IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v205y2025i3d10.1007_s10957-025-02659-8.html
   My bibliography  Save this article

Measuring Efficiency in Data Envelopment Analysis Under Conditional Convexity

Author

Listed:
  • Juan F. Monge

    (Universidad Miguel Hernández)

  • José L. Ruiz

    (Universidad Miguel Hernández)

Abstract

Data Envelopment Analysis (DEA) assesses relative efficiency of decision-making units (DMUs) considering a production possibility set that is built from the observations maintaining some assumptions, like convexity, constant or variable returns to scale and free disposability. Alternative approaches have been developed assuming only some of those postulates or relaxing them. In particular, Kuosmanen (EJOR, 132:326–342, 2001) proposes the so-called conditional convexity (CC), which relaxes the convexity to allow only for convex combinations of DMUs that do not dominate efficient units. The aim is to preserve the efficiency classification. Despite its potential advantages, CC has hardly attracted the attention of users and researchers, perhaps because of difficulties in computation. The implementation of CC requires solving a series of linear programming (LP) problems with disjunctive constraints, which is often computationally expensive. In this paper, we develop a single LP model that allows to measuring efficiency under CC, which results from the reformulation of a bi-level linear programming problem. Once the computational difficulties have been sorted out, we are able to use in practice this approach for the evaluation of performance of organizations, and explore enhancements of CC over DEA and Free Disposal Hull together with its possible extensions.

Suggested Citation

  • Juan F. Monge & José L. Ruiz, 2025. "Measuring Efficiency in Data Envelopment Analysis Under Conditional Convexity," Journal of Optimization Theory and Applications, Springer, vol. 205(3), pages 1-21, June.
    • Handle: RePEc:spr:joptap:v:205:y:2025:i:3:d:10.1007_s10957-025-02659-8
    DOI: 10.1007/s10957-025-02659-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-025-02659-8
    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/s10957-025-02659-8?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. AGRELL, Per J. & BOGETOFT, Peter & BROCK, Michael & TIND, Jorgen, 2005. "Efficiency evaluation with convex pairs," LIDAM Reprints CORE 1828, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Walter Briec & Kristiaan Kerstens & Philippe Venden Eeckaut, 2004. "Non-convex Technologies and Cost Functions: Definitions, Duality and Nonparametric Tests of Convexity," Journal of Economics, Springer, vol. 81(2), pages 155-192, February.
    3. Henry Tulkens, 2006. "On FDH Efficiency Analysis: Some Methodological Issues and Applications to Retail Banking, Courts and Urban Transit," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 311-342, Springer.
    4. Niels Christian Petersen, 1990. "Data Envelopment Analysis on a Relaxed Set of Assumptions," Management Science, INFORMS, vol. 36(3), pages 305-314, March.
    5. Kuosmanen, Timo, 2001. "DEA with efficiency classification preserving conditional convexity," European Journal of Operational Research, Elsevier, vol. 132(2), pages 326-342, July.
    6. Léopold Simar & Valentin Zelenyuk, 2018. "Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores," CEPA Working Papers Series WP072018, School of Economics, University of Queensland, Australia.
    7. 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.
    8. Aparicio, Juan & Cordero, Jose M. & Pastor, Jesus T., 2017. "The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects," Omega, Elsevier, vol. 71(C), pages 1-10.
    9. Podinovski, V. V., 2005. "Selective convexity in DEA models," European Journal of Operational Research, Elsevier, vol. 161(2), pages 552-563, March.
    10. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    11. Dominique Deprins & Léopold Simar & Henry Tulkens, 2006. "Measuring Labor-Efficiency in Post Offices," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 285-309, Springer.
    12. 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.
    13. Peter Bogetoft & Joseph M. Tama & Jørgen Tind, 2000. "Convex Input and Output Projections of Nonconvex Production Possibility Sets," Management Science, INFORMS, vol. 46(6), pages 858-869, June.
    14. Simar, Léopold & Zelenyuk, Valentin, 2020. "Improving finite sample approximation by central limit theorems for estimates from Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1002-1015.
    15. Ehrgott, Matthias & Tind, Jørgen, 2009. "Column generation with free replicability in DEA," Omega, Elsevier, vol. 37(5), pages 943-950, October.
    16. 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.
    17. Kazemi Matin, Reza & Kuosmanen, Timo, 2009. "Theory of integer-valued data envelopment analysis under alternative returns to scale axioms," Omega, Elsevier, vol. 37(5), pages 988-995, October.
    18. Podinovski, Victor V. & Kuosmanen, Timo, 2011. "Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions," European Journal of Operational Research, Elsevier, vol. 211(3), pages 577-585, June.
    19. 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.
    20. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    21. 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.
    22. An, Qingxian & Zhang, Qiaoyu & Tao, Xiangyang, 2023. "Pay-for-performance incentives in benchmarking with quasi S-shaped technology," Omega, Elsevier, vol. 118(C).
    23. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, March.
    24. 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.
    25. Wu, Desheng Dash, 2010. "BiLevel programming Data Envelopment Analysis with constrained resource," European Journal of Operational Research, Elsevier, vol. 207(2), pages 856-864, December.
    26. Cook, Wade D. & Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2019. "DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans," Omega, Elsevier, vol. 84(C), pages 45-54.
    27. Ruiz, José L. & Sirvent, Inmaculada, 2019. "Performance evaluation through DEA benchmarking adjusted to goals," Omega, Elsevier, vol. 87(C), pages 150-157.
    Full references (including those not matched with items on IDEAS)

    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. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    2. Podinovski, Victor V. & Kuosmanen, Timo, 2011. "Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions," European Journal of Operational Research, Elsevier, vol. 211(3), pages 577-585, June.
    3. Borrás, Fernando & Ruiz, José L. & Sirvent, Inmaculada, 2024. "Planning improvements through data envelopment analysis (DEA) benchmarking based on a selection of peers," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    4. 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.
    5. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.
    6. Podinovski, V. V., 2005. "Selective convexity in DEA models," European Journal of Operational Research, Elsevier, vol. 161(2), pages 552-563, March.
    7. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Sung Ko Li & Chun Kei Tsang & Shu Kam Lee, 2024. "Partially Convex Production Technology and Efficiency Measurement," Journal of Productivity Analysis, Springer, vol. 62(3), pages 303-320, December.
    9. Jean-Paul Chavas & Kwansoo Kim, 2015. "Nonparametric analysis of technology and productivity under non-convexity: a neighborhood-based approach," Journal of Productivity Analysis, Springer, vol. 43(1), pages 59-74, February.
    10. Fukuyama, Hirofumi & Shiraz, Rashed Khanjani, 2015. "Cost-effectiveness measures on convex and nonconvex technologies," European Journal of Operational Research, Elsevier, vol. 246(1), pages 307-319.
    11. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    12. Pham, Manh D. & Zelenyuk, Valentin, 2019. "Weak disposability in nonparametric production analysis: A new taxonomy of reference technology sets," European Journal of Operational Research, Elsevier, vol. 274(1), pages 186-198.
    13. Kuosmanen, Timo & Post, Thierry, 2001. "Measuring economic efficiency with incomplete price information: With an application to European commercial banks," European Journal of Operational Research, Elsevier, vol. 134(1), pages 43-58, October.
    14. Walter Briec & Kristiaan Kerstens & Ignace Van de Woestyne, 2022. "Nonconvexity in Production and Cost Functions: An Exploratory and Selective Review," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 18, pages 721-754, Springer.
    15. 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.
    16. Timo Kuosmanen, 2003. "Duality Theory of Non-convex Technologies," Journal of Productivity Analysis, Springer, vol. 20(3), pages 273-304, November.
    17. Mehdiloo, Mahmood & Podinovski, Victor V., 2021. "Strong, weak and Farrell efficient frontiers of technologies satisfying different production assumptions," European Journal of Operational Research, Elsevier, vol. 294(1), pages 295-311.
    18. 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.
    19. Kuosmanen, Timo, 2001. "DEA with efficiency classification preserving conditional convexity," European Journal of Operational Research, Elsevier, vol. 132(2), pages 326-342, July.
    20. Victor Podinovski, 2004. "Efficiency and Global Scale Characteristics on the “No Free Lunch” Assumption Only," Journal of Productivity Analysis, Springer, vol. 22(3), pages 227-257, November.

    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:joptap:v:205:y:2025:i:3:d:10.1007_s10957-025-02659-8. 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.