Universität Bayreuth

Rechts- und Wirtschaftswissenschaftliche Fakultät

Wirtschaftswissenschaftliche Diskussionspapiere

Multilateralism versus Regionalism!?

Bernhard Herz und Marco Wagner

Diskussionspapier 01-10

Januar 2010

ISSN 1611-3837

Adresse:

Prof. Dr. Bernhard Herz

Universität Bayreuth

Lehrstuhl VWL I (Geld und Internationale Wirtschaft)

95440 Bayreuth

Telefon: +49 (0) 921/55-2912

Fax: +49 (0) 921/55-2949

E-Mail: Bernhard.Herz@uni-bayreuth.de

Abstract

The well-known question whether regional trade agreements (RTAs) and the multi-

lateral trading system (MTS) are �strangers, friends, or foes�(Bhagwati and Pana-

gariya, 1996) has gained new importance with the widespread proliferation of RTAs

in recent years. Based on an extensive data set which covers most of world trade over

the past 60 years and about 240 regional trade agreements, we analyze the relation-

ship between RTAs and the MTS by combining the gravity model framework with

vector auto-regression analysis. Impulse-response-functions robustly suggest that

multilateral trade liberalization responds in a signi�cantly positive way to regional

trade liberalization. We also �nd robust evidence that RTA liberalization Granger-

causes GATT/WTO liberalization. Thus our results indicate that RTAs do not

undermine the MTS and serve as building blocs to multilateral trade liberalization.

JEL-Classi�cation: F13

Keywords: Regionalism, multilateralism, trade agreement, gravity model.

1 Motivation

While the number of regional trade agreements (RTAs) grew only slowly until the

beginning of the 1990s, it has remarkably increased since then. In December 2008,

230 RTAs were noti�ed to the World Trade Organization (WTO) and the WTO

(2009c) expects close to 400 RTAs by 2010. At �rst sight, RTAs con�ict with the

most-favored nations clause (GATT Article I, GATS Article II) which prohibits

discriminatory behavior among WTO members. However, WTO members are per-

mitted to establish or enter regional arrangements under speci�c conditions which

are de�ned in GATT Article XXIV, the decision on �Di¤erential and more favorable

treatment reciprocity and fuller participation of developing countries�(the so-called

Enabling Clause), and GATS Article V (WTO, 2009c).

How does this wide proliferation of regionalism relate to global trade liberaliza-

tion? Following Bhagwati (1991) RTAs could be either stumbling or building blocs

to global trade liberalization, i. e. RTAs could contribute to further multilateral lib-

eralization by complementing GATT/WTO or they could impede multilateral trade

liberalization.

Baldwin (2004) summarizes the logic and fears which are associated with RTAs

as stumbling blocs to global trade liberalization and identi�es two key risks of region-

alism. Firstly, regional liberalization might be a substitute for multilateral liberal-

ization since it i) dampens nations�intentness for further multilateral liberalization

and ii) diverts policy makers�attention away from WTO negotiations (e. g. Bhag-

wati, 1992, and Krueger, 1995). Secondly, regionalism might alter the division of

power so that i) small nations are even more dominated by hegemonic powers and

ii) the possibility of tensions (and even trade wars) between trade blocs increases

(Panagariya, 1999). According to Baldwin (2004), these fears are mainly based on

the historical experience during the interwar period.

Summers (1991) rejects this pessimistic view and points out that after World

War II regionalism contributed to tari¤ liberalization and that there is no clear

evidence that regionalism has undermined multilateralism. Creamer (2003) and

Trejos (2005) emphasize that RTAs are not inherently protectionist, but instead

1

can even help reduce political tensions between countries.1 Additionally, RTAs

can stimulate both internal and international political dynamics by providing an

experimental ground for new liberalization ideas.2 Moreover, RTAs can improve the

stability and credibility of countries which should have positive e¤ects on multilateral

negotiations (Paiva and Gazel, 2003).

In contrast to the extensive theoretical literature, empirical research has so far

been rather limited. Limao (2006, 2007) studies the impact of US RTAs on the

evolution of the US external multilateral tari¤s before and after the Uruguay round

negotiations.3 In his regression analysis Limao (2006, 2007) �nds that, on similar

products, the US liberalized external multilateral tari¤s towards non-RTA partners

much more than towards regional trading partners. The rationale of this policy could

be that the US o¤ers preferences to receive concessions from the recipients. Since

the concessions are all the more valuable the larger the preference margin is, the

US tries to prevent the erosion of preferences by resisting multilateral liberalization.

Thus, he concludes that RTAs act as a stumbling bloc to US multilateral trade

liberalization.

Karacaovali and Limao (2008) study how RTAs4 a¤ect the EU�s external multi-

lateral tari¤s basically using the same methodology and time period as Limao (2006,

2007). They �nd that the EU cut its external multilateral tari¤s on imports from

regional trading partners merely by half as much as the external multilateral tari¤s

on products imported from non-members. Karacaovali and Limao (2008) conclude

that EU�s regional arrangements appear to be stumbling blocs to multilateral trade

liberalization.

Estevadeordal et al. (2008) focus on the impact of the Southern Common Mar-

ket (MERCOSUR) and the Andean Community (CAN) on the multilateral tari¤

setting behavior of ten Latin-American countries based on data on preferential an1Bergsten (1997) presents the example of France and Germany which are both members of the

European Union (EU).2See Trejos (2005), Pomfret (2006), Bergsten (1997), and Folsom (2008).3While Limao (2006) incorporates North American Free Trade Agreement (NAFTA), Andean

Trade Preference Act (ATPA), Caribbean Basin Initiative (CBI), Generalized System of Prefer-ences (GSP) and US-Israel in his analysis, Limao (2007) focuses only on CBI and ATPA.

4In particular, the Generalized System of Preferences (GSP), GSP for least developed countries(GSPL), African Caribbean & Paci�c states (ACP), Mediterranean countries (MED), Central &East European states (CEC), European Free Trade Association (EFTX).

2

MFN tari¤s over the period 1990-2001.5 In particular, they regress the change in

external multilateral tari¤s on several control variables, including membership in

MERCOSUR and the Andean Community. Their results indicate that RTAs act as

building blocs for multilateral trade liberalization in Latin America.

Magee and Lee (2001) examine how the formation of the European Economic

Community (EEC) a¤ected external tari¤s of its members. Using the ratio of EEC�s

external tari¤s in 1983 to external tari¤s in 1968 on the imports from 51 industries,

Magee and Lee (2001) �nd that the claim of a �Fortress Europe� is unfounded as

the common external tari¤s of the EEC continuously declined.

Foroutan (1998) carries out a descriptive analysis on the external trading behav-

ior of 50 developing countries over the period 1965-1995. Using trade �ows, import

tari¤s and trade liberalization indicators, she examines whether there is a systematic

relationship between developing countries�membership in a RTA and the external

liberalization of their trade. Her results reject such a systematic relationship.

Taken together, the empirical literature �as well as the theoretical literature �is

inconclusive as RTAs are sometimes found to be stumbling blocs and in some cases

to be building blocs for multilateral trade liberalization.

From a methodological point of view, these approaches are limited in several

ways. The studies investigate selected countries and regions only, thereby ignoring

the interactions with other RTAs these countries or their trading partners are mem-

bers of. The studies cover rather limited time periods which are generally too short

to account for the political dynamics between regionalism and multilateralism sum-

marized by Baldwin (2004) and emphasized by Bhagwati (1992) as well as Summers

(1991). The time periods are also too short to adequately account for the so-called

�rst wave of regionalism in the 1950s and more importantly the new developments

in regionalism since the 1990s and the 2000s (second and third wave of regionalism).

The studies focus only on the external tari¤ setting behavior of countries engaged in

regional arrangements thereby neglecting other dimensions of trade liberalization,

such as non-tari¤ barriers. Foroutan (1998) points out that other indicators, such

5In particular, Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Paraguay, Uruguay,and Venezuela.

3

as actual trade �ows and trade liberalization indicators, are also important.

This study adds to the literature in several ways. We examine a sample of 184

countries with 240 RTAs so that we can control for interactions between various re-

gional arrangements and a country�s membership in more than one RTA. Our data

set covers the period from 1953-2006 which is long enough to adequately account for

the political dynamics between regionalism and multilateralism, and to include the

beginnings of regional arrangements during the 1950s as well as more recent devel-

opments. Based on the building bloc/stumbling bloc discussion, we investigate the

relationship between multilateral and regional trade liberalization. The literature

so far has associated trade liberalization with the countries�external tari¤ setting

behavior. We follow Foroutan (1998) and measure trade liberalization by the actual

impact of regional and multilateral trade liberalization on trade �ows so that we can

account for the whole range of trade liberalizing measures.

To investigate the dynamic e¤ects of RTAs and especially the possibly causal

interrelation between regional and multilateral trade liberalization, we combine a

gravity model framework with vector auto-regressive (VAR) analysis. Using impulse-

response-functions, our study shows that trade liberalization on the multilateral

level responds signi�cantly positive to regional trade liberalization. Additionally, we

�nd that RTA liberalization Granger-causes multilateral liberalization. By contrast,

there is no robust evidence for such an e¤ect in the opposite direction. Thus, the

results suggest that regional trade liberalization does not undermine but rather

contributes to multilateral trade liberalization.

2 General Research Strategy

The �rst wave of regionalism in the 1950s and 1960s was accompanied by a vivid

discussion on the e¤ects of RTAs on trade. More speci�cally, the discussion was

driven by static analysis, i. e. the evaluation of trade creation and trade diversion

(Viner, 1950). With the second wave of regionalism, Summers (1991) and Bhagwati

(1991) initiated an important debate on the interrelation between regional trade

arrangements and the multilateral trading system that focused on the political dy-

4

namic dimension of the issue.6 Bhagwati (1991, p. 77) discussed the dichotomy of

RTAs as stumbling blocs or building blocs for the multilateral trade liberalization.

Bhagwati and Panagariya (1996) further re�ned this question and ask whether RTAs

and the multilateral trading system (MTS) are �strangers, friends, or foes�, adding

the possibility that RTAs and the MTS develop independently from each other.

In our analysis, we proceed in three steps. First, we estimate the time-speci�c

impact of both GATT/WTO and RTAs on international trade for each year us-

ing an extensive gravity model and �xed e¤ects Poisson maximum likelihood (FE-

PML) estimation to derive two time-series that measure the impact of multilateral

and regional liberalization on trade. Secondly, based on the two time-series we use

a vector auto-regressive (VAR) approach to estimate the e¤ect of multilateral on

regional trade liberalization and vice versa. Thirdly, we examine their causal inter-

relation based on impulse-response-functions and Granger-causality analysis, i. e.

we investigate whether and how regional trade liberalization reacts to multilateral

trade liberalization, and vice versa.

The Gravity Model

In a �rst step, we use a standard gravity model to obtain the time-speci�c e¤ects

of both multilateral (represented by GATT/WTO membership) and regional (rep-

resented by RTA membership) agreements on international trade. In particular, we

regress bilateral trade �ows on countries�membership in GATT/WTO and RTAs

together with standard gravity control variables to estimate the time-speci�c impact

of multilateral and regional trade liberalization on trade �ows for each year.

The basic model associates trade �ows with the distance of the trading partners

and their income. This standard model has been supplemented by additional inde-

pendent variables such as cultural, geographic, and historical factors to control for

other �natural�sources of trade. These determinants also include trade agreements

6During the �rst wave of regionalism, the only successful regional trade agreement was rep-resented by the European Community and regional trade agreements were evaluated in staticcomparative analysis. At the beginning of the 1980s, the United States recognized that multi-lateral trade liberalization was di¢ cult to achieve and departed from multilateralism by buildingup bilateral agreements with Israel (1985), Canada (1989) and Mexico (1994) (Panagariya, 1999).With this turn from multilateralism toward regionalism as an alternative way of reaching globalfree trade, Bhagwati (1993) introduced the dynamic time-path dimension.

5

like GATT/WTO and RTAs.7 In formal terms, the model is given by:

Importsijt = �+

2006Xt=1953

�tDt both partners inside GATT/WTO ijt (1)

+

2006Xt=1957

#tDt both partners inside same RTAijt

+polity it + polityjt

+�Xijt + �ij + �t + "ijt

where i and j denote the importing and exporting country, respectively, and t depicts

time. The vector Xijt represents the standard control variables in gravity models.8

� is the common intercept, �ij and �t represent country pair speci�c and time dum-

mies, respectively; "ijt is a white noise error term. The variable both partners inside

the GATT/WTO is a binary dummy variable that is de�ned as one if both trading

partners participate in GATT/WTO in year t, and zero otherwise.Similarly, both

partners inside RTA is a binary dummy variable that equals to one if both trading

partners belong to the same RTA in year t. The termP2006

t=1953 �tDt both partners in-

side GATT/WTO ijt generates 54 dummy variables that represent the time-speci�c

impact of multilateral trade liberalization. In particular, Dt is de�ned as one for

a speci�c year t, else zero, and is multiplied by the binary variable GATT/WTO

membership which is de�ned as one if both trading partners are GATT/WTO mem-

bers in year t, else zero. As an example, D1970 is one in year 1970, else zero. The

combined term D1970 both partners inside GATT/WTO ij1970 is one if both trading

partners are GATT/WTO members in 1970, else zero. Thus, �1970 represents the

impact of multilateral trade liberalization in year 1970.9 The same transformation

is undertaken for membership in RTAs using the termP2006

t=1957 #tDt both partners

inside same RTAijt.10 Generally, �t and #t represent the estimated coe¢ cients re-

7Regarding the theoretical foundation of the gravity model see among others Anderson (1979),Bergstrand (1985), Helpman and Krugman (1985), Deardor¤ (1998), and Anderson and van Win-coop (2003).

8See appendix for a description of the variables.9Note that this speci�cation does not consider the duration of GATT/WTO membership. We

have also taken the duration of GATT/WTO membership into account and found that it does notchange the results of the gravity model estimation signi�cantly.10Note that the variable both partners inside RTA only accounts for mere membership in regional

trade agreements. Additionally, we have also experimented with various dimensions of regional

6

garding the time-speci�c impact of multilateral (GATT/WTO) and regional (RTA)

trade liberalization for each year in the data set, respectively.

We account for these time-speci�c e¤ects of GATT/WTO and RTAs as these

institutions are subject to a continuous change, i. e. GATT/WTO and RTAs in 1970

are qualitatively not the same institutions as in 2009 �independent from the number

of member countries or the number of RTAs as such. For instance, GATT/WTO

currently regulates a much wider range of issues in more depth than in former times

with RTAs having changed in a similar way (WTO, 2007). Regionalism during

the 1950s/1960s is often characterized as �old�regionalism and so-called �shallow�

integration. This type of integration was regional in a geographical sense, did not get

beyond mere tari¤ liberalization and rarely took place between developed and less

developed countries (Limao, 2007). Today, the so-called �new�regionalism is much

more di¤erentiated. It goes well beyond mere tari¤ liberalization and features deeper

forms of integration, such as economic reforms, harmonization and coordination of

economic policies as well as factor market integration among others (Bur�sher et

al., 2003). As the impact of GATT/WTO and RTAs on trade liberalization has

changed over time, we estimate the time-speci�c impact for each year individually.

Note that we do not model membership in GATT/WTO or RTAs according

to Bayer and Bergstrand (2004, 2007, 2009) and Egger et al. (2008) who refer to

the endogeneity of RTAs. While our approach is a �rst solution to the multilater-

alism versus regionalism nexus, the implementation of endogenous membership in

international institutions will be an interesting enhancement for future research.

An immanent problem of estimating the impact of institutional variables is spu-

rious correlation and bias due to omitted (institutional) variables which might also

be important for international trade. The e¤ect of trade liberalization might not

only be driven by international trade agreements but also by national factors. The

trade agreements. In particular, we accounted for de facto RTA membership by using time lags(Tomz et al., 2007, who account for de facto membership of GATT/WTO members). Additionally,we di¤erentiated between pure bilateral (two single countries form a RTA), bilateral (a RTA formsan agreement with a single country) and plurilateral RTAs (two RTAs form an arrangement). Wealso accounted for the number of di¤erent RTAs one country is engaged in. Furthermore, wedi¤erentiated between WTO-noti�ed and non-noti�ed RTAs, regional and cross-regional RTAs,RTAs with only WTO members and RTAs with at least one non-member. Finally, we considerede¤ective RTAs according to Holmes (2005). Again, we �nd that the results of our study do notchange signi�cantly.

7

political system could be such a third factor which might have a positive or negative

impact on international trade, as discussed e. g. by Decker and Lim (2009), Eichen-

green and Leblang (2007), and Yu (2007). To control for this e¤ect, we include the

variable polity which is a measure for the political regime and is scaled between +10

(strongly democratic) and -10 (strongly autocratic).

We estimate the model using �xed e¤ect Poisson maximum likelihood (FE-PML)

estimation. Since comprehensive trade data sets are typically characterized by nu-

merous zero trade �ows, we have to take them into account to avoid biased esti-

mates.11 As the traditional log-linearization of the gravity model cannot account

for zero trade �ows, we follow Verbeek (2008)12 and apply the Poisson maximum

likelihood (PML) estimator.13 The (expected) trade �ows can then be modeled

using an exponential function:

E(yijt j xijt) = exp(x0ijt�); (2)

where yijt represents bilateral trade �ows and xijt denotes a vector of exogenous

variables. The non-negativity of the exponential function ensures that the predicted

values for yijt are also non-negative.14 As this approach does not require a log-

linearization of the variables, the problem of zero trade �ows can be avoided.

Step Two: The VAR-Model

In a second step, we use a vector auto-regressive (VAR) framework based on the

two time-series derived from the gravity model to estimate the e¤ect of GATT/WTO

(multilateral) trade liberalization on RTA (regional) trade liberalization and vice

versa.

As discussed above, the e¤ects of multilateral and regional liberalization on trade

11The current dataset comprises about 46% zero values.12While Verbeek (2008) provides an overview of the Poisson Maximum Likelihood estimation,

Cameron and Trivedi (1998) and Winkelmann (2008) discuss the econometric analysis of countdata more comprehensively.13Regarding the application of gravity models, several authors propose the estimation of the

gravity model in its genuine multiplicative, non-linear form using Poisson maximum likelihoodestimation (Henderson and Millimet, 2008, Westerlund and Wilhelmsson, 2009, Siliverstovs andSchumacher, 2009, Santos Silva and Tenreyro, 2006, or Martínez-Zarzoso et al., 2006).14According to Davidson and MacKinnon (2004), the index function x0ijt� need not be linear

either.

8

may be subject to various interrelations. In particular, we identify the following fac-

tors which might induce a dynamic political link between multilateral and regional

trade liberalization. As potentially negative e¤ects, regionalism might dampen na-

tions�enthusiasm for multilateral liberalization, divert policy makers�attention away

frommultilateral liberalization and create tensions between trading blocs. As poten-

tially positive e¤ects, regionalism might relax political resentments between trading

blocs, serve as an experimental ground for new or controversial issues and improve

a country�s international reputation.15

It is beyond the scope of this paper to solve for a full dynamic and game-theoretic

equilibrium based on these factors. By contrast, we presume two empirical reaction

functions for multilateral and regional liberalization. In particular, we are interested

in the question how multilateral liberalization responds to a regional trade liberaliza-

tion stimulus, and vice versa. Therefore, we construct a VARmodel consisting of the

auto-regressive processes of two time series, namely the yearly time-speci�c impact

of both GATT/WTO �t and RTAs #t on international trade.16 The corresponding

bivariate model can be formulated as24 �t#t

35 =24 c1c2

35+24 d1td2t

35+24 �11(L) �12(L)

�21(L) �22(L)

3524 �t#t

35+24 u1tu2t

35 (3)

where L is the backshift-operator with �ij(L) = �1ijL1 + ::: + �pijL

p for i; j =

1; 2; 3. p denotes the lag order, ci is a constant, while di;t is a time dummy, and ui;t

represents the error term with i = f1; 2g. The time series VAR model is assumed

to be covariance stationary. The error term vector is i.i.d. with mean zero and

unknown non-singular residual covariance matrix E(utu0t) =P

u and existing fourth

moments.17 Consequently, we allow for contemporaneous correlation in the residuals

but no auto-correlation.

Evidently, this is an inherently reduced-form approach. As has been discussed

above, we identify several mechanisms that can a¤ect the interrelation between re-

15For a discussion see e. g. Baldwin (2004), Bhagwati (1992), Krueger (1995), Panagariya (1999),Summers (1991), Creamer (2003), Pomfret (2006), Bergsten (1997), and Folsom (2008).16For a detailed discussion of VAR models see e. g. Lütkepohl and Krätzig (2004) or Hamilton

(1994).17See Lütkepohl and Krätzig, 2004, chapter 3.2.

9

gional and multilateral trade liberalization. We cannot determine the magnitude of

each e¤ect separately, but instead estimate the net e¤ect of the combined mecha-

nisms. For instance, if the sum of coe¢ cients �12(L)#t on �t is negative, then the

negative e¤ects, such as the diversion of policy makers� attention, dominate and

RTAs have a negative e¤ect on multilateral trade liberalization. If they are pos-

itive, then the positive in�uences, such as the generation of reputation, are more

important, i. e. RTAs have a positive e¤ect on multilateral trade liberalization.

Step Three: Impulse-Response-Functions and Granger-causality Analy-

sis

In a third step, we analyze the causal interrelation between regional and multi-

lateral trade liberalization. We investigate the reaction of GATT/WTO trade lib-

eralization on RTA liberalization and vice versa using impulse-response-functions.

Impulse-response-functions trace out the expected response of yit+s to a unit change

in yjt, holding constant all past values of yt.18 In particular, one can use impulse-

response-functions to investigate the response of one event to the impact of the

other.

While the signs of the individual coe¢ cients are of some interest, our primary

focus is testing � overall �whether trade liberalization on one institutional level

causes trade liberalization on the other institutional level. That is, can we reasonably

say that GATT/WTO liberalizes trade as a reaction to RTA trade liberalization,

and vice versa? Our main empirical tool for doing so is the Granger-causality test.

Following Granger (1969), variable X causes variable Y if the forecasting of the

latter is improved by incorporating in the analysis information concerning X and its

past.19 In our case, we test the following two null hypotheses:

H0: RTAs do not Granger-cause GATT/WTO, i. e. the coe¢ cients of

the matrix blocs �12(L) are zero.

H0: GATT/WTO does not Granger-cause RTAs, i. e. the coe¢ cients

of the matrix blocs �21(L) are zero.

18See Lütkepohl and Krätzig, 2004, chapter 4.3.19A Wald test statistic is used in conjunction with an F distribution for testing the restrictions

(Lütkepohl and Krätzig, 2004, chapter 3.7).

10

Data

Our gravity model analysis is based on a sample that covers 184 countries with 240

RTAs over the period from 1953 to 2006 with annual data.20 We de�ne GATT/WTO

membership according to Tomz et al. (2007) and include RTAs following WTO

(2009c) and McGill (2009). The democracy variable polity is taken from the Polity

IV data set by Marshall and Jaggers (2009).

For the VAR analysis, we use the two series obtained from the gravity model

estimation.21 The Akaike information criterion (AIC) (Akaike 1973, 1974) suggests

the endogenous lag order 3 for the bivariate model. In order to save degrees of

freedom, we take advantage of sequential elimination algorithms which preselect

the speci�c lagged variables that are to be estimated in the model (Lütkepohl and

Krätzig, 2004, and Brüggemann and Lütkepohl, 2001). The speci�ed VAR model is

estimated by OLS using the software package JMulti.

3 Empirical Results

Gravity Equation

The gravity equation is estimated with FE-PML with the results being reported

in table 1. Note that the results from the FE-PML estimation are robust to FE-

PML with only non-zero observations (intensive trade margin) in table 9, while

they di¤er from the traditional FE-OLS estimation displayed in table 10 (both in

the appendix).

Regarding the control variables, the coe¢ cient estimates meet the expectations

and are in line with the standard gravity literature. In particular GATT/WTO

membership of only one trading partner has a signi�cantly positive impact on bi-

lateral trade. The imports of GATT/WTO members from non-participants (Im-

porter in GATT/WTO) are about 52% higher (exp(0.42)-1), while the exports of

GATT/WTO members to non-participants (Exporter in GATT/WTO) increase

20The list of countries as well as data sources are reported in tables 6 and 7.21The tests on the two realizations�stationarity can be found in table 11 in the appendix.

11

Dependent Variable:Real imports ij Coef. S. E. Coef. S. E.

Importer in GATT/WTO 0.42*** 0.02 …  c ontinued …Ex porter in  GATT/W TO 0.41*** 0.02 Both in GATT/W TO 1998 0.89*** 0.02GSP­recipient­ex ports ­0.12*** 0.01 Both in GATT/W TO 1999 0.89*** 0.02GSP­donor­exports ­0.04*** 0.01 Both in GATT/W TO 2000 0.79*** 0.02Importer in RTA 0.07*** 0.00 Both in GATT/W TO 2001 0.94*** 0.02Ex porter in  RTA ­0.03*** 0.00 Both in GATT/W TO 2002 0.90*** 0.02Log real GDPi 0.59*** 0.01 Both in GATT/W TO 2003 0.83*** 0.02

Log real GDPj 0.74*** 0.01 Both in GATT/W TO 2004 0.79*** 0.02

Log real GDPPCi 0.22*** 0.01 Both in GATT/W TO 2005 0.66*** 0.03Log real GDPPCj 0.41*** 0.01 Both in GATT/W TO 2006 0.59*** 0.10

Log RER ij ­0.09*** 0.01 Both in RTA 1957 ­0.28*** 0.05Currently  c olonized 0.34*** 0.13 Both in RTA 1958 ­0.21*** 0.05Polity i 0.01*** 0.01 Both in RTA 1959 ­0.11** 0.04

Polity j 0.01*** 0.01 Both in RTA 1960 ­0.13*** 0.03Both in GATT/W TO 1953 0.41*** 0.05 Both in RTA 1961 ­0.10*** 0.03Both in GATT/W TO 1954 0.43*** 0.05 Both in RTA 1962 ­0.08*** 0.03Both in GATT/W TO 1955 0.46*** 0.04 Both in RTA 1963 0.00 0.02Both in GATT/W TO 1956 0.44*** 0.04 Both in RTA 1964 0.01 0.02Both in GATT/W TO 1957 0.42*** 0.04 Both in RTA 1965 0.01 0.02Both in GATT/W TO 1958 0.36*** 0.04 Both in RTA 1966 0.01 0.02Both in GATT/W TO 1959 0.43*** 0.04 Both in RTA 1967 0.02 0.02Both in GATT/W TO 1960 0.87*** 0.04 Both in RTA 1968 0.03 0.02Both in GATT/W TO 1961 0.79*** 0.04 Both in RTA 1969 0.11*** 0.02Both in GATT/W TO 1962 0.80*** 0.04 Both in RTA 1970 0.05*** 0.02Both in GATT/W TO 1963 0.81*** 0.04 Both in RTA 1971 0.08*** 0.02Both in GATT/W TO 1964 0.78*** 0.04 Both in RTA 1972 0.11*** 0.01Both in GATT/W TO 1965 0.88*** 0.04 Both in RTA 1973 0.11*** 0.01Both in GATT/W TO 1966 0.87*** 0.03 Both in RTA 1974 0.06*** 0.01Both in GATT/W TO 1967 0.87*** 0.03 Both in RTA 1975 0.07*** 0.01Both in GATT/W TO 1968 0.86*** 0.03 Both in RTA 1976 0.10*** 0.01Both in GATT/W TO 1969 0.86*** 0.03 Both in RTA 1977 0.09*** 0.01Both in GATT/W TO 1970 1.00*** 0.03 Both in RTA 1978 0.15*** 0.01Both in GATT/W TO 1971 0.99*** 0.03 Both in RTA 1979 0.17*** 0.01Both in GATT/W TO 1972 1.00*** 0.03 Both in RTA 1980 0.15*** 0.01Both in GATT/W TO 1973 0.91*** 0.03 Both in RTA 1981 0.09*** 0.01Both in GATT/W TO 1974 0.78*** 0.02 Both in RTA 1982 0.15*** 0.01Both in GATT/W TO 1975 0.67*** 0.02 Both in RTA 1983 0.19*** 0.01Both in GATT/W TO 1976 0.66*** 0.02 Both in RTA 1984 0.13*** 0.01Both in GATT/W TO 1977 0.69*** 0.02 Both in RTA 1985 0.19*** 0.01Both in GATT/W TO 1978 0.72*** 0.02 Both in RTA 1986 0.22*** 0.01Both in GATT/W TO 1979 0.73*** 0.02 Both in RTA 1987 0.29*** 0.01Both in GATT/W TO 1980 0.64*** 0.02 Both in RTA 1988 0.27*** 0.01Both in GATT/W TO 1981 0.57*** 0.02 Both in RTA 1989 0.38*** 0.01Both in GATT/W TO 1982 0.57*** 0.02 Both in RTA 1990 0.37*** 0.01Both in GATT/W TO 1983 0.64*** 0.02 Both in RTA 1991 0.39*** 0.01Both in GATT/W TO 1984 0.77*** 0.02 Both in RTA 1992 0.36*** 0.01Both in GATT/W TO 1985 0.81*** 0.02 Both in RTA 1993 0.31*** 0.01Both in GATT/W TO 1986 0.96*** 0.02 Both in RTA 1994 0.35*** 0.01Both in GATT/W TO 1987 0.99*** 0.02 Both in RTA 1995 0.36*** 0.01Both in GATT/W TO 1988 1.00*** 0.02 Both in RTA 1996 0.35*** 0.01Both in GATT/W TO 1989 0.88*** 0.02 Both in RTA 1997 0.32*** 0.01Both in GATT/W TO 1990 1.02*** 0.02 Both in RTA 1998 0.32*** 0.01Both in GATT/W TO 1991 1.02*** 0.02 Both in RTA 1999 0.34*** 0.01Both in GATT/W TO 1992 0.99*** 0.02 Both in RTA 2000 0.34*** 0.01Both in GATT/W TO 1993 0.75*** 0.02 Both in RTA 2001 0.29*** 0.01Both in GATT/W TO 1994 0.75*** 0.02 Both in RTA 2002 0.29*** 0.01Both in GATT/W TO 1995 0.76*** 0.02 Both in RTA 2003 0.27*** 0.01Both in GATT/W TO 1996 0.78*** 0.02 Both in RTA 2004 0.32*** 0.01Both in GATT/W TO 1997 0.81*** 0.02 Both in RTA 2005 0.31*** 0.01…  to be continued … Both in RTA 2006 0.40*** 0.01No. of observations 526874No. of country ­pa irs 17332Wald­sta tistic 550196.08Log likel ihood ­303290.15*** denotes significance on 1%­level , ** 5%­level, * 10%­level.Al l estimations enclos e y ear and c ountry­pair dummies. Constants are not reported.

FE­PML

Table 1: Gravity model estimation.

12

by 51%. This result has been interpreted as a public goods or selection e¤ect of

GATT/WTO membership (e. g. Subramanian and Wei, 2007, p. 165).

The Generalized System of Preferences negatively a¤ects both the exports of

GSP recipients (GSP-recipient-exports) as well as the exports of granting countries

(GSP-donor-exports). This result seems to be counter-intuitive since GSP programs

are intended to foster developing countries� exports by granting preferred import

duties on selected products by industrialized countries (UNCTAD, 2008). However,

the literature discusses several problems inherent to GSP schemes which might lead

to disincentives causing distortions in the economic structure and trading patterns

of GSP recipients in the long-run (e. g. Hoekman and Özden, 2005, Dowlah, 2008).

The imports of RTA members from non-members (Importer in RTA) are stimu-

lated by about 7% while the exports of RTA members to non-participants (Exporter

in RTA) are around -3% lower due to trade diversion. It should be noted that the

results average out the e¤ects of the 240 RTAs covered by the data set, where the

impact of the di¤erent RTAs is likely to vary.

The results for the remaining time-variant control variables are generally in line

with expectations. The economic size of the trading partners substantially con-

tributes to bilateral trade. Capital-intensive production, depicted by GDP per

capita, stimulates trade. A high capital-labor ratio indicates a more di¤erentiated

economic structure which should contribute to better trading opportunities. A de-

valuation of the importing country�s real exchange rate has a negative impact on

imports, while current colonial relationships foster contemporaneous trade by about

40%. More democratic nations seem to trade signi�cantly more than autocratic na-

tions as both polity variables indicate �a result which is consistent with the �ndings

of Decker and Lim (2009) and Eichengreen and Leblang (2007).

The yearly time-speci�c e¤ects of both GATT/WTO and RTAs are displayed in

�gure 1 together with the 95% con�dence intervals (also table 1).

The yearly time-speci�c impact of GATT/WTO (solid line) �uctuates around

the average of 0.78 which indicates that trade among GATT/WTO members is

about twice as large (118%)22 as trade between non-members. In contrast, the

22The elasticity expressed in percent is calculated by exp(0.66)-1.

13

Data­ Deterministic No. of ADFtransformation term Lags test­statistic 10% 5% 1%

GATT/WTO­effect level constant L 0 ­2.57* ­2.57 ­2.86 ­3.43level constant L 1 ­2.83*level constant L 2 ­2.65*level constant L 3 ­2.73*

RTA­effect level constant, time trend L 0 ­3.41** ­3.13 ­3.41 ­3.96level constant, time trend L 1 ­3.42**level constant, time trend L 2 ­3.66**level constant, time trend L 3 ­3.22*

*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Critical values

Null­hypothesis Test statistics P­valuePortmanteau­test no residual autocorrelation 8.07 0.78LM­test for autocorrelation   in AR models

no residual autocorrelation 17.84 0.60

Lomnicki­Jarque­Bera­test   for nonnormality

residuals are consistent witha standard normal distribution

   u1 0.89 0.64   u2 0.94 0.62Multivariate ARCH­LM­test no conditional heteroskedasticity 40.60 0.66Univariate ARCH­LM­test no conditional heteroskedasticity   u1 0.72 0.98   u2 1.24 0.94

Figure 1: Yearly time-speci�c impact of GATT/WTO and RTAs on trade.

time-speci�c point estimates of RTAs (dashed line) follow a concave function with

signi�cantly negative values for the 1950s and signi�cantly positive values since the

beginning of the 1960s. The early negative e¤ects might be explained by the fact

that with the exception of the European Common Market most RTAs were inef-

fectively implemented and eventually failed during that period (Pomfret, 2007, and

Panagariya, 1999). In addition, these �rst attempts of the so-called �rst wave of re-

gionalism during the 1950s/1960s were characterized by �shallow�integration which

did not get beyond mere tari¤ liberalization and rarely took place between developed

and less developed countries but rather among countries with similar income level

(Limao, 2007, WTO, 2009c, and McGill, 2009). Generally, the e¤ects of RTAs on

international trade were limited. With the ongoing process of regional liberalization

and the wider scope of RTA liberalization, the e¤ect of RTAs on members�trade

increased signi�cantly.

The time-speci�c impact of GATT/WTO is more volatile and has two periods

with particularly strong e¤ects, namely 1960-1973 and 1985-1992. By contrast, the

point estimates of RTAs develop in a relatively steady way, except for the period

1985-1992, when the RTA-e¤ect increased somewhat more strongly. This coincides

with the so-called second wave of regionalism which was initiated at the beginning

14

of the 1980s when the United States turned away from the multilateral approach

and promoted the North American Free Trade Agreement (NAFTA). The second

wave was also stimulated by the completion of the European Community�s (EC)

internal market in 1992 (Ethier, 1998, Pomfret, 2007, and Panagariya, 1999). The

third wave of regionalism started at the beginning of the 2000s and does not seem

to have any major e¤ects neither on the impact of RTAs on international trade nor

on the e¤ect of GATT/WTO.

VAR Estimation

We use the two time series obtained from the gravity analysis to estimate the e¤ect

of multilateral on regional trade liberalization and vice versa. In particular, we set

up a bivariate VAR model regressing the two variables on their past values. Due

to the limited number of observations, we employ sequential elimination algorithms

which preselect the speci�c lagged variables that are to be estimated in the model

(Lütkepohl and Krätzig, 2004, and Brüggemann and Lütkepohl, 2001).23

The results of the VAR model estimated with three lags are shown in table

2. The left-hand panel shows the trade e¤ect of multilateral trade liberalization

(GATT/WTO-e¤ect) as dependent variable, with lagged values of multilateral and

regional trade liberalization as the explanatory variables. The right-hand panel dis-

plays the trade impact of regional trade liberalization as the dependent variable, with

lagged values of any multilateral and regional trade liberalization as the explanatory

variables.

The results of the system equation regression indicate that both the net e¤ect of

previous multilateral liberalization as well as the net e¤ect of regional liberalization

on contemporaneous multilateral liberalization are signi�cantly positive. Similarly,

the net e¤ects of multilateral and regional liberalization on subsequent regional

liberalization are signi�cantly positive.

23According to standard residual tests, such as the Portmanteau test (Ljung and Box, 1978)and the Breusch-Godfrey LM test (e. g. Godfrey, 1988), residual auto-correlation is not indicated(see table 12 in the appendix). The Lomnicki-Jarque-Bera tests (Lomnicki, 1961, and Jarqueand Bera, 1987) suggest that both u1 and u2 are consistent with a standard normal distribution.Additionally, the ARCH-LM tests (Engle, 1982) assure heteroskedasticity-consistent estimation, i.e. we can continue applying the speci�ed VAR model.

15

GATT/WTO­effect

RTA­effect

GATT/WTO­effectt ­1 0.807*** 0.102**(0.126) (0.050)

GATT/WTO­effectt ­2 ­0.273** ­0.095(0.119) (0.062)

GATT/WTO­effectt ­3 0.081*(0.047)

RTA­effect t ­1 1.600*** 0.635***(0.358) (0.111)

RTA­effect t ­2 ­1.126***(0.361)

RTA­effect t ­3

Constant 0.417*** ­0.086**(0.116) (0.044)

Trend ­0.005 0.003***(0.003) (0.001)

*** denotes significance on 1%­level, ** 5%­level, * 10%­level.Sample range [1957, 2006]. SD in parentheses.

Model 1

Causality direction Model 1GATT/WTO => RTA   Wald­test­statistic1 1.32   P­value 0.27   ηGATT/WTO?RTAs (+)RTA => GATT/WTO   Wald­test­statistic2 5.65***   P­value 0.01   ηRTA s? GATT/WTO +1 Wald­test that GATT/WTO does not Granger­cause RTAs.2 Wald­test that RTAs do not Granger­cause GATT/WTO.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Table 2: VAR estimation, core model.

Impulse-Response-Functions and Granger-causality analysis

We investigate the dynamic interrelations between multilateral and regional liber-

alization by using forecast error impulse-response-functions (IRF). The left panel of

�gure 2 illustrates how the expected e¤ect of multilateral trade liberalization under

GATT/WTO reacts to a unit change of the trade e¤ect of regional liberalization

while the right panel shows the response of regional liberalization to a unit change

in multilateral liberalization.24

The results indicate that multilateral trade liberalization reacts in a statistically

signi�cant way in the �rst and second (and third according to Hall�s percentile) year

after regional trade liberalization has taken place (left-hand panel). Technically

speaking, if RTAs liberalize trade so that the trade volume increases by one unit,

the expected response of GATT/WTO is multilateral trade liberalization associated

with a trade increase by 1.6 units. Intuitively, an RTA-induced increase in trade is

followed by multilateral trade liberalization in the subsequent years, whereby this

GATT/WTO driven liberalization response is even stronger in the �rst subsequent

year than the regional liberalization stimulus. This �nding might not only support

24The plots also contain the 90% Efron and Hall percentiles con�dence intervals which arebootstrapped with 2000 replications (B=2000) over 10 periods (h=10).

16

RTA_coef ­­> WTO_coef WTO_coef ­­> RTA_coef

­1,00

0,00

1,00

2,00

3,00

0 1 2 3 4 5 6 7 8 9 10VAR forecast effor impulse responses90% Efron Percentile CI (B=2000, h=10)90% Hall Percentile CI (B=2000, h=10)

­0,10

­0,05

0,00

0,05

0,10

0,15

0,20

0,25

0 1 2 3 4 5 6 7 8 9 10VAR forecast effor impulse responses90% Efron Percentile CI (B=2000, h=10)90% Hall Percentile CI (B=2000, h=10)

Causality direction Model 23 Model 34 Model 45 Model 56 Model 67

GATT/WTO => RTA   Wald­test­statistic1 5.31*** 1.77 5.78*** 3.01** 3.54**   P­value 0.00 0.16 0.00 0.04 0.02   ηG ATT/WTO? RTAs + (+) + + +RTA => GATT/WTO   Wald­test­statistic2 6.54*** 7.42*** 7.55*** 5.20*** 9.09***   P­value 0.00 0.00 0.00 0.00 0.00   ηR TAs?G ATT/WTO ­ + + + +1 Wald­test that GATT/WTO does not Granger­cause RTAs.2 Wald­test that RTAs do not Granger­cause GATT/WTO.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.3 Added variables: Number of GATT/WTO members, number of RTAs.4 Added variables: Real world GDP.5 Added variables: KOF globalization index6 Added variables: No. of GATT/WTO members, no. of RTAs, real world GDP.7 Added variables: No. of GATT/WTO members, no. of RTAs, real world GDP, globalization index.

Figure 2: Impulse-response-functions.

Model 7 Model 8 Model 9 Model 10GATT/WTO => RTA   Wald­test­statistic1 0.61 1.23 0.97 0.60   P­value 0.54 0.30 0.43 0.72   ηGA TT/WTO?R TA sRTA => GATT/WTO   Wald­test­statistic2 10.06*** 5.10*** 2.66** 2.20**   P­value 0.00 0.00 0.03 0.05   ηRTAs?GA TT/WTO1 Wald­test that GATT/WTO does not Granger­cause RTAs.2 Wald­test that RTAs do not Granger­cause GATT/WTO.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Wald­test­statistic P­value Wald­test­statistic P­valueModel 1 5.65*** 0.01 1.32 0.27*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 2 6.54*** 0.00 5.31*** 0.00

Model 3 7.42*** 0.00 1.77 0.16

Model 4 7.55*** 0.00 5.78*** 0.00

Model 5 5.20*** 0.00 3.01** 0.04

Model 6 9.09*** 0.00 3.54** 0.02*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 7 10.06*** 0.00 0.61 0.54

Model 8 5.10*** 0.00 1.23 0.30

Model 9 2.66** 0.03 0.97 0.43

Model 10 2.20** 0.05 0.60 0.72*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Table 3: Granger-causality tests.

the hypothesis of double trade activism, where countries use regional as well as

multilateral institutions as complements to liberalize trade (Trejos, 2005), but also

indicates that RTA liberalization might be a promotive impulse so that even stronger

multilateral trade liberalization is possible.

The Granger-causality test indicates that RTA liberalization Granger-causes

multilateral trade liberalization under GATT/WTO (table 3).

The lack of any negative and statistically signi�cant coe¢ cients in the impulse-

response-function suggests that regional trade liberalization does not have a net neg-

ative e¤ect on multilateral trade liberalization. This �nding supports the Summers

(1991)-hypothesis which emphasizes the positive impact of regional arrangements on

the MTS. According to Bergsten (1997), RTAs are able to detent political tensions

between nations, e. g. in Europe after WW II, which can also alleviate multilateral

negotiations. Additionally, RTAs can stimulate both internal and international ne-

gotiation dynamics. Since RTAs provide for more �exible and e¢ cient negotiations

than multilateral agreements, RTAs can serve as testing �elds for new liberaliza-

17

tion ideas which can subsequently be negotiated in the multilateral setting, e. g.

services or intellectual property rights (WTO, 2009c, Trejos, 2005, Pomfret, 2006,

Bergsten, 1997, and Folsom, 2008). Moreover, members of RTAs can gain stability

and reputation through their commitment to regional arrangements which is likely

to have positive e¤ects for negotiations on the multilateral level, like in the case of

the Southern Common Market (MERCOSUR) (Paiva and Gazel, 2003).

When analyzing possible e¤ecfs of multilateral on regional trade liberalization,

the right-hand panel of �gure 2 to some degree suggests that regional trade liberal-

ization might respond in a positive way to multilateral trade liberalization. However,

evidence on the causality tests (table 3) indicate that GATT/WTO liberalization

does not Granger-cause RTA liberalization, overall. This result might be interpreted

that regional liberalization additional to multilateral liberalization is not an attrac-

tive option.

Taken together, the Granger-causality analyses indicate an unidirectional causal-

ity relation from GATT/WTO to RTAs. An increase in RTA trade liberalization

stimulates multilateral trade liberalization by GATT/WTO, however not vice versa.

We can respond to the question of Bhagwati and Panagariya (1996) that the rela-

tionship between the multilateral system and regionalism is characterized by an

asymmetric friendship.

Are the Results Robust to Models Controlling for Global Developments?

Generally, causality in a bivariate analysis could be due to omitted variables. To

avoid incorrect inferences, we integrate several additional variables in our vector

auto-regressive (VAR) system and test the causality relations again. In particular,

we complement the VAR model with several variables controlling for the number of

GATT/WTOmembers and the number of RTAs (model 2), and indicators of general

globalization developments, such as world GDP (model 3) or the KOF globalization-

index (model 4). Since the KOF globalization-index is only available for the years

since 1970 and thus restricts the analysis to the period 1970-2006, we include all

control variables except the globalization-index in model 5, while we incorporate all

control variables together with the globalization-index in model 6. The results of

18

Model 7 Model 8 Model 9 Model 10GATT/WTO => RTA   Wald­test­statistic1 0.61 1.23 0.97 0.60   P­value 0.54 0.30 0.43 0.72   ηGA TT/WTO?R TA sRTA => GATT/WTO   Wald­test­statistic2 10.06*** 5.10*** 2.66** 2.20**   P­value 0.00 0.00 0.03 0.05   ηRTAs?GA TT/WTO1 Wald­test that GATT/WTO does not Granger­cause RTAs.2 Wald­test that RTAs do not Granger­cause GATT/WTO.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Wald­test­statistic P­value Wald­test­statistic P­valueModel 1 5.65*** 0.01 1.32 0.27*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 2 6.54*** 0.00 5.31*** 0.00

Model 3 7.42*** 0.00 1.77 0.16

Model 4 7.55*** 0.00 5.78*** 0.00

Model 5 5.20*** 0.00 3.01** 0.04

Model 6 9.09*** 0.00 3.54** 0.02*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 7 10.06*** 0.00 0.61 0.54

Model 8 5.10*** 0.00 1.23 0.30

Model 9 2.66** 0.03 0.97 0.43

Model 10 2.20** 0.05 0.60 0.72*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Table 4: Granger-causality tests for models with additional explanatory variables.

the IRFs are shown in �gure 3 while the corresponding Granger-causality tests are

reported in table 4.25

Generally, the impulse-response-fuctions support the �ndings obtained from the

core section. If RTAs liberalize trade, the expected response of GATT/WTO is mul-

tilateral trade liberalization (left-hand panel, �gure 3). According to the causality

tests, we �nd evidence that regional trade liberalization signi�cantly Granger-causes

multilateral trade liberalization (table 4). However, two results are of particular in-

terest. Regarding model 2, we �nd a signi�cantly positive response of GATT/WTO

liberalization initially, while it becomes signi�cantly negative in periods 3-4 (�gure

3). This might indicate that initially the positive implications of RTA liberalization

outweigh the negative transmission mechanisms so that the net e¤ect is positive,

while the net e¤ect becomes negative in later periods possibly because the e¤orts

associated with double trade activism are hard to maintain over a longer time pe-

riod. With respect to model 6, the reactions seem to emerge in cycles, i. e. in period

1, 4 and 8. This sawtooth pattern might indicate a liberalization process which

alternates between regional and multilateral trade liberalization.

The impulse-response-functions on the right hand-side indicate a positive re-

sponse of RTA liberalization on multilateral liberalization (�gure 3). In contrast

to the core analysis above, we �nd support for the hypothesis that GATT/WTO

liberalization Granger-causes regional trade liberalization (table 4), although the

response of GATT/WTO liberalization to RTA liberalization is much stronger than

vice versa. Three �ndings might be of interest. The IRF of model 2 indicates that

25The corresponding estimation results are reported in table 13 in the appendix.

19

Figure 3: Impulse-response-funcions, models 2-6.

20

the reaction of regional liberalization on multilateral liberalization is positive in

the �rst subsequent year, and additionally in periods 3-5 with a break in period 2.

Regarding model 3, regional liberalization seems to respond to a certain degree to

multilateral liberalization (�gure 3), while we �nd no evidence of Granger-causality

(table 4). Referring to model 6, we �nd an alternating response of regional trade

liberalization on GATT/WTO liberalization �similar to the pattern in the opposite

direction.

Summarizing, we �nd �based on Granger-causality tests �a so-called feedback

relationship between multilateral and regional trade liberalization, overall. The re-

sults indicate that multilateral trade liberalization responds in a signi�cantly posi-

tive way during the �rst years after regional liberalization has taken place. Likewise,

RTA liberalization reactions are signi�cantly positive during the �rst periods after

multilateral liberalization has taken place. However, only in model 2 we �nd a sig-

ni�cantly negative response of GATT/WTO to RTA liberalization in a later period,

while GATT/WTO liberalization reacts positive on RTA liberalization also in later

periods. Additionally, multilateral liberalization seems not to Granger-cause RTA

liberalization in model 3.

Are the Results Robust to Variation in the Model�s Lag-Length?

This section tests whether the results are robust to a variation in the VAR model�s

lag order. In particular, we consider a shorter model accounting for two lags (model

7) as well as models with higher order incorporating 4-6 lags (models 8-10). The IRFs

are displayed in �gure 4 while the corresponding results of the Granger-causality

tests are reported in table 5.26

The impulse-response-functions on the left hand-side clearly indicate that multi-

lateral liberalization responds in a signi�cantly positive way to regional trade liberal-

ization during the �rst two to three subsequent years across the various estimations.

Seemingly, the reaction e¤ect of WTO liberalization to RTA liberalization is not

sensitive to various lag orders of our VAR model, neither is the magnitude of the

reaction e¤ect. Generally, it seems that the duration of the reaction e¤ect tends to

26The estimation results of the corresponding VAR regressions are presented in table 14 (seeappendix).

21

RTA_coef ­­> WTO_coef WTO_coef ­­> RTA_coef

­1,00­0,500,000,501,001,502,002,50

0 1 2 3 4 5 6 7 8 9 10

­0,10

­0,05

0,00

0,05

0,10

0,15

0 1 2 3 4 5 6 7 8 9 10

­0,15­0,10

­0,05

0,00

0,05

0,10

0,15

0 1 2 3 4 5 6 7 8 9 10

­1,00­0,500,000,501,001,502,002,50

0 1 2 3 4 5 6 7 8 9 10

­2,00

­1,00

0,00

1,00

2,00

3,00

0 1 2 3 4 5 6 7 8 9 10

­0,20

­0,10

0,00

0,10

0,20

0,30

0 1 2 3 4 5 6 7 8 9 10

­0,20

­0,10

0,00

0,10

0,20

0,30

0 1 2 3 4 5 6 7 8 9 10VAR forecast effor impulse responses90% Efron Percentile CI (B=2000, h=10)90% Hall Percentile CI (B=2000, h=10)

­2,00

0,00

2,00

4,00

0 1 2 3 4 5 6 7 8 9 10VAR forecast effor impulse responses90% Efron Percentile CI (B=2000, h=10)90% Hall Percentile CI (B=2000, h=10)

Model 7

Model 8

Model 9

Model 10

Figure 4: Impulse-response-functions, model 7-10.

Model 7 Model 8 Model 9 Model 10GATT/WTO => RTA   Wald­test­statistic1 0.61 1.23 0.97 0.60   P­value 0.54 0.30 0.43 0.72   ηGA TT/WTO?R TA sRTA => GATT/WTO   Wald­test­statistic2 10.06*** 5.10*** 2.66** 2.20**   P­value 0.00 0.00 0.03 0.05   ηRTAs?GA TT/WTO1 Wald­test that GATT/WTO does not Granger­cause RTAs.2 Wald­test that RTAs do not Granger­cause GATT/WTO.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Wald­test­statistic P­value Wald­test­statistic P­valueModel 1 5.65*** 0.01 1.32 0.27*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 2 6.54*** 0.00 5.31*** 0.00

Model 3 7.42*** 0.00 1.77 0.16

Model 4 7.55*** 0.00 5.78*** 0.00

Model 5 5.20*** 0.00 3.01** 0.04

Model 6 9.09*** 0.00 3.54** 0.02*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Wald­test­statistic P­value Wald­test­statistic P­valueModel 7 10.06*** 0.00 0.61 0.54

Model 8 5.10*** 0.00 1.23 0.30

Model 9 2.66** 0.03 0.97 0.43

Model 10 2.20** 0.05 0.60 0.72*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

GATT/WTO => RTARTA => GATT/WTO

Table 5: Granger-causality tests according to various lag orders.

22

last longer for VAR models with higher lag order: For models with two or three lags

(model 1 and 7), the response of multilateral liberalization is signi�cantly positive

during the �rst two subsequent years after RTA liberalization. In comparison, for

models with four or more lags (models 8-10), the response of multilateral liberal-

ization is signi�cantly positive during the �rst three subsequent years after RTA

liberalization. Additionally, we �nd robust evidence that regional trade liberaliza-

tion Granger-causes multilateral liberalization, although the signi�cance decreases

with an increase in the lag order.

The IRFs on the right hand-side robustly indicate that regional liberalization

does not respond to multilateral liberalization. In fact, none of the reactions are

signi�cantly di¤erent from zero. Additionally, we �nd no support for the hypothesis

that GATT/WTO liberalization Granger-causes RTA liberalization.

4 Conclusion

Since the �rst wave of regionalism until the 1980s, discussion on regionalism was

characterized by static trade creation and trade diversion e¤ects of regional trade

agreements (RTAs). With the second wave of regionalism during the 1990s, the

debate on regionalism turned to the dynamic interrelation between regional integra-

tion and multilateral trade liberalization. In this context, Bhagwati and Panagariya

(1996) ask whether RTAs and the multilateral trading system are �strangers, friends,

or foes�? We �nd robust evidence that multilateral trade liberalization responds in

a signi�cantly positive way during the �rst years after regional trade liberalization.

Additionally, we �nd robust evidence that RTA liberalization signi�cantly Granger-

causes GATT/WTO liberalization. A sensitivity analysis indicates that these results

are robust to changes in control variables an the VAR model�s lag order. In con-

trast, our results do not robustly indicate that regional trade liberalization responds

in a signi�cantly positive way to multilateral trade liberalization. Summarizing, our

results suggest an unidirectional relationship between multilateralism and regional-

ism. While multilateral trade liberalization reacts signi�cantly positive to regional

trade liberalization whereby Granger-causality is signi�cant, this result does not

23

hold in the opposite direction. Using the terms of Bhagwati and Panagariya (1996),

we might call this relation an asymmetric friendship. At least, we can ensure that

regional trade liberalization does not react in a signi�cantly negative way to multi-

lateral trade liberalization.

24

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A Appendix

The control variables of the gravity model Xijt are de�ned as follows: Importer in

GATT/WTO (exporter in GATT/WTO) equals to one if only the importing (ex-

porting) country is a GATT/WTO member. GSP-recipient-exports (GSP-donor-

exports) accounts for a bilateral relationship under the Generalized System of Pref-

erences and is de�ned as one if the exporting (importing) country is granted the

GSP scheme from the importing (exporting) country. Importer in RTA (exporter in

RTA) is one for a pair of trading countries if only the importing (exporting) country

participates in a regional trade agreement. Log real GDP represents the economic

size of the trading partners measured as GDP in real terms. Log real GDPPC de-

notes real GDP per capita which can be interpreted as the capital-labour ratio. Log

RER depicts the logarithm of the bilateral real exchange rate de�ned in price nota-

tion. Currently colonized is de�ned as one if a country is currently colonized by its

trading partner. Polity is a measure for the polity regime and is scaled from +10

(strongly democratic) to -10 (strongly autocratic). Note that we do not include any

time-invariant variables as these drop out due to the �xed e¤ects estimation which

has emerged as the preferred model.

33

AFGHANISTAN GAMBIA NORWAYALBANIA GEORGIA OMANALGERIA GERMANY PAKISTANANGOLA GHANA PALAUANTIGUA AND BARBUDA GREECE PANAMAARGENTINA GRENADA PAPUA N.GUINEAARMENIA GUATEMALA PARAGUAYARUBA GUINEA PERUAUSTRALIA GUINEA­BISSAU PHILIPPINESAUSTRIA GUYANA POLANDAZERBAIJAN HAITI PORTUGALBAHAMAS HONDURAS QATARBAHRAIN HONG KONG ROMANIABANGLADESH HUNGARY RUSSIABARBADOS ICELAND RWANDABELARUS INDIA SAMOABELGIUM INDONESIA SAO TOME & PRINCIPEBELIZE IRAN SAUDI ARABIABENIN IRAQ SENEGALBERMUDA IRELAND SERBIA MONTENEGROBHUTAN ISRAEL SEYCHELLESBOLIVIA ITALY SIERRA LEONEBOSNIA HERZEGOVINA JAMAICA SINGAPOREBOTSWANA JAPAN SLOVAK REPUBLICBRAZIL JORDAN SLOVENIABRUNEI KAZAKHSTAN SOLOMON ISLANDSBULGARIA KENYA SOUTH AFRICABURKINA FASO KIRIBATI SPAINBURMA(Myanmar) KOREA,SOUTH(R) SRI LANKABURUNDI KUWAIT ST. KITTS&NEVISCAMBODIA KYRQYZ REPUBLIC ST.LUCIACAMEROON LAO PEOPLE'S DEM. REP. ST.VINCENT&GRECANADA LATVIA SUDANCAPE VERDE LEBANON SURINAMECENTRAL AFRICAN REP. LESOTHO SWAZILANDCHAD LIBERIA SWEDENCHILE LIBYA SWITZERLANDCHINA LITHUANIA SYRIACOLOMBIA LUXEMBOURG TAJIKISTANCOMOROS MACAO TANZANIACONGO, DEM. REP. OF (ZAIRE) MACEDONIA THAILANDCONGO, REP. OF MADAGASCAR TOGOCOSTA RICA MALAWI TONGACOTE D'IVORIE (IVORY COAST) MALAYSIA TRINIDAD&TOBAGOCROATIA MALDIVES TUNISIACYPRUS MALI TURKEYCZECH REPUBLIC MALTA TURKMENISTANDENMARK MAURITANIA UGANDADJIBOUTI MAURITIUS UKRAINEDOMINICA MEXICO UNITED ARAB EMIRATESDOMINICAN REP. MOLDVA UNITED KINGDOMECUADOR MONGOLIA UNITED STATESEGYPT MOROCCO URUGUAYEL SALVADOR MOZAMBIQUE UZBEKISTANEQUATORIAL GUINEA NAMIBIA VANUATUERITREA NEPAL VENEZUELAESTONIA NETHERLANDS VIETNAMETHIOPIA NEW CALEDONIA YEMEN, REPUBLIC OFFIJI NEW ZEALAND ZAMBIAFINLAND NICARAGUA ZIMBABWEFRANCE NIGERGABON NIGERIA

Table 6: List of countries.

34

Variable SourceBilateral exports IMF (2007a, 2007b)Nominal GDP (PPP) IMF (2008), Worldbank (2007), Heston et

al. (2006)Consumer price index IMF (2008), Worldbank (2007)(CPI, 2000=100)Population Maddison (2008), IMF (2008), Heston et

al. (2006)GATT/WTO-accession WTO (2009a, 2009b),

Tomz et al. (2007)GSP programs UNCTAD (1973-1986, 2001, 2005)Regional trade agreements WTO (2009c), McGill (2009)Colonial relationships, CIA (2007)common countryNominal exchange rate IMF (2008)Geographic distance, area, borders CEPII (2008)common language,landlocked, islandKOF Globalization-Index KOF (2009)Polity Marshall and Jaggers (2009)

Table 7: Data sources.

35

Variable Mean Std. Dev. Min Max Mean Std. Dev. Min MaxReal Imports 1,85 22,35 0 2628,72Log real imports 15,45 3,40 0,66 26,29Both in GATT/WTO 0,64 0,48 0 1 0,56 0,50 0 1Importer in GATT/WTO 0,16 0,37 0 1 0,19 0,39 0 1Exporter in GATT/WTO 0,16 0,36 0 1 0,19 0,39 0 1­­­­­­­­­­­­      ­­­­­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­GSP­recipient­exports 0,16 0,36 0 1 0,10 0,30 0 1GSP­donor­exports 0,16 0,37 0 1 0,10 0,30 0 1Both in RTA 0,12 0,32 0 1 0,08 0,27 0 1Importer in RTA 0,74 0,44 0 1 0,68 0,47 0 1Exporter in RTA 0,75 0,44 0 1 0,68 0,47 0 1­­­­­­­­­­­­      ­­­­­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­Log real GDPi 24,75 2,08 19,15 30,05 24,13 2,12 17,29 30,05Log real GDPj 24,97 1,99 18,81 30,05 24,21 2,08 18,81 30,05Log real GDPPCi 15,11 1,58 8,09 21,50 14,80 1,60 1,20 21,50Log real GDPPCj 15,10 1,56 8,09 21,50 14,81 1,60 1,20 21,50Log RERij 0,01 3,73 ­13,95 13,95 ­0,05 3,82 ­17,63 17,63­­­­­­­­­­­­      ­­­­­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­Currently colonized 0,00 0,04 0 1 0,00 0,03 0 1Ever colony 0,02 0,14 0 1 0,01 0,11 0 1Common country 0,01 0,08 0 1 0,00 0,06 0 1Log distance 8,58 0,87 2,35 9,89 8,72 0,80 2,35 9,90Log areai 11,93 2,40 3,22 16,65 11,71 2,50 3,22 16,65­­­­­­­­­­­­      ­­­­­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­ ­­­­­­­­­­Log areaj 12,15 2,31 3,22 16,65 11,78 2,47 3,22 16,65Contiguity 0,03 0,18 0 1 0,02 0,14 0 1Landlocked 0,26 0,48 0 2 0,33 0,52 0 2Island 0,34 0,54 0 2 0,41 0,57 0 2Common language 0,18 0,39 0 1 0,18 0,38 0 1

418112 observationsRestricted sample (imports>0) Full sample (imports?0)

776519 observations(imports 0)

Table 8: Summary statistics.

36

Dependent Variable:Real imports ij Coef. S. E. Coef. S. E.

Importer in GATT/WTO 0.26*** 0.02 …  c ontinued …Ex porter in  GATT/W TO 0.25*** 0.02 Both in GATT/W TO 1998 0.80*** 0.03GSP­recipient­ex ports ­0.12*** 0.01 Both in GATT/W TO 1999 0.80*** 0.03GSP­donor­exports ­0.10*** 0.01 Both in GATT/W TO 2000 0.71*** 0.03Importer in RTA 0.05*** 0.00 Both in GATT/W TO 2001 0.85*** 0.03Ex porter in  RTA ­0.04*** 0.00 Both in GATT/W TO 2002 0.82*** 0.03Log real GDPi 0.50*** 0.01 Both in GATT/W TO 2003 0.75*** 0.03

Log real GDPj 0.71*** 0.01 Both in GATT/W TO 2004 0.64*** 0.03Log real GDPPCi 0.23*** 0.01 Both in GATT/W TO 2005 0.52*** 0.03

Log real GDPPCj 0.45*** 0.01 Both in GATT/W TO 2006 0.46*** 0.10

Log RER ij ­0.13*** 0.01 Both in RTA 1957 ­0.33*** 0.05Currently  c olonized 0.74*** 0.07 Both in RTA 1958 ­0.25*** 0.05Polity i 0.01*** 0.00 Both in RTA 1959 ­0.15*** 0.05

Polity j 0.01*** 0.00 Both in RTA 1960 ­0.14*** 0.03Both in GATT/W TO 1953 0.20*** 0.06 Both in RTA 1961 ­0.11*** 0.03Both in GATT/W TO 1954 0.23*** 0.05 Both in RTA 1962 ­0.10*** 0.03Both in GATT/W TO 1955 0.30*** 0.05 Both in RTA 1963 ­0.01 0.03Both in GATT/W TO 1956 0.32*** 0.05 Both in RTA 1964 ­0.01 0.03Both in GATT/W TO 1957 0.31*** 0.04 Both in RTA 1965 0.00 0.03Both in GATT/W TO 1958 0.27*** 0.05 Both in RTA 1966 0.01 0.03Both in GATT/W TO 1959 0.35*** 0.05 Both in RTA 1967 0.02 0.02Both in GATT/W TO 1960 0.62*** 0.04 Both in RTA 1968 0.02 0.02Both in GATT/W TO 1961 0.60*** 0.04 Both in RTA 1969 0.11*** 0.02Both in GATT/W TO 1962 0.60*** 0.04 Both in RTA 1970 0.06*** 0.02Both in GATT/W TO 1963 0.55*** 0.04 Both in RTA 1971 0.08*** 0.02Both in GATT/W TO 1964 0.58*** 0.04 Both in RTA 1972 0.11*** 0.02Both in GATT/W TO 1965 0.61*** 0.04 Both in RTA 1973 0.10*** 0.02Both in GATT/W TO 1966 0.59*** 0.04 Both in RTA 1974 0.05*** 0.02Both in GATT/W TO 1967 0.57*** 0.04 Both in RTA 1975 0.04*** 0.02Both in GATT/W TO 1968 0.62*** 0.04 Both in RTA 1976 0.08*** 0.02Both in GATT/W TO 1969 0.61*** 0.04 Both in RTA 1977 0.07*** 0.01Both in GATT/W TO 1970 0.73*** 0.04 Both in RTA 1978 0.12*** 0.01Both in GATT/W TO 1971 0.75*** 0.03 Both in RTA 1979 0.15*** 0.01Both in GATT/W TO 1972 0.76*** 0.03 Both in RTA 1980 0.12*** 0.01Both in GATT/W TO 1973 0.69*** 0.03 Both in RTA 1981 0.07*** 0.01Both in GATT/W TO 1974 0.57*** 0.03 Both in RTA 1982 0.13*** 0.01Both in GATT/W TO 1975 0.46*** 0.03 Both in RTA 1983 0.17*** 0.01Both in GATT/W TO 1976 0.45*** 0.03 Both in RTA 1984 0.12*** 0.01Both in GATT/W TO 1977 0.48*** 0.03 Both in RTA 1985 0.17*** 0.01Both in GATT/W TO 1978 0.56*** 0.03 Both in RTA 1986 0.19*** 0.01Both in GATT/W TO 1979 0.59*** 0.03 Both in RTA 1987 0.25*** 0.01Both in GATT/W TO 1980 0.51*** 0.03 Both in RTA 1988 0.23*** 0.01Both in GATT/W TO 1981 0.43*** 0.03 Both in RTA 1989 0.33*** 0.01Both in GATT/W TO 1982 0.42*** 0.03 Both in RTA 1990 0.31*** 0.01Both in GATT/W TO 1983 0.49*** 0.03 Both in RTA 1991 0.29*** 0.01Both in GATT/W TO 1984 0.62*** 0.03 Both in RTA 1992 0.28*** 0.01Both in GATT/W TO 1985 0.66*** 0.03 Both in RTA 1993 0.26*** 0.01Both in GATT/W TO 1986 0.81*** 0.03 Both in RTA 1994 0.30*** 0.01Both in GATT/W TO 1987 0.83*** 0.03 Both in RTA 1995 0.31*** 0.01Both in GATT/W TO 1988 0.89*** 0.03 Both in RTA 1996 0.30*** 0.01Both in GATT/W TO 1989 0.74*** 0.03 Both in RTA 1997 0.27*** 0.01Both in GATT/W TO 1990 0.74*** 0.03 Both in RTA 1998 0.27*** 0.01Both in GATT/W TO 1991 0.75*** 0.03 Both in RTA 1999 0.30*** 0.01Both in GATT/W TO 1992 0.73*** 0.03 Both in RTA 2000 0.29*** 0.01Both in GATT/W TO 1993 0.63*** 0.03 Both in RTA 2001 0.24*** 0.01Both in GATT/W TO 1994 0.66*** 0.03 Both in RTA 2002 0.24*** 0.01Both in GATT/W TO 1995 0.68*** 0.03 Both in RTA 2003 0.21*** 0.01Both in GATT/W TO 1996 0.70*** 0.03 Both in RTA 2004 0.26*** 0.01Both in GATT/W TO 1997 0.74*** 0.03 Both in RTA 2005 0.26*** 0.01…  to be continued … Both in RTA 2006 0.35*** 0.01No. of observations 340415No. of country ­pairs 16482Wald­statistic 510323.71Log likel ihood ­284063.38*** denotes significance on 1%­level , ** 5%­level, * 10%­level.Al l estimations enclos e y ear and c ountry­pair dummies. Constants are not reported.

FE­PML (imports>0)

Table 9: Gravity model estimation; FE-PML intensive margin.

37

Dependent Variable:Log real  imports i j Coef. S. E. Coef. S. E.

Importer in GATT/WTO 0.10*** 0.02 …  c ontinued …Exporter in GATT/W TO 0.02 0.02 Both in GATT/W TO 1998 0.35*** 0.03GSP­rec ipient­exports ­0.15*** 0.01 Both in GATT/W TO 1999 0.23*** 0.03GSP­donor­exports ­0.03** 0.01 Both in GATT/W TO 2000 0.24*** 0.03Importer in RTA ­0.04*** 0.01 Both in GATT/W TO 2001 0.36*** 0.03Exporter in RTA ­0.05*** 0.01 Both in GATT/W TO 2002 0.35*** 0.03Log real  GDPi 0.59*** 0.01 Both in GATT/W TO 2003 0.26*** 0.04

Log real  GDPj 0.67*** 0.01 Both in GATT/W TO 2004 0.19*** 0.04

Log real  GDPPCi 0.11*** 0.01 Both in GATT/W TO 2005 0.21*** 0.05

Log real  GDPPCj 0.39*** 0.01 Both in GATT/W TO 2006 0.09*** 0.04

Log RER i j ­0.17*** 0.01 Both in RTA 1957 ­0.70** 0.28Currently colonized ­0.02 0.17 Both in RTA 1958 ­0.63** 0.28Polity i 0.01*** 0.00 Both in RTA 1959 ­0.57** 0.28

Polity j ­0.00 0.00 Both in RTA 1960 ­0.37*** 0.11Both in GATT/W TO 1953 0.31*** 0.09 Both in RTA 1961 ­0.31*** 0.11Both in GATT/W TO 1954 0.33*** 0.08 Both in RTA 1962 ­0.30*** 0.11Both in GATT/W TO 1955 0.44*** 0.08 Both in RTA 1963 ­0.28*** 0.11Both in GATT/W TO 1956 0.37*** 0.08 Both in RTA 1964 ­0.19* 0.11Both in GATT/W TO 1957 0.41*** 0.08 Both in RTA 1965 ­0.17* 0.10Both in GATT/W TO 1958 0.33*** 0.08 Both in RTA 1966 ­0.12 0.10Both in GATT/W TO 1959 0.35*** 0.08 Both in RTA 1967 ­0.11 0.10Both in GATT/W TO 1960 0.44*** 0.06 Both in RTA 1968 ­0.05 0.10Both in GATT/W TO 1961 0.47*** 0.06 Both in RTA 1969 0.02 0.09Both in GATT/W TO 1962 0.39*** 0.06 Both in RTA 1970 ­0.04 0.09Both in GATT/W TO 1963 0.32*** 0.05 Both in RTA 1971 0.01 0.09Both in GATT/W TO 1964 0.27*** 0.05 Both in RTA 1972 0.02 0.09Both in GATT/W TO 1965 0.24*** 0.05 Both in RTA 1973 ­0.15** 0.07Both in GATT/W TO 1966 0.24*** 0.05 Both in RTA 1974 ­0.08 0.07Both in GATT/W TO 1967 0.19*** 0.05 Both in RTA 1975 ­0.10* 0.06Both in GATT/W TO 1968 0.22*** 0.05 Both in RTA 1976 ­0.22*** 0.06Both in GATT/W TO 1969 0.27*** 0.05 Both in RTA 1977 ­0.19*** 0.06Both in GATT/W TO 1970 0.38*** 0.04 Both in RTA 1978 ­0.07 0.06Both in GATT/W TO 1971 0.33*** 0.04 Both in RTA 1979 ­0.09* 0.06Both in GATT/W TO 1972 0.36*** 0.04 Both in RTA 1980 0.02 0.06Both in GATT/W TO 1973 0.29*** 0.04 Both in RTA 1981 0.02 0.05Both in GATT/W TO 1974 0.26*** 0.04 Both in RTA 1982 0.04 0.05Both in GATT/W TO 1975 0.21*** 0.04 Both in RTA 1983 ­0.00 0.05Both in GATT/W TO 1976 0.23*** 0.04 Both in RTA 1984 0.04 0.05Both in GATT/W TO 1977 0.23*** 0.04 Both in RTA 1985 0.07 0.05Both in GATT/W TO 1978 0.29*** 0.04 Both in RTA 1986 0.13*** 0.05Both in GATT/W TO 1979 0.24*** 0.04 Both in RTA 1987 0.13*** 0.05Both in GATT/W TO 1980 0.29*** 0.04 Both in RTA 1988 0.15*** 0.05Both in GATT/W TO 1981 0.23*** 0.04 Both in RTA 1989 0.19*** 0.04Both in GATT/W TO 1982 0.24*** 0.04 Both in RTA 1990 0.26*** 0.04Both in GATT/W TO 1983 0.21*** 0.04 Both in RTA 1991 0.26*** 0.04Both in GATT/W TO 1984 0.29*** 0.04 Both in RTA 1992 0.34*** 0.04Both in GATT/W TO 1985 0.35*** 0.04 Both in RTA 1993 0.33*** 0.04Both in GATT/W TO 1986 0.37*** 0.04 Both in RTA 1994 0.33*** 0.03Both in GATT/W TO 1987 0.39*** 0.04 Both in RTA 1995 0.35*** 0.03Both in GATT/W TO 1988 0.39*** 0.04 Both in RTA 1996 0.32*** 0.03Both in GATT/W TO 1989 0.46*** 0.04 Both in RTA 1997 0.31*** 0.03Both in GATT/W TO 1990 0.38*** 0.04 Both in RTA 1998 0.26*** 0.03Both in GATT/W TO 1991 0.38*** 0.04 Both in RTA 1999 0.32*** 0.03Both in GATT/W TO 1992 0.43*** 0.04 Both in RTA 2000 0.35*** 0.03Both in GATT/W TO 1993 0.37*** 0.03 Both in RTA 2001 0.43*** 0.03Both in GATT/W TO 1994 0.36*** 0.03 Both in RTA 2002 0.39*** 0.03Both in GATT/W TO 1995 0.32*** 0.03 Both in RTA 2003 0.44*** 0.03Both in GATT/W TO 1996 0.29*** 0.03 Both in RTA 2004 0.46*** 0.03Both in GATT/W TO 1997 0.30*** 0.03 Both in RTA 2005 0.47*** 0.03…  to be continued … Both in RTA 2006 0.40*** 0.06No. of observations 341333No. of country­pai rs 17400R­sq 0.5032*** denotes  significance on 1%­level , ** 5%­level, * 10%­level .A ll estimations enclose year and country­pair dummies. Constants are not reported.

FE­OLS

Table 10: Gravity model estimation; traditional FE-OLS.

38

Data­ Deterministic No. of ADFtransformation term Lags test­statistic 10% 5% 1%

GATT/WTO­effect level constant L 0 ­2.57* ­2.57 ­2.86 ­3.43level constant L 1 ­2.83*level constant L 2 ­2.65*level constant L 3 ­2.73*

RTA­effect level constant, time trend L 0 ­3.41** ­3.13 ­3.41 ­3.96level constant, time trend L 1 ­3.42**level constant, time trend L 2 ­3.66**level constant, time trend L 3 ­3.22*

*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Critical values

Null­hypothesis Test statistics P­valuePortmanteau­test no residual autocorrelation 8.07 0.78LM­test for autocorrelation   in AR models

no residual autocorrelation 17.84 0.60

Lomnicki­Jarque­Bera­test   for nonnormality

residuals are consistent witha standard normal distribution

   u1 0.89 0.64   u2 0.94 0.62Multivariate ARCH­LM­test no conditional heteroskedasticity 40.60 0.66Univariate ARCH­LM­test no conditional heteroskedasticity   u1 0.72 0.98   u2 1.24 0.94

Table 11: ADF-test statistics.

Data­ Deterministic No. of ADFtransformation term Lags test­statistic 10% 5% 1%

GATT/WTO­effect level constant L 0 ­2.57* ­2.57 ­2.86 ­3.43level constant L 1 ­2.83*level constant L 2 ­2.65*level constant L 3 ­2.73*

RTA­effect level constant, time trend L 0 ­3.41** ­3.13 ­3.41 ­3.96level constant, time trend L 1 ­3.42**level constant, time trend L 2 ­3.66**level constant, time trend L 3 ­3.22*

*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Critical values

Null­hypothesis Test statistics P­valuePortmanteau­test no residual autocorrelation 8.07 0.78LM­test for autocorrelation   in AR models

no residual autocorrelation 17.84 0.60

Lomnicki­Jarque­Bera­test   for nonnormality

residuals are consistent witha standard normal distribution

   u1 0.89 0.64   u2 0.94 0.62Multivariate ARCH­LM­test no conditional heteroskedasticity 40.60 0.66Univariate ARCH­LM­test no conditional heteroskedasticity   u1 0.72 0.98   u2 1.24 0.94

Table 12: Residual test statistics.

39

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect t­1 0.747*** 0.072* 0.774*** 0.110** 0.764*** 0.153*** 0.583*** 0.105*** 0.422** 0.112***(0.114) (0.039) (0.126) (0.051 ) (0.137) (0.041) (0.116) (0 .039) (0.121) (0.037)

GATT/WTO­effect t­2 ­0.248**  ­0.105* ­0.253** ­0.095 ­0.326*** ­0.291** ­0.131*** ­0.485*** ­0.122**(0.112) (0.056) (0.117) (0.061 ) (0.127) (0.126) (0 .049) (0.126) (0.056)

GATT/WTO­effect t­3 0.135*** 0.076* 0.050 0.270** 0.103** 0.264*** 0.062(0.045) (0.047 ) (0.034) (0.119) (0 .044) (0.107) (0.042)

RTA­effect t­1 0.904*** 0.491*** 1.639*** 0.600*** 1.554*** 0.316** 1.203*** 0.309*** 1.019***(0.362) (0.103) (0.352) (0.116 ) (0.336) (0.141) (0.327) (0 .127) (0.294)

RTA­effect t­2 ­1.287*** ­1.066*** ­0.562 ­0.381*** ­0.966*** ­0.620*(0.324) (0.355) (404 ) (0.151) (0.318) (0.359)

RTA­effect t­3 0.135*** 0.949*** 0.410***(0.045) (0.376) (0.163)

GATT/WTO­membershipt 0.308* ­0.398** 0.284**(0.174) (0.172) 0 .115

GATT/WTO­membershipt­1 ­0.151*(0 .088)

GATT/WTO­membershipt­2

GATT/WTO­membershipt­3 0.616*** 1.081*** 2.455*** 0.685***(0.180) (0.229) (0.432) (0.177)

RTA­membershipt ­0.358*** ­0.097*** ­0.482*** ­0.193*** ­0.540*** ­0.189***(0.115) (0.025) (0.118) (0 .044) (0.169) (0.057)

RTA­membershipt­1 0.085* ­0.366**(0 .048) (0.164)

RTA­membershipt­2

RTA­membershipt­3 ­0.237*** ­0.101*** ­0.241***(0.066) (0 .035) (0.061)

Real world GDPt ­0.381 1.055*** 2.558***(0.272) (0 .386) (0.413)

Real world GDPt­1 ­0.175 ­0.445 3.826***(0.448 ) (0 .371) (1.166)

Real world GDPt­2 0.254 ­1.562*** ­4.651*** ­1.497***(0.429 ) (0.342) (1.042) (0.398)

Real world GDPt­3 1.157***(0.410)

Globalization­Indext ­5.236*** ­6.340***(1.730) (1.172)

Globalization­Indext­1 4.876***(1.690)

Globalization­Indext­2 1.690*** 4.764*** 2.402***(0.599) (1.070) (0.546)

Globalization­Indext­3 ­3.091*** ­2.839***(0.628) (0.519)

Constant ­2.570*** 1.240*** ­0.244 0.947* 4.880*** 1.600** ­1.738*** ­6.207***(0.8.63) (0.598) (0.225 ) (0.542) (0.894) (0.703) (0 .440) (2.264)

Trend 0.031*** 0.013*** 0.008 0.001 0.029*** 0.073*** 0.061*** ­0.041***(0.010) (0.003) (0.009) (0.004 ) (0.005) (0.015) (0.019) (0.016)

Sample range [1957, 2006]. SD in parenthesis.*** denotes significance on 1%­level, ** 5%­level, * 10%­level.

Model 2 Model 3 Model 4 Model 6Model 5

Table 13: VAR estimations according to variable variation.

40

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect

RTA­effect

GATT/WTO­effect t­1 0.805*** 0.48 0.805*** 0.54 0.879*** 0.100* 0.878*** 0.100*(0.126) (0.044) (0.125) (0.044) (0.137) (0.054) (0.141) (0.046)

GATT/WTO­effect t­2 ­0.274** ­0.045 ­0.195 ­0.109* ­0.267** ­0.163*** ­0.279* ­0.163**(0.119) (0.046) (0.121) (0.063) (0.135) (0.066) (0.146) (0.074)

GATT/WTO­effect t­3 0.056 0.117*** 0.115**(0.045) (0.049) (0.056)

GATT/WTO­effect t­4 ­0.075 ­0.075 ­0.067(0.090) (0.090) (0.102)

GATT/WTO­effect t­5

GATT/WTO­effect t­6 0.015(0.040)

RTA­effect t­1 1.602*** 0.625*** 1.457*** 0.753*** 1.370*** 0.757*** 1.369*** 0.773***(0.358) (0.145) (0.352) (0.082) (0.356) (0.125) (0.362) (0.127)

RTA­effect t­2 ­1.118*** 0.172 ­0.848*** ­0.826** ­0.792*(0.361) (0.133) (0.361) (0.361) (0.412)

RTA­effect t­3

RTA­effect t­4

RTA­effect t­5 ­0.236** ­0.271*(0.112) (0.147)

RTA­effect t­6 ­0.045(0.297)

Constant 0.422*** 0.411*** 0.416*** ­0.088* 0.409*** 0.103*(0.116) (0.135) (0.135) (0.047) (0.140) (0.062)

Trend ­0.005 0.002** ­0.005 0.002** ­0.005 0.005*** ­0.005 0.005***(0.003) (0.001) (0.003) (0.001) (0.003) (0.001) (0.004) (0.001)

*** denotes significance on 1%­level, ** 5%­level, * 10%­level.Sample range [1957, 2006]. SD in parentheses.

Model 7 Model 8 Model 9 Model 10(Lag 2) (Lag 4) (Lag 5) (Lag 6)

Table 14: VAR estimations according to lag order variation.

41

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