IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v25y2000i2p189-208.html
   My bibliography  Save this article

Is it efficient to analyse efficiency rankings?

Author

Listed:
  • Uwe Jensen

    (Institut fØr Statistik und ãkonometrie, Christian-Albrechts-UniversitÄt, Olshausenstr. 40, D-24118 Kiel, Germany)

Abstract

When production functions are estimated as frontier functions, the deviations from the frontier can be interpreted as individual inefficiency estimates. Unfortunately, it has recently been shown that efficiency differences across individuals are very often statistically insignificant. In this paper, we will analyse the consequences of the consideration of confidence statements for the reliability of efficiency rankings. The stochastic frontier and confidence intervals derived by Horrace and Schmidt are compared to the COLS approach and bootstrap confidence intervals. The membership function is proposed as a simple Monte-Carlo approximation for the probability for an individual to be the most efficient in the sample.

Suggested Citation

  • Uwe Jensen, 2000. "Is it efficient to analyse efficiency rankings?," Empirical Economics, Springer, vol. 25(2), pages 189-208.
    • Handle: RePEc:spr:empeco:v:25:y:2000:i:2:p:189-208
    Note: received: May 1998/final version accepted: July 1999
    as

    Download full text from publisher

    File URL: http://link.springer.de/link/service/journals/00181/papers/0025002/00250189.pdf
    Download Restriction: Access to the full text of the articles in this series is restricted
    ---><---

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

    Citations

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


    Cited by:

    1. 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.
    2. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," CER-ETH Economics working paper series 13/171, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2006. "Cost Efficiency in Regional Bus Companies: An Application of Alternative Stochastic Frontier Models," Journal of Transport Economics and Policy, University of Bath, vol. 40(1), pages 95-118, January.
    4. Lakshmi Balasubramanyan & Spiro Stefanou & Jeffrey Stokes, 2012. "An entropy approach to size and variance heterogeneity in U.S. commercial banks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(3), pages 728-749, July.
    5. Moran, Valerie & Jacobs, Rowena, 2013. "An international comparison of efficiency of inpatient mental health care systems," Health Policy, Elsevier, vol. 112(1), pages 88-99.
    6. Andrew Street, 2003. "How much confidence should we place in efficiency estimates?," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 895-907, November.
    7. Hasan, Hamid, 2009. "Capabilities measurement: an empirical investigation," MPRA Paper 16742, University Library of Munich, Germany, revised 12 Aug 2009.
    8. Joseph G. Hirschberg & Jenny N. Lye, 2001. "Clustering in a Data Envelopment Analysis Using Bootstrapped Efficiency Scores," Department of Economics - Working Papers Series 800, The University of Melbourne.
    9. Mehdi Farsi & Massimo Filippini, 2008. "Effects of ownership, subsidization and teaching activities on hospital costs in Switzerland," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 335-350, March.
    10. Mehdi Farsi & Aurelio Fetz & Massimo Filippini, 2007. "Benchmarking and Regulation in the Electricity Distribution Sector," CEPE Working paper series 07-54, CEPE Center for Energy Policy and Economics, ETH Zurich.
    11. Michael Koetter, 2006. "Measurement Matters—Alternative Input Price Proxies for Bank Efficiency Analyses," Journal of Financial Services Research, Springer;Western Finance Association, vol. 30(2), pages 199-227, October.
    12. Scrogin, David & Hofler, Richard & Boyle, Kevin J. & Milon, J. Walter, 2004. "On The Frontier Of Generating Revealed Preference Choice Sets: An Efficient Approach," 2004 Annual meeting, August 1-4, Denver, CO 20134, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    13. Neil Soderlund & Rowena Jacobs, 2001. "Towards panel data specifications of efficiency measures for English acute hospitals," Working Papers 185chedp, Centre for Health Economics, University of York.
    14. Mehdi Farsi & Massimo Filippini, 2005. "A Benchmarking Analysis of Electricity Distribution Utilities in Switzerland," CEPE Working paper series 05-43, CEPE Center for Energy Policy and Economics, ETH Zurich.
    15. Mehdi Farsi & Massimo Filippini & William Greene, 2006. "Application Of Panel Data Models In Benchmarking Analysis Of The Electricity Distribution Sector," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 77(3), pages 271-290, September.
    16. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2005. "Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2127-2141.
    17. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    18. Uwe Jensen, 2005. "Misspecification Preferred: The Sensitivity of Inefficiency Rankings," Journal of Productivity Analysis, Springer, vol. 23(2), pages 223-244, May.

    More about this item

    Keywords

    Bootstrap; confidence intervals; frontiers; inefficiency; membership function; ranking;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs

    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:empeco:v:25:y:2000:i:2:p:189-208. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.