Bayesian Ranking and Selection of Fishing Boat Efficiencies
AbstractThe 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) methodsâ€”Gibbs sampling, in particularâ€”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 1987â€“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.
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Bibliographic InfoArticle provided by Marine Resources Foundation in its journal Marine Resource Economics.
Volume (Year): 21 (2006)
Issue (Month): 4 ()
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Web page: http://www.uri.edu/cels/enre/mre/mre.htm
Ranking and selection; hierarchical composed-error model; Markov Chain Monte Carlo; Pacific hake fishery; Resource /Energy Economics and Policy; Q2; L5; C1;
Find related papers by JEL classification:
- Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
- L5 - Industrial Organization - - Regulation and Industrial Policy
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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