Measure Productivity Change Research Articles

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications.

Evaluating energy cost performance in China's thermal power industry: A global cost Malmquist approach

AbstractWith the inclusion of carbon neutrality in the Government Work Report for the first time at the two sessions held in March 2021 and the strengthening of environmental regulation as well as the increase of limits on polluting gas emissions, it has become increasingly important for China to effectively save energy and reduce carbon for sustainable growth under the “dual carbon” target. Since thermal power generation is the most important source of carbon emissions in China, the thermal power industry is the key to achieving the goal of carbon peaking and carbon neutrality. Therefore, it is of great theoretical and practical importance to study the performance of thermal power generation from the micro perspective of power plants. This paper provides a new perspective on the environmental efficiency measurement of thermal power plants by introducing the cost factors of inputs into the model and constructing a global cost Malmquist productivity index, which can effectively solve the problem of the infeasibility of linear programming and different measures of productivity change, to obtain the productivity performance of China's thermal power industry from 2013 to 2017. The empirical results indicate that: (1) The significant decrease in price effect (PE) is the main driving factor of this situation. (2) Except for the central region, the production technology, technical efficiency, and allocative efficiency related to thermal power generation have increased in the eastern and western regions. Therefore, improving CMG productivity in the central region is more challenging than in the other two regions and improving the PE can significantly improve CMG productivity in China's thermal power industry. This study reveals the spatiotemporal characteristics and influencing factors of production efficiency in China's thermal power industry, which can provide targeted policy guidance for managers to improve the sustainable development of power enterprises.

Assessment of national circular economy performance using super-efficiency dual data envelopment analysis and Malmquist productivity index: Case study of 27 European countries

The global commitment toward carbon neutrality and net zero emissions has substantially pressed the needs for all countries to accelerate the adoption of the circular economy paradigm. Tracking the national progress in terms of circular economy performance would provide valuable insights that could aid the effective design of improvement strategies toward sustainability. The current research proposes the combination of super-efficiency dual Data Envelopment Analysis and Malmquist productivity index to provide a full ranking and measure productivity changes in terms of circular economy of 27 European countries. The assessment involved six circular economy indicators encompassing waste generation per capita, waste intensity of the economy, recycling rate of waste in both overall and specific types of waste, i.e., packaging waste and biowaste, and circular material use rate. Our study indicates about one-half of the European countries were efficient in terms of circularity in 2018, where Netherlands, Germany, Austria, and Belgium were the front runner. The proposed approach suggests the European countries enhance their overall circular economy performance by prioritizing improvements strategies through promoting the recycling of biowaste and the circular material use rate. The MPI results over 2012–2018 indicate that Luxembourg exhibited the highest circularity advancement by 6%. Overall, the European countries have slightly enhanced their progression towards circular economy around 0.2% improvement. This suggests the European countries strengthen their policy and regulatory frameworks in support of the transition towards circular economy and encourage progressive movements in such a collaborative manner with the relevant stakeholders to build the momentum for change.

Performance Assessment of Malaysian Fossil Fuel Power Plants: A Data Envelopment Analysis (DEA) Approach

This paper investigated the performance of Malaysian power plants from the year 2015 to 2017 using Malmquist Total Factor Productivity (TFP) index, which is based on Data Envelopment Analysis (DEA). This approach offers substantial advantages as compared to other existing methods as it can measure productivity changes over time for a variety of inputs and outputs. Moreover, it comprises two primary components: the technical efficiency change and the technological change indexes that provide clearer insight into the factors that are responsible for shifts in total factor productivity. This study uses a single input, installed generation capacity (MW), and two outputs, average thermal efficiency (%) and average equivalent availability factor (%). These output-input data included ten main power plants: TNB Natural Gas, SESB Natural Gas, SESB Diesel, SEB Natural Gas, SEB Coal, SEB Diesel, IPP Semenanjung Natural Gas, IPP Semenanjung Coal, IPP Sabah Natural Gas, and IPP Sabah Diesel. The results have two significant implications for fossil fuel power plants in Malaysia. First, technological change was the primary factor in boosting the TFP performance of the fossil fuel power plants in Malaysia. Meanwhile, the decline in TFP performance in Malaysian fossil fuel power plants may be attributed, in part, to a lack of innovation in technical components as the results found that the average technical efficiency changes in 2015 – 2016 were at 146% and then dropped significantly to 2% in 2016 – 2017. Second, the average scale efficiency changes rose dramatically from -53% to 3% providing a significant contribution to the improvement of technical efficiency changes. The fossil fuel power plants become efficient as the power plants’ size increases. This indicates that the size of a power plant positively impacts the performance of the TFP.

Sequential Malmquist-Luenberger productivity index for interval data envelopment analysis

<p style='text-indent:20px;'>Data envelopment analysis (DEA) based productivity indexes models are widely applied to evaluate the productivity of decision-making units over a period. This study proposes a productivity index for evaluating environmentally sensitive productivity growth while excluding the possibility of spurious technical regress. This innovative index has been created by combining directional distance functions, sequential DEA, undesirable data, and the concept of interval DEA. With this combination, the traditional sequential Malmquist-Luenberger productivity index (SMLPI) has been reformulated as an interval DEA problem to present a novel productivity index named interval SMLPI. We propose a decomposition of the developed index utilizing both constant returns to scale and variable returns to scale frontiers as the benchmark, which allows us to quantify scale efficiency change with interval data. To exhibit the capability of the proposed extension of SMLPI, we model a framework for Indian commercial banks and measure productivity change intervals for twenty-one banks from 2011 to 2018. The empirical findings elucidate that the scale efficiency change plays an essential role in driving productivity change. ICICI Bank had the highest average marginal productivity gain of 1.5007 between 2011 and 2018, whereas Karur Vysya bank had the highest average marginal productivity decline of 0.9411.</p>

Solid waste management for the eco-efficiency assessment of energy recovery from waste in incinerators

This study examines energy recovery, specifically electricity generation, from waste in incineration plants. This study decomposes the incinerator system into waste treatment process and energy generation process via a two-stage network structure. We build a Russell multi-activity network data envelopment analysis (RMNDEA) model considering the weak disposability technology, and measure productivity changes over time and assess the plants’ returns-to-scale. We validate the model with an empirical study of 23 incinerators operating between 2011 and 2020 in Taiwan. Our finding that the waste treatment process itself is the major cause of inefficiency indicates that incinerator operators should reallocate input resources. Throughout the study period, there are minor productivity changes and most plants maintain constant returns-to-scale. However, some incinerators in Southern Taiwan having inefficient scale sizes should be rescaled for productivity improvement. The results validate the need to conduct routine eco-efficiency evaluations of solid waste management (SWM) in incinerators.

Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index

Determining energy productivity change during a time interval is an important issue in many production lines. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to measure productivity change and widely used by researchers to analyze the performance of decision making units. In this regard, the modified Enhanced Russell Measure (ERM), a non-radial DEA-based efficiency model, is applied to develop new models for measuring the Malmquist productivity index (MPI). To present productivity changes of decision making units (DMUs) over time more truly and more comprehensively than the conventional MPI method, this paper proposed three new approaches by using optimistic, pessimistic, and general viewpoints of data envelopment analysis. However, in many production processes, undesirable outputs such as smoke or waste pollution may be generated. Thus, this paper has further developed the proposed approaches in the presence of an undesirable output. The proposed methodology is applied to evaluate the productivity changes and efficiencies of chickpea production farms in 16 provinces in Iran.

Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data

The data envelopment analysis (DEA) technique is well known for computing the Malmquist-Luenberger productivity index (MLPI) in measuring productivity change in the decision-making units (DMUs) over two consecutive periods. In this research, we detect infeasibility of the directional distance function (DDF) based DEA model of MLPI under the variable returns to scale technology when data takes on negative values. We address this problem by developing a novel DDF-based DEA model that computes an improved MLPI. We extend the DDF approach to the dynamic network structure and introduce the dynamic MLPI for analyzing the performance of DMUs over time. We also develop the dynamic sequential MLPI to detect shifts in the efficient frontiers due to random shocks or technological advancements over time. The dynamic network structure in the two indexes comprises multiple divisions in DMUs connected vertically by intermediate productivity links and horizontally over time by carryovers. The proposed models are feasible and bounded with undesirable features and negative and non-negative data values. Real data of 39 Indian commercial public and private banks from 2008 to 2019 used to illustrate the two indexes.

Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata

In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal with undesirable outputs have been developed and applied in empirical studies for assessing the sustainability of decision-making units. These models are getting more and more attention from researchers and managers. The teddf command discussed in the present article allows users to measure technical efficiency, both radial and nonradial, when some outputs are undesirable. Technical efficiency measures are obtained by solving linear programming problems. The gtfpch command we also describe here provides tools for measuring productivity change, for example, the Malmquist–Luenberger index and the Luenberger indicator. We provide a brief overview of the nonparametric efficiency and productivity change measurement accounting for undesirable outputs, and we describe the syntax and options of the new commands. We also illustrate with examples how to perform the technical efficiency and productivity analysis with the newly introduced commands.

A dynamic by‐production framework for measuring productivity change in the presence of socially responsible and undesirable outputs: Evidence from European food processors

AbstractIn the past decade, corporate firms have globally increasingly adopted corporate social responsibility (CSR) practices; however, so far less is known about how productivity change in the presence of socially responsible and undesirable output can be evaluated. This paper develops a framework that integrates CSR in a dynamic by‐production technology which captures the transformation of multiple inputs into marketed outputs, socially responsible and undesirable outputs, while accounting for adjustment costs in quasi‐fixed inputs. The paper illustrates the method using a sample of European food and beverage manufacturing firms. The results of the empirical application show that there is a decline in dynamic Luenberger productivity indicators mainly due to technical inefficiency change. Hence, firms should devise strategies to enhance utilization of resources and reduce technical inefficiency. The regression of productivity change indicators on firm‐specific factors showed that more indebted firms experienced a lower growth of the productivity of undesirable output, whereas more profitable firms have a lower productivity growth of variable inputs and a higher growth of the productivity of investments. [EconLit Citations: C02, C14, C6, D24]

Evaluation of Tobacco Control Measures in the Organization for Economic Co-operation and Development Countries: A Comparative Study Using Data Envelopment Analysis.

Background: This study aimed to measure the efficiency and productivity of tobacco control policies across 16 selected Organization for Economic Co-operation and Development (OECD) countries from 2008 to 2014. Study design: A panel-data study. Methods: Data envelopment analysis was used in this study. Taxation on tobacco products and pictorial warning labels were chosen as the inputs. Percentage of the population of daily smokers above 15 years old and the number of cigarettes used per smoker per day were output variables. Additionally, the Malmquist total factor productivity (TFP) was used to analyze the panel data and measure productivity change and technical efficiency changes over time. Results: The highest technical efficiency score (1.05) was attributed to Norway, while the lowest (0.91) belonged to the UK. Technological change with a total mean of 1.06 implied that the technology and creativity have increased, while countries have been able to promote their creativity over the studied period. Norway with the TFP score of 1.15 was the most productive country, while the UK and Turkey with TFP scores of 0.95 and 0.98, respectively, were the least productive countries in terms of the implementation of the tobacco control policies. Conclusions: Most OECD countries have productively implemented tax and pictorial warning policies to reduce tobacco use. To achieve the optimum outcome of the tobacco control policies and overcome the challenges of smoking use, countries need to tackle the difficult underlying factors, i.e. tobacco industry opposition and lobbyists, smuggling, and low socioeconomic status.

A new SBM-based global Malmquist productivity index and target setting

The global Malmquist productivity index is a tool for measuring productivity change. It has two advantages with respect to the conventional Malmquist productivity index: it is circular and the infeasibility of the mixed period models does not happen. One drawback of the global Malmquist productivity index is to ignore slacks in its computations. In this paper, we follow four main goals: (1) we show that ignoring the slacks sometimes leads to misleading results; (2) we introduce a new slack-based measure (SBM)-based global Malmquist productivity index to overcome this drawback; (3) we will use the new index to determine a target for each inefficient unit in a dynamic way so that the proposed target has the most progress in its productivity among all the observed possible targets; (4) we propose a new mix-integer linear programming model to determine all the observed possible targets. Finally, we apply the proposed approach to the dataset of eighteen Canadian power companies to demonstrate the applicability of the method.

Evaluation of Tobacco Control Measures in the Organization for Economic Co-operation and Development Countries: A Comparative Study Using Data Envelopment Analysis

Background: This study aimed to measure the efficiency and productivity of tobacco control policies across 16 selected Organization for Economic Co-operation and Development (OECD) countries from 2008 to 2014. Study design: A panel-data study. Methods: Data envelopment analysis was used in this study. Taxation on tobacco products and pictorial warning labels were chosen as the inputs. Percentage of the population of daily smokers above 15 years old and the number of cigarettes used per smoker per day were output variables. Additionally, the Malmquist total factor productivity (TFP) was used to analyze the panel data and measure productivity change and technical efficiency changes over time. Results: The highest technical efficiency score (1.05) was attributed to Norway, while the lowest (0.91) belonged to the UK. Technological change with a total mean of 1.06 implied that the technology and creativity have increased, while countries have been able to promote their creativity over the studied period. Norway with the TFP score of 1.15 was the most productive country, while the UK and Turkey with TFP scores of 0.95 and 0.98, respectively, were the least productive countries in terms of the implementation of the tobacco control policies. Conclusions : Most OECD countries have productively implemented tax and pictorial warning policies to reduce tobacco use. To achieve the optimum outcome of the tobacco control policies and overcome the challenges of smoking use, countries need to tackle the difficult underlying factors, i.e. tobacco industry opposition and lobbyists, smuggling, and low socioeconomic status.

Efficiency drivers of insurers in GCC: an analysis incorporating company-specific and external environmental variables

The study aims to a comprehensive measure of listed insurance companies’ technical efficiency in GCC countries and benchmarking, peer count summary, and measuring productivity changes in the first ...

Generalized estimation of productivity with multiple bad outputs: The importance of materials balance constraints

Previous research has frequently estimated the directional technology distance function (DTDF) to more flexibly model multiple-input and multiple-output production, firm inefficiency, and productivity growth. For example, with firms such as electric utilities, one must model the production of good and bad outputs using good and bad inputs. Typically, all inputs and outputs are potentially endogenous. In previous work, we show how to identify a DTDF system using price equations based on profit maximization and compute optimal directions for measuring productivity change. However, this work has not imposed restrictions that limit substitution possibilities among inputs and outputs to a feasible set that is consistent with materials-balance constraints. Such constraints require that the weight of all inputs equals the weight of all outputs. The major innovation of this paper is that we include two types of functional relationships that impose the parametric analog of materials balance by modeling the generation of bad outputs and the use of bad inputs. The first requires that bad outputs are functionally related to good inputs and bad inputs. The second requires that bad inputs are functionally related to good inputs. We illustrate these methods using a balanced panel of 80 U.S. coal-fired electric generating plants from 1995–2005. Substantial differences are observed between the specification that includes the materials-balance constraints and the conventional approach that omits them, based on Bayes factors as well as measures of productivity and inefficiency. For many plants, improved management practices can reduce substantial inefficiencies in meeting emission constraints without reducing productivity growth.

Unrestricted geometric distance functions and the Geometric Young productivity index: an analysis of Indian manufacturing

In this paper, we employ the less frequently used Geometric Young index to measure productivity change. We reformulate the Geometric Distance Function of Portela and Thanassoulis (J Product Anal 25:25–41, 2006) by removing the restrictions on the individual input scaling factors and the output scaling factors to obtain the benchmark input–output bundle on the frontier. In this reformulated model, the Geometric Young index is exhaustively decomposed into the technical efficiency change and technical change factors even under variable returns to scale. Further, between any two periods, the measured rate of technical change is identical across all units. In our empirical analysis, we use state-level data from the Annual Survey of Industries for the period 2010–2011 through 2016–2017 to measure total factor productivity growth in Indian manufacturing and provide its decomposition for the major manufacturing states in India. At the All-India level, the annual average Geometric Young productivity index shows a modest growth over the sample period. This resulted from an improvement in technical efficiency reinforced by a very modest increase in the Technical Change Index.

Dual productivity analysis: A Konüs/Shephard approach

A primal (or direct) productivity index is conventionally defined as the ratio of an output quantity index to an input quantity index. There have been attempts in the literature to define and implement dual and indirect productivity indexes based on price changes rather than quantity changes. Although dual and indirect productivity indexes share a common motivation, the measurement of productivity change when prices are easier to measure, or are measured more accurately, than quantities, they differ analytically, from one another and from primal productivity indexes. We introduce a new dual productivity index, inspired by contributions of Konüs and Shephard, and we compare our dual productivity index with a primal productivity index inspired by the work of Malmquist. We also compare these two theoretical productivity indexes with an analogous pair of empirical Fisher productivity indexes. We provide an empirical application to US agricultural productivity growth.

Measurement of Total Factor Productivity in Commercial Airlines Sector using Malmquist Index and Data Envelopment Model

This study attempts to measure productivity change of Airlines companies in private and public sector in India for a period of four years (2011-2016). In this study the nature and productivity change is probed using the Malmquist Productivity Index. This index has the constituents which are used to measure the performance in terms of change in Scale Efficiency, change in Technical Efficiency, change in Technological Change and Total Factor Productivity. The paper compares efficiencies for the companies in public and private commercial airlines sector in India. Five Airlines companies are included in the study. The research includes Total Annual Income as an output variable and Total Expenditure, Employee Compensation, Sales & Distribution Expenditure and Marketing expenses as Input variables. A panel data with 30 observations has been used for analysis. The panel data is used to arrive to MPI estimates, with a total of five commercial airlines companies in India. The Total Factor Productivity change in the airlines sector depends upon the change in the efficiency and productivity of the companies. From the study it is evident that the Total Factor Productivity change has not changed significantly over the last six years for all the companies under study. The Technical Efficiency was the highest in the year 2013-14 which then dropped in the subsequent year. The Total Factor Productivity change is mainly due to change in scale efficiency of the companies since the pure efficiency has shown no significant change during the period under study. The Total Factor Efficiency dropped by almost 50% in the case of Air India in the year 2015-16. This drop is attributed to the deterioration in the technical efficiency of the company. The overall Total Factor Productivity of Air India is the highest. This can be attributed to positive change in the company’s Technical Efficiency especially in the year 2013-14. It is evident that all the airlines companies under study have not emphasized on improving scale efficiency as well as pure efficiency. These companies can improve their overall productivity by bringing in efficiency in the scale of operations as well as focus on improving efficiency on factors other than scale of operations. The commercial airlines companies in India need to improve their scale efficiency and pure efficiency to improve their total factor productivity.

Productivity patterns in Europe: adaptation of the Malmquist index to measuring group performance and productivity change over time

The paper proposes an extension of the methodology for measuring productivity change based on the Malmquist index that now affords consistent comparisons of productivity change and productivity levels in situations when there are groups of several units monitored in periods of several years. The extended measurement of productivity change is handled in the framework of data envelopment analysis and is applied in two respects in order to analyze productivity patterns of 17 European countries divided into two groups: higher-productivity economies and lower-productivity economies. First, their group-wise productivity changes and productivity differences are examined for the periods 2003–2008 and 2010–2015 divided by the critical crisis year 2009. Second, their absolute and conditional convergence is studied accommodating several specifications of growth regressions. The results confirm that higher-productivity economies preserved their lead in productivity despite the crisis, albeit the productivity differential between higher-productivity and lower productivity economies slightly waned. This is obviously owing to the fact that lower-productivity economies display faster convergence tendencies.