Economies of Spatial Scale in the Fisher

In this dissertation, I investigate the nature of spatial scale of centralized and decentralized fisheries management. With respect to decentralized fisheries management, I challenge the long-held rule of thumb from landscape ecologists that resource policies should aim to establish complete coverage of species dispersal range. Using a conceptual model of a territorial user rights for fisheries (TURF) network, I demonstrate how accounting for variable costs to cooperative exploitation within a TURF change conclusions derived from traditional models which assume sole ownership of TURFs. Moreover, I demonstrate spatial spillovers resulting from strategic interactions between TURFs can cause the costs to cooperative exploitation in any single TURF to either increase or decrease, depending on relative spatial scales of neighboring TURFs. These results highlight complex political economy dimensions of spatial property rights that are important to spatial policy design and impact evaluations.;With respect to more centralized fisheries management, I compile a fisheries harvest dataset for the Great Barrier Reef (GBR) Marine Park commercial coral trout fishery that is both publicly available and spatially resolved. Imputation methods are developed to adjust for censoring of the spatial dataset by the regulator. I use the resulting spatial panel to estimate how the fishing fleet deploys harvest effort as a function of spatial patterns of expected revenue. The key explanatory variable in the empirical model is potentially endogenous because of spatial spillovers and positive spatial autocorrelation in biological productivity. I estimate an unconstrained Spatial Durbin Model (SDM) which controls for this endogeneity. I find that over half the total expected revenue elasticity of demand is contributed by spatial spillover effects. This work demonstrates a reconciliation between data demands in estimating spatial econometric models with limitations inherent in publicly available datasets and contributes to ongoing debates concerning the appropriate scale of spatially differentiated fisheries policy.;Finally, I explore the potential revenue gains from shifting from a spatially uniform fishery policy to a spatially differentiated fishery policy using the GBR coral trout fishery as a case study. Using estimates from my spatial econometric model, I simulate the increase in license revenue gains that could be achieved if the fishery manager used spatially discriminatory license pricing. I find that the first best spatially resolved policy increases fisheries license revenues by over 500%. Moreover, I find evidence of diminishing returns to revenues generation as spatial resolution of the policy increases. A simple increase in spatial resolution of the current GBR coral trout management is expected to result in substantial fishery revenue gains.