Identifying the importance of the “skipper effect” within sources of measured inefficiency in fisheries through data envelopment analysis (DEA)

Technical efficiency, uncertainties in data quality and natural fluctuations in fishing stocks constitute potential sources of fishing vessel inefficiency. Moreover, debate is on-going as to whether the skill of the fishermen (‘‘skipper effect’’) is an underlying actor in fishing efficiency. Therefore, this article monitors, calculates and quantifies the inefficiency caused by the ‘‘skipper effect’’, if any, through the use of data envelopment analysis (DEA), with the aim of determining whether best practice target operational values in DEA, and their associated environmental impact reductions through Life Cycle Assessment (LCA) + DEA methodology, are achievable beyond the theoretical baseline they involve. A window analysis model is applied to the US menhaden fishery, a purse seining fleet with high homogeneity, since it is owned by the same company, with similar vessel and management characteristics. Results revealed relevant inefficiency levels in the four ports assessed, suggesting the existence of a ‘‘skipper effect’’ in all of them. Strong variances between vessels were identified, not only on an annual mean basis, but also per week of study. These variances could be attributed to random variation through time, if it were not for the fact that best performing vessels managed to repeatedly perform at high efficiency rates throughout the period. Moreover, standard deviations of low efficiency vessels were higher in all ports. Consequently, best performing targets calculated in LCA + DEA maybe difficult to achieve in fleets where skipper skill strongly influences the sources of inefficiency. In these cases, the results suggest that resource minimization should be linked to specific measures to improve the individual skills of low performing vessels to attain best practice targets.