Trip-Level Analysis of Efficiency Changes in Oregon’s Deepwater Trawl Fishery
David Tomberlin, Garth Holloway
Fishery Buyback,Technical Efficiency,Stochastic Production Frontier,Bayesian Inference,Markov Chain Monte Carlo
Climate Change and Sustainable Development
In 2003, an industry-financed, government-administered buyback of trawl fishing permits and vessels took place on the US West Coast, resulting in the retirement of about one-third of the limited-entry trawl fleet. The lack of cost data in this fishery precludes an analysis of how the buyback has affected profitability, but changes in technical efficiency can provide some insight into the program’s effects. This paper, the first of a planned series of analyses of the buyback’s effect on technical efficiency in the trawl fleet, applies stochastic frontier analysis to assess whether technical efficiency changed perceptibly after 2003. We adopt a hierarchical modeling approach estimated with Markov Chain Monte Carlo methods, and present results from both Cobb-Douglas and translog specifications. The analysis is limited to 13 boats active in Oregon’s deepwater ‘DTS’ fishery, which targets dover sole, thornyheads, and sablefish. The results suggest that the buyback has had little impact on trip-level technical efficiency in the study fishery. However, departures from the frontier are markedly bi-modal, indicating that a mixed-density approach to estimation may be more appropriate.