# Trade Agreements and Sustainable Fisheries

Basak Bayramoglu, Estelle Gozlan, Clément Nedoncelle, Thibaut Tarabbia

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Basak Bayramoglu, Estelle Gozlan, Clément Nedoncelle, Thibaut Tarabbia. Trade Agreements and Sustainable Fisheries. 2023. hal-04101044v2

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# Trade Agreements and Sustainable Fisheries

Basak Bayramoglu, Estelle Gozlan, Clément Nedoncelle, Thibaut Tarabbia

► To cite this version:

Basak Bayramoglu, Estelle Gozlan, Clément Nedoncelle, Thibaut Tarabbia. Trade Agreements and Sustainable Fisheries. 2025. hal-04101044v5

HAL Id: hal-04101044

https://hal.inrae.fr/hal-04101044v5

Preprint submitted on 27 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Trade Agreements and Sustainable Fisheries

Basak Bayramoglu*, Estelle Gozlan*, Clément Nedoncelle*, Thibaut Tarabbia†

*Université Paris-Saclay, INRAE, AgroParisTech, Paris-Saclay Applied Economics, 91120, Palaiseau, France.
†ENPC, France.

*This version: November 2023.*

**Abstract:**

This study examines the impact of trade agreements and their specific provisions on the decline of marine fisheries resources. Using global data on the status of fish stocks and a comprehensive dataset of environmental provisions from trade agreements signed between 1947 and 2018, the impact of signing a free trade agreement and of the presence of fishery-related provisions on the status of fish stocks is estimated. To address potential endogeneity problems associated with fisheries-related provisions, we use a difference-in-differences (DID) propensity score matching method. Our results show that while trade agreements tend to have a negative impact on the status of fish stocks, the inclusion of fisheries-related provisions offsets this negative impact among signatory countries. However, our results indicate that these provisions do not encourage the adoption of better resource management practices but rather tend to reduce trade opportunities.

**Keywords:** Trade agreements, environmental provisions, natural resources, fisheries.

**JEL codes:** F18, Q22.
# Trade Agreements and Sustainable Fisheries

Basak Bayramoglu<fnref n="1" />, Estelle Gozlan<fnref n="1" />, Clément Nedoncelle<fnref n="2" />, Thibaut Tarabbia<fnref n="3" />

**Abstract**

This study examines the impact of trade agreements and their specific provisions on the sustainability of marine fisheries resources. Using global data on the Mean Trophic Level (MTL) between 1950 and 2018 and a comprehensive dataset of environmental provisions from trade agreements signed between 1947 and 2018, we estimate the impact on the MTL of signing (i) a free trade agreement and (ii) a free trade agreement including fishery-related provisions. To address potential endogeneity problems associated with fisheries-related provisions, we use a difference-in-differences (DID) propensity score matching method. Our results show that while trade agreements tend to negatively impact the MTL, including fisheries-related provisions offsets this negative impact among signatory countries. By examining the potential mechanisms underlying this result, we are able to temper the optimistic findings in the existing literature on the beneficial environmental outcomes of environmental provisions. Our findings suggest that these provisions do not foster the adoption of more effective resource management practices. Instead, they appear to reduce trade opportunities, which is contrary to the objective of trade creation in trade agreements.

**Keywords:** Trade agreements, environmental provisions, fisheries, difference-in-differences method, propensity score matching.

**JEL codes:** F18, Q22.

This version: Monday 27th October, 2025 at 21:27 GMT

**Funding:** none.

**Declaration of generative AI and AI-assisted technologies in the writing process:** During the preparation of this work, the author(s) used ChatGPT to improve English clarity and to assist with the initial classification of provisions by function. The function classification was reviewed by the research team to ensure accuracy and consistency. No other classifications or analyses in the paper relied on AI tools. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the publication.

<footnote n="1">¹Université Paris-Saclay, INRAE, AgroParisTech, Paris-Saclay Applied Economics, 91120 Palaiseau, France</footnote>

<footnote n="2">²Université de Lorraine, AgroParisTech-INRAE, BETA, F-54000 Nancy, France</footnote>

<footnote n="3">³Paris School of Economics, France</footnote>
# 1. Introduction

Fisheries are increasingly being recognized for their essential contribution to global food security and nutrition in the twenty-first century (FAO (2022)). However, marine fishery resources are declining. In 2017, 94% of the world's marine fish stocks were maximally exploited or overfished, and the proportion of fish stocks that remained within biologically sustainable levels declined from 90% in 1974 to 64.6% in 2019 (FAO (2020)). This decline threatens the equitable and sustainable food security of a growing world population. Moreover, it has implications beyond collapsed populations, for marine ecosystems and biodiversity (Pauly et al. (1998)).

Many policies have been implemented to mitigate the decline of fishery resources. Among the available instruments, trade policies (Fugazza and Ok (2019); Costello et al. (2021)) and environmental provisions contained in trade agreements could be credible complementary tools for addressing this nontrade issue.<fnref n="1" /> Indeed, because fishery products are largely traded (Asche et al. (2015)), trade policy exerts a wide influence on the sector (FAO (2020)). In addition, trade agreements contain an increasing number of provisions addressing a wide range of nontrade issues (Mattoo, Rocha, and Ruta (2020)) including environmental issues. The purpose of environmental provisions is usually the promotion of environmental cooperation and the provision of a level-playing field (Francois et al. (2022)). In our context, fishery-related provisions aim to mitigate the possible negative impacts of trade liberalization on fishery resources and marine ecosystems.

In some instances, environmental provisions have been shown to be effective in achieving their stated objectives. Provisions tend to mitigate the negative effects of trade liberalization on greenhouse gas emissions (Baghdadi, Martinez-Zarzoso, and Zitouna (2013); Apergis and Payne (2020)), air pollution (Zhou, Tian, and Zhou (2017); Martínez-Zarzoso and Oueslati (2018); Brandi et al. (2020)), and

<footnote n="1">¹ These include quantity-based policies -- such as marine protected areas (Edgar et al. (2014)), fisheries management practices and quotas (Costello, Gaines, and Lynham (2008)), seasonal closures (Bostedt et al. (2020)) -- and price-based policies, such as fishery taxes (Clark (2006)) and subsidies (Bayramoglu, Copeland, and Jacques (2018); Costello et al. (2021)).</footnote>
1. Introduction

Fisheries are increasingly being recognized for their essential contribution to global food security and nutrition in the twenty-first century (Béné et al., 2016; Golden et al., 2021; FAO, ed, 2022). However, marine fishery resources are declining. The proportion of fish stocks that remained within biologically sustainable levels declined from 90% in 1974 to 64.6% in 2019 (FAO, 2020). This decline threatens a growing world population's equitable and sustainable food security. Moreover, it has implications beyond collapsed populations, for marine ecosystems and biodiversity (Pauly et al., 1998).

Many policies have been implemented to mitigate the decline in fisheries resources.<fnref n="4" /> Among the available instruments, trade policies (Fugazza and Ok, 2019) and environmental provisions contained in trade agreements could be credible complementary tools for addressing this non-trade issue. Indeed, because fishery products are largely traded (Asche et al., 2015), trade policy greatly influences the sector.<fnref n="5" /> In addition, trade agreements contain an increasing number of provisions addressing a wide range of non-trade issues (Mattoo et al., 2020), including environmental issues. The purpose of environmental provisions is usually the promotion of environmental cooperation and the provision of a level-playing field (Francois et al., 2025). In our context, fishery-related provisions aim to mitigate the possible negative impacts of trade liberalization on fishery resources and marine ecosystems.

This paper addresses the following question: are the fishery-related provisions in trade agreements effective for mitigating the decline of marine fisheries resources? We answer this question by providing a quantitative assessment of the impact of fishery-related provisions on the sustainability of marine fisheries resources and by investigating the mechanisms through which this impact occurs. We examine whether including fishery-related provisions in a trade agreement exerts the expected positive impact on the sustainability of marine fisheries resources in the signatory countries.

As an indicator of the sustainability of fisheries resources, we use the Mean Trophic Level (MTL) (Pauly et al., 1998). The trophic level refers to an organism's position in the food chain, which is determined by its feeding behavior. In marine ecosystems, primary producers like plankton are at the lowest trophic level (1), followed by herbivores (such as small fish or krill) at level 2, and then carnivores (such as predatory fish, sharks, and large marine mammals) at higher levels. The MTL measures the average trophic level of catches in each area (whether a country or an Exclusive Economic Zone): a decline in the mean trophic level of catches over years could be an indication of "fishing down marine food webs", which is a sign of unsustainable fishing, as larger fishes are removed from the food web (Fey-Hofstede and Meesters, 2007).

<footnote n="4">⁴These include quantity-based policies – such as marine protected areas (Edgar et al., 2014), fisheries management practices and quotas (Costello et al., 2008), seasonal closures (Bostedt et al., 2020)– and price-based policies, such as fishery taxes (Clark, 2006) and subsidies (Bayramoglu et al., 2018).</footnote>

<footnote n="5">⁵In 2009, the trade value of seafood exceeded the combined trade value of sugar, maize, coffee, rice, and cocoa (Asche et al., 2015). In 2020, 33.7% of total fisheries and aquaculture production was traded internationally (FAO, ed, 2022).</footnote>
deforestation (Abman, Lundberg, and Ruta (2021)). For fisheries and marine biodiversity, however,
no such evidence has been provided<fnref n="2" />.

This paper addresses the following question: are the fishery-related provisions contained in trade
agreements an effective tool for the mitigation of trade-related marine biodiversity loss and decline
of marine fisheries resources? We answer this question by providing a quantitative assessment of the
impact of fishery-related provisions on the status of fish stocks and by investigating the mechanisms
through which this impact occurs. We examine whether the inclusion of fishery-related provisions in
a trade agreement exerts the expected positive impact on fish stock status in the signatory countries.

We use the marine trophic level (Pauly et al. (1998)) to measure the status of fish stocks on a global
scale. The marine trophic level measures the average trophic level of catches in each area (whether a
country or an Exclusive Economic Zone). Crucially, this measure captures not only catches but also
the dynamics of the ecosystem. This measure was identified by the Conference of the Parties to the
Convention on Biological Diversity (2004) as an indicator of functional biodiversity, and it is used
also in economic analyses (e.g., Chesnokova and McWhinnie (2019)). For the specific content of trade
agreements, we rely on the Trade and Environment Database (TREND) (Morin, Dür, and Lechner
(2018)) (726 agreements from 1947 to 2018) which is the most comprehensive dataset on
environmental provisions in RTAs (Brandi et al. (2020)). From this dataset, we identify the set of trade
agreements containing a fishery-related provision (FRP) (175 agreements).

Our empirical strategy is based on the comparison of the marine trophic level of signatories of a trade
agreement that includes an FRP with that of an agreement that does not include an FRP. The main
empirical difficulty inherent in this strategy lies in the endogeneity of FRPs. As the inclusion of an
FRP may be endogenous to the pre-agreement fish stock status of signatories, we mitigate this bias
by estimating the impact of the provision on a matched sample in which both those agreements with
and without an FRP can be compared. Methodologically, our study is similar to a recent article that
focuses on the effectiveness of environmental provisions in preventing deforestation (Abman,
Lundberg, and Ruta (2021)) which also uses this “matched difference-in-difference” strategy.

<footnote n="2">² In 2009, the trade value of seafood exceeded the combined trade value of sugar, maize, coffee, rice, and cocoa (Asche et al. (2015)). In 2018, 38% of total fisheries and aquaculture production was traded internationally (FAO (2020)).</footnote>
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Our empirical strategy is based on comparing the MTL of signatories to a trade agreement containing at least one fishery-related provision (FRP, hereafter) with that of an agreement that does not contain any FRP.

The main empirical difficulty inherent in this strategy lies in the endogeneity of FRPs. As the inclusion of an FRP may be endogenous to the pre-agreement MTL of signatories, we mitigate this bias by estimating the impact of the provision on a matched sample in which both agreements with and without an FRP can be compared. Methodologically, our study is similar to a recent article that focuses on the effectiveness of environmental provisions in preventing deforestation (Abman et al., 2024), which also uses this “matched difference-in-difference” strategy.

Our analysis yields four main findings. First, fishery-related provisions effectively neutralize the negative ecological effects of trade liberalization. While trade agreements generally reduce the MTL—indicating pressure on marine ecosystems—agreements that include FRPs prevent this decline. In terms of magnitude, the positive effect of FRPs is nearly equivalent to the negative impact of trade liberalization itself. As a result, agreements with FRPs leave the MTL stable, while those without FRPs drive ecological degradation.

Second, these ecological benefits are not immediate. Most of the positive effect of FRPs appears gradually, between five and eight years after an agreement is signed, consistent with ecological adjustment lags and delayed policy implementation. Third, the effectiveness of FRPs depends heavily on the economic and institutional context of the agreement. Agreements involving developed countries, or those with higher governance quality, show much stronger and more consistent ecological benefits. Besides, within the same agreement, the effect of FRPs also varies across countries—again driven by differences in governance capacity.

Strong institutions increase ecological gains.

Fourth, this good news is somewhat tempered by examining the mechanisms through which this effect occurs.

On the one hand, we estimate that FRPs reduce fish landings and fish exports. On the other hand, we do not find a significant effect of FRPs on fishing techniques (e.g., the proportion of fish caught by trawling, which is particularly harmful to the sustainability of fisheries resources). In other words, ecological gains arise mainly from restrictions on fishing volumes and trade, rather than from changes in fishing practices.

This set of results hence suggests that provisions do not foster the adoption of better resource-management practices but rather serve to reduce trade opportunities.

Our article contributes to the literature in several ways.

First, to the best of our knowledge, our article is the first to assess the effectiveness of environmental provisions on the sustainability of marine natural resources: our main outcome variable is original because it is an indicator of both fisheries resource sustainability and ecosystem integrity. Our analysis hence complements existing research that assesses the effectiveness of environmental provisions on the environment.<fnref n="6" /> This research has systematically shown that these provisions have been effective in achieving their stated objectives: to reduce greenhouse gas emissions (Apergis and Payne, 2020; Baghdadi et al., 2013), air pollution (Zhou et

<footnote n="6">⁶The reader can refer to Brandi and Morin (2023) for a comprehensive literature review on this subject.</footnote>
Two main findings emerge from our analysis. First, we estimate that FRPs mitigate the negative impact of trade liberalization on the status of fish stocks. While trade agreements tend to deteriorate fish stock status, the presence of FRPs tends to compensate for the negative effect of trade liberalization. In terms of magnitude, we estimate that including an FRP in a trade agreement has a sizable effect on fish stock status, equivalent to the negative effect of trade liberalization. Our estimations show that trade agreements containing FRPs do not deteriorate fish stock status, in contrast to those without FRPs, which do. Most of the effect of FRPs on fish stock status occurs 5 to 8 years after the agreement is signed. This broad result holds when controlling for other domestic and international policies (marine protected areas, fishery quotas, number of international environmental agreements signed, etc.). Conversely, the “depth” of such provisions<fnref n="3" /> does not seem to shape their effectiveness.

Second, this good news is somewhat tempered by examining the mechanisms through which this effect occurs. On the one hand, we estimate that FRPs reduce fish landings and fish exports. On the other hand, we do not find a significant effect of FRPs on fishing techniques (e.g., proportion of fish caught by trawling, which is particularly harmful to biodiversity). This suggests that provisions do not foster the adoption of better resource-management practices but rather serve to reduce trade opportunities.

All these results are robust to the use of alternative estimation strategies, alternative sets of provisions, alternative matching procedures, and other estimation issues.

Our article contributes to the literature in several ways. First, to the best of our knowledge, our article is the first to assess the effectiveness of environmental provisions on biodiversity, in particular on marine biodiversity. Our analysis also complements existing research that assesses the effectiveness of environmental provisions on natural resources (Abman, Lundberg, and Ruta (2021)) by considering another renewable resource other than forests, namely, fisheries, and by considering heterogeneous treatment effects. Our main outcome variable is original and rich, as it is both an indicator of sustainable fisheries and marine biodiversity.

<footnote n="3">³ i.e., their number, as an agreement may contain several different FRPs.</footnote>
al., 2017; Martínez-Zarzoso, 2018; Brandi et al., 2020), deforestation (Abman et al., 2024), and improving the composite environmental quality index for developing countries, as measured by the Environmental Performance Index (EPI) (Bastiaens and Postnikov, 2017). Brandi et al. (2019) show that these provisions are associated with the adoption of domestic environmental regulations in developing countries regarding certain issue areas, such as air pollution. To a lesser extent, they are associated with regulations concerning climate and energy, genetic resources, and waste. Francois et al. (2025) also highlight the ambiguous effects of these provisions on environmental outcomes. They show that binding environmental provisions are associated with a modest yet significant increase only for the following environmental outcomes: forest coverage, as well as improvements in air pollution and the ozone exposure index. However, Francois et al. (2025) do not consider fisheries or the health of oceans within their range of environmental outcomes. Our results temper the optimistic findings of the existing literature on the beneficial environmental outcomes of environmental provisions: fishery-related provisions do not foster the adoption of sustainable fishery management practices. Instead, they appear to reduce trade opportunities, which is contrary to the objective of promoting trade in trade agreements. These results echo those of Brandi et al. (2019), who found that environmental provisions are not linked to the adoption of domestic environmental legislation for fisheries, oceans, and coasts.

Prior research has also examined the trade effects of environmental provisions in preferential trade agreements, with mixed findings. For example, Hoekman et al. (2023) find that, controlling for foreign and environmental aid, legally enforceable environmental provisions can increase bilateral exports of environmental- and labor-intensive products from donor countries, as well as exports of environment-intensive goods from aid recipients to donors. Differently, Nuñez-Rocha et al. (2024) show that the environmental provisions reduce exports for dirty-footloose goods, particularly when the provisions are legally binding. While these studies highlight important trade-related consequences of environmental provisions, they do not assess ecological outcomes for fisheries, which is the focus of our analysis.

In line with this research focus, we assume that trade in fish is liberalized under the RTAs considered, allowing us to concentrate on the presence and design of fishery-related provisions rather than on detailed tariff or quota changes triggered by trade agreements. This assumption is broadly supported by existing evidence showing that tariffs on fishery products are generally low and have declined over time at the global level (Eugui et al., 2016).<fnref n="7" />

Second, our analysis contributes to the literature on the impact of trade on the sustainability of fishery resources. Theoretically, trade can lead to the over-exploitation of renewable resources for unmanaged or ill-managed fisheries (Brander and Taylor, 1997, 1998; Rus, 2012, 2016; Takarada et al., 2013; Dube and Quaas, 2024), and even to the complete depletion of a resource (Gars and Spiro, 2018). Empirically,

<footnote n="7">⁷Using the UNCTAD–World Bank–WTO TRAINS database, Eugui et al. (2016) report that the globally averaged applied tariff for fishery products was only 4.8% in 2014, down from 6.7% in 2009. While these low tariff levels are consistent with the assumption of widespread liberalization, they do not establish a causal link between tariff reductions and the actual liberalization of fish trade, which remains beyond the scope of this study.</footnote>
Second, our analysis contributes to the literature on the impact of trade on the sustainability of fishery resources. Theoretically, trade can lead to the overexploitation of renewable resources under open-access conditions (Brander and Taylor (1997a); Brander and Taylor (1997b); Brander and Taylor (1998); Rus (2012); Rus (2016); Takarada, Dong, and Ogawa (2013)) and even to the complete depletion of a resource (Gars and Spiro (2018)). Empirically, international trade and export opportunities increase the likelihood of fishery collapse (Eisenbarth (2022)), but better institutions and governance serve to shape this relationship (Erhardt (2018)). Our results confirm that export opportunities created by cooperative trade agreements and fish stock depletion are closely linked and correlate with each other.

Finally, our work has important policy implications. Our paper shows that the content of trade agreements is not sufficient for the alignment of trade-related objectives and environmental concerns. The environmental improvement for signatories in a trade agreement including environmental provisions occurs at the expense of trade opportunities, thus trade flows decline.

The rest of this article is structured as follows. The next section details the data. The empirical strategy is presented in Section 3. The main results are displayed in Section 4, and Section 5 details the robustness checks. Finally, Section 6 offers concluding remarks and discusses policy implications.

## 2. Data

### 2.1 Dependent variable: marine trophic level

To capture the status of fish stocks, we rely on the Marine Trophic Level (MTL) developed by (Pauly et al. (1998)) and provided by Sea Around US (Pauly, Zeller, and Palomares (2015)). This measure of fish stock condition has been used in the empirical economics literature (e.g. Chesnokova and McWhinnie (2019)) because of its global coverage, its consistency across countries and years, and its importance to the environmental science literature (Pauly et al. (1998)). As mentioned earlier, MTL is also a biodiversity indicator and was included by the Conference of the Parties to the Convention on Biological Diversity (2004) as one of the eight alternative indicators of biodiversity loss. MTL measures functional biodiversity: unlike specific biodiversity, which can be measured by species diversity (e.g., number of endangered species), functional biodiversity considers the structure and
international trade and export opportunities increase the likelihood of fishery collapse (Eisenbarth, 2021), but better institutions and governance shape this relationship (Erhardt, 2018). Our research contributes to this literature by highlighting the role of trade agreements on the sustainability of fisheries resources. We show that export opportunities created by trade agreements and the damage to the health of marine ecosystems are closely linked and correlate with each other.

Thirdly, our analysis contributes to the existing body of literature on the use of trade measures as a tool to conserve fisheries resources and combat illegal, unreported and unregulated (IUU) fishing. Given the failure to reduce the extent of IUU fishing, there has been a discussion about the potential for trade measures to be implemented in place of traditional domestic fishery regulation in relation to monitoring and surveillance of fishing activities (Le Gallic, 2008; Leroy et al., 2016). Le Gallic (2008) distinguishes these trade measures into three categories.<fnref n="8" /> For these measures to be effective, they must be compatible with the WTO rules and primarily implemented by large importing countries with substantial market power, such as the EU and the US (Fang and Asche, 2021). Furthermore, large countries include sustainable trade measures, including environmental provisions, in their RTAs. It is therefore important to evaluate the effectiveness of these measures in ensuring the sustainability of fisheries resources.

Fourth, our analysis contributes to the literature that conducts ex-post policy evaluation to provide insights on the effects of regulation on the performance of fisheries. For instance, there is recent work on the ex-post evaluation of catch-share programs on fisheries (Hsueh, 2017; Kroetz et al., 2015; Pincinato et al., 2022). To the best of our knowledge, our article is the first to undertake an ex-post evaluation of trade measures on the sustainability of marine fisheries.

Finally, our contribution is methodologically closest to that of Abman et al. (2024), but differs in several key aspects. On the one hand, it focuses on a renewable resource other than forests, specifically fisheries. Besides, we use the TREND data set for the specific content of trade agreements. Finally, we consider heterogeneous treatment effects (de Chaisemartin and D'Haultfœuille, 2022). Indeed, recent studies highlight the potential bias in two-way fixed effects estimations due to variations in treatment effects across groups and over time. This issue is particularly relevant when countries sign multiple trade agreements at different times. We apply these heterogeneity-robust estimators to reinforce the validity of the results.

Our work has important policy implications. The fishery-related provisions included in trade agreements benefit fishery resources' sustainability, albeit for the wrong reasons. These provisions function as "trade barriers," impeding trade opportunities among the signatories. Contrary to our initial expectations, these environmental provisions do not help reduce or phase out capacity-enhancing fishery techniques that contribute to the depletion of global fishery resources.

The rest of this article is structured as follows. The next section details the data. The empirical strategy

<footnote n="8">⁸(i) embargoes and tariffs, (ii) trade documentation and labels, and (iii) restrictions on goods and services (such as a ban on landing in a given port).</footnote>
functioning of ecosystems through the abundance and role of different species in the food chain (Gascuel (2019)).

Each species of fish can be associated with a trophic level (TL), indicating where this species is in the food chain. The TL begins with 1 for algae by definition, and then each species' trophic level is 1+ the average trophic level of the species on which it feeds: the trophic level of herbivorous fish is 2, and so on. Cod are at the 4.4 level on average, and the largest predators, large sharks and rorquals, can exceed level 5.

The species-specific trophic level can be aggregated at the level of exclusive economic zone (EEZ) z by taking the average of the trophic level of each species s as weighted by the fish landings of the fish species s:

$$MTL_{zt} = \frac{\sum_s TL_s Y_{zst}}{\sum_s Y_{zst}}$$

where TLs is the trophic level of species s, and Yzst is the landings of species s in EEZ z at time t, as provided by Sea Around Us.

A decline in MTL indicates a decline in the proportion of the fish population that resides at the top of the food chain, i.e., mainly large fish, which have a longer reproductive cycle. This phenomenon, referred to as “fishing down marine food webs” (Pauly et al. (1998)), results in catches gradually occurring at lower marine trophic levels, which is the main characteristic of overexploited fish stocks (Pauly, Zeller, and Palomares (2015)). The average trophic level of global catches has declined from above 3.3 in the early 1950s to below 3.1 in the 1990s (Pauly et al. (1998)). Pauly et al. (1998) revealed the overall impact of fisheries not only on the abundance of a given exploited stock but also on the structure of marine ecosystems at the global level. This impact has also been observed in European fisheries. Between 1985 and 2010, the maximum size of the species found on the seafloor decreased from 90 to 74 cm, illustrating the decline of large predatory species in favor of smaller species. This phenomenon has been observed in many ecosystems around the world (Gascuel (2019)).

Our objective is to measure whether the implementation of a Regional Trade Agreement (RTA) has a negative effect on MTL at the agreement level, i.e., for all signatory countries, and whether its fishery-related provisions help to mitigate this negative effect. Thus, we aggregate this index at the country level and then at the agreement level. To aggregate this index at the country level
is presented in Section 3. The main results are displayed in Section 4, and Section 5 details the robustness checks. Finally, Section 6 offers concluding remarks and discusses policy implications.

## 2. Data

### 2.1. Dependent variable: Mean Trophic Level

Data for calculating the Mean Trophic Level (MTL) developed by (Pauly et al., 1998) are provided by Sea Around US (Pauly et al., 2015). The Sea Around Us data set is based on a catch-reconstruction of fisheries catches data initially assembled and harmonized by the FAO. The Sea Around Us re-estimates total fisheries catches (reported catches and estimated unreported catches, including discards) at the level of Exclusive Economic Zones of all countries in the world for different years. This rich data set is freely accessible and downloadable via the Sea Around Us website.<fnref n="9" /> More information on the data set construction can be found in Zeller et al. (2016).

The methodology used to construct the Mean Trophic Level (MTL) is as follows. Each marine species *s* is assigned a specific trophic level (TL), which represents its position within the food web(TL=1 for primary producers such as algae, and each subsequent species is assigned a TL that is 1 plus the average trophic level of the species it preys upon). Herbivorous fish are assigned a TL of 2. Cod, on average, are categorized at a TL of 4.4, while apex predators, including large sharks and rorquals, may exceed a TL of 5. To compute the aggregated trophic level at the level of a specific Exclusive Economic Zone (EEZ) *z*, the species-specific trophic levels are weighted by the fish catches of each species, with the resulting average representing the MTL for the given EEZ:

$$MTL_{zt} = \frac{\sum_{s} TL_{s} Y_{zst}}{\sum_{s} Y_{zst}} \qquad (1)$$

where *MTL*<sub>*zt*</sub> is the Mean Trophic Level of EEZ *z* at time *t*, *TL*<sub>*s*</sub> is the trophic level of species *s*, and *Y*<sub>*zst*</sub> is the catches of species *s* in EEZ *z* at time *t*.

A decline in the Mean Trophic Level over time reflects a decrease in the proportion of the fish population that occupies higher trophic levels, i.e., mainly large fish with longer reproductive cycles. This phenomenon, referred to as “fishing down marine food webs” (Pauly et al., 1998), means catches gradually occur at lower marine trophic levels, which is the main characteristic of overexploited fish stocks (Pauly et al., 2015). Beyond its relevance to fisheries sustainability, the Mean Trophic Level (MTL) also underpins global marine biodiversity indicators. For instance, the Convention on Biological Diversity (CBD) adopted the "Marine Trophic Index" (a version of MTL with a specific cutoff) as one of seven alternative indicators of biodiversity loss within the focal area of "ecosystem integrity and ecosystem goods and services" during the 2004 Conference of the Parties. MTL serves as a measure of functional biodiversity, which differs from specific

<footnote n="9">⁹See https://www.seaaroundus.org</footnote>
(Chesnokova and McWhinnie (2019)), we take the average of the MTLs of each country's EEZs, weighted by fish landings in each EEZ:

$$MTL_{it} = \frac{\sum_{z \in Z_i} MTL_{zt} Y_{zt}}{\sum_{z \in Z_i} Y_{zt}}$$

where $MTL_{it}$ is the Marine Trophic Level of country $i$ at time $t$, $Z_i$ is the set of all EEZs belonging to country $i$, and $Y_{zt}$ is the fish landings in EEZ $z$ at time $t$.

At each step, we excluded the distant EEZs of countries and mainly considered the different coastal EEZs off the mainland of each country. This is a conservative assumption that is justified by the fact that overseas territories are not always included in trade agreements. In the case of the United Kingdom, for example, overseas territories have the option of requesting a treaty extension, which is not always granted.

The agreement-specific MTL is constructed by aggregating the MTLs of each signatory country, as weighted by their fish catches:

$$MTL_{gt} = \frac{\sum_{i \in I_g} MTL_{it} Y_{it}}{\sum_{i \in I_g} Y_{it}}$$

where $MTL_{gt}$ is the Marine Trophic Level of agreement $g$ at time $t$, $I_g$ is the set of countries that have signed agreement $g$, $MTL_{it}$ is the MTL of country $i$ at time $t$, and $Y_{it}$ is the fish landings of country $i$, which is the sum of the country's landings in all its EEZs.

The aggregation of EEZ-specific MTLs assigns important weight to the fish catches of each country. Indeed, the collapse of a small fishing country's stock may not have a significant effect on the aggregate MTL of an agreement among many larger fishing countries. Nevertheless, this approach allows us to interpret the variation in fish stock sustainability among those countries that signed the agreement. If the average MTL of the signatory countries does not decrease, then it means that the group of signatory countries has a good status of fish stocks even though some of them may be individually facing a decrease in their MTL.

## Dynamics of the MTL

Figure 1 plots the MTL of the top 4 fish-harvesting countries and the world average over the last 50 years. Over that time, the world MTL decreased during two waves, one in the 1960s and one in the 1980s, indicating unsustainable exploitation patterns. However, the evolution of MTL varies by
biodiversity typically assessed through species diversity (e.g., the number of endangered species). Functional biodiversity captures the structure and functioning of ecosystems by considering the abundance and ecological roles of species within the food chain (Gascuel, 2019).

However, the catch-based MTL has important limitations. It is derived from reported catch data, which may be influenced by factors unrelated to ecosystem health such as market demand, reporting biases, and policy enforcement variability. The MTL is also scale-invariant, meaning uniform changes in catches across trophic levels may not be detected as changes in MTL. Additionally, aggregating data at the species and EEZ level can mask species-specific collapses or ecosystem shifts critical to sustainability. Variability in data quality across countries further complicates interpretation, particularly when comparing regions with differing monitoring capacities. These limitations mean that MTL should be interpreted cautiously, especially in cross-country or policy impact analyses.

Despite its widespread use, the catch-based MTL has been the subject of criticism from scholars, particularly in the context of its "fishing down marine food webs" interpretation (Branch et al., 2010; Shannon et al., 2014). Critics have noted that species at low trophic levels are often sensitive to environmental changes unrelated to fishing pressure<fnref n="10" />. A reduction in MTL could also result from a voluntary strategy to increase the catch of small fish that are used as inputs for aquaculture production ("fishing through the food web"). Pauly and Watson (2005) addressed these critiques, emphasizing that a drop in MTL driven by increased catches of low trophic level species would, by construction, require substantial increases in total catches.

While acknowledging the limitations of the catch-based MTL, we note that it offers critical advantages. The MTL provides global coverage and consistency across countries and years, making it particularly valuable for assessing the effectiveness of trade agreements in promoting sustainable fisheries. In contrast, species-level stock status data, though valuable, are inconsistent and unavailable for many countries (Chesnokova and McWhinnie, 2019). Also, the stock status is constructed from data on fish catches, which are subject to reporting and re-estimation biases. As such, the MTL remains an essential tool for international comparisons and dynamic assessments (Fey-Hofstede and Meesters, 2007). Yet, to ensure data reliability, we restrict the sample to the post-1980 period, as reconstructed catch data are considered more accurate from this point onward, particularly for developing countries.

Our objective is to measure whether the implementation of an RTA has an effect on MTL at the agreement level, i.e., for all signatory countries, and whether its fishery-related provisions moderate or amplify this effect. Because Sea Around Us provides information on MTL by ocean areas, we aggregate this index at the country level and then at the agreement level. To aggregate this index at the country level, we follow the methodology proposed by Chesnokova and McWhinnie (2019) whereby the average of the MTLs of each

<footnote n="10">¹⁰Branch et al. (2010) found that catch-based MTL does not reliably predict changes in marine ecosystems, and diverges from ecosystem MTL trends obtained from trawl surveys and fisheries stock assessments. Shannon et al. (2014) compared catch-based, model-based, and survey-based trophic level indicators and found that "all 3 data sources provide useful information about the structural changes in the ecosystem as a result of fishing, but results indicate that only model-based indicators represent fishing impacts at the whole ecosystem level" (p. 115).</footnote>
country. For example, Japan experienced a collapse in the 1980s and 1990s but now has an MTL close
to its original level. Therefore, the evolution of the national MTL varies greatly depending on the
exploitation regime and resource management of the country. That is why it makes sense to
determine the impact of the implementation of a trade agreement and its environmental provisions
on the MTL. Figure 2 shows the evolution of quantities harvested around the world. Total annual
catches reached a peak in the 1970s and again at the end of the 1990s and have continued to decrease
since then.

## 2.2 Trade agreements and environmental provisions

Recent trade liberalization shifted from multilateral to bilateral and regional fronts. Recent trade
agreements are also deeper agreements that tackle beyond-borders policy areas. RTAs often go
beyond the levels of commitment agreed to in the WTO (Mattoo, Rocha, and Ruta (2020)). In recent
years, the scope of RTAs has been broadened, increasing from an average of 8 to 17 policy areas
covered since the 1950s. The deepening of RTAs followed an extensive process of establishing new
policies as well as an intensive process of implementing new specific commitments within existing
policy areas (Mattoo, Rocha, and Ruta (2020)). Mattoo, Rocha, and Ruta (2020) highlight the wide
diversity of provisions and the difficulty to identifying their effect. In addition, provisions differ in
their enforcement mechanisms, and they may not have the same impact depending on the initial level
of regulation prior to the RTA.

To identify fishery-related provisions in trade agreements, we use the Trade and Environment
Database (TREND) (Morin, Dür, and Lechner (2018)). This is the most comprehensive and fine-
grained dataset on environmental provisions in RTAs (Brandi et al. (2020))<fnref n="4" />. It is based on trade
agreements provided by the Design of Trade Agreements (DESTA) dataset, which is by far the most
comprehensive collection of RTAs (Dür, Baccini, and Elsig (2014)). It contains 726 RTAs that were
signed between 1947 and 2018.

<footnote n="4">⁴ An alternative source would be the Deep Trade Agreement database provided by the World Bank, including 283 Preferential Trade Agreements signed between 1957 and 2016, and their provisions grouped into seven categories. We preferred to rely on the TREND database because (i) it accounts for a greater number of trade agreements and (ii) the provisions are coded with more detail, enabling to precisely identify fishery-related provisions.</footnote>
country's EEZs, weighted by fish catches in each EEZ, is calculated:

$$MTL_{it} = \frac{\sum_{z \in Z_i} MTL_{zt} Y_{zt}}{\sum_{z \in Z_i} Y_{zt}} \qquad (2)$$

where $MTL_{it}$ is the Mean Trophic Level of catches in country $i$ at time $t$, $Z_i$ is the set of all EEZs belonging to country $i$, and $Y_{zt}$ is the fish catches in EEZ $z$ at time $t$.<fnref n="11" />

The agreement-specific MTL is then constructed by aggregating the MTLs of each signatory country, as weighted by their fish catches:

$$MTL_{gt} = \frac{\sum_{i \in I_g} MTL_{it} Y_{it}}{\sum_{i \in I_g} Y_{it}} \qquad (3)$$

where $MTL_{gt}$ is the Mean Trophic Level of agreement $g$ at time $t$, $I_g$ is the set of countries that have signed agreement $g$ and $Y_{it}$ is the fish catches of country $i$.

**Dynamics of the MTL.** Figure 1 plots the MTL of the main fish-harvesting countries and the world average over the last 50 years. The world MTL decreased during two waves, one in the 1960s and one in the 1980s, indicating unsustainable exploitation patterns. However, the evolution of MTL varies by country. For example, Japan experienced a collapse in the 1980s and 1990s but now has an MTL close to its original level. Therefore, the evolution of the national MTL varies greatly depending on the exploitation regime and resource management of the country. It is, therefore, pertinent to determine the impact of the implementation of a trade agreement and its environmental provisions on the MTL.

<figure><img src="image_2.png" /><figcaption>Figure 1: Country-specific MTL.</figcaption></figure>

Figure 2 shows the co-evolution of quantities harvested around the world and the world MTL. Total annual catches reached a peak in the 1970s and again at the end of the 1990s and have continuously decreased since

<footnote n="11">¹¹At each step, we excluded the distant EEZs of countries and mainly considered the different coastal EEZs off the mainland of each country. This conservative assumption is justified by the fact that overseas territories are not always included in trade agreements. In the case of the United Kingdom, for example, overseas territories can request a treaty extension, which is not always granted.</footnote>
In this sample of agreements, Morin, Dür, and Lechner (2018) identify the set of provisions included in the agreements, including environmental provisions. For our study, we identify the provisions related to fisheries to empirically evaluate the impact of these specific provisions on the status of fish stocks. Table 1 provides an overview of the benchmark set of provisions that we consider in the analysis. The first column displays the provision identifier from Morin, Dür, and Lechner (2018). For the empirical analysis, in the second column, we classified the provisions into five types to assess their potentially heterogeneous effects. The third column displays the number of agreements (out of 726) that are related to a specific provision. The last column displays the name of the provision.

Using this set of provisions, Figure 3 plots the number of RTAs and the number of RTAs with fishery-related provisions in our sample, ranked by the year of signature of the RTA, and shows the increase in the number of RTAs since the 1990s. Almost all recent RTAs include an FRP.

Panel A in Table 2 shows some statistics on the TREND sample of trade agreements with fishery-related provisions. Out of the 726 RTAs, we consider that 175 of them include a fishery-related provision. Together, these 175 RTAs include 322 provisions, meaning that each RTA with fishery-related provisions includes slightly fewer than 2 provisions.

Based on our classification of the provisions into five types (second column in Table 1), Figure 4 shows the composition of fishery-related provisions (detailed in Table 2). It plots the share of each type for each year in the total number of provisions in all agreements signed in that particular year. Excluding the first years in the sample, provisions related to fishery regulation have become the major type of provisions, while the importance of species and ecosystem protection has declined over time. Moreover, the regulation of subsidies harmful to the environment has only recently been included in RTAs, and very few agreements have included at least one so far.

## 2.3 Additional data

We also consider several control variables for individual countries: GDP (in current USD) as provided by the World Bank to account for the level of development of countries; the OECD classification to distinguish between developed and developing economies; fish catches (in tons) as provided by Sea Around US; exports of fish products (in current USD) as provided by FAO, and protected marine
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then.

<figure><img src="image_3.png" /><figcaption>Figure 2: MTL and catches.</figcaption></figure>

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## 2.2. Trade agreements and environmental provisions

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There has been a clear increase in bilateral and regional trade cooperation, with the number of RTAs steadily increasing over time to reach 726 by 2018 (see Figure 3). What effect does this cooperation have on the trade of fish and fishery products? This is a challenging question, as it is difficult to distinguish the role of RTAs from that of other forms of international cooperation, such as agreements on access to international fisheries (Gagern and Van Den Bergh, 2013; Zimmermann, 2017). For example, the EU grants trade concessions in exchange for access to foreign fisheries (Vatsov, 2019). To our knowledge, no empirical work has estimated the impact of RTAs on the trade of fishery products specifically. Similarly, no existing empirical work informs us of the extent to which RTAs have played a role in the observed global tariff reduction for fish products. However, we have information on specific RTAs. For example, the EU-Chile Association Agreement (2002) eliminated EU tariffs on various Chilean fish products, such as salmon, trout and mussels. Likewise, the US-Chile Free Trade Agreement (2003) eliminated US tariffs on various Chilean fish products, including salmon, trout and tilapia. In both cases, Chile reciprocated by liberalising fish imports from the EU and the US.

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### 2.2.1. Identifying the fishery-related provisions

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To identify fishery-related provisions in trade agreements, we use the Trade and Environment Database (TREND) (Morin et al., 2018). This is the most comprehensive and fine-grained dataset on environmental provisions in RTAs (Brandi et al., 2020).<fnref n="12" /> It is based on trade agreements provided by the Design of Trade Agreements (DESTA) dataset, which is by far the most comprehensive collection of RTAs (Dür et al., 2014).

<footnote n="12">¹²An alternative source would be the Deep Trade Agreement database provided by the World Bank, including 283 Preferential Trade Agreements signed between 1957 and 2016, and their provisions grouped into seven categories. We preferred to rely on the TREND database because (i) it accounts for a greater number of trade agreements and (ii) the provisions are coded with more detail, enabling to precisely identify fishery-related provisions. Recently, the FAO constructed the Ag-ERP database (Avesani et al., 2024) by compiling environmental provisions from the TREND database across agriculture, fisheries and forestry. We chose not to use this database because it makes it difficult to identify fishery-specific provisions precisely, and it covers fewer agreements (318) over a shorter time span (1995–2022), which is limited to WTO-notified RTAs.</footnote>
areas as provided by the International Union for Conservation of Nature. We deflate all the variables in value by the US consumer price index for 2010 (from the World Development Indicators).

## 3. Methodology

We adopt a matched agreement-level approach that estimates the impact of RTAs and an FRP on fish stock status, accounting for the potential endogeneity of fishery-related provisions. Our approach is similar to that of Abman, Lundberg, and Ruta (2021) regarding the effectiveness of environmental provisions in RTAs in preventing deforestation.

### 3.1 General model

We start by examining the effect of fishery-related provisions included in RTAs on the aggregated fish stock status and other outcomes in all the signatory countries (i.e., at the agreement level). We thus aggregate in a panel dataset the outcomes and controls at the agreement-year level.

The effects of fishery-related provisions are estimated using the following general specification at the RTA g x year t level:

$$y_{gt} = \alpha_0 + \alpha_1 1[PostRTA_{gt}] + \alpha_2(1[PostRTA_{gt}] \times FRP_g) + \alpha_3 C_{gt} + \lambda_t + \lambda_g + \varepsilon_{gt} \quad (1)$$

This model estimates the joint impact of signing RTAs and fishery-related provisions on the agreement-level MTL in period t for all signatories of agreement g.

1[PostRTAgt] is a dummy variable that is equal to 1 after the implementation of the agreement and 0 otherwise. FRPg denotes the general measure of the presence of fishery-related provisions in agreement g. Empirically, this variable is a dummy that is equal to 1 if the agreement includes at least one FRP and 0 otherwise. We focus on the interaction between agreement implementation and the existence of FRPs in that agreement. In the following, we also estimate the impact of the number of provisions on the agreement-level MTL.
It contains 726 RTAs that were signed between 1947 and 2018. In this sample of agreements, Morin et al. (2018) identify the set of provisions included in the agreements, including environmental provisions. For our study, we identify the provisions related to fisheries to empirically evaluate the impact of these specific provisions on the MTL.

For the completeness of the analysis, we consider three alternative sets of fishery-related provisions, as described in Table 1. The first column displays the provision identifier from Morin et al. (2018) and its description in the second column. The other columns provide the lists of provisions used in the analysis. The final column displays the number of agreements (out of 726) that are related to a specific provision. In Set 1, the starting point is the set of provisions listed under the fisheries category of TREND data set ("10.04 Fisheries") such as "10.04.01 Conservation of fishery resources". We augment this list with the provisions related to coastal areas such as "10.01.03 Seas and oceans", as well as marine fisheries resources such as "10.08 Whales and seals". Set 2 further enriches Set 1 by including the provisions related to Whaling Convention (1946) such as "14.01.09 ratification Whaling Convention", that is, reference to international environmental agreements that are exclusively related to marine resources. Finally, Set 3 encompasses a more extensive range of provisions than the preceding sets, encompassing those related to environmental regulations or international agreements on the environment in general, rather than on fisheries specifically. Set 3 thus includes provisions i) aiming to protect biodiversity such as "10.06 Endangered species and their illegal trade", ii) on other international environmental agreements such as "14.02.01.01 Implementation of CITES (whole treaty)", and iii) on other environmental regulations such as "80.601 Norms allowing subsidies granted for environmental purposes". In total, the three sets of provisions include respectively 162, 166, and 250 agreements.
A set of controls at the agreement-year level, $C_{gt}$, are included to capture other determinants of MTL (these variables are also aggregated at the agreement-year level). For instance, as fish stocks evolve through the reproduction of fish and fish catches, stocks in a particular period depend on the previous levels of stocks. To represent these dynamics of fish stocks, we include in $C_{gt}$ a lagged variable for the MTL, with a lag of 2 years, $MTL_{g(t-2)}$.

We include year and agreement fixed effects to address unobserved heterogeneity. The year fixed effect absorbs trends in variables across all agreements, and the agreement fixed effect ensures that the estimates are derived from variation across time for the set of countries that signed the agreement. Note that the unconditional effect of fishery-related provisions cannot be included and estimated, as it is absorbed by the agreement fixed effect. $\varepsilon_{gt}$ denotes the error term.

In this general model, $\alpha_1$ measures the impact of the RTA on the agreement-level outcome (MTL), whereas $\alpha_2$ captures the differential effect of the presence of fishery-related provisions on the outcome of interest. Focusing on MTL, in line with existing evidence, we expect to estimate $\alpha_1 < 0$, which suggests a negative impact of trade liberalization on fish stock status. Importantly, estimating a significant and positive $\alpha_2$ would mean that the presence of fishery-related provisions works to increase fish stock status and counteracts the unconditional effect of trade liberalization. In that case, the fish stock status would decrease less, ceteris paribus, after trade liberalization if the RTA includes an FRP. In that specification, the net effect of trade liberalization and fishery-related provisions is obtained by adding $\alpha_1$ and $\alpha_2$. Regarding inference, we cluster the standard errors at the agreement level.

## 3.2 General dynamic model

In addition to this general model, we also estimate a dynamic version of the model, in which we allow the FRP to have time-varying effects. After signing the RTA, the FRP may affect fish stock status and other outcomes with some delay. We thus transition from a static version of the model (identifying the average effect across years) to a dynamic model:

$$y_{gt} = \alpha_0 + \alpha_1 1[\text{PostRTA}_{gt}] + \sum_{\tau=-5}^{15} \delta_{\tau} (1[\text{PostRTA}_{gt}] \times \text{FRP}_g \times [t = \tau]) + \alpha_3 C_{gt} + \lambda_t + \lambda_g + \varepsilon_{gt}. \quad (2)$$
<table><caption>Table 1: Fishery-related Provisions considered in the analysis</caption><thead><tr><th>Prov. ID</th><th>Prov. Description</th><th>Set 1</th><th>Set 2</th><th>Set 3</th><th>Nb. RTA</th></tr></thead><tbody><tr><td>1006</td><td>Endangered species and their illegal trade</td><td></td><td></td><td>1</td><td>38</td></tr><tr><td>1008</td><td>Whales and seals (e.g., import ban based on CITES)</td><td>1</td><td>1</td><td>1</td><td>32</td></tr><tr><td>1011</td><td>Shared species</td><td></td><td></td><td>1</td><td>13</td></tr><tr><td>1013</td><td>Protected areas</td><td></td><td></td><td>1</td><td>56</td></tr><tr><td>10703</td><td>Sovereignty over hydrobiological and fishery resources</td><td></td><td></td><td>1</td><td>7</td></tr><tr><td>80602</td><td>Prevention of subsidies harmful to the environment</td><td></td><td></td><td>1</td><td>4</td></tr><tr><td>100102</td><td>Specific environmental issues: Coral reefs</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>100103</td><td>Specific environmental issues: Seas and oceans</td><td>1</td><td>1</td><td>1</td><td>111</td></tr><tr><td>100106</td><td>Specific environmental issues: Protection of coastal areas</td><td>1</td><td>1</td><td>1</td><td>32</td></tr><tr><td>100109</td><td>Marine plastic pollution</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>100401</td><td>Specific environmental issues: Conservation of fishery resources</td><td>1</td><td>1</td><td>1</td><td>78</td></tr><tr><td>100402</td><td>Specific environmental issues: Sustainable trade in fishery products</td><td>1</td><td>1</td><td>1</td><td>18</td></tr><tr><td>100403</td><td>Specific environmental issues: Combat illegal fishing</td><td>1</td><td>1</td><td>1</td><td>23</td></tr><tr><td>100404</td><td>Prevent pollution from fishing activities</td><td>1</td><td>1</td><td>1</td><td>8</td></tr><tr><td>100405</td><td>Bycatch prevention</td><td>1</td><td>1</td><td>1</td><td>6</td></tr><tr><td>100406</td><td>Prevent harmful subsidies</td><td>1</td><td>1</td><td>1</td><td>3</td></tr><tr><td>140221</td><td>Implementation other agreements related to the environment</td><td></td><td></td><td>1</td><td>191</td></tr><tr><td>140301</td><td>Relation with international institutions: Prevalence CITES</td><td></td><td></td><td>1</td><td>21</td></tr><tr><td>140304</td><td>Prevalence MARPOL</td><td></td><td></td><td>1</td><td>4</td></tr><tr><td>140401</td><td>Other references CITES</td><td></td><td></td><td>1</td><td>48</td></tr><tr><td>140409</td><td>Relation with international institutions: Whaling Convention</td><td></td><td>1</td><td>1</td><td>4</td></tr><tr><td>14020101</td><td>Implementation of CITES (whole treaty)</td><td></td><td></td><td>1</td><td>15</td></tr><tr><td>14020102</td><td>Implementation of CITES (specific parts)</td><td></td><td></td><td>1</td><td>7</td></tr><tr><td>14020901</td><td>Implementation Whaling Convention (Whole treaty)</td><td></td><td>1</td><td>1</td><td>4</td></tr><tr><td colspan="2">Number of RTAs (total by set)</td><td>162</td><td>166</td><td>250</td><td></td></tr></tbody></table>
where $\tau$ denotes both the time since RTA signing ($\tau > 0$) and the time before RTA signing ($\tau < 0$). In this specification, our main interest is in the set of $\{\delta_{\tau}\}_{\tau=-5,...,15}$ that identify the pre-treatment and post-treatment effects of the FRP.

### 3.3 Endogeneity of the provisions: A matching approach

The main econometric issue in our models is the endogeneity of the FRP in the signed RTA. Indeed, a country may be interested in including fishery-related provisions in an RTA because its fishery stocks are depleting, and they want to protect them from increased exploitation pressure. As a result, the presence of an FRP is not random across agreements.

To correct this endogeneity issue, we use the difference in differences (DID) matching method (Baghdadi, Martinez-Zarzoso, and Zitouna (2013); Abman, Lundberg, and Ruta (2021)). First, we match the agreements that include a fishery-related provision with those agreements without a fishery-related provision according to their probability of including an FRP. Then, we estimate a DID model on the set of matched agreements. The matching procedure excludes non-credible counterfactual RTAs (without FRPs) from the sample.

First, we estimate the propensity score for the inclusion of FRPs in the cross-section of agreements through a regression on all RTAs included in the TREND database. This corresponds to the estimation of:

$$FRP_g = F(\beta_0 + \beta X_g)$$

with $FRP_g$ set as the general measure of the presence of fishery-related provision in agreement g and $X_g$ as a set of covariates describing the characteristics of the agreement and the countries signing it. In our case, $X_g$ includes the covariates that translate the propensity for the countries signing the agreement to include an FRP. Therefore, it includes the log of the number of signatories, the log of the squared year of signature, the quantities fished the 5 years before the signature of the agreement by all signatories, the exports of fishery products from all signatories in the 5 years preceding the agreement and the sum of marine protected areas of all signatories.

These variables capture in particular political economy motives to include FRP in trade agreements, such as the importance of the fishery sector in signatory countries and its potential lobbying power on governments. Indeed political economy motives have been shown to be an important driver for
<table><caption>Table 2: Descriptive Statistics for Sample Agreements</caption><thead><tr><th colspan="2">Panel A: Raw Sample</th></tr></thead><tbody><tr><td>Number of Agreements (RTA)</td><td>726</td></tr><tr><td>Number of Agreements with Environmental Provisions</td><td>630</td></tr><tr><td>Number of Agreements with FRP (set 1)</td><td>162</td></tr><tr><td>Number of FRP</td><td>315</td></tr><tr><td>Average Number of FRP per RTA</td><td>0.43</td></tr><tr><td>Average Number of Provisions per RTA with FRP</td><td>1.94</td></tr></tbody><thead><tr><th colspan="2">Panel B: Matched Sample</th></tr></thead><tbody><tr><td>Number of Matched Agreements with FRP</td><td>130</td></tr><tr><td>Average Length After Signature</td><td>12.22 years</td></tr><tr><td>Average Difference in Signature Years (Control vs. Treated)</td><td>10.29 years</td></tr><tr><td>Median Year</td><td>1999</td></tr><tr><td>Average Number of FRP</td><td>.99</td></tr><tr><td>Share of Agreements with the US and/or Canada</td><td>7%</td></tr><tr><td>Share of Agreements with the EU</td><td>31%</td></tr></tbody></table>
the inclusion of environmental provisions (Lechner (2016); Morin, Dür & Lechner (2018)). Beyond political economy motives, other reasons to include environmental provision in a trade agreement are (i) to provide exceptions to trade liberalization for environmental reasons, (ii) to support environmental policy objectives, sometimes to improve compliance with environmental treaties and (iii) to promote environmental cooperation (Francois et al, (2022)).

As our main measure, we use $FRP_g$ which is a dummy equal to 1 if the agreement includes at least a fishery-related provision and 0 otherwise, and we use a logit estimator to estimate this equation. Table A2 in Appendix provides the logit estimation results. Column (1) is the benchmark estimation and is the support of most of the results hereafter.<fnref n="5" /> Note also that including a wide array of observables in this estimation also reduces the probability to have a selection issue due to unobservables.

Once $\beta_0$ and $\beta$ are estimated, propensity scores are derived for each agreement, capturing the probability that this specific agreement includes a fishery-related provision. Appendix B provides supporting results to confirm the matching quality. First, Figure B1 plots the estimated propensity scores for the two groups of agreements (with and without FRPs). On average, propensity scores are larger for those agreements that contain a fishery-related provision than for agreements that do not include these provisions. Second, Figure B2 plots the trends of MTL for both treated and control agreements in the unmatched and matched samples. Whereas the parallel trends assumption is violated in the unmatched sample, the assumption is likely to hold in the matched sample.

After estimating the propensity score of each agreement to include FRPs, we match each agreement that includes an FRP with an agreement that has the closest propensity score but does not include an FRP. This allows the matching of agreements including FRPs with neighboring agreements that do not include FRPs. We then run the regressions of Equations (1) and (2) on the set of matched agreements.

<footnote n="5">⁵ To check the robustness of results, used in section 5, we include additional variables to explain the likelihood of including FRP in trade agreements (Table A2, col.2 and onwards).</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 3: RTAs and FRPs</figcaption></figure>

The benchmark set of fishery-related provisions is Set 1, as it more accurately reflects the inclusion of the fishery sector in environmental provisions. Other sets of provisions are used for robustness checks. Panel A in Table 2 shows some statistics on the TREND sample of trade agreements with fishery-related provisions (Set 1). Out of the 726 RTAs, we consider that 162 of them include a fishery-related provision. Together, these 162 RTAs include 315 provisions, meaning that each RTA with fishery-related provisions includes slightly fewer than 2 provisions.

Using the benchmark set of provisions (Set 1), Figure 3 plots the number of RTAs and the number of RTAs with fishery-related provisions in our sample, ranked by the year of signature of the RTA. It shows the increase in the number of RTAs since the 1990s. Besides, almost all recent RTAs include an FRP which reinforces the importance of assessing their effectiveness.

### 2.2.2. Content of provisions and their potential effects

To account for the heterogeneity across provisions, we classify each provision along three dimensions. Table A.2 details the categorization for each provision.

First, to characterize the objective of environmental provisions, we categorized each FRPs by its function<fnref n="13" />.
Provisions were grouped into four functional types: Environmental Cooperation, Institutional Alignment, Fishery Regulation, Trade Restrictive. This classification reflects the intended operational logic of the provision—whether it encourages information sharing (environmental cooperation), imposes bans or restrictions,

<footnote n="13">¹³We used large language model-based tools (ChatGPT) to assist in the preliminary classification of provisions based on their text. All classifications were then systematically cross-checked and verified to ensure accuracy and consistency. The other classifications used in this paper—such as those related to themes or legal enforceability— do not rely on large language models; these were based solely on our coding.</footnote>
**Characteristics of this matched sample.** Panel B in Table 2 provides some statistics regarding the matched sample. On average, we can follow trade agreements 15 years after signature, the median RTA is signed in 1984, and an important number of agreements are signed by the EU.

# 4. Results

## 4.1 Impact of provisions on MTL

### Average impact of FRPs on MTL

Table 3 presents the baseline results derived from the estimation of Equation 1, which focuses on the impact of the existence of an FRP. Using the matched sample, trade liberalization can be seen to decrease the average MTL of signatories, controlling for the lagged MTL level (Column 1). However, this negative effect of trade liberalization on fishery stock status is dampened for agreements that include a fishery-related provision (Column 2). Whereas the average trade agreements decrease fish stock status by 1% (Column 4), those agreements that include FRPs do not result in such a decrease and drive no change in fish stock status (compared to the counterfactual). The inclusion of fishery-related provisions compensates for the decrease in MTL. This result holds across specifications controlling for other determinants of MTL at the agreement level, i.e., when controlling GDP level and the change in marine protected areas.

Marine protected areas have an unexpected negative and significant sign on MTL in some specifications, while its coefficient is close to zero. It should be noted that the environmental constraints imposed on marine protected areas are usually low, and those areas including a ban on fishing are extremely limited (Gascuel (2019)).

The main explanatory variable of the specification is the lagged level of MTL, which leaves almost no variance and no potential pathway for trade agreements or FRPs to exert an impact on stocks. When this demanding assumption is relaxed in the specification and the 5-year lag of the MTL level is controlled (Column 4), the point estimates are larger, and their significance levels are unaffected. The main result is confirmed in this specification: the presence of an FRP offsets the decline in fish stock status from trade liberalization.

### Dynamic impact of an FRP on MTL
or establishes new governance frameworks.

Second, we classified each environmental provision based on its bindingness into two types: cooperation and enforcement. This classification follows the coding in the TREND provision-level dataset, where provisions explicitly requiring implementation, enforcement, or compliance monitoring (e.g., bans, regulatory mandates, institutional commitments) were labeled as "enforcement", while those promoting dialogue, collaboration, or alignment with international agreements were labeled as "cooperation".

Third, to capture the thematic focus of environmental provisions, we assigned each to a specific theme from: Species, Ecosystems, Fishery, and Other International Environmental Agreements (IEAs). These categories reflect the primary environmental concern addressed by the provision—for example, whether it targets the protection of marine species (e.g., whales or bycatch), the conservation of ecosystems (e.g., coral reefs or marine pollution), the regulation of fisheries practices, or alignment with existing international agreements such as CITES or MARPOL. This thematic classification allows us to evaluate whether certain environmental priorities are more strongly associated with improved marine sustainability outcomes.

This classification allows us to formulate a number of hypotheses concerning the effects that specific types of FRPs could have on our outcome variable for signatory countries. Indeed the MTL is a function of catches at each trophic level. From the literature we retain the following stylized facts capturing the dynamics of the whole system:

* Catches are increasing with fish stocks and with the fishing effort -provided stocks are not over-exploited (Ekerhovd and Gordon, 2013);

* At lower trophic levels, stocks depend critically on the ecosystem carrying capacity (temperature, preservation of habitat such as coastal ecosystems and coral reefs (Smith et al., 2011; Huang et al., 2023)), but decrease with predation and fishing (Hannesson, 1983);

* At higher trophic levels, stocks depend critically on the availability of preys (fishes of lower trophic levels), and also decrease with fishing (Hannesson, 1983);

* Tariff reductions following the enactment of RTAs tend to increase prices received by exporters, and thus fishing effort (Brander and Taylor, 1997).

From our sample of FRPs, the group of provisions classified under the theme "Ecosystem" (in link with carrying capacity) are therefore expected to have a positive impact on fish populations at low trophic levels, with cascading positive effects at higher trophic levels, and thus on catches at all trophic levels. The expected overall effect on the MTL is ambiguous, depending on the relative increase in catches at each TL. For the FRPs classified under the theme "Species" (illegal trade, illegal fishing), the analysis of their potential impact can be based on their functional type ("Fishery Regulation" or "Trade Restrictive") because details regarding the trophic level of protected species are not available.
Figure 5 plots the dynamic response of fishery stocks to the inclusion of FRPs based on the estimation of Equation 2. Methodologically, we allow the inclusion of FRPs to exert a delayed response over time and to have an anticipated effect. On the matched sample, we allow the fish stock status response to FRPs to be heterogeneous over time. Figure 5 plots the OLS effect per year prior and after treatment. We find that the positive impact of FRPs on fish stock status mainly comes into play approximately 5 to 9 years after the RTA signature and FRP inclusion. The average positive impact of an FRP is driven by a medium-run effect. In addition, we find that there is no clear pre-treatment effect.

This medium-run effect may be determined by various factors. First, trade agreements take time to be implemented. Whereas we identify the signature year, the proper implementation may happen a few years after signature. Second, if FRP generate changes in behavior (such as fishing practices), these changes are not immediate either. Third, fish stock status is the result of dynamic ecological process and adjustments take time: there is a large autocorrelation over time in MTL level.

## 4.2 Potential mechanisms: impact of provisions on other outcomes

Whereas trade agreements tend to decrease MTL, the impact of fishery-related provisions almost offsets this negative impact. There are two broad ways of explaining this result: provisions either reduce trade opportunities (counteracting the RTA effects) or foster the adoption of better fishery resource management practices without affecting trade opportunities. We investigated both potential mechanisms.

### Fish catches.

Figure 6 plots the time-varying effect of FRPs on fish catches. We estimate that FRPs decreases fish catches on average, and the main effect occurs 5 to 10 years after the RTA signature, which is in line with the effect on MTL.

### Fish exports.

Figure 7 plots the time-varying effect of FRPs on fish exports. We estimate that FRPs massively decreases fish exports on average. The treatment effect associated with an FRP is sizable, and the main coefficient is close to -0.1. Exports are decreased by 10% in those RTAs that contain FRPs compared to those that do not.
When examining the functional types of our provisions, two types of functions, "Fishery Regulation" or "Trade Restrictive" are de facto constraints that reduce opportunities from trade - whether through an increase in production costs (regulation) or a limit on the possibilities to export (e.g. from illegal fishing or endangered species). This means that the expected impact from this group of FRPs is to limit the increase in the fishing effort expected after the enactment of a trade agreement. Interestingly, if the composition of catches does not change after the enactment of an RTA with these provisions (i.e. fishing efforts do not target fish species differently before and after the RTA), it is possible to show theoretically from the simple dynamics sketched above that a decrease in the aggregate fishing effort unambiguously increases the MTL. But this assumption is unrealistic (in particular, the relative commercial value of species driving relative fishing efforts is influenced by changes in tariffs and species-level trade bans), so again theory does not allow to predict the sign of the impact on the MTL.

In short, while theory does not allow to predict the overall effects of FRPs on the MTL, it helps clarifying that these effects, if any, must be mediated either by changes in the fishing effort (either in scale or in composition), or by changes in marine ecosystem quality.

Finally, regardless of the mechanism by which a FRP might affect stock evolution and catches at any trophic level, their effectiveness is likely to depend on their type of enforcement mechanism (cooperation, enforcement) and whether they are binding or not.

Section 4.2.1 examines empirically the MTL-impact of FRPs by category (theme, function and type).

## 2.3. Additional data

We incorporate several macroeconomic variables for individual countries in our analysis. To account for the level of development, we use GDP (in current USD) as provided by the World Bank, along with the OECD classification to distinguish between developed and developing economies. Protected marine areas, as reported by the International Union for Conservation of Nature (IUCN), are also considered. Additionally, fish catches (in tons) from Sea Around Us and fish product exports (in current USD) from the Food and Agriculture Organization (FAO) are included as covariates in the propensity score matching. All value-based variables are deflated using the US Consumer Price Index for 2010, sourced from the World Development Indicators.

For the empirical exercise, we further include data on marine protected areas (UNEP-WCMC and IUCN, 2025), governance quality and international environmental agreements (Teorell et al., 2019; Mitchell et al., 2020; Mitchell, 2020), catch-share programs (Eisenbarth, 2021), fishery subsidies (Sumaila et al., 2019a), fishing ground biocapacity per capita, and fish caught by trawling from EPI (Block et al., 2024).

## 3. Methodology

We adopt a matched agreement-level approach that estimates the impact of RTAs and an FRP on the MTL, accounting for the potential endogeneity of fishery-related provisions. Our approach is similar to
## Fishing technology.

Figure 8 investigates the impact of FRPs on fishing techniques. We use data on the fish caught by trawling, measured as the percentage of a country's fish that were caught by bottom or pelagic trawling, which is a procedure where a fishing net is pulled through the water behind a boat. If better resource management practices are incentivized by FRPs, this should be reflected by a decrease in the share of fish caught by trawling. Indeed, this fishing technique is particularly harmful to marine biodiversity. The trawl favors short-lived species with high turnover that are capable of reproducing quickly and recolonizing the environment after the passage of a trawl. Fishing by trawling has contributed to the transition from an ecosystem dominated by demersal fish to one dominated by mollusks (that are on lower food webs). The adverse impacts of trawling on marine ecosystems include a reduction in the productivity of these ecosystems and the homogenization of habitats, as nets have a large bycatch impact and may harm non-target species (Gascuel 2019)). These impacts have contributed to the decrease in the state of fish stocks in marine ecosystems. Figure 8 shows no significant change in the share of fish caught by trawling after an FRP. The results are mostly non-significant and point toward no increase to a slight increase. Overall, we infer that an FRP tends to decrease catches and fish exports but does not lead to a change in fishing techniques. FRPs are thus effective in increasing MTL by simply counteracting trade liberalization effects.

## 4.3. Omitted Variables

### Omitted Variables: Other environmental provisions.

The existence of an FRP and other environmental provisions, within a single agreement, is highly correlated (Corr.= 0.835, N=726). This represents a threat to the validity of our estimation, as the effect of FRPs on fish stock status could also result from the existence of other provisions related to environmental topics and concerns.

Environment-related provisions (ERPs) are thus an omitted variable in our main specification. Table 4 challenges our main result by including the interaction between trade liberalization and a dummy that captures whether the agreement includes an ERP, as well as the interaction between the existence of any FRP and trade liberalization. We consider the existence of an ERP that excludes FRPs (Columns 1 to 3). The results suggest that the differential effects associated with an FRP are not threatened by the inclusion of an ERP in the same agreement. Across specifications, most results confirm that fish stock status improves when trade agreements include an FRP.
that of Abman et al. (2024), regarding the effectiveness of environmental provisions in RTAs in preventing deforestation.

## 3.1. General model

We start by examining the effect of fishery-related provisions included in RTAs on the aggregated MTL and other outcomes in all the signatory countries (i.e., at the agreement level). We thus aggregate in a panel dataset the outcomes and controls at the agreement-year level.

The effects of fishery-related provisions are estimated using the following general specification at the RTA g x year t level:

$$y_{gt} = \alpha_0 + \alpha_1 \mathbf{1}[PostRTA_{gt}] + \alpha_2 (\mathbf{1}[PostRTA_{gt}] \times FRP_g) + \alpha_3 C_{gt} + \lambda_t + \lambda_g + \varepsilon_{gt} \quad (4)$$

This model estimates the joint impact of signing RTAs and fishery-related provisions on the agreement-level MTL in period t for all signatories of agreement g.<fnref n="14" /> $\mathbf{1}[PostRTA_{gt}]$ is a dummy variable that is equal to 1 after the implementation of the agreement and 0 otherwise. $FRP_g$ denotes the general measure of the presence of fishery-related provisions in agreement g. Empirically, this variable is a dummy that is equal to 1 if the agreement includes at least one FRP and 0 otherwise. We focus on the interaction between agreement implementation and the existence of FRPs in that agreement. In the following, we also estimate the impact of the number of provisions on the agreement-level MTL.

A set of controls at the agreement-year level, $C_{gt}$, are included to capture other determinants of MTL (these variables are also aggregated at the agreement-year level). For instance, as fish stocks evolve through the reproduction of fish and fish catches, stocks in a particular period depend on the previous levels of stocks. To represent these dynamics of fish stocks, we include in $C_{gt}$ a lagged variable for the MTL, with a lag of 2 years, $MTL_{g(t-2)}$.

We include year and agreement fixed effects to address unobserved heterogeneity. The year fixed effect absorbs trends in variables across all agreements, and the agreement fixed effect ensures that the estimates are derived from variation across time for the set of countries that signed the agreement. Note that the unconditional effect of fishery-related provisions cannot be included and estimated, as it is absorbed by the agreement fixed effect. $\varepsilon_{gt}$ denotes the error term.

In this general model, $\alpha_1$ measures the impact of the RTA on the agreement-level outcome (MTL), whereas $\alpha_2$ captures the differential effect of the presence of FRP on the outcome of interest. Focusing on MTL, in line with existing evidence, we expect to estimate $\alpha_1 < 0$, which suggests a negative impact of trade liberalization on the MTL. Importantly, estimating a significant and positive $\alpha_2$ would mean that the presence of FRP works to increase the MTL and counteracts the unconditional effect of trade liberalization. In that case, the MTL would decrease less, ceteris paribus, after trade liberalization if the RTA includes an FRP. In that

<footnote n="14">¹⁴While specific tariff reductions or quota liberalizations vary across agreements, we assume that all RTAs act as liberalizing fish trade, and our analysis evaluates the additional effects of fishery-related provisions conditional on these agreements.</footnote>
Beyond trade agreements, Columns 4 to 6 explore the impact of FRPs controlling for the existence of International Environment Agreements (IEAs) signed by countries. Trade agreement signatories may also be IEA signatories, which specifically target environmental issues. The change in MTL could then be driven by IEAs signed by trade agreement signatories. We do not observe this pattern: the positive impact of FRPs on fish stock status is unaffected when controlling for the number of IEAs signed by the trade agreement signatories.

Overall, our results suggest that the effect of FRPs is positive and significant. If there is any negative effect of trade liberalization on fishery stock status, FRPs reduces this impact, even if the agreement also includes other environmental-related provisions or if signatories are also engaged in international environmental agreements.

**Omitted Variables: Other domestic regulations.**

In addition to provisions included in international trade agreements, changes in fish stock status and fishery outcomes could be related to changes in domestic regulations that are unrelated to trade issues. Indeed, there is a global trend toward the protection of the environment, and this is occurring at the same time as the increase of provisions in trade. In addition, we cannot exclude the idea that both types of regulations (domestic, unrelated to trade and trade-related regulations) are correlated via, for instance, increasing revenues and demand for protection. Controlling for changes in other sources of regulation is thus important.

Table 5 verifies that our results are not determined solely by changes in other regulations at the agreement level. We consider five measures of domestic regulations outside of trade. First, we allow the impact of trade liberalization to have a differential impact due to provisions and an increase in marine protected areas (aggregated at the agreement level)<fnref n="6" />. National governments could indeed unilaterally decide to increase marine protected areas, which would result in a change in fishing efforts. Second, we allow the effect of trade liberalization to vary with changes in GDP (aggregated at the agreement level). This measure serves as a proxy for national, unrelated to trade, demand for environmental regulation. For a given trade liberalization agreement, results imply that the initial

<footnote n="6">⁶ As the specification includes an interaction term between trade liberalization and other regulations, the coefficient on the "Post RTA" variable may be hardly interpretable. The coefficient captures the effect of liberalization on MTL evaluated for a zero regulation (such as GDP for instance). As for quotas, since they can realistically be 0, the coefficients are more directly readable.</footnote>
specification, the net effect of trade liberalization and FRP is obtained by adding $\alpha_1$ and $\alpha_2$. Regarding inference, we cluster the standard errors at the agreement level.

## 3.2. General dynamic model

In addition to this general model, we also estimate a dynamic version of the model, in which we allow the FRP to have time-varying effects. After signing the RTA, the FRP may affect the MTL and other outcomes with some delay. We thus transition from a static version of the model (identifying the average effect across years) to a dynamic model:

$$y_{gt} = \alpha_0 + \alpha_1 \mathbf{1}[PostRTA_{gt}] + \sum_{\tau=-15}^{15} \delta_{\tau} (\mathbf{1}[PostRTA_{gt}] \times FRP_g \times [t = \tau]) + \alpha_3 C_{gt} + \lambda_t + \lambda_g + \varepsilon_{gt}. \quad (5)$$

where $\tau$ denotes both the time since RTA signing ($\tau > 0$) and the time before RTA signing ($\tau < 0$). In this specification, our main interest is in the set of $\{\delta_{\tau}\}_{\tau=-15,...,15}$ that identify the pre-treatment and post-treatment effects of the FRP.

## 3.3. Endogeneity of the provisions: A matching approach

The main econometric issue in our models is the endogeneity of the FRP in the signed RTA. Indeed, a country may be interested in including fishery-related provisions in an RTA because of its decreased level of MTL. As a result, the presence of an FRP is not random across agreements.

To correct this endogeneity issue, we use the difference in differences (DID) matching method (Baghdadi et al., 2013; Abman et al., 2024). First, we match the agreements that include a FRP with those agreements without a FRP according to their probability of including an FRP. Then, we estimate a DID model on the set of matched agreements. The matching procedure excludes non-credible counterfactual RTAs (without FRPs) from the sample.

First, we estimate the propensity score for the inclusion of FRPs in the cross-section of agreements through a regression on all RTAs included in the TREND database. This corresponds to the estimation of:

$$FRP_g = F(\beta_0 + \beta X_g) \quad (6)$$

with $FRP_g$ denoting the general measure of the presence of fishery-related provision in agreement g and $X_g$ as a set of covariates describing the characteristics of the agreement and the countries signing it. In our case, $X_g$ includes the covariates that translate the propensity for the countries signing the agreement to include an FRP. Therefore, it includes the log of the number of signatories, the log of the squared year of signature, the quantities fished the 5 years before the signature of the agreement by all signatories, the exports of fishery products from all signatories in the 5 years preceding the agreement and the sum of marine protected areas of all signatories.

These variables capture, among others, political economy motives to include FRP in trade agreements, such as the importance of the fishery sector in signatory countries and its potential lobbying power on governments.
drop in MTL is hardly magnified when countries increase their marine protected areas, or their GDPs increase simultaneously. Third, governments may also set up catch-share programs (quotas) to use as fishery management tools. We use data from Eisenbarth (2022) and control for this potential government tool without affecting our conclusions. Fourth, the ability of governments to impose fishery management tools could be determined by their quality of governance (Teorell et al. (2019)). Including a measure of government effectiveness in the analysis does not change the main result.

Fifth, as the worldwide fishery subsidies are large (USD 35.4 billion in 2018 dollars) (Sumaila, Ebrahim, et al. (2019)) and are at the origin of the degradation of fish stocks, we include them as a control in the estimation of the MTL. We use the most recent data provided by the Sea Around Us Project (Sumaila, Skerritt, et al. (2019)) for the year 2018. The Sea Around Us Project contains information on many countries and provides an interesting categorization of subsidies in terms of fish stock sustainability (beneficial, ambiguous, and harmful subsidies for fish stocks, the latter being the highest category provided, at over USD 22.2 billion).

This data set is however time-invariant. This may be a drawback for our analysis as the level of global subsidies has decreased over time (OECD, 2022) and for a given country, the temporal variation of granted subsidies can be large. But we believe that it is more important to describe in a more relevant way the individual heterogeneity among countries in granted subsidies. The seven leading subsidizing entities, including China, the European Union, the United States, South Korea, Japan, Russia, Thailand, and Indonesia, collectively contribute to over $23 billion (USD) in fisheries subsidies per annum, representing over 65% of the overall global total. The results suggest that most of these omitted variables affect MTL but do not affect the effectiveness of FRPs. The previous results regarding the effect of FRPs are confirmed when accounting for these other domestic regulations.

## 4.4 Heterogeneity

### Depth of FRPs

Table 6, Columns 1 to 3, displays the number of FRPs per agreement. Indeed, many trade agreements include more than one FRP. Some agreements include, for instance, both ecosystems protection-related FRPs and fish trade regulation-related FRPs. The main variable of interest is the interaction between the existence of a trade agreement and the number of provisions in that agreement, and such
Indeed political economy motives have been shown to be an important driver for the inclusion of environ-
mental provisions (Lechner, 2016; Morin et al., 2018). Beyond political economy motives, other reasons to
include environmental provision in a trade agreement are (i) to provide exceptions to trade liberalization for
environmental reasons, (ii) to support environmental policy objectives, sometimes to improve compliance
with environmental treaties and (iii) to promote environmental cooperation (Francois et al., 2025).

For the main measure, we use $FRP_g$, which is a dummy equal to 1 if the agreement includes at least a FRP
and 0 otherwise, and we use a logit estimator to estimate this equation. Table A.1 in Appendix provides
the logit estimation results. Column (1) is the benchmark estimation and is the support of most of the
results hereafter.<fnref n="15" /> Note also that including a wide array of observables in this estimation also reduces the
probability of having a selection issue due to unobservables.

Once $\beta_0$ and $\beta$ are estimated, propensity scores are derived for each agreement, capturing the probability that this specific agreement includes a FRP. Appendix B provides supporting results to confirm the matching quality. First, Figure B.1 plots the estimated propensity scores for the two groups of agreements (with and without FRPs). On average, propensity scores are larger for those agreements that contain a fishery-related provision than for agreements that do not include these provisions. Second, Figure B.3 plots the trends of MTL for both treated and control agreements in the unmatched and matched samples. Whereas the parallel trends assumption is violated in the unmatched sample, the assumption is likely to hold in the matched sample.

After estimating the propensity score of each agreement to include FRPs, we match each agreement that
includes an FRP with an agreement that has the closest propensity score but does not include an FRP. This
allows the matching of agreements including FRPs with neighboring agreements that do not include FRPs.
We then run the regressions of Equations (1) and (2) on the set of matched agreements.

**Characteristics of this matched sample.** Panel B in Table 2 provides some statistics regarding the matched sample. On average, we can follow trade agreements 12 years after signature, the median RTA is signed in 1999, and an important number of agreements are signed by the EU. Besides, Figures B.4 to B.8 provide a view on summary statistics of countries with and without fisheries provisions in the RTAs. Further, whereas structural differences between treated and untreated agreements were a concern, our matching strategy seems to overcome this issue. Figures B.4 to B.8 show close comparability between treated and untreated sample across a variety of observables.

# 4. Results

## 4.1. Impact of provisions on MTL

<footnote n="15">¹⁵To check the robustness, we include additional variables to explain the likelihood of including FRP in trade agreements (Table A.1, col.2 and onwards).</footnote>
estimation relies on the variation across agreements (that all include at least one provision, hence
affecting the sample) as to the number of provisions. The results in Table 6 Columns 1 to 3 show that
the number of FRPs is not associated with any differential effect on the status of fish stocks after trade
liberalization and even indicates a (slightly significant) negative impact of including an increasing
number of FRPs.

Number of agreements containing FRPs

Table 6 Columns 4 to 6 reveal the differential effects of the number of agreements that include at least
one FRP on fish stock status ("Nb. Agreements FRP"). MTL values are indeed harmed by trade
liberalization, but the effect vanishes when signatories have signed many agreements including FRPs.
Note that in the last specification, controlling for a 5-year lag of the MTL, neither trade liberalization
nor provisions affect current MTL. We infer that the existence of the FRP is what matters most, rather
than the number of FRPs. Aggregated over many agreements, these provisions mitigate the negative
impact of trade liberalization.

Types of FRPs

For the purpose of the empirical analysis, we classified the FRPs into five types (see Table 2 and Fig. 3). The type of environmental provision included could also be important in shaping MTL variations after trade liberalization. We thus use the different types of FRPs and allow the impact of RTAs to differ along the types of provisions included in the RTA. The results displayed in Figure 10 show a strong differential effect across FRP types. The positive impacts of fishery-related provisions on MTL seem to be mainly driven by the presence of a single provision.

Figure 10 displays only four provision types, as the provision on the Whaling Convention has very
few observations. Our empirical results show that the main effect derives from FRPs related to the
Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). CITES
is an international agreement regulating international trade in wildlife to prevent its decline. It is
interesting to note that the CITES Convention has broader coverage than marine wildlife but seems
to be at play in the marine context as well. In our context, the fact that MTL is affected solely by
banning and restricting trade in endangered species suggests that the functional biodiversity channel
is effective in that the absence of trade of some species has a positive impact on other species. This
result is in line with the evidence supporting the effectiveness of the CITES Convention in preserving
<table><caption>Table 3: Benchmark results</caption><thead><tr><th></th><th colspan="3">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Post RTA</td><td>-0.002<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.009*<br>(0.005)</td></tr><tr><td>Post RTA × Fish Provision</td><td></td><td>0.010***<br>(0.003)</td><td>0.017**<br>(0.008)</td></tr><tr><td>MTL (t-2)</td><td>0.913***<br>(0.010)</td><td>0.913***<br>(0.010)</td><td></td></tr><tr><td>Ln GDP</td><td>0.003<br>(0.002)</td><td>0.004*<br>(0.002)</td><td>0.018**<br>(0.007)</td></tr><tr><td>Ln Marine Prot. Area</td><td>-0.002***<br>(0.001)</td><td>-0.002***<br>(0.001)</td><td>-0.002<br>(0.001)</td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td>0.531***<br>(0.043)</td></tr><tr><td>Observations</td><td>7554</td><td>7554</td><td>7089</td></tr><tr><td>R<sup>2</sup></td><td>0.990</td><td>0.990</td><td>0.968</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
species (Heid and Márquez-Ramos (2023)). Those findings show a positive effect of CITES (but with
a considerable lag) almost exclusively for populations located in those member countries with strong
enforcement. The positive effect of CITES in member countries with weak enforcement only exists
for large species such as elephants, rhinos, or whales.

In contrast, the other types of FRPs do not seem to play a mitigating role on MTL after trade liberalization. In particular, we would expect the provision of fishery regulations to exert a positive significant impact on MTL. These findings suggest that these provisions are not necessarily binding.<fnref n="7" />

**5. Robustness checks**

This section focuses on robustness checks regarding the main joint effect of trade liberalization and
the existence of FRPs on fish stock status, catches and exports. The results are displayed in Appendix
C.

**Alternative sets of fishery-related provisions**

We verified the validity of our conclusions using alternative sets of provisions. Indeed, based on our
understanding and knowledge, we made choices in selecting the set of provisions to be considered.
To overcome this bias, we use two additional sets of provisions. Table C1 describes the additional
provisions provided by Morin, Dür, and Lechner (2018) that we use for our robustness check.
Compared to the benchmark set, alternative list 1 adds potential but minor provisions that only relate
to fisheries. Alternative list 2 adds provisions that could affect fisheries, but whose objective is not
restricted to fisheries.

We replicate our analysis using these alternative sets of provisions. Figure C2 confirms that our choice
was not arbitrary and that our conclusions are independent of the set of provisions considered. If
any, increasing the set of FRPs in the analysis decreases the magnitude of the estimated impact, but
the pro-MTL effect of FRPs remains significant 5 to 9 years after RTA signature.

<footnote n="7">⁷ Additional results (not displayed in the article) support no significant difference in the trajectory of binding agreements (N=96) compared to non-binding ones. A provision is considered as “binding” if the related agreement mentions some binding obligations (i.e., the agreement mentions “shall”, “have to”, “must”), see (Morin, Dür, and Lechner (2018)) as opposed to voluntary actions (“best efforts”, ‘wishes”...).</footnote>
**Trade liberalization reduces mean trophic level, but FRPs overcome this decline** Table 3 presents the baseline results derived from the estimation of Equation 1, which focuses on the impact of the existence of an FRP. Using the matched sample, trade liberalization can be seen to decrease the average MTL of signatories, controlling for the lagged MTL level (Column 1). However, this negative effect of trade liberalization on the MTL is dampened for agreements that include a FRP (Column 2). Whereas the average trade agreements decrease the MTL by 1% (Column 4), those agreements that include FRPs do not result in such a decrease and drive no change in the MTL (compared to the counterfactual). The inclusion of FRP compensates for the decrease in MTL, and maybe increases MTL. This result holds across specifications controlling for other determinants of MTL at the agreement level, i.e., when controlling GDP level and the change in marine protected areas.

Marine protected areas have an unexpected negative and significant effect on MTL in some specifications, while its coefficient is close to zero. It should be noted that the environmental constraints imposed on marine protected areas are usually low, and those areas including a ban on fishing are extremely limited (Gascuel, 2019).

The main explanatory variable of the specification is the lagged level of MTL, which leaves almost no variance and no potential pathway for trade agreements or FRPs to exert an impact on MTL. When this demanding assumption is relaxed in the specification and the 5-year lag of the MTL level is controlled for (Column 4), the point estimates are larger, and their significance levels are unaffected. The main result is confirmed in this specification: the presence of an FRP offsets the decline in the MTL from trade liberalization.

**Provisions' positive effect on mean trophic level emerges gradually** Figure 4 shows the dynamic response of the MTL to the inclusion of FRPs, based on the estimation of Equation 2. This approach allows for both delayed and anticipated effects over time. We find no evidence of pre-treatment effects, as all pre-treatment coefficients are statistically insignificant. The positive effects of FRPs on MTL emerge clearly around 5 to 9 years after the agreement, suggesting that provisions take time to materialize, likely due to implementation delays and ecological adjustment processes. Positive effects persist around 11 to 14 years after signature; however, data limitations—stemming from the limited number of long-term observations—make it difficult to fully assess the longer-run effects beyond that horizon. This medium-run effect may be determined by various factors. First, trade agreements take time to be implemented. Whereas we identify the signature year, the proper implementation may happen a few years after the signature. Second, if FRPs generate changes in behavior (such as fishing practices), these changes are not immediate either. Third, the MTL is the result of a dynamic ecological process, and adjustments take time: there is a large autocorrelation over time in the MTL level.
## Alternative estimation procedures

### *Alternative clustering level.*

Baseline results allow for the serial correlation of errors for observations within a single agreement. We check that our results hold by using alternative clustering levels of standard errors. Figure C3 shows the results when clustering the errors at the observation year x decade of the RTA signature.

In addition, one country's fish stock status will be used as the dependent variable multiple times if that country has multiple agreements. This induces a relationship among the different observations of the dependent variable that could affect inference. We allow errors to be correlated across groups of observations along the number of agreements with FRPs in place. We thus group observations into clusters, depending on the number of agreements with FRPs in place across signatories in the year of observation. By using these groups (N =387), Figure C4 shows a confirmation of the inference previously discussed.

### *Alternative fixed effects*

We take full advantage of the matched diff-in-diff strategy. Whereas baseline results use the matched sample without any further constraints, we constrain our estimation to include more demanding fixed effects to ensure comparability across agreements. Precisely, once a matched sample is created, we can identify the exact match of each agreement with an FRP, namely, the most similar agreement without an FRP. In the baseline matched sample strategy, the treated agreement is compared to the average untreated agreement (in which treated and untreated agreements can be compared). We now explicitly condition the estimation to compare any agreement with its own, untreated neighbor. Each treated agreement - untreated agreement (neighbor) couple is assigned as a match, and we include this match fixed effect in our estimation. Note that match fixed effects absorb agreement fixed effects. Figure C5 plots the results. Estimated effects are slightly lower in this specification, but the significance remains unchanged. FRPs increase the MTL on average, and the main effect occurs between 5 and 9 years after RTA.

### *Dynamic specification of MTL.*

Our main estimation either includes 2-year or 5-year lags of the MTL as independent variables. We first check that considering other lags does not alter the main finding. Table C6, Columns 1 to 5 use
<figure><img src="image_5.png" /><figcaption>Figure 4: Time-Varying Impact of FRP on MTL</figcaption></figure>

## 4.2. Heterogeneity

### 4.2.1. Across-Agreement Heterogeneity

Figure 5 leverages the variation across agreements and provisions to assess how the effects of fishery provisions depend on their composition, function, theme, and legal enforceability. This analysis allows us to estimate empirically the theoretical effects discussed in Section 2.2.2.

First, we examine heterogeneity in terms of the composition of signatories—specifically, whether agreements are signed by developed countries only, developing countries only, or a mix of both (Fig. 5a). We find that agreements involving only developed countries or mixed development contexts tend to yield more consistent and precisely estimated increases in MTL. In contrast, agreements signed exclusively among developing countries exhibit larger estimated effects, but with much wider confidence intervals. This is likely due to both the smaller and more variable initial levels of MTL in these countries, and to weaker baseline monitoring and enforcement capacity.
the various lags of MTL in the specification without affecting the signs and magnitude of the effect
of an FRP. Finally, by including a lagged dependent variable, the model becomes a dynamic panel
model (Arellano and Bond (1991)). We thus consider a dynamic panel estimator in Column 6 of Table
C6. The conclusions are unaffected by the use of this alternative estimator.

Alternative propensity scores

Our results resist the use of alternative propensity scores. We have used an alternative set of variables
in X_g that determines any probability of an agreement including an FRP. In addition to the variables
already included (see Section Methodology), X_g also includes the maximum MTL level by
signatories, the aggregate land area of signatories, the number of environmental provisions already
signed by all signatories of the agreement, the mean GDP of signatories, the total amount of protected
areas (including land protected areas), the share of signatories from developed countries, and the
fishing ground biocapacity per capita (as a measure of the ability of the signatories to produce
seafood). The alternative propensity scores (see Figure B3) lead to a different matched sample, with
which we replicate the main tables. The results in Figure C7 confirm the baseline effects we
highlighted using our former set of matching variables. Our results are not dependent on these
assumptions.

Heterogeneity-robust treatment effect

Recent studies (de Chaisemartin and D'Haultfœuille (2022)) argue that the treatment effect from the
two-way fixed effects estimations can be biased due to heterogeneity in the treatment effect across
groups and across time. Recent papers acknowledge this bias and propose unbiased heterogeneity-
robust estimators (de Chaisemartin and D'Haultfœuille (2020); Borusyak, Jaravel, and Spiess (2021);
Sun and Abraham (2021)). We use these estimators and check the sensitivity of our results. The
dynamic, heterogeneity-robust estimations are plotted in Figure C8. Overall, most results remain
unaffected by using alternative estimators. We consistently estimate a positive and significant effect
of FRPs on fish stock status, no pre-treatment effect and of the majority of the impact to come into
play approximately 5 to 10 years after the RTA. Different estimators, however, provide different time
profiles of the effect across years: the Borusyak, Jaravel, and Spiess (2021) estimator suggests that the
effect comes into play earlier than the estimator in de Chaisemartin and D'Haultfœuille (2020).

Constrained matching
<figure><img src="image_6.png" /><figcaption>(a) Composition of signatories</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) Provision theme</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Provision function</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) Provision type</figcaption></figure>

Figure 5: Heterogeneity across provisions and agreements
Recent trade agreements almost all include FRPs (Figure 4). As a result, finding an untreated agreement for these recent RTAs is challenging and can lead to inaccurate doubtful comparisons between recent RTAs (with FRPs) and very old RTAs without FRPs. To overcome this potential bias, we restrict our sample to matches that have less than 15 years of difference, and the propensity score estimation remains unchanged, implying that treated agreements and untreated agreements must have been signed in a 15-year window. From this additional matched dataset, Figure C9 plots the estimations from this constrained-matching sample and compares them to the benchmark estimates. All our conclusions are confirmed using this constrained estimation.

Another possible concern regarding our research design is that agreements composition may overlap across agreements. Indeed, it is possible that treatment affects the control group because of country overlap: including an FRP in an agreement between countries *i* and *j* affects the average trophic level of all agreements that include country *i* or *j*, which may serve as counterfactuals. First, note that this it mechanically reduces the difference between control and treatment average MTL. Second, we constrained the estimation to match treated and control agreements with no overlap in country composition. In this estimation on a reduced sample, treated and control agreements cannot be signed by any common country. Figure C10 plots the effects: we estimate similar patterns, with lower precision of estimates however, which is driven by lower matching quality.

## 6. Conclusions

In this paper, we evaluate the effectiveness of fishery-related provisions (FRPs) in regional trade agreements (RTAs) in mitigating the depletion of fisheries that results from trade liberalization. Our empirical strategy addresses the potential endogeneity of the inclusion of environmental provision in RTAs. For the 726 RTAs signed during the period 1947-2018, we estimate the impact both of signing a trade agreement and that of the FRPs on fish stock status using a difference-in-differences (DID) propensity score matching method. Our empirical results thus reflect causal relationships between the inclusion of environmental provision and fish stock status.

Our empirical results provide interesting insights. First, we find that the average trade agreements reduce fish stock status by 1%, while the agreements that include FRPs result in no change in the status of fish stocks (compared to the agreements without these provisions). This means that the FRPs
Second, we examine functional differences among provisions by classifying them as either "Environmental Cooperation", "Institutional Alignment", "Fishery Regulation", or "Trade Restrictive" (Table A.2 and Fig. 5b). Trade Restrictive provisions stand out as the most consistent and statistically significant positive effects on MTL, with improvements emerging around five years after implementation. In contrast, provisions related to Institutional Alignment and Environmental Cooperation show some moderate effects over the medium term. We find no measurable impact from fishery regulation provisions, as their estimated effects are large but statistically insignificant.

Third, we analyze thematic heterogeneity by grouping provisions according to whether they address Fisheries, Ecosystems, Species, or reference to International Environmental Agreements (IEAs) (Table A.2 and Fig. 5c). In the short term, provisions targeting fisheries directly or referencing broader environmental commitments tend to correlate with increases in MTL. In the longer run, species-focused provisions are associated with particularly meaningful gains—consistent with the idea that protecting specific marine species takes time to affect trophic structures. Provisions targeting ecosystems also show a delayed but positive effect, reflecting the longer temporal dynamics of ecosystem-level recovery.

Lastly, we differentiate between provisions that are legally enforceable—i.e., backed by formal dispute settlement or conditionality clauses—and those based on voluntary or cooperative mechanisms (Table A.2 and Fig. 5d). Enforceable provisions generally exhibit statistically significant positive effects on MTL, whereas cooperative provisions display smaller and less precisely estimated effects. However, the difference between the two types of provisions is not statistically significant, and a noticeable pre-trend for enforceable provisions suggests that part of the observed effect may reflect concurrent non-trade cooperation or domestic policy reforms rather than the RTA itself. These findings imply that legal enforceability may accelerate or amplify ecological responses on average, but both enforceable and cooperative mechanisms can contribute to sustainability improvements over time, albeit with heterogeneous timing and intensity.<fnref n="16" />

This interpretation aligns with prior work emphasizing the potential importance of legal obligations (Hoekman et al., 2023; Francois et al., 2025), but also reflects the ongoing debate in the literature on whether enforceable provisions are more effective in practice. As noted by Hoekman et al. (2023) and Francois et al. (2025), enforcement mechanisms are often weakly implemented (i.e., provisions may not be enforced in practice), and cooperation-based provisions—particularly when linked to development assistance or technical support—may achieve more durable environmental outcomes. Our results are consistent with this view: enforceability may strengthen commitment, but cooperation and capacity-building appear equally crucial for translating trade-related commitments into tangible ecological outcomes.

<footnote n="16">¹⁶The raw TREND dataset provides complementary information on the legal bindingness of each agreement (Morin et al., 2018). Using this classification, we separately estimate the effects of binding and non-binding agreements. As shown in Figure C.1, results closely mirror our benchmark findings based on our direct classification of provisions: binding agreements in the raw TREND dataset and our classification coincide more than 80% of cases.</footnote>
in RTAs offset the negative effects of trade liberalization on fish stock status. This result is robust to different specifications, controlling for other determinants of fish stock status at the agreement level, and to a variety of additional robustness checks, including the use of alternative sets of FRPs, alternative clustering of standard errors, alternative fixed effects, alternative propensity scores, constrained matching, and heterogeneity-robust treatment effects.

Regarding the impact of the number and type of provisions, our results show that the number of FRPs included in an agreement is not associated with any differential impact on fish stock status following trade liberalization. Thus, we conclude that it is the existence of FRPs that matters most, rather than the number of FRPs. The next issue is revealing which FRPs have the greatest impacts on fish stock status. We have therefore used five different types of FRPs and allowed the impact of RTA to differ along the types of the provisions included in the RTA. Our results indicate that the positive impacts of FRPs on fish stock status seem to be mainly driven by one particular type of provision, namely, the CITES Convention on endangered species. This result is in line with evidence supporting the effectiveness of the CITES Convention in preserving especially large species such as whales (Heid and Márquez-Ramos (2023)). These results suggest that trade agreements, including a provision on the CITES Convention, do not harm functional biodiversity and the marine trophic level.

Our overall results suggest that the signature of a trade agreement leads to a depletion in fishery resources among signatory countries, which is consistent with the literature on trade and renewable resources, and that relevant environmental provisions (related to marine resource conservation) tend to offset this negative outcome, in line with evidence regarding deforestation (Abman, Lundberg, and Ruta (2021)).

Regarding the possible mechanisms, the effectiveness of FRPs could result from improved resource management and/or enforcement linked to the FRPs or decreased trade opportunities in fishery products that occurs following the adoption of a trade agreement with an FRP. On the one hand, our empirical results show that FRPs decrease both fish catches and exports at the agreement level. On the other hand, we do not find a significant effect of FRPs on fishing techniques (e.g., the proportion of fish caught by trawling, which is particularly harmful to biodiversity). These findings suggest that fishery-related provisions do not foster the adoption of better fishery resource-management practices but rather serve to reduce trade opportunities. Furthermore, the provision of the CITES Convention
4.2.2. Within-Agreement Heterogeneity

Figure 6 investigates heterogeneity within agreements, focusing on how the effect of FRPs varies across signatory countries based on their level of development, quality of governance, and initial ecological conditions (namely, biocapacity and baseline MTL). This analysis identifies where the average treatment effects we observe are actually concentrated—and for whom the provisions are most effective.

First, when disaggregating by development level (Fig. 6a), we find that developed countries experience relatively immediate and significant improvements in MTL following the adoption of environmental provisions. This likely reflects stronger enforcement institutions, better monitoring infrastructure, and pre-existing regulatory capacity. In contrast, developing countries exhibit larger effects in the medium to long run, suggesting that while provisions do eventually yield sustainability gains, institutional and capacity constraints may delay their impact.

Second, looking at governance quality (Fig. 6b), we find a clear pattern: countries with higher government effectiveness—measured using the World Bank's Government Effectiveness Index (Dahlberg et al., 2018)—tend to experience larger and more positive effects from FRPs. Conversely, countries with lower governance scores exhibit weaker or even negative effects, likely due to implementation gaps or lack of enforcement.

Finally, we assess whether results differ depending on a country's initial (at the signature year) biocapacity (Fig. 6c) or baseline MTL levels (Fig. 6d). We do not observe strong differential patterns, suggesting that the effectiveness of environmental provisions does not hinge on baseline ecological richness or trophic structure. This supports the idea that governance and institutional readiness are more critical determinants of impact than initial ecological conditions.
seems to have a non-negligible effect through the effective enforcement of trade bans on endangered species, especially those on larger species.

This work has important policy implications. Our paper shows that the content of trade agreements is not sufficient to align trade-related objectives and environmental concerns. In our context, the increase in biodiversity after trade liberalization results in a reduction in trade due to the inclusion of provisions. Provisions related to fishery in trade agreements may require harmonization of product standards and adoption of labels, which are costly especially for exporters from developing countries. This may reduce fish trade opportunities between signatories of trade agreements including these provisions.

The lack of impact of fishery-related provisions on fisheries management, such as the use of more sustainable fishing methods, may also be linked to the lack of enforcement of the environmental provisions contained in trade agreements. This issue is also highlighted by European Parliament. Directorate General for External Policies of the Union (2020), which recommend "*to reduce the number of clauses but make the remaining ones binding*" (p.32). The key requirement for an environmental provision to be enforceable is the imposition of transparent and automatic sanctions for non-compliance. In the context of the European Union, European Parliament. Directorate General for External Policies of the Union (2020) recommend that the EU could make greater use of dispute settlement mechanisms with the help of the Chief Trade Enforcement Officer, a role established by the EU Green Deal in late 2019.

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<figure><img src="image_10.png" /></figure>

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<figure><img src="image_11.png" /></figure>

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<figure><img src="image_12.png" /></figure>

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<figure><img src="image_13.png" /></figure>

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<caption>Figure 6: Heterogeneity within agreements across signatories</caption>
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## 4.3. Omitted Variables

We now document that conclusions are robust to omitted variables inclusion.

**General environmental provisions.** The existence of an FRP and other environmental provisions, within a single agreement, is highly correlated (Corr.= 0.835, N=726). This represents a threat to the validity of our estimation, as the effect of FRPs on the MTL could also result from the existence of other provisions related to more general environmental topics and concerns. Environment-related provisions (ERPs) are thus an omitted variable in our main specification. Table 4 challenges our main result by including the interaction between trade liberalization and a dummy that captures whether the agreement includes an ERP, on top of the interaction of interest (between FRP and trade liberalization). We consider the existence of an ERP that excludes FRPs (Columns 1 to 3). The results suggest that the differential effects associated with an FRP are not threatened by the inclusion of an ERP in the same agreement. The MTL improves when trade agreements include an FRP, even accounting for the existence of more general environmental provisions in the agreement.

Beyond trade agreements, Columns 4 to 6 explore the impact of FRPs controlling for the existence of International Environment Agreements (IEAs) signed by countries and entered into force. Trade agreement signatories may also be IEA signatories, which specifically target environmental issues. The change in MTL could then be driven by IEAs signed by trade agreement signatories. We do not observe this pattern: the positive impact of FRPs on the MTL is unaffected when controlling for the number of IEAs signed by the trade agreement signatories.

**Other domestic regulations.** In addition to provisions included in trade agreements, changes in the MTL and fishery outcomes could be related to changes in domestic regulations unrelated to trade issues. Furthermore, we cannot exclude the idea that both types of regulations ((i) domestic, unrelated to trade and (ii) trade-related regulations) are correlated via, for instance, increasing revenues and demand for protection. Controlling for changes in other sources of regulation is thus important.

In fishery economics, it is well known that increased fish prices, driven either through greater domestic demand or opening up to trade, do not necessarily threaten the sustainability of fishery resources well-managed at the domestic level (see, for instance, Li et al. (2021)). Table 5 verifies that our results are not determined solely by changes in other domestic (fishery) regulations at the agreement level. We now comment on these results.
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<table><caption>Table 4: Omitted Variables: other environmental concerns.</caption><thead><tr><th></th><th colspan="6">Dep. Variable: MTI<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Post RTA</td><td>-0.011*<br>(0.006)</td><td>-0.011*<br>(0.006)</td><td>-0.043***<br>(0.015)</td><td>-0.073**<br>(0.028)</td><td>-0.059**<br>(0.027)</td><td>-0.114<br>(0.078)</td></tr><tr><td>Post RTA × Fish Provision</td><td></td><td>0.009***<br>(0.003)</td><td>0.014*<br>(0.008)</td><td></td><td>0.009***<br>(0.003)</td><td>0.016**<br>(0.008)</td></tr><tr><td>MTL (t-2)</td><td>0.911***<br>(0.010)</td><td>0.912***<br>(0.010)</td><td></td><td>0.906***<br>(0.010)</td><td>0.905***<br>(0.010)</td><td></td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td>0.527***<br>(0.043)</td><td></td><td></td><td>0.501***<br>(0.037)</td></tr><tr><td>Post RTA × Other Env. Prov. (0/1)</td><td>0.009<br>(0.006)</td><td>0.005<br>(0.007)</td><td>0.036**<br>(0.016)</td><td></td><td></td><td></td></tr><tr><td>Post RTA × Ln Nb. IEA</td><td></td><td></td><td></td><td>0.013**<br>(0.005)</td><td>0.010*<br>(0.005)</td><td>0.020<br>(0.015)</td></tr><tr><td>Observations</td><td>7554</td><td>7554</td><td>7089</td><td>7512</td><td>7512</td><td>7065</td></tr><tr><td>R<sup>2</sup></td><td>0.990</td><td>0.990</td><td>0.968</td><td>0.990</td><td>0.990</td><td>0.968</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
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<table><caption>Table 5: Omitted Variables: other domestic regulations.</caption><thead><tr><th></th><th colspan="8">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Post RTA</td><td>-0.001<br>(0.006)</td><td>0.004<br>(0.013)</td><td>0.143***<br>(0.028)</td><td>0.467***<br>(0.067)</td><td>-0.009***<br>(0.002)</td><td>-0.010*<br>(0.005)</td><td>-0.003<br>(0.004)</td><td>0.009<br>(0.011)</td></tr><tr><td>Post RTA × Fish Provision</td><td>0.010***<br>(0.003)</td><td>0.017**<br>(0.008)</td><td>0.012***<br>(0.003)</td><td>0.023***<br>(0.007)</td><td>0.009***<br>(0.003)</td><td>0.016**<br>(0.008)</td><td>0.012***<br>(0.004)</td><td>0.028***<br>(0.010)</td></tr><tr><td>Post RTA × Protected Area</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Post RTA × GDP</td><td></td><td></td><td>-0.006***<br>(0.001)</td><td>-0.018***<br>(0.002)</td><td></td><td></td><td></td><td></td></tr><tr><td>Post RTA × Quota Program</td><td></td><td></td><td></td><td></td><td>0.011***<br>(0.004)</td><td>0.007<br>(0.007)</td><td></td><td></td></tr><tr><td>Post RTA × Government Effect.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.000<br>(0.000)</td><td>-0.002*<br>(0.001)</td></tr><tr><td>MTL (t-2)</td><td>0.912***<br>(0.010)</td><td>0.898***<br>(0.009)</td><td></td><td>0.913***<br>(0.010)</td><td></td><td>0.901***<br>(0.014)</td><td></td><td></td></tr><tr><td>MTL (t-5)</td><td></td><td>0.531***<br>(0.042)</td><td>0.492***<br>(0.033)</td><td></td><td>0.531***<br>(0.043)</td><td></td><td>0.442***<br>(0.038)</td><td></td></tr><tr><td>Observations</td><td>7554</td><td>7089</td><td>7554</td><td>7089</td><td>7554</td><td>7089</td><td>4340</td><td>4340</td></tr><tr><td>R<sup>2</sup></td><td>0.990</td><td>0.968</td><td>0.990</td><td>0.969</td><td>0.990</td><td>0.968</td><td>0.991</td><td>0.972</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
# Tables

<table><caption>Table 1: Fishery-related Provisions. Benchmark list.</caption><thead><tr><th>Provision identifier <sup>a</sup></th><th>Type <sup>b</sup></th><th>#<br>Agreement</th><th>Provision Name <sup>a</sup></th></tr></thead><tbody><tr><td>1008</td><td>Species Protection</td><td>32</td><td>Whales and seals (ex: import ban based on CITES)</td></tr><tr><td>100102</td><td>Ecosystems</td><td>3</td><td>Specific environmental issues: Coral reefs</td></tr><tr><td>100103</td><td>Ecosystems</td><td>111</td><td>Specific environmental issues: Seas and oceans</td></tr><tr><td>100106</td><td>Ecosystems</td><td>32</td><td>Specific environmental issues: Protection of coastal areas</td></tr><tr><td>100401</td><td>Fishery Regulation</td><td>78</td><td>Specific environmental issues: Conservation of fishery resources</td></tr><tr><td>100402</td><td>Fishery Regulation</td><td>18</td><td>Specific environmental issues: Sustainable trade in fishery products</td></tr><tr><td>100403</td><td>Fishery Regulation</td><td>23</td><td>Specific environmental issues: Combat illegal fishing</td></tr><tr><td>140301</td><td>CITES</td><td>21</td><td>Relation with international institutions: Prevalence CITES</td></tr><tr><td>140409</td><td>Whaling Convention</td><td>4</td><td>Relation with international institutions: Whaling Convention</td></tr></tbody></table>

a From Morin, Dür, and Lechner (2018).

b Authors typology.
We consider five measures of domestic regulations outside of trade. First, we allow the impact of trade liberalization to have a differential impact due to provisions and an increase in marine protected areas (aggregated at the agreement level).<sup>17</sup> National governments could indeed unilaterally decide to increase marine protected areas, which would result in a change in fishing efforts. Second, we allow the effect of trade liberalization to vary with changes in GDP (aggregated at the agreement level). This measure serves as a proxy for national, unrelated to trade, demand for environmental regulation. For a given trade agreement, results imply that the initial drop in MTL is hardly magnified when countries increase their marine protected areas, or their GDPs increase simultaneously. Third, governments may also set up catch-share programs (quotas) to use as fishery management tools. We use data from Eisenbarth (2021) and control for this potential government tool without affecting our conclusions. Fourth, the ability of governments to impose fishery management tools could be determined by their quality of governance (Teorell et al., 2019). Including a measure of government effectiveness in the analysis does not change the main result. Fifth, as the worldwide fishery subsidies are large (USD 35.4 billion in 2018 dollars) (Sumaila et al., 2019a) and are at the origin of the degradation of fish stocks, we include them as a control in the estimation of the MTL. We use the most recent data provided by the Sea Around Us Project (Sumaila et al., 2019b) for the year 2018: it provides information on many countries and an interesting categorization of subsidies in terms of fish stock sustainability (beneficial, ambiguous, and harmful subsidies for fish stocks, the latter being the highest category provided, at over USD 22.2 billion). While this data set is time-invariant, it describes the individual heterogeneity among countries in granted subsidies.<sup>18</sup> The results suggest that most of these omitted variables affect MTL but do not affect the effectiveness of FRPs. The previous results regarding the effect of FRPs are confirmed when accounting for these other domestic regulations.

**Depth of FRPs.** Table 6, Columns 1 to 3, focus on the number of FRPs per agreement. Indeed, many trade agreements include more than one FRP. The main variable of interest is the interaction between the existence of a trade agreement and the number of provisions in that agreement, and such estimation relies on the variation across agreements (that all include at least one provision, hence affecting the sample) as to the number of provisions. The results in Table 6 Columns 1 to 3 show that the number of FRPs is not associated with any differential effect on the MTL after trade liberalization.

**Number of agreements containing FRPs.** Table 6, columns 4 to 6 reveal the differential effects of the number of agreements that include at least one FRP on the MTL ("Nb. Agreements FRP"). MTL values are indeed harmed by trade liberalization, but the effect vanishes when signatories have signed many agreements

<footnote n="17"><sup>17</sup>Because the analysis includes an interaction between trade liberalization and other regulations, the "Post RTA" variable's coefficient might be difficult to interpret. It reflects the effect of liberalization on MTL when there are no regulations (like a non-credible zero value for GDP). In the case of quotas, which can realistically be zero, the coefficients are easier to understand directly.</footnote>

<footnote n="18"><sup>18</sup>The seven leading subsidizing entities, including China, the European Union, the United States, South Korea, Japan, Russia, Thailand, and Indonesia, collectively contribute to over $23 billion (USD) in fisheries subsidies per annum, representing over 65% of the overall global total.</footnote>
<table><caption>Table 2: Sample characteristics</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td colspan="2"><strong>Panel A: Raw Sample</strong></td></tr><tr><td>Number of Agreements (RTA)</td><td>726</td></tr><tr><td>Number of Agreements with environmental provisions</td><td>630</td></tr><tr><td>Number of Agreements with FRP</td><td>175</td></tr><tr><td>Number of FRP</td><td>322</td></tr><tr><td>Average number of FRP per RTA</td><td>0.44</td></tr><tr><td>Average number of provisions per RTA with FRP</td><td>1.84</td></tr><tr><td>Number of Agreements with FRP related to ...</td><td></td></tr><tr><td>CITES</td><td>66</td></tr><tr><td>Ecosystems</td><td>122</td></tr><tr><td>Fishery Regulation</td><td>79</td></tr><tr><td>Species Protection</td><td>63</td></tr><tr><td>Whaling Convention</td><td>4</td></tr><tr><td colspan="2"><strong>Panel B: Matched sample</strong></td></tr><tr><td>Number of matched agreements with FRP</td><td>134</td></tr><tr><td>Average length after signature</td><td>15.07 yrs</td></tr><tr><td>Average difference in signature years btw control and treated agreements</td><td>6.6 yrs</td></tr><tr><td>Median year</td><td>1984</td></tr><tr><td>Average number of FRP</td><td>0.86</td></tr><tr><td>Share of agreements with the US and/or Canada</td><td>6%</td></tr><tr><td>Share of agreements with the EU</td><td>27%</td></tr></tbody></table>
<table><caption>Table 6: Depth of FRP and number of agreements with FRP</caption><thead><tr><th></th><th colspan="6">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Post RTA</td><td>0.006*<br>(0.003)</td><td>0.006*<br>(0.003)</td><td>0.016**<br>(0.007)</td><td>-0.000<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.011*<br>(0.006)</td></tr><tr><td>Post RTA × Nb. FRP</td><td>-0.009**<br>(0.004)</td><td>-0.009**<br>(0.004)</td><td>-0.023***<br>(0.009)</td><td></td><td></td><td></td></tr><tr><td>MTL (t-2)</td><td>0.902***<br>(0.012)</td><td>0.902***<br>(0.012)</td><td></td><td>0.908***<br>(0.012)</td><td>0.906***<br>(0.013)</td><td></td></tr><tr><td>Ln GDP</td><td>0.007***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.029***<br>(0.007)</td><td>0.001<br>(0.005)</td><td>0.003<br>(0.005)</td><td>0.016<br>(0.016)</td></tr><tr><td>Ln Marine Prot. Area</td><td>-0.003***<br>(0.001)</td><td>-0.003***<br>(0.001)</td><td>-0.005**<br>(0.002)</td><td>-0.001**<br>(0.001)</td><td>-0.002***<br>(0.001)</td><td>-0.003**<br>(0.001)</td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td>0.508***<br>(0.054)</td><td></td><td></td><td>0.506***<br>(0.049)</td></tr><tr><td>Nb. Agreements FRP</td><td></td><td></td><td></td><td>-0.002***<br>(0.001)</td><td>-0.003***<br>(0.001)</td><td>-0.007***<br>(0.002)</td></tr><tr><td>Post RTA × Nb. Agreements FRP</td><td></td><td></td><td></td><td></td><td>0.004***<br>(0.001)</td><td>0.008***<br>(0.002)</td></tr><tr><td>Observations</td><td>3788</td><td>3788</td><td>3557</td><td>6196</td><td>6196</td><td>6018</td></tr><tr><td>R<sup>2</sup></td><td>0.990</td><td>0.990</td><td>0.969</td><td>0.991</td><td>0.991</td><td>0.972</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
<table><caption>Table 3: Benchmark results.</caption><thead><tr><th></th><th colspan="4">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Post RTA</td><td>-0.005***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.012***<br>(0.005)</td></tr><tr><td>Post RTA x Fish Provision</td><td></td><td>0.005**<br>(0.002)</td><td>0.006***<br>(0.002)</td><td>0.011**<br>(0.005)</td></tr><tr><td>MTL (t-2)</td><td>0.903***<br>(0.009)</td><td>0.903***<br>(0.009)</td><td>0.894***<br>(0.009)</td><td></td></tr><tr><td>Ln GDP</td><td></td><td></td><td>-0.000<br>(0.001)</td><td>0.005<br>(0.003)</td></tr><tr><td>Ln Marine Prot. Area</td><td></td><td></td><td>-0.001***<br>(0.000)</td><td>-0.001<br>(0.001)</td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td></td><td>0.584***<br>(0.020)</td></tr><tr><td>Observations</td><td>17956</td><td>17956</td><td>11728</td><td>11728</td></tr><tr><td>R<sup>2</sup></td><td>0.980</td><td>0.980</td><td>0.987</td><td>0.956</td></tr></tbody></table>

Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
including FRPs. Note that in the last specification, controlling for a 5-year lag of the MTL, neither trade
liberalization nor provisions affect current MTL. We infer that the existence of the FRP is what matters
most, rather than the number of FRPs. Aggregated over many agreements, these provisions mitigate the
negative impact of trade liberalization.

4.4. Mechanisms

We finally investigate the potential mechanisms through which FRP affect the MTL. Provisions either
reduce trade opportunities (counteracting the RTA effects) or foster the adoption of better fishery resource
management practices without affecting trade opportunities. Results are presented in Fig. 7.

**Fish catches.** Figure 7a plots the time-varying effect of FRPs on fish catches aggregated at the agreement-level. The agreement-level catches are constructed by summing the catches of each signatory country. FRPs decrease fish catches on average, and the main effect occurs 5 to 10 years after the RTA signature, which is in line with the effect on MTL.

**Fish exports.** Figure 7b plots the time-varying effect of FRPs on fish exports aggregated at the agreement level. The agreement-level exports are constructed by aggregating the exports of each signatory country, as weighted by their fish catches. We estimate that FRPs massively decrease fish exports on average. The treatment effect associated with an FRP is sizable, and the main coefficient is close to -0.1. Fish exports decrease by 10% in those RTAs that contain FRPs compared to those that do not.

**Fishing technology.** Finally, we use data on the fish caught by trawling (Block et al., 2024), measured as the percentage of a country's fish that were caught by bottom or pelagic trawling, which is a procedure where a fishing net is pulled through the water behind a boat. If better resource management practices are incentivized by FRPs, this should be reflected by a decrease in the share of fish caught by trawling. Indeed, this fishing technique is particularly harmful to marine biodiversity. The trawl favors short-lived species with high turnover that are capable of reproducing quickly and recolonizing the environment after the passage of a trawl. Fishing by trawling has contributed to the transition from an ecosystem dominated by demersal fish to one dominated by mollusks (that are on lower food webs). The adverse impacts of trawling on marine ecosystems include a reduction in the productivity of these ecosystems and the homogenization of habitats, as nets have a large bycatch impact and may harm non-target species (Gascuel, 2019). Figure 7c shows no significant change in the share of fish caught by trawling after an FRP. The results are mostly non-significant and point toward no increase to a slight increase. Overall, we infer that an FRP tends to decrease catches and fish exports but does not lead to the adoption of sustainable fishing techniques. FRPs are thus effective in increasing MTL by simply counteracting trade liberalization effects.
<table><caption>Table 4: Omitted Variables: other environmental concerns.</caption><thead><tr><th></th><th colspan="6">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Post RTA</td><td>0.009<br>(0.008)</td><td>0.009<br>(0.008)</td><td>0.008<br>(0.020)</td><td>-0.025*<br>(0.014)</td><td>-0.027**<br>(0.014)</td><td>-0.019<br>(0.033)</td></tr><tr><td>Post RTA x Fish Provision</td><td></td><td>0.007***<br>(0.002)</td><td>0.012**<br>(0.005)</td><td></td><td>0.006***<br>(0.002)</td><td>0.012**<br>(0.005)</td></tr><tr><td>MTL (t-2)</td><td>0.894***<br>(0.009)</td><td>0.894***<br>(0.009)</td><td></td><td>0.903***<br>(0.009)</td><td>0.902***<br>(0.009)</td><td></td></tr><tr><td>Post RTA x Other Env. Prov. (0/1)</td><td>-0.012<br>(0.008)</td><td>-0.016**<br>(0.008)</td><td>-0.021<br>(0.021)</td><td></td><td></td><td></td></tr><tr><td>Post RTA x Ln Nb. IEA</td><td></td><td></td><td></td><td>0.004<br>(0.003)</td><td>0.004<br>(0.003)</td><td>0.001<br>(0.006)</td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td>0.585***<br>(0.020)</td><td></td><td></td><td>0.583***<br>(0.020)</td></tr><tr><td>Observations</td><td>11728</td><td>11728</td><td>11728</td><td>10961</td><td>10961</td><td>10961</td></tr><tr><td>R<sup>2</sup></td><td>0.987</td><td>0.987</td><td>0.956</td><td>0.987</td><td>0.987</td><td>0.956</td></tr></tbody></table>

Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
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# 5. Robustness checks

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This section focuses on robustness checks regarding the main joint effect of trade liberalization and the existence of FRPs on the MTL. The results are displayed in Appendix C.

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**Alternative proxies for the sustainability of marine fishery resources.** MTL has known limitations as a proxy for the sustainability of marine fishery resources, as already discussed in the Data section. In light of these concerns, we complement our analysis with alternative outcome measures more directly linked to resource overexploitation. We focus on two alternative indicators: the EPI "Fish Stock Status" variable (Block et al., 2024) capturing the share of catches that come from overexploited fish stocks, and the total catches of overexploited fish caught, from Sea Around Us. These measures aim to more directly capture the extent of unsustainable harvesting. As shown in Figure C.2, we observe negative trends in both indicators following the adoption of FRPs, consistent with the main idea that such provisions mitigate overexploitation. However, most treatment effects are statistically insignificant. This likely stems from the reduced sample size and higher noise in these alternative datasets, which are less comprehensive and consistent than the catch data used to compute MTL. Despite the statistical imprecision, the direction of the effects supports our main findings.

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**Alternative weighting schemes of individual countries to agreements** To assess the sensitivity of our findings to alternative aggregation schemes—particularly regarding the influence of large fishing nations—we re-estimate the main specification using several weighting approaches (Figure C.3). In the benchmark case, agreement-level MTL is aggregated based on countries’ total catch volumes. We test three alternatives: (i) using production data (i.e., which includes aquaculture and excludes discards) as weights, (ii) applying a square root transformation to catch volumes to reduce the influence of very large producers, and (iii) using unweighted averages across signatories. Across all specifications, the results remain consistent: FRPs continue to mitigate the negative effect of trade liberalization on MTL, confirming a positive average impact regardless of the aggregation method. This suggests our findings are not driven by a few large countries and are robust to alternative weighting schemes.

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**Alternative sets of fishery-related provisions** We verified the validity of our conclusions using alternative sets of provisions. Indeed, based on our understanding and knowledge, we made choices in selecting the set of provisions to be considered. To overcome this bias, we use two additional sets of provisions. Table 1 describes the additional provisions provided by Morin et al. (2018) that we use for our robustness check. Compared to the benchmark set, set 2 adds potential but minor provisions that only relate to fisheries. Set 3 adds provisions that could affect fisheries, but whose objective is not restricted to fisheries.

We replicate our analysis using these alternative sets of provisions. Figure C.4 confirms that our choice was not arbitrary and that our conclusions are independent of the set of provisions considered. If any, increasing

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<table><caption>Table 5: Omitted Variables: other domestic regulations.</caption><thead><tr><th></th><th colspan="10">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Post RTA</td><td>-0.001<br>(0.005)</td><td>-0.002<br>(0.012)</td><td>0.097***<br>(0.022)</td><td>0.333***<br>(0.056)</td><td>-0.007***<br>(0.002)</td><td>-0.014***<br>(0.005)</td><td>-0.003<br>(0.003)</td><td>-0.000<br>(0.007)</td><td>0.045**<br>(0.021)</td><td>0.176***<br>(0.055)</td></tr><tr><td>Post RTA x Fish<br>Provision</td><td>0.006***<br>(0.002)</td><td>0.011**<br>(0.005)</td><td>0.006***<br>(0.002)</td><td>0.012**<br>(0.005)</td><td>0.005**<br>(0.002)</td><td>0.010*<br>(0.005)</td><td>0.009**<br>(0.004)</td><td>0.028**<br>(0.011)</td><td>0.006**<br>(0.003)</td><td>0.011*<br>(0.007)</td></tr><tr><td>Post RTA x<br>Protected Area</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MTL (t-2)</td><td>0.893***<br>(0.009)</td><td></td><td>0.885***<br>(0.008)</td><td></td><td>0.893***<br>(0.009)</td><td></td><td>0.915***<br>(0.013)</td><td></td><td>0.899***<br>(0.008)</td><td></td></tr><tr><td>MTL (t-5)</td><td></td><td>0.582***<br>(0.020)</td><td></td><td>0.559***<br>(0.019)</td><td></td><td>0.583***<br>(0.021)</td><td></td><td>0.483***<br>(0.035)</td><td></td><td>0.576***<br>(0.022)</td></tr><tr><td>Post RTA x GDP</td><td></td><td></td><td>-0.004***<br>(0.001)</td><td>-0.013***<br>(0.002)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Post RTA x<br>Quota Program</td><td></td><td></td><td></td><td></td><td>0.007*<br>(0.004)</td><td>0.013<br>(0.010)</td><td></td><td></td><td></td><td></td></tr><tr><td>Post RTA x<br>Government Effect.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.003<br>(0.003)</td><td>-0.015**<br>(0.007)</td><td></td><td></td></tr><tr><td>Post RTA x Fishery<br>Subsidies</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.003**<br>(0.001)</td><td>-0.010***<br>(0.003)</td></tr><tr><td>Observations</td><td>11728</td><td>11728</td><td>11728</td><td>11728</td><td>11728</td><td>11728</td><td>5330</td><td>5330</td><td>10116</td><td>10116</td></tr><tr><td>R<sup>2</sup></td><td>0.987</td><td>0.956</td><td>0.987</td><td>0.957</td><td>0.987</td><td>0.956</td><td>0.989</td><td>0.963</td><td>0.955</td><td>0.956</td></tr></tbody></table>

Standard Errors (in parentheses) are clustered at the agreement level. All estimations include agreement fixed effects and year fixed effects. Matched sample. See text for details.
the set of FRPs in the analysis decreases the magnitude of the estimated impact, but the pro-MTL effect of FRPs remains significant 5 to 9 years after RTA signature.

**Alternative propensity scores** Our results resist to the use of alternative propensity scores. We have used an alternative set of variables ($X_g$) that determines any probability of an agreement including an FRP. In addition to the variables already included (see Section Methodology), $X_g$ also includes the maximum MTL level by signatories, the aggregate land area of signatories, the number of environmental provisions already signed by all signatories of the agreement, the mean GDP of signatories, the total amount of protected areas (including land protected areas), the share of signatories from developed countries, and the fishing ground biocapacity per capita (as a measure of the ability of the signatories to produce seafood). The alternative propensity scores (see Figure B.2) lead to a different matched sample, with which we replicate the main tables. Figure C.5 confirms the baseline effects using our former set of matching variables. Our results are not dependent on these assumptions.

**Heterogeneity-robust treatment effect** Accounting for treatment heterogeneity reinforces our main findings. Recent studies (de Chaisemartin and D'Haultfœuille, 2022) argue that the treatment effect from the two-way fixed effects estimations can be biased due to heterogeneity in the treatment effect across groups and across time. This concern is particularly relevant in our case in which (i) countries sign multiple trade agreements and (ii) trade agreement signature is staggered over time. To address this bias, we apply recent heterogeneity-robust estimators (Borusyak et al., 2021; de Chaisemartin and D'Haultfœuille, 2020; Sun and Abraham, 2021); dynamic results are shown in Figure C.6. Overall, most results remain unaffected by using alternative estimators. We consistently estimate a positive and significant effect of FRPs on the MTL, no pre-treatment effect and of most of the impact to come into play approximately 5 to 10 years after the RTA. The heterogeneity-robust estimators provide very close estimates and time profiles of the effect. The benchmark OLS estimates yet tend to be slightly smaller but more precise, while all heterogeneity-robust estimators yield larger and closely aligned point estimates with wider confidence intervals.

**Unobserved Trends** To address the possibility that our results may be driven by pre-existing group-specific trends, we estimate a specification that includes group-specific linear time trends (i.e., $\lambda_{group} \times t$). Groups are defined based on the development status of agreement signatories: (1) all countries developed, (2) all countries developing, and (3) mixed agreements involving both developed and developing countries. Interacting these group indicators with a linear time trend allows the MTL trajectory of each group to follow its own path over time. As shown in Figure C.7, the inclusion of these flexible trends does not alter the main results, indicating that the observed dynamics are unlikely to be driven by differential pre-treatment trends.

**Falsification Test** A falsification test strengthens the causal interpretation of our estimates. Specifically, we re-estimate the treatment effects using manufacturing exports as a placebo outcome. Since FRPs should
<table><caption>Table 6: Depth of FRP and number of agreements with FRP</caption><thead><tr><th></th><th colspan="6">Dep. Variable: MTL<sub>gt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Post RTA</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.006<br>(0.006)</td><td>-0.003**<br>(0.002)</td><td>-0.008***<br>(0.002)</td><td>-0.011<br>(0.007)</td></tr><tr><td>Post RTA x Nb. FRP</td><td>-0.004<br>(0.003)</td><td>-0.004<br>(0.003)</td><td>-0.011<br>(0.008)</td><td></td><td></td><td></td></tr><tr><td>MTL (t-2)</td><td>0.890***<br>(0.011)</td><td>0.890***<br>(0.011)</td><td></td><td>0.925***<br>(0.010)</td><td>0.927***<br>(0.010)</td><td></td></tr><tr><td>MTL (t-5)</td><td></td><td></td><td>0.577***<br>(0.028)</td><td></td><td></td><td>0.600***<br>(0.025)</td></tr><tr><td>Nb. Agreements FRP</td><td></td><td></td><td></td><td>0.002*<br>(0.001)</td><td>0.001<br>(0.001)</td><td>-0.002<br>(0.002)</td></tr><tr><td>Post RTA x Nb. Agreements<br>FRP</td><td></td><td></td><td></td><td></td><td>0.003***<br>(0.001)</td><td>0.004<br>(0.003)</td></tr><tr><td>Observations</td><td>5778</td><td>5778</td><td>5778</td><td>8579</td><td>8579</td><td>8579</td></tr><tr><td>R<sup>2</sup></td><td>0.988</td><td>0.988</td><td>0.958</td><td>0.988</td><td>0.988</td><td>0.958</td></tr></tbody></table>
not plausibly affect non-fish sectors, we expect no impact—and indeed, Figure C.8 shows that all coefficients
are close to zero and statistically insignificant.

**Other concerns** Appendix C provides additional results regarding our choice of clustering level, and other estimation strategies accounting for the dynamic impact of MTL, as well as a subsample (excluding the EU countries, for instance). Our initial results are robust to these changes.

# 6. Conclusions

In this paper, we evaluate the effectiveness of fishery-related provisions (FRPs) in regional trade agreements (RTAs) in mitigating the decline in the sustainability of fisheries resources that results from trade liberalization. As an indicator of the sustainability of fisheries resources, we use the Mean Trophic Level (MTL) (Pauly et al., 1998). This measure has the advantage of having global coverage and being consistent across countries and years, which is crucial for the assessment of the effectiveness of trade agreements on the sustainability of fisheries resources. Our empirical strategy addresses the potential endogeneity of the inclusion of FRPs in RTAs. For the 726 RTAs signed during the period 1947-2018, we estimate the impact both of signing a trade agreement and that of the FRPs on the MTL using a difference-in-differences (DID) propensity score matching method. Our empirical results thus reflect causal relationships between the inclusion of environmental provision and the MTL.

Our empirical results provide interesting insights. First, we find that the average trade agreements reduce the MTL by 1%, while the agreements that include FRPs result in no change in the MTL (compared to the agreements without these provisions). This means that the FRPs in RTAs offset the negative effects of trade liberalization on the MTL. This result is robust to different specifications, controlling for other determinants of the MTL at the agreement level, and to a variety of additional robustness checks, including the use of alternative sets of FRPs, alternative propensity scores, and heterogeneity-robust treatment effects.

Results suggest that the signature of a trade agreement leads to a depletion in fishery resources among signatory countries, which is consistent with the literature on trade and renewable resources, but that relevant environmental provisions (related to marine resource conservation) tend to offset this negative outcome, in line with evidence regarding deforestation (Abman et al., 2024). Regarding the possible mechanisms, the effectiveness of FRPs could result from improved resource management and/or enforcement linked to the FRPs or decreased trade opportunities in fishery products that occur following the adoption of a trade agreement with an FRP. On the one hand, our empirical results show that FRPs decrease both fish catches and exports at the agreement level. On the other hand, we do not find a significant effect of FRPs on fishing techniques (e.g., the proportion of fish caught by trawling, which is particularly harmful to biodiversity). These findings suggest that fishery-related provisions do not foster the adoption of better fishery resource-management practices but rather serve to reduce trade opportunities.
Figures

<figure><img src="image_2.png" /><figcaption>Figure 1: Evolution of MTL</figcaption></figure>
Our empirical results thus indicate that the content of trade agreements is insufficient to align trade-related objectives with environmental concerns. Although the fishery-related provisions in trade agreements contribute to the sustainability of fishery resources, this is not for the reasons one might expect. Contrary to our expectations, these environmental provisions do not effectively reduce or eliminate capacity-enhancing fishing practices that deplete global fishery resources. This may also explain why the effects of these provisions do not endure over a longer period in our econometric estimates (up to nine years). We find that these provisions act as 'trade barriers', limiting trade opportunities between the signatories. Fishery-related provisions in trade agreements may require the harmonisation of product standards and the adoption of labels, both of which are costly, especially for exporters from developing countries. This could reduce opportunities for fish trade between signatories of trade agreements containing these provisions, particularly for species of fish that are more subject to labeling and product standards.

This work has important policy implications. Our empirical results indicate that provisions that restrict trade are more effective in improving the sustainability of fishery resources than provisions related to institutional alignment, environmental cooperation, or fishery regulation. Examples of trade-restrictive provisions include 'Endangered species and their illegal trade', 'Whales and seals (e.g., import ban based on CITES)', and 'Combat illegal fishing'. Our empirical results also indicate that fishery provisions that are legally enforceable are more effective in improving the sustainability of fishery resources than provisions based on incentives for cooperation. The European Parliament also highlights this issue, recommending 'reducing the number of clauses but making the remaining ones binding' (European Parliament. Directorate General for External Policies of the Union., 2020, p.32) and recommends that the EU make greater use of dispute settlement mechanisms with the help of the Chief Trade Enforcement Officer, a role established by the EU Green Deal in late 2019.

Finally, our study has some limitations. First, our approach is based on an agreement-level analysis, evaluating comparable agreements with and without fishery-related provisions. An interesting avenue for future research would be to extend the analysis to the country level. This would allow us to investigate which countries—developed or developing—are improving or worsening the sustainability of their fishery resources. Second, our outcome variable, the mean trophic level, is subject to criticism, as with all alternative indicators, such as stock status indicators based on catch data. A more disaggregated analysis focusing on export-oriented fishery products that benefit from stock assessments, such as tuna, cod, and salmon, and that are subject to labeling and product standards, would be a worthwhile area for future research. Third, a caveat of our study is that some environmental provisions in trade agreements may overlap with commitments already present in fisheries-specific international agreements between the same parties. If provisions are merely reiterating pre-existing obligations, the effects we observe could reflect compliance with those prior agreements rather than additionality from the RTA itself. While we control for countries' participation in international environmental agreements, our measure is broad and not limited to fisheries. This represents an important limitation of our analysis and highlights the need for further research using finer-grained
<figure><img src="image_3.png" /><figcaption>Figure 3: RTAs and FRPs</figcaption></figure>

<figure><img src="image_4.png" /></figure>
<figure><img src="image_14.png" /></figure>

data on fisheries-specific treaties to better isolate RTA-specific effects. More generally, our results should be
interpreted as evidence that FRPs in RTAs coincide with improvements in marine trophic levels, without
necessarily distinguishing whether these improvements are due to new enforcement capacity, symbolic signal-
ing, or the reinforcement of prior policy commitments. Finally, while our analysis assumes that trade in fish
is liberalized under RTAs, future research could investigate the actual extent and timing of tariff reductions
and quota liberalizations. Such work would allow a more precise assessment of how trade liberalization
interacts with fishery-related provisions to affect marine sustainability.

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>
<figure><img src="image_5.png" /><figcaption>Figure 4: Distribution and type of FRPs over time</figcaption></figure>

Source: TREND dataset.
<figure><img src="image_21.png" /><figcaption>(a) Time-Varying Impact of FRP on fish catches</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>(b) Time-Varying Impact of FRP on fish exports</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>(c) Time-Varying Impact of FRP on fishing technique</figcaption></figure>

Figure 7: Time-Varying Impacts of FRP on fish catches, exports, and fishing techniques.
<figure><img src="image_6.png" /><figcaption>Figure 5: Time-Varying Impact of FRP on MTL</figcaption></figure>
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# Trade agreements and sustainable fisheries.

Basak Bayramoglu*, Estelle Gozlan*, Clément Nedoncelle*, Thibaut Tarabbia†

*Université Paris-Saclay, INRAE, AgroParisTech, Paris-Saclay Applied Economics, 91120, Palaiseau, France.
†ENPC, France.

# Appendix A: Information about fishery-related provisions

Figure A1: Set of agreements with FRP (benchmark list): next page
936 Zeller, D., M.L.D. Palomares, A. Tavakolie, M. Ang, D. Belhabib, W.W.L. Cheung, V.W.Y. Lam, E. Sy, G. Tsui, K. Zylich, and D Pauly, “Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility,” *Marine Policy*, August 2016, 70, 145–152.

937
938
939

940 Zhou, Li, Xi Tian, and Zhengyi Zhou, “The effects of environmental provisions in RTAs on PM2. 5 air pollution,” *Applied Economics*, 2017, 49 (27), 2630–2641. Publisher: Taylor & Francis.

941

942
943 Zimmermann, Hubert, “Balancing sustainability and commerce in international negotiation: the EU and its fisheries partnership agreements,” *Journal of European Public Policy*, January 2017, 24 (1), 135–155.
<figure><img src="image_12.png" /><figcaption>Number of Provisions</figcaption></figure>
# Appendix
<table><caption>Table A2- First-stage results.</caption><thead><tr><th rowspan="2">Dep. Variable:</th><th colspan="2">Baseline FRP</th><th colspan="2">FRP Alternative 1</th><th colspan="2">FRP Alternative 2</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Nb. Signatories</td><td>0.047<br>(0.175)</td><td>-0.143<br>(0.224)</td><td>-0.004<br>(0.181)</td><td>-0.207<br>(0.227)</td><td>-0.085<br>(0.181)</td><td>-0.156<br>(0.222)</td></tr><tr><td>Signature Year<br>(squared)</td><td>64.165***<br>(20.731)</td><td>39.267<br>(25.668)</td><td>67.322***<br>(20.980)</td><td>39.031<br>(25.974)</td><td>90.081***<br>(17.910)</td><td>74.999***<br>(24.468)</td></tr><tr><td>Total catches (t to t-5)</td><td>-0.354***<br>(0.106)</td><td>-0.193<br>(0.133)</td><td>-0.339***<br>(0.107)</td><td>-0.179<br>(0.136)</td><td>-0.384***<br>(0.098)</td><td>-0.219**<br>(0.109)</td></tr><tr><td>Total Exports (t to t-5)</td><td>0.707***<br>(0.104)</td><td>0.538***<br>(0.116)</td><td>0.730***<br>(0.107)</td><td>0.539***<br>(0.118)</td><td>0.572***<br>(0.084)</td><td>0.418***<br>(0.093)</td></tr><tr><td>Ln Marine Prot. Area</td><td>-0.044<br>(0.053)</td><td>-0.020<br>(0.061)</td><td>-0.033<br>(0.053)</td><td>-0.015<br>(0.062)</td><td>0.051<br>(0.044)</td><td>0.067<br>(0.051)</td></tr><tr><td>Biocapacity</td><td></td><td>0.009<br>(0.131)</td><td></td><td>0.044<br>(0.133)</td><td></td><td>0.086<br>(0.123)</td></tr><tr><td>% of developed countries</td><td></td><td>0.351<br>(0.695)</td><td></td><td>0.455<br>(0.702)</td><td></td><td>0.277<br>(0.686)</td></tr><tr><td>Max. MTL</td><td></td><td>0.345<br>(0.568)</td><td></td><td>0.387<br>(0.576)</td><td></td><td>0.313<br>(0.512)</td></tr><tr><td>Total land area</td><td></td><td>0.000<br>(0.000)</td><td></td><td>0.000<br>(0.000)</td><td></td><td>-0.000<br>(0.000)</td></tr><tr><td>Nb. FRP (t-1)</td><td></td><td>0.040**<br>(0.016)</td><td></td><td>0.049***<br>(0.017)</td><td></td><td>0.039**<br>(0.018)</td></tr><tr><td>GDP</td><td></td><td>-0.114<br>(0.153)</td><td></td><td>-0.145<br>(0.155)</td><td></td><td>-0.113<br>(0.138)</td></tr></tbody></table>

Standard errors in parentheses. Robust S.E.

* p<0.1, ** p<0.05, *** p<0.01
<footnote n="1">¹Université Paris-Saclay, INRAE, AgroParisTech, Paris-Saclay Applied Economics, 91120 Palaiseau, France</footnote>

<footnote n="2">²Université de Lorraine, AgroParisTech-INRAE, BETA, F-54000 Nancy, France</footnote>

<footnote n="3">³Paris School of Economics, France</footnote>
Appendix B: Matching

<figure><img src="image_13.png" /><figcaption>Figure B1: Propensity scores for the two groups (treated and control).</figcaption></figure>
947 A. Information about fishery-related provisions
<figure><img src="image_14.png" /><figcaption>Figure B2: Trends in MTL, unmatched and matched samples</figcaption></figure>
<table><caption>Figure A.1: Set of agreements with FRP (benchmark list)</caption><thead><tr><th></th><th>Number of Provisions</th><th></th></tr></thead><tbody><tr><td>Lomé III 1984</td><td>1</td><td>East African Community Common Market 2009</td></tr><tr><td>Andean Group Quilo Protocol 1987</td><td>2</td><td>China Peru 2009</td></tr><tr><td>EC (12) Enlargement 1987</td><td>3</td><td>Malaysia New Zealand 2009</td></tr><tr><td>Lomé IV 1989</td><td>4</td><td>China Costa Rica 2010</td></tr><tr><td>African Economic Community 1991</td><td>5</td><td>EFTA Peru 2010</td></tr><tr><td>Economic Community Of West African States (ECOWAS) 1993</td><td>6</td><td>EC Korea 2010</td></tr><tr><td>Common Market for Eastern and Southern Africa (COMESA) 1993</td><td>7</td><td>EC (28) Enlargement 2011</td></tr><tr><td>Economic and Monetary Community of Central Africa (CEMAC) 1994</td><td>8</td><td>Japan Peru 2011</td></tr><tr><td>EC Maastricht (15) Enlargement 1994</td><td>9</td><td>Korea Peru 2011</td></tr><tr><td>EC Estonia Europe Agreement 1995</td><td>10</td><td>India Japan 2011</td></tr><tr><td>EC Lithuania Europe Agreement 1995</td><td>11</td><td>EFTA Hong Kong 2011</td></tr><tr><td>EC Latvia Europe Agreement 1995</td><td>12</td><td>Korea Turkey 2012</td></tr><tr><td>Canada Chile 1996</td><td>13</td><td>Colombia Peru EC 2012</td></tr><tr><td>EC South Africa 1999</td><td>14</td><td>Central America EC 2012</td></tr><tr><td>East African Community (EAC) 1999</td><td>15</td><td>Colombia Korea 2013</td></tr><tr><td>New Zealand Singapore 2000</td><td>16</td><td>Central America EFTA 2013</td></tr><tr><td>Cotonou Agreement 2000</td><td>17</td><td>China Korea 2014</td></tr><tr><td>Caribbean Community (CARICOM) revised 2001</td><td>18</td><td>EC West African states 2014</td></tr><tr><td>Algeria EC Euro-Med Association Agreement 2002</td><td>19</td><td>EC Ukraine 2014</td></tr><tr><td>Chile Korea 2003</td><td>20</td><td>EC Moldova 2014</td></tr><tr><td>Morocco US 2004</td><td>21</td><td>EC Georgia 2014</td></tr><tr><td>EFTA Tunisia 2004</td><td>22</td><td>Australia Korea 2014</td></tr><tr><td>Bahrain US 2004</td><td>23</td><td>EC Kosovo SAA 2015</td></tr><tr><td>Australia US 2004</td><td>24</td><td>EC Kazakhstan 2015</td></tr><tr><td>Japan Malaysia 2005</td><td>25</td><td>Korea New Zealand 2015</td></tr><tr><td>Trans Pacific Strategic EPA 2005</td><td>26</td><td>EFTA Georgia 2016</td></tr><tr><td>New Zealand Thailand 2005</td><td>27</td><td>EC SADC 2016</td></tr><tr><td>Albania EC SAA 2006</td><td>28</td><td>Canada EC (CETA) 2016</td></tr><tr><td>Oman US 2006</td><td>29</td><td>EC Vietnam 2016</td></tr><tr><td>Chile Colombia 2006</td><td>30</td><td>EC EAC 2016</td></tr><tr><td>EC Montenegro SAA 2007</td><td>31</td><td>Transpacific Partnership 2016</td></tr><tr><td>Brunel Japan 2007</td><td>32</td><td>Argentina Chile 2017</td></tr><tr><td>Indonesia Japan 2007</td><td>33</td><td>Armenia EC 2018</td></tr><tr><td>EC Lisbon 2007</td><td>34</td><td>EC Singapore 2018</td></tr><tr><td>Japan Thailand 2007</td><td>35</td><td>EC Japan 2018</td></tr><tr><td>Bosnia and Herzegovina EC SAA 2008</td><td>36</td><td>Comprehensive and Progressive Agreement for Transpacific Partnership USMCA 2018</td></tr><tr><td>EC Serbia SAA 2008</td><td>37</td><td></td></tr><tr><td>Japan Vietnam 2008</td><td>38</td><td></td></tr><tr><td>Canada Colombia 2008</td><td>39</td><td></td></tr><tr><td>CARIFORUM EC EPA 2008</td><td>40</td><td></td></tr></tbody></table>
<figure><img src="image_15.png" /><figcaption>Figure B3: Alternative propensity scores</figcaption></figure>