Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/303653 
Year of Publication: 
2022
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 10 [Issue:] 1 [Article No.:] 2074627 [Year:] 2022 [Pages:] 1-15
Publisher: 
Taylor & Francis, Abingdon
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%.
Subjects: 
Bayesian stochastic frontier model
posterior distributions
Markov Chain Monte Carlo Methods
return to scale
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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