Scale-dependency in discrete choice models: A fishery application

Type Article
Date 2021-01
Language English
Author(s) Dépalle Maxime1, Sanchirico James N.1, 2, 3, Thébaud OlivierORCID4, O’farrell Shay2, 3, Haynie Alan C.5, Perruso Larry6
Affiliation(s) 1 : Department of Agricultural & Resources Economics, University of California, Davis, USA
2 : Department of Environmental Science and Policy, University of California, Davis, USA
3 : Coastal and Marine Sciences Institute, University of California, Davis, USA
4 : Ifremer, UMR 6308, AMURE, Unité d’Economie Maritime, France
5 : NOAA Alaska Fisheries Science Center, USA
6 : NOAA Southeast Fisheries Science Center, USA
Source Journal Of Environmental Economics And Management (0095-0696) (Elsevier BV), 2021-01 , Vol. 105 , P. 102388 (16p.)
DOI 10.1016/j.jeem.2020.102388
WOS© Times Cited 4
Keyword(s) Spatial modeling, Discrete-choice model, VMS data, Fisher behavior, Monte Carlo experiments

Modeling the spatial behavior of fishers is critical in assessing fishery management policies and has been dominated by discrete choice models (DCM). Motivated by the widespread availability of micro-data on fishing vessel locations, this paper examines the complexity associated with the choice of the spatial scale in a DCM of fishing locations. Our empirical approach estimates the standard DCM at varying spatial resolutions using both simulated data and vessel monitoring system data from the Gulf of Mexico longline fishery. We assess model performance using goodness-of-fit, predictive capacity, parameter estimates, and the assessment of the fishery response to a hypothetical marine protected area. Results show that, even when the specification of the decision-making process is correct, models can be structurally biased because of the aggregation of spatial scale that neglects the value of many fishing locations. The extent of such biases can only be detected by considering various spatial aggregation levels.

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