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An overall measure of technical inefficiency at the firm and at the industry level: The ‘lost profit on outlay’

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  • Aparicio, Juan
  • Pastor, Jesus T.
  • Ray, Subhash C.

Abstract

As a measure of overall technical inefficiency, the Directional Distance Function (DDF) introduced by Chambers, Chung, and Färe ties the potential output expansion and input contraction together through a single parameter. By duality, the DDF is related to a measure of profit inefficiency, which is calculated as the normalized deviation between optimal and actual profit at market prices. As we show, in the most usual case, the associated normalization represents the sum of the actual revenue and the actual cost of the assessed firm. Consequently, the corresponding profit inefficiency measure associated with the DDF has no obvious economic interpretation. In contrast, in this paper we allow outputs to expand and inputs to contract by different proportions. This results in a modified DDF that retains most of the properties of the original DDF. The corresponding dual problem has a much simpler interpretation as the lost profit on (average) outlay that can be decomposed into a technical and an allocative inefficiency component. In addition, an overall measure of technical inefficiency at the industry level is introduced resorting to the direction corresponding to the average input–output bundle.

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  • Aparicio, Juan & Pastor, Jesus T. & Ray, Subhash C., 2013. "An overall measure of technical inefficiency at the firm and at the industry level: The ‘lost profit on outlay’," European Journal of Operational Research, Elsevier, vol. 226(1), pages 154-162.
  • Handle: RePEc:eee:ejores:v:226:y:2013:i:1:p:154-162
    DOI: 10.1016/j.ejor.2012.10.028
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    2. Hien Thu Pham & Antonio Peyrache, 2015. "Industry Inefficiency Measures: A Unifying Approximation Proposition," CEPA Working Papers Series WP102015, School of Economics, University of Queensland, Australia.
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    7. Aparicio, Juan & Ortiz, Lidia & Pastor, Jesus T., 2017. "Measuring and decomposing profit inefficiency through the Slacks-Based Measure," European Journal of Operational Research, Elsevier, vol. 260(2), pages 650-654.
    8. Peyrache, Antonio, 2015. "Cost constrained industry inefficiency," European Journal of Operational Research, Elsevier, vol. 247(3), pages 996-1002.
    9. Olesen, Ole B. & Ruggiero, John, 2014. "Maintaining the Regular Ultra Passum Law in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 235(3), pages 798-809.
    10. Mocholi-Arce, Manuel & Sala-Garrido, Ramon & Molinos-Senante, Maria & Maziotis, Alexandros, 2023. "Profit productivity change in the English and Welsh water sector: Impact of the price reviews," Utilities Policy, Elsevier, vol. 82(C).
    11. Subhash C. Ray, 2018. "Data Envelopment Analysis with Alternative Returns to Scale," Working papers 2018-20, University of Connecticut, Department of Economics.
    12. Aparicio, Juan & Monge, Juan F. & Ramón, Nuria, 2021. "A new measure of technical efficiency in data envelopment analysis based on the maximization of hypervolumes: Benchmarking, properties and computational aspects," European Journal of Operational Research, Elsevier, vol. 293(1), pages 263-275.
    13. Subhash C. Ray & Kankana Mukherjee, 2017. "A reverse directional distance function to reconcile between competing efficiency goals: an application to Indian manufacturing," Indian Economic Review, Springer, vol. 52(1), pages 103-126, December.
    14. Halická, Margaréta & Trnovská, Mária & Černý, Aleš, 2024. "A unified approach to radial, hyperbolic, and directional efficiency measurement in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 312(1), pages 298-314.
    15. Camanho, Ana Santos & Silva, Maria Conceicao & Piran, Fabio Sartori & Lacerda, Daniel Pacheco, 2024. "A literature review of economic efficiency assessments using Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 315(1), pages 1-18.
    16. Valentin Zelenyuk, 2018. "Profit Efficiency, DEA, FDH and Big Data," CEPA Working Papers Series WP042018, School of Economics, University of Queensland, Australia.
    17. 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.
    18. Kenneth Løvold Rødseth, 2017. "Environmental regulations and allocative efficiency: application to coal-to-gas substitution in the U.S. electricity sector," Journal of Productivity Analysis, Springer, vol. 47(2), pages 129-142, April.
    19. Barbero, Javier & Zofío, José L., 2023. "The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    20. Färe, Rolf & Grosskopf, Shawna & Karagiannis, Giannis, 2018. "On technical inefficiency indicators at the industry level," International Journal of Production Economics, Elsevier, vol. 196(C), pages 333-334.
    21. Valiyattoor, Vipin & Bhandari, Anup Kumar, 2020. "Outsourcing and firm performance nexus: An analysis using the conventional and panel double-bootstrap procedure," Research in International Business and Finance, Elsevier, vol. 54(C).
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    23. Rolf Färe & Xinju He & Sungko Li & Valentin Zelenyuk, 2019. "A Unifying Framework for Farrell Profit Efficiency Measurement," Operations Research, INFORMS, vol. 67(1), pages 183-197, January.

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