INCORPORATING STOCHASTIC HARVESTS INTO AN ANALYSIS OF PRODUCTION: THE U.S. ATLANTIC AND GULF OF MEXICO PELAGIC LONGLINE FLEET

Vessel operators maximize expected utility indirectly through cost-minimizing production decisions subject to stochastic harvests. Data on the Atlantic longline fleet, available from NMFS logbooks, is used for the empirical analysis. An ex ante multi-input cost function that incorporates expected rather than realized output levels is estimated and results are reported.Resource /Energy Economics and Policy,

Fishing behavior across space, time and depth: with application to the Gulf of Mexico reef fish fishery [Fishing behavior across space and time]

We introduce a model of fishing behavior that features costly targeting of a spatially and temporally heterogeneous, multiple-species fish stock. We characterize fishing behavior under species-specific regulations including time-area-depth closures, per-trip landings limits and tradable harvest permits. Our behavioral model yields a system of Kuhn-Tucker necessary conditions which form the basis of our empirical estimation. Data from the Gulf of Mexico commercial reef fish fishery are used to estimate the model. The estimated harvest technology exhibits local weak output disposability which are linked to spatially and temporally dependent stock conditions in the reef fish fishery. The model predicts harvests, discards and fishing profit across multiple species, and importantly across continuous space and time dimensions. Policy simulations further identify behavioral responses to closure regulations, individual tradeable quota management and recent sea turtle bycatch management rules which impose limits on fishing depth. Our model overcomes limitations of discrete choice spatial fishing behavioral models, and offers a powerful tool for improving regulation of spatially and temporally heterogeneous, multi-species fisheries

A Dual Production Analysis of a Multispecies Fishery: The Case of the U.S. Atlantic Longline Fleet

The harvest technology of several multispecies fisheries has been explained in the recent literature using dual-based models. These studies are useful for explaining rent dissipation, estimating input and output elasticities, as well as describing other aspects of fisherman behavior. Most of these analyses have assumed that inputs are fixed at the trip level. Consequently researchers have argued that firms participating in multiple fisheries behave as revenue-maximizers. This paper describes an empirical model of a multispecies fishery that assumes decision-makers are cost-minimizers at the trip level. A theoretical argument justifying this assumption is presented. Using duality theory, optimal input demands are estimated using data from the U.S. Atlantic pelagic longline fleet using iterative seemingly unrelated regression (SUR) and a recently suggested approach. These results are used to describe the economic characteristics of the industry and the economic consequences of proposed regulations, with focus on the demand for fuel. Additionally, an EM (Expectation-Maximization) algorithm is employed to correct for missing data problems. The efficiency of the algorithm is explored by rerunning the original analysis using the newly created data set and comparing results.Keywords: Fisheries Economics, swordfish, Theoretical and Empirical Bio-Economic Modelling, production economics, duality, longlin

Identification of resource extraction technologies when the resource stock is unobservable

This paper consistently estimates key structural properties of a multiple-species fishing technology. We overcome two ubiquitous features of fisheries data generating processes that invalidate classical estimation of fishing technologies: unobservability by the researcher but partial observability of the fish stock by fishermen and endogenous production decisions that vary with fishermen’s private knowledge of true stock abundance. Our identification strategy exploits timing and available information when production decision are made, technological constraints, and natural, exogenous variability of fish stock abundance. Consistency in estimation obtains under reasonable assumptions for fisheries data generating processes. An application to the U.S. Gulf of Mexico commercial reef fish fishery is presented to demonstrate our approach and reveal substantial bias under estimators that ignore the problem of omitted stock abundance. Implications for improved fisheries management are discussed

Spatial temporal fishing technologies: Specification and estimation

Data generating processes for trip-level commercial fishing data feature endogenous targeting of individual fish species and spatially, temporally varying stock conditions that are unobserved by the researcher but partially observed by fishermen. We present a model and identification strategy to address serious challenges that arise in this setting for obtaining consistent estimates of the structural properties of fishing technologies. Our estimation strategy exploits (1) the timing and information available to fishermen when factor inputs and output targeting decisions are made, and (2) exogenous, natural variation in stock abundance at the trip-level spatial scale. In a first stage, estimation methods used by stock assessment scientists are adopted to account for endogenous fishing power and spatially-temporally varying abundance. Parameters of our multiple-species (trip-level) cost function are then estimated through a decomposition of costs into an endogenous targeting component and a component that varies exogenously due to random fluctuations in the marine environment. Nonparametric series estimation methods are used to control for endogenous targeting and unobserved multiple-species abundance. An application to the Gulf of Mexico commercial reef fish fishery is presented to illustrate the model. The approach solves an identification problem that pervades previous empirical analysis of fishing technologies and offers a new tool for managing spatial-temporal fishing

The Impact of Catch Shares on Fishing Income Diversification and Variation in Annual Revenue

Many fishermen diversify their income by fishing in more than one fishery which can significantly reduce year-to-year variation in income. However, opportunities to diversify have become more limited as access to fisheries has become more restricted. The implementation of catch share systems could further reduce diversification if those who remain in a fishery consolidate catch privileges and specialize, and those who exit lose a component of their fishing portfolio. However, catch shares, particularly in the form of IFQs, offer individuals who had not been part of fishery the opportunity to enter by purchasing or leasing quota. Thus the net effect of catch shares on diversification is uncertain. Furthermore, for fishermen that remain in the catch share fishery, the secure privilege to harvest a set share of the TAC may provide opportunities to reduce variation in income offsetting increased risk associated with reduced diversification. Thus it remains an empirical question whether and how catch shares affect diversification and variation in income. We present an empirical study of diversification in 14 catch share fisheries with a diversity of species from different regions of the US, including both IFQs and cooperative-based catch share systems. For each of these fisheries we test whether diversification levels and trends in diversification changed after implementation of IFQs both for fishermen that remained in the catch share system and for those that exited but remained active in other fisheries. We also test whether variation in fishing revenues changed for fishermen in these groups.Proceedings of the Eighteenth Biennial Conference of the International Institute of Fisheries Economics and Trade, held July 11-15, 2016 at Aberdeen Exhibition and Conference Center (AECC), Aberdeen, Scotland, UK

Fishing behavior across space, time and depth: with application to the Gulf of Mexico reef fish fishery [Fishing behavior across space and time]

We introduce a model of fishing behavior that features costly targeting of a spatially and temporally heterogeneous, multiple-species fish stock. We characterize fishing behavior under species-specific regulations including time-area-depth closures, per-trip landings limits and tradable harvest permits. Our behavioral model yields a system of Kuhn-Tucker necessary conditions which form the basis of our empirical estimation. Data from the Gulf of Mexico commercial reef fish fishery are used to estimate the model. The estimated harvest technology exhibits local weak output disposability which are linked to spatially and temporally dependent stock conditions in the reef fish fishery. The model predicts harvests, discards and fishing profit across multiple species, and importantly across continuous space and time dimensions. Policy simulations further identify behavioral responses to closure regulations, individual tradeable quota management and recent sea turtle bycatch management rules which impose limits on fishing depth. Our model overcomes limitations of discrete choice spatial fishing behavioral models, and offers a powerful tool for improving regulation of spatially and temporally heterogeneous, multi-species fisheries.</p

INCORPORATING STOCHASTIC HARVESTS INTO AN ANALYSIS OF PRODUCTION: THE U.S. ATLANTIC AND GULF OF MEXICO PELAGIC LONGLINE FLEET

Vessel operators maximize expected utility indirectly through cost-minimizing production decisions subject to stochastic harvests. Data on the Atlantic longline fleet, available from NMFS logbooks, is used for the empirical analysis. An ex ante multi-input cost function that incorporates expected rather than realized output levels is estimated and results are reported

Linking real world fisheries datasets for mapping of revenue from fishing grounds to dependent communities

Mapping the economic value of the ocean is pivotal to understand how marine ecosystems contribute to human wellbeing and to support fisheries management. We present a framework to analyse fisheries data and map fishing revenues by linking Vessel Monitoring System (VMS) information to logbooks and observer data. We provide a detailed step-by step methodology and describe different approaches available to fulfill each step, with special notes for the processing of real world messy data. The framework consists of six processing steps: (1) identifying the target fishery and subsetting VMS data; (2) extracting relevant variables; (3) linking observer and VMS data; (4) identifying fishing activity; (5) linking VMS and logbook data; (6) extracting derived variables and mapping revenue back to the communities that extracted the resources. Building this framework opens a broad range of applications including marine spatial planning, rapid response analyses, high-resolution stock assessments and spatially explicit-socioeconomic analyses. We demonstrate the framework in the reef fishery of the Gulf of Mexico, where spatial planning for aquaculture is currently underway.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author

Classifying fishing behavioral diversity using high-frequency movement data

Effective management of social-ecological systems (SESs) requires an understanding of human behavior. In many SESs, there are hundreds of agents or more interacting with governance and regulatory institutions, driving management outcomes through collective behavior. Agents in these systems often display consistent behavioral characteristics over time that can help reduce the dimensionality of SES data by enabling the assignment of types. Typologies of resource-user behavior both enrich our knowledge of user cultures and provide critical information for management. Here, we develop a data-driven framework to identify resource-user typologies in SESs with high-dimensional data. To demonstrate policy applications, we apply the framework to a tightly coupled SES, commercial fishing. We leverage large fisheries-dependent datasets that include mandatory vessel logbooks, observer datasets, and high-resolution geospatial vessel tracking technologies. We first quantify vessel and behavioral characteristics using data that encode fishers' spatial decisions and behaviors. We then use clustering to classify these characteristics into discrete fishing behavioral types (FBTs), determining that 3 types emerge in our case study. Finally, we investigate how a series of disturbances applied selection pressure on these FBTs, causing the disproportionate loss of one group. Our framework not only provides an efficient and unbiased method for identifying FBTs in near real time, but it can also improve management outcomes by enabling ex ante investigation of the consequences of disturbances such as policy actions