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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 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 measure the Malmquist Productivity Index and its different components such as technological change, technical efficiency change and the change in scale efficiency in the Indian food industry during the period spanning 1988‐1989 to 2004‐2005. Further, it examines the variation in productivity and its components with respect to the factors internal to the firms.

The technique of data envelopment analysis has been used to measure productivity index and its different components under the assumption of variable returns to scale. Further, log‐linear regression model has been used to explain the variation in productivity and its components with respect to certain factors internal to the firms.

In spite of a strong agricultural base and being the third largest producer of food products in the world, India's food processing industry is far from tapping its full potential as a result of a low rate of technological progress/regress on the one hand and increasing inefficiencies of the firms on the other hand. It is necessary to encourage imports along with R&D to ensure faster technological progress in the Indian food industry. However, the technological possibilities depend on the mode of organization and various economic and institutional factors. Therefore, bold institutional changes are to be made side‐by‐side in order that inefficiency is substantially reduced.

The present study evaluates the contribution of technological change, technical efficiency change and scale efficiency change to total factor productivity growth in the Indian food processing industry by using the firm‐level data, collected from the Centre for Monitoring Indian Economy (CMIE). It further examines the impact of some common factors internal to the firms on their performance.

The Indian garment industry has witnessed a significant change since the inception of the New Textile Policy 2000 that suggests removing the industry from the list of small‐scale industries with a view to improving its competitiveness in the global market. As productivity is the driving factor in enhancing the competitiveness of any decision‐making entity (firm), a study of total factor productivity (TFP) and its sources can provide vital inputs to a firm for improving its competitiveness. Keeping this as a backdrop, the paper attempts to measure the TFP in the Indian garment‐manufacturing firms; identify sources of the TFP; and suggest measures for the firms to enhance their productivity.

The study is based on the firm‐level panel data collected from the Centre for Monitoring Indian Economy for the years 2002‐2007. One output variable, namely, gross sale and four input variables, namely, net fixed assets, wages & salaries, raw material, and energy & fuel, have been selected. The DEA‐based Malmquist Productivity Index (MPI) approach has been applied to measure the TFP.

The Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent per annum during the study period. The small‐scale firms are found to be more productive than the medium‐ and large‐scale firms. The decomposition of TFP growth into technical efficiency change (catch‐up effect) and technological change (frontier shift) reveals that the productivity growth is contributed largely by technical efficiency change rather than by technological change.

Earlier studies on the Indian garment industry have applied the partial factor productivity approach, which has several limitations. This paper measures the TFP and identifies its sources through applying a non‐parametric DEA‐based MPI approach. Through this approach, the productivity growth is decomposed into technical efficiency change and technological change. Further, an attempt has also been made to study the variation in the productivity growth rates across location, scale‐size and type of garments.

The purpose of this paper is to longitudinally assess the technical efficiency and productivity, considering investment projects and technological change, in a second-generation petrochemical company.

The study uses data envelopment analysis (DEA) together with the Malmquist index to measure efficiency during the analysis periods. The working method consists of four main phases, namely development of the conceptual model, construction of the mathematical model, application of model to the case, and analysis of the results. The study utilizes a quantitative approach with descriptive goals seeking to evaluate the impacts of technical changes on the operational efficiency and productivity of the production process.

The use of DEA associated with the Malmquist index proved to be viable for analyzing a single company and identifying efficiency improvements, as well as the impacts of the learning process and the implementation of improvement projects. However, the results of the improvement projects and learning process were not representative and had no statistical significance on the actual change in efficiency of the company during the periods analyzed. For the case in question, the learning process and continuous improvement were not observed during all study periods.

The proposition that the improvement projects and investments implemented increased the efficiency of the company was rejected. Hence, with this work, it was possible to determine that the company unnecessarily invested resources in projects to increase efficiency. Furthermore, the company could have explored more internal resources before making significant investments in increased efficiency.

As for the value of this research in the theoretical and academic scope, this paper advances knowledge on the application of DEA because it proposes to establish an internal reference benchmarking for comparison. The literature contains few studies that analyze organizations using continuous processes, such as petrochemical processes, in longitudinal studies as a function of time, especially with the use of DEA.

This study aims to undertake the evaluation and examination of the productivity change of the Egyptian banking sector. Using a novel data set covering 14 banks operating in the Egyptian market from 1997 to 2013. We use a nonparametric approach (based on data envelopment analysis (DEA)) to investigate the productivity change in the Egyptian banking sector. Input-oriented Malmquist indices of productivity change are estimated with DEA to measure total factor productivity (TFP) change. The TFP changes are decomposed into the product of technological change and technical efficiency change (catch-up). In the second stage, we study potential determinants of productivity change using a regression model. We find that the Egyptian banking sector as a whole shows a productivity regress of 0.9% per year, mainly due to the technological improvements. The estimated regression model identifies some variables that significantly influence the productivity of banks in Egypt. The banks with higher loan to deposit ratio and higher returns on equity have higher productivity growth reflecting on their strong strategic and managerial skills. The size of a bank seems to be associated with an increase in productivity. The maturity of a bank (measured by age) is associated with higher productivity. The NIM and NIETA variables do not seem to be affecting the productivity of banks. Surprisingly, our results reveal that the financial crisis was negatively and statistically insignificant, hence it had no effect on the Egyptian banks.

The nature and extent of productivity change of ten commercial banks in Malaysia is analysed over the period 2000‐2001. Utilising non‐parametric methodology, Data Envelopment Analysis (DEA) and Malmquist total factor productivity index (MPI), individual bank efficiency and productivity changes which took place within this period is estimated. The MPI calculated within the framework of DEA further decompose productivity growth into technical efficiency change and technological change. The results from this analysis indicate that total factor productivity increased in all eight banks except for EON, which remain the same while PBB, recorded a decrease in productivity. AFB recorded the highest growth in total factor productivity. The growth in productivity is attributed to technological change rather than technical efficiency change.

The purpose of this paper is to empirically study the impact of product patent regime on the productivity of different categories such as ownership, R&D, size and product-wise of Indian pharmaceutical firms using non-parametric data envelopment analysis.

The present study has applied Ray and Desli’s Malmquist productivity index and its decomposition to measure total factor productivity (TFP) change, pure technical efficiency change, scale efficiency change and technical change under variable returns to scale (VRS) technology assumption for 141 Indian pharmaceutical firms during 2000-2001 to 2014-2015.

The study found the negligible impact of product patent regime on productivity. The technological change has played a positive role in the growth of productivity, whereas technical efficiency change depicts the judicious utilization of resources for improving performance. From the results, it is found that R&D intensive firms depict better stability in the TFP than the non-R&D firms. However, Granger causality between R&D and productivity found no relationship. Productivity is more directly affected by investment in fixed assets rather than in R&D, which focusses on incremental value additions in a largely branded/plain generic product market. In case of ownership, private foreign firms found to have registered progress in TFP while others have recorded marginal regress, which probably could be attributed to the superior marketing and management skills of the foreign firms, besides possessing proprietary technology. Both small and large firms have shown positive growth in the new regime as compared to the pre-patent regime. These small firms are able to compete with large firms because of their up-gradation of the technological base by improving access to better foreign technology. TFP growth for all the firms can be attributed to improvement in technology, and innovation in terms of high capital-output ratio. Further, the paper tried to identify the determinants of productivity from panel random effect regression, and it is found that export intensity, age and the new patent regime have negative and significant relationship with productivity, whereas other variables such as R&D, ownership, size and capital imports are insignificant. In the end, the results of sensitivity analysis have confirmed the validity of the selected variables.

The results suggest that Indian pharmaceutical firms need substantive improvement in TFP by improving managerial and scale efficiency. Indian pharmaceutical industry (IPI) needs to improve productivity across the network and drive cost excellence initiatives across the spend base through operational excellence and digital initiatives. The results of this paper can be applied in framing policies for future growth and improvement in the productivity of IPI.

The paper aims to make several new contributions to the existing literature. Most of the research papers only analysed TFP of the industry as a whole and detailed firm-wise analysis is needed to capture the true impact at a unit level. This study has analysed the impact of different categories such as ownership, R&D, size and product-wise, and determinants of productivity. The study has used a broader time period and larger panel data to predict the better picture.

This paper aims to study the changes in productivity for 25 state‐owned coal‐fired power plants (CFPPs) over a period of seven years (2003‐2010).

The methodology that is utilized in the study follows a non‐parametric approach of data envelopment analysis (DEA) and uses the Malmquist index to estimate the change in productivity of panel samples. In the calculations, the study considers installed capacity, fuel, labour, electricity used, and average operational time as inputs and considers net electricity produced as output.

The results indicate that the average total factor productivity regressed during the investigation period at an annual rate of 2 percent. The decrease in productivity is equally attributed to the technical efficiency change and technological change components, with an average decline in productivity of 1 percent per year. A plant‐wise analysis demonstrates that the Parichha plant recorded an average increase in productivity of 3.9 percent per year that was mainly driven by the technical efficiency change component (4.2 percent).There is little variation in the productivity of small‐size plants when compared with medium and large‐size plants. The productivity of multivaried plants is comparatively lower than BHEL (Bharat Heavy Electricals Limited) make plants.

The impact of size, make and region on change in productivity is examined. This study recommends specific policies that can be implemented to increase the productivity of power plants. The study also provides a contemporary overview of Indian CFPPs that can aid energy planners and plant operators in the monitoring and detection of changes in productivity.

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.