Average-cost efficiency and optimal scale sizes in non-parametric analysis

Cesaroni, Giovanni; Giovannola, Daniele

doi:10.1016/j.ejor.2014.09.062

Cited by 21 publications

(17 citation statements)

References 25 publications

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“…The following lemma provides some relations between RAC and the cost function: 14 Our approach is somewhat similar to the one developed in Cesaroni and Giovannola (2015).…”

Section: Characterizing Returns To Scale and Economies Of Scalementioning

confidence: 99%

Cost functions are nonconvex in the outputs when the technology is nonconvex: convexification is not harmless

Kerstens

Woestyne

2021

Ann Oper Res

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This contribution focuses on testing the empirical impact of the convexity assumption in estimating costs using nonparametric specifications of technology and cost functions. Apart from reviewing the scant available evidence, the empirical results based on two publicly available data sets reveal the effect of the convexity axiom on cost function estimates: cost estimates based on convex technologies turn out to be on average between 21% and 38% lower than those computed on nonconvex technologies. These differences are statistically significant when comparing kernel densities and can be illustrated using sections of the cost function estimates along some output dimension. Finally, also the characterization of returns to scale and economies of scale using production and cost functions for individual units yields conflicting results for between 19% and 31% of individual observations. The theoretical known potential impact as well as these empirical results should make us reconsider convexity in empirical production analysis: clearly, convexity is not harmless.

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“…The following lemma provides some relations between RAC and the cost function: 14 Our approach is somewhat similar to the one developed in Cesaroni and Giovannola (2015).…”

Section: Characterizing Returns To Scale and Economies Of Scalementioning

confidence: 99%

Cost functions are nonconvex in the outputs when the technology is nonconvex: convexification is not harmless

Kerstens

Woestyne

2021

Ann Oper Res

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“…In order to calculate the overall and stage efficiency scores based on dissimilar RTS assumptions, some closed form models are applied. Finally, some recent FDH models studies which have been proposed by many scholars can be addressed in literature [39][40][41][42][43].…”

Section: Two-stage Fdh Model In Production Technologiesmentioning

confidence: 99%

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

et al. 2020

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Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.

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“…Although the study related to Cesaroni and Giovannola (2015) was somewhat complicated to calculate OSS, it provided a basis for the discovery of OSS using these concepts in a new and two-step way. Haghighatpisheh et al (2022), believed that a more accurate concept of OSS can be provided if the output price is considered in addition to the input cost.…”

Section: Introductionmentioning

confidence: 99%

“…Haghighatpisheh et al (2022), believed that a more accurate concept of OSS can be provided if the output price is considered in addition to the input cost. Cesaroni and Giovannola (2015) had introduced the average-cost efficiency (ACE) based on the input cost. Further, Haghighatpisheh et al (2022) examined a new step towards the concept of OSS and stated their definition of OSS is accurate when the prices of inputs and outputs are dealt with.…”

Section: Introductionmentioning

confidence: 99%

Average-profit efficiency and optimal scale sizes with integer data and undesirable outputs

Omshi

Kordrostami

Amirteimoori

et al. 2023

JES

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PurposeData envelopment analysis (DEA) is a significant method for measuring the relative efficiency of decision making units (DMUs) that use the least inputs, produce the most desirable outputs and emit the least undesirable outputs in order to maximize their profits. In DEA, detecting an optimal scale size (OSS) is also vital and could be more applicable in economic activities when there are integer and undesirable measures. The purpose of this research is to measure average-profit efficiency (APE) and OSSs with integer data and undesirable outputs.Design/methodology/approachThis study presents an alternative concept of APE using the concepts of most productive scale size (MPSS), profit efficiency and scales, containing desirable and undesirable outputs along with integer and non-integer measures. In fact, the OSS minimizes APE as the optimal scale, which is the ratio of the profit efficiency to the radial average output. Considering the prices of the inputs and desirable outputs, as well as the lack of any specific weight for the undesirable outputs, a two-step model for the numerical calculation of OSS is presented. In addition, the proposed approach is applied to a real data set of Iranian gas companies while there are integer measures and undesirable outputs.FindingsThe results show the introduced approach is beneficial to estimate OSSs from the aspect of maximizing profits of firms with undesirable outputs and integer values.Originality/valueEstimating OSSs is the significant issue for managers, but its investigation in the presence of integer measures and undesirable outputs is presently under-considered.

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Average-cost efficiency and optimal scale sizes in non-parametric analysis

Cited by 21 publications

References 25 publications

Cost functions are nonconvex in the outputs when the technology is nonconvex: convexification is not harmless

Cost functions are nonconvex in the outputs when the technology is nonconvex: convexification is not harmless

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

Average-profit efficiency and optimal scale sizes with integer data and undesirable outputs

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