IDEAS home Printed from https://ideas.repec.org/a/spr/jbecon/v88y2018i7d10.1007_s11573-018-0906-8.html
   My bibliography  Save this article

CCR or BCC: what if we are in the wrong model?

Author

Listed:
  • Andreas Dellnitz

    (FernUniversität in Hagen (University of Hagen))

  • Andreas Kleine

    (FernUniversität in Hagen (University of Hagen))

  • Wilhelm Rödder

    (FernUniversität in Hagen (University of Hagen))

Abstract

The CCR model by Charnes et al. (Eur J Oper Res 2:429–444, 1978) together with the BCC model by Banker et al. (Manag Sci 30:1078–1091, 1984) are the most popular approaches of measuring efficiency among a group of decision making units, DMUs, in data envelopment analysis, DEA. The right choice of a DEA model—CCR or BCC—often, if not always, is a difficult decision. To evaluate a DMU’s efficiency for both models might be helpful, but it does not always capture the essential issues at stake. In this paper we propose a comparative analysis of both concepts: How does activity scaling under constant BCC-efficiency influence CCR-efficiency. And inversely, how does BCC-efficiency behave when activity scaling under constant CCR-efficiency is applied. Such findings of mutual effects improve a DMU’s ability to reassess upsizing and downsizing of activities. Moreover, it allows for exact calculations of the resulting economic effects, and these effects give new insights beyond classical DEA. Finally, scale efficiency turns out to be the ideal concept to control these activity changes, rather than just CCR- or BCC-efficiency. We use a little numerical example to emphasize advantages of the new concept and sketch the new findings for a theater scenery.

Suggested Citation

  • Andreas Dellnitz & Andreas Kleine & Wilhelm Rödder, 2018. "CCR or BCC: what if we are in the wrong model?," Journal of Business Economics, Springer, vol. 88(7), pages 831-850, September.
    • Handle: RePEc:spr:jbecon:v:88:y:2018:i:7:d:10.1007_s11573-018-0906-8
    DOI: 10.1007/s11573-018-0906-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11573-018-0906-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11573-018-0906-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Banker, Rajiv D. & Thrall, R. M., 1992. "Estimation of returns to scale using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 62(1), pages 74-84, October.
    2. Jahanshahloo, G.R. & Soleimani-damaneh, M. & Ghobadi, S., 2015. "Inverse DEA under inter-temporal dependence using multiple-objective programming," European Journal of Operational Research, Elsevier, vol. 240(2), pages 447-456.
    3. Fandel, Gunter & Gal, Tomas, 2001. "Redistribution of funds for teaching and research among universities: The case of North Rhine-Westphalia," European Journal of Operational Research, Elsevier, vol. 130(1), pages 111-120, April.
    4. Golany, Boaz & Yu, Gang, 1997. "Estimating returns to scale in DEA," European Journal of Operational Research, Elsevier, vol. 103(1), pages 28-37, November.
    5. Andreas Kleine & Andreas Dellnitz & Wilhelm Rödder, 2014. "Sensitivity Analysis of BCC Efficiency in DEA with Application to European Health Services," Operations Research Proceedings, in: Dennis Huisman & Ilse Louwerse & Albert P.M. Wagelmans (ed.), Operations Research Proceedings 2013, edition 127, pages 243-248, Springer.
    6. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
    7. 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.
    8. Rajiv D. Banker & Ajay Maindiratta, 1986. "Piecewise Loglinear Estimation of Efficient Production Surfaces," Management Science, INFORMS, vol. 32(1), pages 126-135, January.
    9. Kleine, A., 2004. "A general model framework for DEA," Omega, Elsevier, vol. 32(1), pages 17-23, February.
    10. 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.
    11. F R Førsund & L Hjalmarsson, 2004. "Calculating scale elasticity in DEA models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1023-1038, October.
    12. Finn Førsund & Lennart Hjalmarsson & Vladimir Krivonozhko & Oleg Utkin, 2007. "Calculation of scale elasticities in DEA models: direct and indirect approaches," Journal of Productivity Analysis, Springer, vol. 28(1), pages 45-56, October.
    13. Matthias Klumpp, 2017. "Do Forwarders Improve Sustainability Efficiency? Evidence from a European DEA Malmquist Index Calculation," Sustainability, MDPI, vol. 9(5), pages 1-33, May.
    14. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    15. Podinovski, Victor V., 2017. "Returns to scale in convex production technologies," European Journal of Operational Research, Elsevier, vol. 258(3), pages 970-982.
    16. Fandel, Gunter, 2007. "On the performance of universities in North Rhine-Westphalia, Germany: Government's redistribution of funds judged using DEA efficiency measures," European Journal of Operational Research, Elsevier, vol. 176(1), pages 521-533, January.
    17. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    18. Podinovski, Victor V. & Førsund, Finn R. & Krivonozhko, Vladimir E., 2009. "A simple derivation of scale elasticity in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 197(1), pages 149-153, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martin Eling & Martin Lehmann & Philipp Schaper, 2022. "Optimal labor and capital utilization by financial firms: evidence from the German property and casualty insurance industry," Journal of Business Economics, Springer, vol. 92(5), pages 853-897, July.
    2. Xun Liu & Xiaoliang Yu & Simon Gao, 2019. "A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis," Business Strategy and the Environment, Wiley Blackwell, vol. 28(5), pages 858-871, July.
    3. Zarrin, Mansour & Brunner, Jens O., 2023. "Analyzing the accuracy of variable returns to scale data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1286-1301.
    4. Dellnitz, Andreas & Tavana, Madjid, 2024. "Data envelopment analysis: From non-monotonic to monotonic scale elasticities," European Journal of Operational Research, Elsevier, vol. 318(2), pages 549-559.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sahoo, Biresh K & Khoveyni, Mohammad & Eslami, Robabeh & Chaudhury, Pradipta, 2016. "Returns to scale and most productive scale size in DEA with negative data," European Journal of Operational Research, Elsevier, vol. 255(2), pages 545-558.
    2. Victor V. Podinovski & Finn R. Førsund, 2010. "Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis," Operations Research, INFORMS, vol. 58(6), pages 1743-1754, December.
    3. Dellnitz, Andreas & Tavana, Madjid, 2024. "Data envelopment analysis: From non-monotonic to monotonic scale elasticities," European Journal of Operational Research, Elsevier, vol. 318(2), pages 549-559.
    4. Zelenyuk, Valentin, 2015. "Aggregation of scale efficiency," European Journal of Operational Research, Elsevier, vol. 240(1), pages 269-277.
    5. Victor V. Podinovski & Robert G. Chambers & Kazim Baris Atici & Iryna D. Deineko, 2016. "Marginal Values and Returns to Scale for Nonparametric Production Frontiers," Operations Research, INFORMS, vol. 64(1), pages 236-250, February.
    6. Zarepisheh, M. & Soleimani-damaneh, M., 2008. "Global variation of outputs with respect to the variation of inputs in performance analysis; generalized RTS," European Journal of Operational Research, Elsevier, vol. 186(2), pages 786-800, April.
    7. Vladimir Krivonozhko & Finn Førsund & Andrey Lychev, 2012. "Returns-to-scale properties in DEA models: the fundamental role of interior points," Journal of Productivity Analysis, Springer, vol. 38(2), pages 121-130, October.
    8. Walheer, Barnabé, 2018. "Scale efficiency for multi-output cost minimizing producers: The case of the US electricity plants," Energy Economics, Elsevier, vol. 70(C), pages 26-36.
    9. Afsharian, Mohsen & Podinovski, Victor V., 2018. "A linear programming approach to efficiency evaluation in nonconvex metatechnologies," European Journal of Operational Research, Elsevier, vol. 268(1), pages 268-280.
    10. M. Zarepisheh & E. Khorram & G. Jahanshahloo, 2010. "Returns to scale in multiplicative models in data envelopment analysis," Annals of Operations Research, Springer, vol. 173(1), pages 195-206, January.
    11. Michael Zschille, 2014. "Nonparametric measures of returns to scale: an application to German water supply," Empirical Economics, Springer, vol. 47(3), pages 1029-1053, November.
    12. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
    13. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "Scale characteristics of variable returns-to-scale production technologies with ratio inputs and outputs," Annals of Operations Research, Springer, vol. 318(1), pages 383-423, November.
    14. Cesaroni, Giovanni & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2017. "Global and local scale characteristics in convex and nonconvex nonparametric technologies: A first empirical exploration," European Journal of Operational Research, Elsevier, vol. 259(2), pages 576-586.
    15. Andreas Dellnitz & Elmar Reucher & Andreas Kleine, 2021. "Efficiency evaluation in data envelopment analysis using strong defining hyperplanes," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(2), pages 441-465, June.
    16. Zarepisheh, M. & Soleimani-damaneh, M., 2009. "A dual simplex-based method for determination of the right and left returns to scale in DEA," European Journal of Operational Research, Elsevier, vol. 194(2), pages 585-591, April.
    17. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    18. K. Tone & M. Tsutsui, 2015. "How to Deal with Non-Convex Frontiers in Data Envelopment Analysis," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 1002-1028, September.
    19. M Soleimani-damaneh, 2009. "A fast algorithm for determining some characteristics in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(11), pages 1528-1534, November.
    20. Zelenyuk, Valentin, 2013. "A scale elasticity measure for directional distance function and its dual: Theory and DEA estimation," European Journal of Operational Research, Elsevier, vol. 228(3), pages 592-600.

    More about this item

    Keywords

    Data envelopment analysis; Returns to scale; Scaling of activities; Stability ranges; Scale efficiency;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jbecon:v:88:y:2018:i:7:d:10.1007_s11573-018-0906-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.