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A Meta-Analysis on Mislabeling and Price

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A Meta-Analysis on Mislabeling and Price Marine Policy 100 (2019) 258–264 Contents lists available at ScienceDirect Marine Policy journal homepage: www.elsevier.com/locate/marpol Exploring the causes of seafood fraud: A meta-analysis on mislabeling and T price ⁎ C. Josh Donlana,b, ,1, Gloria M. Luquec,1 a Advanced Conservation Strategies, Midway, UT 84049, USA b Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA c Advanced Conservation Strategies, Córdoba 140011, Spain ARTICLE INFO ABSTRACT Keywords: Seafood mislabeling is receiving increased attention by civil society, and programs and policies to address it are Seafood fraud being implemented widely. Yet, evidence for the causes of mislabeling are largely limited to anecdotes and Mislabeling untested hypotheses. Mislabeling is commonly assumed to be motivated by the desire to label a lesser value Meta-analysis product as a higher value one. Using price data from mislabeling studies, Δmislabel is estimated (i.e., the difference Incentive between the price of a labeled seafood product and its substitute when it was not mislabeled) and a meta-analysis Illegal seafood is conducted to evaluate the evidence for an overall mislabeling for profit driver for seafood fraud. Evidence is Tuna Salmon lacking; rather, Δmislabel is highly variable. Country nor location in the supply chain do not account for the observed heterogeneity. The Δmislabel of substitute species, however, provides insights. Some species, such a sturgeon caviar, Atlantic Salmon, and Yellowfin Tuna have a positive Δmislabel, and may have the sufficient characteristics to motivate mislabeling for profit. Atlantic Bluefin Tuna and Patagonian Toothfish have a negative Δmislabel, which could represent an incentive to mislabel in order to facilitate market access for illegally-landed seafood. Most species have price differentials close to zero—suggesting other incentives may be influencing seafood mislabeling. Less than 10% of studies report price information; doing so more often could provide insights into the motivations for fraud. The causes of mislabeling appear to be diverse and context dependent, as opposed to being driven primarily by one incentive. 1. Introduction illegal, unregulated and unreported (IUU) fishing. It is being rolled out in stages, with the first stage covering sixteen groups of products that With the advent of accessible tools for food forensics [1], seafood were deemed priorities [9]. Yet, the program is controversial due to the fraud is receiving increased attention by governments, academics, and potential costs and complexities involved in implementing a trace- civi society [2–4]. Over 100 studies investigating seafood mislabeling ability program. Some even claim that it could violate legal obligations have been conducted in dozens of countries. In sum, tens of thousands under the World Trade Organization [10]. Insights into why seafood of samples have been tested covering hundreds of species [5,6]. While mislabeling is occurring can help elucidate the incentives for seafood its potential impacts on policies, markets, and ecosystems are con- fraud, which can be used to inform policies and programs. cerning, the causes and consequences of seafood mislabeling remain Seafood mislabeling is commonly assumed to be primarily moti- elusive. The current evidence for the causes of mislabeling, for example, vated by the desire to label a lesser value product as a higher value one are largely limited to anecdotal observations and untested hypotheses. [4,6,11–13]. Economic theory predicts that information asymmetry in Understanding the causes of seafood mislabeling is a critical step to seafood products (i.e., sellers have more information about the true explore the consequences of seafood fraud, as well as to design solu- quality than buyers) can motivate mislabeling [12]. While intentional tions to reduce it. This is particularly important as existing national mislabeling of lower-value product for more expensive one has been policies are being revised and new ones implemented [7,8]. In the documented [14–16], how widespread this incentive is has not been United States, for example, a new seafood import monitoring program rigorously evaluated. Alternative drivers for seafood mislabeling are was recently implemented focused on reducing seafood fraud and plausible [6,17]. This includes more complex incentives that are ⁎ Correspondence to: Advanced Conservation Strategies, 2261 Success Trail, Midway, UT 84049, USA. E-mail address: [email protected] (C.J. Donlan). 1 Authors contributed equally. https://doi.org/10.1016/j.marpol.2018.11.022 Received 18 June 2018; Received in revised form 10 October 2018; Accepted 13 November 2018 Available online 11 December 2018 0308-597X/ © 2018 Elsevier Ltd. All rights reserved. C.J. Donlan, G.M. Luque Marine Policy 100 (2019) 258–264 ultimately connected to profit, such as regulation avoidance (e.g., tar- item as another to increase profit. If prevalent, one would expect a clear iffs) and market access. But, seafood mislabeling could also result from signal in the prices of mislabeled seafood (i.e., Δmislabel > 0). A meta- factors unrelated to profit, such as confusing and weak regulations on analysis requires a set of effect size estimates with their corresponding product labels, mixed fisheries of similar species, and informal supply sampling variance. In this case, the effect size estimates are for an in- chains [18,19]. Yet, information on causes and motivations for seafood dividual group (i.e., there is no control group): the standardized mean mislabeling are currently limited to observations on case-by-case basis. difference in price for each studymislabel (Δ = Plabel - Psubstitute), along This study evaluates the current evidence for a simple mislabeling for with the corresponding standard deviation and sample size. profit driver for seafood fraud: a lower-value product masquerading as a Seafood mislabeling studies are not exactly identical in their higher-value one. Using price data from mislabeling studies, differences methods nor the characteristics of the samples tested, and those dif- are explored in the labeled price of a seafood item and the estimated ferences may introduce variability (i.e., heterogeneity) among any true price of its real identity. This price differential provides some evidence effect in Δmislabel. Thus, a random-effects model is used to model that for the different incentives for seafood mislabeling. In this study,a heterogeneity [25]. The model starts with i = 1,…,k independent effect meta-analysis is conducted on mislabeling and price, and the results are size estimates, each estimating the true effect size. The model assumes discussed within the broader contexts of seafood fraud and designing that, cost-effective policies and programs to reduce it. yi =i + e i, (1) 2. Methods and materials where yi denotes the observed effect in the i-th study, i is the corre- sponding (unknown) true effect, ei is the sampling error, and ei N(0, v i ). A systematic literature review was conducted using the Web of The random-effects model is represented by Science to compile all published literature on seafood fraud up through i =µ + ui, (2) December 2017. A total of 331 publications were identified related to 2 seafood fraud (See Supplementary materials for details). A review was where ui N (0, ). The goal of the meta-analysis is to estimate µ, the 2 also conducted for reports and articles on mislabeling that did not un- average true effect, and , the amount of heterogeneity among the true dergo a formal peer-review process. An additional 69 publications were effects. A restricted maximum-likelihood estimator is used to estimate identified related to seafood mislabeling. Each publication was the residual heterogeneity ( ) [26]. A mixed-effects model is useful in screened for cost data on seafood mislabeling, which resulted in only 24 order to include a moderator that may account for at least part of the publications. For studies with primary data and using a seafood sample heterogeneity in the true effects [25]. In particular, to complement the tested for mislabeling as the replicate, the following information was random-effects model, a mixed-effects model is used to test ifthe compiled and coded when possible: 1) content of the label, 2) genus country or supply chain location where the seafood was sampled ex- reported, 3) species reported, 4) the product form (e.g., whole, filet), 4) plains any of the observed heterogeneity. the location in the supply chain (e.g., restaurant, grocery store), 5) Since the statistical models assume the effect sizes are normally country where the sample was collected, 6) year collected, and 7) the distributed, multiple tests were conducted to assess normality and the true identity (i.e., genus and species) of the sample (See Supplementary presence of outliers. First, Shapiro-Wilk's and Anderson-Darling nor- materials for details). Fishbase and Sealifebase were used as taxonomic mality tests were conducted. Second, model performance was evaluated authorities and for common names reported here [20,21]. with a suite of case deletion and residual diagnostics to identify any Of the limited mislabeling studies that reported information on studies that strongly deviate (i.e., outliers) [27]. Before and after re- prices, data were presented in several ways. Some studies reported the moving any potential outliers from the dataset, model assumptions price of each seafood sample, while others reported averages or ranges were also assessed with normal quantile plots [28]. Lastly, the effect for a particular seafood product. For each seafood sample, the differ- size was estimated using permutation tests as a complementary ap- ence between the price of the labeled item and its substitute when it proach to the maximum-likelihood estimator [29,30]. All analysis were was not mislabeled was estimated (Δmislabel = Plabel - Psubstitute). The conducted in the statistical language R and adopted an α-level of 0.05 price of a seafood sample was recorded as reported by the source [31]. The meta-analysis and associated analysis were conducted using (Plabel). In some cases, the cost of a substitute species when it was not the R package metafor [27].
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