  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.
