EXPLORING THE HETEROGENEOUS IMPACT OF TRADE AGREEMENTS ON TRADE: DEPTH MATTERS

Abstract

Intra-and inter-national trade flows, deep trade agreements, gravity models.

Manuscript Region of Origin: SPAIN
Abstract: One of the most relevant features in the context of international trade in recent decades is the increase in the depth of trade agreements.The aim of this paper is to explore the heterogeneous effect of preferential trade agreements (PTAs) on bilateral trade flows including their depth in addition to other agreement characteristics such as the geographical scope of the member countries, their degree of development, or their nature.To measure depth, we follow the most recent works that propose indirect instead of direct measures.Once we control for depth, our results reveal that: (i) the positive effect of regional PTAs is notably larger for the deepest agreements whereas the shallow interregional agreements do not seem to increase bilateral trade flows; (ii) North-North PTAs only boost trade when they exhibit a high depth level; (iii) the depth is not a relevant factor for plurilateral agreements and those that consist of the adhesion of a country to an existent PTA.
Manuscript Classifications: 6.4.4: Trade Policy • International Trade Organizations; 6.4.5:Empirical Studies of Trade; 6.9.3: Macroeconomic Impacts Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation

Introduction
Undoubtedly one of the most relevant features in the context of international trade in recent decades is the increase in the number and depth of trade agreements, so that they extend beyond traditional trade areas and cover new aspects such as services, investment, and competition policy (Ruta, 2017).The expansion of agreements scope is clear attending to the number of policy areas covered: in the 1950s the average PTA covered 8 policy areas whereas in recent years they have averaged 17 (Mattoo et al., 2020). 1 The existence of differences in the scope and the level of integration commitments between the agreement parties introduces a new source of heterogeneity in the impact of agreements on trade.This is a topic that has not been addressed in the literature until very recently, when databases on the depth of trade agreements have been developed. 2  This literature points out that controlling for the depth of trade agreements is required for a proper quantification on the trade effects of trade agreements.The aim of this paper is to study the heterogeneous effect of trade agreements on bilateral trade flows through the estimation of a gravity equation including the depth of trade agreements in addition to other characteristics that define the type of agreement (the geographical scope of the member countries, their degree of development, or the nature).To measure depth, we follow the most recent works that propose indirect instead of direct measures.To the 1 The expression PTA in this paper refers to several types of trade agreements including, according to the WTO classification, Customs Unions, Free Trade Agreements, Partial Scope Agreements, Economic Integration Agreements, Free Trade Agreements & Economic Integration Agreements, and Customs Unions & Economic Integration Agreements.
2 Seminal empirical studies explored the impact of the depth of trade agreements using their level of economic integration based on the traditional definition by Frankel et al. (1997) as a depth measure.Magee (2008) and Roy (2010) found that customs unions had larger aggregate trade flow effects than free trade agreements.Baier et al. (2014) and Baier et al. (2018) found that more comprehensive trade agreements such as customs unions, common markets and economic unions have larger trade effects than free trade agreements, and the latter have larger effects than two-way and one-way preferential trade agreements.These last two works also provided evidence of the differential (partial) effects of those types of EIAs on both intensive and extensive margins of trade.
best of our knowledge, we are the first in implementing such approach for exploring the existence of sizeable different effects of trade agreements on trade depending on both the depth of the agreement and other agreement characteristics.This research goes a step further than Díaz-Mora et al. (2023) where only those other agreement characteristics but not depth are included as a source of heterogeneity.Moreover, our empirical analysis implements the best practices and recommendations for estimating gravity equations suggested by Yotov et al. (2016) and Yotov (2021).These best practices include, in addition to consider theory-motivated intra-national as well as international trade flows, the need to use panel data techniques (to account for unobserved bilateral heterogeneity and endogeneity), the inclusion of controls for multilateral resistance terms, and the use of the PPML estimator to deal with econometric problems resulting from heteroskedastic residuals and the prevalence of zeros in bilateral trade flows.
As above-mentioned, advances in this topic have been hand in hand to the development of new databases that capture the agreements' depth.Considering that deep trade agreements cover heterogeneous areas and many provisions, the quantification of the effect of agreements' depth on trade flows is a challenge for researchers.The initial contributions rely on directly constructed indices that sum up the number of provisions and the individual or subjective assessment of the researchers of the key provisions in each policy area.Among these initial contributions are or WTO-X areas, which are those that are outside the current mandate of the WTO, such as investment policy, competition, environment or nuclear safety.Based on this dataset, several research papers construct various measures of depth and their effect on trade using a gravity model (Kohl et al., 2016;Rubínová, 2017;Ahcar and Siroën, 2019;Osnago et al., 2019;Boffa et al., 2019;Laget et al., 2020;Dhingra et al., 2021;Falvey and Foster-McGregor, 2022).Their findings confirm that trade agreement heterogeneity matters for international trade and deeper, rather than shallow RTAs promote trade.That is, the extensive margin (number of policy areas covered) has a positive effect on trade creation.
More recently, a number of studies have used indirectly estimated measures relying on new statistical methods such as machine learning to restrict the number of provisions and better identify those provisions more effective to promote international trade.These studies use the Deep Trade Agreements database 2.0 which provides detailed information on the content of a sub-sample of 18 policy areas most frequently covered in a set of 283 agreements currently notified to the WTO between 1958 and 2017 (Mattoo et al., 2020).Policy areas differ widely from each other in complexity and, consequently, in the number of provisions each comprise, the total number of provisions being 937.
Hence, the focus here is on the intensive margin of integration, that is, on the specific commitments within a policy area.This detailed coverage of policy areas is referred to as vertical depth.Based on the experts' knowledge, Mattoo et al. (2020) identify the set of provisions within each policy area that are essential to achieve the objectives of the agreement and the non-essential provisions which are referred to as "corollary." Examples of these works are Baier and Regmi (2021) and Breinlich et al. (2021).
The first one employs an unsupervised learning method such as k-means clustering to identify the clusters of provisions that are typically grouped together and then classify trade agreements into different clusters.Detailed information on each treaty is taken from the NSF-Kellogg Institute EIA (economic integration agreement) database.Given the provisions scores for each of the clusters, they can be classified into shallower agreements (those with lower provision scores) and deeper agreements (those with higher provision scores).Later, by running a gravity model with exporter-time, importer-time, and pair fixed effects (often called the three-way gravity model) using panel data from Comtrade, these authors find that deeper agreements result in larger estimates of the impact of trade agreements.Moreover, by looking at the prominent provisions in the deepest cluster, 18 provisions are identified as those with the most successful in trade creation.Breinlich et al. (2021) complement the approach adopted by Baier and Regmi (2021) by selecting the provisions using a supervised method that also consider the impact of the provisions on trade.More precisely, these authors propose to adapt a specific matching learning technique-the least absolute shrinkage and selection operator (Lasso)to the context of trade agreements.They first specify a three-way panel data gravity to be estimated in its original nonlinear form using PPML.Data come from Comtrade and comprises merchandise trade flows for the period 1964 to 2016.Including a large number of provisions as covariates implies a high dimensionality that, combined with the relatively small number of PTAs, leads to implausibly large and uninterpretable estimates due to multicollinearity.To reduce the number of provisions, these authors use the lasso technique to shrink the impacts of individual provisions toward zero and progressively remove those that do not have a significant effect in the fit of the model.More precisely, they adopt a penalized regression approach that involves appending a penalty term to the Poisson pseudo-likelihood one would use to estimate the unpenalized gravity model.The higher this penalty term, the higher the variables forced to have zero coefficients and the fewer the variables that are selected which should be those with the strongest effect.Using this technique, the authors select 6 essential provisions (among the total 303 essential provisions from the World Bank Deep Trade Agreements database 2.0) that are associated with strong increases in trade flows.They are related to antidumping, competition policy, technical barriers to trade and trade-facilitation procedures.However, the authors argue that the selection of these provisions could be due to the fact that they are included in agreements together with other provisions that are the real responsible for the increase in trade flows.For that reason, in a second step, the authors identify those other provisions which are related to each of the selected provisions in the first step.They term this novel methodology as "iceberg lasso".The additional set of provisions that may be associated with enhancing the trade-increasing effect of trade agreements comprises 43 provisions.
An additional paper that explores the trade impacts of trade agreements focusing on the provisions they contain using the World Bank Deep Trade Agreements database 2.0 is Fontagné et al. (2021).Like Baier and Regmi (2021), these authors rely on a clustering approach (specifically the iterative kmean++ algorithm developed by Arthur and Vassilvitskii, 2007)  To preview our results, using data form World Bank's Trade Agreement database (Horizontal Depth) and indirect measures of depth proposed by Fontagné et al. (2021) and Breinlich et al. (2021) to classify PTAs, we find that the depth of the agreements is also a source of heterogeneity when the impact of different types of agreements on trade flows is taken into account.In particular, we find that: (i) the positive effect of regional PTAs is notably larger for the deepest agreements whereas the shallow interregional agreements do not seem to increase bilateral trade flows; (ii) North-North PTAs only boost trade when they exhibit a high depth level; (iii) the depth is not a relevant factor for plurilateral agreements and those that consist of the adhesion of a country to an existent PTA.
The remainder of the paper is organized as follows.After this Introduction section, Section 2 briefly outlines the empirical methodology and describes the data sources.
Section 3 presents and discusses the main findings of the empirical model.Section 4 concludes.

Methodology and data
As explained above, some recent works have proposed indirect indicators to measure the depth of trade agreements using new statistical methods to address the high heterogeneity and number of provisions covered by trade agreements.We follow the proposed indicators for two of those works to classify the different trade agreements according to their depth.
First, we use Fontagné et al. (2021) three clusters of trade agreements with different depth and, according to their results, different impact on trade flows.We create a dummy variable for the deepest agreements which takes the value one for the 28 trade agreements included in the cluster that exhibits a statistically largest impact of trade and zero otherwise.In this paper we consider those agreements as deep and the remaining agreements as shallow.Second, we use the 43 provisions identified as those with strongest trade-enhancing effect by Breinlich et al. (2021) through their iceberg lasso procedure.
Depending on how many of these provisions are contained in an agreement, we classify the different agreements into deep and shallow following a percentage distribution similar to that of Fontagné et al. (2021).Specifically, we consider the deepest agreements to be the 10% of total agreements, those with the highest number of those 43 provisions included.The maximum number of those provisions that an agreement includes is 19 and there is only an agreement (COMESA) that reaches that maximum number and, consequently, would be the deepest agreement.The successive EU enlargements would also be among the deepest agreements by adding 18 of those provisions.Other agreements that include more than 8 of those provisions have classified as deepest agreements.
Shallow agreements are 90% of total agreements, those with the lowest number of those selected provisions (less than 8 provisions).In a last step, to reconcile both approaches to measure the depth of trade agreements, we classify as deep and shallow agreements those agreements that coincide in each of these two categories by the two approaches.
To quantify the impact of trade agreements depending on their depth and simultaneously on other characteristics of the agreement, we estimate a gravity model.
The gravity equation has been extensively used to quantify the effects on bilateral trade flows of PTAs (e.g., Baier and Bergstrand, 2007;Baier et al., 2014;Bergstrand et al., 2015;Anderson and Yotov, 2016). 4Relying on the latest developments both on theoretical and empirical gravity literature, we specify an econometric model that allows us to estimate the potential differential impact of PTA on trade by depth level and type of agreements.The proposed gravity model is estimated in multiplicative form (instead of logarithmic form) with the Poisson Pseudo Maximum Likelihood (PPML) estimator.This estimator is the preferred option to deal with heteroskedasticity in trade data and to take 4 For a comprehensive review of the empirical literature on the effect of PTAs on trade, see Limão (2016).
advantage of the information that is contained in zero trade flows (Santos Silva and Tenreyro, 2006).
Considering the specification for estimating the overall impact of deep trade agreements on trade as reference, our econometric model takes the following form: This specification estimates the elasticity of trade separately with respect to deep and shallow agreements.The depth variable is measured using the above explained dummy variable.We further estimate variations of Eq. ( 1) for the additional classifications of PTAs considered in this paper (by geographical scope, degree of development of the members states or the nature of the agreement).
In all specifications, the dependent variable is the nominal value of bilateral trade flows (in levels) from a country i (exporter) to a country j (importer) at time t.Data comes from the Structural Gravity Manufacturing Database (Monteiro, 2020), which provides consistent data on bilateral international and domestic trade of manufactured goods, for Regarding explanatory variables, PTAij,t is a variable that takes the value of one when i and j are members of the same Preferential Trade Agreement in force at time t, and it is equal to zero otherwise.GATT/WTOij,t is a variable that takes the value of one when i and j belong the GATT (until 1994) or WTO (from 1995) in year t, and zero otherwise.Following the recommendations by Bergstrand et al. (2015), a set of timevarying border dummy variables (INTERij,t) to account for common globalization effects are included.To obtain these dummy variables, we interact a binary indicator for each year t (Dt) with a time-invariant dummy variable (INTERij), which takes the value 1 for international trade flows (i≠j) and the value 0 for intra-national trade (i=j).Moreover, as suggested by Baltagi et al. (2003), Baier and Bergstrand (2007) and Baldwin and Taglioni (2007), three additional types of fixed effects (country-pair fixed effects (ηij), exportertime fixed effects (χit) and importer-time fixed effects (jt)) are also included in order to deal with two sources of omitted variables bias.Firstly, country-pair fixed effects control for the impact of both observed variables such as distance, common language, contiguity, colonial ties, etc. and unobserved time-invariant determining factors of bilateral trade that may be correlated with the regressors.Furthermore, these country-pair fixed effects alleviate endogeneity concerns regarding our policy variable of interest (PTAij,t).
Secondly, country-year fixed effects (both for exporting and importing countries) capture the unobservable multilateral resistance (price) terms described by Anderson and van Wincoop (2003).This set of exporter-time and importer-time fixed effects control for trade barriers of each country with their remaining trade partners and for any other country-specific time-varying variables that may affect bilateral trade on exporter or importer side.
Regarding the explanatory variables, our primary data source for regional trade agreements is the World Bank's Trade Agreements Database (Horizontal Depth) constructed by Hofmann et al. (2017).From this dataset, we rely on several databases for classifying the trade agreements according to their type.First, we use the World Bank classification to categorize a PTA as Regional, when all its member countries belong to the same region, or as Interregional, when countries from more than one region are part of the agreement.To construct the latter variable, six regions are considered: East Asia, South Asia and Pacific; Europe and Central Asia; Latin America and Caribbean; Middle

Results
Drawing on most recent developments on gravity modelling of trade flows, which recommend the inclusion of both intra and international trade flows, we employ the PPML estimator with the dependent variable defined in levels and including exporter-and importer-time fixed effects as well as country-pair time-invariant fixed effect in all the specifications.This constitutes the best estimator and specification to obtain unbiased and theory-consistent estimates.
The estimation results for the PTA effect on trade depending on the depth level of the agreement appear in Table 1.As explained in previous section, we classify the agreements in deep and shallow using jointly Fontagné et al. (2021) and Breinlich et al.
(2021) approaches.Our estimates, which are displayed in column (1), show a positive and statistically significant coefficient for shallow PTAs with a coefficient value of 0.090, which involves a positive effect on bilateral trade of 9.3% ([exp(0.090)-1]*100=9.3%).
Moreover, we find that deep trade agreements boost trade flows by 49% ([exp(0.309+0.090)-1]*100=49%).This estimated increase in trade is much lower than that obtained by Fontagné et al. (2021) which is around 70% but slightly higher than that obtained by Dhingra et al. (2021) which is around 40%.
In these estimates, the coefficient for belonging to GATT/WTO are also positive and statistically significant, suggesting an increase of trade flows by around 38%.The estimated coefficients of all international border dummies which are included in the model to capture the globalization effects are negative and almost all of them are statistically significant.As the international border dummy for 2015 is dropped from the specification, this is the reference group and the estimates coefficients of the remaining border dummy variables should be interpreted as deviations from that international effect in 2015.Our results suggest that the effects of borders on trade have fallen significantly over the sample period.These results are consistent with Bergstrand et al. (2015) and Yotov et al. (2016) confirming the existence of a clear effect of globalization on trade over time.
Next, we present the results when, in addition to depth, we classify the agreements according to the following criteria: (i) by their geographical scope, distinguishing between regional and interregional agreements (columns 2 and 3 of Table 1); (ii) by the income level of the trading partners, grouping them as North-North, North-South and South-South (Table 2); and, (iii) by their "nature", splitting them into bilateral, plurilateral, whether they consist of the adhesion of a country to an existent PTA, and agreements between an existent PTA and a country or between two existing PTAs (Table 3).
When we split PTAs according to their regional scope, the point estimate of the depth variable is still statistically significant (column 2 of Table 1).The coefficient is very similar (0.290) than that obtained in column 1 of Table 1 and, consequently, the impact of deep PTAs on trade is similar in magnitude: an increase of 33.6%.Regarding the regional scope, when the depth of the agreements is controlled for, our results find that regional PTAs do have a statistically significant effect on trade flows whereas interregional PTAs do not.Our estimates show that bilateral trade between partners with a regional agreement in force increases by 19.2%.
These results contrast with those reported by Díaz-Mora et al. (2023), who conclude that both continental and intercontinental trade agreements boost trade when the depth of the agreement is not controlled for, although the increase is clearly higher for regional agreements (32%) than of interregional agreements (9.3%).That is, when the depth of the agreement is included in the specification, the positive effect of interregional agreements disappears and that of the regional agreements shrinks, pointing out the importance of measuring the depth of PTAs.
To try to infer how the elasticity of trade changes depending on whether the regional and interregional agreements are deep or shallow, we interact the dummy variables capturing the regional scope with the dummy variable capturing deep agreements.We find that for both regional and interregional agreements, deep agreements have a positive and significant impact on trade flows (column 3 of Table 1).In the case of regional PTAs, even for shallow agreements, their effect is positive and statistically significant rising trade flows by 17%.That is, regional PTAs have a positive and significant impact although the effect is notably larger for deep regional agreements boosting trade flows by an additional 38%, leading to an overall effect of 61.8% increase in bilateral trade flows.In the case of interregional PTAs, a positive and statistically significant effect only takes place for deep agreements, which boost trade flows by 27%.
That is, shallow interregional agreements do not seem to increase bilateral trade flows.
Next, we examine the effects of PTAs by partners' income levels and depth of the agreement (Table 2).We find positive and statistically coefficients for deep agreements, which increase trade flows by around 38%. Ceteris paribus, North-North PTAs do not show a significantly impact on trade flows and North-South and South-South ones have a positive and significant effect (column 1).The magnitude of the impact is notably higher for South-South agreements (36%) than for North-South agreements (10%).
When we also take into account the depth of each type of agreement by income levels using interactions (column 2 of Table 3), we find a positive and statistically significant effect when those agreements are deep for North-North PTAs.Deep North-North PTAs are associated with an increase of 41% in trade flows.However, the effect of shallow North-North agreements is not statistically significant.In the case of North-South agreements, both shallow and deep PTAs boost trade flows, although the magnitude of the effect is lower for the former (9,6%) than for the latter (52.4%).
Regarding the South-South PTAs, our results show that their impact on trade flows is positive and statistically significant when they are shallow agreements (with an increase of around 38%) whereas the impact is not statistically significantly different for deep agreements.That is, the elasticity of trade to the entry into force of South-South agreements is not higher for deep agreements.
These results contrast with those of Díaz-Mora et al. ( 2023) who find that the elasticity of trade to PTAs is positive and significant for the three different PTAs according to the income levels of the member states.That is, our results suggest that the depth of the agreements also matters for increasing trade flows and not merely the type of agreement by income levels.
Lastly, we explore how the influence of depth of PTAs on bilateral trade flows changes when, in addition, we classify the agreements between bilateral PTAs, plurilateral PTAs, enlargements of existent PTAs, and new agreements in which participate PTAs already in force (with another PTA or with another country).These results which are displayed in Table 3 are notably different from those previously obtained for other types of agreements.The main difference is that here deep agreements do not exhibit a statistically significant coefficient (column 1).The impact of bilateral agreements is also no significant whereas is positive and significant for the other three types of agreements.The larger effect (increase of trade flows of 54.5%) is found for agreements that consist of the adhesion of a country to an existent PTA (for example, the accession of Armenia and the accession of Kyrgyz Republic to the Eurasian Economic Union).The magnitude of the increase of trade flows is more moderate for plurilateral agreements (20%) and even more moderate for those agreements between an existent PTA and a country or between two existing PTAs (11%).In summary, for this classification criterion the depth of the agreements does not seem to affect the results.
When the estimates include interaction variables between the type of agreements and the dummy variable for deep agreements, the association between the depth and trade flows clear-cut.More specifically, the results in column 2 of Table 3 suggest that there is not a significant impact on bilateral trade flows for both deep plurilateral and deep agreements that consist of the adhesion of a country to an existent PTA.Plurilateral PTAs stimulate bilateral trade flows when they are shallow agreements with an increase of 20%.
For shallow agreements that consist of the adhesion of a country to an existent PTA, the statistically significance is weak but the magnitude of the increase of trade flows is high (71%).Since there are not deep agreements for the other two types of agreements, we cannot calculate their differentiated effects according to their depth.Hence, these results are quite similar to those of Díaz-Mora et al. (2023), suggesting that the depth of agreements that differ in their "nature" (bilateral, plurilateral, whether they consist of the adhesion of a country to an existent PTA, and agreements between an existent PTA and a country or between two existing PTAs) does not matter for increasing trade.

Conclusions
This paper examines the heterogeneity potential differential effect of both depth of the agreement and different types of PTAs on trade.To measure depth, we follow the most recent works that propose indirect instead of direct measures relying on new statistical methods such as machine learning to restrict the number of provisions and better identify those provisions more effective to promote international trade.By estimating theory-grounded specifications of the gravity equation using domestic and international trade flows and following all best practices and recommendations for these estimates, we find that depth is a source of heterogeneity in PTAs' impact on bilateral trade flows.Consequently, previous estimates of PTAs' partial effects are probably biased due to the omission of the level of depth of the agreements in the analysis.
Once we consider that trade agreements differ also in depth, some interesting results stand out.First, we find that deep PTAs have a higher positive effect on bilateral trade than that of shallow PTAs.Second, according to the geographical scope of the agreements, we find that, when the depth of these agreement is controlled for, regional but not interregional agreements boost trade.Moreover, a positive effect of regional PTAs is found for both deep and shallow agreements, although notably larger for the former than for the latter.However, interregional agreements only boost trade when they exhibit a high level of depth.Shallow interregional agreements do not seem to increase bilateral trade flows.Third, according to the type of agreement by income levels of their trade partners, our results show that in the case of North-North PTAs, their positive and statistically significant effects only take places when they are deep agreements.For North-South agreements, both deep and shallow PTAs boost trade flows, although again the magnitude of the effect is higher for the former than for the latter.The results are somewhat different for South-South PTAs since the elasticity of trade to the entry into force of these South-South agreements is not higher for deep agreements.Finally, the depth is not a factor that positively affect trade flows when the impact of other types of agreements are analyzed.Specifically, a high level of depth for plurilateral and those agreements that consist of the adhesion of a country to an existent PTA does not seem to increase their positive effect on trade flows.
Our paper uncovers significant heterogeneity in the impact of depth across different types of trade agreements.Our results have clear and important policy implications given the increasing depth and complexity of trade agreements in most recent years.
to identify groups of trade agreements based on their provisions' content.Three clusters are proposed in such a way that each group of trade agreements has a similar content of provisions by policy subject and different from that of the other groups.The first cluster comprises 28 trade agreements, the second one 87 and the third 167.Thus, the diverse provisions contained in the agreements may explain heterogenous trade effects of those agreements.To test it, in a second stage, the authors estimate the mean impact on trade of belonging to a trade agreement positioned in each of the three clusters using a three-way fixed effect panel PPML procedure of a gravity model.The trade flows data include both domestic and international trade, based on CEPII BACI and TradeProd databases combined with UNIDO INDSTAT2 2019.One of the clusters exhibits a statistically largest impact on trade, revealing the agreements included in it as the deepest agreements.The other two clusters obtain parameter estimates that are considerably lower and close to each other.
186 trading partners over the period 1980-2016.This dataset uses export flows which are expressed as free on board (FOB) and complemented by mirrored import data flows after adjusting for insurance and freight costs (CIF).The panel dimension of Xij,t improves estimation efficiency.Moreover, as suggested by Yotov (2021), the dependent variable includes both international and intra-national trade flows.Domestic trade flows are computed as the difference between gross output and exports of manufacturing goods.Of the aforementioned works that examine the impact of PTAs according to their depth on trade flows, only Fontangé et al. (2021) and Dhingra et al. (2021) include intra-national flows.
East and North Africa; North America; and Sub-Saharan Africa.Second, we use the World Bank's Classification of Countries by Income to categorize PTAs by income levels of their partners at the date of entry into force of each specific agreement.Here, three types of PTAs are distinguished which are North-North, South-South and North-South.When all their member countries are classified as high-income countries, a PTA is categorized as a North-North PTA (N-N PTA) whereas it is categorized as a South-South PTA (S-S PTA) when all member countries belong to groups of low, low-middle or uppermiddle income.When both high and non-high income countries are part of an agreement, this is classified as a North-South PTA (N-S PTA).Thirdly, with regard to the classification of PTAs according to their nature, we split PTAs into four categories: bilateral agreements, plurilateral agreements, agreements consisting in the adhesion of a country to an existing PTA, and agreements between an existing PTA and a country or between two existing PTAs.Finally, data on membership in GATT/WTO come from the World Trade Organization.
and Hofmann, Osnago and Ruta (2019)o and Ruta (2019).The former is based on one of the first databases named Design of Trade Agreements (DESTA) Database, which codifies trade agreements by including fine-grained data with information related to 17 cooperation areas.3Theauthors used two depth indicators which are highly correlated: an additive index that combines the seven provisions that these authors consider key in deep trade agreements and a synthetic index using a type of factorial analysis (latent trait analysis) on a total of 48 variables that are theoretically related to the depth of an agreement but with different importance in establishing the extent of countries' commitments.When these variables of depth for 536 PTAs signed between 1945 and 2009 are included in a gravity model to explain bilateral merchandise trade for 179 countries, they found positive and statistically significant at the 1 percent level coefficients, confirming that the deeper an agreement is, the larger its effect on trade flows between member countries.

Table 1 .
The impact on trade of preferential trade agreements by depth level and type of agreement (regional v. interregional).PPML estimates.The regressand is the value of bilateral exports plus domestic trade flows, measured by dyad-year.Robust standard errors, clustered by dyad are in parentheses.***p< 0.01, **p < 0.05, *p < 0.1.All regressions include countrypair fixed effects, as well as exporter-time and importer time-fixed effects.The fixed effects are not reported for brevity.To control for global trends in international trade, INTERij,t dummies are also included.The sample includes annual data for consecutive years and covers manufacturing trade for 186 countries over the period1980-2015.