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Do Environmental Provisions in Trade Agreements Make Exports from Developing Countries Greener?

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Environmental provisions in preferential trade agreements (PTAs) are increasing in terms of their number and variety. The economic effects of these environmental provisions remain largely unclear. It is, therefore, necessary to determine whether the trend to incorporate environmental provisions in PTAs counteracts the goal to spur economic development through trade via these PTAs. This is the first article in which the trade effects of environmental provisions in PTAs are thoroughly investigated. The spotlight is put on developing countries for which the assumed trade-off between economic development and environmental protection is particularly acute. This article uses a new fine-grained dataset on a broad range of environmental provisions in 680 PTAs, combined with a panel of worldwide bilateral trade flows from 1984 to 2016. We show that environmental provisions can help reduce dirty exports and increase green exports from developing countries. This effect is particularly pronounced in developing countries with stringent environmental regulations. By investigating how environmental provisions in PTAs affect trade flows, this article contributes to the literature on the following topics: international trade and the environment; design and impacts of trade agreements; and greening the economy in developing countries. It also shows that the design of trade agreements matters. Environmental provisions can be used as targeted policy tools to promote the green transformation and to leverage synergies between the economic and environmental effects of including environmental provisions in trade agreements.
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Do Environmental Provisions in Trade Agreements
Make Exports from Developing Countries Greener?
Clara Brandi, Jakob Schwab, Axel Berger, and Jean-Frédéric Morin
Accepted for publication in World Development
1. Introduction
One of the key impediments for the promotion of a green transformation is the
alleged trade-off between growing versus greening the economy.1 This trade-off is acute for
developing countries, which face the immediate challenge of fostering economic growth to
combat poverty, while their ecological footprints are typically much smaller compared to
developed countries. This alleged trade-off is especially evident in debates about trade
policy: although preferential trade agreements (PTAs) are typically signed with the objective
to boost trade between contracting parties, environmental provisions are increasingly being
incorporated into them (Morin et al., 2018). These provisions are becoming more far-
reaching and cover such issues as the regulation of hazardous waste, deforestation and the
protection of fish stock.
Recent research shows that environmental provisions in PTAs have the potential to
contribute towards environmental sustainability by promoting domestic environmental
legislation and reducing air pollution and carbon dioxide (CO2) emissions (Baghdadi et al.,
2013; Bastiaens & Postnikov, 2017; Brandi et al., 2019; Martínez-Zarzoso & Oueslati, 2016;
Zhou et al., 2017; Kolcava et al., 2019). At the same time, there are concerns that
environmental provisions can run counter to the core objective of PTAs, resulting in a
reduction of trade flows. Research also shows that environmental provisions in PTAs and
other non-trade issues are partly motivated by protectionist interests (Lechner, 2016).
However, while environmental provisions in PTAs are more prominent than ever, very few
studies have investigated their economic consequences. Accordingly, the question arises
whether the recent trend of incorporating environmental provisions into PTAs exacerbates
the alleged trade-off between protecting the environment and generating economic
development, particularly in developing countries. Despite the high political relevance of the
trade and environment interface, the actual effects of environmental provisions on trade
flows remain under-researched.
This article focuses on the effect of environmental provisions incorporated into PTAs
on (the composition of) exports, with a particular emphasis on developing countries facing
the above-mentioned trade-off between economic development and environmental
protection. This research focus is particularly relevant as developing countries want to use
PTAs to increase trade while facing increasing demands from their negotiation partners, in
particular high-income countries, such as the United States and the European Union, to
incorporate ever more environmental provisions.
One key question is whether environmental provisions in PTAs can promote
environmental-friendly trade relations. Can they contribute to limiting trade in “dirty”, i.e.
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1The term “green transformation” refers to the radical shift towards a green economy in light of todays
environmental challenges “to achieve a transformation similar in scope to the Neolithic and industrial
revolutions” (WBGU, 2011, p.1). For a discussion of the term “green economy” and related concepts, see
Loiseau et al. (2016).
polluting goods, and can they promote trade in “green” goods, i.e. goods that reduce or
remedy environmental damage? As it appears that no study has yet been conducted to
investigate the trade effect of environmental provisions in PTAs at the sectoral level, this
article is the first to address this important gap in the literature.
We analyse sectoral bilateral trade data and fine-grained data on environmental
provisions included in PTAs to inspect whether these provisions affect the sectoral
composition of trade flows. We find that including environmental provisions in PTAs, and
particularly markedly trade-restrictive provisions, contribute to reducing the share of
environmentally harmful dirty goods in exports. On the other hand, explicitly liberal
environmental provisions are associated with an increased share of environmentally
beneficial green goods exports.
By asking how environmental provisions in PTAs affect sectoral trade flows, this
study contributes to the literature on economic impacts of deep trade agreements, which
increasingly cover non-trade issues, and the consequences of their specific design features.
Moreover, by providing new evidence on the trade effects of environmental provisions in
PTAs, this study contributes to the debate on trade and environment and the links between
greening the economy and the implications for competitiveness. Last but not least, by
providing evidence that the trade effects of environmental provisions depend on their design,
the study offers policy recommendations for shaping PTAs in ways that help to create
synergies and manage trade-offs between the green transformation and competitiveness.
The remainder of this article is organized as follows: Section 2 provides a review of
the relevant strands of literature and contains our hypotheses; Section 3 includes a
description of the data and methodology used for the empirical analysis; Section 4 presents
and discusses the empirical findings; Section 5 includes the robustness checks; and Section
6 concludes with a discussion of the contributions of study.
2. Literature and hypotheses
To date, there is only limited research on the role of PTAs and their environmental
content in the context of debates about the trade and environment interface and little is
known about the trade effects of environmental provisions in trade agreements. The literature
on the effects of trade agreements is mainly centered on investigating how PTAs in general
affect the levels of trade flows between their parties.2 This literature has traditionally focused
on the World Trade Organization (WTO).3 However, in light of the slow pace of multilateral
negotiations and the surge in PTA negotiations, more recent studies have focused on the
trade effects of bilateral and regional PTAs. These studies usually indicate that PTAs lead to
overall higher trade between their members (Baier & Bergstrand, 2007, 2009; Egger et al.,
2008, 2011; Freund & Ornelas, 2010; Fugazza & Nicita, 2013; Magee, 2008). Some studies
investigate the effects of trade liberalization at the sectoral level.4 For example, Baggs and
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2 For a recent review of the literature on the formation of PTAs and their effects, see Baccini (2019).
3 While the literature finds a positive impact for countries that acceded the WTO (Rose, 2005; Subramanian &
Wei, 2007; Tang & Wei, 2009), the findings about a more general trade effect of WTO membership is less
clear. In the well-known study by Rose (2004), a positive impact is indicated, while, Subramanian and Wei
(2007) find that WTO membership increases trade but only for the members that are participating in reciprocal
tariff reductions.
4 Only recently has research been conducted to assess the trade effects of PTAs across sectors and firms. The
empirical insight that not all firms benefit equally when trade barriers are reduced (e.g. Bernard et al., 2003;
Bernard & Jensen, 1999; Eaton et al., 2004) is mirrored in models of new trade theory (Melitz, 2003). These
models show that trade liberalization generates gains for those, typically large, firms that are very productive,
while less productive firms are frequently not sufficiently competitive in foreign markets and accordingly
cannot benefit from reduced trade barriers.
Brander (2006) find that reduced domestic tariffs are associated with lower profits for
import-competing firms, while reduced foreign tariffs are associated with higher profits for
exporting firms. Baier et al. (2014) find that the intensive margin effects (goods that were
already previously exported) of PTAs are larger than extensive margin effects (goods that
were not previously traded). Baccini et al. (2017) find that the distribution of the gains from
trade is highly uneven, with more competitive firms benefiting disproportionally more.
Spilker et al. (2018) find that firms exporting heterogeneous products, such as textiles,
benefit from PTAs, as they can export more varieties of their products, but that their trade
volume decreases; they find the opposite pattern for firms exporting homogenous products.
Newly available data on the design of PTAs (Dür et al., 2014) make it possible to
study how the effects of PTAs vary in light of their design. While PTAs used to focus mainly
on reducing at-the-border measures, such as tariffs and quotas, negotiating parties are now
tending to focus more on behind-the-border measures in trade agreements. The latest PTAs
incorporate a wide array of behind-the-border issues, including, among them, investment,
services, intellectual property and regulatory cooperation. Existing studies indicate that such
deep PTAs tend to generate more trade than shallower agreements (Baier et al., 2014; Dür
et al., 2014; Mattoo et al., 2017).
In recent years, environmental provisions are more than ever being incorporated in
PTAs.5 Figure 1 shows that the average number of environmental provisions per PTA has
increased sharply since the end of the 1990s. In 2016, each new PTA contained, on average,
approximately 100 different environmental provisions (Morin et al., 2018). Environmental
provisions are becoming more and more diverse and extensive. Multiple environmental
provisions are relevant for the trade flows between PTA partner countries. Some provisions,
for example, aim at reducing trade barriers for environmental goods or justify trade barriers
for hazardous waste; other provisions prescribe environmental regulations which in turn are
likely to affect trade flows by impacting firm’s competitiveness (see also the discussion
below).
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5 For an overview of the uptake of environmental provisions, see www.trendanalytics.info.
Figure 1: Average number of environmental provisions per PTA
The literature points to political and economic explanations for the growing number
of environmental provisions per PTA (Lechner, 2016; Milewicz et al., 2016; Morin et al.,
2018; Blümer et al., 2019; Morin et al., 2019). A first strand of political reasoning makes the
case that the inclusion of environmental provisions in PTAs is used as a strategy to get the
backing of political parties and non-state actors, which are critical for implementing trade
liberalization and would otherwise block the adoption of trade agreements (Gallagher, 2004;
Hufbauer et al., 2000). The inclusion of environmental provisions in trade agreements enjoys
strong public support; a majority of citizens in many countries are in favor of “greening”
PTAs (Esty, 2001; Bernauer & Nguyen, 2015). A second political explanation is that
countries use PTAs as an instrument of environmental diplomacy in order to set higher
environmental standards (Johnson, 2015; Jinnah & Lindsay, 2016). As PTAs enable trade-
offs across different issue areas and can include stringent dispute settlement clauses, they
might be regarded as being more effective for environmental diplomacy than multilateral,
regional or bilateral negotiations that focus solely on environmental issues. A third potential
driver for the inclusion of environmental provisions in PTAs is motivated by economic
considerations (Bechtel et al., 2012; Bhagwati & Hudec, 1996; Krugman, 1997). Countries
with higher environmental standards might want to level the playing field with competitors
by reducing differences in regulatory environments across countries (George, 2014).
Moreover, a number of studies suggest that there might be a link between protectionist
interests and environmental provisions in PTAs (Ederington & Minier, 2003; Lechner, 2016;
Runge, 1990; Subramanian, 1992).
While existing research sheds light on the motivations for including environmental
provision in PTAs, their actual economic effects remain largely unclear. One exception is a
recent study by Lechner (2018) which analyzes how non-trade issues, such as environmental
and labor provisions, affect the behavior of US investors. Lechner finds that their effects
vary across sectors: environmental provisions in PTAs reduce FDI in polluting industries
while they have a promoting effect in environmentally clean industries. Yet, it remains
unknown whether and how the trade effects of environmental provisions vary across
different parts of the economy, how environmental provisions in PTAs affect trade flows at
the sectoral level, and to what extent their sectoral implications generate synergies or rather
trade-offs between trade and the environment.
Several studies show that environmental regulations can affect the composition of
exports and investment. Levinson and Taylor (2008) study the effect of US environmental
regulations on bilateral trade flows with Canada and Mexico and find that they lead to an
increase in imports from these countries. Hanna (2010) assesses the U.S. Clean Air Act and
finds that more stringent US regulation leads to a shift of production out of the country. A
recent review of the literature (Cherniwchan et al., 2017) furthermore indicates that there is
a link between more stringent environmental regulations and reduced exports in polluting
sectors. International environmental regulation can also affect trade flows. Aichele and
Felbermayr (2015) investigate the impact of the Kyoto Protocol on the carbon content of
trade for 15 industries in 40 countries and find that the Kyoto Protocol generated a significant
increase in the carbon content of imports.
Moreover, the literature has typically investigated the relation between trade
liberalization and environmental protection from the perspective of the comparative
advantage that results from varying levels of national environmental regulation. One key
concern is the potential rise of pollution havens in developing countries. According to the
pollution haven hypothesis, formulated for the first time by Copeland and Taylor (1994), the
removal of barriers to trade and investment leads to a relocation of environmental harmful
production stages from (high-income) countries with stringent environmental regulation to
(developing) countries with less stringent environmental regulation. Empirical evidence
remains ambiguous, but several studies provide some support for the pollution haven
hypothesis (e.g. Li & Zhou, 2016; Cherniwchan, 2017). One of these studies was recently
conducted by Kolcava et al. (2019). In this study, the authors find that trade liberalization
via PTAs is associated with an increase in the ecological footprint of developing countries’
exports. According to their results, environmental provisions in PTAs even increase this
effect.
In this article, we analyze the effect of different environmental provisions on the
overall level of exports and a shift in its sectoral composition. In contrast to Kolcava et al.
(2019), we investigate sectoral trade flows instead of ecological footprint exports as the
dependent variable. Also, we use bilateral panel data, which allows us to focus on exports
between the contracting partner countries and control for country specific fixed effects in
our estimation whereas Kolcava et al. (2019) use country-level observations. Moreover,
while Kolcava et al. (2019) use a proxy for the strength of environmental provisions (ranging
from 1 to 6) as explanatory variable for their model extension, our measure of environmental
provisions is not only more fine-grained (see the description in Section 3) but also directly
refers to the affected bilateral trade flows in order to illuminate the trade effects of trade-
restrictive and liberal environmental provisions in PTAs.
In our analysis of sectoral trade flows, we disentangle the effect of different
environmental provisions on dirty and green goods. We refer to goods as “dirty” when they
incur high levels of pollution abatement costs and as “green” when they reduce or remedy
environmental damage (for more details, see Section 3). There are also environmentally
‘neutral’ goods (constituting the majority of traded goods), which are neither particularly
harmful nor beneficial for the environment.
Based on existing research on the protectionist motivations for introducing
environmental provisions in PTAs and on the trade restrictive effects of environmental
regulation, we expect that these provisions will decrease exports in dirty sectors. Echoing
the view of their environmental non-governmental organizations (NGOs) and businesses,
high-income countries are expected to promote environmental provisions in PTAs that
restrict the exports of developing countries’ polluting industries. High-income countries’
businesses prefer to avoid this competition and environmental NGOs want to avoid the
creation of pollution havens in developing countries. In this political context, developed
countries have strong political incentives to design environmental provisions that restrict
developing countries’ dirty exports. If entering into a PTA increases the relative importance
of dirty sectors in developing countries, we expect that the inclusion of environmental
provisions in this PTA will counterbalance this effect. We thus hypothesize:
H1a: Environmental provisions in PTAs reduce exports in dirty goods (from developing
countries).
The Porter hypothesis paints a very different picture than the pollution haven
perspective. According to the Porter hypothesis, environmental regulation does not
undermine competitiveness but acts as an incentive for companies to innovate, which, in
turn, enhances productivity (Porter, 1991; Porter & van de Linde, 1995). In light of the Porter
Hypothesis, environmental provisions can be expected to promote (at least certain types of)
trade flows. Environmental provisions lead to more domestic environmental regulations
(Brandi et al., 2019). These environmental regulations in turn are expected to push firms to
develop more environmentally-friendly technologies, thereby prompting innovations that
compensate or even surpass the costs of complying with new regulations (Porter 1991; Porter
& van der Linde 1995).
While the so-called “weak” Porter Hypothesis posits that innovations induced by
regulation offset the compliance costs, the “strong” Porter hypothesis goes beyond this by
arguing that stringent regulations can lead to changed patterns of competitive specialization
(Lanoie, 2008; Ambec et al., 2013). According to this latter variant, strict regulations lead to
technological learning and trigger innovations that generate new areas of specialization. In
light of stringent regulations, companies are expected to develop green innovations that
become an early mover advantage once other countries enforce comparable environmental
regulations at a later point in time; the strong Porter Hypothesis is thus be mirrored, for
instance, by increasing competitiveness, market shares and exports in green sectors (Pegels
& Altenburg 2019).
There is inconclusive evidence regarding the Porter Hypothesis. Whereas several
studies find that regulation tends to promote innovation (Johnstone et al., 2012), it is unclear
how environmental regulation affects competitiveness (Palmer et al., 1995; Berman & Bui,
2001; Lanoie et al., 2008; Dechezleprêtre & Sato, 2017). A recent meta-analysis suggests
that a positive effect of regulation is more likely at the state, regional or country levels than
at the firm or industry levels (Cohen & Tubb, 2018). Mealy and Teytelboym (2019) find that
countries with stricter environmental policies do indeed export a larger number and more
sophisticated green goods. Environmental provisions in PTAs can either directly function as
environmental regulation of exports or demand such regulation more generally at the
domestic level. In either case, they regulate exports and their effects on exports can thus be
analyzed in view of the Porter hypothesis.
In light of these links between environmental provisions in PTAs as proxy for
environmental regulation and their expected effects for the competitiveness of green sectors,
we expect the following:
H1b: Environmental provisions in PTAs increase exports in green goods (from
developing countries).
While it is important to investigate the effect of environmental provisions more
generally, environmental provisions in PTAs are very diverse and might thus have
heterogeneous effects on trade across sectors. To the best of our knowledge, this varying
nature of environmental provisions concerning expected trade effects has not been assessed
yet. Whereas some environmental provisions are likely to limit trade, due to their very
nature, others have the potential to foster trade flows. We, therefore, distinguish between
trade-restrictive and liberal environmental provisions and assess their effects at the sectoral
level. On the one hand, trade-restrictive provisions are intended to limit environmentally
unsustainable trade flows. These restrictive environmental provisions can affect trade flows
in two different ways. First, countries with stringent environmental regulations can use
environmental provisions in PTAs to “level the playing field” with countries that have weak
environmental regulations (Bhagwati, 1995). Indeed, for example, some environmental
provisions require parties to enhance the level of environmental protection and implement a
list of environmental agreements (Bluemer et al., 2019). These types of environmental
provisions can be used to diminish the competitive advantage of countries with previously
less stringent environmental regulations. This is likely to be of special relevance to
developing countries who tend to have a comparative advantage in dirty sectors and fewer
and less stringent environmental regulations. Second, other trade-restrictive environmental
provisions aim at directly restricting environmentally harmful trade flows. For instance, the
members of the Caribbean Community agreed “to protect the Region from the harmful
effects of hazardous materials transported, generated, disposed of or shipped through or
within the Community” (CARICOM, 2001).
Liberal provisions, on the other hand, intend to strengthen “green” trade. They
include requirements to reduce trade barriers specifically for environmental goods and
services. For instance, the PTA between New Zealand and Taiwan from 2013 requires the
elimination of all tariffs on environmental goods. The EU-Georgia PTA (2014) demands the
parties “to facilitate the removal of obstacles to trade or investment concerning goods and
services of particular relevance to climate change mitigation, such as sustainable renewable
energy and energy efficient products and services.” Liberal environmental provisions also
include clauses that promote international standards, harmonize domestic measures and
indicate the prevalence of trade in cases of inconsistencies with other issue areas. Liberal
environmental provisions that promote economic openness can facilitate the diffusion of
more advanced technologies and environmentally friendly innovations (Prakash & Potoski,
2006), thereby further promoting the competitiveness of the green sectors of the economy.
Overall, environmental provisions can be expected to reduce “dirty” trade flows and
to promote “green” trade flows due to their very nature in terms of aiming at liberalizing
environmentally sustainable and restricting unsustainable trade. In light of the liberalizing
and trade-restricting character of environmental provisions, we expect the following:
H2a: Trade-restrictive environmental provisions in PTAs reduce exports in dirty goods
(in developing countries).
H2b: Liberal environmental provisions in PTAs promote exports in environmental goods
(in developing countries).
3. Data and methodology
We base our analysis of the effects of environmental provisions on exports on a panel
dataset of sectoral bilateral merchandise exports from 1984 to 2016 (UN Comtrade).6 We
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6 Although it would also be interesting to analyze the effect on services trade, due to limited data availability
we remain in line with the majority of studies on the trade effects of PTAs, which restrict the analysis to
merchandise trade.
combine these data with information on trade agreements between the trading partners and
the environmental provisions contained therein.
Information on environmental provisions in PTAs is obtained from the Trade and
Environment Database (TREND). TREND, introduced by Morin et al. (2018), is the most
comprehensive and fine-grained dataset of environmental provisions in PTAs. This list of
PTAs is based on the Design of Trade Agreements (DESTA) dataset, which is by far the
most comprehensive collection of PTAs (Dür et al., 2014). TREND identifies a variation of
286 different types of environmental provisions in 568 PTAs, which have entered into force
and for which complete data are available. These PTAs include 505 agreements in which at
least one partner is a developing country.7 We use the overall number of environmental
provisions included in a PTA as the main dependent variable. The number of environmental
provisions should be a good proxy for the concern of partnering countries to environmental
issues in the PTA, and thus also the breadth and stringency of environmental regulations in
the PTA. PTAs include 14.4 environmental provisions on average (14.7 in PTAs in which
developing countries are involved). However, this number varies widely, with a maximum
of 120 provisions (the 2014 agreement between the EU countries and Moldova) and a median
number of five provisions. More recently signed PTAs tend to include more environmental
provisions (see also Figure 1).
We assess the number of environmental provisions in general and also identify those
environmental provisions that are likely to restrict trade and those that are likely to liberalize
trade and investigate their different effects. Table A2 in the Annex includes a list of the
respective trade-restrictive (e.g. concerning specific restrictions of environmentally harmful
trade) and liberal provisions (e.g. concerning the reduction of trade barriers for
environmental goods) (see also the examples mentioned in Section 2). On average, each PTA
includes 1.58 restrictive and 0.41 liberal environmental provisions.
Given that WTO agreements concern almost every country in the trade flow sample,
they are not included in our analysis. We assume that external EC/EU treaties involve all
members and the respective partner country.
We combine these data with the data on bilateral exports and obtain a sample of
476,152 exporter-importer relationships over 33 years, of which 140,457 are under a PTA.
Between some trading partners, there is more than one PTA in place at a given point in time.
If this is the case, we assume that the environmental provisions in the PTA that contains the
most of them have a stronger effect on trade flows and that provisions in a PTA with less
provisions accordingly do not have any additional effects. We thus take the maximum
number of a respective type of environmental norms (overall, trade-restrictive, liberal) in
place between two countries in a given year as our main independent variable. The results
are robust to this choice.
As main dependent variables, we use both the shares of dirty and green goods in
overall exports. To this end, we sum all sectoral flows in sectors that are either classified as
dirty or green and relate them to overall exports. This is simply the sum of all sectoral
exports. For the goods classifications, we build on the literature that assesses trade in so-
called “dirty” goods and “green” or “environmental” goods. While the former are
particularly polluting, for example steel, cement or chemicals, the latter can be defined as
goods that can be used “to measure, prevent, limit, minimize or correct environmental
damage” (OECD & Eurostat, 1999).
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7 The classification of developing countries is based on the country income group classification of the World
Bank and includes all countries that are not listed as high-income countries.
For data on environmentally dirty sectors, we make use of Low’s and Yeats’ (1992)
approach, which has been used in several studies. Dirty sectors are identified as those
incurring the highest level of pollution abatement and control expenditures (see Annex). On
average, across countries, these dirty products comprise 15 percent of all worldwide exports
over our sample, and 14 percent of exports of developing countries.
For green goods, many attempts have been made to come up with lists of
environmental goods that could be used in trade negotiations. An early list that is frequently
used and comprises 132 items covering, issues such as wastewater treatment and air
pollution control, was drawn up in the context of the Organisation for Economic Cooperation
and Development (OECD) (OECD & Eurostat, 1999). Lists of green goods are not just
prepared for negotiations but they are also themselves part of the negotiations. For instance,
in the Doha negotiations, the members of the so-called “Friends Group” developed a list of
154 products (WTO, 2009). In plurilateral negotiations, the Asia-Pacific Economic
Cooperation (APEC) countries agreed on tariff reductions for environmental goods based on
a list of 54 products (APEC, 2012). These lists are generated by negotiators and thus more
strongly politically determined than the OECD list, which is compiled by OECD experts.
For our classification of green goods, we use a combination of the OECD and APEC lists,
which are “the most commonly accepted lists” (Zugravu-Soilita, 2018). The combined list
includes goods used directly in the provision of environmental services, such as waste
management and air pollution control, and comprises 142 items (see Annex). These green
products constitute 2.8 percent of worldwide exports and 2.3 percent of the exports from
developing countries. For robustness, we also report the results using the WTO Friends’ list,
which are very similar.
The classification of dirty products is based on the three-digit Standard International
Trade Classification (SITC) level, while the classification of green products is on the six-
digit Harmonized System (HS) level. We include only those observations for which
countries have reported data in both product classifications, to keep the samples of the
estimations on dirty and green goods comparable.
We distinguish countries not only by their level of income, but also by their
“greenness”, as measured by the Yale Environmental Performance Index (EPI), which ranks
countries according to their performance concerning environmental quality based on several
indicators of environmental health and ecosystem vitality (Wendling et al., 2018) on a score
from 0 to 100. Countries are classified as “brown” when they rank below the median of 58.8
in the EPI, and as “green” if they rank above the median.8 Since the EPI data is not well
covered over time and we use it only in order to split the sample into two groups of countries,
we use the data from 2018 for the classification of countries, thereby assuming that it is a
good proxy for earlier levels of environmental performance as well. There is little difference
in the share of dirty or environmental exports between brown and green countries on average.
Furthermore, while the EPI is positively correlated with the level of income, of all export
flows from developing countries in the sample, 64 percent of them are considered to have
come from a brown developing country, which means that there is variation in the
classification.
With these data, we estimate a gravity equation (Baier & Bergstrand, 2007) with the
number of (overall, trade-restrictive or liberal) environmental provisions as an explanatory
variable for the composition of exports. Our identification strategy is to compare the change
in the composition of exports between two countries induced by a PTA that includes more

8 The results are robust to choosing another cut-off value of the EPI for the classification of brown countries,
such as the median EPI score of only developing countries, which is, with a value of 54.2, also very similar to
that of all countries.
environmental provisions to the change in the composition of exports between two countries
induced by a PTA with less environmental provisions. To this end, in the panel data, we first
control for whether there is a PTA in place between the two countries, and second also for
the general depth of the PTAs in place between the countries. The information on the depth
of the trade agreements is based on the DESTA depth index (Dür et al., 2014).9 The depth
of a PTA is relatively strongly correlated with the number of environmental provisions, with
a correlation coefficient of 0.67. It is essential to ensure that the effect of the overall depth
of an agreement is not falsely captured as the effect of the inclusion of environmental
provisions. Again, we use the maximum depth of any PTA between a country pair to measure
the depth of the PTAs between a country pair. The depth index in the sample ranges from -
1.4 to 2.2, which we normalize to range from zero to 3.6.
Table A1 in the Appendix contains a list of the countries included in the sample as
either exporters or importers and their classification by income (high-income and developing
countries) and into brown and green countries. The summary statistics of all variables at the
PTA level are listed in Table A3. Table A4 includes a list of the summary statistics for all
trade flow variables.
Our main interest is how environmental provisions affect the composition of trade
flows between partner countries. We exploit the trade data’s panel structure by using
country-pair fixed effects in order to control for unobserved heterogeneity and the time-
invariant characteristics of a trading relationship, such as distance and common-border fixed
effects. By using country-pair fixed effects, we can also control for many selection effects
into signing PTAs and the inclusion of environmental provisions. We include exporter- and
importer-year fixed effects to capture time-variant multilateral resistance and country-
specific developments. Thus, our baseline regression equation is as follows:
𝑆𝐻𝐴𝑅𝐸 𝛽∗𝐸𝑁𝑉𝑃𝑅𝑂𝑉𝑆
 𝛾∗𝑃𝑇𝐴
 𝛿∗𝐷𝐸𝑃𝑇𝐻
 𝛼
 𝛼
 𝛼
  𝜀
1
where e is the index for the exporter, i for the importer and t for the respective year.
𝛼 , 𝛼 and 𝛼 are the country-pair and exporter- and importer-year fixed effects,
respectively, and 𝜀 is an error term. SHARE is the share of dirty (DIRTSHARE) and
environmental (GREENSHARE) products in overall exports. The shares of dirty and
environmental goods in overall trade take on values between 0 and 1, so that the coefficients
can be interpreted as changes in percentage points. The coefficient of interest is 𝛽, where
ENVPROVS can be either the absolute number of environmental provisions, or the number
of restrictive or liberal provisions, respectively. When including liberal and restrictive
provisions, we include them jointly, along with the number of absolute provisions, because
the respective numbers of provisions are positively correlated (see a discussion of the
potential challenge of multicollinearity below). In all estimations, standard errors are
clustered at the country-pair level in order to account for the possibility that country pairs
are subject to idiosyncratic, correlated shocks.
The fixed effects approach exploits the dyadic panel structure of the data and allows
us to control for many sources of endogeneity: Firstly, the country-pair fixed effects capture
all time-invariant country-pair specific variables that may lead to countries signing a PTA
and including more or less environmental provisions, such as distance and a common border
or culture, and thus also the general (average) level of trade between the countries. Secondly,
the exporter- and importer-year fixed effects capture all time-variant country-specific

9 The DESTA depth index does not include information about environmental provisions in PTAs.
variables that may be correlated with both environmental provisions and trade levels, such
as exporters’ and importers’ GDP. A potential source of endogeneity that this approach
cannot control for is that (political actors in) a particular country know(s) that trade levels
and compositions with another country (imports or exports) will change in the future and
therefore include(s) more or less environmental provisions in the respective PTA. This
problem is, however, common to the literature on the trade effects of PTAs, and the multiple
fixed effects approach on panel data taken in this article is arguably the best one that can be
pursued using observational data. In addition, we furthermore conduct some robustness
checks with regard to the control variables, the inclusion of fixed effects, and the estimation
method (see Section 5).
4. Empirical analysis and findings
The hypotheses to be tested formulated above refer to the share of dirty and green
goods in overall exports. In order to be able to interpret these findings, it is helpful to
understand how the levels of overall exports between partner countries are affected by the
inclusion of environmental provisions in PTAs. We therefore estimate whether
environmental provisions affect the overall level of exports between the partner countries of
the PTAs they are included in by estimating Equation (1) with the log of exports (EXPORTS)
as dependent variable. Since the inclusion of environmental provisions often follows
protectionist interests (Lechner, 2016), particularly in relation to developing countries, if
anything, we would expect to find a negative coefficient, indicating that the inclusion of
environmental norms mitigates the trade-creating effect of PTAs (the number of
environmental provisions never changes over time for a certain PTA).
The results of the estimation with overall exports as dependent variable are reported
in Table 1. In all tables presenting the estimations results, we always first depict the results
for all countries for comparison, and then on the sample of developing country exporters
explicitly. The shares of trade flow observations in each sample in which exporter and
importer had an active PTA, and the average numbers of the respective environmental
provisions in each, are reported in the results tables. Complete regression results for the
whole sample, including developed country exporters, are presented in the Appendix.
Table 1: The effect of environmental provisions on the level of exports
(1) (2) (3)
All countries Developing
country exporters
Developing
country exporters
EXPORTS EXPORTS EXPORTS
ENVPROVS -0.000 -0.000 -0.001
(0.001) (0.001) (0.001)
RESTRICTIVE 0.008
(0.009)
LIBERAL -0.007
(0.032)
PTA 0.181*** 0.148*** 0.148***
(0.041) (0.052) (0.052)
DEPTH -0.044** -0.051** -0.048*
(0.019) (0.025) (0.027)
Constant 14.263*** 13.696*** 13.698***
(0.009) (0.012) (0.012)
Exporter-Importer Fixed Effects Yes Yes Yes
Exporter-Year and Importer-Year Fixed
Effects Yes Yes Yes
Observations 476,152 348,844 348,844
Share of Exports under PTA 0.29 0.3 0.3
Average ENVPROVS for exports under
PTA 27.6 24.5 24.5
Average RESTRICTIVE for exports
under PTA
0.78
Average LIBERAL for exports under
PTA
0.84
R2 0.884 0.861 0.861
This table shows the results from running a panel regression of the log of bilateral exports (EXPORTS) between 1984 and
2016 on whether a PTA was signed and overall environmental provisions (ENVPROVS, Columns 1-3) and trade-restrictive
(RESTRICTIVE) and trade-liberalizing provisions (LIBERAL, both Column 3) included in the PTA. Column 1 reports the
results for the entire sample of directed bilateral trade flows, Columns 2-3 report the result on only the sample of developing
country exporters. Robust standard errors clustered at the exporter-importer level are reported in parentheses. p<0.01***;
p<0.05**; p<0.1*.
The results indicate that, contrary to common expectation, including environmental
provisions in trade agreements does not reduce the level (or PTA-induced-increase) of trade
significantly. This finding does not only hold for all countries in our sample, as shown in
Column 1 in Table 1, but also for developing country exporters, as shown in Column 2. In
neither case do we find a significant effect of the amount of environmental provisions on
exports. This result indicates that the overall trade enhancing effect of PTAs is not
necessarily undermined by the inclusion of environmental provisions.
Column 3 of Table 1 shows the results for the inclusion of trade-restrictive and liberal
provisions in PTAs on overall trade. Neither of them has a statistically nor economically
significant effect on overall trade flows (of all countries and of developing countries). As the
level of trade is positively affected by the conclusion of a PTA, which is in line with previous
results in the literature, we can conclude that signing a PTA with environmental provisions
increases exports as much as one with no environmental provisions.10, The estimations
including trade-restrictive and liberal environmental provisions for the entire sample can be
found in Table A5 of the Appendix, and generally reveal the same results as for developing
country exporters. 11
Given that overall levels of exports, even from developing countries, do not seem to
be affected by environmental norms in PTAs, we now analyze whether they affect the
composition of these trade flows. In particular, we empirically assess whether they promote
trade in green goods and restrict trade in dirty goods. To test Hypothesis 1a, we estimate
whether environmental provisions reduce the export of environmentally harmful products.

10 The number of environmental provisions never changes for a given PTA. Therefore, the overall effect of the
provisions can only be compared between, but not within PTAs. This also implies that the effect of
environmental provisions can be assumed to become effective at the same time the PTA does.
11 Our estimations also reveal a negative effect of the depth of a PTA on the overall level of trade flows, which
runs contrary to previous findings in Dür et al. (2014). This surprising side result does not stem from the
correlation with environmental provisions, but rather from the extension of the sample by the period of 2010
to 2016. If we analyze the same time frame as Dür et al. (2014) in their study (with or without including
environmental provisions in the estimation), we find the same results. This turnaround of the effect of a PTA’s
depth is interesting and deserves further investigation but is beyond the scope of this paper.
Table 2 shows the results of estimating Equation (1) with the share of dirty goods in overall
exports as a dependent variable.
Table 2: The effect of environmental provisions on the share of dirty goods in overall
exports
(1) (2) (3)
All countries Developing country
exporters
Developing country
exporters
DIRTSHARE DIRTSHARE DIRTSHARE
ENVPROVS -0.037*** -0.049*** -0.026*
(0.011) (0.015) (0.016)
RESTRICTIVE -0.403***
(0.135)
LIBERAL 0.538
(0.496)
PTA 0.278 0.830 0.877
(0.567) (0.700) (0.699)
DEPTH 0.559** 0.588 0.366
(0.279) (0.371) (0.381)
Constant 15.545*** 14.824*** 14.769***
(0.114) (0.154) (0.152)
Exporter-Importer Fixed Effects Yes Yes Yes
Exporter-Year and Importer-Year
Fixed Effects Yes Yes Yes
Observations 476,152 348,844 348,844
Share of export flows under PTA 0.29 0.3 0.3
Average ENVPROVS for exports
under PTA 27.6 24.5 24.5
Average RESTRICTIVE for
exports under PTA
0.78
Average LIBERAL for exports
under PTA
0.84
R2 0.452 0.454 0.454
This table shows the results from running a panel regression of the share of dirty products in overall merchandise exports
(DIRTSHARE) between 1984 and 2016 on whether a PTA was signed and overall environmental provisions (ENVPROVS,
Columns 1-3) and trade-restrictive (RESTRICTIVE) and trade-liberalizing provisions (LIBERAL, both Column 3) included
in the PTA. Column 1 reports the results for the entire sample of directed bilateral trade flows, Columns 2-3 report the
result on only the sample of developing country exporters. Robust standard errors clustered at the exporter-importer level
are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
The results show that environmental provisions indeed restrict exports of dirty goods
for all countries (Column 1). This effect is even stronger in the case of developing countries
(Column 2). The effect on developing country exports is also economically significant: the
share of exports of dirty products from a developing country that take place under a PTA
with the average number of environmental provisions is lower by 0.72 percentage points
(than the average share of dirty exports of 14 percent in developing countries), which
amounts to an average decrease of approximately 5 percent.
In line with Hypothesis 2a, we also find that trade-restrictive provisions significantly
reduce the share of dirty goods in exports (Column 3). One restrictive provision alone
reduces the overall share of dirty products by 0.4 percentage points. The results indicate that
including environmental provisions in PTAs can be a promising approach to change the
composition of trade flows in terms of making them greener. The inclusion of restrictive
environmental provisions has a particularly strong effect by significantly reducing dirty
goods relative to overall trade.
These results are also interesting in light of the pollution haven hypothesis (Copeland
& Taylor, 1994). We do not find evidence supporting the argument that liberalizing trade,
as a result of the conclusion of a PTA, leads to an increase in exports of dirty products of
developing countries. The estimated effect of PTAs on exports of dirty products is positive,
but not significant. At the same time, the findings suggest that the inclusion of environmental
provisions (Hypothesis 1a), and particularly restrictive ones (Hypothesis 2a) in PTAs can be
a successful strategy to counter pollution haven effects in developing countries.
However, environmental provisions are not only aimed at reducing dirty trade, but
they are also intended to encourage environmentally beneficial trade. To test Hypothesis 1b,
we analyze whether including environmental provisions in PTAs also increases the share of
exports in green goods. We, therefore estimate Equation (1) with the share of green goods
as a dependent variable. Table 3 depicts the results.
Table 3: The effect of environmental provisions on the share of green goods in overall
exports
(1) (2) (3)
All countries Developing country
exporters
Developing country
exporters
GREENSHARE GREENSHARE GREENSHARE
ENVPROVS -0.000 0.000 0.002
(0.004) (0.006) (0.006)
RESTRICTIVE -0.114*
(0.060)
LIBERAL 0.411**
(0.184)
PTA 0.032 0.112 0.156
(0.176) (0.205) (0.204)
DEPTH -0.007 -0.059 -0.143
(0.092) (0.112) (0.111)
Constant 2.820*** 2.346*** 2.343***
(0.040) (0.050) (0.050)
Exporter-Importer Fixed Effects Yes Yes Yes
Exporter-Year and Importer-Year
Fixed Effects Yes Yes Yes
Observations 476,152 348,844 348,844
Share of export flows under PTA 0.29 0.3 0.3
Average ENVPROVS for exports
under PTA 27.6 24.5 24.5
Average RESTRICTIVE for exports
under PTA
0.78
Average LIBERAL for exports
under PTA
0.84
R2 0.225 0.213 0.213
This table shows the results from running a panel regression of the share of environmental products in overall merchandise
exports (GREENSHARE) between 1984 and 2016 on whether a PTA was signed and overall environmental provisions
(ENVPROVS, Columns 1-3) and trade-restrictive (RESTRICTIVE) and trade-liberalizing provisions (LIBERAL, both
Column 3) included in the PTA. Column 1 reports the results for the entire sample of directed bilateral trade flows, Columns
2-3 report the result on only the sample of developing country exporters. Robust standard errors clustered at the exporter-
importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Columns 1 and 2 in Table 3 show that the overall number of environmental
provisions, in contrast to Hypothesis 1b, does not affect the share of green goods in exports,
neither in general, nor for developing countries. However, in line with Hypothesis 2b,
explicitly liberal environmental provisions boost the share of green goods in overall exports
in developing countries (Column 3).12 One liberal provision increases the share of green
goods by 0.4 percentage points, which equates to an average increase of 17 percent.
Restrictive provisions, in contrast, tend to decrease the share of green goods, which suggests
that intended trade-limiting effects pertaining to environmentally harmful trade flows spill
over to green sectors as well. The growing share of green exports is in accordance with the
strong Porter hypothesis, according to which stricter environmental regulations increase
firms’ competitiveness in regulated sectors.
The effect of environmental provisions on the export structure might, however,
depend on the initial conditions in the exporting country. A developing country that already
has greener regulatory frameworks might find it easier to comply with environmental
provisions in PTAs and adapt its production structure and export composition. Moreover,
innovations triggered, for example, by strict environmental regulations are typically
cumulative and characterized by path-dependency because of the network and bandwagon
effects they entail (Pegels & Altenburg, 2019). Regulations and other initial triggers of
innovation and specialization thus tend to shape successive innovations and patterns of
specialization (Dosi, 1988). Furthermore, as socio-technical development is path-dependent,
the early mover advantage posited by the strong Porter hypothesis is strengthened by the fact
that it helps to avoid costly lock-ins in terms of a “non-green” specialization and the
production processes and infrastructure its involves (Pegels & Altenburg, 2019). When a
“non-green” socio-technical development path has become stable, it is economically and
politically very costly to leave this path because the costs of swapping paths rise due to the
lock-in of investments and challenges concerning institutional and behavioural change
(Unruh & Carrillo-Hermosilla, 2006). If, in contrast, a “green” path has been embarked upon,
switching costs are much less relevant. In light of path dependency, green specialization thus
increases the likelihood of further green specialization (Aghion et al., 2016; Mealy &
Teytelboym, 2019).
We therefore also investigate whether the effects of environmental provisions on
trade flows of developing countries might depend on their initial level of “greenness,” i.e.
their prior environmental performance. We expect firms in countries that have already
embarked on the path towards a green transformation to more easily adapt to new
environmental provisions in trade agreements and to more swiftly and substantially modify
their production and export composition to the respective partner countries. We thus also
expect the effects of environmental provisions to be stronger in “green” countries with better
environmental performance than in other countries that do not perform well concerning
environmental quality indicators. Accordingly, we expect liberal environmental provisions
to increase green exports in these countries more strongly than they do in other countries.

12 The results for the sample of all exporters, also including developed countries, on the effects of restrictive
and liberal provisions are presented in Table A5 in the Appendix. The differences among them is driven by
exports of developed countries. The result that liberal provisions increase the share of green goods is also
present for high-income country exporters.
We also expect restrictive provisions to reduce dirty exports more strongly in green rather
than in other countries.
To test this, we estimate the above regressions with the absolute number of
environmental provisions and the number of trade-restrictive and liberal provisions for
brown and green developing country exporters separately. To do so, we interact a dummy
for whether, according to the EPI, an exporting-developing country is brown or green with
the respective number of provisions in a PTA as explanatory variables in the estimation of
Equation (1) with overall exports, and the shares of dirty and environmental products, as
dependent variables, respectively. The coefficients reported thus show the effect of
(absolute, restrictive, and liberal) environmental provisions on overall, dirty, and green
exports for either group separately. The results are shown in Table 4.
Table 4: The effect of environmental provisions by “greenness” of the exporting
country
(1) (2) (3)
Developing
country exporters
Developing
country exporters
Developing
country exporters
EXPORTS DIRTSHARE GREENSHARE
ENVPROVS
--- Green exporters -0.001 -0.050** 0.001
(0.002) (0.021) (0.008)
--- Brown exporters -0.002 -0.006 -0.001
(0.002) (0.017) (0.007)
RESTRICTIVE
--- Green exporters 0.001 -0.300** -0.123*
(0.010) (0.143) (0.069)
--- Brown exporters 0.079** -0.702 -0.015
(0.031) (0.491) (0.088)
LIBERAL
--- Green exporters -0.014 0.570 0.470**
(0.034) (0.532) (0.209)
--- Brown exporters 0.062 0.933 0.177
(0.092) (1.296) (0.200)
PTA 0.145*** 0.795 0.232
(0.052) (0.731) (0.209)
Depth -0.047* 0.429 -0.173
(0.027) (0.395) (0.113)
Constant 13.766*** 14.875*** 2.318***
(0.013) (0.167) (0.051)
Exporter-Importer Fixed Effects Yes Yes Yes
Exporter-Year and Importer-Year
Fixed Effects Yes Yes Yes
Observations 333,507 333,507 333,507
Share of Exports under PTA 0.29 0.29 0.29
Average ENVPROVS for exports
under PTA 23.4 23.4 23.4
Average RESTRICTIVE for exports
under PTA
0.75 0.75 0.75
Average LIBERAL for exports under
PTA 0.76 0.76 0.76
R2 0.863 0.460 0.215
This table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Column 1), the
share of dirty products in overall merchandise exports (DIRTSHARE, Column 2), and the share of environmental products
in overall merchandise exports (GREENSHARE) between 1984 and 2016 on whether a PTA was signed and overall
environmental provisions (ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL)
included in the PTA by whether the developing country exporter is classified as green or brown. Robust standard errors
clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Column 1 of Table 4 shows that overall exports are not affected for brown or green
exporters by the inclusion of either type of environmental provision in a PTA with the
importing country.13 However, when looking at the share of green and dirty products in
overall exports, as expected, the results indicate that the effects of environmental provisions
on the composition of exports found above only hold for green exporters. Column 2 shows
that the inclusion of environmental provisions, particularly restrictive ones, leads to a
reduction in the share of dirty goods in the exports of relatively green developing countries.
The results in Column 3 show that the absolute number of environmental provisions has no
significant effect on the share of environmental goods in the exports of developing countries.
Liberal provisions increase the share of environmental goods in overall exports only in green
developing countries, while restrictive environmental provisions reduce the share of
environmental goods. In sum, only developing countries that already have an
environmentally healthier economy can actually green their exports in response to
environmental provisions in trade agreements.14
Overall, our empirical findings show that PTAs that include environmental
provisions are a promising way to foster trade, while at the same time greening the resulting
trade flows into partner countries of developing economies. For developing countries, they
can be a way to reap benefits of trade and at the same time foster their own structural
transformation towards a more sustainable economy.
5. Robustness checks
In order to make sure that our main results do not critically depend on the specific
model that we use, we conduct several robustness tests.15 We conduct one robustness test at
a time. First, we analyze to what extent the results depend on our use of fixed effects, which
can offer insights into the usefulness of our preferred estimation strategy. Tables A6a
(overall provisions) and A6b (including trade- restrictive and liberal provisions) show the
results with different combinations of fixed effects for the sample of developing country
exporters (the results are similar for the entire sample). Columns 1, 4, and 7 show the

13 At the same time, there is a surprising positive effect of restrictive provisions for exports of brown countries.
14 As the level of the EPI is only elicited at one point in time (2018), it could of course be argued that this result
is driven by reverse causality in that those countries that managed to shift to greener exports in response to
environmental provisions in their PTAs also then obtained higher scores on the EPI scale. As we use a binary
variable for the classification into brown and green countries, it is unlikely that the result stems from those
developing countries that shifted from brown to green countries because of some PTAs that they signed. Also,
the EPI measures rather persistent country characteristics.
15 Whenever possible, we report the results for the entire sample and for the sample of developing countries.
If, due to limited space, we have to restrict ourselves, we report the results for the sample of developing country
exporters only. In these cases, there are no large differences compared to the entire sample. These estimation
results are available from the authors upon request. Moreover, in Section 5, also for reasons of space, we do
not continue to report the shares of trade flows under a PTA and the respective average numbers of overall,
trade-restrictive, and liberal environmental provisions in them, since the sample compositions stay either
completely or largely identical to the above sections.
estimations on the pooled sample without any fixed effects, for the overall level of trade, and
the shares of dirty and green goods, respectively. While there seems to be a negative effect
of provisions on overall trade levels in the absence of fixed effects, even without the use of
any fixed effects, the results indicate that (particularly restrictive) environmental provisions
are associated with relatively less exports of dirty goods. Moreover, liberal provisions are
associated with more exports of green goods. Including country-pair fixed effects (Columns
2, 5, and 8) does not substantially change this picture; although this specification ascribes
the negative effect on dirty exports to the nature of –trade-restrictive and liberal provisions.
Columns 3, 6, and 9 then show the results of our preferred specification for comparison. We
see that including exporter- and importer-year fixed effects, and thus absorbing country-
specific developments over time, is important in order to disentangle the idiosyncratic effects
of environmental provisions and exports from the effects of omitted variables but that the
general relationships can also be seen in the pooled data. However, our main findings are
even robust to this choice.
Second, the potential correlation between PTA characteristics, such as that between
the depth of a PTA and the number of environmental provisions it includes, may give rise to
concerns of multicollinearity in our estimations. Table A7 therefore reports the Variance
Inflation Factors (VIFs) of the estimation of Equation (1), including trade-restrictive and
liberal provisions (VIFs are not affected by the choice of the dependent variable). None of
the explanatory variables exhibits a variance inflation factor higher than 10, such that we can
well assume no problems of multicollinearity in the estimations presented above.
Third, the analyses presented above shows how environmental provisions in a certain
year are related to export structures in the same year. Since all PTAs (and consequently the
environmental provisions they include) are only switching from non-existence to existence
once over the sample period, the choice of the exact timing of assumed effectiveness is not
likely to influence the findings, which basically compare the period before the PTA with the
period after the PTA in force. At the same time, it is interesting to explore whether there
might be phase-in effects due to environmental provisions potentially starting to exert
influence only after a short period of coming into existence. We therefore lag our
independent variables by 1, 2, and 3 years respectively. The results are depicted in Columns
1-3 of Tables A8a, b and c, respectively, for overall exports and the shares of dirty and green
exports. There is no significant phase-in effect observable which confirms that simply using
concurrent variables provides unbiased results, while keeping the sample as large as
possible. Furthermore, in Columns 4 of Table A8a, b, and c, we include the lead variables.
If they were significant, this could either point to the presence of anticipatory effects or to
endogeneity problems. However, none of the lead variables in the estimations for the share
of dirty or green goods are significant. Only for the overall level of trade, the results suggest
that there might be an increase in exports before the PTA with environmental provisions
enters into force. This does not affect our main findings at the sectoral level, however.
Fourth, we investigate the question of enforcement of environmental provisions in
PTAs and whether and how it affects our findings. It is well conceivable that those
environmental provisions for which there is no dispute settlement mechanism for
enforcement might have a weaker effect on the composition of trade flows than those that
do. We therefore classify PTAs according to whether or not they have in place a specific
dispute settlement mechanism for environmental provisions or a general one that applies to
environmental provisions. Roughly 18 percent of all country pairs under a PTA have
included such an enforcement mechanism. We then interact the number of provisions in
place with the dummy for the presence of an enforcement mechanism and include this
interaction term in the estimation. The results are depicted in Table A9. They show that such
a mechanism is not decisive for our empirical findings. Most importantly, all our main results
also hold for those provisions for which there is no dispute settlement mechanism in place.16
Fifth, there are 4,363 cases in the data in which two countries that had already been members
of the same PTA ratified another PTA (often including other countries). In 1,812 of these
cases, the maximum number of environmental provisions in force between such country
pairs increased in that instance (i.e. the new PTA contained more environmental provisions
than the an existing one). To make sure that it is not the additional PTA per se which affects
the composition of exports rather than its environment-related content, we replace the binary
indicator that controls for a PTA in force by the number of PTAs in force between the
exporter and the importer for robustness. The results are shown in Table A10 in the Appendix
and demonstrate that our results are not driven by a potential correlation of our measure of
environmental provisions with the number of PTAs in place.
Sixth, we also run all equations through Poisson Pseudo Maximum Likelihood
(PPML) estimation (Santos Silva & Tenreyro, 2010), as is common practice in the literature
on trade effects of PTAs. The results, depicted in Table A11 in the Appendix, are the same
as the ones shown above. We opted for reporting the results of the linear estimation in
Section 4 because this allows for a more straightforward interpretation, particularly of the
interaction terms. Moreover, the main benefits of PPML, i.e. being able to deal with zeros
(because no log-normalization of the dependent variable is necessary) and with
heteroscedasticity, are not very relevant for our analysis given that our main explanatory
variables range from zero to one and that our heteroscedasticity robust standard errors are
clustered at the country-pair level. Furthermore, PPML has started being under some
discussion in the literature recently (see Pfaffermayr, 2019). However, the results remain the
same when using this approach.
Seventh, in order to be able to compare our results across different methods, we also
run an additional regression that refrains from using any fixed effects. Instead, we now use
the country-pair and country-year variables that are typically used to explain trade levels
between countries, i.e. exporter- and importer-GDP, distance, contiguity, common historical
ties and common language (Mayer & Zignago, 2011) as control variables. The results are
depicted in Table A12 in the Appendix. Even under this completely different, and arguably
less precise way of estimation, the general thrust of the results remains the same as the one
that we presented above.
Eighth, there are alternative ways to control for selection into PTAs and
environmental provisions that differ from the fixed effects approach that we pursue. At the
same time, all of these approaches have to rely on country-pair or country-year variables as
well in order to predict selection. We run two two-stage models to test for the robustness of
our results. First, we predict selection into including environmental provisions in PTAs by
running a regression of these on the gravity variables named above, plus the population
(time-variant), and the EPI (time-invariant) of the exporter and importer. The fit of this
model should be the expected number of environmental provisions that a country-pair will
include in the PTA they conclude, given its characteristics. The residuals from this regression
should thus be the unexpected, or “surprise”, provisions between two countries. If one were
to see these as exogenous, we can use these surprise provisions (the residuals from the first
stage) as independent variables in the second stage. Table A13 reports the results of the
second stage of this estimation. To account for the fact that the independent variables are
themselves estimated, standard errors are bootstrapped in the second stage. The results of
this two-stage estimation, controlling for selection on observables into including

16 This finding is in line with research on environmental provisions in PTAs which shows that not only hard
enforcement approaches, such as dispute settlement mechanisms, but also softer approaches, for example
building on political dialogues, can be effective (Bastiaens & Postnikov, 2017).
environmental provisions, are the same as the ones reported in our main estimations. This
procedure has the drawback that those country pairs that have not concluded a PTA (but
would have potentially included environmental provisions, had they done so) also contribute
to the prediction of environmental provisions included in PTAs but enter with a zero. To
address this shortcoming, in an additional step, we estimate a Heckman (1976, 1979) –
selection model. The relevant treatment (that country pairs select into) is whether they have
entered a PTA. For the exclusion restrictions (i.e. explanatory variables for selection into a
PTA), we use again the country-pair and country-year specific gravity variables mentioned
before. The second stage then controls for the depth of the PTAs. It should be noted that
using the gravity explanatory variables for selection does not generate perfect exclusion
restrictions, i.e. variables that are correlated with selection into a PTA but not with the
outcome variables, because most are correlated with the outcome variables of the share of
dirty and green goods, although only weakly so. Not having a valid exclusion restriction
makes the estimation less robust. The fixed effects are included by taking as outcome
variables the estimated residuals of the regression of the dependent variables of interest on
the fixed effects. The results of the second stage are reported in Table A14. Although the
results on restrictive and liberal provisions are not significant in this estimation, all results
point in the same direction as our main findings.
Lastly, we would also like to test how robust the results are to different definitions
of the classification of dirty and green sectors. Unfortunately, there is no other definition of
dirty sectors common in the literature, that can be connected to the UN COMTRADE data.
The classification based on Low and Yeats (1992) that we base our analysis on is thus used
by almost all studies on trade and the environment. For the definition of green sectors,
however, there is also the WTO Friends’ list available, which consists of a comparable
amount of sectors as the combined OECD and APEC list, but with a different composition
of goods included. To conduct our robustness check, we thus use this WTO classification to
compute the share of green exports to be used as dependent variable in the estimation of
Equation (1). The results are shown in Table A15 in the Appendix. Columns 1 and 2 depict
the results for the entire sample, Columns 3 and 4 show the findings for developing country
exporters. While the findings based on this rather politically determined list (see discussion
above) suggest that it is overall environmental provisions, rather than explicitly liberalizing
ones, that increase the share of products listed in it, the overall results also remain the same
in the context of this robustness check.
6. Conclusion
The effects of environmental provisions in trade agreements on trade flows have to
date not been assessed at the sectoral level even though environmental content in PTAs has
become more relevant than ever. While developing countries are concerned that high-income
countries use environmental provisions in PTAs to promote “green protectionism”, we find
that environmental provisions do not substantially limit the exports of developing countries.
Accordingly, there does not seem to be a general trade-off between the environmental and
the economic implications of including environmental provisions in PTAs.
Moreover, we find that environmental provisions can help to decrease dirty exports
and promote green exports from developing countries. This, in turn, increases the options to
create win-win scenarios for developing countries and leverage synergies between economic
and environmental benefits by signing PTAs with environmental provisions.
Our findings are relevant for academic research on the relationship between
international economic integration and environmental policy. Our empirical results lend
support to the Porter hypothesis. The increasing share of green goods in developing
countries’ exports is in line with the strong Porter hypothesis, which posits that more
stringent environmental regulation enhances the competitiveness of green sectors and
promotes green exports. At the same time, our evidence indicates that environmental
provisions in PTAs, and the higher environmental standards and regulations they induce, can
be effective policy tools to counter potential pollution haven effects.
From a policy perspective, our empirical evidence also suggests that the design of
PTAs is important. We find that PTA provisions can be used as targeted policy tools: while
restrictive environmental provisions reduce dirty exports, liberal environmental provisions
facilitate exports of green goods. To date, only a few meaningful commitments to liberalize
trade in environmental goods and services are included in PTAs. These win-win
opportunities should be exploited more by decision-makers.
At the same time, we find that the effect of environmental provisions is only visible
for exporters from developing countries that have a strong environmental performance.
These “green” developing countries seem to be better positioned to green their exports in
response to environmental provisions in trade agreements than other developing countries.
This, in turn, offers support to those that call for adopting green policies straight away
(“greening now”) rather than a “grow first, cleaning up later” strategy for latecomer
economies (Pegels & Altenburg, 2019). Environmental provisions in PTAs can, therefore,
complement environmental reforms at the country level but they cannot be a substitute for
them.
Future research could shed light on the effects of environmental provisions at the
firm level. Moreover, in light of the importance of global value chains (GVCs) for
development countries, future research could focus on analyzing the effects of
environmental provisions on upgrading in GVCs. Recent empirical evidence suggests that
environmental standards, which can by promoted by environmental provisions in PTAs, are
indeed a key factor for GVC upgrading (Kummritz et al., 2017; Taglioni & Winkler, 2016)
but whether environmental provisions can contribute to this upgrading has not been assessed
and merits further attention.
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Appendix
Table A1: List of Countries included in the sample
High Income Countries
Andorra' French Polynesia' New Caledonia'
Argentina* Germany* New Zealand*
Aruba' Greece* Norway*
Australia* Greenland' Portugal*
Austria* Guam' Qatar*
Bahamas Hong Kong' San Marino'
Barbados Iceland* Singapore*
Bermuda' Ireland* Slovenia*
Brunei* Israel* Spain*
Canada* Italy* Sweden*
Cayman Islands' Japan* Switzerland*
Cyprus* Kuwait* United Arab Emirates*
Denmark* Luxembourg* United Kingdom*
Faeroe Islands' Macao' USA*
Finland* Malta*
France* Netherlands*
Non- High Income Countries
Afghanistan Georgia Paraguay
Albania* Ghana Peru*
Algeria Grenada Philippines
American Samoa' Guatemala Poland*
Angola Guinea Republic of Congo
Antigua and Barbuda* Guinea-Bissau Republic of Moldova
Armenia* Guyana Romania*
Azerbaijan* Haiti Russian Federation*
Bahrain Honduras Rwanda
Bangladesh Hungary* Saint Kitts and Nevis'
Belarus* India Saint Lucia
Belgium* Indonesia Saint Vincent and the
Grenadines*
Belize Iran Samoa
Benin Iraq São Tomé and Príncipe'
Bhutan Jamaica* Saudi Arabia
Bolivia Jordan* Senegal
Bosnia Herzegovina Kazakhstan Serbia'
Botswana Kenya Serbia and Montenegro'
Brazil* Kyrgyzstan Seychelles*
Bulgaria* Latvia* Sierra Leone
Burkina Faso Lebanon* Slovakia*
Burundi Lesotho Solomon Islands
Cabo Verde Liberia Somalia'
Cambodia Libya South Africa
Cameroon Lithuania* South Korea'
Central African Republic Madagascar Sri Lanka*
Chad Malawi Sudan
Chile Malaysia* Suriname
China Maldives Swaziland
Colombia* Mali Syria'
Comoros Marshall Islands' Tajikistan
Costa Rica* Mauritania Tanzania
Côte d'Ivoire Mauritius Thailand
Croatia* Mayotte' Togo
Cuba* Mexico* Tonga*
Czech Republic Mongolia Trinidad and Tobago*
Democratic Republic of the
Congo' Morocco* Tunisia*
Djibouti Mozambique Turkey
Dominica* Myanmar Turkmenistan*
Dominican Republic* Namibia Uganda
Ecuador Nepal Ukraine
Egypt* Nicaragua Uruguay*
El Salvador Niger Uzbekistan
Equatorial Guinea* Nigeria Vanuatu
Eritrea North Korea' Venezuela*
Estonia* North Macedonia' Viet Nam
Ethiopia Oman Yemen'
Fiji Pakistan Zambia
Gabon Palau' Zimbabwe
Gambia Panama*
This Table lists all countries that are included in the sample as exporting countries by their classification as High-Income
or non-High-Income countries according to the World Bank classification in the year 2000, which is in the middle of the
time span covered by the sample. “*” marks countries that are considered “green”, according to whether they are above the
median of all countries in the sample of the Environmental Performance Index (EPI, Wendling et al., 2018). “ ‘ ” marks
countries for which there is no EPI information is available.
Table A2: List of Restrictive and Liberal Environmental Provisions
(Details are available in the codebook: http://www.chaire-epi.ulaval.ca/en/trend)
Restrictive environmental provisions
Specific trade restrictions
Prohibit the export if import is prohibited
Prohibit the import if export is prohibited
Restrictions on trade in hazardous waste
Illegal trade of endangered species
Exclusion of water from the trade agreement
High level of protection
Laws and regulations should provide for high levels of protection
Commitment to enhance levels of environmental protection
Precaution principle
Precaution principle
Not environmentally harmful
Trade measures should not be environmentally harmful
Harmonization not to be used to lower environmental protection
Environmental consideration in legal dispute
Environmental experts as panelists for state-state dispute
Environmental experts as panelists in investor-state dispute
Environmental report in state-state dispute
Environmental report in investor-state dispute
Panel shall consult or defer to relevant entity
Consent to use the DSM of a MEA
Assessment
Requirement to conduct environmental assessment
Environmental impact assessment of the agreement
Genetic resources
Disclosure of the source of genetic material
Prior informed consent
Equitable sharing of benefits arising from use of genetic resources
Coherence with economic sector
Interaction between tourism and the environment
Interaction between rural development and the environment
Interaction between urban development and the environment
Interaction between land-use planning and the environment
Interaction between construction activities and the environment
Interaction between agriculture and the environment
Interaction between industrial activities and the environment
Interaction between transport and the environment
Interaction between energy policies and the environment
Interaction between mining and the environment
Combat illegal exploitation
Combat illegal fishing
Combat illegal forest exploitation
Ratification and implementation of trade-related MEA
Ratification of CITES
Ratification of Montreal Protocol
Ratification of Basel Convention
Ratification of Rotterdam
Ratification of Stockholm
Ratification of Kyoto
Ratification of CBD
Ratification of Cartagena
Ratification of Nagoya
Implementation of CITES
Implementation of Montreal
Implementation of Basel
Implementation of Rotterdam
Implementation of Stockholm
Implementation of Kyoto
Implementation of CBD
Implementation of Cartagena
Implementation of Nagoya
Prevalence of trade-related MEA
Prevalence CITES
Prevalence Montreal Protocol
Prevalence Basel Convention
Prevalence Rotterdam Convention
Prevalence Stockholm Convention
Prevalence Kyoto
Prevalence CBD
Prevalence Cartagena
Prevalence Nagoya
Liberal environmental provisions
Environmental goods and services
Encourage production of environmental goods and services
Encourage trade or investment in goods and services
Encouragement for specific goods and services
Harmonization of domestic environmental measures
Harmonization of environmental measures
Alignment of a Party’s legislation to the other Party’s
Avoid exceptional national environmental standards
Mutual recognition
Promotion of international standards
International standards are presumed to be in conformity
International standards should be used
Party should use IOs’ methods of risk assessment
Prevalence of trade
Prevalence of trade agreement in case of inconsistency
Exclusion of multilateral environmental agreements’ DSM
Not for protectionist purposes
Environmental measures should not be adopted for protectionist purposes
Promotion of voluntary measures
Promotion of unspecified voluntary measures
Promotion of specific voluntary measures
Use of market instruments
Unspecified economic or market instruments
Specific economic or market instruments
Scientific basis
Scientific knowledge when designing environmental measures
Scientific knowledge when making risk assessment
Table A3: Summary Statistics PTAs
All PTAs
Variable Obs Mean Std. Dev. Min Max
ENVPROVS 567 14.44444 21.61901 0 120
RESTRICTIVE 567 1.583774 3.481341 0 21
LIBERAL 567 0.4091711 0.9813385 0 6
DEPTH 567 1.582936 1.02003 0 3.687593
P
TAs that include Develo
p
in
g
Countries
Variable Obs Mean Std. Dev. Min Max
ENVPROVS 505 14.73267 21.97604 0 120
RESTRICTIVE 505 1.653465 3.578029 0 21
LIBERAL 505 0.4178218 0.992871 0 6
DEPTH 505 1.585889 1.023272 0 3.687593
Table A4: Summary Statistics Trade Flow Observations
All Country Pairs
Variable Obs Mean Std. Dev. Min Max
EXPORTS 476,152 14.29924 4.236119 0 26.9459
DIRTSHARE 476,152 15.5249 25.51327 0 100
GREENSHARE 476,152 2.822848 10.06015 0 100
ENVPROVS 476,152 8.424083 20.08451 0 120
RESTRICTIVE 476,152 0.6717162 2.607578 0 29
LIBERAL 476,152 0.1453086 0.6126786 0 6
PTA 476,152 0.2949835 0.4560358 0 1
#PTAs 476,152 0.6305465 1.295037 0 9
DEPTH 476,152 0.37225 0.8414226 0 3.687593
Brown Exporter 439,566 0.5010101 0.4999995 0 1
Developing Country Exporters
Variable Obs Mean Std. Dev. Min Max
EXPORTS 348,844 13.72279 4.106227 0 26.9459
DIRTSHARE 348,844 14.88556 26.50783 0 100
GREENSHARE 348,844 2.362553 10.28965 0 100
ENVPROVS 348,844 7.238579 18.52078 0 120
RESTRICTIVE 348,844 0.2291626 1.352832 0 29
LIBERAL 348,844 0.0638968 0.3887953 0 6
PTA 348,844 0.2951434 0.4561078 0 1
#PTAs 348,844 0.5872711 1.210724 0 8
DEPTH 348,844 0.2936409 0.7509712 0 3.687593
Brown Exporte
r
333,507 0.6466311 0.4780167 0 1
Table A5: The Effect of Restrictive and Liberal Environmental Provisions in PTAs
for all Countries, including Developed Countries
(1) (2) (3)
All Countries All Countries All Countries
EXPORTS DIRTSHARE GREENSHARE
ENVPROVS -0.003** -0.021 0.002
(0.001) (0.013) (0.005)
RESTRICTIVE 0.027*** -0.071 -0.050*
(0.005) (0.067) (0.029)
LIBERAL -0.021 -0.362* 0.176**
(0.014) (0.215) (0.079)
PTA 0.159*** 0.146 0.135
(0.042) (0.578) (0.176)
DEPTH -0.016 0.582** -0.092
(0.021) (0.297) (0.092)
Constant 14.266*** 15.542*** 2.811***
(0.009) (0.115) (0.040)
Exporter-Importer Fixed Effects Yes Yes Yes
Exporter-Year and Importer-Year Fixed Effects Yes Yes Yes
Observations 476,152 476,152 476,152
R2 0.884 0.452 0.225
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Column 1), the
share of dirty products in overall merchandise exports (DIRTSHARE, Column 2), and the share of environmental products
in overall merchandise exports (GREENSHARE, Column 3) between 1984 and 2016 on whether a PTA was signed and
overall environmental provisions (ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions
(LIBERAL) included in the PTA for the sample of all exporters. Robust standard errors clustered at the exporter-importer
level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Table A6a: Estimations with varying Fixed Effects included – Overall Provisions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
EXPORTS EXPORTS EXPORTS DIRTSHARE DIRTSHARE DIRTSHARE GREENSHARE GREENSHARE GREENSHARE
ENVPROVS -0.086*** -0.013*** -0.000 -0.212*** -0.058*** -0.049*** 0.000 0.008 0.000
(0.002) (0.001) (0.001) (0.011) (0.014) (0.015) (0.002) (0.005) (0.006)
PTA 1.288*** 0.528*** 0.148*** 4.903*** -0.380 0.830 0.013 0.236 0.112
(0.065) (0.058) (0.052) (0.366) (0.683) (0.700) (0.079) (0.187) (0.205)
DEPTH 2.209*** 0.508*** -0.051** 2.399*** 0.496 0.588 -0.035 0.170* -0.059
(0.049) (0.027) (0.025) (0.279) (0.354) (0.371) (0.062) (0.096) (0.112)
Exporter-
Importer
Fixed Effects
No Yes Yes No Yes Yes No Yes Yes
Exporter-
Year and
Importer-
Year Fixed
Effects
No No Yes No No Yes No No Yes
Observations 348,844 348,844 348,844 348,844 348,844 348,844 348,844 348,844 348,844
R2 0.070 0.821 0.861 0.008 0.421 0.454 0.000 0.188 0.213
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE, Columns
4-6), and the share of environmental products in overall merchandise exports (GREENSHARE, Columns 3) between 1984 and 2016 on whether a PTA was signed and overall environmental provisions
(ENVPROVS) included in the PTA for the sample of developing country exporters. Columns 1, 4, and 7 include no fixed effects, Columns 2, 5, and 8 include only country-pair fixed effects, and
Columns 3, 6, and 9 include all fixed effects as in the main text for comparison. Robust standard errors clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**;
p<0.1*.
Table A6b: Estimations with varying Fixed Effects included – Restrictive and Liberal Provisions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
EXPORTS EXPORTS EXPORTS DIRTSHARE DIRTSHARE DIRTSHARE GREENSHARE GREENSHARE GREENSHARE
ENVPROVS -0.086*** -0.019*** -0.001 -0.232*** -0.009 -0.026* -0.002 0.006 0.002
(0.002) (0.001) (0.001) (0.011) (0.014) (0.016) (0.003) (0.005) (0.006)
RESTRICTIVE 0.076*** 0.093*** 0.008 0.691*** -0.424*** -0.403*** -0.001 -0.074 -0.114*
(0.026) (0.009) (0.009) (0.134) (0.147) (0.135) (0.028) (0.060) (0.060)
LIBERAL -0.468*** -0.040 -0.007 -0.009 -1.213** 0.538 0.497*** 0.456** 0.411**
(0.082) (0.034) (0.032) (0.465) (0.493) (0.496) (0.114) (0.181) (0.184)
PTA 1.306*** 0.531*** 0.148*** 4.772*** -0.554 0.877 -0.021 0.282 0.156
(0.065) (0.057) (0.052) (0.367) (0.679) (0.699) (0.080) (0.186) (0.204)
DEPTH 2.237*** 0.550*** -0.048* 2.489*** 0.396 0.366 -0.054 0.110 -0.143
(0.050) (0.027) (0.027) (0.283) (0.356) (0.381) (0.062) (0.094) (0.111)
Exporter-
Importer Fixed
Effects
No Yes Yes No Yes Yes No Yes Yes
Exporter-Year
and Importer-
Year Fixed
Effects
No No Yes No No Yes No No Yes
Observations 348,844 348,844 348,844 348,844 348,844 348,844 348,844 348,844 348,844
R2 0.071 0.821 0.861 0.009 0.422 0.454 0.000 0.188 0.213
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE, Columns
4-6), and the share of environmental products in overall merchandise exports (GREENSHARE, Columns 3) between 1984 and 2016 on whether a PTA was signed and overall environmental provisions
(ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL) included in the PTA for the sample of developing country exporters. Columns 1, 4, and 7 include no fixed
effects, Columns 2, 5, and 8 include only country-pair fixed effects, and Columns 3, 6, and 9 include all fixed effects as in the main text for comparison. Robust standard errors clustered at the exporter-
importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Table A7: Variance Inflation Factors
Variable VIF 1/VIF
ENVPROVS 4.62 0.216417
DEPTH 4.44 0.225363
RESTRICTIVE 2.93 0.341335
LIBERAL 2.82 0.355236
PTA 1.93 0.516902
Mean VIF 3.35
This Table shows the variance inflation factors in the panel regression of Equation (1) with bilateral trade information from between 1984 and 2016
on whether a PTA was signed and overall environmental provisions (ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions
(LIBERAL) included in the PTA for the full sample.
Table A8a: Estimations with lags and lead of explanatory variables – Level of Exports
(1) (2) (3) (4)
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
EXPORTS EXPORTS EXPORTS EXPORTS
ENVPROVS 0.002 0.001 0.000 -0.002*
(0.001) (0.001) (0.001) (0.001)
-- L1. -0.002 -0.003** -0.002*
(0.001) (0.001) (0.001)
-- L2. 0.001 0.002
(0.001) (0.001)
-- L3. -0.001
(0.001)
-- F. 0.002*
(0.001)
RESTRICTIVE -0.012 -0.011 -0.010 0.012
(0.010) (0.009) (0.009) (0.010)
-- L1. 0.016* 0.005 0.004
(0.009) (0.009) (0.009)
-- L2. 0.012 -0.006
(0.009) (0.008)
-- L3. 0.028***
(0.010)
-- F. -0.013
(0.009)
LIBERAL 0.020 0.032 0.038 0.041
(0.037) (0.037) (0.037) (0.045)
-- L1. -0.021 -0.057 -0.037
(0.035) (0.038) (0.037)
-- L2. 0.052 -0.003
(0.036) (0.038)
-- L3. 0.027
(0.029)
-- F. -0.040
(0.045)
PTA 0.119* 0.138** 0.177*** 0.155***
(0.061) (0.060) (0.060) (0.059)
-- L1. 0.030 -0.018 -0.065
(0.057) (0.052) (0.051)
-- L2. 0.017 -0.046
(0.053) (0.048)
-- L3. 0.061
(0.051)
-- F. -0.018
(0.064)
DEPTH -0.084*** -0.082*** -0.083*** -0.026
(0.029) (0.029) (0.028) (0.029)
-- L1. 0.037 0.052** 0.046*
(0.029) (0.025) (0.025)
-- L2. -0.015 0.023
(0.028) (0.025)
-- L3. -0.029
(0.027)
-- F. -0.021
(0.027)
Exporter-Importer Fixed
Effects Yes Yes Yes Yes
Exporter-Year and Importer-
Year Fixed Effects Yes Yes Yes Yes
Constant 14.264*** 14.603*** 14.856*** 14.170***
(0.013) (0.014) (0.014) (0.013)
Observations 303,475 276,468 255,946 311,287
R2 0.871 0.878 0.884 0.868
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS) between 1984 and 2016 on the first (Column
1), second (Column 2), and third (Column 3) lag, respectively, of whether a PTA was signed and overall environmental provisions (ENVPROVS),
trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL) included in the PTA for the sample of developing country exporters.
Column 4 shows the results when including the on-year leads of the explanatory variables. Robust standard errors clustered at the exporter-importer
level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Table A8b: Estimations with lags and lead of explanatory variables – Share of dirty exports
(1) (2) (3) (4)
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
DIRTSHARE DIRTSHARE DIRTSHARE DIRTSHARE
ENVPROVS -0.030 -0.031 -0.032 -0.021
(0.020) (0.020) (0.020) (0.018)
-- L1. 0.001 -0.004 -0.006
(0.020) (0.022) (0.022)
-- L2. 0.017 0.017
(0.021) (0.022)
-- L3. 0.000
(0.020)
-- F. -0.008
(0.015)
RESTRICTIVE 0.075 0.092 0.077 -0.539***
(0.182) (0.178) (0.177) (0.189)
-- L1. -0.615*** -0.437** -0.433**
(0.182) (0.176) (0.172)
-- L2. -0.268* -0.135
(0.162) (0.150)
-- L3. -0.186
(0.191)
-- F. 0.138
(0.166)
LIBERAL -0.670 -0.896 -1.010 1.200
(0.730) (0.713) (0.718) (0.741)
-- L1. 1.521** 0.708 0.932
(0.681) (0.684) (0.684)
-- L2. 1.214** 0.093
(0.536) (0.582)
-- L3. 1.245**
(0.540)
-- F. -0.639
(0.770)
PTA 1.640* 1.989** 1.399 0.353
(0.956) (0.965) (0.951) (0.879)
-- L1. -0.621 -0.808 -0.109
(0.880) (0.930) (0.893)
-- L2. -0.073 0.394
(0.827) (0.849)
-- L3. -0.622
(0.767)
-- F. 0.811
(0.940)
DEPTH 0.041 -0.017 0.160 0.308
(0.486) (0.482) (0.473) (0.436)
-- L1. 0.347 0.424 0.154
(0.442) (0.463) (0.453)
-- L2. -0.217 -0.049
(0.443) (0.451)
-- L3. -0.123
(0.405)
-- F. 0.066
(0.430)
Exporter-Importer Fixed
Effects Yes Yes Yes Yes
Exporter-Year and Importer-
Year Fixed Effects Yes Yes Yes Yes
Constant 14.937*** 14.997*** 15.090*** 14.878***
(0.167) (0.178) (0.189) (0.175)
Observations 303,475 276,468 255,946 311,287
R2 0.503 0.530 0.551 0.495
This Table shows the results from running a panel regression of the share of dirty products in overall merchandise exports (DIRTSHARE) between
1984 and 2016 on the first (Column 1), second (Column 2), and third (Column 3) lag, respectively, of whether a PTA was signed and overall
environmental provisions (ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL) included in the PTA for the
sample of developing country exporters. Column 4 shows the results when including the on-year leads of the explanatory variables. Robust standard
errors clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Table A8c: Estimations with lags and lead of explanatory variables – Share of green exports
(1) (2) (3) (4)
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
Developing Country
Exporters
GREENSHARE GREENSHARE GREENSHARE GREENSHARE
ENVPROVS 0.004 0.005 0.008 0.004
(0.010) (0.010) (0.010) (0.007)
-- L1. -0.005 0.012 0.005
(0.010) (0.013) (0.013)
-- L2. -0.017* -0.017*
(0.010) (0.010)
-- L3. 0.006
(0.008)
-- F. -0.000
(0.005)
RESTRICTIVE -0.135 -0.137 -0.132 -0.111**
(0.088) (0.090) (0.086) (0.047)
-- L1. 0.025 0.051 0.075
(0.075) (0.091) (0.090)
-- L2. -0.032 0.036
(0.065) (0.064)
-- L3. -0.106*
(0.060)
-- F. -0.024
(0.054)
LIBERAL 0.748* 0.624 0.682* 0.525***
(0.402) (0.397) (0.398) (0.174)
-- L1. -0.300 -0.408 -0.417
(0.387) (0.456) (0.460)
-- L2. 0.100 -0.214
(0.302) (0.308)
-- L3. 0.295
(0.220)
-- F. -0.153
(0.186)
PTA -0.100 0.026 0.293 0.273
(0.319) (0.332) (0.335) (0.295)
-- L1. 0.276 0.095 -0.277
(0.297) (0.407) (0.389)
-- L2. 0.099 0.204
(0.310) (0.372)
-- L3. 0.034
(0.317)
-- F. 0.016
(0.316)
DEPTH 0.187 0.110 -0.075 -0.187
(0.178) (0.182) (0.177) (0.134)
-- L1. -0.325* -0.413* -0.132
(0.170) (0.221) (0.199)
-- L2. 0.180 0.041
(0.161) (0.182)
-- L3. 0.010
(0.146)
-- F. 0.021
(0.140)
Exporter-Importer Fixed
Effects Yes Yes Yes Yes
Exporter-Year and
Importer-Year Fixed
Effects
Yes Yes Yes Yes
Constant 2.236*** 2.154*** 2.093*** 2.189***
(0.053) (0.055) (0.055) (0.053)
Observations 303,475 276,468 255,946 311,287
R2 0.248 0.270 0.284 0.239
This Table shows the results from running a panel regression of the share of environmental products in overall merchandise exports (GREENSHARE)
between 1984 and 2016 on the first (Column 1), second (Column 2), and third (Column 3) lag, respectively, of whether a PTA was signed and overall
environmental provisions (ENVPROVS), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL) included in the PTA for the
sample of developing country exporters. Column 4 shows the results when including the on-year leads of the explanatory variables. Robust standard
errors clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
Table A9: Interactions with Enforcement Clauses
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
Developing
Country
Exporters
(1) (2) (3) (4) (5) (6)
EXPORTS DIRTSHARE GREENSHARE EXPORTS DIRTSHARE GREENSHARE
ENVPROVS 0.000 -0.050*** 0.005 0.002 0.021 0.003
(0.001) (0.019) (0.007) (0.002) (0.025) (0.008)
ENVPROVS X ENFORCEMENT -0.001 0.002 -0.008 -0.002 -0.043** -0.005
(0.001) (0.016) (0.006) (0.002) (0.019) (0.007)
RESTRICTIVE -0.005 -0.546*** -0.122
(0.011) (0.165) (0.079)
RESTRICTIVE X ENFORCEMENT 0.031 0.151 -0.016
(0.019) (0.292) (0.104)
LIBERAL -0.016 0.234 0.437**
(0.036) (0.561) (0.218)
LIBERAL X ENFORCEMENT 0.118* 1.848* -0.594**
(0.065) (1.034) (0.294)
PTA 0.145*** 0.839 0.079 0.129** 0.553 0.172
(0.051) (0.703) (0.202) (0.052) (0.707) (0.200)
DEPTH -0.052** 0.591 -0.068 -0.068** 0.114 -0.103
(0.025) (0.372) (0.114) (0.028) (0.394) (0.114)
Constant 13.697*** 14.821*** 2.356*** 13.699*** 14.798*** 2.341***
(0.012) (0.153) (0.050) (0.012) (0.153) (0.051)
Exporter-Importer Fixed Effects Yes Yes Yes Yes Yes Yes
Exporter-Year and Importer-Year
Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 348,844 348,844 348,844 348,844 348,844 348,844
R2 0.861 0.454 0.213 0.861 0.454 0.213
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Columns 1 and 4), the share of dirty products
in overall merchandise exports (DIRTSHARE, Columns 2 and 5), and the share of environmental products in overall merchandise exports
(GREENSHARE, Columns 3 and 6) between 1984 and 2016 whether a PTA was in in force between countries and the maximum number of overall
environmental provisions (ENVPROVS, Columns 1-6), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL, both Columns
4-6) included in the PTAs, and their interaction with a dummy variable on whether an enforcement clause was included in a PTA, for the sample of
developing country exporters. Robust standard errors clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**;
p<0.1*.
Table A10: The Effect of Environmental Provisions in PTAs – Controlling for # of PTAs
(1) (2) (3) (4) (5) (6) (7) (8) (9)
All
Countries
Developing
Country
Exporters
Developing
Country
Exporters
All Countries
Developing
Country
Exporters
Developing
Country
Exporters
All
Countries
Developing
Country
Exporters
Developing
Country
Exporters
EXPORTS EXPORTS EXPORTS DIRTSHARE DIRTSHARE DIRTSHARE GREENSHARE GREENSHARE GREENSHARE
ENVPROVS -0.000 -0.000 -0.001 -0.037*** -0.049*** -0.026 -0.000 0.000 0.002
(0.001) (0.001) (0.001) (0.011) (0.015) (0.016) (0.004) (0.006) (0.006)
RESTRICTIVE
0.006 -0.400*** -0.099*
(0.009) (0.135) (0.060)
LIBERAL
-0.004 0.520 0.363**
(0.032) (0.492) (0.183)
# of PTAs 0.056*** 0.059*** 0.059** 0.001 0.019 0.093 -0.272*** -0.334*** -0.304***
(0.016) (0.023) (0.023) (0.217) (0.316) (0.315) (0.064) (0.088) (0.087)
DEPTH -0.010 -0.033 -0.029 0.646*** 0.841*** 0.600* 0.119 0.133 0.059
(0.015) (0.022) (0.023) (0.223) (0.326) (0.337) (0.073) (0.099) (0.098)
Constant 14.268*** 13.699*** 13.701*** 15.593*** 14.981*** 14.902*** 2.951*** 2.519*** 2.511***
(0.009) (0.011) (0.012) (0.115) (0.157) (0.157) (0.034) (0.044) (0.046)
Exporter-
Importer Fixed
Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Exporter-Year
and Importer-
Year Fixed
Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observations 476,152 348,844 348,844 476,152 348,844 348,844 476,152 348,844 348,844
R2 0.884 0.452 0.225 0.884 0.452 0.225 0.884 0.452 0.225
This Table shows the results from running a panel regression of the log of bilateral exports (EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE, Columns
4-6), and the share of environmental products in overall merchandise exports (GREENSHARE, Columns 7-9) between 1984 and 2016 on the number (#) of PTAs in force between countries and the
maximum number of overall environmental provisions (ENVPROVS, Columns 1-9), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL, both Columns 3, 6, and 9) included
in the PTAs for the samples of all exporters (Columns 1, 4, and 7) and developing country exporters (Columns 2, 3, 5, 6, 8, and 9). Robust standard errors clustered at the exporter-importer level are
reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
1
Table A11: The Effect of Environmental Provisions in PTAs – PPML Regressions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
All
Countr
ies
Develo
ping
Countr
y
Export
ers
Develo
ping
Countr
y
Export
ers
All
Countrie
s
Develop
ing
Country
Exporter
s
Develop
ing
Country
Exporter
s
All
Countries
Developin
g Country
Exporters
Developin
g Country
Exporters
EXPO
RTS
EXPO
RTS
EXPO
RTS
DIRTSH
ARE
DIRTSH
ARE
DIRTSH
ARE
GREENS
HARE
GREENS
HARE
GREENS
HARE
ENVPRO
VS 0.000 0.000 0.000
-
0.002*** -0.002** -0.000 -0.000 -0.001 -0.000
(0.000) (0.000) (0.000) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002)
RESTRIC
TIVE
-0.000
-
0.018*** -0.033***
(0.001) (0.006) (0.012)
LIBERA
L
0.002 0.007 0.170***
(0.002) (0.023) (0.045)
PTA
0.011*
** 0.009** 0.009** 0.035 0.053 0.058 0.016 0.044 0.086
(0.003) (0.003) (0.003) (0.029) (0.036) (0.036) (0.062) (0.089) (0.090)
DEPTH
-
0.006*
**
-
0.007**
*
-
0.008**
* 0.009 0.005 -0.007 -0.006 -0.017 -0.078
(0.001) (0.002) (0.002) (0.014) (0.018) (0.019) (0.028) (0.044) (0.049)
Observati
ons
476,15
2 348,844 348,844 455,087 330,616 330,616 425,000 304,472 304,472
Exporter-
Importer
Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Exporter-
Year and
Importer-
Year
Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
R2 0.882 0.859 0.859 0.457 0.46 0.46 0.306 0.323 0.323
This Table shows the results from running a panel pseudo maximum likelihood regression of the log of bilateral exports
(EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE, Columns 4-6), and
the share of environmental products in overall merchandise exports (GREENSHARE, Columns 7-9) between 1984 and 2016
on whether a PTA was signed and overall environmental provisions (ENVPROVS, Columns 1-9), trade-restrictive
(RESTRICTIVE), and trade-liberalizing provisions (LIBERAL, both Columns 3, 6, and 9)) included in the PTA for the
samples of all exporters (Columns 1, 4, and 7) and developing country exporters (Columns 2, 3, 5, 6, 8, and 9). Robust
standard errors clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
2
Table A12: The Effect of Environmental Provisions in PTAs – Gravity with country-
pair and country-year explanatory variables
(1) (2) (3) (4) (5) (6) (7) (8) (9)
All
Count
ries
Devel
oping
Countr
y
Export
ers
Devel
oping
Countr
y
Export
ers
All
Countri
es
Develop
ing
Country
Exporte
rs
Develop
ing
Country
Exporte
rs
All
Countries
Developi
ng
Country
Exporters
Developi
ng
Country
Exporters
EXPO
RTS
EXPO
RTS
EXPO
RTS
DIRTS
HARE
DIRTS
HARE
DIRTS
HARE
GREENS
HARE
GREENS
HARE
GREENS
HARE
ENVPROVS
-
0.065*
**
-
0.063*
**
-
0.062*
**
-
0.094***
-
0.141***
-
0.151*** -0.005** -0.002 -0.005*
(0.002) (0.002) (0.002) (0.009) (0.012) (0.012) (0.002) (0.003) (0.003)
RESTRICTIV
E
0.019 0.194 0.013
(0.024) (0.131) (0.029)
LIBERAL
-
0.299*
** 0.588 0.471***
(0.072) (0.463) (0.121)
PTA
0.706*
**
0.958*
**
0.970*
** 2.105*** 2.462*** 2.426*** -0.243*** 0.044 0.022
(0.062) (0.066) (0.066) (0.355) (0.391) (0.392) (0.084) (0.090) (0.090)
DEPTH
1.743*
**
1.515*
**
1.523*
** 0.507** 0.605** 0.642** 0.373*** 0.022 0.018
(0.039) (0.046) (0.047) (0.229) (0.294) (0.299) (0.056) (0.066) (0.067)
GDP(EXP)
0.000*
**
0.000*
**
0.000*
** 0.000*** 0.000*** 0.000*** 0.000*** -0.000** -0.000**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
GDP(IMP)
0.000*
**
0.000*
**
0.000*
** 0.000 0.000 0.000 -0.000*** -0.000*** -0.000***
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
DISTANCE
-
0.859*
**
-
0.863*
**
-
0.870*
**
-
3.790***
-
4.087***
-
4.025*** 0.144*** 0.129*** 0.148***
(0.027) (0.032) (0.032) (0.159) (0.192) (0.194) (0.034) (0.039) (0.039)
CONTIGUITY
1.142*
**
1.226*
**
1.247*
** 2.155*** 2.608*** 2.445** -0.255** -0.002 -0.057
(0.132) (0.149) (0.149) (0.834) (0.978) (0.975) (0.125) (0.136) (0.137)
COMMON
LANGUAGE 0.073 0.102 0.113* -0.119 0.133 0.091 -0.309*** -0.175** -0.196**
(0.062) (0.068) (0.068) (0.320) (0.385) (0.385) (0.070) (0.084) (0.084)
COLONY
2.785*
**
2.827*
**
2.774*
**
-
2.263***
-
3.963***
-
3.658*** -0.073 -0.921*** -0.800***
3
(0.136) (0.189) (0.190) (0.713) (1.065) (1.067) (0.139) (0.131) (0.131)
COMMON
COLONIZER
-
1.020*
**
-
0.841*
**
-
0.832*
** -0.889**
-
1.444***
-
1.526*** -0.509*** -0.326*** -0.352***
(0.070) (0.074) (0.074) (0.403) (0.455) (0.456) (0.081) (0.093) (0.093)
Exporter-
Importer Fixed
Effects
No No No No No No No No No
Exporter-Year
and Importer-
Year Fixed
Effects
No No No No No No No No No
Observations
415,61
4
306,79
3
306,79
3 415,614 306,793 306,793 415,614 306,793 306,793
R2 0.292 0.271 0.271 0.020 0.025 0.025 0.002 0.001 0.001
This Table shows the results from running a regression of the log of bilateral exports (EXPORTS, Columns 1-3), the share
of dirty products in overall merchandise exports (DIRTSHARE, Columns 4-6), and the share of environmental products in
overall merchandise exports (GREENSHARE, Columns 7-9) between 1984 and 2016 on whether a PTA was in force and
overall environmental provisions (ENVPROVS, Columns 1-9), trade-restrictive (RESTRICTIVE), and trade-liberalizing
provisions (LIBERAL, both Columns 3, 6, and 9)) included in the PTA for the sample of all exporters. Instead of country-
pair or country year fixed effects, the reported regressions include the exporter’s and importer’s GDP, the DISTANCE
between their capitals, and dummy variables on whether they share a common border (CONTIGUITY), a COMMON
LANGUAGE, a direct (COLONY) or indirect (COMMON COLONIZER) colonial link after 1945. Robust standard errors
clustered at the exporter-importer level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
4
Table A13: Two-Stage Regressions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
All
Countr
ies
Develo
ping
Countr
y
Export
ers
Develo
ping
Countr
y
Export
ers
All
Countrie
s
Develop
ing
Country
Exporter
s
Develop
ing
Country
Exporter
s
All
Countries
Developin
g Country
Exporters
Developin
g Country
Exporters
EXPO
RTS
EXPO
RTS
EXPO
RTS
DIRTSH
ARE
DIRTSH
ARE
DIRTSH
ARE
GREENS
HARE
GREENS
HARE
GREENS
HARE
ENVPRO
VS' 0.001* 0.002 0.001 -
0.040***
-
0.052*** -0.024 -0.004 -0.005 -0.002
(0.001) (0.001) (0.001) (0.012) (0.016) (0.017) (0.005) (0.006) (0.007)
RESTRIC
TIVE'
0.007
-0.313**
-0.136**
(0.010) (0.145) (0.066)
LIBERAL
'
0.005
-0.149
0.466**
(0.035) (0.533) (0.197)
PTA
0.113*
** 0.081 0.079 0.043 0.355 0.441 0.124 0.180 0.227
(0.043) (0.055) (0.055) (0.623) (0.772) (0.773) (0.199) (0.235) (0.233)
DEPTH -0.014 -0.031 -0.027 -0.215 -0.437 -0.629* -0.111 -0.175* -0.263**
(0.018) (0.023) (0.023) (0.250) (0.316) (0.329) (0.081) (0.104) (0.105)
Exporter-
Importer
Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Exporter-
Year and
Importer-
Year
Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observati
ons
390,30
8 294,392 294,392 390,308 294,392 294,392 390,308 294,392 294,392
R2 0.888 0.864 0.864 0.461 0.462 0.462 0.233 0.216 0.216
This Table shows the results of the second stage regression from running a panel regression of the log of bilateral exports
(EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE, Columns 4-6), and
the share of environmental products in overall merchandise exports (GREENSHARE, Columns 7-9) between 1984 and 2016
on whether a PTA was in force between countries and the residuals from a first stage regression. In this, the number of
overall environmental provisions (ENVPROVS, second stage results reported in Columns 1-9), trade-restrictive
(RESTRICTIVE), and trade-liberalizing provisions (LIBERAL, second stage results reported in Columns 3, 6, and 9) was
regressed on the exporter’s and importer’s GDP, their POPULATION, their EPI in 2018, the DISTANCE between their
capitals, and dummy variables on whether they share a common border (CONTIGUITY), a COMMON LANGUAGE, a
direct (COLONY) or indirect (COMMON COLONIZER) colonial link after 1945, The residuals of these regressions (and
thus the unpredicted number of the respective environmental provisions) are used as explanatory variables (marked with
an “ ‘ “) in the second stage. Because the estimations thus use estimated variables as explanatory variables, the standard
errors in the second stage, which are reported in parentheses, are bootstrapped. Results are shown for the sample of all
exporters (Columns 1, 4, and 7) and that of developing country exporters (Columns 2, 3, 5, 6, 8, and 9). p<0.01***;
p<0.05**; p<0.1*.
5
Table A14: Heckman selection model, second stage regressions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
All
Countri
es
Develo
ping
Country
Exporte
rs
Develo
ping
Country
Exporte
rs
All
Countries
Developi
ng
Country
Exporters
Developi
ng
Country
Exporters
All
Countries
Developing
Country
Exporters
Developing
Country
Exporters
EXPO
RTS
EXPOR
TS
EXPOR
TS
DIRTSH
ARE
DIRTSH
ARE
DIRTSH
ARE
GREENSH
ARE
GREENSH
ARE
GREENSH
ARE
ENVPRO
VS 0.000* 0.000 0.000 -0.004* -0.005 -0.003 -0.001 -0.001 -0.000
(0.000) (0.000) (0.000) (0.002) (0.004) (0.003) (0.001) (0.001) (0.001)
RESTRIC
TIVE
0.000
-0.069
-0.026
(0.003) (0.050) (0.019)
LIBERAL
-0.002 -0.105 0.100
(0.011) (0.166) (0.080)
DEPTH
-0.007* -
0.012**
-
0.012**
*
0.080 0.072 0.057 -0.000 -0.006 -0.013
(0.004) (0.006) (0.004) (0.060) (0.090) (0.067) (0.027) (0.032) (0.028)
Exporter-
Importer
Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Exporter-
Year and
Importer-
Year Fixed
Effects
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Observatio
ns
390,40
4 294,488 294,488 390,404 294,488 294,488 390,404 294,488 294,488
This Table shows the results of the second stage of a Heckman (1976, 1979) selection model estimation with the log of
bilateral exports (EXPORTS, Columns 1-3), the share of dirty products in overall merchandise exports (DIRTSHARE,
Columns 4-6), and the share of environmental products in overall merchandise exports (GREENSHARE, Columns 7-9)
between 1984 and 2016 as outcome variables, using as explanatory variables the maximum sum of overall environmental
provisions (ENVPROVS, Columns 1-9), trade-restrictive (RESTRICTIVE), and trade-liberalizing provisions (LIBERAL,
both Columns 3, 6, and 9)) included in PTAs between exporter and importer. The first stage controls for selection into
signing a PTA, predicted by the exporter’s and importer’s GDP, their POPULATION, their EPI in 2018, the DISTANCE
between their capitals, and dummy variables on whether they share a common border (CONTIGUITY), a COMMON
LANGUAGE, a direct (COLONY) or indirect (COMMON COLONIZER) colonial link after 1945. Because the fixed
effects are controlled for by regressing the dependent variables on the fixed effects first, and then using the residuals as
dependent variables, the regressions are on estimated variables, and the standard errors in the second stage, which are
reported in parentheses, are bootstrapped. Results are shown for the sample of all exporters (Columns 1, 4, and 7) and that
of developing country exporters (Columns 2, 3, 5, 6, 8, and 9). p<0.01***; p<0.05**; p<0.1*.
6
Table A15: Green Sector Classification based on WTO Friends’ List
(1) (2) (3) (4)
All
Countries
All
Countries
Developing
Country
Exporters
Developing
Country
Exporters
GREENSHAREWTO GREENSHAREWTO GREENSHAREWTO GREENSHAREWTO
ENVPROVS 0.030* 0.029 0.045** 0.050**
(0.016) (0.020) (0.022) (0.025)
RESTRICTIVE -0.095 -0.065
(0.096) (0.185)
LIBERAL 0.598** 0.029
(0.290) (0.670)
PTA 1.712** 2.028** 2.458** 2.459**
(0.848) (0.862) (1.065) (1.066)
DEPTH -1.879*** -2.103*** -2.811*** -2.842***
(0.412) (0.440) (0.564) (0.586)
Constant 43.865*** 43.843*** 36.229*** 36.217***
(0.169) (0.170) (0.233) (0.234)
Exporter-Importer
Fixed Effects Yes Yes Yes Yes
Exporter-Year and
Importer-Year
Fixed Effects
Yes Yes Yes Yes
Observations 476,152 476,152 348,844 348,844
R2 0.645 0.645 0.602 0.602
This Table shows the results from running a panel regression of the share of environmental products, as classified by the
WTO Friends’ List, in overall merchandise exports (GREENSHAREWTO) between 1984 and 2016 on whether a PTA was
signed and overall environmental provisions (ENVPROVS, Columns 1-4)), trade-restrictive (RESTRICTIVE), and trade-
liberalizing provisions (LIBERAL, both Columns 2 and 4)) included in the PTA for the samples of all exporters (Columns
1-2) and that of developing country exporters only (Columns 3-4). Robust standard errors clustered at the exporter-importer
level are reported in parentheses. p<0.01***; p<0.05**; p<0.1*.
... Bastiaens and Postnikov (2017) investigated the European Union and the USA and found that the environmental provision played a penalty and incentive role in prompting Member States to carry out reform in environmental protection. Brandi et al. (2020) believe that environmental provisions are conducive to reducing the export of pollutants from developing countries and increasing their exports of green products. Another point of view holds that the environmental provisions are motivated by non-environmental protection. ...
... Independent variable: Referring to Brandi et al. (2020), this paper uses the number of effective PTA agreements between a country and other B&R countries as an independent variable to measure the trade intensity between them. 4 This variable is a year-by-year rolling sum, increasing as new PTAs is signed and decreasing when a PTA is invalid. ...
... As for the environmental provisions, referring to Brandi et al. (2020), this paper uses the quantity of provisions in the environmental agreement between a country and other BR countries as the independent variable. 5 The environmental provision will affect trade between B&R countries about two years after its signing, and as a result, the provision will be delayed for two years. ...
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What causes the shift in pollution burden among different high-income Belt and Road (B&R) countries? According to the pollution haven hypothesis, international trade (via Preferential Trade Agreements) and foreign direct investment are the key influencing mechanisms of international pollution burden shifting, while technological innovation and environmental provisions also have a significant impact on pollution burden transfer. The purpose of this paper is to examine how pollution burden transfer occurs in B&R countries through studying the impact of these mechanisms on the import and export of ecological footprints. The relevant evidence of pollution transfer is based on balance panel data analysis of 61 countries along B&R from 2009 to 2016. In terms of the different incomes of B&R countries, it has been found: Firstly, under the impetus of preferential trade agreements, the two-way transfer of environmental pollution burden exists in low-income and middle-income of B&R countries, and it also promotes the two-way transfer of pollution burden in middle-income countries through foreign direct investment. Secondly, the environmental provisions have no binding effect on the import and export of ecological footprint of B&R countries. Thirdly, technological innovation has negatively affected the environment of middle-income countries. These conclusions enrich the research on the transfer and influence mechanism of pollution burden among B&R countries. Graphical abstract
... Second, we contribute to the recent literature investigating the non-trade content of trade agreements (Lechner, 2016;Morin et al., 2018;Raess and Sari, 2018;Borchert et al., 2021) and its impact on trade Brandi et al., 2020;Lopez Vicente et al., 2020;Osnago et al., 2020;Carrère et al., 2021) and FDI (Lechner, 2018). Third, we add also to a rich literature on the determinants of FDI 19 , in particular to those studies concerned with how investors respond to 16 We use the expression 'risk' strictly to refer to a reduced inflow of FDI. ...
... Unlike ESR provisions, we find a positive effect of EP provisions for 'South-North' manufacturing investments: this suggests that EP provisions might help firms in low-and middle-income countries to penetrate high-income markets. This could be driven by supply-side effects, if EP provisions push firms in developing countries to become more productive (Porter and van der Linde, 1995;Brandi et al., 2020) or demand side effects, if consumers in the 'North' reward 'greener' production. The data at hand do not allow us to investigate which of these two effects is at work, however. ...
... We decided to follow this approach as it is very common in the literature (e.g.Harris et al., 2002;Brandi et al., 2020). The list of 'dirty' sectors in provided in Appendix. ...
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Preferential trade agreements (PTAs) increasingly feature a variety of provisions related to economic and social rights (ESR), environmental protection (EP), and civil and political rights (CPR). In this paper we estimate the effect of the degree of legalization of PTAs in these non-trade provisions on the flow of bilateral greenfield FDI. We explore this relation in a structural gravity setting and find that all three types of non-trade provisions affect the flow of FDI negatively. This effect is driven by investments directed to middle- and low-income countries, in particular for more directly economically relevant (ESR and EP) provisions. These latter are also found to have a larger negative impact on FDI between origin-destination country-pairs which differ the most in their commitment on non-trade issues in PTAs with third parties.