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Economics & Social Sciences Research Program

Survey Form Developed for Southwest Region Economic Data Collection Project

Published regional economic data for Alaska fisheries are highly aggregated and do not provide detailed and reliable information needed for regional economic analysis of Alaska fisheries. For this reason, the AFSC has hired Hans Geier and Bill Hall (contractors) at the University of Alaska, Fairbanks (UAF) to conduct a regional economic data collection project for Southwest Alaska fisheries. Since the contractors are concerned about the low response rate of a voluntary survey, they will use an approach slightly different from those used by previous data collection studies.

First, for gathering information on employment and earnings, they will rely on survey of vessel owners and interview focus groups. Second, for gathering information on costs of intermediate inputs used on harvesting vessels, they will survey the suppliers of the inputs rather than the vessels owners. This is because the suppliers of inputs are likely to be more cooperative than vessel owners.

Using the information from the suppliers, the contractors will estimate the cost information for an average boat in a harvesting sector. Based on the estimated cost information for the average boat, the contractors will estimate the same information for all the vessels in the harvesting sector, by comparing factors such as horsepower, weight, gear types, etc. This approach will allow the number of questions on the voluntary survey to be minimized, which will help to increase the response rate.

To accomplish this task the contractors have developed and revised a draft vessel survey and its cover letter with assistance from REFM economists, identifying the types of questions to be asked on vessel survey (questions on employment and income). The contractors will have a meeting with a focus group of fishermen on 7 April 2006 to gauge the reactions of the fishermen to the questions on the survey form and obtain their comments. The goal of this project is to improve our ability to conduct the requisite regional economic analyses. A similar data collection project will be conducted for the Gulf Coast and Southeast regions of Alaska when the Southwest region project is completed.

By Chang Seung


Second Phase of Integrated Economic-Ecosystem Modeling Project Launched

Commercially valuable fish species are dependent on many other species and organisms dispersed throughout their habitat. Understanding ecological relationships between these species is important when formulating renewable fishery resource policies. It is also important to have a good understanding of how these fishery resource policies impact human activity and the economy and how human activity affects these species in a marine ecosystem.

The objective of this project is to develop an integrated ecological/economic model for Alaska fisheries that can track both ecological relationships and human activities. The ecosystem model being developed is a general equilibrium ecosystem model (GEEM) which combines an ecosystem model with an economic general equilibrium model called a computable general equilibrium (CGE) model. Such an integrated ecosystem approach will provide more useful information to policy-makers than stand-alone regional economic or ecological models for fisheries and will better satisfy the National Standard 8.

For the second phase of the project, a workshop was held at the AFSC in February. The contractors (Drs. David Finnoff and John Tschirhart from the University of Wyoming) presented their work based on on their report on the first phase of the integrated modeling project. In subsequent small group meetings, AFSC scientists with diverse research foci provided useful comments on the ecosystem and economics components of the model. Since then, the contractors have worked to improve the model using the comments from the AFSC scientists.

By Chang Seung


Estimating Economic Base Relationship for Alaska Fisheries Within Panel Cointegration Framework

Virtually all regional economic impact models developed to date for analysis of U.S. fisheries are static models. For example, frequently used input-output (IO) models that have been implemented for calculating regional economic impacts of fisheries are static models. However, the regional economic impacts of fishery management actions calculated based on a single period static model can be misleading, because most fishery management policies have permanent effects over time and the impacts occur over a number of periods.

With static models, it is impossible to address the timing of the impacts, which needs to be considered in formulating fishery management policies; IO models always predict positive (negative) impacts with positive (negative) shocks to seafood industries. In addition, the IO models do not allow adjustment by other supporting industries to long-run equilibrium of a fishery-dependent regional economy.

An alternative to the static model is a dynamic economic base model, which is often implemented with a vector autoregressive error correction (VECM) model. The VECM model provides the time and magnitudes of regional economic impacts in response to shocks to seafood industries as well as the long-run relationships between basic industries (including seafood industry) and nonbasic (supporting) industries (which cannot be done in a static IO framework).

Using monthly employment data at a regional level from 1990 to 2000, Dr. Chang Seung developed VECM models for two fishery-dependent regions in Alaska - the Southwest and Gulf Coast regions. In these models, the dynamic impacts of the seafood industry on the economies of the two regions were investigated. A very recent development in VECM modeling - "panel cointegration" - made it possible to apply this model with panel (a cross-sectional time series) data. Therefore, using panel employment data obtained via a project by Alaska Department of Labor and Workforce Development Dr. Seung is planning to develop a panel cointegration model of Alaska fisheries.

By Chang Seung
 

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