Aquaculture International

, Volume 20, Issue 4, pp 605–618 | Cite as

Implicit price of mussel characteristics in the auction market

  • Thong Tien NguyenEmail author


This study explores desired and undesired characteristics of mussels in wholesale market by applying hedonic price analysis. Transaction data in auction market in Yerseke, the Netherlands, was used to estimate linear and semi-log price models. Meat content and size count, which are measured as the ratio of the weight of cooked meat to the total weight and the number of mussel per kg of raw mussels, respectively, are the most important characteristics determining the price. At the sample mean, if the meat content increases by 1%, farmers can get a premium price of 5.5 eurocents kg−1 of raw mussel. Mussel lots with size counts below 50 pieces kg−1 can command the highest implicit price of size. Processors prefer mussel lots in which the size of mussels is more or less heterogeneous. The impurity of mussel lots, which is measured by the percentage of tare, the amount of barnacles and limpets per kg of raw mussels are significant discounting factors on the price. The study also investigates the impact of farming locations and seasons on the price and the price trend during the period of 2002–2009.


Hedonic model Meat content Mussel Price formation Size count The Netherlands Yerseke auction 



The author gratefully acknowledges Jaap Holstein in the Yerseke auction market, the Netherlands, for providing data and consulting. The author also would like to thank Lars Ravn-Jonsen, Hans Stubbe Solgaard, Eva Roth, Peter Sandholt Jensen, and the people in the Market and Competition research group (SDU, Denmark), two anonymous referees and the co-editors for their comments and discussion. This paper is financed by the MarBioShell project at Southern Denmark University in Denmark.


  1. Anderson JL (1991) Seafood quality: issues for consumer research. J Consum Aff 25(1):144–163CrossRefGoogle Scholar
  2. Benson BL, Faminow MD (1985) An alternative view of pricing in retail food markets. Am J Agr Econ 67:296–306CrossRefGoogle Scholar
  3. Cochrane D, Orcutt GH (1949) Application of least squares regression to relationships containing auto- correlated error terms. J Am Stat Assoc 44:32–61Google Scholar
  4. Dankers N, Zuidema DR (1995) The role of the mussel (Mytilus edulis L.) and mussel culture in the Dutch Wadden Sea. Estuar Coast 18(1):71–80CrossRefGoogle Scholar
  5. Eurostat (2010) Traditional external trade database access. Statistical office of the European commission.
  6. FAO (2009a) State of world fishery and aquaculture production. Food and agriculture organization of the United Nations, RomeGoogle Scholar
  7. FAO (2009b) Fishstat plus database. Food and agriculture organization of the United Nations Rome, ItalyGoogle Scholar
  8. Gibbs J, Shaw SA, Gabbott M (1997) An analysis of price formation in the Dutch mussel industry. Aquac Int 2(2):91–103CrossRefGoogle Scholar
  9. Globefish (2008) Monthly market report-mussels-Feb 2008. FAO Globefish. Rome. Cited 15 Dec 2010Google Scholar
  10. Greene WH (2007) Econometric analysis. Prentice Hall, USAGoogle Scholar
  11. Grunert KG (2005) Food quality and safety: consumer perception and demand. Eur Rev Agri Eco 32(3):369–391CrossRefGoogle Scholar
  12. Gujarati DN (1995) Basic econometrics. McGraw-Hill, New YorkGoogle Scholar
  13. Halvorsen R, Palmquist R (1980) The interpretation of dummy variables in semilogarithmic equations. Am Eco Rev 70(3):474–475Google Scholar
  14. Halvorsen R, Pollakowski HO (1981) Choice of functional form for hedonic price equations. J Urban Econ 10:37–47CrossRefGoogle Scholar
  15. Hew CL, Fletcher GL (2001) The role of aquatic biotechnology in aquaculture. Aquaculture 197:191–204CrossRefGoogle Scholar
  16. Kleijnen J, Schaik F (2007) Sealed-bid auction of Dutch mussels: statistical analysis. University site. Cited 15 Jan 2011
  17. La Rosa T, Mirto S, Marino A, Alonzo V, Maugeri TL, Mazzola A (2001) Heterotrophic bacteria community and pollution indicators of mussel-farm impact of the Gulf of Gaeta (Tyrrhenian Sea). Mar Environ Res 52:310–321CrossRefGoogle Scholar
  18. Lancaster KJ (1966) A new approach to consumer theory. J Politi Econ 74(2):132–157CrossRefGoogle Scholar
  19. Larkin SL, Sylvia G (1999) Firm-level hedonic analysis of US produced surimi: implications for processors and resource managers. Mar Res Eco 14(3):179–198Google Scholar
  20. Mackinnon J, White H, Davidson R (1983) Tests for model specification in the presence of alternative hypothesis: some further results. J Econ 21:53–70Google Scholar
  21. McConnell KE, Strand IE (2000) Hedonic prices for fish: tuna prices in Hawaii. Am J Agri Econ 82(1):133–144CrossRefGoogle Scholar
  22. Parker DD, Zilberman D (1993) Hedonic estimation of quality factors affecting the farm-retail margin. Am J AgricEcon 75:458–466CrossRefGoogle Scholar
  23. Rosen S (1974) Hedonic prices and implicit markets: product differentiation in pure competition. J Poli Econ 82(1):34–55CrossRefGoogle Scholar
  24. Salayo ND, Voon TJP, Selvanathan S (1999) Implicit prices of prawn and shrimp attributes in the Philippine domestic market. Mar Res Econ 14:65–78Google Scholar
  25. Smaal A (2002) European mussel cultivation along the Atlantic coast: production status, problems and perspectives. Hydrobiologia 484:89–98CrossRefGoogle Scholar
  26. Spencer BE (2002) Molluscan shellfish farming. Blackwell Science, OxfordCrossRefGoogle Scholar
  27. Wang SDH, Kellogg CB (1988) An econometric model for American lobster. Mar Res Econ 5(1):61–70Google Scholar
  28. Zeithaml VA (1988) Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J Mark 52(3):2–22CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Environmental and Business Economics, Centre for Fisheries and Aquaculture Management and Economics (FAME)Southern Denmark University (SDU)EsbjergDenmark

Personalised recommendations