IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v15y2001i2p129-137.html
   My bibliography  Save this article

The Effect of Sample Size on the Mean Efficiency in DEA: Comment

Author

Listed:
  • Matthias Staat

Abstract

Zhang and Bartels (1998) show formallyhow DEA efficiency scores are affected by sample size. They demonstratethat comparing measures of structural inefficiency between samplesof different sizes leads to biased results. This note arguesthat this type of sample size bias has much wider implicationsthan suggested by their example. Models which implicitly restrictthe comparison set like some models for non-discretionary variableslead to biased efficiency scores as well. A reanalysis of theBanker and Morey (1986b) data shows that the efficiency scoresderived there are significantly influenced by the variation insample size implicit in their model. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Matthias Staat, 2001. "The Effect of Sample Size on the Mean Efficiency in DEA: Comment," Journal of Productivity Analysis, Springer, vol. 15(2), pages 129-137, March.
  • Handle: RePEc:kap:jproda:v:15:y:2001:i:2:p:129-137
    DOI: 10.1023/A:1007826405826
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1007826405826
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1023/A:1007826405826?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. Ray, Subhash C., 1988. "Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation," Socio-Economic Planning Sciences, Elsevier, vol. 22(4), pages 167-176.
    2. Yu, Chunyan, 1998. "The effects of exogenous variables in efficiency measurement--A monte carlo study," European Journal of Operational Research, Elsevier, vol. 105(3), pages 569-580, March.
    3. Yun Zhang & Robert Bartels, 1998. "The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand," Journal of Productivity Analysis, Springer, vol. 9(3), pages 187-204, March.
    4. 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.
    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. Van Puyenbroeck, Tom & Rogge, Nicky, 2020. "Comparing regional human development using global frontier difference indices," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    2. Aristovnik, Aleksander & Yang, Guo-liang & Song, Yao-yao & Ravšelj, Dejan, 2023. "Industrial performance of the top R&D enterprises in world-leading economies: A metafrontier approach," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    3. Harrison, Julie & Rouse, Paul, 2014. "Competition and public high school performance," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 10-19.
    4. M I Gonzalez-Bravo, 2007. "Prior-Ratio-Analysis procedure to improve data envelopment analysis for performance measurement," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(9), pages 1214-1222, September.
    5. Bracalente, Bruno & Polinori, Paolo, 2010. "L’efficienza tecnico-economica dei servizi pubblici locali: i casi delle farmacie comunali e dei servizi di igiene urbana [Technical And Economic Efficiency Of Local Public Services: The Cases Of T," MPRA Paper 34455, University Library of Munich, Germany.
    6. Julie Harrison & Paul Rouse & Jamie Armstrong, 2012. "Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models," Journal of Productivity Analysis, Springer, vol. 37(3), pages 261-276, June.
    7. Nicky Rogge & Alena Kolyaseva, 2022. "Measuring and comparing World Bank regions’ ‘ease of doing business’ opportunity sets," Journal of Productivity Analysis, Springer, vol. 57(2), pages 131-155, April.
    8. Diep Thanh Tung, 2014. "Regional Differences in Measuring the Technical Efficiency of Rice Production in Vietnam: A Metafrontier Approach," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 6(10), pages 147-147, September.
    9. Zervopoulos, Panagiotis, 2012. "Dealing with small samples and dimensionality issues in data envelopment analysis," MPRA Paper 39226, University Library of Munich, Germany.
    10. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    11. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    12. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    13. Brown, Rayna, 2006. "Mismanagement or mismeasurement? Pitfalls and protocols for DEA studies in the financial services sector," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1100-1116, October.
    14. Arjomandi, Amir & Dakpo, K. Hervé & Seufert, Juergen Heinz, 2018. "Have Asian airlines caught up with European Airlines? A by-production efficiency analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 389-403.

    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. Julie Harrison & Paul Rouse & Jamie Armstrong, 2012. "Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models," Journal of Productivity Analysis, Springer, vol. 37(3), pages 261-276, June.
    2. José Manuel Cordero & Cristina Polo & Daniel Santín & Gabriela Sicilia, 2016. "Monte-Carlo Comparison of Conditional Nonparametric Methods and Traditional Approaches to Include Exogenous Variables," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 483-497, October.
    3. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    4. Syrjanen, Mikko J., 2004. "Non-discretionary and discretionary factors and scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 158(1), pages 20-33, October.
    5. Orea, Luis & Wall, Alan, 2015. "A parametric frontier model for measuring eco-efficiency," Efficiency Series Papers 2015/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    6. Kenneth Rødseth, 2014. "Efficiency measurement when producers control pollutants: a non-parametric approach," Journal of Productivity Analysis, Springer, vol. 42(2), pages 211-223, October.
    7. Ruggiero, John, 2004. "Performance evaluation when non-discretionary factors correlate with technical efficiency," European Journal of Operational Research, Elsevier, vol. 159(1), pages 250-257, November.
    8. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.
    9. Rogge, Nicky & De Jaeger, Simon, 2013. "Measuring and explaining the cost efficiency of municipal solid waste collection and processing services," Omega, Elsevier, vol. 41(4), pages 653-664.
    10. Kohl, Sebastian & Brunner, Jens O., 2020. "Benchmarking the benchmarks – Comparing the accuracy of Data Envelopment Analysis models in constant returns to scale settings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1042-1057.
    11. Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
    12. Abdel Latef Anouze & Imad Bou-Hamad, 2021. "Inefficiency source tracking: evidence from data envelopment analysis and random forests," Annals of Operations Research, Springer, vol. 306(1), pages 273-293, November.
    13. Bolli, Thomas & Olivares, Maria & Bonaccorsi, Andrea & Daraio, Cinzia & Aracil, Adela Garcia & Lepori, Benedetto, 2016. "The differential effects of competitive funding on the production frontier and the efficiency of universities," Economics of Education Review, Elsevier, vol. 52(C), pages 91-104.
    14. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    15. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    16. Michael K. Fung, 2019. "Fraudulent Financial Reporting and Technological Capability in the Information Technology Sector: A Resource-Based Perspective," Journal of Business Ethics, Springer, vol. 156(2), pages 577-589, May.
    17. Cherchye, Laurens & De Rock, Bram & Kerstens, Pieter Jan, 2018. "Production with storable and durable inputs: Nonparametric analysis of intertemporal efficiency," European Journal of Operational Research, Elsevier, vol. 270(2), pages 498-513.
    18. Daraio, Cinzia & Simar, Leopold, 2006. "A robust nonparametric approach to evaluate and explain the performance of mutual funds," European Journal of Operational Research, Elsevier, vol. 175(1), pages 516-542, November.
    19. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    20. Miningou, Élisé Wendlassida & Vierstraete, Valérie, 2013. "Households' living situation and the efficient provision of primary education in Burkina Faso," Economic Modelling, Elsevier, vol. 35(C), pages 910-917.

    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:kap:jproda:v:15:y:2001:i:2:p:129-137. 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.