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Economics & Social Sciences Research Program

Steller Sea Lion Economic Survey Data Collection Completed

Little is known about the public’s preferences for providing additional protection to the threatened and endangered stocks of Steller sea lions. These preferences are primarily the result of the nonconsumptive value people attribute to such protection, which can take the form of active use values (such as the benefits of viewing Steller sea lions in the wild) or non-use values (such as the value associated with protecting Steller sea lions for future generations or the personal satisfaction of knowing they will continue to exist in the future). Since these types of values are not observed in markets, nonmarket valuation approaches must be used.

To understand these public preferences Dr. Dan Lew (Economics & Social Sciences Research (ESSR) Program), working with David Layton (University of Washington), and Stratus Consulting, has developed and implemented a non-market valuation survey that collects the information necessary to estimate the public’s preferences and values for providing additional protection to Steller sea lions. In the survey, information about preferences for protecting Steller sea lions are primarily obtained through a series of stated preference choice experiment (SPCE) questions.

This type of question is commonly used in marketing and transportation research, health economics, and environmental economics to understand economic preferences and values. In this application, each SPCE question presents the respondent with a choice between maintaining the current set of protection actions and two options that involve doing more and spending more to protect Steller sea lions. Each option is described in terms of the expected population sizes and Endangered Species Act statuses for each stock in 60 years and the potential cost on the respondent’s household each year. In this way, the SPCE questions are intended to emulate a market decision. Furthermore, different versions of the survey present different combinations of expected results and costs. This is necessary to ensure there is sufficient variation in the data for the econometric models to estimate the effect on choice behavior of the individual attributes describing each option (i.e., expected population sizes and statuses).

Following clearance under the Paperwork Reduction Act, the final survey implementation was begun in January and concluded in August. The mail survey was initially sent to a stratified random sample of 5,000 U.S. residents—800 Alaska residents and 4,200 other U.S. residents. A modified Dillman mail-telephone protocol was followed that involved multiple follow-up contacts (i.e., multiple mailings and telephone contacts). The implementation achieved response rates, excluding undeliverables, of 70% for Alaska residents and 60% for other U.S. residents, which are extremely good response rates for stated preference mail-based surveys. At present, the survey data are being summarized and the analysis of the SPCE question responses has begun.

By Dan Lew


A Quantitative Model for Ranking and Selecting Communities Most Involved in Commercial Fisheries

In an article for NAPA Bulletin (an applied anthropology journal), Drs. Jennifer Sepez and Ron Felthoven of the ESSR Program, along with colleague Dr. Karma Norman of the Northwest Fisheries Science Center, propose a quantitative model for ranking commercial fisheries involvement by communities. The model was developed for the purpose of evaluating the level of participation of communities involved in North Pacific and West Coast fisheries, using multiple indicators including vessel ownership, landings, and permit holdings.

Analysis of recent fishing community profiling projects from other locations in the United States shows there have been four basic approaches to selecting a manageable number of communities for analysis, including focusing only on major ports, aggregating communities by region, selecting representative examples, and focusing only on the top of a ranked list. Falling within the ranked list approach, the proposed model uses data envelopment analysis (DEA) as a nonparametric, multidimensional modeling method appropriate for evaluating and ranking fishing communities based on an array of quantitative indicators of fisheries involvement.

The results of applying this model to communities involved in West Coast and North Pacific fisheries are summarized in the article. Nineteen indicators of fisheries dependence and 92 indicators of fisheries engagement were modeled, yielding ranked lists of 1,564 and 1,760 U.S. communities, respectively. Communities assigned the highest possible score in one or more of the ranked lists by the data envelopment analysis (DEA) model for commercial fisheries dependence or engagement are listed in Table 1 below.

Table 1.  Top ranked fishing communities involved in North Pacific and/or West Coast fisheries based
on multiple quantitative indicators.
Alaska Washington Oregon California Other States
Akutan, Anchorage, Chignik, Cordova,
Dillingham, Dutch Harbor, Egegik,
Elfin Cove, Excursion Inlet,
Halibut Cove, Homer, Kasilof,
King Cove, King Salmon, Kipnuk,
Kodiak, Naknek, Pelican,
Petersburg, Point Baker,
Sand Point, Sitka, Togiak, Unalaska
Anacortes,
Bellingham,
Blaine,
Cathlamet,
Olympia,
Seattle,
Tokeland,
Westport
Astoria,
Bandon,
Garibaldi,
Hammond,
Harbor,
Newport,
Port Orford,
Roseburg
Bodega Bay,
Crescent City,
Fields Landing,
Fort Bragg,
Moss Landing,
San Diego, San Pedro,
Santa Barbara, Tarzana,
Terminal Island
Seaford, VA

The strengths and weaknesses of the DEA modeling approach are discussed in the article, along with an evaluation of those situations in which it would be most beneficial to apply, and those in which it would not be appropriate. DEA modeling is not a substitute for ethnographic analysis of communities based on field work, but it does present an enticing way to consider which communities might be selected for fieldwork or profiling or as “fishing communities.” Comparison of the DEA method’s top-ranked communities in Alaska to those selected by an indicators-based, threshold-trigger model for Alaska showed 71% overlap of selected communities, indicating a reasonable level of robustness of the rankings.

The threshold-trigger model communities in Alaska were profiled in NOAA Technical Memorandum NMFS-AFSC-160 as described in the AFSC Quarterly Report for Jan-Feb-Mar 2006. Profiles of the DEA model communities for Washington, Oregon, and California and other states, described in the AFSC Quarterly Report for Apr-May-Jun 2006, will soon be released as NOAA Technical Memorandum NMFS-NWFSC-84. Drafts of these profiles are available at http://www.nwfsc.noaa.gov/research/divisions/sd/communityprofiles/index.cfm.

By Jennifer Sepez
 

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