Reconstructing global ex-vessel prices of fished species

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Authors Michael C. Melnychuk, Tyler Clavelle, Brandon Owashi, Kent Strauss
Journal/Conference Name ICES Journal of Marine Science
Paper Category
Paper Abstract Economic dimensions of global fishery analyses are often limited by unavailable or inconsistent ex-vessel price data from the world’s fishing nations. We describe a novel method for estimating ex-vessel price time series for individual species by converting export prices of fishery commodities into ex-vessel prices and pairing these with species. The method relies exclusively on global, publicly-available data from the Food and Agricultural Organization of the United Nations (FAO). National datasets of ex-vessel prices are not used as inputs for the method, but comparisons of reconstructed ex-vessel prices with actual prices from national datasets showed strong correspondence. Correlation coefficients for paired reconstructed prices and actual prices of the same species were typically between 0.60 and 0.75 annually in the past two decades. There was a tendency for reconstructed prices to be less variable than actual prices, over-estimating actual prices at low values of actual prices and under-estimating actual prices at high values, likely the result of incomplete price transmission or assigning a given price time series to multiple species. However, there was no evidence of overall bias between reconstructed prices and actual prices, and correlations were strongest for comparisons involving multiple taxonomic groups. The method described carries advantages of global comprehensiveness and consistency across countries in reconstructed ex-vessel prices, reflecting the comprehensiveness and consistency of export price information. The method described links to species from the global FAO landings database, but can be modified to pair with other species lists or to focus on specific regions or countries. Data tables and source code are publicly available and ex-vessel price estimates can be updated annually following annual releases of the FAO fishery commodities database.
Date of publication 2016
Code Programming Language R
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