IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v221y2014i1p107-13210.1007-s10479-011-1007-z.html
   My bibliography  Save this article

Handling estimated proportions in public sector data envelopment analysis

Author

Listed:

Abstract

Periodically data envelopment analysis (DEA) is conducted on values that include estimated proportions, such as defect, satisfaction, mortality, or adverse event rates computed from samples. This occurs frequently in healthcare and public sector analysis where proportions frequently are estimated from partial samples. These estimates can produce statistically biased and variable estimates of DEA results, even as sample sizes become fairly large. This paper discusses several approaches to these problems, including Monte Carlo (MC), bootstrapping, chance constrained, and optimistic/pessimistic DEA methods. The performance of each method was compared using previously published data for fourteen Florida juvenile delinquency programs whose two of three inputs and one output were proportions. The impact of sample size and number of estimated rates also were investigated. In most cases, no statistically significant differences were found between the true DEA scores and the midpoints of optimistic/pessimistic, MC, and bootstrap intervals, the latter two after bias correction. True DEA results are strongly correlated with those produced by the MC (r=0.9865, p>0.001), chance constraint (r=0.9536,p>0.001), bootstrapping (r=0.9368,p>0.001), and optimistic/pessimistic (r=0.6799,p>0.001) approaches. While all methods perform fairly well, the MC approach tends to produce slightly better results and be fairly easy to implement. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • Mehmet Ceyhan & James Benneyan, 2014. "Handling estimated proportions in public sector data envelopment analysis," Annals of Operations Research, Springer, vol. 221(1), pages 107-132, October.
    • Handle: RePEc:spr:annopr:v:221:y:2014:i:1:p:107-132:10.1007/s10479-011-1007-z
    DOI: 10.1007/s10479-011-1007-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-011-1007-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-011-1007-z?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. Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
    2. 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.
    3. Beasley, J. E., 1990. "Comparing university departments," Omega, Elsevier, vol. 18(2), pages 171-183.
    4. Fried, Harold O. & Lambrinos, James & Tyner, James, 2004. "Evaluating the performance of professional golfers on the PGA, LPGA and SPGA tours," European Journal of Operational Research, Elsevier, vol. 154(2), pages 548-561, April.
    5. Zhu, Joe, 2001. "Multidimensional quality-of-life measure with an application to Fortune's best cities," Socio-Economic Planning Sciences, Elsevier, vol. 35(4), pages 263-284, December.
    6. Desai, Anand & Ratick, Samuel J. & Schinnar, Arie P., 2005. "Data envelopment analysis with stochastic variations in data," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 147-164, June.
    7. Boame, Attah K., 2004. "The technical efficiency of Canadian urban transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(5), pages 401-416, September.
    8. Wade D. Cook & Joe Zhu, 2005. "Modeling Performance Measurement," Springer Books, Springer, number 978-0-387-24138-8, October.
    9. Wai Peng Wong & Wikrom Jaruphongsa & Loo Hay Lee, 2008. "Supply chain performance measurement system: a Monte Carlo DEA-based approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 162-188.
    10. William Cooper & Zhimin Huang & Vedran Lelas & Susan Li & Ole Olesen, 1998. "Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA," Journal of Productivity Analysis, Springer, vol. 9(1), pages 53-79, January.
    11. Entani, Tomoe & Maeda, Yutaka & Tanaka, Hideo, 2002. "Dual models of interval DEA and its extension to interval data," European Journal of Operational Research, Elsevier, vol. 136(1), pages 32-45, January.
    12. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    13. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
    14. Gary Ferrier & Joseph Hirschberg, 1997. "Bootstrapping Confidence Intervals for Linear Programming Efficiency Scores: With an Illustration Using Italian Banking Data," Journal of Productivity Analysis, Springer, vol. 8(1), pages 19-33, March.
    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. Jesús A. Tapia & Bonifacio Salvador, 2022. "Data envelopment analysis efficiency in the public sector using provider and customer opinion: An application to the Spanish health system," Health Care Management Science, Springer, vol. 25(2), pages 333-346, June.
    2. Panagiotis Mitropoulos & Ioannis Mitropoulos, 2020. "Performance evaluation of retail banking services: Is there a trade‐off between production and quality?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(7), pages 1237-1250, October.
    3. Sandiford, P. & Vivas Consuelo, D. & Rouse, P. & Bramley, D., 2018. "The trade-off between equity and efficiency in population health gain: Making it real," Social Science & Medicine, Elsevier, vol. 212(C), pages 136-144.
    4. Panagiotis Mitropoulos & Panagiotis D. Zervopoulos & Ioannis Mitropoulos, 2020. "Measuring performance in the presence of noisy data with targeted desirable levels: evidence from healthcare units," Annals of Operations Research, Springer, vol. 294(1), pages 537-566, November.
    5. Polcyn, Jan, 2017. "Edukacja jako dobro publiczne - próba kwantyfikacji [Education as a public good – an attempt at quantification]," MPRA Paper 76606, University Library of Munich, Germany, revised 2017.

    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. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    2. 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.
    3. R G Dyson & E A Shale, 2010. "Data envelopment analysis, operational research and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 25-34, January.
    4. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    5. Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
    6. Prakashan Veettil & Stijn Speelman & Guido Huylenbroeck, 2013. "Estimating the Impact of Water Pricing on Water Use Efficiency in Semi-arid Cropping System: An Application of Probabilistically Constrained Nonparametric Efficiency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(1), pages 55-73, January.
    7. Li, Susan X., 1998. "Stochastic models and variable returns to scales in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 104(3), pages 532-548, February.
    8. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    9. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    10. Imad Bou-Hamad & Abdel Latef Anouze & Denis Larocque, 2017. "An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment," Annals of Operations Research, Springer, vol. 253(1), pages 77-95, June.
    11. Sun, Qinghe & Chen, Li & Meng, Qiang, 2022. "Evaluating port efficiency dynamics: A risk-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 333-347.
    12. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    13. Bruni, M.E. & Conforti, D. & Beraldi, P. & Tundis, E., 2009. "Probabilistically constrained models for efficiency and dominance in DEA," International Journal of Production Economics, Elsevier, vol. 117(1), pages 219-228, January.
    14. Zhimin Huang & Susan Li, 2001. "Stochastic DEA Models With Different Types of Input-Output Disturbances," Journal of Productivity Analysis, Springer, vol. 15(2), pages 95-113, March.
    15. HATAMI-MARBINI, Adel & TAVANA, Madjid & EMROUZNEJAD, Ali & SAATI, Saber, 2012. "Efficiency measurement in fuzzy additive data envelopment analysis," LIDAM Reprints CORE 2393, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Mohammad Jamshidi & Masoud Sanei & Ali Mahmoodirad & Farhad Hoseinzadeh Lotfi & Ghasem Tohidi, 2021. "Uncertain SBM data envelopment analysis model: A case study in Iranian banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2674-2689, April.
    17. Andrew Worthington, 2001. "An Empirical Survey of Frontier Efficiency Measurement Techniques in Education," Education Economics, Taylor & Francis Journals, vol. 9(3), pages 245-268.
    18. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    19. 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.
    20. Vincent Charles & Ioannis E. Tsolas & Tatiana Gherman, 2018. "Satisficing data envelopment analysis: a Bayesian approach for peer mining in the banking sector," Annals of Operations Research, Springer, vol. 269(1), pages 81-102, October.

    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:annopr:v:221:y:2014:i:1:p:107-132:10.1007/s10479-011-1007-z. 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.