WPS4220
Evaluating the Trade Effect of Developing Regional
Trade Agreements: A Semi-parametric Approach
Souleymane Coulibaly
World Bank*
1818 H Street NW, 20433 Washington DC, USA
Tel: +1 202 473 9845
Email: scoulibaly2@worldbank.org
Abstract
Many recent papers have pointed to ambiguous trade effects of developing regional trade
agreements (RTAs), calling for a reassessment of their economic merits. We focus on seven such
agreements currently in force in Sub-Saharan Africa (ECOWAS and SADC), Asia (AFTA and
SAPTA) and Latin America (CACM, CAN and MERCOSUR), estimating their impacts on their
members' trade flows. Instead of the usual dummy variables for RTAs, we propose a variable
taking into account the number of years of membership. We then combine a gravity model with
kernel estimation techniques so as to capture the non-monotonic trade effects while imposing
minimal structure on the model.
The results indicate that except for SAPTA, all these RTAs have had a positive impact on
their members' intra-trade over the estimation period (1960-1999). AFTA seems to be the most
successful among them with an estimated positive impact on its members' imports from the rest
of the world (ROW), but its impact on their exports to the ROW is rather limited. During its first
ten years of existence, ECOWAS appears to have had a positive impact on its members' imports
from the ROW, but this positive impact vanished over time. SAPTA's negative impact on its
members' intra-trade is probably an implicit effect of the India-Pakistan tensions over the
estimation period.
J.E.L Classification: F11, F15, O50
Keywords: regional trade agreement, kernel regression, trade impact
World Bank Policy Research Working Paper 4220, May 2007
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
Acknowledgments: I thank Jeffrey Bergstrand, Paul Brenton, Marius Brülhart, Celine Carrère, Renato
Flores, Lionel Fontagné, Guillaume Gaulier, Thierry Mayer, Daniel Mirza, Eric Toulemonde and an
anonymous referee for helpful comments and suggestions.
1 Introduction
According to official rhetoric, countries involved in a regional trade agreement
(RTA) expect a welfare gain. This expectation is so strong that most engage in many
different agreements leading to what Bhagwati called the "spaghetti bowl" phenomenon,
that is the crisscrossing of many regional agreements differing in their schedules of
phasing out tariffs, rules of origin and excluded products. Recent studies of trade effects
of developing RTAs come to different conclusions, sometimes for the same RTAs, as
depicted in Table 1.
Table 1: Trade impact of some developing RTAs
Net trade creation Net trade diversion
AFTA/ASEAN Carrère (2004) Dee & Gali (2003)
Elliott & Ikemoto (2004) Soloaga & Winters (2000)
Gosh & Yamarik (2004)
Cernat (2001)
LAFTA/LAIA Dee & Gali (2003) Carrère (2004)
Gosh & Yamarik (2004) Soloaga & Winters (2000)
Soloaga & Winters (2000)
MERCOSUR Gosh & Yamarik (2004) Carrère (2004)
Cernat (2001) Dee & Gali (2003)
Soloaga & Winters (2000) Krueger (1999)
For instance, AFTA, LAIA and MERCOSUR appear to have been net trade
creating in some studies and net trade diverting in others. These studies use different
estimation methods, different databases and different dynamic specifications to measure
trade effects, and they focus on the number of years these RTAs have existed to estimate
their trade impact.
Freund and McLaren (1999) introduced an alternative way of looking at RTAs
trade effect by focusing on the dynamic of trade orientation when a country joins a
regional trade agreement and over the number of years of membership. This paper
follows this idea of evaluating the participation effect of each RTA's member. To carry
out such analysis, we propose an RTA variable taking into account the number of years
of participation of each member, and we use a two-step estimation approach combining a
gravity model estimation and a kernel regression of the estimated trade residuals. We
focus on seven developing RTAs covering Sub-Saharan Africa (ECOWAS and SADC),
Asia (AFTA and SAPTA) and Latin America (CACM, CAN and MERCOSUR) over the
period 1960-1999.1
The results indicate that except for SAPTA, all these RTAs have had a positive
impact on their members' intra-trade over the estimation period (1960-1999). AFTA
1Appendix 1 describes these RTAs.
seems to be the most successful among them with an estimated positive impact on its
members imports from the ROW (hence no trade diversion), but its impact on their
exports to the ROW is rather limited. During its first ten years of existence, ECOWAS
appears to have had a positive impact on its members imports from the ROW (hence no
trade diversion), but this positive impact vanished over time. SAPTA's negative impact
on its members' intra-trade is probably an implicit effect of the India-Pakistan tensions
over the estimation period.
The remainder of the paper contains a theoretical and an empirical part. In the
theoretical part (section 2), we first describe the RTA variable, then we present the two-
step estimation approach. In the empirical part (section 3), we estimate and discuss the
trade effect of the selected developing RTAs. Section 4 concludes the paper.
2 Theoretical investigation
XRTA-RTA
RTA
XRTA-ROW XROW-RTA
ROW
XROW-ROW
export flows
Figure 1: Geography of World Trade Flows
To properly measure the RTAs' trade effect, we focus on export flows of the
trading partners in a general equilibrium framework as described in Figure 1. The subset
RTA comprises the member countries of one of the seven RTAs under consideration and
the subset ROW represents all the remaining countries in the world.
2.1 The RTA variable
The usual RTA's dummy variable assesses the impact of the RTA year after year.
In this paper, we propose a variable designed to assess the impact of the RTA after a
given period of membership. The variable we propose is based on the count of the
number of years each member has participated. We thus combine the expansion
dimension of the RTA (the evolution of the membership over time) and the cumulative
cooperation experience of the members over time.
2
For instance, let us consider the membership of the Central American Common
Market (CACM): El Salvador, Guatemala, Honduras and Nicaragua created this RTA in
1960, and Costa Rica joined in 1962. Let us call YP(i,t) the number of years of
participation of member country i in the RTA at date t. Table 2 illustrates CACM
member participation in 1988, 1990 and 1992.
Table 2: Number of years CACM members have participated
Years of participation: YP(i,t)
1988 1990 1992 Year: t
El Salvador 29 31 33
Guatemala 29 31 33
Honduras 29 31 33
Nicaragua 29 31 33
Costa Rica 27 29 31
Member: i
To compute the RTA variable, we distinguish between the exporter (country i)
and the importer (country j). Each RTA is thus characterized by three variables
representing respectively export flows from a member to a non-member (VRTA-ROW),
export flows from a non-member to a member (VROW-RTA), and export flows between
members (VRTA-RTA). These variables depend on i, j and t:
VRTA (i, j,t)= YP(i,t) if i belongs to RTA and j does not, 0 otherwise
-ROW (1)
VROW (i, j,t)= YP( j,t) if j belongs to RTA and i does not, 0 otherwise
-RTA (2)
VRTA (i, j,t)= Min{YP(i,t),YP(j,t)}if i and j belong to RTA, 0 otherwise
-ROW (3)
To take account of anticipation effects from the beginning of the negotiation of
the RTA to the end of the first year of existence, we can start the analysis a certain
number of years ahead of the date of entry into force. We arbitrarily choose ten years.
This is sufficient to capture any anticipation effect following Freund and McLaren (1999)
who estimate this period to be approximately 12 years. Under this hypothesis, the RTA
variables become:
V~RTA (i, j,t)= YP(i,t)+10 if i belongs to RTA and j does not, 0 otherwise
-ROW (4)
V~ROW (i, j,t)= YP( j,t)+10 if j belongs to RTA and i does not, 0 otherwise (5)
-RTA
V~RTA (i, j,t)= Min{YP(i,t),YP(j,t)}+10 if i and j belong to RTA, 0 otherwise (6)
-ROW
These measures help to take into account the variation in membership and the cumulative
3
cooperation effect over time of the RTA.
2.2 The two-step estimation approach
The gravity equation is the most used tool to analyze the trade impact of RTAs.
However, regardless of any theoretical base, most of the empirical papers addressing
RTAs' trade impact impose a linear relationship between RTAs and trade flows through
the inclusion of dummy variables. A non-parametric approach would let the data impose
the relevant structure to the RTA-Trade flows relation and this paper proposes an
estimation approach in this vein. We proceed in two steps.
2.2.1 First step: the gravity equation estimation
First, we have to estimate a simple gravity model not including any RTA measures. In
the empirical trade literature, many recent papers have revisited the formulation of the
gravity equation by proposing different set of dummy variables to be included to control
for the price and the remoteness term. Among these papers, we can mention Baier and
Bergstrand (2002), Anderson and van Wincoop (2003), Martinez-Zarzoso and Nowak-
Lehmann (2003), and Cheng and Wall (2005). The recent paper by Baldwin and Taglioni
(2006) summarizes this debate and describes the common errors made in this empirical
literature. Basically, three current errors are made: the inadequate deflation of trade flows
by CPI, a misleading bilateral trade average (taking the log of average bilateral trade
instead of the average of the log of bilateral trade), and the omission or the incorrect
inclusion of the multi-lateral resistance term. All these errors lead to biased estimates of
the trade impact of any trade policy.
Baldwin and Taglioni propose some improvements of the empirical estimation of
gravity equations: use unidirectional trade flows and include country-pair and time
dummy variables, or country-time dummy variables. Both options correct for the
inadequate deflation of the trade flows, but correct for omission of the multi-lateral
resistance only partially. Baldwin and Taglioni's preferred specification is to include both
country-pair and country-time dummy. However, they acknowledge that since most of
the trade policies examined by trade economists are country-pair specific, this approach
alters the estimation of the trade impact of these policies.
Against this backdrop, we propose two specifications incorporating most of the
suggestions of Baldwin and Taglioni. The first specification includes country-pair and
time dummies, the second includes country-time dummies and some bilateral
geographical variables to partially control for the omitted country-pair dummies:
LnXijt =1LnGDPit +2LnGDPjt + 1LnPOPit + 2LnPOPjt
+LnRERijt +t+0 + FEij +FEt +ijt (7)
4
LnXijt =0LnDistij +1LnGDPit +2LnGDPjt + 1LnPOPit + 2LnPOPjt
+LnRERijt +t+0 + FEit + Geoij +ijt k (8)
k
where Xijt is country i's export to country j at period t, Distij is the distance between
country i and j, GDPit is the GDP of country i in year t, POPit is the population of country
i in year t, RERijt is a measure of the real exchange rate between country i and j in year t, t
is the time trend so that measures the long term effect of time on trade flows, 0 is an
intercept common to all years and country-pairs, FEij (with FEij FEji) is the country-
pair fixed effects, FEit is the exporter-year fixed effects, FEt is year fixed effects, and
Geoij is a set of k bilateral geographical variables.2 Following Rose (2003), we consider
k
the following bilateral geographical variables: Border (sharing a common border),
Colony (colonizer-colony relationship), Comcol (sharing a common colonizer), Comlang
(sharing a common language), and Curcol (currently in a colony-colonizer relationship).
ijt is the error term.
2.2.2 Second step: the non-parametric estimation
The estimated residuals of these two equations are extracted and used in the second step
for the non-parametric part of the estimation.
Imagine a scatter plot depicting the estimated trade residuals (^ijt ) against one of
the three RTA variables described in the previous section
(V~RTA (i, j,t)). The point is to evaluate the non-
-ROW (i, j,t),V~ROW -RTA(i, j,t), or V~RTA -RTA
parametric function f () underlining the variation of ^ijt in accordance with
.
V~RTA (i, j,t)) by using a kernel estimator:
-ROW (i, j,t),V~ROW -RTA(i, j,t), or V~RTA -RTA
E ijt VRTA
( (i, j,t))
-ROW (i, j,t))= f^(VRTA -ROW (9)
E ijt VROW
( (i, j,t))
-RTA (i, j,t))= f^(VROW -RTA (10)
E ijt VRTA
( (i, j,t))
-RTA (i, j,t))= f^(VRTA -RTA (11)
where:
2Our real exchange rate variable is inspired by Soloaga and Winters (2000):
RERijt = e× US / i . e× US /
( )( ) where
,t ,t ,t j,t e is the value of 1 US $ evaluated in the currency
of country i and is the GDP deflator.
5
f^(x) = n
i=1K(xi ).^ijt / n
(12)
n
i=1K(xi )/ n
where n is the number of observations, n is an a-priori chosen sequence of positive
numbers called the window width parameter and K() . is an a-priori chosen real function
called the kernel, and satisfying K(x)dx < and K(x)dx = 1.
Bieriens (1994) analyses the asymptotic property of this estimator, and shows that it is
asymptotically normal, that is:
n f (x)- f (x) N(0,V (x))
[^ ] (13)
where V (x) depends on the characteristics of the kernel function K(x).
Bierens shows that the specific choice of the kernel function is not crucial: any
Gaussian kernel is relevant. More important is the choice of the bandwidth that controls
the trade-off between bias and variance of the estimated trade effects. Since the RTA
variables are discrete variables (number of years of participation), we choose a bandwidth
n =1so as to smooth trade effects over a one-year period. Bierens (1994) describes in
detail how to use equation (13) to directly build the Confidence Interval of the estimated
trade effects.
3 Empirical Analysis
In this section, we present and discuss the data used to evaluate the trade effect of the
seven developing RTAs under consideration.
3.1 Data and estimation issues
Our database comes from Rose (2003) completed with data on export price index
from IFS. We divided the export values by the export price indices to obtain export
quantities. The final database is an unbalanced panel containing 56 exporter and 90
importer countries over the period 1960-1999 (see the list in Appendix 2). It contains no
zero trade flow and only 8% of export values are missing. We thus use a simple
regression model to estimate the gravity models (7) and (8). Since we are using fixed
effects, the estimators are not biased because of the unbalancedness of the database;
however, we use the Huber/White estimator of the variance to correct for the potential
heteroscedasticity problem.
6
The estimation results of the gravity equations are reported in Appendix 3.3
Specification 1 corresponds to equation (7) including country-pair and year fixed effects
and Specification 2 corresponds to equation (8) including exporter-year fixed effects. In
Appendix 3, a parameter with an upper index a is significant at the 1% level, that with an
upper index b is significant at the 5% level and that with an upper index c is significant at
the 10% level.
The traditional gravity variables (distance, GDP and Population) depict the
expected sign and magnitude in the two specifications. The estimated coefficients of the
real exchange rate variable are negative, indicating a slight decreasing competitiveness
among trading partners over the period 1960-1999 after controlling for the traditional
gravity variables.
The time trend is not statistically significant in Specification 1. The bilateral
geographical variables' coefficients are statistically significant with the expected signs
and magnitudes.
In the second step, we extracted the estimated trade residuals from equations (7)
and (8), and run a kernel regression as described in Section 2.2. We then used equation
(13) to build the confidence interval as follows: for each grid point, we consider the
standard deviation () from equation (13) and use it to compute the 95% confidence
interval of the trade effects defined as ±1.96× , where = 1/(12n) , n being the total
number of years of existence of a given RTA and 1/12 being the variance of the uniform
distribution.4 The results are presented graphically in Appendix 4.
Following Baldwin and Taglioni (2006), we choose the specification including the
exporter-year fixed effects as our preferred estimation and comment the results in the
next section.
3.2 The trade effect of some developing RTAs
The ASEAN Free Trade Agreement (AFTA) was created in 1992 by six members
of the Association of South East Asian Nations (Brunei Darussalam, Indonesia, Malaysia,
Philippines, Singapore and Thailand), four other members joined subsequently (Vietnam
in 1995, Laos and Myanmar in 1997, Cambodia in 1999). The AFTA members included
in the estimation as exporter and importer are Indonesia, Malaysia, Philippines Singapore
and Thailand, the remaining members being included only as importers. Figure 2 of
3We do not report exporter-year, country-pair and year fixed effects to save space. Note also that since
equation (8) introduces exporter-years fixed effects, the variables with the index it are absorbed in this
specification.
4See Bierens (1994) for a full explanation of this process.
7
Appendix 4 plots the estimated trade residuals against the AFTA membership evolution
over time: the top panel focuses on intra-AFTA exports ( X AFTA-AFTA ), the middle panel
focuses on AFTA's imports flows from the ROW ( X ROW -AFTA ) and the bottom panel
focuses on AFTA's exports to the ROW ( X AFTA -ROW ). The dashed lines represent the
estimated 95% confidence interval. These graphs clearly show an anticipation effect of
AFTA members which started increasing their intra-trade five years before the official
year of joining this RTA. In addition, the trade effect of AFTA seems to be globally
positive over the estimation period since its effect on intra-AFTA exports and imports
from the ROW are estimated to be positive and increasing. However, its impact on export
flows remained neutral.
The Central American Common Market (CACM), was created in 1960 by El
Salvador, Guatemala, Honduras, Nicaragua. Costa Rica joined in 1962. It is notified at
the WTO as a Customs Union. Except for El Salvador included as importer only, all the
CACM members are both exporter and importer in the database. Figure 3 of Appendix 4
plots the estimated trade residuals against the number of years of each CACM member's
participation. The RTA's impact on intra-CACM exports are estimated to be negative
during the first years of its existence, and then it became positive and increasing over
time. The tensions between El Salvador and Honduras in the late sixties may explain this
initial negative impact.5 The RTA's impact on its members' exports to and imports from
the ROW are estimated to be negative and sometimes decreasing, a result suggesting an
overall ambiguous trade effect of the CACM.
The Andean Community (CAN) is a preferential agreement signed in 1988 by
Bolivia, Colombia, Ecuador, Peru and Venezuela. Except for Venezuela included as
importer only, all the other CAN members are both exporter and importer in the database
used for the estimations. Figure 4 of Appendix 4 plots the estimated trade residuals
against the number of years of CAN members' participation. Intra-CAN exports seem to
have started increasing three years before the official date of entry into force of this RTA.
It remained positive and increasing over the estimation period. However, the RTA's
effects on imports from and exports to the ROW are estimated to be negative or neutral.
The Economic Community of West African States (ECOWAS) is a political
association created in 1975 by fifteen members (Mauritania withdrew in 1999): Benin,
Burkina Faso, Cape Verde, Côte d'Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau,
Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Togo. Except for Burkina Faso,
Côte d'Ivoire, Liberia, Nigeria, Senegal and Togo that are included as exporter and
5In fact, the CACM collapsed in 1969 after a five-day war that had been known as the "soccer war"
between El Salvador and Honduras. After this episode, the partners tried to slowly re-establish their
collaboration. This may explain the abnormal trade effects observed. We may also notice that in Figure 4 of
appendix 5, the CACM trade flows are limited to two years before the official date of entry into force (1962
for Costa Rica) because the database used is limited on the period 1960-1999.
8
importer, the remaining members are included in the database as importers only. Figure 5
in Appendix 4 plots the estimated trade residuals against the number of years these
countries have participated in the ECOWAS. These graphs indicate a slight anticipation
effect of ECOWAS members five years before the official date of its creation. The RTA's
impact on intra-ECOWAS trade flows is estimated to be positive and increasing over the
estimation period, while its impact on its members exports to the ROW is negative and
decreasing over time. During the first ten years of the existence of the RTA, its impact on
its members imports from the ROW was estimated to be positive, but this result was
reversed after. The overall trade impact of the ECOWAS is thus ambiguous.
The Southern Common Market (MERCOSUR) was established in 1991 between
Argentina, Brazil, Paraguay and Uruguay. Except for Uruguay included as importer only,
the other MECOSUR members are included as exporter and importer in the database.
Figure 6 of Appendix 4 plots the estimated export volume residuals against the number of
years of member participation. These graphs indicate that MERCOSUR members were
very involved in intra-trade five years before the official date of implementation of this
RTA. The RTA's impact on its members intra-trade is estimated to be positive and
increasing over time, while its impact on their imports from the ROW is negative. The
RTA appears to have had no impact on its members exports to the ROW.
The South African Development Community (SADC) is a political association
created in 1992 by fourteen members: Angola, Botswana, Democratic Republic of
Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa,
Swaziland, Tanzania, Zambia, Zimbabwe. Except for Malawi, Mauritius, Seychelles,
South Africa, Zambia and Zimbabwe included as importer and exporter in the database,
the other SADC members are included as importers only. Figure 7 in Appendix 4 plots
the estimated trade residuals against the number of years of SADC member participation.
Figure 7 reveals an anticipation effect of SADC members depicted by a continuous
increase in the intra-SADC trade flows five years before the official implementation date.
The RTA's impact on its members' intra-trade is estimated to be positive and increasing.
However, its impact on their exports to or imports from the ROW are estimated to be
slightly negative.
The last RTA analyzed is the South Asian Preferential Trade Agreement
(SAPTA) comprising Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri-
Lanka. The treaty creating the SAPTA was signed on April 1993, and it enters into force
in December 1995. Except for India, Pakistan and Sri-Lanka included as importers and
exporters, the other members are included only as importers in the database. Figure 8 of
Appendix 4 plots the estimated trade residuals against the number of years of SAPTA
members' participation. The RTA's impacts on its members intra-trade and imports from
the ROW are estimated to be negative, while its impact on the members exports to the
ROW is estimated to be neutral. The recurrent tensions between India and Pakistan over
the estimation period may explain the negative impact on intra-SAPTA trade flows.
4 Conclusion
9
This paper proposes two contributions to the evaluation of RTAs' trade impacts. First, we
use an RTA variable that takes into account the number of years each member has
participated instead of the usual RTA dummy variable. Second, we combine traditional
gravity regressions with non-parametric estimation techniques so as to capture the non-
monotonic trade effects while imposing minimal structure on the model.
We focus on a panel of seven developing RTAs covering Africa, Asia and Latin America.
Except for SAPTA, all these RTAs appear to have had a positive impact on their
members' intra-trade over the estimation period (1960-1999). AFTA seems to be the most
successful of these RTAs with an estimated positive impact on its members imports from
the ROW (hence no trade diversion), but its impact on their exports to the ROW is rather
limited. During its first ten years of existence, ECOWAS has had a positive impact on its
members imports from the ROW (hence no trade diversion), but this positive impact
vanished over time. SAPTA's negative impact on its members' intra-trade is probably an
implicit effect of the India-Pakistan tensions over the estimation period.
This work is based on the up-to-date formulation of the gravity model and the proposed
semi-parametric estimation approach can be easily implemented to rigorously assess the
trade impact of developing RTAs. It could be improved and used as a key diagnostic tool
to evaluate the trade impact of the various RTAs signed between many World Bank
clients.
10
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11
Appendix
Appendix 1: A Panel of Developing RTAs
Agreement Full name Membership evolution Type
ECOWAS Economic 1975: Benin Political
Community 1975: Burkina Faso Association
Of West Africa 1975: Cape Verde
1975: Côte d'Ivoire
1975: Gambia
1975: Ghana
1975: Guinea
1975: Guinea Bissau
1975: Liberia
1975: Mali
1975: Niger
1975: Nigeria
1975: Senegal
1975: Sierra Leone
1975: Togo
Agreement Full name Membership evolution Type
SADC South African 1992: Angola Political
Development 1992: Botswana Association
Community 1992: DR Congo
1992: Lesotho
1992: Malawi
1992: Mauritius
1992: Mozambique
1992: Namibia
1992: Seychelles
1992: South-Africa
1992: Swaziland
1992: Tanzania
1992: Zambia
1992: Zimbabwe
Agreement Full name Membership evolution Type
CAN Andean 1988: Bolivia Preferential
Community 1988: Columbia Arrangement
1988: Ecuador
1988: Peru
1988: Venezuela
12
Agreement Full name Membership evolution Type
CACM Central 1960: El Salvador Customs
American 1960: Guatemala Union
Common 1960: Honduras
Market 1960: Nicaragua
1962: Costa Rica
Agreement Full name Membership evolution Type
MERCOSUR Southern 1991: Argentina Customs
Common 1991: Brazil Union
market 1991: Paraguay
1991: Uruguay
Agreement Full name Membership evolution Type
AFTA ASEAN 1992: Brunei Darussalam Political
Free Trade 1992: Indonesia Association
Agreement 1992: Malaysia
1992: Philippines
1992: Singapore
1992: Thailand
1995: Vietnam
1997: Laos
1997: Myanmar
1997: Cambodia
Agreement Full name Membership evolution Type
SAPTA South Asia 1995: Bangladesh Preferential
Preferential 1995: Bhutan Agreement
Trade 1995: India
Agreement 1995: Maldives
1995: Nepal
1995: Pakistan
1995: Sri Lanka
13
Appendix 2: Exporter and importer countries
Code Country Exporter Importer Code Country Exporter Importer
111 United States yes yes 522 Cambodia no yes
112 United Kingdom yes yes 524 Sri Lanka yes yes
122 Austria no yes 534 India yes yes
124 Belgium yes yes 536 Indonesia yes yes
128 Denmark yes yes 542 Republic of Korea yes yes
132 France yes yes 544 Lao People's Dem Rp no yes
134 Germany yes yes 548 Malaysia yes yes
136 Italy yes yes 556 Maldives no yes
137 Luxembourg no yes 558 Nepal no yes
138 Netherlands yes yes 564 Pakistan yes yes
142 Norway yes yes 566 Philippines yes yes
144 Sweden yes yes 576 Singapore yes yes
146 Switzerland yes yes 578 Thailand yes yes
156 Canada yes yes 582 Viet Nam no yes
158 Japan yes yes 614 Angola no yes
172 Finland yes yes 616 Botswana no yes
174 Greece yes yes 624 Cape Verde no yes
176 Iceland yes yes 636 Congo, Dem Rep of no yes
178 Ireland yes yes 638 Benin no yes
181 Malta yes yes 648 Gambia no yes
182 Portugal yes yes 652 Ghana no yes
184 Spain yes yes 654 Guinea-Bissau no yes
186 Turkey yes yes 656 Guinea no yes
193 Australia yes yes 662 Côte D'Ivoire yes yes
196 New Zealand yes yes 664 Kenya yes yes
199 South Africa yes yes 666 Lesotho no yes
213 Argentina yes yes 668 Liberia yes yes
218 Bolivia yes yes 676 Malawi yes yes
223 Brazil yes yes 678 Mali no yes
233 Colombia yes yes 682 Mauritania no yes
238 Costa Rica yes yes 684 Mauritius yes yes
248 Ecuador yes yes 688 Mozambique no yes
253 El Salvador no yes 692 Niger no yes
258 Guatemala yes yes 694 Nigeria yes yes
268 Honduras yes yes 698 Zimbabwe yes yes
273 Mexico no yes 718 Seychelles yes yes
278 Nicaragua yes yes 722 Senegal yes yes
288 Paraguay yes yes 724 Sierra Leone no yes
293 Peru yes yes 728 Namibia no yes
298 Uruguay no yes 734 Swaziland no yes
299 Venezuela no yes 738 Tanzania no yes
513 Bangladesh yes yes 742 Togo yes yes
514 Bhutan no yes 746 Uganda no yes
Brunei
516 Darussalam no yes 748 Burkina Faso yes yes
518 Myanmar no yes 754 Zambia no yes
14
Appendix 3: Gravity equation estimations
Dependent variable: LnXijt
1 2
LnDistij -1.23a
LnGDPit 1.34a
LnGDPjt 0.90a 1.39a
LnPOPit -0.06c
LnPOPjt -0.53a -0.57a
LnRERijt -0.002a -0.004a
t -0.008
Border 0.16a
Colony 1.29a
Comcol 0.86a
Comlang 0.37a
Curcol 1.50a
Constant -23.44a 1.96a
N 123,205 123,205
R2 0.45 0.29
P-value 0.00 0.00
15
Appendix 4: Figures
Exporter-time fixed effects Country-pair and year fixed effects
1 1
rt t
poxe expor
ATFA 0
a-
ntrI ATFAartnI 0
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR WOR
omrf 0 omrft 0
port
mI pormI
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR WOR
totropxE 0 totrop 0
Ex
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years Years
Figure 2: AFTA trade effects
16
Exporter-time fixed effects Country-pair and year fixed effects
3 3
2 2
tropxe 1 tropxe 1
ATFA 0
a-tr
In -1 MCACatrIn 0
-1
-2
-2
-3
-3
-9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
3 3
2 2
WOR 1 1
WOR
omrftr 0 omrft 0
po
Im -1 pormI -1
-2 -2
-3 -3
-9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
3 3
2 2
1 1
WOR WOR
tot 0
porxE totropxE 0
-1 -1
-2 -2
-3 -3
-9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30 33 36 39
Years Years
Figure 3: CACM trade effects
17
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
tropxe tropxe
NAC-atr 0
In NACatrIn 0
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
WOR WOR
mofrtrop 0 morftrop 0
mI Im
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
WOR
tot 0 WORott 0
porxE porxE
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12
Years Years
Figure 4: CAN trade effects
18
Exporter-time fixed effects Country-pair and year fixed effects
2 1
tro 1
xpe tropxe
SA SA
WOCE-art 0 WOCEatrIn 0
In
-1 -1
-9 -6 -3 0 3 6 9 12 15 18 21 24 -9 -6 -3 0 3 6 9 12 15 18 21 24
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR WOR
romftr 0 omrft 0
po
mI pormI
-1 -1
-9 -6 -3 0 3 6 9 12 15 18 21 24 -9 -6 -3 0 3 6 9 12 15 18 21 24
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR WOR
totropxE 0 totr 0
poxE
-1 -1
-9 -6 -3 0 3 6 9 12 15 18 21 24 -9 -6 -3 0 3 6 9 12 15 18 21 24
Years Years
Figure 5: ECOWAS trade effects
19
Exporter-time fixed effects Country-pair and year fixed effects
2 2
tropxe 1 tropxe 1
RUS RUS
OCRE 0 OCRE 0
M-a M-atr
Intr -1 In -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
OWR WOR
omrftrop 0 omrft 0
por
Im mI
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
WOR WOR
totropxE 0 ott 0
porxE
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
Years Years
Figure 6: MERCOSUR tradeeffects
20
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
tr t
poxe porxe
CDAS-atr 0
In CDASarntI 0
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
WOR WOR
omrftr 0 omrft 0
po
mI pormI
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
2 2
1 1
WOR
ottrop 0 WORott 0
Ex porxE
-1 -1
-2 -2
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Years Years
Figure 7: SADC trade effects
21
Exporter-time fixed effects Country-pair and year fixed effects
1 1
tr tr
poxe poxe
ATPAS 0 ATPAS 0
a-rtnI a-rtnI
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR
mofrtrop WOR
0 ottrop 0
mI Im
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Years Years
Exporter-time fixed effects Country-pair and year fixed effects
1 1
WOR WOR
totropxE 0 totropxE 0
-1 -1
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Years Years
Figure 8: SAPTA trade effects
22