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The purpose of this paper is to analyze the total factor productivity (TFP)[1] change and to investigate its determinants in the case of MENA Islamic banks.

In the first stage, bootstrapped Malmquist index approach is used to provide a robust analysis of the changes in the productivity of 33 Islamic banks operating in 10 MENA countries during the period 2006-2011. In the second stage, panel data models are used to investigate the determinants of TFP change.

The results of the first stage show that Gulf Cooperation Council (GCC) banks have known a productivity decline between 2006 and 2011 due to technical regress and scale inefficiency. In contrast, non-GCC banks have improved their productivity by benefiting from scale economies. The results of the second stage show that the productivity growth of MENA Islamic banks was mainly determined by bank-specific factors and that TFP indices decreased in the period of global financial crisis.

This paper provides relevant recommendations for improving the productivity of Islamic banks operating in the MENA countries.

This paper attempts to fill a demanding gap in the literature by examining productivity change and investigating its determinants using cross-country data of MENA Islamic banks. In addition, it is one of the few studies that have applied the bootstrapped Malmquist index approach in the case of Islamic banking.

The purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of efficiency, productivity and returns-to-scale economies. In addition to this, it identifies the determinants of efficiency.

This study employs a two-stage data envelopment analysis (DEA) bootstrap approach to estimate the level and determinants of efficiency. In the first stage, the DEA bootstrap approach is employed to estimate bias-corrected efficiency scores. In the second stage, the truncated bootstrapped regression is used to identify the effect of firm-level characteristics on the efficiency of insurers. Moreover, the bootstrapped Malmquist index is used to examine the productivity growth over the observation period 2005–2016.

The bootstrapped DEA results show that the Indian non-life insurance sector is moderately technical, scale, cost and allocative efficient, and there is a large opportunity for improvement. Moreover, the results reveal that the public insurers are more cost efficient than the private insurers. It is also evident that all the insurers irrespective of size and ownership type are operating under increasing returns to scale. Malmquist index results divulge an improvement in productivity of insurers, which is attributable to the employment of the best available technology. Bootstrapped DEA and bootstrapped Malmquist index results also show that the global financial crisis of 2008 has not severely affected the efficiency and productivity of the Indian non-life insurance sector. The truncated regression results spell that size and reinsurance have a statistically significant negative relationship with efficiency. It also shows a statistically significant positive age–efficiency relationship.

The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies.

This study is the first of its kind that comprehensively investigates different types of robust efficiency measures, determinants of efficiency, productivity growth and returns-to-scale economies in the Indian non-life insurance market for an extended time period.

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 current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.

The purpose of this paper is to present a productivity measure for hospital services in Ontario.

The study applied the Malmquist Productivity Index (MPI) to assess the efficiency of hospital services in Ontario, Canada, over the period 2003‐2006. The MPI was decomposed into efficiency change and technological change. Efficiency change was further decomposed into pure efficiency change and scale efficiency change. A bootstrapping technique was also used to obtain confidence intervals for the output oriented MPI and its decompositions.

By estimating confidence intervals it was found that a large number of hospitals did not achieve significant progress in terms of productivity. By taking geometric means of estimates for all years it was observed that while overall productivity and efficiency of hospitals in Ontario declined during the study period, technological progress increased at a rate of 5.95 percent on average.

The present study helps to understand the productivity and technological change and change in technical efficiency in this vital sector of the economy, which is important for policy making identifying improvement opportunities in resource allocation. It was observed that Ontario hospitals did not improve the efficiency with which they employed their inputs (i.e. staff and supplies) over the study period; they did achieve gains through application of technologies.

The paper provides a thorough study on productivity growth of health care services in Ontario using a non‐parametric framework with bootstrapping. It also provides a robust measurement and analysis of the contributions of technology, size of operation and use of inputs to the performance of hospitals in Ontario.

The purpose of this work is twofold: on the one hand, recent methodologies will be used to estimate efficiency and productivity in Spain's non‐specialized retail sector for the period of 1997‐2007. In particular, the order‐m approach proposed by Cazals et al., which is based on the concept of expected minimum input function. On the other hand, the results obtained applying the methods mentioned in the Spanish retail sector can contribute to opening up a new field of analysis since the results may be compared by means of the methodologies proposed as well as those which already exist in the literature.

The paper used data envelopment analysis stochastic (order‐m) and bootstrapping Malmquist index to measure productivity and efficiency in 12 sectors in Spanish retail trade 1997‐2007.

In order to illustrate the methodology proposed in this paper different phases involved; first, we have estimated the efficiency in 12 sectors of the retail sector four digits NACE, we found high levels of inefficiency in most of the sectors analyzed over the period of analysis. Next, we will deepen and simplify the analysis by concentrating on food‐predominant sectors in non‐specialised shops (5211). The evolution of the efficiency of firms belonging to this sector decreases over the period of analysis. Analyzing the relationship between firms and size, the results obtained in this work shows that the firm's size have a positive influence on efficiency that suggest that the management may have incentives to grow in order to improve their efficiency levels. Our second contribution has to do with the use of bootstrapping Malmquist productivity indices. Productivity decreased at an average rate of −4.1 percent over the entire period of 1997‐2007. On average, this deterioration was due to efficiency change −6.1 percent. Technical progress is increased at an average rate of 2.1 percent. All rates at global level are statistically significant at 95 percent.

The main contribution of this paper is to provide an efficiency analysis using a non‐parametric approach with a robust estimator that has been suggested recently by Cazals et al. This methodology is that the first time that is applied in the analysis of retail sector. In addition, we analyze productivity growth using bootstrapping Malmquist indices. This methodology allows for a more careful analysis of what happens at firm level. Differences in conclusions between the original estimates and the bootstrap results are more evident when we scrutinize the sample firms and individual levels.

This paper aims to measure the change and the sources of change in total factor productivity (TFP) of the Indian non-life insurance sector over the period 2005–2016.

This study employs the bootstrapped Malmquist index (MI) to assess the changes in the TFP and adopts a decomposition approach proposed by Balk and Zofío (2018). Moreover, it utilises truncated regression to identify the determinants of the TFP. In addition, it employs Wilcoxon-W test and t-test to scrutinise the difference between the state-owned and the private insurers in terms of variations in TFP and its various components.

The results divulge a miniature improvement in TFP of the insurance sector, which is primarily attributable to the improvement in scale efficiency (economies of scale). The results also reveal that there are no significant TFP differences across the ownership. However, private insurers have better scale efficiency and lower input-mix efficiency than state-owned insurers. In addition, the results unveil that size, diversification and reinsurance have a negative impact on the TFP, while age has a positive impact on it.

The results may help the policymakers to frame new consolidation policies. Moreover, the findings may guide the decision-makers of the Indian non-life insurance companies to abate inefficiency and improve TFP.

This study estimates bias-corrected changes in TFP and efficiency in the non-life insurance sector. Moreover, it adopts an elaborated decomposition of the MI to identify the true sources of change in the TFP.

The purpose of this paper is to provide lines of action based on the results aimed at improving the future levels of productivity of the travel agencies in Alicante.

Estimates of output‐oriented Malmquist Productivity Indices and the values of their components will be calculated. Furthermore, in order to avoid problems that may arise from these results (which are based on finite samples that are sensitive to sampling variations) bootstrapping techniques (smoothed bootstrap) are used. Lastly, the Mann‐Whitney‐U test is used in order to accept or reject the influence that different factors have on productivity levels.

This study is a continuation of previous studies analysing the variation productivity of 22 travel agencies located in Alicante (Spain) over the period 2008‐2010. The results obtained from applying the afore‐mentioned methods do not provide substantially different conclusions to those obtained in previous studies. The authors can observe that the technological component (T) is the main factor, which these companies use to sustain their productivity levels and, thanks to this, the negative effects of a poor application of economies of scale are neutralised. In light of this, and for the whole of the period 2008‐2010, the inappropriate management of resources and the inability to obtain higher output levels played an insignificant role. Perhaps, a higher amount of information online about both economics and efficiency could be a good strategy to help these agencies to improve their results.

The impossibility of increasing the amount of data for carrying out the study, given the scarcity of statistical sources available and the reticence of the travel agencies to provide us with more information.

Although other studies have been previously carried out using Malmquist indices and bootstrapping techniques to analyse the productivity of travel agencies in other countries or cities, this is the first to be undertaken in Spain using the smoothed bootstrap method based on output‐oriented models, so as to examine this field of study from a new perspective.

The purpose of this paper is to explore the effects of cross-border acquisitions on the efficiency and productivity of acquired companies in the cement industry in the context of a transitional economy.

The Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used to assess the efficiency and productivity of the acquired companies over the period 2000–2018. DEA and Malmquist index are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. The study assesses partial efficiency and productivity of three inputs: material, capital and labour, as well as the total factor efficiency and productivity of the acquired companies in the short and long term after the acquisitions.

The research results suggest that efficiency of material, efficiency of labour and the total factor efficiency of the acquired companies are higher after the acquisitions than before, while efficiency of capital is lower. In addition, the results show that the acquisitions had a positive impact on total factor productivity of the acquired companies.

The results of this study have practical implications for managers, especially for policy-makers and industry analysts in deciding whether to encourage or discourage cross-border acquisitions in transitional economies.

The study contributes to a better understanding of the impact of cross-border acquisitions on efficiency and productivity of acquired companies in the manufacturing industry. Research in transitional economies related to subject matter is limited, and this study is the first empirical investigation of the effect of cross-border acquisitions on the efficiency and productivity in the cement industry in Serbia by applying the Data Envelopment Analysis.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.