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Economics & Social Sciences Research Program

Alaska Fisheries and Global Trade

This project is aimed at integrating international trade data that are associated with Alaska fisheries (see AFSC Quarterly Report, Oct.-Dec. 2006) into a global economic growth model that represents international trade, which has a regional component for Alaska. Recent work on the global model is described above in the section on environment and demography by Sepez and Dalton. In addition, Dr. Chang Seung of the ESSR Program prepared benchmark economic data for Alaska and the United States that will be used in the global model. Dr. Shonali Pachauri of the International Institute for Applied Systems Analysis (IIASA) prepared supplemental information, obtained from the International Energy Agency to complete the U.S. benchmark. Dr. Leiwen Jiang of Brown University is preparing benchmark data for the rest of the world from the Global Trade Analysis Project (GTAP).

While exports of certain seafood products from Alaska are available in the U.S. Merchandise Trade Statistics, a consistent set of trade flows (including re-exports) among other countries (e.g., China, Japan, Korea) for seafood products that are not otherwise available from the GTAP will be estimated from the U.N. Merchandise Trade Statistics, and Food and Agriculture Organization data on fish production and trade, using a new procedure developed by Dalton.

By Mike Dalton

Developing a Computable General Equilibrium Model for Alaska Fisheries

Fixed-price models such as input-output (IO) and social accounting matrix (SAM) models are often used for analysis of fisheries. However, these models have several important limitations.

In these models, prices are assumed to be fixed, and no substitution is allowed between factors in production or commodities in consumption. As a result, in cases where the fixed-price assumption may not be realistic, these models tend to overestimate impacts. Computable General Equilibrium (CGE) models overcome these limitations. In CGE models, prices are allowed to vary, triggering substitution effects in production and consumption. The CGE model therefore enables analysts to more easily examine the economic welfare implications of a policy change. Furthermore, the CGE approach is generally more appropriate than other regional economic models for analyzing the impacts of a change in productive capacity of resource-based industries. Therefore, Drs. Chang Seung (ESSR Program) and Edward Waters (contractor) will build the first Alaska fisheries CGE model, and use the model to estimate the distribution and magnitude of economic impacts associated with harvesting, processing and support activities related to Alaska fisheries.

The investigators will use IMPLAN, a commercially available set of data for conducting regional economic analyses, and other available data. Once developed, the CGE model will be used to estimate economic impacts of selected, relevant policy issues affecting commercial fishing and related activities in Alaska.

By Chang Seung

Estimating Heterogeneous Capacity and Capacity Utilization in a Multispecies Fishery

ESSR scientist Dr. Ronald Felthoven has been working with Professors Kurt Schnier and Bill Horrace at the University of Rhode Island and Syracuse University, respectively, to develop a stochastic production frontier model that accommodates heterogeneous fishing production technologies within a fishery and internally partitions these different technologies into identifiable groups.

One of the goals of this research is to investigate the impact of this more flexible model on measures of fleet capacity and capacity utilization in a multi-species fishery. In our research 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 vesselís 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 inform policy makers.

A paper on the subject will be submitted for publication in a peer-reviewed journal this spring.

By Ron Felthoven

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