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annotated bibliography of fisheries economics literature - Office of ...

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Fishery <strong>of</strong>f Texas and Louisiana." NOAA Technical Memorandum NMFS-<br />

SEFC-136, National Oceanic and Atmospheric Administration,<br />

National Marine Fisheries Service, Galveston Laboratory,<br />

Galveston, Texas, March, 28 pp.<br />

The objectives <strong>of</strong> whether the Texas Closure regulation increased shrimp<br />

yield and reduced discarding <strong>of</strong> undersized shrimp were determined for 1982 and<br />

1983. This report reviews and analyzes the characteristics <strong>of</strong> the Texas and<br />

Louisiana <strong>fisheries</strong> west <strong>of</strong> the Mississippi River and describes the catch,<br />

fishing effort, relative abundance and recruitment to the <strong>of</strong>fshore fishery<br />

from June 1982-August 1983.<br />

Klima, Edward F., James M. Nance, Eduardo X. Martinez, and Terrance<br />

Leary (1990). "Workshop on Definition <strong>of</strong> Shrimp Recruitment<br />

Overfishing." NOAA Technical Memorandum, NMFS-SEFC-264, 21 p.<br />

This report summarizes the findings <strong>of</strong> a two day workshop undertaken to<br />

(1) draft scientific definitions <strong>of</strong> overfishing for each <strong>of</strong> the shrimp species<br />

in the management unit <strong>of</strong> the Fishery Management Plan and (2) to recommend<br />

action that might be taken if overfishing occurred in any <strong>of</strong> these stocks.<br />

Kling, Catherine L. (1987). "A Simulation Approach to Comparing<br />

Multiple Site Recreation Demand Models Using Chesapeake Bay Survey<br />

Data." Marine Resource Economics, 4:95-109.<br />

This paper's purpose is to implement a methodology that can be used to<br />

suggest a model (or models) appropriate for valuing quality improvements in<br />

the Chesapeake Bay. To compare these approaches, a series <strong>of</strong> outdoor<br />

recreation user populations is constructed by choosing a utility function, its<br />

parameter values and an error distribution. This information is combined with<br />

the characteristics <strong>of</strong> individuals and recreation sites from a Chesapeake Bay<br />

recreation demand survey to solve the individual's maximization problem. Each<br />

<strong>of</strong> the models is estimated using these data, and the compensating variation <strong>of</strong><br />

a quality change is calculated. Benefit estimates are compared with simulated<br />

welfare change to evaluate the models.<br />

Kling, Catherine L. (1988). "The Reliability <strong>of</strong> Estimates <strong>of</strong><br />

Environmental Benefits from Recreation Demand Models." Journal <strong>of</strong><br />

Agricultural Economics, 70(4):892-901.<br />

Recreation demand models are commonly employed tools <strong>of</strong> economists<br />

interested in valuing improvements in environmental amenities. Despite their<br />

importance, little comparative work has been undertaken to examine the ability<br />

<strong>of</strong> the models to accurately estimate welfare changes. A simulation study<br />

designed to compare the reliability <strong>of</strong> estimated welfare measures<br />

(compensating variation and consumer surplus) from several commonly employed<br />

recreation demand models is presented. Results <strong>of</strong> the study indicate that<br />

choice <strong>of</strong> functional forma nd model specification are important determinants<br />

<strong>of</strong> the resulting estimates <strong>of</strong> benefits.<br />

Kling, Catherine L. (1988). "Comparing Welfare Estimates <strong>of</strong><br />

Environmental Quality Changes from Recreation Demand Models."<br />

Journal <strong>of</strong> Environmental Economics and Management, 15:331-340.<br />

This paper presents a procedure for examining the reliability <strong>of</strong> welfare<br />

estimates resulting from the estimation <strong>of</strong> multiple site recreation demand<br />

models. A simulation approach is suggested where a utility function is<br />

combined with observations <strong>of</strong> individual and site characteristics to generate<br />

simulated data sets. Welfare measures associated with an improvement in site<br />

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