Non-discretionary Inputs Research Articles

A hybrid returns to scale-DEA model for sustainable efficiency evaluation of urban transportation systems

The urban transportation network has an undeniable role in addressing the economic, social and environmental issues caused by the traffic. Transportation managers seek to use the existing facilities and capacities in an optimal way to increase customer satisfaction. Therefore, it is necessary to develop an approach to evaluate the performance of the urban transportation system to provide service to citizens effectively. This study develops an approach based on the extended version of the data envelopment analysis (DEA) model to measure the nonradial efficiency and super-efficiency of metro-stations considering the sustainability concept. The developed non-radial DEA model considers the hybrid returns to scale the form of technology by combining constant and variable returns to scale assumptions to improve its applicability to identify efficient and inefficient stations. This DEA model also incorporates the non-discretionary inputs and different types of outputs (i.e. undesirable, negative and non-negative) to improve discrimination power and the ability to interpret the results. The findings help decision-makers identify super-efficient stations as a benchmark for future planning and finding the best location to construct metro-stations. Furthermore, this research enables managers to optimally use the resources to increase the transferred passengers, reduce customer dissatisfaction and optimise the annual profit.

Performance Evaluation of Water Services in Italy: A Meta-Frontier Approach Accounting for Regional Heterogeneities

Data relative to the water services industry in Italy indicate that there is a serious infrastructure gap between the southern regions and isles and the rest of the country. In these geographical areas, water utilities are provided with substantial public grants from the central and local governments to support investments necessary to mitigate the infrastructure divide by increasing capacity and improve service quality. This paper implements a meta-frontier non-parametric approach based on a data envelopment analysis (DEA) to evaluate the efficiencies of 71 Italian water utilities, accounting for the differentiated contexts in which they operate. A short-term perspective was assumed to estimate efficiency, considering the production factors associated with the infrastructure assets as non-discretionary inputs in the specification of the meta-frontier model. The results showed that water utilities operating in the southern regions and isles suffer from an efficiency gap in comparison to those in the northern and central regions. The average efficiency gap was 9.7%, achieving 24.9% in the worst case. Moreover, a more in-depth analysis focusing on the water utilities in the southern regions and isles indicated that scale inefficiencies might be an important determinant of such an efficiency gap. Indeed, slightly more than 69% of the water utilities operated at increasing returns to scale. Evidence from this study raises concern about the appropriate structure of the Italian water service industry and, particularly, the optimal size of the utilities and the financial sustainability of water services in the southern regions and isles.

Eco-efficiency of Chinese transportation industry: A DEA approach with non-discretionary input

In the context of the rapid development of Chinese economy and the advancement of urbanization and industrialization, the transportation industry caused serious environmental pollution and resource waste problems. The eco-efficiency of Chinese transportation industry has become a hot spot of society. Recently, China announced several initiatives to stabilize and expand employment, so that the efficient and sustainable development of industries cannot be based on the premise of reducing the labor force. However, traditional efficiency measures usually assume that inputs can be reduced at will, which is insufficient to correctly represent the observed practice. In this paper, we investigate the eco-efficiency of the transportation industry in 30 Chinese provinces incorporating the non-discretionary input labor and undesirable output CO2 emission. Data envelopment analysis and directional distance function approaches are employed in constructing the measurement model. Our empirical results reveal explicit spatial features of the eco-efficiency in various provinces and show significant correlation between eco-efficiency and industrial structure, technological, and management factors. To examine the validity of our proposed models, comparative studies were further conducted and illustrated that the introduction of non-discretionary inputs and the selection of direction vectors have a crucial impact on the results of eco-efficiency. Based on the empirical results and our summarized policy implications, we found several deficiencies in the real policies and put forward corresponding policy suggestions for the industry and government.

Assessing the sustainability of transport supply chains by double frontier network data envelopment analysis

The transport industry is one of the main contributors to environmental pollution. Sustainable supply chain management (SSCM) is an essential subject in the transportation industry. Today, one of the important goals of organizations is to evaluate the sustainability of supply chain (SC) because assessing the efficiency of SCs helps organizations to enhance their awareness of performance and develop managerial strategies. Data envelopment analysis (DEA) is a common technique to assess the sustainability. The objective of this paper is to propose a Malmquist productivity index (MPI) based on network data envelopment analysis (NDEA) model in the presence of integer data, undesirable outputs, and non-discretionary inputs. The NDEA models deal with the internal structure of decision making units (DMUs). The MPI reflects the productivity change over time. The proposed model can fully rank DMUs. To prove the applicability of the proposed model, the sustainability of intercity passenger transportation is evaluated.

Efficiency of Green Supply Chain in the presence of non-discretionary and undesirable factors, using Data Envelopment Analysis

A new approach that has dominated the production operations management field in recent years is supply chain management. A supply chain includes all the facilities, tasks and activities involved in manufacturing a product from suppliers to customers. Its various elements are planning, supply and demand management, procurement of raw materials, production scheduling, distribution and delivery of products to the customer. Special structures in the supply chain have been less studied in previous research. In this paper, the supply chain and its performance evaluation are examined in the presence of non-discretionary, undesirable and negative data. For this purpose, another model of the network DEA is presented which evaluates performance of the chain in the presence of non-discretionary inputs and outputs, undesirable outputs and negative outputs even in its internal structure. The efficiency of the chain stages is also calculated using a dual model. Subsequently, 42 cement companies listed on the Tehran stock exchange were evaluated, each of which has a chain of four stages including suppliers, manufacturers, distributors and customers. Based on the implementation of the model, six companies were found to be efficient and the rest were introduced as inefficient. Moreover, 25 cement companies in the Supplier sector, 18 companies in the manufacturing sector, seven companies in the distribution sector and finally 17 companies in the customer service sector were found to be efficient.

Efficiency evaluation in hybrid two-stage network DEA with non- discretionary inputs and shared discretionary inputs

In the network DEA models, it is assumed that all inputs are varied according to decisions of manager. However, in many cases, there are non-discretionary inputs that are beyond the control of a sub-DMUs and DMUs management in the efficiency evaluation, and this problem has not been discussed considerably in network DEA so far. This study proposed a multiplier model to treat a two-stage network system comprised of three sub-DMUs in hybrid form with non- discretionary inputs and shared discretionary inputs, while this issue is not sufficiently rich in the network systems, here, it is being analyzed seriously in this study. The proposed model simulate precisely the parallel-series internal structure of network system with non-discretionary inputs and shared discretionary inputs between sub-DMUs, and also uses synthetically additive and multiplicative DEA approaches for evaluating the efficiency of two-stage network system. An example of measuring the efficiency of the heart surgery hospitals in Iran, that has been discussed in this study, illustrates the idea presented in this study.

The role of fixed cost and non-discretionary variables in fisheries: The case of South Australian rock lobster

Fishing industry sustainability matters for policy-makers and communities as well as the industry itself and understanding profitability is an important component of long-run viability. Our analysis investigates the effects of key fixed and non-discretionary inputs, vessel size and biomass, on profits. We first set up a theoretical model with an input that is fixed in the short-run but that can be used with a variable input at sub-optimal capacity. We use this model to get theoretical insights and predictions for the impact of exogenous changes in biomass, output price and vessel size on profits. We subsequently conduct an empirical investigation to gain an understanding of the effects of these non-discretionary factors on profit efficiency. In particular, we apply a truncated regression with bootstrap methodology to data on individual fisher profit efficiency from the South Australian Rock Lobster Fishery. The non-discretionary variables we consider are biomass, fishing zone, quota management, vessel age, vessel length, engine age, electrical equipment age, and exchange rate. We find evidence that increased biomass and quota management are associated with higher profit efficiency, exchange rate appreciation is associated with lower profit efficiency, and vessel characteristics have limited effects.

Achieving agricultural revitalization: Performance of technical innovation inputs in farmland and water conservation facilities

This article aims to explore the impact of fixed input or the non-discretionary inputs on estimating the input of individual farmland water conservation facilities and its effect on the performance of regional agricultural area in China. The present study developed the production function model and the occasional cost boundary function from the duality of the production, to estimate the technical efficiency of the benefitted farmland area from constructing and improving irrigation and drainage facilities by using Statistics in Taiwan and Zhejiang Province. Considering the presence of fixed inputs or non-discretionary inputs in the investment, construction, and operation of water conservation facilities, the present study estimated the production efficiency of individual farmland water conservation facilities. The empirical results showed that fixed input, or non-discretionary inputs, had a significant impact on the efficiency estimation of farmland water conservation facilities. Technical efficiency would be underestimated if fixed inputs is not considered.

Performance measurement and resource sharing among business sub-units in the presence of non-discretionary factors

PurposeThis paper aims to apply a new approach for resource sharing and efficiency estimation of subunits in the presence of non-discretionary factors and partial impacts among inputs and outputs in the data envelopment analysis (DEA) framework.Design/methodology/approachFirst, inspired by the Imanirad et al.’s model (2013), the authors consider that each decision-making unit (DMU) may consist of several subunits, that each of which can be affected by non-discretionary inputs. After that, the Banker and Morey’s model (1996) is used for modeling non-discretionary factors. For measuring performance of several subunits, which can be considered as DMUs, the aggregate efficiency is suggested. At last, the overall efficiency is computed and compared with each other.FindingsOne of the important features of proposed model is that each output in this model applies discretionary input according to its need; therefore, the result of this study will make it easier for the managers to make better decisions. Also, it indicates that significant predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the influence of non-discretionary input. Therefore, the proposed model provides a more reasonable and encompassing measure of performance in participating non-discretionary and discretionary inputs to better efficiency. An application of the proposed model for gaining efficiency of 17 road patrols is provided.Research limitations/implicationsMore non-discretionary and discretionary inputs can be taken into consideration for a better analysis. This study provides us with a framework for performance measures along with useful managerial insights. Focusing upon the right scope of operations may help out the management in improving their overall efficiency and performance. In the recent highway maintenance management systems, the environmental differences exist among patrols and other geotechnical services under the climate diverse. Further, in some cases, there might exist more than one non-discretionary factor that can have different effects on the subunits’ performance.Practical implicationsThe purpose of this paper was to measure the performance of a set of the roadway maintenance crews and to analyze the impact of non-discretionary inputs on the efficiency of the roadway maintenance. The application of the proposed model, on the one hand, showed that each output in this model uses discretionary input according to its requirement, and on the other hand, the result showed that meaningful predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the impact of non-discretionary input.Originality/valueProviding information on resource sharing by taking into account non-discretionary factors for each subunit can help managers to make better decisions to increase the efficiency.

Educational expenditure efficiency in China: the role of the governor’s characteristics

ABSTRACT This article studies educational efficiency in Chinese provinces following a two-step approach. Specifically, we take advantage of the selecting pattern of local officials in China to investigate the relationship between governors’ characteristics and local government educational efficiency, which has been previously ignored in the literature. First, we use a robust order-m partial frontier approach to evaluate the efficiency of the Chinese provinces in the provision of education, which represents the main sector of expenditure for local governments. Second, we estimate the relationship between efficiency and the provinces’ observable characteristics, with a special focus on the governor’s characteristics. We consider economic, institutional, and demographic variables as non-discretionary inputs. Finally, we use the Spatial Lag of X (SLX) and Spatial Durbin Model (SDM) to evaluate the possible spatial interdependence in the educational efficiency between Chinese provinces. The results show that East region provinces are the most efficient in the provision of education, while the provinces in the West region are the least efficient. Moreover, the governor’s observable characteristics, such as age, education, and experience abroad, have a significant effect on educational efficiency.

A common weights model for investigating efficiency-based leadership in the russian banking industry

In this race for productivity, the most successful leaders in the banking industry are those with high-efficiency and a competitive edge. Data envelopment analysis is one of the most widely used methods for measuring efficiency in organizations. In this study, we use the ideal point concept and propose a common weights model with fuzzy data and non-discretionary inputs. The proposed model considers environmental criteria with uncertain data to produce a full ranking of homogenous decision-making units. We use the proposed model to investigate the efficiency-based leaders in the Russian banking industry. The results show that the unidimensional and unilateral assessment of leading organizations solely according to corporate size is insufficient to characterize industry leaders effectively. In response, we recommend a multilevel, multicomponent, and multidisciplinary evaluation framework for a more reliable and realistic investigation of leadership at the network level of analysis.

Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production.

A bargaining game model for measuring efficiency of two-stage network DEA with non-discretionary inputs

Conventional data envelopment analysis (DEA) is a method for measuring the efficiency of decision-making units (DMUs). Recently, to measure the efficiency of sub-DMUs (Stages), several network DEA models have been developed, in which the results of network DEA models not only provide the overall efficiency of the whole system but also provide the efficiency of the individual stages. This study develops a bargaining game model for measuring the efficiency of DMUs that have a two-stage network structure with non-discretionary inputs, that the model as a method of dealing with the conflict arising from the intermediate measures. Under the Nash bargaining game theory, the two stages in the network DEA are considered as players and network DEA model is a cooperative game model. Here, the non-discretionary additional inputs in the second stage make changes in the cooperative game model, so that managers of units cannot change the value of non-discretionary inputs in measuring the efficiency of the bargaining game model, and this causes the desired and expected output of the managers not to be produced. In addition, it can be stated that the presence of such inputs is capable, significantly affecting the system efficiency score and stages. So that the existence of the inputs in the measuring efficiency of decision-making units reduces the efficiency score of cooperative game. In this study, linearizing the model in the presence of the non-discretionary input is a new idea in the bargaining game model. A numerical example shows the applicability of the new model.

A two-stage DEA model for resource allocation in industrial pollution treatment and its application in China

An increasing amount of resources is being utilized to combat the pollution problem in China. Measuring and improving the efficiency of this resource allocation is an effective way to promote pollution treatment at the present technical level. This paper utilizes a two-stage data envelopment analysis (DEA) model to attain allocation results based on the zero-sum gain and fixed-sum gain assumptions. We divide all the inputs into discretionary and non-discretionary inputs and construct two models for allocating resources based on the environmental efficiency scores. The new models are applied to calculate the performance and resource allocation scheme of 30 Chinese provinces from 2011 to 2015. Most provinces are inefficient at controlling pollution emission or treating pollution while using the given level of input resources. Based on the production possibility set of each province in 2015, the processing capacity of wastewater and gas could be increased by 1% and 3%, respectively, by reallocating resources for pollution treatment among different provinces. Environmental efficiency scores and resource allocation plans based on various assumptions are obtained in this paper to enlighten the corresponding decision-makers.

The regional green growth and sustainable development of China in the presence of sustainable resources recovered from pollutions

The rapid economic development of China has intensified the country’s many problems. Among them, energy shortage and environmental pollution are two main problems, which highly affects the economic growth and sustainable development. To achieve more rapid green growth, the innovative technology by reusing the environmental wastes has been widely used since doing so not only decreases the environment pollution, but also further brings more natural resource. The present paper establishes a two-stage structure for evaluating the regional green growth and sustainable development in China by calculating the efficiency of “energy saving” and “pollution treatment” separately. Specifically, a set of models based on slack-based measure approach are constructed in which non-discretionary inputs can be calculated in both resource utilization stage and pollution treatment stage. Comparing with the traditional models, the new proposed models can measure the performance of resources saving and pollution treatment with considering the influence of non-discretionary inputs. An empirical application on Chinese 30 regions during 2011–2015 have been done to illustrate the use of our framework and the performance of regional green growth and sustainable development. Based on the efficiency results, we find that the efficiency scores of the provinces in central and northeast area are lower, which is mostly caused by their poor performance on “pollution treatment”. Both the environmental efficiency scores and target values for performance improvement are obtained in this paper to enlighten the corresponding decision-makers.

Educational Efficiency of State Boards of Secondary Education in India: SFA and D&ND DEA Analysis

The paper, using both stochastic Cobb-Douglas production frontier and the discretionary and non-discretionary DEA, empirically evaluated the performance of the BSEs of 28 states of India using the number of students passed Class X as the output. The mean efficiency score in SPF was more (0.792) compared to the DEA score (0.695). Around 43 per cent of the total DMUs were efficient in the range of 80 per cent and above. Gamma value of the SPF model inferred that 98 per cent of the variation in the efficiency score was due to technical inefficiency. The major factors determining the total students passed under a DMU are SCRR, PTR and RT. Out of GSE, SCSTE and PSRA, the SCSTE was positively and significantly affected the technical inefficiency. In the case of DEA introduction of nondiscretionary inputs reduced the mean score to 0.628 keeping the number of fully efficient states the same. The fully efficient states gave less weight to RT and PTR and all other inputs were assigned higher weights reflecting the relatively larger scarcity of these inputs. Due to non-discretionary inputs, the states affected most were Arunachal Pradesh, Assam, Goa, Manipur, Punjab and Sikkim. The fully efficient DMUs under DEA had also scored more than 90 per cent under SFA. On the basis of the result, the study concluded that appointment of regular teacher with appropriate benefits and perks, school infrastructure development, adoption of public-private partnership for bringing more investment in the infrastructure, at par treatment of the schools in the rural areas, uniform policy for all categories of students in the admission into the secondary schools and regular selfappraisal of the regular teachers will not only help in identifying the states with least performances but also the states who preferred to be peers for many inefficient performers but also about cost implication of the use of the inputs thereby warranting an effective management of the respective DMUs.

Measuring the relative performance of forest management units: a chance-constrained DEA model in the presence of the nondiscretionary factor

In this study, we develop a marginal chance-constrained data envelopment analysis (DEA) model in the presence of nondiscretionary inputs and hybrid outputs for the first time. We call it a stochastic nondiscretionary DEA model (SND-DEA), and it is developed to measure and compare the relative efficiency of forest management units under different environmental management systems. Furthermore, we apply an output-oriented DEA technology to both deterministic and stochastic scenarios. The required data are collected from 24 forest management plans (as decision-making units) and included four inputs and an equal amount of outputs. The findings of this practical research show that the modified SND-DEA model in different probability levels gives us apparently different results compared with the output from pure deterministic models. However, when we calculate the correlation measures, the probability levels give us a strong positive correlation between stochastic and deterministic models. Therefore, approximately 40% of the forest management plans based on the applied SND-DEA model should substantially increase their average efficiency score. As the major conclusion, our developed SND-DEA model is a suitable improvement over previous developed models to discriminate the efficiency and (or) inefficiency of decision-making units to hedge against risk and uncertainty in this type of forest management problem.

Accounting for rainfall and the length of growing season in technical efficiency analysis

The physical environment of farming systems is rarely considered when conducting farm level efficiency analysis, which is likely to lead to bias of performance measurements based on benchmarking methods such as Data Envelopment Analysis (DEA). We incorporate variations of the physical environment (rainfall and length of growing season) through the specifications of the linear programming in DEA to investigate performance measurement bias. The derived technical efficiency estimates are obtained using a sub-vector DEA which ensures farms are compared in a homogenous environment (i.e. accounting for differences in rainfall levels amongst distinct farm units). We use the Farm Business Survey to analyse a representative sample of 245 cereal farms in the East Anglia region between 2009 and 2010. Efficiency rankings obtained from a standard DEA model and a non-discretionary DEA model that incorporates the variations in the physical environment. We show that incorporating rainfall and the length of the growing season as non-discretionary inputs into the production function had significantly altered the farm efficiency ranking between the two models. Hence, to improve extension services to farmers and to reduce biased estimates of farm technical efficiency, variations in environmental conditions need to be integral to the analysis of efficiency.

A DEA-based approach for competitive environment analysis in global operations strategies

While competitive environment analysis is critical to a global retailing operations strategy, there exist research gaps from perspectives of operational performance, retailing industrial environment, and nondiscretionary factors. Therefore, our research objective is to propose a new approach to conduct competitive environment analysis for a global operations strategy in retailing, by examining relationships between discretionary inputs of the supply chain, nondiscretionary inputs of the environment, and performance of retailing. We develop a nondiscretionary data envelopment analysis model to assess performance in retailing and integrate it with econometric analysis. Using multisource data of 124 organizations in the global retailing industry, it is interesting to find: while nondiscretionary factors significantly influence the operational performance of global retailers, firms in an environment with a higher market concentration, larger consumer spending per capita, and smaller inhabitants’ population are more likely to achieve a higher operational efficiency in retailing. Another interesting finding with practical implication is: inputs relevant to outside environment (e.g., suppliers in upstream and outlets in downstream supply chain) can influence operational efficiency more than inputs in internal supply chain (e.g., warehouses).

Special District Libraries and Operating Costs: An Application of Data Envelopment Analysis (DEA) with Discretionary and Non-Discretionary Inputs

ABSTRACTData envelopment analysis (DEA) was used to assess the technical efficiency of special district libraries in the United States with respect to three cost-related outputs: operating cost per hour, program costs per visit, and program costs per attendance. A set of discretionary and non-discretionary inputs were used to obtain the technical efficiency scores of 999 special district libraries. The DEA input-oriented analysis shows that 30% of the libraries were technically efficient with respect to the use of the inputs to achieve their level of operating costs in 2015. The DEA analysis also shows that inefficient libraries need to make proportional reductions in the level of discretionary inputs to become technically efficient.