Identifying technically efficient fishing vessels: a non‐empty,minimal subset approach

Stochastic frontier models are often employed to estimate fishing vessel technical efficiency. Under certain assumptions, these models yield efficiency measures that are means of truncated normal distributions. We argue that these measures are flawed, and use the results of Horrace ( 2005 ) to estimate efficiency for 39 vessels in the Northeast Atlantic herring fleet, based on each vessel's probability of being efficient. We develop a subset selection technique to identify groups of efficient vessels at pre‐specified probability levels. When homogeneous production is assumed, inferential inconsistencies exist between our methods and the methods of ranking the means of the technical inefficiency distributions for each vessel. When production is allowed to be heterogeneous, these inconsistencies are mitigated. Copyright © 2007 John Wiley & Sons, Ltd.     

Strategic substitutes or complements? The game of where to fish

The “global game with strategic substitutes and complements” of Karp et al. (2007) is used to model the decision of where to fish. A complete information game is assumed, but the model is generalized to S>1$S>1$ sites. In this game, a fisherman’s payoff depends on fish density in each site and the actions of other fishermen which can lead to congestion or agglomeration effects. Stable and unstable equilibria are characterized, as well as notions of equilibrium dominance. The model is applied to the Alaskan flatfish fishery by specifying a strategic interaction function (response to congestion) that is a non-linear function of the degree of congestion present in a given site. Results suggest that the interaction function may be non-monotonic in congestion.     

Estimating heterogeneous capacity and capacity utilization in a multi-species fishery

We use a stochastic production frontier model to investigate the presence of heterogeneous production and its impact on fleet capacity and capacity utilization in a multi-species fishery. We propose a new fleet capacity estimate that incorporates complete information on the stochastic differences between vessel-specific technical efficiency distributions. Results indicate that ignoring heterogeneity in production technologies within a multi-species fishery as well as the complete distribution of a vessel’s technical efficiency score, may lead to erroneous fleet-wide production profiles and estimates of capacity. Our new estimate of capacity enables out-of-sample production predictions which may be useful to policy makers.     

Spatial regulations and endogenous consideration sets in fisheries

The implementation of spatial regulations has become a mainstay in fisheries management. These regulations have generated a sizable economics literature focused on the spatial behavior of fishermen. Fundamental to these studies is the consideration set (spatial alternatives) assumed by the researcher to be possessed by the decision agent. Often times this consideration set is assumed to be the entire spatial extent of the fishery. This research proposes the use of finite mixture modeling to endogenously estimate the formation of consideration sets and the method is applied to a unique spatial decision environment, the Atka mackerel fishery in the Aleutian Islands. Consideration sets are modeled using different macro-definitions of spatial regions to focus the micro-level spatial decision making within the fishery and to investigate the sensitivity of the results to alternative macro-level spatial definitions. Results illustrate the biases associated with traditional consideration set assumptions by estimating fishermen's valuations for different high-value sites within the Aleutian Islands under alternative consideration set assumptions. Furthermore, our results demonstrate how a model that assumes some structure on potential consideration sets reduces the dimensionality problems associated with other endogenous approaches to choice set definition.     

Dynamic Random Utility Modeling: A Monte Carlo Analysis

Applied studies of commercial fishing have largely ignored the intertemporal aspects of repeated site choices. For many fisheries, fishermen might choose a dynamically optimal cruise trajectory rather than myopic day-to-day strategies and a model that ignores these considerations will likely lead to biased parameter estimates and poor policy guidance. A dynamic random utility model is developed that utilizes the same information as static site-choice models but is entrenched in the principles of dynamic optimization. Using Monte Carlo analysis, we evaluate the performance of this estimator as compared to the static model for a variety of simulated fishery types.     

Estimation of sample selection models with spatial dependence

We consider the estimation of a sample selection model that exhibits spatial autoregressive errors (SAE). Our methodology is motivated by a two‐step strategy where in the first step we estimate a spatial probit model and in the second step (outcome equation) we include an estimated inverse Mills ratio (IMR) as a regressor to control for selection bias. Since the appropriate IMR under SAE depends on a parameter from the second step, both steps are jointly estimated employing the generalized method of moments. We explore the finite sample properties of the estimator using simulations and provide an empirical illustration. Copyright © 2010 John Wiley & Sons, Ltd.