IDEAS home Printed from https://ideas.repec.org/p/acb/cbeeco/2012-597.html
   My bibliography  Save this paper

Estimating Revenue Under Collusion-Proof Auctions

Author

Listed:
  • Gaurab Aryal
  • Maria F. Gabrielli

Abstract

We propose a method to nonparametriclly estimate the revenue under a auction that is efficient and resilient to collusion [Chen and Micali, 2012]. Efficiency is achieved on account of a lower revenue and we propose a method to quantify this efficiency-revenue trade-off, i.e. the extra cost for which efficient allocation can be guaranteed even when bidders collude. We implement a local polynomial estimation method on sample of California highway procurements data and find that to achieve efficiency the cost of procurement must increase by at lest 10.8% and can go up to 48.8% depending on the size of bidding-ring.

Suggested Citation

  • Gaurab Aryal & Maria F. Gabrielli, 2012. "Estimating Revenue Under Collusion-Proof Auctions," ANU Working Papers in Economics and Econometrics 2012-597, Australian National University, College of Business and Economics, School of Economics.
  • Handle: RePEc:acb:cbeeco:2012-597
    as

    Download full text from publisher

    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp597.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Anil Chorppath & Tansu Alpcan & Holger Boche, 2015. "Adversarial Behavior in Network Games," Dynamic Games and Applications, Springer, vol. 5(1), pages 26-64, March.
    2. Maria Florencia Gabrielli, 2023. "Detecting Collusion on Highway Procurement," Working Papers 263, Red Nacional de Investigadores en Economía (RedNIE).
    3. Florencia M. Gabrielli, 2013. "Detecting Collusion on Highway Procurement," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 59, pages 127-165, January-D.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L4 - Industrial Organization - - Antitrust Issues and Policies

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:acb:cbeeco:2012-597. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/feanuau.html .

    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.