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Efficiency analysis with ratio measures

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  • Olesen, Ole Bent
  • Petersen, Niels Christian
  • Podinovski, Victor V.

Abstract

In applications of data envelopment analysis (DEA) data about some inputs and outputs is often available only in the form of ratios such as averages and percentages. In this paper we provide a positive answer to the long-standing debate as to whether such data could be used in DEA. The problem arises from the fact that ratio measures generally do not satisfy the standard production assumptions, e.g., that the technology is a convex set. Our approach is based on the formulation of new production assumptions that explicitly account for ratio measures. This leads to the estimation of production technologies under variable and constant returns-to-scale assumptions in which both volume and ratio measures are native types of data. The resulting DEA models allow the use of ratio measures “as is”, without any transformation or use of the underlying volume measures. This provides theoretical foundations for the use of DEA in applications where important data are reported in the form of ratios.

Suggested Citation

  • Olesen, Ole Bent & Petersen, Niels Christian & Podinovski, Victor V., 2015. "Efficiency analysis with ratio measures," European Journal of Operational Research, Elsevier, vol. 245(2), pages 446-462.
  • Handle: RePEc:eee:ejores:v:245:y:2015:i:2:p:446-462
    DOI: 10.1016/j.ejor.2015.03.013
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    References listed on IDEAS

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    1. Podinovski, V. V., 2005. "Selective convexity in DEA models," European Journal of Operational Research, Elsevier, vol. 161(2), pages 552-563, March.
    2. B. Hollingsworth & P. Smith, 2003. "Use of ratios in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 733-735.
    3. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
    4. Olesen, O. B. & Petersen, N. C., 1995. "Incorporating quality into data envelopment analysis: a stochastic dominance approach," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 117-135, April.
    5. Martin Gaynor & Harald Seider & William B. Vogt, 2005. "The Volume–Outcome Effect, Scale Economies, and Learning-by-Doing," American Economic Review, American Economic Association, vol. 95(2), pages 243-247, May.
    6. 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.
    7. Thanassoulis, E. & Boussofiane, A. & Dyson, R. G., 1995. "Exploring output quality targets in the provision of perinatal care in England using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 80(3), pages 588-607, February.
    8. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    9. 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.
    10. Golany, Boaz & Thore, Sten, 1997. "The Economic and Social Performance of Nations: Efficiency and Returns to Scale," Socio-Economic Planning Sciences, Elsevier, vol. 31(3), pages 191-204, September.
    11. Ruggiero, John, 1996. "On the measurement of technical efficiency in the public sector," European Journal of Operational Research, Elsevier, vol. 90(3), pages 553-565, May.
    12. Joseph Paradi & Mette Asmild & Paul Simak, 2004. "Using DEA and Worst Practice DEA in Credit Risk Evaluation," Journal of Productivity Analysis, Springer, vol. 21(2), pages 153-165, March.
    13. Rajiv D. Banker & Richard C. Morey, 1986. "The Use of Categorical Variables in Data Envelopment Analysis," Management Science, INFORMS, vol. 32(12), pages 1613-1627, December.
    14. O. Olesen & N. Petersen, 2009. "Target and technical efficiency in DEA: controlling for environmental characteristics," Journal of Productivity Analysis, Springer, vol. 32(1), pages 27-40, August.
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    Citations

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    Cited by:

    1. Silva, Maria Conceição A., 2018. "Output-specific inputs in DEA: An application to courts of justice in Portugal," Omega, Elsevier, vol. 79(C), pages 43-53.
    2. Mehdiloozad, Mahmood & Podinovski, Victor V., 2018. "Nonparametric production technologies with weakly disposable inputs," European Journal of Operational Research, Elsevier, vol. 266(1), pages 247-258.
    3. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    4. Olesen, Ole Bent & Petersen, Niels Christian & Podinovski, Victor V., 2017. "Efficiency measures and computational approaches for data envelopment analysis models with ratio inputs and outputs," European Journal of Operational Research, Elsevier, vol. 261(2), pages 640-655.
    5. Maria Conceição Portela, 2016. "Output-specific inputs in DEA: an application to courts," Working Papers de Gestão (Management Working Papers) 02, Católica Porto Business School, Universidade Católica Portuguesa.
    6. Victoria Wojcik & Harald Dyckhoff & Marcel Clermont, 2019. "Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts?," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 559-595, December.
    7. Jesus T. Pastor & Juan Aparicio & Javier Alcaraz & Fernando Vidal & Diego Pastor, 2018. "Bounded directional distance function models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 985-1004, December.
    8. Necmi Kemal Avkiran, 2017. "An illustration of multiple-stakeholder perspective using a survey across Australia, China and Japan," Annals of Operations Research, Springer, vol. 248(1), pages 93-121, January.
    9. Joe Zhu, 0. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 0, pages 1-23.
    10. Cook, Wade D. & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2017. "Within-group common benchmarking using DEA," European Journal of Operational Research, Elsevier, vol. 256(3), pages 901-910.
    11. Fritz Schiltz & Kristof Witte & Deni Mazrekaj, 2020. "Managerial efficiency and efficiency differentials in adult education: a conditional and bias-corrected efficiency analysis," Annals of Operations Research, Springer, vol. 288(2), pages 529-546, May.
    12. R. K. Jha & B. S. Sahay & P. Charan, 2016. "Healthcare operations management: a structured literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 259-279, September.
    13. Víctor Giménez & Claudio Thieme & Diego Prior & Emili Tortosa-Ausina, 2017. "An international comparison of educational systems: a temporal analysis in presence of bad outputs," Journal of Productivity Analysis, Springer, vol. 47(1), pages 83-101, February.

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