IDEAS home Printed from https://ideas.repec.org/a/oup/ajagec/v73y1991i2p421-428..html
   My bibliography  Save this article

Dairy Farm Efficiency Measurement Using Stochastic Frontiers and Neoclassical Duality

Author

Listed:
  • Boris E. Bravo-Ureta
  • Laszlo Rieger

Abstract

This paper presents a stochastic efficiency decomposition model based on Kopp and Diewert's deterministic methodology. The stochastic model is used to analyze technical, economic, and allocative efficiency for a sample of New England dairy farms. The results suggest that mean economic efficiency for the farmers in the sample is about 70% and that, on average, there is little difference between technical (83.0%) and allocative (84.6%) efficiency. Analyses of the relationship between efficiency and four socioeconomic variables—farm size, education, extension, and experience—reveal that, despite some statistically significant associations, efficiency levels are not markedly affected by these variables.

Suggested Citation

  • Boris E. Bravo-Ureta & Laszlo Rieger, 1991. "Dairy Farm Efficiency Measurement Using Stochastic Frontiers and Neoclassical Duality," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 421-428.
    • Handle: RePEc:oup:ajagec:v:73:y:1991:i:2:p:421-428.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2307/1242726
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:oup:ajagec:v:73:y:1991:i:2:p:421-428.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.