation of the technical efficiency indicators for beneficiaries and nonbeneficiaries of agricultural credits on a sample of 130 cocoa farming households and four MFIs of the same area between 2008 and 2011. The censored tobit model is used to assess the determinants of technical efficiency.

The results show that the SFF beneficiaries of agricultural credit have an average technical efficiency of 0.68 inferior to that of nonbeneficiaries (0.72) as expected. They are, respectively, at 0.32 and 0.28 of their full productive capacities. The results of the censored Tobit model show that socioeconomic characteristics of the producer such as age and gender explain negatively, while experience explains positively the technical efficiency of SFFs.

Although without any selectivity bias, this study indicates the essential character of the socioeconomic factors in the amplification of the role of the MFIs credit on the efficiency of SFFs.

Strategies to improve the efficiency of SFFs require an increase in MFI credits, primarily targeting young, experienced and female farmers.

This study examines the efficiency of SFFs by highlighting the interaction between the socio-economic factors of farmers and the credit of MFIs. It also points to the problem of monitoring the implementation of agricultural financing.

The purpose of this study is to explore the link between aggregate production efficiency and the extent of linguistic clustering in Indonesia.

The author draws on the stochastic frontier model and applies it to the data on Indonesian provinces to compute the effects of various determinants on these provinces' aggregate production efficiency. The key determinant is the spatial index of linguistic clustering that the author believes has never been applied before in this context.

Linguistic clustering is an important determinant of aggregate production efficiency. Linguistic diversity is positively associated with productive efficiency if members of a specific linguistic group are not clustered beyond a certain level.

To the best of the author’s knowledge, this is the first study that links the spatial index of linguistic clustering (because of Massey and Danton) to production efficiency. In other words, the contribution of this study is to introduce a geographical dimension to the mainstream analysis of the association between ethnic diversity and economic performance.

The purpose of this paper is to examine the relationship between credit access and technical efficiency of smallholder crop farmers in northern Ghana.

The study uses a random sample of 445 farming households in the three northern regions of Ghana. The two-stage double bootstrap DEA approach was used to consistently estimate technical efficiency scores as well as the determinants.

The results revealed that, given the current technology, there is substantial yield or productivity gap among the sample of producers in northern Ghana used for the study. This is because producers can reduce input use by over 50.0 percent while still achieving the same output levels. It is further revealed that proportion of household income from off-farm activities, distance of farm from homestead, location and credit access are significant determinants of technical efficiency.

The current study differs from previous studies in two basic ways. First, it takes into account the fact that smallholder farmers practise mixed or inter-cropping by using value of output so that various crops on a given plot of the farmer can be aggregated; and second, a nonparametric approach is adopted so that the inherent inconsistencies in using the two-step model within a parametric framework can be avoided.

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

The purpose of this paper is to estimate the degree of technical efficiency, determinants of technical inefficiencies and driving forces behind the production growth for a panel data set collected during the 1998/1999 and 2004/2006 Kharif cropping season, from 452 small-scale rice farming households in the Giridih and Purulia districts of Eastern India.

The estimations of technical efficiency utilize stochastic frontier production function with a sub-model of inefficiency effects at both aggregated farm level and disaggregated plot level where traditional varieties (TVs) and high-yielding varieties (HYVs) are differentiated. The output growth decomposition analysis identifies the main contributor to the total rice production growth.

The results indicate that the sampled farms are operated at moderate levels of technical efficiency. The production of HYV rice is associated with higher technical efficiency compared to TV rice. Farming experience, education attainment, landholding size, the share of non-agricultural income and the share of land in the lower terraces account for the differences in technical inefficiencies across the sampled farms. The decomposition analysis suggests that as technical efficiency decreased, technical change is the main source of production growth during the survey period.

The small sample size applied in the analysis will result in an insufficient representativeness of the study area.

This paper fills the literature gap as estimations of technical efficiency that account for subtle differences in adopted rice varieties are still rare in India.