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Decomposing technical inefficiency using the principle of least action

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  • Aparicio, Juan
  • Mahlberg, Bernhard
  • Pastor, Jesus T.
  • Sahoo, Biresh K.

Abstract

In for-profit organizations, profit efficiency decomposition is considered important since estimates on profit drivers are of practical use to managers in their decision making. Profit efficiency is traditionally due to two sources – technical efficiency and allocative efficiency. The contribution of this paper is a novel decomposition of technical efficiency that could be more practical to use if the firm under evaluation really wants to achieve technical efficiency as soon as possible. For this purpose, we show how a new version of the Measure of Inefficiency Proportions (MIP), which seeks the minimization of the total technical effort by the assessed firm, is a lower bound of the value of technical inefficiency associated with the directional distance function. The targets provided by the new MIP could be beneficial for firms since it specifies how firms may become technically efficient simply by decreasing one input or increasing one output, suggesting that each firm should focus its effort on a specific dimension (input or output). This approach is operationalized in a data envelopment analysis framework and applied to a dataset of airlines.

Suggested Citation

  • Aparicio, Juan & Mahlberg, Bernhard & Pastor, Jesus T. & Sahoo, Biresh K., 2014. "Decomposing technical inefficiency using the principle of least action," European Journal of Operational Research, Elsevier, vol. 239(3), pages 776-785.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:3:p:776-785
    DOI: 10.1016/j.ejor.2014.06.006
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    3. Juan Aparicio & Magdalena Kapelko & Juan F. Monge, 2020. "A Well-Defined Composite Indicator: An Application to Corporate Social Responsibility," Journal of Optimization Theory and Applications, Springer, vol. 186(1), pages 299-323, July.
    4. Bogetoft, Peter & Ramírez-Ayerbe, Jasone & Romero Morales, Dolores, 2024. "Counterfactual analysis and target setting in benchmarking," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1083-1095.
    5. Chao Lu & Jie Tao & Qiuxian An & Xiaodong Lai, 2020. "A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry," Annals of Operations Research, Springer, vol. 292(1), pages 321-339, September.
    6. Juan Aparicio & Magdalena Kapelko & Bernhard Mahlberg & Jose L. Sainz-Pardo, 2017. "Measuring input-specific productivity change based on the principle of least action," Journal of Productivity Analysis, Springer, vol. 47(1), pages 17-31, February.
    7. Aparicio, Juan & Pastor, Jesús T. & Vidal, Fernando & Zofío, José L., 2017. "Evaluating productive performance: A new approach based on the product-mix problem consistent with Data Envelopment Analysis," Omega, Elsevier, vol. 67(C), pages 134-144.
    8. Alcaraz, Javier & Anton-Sanchez, Laura & Aparicio, Juan & Monge, Juan F. & Ramón, Nuria, 2021. "Russell Graph efficiency measures in Data Envelopment Analysis: The multiplicative approach," European Journal of Operational Research, Elsevier, vol. 292(2), pages 663-674.
    9. Lozano, Sebastián & Calzada-Infante, Laura, 2018. "Computing gradient-based stepwise benchmarking paths," Omega, Elsevier, vol. 81(C), pages 195-207.
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    11. Ando, Kazutoshi & Minamide, Masato & Sekitani, Kazuyuki & Shi, Jianming, 2017. "Monotonicity of minimum distance inefficiency measures for Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 260(1), pages 232-243.
    12. Henriques, C.O. & Chavez, J.M. & Gouveia, M.C. & Marcenaro-Gutierrez, O.D., 2022. "Efficiency of secondary schools in Ecuador: A value based DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
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