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Measuring Willingness to Pay for Environmental Attributes in Seafood

  • James Hilger
  • Eric Hallstein
  • Andrew W. Stevens
  • Sofia B. Villas-Boas
Article
  • 238 Downloads

Abstract

We investigate whether consumers are willing to pay for sustainability in seafood. To do this, we estimate a logit random utility model (RUM) of seafood purchases using a product-level scanner dataset from a quasi-experimental setting that includes data both before and after the implementation of a seafood advisory and sustainability label. Each seafood product is defined as a bundle of attributes, including price, species, and sustainability rating. The sustainability rating is communicated to consumers through the use of a color-based traffic light label system, where a color rating is assigned to each seafood stock-keeping unit. Combining a structural demand model with a difference-in-differences approach allows us to take advantage of the implementation of the labeling treatment in a subset of stores in the local retail chain to estimate consumers’ willingness to pay (WTP) for green, yellow, and red sustainability labels. We find that the addition of a yellow sustainability label negatively impacts consumer’s WTP for seafood products, however this simple average effect does not fully capture many independent underlying mechanisms, such as consumer preferences for wild-caught versus farmed products, and the color-distribution of available labeled products within a species, which are empirically explored. Additional results from a second stage generalized least squares regression of RUM product fixed effects on product characteristics indicate that consumers prefer selective harvest methods, wild caught seafood, and U.S. caught seafood.

Keywords

Eco-labels Traffic-light labels Sustainable seafood Random utility model Quasi-experimental Information provision Environmental policy 

Notes

Funding

Funding was provided by California Sea Grant College Program (Grant No. Project: R/RCC-02, NA10OAR417 0060).

References

  1. Alfnes F, Guttormensen AG, Steine G, Kolstad K (2006) Consumer’s willingness to pay for the color of salmon: a choice experiment with real economic incentives. Am J Agric Econ 88:1050–1061CrossRefGoogle Scholar
  2. Asche F, Guillen J (2012) The importance of fishing method, gear and origin: the Spanish hake market. Mar Policy 36:365–369CrossRefGoogle Scholar
  3. Asche F, Larsen TA, Smith MD, Sogn-Grundvåg G, Young JA (2015) Pricing of eco-labels with retailer heterogeneity. Food Policy 53:82–93CrossRefGoogle Scholar
  4. Batte MT, Hooker NH, Haab TC, Beaverson J (2007) Putting their money where their mouths are: consumer willingness to pay for multi-ingredient, processed organic food products. Food Policy 32:145–149CrossRefGoogle Scholar
  5. Berry S (1994) Estimating discrete-choice models of product differentiation. RAND J Econ 25–2:242–262CrossRefGoogle Scholar
  6. Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63:841–890CrossRefGoogle Scholar
  7. Bjørner TB, Hansen LG, Russell CS (2004) Environmental labeling and consumers’ choice—an empirical analysis of the effect of the Nordic Swan. J Environ Econ Manag 47–3:411–434CrossRefGoogle Scholar
  8. Blomquist J, Bartolino V, Waldo S (2015) Price premiums for providing eco-labelled seafood: evidence from MSC-certified cod in Sweden. J Agric Econ.  https://doi.org/10.1111/1477-9552.12106 Google Scholar
  9. Carroll M, Anderson J, Martinez-Garmendia J (2001) Pricing U.S. North Atlantic Bluefin Tuna and implications for management. Agribusiness 17:243–254CrossRefGoogle Scholar
  10. Chamberlain G (1982) Multi variate regression models for panel data. J Econom 18:5–46CrossRefGoogle Scholar
  11. Costello C, Gaines SD, Lynham J (2008) Can catch shares prevent fisheries collapse? Science 321:1678–1681CrossRefGoogle Scholar
  12. Delgado CL, Wada N, Rosegrant MW, Meijer S, Ahmed M (2003) Outlook for fish to 2020: meeting global demand. Technical report, International Food Policy Research InstituteGoogle Scholar
  13. Fischer C, Lyon TP (2014) Competing environmental labels. J Econ Manag Strategy 23–3:692–716CrossRefGoogle Scholar
  14. Fischer C, Lyon TP (2015) A theory of multi-tier ecolabel competition. Ross school of business paper no. 1319. University of Michigan, Ann ArborGoogle Scholar
  15. Hallstein E, Villas-Boas SB (2013) Can household consumers save the wild fish? Lessons from a sustainable seafood advisory. J Environ Econ Manag 66–1:52–71CrossRefGoogle Scholar
  16. Hensher DA, Bradley M (1993) Using stated response data to enrich revealed preference discrete choice models. Mark Lett 4–2:139–152CrossRefGoogle Scholar
  17. Hilger J, Rafert G, Villas-Boas SB (2011) Expert opinion and the demand for experience goods: an experimental approach in the retail wine market. Rev Econ Stat 93–4:1289–1296CrossRefGoogle Scholar
  18. Hoffman D (1987) Two-step generalized least squares estimators in multi-equation generated regressor models. Rev Econ Stat 69–2:336–346CrossRefGoogle Scholar
  19. Ippolito P, Mathios AD (1995) Information and advertising: the case of fat consumption in the United States. Am Econ Rev 85–2:91–95Google Scholar
  20. Jaffry S, Pickering H, Ghulam Y, Whitmarsh D, Wattage P (2004) Consumer choices for quality and sustainability labeled seafood products in the U.K. Food Policy 29:215–228CrossRefGoogle Scholar
  21. Jin G, Leslie P (2003) The effect of information on product quality: evidence from restaurant hygiene grade cards. Q J Econ 118:409–451CrossRefGoogle Scholar
  22. Johnston RJ, Roheim CA (2006) A battle of taste and environmental convictions for eco-labeled seafood: a contingent ranking experiment. J Agric Resour Econ 31:283–300Google Scholar
  23. Johnston RJ, Wessells CR, Donath H, Asche F (2001) Measuring consumer preferences for eco-labeled seafood: an international comparisons. J Agric Resour Econ 26:20–39Google Scholar
  24. Kiesel K, Villas-Boas SB (2007) Got organic milk? Consumer valuations of milk labels after the implementation of the USDA organic seal. J Agric Food Ind Organ 5:4Google Scholar
  25. Mathios AD (2000) The impact of mandatory disclosure laws on product choices: an analysis of the salad dressing market. J Law Econ 42–2:651–677CrossRefGoogle Scholar
  26. McConnell K, Strand I (2000) Hedonic prices for fish: Tuna prices in Hawaii. Am J Agric Econ 82:133–144CrossRefGoogle Scholar
  27. McFadden D (1974) The measurement of urban travel demand. J Public Econ 3:303–328CrossRefGoogle Scholar
  28. McFadden D, Train KE (2000) Mixed MNL models for discrete response. J Appl Econom 15:447–470CrossRefGoogle Scholar
  29. Nevo A (2000) A practitioner’s guide to estimation of random-coefficients logit models of demand. J Econ Manag Strategy 9:513–548CrossRefGoogle Scholar
  30. Nevo A (2001) Measuring market power in the ready-to-eat cereal industry. Econometrica 69(2):307–342CrossRefGoogle Scholar
  31. Nevo A (2003) New products, quality changes and welfare measures computed from estimated demand systems. Rev Econ Stat 85:266–275CrossRefGoogle Scholar
  32. Nimon W, Beghin J (1999) Are eco-labels valuable? Evidence from the apparel industry. Am J Agric Econ 81–4:801–811CrossRefGoogle Scholar
  33. Pagan A (1984) Econometric issues in the analysis of regressions with generated regressors. Int Econ Rev 25–1:221–247CrossRefGoogle Scholar
  34. Roheim CA (2003) Early indications of market impacts from the marine stewardship council’s eco-labeling of seafood. Mar Resour Econ 18:95–104CrossRefGoogle Scholar
  35. Roheim CA, Gardiner L, Asche F (2007) Value of brands and other attributes: hedonic analysis of retail frozen fish in the UK. Mar Resour Econ 22–3:239–253CrossRefGoogle Scholar
  36. Roheim CA, Asche F, Santos JL (2011) The elusive price premium for eco-labeled products: evidence from seafood in the UK market. J Agric Econ 62–3:655–668CrossRefGoogle Scholar
  37. Seafoodwatch (2014) Fishing and farming methods. Monterey Bay Aquarium. May 6th, 2014. http://www.seafoodwatch.org/cr/cr_seafoodwatch/sfw_gear.aspx
  38. Shimshack J, Ward M, Beatty T (2007) Mercury advisories: information, education, and fish consumption. J Environ Econ Manag 53–2:158–179CrossRefGoogle Scholar
  39. Smith MD, Roheim CA, Crowder LB, Halpern BS, Turnipseed M, Anderson JL, Asche F, Bourillón L, Guttormsen AG, Khan A, Liguori LA, McNevin A, O’Connor MO, Squires D, Tyedmers P, Brownstein C, Carden K, Klinger DH, Sagarin R, Selkoe KA (2010) Sustainability and global seafood. Science 327:784–786CrossRefGoogle Scholar
  40. Sogn-Grundvåg G, Larsen T, Young J (2013) The value of line-caught and other attributes: an exploration of price premiums for chilled fish in UK supermarkets. Mar Policy 38:41–44.  https://doi.org/10.1016/j.marpol.2012.05.017 CrossRefGoogle Scholar
  41. Sun J, Chiang F, Owens M, Squires D (2017) Will American consumers pay more for eco-friendly labeled canned tuna? Estimating US consumer demand for canned tuna varieties using scanner data. Mar Policy 79:62–69CrossRefGoogle Scholar
  42. Swait J, Adamowicz W, van Bueren M (2004) Choice and temporal welfare impacts: incorporating history into discrete choice models. J Environ Econ Manag 47–1:94–116CrossRefGoogle Scholar
  43. Teisl MF, Roe B, Hicks RL (2002) Can eco-labels tune a market? Evidence from dolphin-safe labeling. J Environ Econ Manag 43–3:339–359CrossRefGoogle Scholar
  44. Teisl MF, Fromberg E, Smith AE, Boyle KJ, Engelberth HM (2011) Awake at the switch: improving fish consumption advisories for at-risk women. Sci Total Environ 409–18:3257–3266CrossRefGoogle Scholar
  45. Train K (2003) Discrete choice methods with simulation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  46. Watson R, Pauly D (2001) Systematic distortions in world fisheries catch trends. Nature 414:534–536CrossRefGoogle Scholar

Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  • James Hilger
    • 1
  • Eric Hallstein
    • 2
  • Andrew W. Stevens
    • 3
  • Sofia B. Villas-Boas
    • 4
  1. 1.NOAA FisheriesSouthwest Fisheries Science CenterLa JollaUSA
  2. 2.The Nature ConservancySan FranciscoUSA
  3. 3.Department of Agricultural EconomicsMississippi State UniversityMississippi StateUSA
  4. 4.Department of Agricultural and Resource EconomicsUniversity of California at BerkeleyBerkeleyUSA

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