Fishing cooperatives around the world have increasingly taken on co-management of commercial fisheries in recent decades, with generally positive results in meeting management targets and increasing economic value. To better understand which commercial fisheries or fleets are likely to form cooperative associations in the future, we utilized a database of management and fleet-level attributes for 106 fisheries-mainly industrial fisheries from the United States, New Zealand, Canada, and the United Kingdom-to develop a predictive model. We considered two alternative definitions of cooperatives: a legal, operational definition that classified 51 of the fisheries as cooperatives, and a more stringent proactive definition that classified 35 of the fisheries as cooperatives. Random forest classification analyses showed that cooperatives of both types were most likely to form in fisheries with greater boat cost, greater level of participation in industry associations, and in fisheries with bycatch limits; strong regional effects were also observed. Cross-validation prediction accuracy levels were high: using 10 predictor variables, 86% and 91% of fisheries were correctly classified under the operational and proactive cooperative definitions, respectively. These predictions suggest which fisheries may be next to create cooperative fishing associations or engage in more proactive arrangements within cooperatives. These results point to which regulatory reforms, such as flexible bycatch restrictions, could lead to more cooperative behavior in fisheries.
Keywords: Economic organization; Fisheries management; Fishing sectors; Machine learning; New institutional economics; Random forest.
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