How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model
Jonathan R. Sweeney,
Richard E. Howitt,
Hing Ling Chan,
Minling Pan and
Papers from arXiv.org
We present a vessel and target-specific positive mathematical programming model (PMP) for Hawaii's longline fishing fleet. Although common in agricultural economics, PMP modeling is rarely attempted in fisheries. To demonstrate the flexibility of the PMP framework, we separate tuna and swordfish production technologies into three policy relevant fishing targets. We find the model most accurately predicts vessel-specific annual bigeye catch in the WCPO, with an accuracy of 12% to 35%, and a correlation between 0.30 and 0.53. To demonstrate the model's usefulness to policy makers, we simulate the economic impact to individual vessels from increasing and decreasing the bigeye catch limit in the WCPO by 10%. Our results suggest that such policy changes will have moderate impacts on most vessels, but large impacts on a few generating a fat tailed distribution. These results offer insights into the range of winners and losers resulting from changes in fishery policies, and therefore, which policies are more likely to gain widespread industry support. As a tool for fishery management, the calibrated PMP model offers a flexible and easy-to-use framework, capable of capturing the heterogeneous response of fishing vessels to evaluate policy changes.
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Published in Natural Resource Modeling, 30(2) (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1707.03960
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