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Measuring Willingness to Pay for Environmental Attributes in Seafood


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.

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  1. 1.

    One has to look outside the seafood industry for additional empirically based studies on the efficacy of product labels. Studies on restaurants hygiene (Jin and Leslie 2003), organic milk certification (Kiesel and Villas-Boas 2007; Batte et al. 2007), wine quality (Hilger et al. 2011) and apparel (Nimon and Beghin 1999) estimate significant relationships between attributes covered by these labels and price premiums or market share gains.

  2. 2.

    As shown in a variety of settings, consumers do not always incorporate all available information (Ippolito and Mathios 1995; Mathios 2000). Teisl et al. (2002) is one of the few studies on the seafood industry using consumer purchase data to confirm that the dolphin-safe tuna label increased the market share of canned tuna. More recently, Shimshack et al. (2007) and Teisl et al. (2011), investigate the impact of seafood risk advisories for certain population groups.

  3. 3.

    Subsequent to our data collection, the yellow label’s definition was changed from “proceed with caution” to “good alternative.” We discuss this re-definition in our results and conclusion Sects. 5 and 6, respectively. Perhaps most importantly, we note that some consumers may have interpreted “proceed with caution” as a signal of increased health risk, rather than a signal about environmental sustainability.

  4. 4.

    FishWise is a sustainable seafood consultancy that promotes the health and recovery of ocean ecosystems through environmentally responsible practices. FishWise scores are assigned based on the Monterey Bay Aquarium methodology while taking into account additional third-party standards from organizations including the Marine Stewardship Council, the Aquaculture Stewardship Council, the Environmental Defense Fund, and various government agencies.

  5. 5.

    These data were previously analyzed in a reduced-form analysis by Hallstein and Villas-Boas (2013).

  6. 6.

    The data do not contain customer information.

  7. 7.

    We assume that the sustainability label system weakly increased consumers’ knowledge (information) about the environmental sustainability and healthiness of the available seafood products. Other possible sources of information include popular media, other sustainability labeling programs, and scientific literature. For example, starting in 1999, the Monterey Bay Aquarium's Seafood Watch program began distributing pocket guides about seafood sustainability to consumers. Beyond the labeling program, which is the focus of this study, we do not have information about the utilization or provision of additional information by consumers shopping at the Retailer.

  8. 8.

    Figures A1 and A2 in the Appendix present the share of seafood sales and units of seafood sold over time for all seafood products and stratified by green, yellow, and red ratings.

  9. 9.

    We define the following to be selective catch methods: midwater trawl, hand line, pole, troll, setline, bottom longline, traps, and salmon gear (Seafoodwatch 2014).

  10. 10.

    Recall that the descriptions for green labels (“best choice”) and red labels (“worst choice”) were much clearer in comparison.

  11. 11.

    We hypothesize that consumers may interpret full-price seafood as of higher quality than discounted seafood.

  12. 12.

    We estimate single-species regressions for other species as well. In all other cases, our results show no negative and significant impact on yellow labeled products.


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

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Corresponding author

Correspondence to James Hilger.



Fig. 1

Event study \( \varvec{\beta}_{{ - \varvec{t}}} \) and 95% confidence intervals, t = − 2, − 3, and − 4 weeks

Seafood Sales Shares and Units

The share of seafood sales and units of seafood sold over time for all seafood products and stratified by green, yellow, and red ratings are presented in Figure A1 and Figure A2.


Total Traffic and Total Seafood Sales

Table 9 reports results of two difference-in-differences specifications on the total store traffic at the store-week level and total seafood purchases at the store-week level. Estimation results indicate that total store traffic was constant when controlling for both store-level and time-periods (Column 1). However, results indicate that seafood sales decreased when controlling for both store-level and time period (Column 2). Taken together, these two results suggest that the total share of non-seafood purchases in the treatment store increased during the treatment period.

Table 9 Total traffic and total seafood sales store level difference-in-difference

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Hilger, J., Hallstein, E., Stevens, A.W. et al. Measuring Willingness to Pay for Environmental Attributes in Seafood. Environ Resource Econ 73, 307–332 (2019).

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