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A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture

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  • Mosbah, Ezzeddine
  • Zaibet, Lokman
  • Dharmapala, P. Sunil

Abstract

This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.

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  • Mosbah, Ezzeddine & Zaibet, Lokman & Dharmapala, P. Sunil, 2020. "A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:soceps:v:72:y:2020:i:c:s0038012119303891
    DOI: 10.1016/j.seps.2020.100857
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    1. Mehdi Toloo & Madjid Tavana, 2017. "A novel method for selecting a single efficient unit in data envelopment analysis without explicit inputs/outputs," Annals of Operations Research, Springer, vol. 253(1), pages 657-681, June.
    2. Tarja Joro & Pekka Korhonen & Jyrki Wallenius, 1998. "Structural Comparison of Data Envelopment Analysis and Multiple Objective Linear Programming," Management Science, INFORMS, vol. 44(7), pages 962-970, July.
    3. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    4. Zaibet, L. & Dharmapala, P. S., 1999. "Efficiency of government-supported horticulture: the case of Oman," Agricultural Systems, Elsevier, vol. 62(3), pages 159-168, December.
    5. Emrouznejad, Ali & Yang, Guo-liang, 2018. "A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 4-8.
    6. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    7. Victor Podinovski & Emmanuel Thanassoulis, 2007. "Improving discrimination in data envelopment analysis: some practical suggestions," Journal of Productivity Analysis, Springer, vol. 28(1), pages 117-126, October.
    8. William W. Cooper & Kyung Sam Park & Gang Yu, 1999. "IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA," Management Science, INFORMS, vol. 45(4), pages 597-607, April.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. P. Sunil Dharmapala & Lokman Zaibet, 2006. "Analysis of farmers' efficiency and growth factors in oil exporting Arabian gulf countries: the case of Oman," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 7(4), pages 377-387.
    11. Benjamin Tetteh Anang & Stefan Bäckman & Antonios Rezitis, 2016. "Does farm size matter? Investigating scale efficiency of peasant rice farmers in northern Ghana," Economics Bulletin, AccessEcon, vol. 36(4), pages 2275-2290.
    12. Eliane Gomes & João Soares de Mello & Geraldo Souza & Lidia Angulo Meza & João Mangabeira, 2009. "Efficiency and sustainability assessment for a group of farmers in the Brazilian Amazon," Annals of Operations Research, Springer, vol. 169(1), pages 167-181, July.
    13. Podinovski, V. V., 1999. "Side effects of absolute weight bounds in DEA models," European Journal of Operational Research, Elsevier, vol. 115(3), pages 583-595, June.
    14. María Molinos-Senante & Manuel Mocholi-Arce & Ramón Sala-Garrido, 2016. "Efficiency Assessment of Water and Sewerage Companies: a Disaggregated Approach Accounting for Service Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4311-4328, September.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Pekka Korhonen & Mikko Syrjänen, 2004. "Resource Allocation Based on Efficiency Analysis," Management Science, INFORMS, vol. 50(8), pages 1134-1144, August.
    17. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    18. OW Maietta, 2000. "The decomposition of cost inefficiency into technical and allocative components with panel data of Italian dairy farms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 27(4), pages 473-495, December.
    19. Sahar Khoshfetrat & Masoud Rahiminezhad Galankashi, 2015. "Efficiency improvement of decision making units: a new data envelopment analysis model," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 7(6), pages 720-734.
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    2. Leonidas Sotirios Kyrgiakos & Georgios Kleftodimos & George Vlontzos & Panos M. Pardalos, 2023. "A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability," Operational Research, Springer, vol. 23(1), pages 1-38, March.
    3. Haokun Wang & Hong Chen & Tuyen Thi Tran & Shuai Qin, 2022. "An Analysis of the Spatiotemporal Characteristics and Diversity of Grain Production Resource Utilization Efficiency under the Constraint of Carbon Emissions: Evidence from Major Grain-Producing Areas ," IJERPH, MDPI, vol. 19(13), pages 1-25, June.
    4. Hepei Zhang & Zhangbao Zhong, 2022. "How Does Environmental Regulation Affect the Green Growth of China’s Citrus Industry? The Mediating Role of Technological Innovation," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    5. Mostafa Mardani Najafabadi & Hanieh Kazmi & Somayeh Shirzadi Laskookalayeh & Abas Abdeshahi, 2023. "Investigating the ability of fuzzy and robust DEA models to apply uncertainty conditions: an application for date palm producers," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 776-801, June.

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