IDEAS home Printed from https://ideas.repec.org/a/spr/jbecon/v89y2019i1d10.1007_s11573-018-0901-0.html
   My bibliography  Save this article

Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking

Author

Listed:
  • Heinz Ahn

    (Technische Universität Braunschweig)

  • Peter Bogetoft

    (Copenhagen Business School)

  • Ana Lopes

    (Universidade Federal de Minas Gerais)

Abstract

The paper deals with benchmarking cases where highly aggregated decision making units are in the data set. It is shown that these units—consisting of sub-units which are not further known by the evaluator—are likely to receive an unjustifiable harsh evaluation, here referred to as aggregation bias. To counter this bias, we present an approach which allows to calculate the potential sub-unit efficiency of a decision making unit by taking into account the possible impact of its sub-units’ aggregation without having disaggregated sub-unit data. Based on data envelopment analysis, the approach is operationalized in several ways. Finally, we apply our method to the benchmarking model actually used by the Brazilian Electricity Regulator to measure the cost efficiency of the Brazilian distribution system operators. For this case, our results reveal that the potential effect of the aggregation bias on the operators’ efficiency scores is enormous.

Suggested Citation

  • Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    • Handle: RePEc:spr:jbecon:v:89:y:2019:i:1:d:10.1007_s11573-018-0901-0
    DOI: 10.1007/s11573-018-0901-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11573-018-0901-0
    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/s11573-018-0901-0?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. Kevin Fox, 2012. "Problems with (dis)aggregating productivity, and another productivity paradox," Journal of Productivity Analysis, Springer, vol. 37(3), pages 249-259, June.
    2. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    3. Loren Tauer, 2001. "Input aggregation and computed technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 295-297.
    4. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    5. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
    6. Charnes, A. & Neralic, L., 1990. "Sensitivity analysis of the additive model in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 48(3), pages 332-341, October.
    7. Peter Bogetoft, 2000. "DEA and Activity Planning under Asymmetric Information," Journal of Productivity Analysis, Springer, vol. 13(1), pages 7-48, January.
    8. Frank, Charles R, Jr, 1969. "A Generalization of the Koopmans-Gale Theorem on Pricing and Efficiency," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(3), pages 488-491, October.
    9. Sebastián Lozano & Gabriel Villa, 2004. "Centralized Resource Allocation Using Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 22(1), pages 143-161, July.
    10. Peter Bogetoft & Dexiang Wang, 2005. "Estimating the Potential Gains from Mergers," Journal of Productivity Analysis, Springer, vol. 23(2), pages 145-171, May.
    11. Afsharian, Mohsen & Ahn, Heinz & Thanassoulis, Emmanuel, 2017. "A DEA-based incentives system for centrally managed multi-unit organisations," European Journal of Operational Research, Elsevier, vol. 259(2), pages 587-598.
    12. Raha Imanirad & Wade D. Cook & Joe Zhu, 2013. "Partial input to output impacts in DEA: Production considerations and resource sharing among business subunits," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(3), pages 190-207, April.
    13. Rolf Fare & Valentin Zelenyuk, 2002. "Input aggregation and technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 9(10), pages 635-636.
    14. Fox, Kevin J., 1999. "Efficiency at different levels of aggregation: public vs. private sector firms," Economics Letters, Elsevier, vol. 65(2), pages 173-176, November.
    15. Peter Bogetoft & Kristoffer Boye & Henrik Neergaard-Petersen & Kurt Nielsen, 2007. "Reallocating sugar beet contracts: can sugar production survive in Denmark?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 34(1), pages 1-20, March.
    16. Rolf Fare & Shawna Grosskopf & Valentin Zelenyuk, 2004. "Aggregation bias and its bounds in measuring technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 657-660.
    17. Jesper Levring Andersen & Peter Bogetoft, 2007. "Gains from quota trade: theoretical models and an application to the Danish fishery," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 34(1), pages 105-127, March.
    18. Peter Bogetoft & Lars Otto, 2011. "Benchmarking with DEA, SFA, and R," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7961-2.
    19. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    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. Afsharian, Mohsen & Bogetoft, Peter, 2023. "Limiting flexibility in nonparametric efficiency evaluations: An ex post k-centroid clustering approach," European Journal of Operational Research, Elsevier, vol. 311(2), pages 633-647.
    2. Núñez, F. & Arcos-Vargas, A. & Villa, G., 2020. "Efficiency benchmarking and remuneration of Spanish electricity distribution companies," Utilities Policy, Elsevier, vol. 67(C).
    3. 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.
    4. An, Qingxian & Tao, Xiangyang & Chen, Xiaohong, 2023. "Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework," European Journal of Operational Research, Elsevier, vol. 306(1), pages 269-285.

    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. Afsharian, Mohsen & Ahn, Heinz & Bogetoft, Peter & Kamali, Sara & Lopes-Ahn, Ana, 2025. "Endogenous system-wide output prices in incentive regulation," European Journal of Operational Research, Elsevier, vol. 320(1), pages 188-204.
    2. Ali Homayoni & Reza Fallahnejad & Farhad Hosseinzadeh Lotfi, 2022. "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR, Springer, vol. 20(4), pages 567-602, December.
    3. Bogetoft, Peter & Wittrup, Jesper, 2021. "Benefit-of-the-doubt approach to workload indicators: Simplifying the use of case weights in court evaluations," Omega, Elsevier, vol. 103(C).
    4. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    5. An, Qingxian & Tao, Xiangyang & Xiong, Beibei & Chen, Xiaohong, 2022. "Frontier-based incentive mechanisms for allocating common revenues or fixed costs," European Journal of Operational Research, Elsevier, vol. 302(1), pages 294-308.
    6. Ang, Sheng & Liu, Pei & Yang, Feng, 2020. "Intra-Organizational and inter-organizational resource allocation in two-stage network systems," Omega, Elsevier, vol. 91(C).
    7. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    8. Lozano, S., 2013. "DEA production games," European Journal of Operational Research, Elsevier, vol. 231(2), pages 405-413.
    9. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    10. Valentin Zelenyuk, 2022. "Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 25, pages 1039-1079, Springer.
    11. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    12. Amirteimoori, Alireza & Kazemi Matin, Reza & Yadollahi, Amir Hossein, 2024. "Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    13. Andreas C. Georgiou & Konstantinos Kaparis & Eleni-Maria Vretta & Kyriakos Bitsis & George Paltayian, 2024. "A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions," Mathematics, MDPI, vol. 12(4), pages 1-21, February.
    14. Aldanondo, Ana M. & Casasnovas, Valero L., 2016. "A note on the impact of multiple input aggregators in technical efficiency estimation," MPRA Paper 75290, University Library of Munich, Germany.
    15. Dai, Qianzhi & Li, Yongjun & Lei, Xiyang & Wu, Dengsheng, 2021. "A DEA-based incentive approach for allocating common revenues or fixed costs," European Journal of Operational Research, Elsevier, vol. 292(2), pages 675-686.
    16. Sanjeet Singh & Prabhat Ranjan, 2018. "Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education," Annals of Operations Research, Springer, vol. 269(1), pages 641-666, October.
    17. A. M. Aldanondo & V. L. Casasnovas, 2015. "Input aggregation bias in technical efficiency with multiple criteria analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 430-435, April.
    18. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    19. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    20. Davtalab-Olyaie, Mostafa & Begen, Mehmet A. & Yang, Zijiang & Asgharian, Masoud, 2024. "Incentivization in centrally managed systems: Inconsistencies resolution," Omega, Elsevier, vol. 129(C).

    More about this item

    Keywords

    Benchmarking; Data envelopment analysis; DEA; Aggregation bias; Potential sub-unit efficiency; Regulation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • 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:89:y:2019:i:1:d:10.1007_s11573-018-0901-0. 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.