DP2023-09

Do Deep Regional Trade Agreements
Facilitate Regional Production
Networks in Latin American and
Caribbean Countries?

Yoshimichi MURAKAMI

Revised June 14, 2023

Revised June 14, 2023

Do Deep Regional Trade Agreements Facilitate Regional Production
Networks in Latin American and Caribbean Countries?
Yoshimichi Murakamia*aResearch Institute for Economics and Business Administration (RIEB), Kobe

University, Kobe, Japan
Associate Professor, Research Institute for Economics and Business Administration(RIEB), Kobe University, 2-1, Rokkodai, Nada-ku, Kobe 657-8501, Japan. Email: ymurakami@rieb.kobe-u.ac.jp*corresponding author

Acknowledgments
The author’s special thanks go to Mikio Kuwayama, who provided very detailed comments andconstructive suggestions. The author is also grateful to Manabu Furuta, Hiroyuki Nishiyama,
Nobuaki Hamaguchi, Yoshiaki Hisamatsu, Isao Kamata, Atsuyuki Kato, and Takahiro Sato, fortheir insightful comments and constructive suggestions, and to Yoshihiko Hashiguchi and
Xiangwei Kong for their assistance with this research.

Declaration of interest statement
There are no potential conflicts of interest to declare.

Funding
This work was supported by the JSPS KAKENHI under Grant numbers 20K13482.

1

Do Deep Regional Trade Agreements Facilitate Regional Production
Networks in Latin American and Caribbean Countries?
Abstract: This study analyzes the causal effects of the depth of regional trade
agreements (RTAs) measured by the coverage and legal enforceability of WTOplus and WTO-extra policy areas on the production networks trade in all 33 Latin
American and Caribbean (LAC) countries from 1990 to 2016, using a structural
gravity model. The study constructs a unique dataset on the indexes of the depth,
breadth, and core depth of all RTAs in force that include at least two LAC
countries, based on a World Bank database on RTAs’ contents. Results indicatethat both depth and breadth of RTAs have positive effects on the intra-regionalparts and components exports in the LAC region. However, the effects aresubstantially heterogeneous by the type of agreements and the characteristics ofcountry-pairs. The depth of custom unions among Latin American countries,
mainly the Southern Common Market (MERCOSUR), has positive effects,
whereas the depth and breadth of plurilateral free trade agreements with
developed countries outside the region (e.g., the United States or European
countries) have negative effects. These findings are robust to the use of the mirror
import data, the use of three-year interval data, and the inclusion of future values
that controls for reverse causality.
Keywords: depth and breadth of regional trade agreements; parts and components
exports; heterogeneous effects; Latin American and Caribbean countries;
structural gravity model
JEL classification codes: F13, F14, F15, O54

1 Introduction
A prominent feature of recent regional trade agreements (RTAs) has been the“deepened” and “widened” nature of the integration, including a wide segment of nontariff policy areas, such as foreign direct investment (FDI), trade in services, intellectual
property rights (IPR) protection, and technical norms and standards, beyond theelimination of tariffs and other obstacles to trade in goods (ECLAC 2014; Jinji, Zhang,and Haruna 2022a; Orefice and Rocha 2014).
2

RTAs, particularly deep and wide ones, are expected to be pivotal in theformation of international production networks because they tend to address several
critical dimensions for the sound functioning of supply chains (Estevadeordal et al.
2013). For example, Antràs and Staiger (2012) theoretically show that trade involving
the exchange of customized intermediate inputs requires deep integration, which isbeyond integration based on the traditional General Agreement on Tariffs and Trade(GATT) / World Trade Organization (WTO) rules. The depth of integration is typicallymeasured by the coverage of WTO-plus (WTO+) and WTO-extra (WTO-X) areas(Horn, Mavroidis, and Sapir 2010; Hofmann, Osnago, and Ruta 2019).
The Latin American and Caribbean (LAC) region has a long tradition of de jure(government-led) regional integration through RTAs since the era of state-led
industrialization in the 1960s (Kuwayama 2019). For example, the Latin American Free
Trade Association (LAFTA)1 and Central American Common Market (CACM) enteredinto force in 1961. The Andean Pact entered into force in 1969, and the Caribbean
Community and Common Market (CARICOM) entered into force in 1973. Later, theAndean Pact and CACM became moribund during the late 1970s and 1980s owing topolitical and economic turmoil as well as armed conflicts between the member
countries. Following the unilateral liberalization in the 1980s, LAC countries have,since the 1990s, actively engaged in pursuing reciprocal trade liberalization. Following
the revitalization of the Andean Pact and CACM,2 and establishing the Southern

1

LAFTA reorganized into the Latin American Integration Association (LAIA), which enteredinto force in 1981.

2

In an attempt to revitalize the integration process of the Andean Pacto, the Quito Protocol was
signed on May 12, 1987 and entered into force on May 25, 1988 (O'Keefe 1996).

3

Common Market (MERCOSUR) in 1991, LAC countries have executed various
bilateral or plurilateral free trade agreements (FTAs). Until 2015, 22 RTAs that include
at least two LAC countries and provide at least one WTO-plus (WTO+) or WTO-extra(WTO-X) area entered into force, excluding partial scope agreements (PSAs) that cover
only certain goods (see Table A1 in the Appendix for a full list of the 22 RTAs).3
Nevertheless, it has been repeatedly pointed out that the LAC’s intra-regional
trade has been lackluster relative to that of other regions. In particular, the LAC region
has been characterized as having a low degree of regional production integration andlimited progress in constructing regional value chains (ECLAC 2014; Kuwayama2019). The share of parts and components in intra-regional trade has been
approximately 10% since 2000 (Kuwayama 2019, 11; ECLAC 2014, 42).

Subsequently, the member countries established an intra-regional free trade area with
common external tariff. The Trujillo protocol was signed on March 10, 1996, which formallylaunched the Andean community (CAN), and entered into force on August 1, 1997 (O'Keefe
2009). As for CACM, the Tegucigalpa Protocol was signed on December 13, 1991, which
formally launched the Central American Integration System (SICA), and entered into force
on July 23, 1992 (O'Keefe 2009). Note that Baier, Bergstrand, and Vidal (2007) and
Sánchez-Albornoz and Timini (2021) consider that CACM was revived on July 30, 1990,
when the meeting for reviving CACM started. However, it is more plausible and consistent
to consider the entry into force as the revitalization.
3

The data are from the Regional Trade Agreements Database(https://rtais.wto.org/UI/PublicMaintainRTAHome.aspx, accessed on August 8, 2022). We
exclude two RTAs (EU-CARIFORUM States and EU-Central America) are not included forwhich “Deep Trade Agreements Database 1.0” does not provide the information on the depth
indexes.

4Notably, intra-regional trade among LAC countries comprises differentiatedmanufacturing products with higher value added and technological intensity. Thus,
intra-regional trade can be a platform for exporting such products to extra-regional
markets (Kuwayama 2019). Therefore, the promotion of regional production linkages
through regional integration is expected to provide member countries with more
opportunities for diversifying their exports toward higher value-added goods and tasks,
thereby bring about structural changes in LAC countries (ECLAC 2014; Moreira 2018).
Accordingly, from a policy perspective, an empirical analysis revealing thedeterminants of regional production network formation is urgently required.
Several studies analyze the effects of major RTAs in the LAC region on trade
between member countries (i.e., well-known trade creation effects). For example,
Martin-Mayoral, Carofilis, and Guijarro (2016) find trade creation effects for CACM,MERCOSUR, and CAN (in this order of magnitude) in bilateral exports from 19
Western hemisphere countries to their main trading partners in the world (40 countries)
from 1970 to 2014. Additionally, they find that export diversification contributes themost to trade creation in MERCOSUR, followed by CACM and CAN, while intraindustry trade contributes to trade creation in CAN. Similarly, Stender (2018) finds
trade creation effects for MERCOSUR among the MERCOSUR’s four original member
countries and their 26 major import partners from 1989 to 2012. However, these studies,
which use binary dummy variables taking a value of 1 if both exporter and importer
countries have an RTA in force, to measure the effects of RTAs, fail to consider
potential heterogeneity in the contents of those major RTAs. Furthermore, they do not
analyze the effects on production networks trade, typically measured by parts andcomponents trade.

5Other studies analyze the effects of intermediate goods imports on intra-regional
exports among selected LAC countries. For example, Florensa et al. (2015) find thatimports of intermediate goods from MERCOSUR and LAIA member countries are
positively correlated to intra-bloc exports of intermediate and final goods in the 11
LAIA countries from 1991 to 2008. Similarly, Chang (2017) finds that imports ofmachinery parts and components from other countries in the region are positively
correlated with intra-regional machinery exports in 17 Latin American countries from1996 to 2011. Although those studies focus on production networks trade in the LAC
region, the effects of the depth of those RTAs on the intra-regional intermediate goods
exports are not the focal point of their main analysis.4 Additionally, Gómez-Mera andVarela (2021) find that the depth of RTAs has positive effects on the country’s FDI
inflows in the 29 LAC countries from 2001 to 2015. Although Gómez-Mera and Varela(2021) focus on the depth of RTAs measured by the number of provisions of WTO+
and WTO-X policy areas, the effects on production network trade are beyond the scopeof their analysis.
It is worth noting that the following three studies are particularly related to ourstudy as they systematically analyze the effects of all RTAs involving at least one of
selected LACs. First, Florensa, Márquez-Ramos, and Recalde (2015) analyze the effectsof the depth of all economic integration agreements (including nonreciprocal
preferential trade agreements such as the generalized system of preferences) involving
at least one of 11 LAIA countries on bilateral exports from the 11 LAIA countries to4

Although Chang (2017) includes the depth of RTAs between member countries, measured by
the number of legally enforceable provisions, to control for non-tariff barriers, he does notfind any significant effect on the intra-regional exports.

6their trading partners in the world (161 countries) from 1962 to 2009. They find that thecustom unions (CUs) in the 11 countries (i.e., CAN and MERCOSUR) have the largest
positive effects, particularly on the exports of manufacturing goods. Similarly,
Sanguinet, Alvim, and Atienza (2022) analyze the effects of the depth of all RTAs
involving at least one of 20 LAC countries on trade in value added among the 20
countries and their 141 trading partners from 1995 to 2015. They find that RTAs with
European countries induce the LAC countries to increase value-added imports fromthose countries, while deeper agreements do not have significant effects on intraregional value-added trade among the LAC countries. However, given that both studies
define the depth of RTAs based on a simple categorization by the type of RTAs (e.g.,CUs, FTAs, and PSAs), rather than the coverage and legal enforceability of WTO+ and
WTO-X policy areas. Sánchez-Albornoz and Timini (2021) analyze the effects of all
RTAs involving at least one of 21 LAC countries on bilateral exports among the 21
countries and their 32 major trading partners from 1984 to 2015. Considering
heterogeneous effects of those RTAs by the characteristics of country-pairs, they findthat RTAs among the LAC countries, particularly MERCOSUR and CAN, havepositive and economically significant effects. However, the heterogeneity in the depthof those RTAs and the effects on the production networks trade are beyond the scope of
their analysis. Furthermore, given that these three studies include many non-LAC
import partner countries while excluding more than 10 LAC countries, they do not
analyze the determinants of intra-regional trade among the full set of LAC countries.
Finally, several recent studies analyze the effects of the depth of RTAs measured
by the coverage and legal enforceability of WTO+ and WTO-X policy areas on bilateralexports (Falvey and Foster-McGregor 2022; Kohl, Brakman, and Garretsen 2016;
Mattoo, Mulabdic, and Ruta 2022), international technology spillovers (Jinji, Zhang,

7

and Haruna 2019; 2022a), and trade in value added (Boffa, Jansen, and Solleder 2019;
Laget et al. 2020). However, those studies do not systematically focus on the LAC
region.
Therefore, this study aims to analyze the causal effects of the depth of RTAs
measured by the coverage and legal enforceability of WTO+ and WTO-X policy areas
on production networks trade in all 33 LAC countries from 1990 to 2016. For thispurpose, it employs the recently developed Deep Trade Agreements Database 1.0 by theWorld Bank,5 which provides information on the coverage and legal enforceability of
WTO+ and WTO-X policy areas for all RTAs notified to the WTO and in force as ofDecember 2015 (Hofmann, Osnago, and Ruta 2019). With this database, this study
constructs a unique dataset on the indexes on the depth of all RTAs in force that include
at least two LAC countries. Furthermore, this study measures production network tradeby trade in parts and components rather than trade in value added, because the data are
available for all the 33 LAC countries.6 Consequently, this study is first, to the best of
our knowledge, to systematically focus on the heterogeneous effects of RTAs on
production networks trade in all the 33 LAC countries, including the period before andafter most of RTAs in LAC countries entered into force. This study focuses on theheterogeneous effects by the coverage and legal enforceability of WTO+ and WTO-X
policy areas, type of agreements, and characteristics of country-pairs, which is a novel
contribution of this study to the literature. Furthermore, this study systematically applies

5

https://datacatalog.worldbank.org/search/dataset/0039575, accessed on June 27, 2022.

6

In general, the data on trade in parts and components are available for more countries than thedata on trade in value added (Laget et al. 2020). The data on trade in value added are notavailable for all the 33 LAC countries from UNCTAD-Eora GVC Database.

8

recent methodological improvements in gravity equations to the estimation of theheterogeneous effects of RTAs, thereby showing the robustness of our estimation
results.7
The remainder of this study is organized as follows. Section 2 explains the data
used in the analysis and presents the descriptive statistics. Section 3 presents theempirical specifications of the study. Section 4 presents the estimation results. Section 5
performs several robustness checks, and the final section concludes the study andpresents some policy implications.

2 Data and descriptive statistics

2-1. Data on production network trade
Following empirical studies that capture production networks trade using parts andcomponents trade (e.g., Hayakawa and Yamashita 2011; Martinez-Zarzoso, Voicu, andVidovic 2015; Orefice and Rocha 2014), this study defines parts and components ascodes 22 (processed industrial suppliers not elsewhere specified), 42 (parts andaccessories of capital goods [except transport equipment]), and 53 (parts andaccessories of transport equipment) of the Broad Economic Categories (BEC).8 It

7Kohl (2014) points out that studies that pursue a “specialist approach,” which focus on specificgeographical area and heterogeneous effects of individual RTAs, like this study, do not tend
to systematically apply the methodological improvements in the estimation of the effects of
RTAs at an aggregate level.

8Note that Hayakawa and Yamashita (2011) and Martinez-Zarzoso, Voicu, and Vidovic (2015)
define parts and components as codes 22, 42, and 53 of the BEC, which also corresponds tocodes 7 (machinery and transport equipment) and 8 (miscellaneous manufactured articles) of

9sources the data from the World Integrated Trade Solution (WITS) database.9 Given
that this study conducts a causal analysis on the effects of RTAs by analyzing theperiods before and after major RTAs entered into force, the focus is on the period from1989 to 2015. Moreover, as we employ one-year lagged variables for the depth of RTAs
to address potential endogeneity issues and allow time for parts and components trade toadjust changes in the depth of RTAs, we make use of the data on parts and components
trade from 1990 to 2016.
Although the database provides the data for all the 33 LAC countries, there are
missing values in some years. Following Linders and Groot (2006), we replaceobservations originally reported as missing with zero, unless a country in a given year
does not report any export data in the database.10 This replacement can be justified
because trade flows below minimum reporting levels (thus very small values), varying
across countries, are usually unreported (Kehoe and Ruhl 2013). Alternatively, we canuse mirror import flows (i.e., imports from partner countries) as the measure of exports

the Standard International Trade Classification (SITC) Revision 3. However, trade flowsclassified by SITC are missing in LAC countries especially before 1994 in the WITS
database.
9

https://wits.worldbank.org/, accessed on July 23, 2022.

10

Consequently, the observations of following years in given countries are missing: Antigua
and Barbuda: 1990-1998, 2001-2004, 2006, and 2008; Bahamas: 1990-1994 and 1996;
Belize: 1990-1991; Cuba: 1990-1998 and 2007-2016; Dominica: 1990-1992, 1998, 2011,
and 2013-2016; Dominican Republic: 1990-1991 and 1998-2000; Grenada: 1992 and 2009;
Guyana: 1990-1996; Haiti: 1998-2016; Honduras: 2008 and 2013; Saint Kitts and Nevis:
1990-1992 and 1998; Saint Vincent and the Grenadines: 1990-1992; Suriname: 1993; andVenezuela: 2007 and 2014-2016.

10

from origin countries, because import data are usually recorded with more accuracy andfewer missing observations than export data (Feenstra et al. 2005; Linders and Groot2006). However, the use of mirror data is not recommended in cases where the importcountry applies high tariffs and has weak monitoring capability at customs (Yotov et al.
2016).11 Considering both cases apply to some LAC countries, we use only export datain our main analysis. However, to show that our findings are not affected by the missing
observations, following, Falvey and Foster-McGregor (2022), Kohl, Brakman, andGarretsen (2016), Orefice and Rocha (2014), we use the mirror import data in therobustness check performed in Section 5-1.
After the replacement, 8,842 observations in code 22, 15,434 observations incode 42, and 16,890 observations in code 53 have zero values, including the original
zero values. Consequently, 8585 (30.1%) and 3136 (11.0%) observations of this study’s
dependent variable (the sum of export values in codes 22, 42, and 53) take on zero
values and remain missing.
Table A3 in the Appendix shows the descriptive statistics of the parts andcomponents exports from 1990 to 2016 by country. From the table, we find thatcountries with larger economies, such as Brazil, Argentina, Mexico, and Chile, havelarger intra-regional parts and components exports in the LAC region.

2-2. Data on the depth of RTAs
This study sources the data on the depth of RTAs from the “Deep Trade Agreements

11

Imports are reported at insurance and freight (CIF) prices, while exports are reported at freeon board (FOB) prices, in the WITS database. Thus, if import countries apply higher tariffs,
the difference between imports and exports is larger.

11

Database 1.0” by the World Bank. Recent studies including Boffa, Jansen, and Solleder(2019), Falvey and Foster-McGregor (2022), Jinji, Zhang, and Haruna (2019; 2022a),
Laget et al. (2020), and Mattoo, Mulabdic, and Ruta (2022) use the data from thisdatabase for the measures of the depth of RTAs. The database was originally provided
by Horn, Mavroidis, and Sapir (2010) and was extended by Hofmann, Osnago, and Ruta
(2017; 2019). Thus, the database follows Horn, Mavroidis, and Sapir (2010), who
propose a systematic method for measuring the depth and nature of RTAs. They
identify 52 policy areas covered by RTAs and classify them into WTO+ and WTO-X
categories. The WTO+ category, containing 14 policy areas, corresponds to provisions
that fall under the current mandate of the WTO but go beyond the commitments
accepted at the multilateral level. The WTO-X category, containing 38 policy areas,
comprises RTA provisions that address issues beyond the current WTO mandate (Horn,
Mavroidis, and Sapir 2010).
Furthermore, Horn, Mavroidis, and Sapir (2010) propose a method to evaluate
the coverage and legal enforceability of the 52 policy areas by using legally enforceable(LE) index. The LE index evaluates the legal enforceability of a policy area on a threepoint scale: 0 for not being mentioned in the agreement or not legally enforceable, 1 forlegally enforceable but explicitly excluded by a dispute settlement provision, and 2 forlegally enforceable (Hofmann, Osnago, and Ruta 2017, 10 Figure 1; Jinji, Zhang, andHaruna 2022a). Therefore, the database provides detailed information on the LE indexesof the 52 policy areas in each of the 279 RTAs from 1958 to 2015 (Hofmann, Osnago,
and Ruta 2017; 2019).
Moreover, Limão (2016) re-categorizes WTO+ and WTO-X policy areas interms of the depth and breadth of RTAs. He considers that the depth of RTAs evaluates
the level of bilateral economic policy cooperation, while the breadth evaluates the width12

of its coverage. Among the 52 policy areas, he identifies 29 policy areas as related tothe depth, and 23 policy areas as related to the breadth. Regarding the depth, lower
applied tariffs are typically considered a deeper level of economic policy cooperation.
Various non-tariff measures including contingent protection (e.g., anti-dumping,countervailing measures, and export taxes), which affect market access, are also
considered to characterize the depth of economic policy cooperation. Similarly, giventhat behind-the-border policies that invalidate national treatment (e.g., state aid,
procurement, and competition policies) affect market access, they are considered tocharacterize the depth. Finally, other policies that may also affect market access (e.g.,
regional, industrial, and agricultural cooperation and financial assistance) are considered
to be related to its depth (Limão 2016, 288). On the breadth of economic policy
cooperation, Limão (2016) divides the policy areas into the following four fields basedon their impact: the type of trade (trade in services), technology (IPR andinnovation/diffusion), production factors (investment/capital and labor), and noneconomic policies (environmental laws, health, human rights, political dialogue, illicit
drugs, money laundering, and terrorism) (Limão 2016, 290-292). Table A2 shows thelist of the policy areas of the depth and breadth in RTAs.12
Consequently, in line with Jinji, Zhang, and Haruna (2022b), this study
constructs the following indexes of the depth and breadth of RTAs to which exporter
country i and importer country j belong in year t:

𝑅𝑇𝐴_𝑑𝑒𝑝𝑡ℎ

12

∑

_
∗

,

Table A2 divides the field of production factors into investment/capital and labor.

13

(1)

∑

𝑅𝑇𝐴_𝑏𝑟𝑒𝑎𝑑𝑡ℎ

_

,

∗

where p indexes the policy areas and 𝑀𝑎𝑥_𝐿𝐸(2)∈ 0,1,2 is the maximum point of the

LE index of p in all RTAs to which countries i and j belong in year t. If the exporter andimporter countries do not belong to any RTAs in force that provide at least one WTO+
or WTO-X area, the variable is zero. Given that the maximum point of the LE index foreach policy area is 2, the denominator is the number of policy areas (29 for the depth
and 23 for the breadth) multiplied by 2. Thus, the depth and breadth indexes take values
between 0 and 1.13
In the above Equations (1) and (2), the individual policy areas are equallyweighted. However, we can construct the indexes based on the policy areas that haveclear economic contents, meanwhile excluding policy areas that do not (Laget et al.
2020). Thus, according to Falvey and Foster-McGregor (2022), Hofmann, Osnago, andRuta (2017), Laget et al. (2020), and Mattoo, Mulabdic, and Ruta (2022), we construct
the following alternative index on the depth of RTAs based on the policy areas that theliterature identifies as more economically relevant (core provisions):∑

𝑅𝑇𝐴_𝑐𝑜𝑟𝑒_𝑑𝑒𝑝𝑡ℎ

13

_
∗

,

Although Kohl, Brakman, and Garretsen (2016) and Mattoo, Mulabdic, and Ruta (2022)
construct the depth indexes in a similar way, they do not separate depth from breadth.

14

(3)

The core provisions include all 14 WTO+ provisions and four WTO-X provisions(competition policy, investment, movement of capital and intellectual property rights),
as shown in Table A2 in the Appendix.14
Considering that previous studies find the heterogeneous effects of RTAs
including LAC countries by the type of agreements (i.e., FTAs versus CUs: Florensa,
Márquez-Ramos, and Recalde 2015; Sanguinet, Alvim, and Atienza 2022), number ofcountries (i.e., bilateral versus plurilateral: Sánchez-Albornoz and Timini 2021), andcharacteristics of country-pairs (i.e., among LAC countries versus between LAC
countries and non-LAC countries: Sánchez-Albornoz and Timini 2021), this study also
constructs the three indexes based on the following disaggregated RTA categories, aspresented in Table A1 in the Appendix. First, we disaggregate RTAs into CUs and
FTAs. Next, we disaggregate the plurilateral CUs into those among Latin Americancountries (CU_Latin America) and those among Caribbean countries (CU_Caribbean).
As for FTAs, we disaggregate them in two ways. First, we disaggregate FTAs intobilateral FTAs (Bilateral_FTA) and plurilateral FTAs. Furthermore, we disaggregate theplurilateral FTAs into those among three or more LAC countries(Plurilateral_FTA_LAC) and those between two or more LAC countries and at least one
non-LAC countries (Plurilateral_FTA_with outside). For each of those disaggregated14

Similarly, Kohl, Brakman, and Garretsen (2016) identify 17 policy areas (13 WTO+
provisions and four WTO-X provisions). However, the 17 areas are slightly different fromthe 18 areas. As for WTO+ provisions, they exclude FTA industrial goods. As for WTO-X
provisions, they exclude intellectual property right and investment, while including
environmental laws and labor market regulations, both of which are not included in our coreprovisions.

15

RTA categories, this study constructs the indexes of the depth, breadth, and core depth
as per Equations (1) to (3).
Tables A4 to A6 in the Appendix show the descriptive statistics of the indexesof the depth, breadth, and core depth of RTAs from 1989 to 2015 by country in the casewhere the country has RTAs in force that provide at least one WTO+ or WTO-X area.
Additionally, Table A1 shows the three indexes by individual RTAs. In those tables,
following Laget et al. (2020), we consider that CACM has same indexes before therevitalization (henceforth, the assumption on the revitalization (1), see footnote 2 formore detail). However, given that the assumption undoubtedly over-estimates theindexes before the revitalization, we also present estimation results in Sections 4 and 5
using the depth and core depth indexes under the alternative assumption that thoseindexes has given values since its revitalization in 1992 (henceforth the assumption on
the revitalization (2), see footnote 2 for more detail). Moreover, given that the breadthindex of CACM is equal to zero, we show the additional estimation results under theassumption (2) for the depth and core depth indexes only.
We find that the index of the depth is higher than that on the breadth, given thelower coverage and enforceability of labor and non-economic policies in the breadthindex (see Tables A4 and A5 in the Appendix, and also see Table 1 of Mattoo,
Mulabdic, and Ruta 2022, 1606 for all 279 RTAs). We find that three MERCOSUR
member countries (Argentina, Brazil, and Paraguay) have the highest indexes on thedepth (0.414) and core depth (0.944) (see Tables A4 and A6), given that MERCOSUR
has relatively high values in those indexes (see Table A1). Indeed, MERCOSUR reports
a maximum of 2 points of the LE index in 12 of 13 areas in the first three policy areas(import tariffs, non-tariff barriers, and behind-the-border policies) in the depth index

16

and 17 of 18 areas in the core depth index since its enforcement in 1991.15 Additionally,
we find that Mexico has the highest mean index on the breadth (0.251) (see Table A5),
given that FTAs that Mexico executed (i.e., FTAs between Chile and Mexico, Colombia
and Mexico, Mexico and Central America, Mexico and Uruguay, and Peru and Mexico
has relatively high values in the breadth indexes (see Table A1).
Importantly, we confirm that the depth defined by the simple categorization by
the type of agreements (i.e., FTAs versus CUs) does not necessarily coincide with thedepth defined by the coverage and legal enforceability of WTO+ and WTO-X policy
areas in the LAC region. The CUs except for MERCOSUR (i.e., CAN and CACM) are
the shallowest agreements in the LAC region in terms of the coverage and legal
enforceability of WTO+ and WTO-X policy areas, whereas the above mentioned FTAs
that Mexico executed are deeper agreements. Therefore, the construction of the indexes
based on the combination of the type of agreements and the coverage and legal
enforceability, which this study employs, are more appropriate.
Finally, Tables 1 and 2 show the descriptive statistics and correlation matrix ofall variables used in this study. The three indexes are highly correlated with each other,suggesting that they should be included separately in the gravity equation explained in
Section 3. As expected, the parts and components trade is positively correlated with thethree indexes. Furthermore, the variable is particularly positively correlated with theindexes of CUs among Latin American countries.

15

Using the same database, Falvey and Foster-McGregor (2022) also report the number of thecore provisions of MERCOSUR to be 17 out of 18 (see Table 9 on p. 222).

17

3 Empirical specifications
Following Laget et al. (2020) and Mattoo, Mulabdic, and Ruta (2022), who analyze theeffects of depth of RTAs on trade, this study employs a structural gravity model. In themodel, time-varying exporter and importer fixed effects are included to control for thechanges in the multilateral resistance terms (Anderson and van Wincoop 2003; Baldwin
and Taglioni 2007)16 and time-varying exporter and importer-specific characteristics,
such as their economic size and population or per capita income. Time-invariant
country-pair fixed effects are also included to control for time-invariant unobserved
bilateral heterogeneity that may be correlated with trade flows and the probability offorming RTAs (Baier and Bergstrand 2007) as well as time-invariant bilateral-pair
specific characteristics, such as geographical distance, the use of a common language,
and sharing common land borders. Furthermore, we apply a pseudo-Poisson maximum
likelihood (PPML) estimator to the structural gravity model to addresses the issues
related to heteroscedasticity and allows the inclusion of zero values of the dependent
variable (Santos Silva and Tenreyro 2006). This issue is particularly relevant for thisstudy because the dependent variable—parts and components trade—includes
substantial parts with zero values, as noted in Section 2-1. Thus, the ordinary leastsquares (OLS) estimates of the log-linearized model, excluding zero values, can bebiased and inefficient.17 Consequently, the structural gravity model with the PPML

16

Anderson and van Wincoop (2003) refer to cross-country price variations in price asmultilateral resistance terms.

17

However, observations for which all values of the dependent variable for a given country-pairor exporter-year are zero are excluded in the PPML estimation with time-invariant countrypair fixed effects and time-varying exporter fixed effects. For this reason, the number of18

estimator is the most accepted technique for identifying the determinants ofinternational trade flows (Chang 2017; Larch et al. 2019; Yotov et al. 2016).
Therefore, this study estimates the following structural gravity equation:
𝑋
where 𝑋

exp 𝛽

𝛽 𝑅𝑇𝐴

𝜆

𝜑

𝜇

𝜀 ,

(4)

is the nominal value of bilateral parts and components exports from exporter

country i to importer country j in year t, expressed in millions of current US dollars;
𝑅𝑇𝐴

is one of the three indexes defined in Section 2-2; 𝜆 is the time-varying

exporter fixed effects; 𝜑 is the time-varying importer fixed effects; 𝜇 is the timeinvariant country-pair fixed effects; and 𝜀is the error term.18 Note that, following

Boffa, Jansen, and Solleder (2019), Falvey and Foster-McGregor (2022), Kohl,
Brakman, and Garretsen (2016), and Orefice and Rocha (2014), we do not include RTA
dummy (taking a value of 1 if both exporter and importer countries have an RTA inforce) in Equation (4). Alternatively, it is possible to include the RTA dummy inaddition to one of the three indexes of the depth of the RTA in the equation, as Jinji,Zhang, and Haruna (2019; 2022a), Laget et al. (2020), and Mattoo, Mulabdic, and Ruta
(2022). However, the coefficients on the depth indexes only capture the additional

observations in the full sample (24,806) in Tables 3 and 4 and Table A7 in the Appendix issmaller than the number of observations of the dependent variable (25,376) in Table 1.
18

In Equation (4), we do not control for other country-pair time-varying variables such as
bilateral applied tariffs, because the two policy areas (import tariffs and non-tariff barriers)
included in our indexes deal with the tariff liberalization and elimination of non-tariffbarriers on industrial and agricultural goods. Moreover, the missing data on the applied
tariffs in the WITS database leads to the exclusion of substantial part of observations. Thus,
we decide to keep our sample in the estimation without including applied tariffs.

19effects of the RTA depth in this specification, which is different from what this study
wants to estimate.
Finally, we consider the following specification where the effects of RTAs areallowed to vary at the disaggregated RTA categories explained in Section 2-2:
𝑋

exp 𝛽

∑ 𝛽 𝑅𝑇𝐴

𝜆

𝜑

𝜇

𝜀

,

(5)

where superscript k indexes each of the five categories (CU_Latin America,
CU_Caribbean, Plurilateral_FTA_LAC, Plurilateral_FTA_with outside, andBilateral_FTA).

4 Baseline results
Table 3 reports the estimation results of Equation (4). We confirm that the PPMLestimated coefficients are remarkably similar using the whole sample and the sample
with non-zero export values. Thus, we show only the estimation results using the wholesample henceforth.
The coefficients on the indexes of the depth, breadth and core depth are
expectedly positive and significant. The estimated coefficient shows that if a country
has an RTA in force with full coverage and enforceability for all policy areas in thegiven indexes, parts and components exports from the country to the partner country
will increase by 72.9%, 63.4%, and 27.2% on average.19 The magnitudes of the effects
are comparable to those estimated by Orefice and Rocha (2014), who find that the19

As the indexes are normalized to take values between 0 and 1, as explained in Section 2-2, thepercentage effect on the parts and components exports is provided by 100 exp 𝛽

20

1 .

coefficients on the total number of WTO+ and WTO-X provisions is 0.008 for 200
countries from 1980 to 2007.20
Table 4 reports the estimation results of Equation (5) for disaggregated RTA
categories. We find that the effects of the indexes are substantially heterogeneous acrossthe RTA categories. We find that the three indexes of CUs among Latin Americancountries have positive and highly significant effects on the intra-regional parts andcomponents exports. We confirm that the positive and significant effects of CUs among
Latin American countries are robust to the use of the depth and core depth indexes
based on the two different assumptions on the revitalization of CACM.
Among the CUs among Latin American countries, CACM came into force
before the period under the analysis under the assumption on the revitalization (1), andCAN came into force under the assumptions on the revitalization (1) and (2).21 Thus,
the positive effects are likely to capture the enforcement of MERCOSUR in 1991. To
find out the effects, following Baier, Yotov, and Zylkin (2019), Table A7 in the
Appendix presents the estimation results of the specification where the effects of RTAs
are allowed to vary at individual RTAs. We find that MERCOSUR has large positiveeffects on the intra-regional parts and components exports: the estimated coefficient in
the first column shows that MERCOSUR increases intra-regional parts and components

20

As Orefice and Rocha (2014) construct the depth index based on the number of provisions,the estimated coefficient in the case that the indexes are normalized can be 0.008 ∗ 52
0.416.

21

Even under those assumptions, we can obtain the coefficients on the depth and core depth
indexes of CAN, because Venezuela withdrew from CAN in 2006.

21

exports by 92.4%.22 Thus, the results support the findings of Baier, Yotov, and Zylkin(2019), Martin-Mayoral, Carofilis, and Guijarro (2016), Sánchez-Albornoz and Timini(2021), who find that the enforcement of MERCOSUR has large positive andeconomically significant effects on bilateral trade. However, the novel contribution ofthis study is to show the significant effects of the depth of MERCOSUR rather than its
enforcement itself on intra-regional parts and components trade instead of total tradeflows.
Additionally, we find that the three indexes of CUs among the Caribbean
countries (CARICOM) have positive and weakly significant effect, which support thefinding of Baier, Bergstrand, and Vidal (2007). Note that given that CARICOM had
already entered into force in 1973, the effects capture the accession of Suriname andHaiti to CARICOM in 1995 and 2002, respectively.23
By contrast, we find that the three indexes of plurilateral FTAs with countries
outside the region have negative and highly significant effects on the intra-regionalparts and components exports (see Table 4). The estimation results for individual RTAs
reveal that the negative effects are primarily derived from CAFTA-DR and EUColombia and Peru FTA (see Table A7 in the Appendix). Thus, the finding suggeststhat growing production networks with the United States or EU courtiers induce anincrease in the parts and components imports of partner countries from those developed

22

Given that the index on the depth of MERCOSUR is 0.4138 (see Table A1 in the Appendix),
the effect is calculated by exp 1.173

10.4138. As for this calculation, see Jinji,Zhang, and Haruna (2022b, 139), note 11 to 16.
23

https://caricom.org/country_profiles/suriname/; https://caricom.org/country_profiles/haiti/,
accessed on March 31, 2023.

22

countries, which results in decreasing intra-regional parts and components exports. The
finding is not captured by Sánchez-Albornoz and Timini (2021), who find positive andsignificant effects of RTAs between LAC countries and EU (including EU-Colombia
and Peru FTA), because they include LAC courtiers’ major trading partners in theiranalysis. Indeed, we find that a regression of log of imports of CAFTA-DR’s member
countries in the LAC region (Costa Rica, Dominican Republic, El Salvador, Guatemala,Honduras, and Nicaragua) from the United States during 1990 to 2016 on the timetrend, post CAFTA-DR dummy taking a value of 1 after 2006, interaction term between
the time trend and post CAFTA-DR dummy, and importer-country fixed effects yields a
positive and highly significant coefficient of 1.327 for the post CAFTA-DR dummy (thestandard error is 0.252).24 This finding is consistent with Sanguinet, Alvim, and Atienza(2022), who find that RTAs with European countries induce LAC countries to increasevalue-added imports.
Finally, the three indexes of plurilateral and bilateral FTAs in the LAC region do
not have significant effects on intra-regional parts and components exports,
unexpectedly. The estimation results for individual RTAs show that there isconsiderable heterogeneity across those RTAs, making it difficult to identify clear
patterns among them. One possibility is that FTAs between geographically distantcountries might not contribute to the formation of regional production networks becauseof bottlenecks such as high transportation costs owing to insufficient infrastructure in
the region. Thus, inspired by Baier, Yotov, and Zylkin (2019) and Kohl (2014), whoanalyze the determinants of the effects of individual RTA, we regress the estimatedeffects of individual RTAs on the average of log distance between member countries for24

The detailed result is available upon request.

23

the 14 plurilateral and bilateral FTAs in the LAC. We source the data on the distance
between most populated cities (km) of countries i and j from GeoDist of CEPII
database.25 We estimate the equation using weighted least squares, with the inverse of
the robust clustered standard error of the effects of individual FTAs as the weight.
However, the coefficients on the average of log distance are not significant at all for thethree indexes.26 Interestingly, the effects of three of five FTAs that Chile executed(FTAs between Chile and Central America, Chile and Mexico, and Chile and Peru) are
positive and significant. Considering that Chile is one of most distant countries fromother LAC countries, we conclude that geographical distance is not the main factor
explaining the effects of FTAs among LAC countries on intra-regional parts andcomponents exports.

5 Robustness checks

5-1. Use of mirror import data
To show that the baseline results in Section 4 are not affected by the missing
observations explained in Section 2-1, we use imports of a country j from a country i tomeasure the exports of the country i to country j in this section. Following Falvey andFoster-McGregor (2022), Feenstra et al. (2005), and Linders and Groot (2006), whenobservations in the mirror imports are recorded as missing but the corresponding
exports are reported as non-zero, we replace the missing import values with the non-25

http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=6, accessed on February
10, 2023.

26

The detailed result is available upon request.

24

zero export values. After this replacement, we replace the remaining missing
observations with zero, because all countries under the analysis period report some
import data in the database. Consequently, the mirror import data have no missing
observations and only 6853 zero values, which are substantially smaller than the export
data in Section 4.27
Tables 5 and 6 show the estimation results of the use of the mirror import data.
Except for the insignificant effects of the aggregate breadth index in Table 5, theestimation results obtained using the mirror data are remarkably similar to the results
obtained using the export data in Section 4. The three indexes of the CUs among Latin
American countries have significantly positive effects, while the three indexes of theplurilateral FTAs with developed countries outside the region have significantly
negative effects. The three indexes of the plurilateral and bilateral FTAs in the LAC
region do not have significant effects. Therefore, we conclude that our main findings in
Section 4 are robust to the use of mirror import data with full observations and are notaffected by the missing observations in the export data.

5-2. Use of data at three-year intervals
Trefler (2004) argues that the adjustment of trade flows after the enforcement of RTAs
is not instantaneous and thus criticizes the use of consecutive-year data. Moreover,
RTAs often have a 5-to-10-year phase-in period until the full enforcement of the27

However, 54 observations for which all vales of the dependent variable for a given countrypair are zero are excluded in the PPML estimation of the structural gravity equation. For thisreason, the number of observations (28,458) in Tables 5 and 6 is smaller than the number ofthe full observations (28,512) in Table 1.

25

provisions, with different times at different provisions (Baier and Bergstrand 2007;
Falvey and Foster-McGregor 2022; Kohl 2014). Thus, we check whether the results arerobust to the specification that allows more time for the dependent variable to adjust tochanges in the depth of RTAs. Given that we exclude PSAs, which usually have longer
phase-in period, from our analysis, we consider that the use of data with wider intervals(e.g., more than five year) is unnecessary. Consequently, following Laget et al. (2020)
and Mattoo, Mulabdic, and Ruta (2022), we use the panel data at three-year intervals
from 1992 to 2016.
Tables 7 and 8 show the estimation results of the use of the data with three-year
intervals from 1992 to 2016. We find that the coefficients on the breadth and core depth
indexes are smaller and less significant than the coefficients obtained using theconsecutive data. However, for the disaggregated RTA categories, the estimation results
are remarkably similar to the results obtained using the consecutive-year data in Section
4. The depth and core depth indexes of the CUs among Latin American countries havesignificantly positive effects, while the three indexes of the plurilateral FTAs with
developed countries outside the region have significantly negative effects. The threeindexes of the plurilateral and bilateral FTAs in the LAC region do not have significant
effects. Therefore, we conclude that our main findings are robust to the use of data at
three-year intervals.

5-3. Control for reverse causality
Our final concern is that the estimation results in Section 4 might be biased owing toreverse causality (i.e., trade causes future RTAs). In general, reverse causality arises
because countries that have been already significant trade partners (i.e., natural trading
partners) are more likely to sign deeper agreements, given that the inclusion of specific26

provisions depends on the trade potentials (Mattoo, Mulabdic, and Ruta 2022;
Piermartini and Yotov 2016). Particularly, countries participating in production
networks often tend to sign deeper agreements to complement the development of value
chains through de jure integration (Kuwayama 2019). This decision may be affected bylobbying activities by firms that are involved in production networks trade and need tosecure the supply of intermediate goods from partner countries (Laget et al. 2020;
Sanguinet, Alvim, and Atienza 2022).
To control for the possible reverse causality, following Laget et al. (2020),
Mattoo, Mulabdic, and Ruta (2022), we add future values (leads) of the depth indexes,in addition to the lags that capture the adjustment of trade over time explained in
Section 5-2. Specifically, we include two leads and three lags of the depth indexes. In
the absence of reverse causality (i.e., in the assumption of strict exogeneity), thecoefficients of the leads should not be statistically different from zero (Kohl 2014;
Piermartini and Yotov 2016; Sánchez-Albornoz and Timini 2021; Yotov et al 2016). In
this specification, we estimate Equation (4) using consecutive-year data from 1987 to
2013, because we need to include the periods before and after the enforcement of the
RTA of particular interest (i.e., the enforcement of MERCOSUR in 1991).28 In thisperiod of analysis, the depth and core depth indexes of CAN have same indexes before
the revitalization under the assumption on revitalization (1), whereas they have given
values since its revitalization in 1988 under the assumption on revitalization (2) (seefootnote 2 for more detail), as in the case of CAN.
Tables 9 and 10 show the estimation results. For the aggregated effects, we findthat the third lag remains positive and significant, except for the breadth index.

28

The second lead of the three indexes of MERCOSUR include the values since 1989.

27

Unfortunately, the first lead is also statistically significant. However, as the significant
effects are limited to the first lead, they may be explained by the time gap between
signature and entry into force of RTAs, as suggested by Laget et al. (2020).
For the disaggregated effects, the third lag of the depth and core depth indexesof the CUs among Latin American countries remains positive and significant although
the second lead is also statistically significant. Unfortunately, the second lead of theindexes of plurilateral FTAs with countries outside the region is negative andsignificant, whereas the lags are statistically insignificant. However, considering that the
RTA that have main negative effects (CAFTA-DR, see Table A7 in the Appendix)
entered into force two years after the signature, the significant negative effects also may
be explained by the time gap between the signature and entry into force.29
Consequently, although we cannot entirely rule out the concern for the reverse
causality, we show that the long-run positive effects of the CUs among Latin Americancountries are robust to the inclusion of the future values.

6 Concluding remarks
Although the promotion of regional production networks in the LAC region is expected
to contribute to the region’s export diversification and structural changes, the region has
been characterized by a low degree of regional production networks. Although priorstudies analyze the effects of RTAs in the LAC region, they do not analyze the effects
of depth of RTAs, measured by the coverage and legal enforceability of WTO+ and

29

CAFTA-DR was signed on August 5, 2004 and entered into force on March 1, 2006
(http://www.sice.oas.org/Trade/CAFTA/CAFTADR_e/CAFTADRin_e.asp, accessed on
May 31, 2023).

28

WTO-X policy areas, on the production networks trade in the region. Thus, focusing on
the role of the depth of RTAs in the LAC region, this study analyzed the determinantsof the formation of intra-regional production networks. For this purpose, this study
constructed dataset on the indexes of the depth, breadth, and core depth of RTAs,
measured by the coverage and legal enforceability of WTO+ and WTO-X policy areas,
for all RTAs in force that include at least two LAC countries. Using this unique dataset,
this study directly analyzed the causal effects of the depth of RTAs on the intra-regionalparts and components exports in all the 33 LAC countries from 1990 to 2016.
We found that both depth and breadth of RTAs had positive effects on the intraregional parts and components exports in the LAC region. However, the effects were
substantially heterogeneous by the type of agreements and characteristics of countrypairs. The depth of CUs among Latin American countries, mainly MERCOSUR, had
positive effects on the intra-regional parts and components exports. By contrast, thedepth and breadth of plurilateral FTAs with developed countries outside the region (theUnited States or European countries) had negative effects on the intra-regional parts andcomponents exports. Additionally, the depth and breadth of plurilateral and bilateral
FTAs in LAC region did not have statistically significant effects. Finally, our main
findings were robust to the alternative assumption on the revitalization of thetemporarily moribund CUs, the use of mirror import data with full observations, the useof panel data at three-year intervals that allows more time for the adjustment, and theinclusion of the future values that controls for the reverse causality.
A notable policy implication emerges from the findings. As shown in Table A3,
the major parts and components exporters in the LAC region include Brazil, Argentina,
Mexico, Chile, and Colombia. After the period under the analysis of this study, thePacific Alliance, plurilateral FTAs comprising Chile, Colombia, Mexico, and Peru,

29entered into force in 2016. However, until very recently, any RTAs between
MERCOSUR and the Pacific Alliance countries had not existed, except for economic
complementation agreements (ECAs).30 Thus, the findings indicate that the absence ofdeep RTAs between the major parts and component exporters in the LAC region, in
terms of both contents and type of agreements, is likely to pose significant impediments
to promote intra-regional production networks. The full enforcement of deep integration
covering all those countries may contribute to export diversification and structuralchanges in the LAC region.

30

Recently, FTAs between Chile-Uruguay, Argentina-Chile, Brazil-Chile entered into force on
December 13, 2018, May 2, 2019, and January, 25, 2022, respectively.
(http://www.sice.oas.org/ctyindex/CHL/CHLagreements_e.asp, accessed on March 31,2023).

30

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Geneva: World Trade Organization (WTO). https://doi.org/10.30875/abc0167een

36Appendix
Table A1. List of regional trade agreements (RTAs) in force that include at least two Latin American and Caribbean (LAC) countries included in
Deep Trade Agreements Database 1.0 and the mean of the indexes of the depth, breadth, and core depth of RTAs since their enforcement.

Date entered
Type

Agreement

into force

CU_Latin America

Andean Community (CAN)

10/16/1969

Depth

05/25/1988aCentral American Common Market (CACM)

BreadthCore depth

0.20690.00000.22220.13790.00000.2222

06/04/1961
07/23/1992bSouthern Common Market (MERCOSUR)

11/29/1991

0.41380.21740.9444

CU_Caribbean

Caribbean Community and Common Market (CARICOM)

08/01/1973

0.34480.08700.5556

Plurilateral_FTA_LAC

Chile-Central America (Chile-Costa Rica)

02/15/2002

0.34480.1087

0.6667

Chile-Central America (Chile-El Salvador)

06/01/2002

0.34480.15220.6667

Chile-Central America (Chile-Guatemala)03/23/2010

0.37930.1087

0.6667

Chile-Central America (Chile-Honduras)

07/19/2008

0.37930.1087

0.666737

Chile-Central America (Chile-Nicaragua)

10/19/2012

0.41380.10870.7222

Colombia-Northern Triangle

11/12/2009

0.34480.19570.7778

Dominican Republic-Central America

10/04/2001

0.31030.23910.7778Mexico-Central America

09/01/2012

0.27590.23910.6667

Panama-Central America (Panama-Costa Rica)

11/23/2008

0.3966

0.23910.8611

Panama-Central America (Panama-El Salvador)

04/11/2003

0.3276

0.23910.7500Panama-Central America (Panama-Guatemala)06/20/2009

0.3276

0.23910.7500Panama-Central America (Panama-Honduras)

01/09/2009

0.3276

0.23910.7500Panama-Central America (Panama-Nicaragua)

11/21/2009

0.3621

0.23910.7500Plurilateral_FTA_with

Dominican Republic-Central America-United States Free TradeoutsideAgreement (CAFTA-DR)

03/01/2006

0.36210.34780.8611El Salvador-Honduras-Chinese Taipei

03/01/2008

0.41380.28260.7778Rica and Panama)

08/19/2014

0.36210.26090.8611

EU-Colombia and Peru

03/01/2013

0.39660.26090.7778Chile-Colombia

05/08/2009

0.24140.19570.6111

European Free Trade Association (EFTA)-Central America (Costa

Bilateral_FTA

38

Chile-Mexico

08/01/1999

0.32760.2826

0.8611

Colombia-Mexico

01/01/1995

0.44830.23910.8333

Costa Rica-Peru

06/01/2013

0.36210.28260.9167

Mexico-Uruguay

07/15/2004

0.34480.23910.8333

Panama-Chile

03/07/2008

0.20690.08700.4444

Panama-Peru

05/01/2012

0.3276

0.23910.8056Peru-Chile

03/01/2009

0.31030.1957

0.6667Peru-Mexico

02/01/2012

0.41380.28260.8333

Source: Author’s calculations from the Deep Trade Agreements Database 1.0. and Regional Trade Agreements Database.
Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among LAC countries; Plurilateral_FTA_LAC, plurilateral FTAs among three ormore LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC countries and at least one non-LAC
countries. a: Revitalization of the Andean Pact. b: Revitalization of CACM.

39

Table A2. List of policy areas of the depth and breadth of regional trade agreements.

Depth
Field

BreadthPolicy Area(a) Import Tariffs Free Trade Agreement (FTA) Industrial Goods (+)

Field

Policy Area(a) Services

General Agreement on Trade in Services (+)(b) Technology

Trade-related Aspects of Intellectual Property Rights (+)

FTA Agricultural Goods (+)(b) Non-tariff

Customs Administration (+)

barriers

Export Taxes (+)

Intellectual Property Right (X)*

Sanitary and Phytosanitary (+)

Innovation Policies (X)Technical Barriers to Trade (+)

Economic Policy Dialogue (X)Anti-dumping (+)

Information Society (X)Countervailing Measures (+)

Research and Technology (X)(c) Behind theState Trading Enterprises (+)(c) Investment/Capital Trade-related Investment Measures (+)

Border Policies

State Aid (+)

Investment (X)*

Public Procurement (+)

Movement of Capital (X)*

Anti-corruption (X)40Competition Policy (X)*(d) OtherConsumer Protection (X)(d) Labor

Labor Market Regulation (X)Policies

Data Protection (X)Illegal Immigration (X)Agriculture (X)Social Matters (X)Approximation of Legislation (X)Visa and Asylum (X)

Civil Protection (X)(e) Non-economic

Environmental Laws (X)Education and Training (X)Policies

Audio Visual (X)Energy (X)Cultural Cooperation (X)Financial Assistance (X)Health (X)Industrial Cooperation (X)Human Rights (X)Mining (X)Illicit Drugs (X)

Nuclear Safety (X)Money Laundering (X)Public Administration (X)Political Dialogue (X)

Regional Cooperation (X)Terrorism (X)

Small and Medium Enterprises (X)Statistics (X)41

Taxation (X)Source: Hofmann, Osnago, and Ruta (2017: 12) Table 2, Jinji, Zhang, and Haruna (2022a: 26) Table 2.2, and Limão (2016) Table A1.
Note: (+), (X), and * indicate that WTO-plus policy areas, WTO-extra (WTO-X) policy areas, and core provisions in the WTO-X policy areas,
respectively.

42

Table A3. Descriptive statistics of parts and components exports (million dollars) from1990 to 2016 by country.

Standard
Country

Observations

Mean

Deviation

Min

Max

Antigua and Barbuda

384

0.03

0.11 0.00

1.08

Argentina

864 149.33

478.85 0.00

4333.61

Bahamas672

0.09

0.51 0.00

5.00

Barbados

864

1.25

2.21 0.00

19.72

Belize8000.100.61 0.00

11.90

Bolivia

864

10.45

35.14 0.00

326.94

Brazil864 340.59

952.48 0.00 10092.00

Chile

864 102.31

292.08 0.00

2832.06

Colombia

864

66.22

159.47 0.00

2086.60

Costa Rica

864

20.54

45.66 0.00

324.23

Cuba

256

4.74

12.13 0.00

102.58

Dominica

576

0.09

0.24 0.00

2.10

Dominican Republic

704

11.21

60.76 0.00

741.94

Ecuador

864

10.81

37.04 0.00

373.39El Salvador

864

14.33

43.99 0.00

304.50

Grenada800

0.06

0.18 0.00

1.32

Guatemala

864

20.91

56.90 0.00

352.96

Guyana640

0.59

1.99 0.00

36.00

Haiti

256

0.01

0.04 0.00

0.42

Honduras

800

5.13

13.84 0.00

105.93

Jamaica864

0.87

2.10 0.00

24.78

Mexico864 121.29

238.83 0.00

2246.26

43

Nicaragua

864

4.47

36.72 0.00

528.01

Panama864

9.68

32.82 0.00

393.04

Paraguay

864

10.28

33.39 0.00

309.58

Peru

864

44.97

110.10 0.00

740.81Saint Kitts and Nevis

736

0.01

0.04 0.00

0.74Saint Lucia

864

0.15

0.48 0.00

3.67

Grenadines

768

0.17

0.35 0.00

2.29

Suriname

832

0.30

2.46 0.00

37.94

Trinidad and Tobago

864

15.79

35.14 0.00

481.40

Uruguay

864

17.04

59.94 0.00

584.35Venezuela

736

37.89

100.13 0.00

812.12

25376

34.39

224.74 0.00 10092.00

Saint Vincent and the

Total

Source: Author’s calculations from World Integrated Trade Solution (WITS) database.
Note: Counties with fewer than 864 32 partners

27 years

864 observations

have missing data on parts and components exports in the analysis period. The
missing years by country are listed in footnote 10.

44

Table A4. Descriptive statistics of the depth index from 1989 to 2015 by country in thecase where the country has RTAs in force that provide at least one WTO-plus or
WTO-extra area.

Standard
Country

Observations Mean Deviation

Min

Antigua and Barbuda

305 0.345

0.000 0.345 0.345

Argentina

79 0.414

0.000 0.414 0.414

Bahamas305 0.345

0.000 0.345 0.345

Belize305 0.345

0.000 0.345 0.345

Bolivia

99 0.207

0.000 0.207 0.207

Brazil

79 0.414

0.000 0.414 0.414

Chile

85 0.326

0.054 0.207 0.414

Colombia

148 0.268

0.094 0.207 0.466

Costa Rica

152 0.264

0.118 0.138 0.431

Dominica

305 0.345

0.000 0.345 0.345

Dominican Republic

75 0.310

0.000 0.310 0.310

Ecuador

99 0.207

0.000 0.207 0.207

El Salvador

161 0.271

0.123 0.138 0.517

Grenada305 0.345

0.000 0.345 0.345

Guatemala147 0.260

0.118 0.138 0.414

Guyana305 0.345

0.000 0.345 0.345

Haiti

168 0.345

0.000 0.345 0.345

Honduras

149 0.267

0.127 0.138 0.517

Jamaica

305 0.345

0.000 0.345 0.345

Mexico74 0.355

0.068 0.276 0.448

Nicaragua

138 0.255

0.120 0.138 0.414

45

Max

Panama

54 0.326

0.057 0.207 0.431

Paraguay

79 0.414

0.000 0.414 0.414

Peru

117 0.235

0.065 0.207 0.466

Saint Kitts and Nevis

305 0.345

0.000 0.345 0.345

Saint Lucia

305 0.345

0.000 0.345 0.345

Grenadines

305 0.345

0.000 0.345 0.345

Suriname

245 0.345

0.000 0.345 0.345

Trinidad and Tobago

305 0.345

0.000 0.345 0.345

Uruguay

91 0.405

0.023 0.345 0.414

Venezuela

88 0.245

0.080 0.207 0.414

5682 0.327

0.067 0.138 0.517

Saint Vincent and the

Total

Source: Author’s calculations from the Deep Trade Agreements Database 1.0.

46

Table A5. Descriptive statistics of the breadth index from 1989 to 2015 by country inthe case where the country has RTAs in force that provide at least one WTOplus or WTO-extra area.

Standard
Country

Observations Mean Deviation

Min

Antigua and Barbuda

305 0.087

0.000 0.087 0.087

Argentina

79 0.217

0.000 0.217 0.217

Bahamas305 0.087

0.000 0.087 0.087

Belize305 0.087

0.000 0.087 0.087

Bolivia

99 0.000

0.000 0.000 0.000

Brazil

79 0.217

0.000 0.217 0.217

Chile

85 0.163

0.069 0.087 0.283

Colombia

148 0.076

0.105 0.000 0.261

Costa Rica

152 0.151

0.151 0.000 0.348

Dominica

305 0.087

0.000 0.087 0.087

Dominican Republic

75 0.239

0.000 0.239 0.239

Ecuador

99 0.000

0.000 0.000 0.000

El Salvador

161 0.160

0.151 0.000 0.413

Grenada305 0.087

0.000 0.087 0.087

Guatemala147 0.153

0.154 0.000 0.370

Guyana305 0.087

0.000 0.087 0.087

Haiti

168 0.087

0.000 0.087 0.087

Honduras

149 0.155

0.157 0.000 0.413

Jamaica

305 0.087

0.000 0.087 0.087

Mexico74 0.251

0.020 0.239 0.283

Nicaragua

138 0.149

0.155 0.000 0.348

47

Max

Panama

54 0.2200.058 0.087 0.326

Paraguay

79 0.217

0.000 0.217 0.217

Peru

117 0.043

0.095 0.000 0.283

Saint Kitts and Nevis

305 0.087

0.000 0.087 0.087

Saint Lucia

305 0.087

0.000 0.087 0.087

Grenadines

305 0.087

0.000 0.087 0.087

Suriname

245 0.087

0.000 0.087 0.087

Trinidad and Tobago

305 0.087

0.000 0.087 0.087

Uruguay

91 0.2200.007 0.217 0.239

Venezuela

88 0.040

0.084 0.000 0.217

5682 0.105

0.079 0.000 0.413

Saint Vincent and the

Total

Source: Author’s calculations from the Deep Trade Agreements Database 1.0.

48

Table A6. Descriptive statistics of the core depth index from 1989 to 2015 by country
in the case where the country has RTAs in force that provide at least one WTOplus or WTO-extra area.

Standard
Country

Observations

Mean

Deviation

Min

Max

Antigua and Barbuda

305

0.556

0.000

0.556

0.556

Argentina

79

0.944

0.000

0.944

0.944

Bahamas

305

0.556

0.000

0.556

0.556

Belize

305

0.556

0.000

0.556

0.556

Bolivia

99

0.222

0.000

0.222

0.222

Brazil

79

0.944

0.000

0.944

0.944

Chile

85

0.683

0.112

0.444

0.861

Colombia

148

0.418

0.270

0.222

0.833

Costa Rica

152

0.561

0.315

0.222

0.972

Dominica

305

0.556

0.000

0.556

0.556

Dominican Republic

75

0.778

0.000

0.778

0.778

Ecuador

99

0.222

0.000

0.222

0.222

El Salvador

161

0.568

0.309

0.222

1.000

Grenada

305

0.556

0.000

0.556

0.556

Guatemala

147

0.552

0.316

0.222

0.944

Guyana

305

0.556

0.000

0.556

0.556

Haiti

168

0.556

0.000

0.556

0.556

Honduras

149

0.557

0.318

0.222

1.000

Jamaica

305

0.556

0.000

0.556

0.556

Mexico

74

0.795

0.079

0.667

0.861

Nicaragua

138

0.538

0.318

0.222

0.917

49

Panama

54

0.729

0.131

0.444

0.972

Paraguay

79

0.944

0.000

0.944

0.944

Peru

117

0.322

0.218

0.222

0.917

Saint Kitts and Nevis

305

0.556

0.000

0.556

0.556

Saint Lucia

305

0.556

0.000

0.556

0.556

Saint Vincent and the Grenadines

305

0.556

0.000

0.556

0.556

Suriname

245

0.556

0.000

0.556

0.556

Trinidad and Tobago

305

0.556

0.000

0.556

0.556

Uruguay

91

0.930

0.038

0.833

0.944

Venezuela

88

0.354

0.280

0.222

0.944

5682

0.564

0.184

0.222

1.000

Total

Source: Author’s calculations from the Deep Trade Agreements Database 1.0.

50

Table A7. Estimation results for individual regional trade agreements (RTAs).

Assumption on the revitalization(1)
RTA_depth

RTA_breadth(2)
RTA_core_depth

RTA_depth

RTA_core_depth

0.60410.56310.60430.5633(0.9085)(0.8459)(0.9085)(0.8458)-2.7790*-1.7227*(1.5296)(0.9491)

CU_Latin America
CAN

CACM

MERCOSUR

1.1732***2.3815***0.5140***1.1717***0.5133***(0.4394)(0.8075)(0.1925)(0.4391)(0.1924)

5.2530*20.7943*

3.2603*5.2677*3.2695*(3.0373)(12.0464)(1.8853)(3.0407)(1.8874)

1.1382***

3.5449***0.6104***

1.1344***0.6082***

CU_Caribbean
CARICOM

Plurilateral_FTA_LAC
Chile-Central America

51

Colombia-Northern Triangle

Dominican Republic-Central America

Mexico-Central America

Panama-Central America(0.2413)(0.8334)(0.1374)(0.2428)(0.1383)

0.59861.02990.26110.59820.2609(0.5817)(1.0251)(0.2579)(0.5819)(0.2580)

0.47610.60050.19050.45620.1825(0.7817)(1.0156)(0.3127)(0.7800)(0.3120)2.0865**2.3850**0.8625**2.0867**0.8626**(0.8223)(0.9470)(0.3403)(0.8224)(0.3404)-2.0112***-3.1374***-0.9097***-2.0216***-0.9144***(0.7214)(1.1814)(0.3294)(0.7191)(0.3284)-1.3279**-1.3896**-0.5581**-1.2683**-0.5331**(0.5294)(0.5467)(0.2224)(0.5278)(0.2217)0.6251**0.9062**0.3322**0.6235**0.3313**(0.2918)(0.4301)(0.1555)(0.2902)(0.1546)-0.1486-0.3074-0.0699-0.1479-0.0696(0.2339)(0.3473)(0.0997)(0.2339)(0.0996)

Plurilateral_FTA_with outside
CAFTA-DR

El Salvador-Honduras-Chinese Taipei

EFTA-Central America

52

EU-Colombia and Peru

-0.9012***-1.3479***-0.4595***-0.9008***-0.4593***(0.2709)(0.4090)(0.1381)(0.2709)(0.1381)-0.4108-0.4707-0.1627-0.4102-0.1624(0.3818)(0.4807)(0.1508)(0.3818)(0.1508)

0.5871***0.6590***0.2235***0.5916***0.2252***(0.1789)(0.2071)(0.0681)(0.1789)(0.0681)0.22860.46010.12290.23810.1280(0.2534)(0.4722)(0.1363)(0.2539)(0.1366)

0.60270.77770.23860.60240.2385(0.4259)(0.5444)(0.1681)(0.4261)(0.1682)-0.3779*-0.5560*-0.1563*-0.3756*-0.1554*(0.2261)(0.3271)(0.0936)(0.2260)(0.0935)

0.73291.66070.34310.7301

0.3418(1.1309)(2.7605)(0.5263)(1.1313)(0.5265)0.7017

0.94670.28540.70250.2857

Bilateral_FTA
Chile-Colombia

Chile-Mexico

Colombia-Mexico

Costa Rica-Peru

Mexico-Uruguay

Panama-Chile

Panama-Peru53

(0.6130)(0.8543)(0.2496)(0.6132)(0.2496)0.5528**0.8843**0.2572**0.5529**0.2573**(0.2593)(0.4152)(0.1207)(0.2593)(0.1207)-0.1057-0.1579-0.0524-0.1054-0.0523(0.2362)(0.3440)(0.1173)(0.2362)(0.1173)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

24,80624,80624,80624,80624,806Peru-Chile

Peru-Mexico

Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries. Estimations were implemented using the Stata command ppmlhdfe. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively. Numbers in parentheses represent robust standard errors clustered bycountry-pairs. The constant term is included. The reason why the number of observations (24,806) is smaller than the number ofobservations of the dependent variable (25,376) in Table 1 is presented in footnote 17.

54

Table 1. Descriptive statistics of all variables.

Standard
Variable

Observations

Mean

Deviation

Min

Max

(millions dollars)

25,37634.389224.7440.000

10092.000

RTA_depth

28,512

0.065

0.134

0.000

0.517

RTA_breadth

28,512

0.021

0.055

0.000

0.413

RTA_core_depth

28,512

0.112

0.240

0.000

1.000

CU_Latin America

28,512

0.011

0.054

0.000

0.414

CU_Caribbean

28,512

0.046

0.117

0.000

0.345

Plurilateral_FTA_LAC

28,512

0.005

0.041

0.000

0.414

Plurilateral_FTA_with outside

28,512

0.003

0.032

0.000

0.483

Bilateral_FTA

28,512

0.002

0.027

0.000

0.448

CU_Latin America

28,512

0.003

0.023

0.000

0.217

CU_Caribbean

28,512

0.011

0.029

0.000

0.087

Plurilateral_FTA_LAC

28,512

0.003

0.026

0.000

0.239

Plurilateral_FTA_with outside

28,512

0.003

0.030

0.000

0.413

Bilateral_FTA

28,512

0.001

0.018

0.000

0.283

CU_Latin America

28,512

0.019

0.109

0.000

0.944

CU_Caribbean

28,512

0.073

0.188

0.000

0.556

Plurilateral_FTA_LAC

28,512

0.012

0.093

0.000

0.861

Plurilateral_FTA_with outside

28,512

0.006

0.074

0.000

0.944

Bilateral_FTA

28,512

0.004

0.059

0.000

0.917

Parts and components trade

RTA_depth disaggregated

RTA_breadth disaggregated

RTA_core_depth disaggregated55Source: Author’s calculations from the World Integrated Trade Solution (WITS)
database and the Deep Trade Agreements Database 1.0.
Note: CU_Latin America, custom unions (CUs) among Latin American countries;
CU_Caribbean, CUs among Caribbean countries; Bilateral_FTA, bilateral free
trade agreements (FTAs) among Latin American and Caribbean (LAC)
countries; Plurilateral_FTA_LAC, plurilateral FTAs among three or more LAC
countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or
more LAC countries and at least one non-LAC countries.

56

Table 2. Correlation matrix of all variables.

Variables

1

2

3

4

1

Parts and components trade

1.000

2

RTA_depth

0.133

1.000

3

RTA_breadth

0.196

0.835

1.000

4

RTA_core_depth

0.174

0.979

0.920

1.000

5

6

7

8

9

10

11

12

13

14

15

RTA_depth disaggregated5

CU_Latin America

0.342

0.385

0.400

0.432

1.000

6

CU_Caribbean-0.054

0.792

0.438

0.690

-0.0781.000

7Plurilateral_FTA_LAC

0.020

0.281

0.477

0.361

0.003

-0.0491.000

8Plurilateral_FTA_with outside

0.042

0.236

0.529

0.303

0.198

-0.033

0.155

1.000

9

Bilateral_FTA

0.104

0.179

0.305

0.225

-0.017-0.030-0.011-0.007

1.000

RTA_breadth disaggregated10

CU_Latin America

0.369

0.299

0.395

0.393

0.803

-0.042-0.015-0.010-0.0091.000

11CU_Caribbean-0.054

0.792

0.438

0.690

-0.0781.000-0.049-0.033-0.030-0.0421.000

12Plurilateral_FTA_LAC

0.022

0.267

0.494

0.356

0.000

-0.048

0.958

0.137

-0.010-0.015-0.0481.000

13Plurilateral_FTA_with outside

0.040

0.235

0.530

0.304

0.198

-0.033

0.149

0.997

-0.007-0.010-0.033

0.132

1.000

14

Bilateral_FTA

0.106

0.175

0.312

0.227

-0.017-0.029-0.011-0.0070.980-0.009-0.029-0.010-0.007

1.000

0.368

0.373

0.437

0.447

0.965

-0.068

0.000

0.156

-0.0150.924-0.068-0.0020.157-0.014

RTA_core_depth disaggregated15

CU_Latin America

57

1.000

16

17

18

19

16

CU_Caribbean-0.054

0.792

0.438

0.690

-0.0781.000-0.049-0.033-0.030-0.0421.000-0.048-0.033-0.029-0.0681.000

17Plurilateral_FTA_LAC

0.021

0.279

0.489

0.363

0.003

-0.050

0.994

0.155

-0.011-0.015-0.050

0.982

0.149

-0.0110.000-0.0501.000

18Plurilateral_FTA_with outside

0.041

0.235

0.530

0.304

0.198

-0.033

0.149

0.998

-0.007-0.010-0.033

0.132

0.999

-0.0070.156-0.033

0.149

1.000

19

Bilateral_FTA

0.104

0.176

0.310

0.227

-0.017-0.030-0.011-0.0070.990-0.009-0.030-0.010-0.0070.996-0.015-0.030-0.011-0.007

1.000

Source: Author’s calculations from the World Integrated Trade Solution (WITS) database and the Deep Trade Agreements Database 1.0.
Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries.

58

Table 3. Estimation results of Equation (4).

Assumption on the revitalization

RTA_depth

(1)
Xijt

Xijt > 00.5477***0.5604***0.5393***(0.1847)(0.1843)(0.1837)

RTA_breadth

Xijt

(2)
Xijt > 00.4914**0.5105**(0.2375)(0.2371)

RTA_core_depth

Xijt

Xijt > 0

Xijt

Xijt

0.2402***0.2458***0.2373***(0.0806)(0.0804)(0.0802)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

24,806

16,77424,806

16,77424,806

16,77424,80624,80659

Note: Estimations were implemented using the Stata command ppmlhdfe. ∗∗∗ and ∗∗ indicate statistical significance at the 1% and 5% levels,
respectively. Numbers in parentheses represent robust standard errors clustered by country-pairs. The constant term is included. The
reason why the number of observations in the full sample (24,806) is smaller than the number of observations of the dependent variable(25,376) in Table 1 is presented in footnote 17.

60

Table 4. Estimation results of Equation (5).

Assumption on the revitalization

(1)

(2)

RTA_depth

RTA_breadth

RTA_core_depth

RTA_depth

RTA_core_depth

1.1680***3.3883***0.6789***1.1462***0.6702***(0.3415)(0.4061)(0.1077)(0.3458)(0.1092)

5.0405**19.8241**

3.1140**5.0382**

3.1127**(2.4284)(9.5670)(1.5004)(2.4288)(1.5005)

0.67580.93430.28990.67900.2911(0.4801)(0.6727)(0.2093)(0.4800)(0.2093)-0.9349***-1.1727***-0.4440***-0.9458***-0.4492***(0.2344)(0.2794)(0.1088)(0.2353)(0.1091)

0.16420.19140.0686

0.16280.0681(0.1856)(0.2419)(0.0780)(0.1857)(0.0780)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

CU_Latin America

CU_Caribbean

Plurilateral_FTA_LAC

Plurilateral_FTA_with outside

Bilateral_FTA

61

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

24,80624,80624,80624,80624,806

Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries. Estimations were implemented using the Stata command ppmlhdfe. *** and ** indicate
statistical significance at the 1% and 5% levels, respectively. Numbers in parentheses represent robust standard errors clustered bycountry-pairs. The constant term is included. The reason why the number of observations (24,806) is smaller than the number ofobservations of the dependent variable (25,376) in Table 1 is presented in footnote 17.

62

Table 5. Estimation results of Equation (4) using mirror import data.

Assumption on the revitalization
RTA_depth

(1)

(2)

0.4656*0.4551*(0.2434)(0.2443)

RTA_breadth

0.4198(0.2983)

RTA_core_depth

0.2183**0.2146**(0.1022)(0.1025)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

28,45828,45828,45828,45828,458

Note: Estimations were implemented using the Stata command ppmlhdfe. ** and *indicate statistical significance at the 5% and 10% levels, respectively. Numbers
in parentheses represent robust standard errors clustered by country-pairs. The
constant term is included. The reason why the number of observations (28,458)
is smaller than the number of full observations of (28, 512) in Table 1 ispresented in footnote 27.

63

Table 6. Estimation results of Equation (5) using mirror import data.

Assumption on the revitalization

(1)

(2)

RTA_depth

RTA_breadth

RTA_core_depth

RTA_depth

RTA_core_depth

1.0803***3.4627***0.6592***

1.0574***0.6499***(0.3772)(0.5230)(0.1215)(0.3819)(0.1230)

3.8783**15.1995**

2.3927**3.8765**2.3916**(1.8629)(7.3067)(1.1485)(1.8630)(1.1485)

0.46300.59390.19310.4656

0.1942(0.4327)(0.5901)(0.1868)(0.4327)(0.1868)-0.8899***-1.1244***-0.4220***-0.9001***-0.4271***(0.2340)(0.2754)(0.1077)(0.2349)(0.1080)0.18100.18080.07230.18000.0718(0.2148)(0.2811)(0.0916)(0.2149)(0.0916)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

CU_Latin America

CU_Caribbean

Plurilateral_FTA_LAC

Plurilateral_FTA_with outside

Bilateral_FTA

64

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

28,45828,45828,45828,45828,458

Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries. Estimations were implemented using the Stata command ppmlhdfe. *** and ** indicate
statistical significance at the 1% and 5% levels, respectively. Numbers in parentheses represent robust standard errors clustered bycountry-pairs. The constant term is included. The reason why the number of observations (28,458) is smaller than the number of fullobservations of (28, 512) in Table 1 is presented in footnote 27.

65

Table 7. Estimation results of Equation (4) using panel data at three-year intervals,
1992-2016.

Assumption on the revitalization
RTA_depth

(1)

(2)

0.4746**0.4609**(0.2091)(0.2088)

RTA_breadth

0.0615(0.2426)

RTA_core_depth

0.1477*0.1433(0.0881)(0.0879)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

7,973

7,973

7,973

7,973

7,973

Note: Estimations were implemented using the Stata command ppmlhdfe. ** and *indicate statistical significance at the 5% and 10% levels, respectively. Numbers
in parentheses represent robust standard errors clustered by country-pairs. The
constant term is included.

66

Table 8. Estimation results of Equation (5) using panel data with three-year intervals, 1992-2016.

Assumption on the revitalization

(1)

(2)

RTA_depth

RTA_breadth

RTA_core_depth

RTA_depth

RTA_core_depth

1.4030***1.76580.5862**

1.3690***0.5735**(0.4739)(1.2225)(0.2453)(0.4748)(0.2452)

4.263716.9406

2.64474.26112.6439(3.4408)(13.7761)(2.1406)(3.4410)(2.1408)0.7642

1.2972

0.34450.76880.3458(0.6199)(0.8379)(0.2693)(0.6201)(0.2693)-1.2017***-1.4484***-0.5783***-1.2166***-0.5832***(0.4351)(0.5264)(0.2034)(0.4381)(0.2041)0.02210.02000.00100.02120.0010(0.2745)(0.3640)(0.1195)(0.2747)(0.1196)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

CU_Latin America

CU_Caribbean

Plurilateral_FTA_LAC

Plurilateral_FTA_with outside

Bilateral_FTA

67

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

7,973

7,973

7,973

7,973

7,973

Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries. Estimations were implemented using the Stata command ppmlhdfe. *** and ** indicate
statistical significance at the 1% and 5% levels, respectively. Numbers in parentheses represent robust standard errors clustered bycountry-pairs. The constant term is included.

68

Table 9. Estimation results of Equation (4) including 2 leads and 3 lags, 1987-2013.

Assumption on the revitalization

t

t-1

t-2

t-3

t+1

t+2

(1)

(2)

RTA_depth

RTA_breadth

RTA_core_depth

RTA_depth

RTA_core_depth

0.22350.46070.13350.21930.1333(0.2172)(0.3441)(0.1011)(0.2158)(0.1010)-0.0623

0.28470.0155-0.0775

0.0114(0.1861)(0.3048)(0.0837)(0.1841)(0.0834)-0.1926-0.6903-0.1287-0.1952-0.1298(0.2481)(0.4581)(0.1193)(0.2459)(0.1190)0.4542**0.31940.1534**0.4802**0.1579**(0.1852)(0.2212)(0.0757)(0.1871)(0.0761)0.2518***0.2636**0.1000***0.2474***0.0991***(0.0865)(0.1114)(0.0381)(0.0865)(0.0380)0.20600.12900.10750.21320.1085(0.2201)(0.3388)(0.1038)(0.2201)(0.1037)69

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

24,247

24,247

24,247

24,247

24,247

Note: Estimations were implemented using the Stata command ppmlhdfe. *** and ** indicate statistical significance at the 1% and 5% levels,
respectively. Numbers in parentheses represent robust standard errors clustered by country-pairs. The constant term is included.

70

Table 10. Estimation results of Equation (5) including 2 leads and 3 lags, 1987-2013.

Assumption on the revitalization

(1)

(2)

RTA_depth

RTA_breadth

RTA_core_depth

RTA_depth

RTA_core_depth-0.30430.0371-0.0626-0.3167-0.0645(0.4942)(1.0205)(0.2275)(0.4832)(0.2262)-0.0251

0.81030.0917-0.0638

0.0786(0.4376)(0.9217)(0.1958)(0.4266)(0.1941)0.51281.7731*0.36380.4506

0.3351(0.5366)(0.9457)(0.2217)(0.5012)(0.2157)

1.0871***

0.48320.2936*

1.1343***0.3249**(0.4137)(0.7145)(0.1692)(0.3783)(0.1637)

0.34150.38520.12470.34040.1236(0.4940)(1.0411)(0.2307)(0.4863)(0.2296)0.6223*

2.1071***0.3886**

0.6667**

0.3964**

CU_Latin America
t

t-1

t-2

t-3

t+1

t+2

71

(0.3264)(0.7349)(0.1590)(0.3252)(0.1587)

1.00353.98380.62300.99110.6200(4.2379)(16.7901)(2.6298)(4.2360)(2.6301)

3.279313.00422.03533.28072.0364(2.9560)(11.7061)(1.8339)(2.9582)(1.8353)

1.13654.51980.70461.13680.7049(1.5270)(6.0277)(0.9461)(1.5306)(0.9474)-0.4160-1.7546-0.2643-0.4135-0.2633(1.1713)(4.6188)(0.7253)(1.1745)(0.7263)-3.5682-13.9820-2.1982-3.5585-2.1987(3.6155)(14.2531)(2.2381)(3.6139)(2.2389)2.9541

11.4364

1.79962.9274

1.7912(2.0296)(7.9238)(1.2487)(2.0333)(1.2500)

0.84011.27460.3776

0.84120.3778CU_Caribbean
t

t-1

t-2

t-3

t+1

t+2

Plurilateral_FTA_LAC
t

72

t-1

t-2

t-3

t+1

t+2

(0.6054)(0.9047)(0.2712)(0.6052)(0.2712)0.18330.24810.08080.18460.0813(0.8536)(1.2960)(0.3833)(0.8535)(0.3832)-1.0973*-1.7674*-0.4903*-1.0932*-0.4888*(0.6125)(0.9815)(0.2781)(0.6126)(0.2782)

0.0070-0.12240.01040.00950.0115(0.3608)(0.6571)(0.1665)(0.3610)(0.1665)

0.13570.24750.06240.13590.0624(0.3517)(0.5094)(0.1560)(0.3523)(0.1562)0.1128-0.00480.01850.11890.0206(0.2625)(0.3827)(0.1161)(0.2635)(0.1163)-0.5752-0.8414-0.2923-0.5805-0.2951(0.5487)(0.6874)(0.2586)(0.5523)(0.2596)0.61140.9579*0.31560.54240.2952(0.4611)(0.5280)(0.2075)(0.4629)(0.2080)

Plurilateral_FTA_with outside
t

t-1

73

t-2

t-3

t+1

t+2

-0.1884-0.2253-0.0917-0.1885-0.0917(0.6337)(0.6637)(0.2738)(0.6338)(0.2738)-0.0159

0.0699-0.0124-0.0134-0.0115(0.6264)(0.6391)(0.2697)(0.6265)(0.2697)-0.0428-0.1460-0.0355-0.0438-0.0362(0.6122)(0.8010)(0.2946)(0.6142)(0.2953)-0.9340**-1.0980*-0.4294**-0.9660**-0.4412**(0.4448)(0.5866)(0.2136)(0.4462)(0.2140)0.5421

0.69280.25230.53990.2518(0.4863)(0.7106)(0.2306)(0.4864)(0.2306)-0.6374-0.7995-0.2876-0.6369-0.2875(0.4851)(0.7165)(0.2326)(0.4849)(0.2325)-0.0536

0.0183-0.0110-0.0547-0.0120(0.4824)(0.7200)(0.2215)(0.4822)(0.2214)0.37700.34300.13190.38130.1328Bilateral_FTA
t

t-1

t-2

t-3

74

(0.3890)(0.5788)(0.1766)(0.3888)(0.1765)0.23350.35710.10680.23100.1057(0.3984)(0.5791)(0.1821)(0.3985)(0.1820)-0.2040-0.1832-0.0656-0.1873-0.0632(0.2902)(0.4162)(0.1312)(0.2900)(0.1311)

Exporter-year fixed effects

Yes

Yes

Yes

Yes

Yes

Importer-year fixed effects

Yes

Yes

Yes

Yes

Yes

Country-pair fixed effects

Yes

Yes

Yes

Yes

Yes

Observations

24,247

24,247

24,247

24,247

24,247t+1

t+2

Note: CU_Latin America, custom unions (CUs) among Latin American countries; CU_Caribbean, CUs among Caribbean countries;
Bilateral_FTA, bilateral free trade agreements (FTAs) among Latin American and Caribbean (LAC) countries; Plurilateral_FTA_LAC,
plurilateral FTAs among three or more LAC countries; Plurilateral_FTA_with outside, plurilateral FTAs between two or more LAC
countries and at least one non-LAC countries. Estimations were implemented using the Stata command ppmlhdfe. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively. Numbers in parentheses represent robust standard errors clustered bycountry-pairs. The constant term is included.

75