trade creation and diversion effects of regional trade agreements: a product-level analysis

Trade Creation and Diversion Effectsof Regional Trade Agreements:

A Product-level AnalysisShujiro Urata1 and Misa Okabe2

1Graduate School of Asia Pacific Studies, Waseda University, Tokyo, Japan and2Faculty of Economics, Wakayama University, Wakayama, Japan

1. INTRODUCTION

ACCORDING to the World Trade Organization (WTO), as of January 2013, 546 regional

trade agreements (RTAs) have been notified to the General Agreement on Tariffs and

Trade (GATT) and the WTO since 1948, and 354 RTAs are in force.1 While the total number

of notified RTAs was 124 for 36 years from 1948 to 1994, 422 additional RTAs were notified

in the 18 years from 1995 to 2010. In the light of the rapid expansion of RTAs, it is only nat-

ural that a large number of studies concerning their effects on foreign trade have been carried

out from both theoretical and empirical perspectives. Of these studies, ex post-evaluation

plays a central role in empirical studies. Although the main object of these studies is to verify

a very simple question, whether RTAs have trade creation and/or trade diversion effects, there

seems to be little agreement as to the nature of the impacts of an RTA on trade flows.2

There are two main unsettled issues: one concerns the econometric methods used. A num-

ber of studies estimated the gravity equation to find the impacts of RTAs. These encountered

econometric problems, such as the endogeneity of RTA variables, which are used as explana-

tory variables, and the treatment of zero trade flow values. Concerning the former, recent

studies have applied econometric techniques such as the instrumental variables (IV) method,

fixed effect model for panel data and difference-in-difference analysis.3 Baier and Bergstrand

(2007) argue that the most important source of endogeneity relationships is the omitted vari-

able bias. The zero trade flow problem has attracted attention recently, and new estimation

methods, rather than simply replacing zero by small values, have been attempted.

The other issue is the level of aggregation. Many studies examine the impacts of RTAs

using aggregated trade data. However, this does not seem to capture the impacts of RTAs

accurately, since generally the treatment of tariff reduction/elimination differs substantially by

product. An appropriate analysis should examine the impacts at product level.

In this paper, we address these two issues. Concerning the econometric issues,

we use panel data and include fixed effects to deal with endogeneity caused by unobserved

The authors are grateful for the comments received from Mitsuyo Ando, Shiro Armstrong, MasahisaFujita, David Greenaway, Helan T. Naughton, Katsuhide Takahashi and Ryuhei Wakasugi. This researchwas conducted as a part of a project entitled ‘FTA study’ for the Research Institute of Economy, Tradeand Industry.

1 See WTO webpage, http://www.wto.org/english/tratop_e/region_e/rta_pta_e.htm2 Baier and Bergstrand (2007) pointed out ‘fragility’ of estimated treatment effects of RTAs.3 For example, see Egger (2004) and Carrere (2006) for the application of IV method, Baier andBergstrand (2007) for fixed effect model, and Egger et al. (2008) for difference–in-difference matchingtechniques.

© 2013 John Wiley & Sons Ltd 267

The World Economy (2014)doi: 10.1111/twec.12099

The World Economy

heterogeneity of country pairs. To deal with the zero trade flow problem, we apply the Pois-

son pseudo-maximum likelihood (PPML) model. To overcome the problem caused using

aggregated trade data, we undertake the analysis using product-level trade data at SITC 2

digit level.

The structure of the paper is as follows. Section 2 briefly reviews previous studies, which

estimated the effects of RTAs on trade using the gravity model. Section 3 presents the theo-

retical framework and the specification of the gravity equation applied; Section 4 describes

the data used; Section 5 presents the results of estimation; while Section 6 concludes and

draws some policy implications.

2. LITERATURE REVIEW

Our analysis is based on the estimation of the gravity model, which has been applied

extensively to explain bilateral international trade flows for more than four decades. The pio-

neering studies are Tinbergen (1962) and Poyhonen (1963). Although numerous empirical

analyses have been conducted, it was not until the late 1970s that theoretical foundations for

the gravity equation were developed. The first study is Anderson (1979), which derives a sim-

ple theoretical gravity equation from a framework of two countries under complete specialisa-

tion. In the 1980s, ‘new trade theory’ with an assumption of monopolistic competition used to

explain intra-industry trade was applied to test the gravity equation.4

In the context of the estimation of effects of RTAs on international trade, one of the most

serious concerns is the endogeneity of RTA variables. As will be discussed in detail in subsec-

tion 3.b, this arises as RTA variables tend to be correlated with the error term in the estimated

equation. Several studies dealt with this. For example, Egger (2004) and Carrere (2006) applied

the IV method proposed by Hausman and Taylor (1981). Baier and Bergstrand (2007) sug-

gested that a first differential fixed effect model may be effective in dealing with it.

The main concern of this paper is to estimate the effects of bilateral and regional RTAs on

trade at product level. Many studies have applied the gravity equation to estimate the effect

of RTAs at product level. Fukao et al. (2003) estimated trade creation and trade diversion

effects of the North American Free Trade Agreement (NAFTA) using product-level trade

data. They estimated an equation based on a partial-equilibrium model under monopolistic

competition using the fixed effect model, while the problems of endogeneity and zero trade

flows are not controlled explicitly. Gilbert et al. (2004) used sectorally aggregated trade data

to estimate the impacts of major regional RTAs, and Endoh (2005) analysed disaggregated

trade data to verify the effect of the global system of trade preferences. Like the earlier stud-

ies, problems of endogeneity and zero trade flows were not dealt with explicitly. Powers

(2007) is one of the few to have attempted to remove biases caused by endogeneity and zero

trade flows by applying the first-differencing fixed effect model to the theoretically consistent

gravity model. He used panel data consisting of 75 countries and three periods during 1990–2000 for 25 sectors at the ISIC 3-digit level. Although his estimated gravity equation enabled

him to control for biases which result from endogeneity and zero trade flows, the different

impacts of each type of RTAs were not examined.

4 For example, Helpman (1987) tested the hypotheses derived from the model using cross-country andtime series data, and Bergstrand (1989) developed the gravity equation based on monopolistic competi-tion model. Baier and Bergstrand (2009) suggested a way to measure the price term in the gravityequation derived from the monopolistic competition model.

© 2013 John Wiley & Sons Ltd

268 S. URATA AND M. OKABE

While this paper adapts the specification of Powers (2007), we extend his analysis in three

respects: first, expansion of the data periods by covering 1980–2006; second is the estimation

method. Powers (2007) uses the first-differencing system to control for endogeneity and zero

trade flows, while we apply a fixed effect PPML estimator. The third extension is that we

examine the impacts of RTAs from two new angles. One is consideration of the different

characteristics of RTAs by their memberships, and the other is analysis of the impacts of

trade creation and diversion effects of RTAs separately.

3. APPLICATION OF THE GRAVITY EQUATION TO TRADE FLOWS AT PRODUCT LEVEL

a. A Theoretically Consistent Gravity Equation

Like Powers (2007) who applied Anderson and van Wincoop’s (2004) ‘class of trade sepa-

rable model’ to his estimation with sectoral trade data, we construct a gravity equation for

product trade under the assumption of monopolistically competitive firms, and two-stage bud-

geting that separates the allocation of expenditure across product classes (k = 1,…, K) from

the allocation of expenditure within a product class, across countries of origin (j = 1,…, J). Inother words, ‘the class of trade separable’ is grounded on the allocation of production Y and

expenditure E of country i to product k {Yik, Eik}, which is separable from the bilateral alloca-

tion of trade across countries, and the model assumes separable technology and preferences.

The variety of product k has an identical aggregator across countries of origin, which takes

the constant elasticity of substitution (CES) form with elasticity of substitution of product kexpressed as gk. The CES demand function for each variety in country i is determined by pijk,that is, c.i.f. price of country i’s import of product k shipped from country j; Pik, the CES

price index denoted as ðPikÞ1�gk ¼ Pj ðpijkÞ1�gk and total expenditure on variety k. The rela-

tionship between c.i.f. price pijk and its f.o.b. price in country j, pik is given by pik = pjkTijk,where Tijk is the ad-valorem equivalent of the trade cost of product k from country j to

country i.Solving the price pjk from the market clearing conditions pjkYjk ¼

Pi xijkpjkTijk and substi-

tuting the result in the demand function for each variety in country i, and the CES price

index, we can derive the gravity equation, in which country i’s import of k from country jdepends on expenditure on k by country i, production of k by country j, the world production

of k, denoted by YWk and trade cost Tijk and price indexes Pik, Pjk interpreted as the multilat-

eral resistance term, as follows:

xijk ¼ EikYjkYWk

� �Tijk

PikPjk

� �1�gk

: (1)

b. Estimation Approaches to Endogenous Bias and Zero Trade Flows

According to Baier and Bergstrand (2007), explanatory variables of the gravity equation

including RTA terms tend to be correlated with the error term eijk. This is the problem of

endogenous biases. They pointed out the possibility of endogenous bias caused by omitted

variables. The omitted variables problem comes from the correlation between the decision to

form an RTA and unobservable bilateral economic- or policy-related conditions included in

the error term. Baier and Bergstrand (2004) showed empirical evidence that the larger the


TRADE CREATION AND DIVERSION EFFECTS OF RTAs 269

GDP and the more similar economically, the higher the probability of the formation of an

RTA between two countries. The probability of the formation of an RTA is also higher when

the difference between two countries with respect to factor endowments is larger. These fac-

tors are regarded as source of the potential for trade creation and promoting the governments

to RTAs, but they are not included in the estimated equation. Baier and Bergstrand (2007)

pointed out that IV methods applied to cross-section data so as to address the endogenous bias

are not reliable because of difficulties in selection of appropriate IVs. Instead of using IVs,

they suggested an application of the fixed effects model using panel data, because the source

of the endogeneity bias in the gravity equation could be unobserved time-invariant heteroge-

neity. As in Baier and Bergstrand (2007), we apply the fixed effects model using panel data

to control for endogeneity bias.

The zero trade flow problem is also a matter of serious concern. In particular, zero trade

flows may appear frequently in sectoral and product-level trade data.5 For instance, the per-

centage of zero trade flows in the total number of our sample is about 50 per cent. Most

studies on the impact of RTAs on foreign trade, by estimating the gravity equation, omitted

zero trade flows since the log of a zero value is not defined. However, omitting zero trade

flows results in biased results if zero trade flows do not occur randomly. It seems appropriate

to assume that factors such as distance, lack of political and cultural links, and large differ-

ences in production structures lead to zero trade between countries.

Several studies have dealt with this using the Tobit model, the Heckman sample selection

model and the PPML method. Trade flows could not be negative, and import values from the

UN COMTRADE database, which we use for the estimation, are reported when values are

greater than 1 US$. Consequently, the gravity model with zero trade flows is categorised as a

type 2 Tobit model, namely the sample selection model.6 Linders and de Groot (2006) employ

the Heckman sample selection model, which incorporates the decision on trade. Given that

zero trade flows usually do not appear randomly, the sample selection model is appropriate.

However, theoretically founded models of the firm’s decision on trade have not been well

developed, and thus, it is difficult to find specific explanatory variables for the selection

model. In the case where the same variables are used in both the selection and the outcome

equations, multicollinearity could be caused and error variances increase. Santos Silva and

Tenreyro (2006) on the other hand propose the PPML estimation technique which provides a

way to deal with zero trade values. They also show that the PPML estimation can provide

robust estimates in the presence of heteroscedasticity. Although Martin and Pham (2008) dem-

onstrated the PPML estimator yields severely biased estimates when zero trade flows are fre-

quent, Santos Silva and Tenreyro (2011) extended their simulation to the case of a large

proportion of zeros, and confirmed that the PPML estimator is generally well behaved even

when the dependent variable, that is trade flow in the gravity model, has a large proportion of

zeros in overall bilateral trade.

Taking account of these, we apply the fixed effects PPML estimation technique with robust

standard errors to our sectoral gravity equations.

5 Zero trade flow has two meanings; one is the case in which a country pair has no trade, and the otheris a zero-valued trade that is less than the reporting threshold.6 Amemiya (1985) classified Tobit model into five categories according to similarities in the likelihoodfunction. The type 2 Tobit model is applied to the case when an individual makes decision in two steps.



c. Estimation Specification

We specify a gravity equation for each product for the estimation as follows:7

xijt ¼ a0YitYjtðTijt=PitPjtÞ1�g; (2)

where, xijt denotes the import value of country i from country j at time t. Yit and Yjt aredemand and production of product k at time t of importer and exporter, respectively. We use

GDP for Y since both sectoral demand and production data of all sample countries and sample

periods are not available. Tijt denotes the trade cost affecting import directly when country iimports product k from country j. a0 is a constant. Following a number of studies using the

gravity model to estimate RTAs’ impacts on trade, we assume that the bilateral trade cost is

expressed as the following linear combination of observable measures.

Tijt ¼ DISkij expð�dBBORij � dLLANij � dRTARTAijtÞ; (3)

where DISij is the geographical distance between the most populous cities of countries i and jmeasured in kilometres, BORij and LANij are dummy variables that take unity if country i andj share a common border and common official languages, respectively, and RTAij is an RTA

dummy variable that takes unity if the country pair belongs to the same RTA. In our analysis,

we classify RTAs into three groups based on the following characteristics, the form of agree-

ment, the number of members and the level of economic development of members. Addition-

ally, we examine the effects of seven major RTAs, the European Union (EU), North

American FTA (NAFTA), ASEAN FTA (AFTA), Mercado Comun del Sur (MERCOSUR),

Andean Sub-regional Integration Agreement (CAN), Common Market for Eastern and South-

ern Africa (COMESA) and Pan-Arab FTA. Regarding the effects of RTAs on trade cost, two

types of dummy variables are adopted. The first equals unity if both importer and exporter

belong to the same RTA, and the second equals unity if the importer is a member of the

RTA, but the exporter is not. These dummies are used to examine trade creation and diversion

effects, respectively. Substituting equation (3) into (2) and adding country-pair fixed effects

yields the following equation:

xijt ¼ expða0 þ aij þ ð�dBBORij � dLLANij � dRTARTAijtÞ1�gÞDISkijYitYjtPg�1it Pg�1

jt þ eijt: (4)

Following Baier and Bergstrand (2009), we apply multilateral resistance terms that are gen-

erated by a first-order log-linear Taylor-series expansion around the equilibrium values of the

price index. The conditional mean of the exponential form of equation (4) can be written as

follows:

Eðxijtja0; aij;BORij; LANij;RTAij;DISijYitYjtPitPjtÞ¼ expða0 þ aij þ ð�dBBORij � dLLANij � dRTARTAijÞ1�gÞDISkijYitYjtMRg�1

ijt :(5)

For estimation, we keep the multiplicative form of equation (5) to avoid dropping all

observations with zero trade value in the process of log-linearisation.8 We apply the fixed

effects PPML estimation; therefore, all time-invariant variables which are defined as factors

of trade cost such as geographical distance, common border and language dummies are

7 To simplify the expression, we henceforth drop index k.8 Westerlund and Wilhelmsson (2011) describe the exponential regression function as an alternativeapproach of estimating a log-linearised gravity equation with zero value.



eliminated from the regression. In addition, time dummy variables (TDU) are included to

capture unobservable time effects. Taking these into account, we obtain the following

estimating equation, known as an exponential regression function;

Eðxijtja0; aij; Yit; Yjt;Pit;PjtÞ ¼ expða0 þ aij � dRTARTA1�gijt þ c1 lnðYitÞ þ c2 lnðYjtÞ

þXM

kM lnMRMijt þ

X2006d¼1981

ldTDUtÞ: (6)

Multilateral resistance terms are expressed as a function of the weighted average of import-

ers’ trade cost with all exporters, exporters’ trade cost with all importers and trade cost

imposed imports throughout the world. Each multilateral resistance term is described as

follows:

lnMRMijt ¼

Xi

sitTCFMijt þ

Xj

sjtTCFMijt þ

Xi

Xj

sitsjtTCFMijt ; (7)

where TCF denotes the factors associated with trade cost, namely geographical distance,

dummy variable of sharing of border and language and dummy variable of RTA. The super-

script M indicates the type of trade cost. We apply the conventional fixed effects PPML

estimator with robust standard errors to equation (6) using STATA 10 (StataCorp, College

Station, TX).9

4. DATA DESCRIPTION

The data set used is a panel with 20 products for 67 countries/regions and 27 years from

1980 to 2006. The list of sample countries is shown in Table 1. Bilateral product trade data

are taken from the World Integrated Trade Solution (WITS) developed by the World Bank in

collaboration with the UNCTAD. The trade data provided by WITS are compiled from the

United Nation’s Commodity Trade Statistic Database. We use data at SITC 2 digit level.

GDP data are taken from the World Bank’s World Development Indicators. For the GDP of

Taiwan, we use GDP released by the National Statistical Bureau of the Republic of China.

Both nominal trade and GDP are converted in real US dollars using the market exchange rates

and the US consumer price index from the Bureau of Economic Analysis. RTA dummy vari-

ables are created based on the information on the year of establishment, which is available on

the WTO website. Membership of the EU increased during the sample periods; thus, the EU

dummy was created to reflect these developments.

Before discussing results, we must make some observations on the sample data. We use

import value at product level for the dependent variable. Table 2 shows import values of the

world and annual growth rates during the sample period for 20 products. Petroleum (SITC33),

road vehicles (SITC78), electrical machinery (SITC77) account for large shares of total

imports. While total import value for the world increases from 0.16 trillion to 12 trillion at an

annual average growth rate of 7.5 per cent, the growth rates for all machinery (SITC74-77)

and medical and pharmaceutical products (SITC54) during the sample periods are high, each

exceeding 8 per cent. By contrast, import values of agricultural products (SITC01, 04, 05),

9 STATA module to estimate the fixed effects Poisson regression with robust standard errors, xtpqml isprovided by Simcoe (2007)



crude materials (SITC24, 26) and petroleum grew at lower rates, resulting in a decline in their

share in the total. Summary statistics of the variables used for the estimation are shown in

Table 3.

5. ESTIMATION RESULTS

a. Results of Benchmark Estimation

First, for benchmarking purposes, we estimate equation (6) with an ‘all RTA dummy’

which equals unity if a country pair are members of an RTA regardless of its type. Table 4

shows the estimation results. The number of observations varies among the products since

some country pairs with zero import value during all sample periods are not counted. A high

share of zero import values appears for meat and meat preparation (71 per cent), cork and

wood (62.6 per cent) and articles of apparel (62.6 per cent) while low shares are found for

textile yarn and fabrics (36.1 per cent), electrical machinery (36.8 per cent) and general

machinery (37.8 per cent). Machinery and equipment manufactured products, on the whole,

have a relatively low share of zero imports in bilateral trade flows.

Taking a look at the estimated coefficients of importers’ and exporters’ GDP, positive and

statistically significant coefficients are found for many products. The results at product level

imply that importers’ demand is a significant and positive factor that increases trade of agri-

cultural products such as meat and vegetables and materials such as cork, textile fibres and

petroleum, while the magnitude of exporters’ production is a strong factor causing an increase

in import of all machinery products as the coefficients of exporters’ GDP tend to be larger

than those for the importers.

The coefficient of the RTA dummy variable in the benchmarking estimation, namely ‘all

RTA dummy’, denotes the impact of the RTA on imports from members of the same RTA.

Of 20 products, positive coefficients are shown at less than the 5 per cent level of statistical

significance for seven products (meat and meat preparations, cereals and cereal preparations,

TABLE 1Sample Countries/Economies

Algeria Ethiopia Kuwait Saudi ArabiaArgentina Finland Libyan Arab Jamahiriya SingaporeAustralia France Luxembourg South AfricaAustria Germany Malaysia SpainBelgium Ghana Mexico SudanBelgium-Luxembourg Greece Morocco SwedenBolivia Hungary Netherlands SwitzerlandBrazil Iceland New Zealand TaiwanBulgaria India Nigeria ThailandCanada Indonesia Norway TunisiaChile Iran Pakistan TurkeyChina Ireland Paraguay United Arab EmiratesHong Kong Israel Peru United KingdomColombia Italy Philippines United States of AmericaDenmark Japan Portugal UruguayEcuador Kenya Romania VenezuelaEgypt Korea, Republic of Russian Federation



vegetables and fruit, textile fibres and their wastes, textile yarn, fabrics, made-up articles, iron

and steel, non-ferrous metals) and at less than the 10 per cent level of significance for two

products (general industrial machinery, and telecommunications and sound recording equip-

ment). These results indicate that trade creation is found for these nine products. The

estimated coefficients for meat and meat preparations, cereals and cereal preparations, and

textile fibres and their wastes are found to be relatively larger than for other products. Rec-

ognising that MFN tariff rates on agricultural products are relatively high among all countries,

one would suppose it reasonable to find significant trade creation effect of the RTA for these

commodities.10 By contrast, the effect cannot be detected for chemical products, mainly

because tariff rates on these products are relatively low both among developing and developed

countries. The results in Table 4 shows that there are quite a few products for which the

impacts of RTAs are not detected. This may possibly be due to our treatment of all RTAs as

TABLE 2Commodity Import Values in the World During Sample Periods

Commodity Import Value (Million US$) Annual GrowthRate 1980–2006(%)1980 (Share in Total) 2006 (Share in Total)

SITC01 Meat and meat preparations 34,046,627 (1.12) 64,927,036 (0.63) 2.42SITC04 Cereals and cereal preparations 47,524,553 (1.56) 73,513,215 (0.71) 1.63SITC05 Vegetables and fruit 49,261,809 (1.61) 113,778,510 (1.10) 3.15SITC24 Cork and wood 45,479,509 (1.49) 50,813,946 (0.49) 0.41SITC26 Textile fibres and their wastes 25,853,301 (0.85) 25,645,015 (0.25) �0.03SITC33 Petroleum, petroleum products 671,538,300 (22.01) 1,233,383,441 (11.98) 2.28SITC51 Organic chemicals 54,151,301 (1.77) 255,953,232 (2.49) 5.92SITC52 Inorganic chemicals 29,048,151 (0.95) 64,051,862 (0.62) 2.97SITC54 Medicinal and

pharmaceuticalproducts

22,372,107 (0.73) 272,579,658 (2.65) 9.70

SITC63 Cork and wood manufactures 15,073,370 (0.49) 46,277,397 (0.45) 4.24SITC65 Textile yarn, fabrics,

made-up articles84,647,617 (2.77) 170,331,223 (1.65) 2.62

SITC67 Iron and steel 104,279,028 (3.42) 319,486,282 (3.10) 4.23SITC68 Non-ferrous metals 85,590,418 (2.81) 258,592,454 (2.51) 4.18SITC74 General industrial machinery 90,813,832 (2.98) 362,728,894 (3.52) 5.26SITC75 Office machines and auto-data

processing equipment49,419,007 (1.62) 466,848,379 (4.53) 8.67

SITC76 Telecommunications, soundrecording and reproducingequipment

54,724,950 (1.79) 486,809,183 (4.73) 8.43

SITC77 Electric machinery 104,866,535 (3.44) 901,974,746 (8.76) 8.30SITC78 Road vehicles 203,941,392 (6.68) 830,292,982 (8.06) 5.34SITC84 Articles of apparels 68,853,028 (2.26) 265,816,159 (2.58) 5.13SITC87 Professional and scientific

instruments31,863,165 (1.04) 218,319,995 (2.12) 7.39

Note:Values given in parentheses are expressed as percentage.

10 See Table 7.



TABLE 3Summary Statistics

Variable Obs Mean SD Min Max

Real import value 2,287,040 2.93e+07 3.65e+08 0 5.79e+10Log GDP 114,352 25.2588 1.59016 21.53983 30.07049Multilateral resistance-distance 114,352 24.5297 75.9901 0.205088 2312.957Multilateral resistance-adjacency 114,352 0.09250 0.30897 5.18e-06 8.00011Multilateral resistance-language 114,352 0.28033 0.92021 0.000044 25.8818Multilateral resistance-All RTAs 114,352 0.67246 2.28066 8.58e-06 78.2111All RTAs 114,352 0.23645 0.42490 0 1RTA btw. developedTC 114,352 0.08522 0.27921 0 1TD 114,352 0.28391 0.45090 0 1RTA btw. G77TC 114,352 0.10043 0.30057 0 1TD 114,352 0.20805 0.40592 0 1RTA btw. developed importer and G77 exporterTC 114,352 0.02547 0.15756 0 1TD 114,352 0.22771 0.41936 0 1RTA btw. G77 importer and developed exporterTC 114,352 0.02533 0.15711 0 1TD 114,352 0.18922 0.39169 0 1Free trade agreementTC 114,352 0.02277 0.14918 0 1TD 114,352 0.21548 0.41115 0 1Customs unionTC 114,352 0.03975 0.19536 0 1TD 114,352 0.24875 0.43229 0 1Partial scope agreementTC 114,352 0.13082 0.33721 0 1TD 114,352 0.25722 0.43710 0 1Bilateral RTAsTC 114,352 0.00236 0.04853 0 1TD 114,352 0.08727 0.28224 0 1Plurilateral RTAsTC 114,352 0.18554 0.38874 0 1TD 114,352 0.50964 0.49991 0 1EU enlargementTC 114,352 0.03676 0.18816 0 1TD 114,352 0.15926 0.36592 0 1NAFTATC 114,352 0.00068 0.02611 0 1TD 114,352 0.02149 0.14500 0 1AFTATC 114,352 0.00262 0.05115 0 1TD 114,352 0.04001 0.19598 0 1MERCOSURTC 114,352 0.00280 0.05283 0 1TD 114,352 0.04254 0.20181 0 1CANTC 114,352 0.00199 0.04461 0 1TD 114,352 0.04117 0.19869 0 1



one type without considering the differences in membership such as developed and develop-

ing countries. Furthermore, we did not consider the trade diversion effect, which may be

caused by RTAs. We will take up these issues in the next sections.

b. Trade Creation and Diversion Effect by Type of RTAs

Regional trade agreements take various forms and their impacts on product trade are likely

to be different for different types of RTA. To examine differences, we adopt the following

three classifications. First, we apply the WTO’s classification by the form of agreement. Sec-

ond, we divide all RTAs into bilateral and plurilateral. The third classification is according to

the level of economic development of the RTA members. We construct two RTA dummy

variables which capture trade creation and trade diversion effects respectively. The RTA

dummy variable which captures the trade creation effect is the same as that used in the previ-

ous section, while the RTA dummy variable which captures trade diversion equals unity when

the importer country belongs to a RTA but the exporter country does not. If the coefficient is

significantly negative, it indicates that imports from a non-member country decrease as a

result of the formation of RTA.

We first examine the impacts of RTAs on trade by classifying the RTAs based on the

WTO grouping, namely free trade area (FTA), customs union (CU) and partial scope agree-

ment (PSA).11 As Table 5 shows, among the three types, FTA is the most common. CU and

PSA take the form of plurilateral RTA. Examples of CUs are the European Community

(EC), the Mercado del Sur (MERCOSUR) and the Gulf Cooperation Council (GCC). An

example of a PSA is the Global System of Trade Preferences (GSTP).

Table 6 summarises estimation results by type of RTA as classified by the WTO. The fig-

ures shown are the marginal effects of trade creation (TC) and trade diversion (TD) resulting

from the formation of RTAs, which are statistically significant at less than 5 per cent. While

trade creation effect of FTAs are found for only one product, cereals and cereal preparations,

CUs give rise to a trade creation effect for eight products, and the effect is particularly strong

for agricultural products (SITC01 and 04) and textile fibres (SITC 26). As we discuss later,

the trade creation effect for these agricultural products is substantial in the EU, MERCOSUR

and CAN. Strong trade creation observed for the agricultural products and textile materials is

TABLE 3 Continued

Variable Obs Mean SD Min Max

COMESATC 114,352 0.00227 0.04763 0 1TD 114,352 0.03467 0.18295 0 1Pan-Arab FTATC 114,352 0.00331 0.05740 0 1TD 114,352 0.03251 0.17734 0 1

Note:RTAs, regional trade agreements.

11 Although Economic Integration Agreement (EIA) is included in the classification of the WTO, EIA isthought of as a subset of FTA. Therefore, we classify EIA as FTA.



TABLE4

Estim

ationResults:TheBenchmarkSpecification

Commodity

All

RTAs

Importer

GDP

Exporter

GDP

Multilateral

RTA

Multilateral

Distance

Multilateral

Adjacency

Multilateral

Language

Obs.

Log

Likelihood

%of

Zero

SITC

01

Meatandmeat

preparations

0.5253**

*(0.116)

0.5251**

(0.236)

�0.1227

(0.202)

�0.0033

(0.010)

0.0207*

(0.011)

�1.2719

(1.815)

�1.1018*

(0.554)

72,933

�2.1e+

11

71.4

SITC

04

Cereals

and

cereal

preparations

0.6671**

*(0.115)

0.2256

(0.204)

0.0469

(0.211)

�0.0174**

(0.009)

�0.0006

(0.005)

0.5406

(0.622)

�0.1629

(0.505)

95,020

�3.42e+

11

56.5

SITC

05

Vegetables

andfruit

0.1597**

(0.073)

0.4944**

*(0.148)

�0.2982*

(0.175)

�0.0023

(0.007)

0.0078

(0.006)

0.3495

(0.668)

�0.4805

(0.431)

103,282

�2.07e+

11

44.3

SITC

24

Cork

and

wood

�0.0616

(0.111)

1.3286**

*(0.366)

�1.0275**

*(0.340)

�0.0030

(0.007)

�0.0031

(0.009)

0.7290

(2.190)

0.2504

(0.345)

87,916

�1.47e+

11

62.6

SITC

26

Textile

fibres

andtheirwastes

0.4280**

*(0.141)

1.1808**

*(0.182)

0.3036*

(0.162)

0.0171

(0.012)

�0.0229**

*(0.007)

1.0694

(0.997)

1.6252**

*(0.569)

97,401

�1.44e+

11

53.1

SITC

33

Petroleum,

petroleum

products

0.1267

(0.112)

1.2603**

*(0.188)

0.0392

(0.111)

�0.0026

(0.009)

0.0025

(0.010)

�1.5458

(1.302)

0.2760

(0.532)

93,882

�2.17e+

12

56.3

SITC

51

Organic

chem

icals

0.0249

(0.087)

1.0780**

*(0.159)

1.7240**

*(0.307)

�0.0108

(0.007)

0.0059

(0.006)

�0.9665

(0.688)

�0.1553

(0.478)

98,955

�4.27e+

11

47.3

SITC

52

Inorganic

chem

icals

�0.0242

(0.082)

0.8254**

*(0.126)

0.6147**

*(0.142)

�0.0169**

*(0.005)

0.0076**

(0.004)

0.6677

(0.421)

�0.7308**

*(0.290)

99,194

�1.53e+

11

50.4

SITC

54

Medicinal

and

pharmaceutical

products

�0.1236

(0.090)

�0.3472*

(0.194)

1.2433**

(0.512)

0.0004

(0.013)

�0.0158*

(0.009)

4.0148**

*(1.511)

0.2113

(0.589)

99,875

�3.05e+

11

48.2

SITC

SITC63

Cork

andwood

manufactures

0.1517

(0.132)

0.4177

(0.275)

0.0184

(0.313)

0.0282**

(0.014)

�0.0079

(0.011)

0.2497

(1.562)

0.9356

(0.698)

100,928

�1.18e+

11

49.3

SITC

65

Textile

yarn,

fabrics,

made-uparticles

0.3851**

*(0.119)

0.4771**

(0.225)

0.5212**

*(0.138)

0.0097

(0.0130)

�0.0043

(0.008)

0.7439

(0.965)

0.3433

(0.541)

108,199

�4.40e+

11

36.1

SITC

67

Ironandsteel

0.2373**

*(0.078)

0.6120**

*(0.143)

0.6615**

*(0.150)

�0.0335**

*(0.008)

0.0067

(0.006)

�0.0107

(0.708)

�0.3574

(0.379)

100,849

�6.07e+

11

48.3

SITC

68

Non-ferrous

metals

0.1387**

(0.069)

1.2751**

*(0.126)

0.0284

(0.152)

�0.0197**

*(0.006)

0.0062

(0.005)

0.0848

(0.701)

�0.3733

(0.319)

97,632

�3.71e+

11

49.5

SITC

74

General

industrial

machinery

0.1421*

(0.083)

0.6777**

*(0.135)

1.2396**

*(0.176)

0.0020

(0.006)

�0.0001

(0.005)

0.3394

(0.767)

�0.0014

(0.325)

109,218

�4.63e+

11

37.8



TABLE4Continued

Commodity

All

RTAs

Importer

GDP

Exporter

GDP

Multilateral

RTA

Multilateral

Distance

Multilateral

Adjacency

Multilateral

Language

Obs.

Log

Likelihood

%of

Zero

SITC

75

Office

machines

and

auto-data

processingequipment

�0.0953

(0.151)

1.1772**

*(0.160)

2.3173**

*(0.097)

�0.0130

(0.010)

0.0065

(0.009)

0.2111

(1.401)

�0.4070

(0.518)

105,351

�7.76e+

11

44.8

SITC

76

Telecommunications

andsound

recordingequipment

0.2264*

(0.125)

0.2679

(0.196)

1.0135**

*(0.235)

�0.0171**

(0.008)

�0.0029

(0.006)

1.5783*

(0.980)

�0.0133

(0.491)

105,030

�8.24e+

11

44.1

SITC

77

Electricmachinery

0.1318

(0.124)

1.4025**

*(0.186)

1.2041**

*(0.172)

�0.0108*

(0.007)

0.0077

(0.006)

0.1025

(1.005)

�0.5261

(0.426)

109,104

�9.39e+

11

36.8

SITC

78

Road

vehicles

0.0681

(0.167)

0.4723**

(0.215)

1.0453**

*(0.277)

�0.0152

(0.008)

�0.0040*

(0.007)

2.0482**

(0.989)

�0.2324

(0.394)

106,769

�1.31e+

12

43.5

SITC

84

Articlesofapparels

0.4248

(0.304)

1.1104*

(0.606)

0.2429

(0.287)

0.0038

(0.022)

0.0146

(0.017)

�0.4337

(0.900)

�0.8797

(1.332)

73,143

�2.86e+

11

62.6

SITC

87

Professional

and

scientific

instruments

0.0994

(0.127)

1.6646**

*(0.188)

1.6339**

*(0.130)

�0.0099

(0.007)

0.0127**

*(0.005)

�1.5796**

(0.730)

�0.5466

(0.427)

106,223

�2.17e+

11

41.2

Notes:

(i)Coefficients

ofTim

edummiesareabbreviated.Whilethenumber

ofsamplesis

114,352forallcommodities,someobservationsaredropped

because

ofallzero

out-

comes

duringtheestimationprocess.‘PercentageofZero’denoteszero

valued

importratiosofallsamplesofeach

sector.

(ii)*,

**and**

*denotessignificantlevel

at10%,5%

and1%

respectively.



TABLE 5Regional Trade Agreement Classified by WTO, in Estimated Sample

Free Trade Agreement (FTA) Customs Union (CU) Partial Scope Agreement(PSA)

1950s EC (1958)1960s EFTA (1960)1970s APTA (1976)1980s CER (1983)

United States-Israel (1985)EC accession of Greece (1981)EC accession of Portugal (1986)EC accession of Spain (1986)CAN (1988)

SPARTECA (1981)LAIA (1981)GSTP (1989)

1990s AFTA (1992)EFTA-Turkey (1992)EFTA-Israel (1993)NAFTA (1994)COMESA (1994)Canada-Israel (1997),Canada-Chile (1997)Turkey-Israel (1997)Canada-Chile (1997)Pan-Arab Free Trade Area (1998)EC-Tunisia (1998)Chile-Mexico (1999)EFTA-Morocco (1999)

MERCOSUR (1991)EC accession of Austrian (1995)EC accession of Finland (1995)EC accession of (1995)EC-Turkey (1996)

ECOWAS (1993)SAPTA (1995)

2000– EC-South Africa (2000)EC-Morocco (2000)EC-Israel (2000)EC-Mexico (2000)EFTA-Mexico (2000)Israel-Mexico (2000)New Zealand-Singapore (2001)Japan-Singapore (2002)EFTA-Singapore (2003)EC-Chile (2003)Singapore-Australia (2003)China-Hong Kong (2004)United States-Singapore (2004)United States-Chile (2004)Korea-Chile (2004)EC-Egypt (2004)EFTA-Chile (2004)Thailand-Australia (2005)United States-Australia (2005)Japan-Mexico (2005)EFTA-Tunisia (2005)Thailand-New Zealand (2005)Turkey-Tunisia (2005)India-Singapore (2005)EC-Algeria (2005)Turkey-Morocco (2006)United States-Morocco (2006)Korea-Singapore (2006)Trans-Pacific SEP (2006)

GCC (2003)EC 25(2004)

APTA-Accession ofChina (2002)ASEAN-China (2003)



likely to be due to high tariffs imposed before the establishment of the CUs, as can be seen

for many countries in Table 7. The trade creation effect is found in the case of PSAs for 14

products. This effect is particularly strong for professional and scientific instruments

(SITC87), electrical machinery and office machinery and road vehicles.

TABLE 5 Continued

Free Trade Agreement (FTA) Customs Union (CU) Partial Scope Agreement(PSA)

Japan-Malaysia (2006)EFTA-Korea (2006)Chile-China (2006)

TABLE 6Marginal Effects of Trade Creation and Diversion by Type of RTAs, Classification of WTO

Commodity Free TradeAgreement

Customs Union Partial ScopeAgreement

TC TD TC TD TC TD

SITC01 Meat and meat preparations 1.1887SITC04 Cereals and cereal preparations 0.4975 1.0332 �0.4790SITC05 Vegetables and fruit �0.1202 0.3818 0.5098SITC24 Cork and woodSITC26 Textile fibres and their wastes 0.7312 0.5338 0.5162 0.3612SITC33 Petroleum, petroleum productsSITC51 Organic chemicals 0.4835 0.3567SITC52 Inorganic chemicals 0.2744SITC54 Medicinal and pharmaceutical

products�0.3506 �0.3946 �0.3410 �0.2451 �0.6844 �0.3611

SITC63 Cork and wood manufactures �0.2429 �0.6755SITC65 Textile yarn, fabrics, made-up

articles�0.1482 0.3313 0.2639 0.4113

SITC67 Iron and steel �0.2068 0.1702 0.4051SITC68 Non-ferrous metals 0.7548SITC74 General industrial machinery 0.5546SITC75 Office machines and auto-data

processing equipment�0.1346 0.1637 �0.2232 0.9894 0.3892

SITC76 Telecommunications, soundrecording equipment

�0.1436 0.4346 1.0433

SITC77 Electric machinery 0.1926 1.0928 0.3482SITC78 Road vehicles 0.3578 0.9674SITC84 Articles of apparels 0.6555SITC87 Professional and scientific

instruments0.1137 1.8688 0.5979

Notes:(i) Figures are marginal effects of RTAs which are estimated at 1 or 5 percentage significant levels. ‘TC’ and ‘TD’indicate the trade creation effect and the trade diversion effect, respectively.(ii) RTAs, regional trade agreements.



We observe trade diversion effects for six products in the case of FTAs. The number of

products showing trade diversion is smaller for CUs (3) and PSAs (2). The rarity of cases

of the trade diversion for the CUs may be explained by the practice of establishing

common external tariffs, which are generally set equal to the lowest tariffs of the member

countries.

Next, we classify all RTAs into bilateral and plurilateral. A list of bilateral and plurilateral

RTAs is shown in Table 8. Table 9 shows the estimated results with significant marginal

effects at less than the 5 per cent level only. While trade creation is not found for any prod-

ucts in bilateral RTAs, it is detected for three products, namely medical and pharmaceutical

products, iron and steel (SITC67) and telecommunication equipments (SITC76). In contrast,

in the case of plurilateral RTAs, trade creation is found for eight products, and trade diversion

for four. These findings indicate plurilateral RTAs tend to promote bilateral trade between

member countries compared with bilateral RTAs, while its negative impact on the rest of the

world, namely trade diversion, is more or less the same in terms of the number of products

experiencing such an effect.

Lastly, we classify RTAs by members’ level of economic development. We divide the

sample countries into two groups, countries which belong to the Group of Seventy-seven

TABLE 7Simple Average Tariff Rate During 1988–2006

Commodity BetweenDevelopedCountries

Between G77Countries

All Samples

Average SD Average SD Average SD

SITC01 Meat and meat preparations 13.40 17.51 20.51 24.56 17.21 21.60SITC04 Cereals and cereal preparations 11.49 13.09 20.50 18.59 16.19 16.77SITC05 Vegetables and fruit 12.73 12.24 20.48 19.57 16.53 16.79SITC24 Cork and wood 3.06 5.01 8.43 9.02 5.71 8.06SITC26 Textile fibres and their waste 3.78 5.05 11.47 11.38 8.48 10.94SITC33 Petroleum, petroleum products 4.14 3.88 9.46 9.76 7.66 8.46SITC51 Organic chemicals 4.35 3.90 9.39 8.75 7.44 7.84SITC52 Inorganic chemicals 3.52 4.04 9.49 9.46 7.16 8.14SITC54 Medicinal and pharmaceutical products 2.57 3.66 9.17 8.82 6.65 7.98SITC63 Cork and wood manufactures 6.86 5.80 17.69 12.06 13.21 11.80SITC65 Textile yarn, fabrics, made-up articles 10.40 5.58 20.84 14.11 16.79 12.82SITC67 Iron and steel 4.93 4.37 13.33 9.98 10.11 9.29SITC68 Non-ferrous metals 4.63 3.89 11.00 9.29 8.71 8.35SITC74 General industrial machinery 4.82 4.33 12.19 9.55 9.06 8.37SITC75 Office machines and automatic

data processing equipment2.54 4.04 8.83 10.35 6.34 9.98

SITC76 Telecommunications, sound recordingand reproducing equipment

5.69 5.43 15.54 12.05 11.21 11.12

SITC77 Electric machinery 5.03 4.54 14.47 10.51 10.64 9.74SITC78 Road vehicles 6.97 6.63 21.36 19.94 15.10 15.69SITC84 Articles of apparels 16.84 7.73 24.55 16.46 21.86 15.09SITC87 Professional and scientific instruments 3.89 3.97 9.47 7.83 7.22 7.11

Note:Simple average tariff rate of sample countries, during 1988–2006, calculated by data from TRAINS by UNCTAD.



(G77, hereafter) and others. The former consists of developing countries, the latter includes

developed countries. Although it is possible to use other indicators such as per capita GDP to

measure the level of economic development, adoption of such an indicator leads to a situation

where the country composition of the groups changes during the sample periods. We construct

four RTA dummies, RTAs among developed countries, RTAs among developing countries,

TABLE 8RTAs Classified by Number of Members, in Estimated Sample

Bilateral RTAs Plurilateral RTAs

CER (1983)United States-Israel (1985)Canada-Israel (1997)Canada-Chile (1997)Turkey-Israel (1997)Chile-Mexico (1999)Israel-Mexico (2000)New Zealand-Singapore (2001)Japan-Singapore (2002)Singapore-Australia (2003)China-Hong Kong (2004)United States-Singapore (2004)United States-Chile (2004)Korea-Chile (2004)Thailand-Australia (2005)United States-Australia (2005)Japan-Mexico (2005)Thailand-New Zealand (2005)Turkey-Tunisia (2005)India-Singapore (2005)Turkey-Morocco (2006)United States-Morocco (2006)Korea-Singapore (2006)Japan-Malaysia (2006)Chile-China (2006)

EC (1958)EC accession of Greece (1981)EC accession of Portugal (1986)EC accession of Spain (1986)EC accession of Austrian (1995)EC accession of Finland (1995)EC accession of Sweden (1995)EC-Turkey (1996)EC-Tunisia (1998)EC-South Africa (2000)EC-Morocco (2000)EC-Israel (2000)EC-Mexico (2000)EC-Chile (2003)EC 25(2004)EC-Egypt (2004)EC-Algeria (2005)EFTA (1960)EFTA-Turkey (1992)EFTA-Israel (1993)EFTA-Morocco (1999)EFTA-Mexico (2000)EFTA-Singapore (2003)EFTA-Chile (2004)EFTA-Tunisia (2005)EFTA-Korea (2006)APTA (1976)APTA-Accession of China (2002)GSTP (1989)MERCOSUR (1991)SPARTECA (1981)LAIA (1981)CAN (1988)ECOWAS (1993)NAFTA (1994)COMESA (1994)SAPTA (1995)Pan-Arab Free Trade Area (1998)GCC (2003)Trans-Pacific SEP (2006)AFTA (1992)ASEAN-China (2003)



RTAs between developed countries and developing countries. The RTA between developed

countries and developing countries is divided into two cases, one where the importer is a

developed country and exporter is a developing country, and the other is the reverse.

Table 10 reports the estimation results for developed countries and developing countries.

The figures are those with statistical significance at less than the 5 per cent level. RTAs

among developed countries have a trade creating effect for a wide range of products. The

marginal effect for agricultural products (SITC01 and 04), textile yarn (SITC65), telecommu-

nication equipment and articles of apparel (SITC84) is larger compared with those for other

products. The trade diversion effect is found for only one product, medicinal and pharmaceu-

tical products. RTAs among developing countries are found to have a trade creation effect

for eight products, mainly machinery products, while trade diversion is found for six

products.

Average tariff rates in developing countries are relatively high compared with developed

countries. Table 7 shows simple average tariff rates of the products for developed and G77

countries during 1988–2006. The simple average tariff rates of the G77 countries against all

countries on agricultural product (SITC 01-05), textile yarn, road vehicles and articles of

TABLE 9Marginal Effect of Trade Creation Effects and Trade Diversion by Type of RTAs,

Bilateral and Multilateral RTAs

Commodity Bilateral RTA Plurilateral RTA

TC TD TC TD

SITC01 Meat and meat preparations 0.53453 �0.34080SITC04 Cereals and cereal preparations 0.70204SITC05 Vegetables and fruitSITC24 Cork and wood 0.50885SITC26 Textile fibres and their wastes �0.61071 0.66415 0.29714SITC33 Petroleum, petroleum products 0.44036SITC51 Organic chemicals �0.44240 0.20587 0.33817SITC52 Inorganic chemicalsSITC54 Medicinal and pharmaceutical products �0.25192SITC63 Cork and wood manufacturesSITC65 Textile yarn, fabrics, made-up articles �0.43850 0.48525SITC67 Iron and steel �0.55848 �0.12239 0.29289SITC68 Non-ferrous metalsSITC74 General industrial machinery �0.53098SITC75 Office machines and automatic

data processing equipment�0.57611

SITC76 Telecommunications, sound recordingand reproducing equipment

�0.46886 �0.12850 0.49490 �0.25399

SITC77 Electric machinery �0.34313 0.17817SITC78 Road vehicles �0.34105SITC84 Articles of apparels �0.35415 �0.54484SITC87 Professional and scientific instruments 0.16758 0.18556

Notes:(i) Figures are marginal effects of RTAs which are estimated at 1 or 5 percentage significant levels. ‘TC’ and ‘TD’indicate the trade creation effect and the trade diversion effect, respectively.(ii) RTAs, regional trade agreements.



TABLE10

TradeCreationEffects

andTradeDiversionEffects

byTypeofRTAs,Classified

byMem

bership

ofOECD

ortheGroupofSeventy-seven

Commodity

RTABetween

Developed

RTABetween

G77

RTABetween

Developed

Importer

andG77Exporter

RTABetween

G77Im

porter

and

Developed

Exporter

TC

TD

TC

TD

TC

TD

TC

TD

SITC01

Meatandmeatpreparations

0.6712

�2.1502

SITC04

Cerealsandcereal

preparations

0.9354

�0.3590

SITC05

Vegetablesandfruit

�0.1369

�0.6614

SITC24

Cork

andwood

0.3004

SITC26

Textile

fibresandtheirwastes

0.5589

0.4870

0.4413

�0.6400

0.5385

SITC33

Petroleum,petroleum

products

0.4229

0.5569

SITC51

Organic

chem

icals

0.4907

�0.1327

0.3579

SITC52

Inorganic

chem

icals

0.2109

SITC54

Medicinal

andpharmaceuticalproducts

�0.1186

�0.9756

�0.7857

�1.1869

0.3272

SITC63

Cork

andwoodmanufactures

�0.7910

�0.5147

�0.8786

SITC65

Textile

yarn,fabrics,made-uparticles

0.8269

0.3487

�0.2164

�0.2396

SITC67

Ironandsteel

0.3277

0.3003

�0.4664

�0.1985

�0.7418

SITC68

Non-ferrousmetals

0.3828

0.3462

SITC74

General

industrial

machinery

0.3612

0.2132

0.2939

�0.4533

�0.4133

�0.5717

0.4814

SITC75

Office

machines

andauto-dataprocessingequipment

0.8571

SITC76

Telecommunications,soundrecordingequipment

0.6393

0.5961

�0.4290

�0.6746

SITC77

Electricmachinery

0.3738

0.9876

�0.6830

�0.2420

1.1344

SITC78

Road

vehicles

SITC84

Articlesofapparels

1.2751

�0.4992

SITC87

Professional

andscientificinstruments

0.3656

0.1469

0.8191

�0.3264

�0.2550

1.1119

Notes:

(i)Figuresaremarginal

effectsofRTAswhichareestimated

at1or5percentagesignificantlevels.

‘TC’and‘TD’indicatethetradecreationeffect

andthetrade

diversioneffect,respectively.

(ii)RTAs,regional

tradeagreem

ents.



TABLE11

TradeCreationEffects

andTradeDiversionEffects

inRTAs

Commodity

EU

NAFTA

AFTA

MERCOSUR

CAN

COMESA

Pan-ArabFTA

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

SITC01

Meatandmeat

preparations

1.51

0.55

�0.65

�0.73

�0.82

SITC04

Cereals

andcereal

preparations

0.94

�1.23

1.13

0.65

1.25

1.42

1.54

SITC05

Vegetablesandfruit

0.31

0.37

0.58

1.02

SITC24

Cork

andwood

0.36

0.26

1.67

1.87

�0.78

�0.39

1.00

SITC26

Textile

fibresand

theirwastes

�0.48

1.16

0.61

1.03

1.10

0.77

2.30

SITC33

Petroleum,

petroleum

products

0.62

�0.61

�0.40

1.29

SITC51

Organic

chem

icals

0.51

�0.50

0.26

�0.42

�0.35

2.69

�0.41

SITC52

Inorganic

chem

icals

�0.40

0.66

2.15

�0.33

0.45

SITC54

Medicinal

and

pharmaceutical

products

�0.39

�0.32

0.47

�1.36

�0.57

0.69

�0.54

�0.82

2.60

�0.93

�0.83

SITC63

Cork

andwood

manufactures

0.49

0.67

2.94

�0.42

�0.59

SITC65

Textile

yarn,

fabrics,made-up

articles

1.09

1.52

0.99

1.46

0.76

2.23

SITC67

Ironandsteel

0.22

0.57

�0.30

0.38

1.13

2.01

�0.44

�0.35

SITC68

Non-ferrousmetals

1.26

0.31

�0.38

2.98

SITC74

General

industrial

machinery

�0.38

0.75

0.28

�0.69

�0.57

1.82

�0.47

0.84

�0.43

SITC75

Office

machines

andauto-data

processing

equipment

1.84



TABLE11Continued

Commodity

EU

NAFTA

AFTA

MERCOSUR

CAN

COMESA

Pan-ArabFTA

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

TC

TD

SITC76

Telecommunications,

soundrecording

equipment

0.34

0.57

1.00

2.13

�0.54

1.55

SITC77

Electricmachinery

�0.22

0.59

�0.72

2.59

�0.83

�0.54

SITC78

Road

vehicles

1.34

�0.56

1.46

2.04

�0.64

�0.63

SITC84

Articlesofapparels

1.28

�0.59

�0.68

0.75

2.40

1.15

2.69

SITC87

Professional

and

scientific

instruments

�0.50

0.93

0.34

0.59

�0.37

3.02

1.49

�0.35

Note:

Figuresaremarginal

effectsofRTAswhichareestimated

at1or5percentagesignificantlevels.‘TC’and‘TD’indicatethetradecreationeffect

andthetradediversion

effect,respectively.



apparel are about 20 per cent and higher, and cork and wood manufactures (SITC63), tele-

communications equipment, electrical machinery are also relatively high. In the light of these

high tariff rates, it seems natural that the trade diversion effect of RTAs involving only devel-

oping countries is found for many products. One would expect trade creation and diversion

effects would be found for products with high tariff rates; however, this cannot be detected.

One reason is that RTAs do not necessarily lead to immediate tariff elimination. Indeed, many

adopt gradual tariff elimination and even exclude some products from tariff reduction.

c. Trade Creation and Diversion Effects in Specific RTAs

Table 11 shows a summary of the marginal effects of trade creation and diversion for the

seven RTAs.12 In terms of the trade creation effects, MERCOSUR and COMESA are found

to increase trade among members for more than half of the products, and marginal effects are

relatively strong. The EU is found to have a trade creation effect mainly in agricultural prod-

ucts, while AFTA has a trade creation effect only for textile fibres (SIT26) and road vehicles.

NAFTA has trade creation effects in a wide range of products, and relatively higher estimated

values are obtained for textiles materials and apparel.

Trade diversion effects vary among the RTAs under study. Similar to the findings in

Table 10, the trade diversion effect tends to be detected for many products in the RTAs

among developing countries. The number of products showing trade diversion is large for

AFTA, CAN and COMESA. In the case of COMESA, the trade diversion effect is detected

for most products, while for AFTA, the trade diversion effect is found for all chemical prod-

ucts (SITC51-54), petroleum, iron and steel and road vehicles. Taking account of the possibil-

ity that the trade diversion effect reduces the economic welfare of the RTA members, AFTA,

CAN and COMESA countries are advised to lower their most-favoured nation (MFN) tariff

rates to minimise the trade diversion effect.

6. CONCLUSIONS

We analysed the impacts of RTAs on trade flows, with a particular focus on their trade

creation and diversion effects, by estimating the gravity equation covering 67 countries/

regions for 27 years from 1980 to 2006 at a disaggregated level of 20 products. In our estima-

tion, we dealt with the problems of endogeneity bias and zero trade flows by applying the

fixed effects PPML estimator.

We found that the impacts of RTAs on trade flows differ by product and type of RTA.

Trade creation is found for many products in customs unions (CUs) compared with FTAs,

while the trade diversion effect is found for fewer products in CUs than in FTAs. We also

found that plurilateral RTAs give rise to trade creation for many more products compared

with bilateral RTAs. We observed that RTAs among developed countries generate the trade

creation effect for a half of all products while the trade diversion effect is not found for any

products except medical and pharmaceutical products. In contrast, RTAs among developing

countries give rise to trade diversion for many more products compared with the RTAs

among developed countries. These results suggest that high tariffs imposed on imports from

12 As for the EU and AFTA, the dummy variables are constructed to reflect their changing membershipsor their enlargement.



non-members by developing countries could be a primary factor causing the trade diversion.

The analysis of seven specific FTAs yielded similar observations to those obtained from the

analysis of RTAs by taking account of differences in the levels of economic development.

Based on these results, we can derive the characteristics of desirable RTAs that maximise

the trade creation effect and minimise the trade diversion effect. The desirable RTA is the

customs union with a large number of members and low external tariffs. Observing a sharp

increase in bilateral FTAs in recent decades, the countries are advised to transform FTAs to

CUs and to expand their membership by merger or coordination with other FTAs.

REFERENCES

Amemiya, T. (1985), Advanced Econometrics (Cambridge, MA: Harvard University Press).Anderson, J. E. (1979), ‘A Theoretical Foundation for the Gravity Equation’, American Economic

Review, 69, 1, 106–16.Anderson, J. E. and E. van Wincoop (2004), ‘Trade Cost’, Journal of Economic Literature, 42, 3, 691–

751.Baier, S. L. and J. H. Bergstrand (2004), ‘On the Economic Determinants of Free Trade Agreements’,

Journal of International Economics, 64, 1, 691–751.Baier, S. L. and J. H. Bergstrand (2007), ‘Do Free Trade Agreements Actually Increase Members’

International Trade?, Journal of International Economics, 71, 1, 72–95.Baier, S. L. and J. H. Bergstrand (2009), ‘Bonus Vetus OLS: A Simple Method for Approximating

International Trade-Cost Effects Using the Gravity Equation’, Journal of International Economics,77, 1, 77–85.

Bergstrand, J. H. (1989), ‘The Generalized Gravity Equation, Monopolistic Competition and the Factor-Proportions Theory in International Trade’, The Review of Economics and Statistics, 71, 1, 143–53.

Carrere, C. (2006), ‘Revising the Effects of Regional Trading Agreements on Trade Flows with ProperSpecification of the Gravity Model’, European Economic Review, 50, 2, 223–47.

Egger, P. (2004), ‘On the Problem of Endogenous Unobserved Effects in the Estimation of GravityModels’, Journal of Economic Integration, 19, 1, 182–91.

Egger, H., P. H. Egger and D. Greenaway (2008), ‘The Trade Structure Effects of Endogenous RegionalTrade Agreements’, Journal of International Economics, 74, 2, 278–98.

Endoh, M. (2005), ‘The Effects of the GSTP on Trade Flow: Mission Accomplished?’, Applied Economics,37, 5, 487–96.

Fukao, K., T. Okubo and R. M. Stern (2003), ‘An Econometric Analysis of Trade Diversion UnderNAFTA’, The North American Journal of Economics and Finance, 14, 1, 3–24.

Gilbert, J. P., R. Scollay and B. Bora (2004), ‘New Regional Trading Developments in the Asia-PacificRegion’, in S. Yusuf, M. A. Altaf and K. Nabeshima (eds.), Global Change and East Asian PolicyInitiatives (New York: The World Bank, Oxford University Press), 121–90.

Hausman, E. and E. Taylor (1981), ‘Panel Data and Unobservable Individual Effects’, Econometrica, 49,6, 1377–98.

Helpman, E. (1987), ‘Imperfect Competition and International Trade: Evidence from Fourteen IndustrialCountries’, Journal of the Japanese and International Economics, 1, 1, 62–81.

Linders, G. M. and H. L. F. de Groot (2006), ‘Estimation of the Gravity Equation in the Presence ofZero Flow’, Tinbergen Institute Discussion Paper, No. 06-072-3 (Amsterdam: Tinbergen Institute).

Martin, W. and C. S. Pham (2008), ‘Estimating the Gravity Equation When Zero Trade Flows areFrequent’, Available at: http://mpra.ub.uni-muenchen.de/9453/ (accessed 1 December 2010).

Powers, W. M. (2007), ‘Endogenous Liberalization and Sectoral Trade’, Office of Economics WorkingPaper, No. 2007-06-B (Washington, DC: United States International Trade Commission).

Poyhonen, P. (1963), ‘A Tentative Model or the Volume of Trade Between Countries’, Weltwirschaf-tliches Archive, 90, 1, 93–9.

Santos Silva, J. M. C. and S. Tenreyro (2006), ‘The Log of Gravity’, The Review of Economics andStatistics, 88, 4, 641–58.

Santos Silva, J. M. C. and S. Tenreyro (2011), ‘Further Simulation Evidence on the Performance of thePoisson Pseudo-Maximum Likelihood Estimator’, Economic Letters, 112, 2, 220–22.



Simcoe, T. (2007), ‘XTPQML: Stata Module to Estimate Fixed-effects Poisson Quasi-ML Regressionwith Robust Standard Errors’, Available at: http://fmwww.bc.edu/repec/bocode/x/xtpqml.ado(accessed 1 December 2010).

Tinbergen, J. (1962), ‘Shaping the World Economy: Suggestions for an International Economic Policy’,(New York: Twentieth Century Fund).

Westerlund, J. and F. Wilhelmsson (2011), ‘Estimating the Gravity Model Without Gravity Using PanelData, Applied Economics, 43, 6, 641–49.



trade creation and diversion effects of regional trade agreements: a product-level analysis

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