Consumer preferences for sustainably sourced seafood: Implications for fisheries dynamics and management

Dube, Isha; Quaas, Martin; Sagebiel, Julian; Voss, Rudi Article — Published Version Consumer preferences for sustainably sourced seafood: Implications for fisheries dynamics and management American Journal of Agricultural Economics Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Dube, Isha; Quaas, Martin; Sagebiel, Julian; Voss, Rudi (2025) : Consumer preferences for sustainably sourced seafood: Implications for fisheries dynamics and management, American Journal of Agricultural Economics, ISSN 1467-8276, Wiley, Hoboken, NJ, Iss. Early View, pp. 1-23, https://doi.org/10.1111/ajae.12544 This Version is available at: https://hdl.handle.net/10419/318204 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ ARTICLE Consumer preferences for sustainably sourced seafood: Implications for fisheries dynamics and management Isha Dube 1 | Martin Quaas 2,3,4 | Julian Sagebiel 2,3 | Rudi Voss 2,5 1 Department of Business Administration, Economics and Law, Carl von Ossietzky University Oldenburg, Oldenburg, Germany 2 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany 3 Department of Economics, University of Leipzig, Leipzig, Germany 4 Kiel Institute for the World Economy, Kiel, Germany 5 Center for Ocean and Society, University of Kiel, Kiel, Germany Correspondence Martin Quaas, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. Email: [email protected] Abstract Many fish consumers reveal a preference for sustainably sourced seafood in their purchasing decisions. We propose a bioeconomic modeling approach and an empirical strategy, based on a discrete choice experiment, to quantify the resulting effects on fishery dynamics and to derive implications for efficient fishery management. We show that a “consumer stock effect”arises, which stabilizes a fishery under open access and which decreases catches under economically efficient management. We quantify these effects for the Western Baltic cod fishery. KEYWORDS bioeconomic model, discrete choice experiment, fisheries, renewable resource management, sustainability label JEL CLASSIFICATION Q11, Q22 1|INTRODUCTION Worldwide, fish and fishery products form an important source of protein and income for millions of people (FAO, 2024). However, increasing exploitation as well as environmental stressors pose serious threats to fish stocks, and the percentage of global stocks being classified as overused by the FAO has been increasing for decades (FAO, 2024). Fish consumers are aware of this and increasingly pay attention to the sustainability of the fisheries in their purchasing decisions (Asche & Bronnmann, 2017; Bronnmann et al., 2021; Bronnmann & Asche, 2017; Zheng et al., 2021). Accordingly, seafood labels such as the Marine Stewardship Council (MSC) label are gaining traction, with nearly 20% of global fish catches being MSC certified (MSC, 2024). Ecolabeled seafood can receive a Received: 12 December 2023 Accepted: 21 February 2025 DOI: 10.1111/ajae.12544 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). American Journal of Agricultural Economics published by Wiley Periodicals LLC on behalf of Agricultural & Applied Economics Association. Amer J Agr Econ. 2025;1–23. wileyonlinelibrary.com/journal/ajae 1 substantial price premium, showing that consumers are willing to pay more for fish from a sustainability-certified fishery (Asche et al., 2015; Asche & Bronnmann, 2017; Bronnmann et al., 2021; Hori et al., 2020). This higher willingness to pay is mostly motivated by a concern for stock status and environmental impact of fishing (Bronnmann et al., 2021). However, in the current markets, the potential of sustainable fish products is not fully exploited (Altintzoglou & Nøstvold, 2014; Brécard et al., 2009; Pieniak et al., 2013). According to bioeconomic theory, the fact that fishing costs decrease with fish stock size results in increases in the size of the fished population in steady state, both under open access and under economically optimal harvesting (Clark & Munro, 1975; Hannesson, 2007). This so called “stock effect”results in the conclusion that the “maximum economic yield”stock size, that is, the economically optimal stock size in the long run without discounting, is larger than the stock size that would generate the maximum sustainable yield (Clark, 1991; Clark & Munro, 1975; Grafton et al., 2007; Hannesson, 2007), which considers yield alone but disregards harvesting costs. Here, we discuss a new variant of stock effect, which arises as the value of fish increases with a larger (more sustainable) stock status. This is justified with the higher willingness to pay of consumers for fish from sustainable fisheries, leading to an upward shift of demand if the stock size is in a health shape. We call this effect the “consumer stock effect.” We develop and apply a bioeconomic model that seamlessly integrates the statistical analysis of stock assessment data and a demand model that is based on both time series of market price data and choice experiment data on preferences for different attributes of fish products. We assess the role of the consumer stock effect by contrasting results of model variants with and without considering the consumer stock effect, both in a setting of open access and under economically optimal fisheries management. We also quantify the resilience of the steady state in an open access setting by computing the characteristic time to approach the steady state (Pimm, 1984). As a case study, we quantify the model based on detailed data for the Western Baltic cod fishery. This stock is attracting high interest, in particular recently, as it is outside safe biological limits (ICES, 2022a,2022b; Möllmann et al., 2021; Voss et al., 2021). The bioeconomic model we develop for the Western Baltic cod fishery is based on Tahvonen et al. (2018). To be as close as possible to actual fisheries management, we use a single species age-structured fish population model, following the standard ICES (2022a,2022b) stock assessment, and discuss sustainability reference points based on the scientific advice from ICES (2022a,2022b). Currently, the Western Baltic cod fishery is best described as a restricted open access fishery: The fishery is subject to a number of input restrictions, including limited entry, gear restrictions, and seasonal closures. Yet, quotas have not been sufficiently restrictive in the past. For the German fleet, the actual catches have been considerably lower than the fishing quota for 9 out of the 10 years in the period 2012–2022 (ICES, 2022a,2022b). This indicates that it has not been profitable for the fishermen to fully exhaust the quota (Quaas & Skonhoft, 2022). One reason might be a consumer concern for the sustainability of marine fisheries, which is prevalent among German fish consumers (Asche & Bronnmann, 2017; Bronnmann et al., 2021; Bronnmann & Hoffmann, 2018). The reduced demand from consumers may have reduced the incentives to continue fishing on the already overfished stock. At the same time, this consumer concern for sustainability may provide an extra economic reason to rebuild the stock. The aim of this paper is to quantify these effects for both settings, (restricted) open access and optimal management, for a real-world fishery. For the case of the Western Baltic cod fishery, we find that the implications of the consumer stock effect are of large magnitude in the (restricted) open access fishery and would have significant implications for optimal fisheries management. We find that the stronger the consumer preferences for fish stock sustainability is, the lower is the characteristic time to approach the steady state, implying a higher resilience. Whereas the characteristic time with the consumer stock effect estimated from the data of the actual fishery is about 5 years, the hypothetical characteristic time without a consumer stock effect would be more than 25 years, that is, more than five times longer. However, we also find that the consumer concern for seafood sustainability is not sufficient to achieve an 2PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License efficient outcome of the fishery without proper regulation. Rather, the economically optimal management should take the consumer concern for stock status into account. At any given fish population size, the efficient catches with the consumer stock effect are lower than the actual catches under open access and also than the catches that would be efficient without a consumer stock effect. Whereas the efficient catches without a consumer stock effect would be close to maximum sustainable yield management, the efficient catches that take into account the consumer stock effect are less than half as large. These quantitative results are obtained for the special case of the Western Baltic cod fishery, and uncertainties in both stock assessment and empirical quantification of consumer preferences translate into uncertainties in the magnitude of results. Yet, at least for this fishery, our results suggest that fisheries management that adequately reflects consumer preferences for sustainably sourced seafood should be more conservative than current management. 2|RELATED LITERATURE We build on the extensive literature that studies how the management of living resources should take into account use and non-use values of the ecosystem. In this literature, the non-use values are often attached to stocks that are different from the harvested resource itself. Armstrong et al. (2017) include the value of habitat in a bioeconomic analysis of fishing with gears that are destructive versus fishing with gears that are nondestructive to cold-water corals. The stock with non-use benefits, in this case, are cold-water corals, whereas the harvested resource is the fish population. Using data from a choice experiment and a bioeconomic model for the Northeast Arctic cod fishery, they show that the non-use value of cold-water corals for the Norwegian general population strongly affects optimal fishing activities. Ansuategi et al. (2019) consider local communities fishing on a shrimp stock in Baja, México, and nature-based tourism, in particular whale-watching trios, as a nonextractive activity. They show that fishing activity moderately decreases with the stock size of the whale population. Similar in spirit to our paper but considering other types of natural resources are Manning et al. (2020) and Enriquez and Finnoff (2021). In both of these studies, it is the stock of the harvested resource that has a non-use value. Manning et al. (2020) use results from a dichotomous choice contingent valuation survey in an integrated assessment model of groundwater use in Kansas. They use this approach to estimate the value of a water right retirement program that aims at increasing the stock of groundwater. Enriquez and Finnoff (2021) develop a bioeconomic model for hunting and conservation of grizzly bears in the Greater Yellowstone Ecosystem, including non-use values of the grizzly bear population as well as damages from bear–human conflicts that increase with the grizzlybear population. Enriquez and Finnoff (2021) build on the broader literature on “multi-use”wildlife populations, which analyzes how to manage populations that are both a value and a nuisance on a more conceptual level (Rondeau, 2001), including African elephants (Horan & Bulte, 2004), moose in Norway (Skonhoft & Olaussen, 2005), and the red king crab in the Barents Sea (Skonhoft & Kourantidou, 2021). Most closely related to our paper are Bulte and Kooten (1999), Arnason (2008), and Kersulec et al. (2024), as they consider the management of a living marine resource, which at the same time has a consumptive value from harvesting and a non-use value attached to the stock. Bulte and Kooten (1999) integrate non-use benefits of preserving the stock of minke whales in a bioeconomic analysis of harvesting these whales for their consumption value. They find that including the nonuse value substantially increases the optimal steady-state whale population size. Bulte and Kooten (1999) consider the preservation value of whales as a pure public good, and accordingly the objective function is additively separable in the consumption benefit and non-use value of whales. In contrast, the consumer preferences for sustainably sourced seafood, considered here, is a private value of a more healthy stock size, which shifts the demand function for resource harvest up or down. Arnason (2008) includes “conservationists,”who only care about the stock status, as one stakeholder group in DUBE ET AL.3 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License the analysis of optimal fisheries management, and studies the efficiency of an individual transferable quota system in this setting. Here, we focus on consumers of the resource who have a preference for consuming fish from a sustainably managed stock. Kersulec et al. (2024) study how consumer preferences affect the sustainability of a coastal multispecies fishery in French Guiana. They consider the demand model proposed by Quaas and Requate (2013) and use it to derive conditions for biologically sustainable consumer preferences while at the same time maintaining viable economic profits. We differ from Kersulec et al. (2024) as we explicitly derive demand for fish from a discrete choice experiment, which takes into account stock status of the resource. In many studies that consider environmental preferences, the direct use value from harvesting and the non-use value from the ecosystem stock enter the societal objective linearly. This implies that under open access, when the non-use value is an externality in the decision making of resource harvesters, it has no effect on resource dynamics. In contrast, our focus is on the non-use value that directly interacts with the use value, as consumer willingness to pay for resource consumption increases with the resource stock size. This interaction between use value and non-use value has an effect on resource dynamics also under open access, as changes in the resource stock size affect the value of resource harvest. Under economically optimal management, the interaction between use value and non-use value has a nontrivial effect, as it affects not only the value of the stock but also demand for resource harvest. This is an effect on the demand system that comes in addition to the usual downward-sloping demand. A downward-sloping demand function means that consumers are willing to pay a relatively high price if fish is getting scarce. In an open-access fishery, this implies that incentives to catch remain relatively high if the stock is decreasing. This effect may be strong enough that the fishery becomes unstable at low stock sizes (Dao et al., 2023; Holden & McDonald-Madden, 2017; Quaas & Requate, 2013; Smith, 1969), and it also tends to decrease economically optimal harvest at high stock sizes (Zimmermann et al., 2011b). Our analysis takes this effect into account and therefore includes both effects: Demand is a decreasing function of the fish quantity available on the market and also an increasing function of the current size of the fish population size. Our study also builds on previous work that includes a positive effect of fish population size on seafood demand in bio-economic analysis. These studies come to similar conclusions as we do but for reasons other than a consumer concern for fish stock sustainability. Several studies include the effect that the quality of landed fish is increasing with the fish stock size, which increases the market price that consumers are willing to pay. One aspect is that larger fish of the same species get a higher market price (Quaas et al., 2013; Zimmermann & Heino, 2013). Zimmermann et al. (2011a) show that this effect reduces harvest rates and implies a larger optimal stock size. World Bank (2016) present a bio-economic model of the global marine fisheries, where the fish price is an increasing function of fish biomass. This is supposed to capture the effects that a larger global fish population biomass also means that landings increasingly consist of more valuable species and larger individual fish, which get a higher market price, as also discussed in Grafton et al. (2005) and Costello et al. (2016). These effects amplify the benefits of more effective fisheries management. We use exactly the same formulation of the demand model as World Bank (2016) but in a single-species age-structured population model, with the aim to capture a consumer concern for sustainably sourced seafood, not an increasing quality of the seafood product itself. Whereas this distinction does not matter for general theoretical results, it is important for the quantification of the effects, which is the main purpose of the present paper. 3|THEORY 3.1 |Model of seafood demand with consumers caring for stock status We consider a representative consumer making a choice over the quantity qof seafood consumption, which may depend on the stock size (or stock status) Band on a vector of other characteristics 4PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License of the fishery A, in addition to the fish price p. The consumer has a budget mavailable for the consumption of fish and the numeràire z, that is, a composite good of price normalized to one, so that m¼pqþz. We specify an iso-elastic inverse demand function PqðÞ¼aAχBσqη:ð1Þ In this equation, Bdenotes stock biomass, with σbeing the stock elasticity of demand; qdenotes the quantity of fish consumption, and accordingly, ηdenotes price flexibility. Given η> 0, the demand function (1) has the usual downward-sloping property. We further assume η< 1, which means that expenditures increase with the consumed quantity. Finally, aAχ> 0 is a demand shifter that captures potentially observable as well as unobserved effects on demand, including other fishery-related variables Athe consumer may care about, which enter with an elasticity χ. Whereas the empirical analysis requires us to specify the demand function, such a specification naturally comes with restrictions. One is that the iso-elastic form can only be regarded as an approximation over a limited interval of prices and quantities of fish consumption. Second, the iso-elastic relationship of the demand shifter on the variables of interest may also become problematic if these variables exceed certain limits. Third, a multiplicative demand shifter implies a particular complementarity between stock status and consumed quantity, such that the effect of the demand shift—in absolute quantities—is particularly large as the consumed quantity is small. The introduction of the demand shifter allows capturing demand effects of credence attributes of the fish such as the sustainability of the fishery. In our case, we use the demand shifter to capture preferences for a healthy stock. As found for example in Bronnmann et al. (2021), demand shifts upward if the product comes from a fishery with a healthy stock (Figure 1, using data for the case of Western Baltic cod). The demand function (1) implies an indirect utility function, which can be derived using Roy’s Identity under the assumption of constant income elasticity. Specifically, we integrate (1) to obtain (see online Appendix S1) Vp,A,BðÞ¼mþ1 1ηaAχBσ ðÞ 1 ηp11 η:ð2Þ overfished stock slightly overfished not overfished q uantit y [1000 tons] price [EUR/kg] 40 35 30 25 20 15 10 5 0 5 4 3 2 1 0 FIGURE 1 Demand function for cod for three levels of the stock status: overfished (Blim), slightly overfished (Bpa), and not overfished (Bmsy)–of Western Baltic cod. DUBE ET AL.5 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License In this equation, mis a constant of integration that captures all effects on utility not directly interacting with fish consumption. This includes income, as we have to ignore income effects in the model, due to lack of data. 3.2 |Fishery economic behavior Fishers are assumed to maximize profits, taking as given market prices, fishing technology, and regulatory constraints. Using Htto denote total catches and Btto denote fish population biomass in year t, we model the fishing cost function as CH t,Bt ðÞ¼ c 1þε H1þε t Bt :ð3Þ Here, c> 0 is a cost parameter, which possibly includes the costs of technical constraints, such as mesh size restrictions or seasonal closures, and 1þε> 1 is the elasticity of the fishing cost function with respect to catch. This captures an effect that unit fishing cost increase with harvest Htdue to congestion externalities (Smith, 1969). The cost function specified in Equation (3) also features the classical stock effect that fishing costs decrease with increasing stock size Bt(Clark & Munro, 1975; Hannesson, 2007), which is a sensible assumption for a search fishery, as the Western Baltic cod fishery is. For the purposes of model identification and having lack of more precise information on cost elasticity with respect to stock size, we assume that the unit fishing costs are inversely proportional to fish population biomass. We note that this is a restrictive assumption, which possibly overestimates the classical cost-based stock effect, as this elasticity may differ from (minus) one (Steinshamn, 2011), and also that we ignore any (quasifixed) costs that are independent of the harvest and biomass. 3.3 |Fish population dynamics We consider an age-structured population model with Sage classes, using the notation of Tahvonen et al. (2018), where xst denotes the stock numbers of age sin year t, and αsthe survival rate from age sto age sþ1. Recruitment at age 1 is given by the stock-recruitment function φx0t ðÞ, which models recruitment as a function of spawning stock biomass x0t. Spawning stock biomass is defined as x0t¼X S s¼1 wsγsxst:ð4Þ In this equation, wsis the average weight of an individual of age sand γsis the fraction of fish of age sthat is mature. Using hst to denote the harvest of fish aged sin year t, the population dynamics can be summarized as x1,tþ1¼φx0t ðÞ ð5aÞ xsþ1,tþ1¼αsxst hst fors¼1,…,S1ð5bÞ xS,tþ1¼αS1xS1,tþαSxSt hSt:ð5cÞ 6PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License We use Sto denote the oldest age class. For the case of Western Baltic cod we specify S¼7 years, following the ICES (2022a,2022b) stock assessment. Following Tahvonen et al. (2018) and Stoeven et al. (2021), the number of fish that is harvested from age sis given by hst ¼qs Ht Be t xst:ð6Þ The constants qsdenote age-specific catchability coefficients. These constants depend on mesh size, which we consider to be fixed. Moreover, Ht≔PS s¼1wshst is aggregate catch, and Be t≔PS s¼1qswsxst is the “efficient biomass”(Tahvonen et al., 2018; Zimmermann & Jørgensen, 2015), such that Ht=Be t can be interpreted as the exploitation rate of the fishery in year t. 3.4 |Fishery dynamics under open access One of the two management scenarios we consider is (restricted) open access: the fishery is subject to technical regulations such as gear restrictions and seasonal closures but without an effective quota management (Quaas & Skonhoft, 2022; Reimer & Wilen, 2013). The technical regulations effectively increase (marginal) fishing costs, and only this is restricting catches compared to pure open access. We refer to this situation as restricted open access, or simply open access. Under restricted open access, the harvested quantity is determined by the zero-profit condition that the price equals fishing cost (Quaas & Skonhoft, 2022). Using the demand function (1) and the cost function (3), this condition becomes aAχBσ tHη t¼pt¼cB1 tHε tð7Þ From this we obtain the fish catch under open access as a function of the current biomass, Ht¼aAχ c  1 εþη B 1þσ εþη t:ð8Þ Using (8) and (6)in(5a)–(5c), we can write the dynamics of the fish population harvested under open access in matrix form x1,tþ1 . . . xsþ1,tþ1 . . . xS,tþ1 0 B B B B B B B B @ 1 C C C C C C C C A ¼ φx0t ðÞ0  0 . . ... ... ... .. . . 0 αs 0 . . ... ... ... .. . . 0  αS1αS 0 B B B B B B B B @ 1 C C C C C C C C A 1 . . . xst . . . xSt 0 B B B B B B B @ 1 C C C C C C C A  q1aAχ c  1 εþηB 1þσ εþη1 tx1t . . . qsaAχ c  1 εþηB 1þσ εþη1 txst . . . qSaAχ c  1 εþηB 1þσ εþη1 txSt 0 B B B B B B B B B B @ 1 C C C C C C C C C C A :ð9Þ Here, x0tis the spawning stock biomass (SSB; Equation 4), and Btis total stock biomass. The dynamics of the fishery under (restricted) open access can be simulated, starting from the current state described by the age-specific stock numbers in the transition toward a steady state. The eigenvalues associated with the Jacobian of (9) evaluated at steady state provide information about the stability properties of the steady state. If the steady state is asymptotically stable, the characteristic time at which the fishery approaches the steady state is given by the leading eigenvalue (Pimm, 1984). DUBE ET AL.7 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3.5 |Fishery dynamics under economically optimal management The second of the two management scenarios we consider is that of a fishery that is managed such as to maximize the present value of economic surplus, which is the sum of consumer and producer surpluses (Anderson, 1980; Copes, 1972; Jensen et al., 2019; Kroetz et al., 2022; Quaas et al., 2018). We refer to this as economically optimal or efficient management. Using δto denote the social discount rate, the economically optimal catches Htare determined by the solution to the dynamic optimization problem max Ht fg X ∞ t¼0 1 1þδðÞ t aAχ 1ηBσ tH1η tc 1þεB1 tH1þε t  ð10Þ subject to fish population dynamics (5a)–(5c), given initial fish population size, and non-negativity constraints Ht≥0 and xst ≥0, s¼1,…,S,t¼0,…. Consumer surplus is derived from the demand function (1) or equivalently from the indirect utility function (2); producer surplus from the cost function (3). As expenditures for the consumers equal revenues for the fishers, they drop out from the welfare function. As the price of fish depends on harvest and biomass, which both are controlled in the optimal fishery, the objective entangles the positive effect of increasing catch and stock size for consumers and for fishers, who also benefit from higher prices. We quantify the model parameters empirically using data for the Western Baltic Sea and solve the optimization problem (10) numerically. The time horizon is set to an arbitrary value long enough that a steady state is reached, and results are presented only for the period of the transition toward the steady state, which is after about 30 years. The numerical optimization is performed using the state-of-the art interior point algorithm implemented in Knitro (version 14.0) with AMPL (Byrd et al., 2006). Programming codes are provided in the online Appendix S1. 4|DATA AND METHODS 4.1 |Population dynamics of Western Baltic cod stock The cod population in the Western Baltic Sea has been subject to overfishing for many years and recently has been assessed as ecologically collapsed (Möllmann et al., 2021). According to the stock assessment by the International Council for the Exploration of the Sea (ICES), the stock has been for several years below the spawner biomass Blim where recruitment starts to be impaired (ICES, 2022a, 2022b). We quantify the parameters of the age-structured fish population model (5a)–(5c) based on the data provided by the ICES (2022a,2022b) stock assessment report. The age-specific survival rates are computed from the age-specific mortality rates Ms, which are given in ICES (2022a,2022b), as α¼exp Ms ðÞ. Weights at age ws, which are used to compute the biomass, and the age specific fractions of mature fish γsused to compute spawning stock biomass (4), are directly given in ICES (2022a,2022b). Age-specific catchabilities qsare derived from age-specific fishing mortalities estimated by ICES (2022a,2022b), normalizing the fishing mortality for the largest fish to one. The specifications of these parameters can be found in the programming codes in the online Appendix S1. For the stock-recruitment model, we follow ICES and assume that recruitment monotonically increases with spawning stock biomass if it is below Blim . In line with ICES (2022a,2022b), Blim is determined as the average of lowest SSB in years with above average recruitment (1990, 1991, 1993, 2016). The corresponding estimate for the Western Baltic cod fishery is Blim ¼15,067 tons. According to the model used by ICES (2022a,2022b), recruitment is constant above the limiting 8PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Contrasting fishery dynamics with and without consumer preferences for stock sustainability in open access, we first contrast the fishery dynamics of the Western Baltic cod fishery for the actual management regime, which is best characterized as restricted open access (Quaas & Skonhoft, 2022), for the actual demand function and for a hypothetical demand function without a consumer preferences for sustainably sourced seafood. The results are shown as a phase diagram (Figure 2), plotting catches and biological growth as functions of spawning stock biomass. The green curve shows the equilibrium yield of the fishery. This curve is not symmetric as it would be in a standard logistic growth equation, but rather, it shows a typical skewness with a slow decrease of equilibrium yield at high stock sizes. The dots in Figure 2show data from ICES (2022a,2022b) stock assessment. The red curve shows the catch under restricted open access as simulated from the full bioeconomic model, as summarized in Equation (9). As the model is estimated from the data under the assumption of restricted open access, it is no surprise that the model closely follows the data. The blue curve shows the catch under restricted open access in the hypothetical situation where consumer demand would not change with fish stock status. The demand shifter for this model variant is calibrated such that the steady state is the same as for the full model. The blue curve that shows the catch without consumer preferences for sustainability has a strikingly different shape from the model with the feedback from stock status to demand. In particular, catches would be much lower at high stock sizes and much higher at small stock sizes, as prices would be rather high. The overall result is that the fishery would have been much less resilient if the effect of consumers decreasing demand in response to declining fish population was not present. To more rigorously explore this effect of consumer preferences for seafood sustainability in the restricted open-access fishery, we consider the dynamics as described by (9) in the neighborhood of the steady state. The steady state is asymptotically stable if all eigenvalues of the Jacobian have real parts that are less than one in absolute value. In particular, stability requires that the largest of the negative eigenvalues (i.e., the smallest in absolute value) is still smaller than one (in absolute terms). Pimm (1984) proposes to measure the resilience by this largest eigenvalue. The smaller it is, the more resilient is the steady state of the fishery under restricted open access. Somewhat more intuitive is to compute, from the largest eigenvalue, the characteristic time at which the fishery asymptotically approaches the steady state. The larger the characteristic time, the less stable the steady state is. We measure the consumer preferences for seafood sustainability by the elasticity σat which demand increases with stock size. Empirically it is σ¼0:45 for cod. We ask how the stability of the steady state, as measured by the characteristic time, changes with σ, keeping the steady-state fish population size constant. The results are shown in Figure 5. We find that the stronger the consumer preferences for a healthy stock status, that is, the larger the stock elasticity of demand, σ, the faster is the characteristic time at which the fishery approaches the steady state. In particular contrasting the actual fishery with σ¼0:45 to a fishery without the consumer preferences for sustainable sourced seafood, σ¼0, the characteristic time to approach the steady state would be more than six times longer, that is, more than 30 years instead of 5 years in the actual fishery. This suggests that consumer preferences for a healthy stock status play an important role in stabilizing the fishery under restricted open access. Contrasting fishery dynamics with and without consumer preferences for stock sustainability under optimal fishery management, we next contrast the fishery dynamics of the Western Baltic cod fishery for optimal fishery management. Optimal fishery management, thereby, is defined as fishing that maximizes the net present value of economic surplus from the fishery, Equation (10). We use a social discount rate of 2%per year. Again, we summarize the results in a phase diagram, shown in Figure 6, plotting catches and fish population growth as functions of spawning stock biomass. As in Figure 2, the green curve shows the equilibrium yield of the fishery also in Figure 5but now over a larger range of stock sizes. This shows that the unfished biomass for the Western Baltic cod population is estimated to be 270,000 tons. The dots again show the data from ICES (2022a, DUBE ET AL.15 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2022b) stock assessment, and the red curve shows the catch under restricted open access, as simulated from the full bioeconomic model. The three other curves show results of dynamic optimization starting from the actual fish population in 2021 for three scenarios. The full model includes both a cost-based stock effect—as fishing Characteristic time to approach steady state Consumer preference for seafood sustainability, σ slower approach less resilient faster approach more resilient Characteristic time [years] 0.5.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 30 25 20 15 10 5 0 FIGURE 5 Resilience of the fishery in under restricted open access, as measured by the characteristic time at which the fishery approaches the steady state (Pimm, 1984). optimal catch, full model no consumer stock effect no consumer or cost stock effects catch under restricted open access equilibrium yield ICES stock assessment s p awner biomass [1000 tons] catch [1000 tons] 250 200 150 100 50 0 35 30 25 20 15 10 5 0 FIGURE 6 Phase diagram summarizing the dynamics of the Western Baltic cod fishery under restricted open access and under economically optimal management. The green curve shows the equilibrium yield of the fishery derived from the agestructured fish population model with fixed age-specific catchabilities. The dots show data from ICES (2022a,2022b) stock assessment. The red curve shows the catch under restricted open access as simulated from the full bioeconomic model. The other curves show the outcome of dynamic optimization starting from the actual fish population in 2021 for three scenarios, namely the full model (black curve), only cost-based stock effect (i.e., setting σ¼0, blue curve), and no stock effect (i.e., setting σ¼0 and assuming a constant stock biomass in the cost function 3, yellow curve). 16 PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License costs (3) decrease with stock size—and the consumer stock effect. This is contrasted with a model where we hypothetically switch off the consumer stock effect, by setting σ¼0, and a third one where we also switch off the usual cost-based stock effect, additionally replacing Btin the cost function (3) by a constant. In this latter model, the steady state is close to the maximum sustainable yield, as could be expected for the relatively low social discount rate of 2% per year. The cost-based stock effect shifts the steady state to a stock size larger than the one that would deliver the maximum sustainable yield. This reduces the fishing costs but at the expense of a slightly reduced equilibrium yield. This model without consumer stock effect closely resembles the standard bioeconomic model where the stock effect comes from harvesting costs only (Clark & Munro, 1975; Grafton et al., 2007). The full model that also includes the consumer stock effect leads to a harvest that is much lower, at any given stock size, than optimal harvesting in the standard model that does not exhibit a consumer stock effect. The reason is that a higher stock size would strongly increase the fish price and thus the economic benefit derived from the fishery. This warrants a strong sacrifice of yield, because catch from the more sustainable fishery is much more valuable for the consumers. To further analyze the dynamics of the fishery under economically optimal management in the different scenarios, Figure 7shows the development of spawning stock biomass (panel a), Simpson diversity of the fish population age structure (panel b), cod catch (panel c), and economic surplus (panel d) as functions of time. The full model results in a faster and more pronounced rebuilding of the stock (Figure 7a). Including the consumer stock effect results in lower catches over the complete time path as compared to ignoring this effect (Figure 7c). Economic surplus shows the interesting pattern that it is lower in the full model for the first three years, as rebuilding of the stock has priority in the beginning of the simulation (Figure 7d). Starting from 2025 onward, the positive outcomes year spawner biomass [1000 tons] (a) restricted open access no stock effects no consumer stock effect full model 54020502 2040 2035 2030 2025 2020 180 160 140 120 100 80 60 40 20 0 year Simpson biodiversity (b) restricted open access no stock effects no consumer stock effect full model 54020502 2040 2035 2030 2025 2020 6 5 4 3 2 1 0 y ear catch [1000 tons] (c) restricted open access no stock effects no consumer stock effect full model 54020502 2040 2035 2030 2025 2020 30 25 20 15 10 5 0 y ear economic surplus [million euros] (d) restricted open access no stock effects no consumer stock effect full model 54020502 2040 2035 2030 2025 2020 300 250 200 150 100 50 0 FIGURE 7 Time path of spawning stock biomass (a), Simpson biodiversity index (b), catch (c), and economic surplus (d) for different model configurations. DUBE ET AL.17 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License in terms of stock size, economic surplus as well as biodiversity (Figure 7c) start to materialize. Under full optimization, economic surplus is lower than in any of the other scenarios for a stock rebuilding phase of approximately 3 to 4 years, and higher only thereafter. Overcoming such a transition phase may be a challenge for fisheries policy. Yet, in present value terms, welfare is more than doubled if management would follow the fully optimal path. 5.3 |Effects on the age structure of the fish population We conclude the analysis by studying the effect of the different model scenarios on the age structure of the fish population. Figure 7shows that, after a transition period of about 5 years, the age structure of the fish population under economically optimal management with consumer preferences for stock sustainability is substantially more diverse than in the other scenarios. This effect becomes particularly clearly visible in steady state. The results for the steady state are shown in Figure 8. The restricted open-access fishery leads to a strongly truncated age structure, dominated by young and very small fish (Figure 8, panel a). Such a population structure is rather susceptible to environmental fluctuations (Barneche et al., 2018) and possibly detrimental effects of climate change (Möllmann et al., 2021). Optimal fishing would lead to a more balanced age structure, even when ignoring consumer preferences for stock sustainability (Figure 8, panel b). Including that preference would lead to an age structure of the fish population where the largest and oldest age class contributes a substantial fraction to the overall stock (Figure 8, panel c). 5.4 |Sensitivity analysis As the main contribution of the present paper is to quantify the magnitude of the consumer stock effect for fisheries outcomes, it is important to get an impression of the uncertainty of the quantitative results. One relevant uncertainty concerns biological fish population dynamics, which arises in particular due to the effects of climate change (Möllmann et al., 2021; Voss et al., 2019). This uncertainty has been studied elsewhere and is not of particular interest for the research question of this paper. We thus rather focus on the uncertainty in the economic part of the model. Specifically, the aim is to assess the sensitivity of results with respect to the elasticities of the demand and cost function that are related to the consumer stock effect, and the ordinary stock effect on fishing costs. Thus, we are interested in the uncertainty of the stock elasticity of demand, σ; the price flexibility, η; and the elasticity of marginal harvesting costs, ε. To assess the parameter uncertainty with respect to these elasticities, we present results from a Monte Carlo sensitivity analysis, based on 1000 randomly drawn parameter sets from normal distributions with means given by the restricted open access a g e[ y ears] number of fish [millions] (a) 12345678 12345678 12345678 45 40 35 30 25 20 15 10 5 0 no consumer stock effect a g e[ y ears] (b) 45 40 35 30 25 20 15 10 5 0 full model a g e[ y ears] (c) 45 40 35 30 25 20 15 10 5 0 FIGURE 8 Age structure of the steady-state fish population in three model scenarios. 18 PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License estimates reported in Tables 2and 3, and standard deviations given by the respective standard errors. For each of these parameter sets, we computed the optimal steady state. Figure 9shows the histogram (with 10 bins) of the resulting values for the optimal spawner biomass. This analysis reveals a considerable uncertainty of results: The standard deviation is 33,400 tons, about 19% of the optimal steady state stock size for the standard parameter set, which is 174,400 tons. Thereby, the distribution of possible optimal steady state stock sizes is skewed: The difference between the upper bound of the confidence interval (about 256,000 tons) to the mean result is larger than the difference between the mean result and the lower bound of the confidence interval (about 112,000 tons). Another relevant parameter is the discount rate, which we have set to δ¼2%per year in the reference parameter set. Varying the discount rate, we find an almost linear decline of the optimal steady state stock size with the discount rate: The optimal steady state spawning stock is 182,000 tons for an interest rate of zero and 150,000 tons for an interest rate of 10% per year. 6|DISCUSSION AND CONCLUSION We have developed a model that seamlessly integrates consumer preferences for stock sustainability estimated from a choice experiment with an empirical age-structured bioeconomic fishery model. We have applied this model to the case of the Western Baltic cod fishery and derived insights into how consumer preferences for sustainably sourced seafood changes fishery dynamics under (restricted) open access and under economically optimal management. We found that a “consumer stock effect”arises, which stabilizes a fishery under open access and which decreases catches under economically efficient management. For the case of the Western Baltic cod fishery, and considering the preferences of German fish consumers, we find that these effects are of large magnitude. Switching off the consumer preferences for stock sustainability, the characteristic time to approach the restricted open access steady state would be more than six times longer than for the actual fishery where consumers care about the sustainability of the stock that provides the fish. Switching off the effect that the price would decrease when the stock is overfished would lead to much higher catches at low stock sizes, possibly leading to a fast collapse of the resource stock. We conclude that the consumer preference for sustainably sourced seafood has the important effect that it enhances the resilience of the poorly managed fishery. o p timal stead y state s p awner biomass [1000 tons] relative frequency 250 200 150 100 50 0 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 FIGURE 9 Histogram showing the results of a Monte-Carlo sensitivity analysis with respect to parameter uncertainty in the elasticities relevant for the stock effect: the stock elasticity of demand, price flexibility, and elasticity of marginal harvesting cost. The graph shows the relative frequency of spawner biomass in optimal steady state in 10 bins. The result for the standard parameter set is an optimal steady state stock size of 174,400 tons, shown as dashed line. DUBE ET AL.19 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Considering the case of economically optimal management, we found that the consumer stock effect strongly reduces optimal catches to less than half the amount that a standard model without consumer stock effect would imply for the particular fishery studied here. Accordingly, the optimal steady state for the Western Baltic cod fishery is much higher than the stock that would deliver the maximum sustainable yield. This would also benefit biodiversity, as it would lead to a more favorable age structure of the fish population, with older individuals more abundant than at present. This, in turn, could also contribute to the resilience of the stock against environmental fluctuations (Barneche et al., 2018). We conclude that economically optimal fisheries management, which adequately takes into account consumer preferences for sustainably sourced seafood, should be more conservative than implied by purely biological models. In case of the Western Baltic cod fishery, management is moving toward ecosystem-based fisheries management (EBFM), and scientific advice increasingly takes into account economic considerations. Our study suggests that this should also consider more thoroughly the consumer preferences for sustainably sourced fish products. It also contributes to the evidence that management of the Western Baltic should set total allowable catches at much more conservative levels than previously and that the key challenge remains to reduce the fishing mortality to sustainable levels, which would eventually allow for a rebuilding of the stock. According to our model, the transition dynamics take 3 to 4 years until economic surplus, including the consumer stock effect, outperforms the alternatives. During this period with low catches and profits, bridging solutions for the fishery need to be found. The demand function in our model has the usual downward-sloping property: The price that consumers are willing to pay for a kilogram of fish is decreasing with the overall quantity on the market. Dao et al. (2023) show that this effect tends to go in the opposite direction as the “consumer stock effect,”which is the focus of the present paper: Generally, catches increase with stock size, but this effect is attenuated if the price flexibility is high. Accordingly, a high price flexibility decreases the resilience of the fishery under open access (Dao et al., 2023). We would expect price flexibility to be high if the fish is primarily sold on a local market or if few substitutes are available for the fish under consideration. In such situations, the consideration of the consumer stock effect may be even more important. Naturally, our analysis comes with a number of limitations. One is that the discrete choice experiment is a stated preference method, which always comes with the question about the external validity of results. We are confident that consumers actually do care for the status of the fish stock, as this is confirmed by revealed preference studies (Asche et al., 2015). The exact magnitude of the effect may be overor underestimated, though. Moreover, our model uses a particular specification of the demand function, in this case an iso-elastic specification. This means that we assume consumers are always willing to pay more if the stock is higher—even if the stock is well within safe biological limits. This means that the quantitative results, especially on optimal management at larger stock sizes, should not be taken too literally. Also, we have ignored that consumers have a preference for larger fish (Quaas et al., 2013; Zimmermann & Heino, 2013), which may have similar effects as the consumer preferences for stock sustainability (Zimmermann et al., 2011a). A similar uncertainty applies to the biological part of the model. Climate change is imposing a serious threat to the cod populations in the Baltic Sea. Optimal management would have to respond to climate change (Voss et al., 2019; Voss et al., 2021). Although this does not qualitatively change our conclusions about the effect of a consumer preferences for stock sustainability, the quantitative results for the Western Baltic cod fishery will likely have to be adjusted in the future due to climate change effects. Also in terms of consumer preferences, the Western Baltic cod fishery has some special characteristics: German households are perhaps more environmentally conscious than others. Therefore, the quantitative results might not be representative for other fisheries. Yet, a recent review found a willingness to pay for sustainable food also for Africa, America, Asia, and Europe outside Germany (Cecchini et al., 2018), indicating that similar effects can be expected for other fisheries as well. 20 PREFERENCES FOR SUSTAINABLY SOURCED SEAFOOD 14678276, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ajae.12544, Wiley Online Library on [23/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License In sum, we conclude that fishery management should take the consumer preferences for sustainably sourced seafood seriously and accordingly set fishing quotas more conservatively to generate the extra value associated with sustainably sourced seafood. ACKNOWLEDGMENTS We are grateful for fruitful discussions at the 7th Workshop on Age-structured Models in Natural Resource Economics, Würzburg, Germany, and the XIV annual BIOECON conference, Santiago de Compostela, Spain. The study was funded by the German Federal Ministry of Education and Research (BMBF) under grant numbers 01UT1410, 01LC1826A and 01LC2326A. Open Access funding enabled and organized by Projekt DEAL. ORCID Martin Quaas https://orcid.org/0000-0003-0812-8829 REFERENCES Altintzoglou, T., and B. H. Nøstvold. 2014. “Labelling Fish Products to Fulfil Norwegian consumers’Needs for Information.” British Food Journal 116: 1909–20. Anderson, L. G. 1980. “Necessary Components of Economic Surplus in Fisheries Economics.”Canadian Journal of Fisheries and Aquatic Sciences 37: 858–870. Ansuategi, A., D. Knowler, T. Schwoerer, and S. 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