metadata: authors: 0: author: Mattoo,Aaditya
1: author: Mulabdic,Alen
2: author: Ruta,Michele
count: World
docna: 0: docna: Trade creation and trade diversion in deep agreements
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Competition Policy,Competitiveness and Competition Policy,Trade Policy,Rules of Origin,Trade and Multilateral Issues,Regulatory Regimes,Judicial System Reform,Intellectual Property Rights,Social Policy,Legislation,Legal Products,Common Property Resource Development,Real & Intellectual Property Law,Legal Reform,Human Rights
teratopic: Social Development,Law and Development,Governance,Private Sector Development,Rural Development,Public Sector Development,International Economics and Trade
abstracts: cdata!: Preferential trade agreements have
            boomed in recent years and extended their reach well beyond
            tariff reduction, to cover policy areas such as investment,
            competition, and intellectual property rights. This paper
            uses new information on the content of preferential trade
            agreements to examine the trade effects of deep agreements
            and revisit the classic Vinerian question of trade creation
            and trade diversion. The results indicate that deep
            agreements lead to more trade creation and less trade
            diversion than shallow agreements. Furthermore, some
            provisions of deep agreements have a public good aspect and
            increase trade also with non-members.
repnme: repnme: Trade creation and trade diversion in deep agreements
display_title: Trade creation and trade diversion
            in deep agreements
listing_relative_url: /research/2017/09/28011319/trade-creation-trade-diversion-deep-agreements
topicv3: Trade m1326924 698,Competition Policy m1326982 406,Intellectual Property Rights m1327290 851,Trade Policy m1327640 776,Discrimination m1505707 3379
publishtoextweb_dt: 2017-09-27T00:00:00Z
historic_topic: Social Development,Law and Development,Governance,Private Sector Development,Rural Development,Public Sector Development,International Economics and Trade
no_of_pages: 44
author_keys: 0: author_key: 429183
1: author_key: 1259324
2: author_key: 1245806
model_extractions: WPS8206 Policy Research Working Paper 8206 _Aaditya Mattoo Alen Mulabdic Michele Ruta_ Development Research Group Trade and International Integration Team & Trade and Competitiveness Global Practice Group September 2017
Policy Research Working Paper 8206

# **Abstract**

Preferential trade agreements have boomed in recent years and extended their reach well beyond tariff reduction, to cover policy areas such as investment, competition, and intellectual property rights. This paper uses new information on the content of preferential trade agreements to examine the trade effects of deep agreements and revisit the classic Vinerian question of trade creation and trade diversion. The results indicate that deep agreements lead to more trade creation and less trade diversion than shallow agreements. Furthermore, some provisions of deep agreements have a public good aspect and increase trade also with non-members.

This paper is a product of the Trade and International Integration Team, Development Research Group and the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amattoo@worldbank.org; amulabdic@ worldbank.org; mruta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Trade Creation and Trade Diversion in Deep Agreements<sup>1</sup>

_By_ AADITYA MATTOO, ALEN MULABDIC AND MICHELE RUTA<sup>_2_</sup>

**Keywords:** Preferential Trade Agreements, Deep Integration, Regionalism. **JEL Codes:** F13, F15.

1 We are grateful to Richard Baldwin, Nuno Limão, Andrés Rodríguez-Clare, Robert Staiger, and seminar participants at the World Bank, the OECD, the Fifth IMF-WB-WTO Trade Workshop, the conference on “The Economics of Trade Agreements” organized by the University of Geneva, the Seventh Washington Area International Trade Symposium (WAITS) Conference at George Washington University and the European Trade Study Group Conference in Florence for helpful comments and suggestions. Errors are our responsibility only. 2 World Bank, 1818 H Street, Washington DC, USA. Aaditya Mattoo, Email: <u>amattoo@worldbank.org;</u> Alen Mulabdic, Email: amulabdic@worldbank.org; Michele Ruta, Email: mruta@worldbank.org. Research for this paper has been supported in part by the World Bank’s Multidonor Trust Fund for Trade and Development, and by the Strategic Research Program. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **I. Introduction**

If a trade economist were abruptly woken up by somebody shouting, “preferential trade agreements” (PTAs), their first thought is likely to be “ _trade creation_ and _trade diversion”_ .<sup>3</sup> That is a measure of the influence of Jacob Viner’s classic book _The Customs Union Issue_ (Viner, 1950) on the profession and the policy debate on the trade effects and, hence, the desirability of preferential arrangements. However, Vinerian analysis was developed in a world where trade agreements were “shallow” and focused only on bilateral tariff liberalization. Today, PTAs are increasingly “deep” and cover also behind-the-border policy areas, such as competition policy, intellectual property rights and other regulatory issues.<sup>4</sup> In this paper, we empirically investigate how far classic Vinerian logic helps us to understand the trade effects of modern preferential trade arrangements.

Do deep agreements simply lead to _more_ trade creation and _more_ trade diversion than shallow agreements? Intuitively, Vinerian logic does not fully apply to deep agreements because their nature is in part different from shallow PTAs. Shallow agreements are controversial because they are inherently discriminatory. Members grant tariff concessions to each other, leaving tariffs on imports from non-members unconstrained. The resulting tariff preferences are likely to increase trade between members (trade creation), but they can also lead members to substitute imports previously sourced from non-members for within PTA products (trade diversion).<sup>5</sup> Deep agreements can reduce trade costs and discrimination beyond tariff liberalization and hence are expected to lead to even more trade creation. But differently from tariffs, provisions relating to competition policy or subsidies tend to be nondiscriminatory in nature and may reduce trade costs and discrimination also vis-à-vis outsiders, creating a positive spillover effect, or “negative” trade diversion (Baldwin and Low, 2009; Baldwin, 2014). Ultimately, the verdict on what forces dominate is empirical and will crucially depend on the content of the trade agreements.

> 3 We refer to PTAs as any trade agreement between a subset of countries (two or more). PTAs have been also referred to in the literature as Free Trade Agreements, Regional Trade Agreements, Economic Integration Agreements, etc. As we will further clarify below, we will also use the term Deep Agreements to stress the fact that many of these arrangements have features that go beyond trade policy and are not preferential in nature. 4 The terms “shallow” and “deep” trade agreements were first defined in Lawrence (1996). There is a voluminous literature on the purpose of shallow trade agreements (e.g. Grossman, 2016). The rationale for deep agreements has not received the same attention. Two references that help explain the changing scope of trade agreements include Ederington and Ruta (2016) and Maggi (2016).

> 5 As it is well known, preferential tariff liberalizations have an ambiguous welfare effect. Trade creation is welfare improving for members. Trade diversion has a negative impact on the welfare of non-members through lower market access as well as on members through reduced tariff revenue. The net welfare effect of shallow PTAs, therefore, depends on which of these two forces dominates.

2
To empirically address the question of the trade effects of deep agreements, we exploit a new database on the content of PTAs (Hofmann et al., 2017). Since the early 1990s, a large number of trade agreements have entered into force. Focusing on the PTAs still in force in 2015, the number of preferential arrangements increased from 20 in 1990 to 279 at the end of 2015. The content of PTAs too has changed. Newer agreements are “deeper” in the sense that they generally expand the set of policy areas covered by older agreements. Specifically, older PTAs focused on fewer than 10 policy areas, mostly commitments on tariffs on industrial and agricultural goods and other border measures such as export taxes. As agreements become deeper, they increasingly extend their reach first to areas such as trade remedies (i.e. countervailing measures, antidumping duties) and subsidies and then to a broader set of behind the border measures such as intellectual property rights and standards.

To assess the impact of deep agreements on members’ and non-members’ trade, we augment a standard gravity model, which is widely used in the literature to assess the effects of PTAs on trade flows (see Head and Mayer, 2014; Limão, 2016). We include a variable of depth of agreements between PTA members, and a variable that captures the depth of the agreements of a trading partner with other countries. Using information from the content of PTAs database, we construct different measures of depth based on the policy areas regulated by the agreements and their legal enforceability. As standard in the literature, we include importer and exporter-year fixed effects to control for country-year specific shocks and for the multilateral resistance terms, and we introduce country-pair fixed effects to partially address endogeneity concerns (Baier and Bergstrand, 2007).<sup>6</sup> We also include measures of bilateral tariffs and preference margins (Kee et al. 2008, 2009; Fugazza and Nicita, 2013) to isolate the impact of changes in depth from changes in tariffs. Finally, we include additional controls, such as dummies to identify shallow PTAs, PTAs that are no longer in force, and the presence of other international agreements that may affect bilateral trade. Our sample covers 96 countries, including all major economies, for the period 2002-2014.<sup>7</sup> During this period, the share of country pairs with PTAs increased from 9 to 29 percent, average tariffs were cut by half, while depth (measured as the count of provisions included in the PTA) increased by a factor of three.

> 6 See Piermartini and Yotov (2016) for a useful guide on estimating trade policy effects with structural gravity models.

> 7 The country coverage is determined by the availability of comprehensive tariff data for the entire period.

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We find that the formation of deep agreements has a meaningful positive impact on the trade flows among members. In particular, we find that trade between country pairs that sign a deep agreement increases by 44 percent. As we control for tariffs and for a PTA dummy, the estimate suggests that deep provisions induce more trade creation than shallow PTAs. When we look at the dynamic effects of deep agreements, we find that future levels of PTA depth are statistically uncorrelated with current levels of trade flows, suggesting that depth of agreements is not determined by the closeness of current trade relations. On average, it takes two years for deep agreements to increase trade flows, consistently with the evidence that reforms of behind the border measures take time to be implemented.

Despite this strong evidence of trade creation, the deepening of trade agreements does not appear to happen at the expense of trade with non-members. Specifically, a standard deviation increase in the depth of the partner’s trade agreements with other countries increases bilateral trade by around 19 percent. As hypothesized in Baldwin and Low (2009) and Baldwin (2014), we find that this “negative” trade diversion of deep agreements is driven by the inclusion of non-discriminatory provisions, such as those that regulate competition policy, subsidies and standards. Tariff preferences (and other preferential provisions) are still found to divert trade with non-members. For instance, a 1 percent increase in the average tariffs faced by a non-member relative to a member (i.e. the relative preference margin) decreases bilateral trade by 4 percent. Furthermore, deep agreements tend to moderate the trade-diverting effect of tariff preferences: the negative impact of relative preferences on trade becomes insignificant and is eventually reversed for deeper agreements.

Some examples may help put these findings in perspective. We focus on three trade agreements with increasing levels of depth, as measured by the number of policy areas covered by the treaty: Peru-Chile, the Republic of Korea-US, and the EU. Based on our preferred specifications, a shallow agreement such as Peru-Chile increased bilateral trade by an estimated 10 percent, but had a negligible impact on non-members. Korea-US, a medium depth PTA, increased trade by 14 percent and also raised exports from outsiders by 4 percent. Finally, our estimates suggest that the deepest agreement in our sample, the EU, increased trade flows among members by 44 percent, while exports from non-EU countries would be around 30 percent lower in the absence of the agreement.

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This paper contributes to a large body of literature on the trade effects of preferential trade arrangements by including the notion of “depth” in the analysis of PTAs.<sup>8</sup> Previous work in this area suffers from a well-known measurement error problem (Baier and Bergstrand, 2007). Due to lack of data, most studies use dummies to identify the presence of a PTA or distinguish between broad types of trade arrangements (e.g. partial scope agreements, free trade agreements or custom unions, as in Baier et al., 2014). This approach does not adequately capture the variation in the content of preferential trade agreements. Indeed, we show that this variation has important implications for the effects of PTAs both on members’ and nonmembers’ trade flows. Our analysis also has relevant implications for the longstanding debate on regionalism versus multilateralism (Bhagwati, 1993). A key question in this debate is whether PTAs are building blocks or stumbling blocks of the multilateral trade system. Both formal models and empirical studies in this literature assume that PTAs are mostly about tariff liberalization.<sup>9</sup> The positive impact of deep PTAs on members’ and non-members’ trade that we find in our analysis supports the view that deep provisions in trade agreements can complement rather than undermine the world trading system (WTO, 2011).

The rest of the paper is organized as follows. The next section discusses the database on the content of trade agreements and the other data used in the analysis. Section 3 presents the empirical strategy. Econometric results are presented in Section 4. Concluding remarks follow.

# **II. Data**

Our measures of depth of preferential trade agreements are based on detailed information on the content of PTAs from a new database (Hofmann et al., 2017). The database covers 279 treaties, which are all the preferential agreements notified to the WTO and in force up to December 2015.<sup>10</sup> Following the methodology proposed by Horn et al. (2014), the focus is on 52 policy areas (see Table A1 in Appendix A), divided into areas that are currently under the mandate of the WTO such as tariffs, antidumping duties and subsidies (referred to as “WTO

> 8 For recent surveys, see Freund and Ornelas (2010), WTO (2011), Head and Mayer (2014) and Limão (2016). There is a small literature on deep agreements. Osnago, Rocha and Ruta (2017a) look at the impact of deep agreements on countries’ participation in global value chains. Mulabdic, Osnago and Ruta (2017) study the effect of Brexit (i.e. the undoing of a deep agreement) on future EU-UK trade relations. Other studies that have looked at the impact of deep agreements based on a more limited database covering around 100 PTAs are Orefice and Rocha (2014) and Osnago, Rocha and Ruta (2015 and 2017b). 9 Informal arguments on the relationship between deep PTAs and the multilateral trade system have been made in Baldwin and Low (2009) and WTO (2011).

> 10 The data are freely accessible at <u>http://data.worldbank.org/data-catalog/deep-trade-agreements.</u>

5
+”) and areas where the WTO has no comprehensive mandate such as investment and competition policy (“WTO X”).<sup>11</sup> For each agreement, the data set identifies whether a policy area is covered by the agreement and whether the provision is legally enforceable.<sup>12</sup> This information allows us to capture the expanding scope of trade agreements beyond a narrowly defined set of traditional trade measures.<sup>13</sup> As noted by Anderson and van Wincoop (2004) “ _[t]here is extensive evidence that free trade agreements and customs unions increase trade and therefore reduce trade barriers ... but it is less clear what elements of these trade agreements play a role (tariffs, NTB’s, or regulatory issues)_ ”. There are three main advantages of using the information on the content of PTAs (instead of dummy variables) to assess their trade effects. First, the new data help us define _deep_ trade agreements more precisely. As discussed below, we define the depth of a PTA based on the extent to which different regulatory issues and policy areas are covered by the agreement and the legal enforceability of such provisions. Second, the information present in the database also allows us to isolate the trade effect of specific sets of provisions. For instance, we dissect the PTAs to assess the impact of provisions based on their economic relevance (named “core provisions”), or the feasibility of preferential treatment (i.e. whether they improve the conditions for PTA members only or for all trading partners). Finally, the data set can capture the evolving nature of trade agreements over time. A notable example is the European Union with its enlargements, which cover an increasing number of members and policy areas.

The data show that the number of trade agreements and their content have changed dramatically since the early 1990s (Figure 1). The number of PTAs in force increased slowly in the 1970s and 1980s and then remained constant until the beginning of the 1990s, after which a large number of agreements entered into force. Focusing on the agreements covered in our database (i.e. those still in force in 2015), the number of PTAs has increased exponentially from 20 agreements in 1990 to 279 in 2015. Along with the number, the content of trade agreements has changed. While older PTAs focused on few policy areas (“shallower” trade

> 11 The WTO’s General Agreement on Trade in Services (GATS) covers commercial presence as a mode of supply but there are currently no rules covering investment in goods.

> 12 See Hofmann et al. (2017) for a detailed description of the methodology and of the data.

> 13 Hofmann et al. (2017) refer to the expanding scope of PTAs as “horizontal depth”. Another dimension of the depth of a trade agreement is “vertical”, reflecting the liberalizing content of commitments or the stringency of rules. This information, however, is widely available only for tariffs (see below) and a small subset of policy areas.

6
agreements covering fewer than 10 policy areas dominated up to the late 1990s), an increasing share of PTAs over time has tended to cover a larger number of policy areas, suggesting a deepening of trade agreements.

Figure 1: Number of legally enforceable provisions in PTAs notified and in force, December 2015 <!-- Start of picture text --> 25 300<br>Cumulative<br>250<br>20<br>200<br>15<br>150<br>10<br>100<br>5<br>50<br>0 0<br>More than 20 Between 10 and 20 Less than 10 Cumulative<br>Number of Agreements<br>Cumulative Number of Agreements<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: Authors' calculations based on the Content of Deep Trade Agreements database.

Table 1 shows that there is an ordering in terms of which provisions are included in trade agreements with different values of depth. Specifically, we divide the agreements into three categories, based on the number of legally enforceable provisions and calculate the share of agreements that include each policy area. We find that policy areas included in shallower agreements (“Less than 10”), are at least as frequent in deeper agreements (cooperation on “statistics” is an exception). The majority of these agreements tend to cover tariffs and other border measures such as export taxes and customs. Competition policy is the only policy area outside the mandate of the WTO appearing in a majority of shallower PTAs. As agreements become deeper (“Between 10-20”), they increasingly extend their reach to a broader set of WTO + areas, including state aid, anti-dumping and countervailing measures. Finally, deeper agreements (“More than 20”) tend to cover areas related to intellectual property rights, movement of capital, and standards, in addition to the areas covered by shallower agreements.<sup>14</sup>

> 14 Figure A1 in Appendix A shows that recent agreements signed by the US and the EU include a larger number of areas than earlier agreements.  These new areas were often covered in other countries’ earlier PTAs, suggesting there may be learning from other countries’ PTAs.

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Based on this evidence, we build several measures of the depth of trade agreements which reflect the extent to which the different policy areas are covered and legally enforceable in a PTA.<sup>15</sup> An area is considered as weakly legally enforceable if the language used is sufficiently precise and binding, but the area has been excluded from dispute settlement procedures under the PTA. While strong legal enforceability refers to areas where the language used is sufficiently precise and binding, and if the area is subject to dispute settlement procedures under the PTA. Using this information, we define alternative measures of depth of an agreement. Specifically, the depth variables are equal to the count of all (“depth all”), weakly legally enforceable (“depth wle”), or legally enforceable (“depth le”) provisions included in an agreement. Each measure is normalized between 0 and 1, with 1 indicating the agreement with the highest number of provisions. In characterizing trade agreements, we also consider the policy areas that have been identified in the literature as being more economically relevant (“core” provisions). These core provisions include all WTO + areas and four areas that fall outside the domain of the WTO: competition policy, rules on investment, movements of capital, and intellectual property rights protection.<sup>16</sup> As shown in Hofmann et al. (2017), these policy areas are also the ones that appear more frequently in PTAs.

A useful distinction for our subsequent discussion is between discriminatory and nondiscriminatory policy areas. Here we follow Baldwin and Low (2009) to classify PTA provisions in these two groups. The traditional view of PTAs is that their benefits accrue only to PTA partners. This is indeed the case for traditional trade policies (i.e. tariffs on industrial goods, tariffs on agricultural goods, export taxes, countervailing measures and antidumping duties) that can be implemented on a discriminatory basis based on the origin of the product. Similarly, government procurement provisions in PTAs tend to open state purchasing to foreign firms on a strictly preferential basis. For other policy areas, however, the traditional view does not appear to hold as PTA provisions may improve the conditions of access in a nondiscriminatory manner (i.e. on a “most-favored-nation,” or MFN basis). According to Baldwin and Low (2009), these areas include customs administration, domestic regulation (SPS and TBT measures), competition (state trading enterprises, competition policy), services (GATS), investment (TRIMS and investment rules), property rights (TRIPS and IPR protection), and

> 15 Given the fact that provisions tend to be highly correlated with each other (see Table A2 of Appendix A), regressions that include individual variables indicating the presence of each provision would suffer from the problem of multicollinearity.

> 16 Core areas have been identified in Damuri (2012) based on Baldwin (2008).

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rules on subsidies and on movements of capital. In some cases, discrimination is simply not possible: if a country limits subsidies to domestic producers or establishes a competition authority in fulfillment of its PTA commitments, these reforms benefit both members and nonmembers of the PTA. In other cases, discrimination is feasible but unlikely for economic or legal reasons: in services, market access is generally granted through reforms of domestic regulation, such as rules on foreign participation or access to essential facilities, which are hard to undertake in a way that grants privileged access.

The sample covers 96 countries, including all major economies, for the period 20022014. The choice of the initial year is due to the poor quality and availability of tariff data before 2002. In addition to the database on the content of deep trade agreements, we use trade and trade policy data from standard sources. Export data at the HS product level are from the _United Nations Commodity Trade Statistics Database_ (UN-COMTRADE). Additional data on bilateral time-invariant covariates, used in a series of robustness checks, come from the CEPII _geodist_ and _gravity_ databases. Tariff data, from the United Nations Conference on Trade and Development TRAINS, and import demand elasticities at the at the 6-digit level, from Kee et al. (2008), are used to construct the Tariff Trade Restrictiveness Index (TTRI) and the Relative Preferential Margin index (RPM). Finally, data on PTAs no longer in force come from Egger and Larch (2008) and Bilateral Investment Treaties (BITs) from the United Nations Conference on Trade and Development’s Investment Policy Hub.<sup>17</sup> Before moving to the econometric analysis, we take a first look at the data. Over the 2002-2014 period, the share of country pairs with PTAs increased from 9 to about 29 percent (Table 2). During the same period, average tariffs (TTRI) were cut by half while depth, irrespective of legal feasibility, increased by a factor of three. As countries reduced bilateral tariffs, the average relative preference margins (RPM) and its standard deviation decreased as well. The two trends together indicate widespread tariff reductions which are less likely to have increased trade diversion. In terms of the content of PTAs, the summary statistics show that there were minor differences (before 2014) between depth constructed using legally enforceable provisions subject to dispute settlement (“Depth LE” or “Depth Legally Enforceable”), and depth constructed on the basis of legally enforceable language (“Depth WLE” or “Depth Weakly Legally Enforceable”). There is also some evidence that the newest

> 17 The data are freely accessible at <u>http://www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html</u> and <u>http://investmentpolicyhub.unctad.org/IIA</u> respectively.

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agreements tend to be deeper. The average maximum depth (“max Depth LE” or “max Depth Core LE”) by importer almost doubled from 2002 to 2014. Part of these increases are due to countries signing agreements for the first time, but this trend is also observed when we restrict the sample to country pairs which already have a PTA.

<u>Table 2: Descriptive Statistics (means and standard deviations in parentheses)</u>

||2002|2005|2008|2011|2014|
|---|---|---|---|---|---|
|PTA (dummy)|.088|.161|.196|.223|.286|
||(.283)|(.368)|(.397)|(.416)|(.452)|
|TTRI (tariffs)|.041|.034|.028|.027|.019|
||(.195)|(.077)|(.066)|(.08)|(.069)|
|RPM (relative tariffs)|.009|.008|.007|.007|.004|
||(.062)|(.036)|(.033)|(.035)|(.029)|
|Depth All|.054|.111|.134|.145|.197|
||(.191)|(.272)|(.293)|(.296)|(.344)|
|Depth WLE(Weakly|.038|.083|.103|.111|.142|
|<br>Legally Enforceable)|(.153)|(.23)|(.251)|(.253)|(.281)|
|Depth LE(Legally|.038|.082|.102|.108|.132|
|<br>Enforceable)|(.153)|(.229)|(.249)|(.25)|(.273)|
|Depth Core LE(Legally<br>Enforceable)|.057<br>(.202)|.118<br>(.288)|.148<br>(.318)|.162<br>(.325)|.199<br>(.342)|
|max Depth LE(Legally|.307|.423|.482|.499|.529|
|<br>Enforceable)|(.295)|(.313)|(.291)|(.283)|(.308)|
|max Depth Core LE|.493|.646|.746|.769|.787|
|<br>(Legally Enforceable)|(.366)|(.319)|(.275)|(.259)|(.246)|
|max MFN LE(Legally|.445|.594|.714|.733|.759|
|<br>Enforceable)|(.365)|(.35)|(.303)|(.289)|(.275)|
|max PREF LE(Legally|.596|.764|.828|.861|.872|
|<br>Enforceable)|(.392)|(.297)|(.248)|(.227)|(.207)|
|Trade (millions of US$)|631.588|994.542|1495.1|1668.34|1680.574|
||(4829.109)|(6974.361)|(9243.604)|(10284.256)|(11246.587)|

Figure 2 plots the distribution of trade flows for different intervals of “depth all”. In the left panel, groups are defined according to different levels of depth in bilateral agreements, while the right panel uses the average depth of the destination country’s agreements with the rest of the world weighted by imports. Figure 2 shows that country-pairs with higher levels of depth trade more on average. The right panel shows that on average, countries export relatively 11
less to partners involved in shallow agreements (i.e. “Low depth”) than partners without PTAs. However, this negative effect is reversed as partners sign deeper agreements (“Medium depth” and “High depth”) which are associated with distributions shifted to the right of the “no PTA”. This suggests that deep agreements tend to benefit excluded countries as well, possibly due to the inclusion of provisions that are de jure or de facto MFN.

Figure 2: Distribution of trade over levels of legally enforceable depth (“Depth LE”) <!-- Start of picture text --> Depth Others Depth<br>5 10 15 5 10 15<br>Total imports (log) Total imports (log)<br>no PTA Low depth no PTA Low depth<br>Medium depth High depth Medium depth High depth<br>.2 .15<br>.15<br>.1<br>.1<br>Density Density<br>.05<br>.05<br>0 0<br><!-- End of picture text -->

# **III. Trade effects of deep agreements: Empirical strategy**

In this section, we begin the empirical investigation of the trade impact of deep agreements. A number of policy-related factors contribute to trade costs between countries, which create a gap between the price in the importing country and the export price. Trade agreements allow members to reduce these costs and hence increase bilateral trade. A concern, well understood since Viner (1950), is that this mechanism could also generate trade diversion, that is, a substitution of trade away from non-members. Deep agreements can reduce trade costs among members by eliminating tariffs _and_ by reducing other frictions. Examples of the latter are contingent protection measures like antidumping, countervailing and safeguard actions, and differences in national regulations that create an adaptation cost for foreign producers. Even other provisions of PTAs, such as disciplines on subsidies or strengthened protection of intellectual property rights, reduce the risk of exporting due to policy uncertainty, and hence can be seen as reducing trade costs (Limão and Maggi, 2015).<sup>18</sup> Therefore, we expect PTAs

> 18 The assumption that trade agreements reduce trade costs helps us to cast the following discussion in the framework of the gravity model. We recognize that other provisions of agreements, such as those relating to labor or environmental standards, do not necessarily lead to a reduction in trade costs. The extent of the aggregate impact of these heterogeneous provisions is, therefore, an empirical question. We come back to this issue below.

12
that cover more areas to have a positive impact on members’ trade that goes beyond the impact of shallow agreements.

The impact of a deep agreement on non-members is more complicated. The rules in a deep PTA can be implemented either to reduce costs only for members (e.g. by exempting only them from burdensome regulatory requirements) or also for non-members (e.g. by simplifying customs procedures for all trading partners). If these rules are implemented in a discriminatory way, they inflict a further competitive disadvantage on third countries. Since member countries must now pay neither tariffs nor frictional costs, they can expand sales in their markets, driving down prices and hurting exports of third countries. However, if frictional barriers are eliminated in a non-discriminatory way, third countries also benefit from the reduction in associated costs.<sup>19</sup> In these circumstances, third countries still suffer from the decline in price in destination markets due to preferential access granted to members of PTAs, but the price they actually receive is closer to the destination price because the elimination of the frictional costs reduces the total trade tax they pay. If the decline in trade costs for non-members is sufficiently large relative to the preferences members receive, then we may observe “negative trade diversion” (Baldwin, 2014): third countries see an increase in the export price they receive and expand quantity exported as a result of a deep PTA.<sup>20</sup> Below, we introduce the empirical model and identification strategy used to analyze the effect of deep agreements on members’ and non-members’ trade. In line with the above discussion, we augment a standard gravity model to include a variable of depth between PTA members and another variable that captures the depth of agreements trading partners conclude with the rest of the world. We also use information on relative tariff preferences (Fugazza and Nicita, 2013) to assess how their impact is affected by existence of deep agreements.

# **a. Trade creation**

Our main specification is based on the gravity model of trade, which is widely used in the literature to assess the effects of policy variables on trade flows (see Head and Mayer, 2014; Limão, 2016). We begin by discussing how the depth of PTAs can be incorporated into the

> 19 There is some evidence of these positive externalities. Chen and Mattoo (2008) examine the consequences of harmonization and mutual recognition of standards within PTAs. They show that when these agreements are concluded with restrictive rules of origin which deny their benefit to non-members, the latter suffer a decline in exports to PTA countries.  However, when the agreements do not have restrictive rules of origin, nonmembers’ exports to PTA countries also increase.

> 20 Appendix B provides a graphical example of these effects.

13
E Xij ~~>~~ XjP ~~ao~~ (Yt Xij _ 5Xi E

> L )

|

Tj |

i Tij

|

i XijtL = xp{By Depthijt + fp, In( 1+ TT ) + Git + Qe + ij + Contro ls} + ei jt Xijt

> . .

> Git |
downward the effects of PTAs, or in our case the effect of ����h��� on trade. Finally, we include several additional controls: dummies to capture the presence of a PTA (i.e. a shallow PTA dummy), of a PTA no longer in force, or of other international agreements that can have an impact on trade flows, such as a Bilateral Investment Treaty (BIT).

An important issue in the estimation of the effects of any policy variable is endogeneity. In the trade literature, it has been shown that countries are more likely to sign agreements with partners with whom they already trade more intensively because of geography or cultural proximity or other common characteristics. If countries tend to sign trade agreements with their “natural” trading partners (Krugman, 1991), this would bias the effects of trade agreements upwards especially with cross-sectional data. This bias may be even stronger for depth to the extent that countries may be more willing to sign deeper agreements with their natural trading partners. The issue of endogeneity of trade policies is well known since Trefler (1993), but it is hard to address due to the lack of reliable instruments for panel data. To partially address the endogeneity problem, we follow Baier and Bergstrand (2007) and introduce country-pair fixed effects, ���, to capture country-pair time-invariant factors determining bilateral trade such as distance or common language. This set of fixed effects accounts for unobserved time-invariant heterogeneity among country pairs which can bias estimates in cross-sectional studies, and hence attenuates the endogeneity bias stemming from omitted variables.

A limitation of previous work is that the use of a dummy variable to identify the trade effect of a PTA is generally associated with a negative bias in the variable’s coefficient. We improve with respect to earlier studies on the bias due to measurement error of the trade policy variables by following the suggestion outlined in Baier and Bergstrand (2007) “ _the best method for eliminating this [measurement error] bias is construction of a continuous variable that would more accurately measure the degree of trade liberalization from various PTAs_ .” First, we include a variable for the depth of trade agreements to capture the degree of trade liberalization between PTA partners. Second, we also include the �������, the tariff trade restrictiveness index, to isolate the effect of changes in tariffs between country _i_ and _j_ (Kee et al. 2008, 2009; Fugazza and Nicita, 2013) from the impact of changes in depth. The index is obtained using the following formula: 15
where ��� is the average product level exports from country _i_ to country _j_ between 1995 and 1997, � is the bilateral import elasticity and � is the applied tariff rate on product h�. We use export weights based on pre-sample data to reduce the potential endogeneity problem of trade to tariff. The obtained index aggregates bilateral product level tariffs to a uniform tariff equivalent that would maintain exports between _i_ and _j_ constant.

As discussed in Section II, the ����h��� variable is defined as the count of provisions included in each agreement normalized between 0 and 1. Our baseline specification relies on the count of legally enforceable provisions, i.e. those which have binding language and are subject to dispute settlement (“depth LE”). We also construct alternative measures of depth by counting the areas covered irrespective of their legal enforceability (“depth all”) and by including provisions that are more likely to be economically relevant (“depth core”).<sup>21</sup> The coefficient of depth captures the effect of changes in the coverage of areas in a PTA net of changes in tariffs. Given the set of fixed effects, the identification strategy relies on the variation in depth within country-pairs variation to identify the effect on exports.

# b. **Trade diversion**

To capture effects on a trading partner i from country �’s trade agreements, we modify the definition of trade cost in equation (2) to where iceberg trade costs ��� are divided into an “MFN” component, ����� when �≠�, which is a destination specific cost common to all exporters, and ������� that can be eliminated between specific country-pairs. Deep agreements affect non-members in two different ways. First, as in the case of shallow PTAs, they make non-members less competitive in members’ countries by reducing bilateral trade costs of members. This effect results both from the preferential reduction in tariffs (���) and of other trade costs (�������). Second, deep agreements

> 21 We also construct a depth variable based on the first component of a Principal Component Analysis (PCA) of the provisions (see Orefice and Rocha, 2014).

16
TjMFN + Dip + bij + Controls} + Eijt RPM Others Depth RPM Others Depth RPM ijt ~~hs Xij(95-97),hs Ejnas(Tijtns — Twjt,ns~~ hs Xij(95-97),hs Ejns with Ty itn, ~~EEEXv Xy j(95-97),hs Ty jt,hs~~ #i wstns dv Xp j(o5-97),hs Others Depth;jt ~~2v XyXvjos-97)~~ <u>4095-97)</u> ~~Depthy;Depthoje~~ #1 Xv Xyj(98-97) Twjt,ns TMEN = f (depth; RPM, TEN
goods that are important for _i_ we would expect country _i_ ’s exports to decrease; similarly, if _j_ signs deep agreements with _i_ competitors, this should have an impact on bilateral trade.

As a second step, we decompose the depth of the PTA into its preferential and MFN components, as suggested in the literature (e.g. Baldwin and Low, 2009). Specifically, an increase in the ��� denotes a loss in market access for the exporter relative to the rest of the world, while increases in ��h��� ����h capture the deepening of importer's trade relations with other partners. The effect of relative tariffs is unambiguously negative since they directly impact the final prices paid by consumers in destination markets, while deep provisions could have ambiguous effects on trade. On the one hand, if countries can set policies to discriminate between members and non-members and reduce costs for PTA member-countries only, as in the case of tariffs, export taxes or other duties, we would expect a negative impact on third countries. On the other hand, if deeper agreements have a public good component, such as improvements in customs, increased competition or the reduction in subsidies to domestic producers, then the effect on excluded countries could be positive. To capture the two opposing effects that deep agreements may have, we include two variables in equation (3’) to capture the depth of preferential and MFN core provisions following the classification provided in Baldwin and Low (2009).

Apart from their direct impact on third countries, deep provisions in agreements may also influence the impact on these countries of conventional tariff preferences. How an MFN reduction in the frictional trade tax for all trading partners influences the marginal effect of tariff preferences on third countries is analytically ambiguous. Therefore, it is worth examining the empirical evidence. We test the following equation: where ����h�� is interpreted in two different ways. The first is, as before, an average of the depth of the importers’ agreements with the rest of the world. The second is the maximum number of provisions that importer _j_ has in its deepest agreement at time _t_ . This captures the idea that MFN provisions, once introduced in a PTA, may have an impact on all partners because of their intrinsic public good nature. Coefficient �� of the interaction term in equation 18
(3’’) identifies the effect of deep agreements on tariff preferences. A negative coefficient would suggest that tariff preferences have a stronger marginal effect once the importing country signs deeper agreements, whereas a positive coefficient would suggest that tariff preferences matter less when trading partners implement deep agreements.

# **IV. Econometric results**

In this section, we present the results of the estimations from the gravity model. The first subsection focuses on the impact of deep agreements on members’ trade. We then study how deep agreements affect trade with non-members.

# a. **Trade creation**

This subsection discusses and presents the estimates from equation (3) and its extensions. The objective is to identify the effect of deep trade agreements on member countries’ trade flows.

Table 3 reports the PPML estimates from the gravity equation (3). Results point to a significant effect of depth on bilateral trade. In the first column, we use the count of all the legally enforceable provisions included in PTAs and normalize the variable between 0 and 1 for ease of interpretation. Results suggest that trade between country pairs that sign an agreement with the highest depth (43 provisions) increases by around 12.5 percent.<sup>23</sup> The effect changes only slightly when we include all provisions whether legally enforceable or not (����h ���). The effects are reduced by half once we count the strictly economically relevant provisions (����h ����). Since the maximum number of provisions in _Depth Core_ is about half the maximum number of provisions in the other variables, the impact of an additional provision is similar across all the depth variables. The finding that even measures which a priori seem peripheral, like cooperation on health and human rights, matter for bilateral trade on average as much as core provisions is puzzling. One explanation could be that the inclusion of non-economic areas in trade agreements facilitates deeper commitments in more directly trade related areas – a form of “issue-linkage” (Maggi, 2016) that is not adequately captured by binary representation of provisions in this paper.<sup>24</sup>

> 23 Since the _Depth_ variables are normalized between 0 and 1, the following formula provides the percentage change in trade flows of signing the deepest agreement: �<sup>������</sup> −1.

> 24 In a series of robustness checks, we find similar results when controlling for the presence of bilateral investment treaties (BITs) and using alternative definitions of depth based on the legal language. Results for _depth_

19
<u>Table 3. PPML Regression: Trade Creation</u>

||Depth PP<br>|ML<br>||||||
|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Trade|(2)<br>Trade|(3)<br>Trade|(4)<br>Trade|(5)<br>Trade|(6)<br>Trade|(7)<br>Trade|
|Depth LE|0.118**||||0.195***|0.366***|0.356***|
||(0.053)||||(0.065)|(0.125)|(0.122)|
|Depth All||0.099**||||||
|||(0.042)||||||
|Depth Core LE|||0.059*<br>(0.034)|||||
|Depth Core All||||0.053*||||
|||||(0.030)||||
|old PTAs|||||0.143***|0.185***|0.171***|
||||||(0.050)|(0.057)|(0.055)|
|PTA||||||-0.074|-0.079*|
|||||||(0.049)|(0.048)|
|ln(1+TTRI)|||||||-0.206<br>(0.562)|
|N|110,739|110,739|110,739|110,739|110,739|110,739|94,057|
|Exp.-Year|yes|Yes|yes|yes|yes|yes|yes|
|Imp.-Year|yes|Yes|yes|yes|yes|yes|yes|
|Exp.-Imp.|yes|Yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

We find that controlling for _old PTAs_ (columns 5 to 7), agreements that are no longer in force and on the content of which we have no information, increases the magnitude and statistical significance of the impact of depth on trade. Specifically, in this specification trade between country pairs that sign an agreement with the highest depth increases by 44 percent. Intuitively, the inclusion of the _old PTA_ variable increases the magnitude and precision of the _depth_ estimates because it allows us to distinguish between country-pairs in the control group that had a PTA at some point in time and those that never had a PTA and for which _depth_ is equal to zero.<sup>25</sup> _core LE_ become insignificant in a specification where we include controls for _old PTA_ , _PTA_ , and bilateral tariffs together, for which there is limited variation within country-pairs. Finally, results are robust to an alternative definition of depth based on the principal component analysis “PCA,” as in Orefice and Rocha (2014). Moreover, we find that results are also robust to the exclusion of crises years (i.e., 2008 and 2009) with coefficients around 10 percent higher than those in specifications using the full sample.

> 25 The old PTA dummy captures the pre-accession agreements for countries that joined the EU after 2002 and a small number of other PTAs no longer in force: the trade agreement between Mexico and the three Northern Triangle countries – El Salvador, Guatemala and Honduras – that was active between 2001 and 2012, the trade agreement between Mexico and Nicaragua (1998-2012), and Closer Economic Partnership Arrangement (CEPA)

20
It is important to note that with the inclusion of a PTA dummy in columns 6 and 7 of Table 3, we capture the effect of depth due to variations within country-pairs and within PTAs. The PTA dummy could be interpreted either as a trade agreement fixed effect or as an interaction variable that captures the effect of an agreement with zero provisions. Therefore, a positive and significant coefficient indicates that country-pairs with deep agreements trade more with respect to those that have shallower agreements. This suggests that results in the first four columns are not merely due to the presence of a PTA. Moreover, results are robust to the inclusion of bilateral tariffs as well, which suggests that the finding that deep trade agreements increase bilateral trade is not driven by tariff liberalization.

Our baseline specification could suffer from several econometric problems. On the one hand, the relatively small effects of ����h on trade we find in Table 3 compared to the literature may suggest a downward bias in our coefficients.<sup>26</sup> On the other hand, the potential endogeneity of deep agreements and trade could bias our estimates in the opposite direction. More specifically, a first econometric issue could be that trade flows tend to adjust slowly to trade cost changes and by using annual data without lags we may not capture the full effect of trade agreements.<sup>27</sup> The issue may be particularly relevant for deep agreements as they tend to have longer implementation phases, which could bias downwards the estimates of depth. Second, coefficients of the anticipation effects of PTAs may also be a confounding factor in our regression analysis. If trade flows increase in anticipation of the agreement even before its entry into force, we would fail to assign these effects to the agreements when using contemporaneous variables. Evidence of anticipatory effects could also raise concerns about the identification strategy and causality because of the difficulty in distinguishing between anticipation effects and pre-existing trends. Finally, a third concern is that the absence of intrabetween China and Hong Kong SAR, China. The coefficient on old PTAs in column 5 is 0.18 which suggests that older PTAs increased bilateral trade by around 20 percent on average during the 2002-2014 period. This impact is equivalent to signing an agreement that includes 30 legally enforceable provisions and reflects the depth of EU pre-accession agreements.

> 26 According to a meta-analysis of the effect of trade agreements on trade by Head and Mayer (2014), the median coefficient of a PTA dummy is 0.28. In the case of deep trade agreements such as the EU, the coefficient found in the literature is 0.98, which is higher than our estimate for the trade effect of the agreement with highest depth.

> 27 Trefler (2004) suggests that trade flows adjust slowly to changes in trade costs and criticizes the use of yearly data. Therefore, we use 3-year intervals to allow more time for trade to adjust to changes in depth of trade agreements. We find that results in Table A3, in Appendix A, are qualitatively and quantitatively similar to results obtained using consecutive years, with the exception for core depth which becomes statistically insignificant. In the rest of the paper, we favor yearly data over 3-year intervals because it is more common in the gravity literature.

21
national trade flows limits the identification to the comparison between PTA member countries and country-pairs without PTAs in a way that is not completely consistent with the theoretical basis of the gravity equation (Larch et al., 2017).

To address these concerns, we extend our baseline specification in equation (3) in several directions. First, we use yearly data and include lags and leads of the depth variables to estimate the dynamic effects of PTAs. Figure 3 presents the results on the dynamic effects of deep agreements. Results are based on specifications (1) to (4) in Table 3, modified to include two leads and four lags of the depth variables to accommodate heterogeneous effects over time and to test for anticipatory effects of agreements. The inclusion of the leads thus also provides for an informal test for the “strict exogeneity” of trade agreements (see Bergstrand et al., 2015).<sup>28</sup> The results suggest that both current and future levels of PTA depth are statistically uncorrelated with current levels of trade flows. It takes at least two years for a deep agreement to increase trade flows and the effects are twice as large as the ones we find in specifications without lags and leads (Table 3). As shown in Figure 3, these results are robust to the use of different measures of depth.

Figure 3: Dynamic effects of Depth

> 28 As an alternative way to address the problem of reverse causality, we check whether previous trade flows predict depth. In results available upon request, we find no significant relationship between past trade flows and depth in both OLS and PPML regressions.

22
<!-- Start of picture text --> Depth All Depth LE<br>©|a|<br>t !_ | ae<br>| et =t “<br>“ a | ff<br>| “ tg le _<br>“ a _ oer| Steere TM ||<br>o+= = o+ ——<br>~~ -__! ee ~ | Pa<br>-2 -1 0 1 2 3 a -2 -1 0 1 2 3 4<br><!-- End of picture text -->

<!-- Start of picture text --> Depth Core All Depth Core LE<br>©|&|<br>||<br>~ ~<br>| |<br>| ae | aoc __<br>° = a ° = os<br>“ N<br>-2 -1 0 1 2 3 = -2 -1 0 1 2 3 4<br><!-- End of picture text -->

Depth 1 year prior to agreement;;, = Depthij(¢41) * IPT Aijt
variable two years prior to an agreement taking effect. Table 4 shows that trade flows are not statistically correlated with future levels of depth. Depth estimates obtained in Table 3 are unaffected by the inclusion of these additional variables. This evidence suggests a causal relationship between depth and trade.<sup>30</sup> <u>Table 4: Trade Creation Falsification Test</u>

||Depth PPML<br>|<br>|||||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Depth LE|(2)<br>Depth LE|(3)<br>Depth All|(4)<br>Depth All|(5)<br>Depth Core LE|(6)<br>Depth Core LE|(7)<br>Depth Core All|(8)<br>Depth Core All|
|Depth|0.133**|0.124**|0.107**|0.104**|0.062|0.062*|0.057*|0.057*|
||(0.064)|(0.058)|(0.049)|(0.045)|(0.039)|(0.036)|(0.034)|(0.031)|
|Falsification test:|0.077||0.052||0.035||0.035||
|Depth 1 year prior to<br>agreement|(0.058)||(0.042)||(0.037)||(0.032)||
|Falsification test:||0.042||0.033||0.019||0.021|
|Depth 2 years prior to<br>agreement||(0.037)||(0.027)||(0.023)||(0.019)|
|Observations|105,107|104,696|105,107|104,696|105,107|104,696|105,107|104,696|
|Importer-Year FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Exporter-Year FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Country-Pair FE|yes|Yes|yes|yes|yes|yes|Yes|yes|
|Period|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|2002-2014|

Note: Robust standard errors, clustered at the country-pair level, are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Another concern with our main specification is that the absence of intra-national trade flows limits the identification to the comparison between members of a PTA and country-pairs without PTAs. To address this concern, we follow Bergstrand et al. (2015) and construct intranational trade flows using GDP data from the Penn World Tables.<sup>31</sup> In this specification, the control group comprises country-pairs without trade agreements and countries' trade with themselves, neither of which sees any change in depth. Results in Table 5 suggest that the exclusion of internal flows plays an important role in explaining the relatively small effects of depth we found earlier, as already documented in the trade gravity literature for the PTA dummy (e.g. Dai et al., 2014; Larch et al., 2017). The coefficients of depth on trade are around three times larger than those presented in Table 3. These results are more in line with the literature in which, for instance, the coefficient for a common currency is 0.98 while we find

> 30 In an additional falsification test, we assign random levels of depth to trade agreements in our sample. Figure A2 of Appendix A shows the distribution of the coefficients of random depth variable obtained from 500 random draws of depth, obtained from a PPML model that includes a PTA dummy, is normally distributed. 31 The main advantage of constructing intra-national flows with GDP data is the extensive time and country coverage compared to gross output data (e.g. CEPII’s TradeProd data are available until 2006). The drawback is that GDP is measured as value added which is an imperfect proxy of gross output.

24
in column 6 that the coefficient for the deepest agreement in our sample (the European Union) is 0.97. Additionally, we find the expected negative and significant impact of tariffs on trade which is not captured in regressions with international flows only. Unfortunately, due to data limitations on product level output, we limit our analysis to international trade when we study the effects of trade diversion.

<u>Table 5. PPML Regression: Trade Creation Internal Flows</u>

||Depth PPM<br>|L Internal Flo<br>|ws<br>|||||
|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Trade|(2)<br>Trade|(3)<br>Trade|(4)<br>Trade|(5)<br>Trade|(6)<br>Trade|(7)<br>Trade|
|Depth LE|0.849***||||1.023***|0.972***|0.996***|
||(0.045)||||(0.070)|(0.132)|(0.130)|
|Depth All||0.722***<br>(0.041)||||||
|Depth Core LE|||0.555***<br>(0.033)|||||
|Depth Core All||||0.483***<br>(0.032)||||
|old PTAs|||||0.282***|0.267***|0.261***|
||||||(0.061)|(0.067)|(0.066)|
|PTA||||||0.026|-0.031|
|||||||(0.055)|(0.055)|
|ln(1+TTRI)|||||||-2.131***<br>(0.551)|
|N|116,134|116,134|116,134|116,134|116,134|116,134|97,825|
|Exp.-Year FE|yes|yes|yes|yes|yes|yes|yes|
|Imp.-Year FE|yes|yes|yes|yes|yes|yes|yes|
|Exp.-Imp. FE|yes|yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

To better understand the impact, and to quantify the effect, of additional provisions in trade agreements, we consider three agreements that are characterized by different levels of depth. First, we calculate the trade impact of the Peru-Chile FTA, a relatively shallow agreement signed in 2009, which includes 11 legally enforceable provisions. Second, we calculate the trade impact of the United States-Korea Free Trade Agreement (KORUS FTA) signed in 2007, an agreement with a medium level of depth which includes 15 provisions. Third, we estimate the impact of the EU which comprises eight agreements, Treaty of Rome and successive enlargements, which cover 43 legally enforceable provisions.<sup>32</sup>

> 32 Details on the policy areas covered by the Peru-Chile FTA, KORUS FTA and the EU Treaties are in Table A4 in the Appendix.

25
Based on the estimates in column 6 of Tables 3 and 5, we find that the Peru-Chile FTA increased members’ bilateral trade between 10 and 30 percent. For the case of KORUS FTA, which includes additional provisions on state trading enterprises, public procurement, and provisions on intellectual property rights, we find a larger effect, ranging between 14 and 40 percent. Finally, we find that the inclusion of all depth core provisions and 25 other provisions spanning from taxation and money laundering to labor market regulation and visa and asylum, increased trade between 44 and 164 percent among EU countries.

# **b. Trade diversion**

Table 6 presents the results on the effect of deep trade agreements on excluded countries. Note first that the depth of PTAs (depth LE) continues to have a consistently significant impact on trade between member countries. Even though the coefficients in Table 6 are slightly different from those in Table 4, the difference is not statistically significant. To ease interpretation all _Others_ variables are standardized and the coefficients capture one standard deviation shocks. We find that the importer's average depth, when counting all the 52 areas, has a positive effect on bilateral trade. In column 2 we limit the analysis to core provisions and find that while the magnitude drops, the estimates increase in statistical significance. The results suggest that a standard deviation increase in partner’s depth (depth core LE) increases trade by around 19 percent. These positive effects on third-countries could potentially explain the difference between trade creation estimates with and without internal flows. If deep trade agreements benefit all trade partners, then the effect of signing a deep trade agreement (or unilaterally reducing tariffs) would be absorbed by the country-year fixed effects when using international trade flows only.

We find that the positive effect of deep agreements on third countries is driven by the inclusion of MFN provisions, while the inclusion of preferential provisions has a negative but insignificant impact (columns 3 and 6). The negative effect of preferential provisions becomes significant once we account for the presence of _old PTA_ , agreements for which we do not have information on their content, in columns 9 and 12. Results in columns 7 to 12 reveal that _old PTA_ is associated with a negative average effect on third-countries’ trade. This finding suggests that early agreements were more trade diverting, which is consistent with the evidence presented in Section II that PTAs have become deeper over time. Both results for depth and 26
older PTAs are robust to the inclusion of relative and bilateral tariff preferences, which are insignificant for different specifications.

A comparison of the effects of trade creation and trade diversion is helpful to put these results in perspective. As before, we focus on three trade agreements with different levels of depth: Peru-Chile, United States-Korea and the EU. Using estimates from column 9, we find that a medium depth agreement such as KORUS FTA increased exports from excluded countries to members by around 4 percent. We find large effects of the European Union (the deepest agreement in our data) for non-member countries. Estimates suggest that exports from non-EU countries would be around 30 percent lower in the absence of the agreement. Finally, we find shallow agreements between smaller countries such as the Peru-Chile agreement increased trade between members but had a negligible impact on non-members trade. In general, the positive impact on non-members’ trade flows is driven by the inclusion of MFN provisions, while preferential provisions have a negative effect.

27
<u>Table 6. PPML Regression: Trade Diversion</u>

||Depth PPM|L Diversion|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|VARIABLES|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|
|Depth LE|0.177***|0.177***|0.157**|0.173***|0.172***|0.149**|0.232***|0.240***|0.238***|0.229***|0.235***|0.229***|
||(0.064)|(0.060)|(0.062)|(0.065)|(0.062)|(0.063)|(0.080)|(0.078)|(0.078)|(0.081)|(0.079)|(0.080)|
|Others Depth LE|0.290*|||0.295*|||0.284*|||0.290*|||
||(0.151)|||(0.153)|||(0.152)|||(0.154)|||
|Others Depth||0.181**|||0.185**|||0.180**|||0.183**||
|Core LE||(0.078)|||(0.078)|||(0.078)|||(0.078)||
|Others MFN LE|||0.661**|||0.670**|||0.769***|||0.780***|
||||(0.315)|||(0.313)|||(0.297)|||(0.295)|
|Others PREF LE|||-0.476|||-0.479|||-0.590**|||-0.594**|
||||(0.312)|||(0.312)|||(0.294)|||(0.294)|
|RPM.||||-0.255|-0.295|-0.515||||-0.240|-0.280|-0.536|
|||||(0.899)|(0.892)|(0.863)||||(0.899)|(0.892)|(0.860)|
|ln(1+TTRI)||||-0.116|-0.112|-0.050||||-0.120|-0.115|-0.046|
|||||(0.689)|(0.686)|(0.678)||||(0.689)|(0.686)|(0.677)|
|old PTAs|||||||0.066|0.078|0.061|0.065|0.077|0.058|
||||||||(0.060)|(0.061)|(0.061)|(0.059)|(0.060)|(0.061)|
|Others old PTAs|||||||-0.044*|-0.044*|-0.116***|-0.045*|-0.045*|-0.117***|
||||||||(0.026)|(0.025)|(0.042)|(0.026)|(0.025)|(0.042)|
|N|100,157|100,157|100,157|94,057|94,057|94,057|100,157|100,157|100,157|94,057|94,057|94,057|
|Exp.-Year FE|Yes|Yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Imp.-Year FE|Yes|Yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Exp.-Imp. FE|Yes|Yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses.

*** p<0.01, ** p<0.05, * p<0.1
Finally, we investigate if the depth of trade agreements concluded by countries influences the marginal effect of trade preferences on third countries. Specifically, we identify the effect of deep PTAs on tariff preferences by estimating equation (3’’). Results are reported in Table 7. We interact the _RPM_ variable with the importer’s average depth of trade agreements with the rest of the world and with the importer's maximum value of depth (i.e. its deepest trade agreement) in a given year. Therefore, we test if commitments, for instance, to improve customs efficiency or to reduce subsidies, soften the consequences of trade preferences for excluded countries.

|Table 7. PPML|Regression<br>Depth PPM<br>|: The Influ<br>L Diversion<br>|ence of Dep<br>Revisited<br>|th on the I<br>|mpact of T<br>|rade Prefer<br>|ences<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|VARIABLES|Trade|Trade|Trade|Trade|Trade|Trade|Trade|Trade|
|Depth LE|0.189***|0.184***|0.234***|0.231***|0.191***|0.185***|0.236***|0.229***|
||(0.064)|(0.060)|(0.080)|(0.079)|(0.064)|(0.063)|(0.080)|(0.079)|
|RPM|-0.254|-0.546|-0.242|-0.532|-2.169|-3.659*|-2.131|-3.609*|
||(0.864)|(0.909)|(0.865)|(0.910)|(1.543)|(1.871)|(1.550)|(1.882)|
|(RPM * Others|1.618***||1.608***||||||
|Depth LE)|(0.617)||(0.619)||||||
|(RPM * Others||1.903***||1.893***|||||
|Depth Core LE)||(0.676)||(0.678)|||||
|(RPM * Max|||||3.776**||3.726**||
|Depth LE)|||||(1.790)||(1.799)||
|(RPM * Max||||||4.174***||4.127***|
|Depth Core LE)||||||(1.554)||(1.564)|
|ln(1+TTRI)|-0.009|0.057|-0.013|0.052|-0.052|-0.063|-0.057|-0.068|
||(0.671)|(0.668)|(0.671)|(0.668)|(0.685)|(0.687)|(0.685)|(0.687)|
|Others Depth LE|0.278*||0.274*||0.292*|0.285*|0.286*|0.281*|
||(0.151)||(0.152)||(0.153)|(0.153)|(0.154)|(0.154)|
|old PTAs|||0.039|0.044|||0.044|0.042|
||||(0.060)|(0.062)|||(0.060)|(0.059)|
|Others old PTAs|||-0.049*|-0.050**|||-0.047*|-0.048*|
||||(0.027)|(0.025)|||(0.027)|(0.027)|
|Others Depth Core LE||0.167**||0.165**|||||
|||(0.076)||(0.077)|||||
|N|94,057|94,057|94,057|94,057|94,057|94,057|94,057|94,057|
|Exp.-Year FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Imp.-Year FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Exp.-Imp. FE|yes|yes|yes|yes|yes|yes|yes|yes|
|Period|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|2002-14|

Note: LE stands for legally enforceable. Robust standard errors, clustered at the country-pair level, are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
We find that the effect of tariff preferences does depend on the depth of trade agreements concluded by an importing country. The interaction of the relative preference margin is significant with measures of both the average depth vis-à-vis the rest of the world ( _Others Depth LE_ or _Others Core Depth LE_ ) and of maximum level of commitments that importers undertake ( _Max Depth LE_ and _Max Depth Core LE_ ) (Table 7). Figure 4, based on results in column 6 of Table 7, shows that when maximum depth core is close to zero, a 1 percent increase in RPM decreases bilateral trade by 4 percent. This negative impact of relative preferences on trade is statistically significant for values of depth core lower than 0.3, while it is completely offset when more than 80 percent of depth core provisions are included. This suggests that tariff preferences have a discriminatory effect in countries that have “shallow” agreements, while the effect is reversed when a country undertakes deep commitments. The statistical insignificance of relative tariff preferences we find in some of our specifications may, therefore, be due to pooling across agreements with different levels of depth.

Figure 4. Marginal Effect of Relative Tariff Preferences (90% C.I.) <!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Depth Core<br>MFX RPM 90% C.I.<br>2<br>0<br>-2<br>-4<br>-6<br><!-- End of picture text --> 30
# **V. Concluding remarks**

Most of the work on PTAs in the literature is based on the implicit assumption that trade agreements are about tariff liberalization. In this literature, the impact of preferential trade agreements is captured by the standard Vinerian analysis of trade creation and trade diversion. Recent data on the content of trade agreements show, however, that PTAs are deepening, in the sense that they include an expanding set of provisions, often covering behind the border policy areas. The evidence presented in this paper confirms the view that Vinerian logic may provide an incomplete guide to the effects of deep agreements. Intuitively, the reason is that deep provisions do not necessarily act as preferential tariffs. In fact, we find that deep agreements create more trade than shallow agreements and that they can have a positive spillover effect on trade with outsiders when they are non-discriminatory in design or implementation.

The increasing number and complexity of preferential trade agreements justifies the growing interest in this area. This paper is only a first step to better understand the trade effects of deep agreements. Many questions remain open. First, we would like to uncover the specific channels through which the depth of PTAs affects trade flows. Deep agreements can influence the ability of firms to produce different products, to engage in global value chains, and to access new markets. They can also have a differing impact on developed and developing economies, particularly as they have different institutional capacities. Second, the detailed content of PTAs, i.e. the legal commitments embedded in different policy areas covered by the agreement, are likely to matter for trade and beyond. Deep provisions on services and competition will influence the ability of countries to integrate in trade markets; investment rules will affect the ability to attract and retain foreign investment; and the protection granted to intellectual property rights will have an impact on the ability to innovate. As new data are collected on the detailed content of PTAs, an exciting research agenda lies ahead.

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