IDEAS home Printed from https://ideas.repec.org/p/ags/ugaeab/387596.html

Bayesian Technical Efficiency Analysis Of Groundnut Production In Ghana

Author

Listed:
  • Chakuri, D.
  • Asem, F.E.
  • Onumah, E.E.

Abstract

This paper considered Bayesian Stochastic Frontier Model to analyse technical efficiency and their determinants of groundnut farmers in Ghana. The paper used a cross-sectional data of three-hundred (300) observations to obtain posterior distributions of the farmers’ technical efficiency levels. All computations were done using Markov Chain Monte Carlo methods (MCMC). Results revealed that the groundnut farmers produce at an increasing return to scale of 1.10. Average technical efficiency of the farmers was found to be 70.5%, ranging from a minimum of 13.0% to a maximum of 95.1%. Frequency of extension visit, educational level and gender of the farmers were identified to significantly explain inefficiency of the farmers. The paper concluded that groundnut farmers in the northern part of Ghana are operating in the first stage of the production function and could increase their frontier output by 29.5%.

Suggested Citation

  • Chakuri, D. & Asem, F.E. & Onumah, E.E., 2022. "Bayesian Technical Efficiency Analysis Of Groundnut Production In Ghana," Department of Agricultural Economics and Agribusiness 387596, University of Ghana.
    • Handle: RePEc:ags:ugaeab:387596
    DOI: 10.22004/ag.econ.387596
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/387596/files/Bayesian%20Technical%20Efficiency%20Analysis%20Of%20Groundnut%20Production%20In%20Ghana.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.387596?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
    ---><---

    More about this item

    Keywords

    ;

    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:ags:ugaeab:387596. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/daughgh.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.