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Friday, May 24, 2019
Session Chair: Lisa Pfeiffer, NOAA Fisheries;
10:00 – 10:18 | 3581334
Somenath Bera1; Quinn Weninger1; email@example.com
1Iowa State University, Department of Economics, Ames, US;
Maximizing the economic value of fisheries resources requires knowledge of the growth potential of the fish stock and the benefits and costs of harvesting fish. Despite its crucial role in the rent maximization problem, stock abundance effects on costs, the stock-cost elasticity, have not previously been estimated. Unobservability of the stock is the main problem; abundance estimates generated by assessment models are rarely spatially delineated and contain unmeasureable noise.
This paper consistently estimates the stock-cost elasticity in the Alaskan longline halibut fishery. We combine recent advances in estimating harvest technologies when abundance is unobserved (Weninger, Perruso and Bunzel, 2018) with industry-independent stock survey data collected by the International Pacific Halibut Commission (IPHC). A generalized linear model is fit to the IPHC data to construct a space-time-varying map of relative halibut stock abundance. We embed the index in an estimation of a structural model of halibut fishing costs. Consistent estimation of the stock-cost elasticity obtains under reasonable assumptions for fisheries data generating processes.
Two stock-cost elasticity estimates are derived. A first measures stock abundance effects under the assumption that the spatial distribution of longline fishing is stock invariant. A second approach exploits the predictive power of the generalized linear model and spatial fishing patterns in our data to simulate profit maximizing spatial longline fishing patterns when halibut abundance and its spatial distribution both change. We estimate additional cost savings that derive when fishermen reoptimize spatial-temporal fishing patterns in response to increased halibut abundance.
A preliminary elasticity estimate of -0.35, with 95% c.i., [-0.61, -0.11] obtains when spatial fishing patterns are assumed to remain constant. Our elasticity estimates is -X.XX (TBD) when fishermen re-optimize their spatial fishing patterns in response to an stock abundance increase.
Our results have profound implications for setting harvest policies in fisheries: stock-cost elasticities in the range estimated in our model favor maintaining abundance at levels well above the maximum sustainable yield benchmark. Doing so will substantially lower halibut fishing costs and increase fishery rent.
10:18 – 10:36 | 3659695
Allen Chen1; Alan Haynie2; firstname.lastname@example.org
1PSMFC, Seattle, WA, USA; 2AFSC/NMFS/NOAA, Seattle, WA, USA;
Stock assessments estimate the health and stability of exploited fisheries and are a vital tool for sustainable marine resource management. When stock assessments use fishery-dependent observations, for example age- or length-composition data or abundance indices, the data should account for endogenous behavior. This accounting should recognize the selectivity and incentives of fishers, who are not randomly sampling, but choosing locations to target different species, densities, and sizes of fish to maximize profits. Economists can help improve assessment methods by providing greater insight into the sampling process that generated the fishery-dependent data, by understanding and explicitly modeling how fishers make tradeoffs. We show how including a model of profit-maximizing economic behavior in combination with recent statistical advances can correct for selection that occurs, if fishers choose to systematically fish at locations with greater expected catches or size-driven values. Then, we examine the performance of assessments conducted with the common assessment software Stock Synthesis that take advantage of fishery data corrected for economic endogeneity. Finally, we discuss how these methods can be applied in data-poor fisheries.
10:36 – 10:54 | 3660433
Alan Haynie1; Corinne Bassin2; Melanie Harsch3; Jordan Watson4; Allen Chen3; email@example.com
1NOAA Fisheries, Seattle, WA, USA; 2ECS Federal Inc., Seattle, WA, USA; 3PSMFC, Seattle, WA, USA; 4NOAA Fisheries AFSC, Juneau, AK, USA;
There are large number of possible approaches to analyzing and modeling the impacts of environmental, stock, and management changes on fisheries. NOAA Fisheries and partners have worked to develop the Spatial Economics Toolbox for Fisheries (FishSET) to provide better information to managers and the public about the economic tradeoffs among different uses of our marine resources. The first versions of the software were based in Matlab, but since 2018 the project has focused on developing R-based software that can be more freely utilized across fisheries.
Since the 1980s, economists have used discrete choice random utility models to assess the factors that influence fishers spatial and participation choices to understand the trade-offs of fishing under different conditions. This knowledge can improve evaluations and predictions of how fishers respond to the creation of marine reserves, to changes in market conditions, to variations in target stock abundance, or to management actions such as the implementation of catch share programs. FishSET includes a suite of discrete choice models as well as a range of related data management and analytical tools. Additionally, the FishSET project has focused on developing other metrics to assess fishery performance. In many cases, metrics such as changes in location, trip length, revenue per day, and CPUE also provide meaningful insights into how behaviors have changed with variations in policy and environmental conditions.
For several years, the FishSET project has worked to make this type of modeling easier. FishSET has worked to standardize best practices and enable robust model development, execution, comparison, and interpretation. Here we discuss the current status of the FishSET project and pilot projects from multi-species fisheries around the U.S. We also discuss how FishSET enables models and indices of fisher behavior to be better integrated with ecosystem and stock assessment models.
10:54 – 11:15
Group discussion will follow oral presentations.
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