IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v48y2016i3p179-189.html
   My bibliography  Save this article

Estimating the effect of monopsony power on elasticity estimates

Author

Listed:
  • Koichi Yamaura
  • Allen M. Featherstone

Abstract

Love and Shumway (1994) developed a nonparametric deterministic test for monopsony market power using a normalized quadratic restricted cost function with one input for which the firm has potential market power. This research examines monopsony power using Lau's Hessian identity relationships based on the empirical properties of duality theory. Lau's Hessian identity shows the Hessian matrices are equal under pure competition using an unrestricted profit function, restricted profit function and production function approach. We examine how market power changes in the monopsony case using Lau's Hessian identity relationships. Results show that there is a difference between the unrestricted and restricted profit function results under monopsony power. The important implication is that if an input or output is potentially in a market subject to market power, that input or output should be modelled as a fixed input or output to correctly recover the underlying technology.

Suggested Citation

  • Koichi Yamaura & Allen M. Featherstone, 2016. "Estimating the effect of monopsony power on elasticity estimates," Applied Economics, Taylor & Francis Journals, vol. 48(3), pages 179-189, January.
    • Handle: RePEc:taf:applec:v:48:y:2016:i:3:p:179-189
    DOI: 10.1080/00036846.2015.1076148
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2015.1076148
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2015.1076148?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.

    Citations

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


    Cited by:

    1. Allen, Keighton R. & Fullerton, Thomas M., Jr., 2018. "Analyzing Small Industrial and Commercial User Demand for Electricity," MPRA Paper 98988, University Library of Munich, Germany, revised 22 Oct 2018.
    2. Rao Fu & Chenguang Li & Liming Wang, 2021. "Market Power in the Irish Beef Processing Industry," Sustainability, MDPI, vol. 13(11), pages 1-18, June.

    More about this item

    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:taf:applec:v:48:y:2016:i:3:p:179-189. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.