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The purpose of this paper is to introduce a novel measure of access to credit suited to estimate the relative change in credit reserves.

A debt possibility frontier is estimated using data envelopment analysis and the Malmquist index is calculated. The Malmquist index is redubbed the Debt Development index and decomposed into “change in debt capacity” and “change in debt capacity utilization”. Bootstrapping is applied for statistical inference. The method is applied to an unbalanced panel of 92,000 Danish farm accounts from 1996 to 2009.

The paper finds that credit capacity roughly doubled for Danish farmers over the period, and that utilization of credit capacity generally was proportional to capacity change, utilization being higher for dairy and pig farms, than for crop farms.

Changes in credit reserves may have important implications for risk management practice, investment and technology adoption and related policy issues. The method is limited by the possibility of strategic behavior of lenders during credit cycle busts. In credit cycle booms, the method gives a good basis for the estimates of change in credit reserves.

In a period of increasing credit reserves, risk management institutions are unlikely to develop. Like agricultural policy, access to credit may crowd out market-based risk management.

The study represents a novel application and interpretation of a well-known method.

The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.

This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.

DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India.

The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression.

This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.

The purpose of this paper is to demonstrate that overcapacity and tariff deficit (external constraints) negatively affect the efficiency of distribution firms in the Spanish electricity sector. To do this, the paper is based on the theory of constraints and theory of economic regulation.

Data envelopment analysis (DEA) window methodology is carried out on the constant scales (I-C) with a sample consisting of five main distribution firms during the period from 2006 to 2015. In turn, an analysis of the Malmquist index is carried out to assess whether it has had a displacement with respect to the efficiency frontier.

The results show that the overcapacity and the tariff deficit negatively affect the efficiency of the distribution firms of the Spanish electricity sector. In addition, there is an existence of external constraints that affect the activities of regulated organisations and the importance of adequate legislation in regulated sectors.

This study defines a model that shows how the efficiency problems associated with electricity distribution companies such as productive overcapacity or tariff deficit can be measured based on the theory of constraints and theory of economic regulation.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

The paper's aim is to assess performance of firms in the UK retail sector.

Economic efficiencies of 41 retail companies working in the UK between 2000 and 2005 are examined in this study using three related methodologies: data envelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regression model. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover, profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) are employed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change over the six year period 2000‐2005. DEA efficiencies are then used to test important hypotheses on the impact of environmental variables, namely head office location, type of ownership, years of incorporation, legal form and retail characteristic, on the functioning of the UK retail sector using bootstrapped Tobit regression.

DEA analysis has shown that only ten retail companies are considered as efficient under CRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about 50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twenty out of 41 retail companies have adopted advanced and efficient retailing technologies during this period. Three environmental variables, namely the type of ownership, legal form and retail characteristic, have been found to play significant roles influencing retail efficiency using bootstrapped Tobit regression.

Data availability has limited the level of analysis in some parts of this study, especially in the bootstrapped Tobit regression.

This study seems to be the first in applying productivity analysis using DEA for the UK retail sector.

This paper aims to investigate the impact of acquisition and organic growth on the operating efficiency and total factor productivity change of retailing networks.

The assessment uses low-frequency data of newly opened stores and acquired stores of a large supermarket (S/M) network in Athens, for a period (financial year 2014) where the network began to refocus on its organic growth after a two-year period of deep recession (financial years 2012–2013). To evaluate the performance effects of both strategies, the authors employ the innovative benchmarking tool of bootstrap data envelopment analysis (DEA) for measuring operational efficiency and the Malmquist productivity index DEA approach for measuring productivity change over time.

The short-run evidence indicates that compared to organic growth, acquisitions lead to lower operating efficiency. However, this difference gradually converges over time as acquired stores show a higher rate of productivity compared to newly opened stores. The authors interpret this as a result of the smooth integration of the acquired chain store into the organizational structure of the existing store network given their significant similarities in terms of products and customers.

The authors inform managers of store chains that during the process of organic growth, a general improvement in efficiency takes place while in the case of acquisitions, the required post-acquisition streamlining actions cause a short delay on the realization of efficiency gains. Therefore, managers should not take it for granted that acquisitions cause a long-term decrease in efficiency.

The study contributes to the literature on growth strategies and retailing performance in general, by offering new evidence regarding the comparative effect of the horizontal growth modes on the efficiency of store chains.

This paper explores the impact of international acquisitions on the efficiency and productivity of the cement industry in an emerging economy.

The data envelopment analysis (DEA) and Malmquist index (MI) are used to calculate the partial efficiency and productivity of individual inputs (materials, labour and fixed assets), as well as the total factor efficiency and productivity during the period 2000–2018. DEA and MI are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. In this paper we apply the input-oriented CCR DEA Window model. With respect to the level of analysis, data was collected from individual companies and then aggregated data at the industry level.

The research results show that international acquisitions positively affect efficiency of the cement industry in the long term. Efficiency of capital is lower in the short period after acquisitions. Additionally, international acquisitions positively affect partial productivity, as well as total factor productivity of the cement industry.

The results of the study may be significant for managers and policy makers to design appropriate strategies for the improvement of the cement industry performance over time.

Research in emerging economies related to subject matter is limited, and this is one of the earliest research studies which explore change in efficiency and productivity at the level of Serbian cement industry.

The purpose of this paper is to provide a robust version of the Malmquist Productivity Index (MPI) in order to evaluate hotels' productivity levels during the Great Recession.

Based on the order-m frontiers, we apply a robust version of an MPI. We decompose the productivity into three robust components. We use a sample of hotels operating in the Balearic Islands and Canary Islands, and we decompose and evaluate their productivity levels during the period 2004–2013. Moreover, we evaluate hotels' productivity performance during the pre-crisis period, the US subprime crisis period, the global financial crisis (GFC), the sovereign debt crisis period and the after-crisis period.

Our findings show that productivity did not deteriorate due to the adverse effects of economic crisis. This is mainly driven by increased technical change and the ability to operate at optimal scales. The long-term investment in innovation policies which are related to services and processes, appear to be the dominating feature behind hotels' productivity levels, which helped the hotel industry to recover quickly from the Great Recession.

The vast majority of empirical studies evaluating the productivity change in the hotel industry apply MPIs which are based on data envelopment analysis (DEA). However, the productivity measurement which is based on the nonparametric framework is sensitive to sample characteristics. In order to avoid such shortcomings, we apply a robust version of the MPI.

This study aims to analyse Gulf Cooperation Council (GCC) Islamic and conventional banks’ productivity and to investigate the impact of Basel III on their productivity change. This study is conducted on 73 GCC banks (45 conventional and 28 Islamic) over the period of 2005-2015.

This study uses the data envelopment analysis-type Malmquist productivity change index and its component indexes to obtain a deep insight into the source of productivity change.

The results show that Islamic banks are less productive than their conventional counterparts. Also, the results indicate that Basel III accord has impeded the GCC banks’ productivity and this negative effect is larger on Islamic banks. However, there is scale efficiency progress in the past years that offsets the production frontier deterioration, which leads to stagnation in total productivity change for both banks.

This study differs from the previous GCC banks’ productivity studies in several ways. Firstly, it covers a recent period that includes major events such as the global crisis and focuses on the influence of Basel III accord on GCC banks’ productivity. Secondly, as opposed to the previous studies, this study will estimate the GCC banks’ productivity index and its components based on separate frontiers for Islamic and conventional banks that will ensure the homogeneity in the sample and the robustness of the results. Thirdly, this study uses a combination of parametric and non-parametric tests to confirm and check the robustness of the findings. Lastly, to the best of the knowledge of the authors, this is the first study that tries to analyse the GCC banking sector productivity around the new Basel III announcement.

The purpose of this paper is to explore the sensitivity of economic efficiency rankings of water businesses to the choice of alternative physical and accounting capital input measures.

Data envelopment analysis (DEA) was used to compute efficiency rankings for government-owned water businesses from the state of Victoria, Australia, over the period 2005/2006 through 2012/2013. Differences between DEA models when capital inputs were measured using either: statutory accounting values (historic cost and fair value), physical measures, or regulatory accounting values, were scrutinised.

Depending on the choice of capital input, significant variation in efficiency scores and the ranking of the top (worst) performing firms was observed.

Future research may explore the generalisability of findings to a wider sample of water utilities globally. Future work can also consider the most reliable treatment of capital inputs in efficiency analysis.

Regulators should be cautious when using economic efficiency data in benchmarking exercises. A consistent approach to account for the capital stock is needed in the determination of price caps and designing incentives for poor performers.

DEA has been widely used to explore the role of ownership structure, firm size and regulation on water utility efficiency. This is the first study of its kind to explore the sensitivity of DEA to alternative physical and accounting capital input measures. This research also improves the conventional performance measurement in water utilities by using a bootstrap procedure to address the deterministic nature of the DEA approach.