A Spatial Model of Dolphin Avoidance in the Eastern Tropical Pacific Ocean

This paper examines the impact of dolphin-safe eco-labeling and how it fundamentally altered the spatial distribution of fishing effort and fishermen's willingness to pay to avoid dolphins. To do this, a dynamic discrete choice econometric model is applied to the Eastern Tropical Pacific tuna fishery. This econometric approach combines a dynamic programming component with the static discrete site choice model. This estimator couples the current period projected profits associated with fishing a specific site with the value of all future location choices on the cruise, assuming choices are made optimally. The key feature of this model is that it recovers behavioral parameters and solves the dynamic programming problem recursively. The dynamic site choice model reveals a markedly higher impact on producers as compared to the commonly used static model following the labeling regime. Further, in all but a few cases the common practice in dynamic choice models of setting discount factors equal to one is rejected.Environmental Economics and Policy,

Fixed-Effect Estimation of Highly-Mobile Production Technologies

We consider fixed-effect estimation of a production function where inputs and outputs vary over time, space, and cross-sectional unit. Variability in the spatial dimension allows for time-varying individual effects, without parametric assumptions on the effects. Asymptotics along the spatial dimension provide consistency and normality of the marginal products. A finite-sample example is provided: a production function for bottom-trawler fishing vessels in the flatfish fisheries of the Bering Sea. We find significant spatial variability of output (catch) which we exploit in estimation of a harvesting function

Estimating Heterogeneous Primal Capacity and Capacity Utilization Measures 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. Furthermore, we propose a new fleet capacity estimate that incorporates complete information on the stochastic differences between each vessel-specific technical efficiency distribution. 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 yield erroneous fleet-wide production profiles and estimates of capacity.Resource /Energy Economics and Policy,

Fixed-Effect Estimation of Highly-Mobile Production Technologies

We consider fixed-effect estimation of a production function where inputs and outputs vary over time, space, and cross-sectional unit. Variability in the spatial dimension allows for time-varying individual effects, without parametric assumptions on the effects. Asymptotics along the spatial dimension provide consistency and normality of the marginal products. A finite-sample example is provided: a production function for bottom-trawler fishing vessels in the flatfish fisheries of the Bering Sea. We find significant spatial variability of output (catch) which we exploit in estimation of a harvesting function

Estimation of Sample Selection Models with Spatial Dependence

We consider the estimation of sample selection (type II Tobit) models that exhibit spatial error dependence or spatial autoregressive errors (SAE). The method considered is motivated by a two-step strategy analogous to the popular heckit model. The first step of estimation is based on a spatial probate model following a methodology proposed by Pinkse and Slade (1998) that yields consistent estimates. The consistent estimates of the selection equation are used to estimate the inverse Mills ratio (IMR) to be included as a regressor in the estimation of the outcome equation (second step). Since the appropriate IMR turns out to depend on a parameter from the second step under SAE, we propose to estimate the two steps jointly within a generalized method of moments (GMM) framework. We explore the finite sample properties of the proposed estimator using a Monte Carlo experiment; discuss the importance of the spatial sample selection model in applied work, and illustrate the application of our method by estimating the spatial production within a fishery with data that is censored for reasons of confidentiality. Working Paper 08-3

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. Furthermore, we propose a new fleet capacity estimate that incorporates complete information on the stochastic differences between each vessel-specific technical efficiency distribution. 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 yield erroneous fleet-wide production profiles and estimates of capacity. Furthermore, our new estimate of capacity enables out-of-sample production predictions predicated on either homogeneity or heterogeneity modeling which may be utilized to facilitate policy

Estimating Heterogeneous Production in Fisheries

Stochastic production frontier models are used extensively in the agricultural and resource economics literature to estimate production functions and technical efficiency, as well as to guide policy. Traditionally these models assume that each agent\u27s production can be specified as a representative, homogeneous function. This paper proposes the synthesis of a latent class regression and an agricultural production frontier model to estimate technical efficiency while allowing for the possibility of production heterogeneity. We use this model to estimate a latent class production function and efficiency measures for vessels in the Northeast Atlantic herring fishery. Our results suggest that traditional measures of technical efficiency may be incorrect, if heterogeneity of agricultural production exists

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 \u3e 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

Identifying Technically Efficient Fishing Vessels: A Non-Empty, Minimal Subset Approach

There is a growing resource economics literature, concerning the estimation of the technical efficiency of fishing vessels utilizing the stochastic frontier model. In these models, vessel output is regressed on a linear function of vessel inputs and a random composed error. Using parametric assumptions on the regression residual, estimates of vessel technical efficiency are calculated as the mean of a truncated normal distribution and are often reported in a rank statistic as a measure of a captain’s skill and used to estimate excess capacity within fisheries. We demonstrate analytically that these measures are potentially flawed, and extend the results of Horrace (2005) to estimate captain skill for thirty nine vessels in the Northeast Atlantic herring fleet, based on homogenous and heterogeneous production functions within the fleet. When homogenous production is assumed, we find inferential inconsistencies 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

Estimating Heterogeneous Production in Fisheries

Stochastic production frontier models are used extensively in the agricultural and resource economics literature to estimate production functions and technical efficiency, as well as to guide policy. Traditionally these models assume that each agent’s production can be specified as a representative, homogeneous function. This paper proposes the synthesis of a latent class regression and an agricultural production frontier model to estimate technical efficiency while allowing for the possibility of production heterogeneity. We use this model to estimate a latent class production function and efficiency measures for vessels in the Northeast Atlantic herring fishery. Our results suggest that traditional measures of technical efficiency may be incorrect, if heterogeneity of agricultural production exists