Pursuit of the triple bottom line of economic, community and ecological sustainability has increased the complexity of fishery management; fisheries assessments require new types of data and analysis to guide science-based policy in addition to traditional biological information and modeling. We introduce the Fishery Performance Indicators (FPIs), a broadly applicable and flexible tool for assessing performance in individual fisheries, and for establishing cross-sectional links between enabling conditions, management strategies and triple bottom line outcomes. Conceptually separating measures of performance, the FPIs use 68 individual outcome metrics—coded on a 1 to 5 scale based on expert assessment to facilitate application to data poor fisheries and sectors—that can be partitioned into sector-based or triple-bottom-line sustainability-based interpretative indicators. Variation among outcomes is explained with 54 similarly structured metrics of inputs, management approaches and enabling conditions. Using 61 initial fishery case studies drawn from industrial and developing countries around the world, we demonstrate the inferential importance of tracking economic and community outcomes, in addition to resource status.

This paper analyzes the causes for regulatory compliance using traditional deterrence variables and potential moral and social variables. We use self-reported data from Tanzanian artisanal fishers in Lake Victoria. The results indicate that fishers adjust their violation rates with respect to changes in the probability of detection and punishment, but they also react to legitimacy and social variables. A small group of persistent violators react neither to normative aspects nor to traditional deterrence variables, but systematically violate the regulation and use bribes to avoid punishment.JEL classification: K42, L51, Q22

We present a model of fishers' gear choice, which allows for heterogeneity both in production technology and risk preferences and apply it on a panel of Swedish trawlers. Stochastic revenue functions are estimated and used to predict the mean and standard deviation of revenue for each trip. In a random-parameters logit model, we test if these predicted values explain gear choice. A majority of fishers respond positively to increased mean and negatively to increased variability of expected landing values, indicating risk aversion, but also show a strong tendency to choose the same gear used on the previous trip. Copyright 2004, Oxford University Press.

Empirical studies of fishers' preferences have found that most fishers are risk-averse, while expected-utility theory predicts risk neutrality even for sizable stakes. We test this prediction using data from a stated choice experiment with Swedish commercial fishers. Our results show that almost 90% of the respondents do not behave as expected-utility maximizers. 48% of the fishers can be broadly characterized as risk-neutral, 26% as modestly risk-averse, while 26% are strongly risk-averse. Fishers are more risk-neutral the higher the fraction of their household's income comes from fishing, while fishers with a positive attitude to individual quotas are more risk-averse. Sensitivity testing implies that decisions with modest stakes like a few days of fishing are not influenced by wealth level.

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