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Bayesian ranking and selection of fishing boat efficiencies

Holloway, G. J. ORCID: and Tomberlin, D. (2007) Bayesian ranking and selection of fishing boat efficiencies. Journal of Agricultural Economics. pp. 13-23. ISSN 0021-857X

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The steadily accumulating literature on technical efficiency in fisheries attests to the importance of efficiency as an indicator of fleet condition and as an object of management concern. In this paper, we extend previous work by presenting a Bayesian hierarchical approach that yields both efficiency estimates and, as a byproduct of the estimation algorithm, probabilistic rankings of the relative technical efficiencies of fishing boats. The estimation algorithm is based on recent advances in Markov Chain Monte Carlo (MCMC) methods—Gibbs sampling, in particular—which have not been widely used in fisheries economics. We apply the method to a sample of 10,865 boat trips in the US Pacific hake (or whiting) fishery during 1987–2003. We uncover systematic differences between efficiency rankings based on sample mean efficiency estimates and those that exploit the full posterior distributions of boat efficiencies to estimate the probability that a given boat has the highest true mean efficiency.

Item Type:Article
Divisions:Life Sciences > School of Agriculture, Policy and Development
ID Code:8681
Uncontrolled Keywords:Ranking and selection, hierarchical composed-error model, Markov Chain Monte Carlo, Pacific hake fishery, Resource /Energy Economics and Policy, Q2, L5, C1

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