FN Archimer Export Format
PT J
TI Scale-dependency in discrete choice models: A fishery application
BT 
AF Dépalle, Maxime
   Sanchirico, James N.
   Thébaud, Olivier
   O’Farrell, Shay
   Haynie, Alan C.
   Perruso, Larry
AS 1:1;2:1,2,3;3:4;4:2,3;5:5;6:6;
FF 1:;2:;3:PDG-RBE-EM;4:;5:;6:;
C1 Department of Agricultural & Resources Economics, University of California, Davis, USA
   Department of Environmental Science and Policy, University of California, Davis, USA
   Coastal and Marine Sciences Institute, University of California, Davis, USA
   Ifremer, UMR 6308, AMURE, Unité d’Economie Maritime, France
   NOAA Alaska Fisheries Science Center, USA
   NOAA Southeast Fisheries Science Center, USA
C2 UNIV CALIF DAVIS, USA
   UNIV CALIF DAVIS, USA
   UNIV CALIF DAVIS, USA
   IFREMER, FRANCE
   NOAA, USA
   NOAA, USA
SI BREST
SE PDG-RBE-EM
UM AMURE
IN WOS Ifremer UMR
   copubli-int-hors-europe
IF 5.84
TC 4
UR https://archimer.ifremer.fr/doc/00661/77272/79206.pdf
LA English
DT Article
DE ;Spatial modeling;Discrete-choice model;VMS data;Fisher behavior;Monte Carlo experiments
AB 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. 
PY 2021
PD JAN
SO Journal Of Environmental Economics And Management
SN 0095-0696
PU Elsevier BV
VL 105
UT 000607089900004
DI 10.1016/j.jeem.2020.102388
ID 77272
ER

EF