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Who Liberalizes? Explaining PreferentialTrade LiberalizationDaniel Y. Kono aa Department of Political Science , University of California at Davis ,Davis, California, USAPublished online: 07 Dec 2007.

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GINI0305-06291547-7444International Interactions, Vol. 33, No. 4, October 2007: pp. 1–32International Interactions

Who Liberalizes? Explaining Preferential Trade Liberalization

Who Liberalizes?Daniel Y. Kono

DANIEL Y. KONODepartment of Political Science, University of California at Davis, Davis, California

Despite the growth in research on preferential trade arrangements(PTAs), few studies have systematically explored why some PTAshave been more successful than others at liberalizing trade amongmembers. In this paper I test four hypotheses concerning intra-PTAliberalization: a regional system structure hypothesis, an interna-tional institutions hypothesis, a domestic institutions hypothesis,and an economic hypothesis. Although all four types of variablesare statistically significant, only international institutions havesubstantively large effects on intra-PTA liberalization. Thissuggests that policymakers have considerable latitude to promoteintegration, as the impact of “choice” variables such as interna-tional institutions far outweighs that of “given” factors such asregional system structure or the nature of member economies.

KEYWORDS regionalism, institutions, cooperation

The recent proliferation of preferential trade arrangements (PTAs) has led toa comparable spread of scholarly articles on this topic. Some examine thedeterminants of PTA formation or expansion (Grossman and Helpman,1995; Mansfield, Milner, and Rosendorff, 2002). Others measure the impactof PTAs on trade (Eichengreen and Irwin, 1998; Mansfield and Bronson,1997), explain variation in formal regional institutions (Haas, 1958; Grieco,1997; Smith, 2000), or explore the relationship between PTAs and multilat-eral trade liberalization (Lazer, 1999; Haftel, 2004; Kono, 2007). Few,

I wish to thank Yoram Haftel, Bob Jackman, Edward Mansfield, Jeannette Money, Gabriella Montinola, Randy Siverson, and three anonymous II reviewers for helpful comments.

Address correspondence to Daniel Y. Kono, Assistant Professor, Department of PoliticalScience, University of California at Davis, One Shields Avenue, Davis, CA 95616-8682, USA.E-mail: dykono@ucdavis.edu

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402 D. Y. Kono

however, have empirically explored why some PTAs have liberalized tradeamong members while others have not.

This inattention to the determinants of intra-PTA liberalization issurprising for several reasons. First, such liberalization is the declared objec-tive of most PTAs: for example, both the European Community (EC) and theEconomic Community of West African States (ECOWAS) called for thecomplete elimination of intra-bloc tariffs within ten years.1 Second, how-ever, some PTAs appear to be much more successful than others at achiev-ing this goal. For example, Mattli (1999, p. 68) describes the EuropeanUnion as “one of the most successful examples of integration” and contrastsits performance with that of “failed arrangements” such as the Latin Ameri-can Free Trade Association. Given the potential impact of PTAs on bothtrade and military conflict (Mansfield and Pevehouse, 2000), such variationhas great practical importance as well as providing an ideal opportunity totest more general theories of international cooperation at the regional level.However, while scholars have developed a substantial body of theory toexplain the success and failure of regional PTAs, they have yet to evaluatethis theory through systematic, comparative empirical research.

This paper is the first to test a wide range of hypotheses concerningthe determinants of intra-PTA trade liberalization. I examine four types ofdeterminants: “regional-systemic,” “international-institutional,” “domestic-institutional,” and “economic.” I find that all four have significant effects onintra-PTA liberalization: specifically, regional economic asymmetries tend toimpede such liberalization, while international dispute settlement mecha-nisms, a higher number of domestic veto players, and similar member factorendowments tend to promote it. However, while all four variables havesignificant effects, only those of dispute settlement mechanisms are substan-tively large. I thus conclude that policymakers have considerable latitude topromote integration, as the impact of “choice” variables such as interna-tional institutions far outweighs that of “given” factors such as regionalsystem structure or member factor endowments.

WHAT DO WE KNOW?

A quick glance at the data suggests that some PTAs have promoted intra-bloc trade more than others. For example, Figure 1 presents intra-blocimport shares for four PTAs—the six-member EC, the Southern CommonMarket (MERCOSUR), the West African Economic Community (CEAO), andECOWAS—for ten years preceding and ten years following PTA formation.2

It presents a clear contrast between the EC and MERCOSUR, on the onehand, and the CEAO and ECOWAS on the other: import shares of theformer rise dramatically following PTA formation, whereas those of the lat-ter fall slightly. Of course, one should not infer too much from these simple

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Who Liberalizes? 403

descriptive statistics, and a primary goal of this paper is to develop a moreinformative measure of intra-PTA liberalization. However, Figure 1 doesseem to support the conventional wisdom that some PTAs have achievedmore intra-bloc liberalization than others. The question is: why?

Mattli (1999) provides the most comprehensive effort to date to answerthis question. Using case studies of 13 successful and failed PTAs, he identi-fies two crucial conditions for successful intra-bloc liberalization.3 First,liberalization is more likely when the potential economic gains from inte-gration are high. Second, liberalization is more likely when there is anundisputed regional leader “able to serve as an institutional focal point andregional paymaster.” (p. 65) Mattli also identifies other factors that influ-enced liberalization in particular PTAs. For example, “commitment institu-tions” such as the European Court of Justice facilitated integration in theEuropean Union (EU), while the “Soccer War” between El Salvador andHonduras led to the demise of the Central American Common Market(CACM).

Mattli’s study makes a major contribution to our understanding ofregional liberalization. However, this paper extends Mattli’s work in impor-tant ways. First, my sample is larger and more representative, including 34regional and bilateral PTAs. Second, I test more hypotheses than Mattli andam able to include a number of important controls. Third, I examine theimpact of all variables systematically across all cases. Hence, for example,while Mattli’s study points to the importance of legal institutions in the EUand military conflict in the CACM, it is not clear whether these variablesaffect the performance of PTAs more generally. Finally, because Mattli’s unitof analysis is the PTA, his independent variables are aggregate PTA charac-teristics such as gains from integration or hegemonic leadership for the bloc

FIGURE 1 Intra-Bloc Import Shares Before and After PTA Formation.

0

0.1

0.2

0.3

0.4

0.5

Year of PTA Formation

Intra

-Blo

c Im

port

Sha

re

EC

MERCOSUR

CEAO

ECOWAS

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404 D. Y. Kono

as a whole. In contrast, I employ a dyadic analysis that allows me also toconsider characteristics of individual members (e.g., the number of domes-tic veto players) and member dyads (e.g., shared alliance membership). Theinclusion of such variables is important because intra-bloc liberalizationmay vary within as well as across PTAs.

THEORY

Four broad sets of factors—regional-systemic, international-institutional,domestic-institutional, and economic—may affect the willingness or abilityof governments to liberalize intra-PTA trade. In this section, I presenthypotheses concerning each set of factors. Due to space constraints, thesehypotheses are not exhaustive. Rather, they are meant to highlight variouspossible determinants of regional trade liberalization.

Regional System Structure

Regional-systemic theories contend that the structure of the system withinwhich PTA members interact affects the prospects for intra-bloc liberaliza-tion. Perhaps the most-analyzed systemic characteristic is the concentrationof economic power within the regional bloc. Most arguments abouteconomic concentration build upon Olson’s (1965) claim that groupscontaining a single dominant actor are more capable of collective action,and Kindleberger’s (1973) subsequent application of this “hegemonic stabil-ity theory” to international economic relations. The core idea is that a singledominant actor or “hegemon” is less subject to free-riding incentives thanare numerous smaller actors. The hegemon is thus more likely both toprovide public goods unilaterally and to enforce cooperation by others. Inthe context of PTAs, we would expect arrangements that include a hege-monic actor to liberalize trade more than those which do not. Mattli (1999,p. 56, my italics) has been the most forceful proponent of this theory, argu-ing that “successful integration requires the presence of an undisputedleader among the group of countries seeking closer ties.” If so, then

H1(a): Intra-PTA trade liberalization will be greater in PTAs withunequal economies.

Although H1(a) is the most prevalent hypothesis concerning the impactof regional asymmetries, it is by no means the only one. Other scholarshave argued, in contrast, that regional asymmetries may impede liberaliza-tion because disproportionate gains to the largest state may cause smallerones to oppose liberalization (Balassa, 1961). The principal reason largerstates might reap disproportionate gains is that industries in these states

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already enjoy economies of scale—thanks to their large domestic markets—and are thus better prepared to exploit the increased economies of scaleprovided by the PTA. Empirical research on PTAs (e.g., Sanguinetti, Traist-aru, and Martincus, 2004) lends some support to this view. If governmentsof smaller states fear that such effects will increase the economic or politicaldominance of larger ones, they may resist further integration. This implies adifferent hypothesis:

H1(b): Intra-PTA trade liberalization will be greater in PTAs with equaleconomies.

International Institutions

International institutions may facilitate trade cooperation in two ways.First, by providing information about government behavior, they enableother governments to pursue reciprocal strategies needed to supportcooperation in an iterated Prisoner’s Dilemma (Keohane, 1984). In otherwords, information about trade policies enables trading partners toreward liberalization with liberalization and to punish protection withprotection. Second, by providing clear behavioral prescriptions andproscriptions, institutions may increase the reputational costs of noncom-pliance and thus promote compliance with international agreements(Keohane, 1984).

A prominent type of institution that serves both functions is interna-tional dispute settlement mechanisms (DSMs). DSMs that provide relativelyimpartial international tribunals help clarify which behaviors constitutetreaty violations. DSM decisions thus help trading partners levy appropriatesanctions against proscribed behavior and may also avert unwarrantedretaliation (and trade wars) against actions that are not proscribed. In addi-tion, cases that reach DSMs tend to be much more public than ones settledthrough quiet bilateral bargaining. By publicizing information on treatyviolations, DSMs increase the reputational costs of noncompliance andfurther increase incentives to comply. For these reasons, Yarbrough andYarbrough (1997), Mattli (1999), and Smith (2000) have all argued thatlegalistic regional DSMs should improve compliance with PTA rules.Hence,

H2: Intra-PTA trade liberalization will be greater in PTAs with legalisticDSMs.

Domestic Institutions

Domestic institutions may also influence preferential liberalization invarious ways. For example, Mansfield, Milner, and Pevehouse (2007)

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406 D. Y. Kono

have found that countries with more domestic veto players are lesslikely to form PTAs because a higher number of veto players makespolicy change (including PTA formation) more difficult (Tsebelis, 2002).Without disputing this point, I argue that a higher number of vetoplayers should promote liberalization within extant PTAs because, to beformed, these PTAs had to receive the support of all veto players at theratification stage. The veto players that ratified PTAs should not impedetheir implementation: in fact, they should use their veto power toensure that the government does not break its commitment to liberalize.A high number of veto players may, in other words, impede PTAratification while increasing the credibility of PTAs that are ratified. Ifso, then

H3: Intra-PTA trade liberalization will be greater when the number ofdomestic veto players is large.

Economic Effects

Because PTAs are economic arrangements, their economic effects shouldinfluence government incentives both to form and to implement them.For example, Levy (1997) argues that PTAs are politically more feasiblewhen factoral income effects are weak relative to differentiated-productvariety gains. The reason is that median voters in all PTA members mustconsent to the arrangement. Although all median voters reap varietygains, at least one suffers factoral income losses. If these losses arelarger than the variety gains, this voter vetoes the arrangement. Levy’smodel generates clear predictions about preferential liberalization:because factoral income effects grow as members’ factor endowmentsdiverge, large factor-endowment differences should impede preferentialliberalization. This may explain why, for example, the Canada–U.S. FreeTrade Agreement encountered less resistance than the North AmericanFree Trade Agreement (which included capital-scarce Mexico), and whyEC enlargements involving poor Southern and Eastern countries havebeen more difficult than enlargements involving richer countries. Moregenerally,

H4: Intra-PTA trade liberalization will be greater when members havesimilar factor endowments.

The above hypotheses are clearly not exhaustive, but they do illustratethe range of possible explanations for intra-PTA trade liberalization. Todate, however, none of these hypotheses has been systematically tested.The remainder of this paper is thus devoted to empirical tests. I proceed intwo stages. In the next section, I develop a measure of intra-PTA trade

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liberalization. In the following section, I employ this measure as my depen-dent variable to test the above hypotheses.

MEASURING INTRA-PTA TRADE LIBERALIZATION

The first step in my empirical analysis is to measure intra-bloc liberalizationin extant PTAs. My sample includes the 34 regional and bilateral PTAsshown in Table 1. This sample is similar to Smith’s (2000) and employs sim-ilar selection criteria. First, the PTAs must be reciprocal, since some of myhypotheses (e.g., dispute settlement mechanisms) are based on a logic ofreciprocity. This excludes the EU’s preferential arrangements with formercolonies in Africa, the Caribbean, and the Pacific. Second, to ensure compa-rability across PTAs, the proposed scope of liberalization must be compre-hensive rather than narrowly sectoral. This excludes the European Coal andSteel Community, which was designed to manage trade in these sectors.Third, I include only trade agreements and exclude ones, for example, theSouthern African Development Community, designed solely to promotejoint infrastructural and other non-trade objectives. Fourth, I exclude frame-work agreements, such as Asia-Pacific Economic Cooperation, that aremeant to facilitate trade negotiations but have no explicit policy targets.Finally, because I wish to examine trends in trade for a number of yearsbefore and after PTA formation—and because my data extend from 1950–2000—I include only PTAs that went into effect between 1958 and 1995. Mysample includes all PTAs that meet these criteria and for which data areavailable.4

I measure intra-PTA liberalization using a modified gravity model, thestandard framework for assessing the effects of PTAs on trade. Gravity mod-els predict dyadic trade on the basis of trading partners’ GDPs, populations,geographic distance, adjacency, and other controls. Dummies indicatingPTA membership are then included to measure the impact of PTAs. If theactual trade of PTA members differs from predicted trade, this difference isascribed to the PTA. I employ dyadic imports rather than dyadic trade(exports + imports) as my dependent variable because the former betterproxy the home country’s trade policies.

To reduce omitted-variable bias, I estimate bilateral import sharesrather than absolute values of bilateral imports. That is, instead of estimatingimportsijt (country i’s imports from country j in year t), I estimate importsijt ÷importsit, (j’s share of i’s total imports in year t). This model specificationreduces omitted-variable bias because i’s total imports control much moreaccurately for i’s overall propensity to import than do variables such as i’sGDP and population (Haveman and Hummels, 1998). This specificationalso eliminates the need to include i’s GDP, population, and other purelymonadic home-country characteristics in the model.

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TA

BLE

1Sa

mple

PTAs

by

Dat

e of Entry

into

Forc

e

PTA

Entry

Into

Forc

eM

ember

s

Euro

pea

n C

om

munity

(EC6)

1958

Bel

gium

, Fr

ance

, G

erm

any,

Ita

ly, Lu

xem

bourg

, N

ether

lands

Euro

pea

n F

ree

Tra

de

Ass

oci

atio

n (

EFT

A)

1960

Aust

ria,

Den

mar

k, F

inla

nd, Ir

elan

d, N

orw

ay, Portuga

l, Sw

eden

, Sw

itzer

land, U

nite

d K

ingd

om

Cen

tral

Am

eric

an C

om

mon M

arke

t (C

ACM

)19

61Cost

a Ric

a, E

l Sa

lvad

or, G

uat

emal

a, H

ondura

s, N

icar

agua

EC-T

urk

ey A

ssoci

atio

n19

63EC6,

Turk

eyEas

t A

fric

an C

om

munity

(EAC)

1967

Ken

ya, Tan

zania

, U

ganda

Andea

n P

act

1969

Boliv

ia, Chile

, Colo

mbia

, Ecu

ador, P

eru, V

enez

uel

aEC-C

ypru

s &

Mal

ta A

ssoci

atio

ns

1972

EC-9

, M

alta

, Cyp

rus

Car

ibbea

n C

om

munity

(CARIC

OM

)19

73A

ntig

ua

& B

arbuda,

Bah

amas

, B

arbad

os,

Bel

ize,

Dom

inic

a,

Gre

nad

a, G

uya

na,

Hai

ti, J

amai

ca, M

onse

rrat

, Trinid

ad &

Tobag

o, St

. K

itts

& N

evis

, St

. Lu

cia,

St.

Vin

cent &

the

Gre

nad

ines

, Su

rinam

Wes

t A

fric

an E

conom

ic C

om

munity

(CEAO

)19

73B

enin

, B

urk

ina

Faso

, Iv

ory

Coas

t, M

ali,

Mau

rita

nia

, N

iger

, Se

neg

alM

ano R

iver

Unio

n19

73Li

ber

ia, Si

erra

Leo

ne,

Guin

eaEC E

nla

rgem

ent 1

1973

EC6,

Den

mar

k, I

rela

nd, U

nite

d K

ingd

om

EC-E

FTA F

TA

1973

EC6,

Aust

ria,

Fin

land, N

orw

ay, Portuga

l, Sw

eden

, Sw

itzer

land, Ic

elan

dEco

nom

ic C

om

munity

of W

est Afric

an S

tate

s (E

CO

WAS)

1975

Ben

in, B

urk

ina

Faso

, Côte

d’Iv

oire,

The

Gam

bia

, G

han

a,

Guin

ea, G

uin

ea-B

issa

u, Li

ber

ia, M

ali,

Mau

rita

nia

, N

iger

, N

iger

ia, Se

neg

al, Si

erra

Leo

ne,

Togo

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] at

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409

Gulf C

ooper

atio

n C

ounci

l (G

CC)

1981

Bah

rain

, K

uw

ait,

Om

an, Q

atar

, Sa

udi A

rabia

, U

nite

dA

rab

Em

irat

esEC E

nla

rgem

ent 2

1981

EC9,

Gre

ece

Aust

ralia

-New

Zea

land C

lose

r Eco

nom

ic R

elat

ions

(AN

ZCERTA

)19

83A

ust

ralia

, N

ew Z

eala

nd

US-

Isra

el F

TA

1985

Isra

el, U

SEC E

nla

rgem

ent 3

1986

EC10

, Portuga

l, Sp

ain

South

ern C

om

mon M

arke

t (M

ERCO

SUR)

1991

Arg

entin

a, B

razi

l, Par

aguay

, U

rugu

ayA

ssoci

atio

n o

f So

uth

east

Asi

an N

atio

ns

FTA (

AFT

A)

1992

Indones

ia, M

alay

sia,

Phili

ppin

es, Si

nga

pore

, Thai

land,

Bru

nei

EC-C

entral

-Eas

t Euro

pea

n C

ountry

Ass

oci

atio

ns

(EC-C

EECs)

(4)

1992

–94

EC12

, B

ulg

aria

, H

unga

ry, Pola

nd, Rom

ania

EFT

A A

ssoci

atio

ns

(6)

1993

EFT

A3

(Ice

land, N

orw

ay, Sw

itzer

land), B

ulg

aria

, H

unga

ry,

Isra

el, Pola

nd, Rom

ania

, Turk

eyN

orth A

mer

ican

FTA (

NAFT

A)

1994

Can

ada,

Mex

ico, U

SEuro

pea

n E

conom

ic A

rea

(EEA)

1994

EC12

, Ic

elan

d, N

orw

ayCom

mon M

arke

t fo

r Eas

tern

and S

outh

ern A

fric

a (C

OM

ESA

)19

95A

ngo

la, B

uru

ndi,

Com

oro

s, D

jibouti,

Eritrea

, Eth

iopia

, K

enya

, Le

soth

o, M

adag

asca

r, M

alaw

i, M

auritiu

s,

Moza

mbiq

ue,

Nam

ibia

, Rw

anda,

Som

alia

, Su

dan

, Sw

azila

nd, Tan

zania

, U

ganda,

Zai

re, Zam

bia

, Zim

bab

we

EC E

nla

rgem

ent 4

1995

EC12

, A

ust

ria,

Fin

land, Sw

eden

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410 D. Y. Kono

I estimate the following model:

where CONTROLS is a vector of control variables and γ is a vector of coef-ficients for the controls. Importing country i is always a PTA member,whereas exporting countries j comprise the universe of i’s PTA and non-PTA trading partners. I calculated import shares for no less than six yearsand no more than ten years before and after PTA formation. The resultingsample contains at least 16 and no more than 20 years for each PTA.5

I include a lagged dependent variable both to reduce autocorrelation(Beck and Katz, 1995) and because the autoregressive model estimates theimpact of other independent variables on changes in, rather than levels of,imports. This is important because our question is not whether PTA mem-bers trade more with each other than with nonmembers, but whether theformation of a PTA causes members to trade more with one another thanthey did before. Answering this question requires a focus on changes ratherthan levels.

I include standard gravity model controls: partner GDP and population,dyadic distance, and dummies for contiguity and common language.Because the model examines changes in import shares, partner GDP andpopulation are entered in first-differenced form. I also include a number ofpolitical controls that previous research has shown to influence trade: Alli-anceijt is a dummy variable coded 1 if i and j belonged to the same militaryalliance at time t; Democratic Dyadijt is the sum of i’s and j’s Polity IVdemocracy scores at time t; MIDijt is a dummy coded 1 if i and j wereinvolved in a militarized interstate dispute at time t; and GATT Dyadijt is adummy coded 1 if i and j belonged to the General Agreement on Tariffsand Trade (GATT) at time t.6 Based on previous research, alliances anddemocratic dyads should be positively signed (Mansfield and Bronson,1997; Mansfield, Milner, and Rosendorff, 2000), while MIDs should be nega-tively signed (Mansfield and Bronson, 1997). GATT membership should, intheory, have a positive impact on trade, although Rose (2004) finds noevidence of this.

To measure the impact of PTAs I include PTAijt dummies, which arecoded 1 if i and j belonged to a PTA at time t and 0 otherwise. They arethus coded 1 for PTA partner dyads following PTA formation but 0 for thesesame dyads prior to that time and for all dyads containing non-PTApartners. Because I wish to measure variation across PTAs, I include a sepa-rate dummy for each arrangement. In separate analyses below, I definePTAs both regionally (e.g., NAFTA) and dyadically (e.g., U.S.–Mexico).

ln Import Share ln(Import Share PTA( ) )ijt ijt ijt= + + ∑b b b0 1 ijt -1 iijt

ijt ijt ijt+ ∑ + +b g ePre-PTA CONTROLS ijt ,

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Because PTAs should boost trade among members, these dummies shouldbe positively signed.

It is often noted that studies of PTA effects face endogeneity problemsbecause states that trade more with one another—or liberalize trade faster—may be more likely to form PTAs. A positive PTA coefficient may thus reflecta higher propensity to trade rather than the effects of the PTA per se. Thispossibility is clearly suggested by Figure 1: both the apparent success of theEC and MERCOSUR and the apparent failure of the CEAO and ECOWAS arecontinuations of pre-PTA trends. Although this does not change our conclu-sion that the former regional blocs are more “cooperative,” it does raisequestions about the causal impact of the PTAs themselves. The same prob-lem arises in testing many of my hypotheses: for example, if DSM design isendogeneous to prior trade policy preferences, then legalistic DSMs may beassociated with rapid trade liberalization even if the latter is merely a contin-uation of pre-DSM trends. To address this problem, and to assess the causalimpact of DSMs, we need a dependent variable that captures the additionalgrowth in trade that occurs after PTA formation.7

To obtain such a measure, I include another set of PTA dummies, Pre-PTAijt, which are coded 1 for eventual PTA partners prior to PTA formationand 0 otherwise. In other words, they are coded 0 for all non-PTA dyads, 0for PTA partner dyads following PTA formation, and 1 for the latter dyadsprior to PTA formation. If an endogeneity problem exists, these dummieswill be positively signed. As before, I generate dummies for both regionaland dyadic PTAs.

To explicate my approach to measuring PTA effects, I first examinevariation in the aggregate effects of trade blocs using regional PTAdummies. I correct for possible serial correlation by using robust-clusterestimators that correct for within-dyad correlation of residuals. Results areshown in Table 2.8

Because the control variable results and model statistics are not of pri-mary interest, I present them in the appendix. Suffice it to say that all con-trols are signed as expected, and nearly all are highly significant. Turning tothe variables of interest, column 1 presents results for the PTA dummies.Fifteen of the 34 dummies have significant positive coefficients, suggestingthat nearly half of the PTAs liberalized trade among members. However,column 2, which presents the pre-PTA coefficients, suggests a need for cau-tion in interpreting this result. Of the 15 arrangements with significant posi-tive PTA coefficients, 6 also have significant positive pre-PTA coefficients.Many blocs that liberalized intra-PTA trade were thus already liberalizingrapidly prior to PTA formation. This suggests that endogeneity is a concernbut does not preclude the possibility of marginal PTA effects, as many PTAcoefficients are larger than pre-PTA ones.

To calculate the net effect of each PTA, I subtract the pre-PTA from thepost-PTA coefficients. Results are shown in column 3. Note that the post-PTA

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estimates alone typically overstate (e.g., the EC) but sometimes understate(e.g., the EC-Turkey Association) the true impact of PTA formation. Bothexamples underscore the importance of examining marginal, rather thantotal, intra-PTA liberalization when assessing the effects of PTAs. To deter-mine whether these marginal effects were significant, I performed Waldtests of the hypothesis that the PTA and pre-PTA coefficients are identical.Results are shown in column 4. In 12 cases, the tests indicate that blocmembers increased trade with each other significantly faster following PTAformation. Hence, even after controlling for prior trade trends, over one-third

TABLE 2 Effects of Regional PTAs

(1) (2) (3) (4)

Name of Arrangement PTAijt Pre-PTAijt

Net PTA Effectijt

(PTAijt – Pre-PTAijt)

Wald TestH0: PTAijt = Pre-PTAijt

P > F =

AFTA .071 (.007)* .035 (.010)* .036* 0.0005Andean Pact .024 (.007)* .000 (.007) .024* 0.0298ANZCERTA .076 (.008)* .043 (.013)* .033* 0.0000CACM .079 (.019)* .014 (.010) .065* 0.0009CARICOM .008 (.021) −.025 (.017) .033 0.3286CEAO −.005 (.007) .016 (.013) −.021 0.0832COMESA .004 (.003) .001 (.002) .003 0.4681EAC .101 (.064) .049 (.038) .052 0.5272EC6 .050 (.006)* .026 (.009)* .024* 0.0163EC-Bulgaria .021 (.007)* .011 (.009) .010 0.1309EC-Cyprus & Malta .002 (.007) .002 (.006) .000 0.9691EC-Hungary .016 (.007)* .008 (.007) .008 0.1882EC-Poland .016 (.008)* .029 (.011)* −.013 0.1955EC-Romania .030 (.010)* −.001 (.007) .031* 0.0000EC-Turkey .012 (.013) −.034 (.011)* .046* 0.0058EC-EFTA .014 (.003)* .001 (.003) .013* 0.0000EC Enlarge 1 .027 (.005)* .003 (.004) .024* 0.0000EC Enlarge 2 .012 (.010) −.004 (.006) .016* 0.0138EC Enlarge 3 .027 (.006)* .007 (.004) .020* 0.0000EC Enlarge 4 .005 (.005) .025 (.003)* −.020* 0.0001ECOWAS −.006 (.003) −.001 (.004) −.005 0.1495EEA −.006 (.005) .005 (.005) −.011* 0.0109EFTA .030 (.004)* .006 (.003) .024* 0.0000EFTA-Bulgaria −.016 (.003)* −.001 (.012) −.015 0.2844EFTA-Hungary −.019 (.009)* −.004 (.010) −.015 0.3862EFTA-Israel −.008 (.006) .007 (.011) −.015 0.0834EFTA-Poland −.002 (.017) .010 (.014) −.012 0.6759EFTA-Romania −.008 (.005) −.006 (.011) −.002 0.8815EFTA-Turkey −.014 (.004)* −.010 (.005)* −.004 0.4847GCC .011 (.013) −.001 (.013) .012 0.5299Mano −.003 (.009) −.006 (.010) .003 0.8035MERCOSUR .038 (.020) .042 (.014)* −.004 0.8528NAFTA .076 (.013)* .058 (.018)* .018 0.0950US-Israel .045 (.017)* .067 (.031)* −.022 0.0957

Dependent Variable: ln(Import Shareijt) *p < .05 Robust (dyad-clustered) standard errors in parentheses.

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of the PTAs led to significantly faster growth in intra-bloc trade. In only twocases was PTA formation associated with significantly slower growth inintra-bloc trade. In the remaining 20 cases, PTAs had no significant effects.For our purposes, the interesting point to note is that there is considerablevariation in the effects of PTAs. The CACM’s net effect of .065 implies thatthe CACM boosted intra-bloc imports by exp(.065): roughly 6.7 percent peryear or 92 percent over ten years. The EC had a more modest effect of 2.4percent per year (27 percent over ten years), while COMESA had a stillsmaller impact of 0.3 percent per year (3 percent over ten years). Table 2thus reinforces the conventional wisdom that some PTAs have been moresuccessful than others at promoting trade among members.

Although the regional analysis provides a useful first cut at measuringthe effects of PTAs, I cannot test my hypotheses with the regional measurebecause some hypotheses are national (e.g., veto players) or dyadic (e.g.,factor endowments). My tests thus employ a dyadic measure of PTA effects,which has the added advantage of allowing me to control for dyadic influ-ences on intra-PTA trade such as shared alliance membership (Mansfieldand Bronson, 1997). To measure dyadic variation in the effects of PTAs, Isimply repeat the above analysis using dyadic dummies. That is, I generatepre- and post-PTA dummies for each intra-PTA dyad, then subtract pre-from post-PTA coefficients to obtain estimates of PTA effects on each dyad’strade. These estimates constitute a measure of dyadic PTA effects, PTAEffectij, that I employ as my dependent variable in the next section. Becausethis procedure generates many coefficients (2560), I do not present themhere. However, dyadic intra-PTA liberalization, like regional liberalization,varies considerably. I now examine the extent to which my hypotheses helpexplain this variation.

EXPLAINING VARIATION IN INTRA-PTA LIBERALIZATION

Independent Variables

I test H1-H4 by regressing PTA Effectij on the following variables:Asymmetryk measures the asymmetry of member GDPs in PTA k and

is included to test the regional-systemic hypotheses (H1[a] and [b]).I employ Smith’s (2000) adjusted proportional asymmetry index:

, where xi is the GDP share of PTA

member i at the time of PTA formation and N is the number of PTA members.Following Smith, I treat the EC/EU as a single actor in association agree-ments between it and other countries. Asymmetry ranges from 0 to 1, withhigher values indicating more asymmetric GDP distributions. Asymmetry will bepositively signed if If H1(a) is correct but negatively signed if H1(b) is correct.

Asymmetry = - 1/ /(1 - 1/ )2k i

i

x N N∑⎛

⎝⎜⎞

⎠⎟

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DSMk is a dummy variable for dispute settlement institutions and iscoded 1 for PTAs with legalistic dispute settlement mechanisms. To constructthis measure, I employ Smith’s (2000) classification of regional DSMs and treatas legalistic any DSMs that provide automatic third-party review. If the inter-national institutions hypothesis (H2) is correct, DSM will be positively signed.

Veto Playersi measures the political constraints imposed by domesticveto players in PTA member i. I measure these constraints using Henisz’s(2000) POLCON V index, which is based on two characteristics of thedomestic polity: “the number of independent [institutional] veto points overpolicy outcomes and the distribution of preferences of the actors thatinhabit them (5).” Henisz employs these characteristics to calculate “theexpected range of policies for which a change in the status quo can beagreed upon by all political actors with veto power (5).” The POLCON Vindex is one minus this expected range. It ranges from 0 where the execu-tive is completely unchecked to about .9 where executives face consider-able obstacles to policy change. If the domestic institutions hypothesis (H3)is correct, veto players will be positively signed.

Factor Similarityij measures the similarity of factor endowmentsbetween PTA members i and j. I first obtain the absolute value of thedifference between their logged real GDPs per capita at the time of PTA for-mation. I then calculate the inverse of this difference in order to convert itinto a similarity score. If the economic hypothesis (H4) is correct, factorsimilarity will be positively signed.

Control Variables

I omit most of the controls from the gravity-model regressions (e.g., MIDs,joint democracy, GATT membership) because the first-stage regressions havepurged my dependent variable of their effects.9 There are only two exceptionsto this rule. First, I include shared alliance membership because previousresearch has found that alliances have larger positive effects on trade withinintra-PTA than non-PTA dyads (Mansfield and Bronson, 1997). Because theeffects of alliances are smaller outside PTAs, the first-stage analysis—whichcombined PTA and non-PTA dyads and was dominated by the latter—may nothave controlled adequately for the effects of alliances within PTAs. Second, Iinclude Democracyi, country i’s Polity IV democracy score. Because democ-racy was included in the first-stage regressions, I have no theoretical predictionconcerning the sign of this variable: it will tell us the impact of home-countrydemocracy after dyadic democracy has already been controlled for in the firststage. I nonetheless have a strong empirical reason for including this variable:democracy scores are highly correlated with the number of domestic vetoplayers, so it is essential to include the former to isolate the effects of the latter.

Mattli (1999) argues that regional liberalization is more likely tosucceed when potential gains from trade are high. The political logic is that

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such gains generate popular and interest-group pressures for regional liber-alization, to which politicians must respond. According to Mattli (1999,p. 46), market size is an important source of such gains because large PTAmarkets allow members not only to export more but also to exploit econo-mies of scale. I thus include Partner Export Shareij, the log of PTA memberj’s share of member i’s exports one year prior to PTA formation, to controlfor the importance of j’s export market to i. If larger markets promoteintegration, partner export shares will be positively signed.

Finally, I include Smith’s (2000) measure of Proposed Integrationk,which describes each PTA’s stated integrationist goals (rather than actualsuccess at achieving them). This variable is coded 0 (“low”) if governmentspropose an FTA or customs union and 1 (“high”) if they propose aneconomic union or common market. Intuitively, one would expect govern-ments that propose deeper integration to achieve more liberalization thanless ambitious ones, in which case proposed integration will be positivelysigned. However, Pevehouse, Hafner-Burton, and Zierler (2002) argue thatlarge gaps between proposed and realized integration may lead to frictionsthat can undermine the arrangement, so that PTAs with ambitious goals endup being less successful than ones with more modest goals. It may also bethe case that multidimensional integration involving not only trade but alsoimmigration and capital flows may be politically more difficult than unidi-mensional trade integration. If either of these arguments is correct,proposed integration will be negatively signed.

Analysis

I test H1-H4 by regressing PTA Effectij on the above variables. Becausedyadic observations within regional PTAs are probably not independent, Iemploy robust-cluster estimators that correct for within-PTA correlation ofresiduals.10 Results are shown in Table 3.

Model 1 presents the results of the basic model. Asymmetry is nega-tively signed and significant, indicating that regional economic asymmetriesimpede intra-PTA liberalization. This result supports H1(b) but not H1(a).DSM is positive and significant, indicating that regional DSMs that providethird-party arbitration promote intra-PTA liberalization. This result supportsH2. Veto players is positive and significant, indicating that an increase in thenumber of domestic veto players promotes intra-PTA liberalization andsupporting H3. Finally, factor similarity is positive and significant, indicatingthat PTA members with similar factor endowments liberalize trade with eachother more than do members with different factor endowments. This resultsupports H4. Model 1 thus indicates that regional system structure, interna-tional institutions, domestic institutions, and economic factors all havesignificant effects on intra-PTA liberalization.

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To ensure that these results do not reflect omitted-variable bias, Model 2includes decade dummies and region dummies to control for omitted time-specific and region-specific variables. Results for DSM, veto players, and fac-tor similarity are unaffected, but the asymmetry result loses significance at the.05 level (although it remains significant at .10). This is due to the high corre-lation between asymmetry and the Europe dummy: the two are correlated at.76 because all of the EC/EU’s association agreements are highly asymmetric.Of course, this association between asymmetry and the Europe dummy doesnot necessarily imply that the asymmetry result is spurious. However, Model 2does cast some doubt on the asymmetry result while reinforcing the others.

It is evident from Table 3 that many PTAs involve the EC and EFTA: inaddition to the original EC6 and EFTA, my sample includes four EC enlarge-ments, six EC association agreements, six EFTA associations, and the EC-EFTA FTA. Readers may thus be concerned that my results are driven by theEC and EFTA cases. To address this concern, Model 3 drops all of the ECenlargements, the EC associations, the EFTA associations, and the EC-EFTAFTA. This reduces the sample by 32 percent, and this sample change is, ofcourse, nonrandom. It is thus striking that all four results of interest arerobust to this sample change. Hence, although the European cases arenumerous, they do not appear to drive my results.

To interpret these results substantively, recall that the dependent variableis the annual growth in logged intra-bloc import shares resulting from PTAformation. A one-standard deviation (.34) shift in the asymmetry scorereduces this growth rate by .34*exp(−.016): roughly .5 percent per year orabout 5 percent over ten years. This seems quite small. The formation of a

TABLE 3 Determinants of Intra-PTA Trade Liberalization

Independent Variable Model 1 Model 2 Model 3

Asymmetryk −.016* (.005) −.018 (.010) −.019* (.008)DSMk .021* (.004) .016* (.006) .020* (.007)Veto Playersi .019* (.007) .022* (.007) .024* (.005)Factor Similarityij .003* (.001) .003* (.001) .003* (.001)Allianceij .029* (.007) .019* (.009) .033* (.009)Democracyi −.001 (.001) −.001* (.0005) −.002* (.0003)ln(Export Shareij) −.001 (.004) −.002 (.004) −.004 (.006)Proposed Integrationk −.028* (.005) −.024* (.007) −.033* (.008)1970s −.007 (.011)1980s .003 (.009)1990s −.009 (.008)Europe −.001 (.010)Latin America .006 (.014)Africa −.008 (.015)Constant .027* (.008) .046* (.021) .035 (.017)Observations 1280 1280 868P > F 0.0000 0.0000 0.0000R-Squared .06 .07 .06

Dependent Variable: PTA Effectij *p < .05 Robust (PTA-clustered) standard errors in parentheses.

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legalistic DSM, in contrast, raises the annual growth rate in import shares byexp(.021): about 2.1 percent per year and 23 percent over ten years. This isquite a large effect, comparable to the difference between the EC and ECO-WAS shown in Figure 1. A one-standard deviation (.35) shift in the veto play-ers index boosts trade growth by .35*exp(.019): .7 percent per year and 8percent over ten years. Domestic veto players, like regional asymmetries, thushave substantively small effects. Finally, because factor similarity is the inverseof the absolute difference in logged GDPs, the impact of factor similarity isexp(αβ), where α = ln([100 + x]/100), x is the percentage change in the ratioof the two countries’ GDPs, and β is the factor similarity coefficient (.003).Reducing factor-endowment differences by, say, 75 percent—roughly the dif-ference between the U.S.–Canada and U.S.–Mexico dyads—thus raises annualgrowth in intra-bloc trade by exp((.56)(.003)) or .17 percent. Over ten years,trade would grow about 1.7 percent more in the former than in the latterdyad. This is a very small effect: factor-endowment differences appear toaccount for little variation in PTA performance. In sum, although all four vari-ables of interest are statistically significant, only international institutions haveclearly important effects on intra-bloc liberalization. It is worth noting, how-ever, that the potential impact of regional asymmetries and veto players—resulting from a minimum-to-maximum rather than a one-standard deviationshift in these variables—is about three times larger than the effects discussedabove and comparable to the impact of regional institutions.

CONCLUSION

My analysis yields several broad conclusions. First, regional system struc-ture—and, by extension, international system structure—does not seem tomatter much for trade cooperation. Although regional asymmetries have sig-nificant effects in some models, these effects are substantively small. More-over, they are inconsistent with the predictions of hegemonic stability theory(Kindleberger, 1973) and recent applications of that theory to regional inte-gration (Mattli, 1999). Whereas these theories posit that the presence of ahegemon eases cooperation problems, my results suggest that such asymme-tries raise distributional concerns that hinder cooperation. The finding thatasymmetries do not promote cooperation is perhaps unsurprising, sincescholars have long observed that the original hegemonic stability argumentdoes not apply to excludable goods such as trade liberalization (Snidal, 1985).The finding that asymmetries actually hinder cooperation suggests that thestudy of international system structure could usefully redirect its attentionaway from collective action problems and toward distributional concerns.

Second, the effects of factor-endowment differences are also substantivelysmall. This augurs well for the prospects of North–South integration schemes,which have proliferated in the last few years. Although these arrangements

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should be slightly less successful than their North–North and South–South prede-cessors, the difference is practically negligible. Although this does not mean thatthe current wave of North–South arrangements will succeed, it does imply thattheir economic composition alone constitutes no great obstacle to their success.

Third, international institutions appear to matter a lot. Of the variablestested here, dispute settlement institutions have by far the largest impact onintra-PTA liberalization. This is encouraging news for those seeking activelyto promote regional integration. Although some “given” factors such asregional system structure and factor endowments may work against regionalintegration, their effects can be more than offset by the appropriate designof international institutions.

Fourth, it is interesting that veto players promote intra-PTA liberaliza-tion, even though previous research (Mansfield, Milner, and Pevehouse,2007) shows that a high number of veto players lowers the probability ofPTA formation. Together, these findings indicate that veto players haveambiguous net effects on international cooperation: they reduce the likeli-hood of cooperative arrangements but increase the credibility and improvethe implementation of arrangements that are made. It would be useful infuture research to measure the relative magnitude of these effects so that wecan assess the net impact of veto players on cooperation.

Finally, it is worth noting that variation in the success of regionalarrangements remains something of a puzzle. Although my analysis pointsto a number of significant influences on PTA implementation, the low R-squared (.06) indicates that we are far from fully understanding this phe-nomenon. This does not weaken my conclusions, but it does mean thatmuch work remains to be done. I hope this paper stimulates such researchand further discussion of how such research might proceed.

CONTRIBUTOR

Daniel Y. Kono is an Assistant Professor of Political Science at the Univer-sity of California at Davis. His research on the political economy of tradepolicy has been published in the American Political Science Review, Businessand Politics, International Studies Quarterly, and The Journal of Politics.

NOTES

1. 1958 Treaty of Rome; 1975 Treaty of Lagos.2. Each bloc’s intra-bloc import share is its imports from bloc members divided by its total imports.3. Mattli refers to “integration” rather than “liberalization,” but it is clear from his analysis that these

terms are interchangeable.4. Some readers may be surprised that the sample is not larger because, according to the WTO, there

are 187 regional trade arrangements in force (http://www.wto.org). However, only 59 of these agreements

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were formed before or in 1995. Many of these 59 agreements are “redundant,” in that they are listed morethan once: for example, NAFTA, the EC, ANZCERTA, and many of the EC’s Association Agreements arelisted first as trade agreements, then again as service agreements. Moreover, the WTO lists all associationagreements separately (e.g., EC-Bulgaria, EC-Hungary, etc.), whereas Table 1 lists them collectively (e.g.,“EC-CEEC Associations”). Although I omit some PTAs, such as the Central European Free Trade Agree-ment, due to data limitations, my sample otherwise includes all that meet the above criteria.

5. Import, GDP, and population data are from Gleditsch’s (2002) Expanded Trade and GDP DataSet. I truncate each PTA’s temporal scope because if I did not, older PTAs would have few pre-PTAobservations but many post-PTA observations, while recently formed PTAs would display the oppositepattern. By limiting each PTA’s temporal scope, I obtain a balanced data set in which all PTAs areequally represented before and after PTA formation.

6. Alliance data are from the COW2 dyadic alliance data set. Polity scores are from the Polity IVdata set. MID data from 1948–92 are from Russett and Oneal (2001), while data from 1993–2001 are fromGhosn and Bennett (2003). GATT membership data are from Reinhardt (1999).

7. And not just the additional trade. A before-after comparison of trade levels could generate thesame spurious conclusions as a purely “after” focus on trade changes because an increase (decrease) inthe level of trade could simply reflect a secular liberalizing (protectionist) trend that preceded, and wasnot caused by, the PTA. It is thus essential to focus on the change in the growth rate of trade.

8. To guard against unit-root problems, I have also performed the analysis in first differences. Thefirst-differenced model generates virtually identical results, substantially easing unit-root concerns (Beckand Katz, 2004).

9. Not surprisingly, these variables are insignificant when included and do not affect my results.10. An alternative approach would be to employ a hierarchical model in which the level-1 vari-

ables are dyadic and the level-2 variables are regional. However, Kam and Franzese’s (forthcoming)Monte Carlo simulations indicate that, while both approaches generate better standard error estimatesthan conventional ordinary least-squares (OLS), they do not differ appreciably from one another.

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APPENDIX

Control Variable Results from Gravity Regressions

VariableRegression with Regional

PTA DummiesRegression with Dyadic

PTA Dummies

ln(Import Shareijt-1) .968* (.001) .965* (.001)dln(GDPjt) .064* (.004) .066* (.005)dln(Populationjt) −.151* (.012) −.163* (.012)ln(Distanceij) −.004* (.0004) −.004* (.0004)Contiguityij .007* (.002) .007* (.002)Common Languageij .0004 (.001) −.0001 (.001)Allianceijt .014* (.001) .016* (.002)Militarized Disputeijt −.016* (.006) −.016* (.006)Democratic Dyadijt .0005* (.00003) .0006* (.00003)GATT Dyadijt .0004 (.0004) .0006 (.0005)Constant .039* (.004) .040* (.004)Observations 539,254 539,254R-Squared .95 .95

Dependent Variable: ln(Import Shareijt) *p < .05 Robust (dyad-clustered) standard errors in parentheses.

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