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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.

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.

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.

This study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.

In the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.

The DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.

The results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.

This study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

The 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.

The 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.

The 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.

The 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.

The 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.

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.

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.

The purpose of this paper is to analyse the performance of Spanish tourism regions for the 2008-2011 period, to obtain a ranking of efficiency and to examine the hypothesis that the efficiency of these regions is determined by a group of contextual variables.

In contrast with monitoring reports based on descriptive methods, this paper uses data envelopment analysis (DEA) methodology and bootstrap semiparametric procedures to correct inherent bias. The significance of a group of exogenous factors is investigated and the importance of each determinant is ordered by its elasticity.

The ranking obtained by radial DEA models and by bias-corrected ones describes two remarkably different settings. The exogenous variables influence hypothesis and confirmed: that estimated coefficients are of the correct sign and statistically significant at 5 per cent.

The statistical significance of the potential attractors can offer an interesting tool for strategic decisions. The two-stage procedure employed has supposed a turning point in the methodology and there are only a handful of very recent studies of this type in the literature on tourism destination performance. In this sense, no previous work has considered the returns to scale test or the separability assumption. Following United Nations of World Tourism Organization recommendations, it is essential to move towards responsible tourism in all aspects. Some final considerations about the link between performance and sustainability of the Spanish tourist model are addressed in this study.

El objetivo de este trabajo es analizar el funcionamiento de las regiones turísticas españolas durante el período 2008-2011, para obtener un ranking de eficiencia y examinar la hipótesis de que la eficiencia viene determinada por un grupo de variables ambientales.

Este estudio emplea la metodología del Análisis Envolvente de Datos (DEA) y un procedimiento bootstrap semiparamétrico para corregir el sesgo inherente de los coeficientes de eficiencia. Se investiga la significatividad de un grupo de variables exógenas, y la importancia de cada una de ellas se ordena mediante su elasticidad.

La clasificación obtenida de los modelos DEA radiales y la derivada de los coeficientes insesgados, describe dos escenarios muy diferentes. Los resultados confirman la hipótesis de influencia de las variables exógenas: los coeficientes estimados tienen el signo esperado y son estadísticamente significativos al 5%.

La significancia estadística de las variables exógenas puede ofrecer una herramienta de interés para la toma de decisiones estratégicas. El procedimiento bietápico empleado supuso un punto de inflexión en la metodología y solamente existe un reducido número de trabajos en la literatura, si bien, ninguno de ellos ha considerado el contraste sobre la escala de los rendimientos o la condición de separabilidad. Siguiendo las recomendaciones de la Organización Mundial del Turismo (OMT), es esencial desplazarse hacia un turismo responsable; el trabajo añade una reflexión final sobre el vínculo entre eficiencia y la sostenibilidad del modelo turístico español.

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 purpose of this paper is to provide preliminary efficiency assessment of Arab microfinance institutions (MFIs) within the period 2002–2012. Microfinance is defined as the provision of financial services to poor and low-income households and their microenterprises on a sustainable basis.

The authors first present the main features of microfinance in the Middle East and North Africa (MENA) region. Second, based on a simple of 72 microfinance institutions issued from ten countries of the region, they develop a bootstrap–data envelopment analysis (bootstrap–DEA) framework to measure Arab MFIs’ efficiency. Finally, they apply parametric and non-parametric tests to compare the performance and identify factors that contribute to the efficiency of Arab Islamic microfinance institutions.

Efficiency scores of the MENA region exhibit high variability, both across time and countries. Significant difference in efficiency was found due to MFI age or regulation. Results also reveal the ability of Arab MFIs to combine social and financial performance and their solidity in time of crisis.

In this paper, the authors apply DEA–bootstrap method on a large sample of Arab MFI with special look at the peer group differences. Unlike most previous relevant studies, the paper overcomes many of the drawbacks of the DEA method by using, in addition to the DEA–bootstrap approach, a test of return to scale and a combination of three procedures to detect outliers. Furthermore, this paper analyses the efficiency of MFI in the MENA region in the light of financial crises and Arab Spring.