Measuring the productivity changes with double frontiers data envelopment analysis for two-stage processes

This study has a twofold. First, it aimed to measure the efficiency of the insurance sector in the Kingdom of Saudi Arabia during the period 1995-2022. Efficiency was measured using the Data Envelopment Analysis (DEA) method, employing three inputs: general and administrative expenses, underwriting costs, and direct written premiums, and one output represented by the insurance operations surplus. The study also relied on the Malmquist Index to calculate and determine the nature of changes in the total factor productivity (TFP) of insurance companies. The results showed that only one company, Inaya Saudi Arabia Cooperative Insurance, achieved the optimal level of efficiency. Furthermore, the Malmquist Index results indicated a slight decrease in overall productivity, accompanied by a decline in pure technical efficiency and a reduction in scale efficiency. The Second objective of the research was the study of the impact of insurance sector efficiency on economic growth using the Autoregressive Distributed Lag (ARDL) model. Three models were estimated, considering three indicators of efficiency: insurance sector efficiency under constant returns to scale (CRS), under variable returns to scale (VRS), and the Malmquist Productivity Index. The findings revealed a long-run relationship between the efficiency of the insurance sector and economic growth. In the short run, the Error Correction Model (ECM) showed that efficiency has a negative and significant effect in both the CRS and VRS models, while its effect was positive and significant in the productivity index model. The causal relationships between insurance sector efficiency and economic growth were apprehended using the Toda-Yamamoto method. The results indicated a bidirectional causal relationship between insurance sector efficiency and economic growth in the constant returns to scale (CRS) model. In contrast, the variable returns to scale (VRS) model showed no causal relationship. The Malmquist Index model demonstrated the broadest connection with economic variables and the largest number of causal relationships directed toward economic growth among the models. Our results would have several prominent implications for policymakers when designing strategies to improve the performance and efficiency of the insurance sector, consistent with the economic diversification strategy outlined by the Kingdom in its Vision 2030.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

Healthcare organizations must operate efficiently to provide affordable care without compromising their sustainability and competitiveness. The purpose of this is to measure the profit efficiency of select Indian Corporate Hospitals during the COVID-19 pandemic. Further, the study attempts to analyze the productivity changes in select corporate hospitals during COVID-19 and postimplementation of the national insurance policy in India. The study used the Data envelopment analysis (DEA) technique to measure the efficiency of corporate hospitals. It utilizes Constant Return to Scale (CRS), and Variable Return to Scale (VRS) to estimate profit inefficiency. DEA-based Malmquist Productivity Index (MPI) was used to examine the productivity change during the last three years since the outbreak of COVID-19. The DEA-MPI model is iterated for panel data for Financial Year 2018–2021. with year-wise employee benefits and operating expenses as the input parameters and year-wise net sales as the output parameter. The study reveals surprising results and requires critical analysis. Out of select hospitals, 80% of hospitals were found inefficient using CRS while 70% of the hospitals were found inefficient using VRS. The findings of this study are useful for healthcare administrators and managers to highlight improvement programs for efficiency maximization .

A traditional DEA (data envelopment analysis) model is useful for estimating the best performances for a successful and a more efficiency DMU (decision-making unit). This study uses a traditional DEA model to estimate the efficiency score of bank z in Taiwan and get a good efficiency score based on the 2006 data, however the bank z was taken over by the government in early 2007. In illustrating this paradox phenomenon, the study reconsiders the tradition DEA model by contributing a worst-practice frontier under a traditional DEA model. Findings: the bank z is located on the intersection of the worst-practice frontier and the traditional DEA model frontier. This study provides a critical determinant of a bank’s failure that a bank has a location such as the bank z.

This study is aimed to examine the efficiency and productivity growth of Initial Public Offerings (IPOs) firms listed on Karachi Stock Exchange (KSE), Pakistan from 2000 to 2012 by using three stages - Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). The analysis and comparison of the efficiency and productivity growth of IPOs on sectoral basis in the pre and post period of IPOs is also investigated. This is ever first study to measure the pre IPO efficiency across the globe in the field of IPOs. The overall efficiency scores of IPO firms dismal as the percentage of optimum level of IPO firms remain between 5% and 20% in all the three stages in pre and post IPOs. In the analysis of broader categories of sectors; private, state-owned enterprises (SOEs), manufacturing, financial, other services sectors, the results of DEA model of three stages suggest that neither of the sector is constant return to scale (CRS) efficient nor and Variable Returns to Scale (VRS) in pre and Post IPO. Even the efficiency scores are decreased in post IPO after one year. However, in detail sector-wise analysis the only oil and gas sector showed optimal level under VRS in stage 2. Further, SOEs showed some better efficiency than private IPO firms. The overall results of declining trend in total productivity growth of IPOs after three years period in KSE are observed. The overall results suggest that, after acquiring further resources, IPO firms did not improve their efficiency level and productivity growth after the period of three years of IPOs. DOI: http://dx.doi.org/10.5755/j01.ee.27.2.10062

The operating efficiency of Taiwanese commercial banks is a key factor on Taiwan's economic development. However, the changes in credit risk of the banks have serious impact on productivity change. In this paper, we use financial ratios to assess credit risk of 34 Taiwanese commercial banks before and during the global financial crisis over the period 2007-08, and investigate the productivity change based on the changes in credit risk with the Malmquist productivity index (MPI) approach, which is calculated from efficiency scores based on data envelopment analysis (DEA). Then we apply Tobit regression to identify the key variables which significantly affect the efficiencies in c 6 pt redit risk and to analyze the correlation between the experience of M & As investment and productivity change. And according to earning per share (EPS) and MPI, we classify the 34 banks into four groups. We find that different groups of banks should have different strategies of credit risk management to survive in this changing environment. Furthermore, the increase of the ratio of total loans to total assets could not improve technical efficiency (TE) and pure technical efficiency (PTE) at the decision making units (DMUs) due to the increase of credit risk. Also, the experience of M & As investment could have poor help on efficiency/productivity change on credit risk at the DMUs.

Efficient healthcare delivery is crucial for achieving optimal health outcomes, particularly in developing countries facing resource allocation challenges. This study evaluates regional healthcare efficiency in Tanzania using Data Envelopment Analysis (DEA) with data from 2017 to 2021, employing both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) methods and analyzing productivity trends through the Malmquist Productivity Index (MPI). The results indicate an average scale efficiency of 56.8%, with Dar es Salaam, Morogoro, Kilimanjaro, Arusha, and Tanga achieving full efficiency, while Ruvuma (26.7%) and Mtwara (28%) were the least efficient, highlighting significant regional disparities. CRS technical efficiency averaged 48.5%, implying a 51.5% input savings potential, while VRS technical efficiency averaged 82.3%, suggesting a 17.7% input savings potential. Additionally, only 19.23% of regions were efficient under CRS, and 30.77% under VRS. The MPI results showed that low technical change, with an average of 0.822, was a key driver of inefficiency despite a 2.063 average pure efficiency change, indicating poor technological progress. Further analysis of returns to scale found that 69.24% of regions operated under Increasing Returns to Scale (IRS), suggesting underutilization of resources, while 11.53% experienced Decreasing Returns to Scale (DRS), signaling inefficiencies from overextension. Recommendations to address these inefficiencies include targeted resource redistribution, improving healthcare worker conditions, staff capacity building, health information system integration, performance-based incentives, and fostering public–private partnerships to enhance service delivery and efficiency. Additionally, evidence-based decision-making frameworks and strong performance monitoring systems should guide reforms, promoting a more equitable and efficient healthcare system in Tanzania and many other developing countries.

Background: We measured productivity changes of regional public hospitals using both global Malmquist productivity index—based on global production possibility set over all the periods— and the traditional Malmquist productivity index and analyzed the factors of productivity change.Methods: The data used in this study is two annual inputs and two annual outputs of 32 regional public hospitals in Korea from 2005 to 2009 and the results such as distances and Malmquist productivity index and global Malmquist productivity index are ob-tained by an R program written for this study.Results: The results can be summarized as follows. Firstly, technical efficiencies of regional public hospitals are affected largely by scale efficiency than pure technical efficiency. Second, productivity progressed and technological change has more significant in-fluence on productivity advance over the period between 2005 and 2009. Third, the circularity problem of the traditional Malmquist index is confirmed, and so the global Malmquist index without this problem are valid for the analysis.Conclusion: Though this study also has some limitations with the data of regional public hospitals with a short time span, it is the first study of hospitals using global Malmquist productivity index and later it can be expanded to private hospitals and longer time periods.Keywords: Data envelopment analysis; Malmquist productivity index; Luenberg productivity index; Regional public hospitals

BackgroundDifferent healthcare reforms could affect the productivity of hospitals. The aim of this study was to track hospital productivity before and after the recent Iranian healthcare reform in Khuzestan province, South West of Iran.MethodsHospital productivity was evaluated through data envelopment analysis (DEA) and Malmquist productivity index (MPI) from 2011 to 2015 for 17 Iranian public hospitals before and after the health sector transformation plan. We assumed an output-oriented model with variable returns to scale (VRS) to estimate the productivity and efficiency of each hospital. The DEAP V.2.1 software was used for data analysis.ResultsAfter the transformation plan, the averages of technical efficiency, managerial efficiency and scale efficiency in the studied hospitals had negative changes, but technology efficiency had positive changes.44.4% of general hospitals, 25% of multi-specialized hospitals, and 100% of specialized hospitals had positive productivity changes after implementing the health sector evolution plan. The Malmquist productivity index (MPI) had low positive changes from 2013 to 2016 (MPI = 0.13 out of 1) but the mean productivity score had no change after the health sector evolution plan.ConclusionsThe total productivity before and after the health sector evolution plan had no change in Khuzestan province. This and the increase in the utilization of impatient services seemed to be a sign of good performance. But apart from technology efficiency, other efficiency indices had negative changes. It is suggested that in health reforms in Iran, more attention should be paid to the allocation of resources in the hospital.

Many governments in the developing world face the social and economic consequences of road accidents and mortalities. Hence, more precise evaluation of regional programs to reduce road fatalities has been a concern for many safety professionals.Road safety performance is often measured using various extensions of Data Envelopment Analysis (DEA), in particular the model proposed by Charnes, Cooper and Rhodes (CCR), which deals only with the radial efficiency as the objective function neither taking into account input excesses nor output shortfalls. The Slacks-Based Measure (SBM) of efficiency overcomes this shortcoming by taking both measurements mentioned above simultaneously. In this regard, the current study aims to employ the SBM in analyzing road safety performance. It is noteworthy that the efficiency of each Decision Making Unit (DMU) can be pessimistically measured using the slacks-based measure of inefficiency such that the anti-efficient DMUs provide the anti-efficient frontier. The results obtained from the optimistic and pessimistic frontiers are nonlinearly aggregated by means of the Evidential Reasoning (ER) algorithm.Furthermore, a Double-Frontier SBM-based Malmquist Productivity Index (DF-SBM- MPI) is provided to analyze the efficiency and technological changes in safety performance from 2014 to 2016. For this purpose, the standard SBM and Super-SBM models are used to compute the optimistic Malmquist Productivity Index (MPI); similarly, the pessimistic MPI is determined by means of the inverted SBM and Super-SBM models. Finally, the obtained MPIs from the two different points of view are geometrically combined to obtain the overall MPI.

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.

An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa

Productivity measurement is assumed as one of the main guidelines to assess the effectiveness and efficiency of organisations. Productivity of the bank branches as main players of financial systems should be measured periodically. In this paper, the productivity and its components (i.e., technical efficiency change and technological frontier change) in 42 profit-making branches of a private bank in Tehran province, Iran, are analysed during the period 2014-2016. Inefficient bank branches are determined. The projection of inefficient bank branches toward efficient frontier is also discussed. Productivity and technical efficiency of production elements of branches of a private bank are investigated by Malmquist productivity index (MPI) and an input-oriented data envelopment analysis (DEA) in constant returns to scale (CRS) and variable returns to scale (VRS) conditions. Based on results, scale inefficiency had the greatest impact on technical efficiency in the case study.

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

This study estimates technical efficiency and total factor productivity change in the Nigerian banking sector for the period 2005–14, which encapsulates the post‐consolidation era and subsequent regime of banking reforms aimed at stabilizing the sector from the effects of financial crisis. The study applies both non‐parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Approach (SFA), using the Malmquist Productivity Index, and error component production function respectively, to ascertain if any significant variation in efficiency exists on a sample of 12 banks covering over 80 percent of total bank assets in Nigeria. The theoretical intermediation approach is applied for selection of input and output variables. The input variables considered are total deposits, total equity and operating expenses including staff costs, and output variables are loans and operating income, which accounts for off‐balance‐sheet items such as non‐interest or fee‐based income. Findings reveal that the mean technical efficiency under SFA and total factor productivity change in DEA decreases as bank output moves toward non‐interest or fee‐based income. Although the magnitude differs, both SFA and DEA follow a similar direction for technical efficiency and total factor productivity change. Study implications suggest that policymakers should be concerned about arbitrariness in bank's ability to earn fee‐based income, which portends high cost of banking services in the long run.