Managing Fleet Capacity Effectively Under Second-Hand Market Redistribution
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Fishing capacity management policies have been traditionally implemented at national level with national targets for capacity reduction. More recently, capacity management policies have increasingly targeted specific fisheries. French fisheries spatially vary along the French coastline and are associated to specific regions. Capacity management policies, however, ignore the capital mobility associated with second-hand vessel trade between regions. This is not an issue for national policies but could limit the effectiveness of regional capacity management policies. A gravity model and a random-effect Poisson regression model are used to analyze the determinants and spatial extent of the second-hand market in France. This study is based on panel data from the French Atlantic Ocean between 1992 and 2009. The trade flows between trading partners is found to increase with their sizes and to be spatially concentrated. Despite the low trade flows between regions, a net impact analysis shows that fishing capacity is redistributed by the second-hand market to regions on the Channel and Aquitaine from central regions. National capacity management policies (constructions/destructions) have induced a net decrease in regional fleet capacity with varying magnitude across regions. Unless there is a change of policy instruments or their scale of implementation, the operation of the second-hand market decreases the effectiveness of regional capacity management policies in regions on the Channel and Aquitaine.
KeywordsGravity model Fishing vessels Second-hand markets Poisson French Atlantic
The authors would like to acknowledge the very helpful comments and suggestions received from the Editor, the Associate Editor, and two anonymous reviewers. We would also like to thank Dr. Sylvain Barde (University of Kent) and Dr. Olivier Thébaud (CSIRO) for their insights and constructive suggestions. We are also very grateful for comments and suggestions received during a seminar presentation at the School of Economics of the University of Kent (UK) and at the 2012 Conference of the Natural Resource Modeling Association as part of a research visit funded by the ARC Centre of Excellence for Environmental Decision (CEED) Australia. In addition, we would like to thank Dr. Fabienne Daurès for her insights into fishery policy, Samuel Le Blond, Mathilde Pitel and the Sextant team at Ifremer for greatly facilitating the building of the database, Christelle Le Grand and Damaris Phélippé. We would also like to thank Tatiana Jousselin (ESRI) and Dr. Hawthorne L. Beyer (Spatial Ecology LLC) for their help with ArcGIS 9.3.1 and Geospatial Modelling Environment.
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