Abedini_Iceberg Trade Cost Measures an Application to the OECD Area Over 1988–2010

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Iceberg Trade Cost Measures: An

Application to the OECD Area over19882010Javad Abedini

a

a

HEC Montral, Montral, CanadaPublished online: 06 Jan 2015.

To cite this article:Javad Abedini (2015) Iceberg Trade Cost Measures: An Applicationto the OECD Area over 19882010, The International Trade Journal, 29:2, 115-141, DOI:10.1080/08853908.2014.990071

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The International Trade Journal, 29:115141, 2015Copyright Taylor & Francis Group, LLCISSN: 0885-3908 print/1521-0545 onlineDOI: 10.1080/08853908.2014.990071

Iceberg Trade Cost Measures: An Applicationto the OECD Area over 19882010

JAVAD ABEDINIHEC Montral, Montral, Canada

This study suggests an augmented trade cost function, with correc-tion for nonstationarity, and applies it to estimate bilateral icebergtrade costs of OECD countries during 19882010. We show thattrade costs have not been reduced during recent decades, but alsoexhibit an upward trend in geographical, institutional, and cul-tural components. Our findings also indicate that bilateral andmultilateral trade resistances are asymmetrically distributed acrossthe OECD area, particularly for intra-continent trade and for tradebetween countries at different levels of economic development.

A convergence process is, however, detected between developed anddeveloping countries.

KEYWORDS iceberg trade costs, trade policy, gravity model, paneldata, nonstationarity, OECD

I. INTRODUCTION

Direct measures of trade costs are dramatically unavailable. This is because alarge variety of trade barriers are unobservable or hardly measurable in mon-etary values. However, the gravity approach of international trade suggests a

revolutionary solution to model these costs. Commonly practiced, empiricalgravity models develop a log linear trade cost function to represent varioussources of bilateral trade costs such as geographical distance and trade policy

variables. In spite of their great success, these empirical specifications havebeen usually criticized as being ad hoc (theoretically ill-defined).

On the other hand, theoretical gravity models assume trade costs tobe in the Samuelsons well-known iceberg form. Iceberg trade costs mean

Author is a recent graduate from HEC Montreal.

Address correspondence to Javad Abedini, HEC Montral, 3000 Chemin de la Cte-Sainte-

Catherine, Montral, QC H3T 2A7, Canada. E-mail: [email protected]

115
mailto:[email protected]:[email protected]


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116 J. Abedini

that, for each good, a certain fraction melt away during the trading pro-cess, as if an iceberg were shipped across the ocean.1 Using the icebergnotation, several papers have attempted to estimate bilateral iceberg tradecosts (Eaton & Kortum 2002; Anderson & van Wincoop [hereafter AvW]

2004; Baier & Bergstrand 2009; among others). For instance, Eaton andKortum (2002) developed a Ricardian-based general equilibrium structureaugmented with geographic barriers. This structure results in a model resem-bling a gravity equation, which in turn is used to estimate tariff equivalents of

various barriers to trade for 19 OECD2 countries (Eaton & Kortum2002, 1766,Table VII).

The direct application of theoretical equations similarly has some short-comings. First, theoretical equations usually fail to provide a suitable basis tocontrol statistical features of data such as fixed effects in a panel context. Thefailure would cause serious inconsistency in the estimated coefficients, as

already noted by Egger (2000), Egger and Pfaffermayr (2003), Magee (2008)and, more recently, by Head and Mayer (2012). Second, theoretical equationsare usually developed in a simplified version which excludes, by definition,the impact of some other relevant factors, resulting in a sort of underspecifi-cation. Third, certain variables, which may appear relevant from a theoreticalpoint of view, may not be correctly or easily measurable from an empiricalpoint of view. In this case, the measurement problem could aggravate thesituation. To this aim, we need instead a method which is reliable from boththeoretical and empirical approaches.

This study aims to estimate bilateral iceberg trade costs related to

exchanges of goods within the OECD area over 19882010. It would con-tribute to an already abundant literature on trade costs in several ways. First,our model exposes important theoretical and empirical developments of thegravity model in order to avoid the previous problems arising from the par-tial inclusion of those factors. In addition, we control for nonstationarity inthe data, which is the main source of previously misleading inferences aboutthe time path of trade costs.

The second contribution comes from a distinction made between geo-graphical, administrative, institutional, and cultural components of trade

costs. The iceberg trade cost related to each component has been separatelyestimated and examined over time. Finally, the large amount of collected data(1089 country pairs over 23 years) and the application of several econometricand sensitivity tests ensure the empirical reliability of our findings.

In light of these improvements, several novel results emerge. First, wefind that previous conclusions about the rapid reduction of trade costs, usu-ally more than 40% during a similar time span, are indeed an exaggeration

1 This is similar to imposing a tariff equivalent on imported goods, due to geographical distance.2 Organization for Economic Co-operation and Development.


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Iceberg Trade Cost Measur es 117

caused by underspecified models and nonstationary variables. Indeed, thisreduction does not exceed 8% during our entire 23-year period. Second, thetime trend is not the same for all components. While administrative tradecosts decrease steadily, upward trends are found for geographical, institu-

tional, and cultural iceberg trade costs, especially in the last years of thesample. This indicates that future barriers to international trade would beinstitutional and cultural, after overcoming the geographical ones.

Finally, we find that bilateral trade costs are highly asymmetrically dis-tributed across country pairs and sub-samples within the OECD area. Thissoundly rejects the hypothesis of symmetric bilateral trade costs commonlyassumed in the theoretical literature. A similar result is found for multilateraltrade resistances,3 but to a lesser extent.

The rest of this article is structured as follows. Section 2 representsthe theoretical framework. Section 3 introduces our trade cost function.

Econometric method is discussed in section 4. Section 5 provides theestimates. Finally, section 6 concludes the current article.

II. MODEL AND METHODOLOGY

AvW (2004)s theoretical gravity model provides multiple advantages forstudying trade costs. Based upon a general equilibrium framework, it rep-resents a solid theoretical structure. The model also has an elegantly simpleform and provides a straightforward connection to empirical gravity equa-

tions with log linear trade cost functions. This last feature greatly simplifiesthe move from a theoretical equation to an efficiently empirical one.

According to AvWs gravity model, each country specializes in the pro-duction of only one good, which is consequently differentiated by its placeof origin. The supply of each good is fixed. Preferences are identical, homo-thetic, and approximated by a CES utility function. Maximizing utility subjectto a budget constraint gives a set of first-order conditions which could besubsequently solved to obtain a demand function, as below, describing thenominal bilateral trade flows from ito j (xij):

xij= yi yj

yW

ij

PiPj

1(1)

where yi and yj are, respectively, the nominal income in regions i and j,Pi and Pj the outward and inward multilateral resistance terms in the same

3 However, note that the multilateral trade resistance here does not exactly match the similar term byAvW(2003). This is rather an aggregate empirical measure of bilateral trade costs as described in detaillater by Equations 13 and 14. I am thankful for the vigilance of the corresponding reviewer.


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118 J. Abedini

regions, yW world nominal income (yW =

iyi=

jyj), ijbilateral icebergtrade costs betweeniand j(ij 1 i = j, andij= 1 i = j, and ij= ji),and (>1) the elasticity of substitution between all goods.

However, the unitary income elasticities in Equation 1 are not consistent

with empirical facts and the model is not adequate to cover all explanatoryvariables. So, it should be modified in this direction. In addition, as suggestedby AvW (2004), price indices could be simply proxied by country effects inan empirical equation.

Non-unitary income elasticities become possible when we take intoaccount the part of goods which is non-tradable (Pridy 2005).4 Adoptingthis and a simple taking log on both sides of Equation 1 gives:

lnxij

= 0 + 1lnyi

+ 2lnyj

( 1) ln

ij

+ ( 1) ln(Pi) + ( 1) lnPj

,

(2)

where 1 and 2 describe the non-unitary coefficients for the incomevariables, and 0 = ln

yW

is a constant measure across country pairs.Apparently, Equation 2 looks very similar to most estimated empirical

gravity equations taking the general form

lnxij

= 0 + 1lnyi

+ 2lnyj

+K

k=1kln

zkij

+ i + j + ij, (3)

where yiand yjare usually replaced by the gross domestic production mea-

sures in i and j. In particular, bilateral iceberg trade costs, ij, have beenrepresented by the intermediate of a set of observable variables zkij (k= 1,. . . , K), to which bilateral trade barriers are related. i and j stand for(unobserved) fixed effects, including price indices Piand Pj, at the exporterand importer levels, respectively. This is in line with the results of Baier andBergstrand (2009)s Monte Carlo analysis, showing that a fixed effect modelgenerates the same output as that expected from a model with endogenousprice factors. Finally,ijrepresents a white noise disturbance term.

Typically, Equation 3 may still be exposed to some common problems

in the literature, such as the endogeneity of variables, unobserved multi-ple heterogeneities (self-selection), and missing variable problem. Relyingon panel data would resolve a great part of those problems. Panel dataallows simultaneous control of unobserved heterogeneities (here, exporter,importer, and bilateral effects), time dynamics, and missing variables in the

4 Anderson (1979) also allows for unrestricted (non-unitary) income elasticities using the Cobb-Douglas expenditure system for traded versus nontraded goods. Based on a similar structure, Pridy

(2005) shows that non-unitary income elasticities are consistent with the AvW(2004)s theoretical gravitymodel if we allow the income elasticities to be dependent on income. For the proof, please refer toPridy (2005).


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model. In addition, it offers multiple other advantages, such as being moreinformative and representing less collinearity in variables and more efficiencyin results (Baltagi2005,48).

So, the next modification which we apply is adding a time index to

Equation 3 in order to extend it towards panel dimensions. The only require-ment here is to assume that the AvWs equation is independently valid ineither year (t).5 The panel specification is then derived as follows.

lnxijt

= 0 + 1lnyit

+ 2lnyjt

+ 3lngijt

+K

k=1kln

zkijt

+ it + jt + ijt,

(4)

where the index t represents yearly measures. In addition, we augment the

set of income variables to include the absolute difference between the twocountries per capita incomes, gijt. This variable is considered as a proxy tocontrol the difference between exporting and importing countries in relativefactor endowments or in preferences for producing and consuming high-quality goods.6

The inclusion of unobserved effects also deserves a special treatment.Magee (2008) indicates that including dyad, exporter-year, and importer-

year specific effects (it and jt, respectively) controls for any (time moving)unobserved heterogeneity and, consequently, it removes the difficult choicesabout which of the hypothesized variables should be included. In other

words, the inclusion ofit and jtensures the consistency of estimates evenin the presence of underspecification. Alternatively, Egger (2000) and Eggerand Pfaffermayr (2003) suggest another formulation of unobserved effects,

which is resumed in Equation 5:

lnxijt

= 0 + 1lnyit

+ 2lnyjt

+ 3lngijt

+K

k=1kln

zkijt

+ i + j + ij + t + ijt.

(5)

5 The violation of this assumption would not, however, cause any problem to our analysis as we latercontrol for the time path dependence in the empirical model through introducing a time trend variable.6 Linder (1961)interprets the inequality between two countries per capita incomes as taste differences

between them. Countries with high per capita income tend to produce and consume high-quality goods,while countries with low per capita income usually produce and consume goods with lower quality.As a result, there is more difference in per capita income between the two partners, less conformitybetween production and consumption baskets from these countries, and then less bilateral trade betweenthem. On the contrary, the same variable is taken by Helpman and Krugman (1987) as a measurefor capital-labor endowment ratio difference between countries. According to them, higher per capitaincome differences induce international trade of the Heckscher-Ohlin type. So, a positive coefficient for

this variable supports the HO theorem, while a negative one is rather in line with the Linder approach.


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120 J. Abedini

As before, i and jstand for the exporter and importer fixed effects, whilethe new components ij and t, respectively, control the bilateral (exporter-by-importer) and time-specific effects.

While the dyad it and jt combines the exporter and importer effects

with time, these effects are separately presented by the triad i, j, and t.However, from an econometric point of view, the triad formulation has twomain advantages for our model. First, though the triad formulation still con-trols for any unobserved country and time effects in the model, it does notfully absorb the panel heterogeneity as the dyad does. We could then expectthat the corresponding panel heterogeneity is captured by the observable

variables in the model, especially the trade resistance terms, Zijt. Second, theinclusion of the triad would largely save for us, especially in large and /orlong panels, the degree of freedom compared to that done by the dyad.These are good reasons to prefer Equation 5 to 4, while also conserving bilat-

eral effects, ij, which is assumed to be important to the proper econometricspecification of the gravity model (Egger and Pfaffermayr2003).

Using Equations 2 and 5,ijt1

Kk=1z

kijt

k. As a result, bilateral icebergtrade costs, ijt, could be drawn as

(1 ) ln ijt=K

k=1kln

zkijt

, (6)

ln ijt=

Kk=1kln

zkijt

1 , (7)

and finally,

ijt= exp

K

k=1kln

zkijt

1

, (8)

whereK

k=1kln

zkijt

0 and ijt 1 for i = j, by expectation. Apparently,

Equation 8 connects ijtto the estimable trade cost function,K

k=1kln

zkijt

.

Empirical literature has used different specifications for this function. But,for the most part, they fail to control adequately for various trade barriers.In response to this inadequacy, the next section develops an augmentedtrade cost function specifying trade barriers in four groups of geographical,administrative, institutional, and cultural components. Some analyses will be

provided as regards the time movement of each one within the OECD area.


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III. EMPIRICAL MODEL USING AN AUGMENTED TRADECOST FUNCTION

Trade costs are those that have to be incurred to overcome various trade

barriers that impede a frictionless transaction of goods and services betweenor within countries. In the absence of direct measures, empirical gravitymodels represent trade costs as a set of variables which are related to thegenerating sources of such costs. In this section, we take advantage of anaugmented trade cost function which covers a relatively full range oftangibleandintangibleobstacles to trade. This range of inclusion is essential to avoidthe common problem of underspecification.

Tangible trade barriers are those which are directly observable in termsof the effect on the costs or quantity of trade (Linders, 2006). It is ratherstraightforward to study these barriers under two major categories: trans-

port and customs obstacles. Transport barriers mainly exist due to physicalremoteness (or geographical distance), lack of a common border, lack of seaor river access, and weak transportation or communication links betweenpartners. While physical remoteness increases transport costs, disposinga common border, maritime transport, and communication infrastructuresreduce this impact thanks to simplifying bilateral relations. For ease of expo-sition, we call all these factors geographical obstacles (or barriers) to trade.

Customs barriers are, however, man-made. They are tariff and non-tariff (hereafter, administrative) barriers such as quotas and various qualitystandards that restrict the free flows of goods across frontiers. In empiricalstudies, administrative restrictions have been usually controlled by an inversetrade cost variable; i.e., free trade arrangements.

On the other hand, intangible trade obstacles are those which cannotbe observed directly in terms of a monetary or quantitative restriction. Thesebarriers might have institutional or cultural aspects.

A good institutional quality enforces commercial contracts by trans-parent and impartial formulation of governance and economic policies(Anderson & Marcouiller 2002). A consistent and transparent law environ-ment reduces the uncertainty in exchange, and lowers transaction costs

(Linders, 2006). In contrast, corrupt officials, unreliable courts, and outrightpredation impose substantial barriers to trade (Anderson,2004). For example,Anderson and Marcouiller (2002) show that if Latin American countries ben-efited from institutional qualities comparable to those of European Unioncountries, predicted Latin American import volumes would rise 30%. Thepositive impact of institutional quality on bilateral trade has been also con-firmed by several other empirical studies (e.g., Moenius & Berkowitz 2011;Rodrik2004;Francois & Manchin2007).

In addition to institutional qualities, the institutional distancebetweenthe two partners would also affect the level of their bilateral trade. Linders

et al. (2008) were the first to include this variable in a gravity specification:


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122 J. Abedini

Institutional distance, or heterogeneity in the perceived quality of institu-tions, raises unfamiliarity with the institutional setting in the trade partnerscountry. This causes adjustment costs and other transaction costs, related todifferences in informal procedures of business, and may reduce mutual trust.

Exchanges of goods are posterior to human contacts and exchanges ofinformation, which are, in turn, influenced by culture.Cultural familiarityinthe sense of sharing common language, social networks, and having histor-ical ties facilitate substantially the exchange of information and knowledgebetween countries. For example, Egger and Lassmann (2012) apply a meta-analysis on 701 language effects collected from 81 academic articles and findthat a common (official or spoken) language increases trade flows directlyby 44% on average.

Familiarity with foreign cultures does not, however, mean that theyaccept and trust each other. Cultural similarity, in the sense of sharing com-

mon norms and values, is required to fill this gap. Cultural similarity makesit easy to understand, control, and predict the behavior of others (Elsass& Veiga 1994). It provides a favorable social and political environment forstrengthening bilateral relations between countries. In contrast, cultural dis-

similarity (or distance)generates new trade costs and reduces the volume oftrade between partners, other things being equal.

The proper specification of our trade cost function implies to control

for all of the four types of trade barriers. As a result,K

k=1kln

zkijt

is

augmented, including 14 trade cost variables which cover, in a significant

way, both the tangible and intangible sources of trade costs. The choice ofvariables, among existing alternatives, is based on their popularity in theempirical literature, and some econometric considerations such as collinear-ity, nonstationarity, and explanatory power of each one. As a result, our finalempirical model to be estimated is obtained as follows.

lnxijt

= 0 + 1lnyit

+ 2lnyjt

+ 3lngijt

+ 1ln

dGij

+ 2cij + 3sij

+ 4lnmijt+ 5lnfjt+ 6aijt + 7uijt + 8qit + 9qjt + 10lndIijt+ 11lij + 12rij + 13hij + 14ln

dCijt

+ i + j + ij + t + ijt, (9)

xijt represents total exports (in all products) from i to j at t in U.S. currentdollars (data: UN Comtrade database). The data for income variables, aspreviously defined, yit, yjtand gijt, come from World Bank indicators.

The first group of trade obstaclesgeographical barriersare controlledby four variables, namely,dGij,cij,sij, and mijt. The first one,d

Gij, is the spatial

weighted geographical distance between exporting and importing countries


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i and j (data: CEPII, DIST database).7 The more physical distance there isbetween countries, the less bilateral trade will be between them, ceteris

paribus. However, the impact of physical distance on trade may be lightenedby the existence of a common border, cij, sea, sij, and good communication

infrastructure between the two countries, mijt.8

cij and sij are two dummyvariables that indicate whether the two countries share the same land bor-der and whether they share the same sea, respectively, coded 1 for yes, and0 forno (data: CEPII, GEO database). mijtdescribes the minimum number offixed telephone lines existing between ior jat t (data: World DevelopmentIndicators, World Bank). Sincecij,sij, andmijtrepresent bilateral trade barriersin an inverse way, their coefficients are expected to be positive.

The next set of variables is devoted to administrative trade barriers,including fjt, aijt, and uijt. fjtmeasures the degree to which government (inthe importing country j) hinders the free flow of foreign commerce. It is

a weighted average tariff rate, applied by that country, with adjustmentsfor non-tariff barriers and corruption in the customs service (data: HeritageFoundation, trade freedom database). The countries are ranked on a 0 to100 scale, where a higher score represents a greater freedom to trade inter-nationally (low barriers to trade). aijtis a dummy variable equal to 1 if thereis any discriminatory trade agreement favoring trade between i and j att, and zero otherwise (data: CEPII, Market Access Map). Finally, uijt con-trols whether there is a common currency (a monetary union) between thetwo partners, coded 1 for yesand 0 for no (data: CEPII, Gravity database).In fact, this variable captures all incurred costs due to currency changes. Thecoefficients of these last three variables are similarly expected to be positive.

Intangible trade barriers have been controlled by another two sets ofvariables. Including qit, qjt, and d

Iijt, the first set detects institutional trade

obstacles. qit and qjt show, respectively, institutional qualities in exportingand importing countries. They are arithmetic averages constructed over sixgovernance indicators, collected by the World Bank: voice and account-ability, political stability and absence of violence, government effectiveness,regulatory quality, rule of law, and control of corruption.

The six indices, and subsequentlyqit and qjt, vary from 2.5 (worst) to

2.5 (best). A better institution reduces insecurity and instability in trade andconsequently lowers transaction costs. As a result, positive coefficients areexpected for these variables. Finally, institutional distance, dIijt, is obtained

7 dGij =

ki

pkpi

lj

plpj

dkl

1/, wherepkdesignates the population of urban agglomeration

k belonging to countryi. The parameter measures the sensitivity of trade flows to inter-city distances,dkl. The key idea is to include all distances between major cities in exporting and importing countries,weighted by the share of each city in the overall countrys population.8 Communication and transportation infrastructures are highly correlated. As a result, we include only

one of them (i.e., communication infrastructure) here.


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124 J. Abedini

using a nonlinear function of the difference in institutional quality betweenthe two partners:

dIijt1

6

6

k=1Ikit Ikjt

2

VIkt, (10)

where Ikit indicates the score of institutional indicator k (k = 1, . . . , 6) forcountryiat t; andVIktrepresents the variance of the indicator across countriesat t. This method of calculation was initially developed by Kogut and Singh(1988), and also used by Linders (2006) for constructing the same variable(but only in a cross-sectional dimension). As discussed before, it is expectedthat institutional distance negatively affects bilateral trade flows.

The second set of intangible trade barriers is devoted to cultural factors.This is represented by cultural familiarity indicators, namely lij, rij, and hij,

and cultural dissimilarity, dCijt. The first three are dummy variables that indi-cate whether the two countries share a common language (spoken by at least9% of the population in each country; data: CEPII), a common chief religion(data: Association of Religion Data Archives), and a common history (colonialrelationship; data: CEPII, GEO database), coded 1 for yes, and 0 for no.

Finally,dCijtdescribes to which extent social norms and values are similarbetween partner countries iand jat t. To this aim, we focus on cultural qual-ities that are relevant for economic environment. Tabellini (2010), Coyne and

Williamson (2012), and Nabamita and Deepraj (2012)are among those whouse four fundamental social valuesnamely, trust, tolerance, individual self-determination, and obedienceto represent this so-called economic culture.People who have more trust in others, more tolerance and respect towardother people, and are more confident in individual self-determination, under-take more trade internationally. In contrast, obedience as a cultural qualityreduces risk-seeking and innovatory attitudes in the society, which wouldnegatively affect international trade.

Similar to the applied method for institutional distance, cultural distancecould be measured as

dCijt 14

4k=1

Ckit Ckjt

2

VCkt, (11)

where Ckit indicates the score of cultural indicator k (k = 1, . . . , 4) forcountry i at t, and VCkt measures the variance of the k

th indicator acrosscountries at t. Data for the cultural indicators come from the five waves ofthe World Values Survey (WVS Data & Documentation).9

9 The survey collects the data only periodically. We have filled up missing years by their nearby

observations. This does not cause a serious bias as the cultural features usually represent a slow andsmooth time trend.


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IV. ESTIMATION OF THE MODEL: OECD CASE STUDY

We now use the final equation (Equation 9) to estimate bilateral iceberg tradecosts between the OECDs over 19882010. A rigorous method is applied

as regards the nonstationarity problem and the sensitivity of the results todistributional and specification choices.Including 34 member states, the OECD is, after the WTO, the largest

multilateral economic arrangement across the globe, and stands for around60% of the world trade in 2010 (UNCTAD10). Our sample includes all bilateraltrade relations of 33 OECD members (Belgium was dropped due to substan-tial lack of data for some variables) over 19882010: 333323 = 25047 obser-

vations, potentially. However, this number of observations reduces to15353 real records after excluding missing observations (9485 obs.), outliers(176 obs.), and observations with high leverage11 (33 obs.).12 The left-hand

side ofTable 1provides some descriptive statistics on the included variablesin Equation 9.

From an econometric point of view, if the included variables are notstationary, Equation 9 may spuriously relate the dependent variable to theexplanatory regressors. In this case, no logical inference is possible based onthe estimated parameters. As a result, it is of crucial importance to check fornonstationarity before proceeding to the estimation. However, this step hasbeen ignored by many empirical studies based on the gravity model.

Several techniques have been quoted in the literature for testing nonsta-tionarity (unit root) in panel data. The distinction is mainly based on whether

the sections are mean reverting with the same speed and whether they areindependent or not (for a survey, see Baltagi2000).

For the purpose of comparison, the right-hand side ofTable 1providesthe results using Maddala and Wu (1999) and Pesaran (2007) panel unit roottests. While the two tests allow for heterogeneous speeds of reversion acrosssections, it is only the second one which controls for the cross-sectionalcorrelation as well. Such a correlation is very likely in the case of a grav-ity model as many international factors simultaneously affect the pattern ofinternational trade in either country.

Pesarans CADF test indicates that controlling for time trend is essentialto the stationarity of some variables in the model. This emphasizes the roleoftin Equation 9 capable of capturing this trend.

13 In contrast, if we fail to

10 United Nations Conference on Trade and Development.11 In linear regression, an outlier is an observation with large residual. The second type of outlier isan observation with an unusual value of an explanatory variable, referred to as a leverage point. If notexcluded, these observations divert the estimates from reflecting the general pattern of the sample.12 Please note that the outliers and leverage points are relatively few in our sample and their inclusiondoes not modify the results in a significant way.13 Replacing time effects, t, in Equation 9 by a linear time trend variable has no significant impact

on the estimated parameters.


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TABLE1DescriptiveStatistic

s(1)andPanelDataUnitRootTests(2)

CADF(12)

MW(13)

Variable

Description(iandj:country

index,t:time

index)

Mean

Std.D

ev.

Min

Max

Without

trend

With

trend

Witho

ut

trend

With

trend

xijt(

3)

Bilateraltradeflo

w

3658

1426

2

0

353783

16.5

8

15.2

1651

2627.7

yit/yjt(

4)

Nationalincomes

841189

18694

48

3860

14600000

4.54

5.23

25.8

71.3

gijt

(5)

Percapitaincomedifference

9726

9133

0

74584

7.63

2.1

1519.8

1696.7

dGij(6)

Geographicaldistance

5377

5301

10

19537

Na.

Na.

Na.

Na.

cij(

7)

Commonborder

0.06

0.2

4

0

1

Na.

Na.

Na.

Na.

sij(

7)

Commonsea

0.67

0.4

7

0

1

Na.

Na.

Na.

Na.

mijt(

8)

Min.

fixedtel.lin

es

34.8

1

14.3

6

5

74

5.02

16.5

2234

1167

fjt(9)

Tradefreedom

78.1

6

6.4

7

49.6

90

3.45

2.68

35.8

4

133

aijt(

7)

Tradeagreement

0.49

0.5

0

1

0.73

1.5

56.8

61.2

uijt(

7)

Commoncurrenc

y

0.08

0.2

7

0

1

0.824

1.794

32

93

qit

/qjt

(10)

Institutionalqualities

1.2

0.5

5

0.42

1.98

1.6

1.96

62.5

4

88.4

dI ij

t(11)

Institutionaldista

nce

2

2.5

0

15

6.1

2.87

1019.8

5

1182.5

lij(7)

Commonlanguage

0.11

0.3

1

0

1

Na.

Na.

Na.

Na.

rij(

7)

Commonchiefre

ligion

0.32

0.4

7

0

1

Na.

Na.

Na.

Na.

hij

(7)

Commonhistory

0.04

0.1

9

0

1

Na.

Na.

Na.

Na.

dC ij

t(11)

Culturaldistance

2

1.5

5

0

10.3

8

4.54

1.29

1002.3

3

1258

Significantat1%;Significant

at5%;Significantat10%.

(1)Usingthevariablesinnon-loga

rithmicform;

(2)Usingthevariablesinlogarithmicform,withtheexceptio

nofqit,qjtandalldummyvariables,whichhavebeen

usedintheiroriginalform.

(3),(4)

MillionU.S.currentdollars;(5)U.S.currentdollars;(6)Kilometers;(7)Dumm

yvariable;

(8)Numberper100peop

le;

(9)Scorefrom1

(close)to100(open);(10)from

2.5(weak)to2.5(strong);(11)Positiv

evaluesbyconstruction;

(12)Pesaran

sCross-sectionalAugmentedDickey-Fullerpanelunit

roottest;

(13)MaddalaandWuspanelunitroottest.

126


15/29


control for such a trend, the effect would be automatically captured by theexplanatory part of the model and misleading results emerge. This could, inpart, explain the much more rapid reduction of trade costs over time, foundby the previous studies but not supported later in our study.

Another verification to be undertaken is to test for multicollinearity.This becomes especially important when the number of variables increasesin a regression analysis. Variance Inflation Factor (VIF) is then calculatedfor each variable of our model. The corresponding measures are not higherthan 2.5 and so are much below the acceptable threshold equal to 10, assuggested by Kennedy (2003). This indicates that multicollinearity is not asevere problem in Equation 9.

Finally, we apply the Wooldridge test for autocorrelation in panel dataas well as the Moran-I test for spatial effect to examine the good behaviorof disturbances. The results indicate the presence of autocorrelation (signif-

icant at 1%) and spatial dependence (significant at 1 or 5% according tothe year) in the residuals. In this case, the use of robust standard errors issuggested when the number of time periods is small compared to the num-ber of sections in a panel data context (Wooldridge 2010), which is largelythe case here: 23 years versus 1089 country-pairs. Table 2provides the esti-mated results of Equation 9 using robust regressions. For the sake of brevity,

we only report the estimates of main coefficients, while exporter, importer,bilateral (pair), and time effects are included in all the regressions.

Column 1 shows the direct estimates from Equation 9, with Adj R-squaredaround 0.96. All results are in line with our theoretical expectations

regarding the sign and significance level of the estimated coefficients. Thetwo last columns of Table 2 (columns 2 and 3) check the robustness ofthe results to the distributional assumption and specification choice, usingbootstrappingand extreme bound analysis, respectively. Based on repeatedempirical sampling (here, 100 replications), bootstrapping regression esti-mates the parameters and the corresponding confidence intervals withoutmaking any distributional assumptions. The great overall similarity betweenthe results from column 1 and column 2 indicates that our estimates arerobust as regards the distributional assumptions.

For the extreme bound analysis, the explanatory variables are firstdivided into two groups of control and primary interest regressors. (Thesetwo groups correspond respectively to income and trade cost variables inEquation 9.) Suggested by Cooley and LeRoy (1981, 825), extreme boundanalysis involves varying the subset of control variables to get a range ofcoefficient estimates (an extreme bound) on each variable of primary inter-est. In other words, the model is estimatedn times, each time with a differentspecification of control variables. Consequently,n estimates will be obtainedfor each variable of primary interest, which are used to make one extremebound for that special variable. An extreme bound is a simple set which is

bounded by the lower and upper values of the n obtained estimates: (lower


16/29

128 J. Abedini

TABLE 2 Estimated Coefficients and Sensitivity Tests

EstimatesBootstrap

RegressionExtreme Bound

AnalysisVariable (1) (2) (3)

ln(yit) 0.861 0.852 (0.0331) (0.0386)

ln(yjt) 0.93 0.953

(0.0335) (0.0092)ln(gijt) 0.009

0.009

(0.0048) (0.0041)ln(dGij) 1.195

1.17 (1.196, 1.173)

(0.0097) (0.0117)cij 0.176

0.158 (0.176, 0.161)

(0.0247) (0.0246)sij 0.492

0.566 (0.486, 0.524)

(0.0266) (0.0212)ln(m

ijt) 0.168 0.208 (0.139, 0.366)

(0.028) (0.0273)ln(fjt) 0.235

0.583 (0.231, 0.765)

(0.0956) (0.1027)aijt 0.229

0.235 (0.222, 0.31)

(0.0178) (0.0196)uijt 0.013 0.039

(0.047, 0.013)(0.0209) (0.0155)

qit 0.644 0.65 (0.587, 0.924)

(0.0536) (0.0538)qjt 0.31

0.06 (0.285, 0.665)

(0.0549) (0.0172)ln(dIijt) 0.011

0.013 (0.011, 0.008)

(0.005) (0.0045)lij 0.443

0.44 (0.418, 0.443)

(0.024) (0.0201)rij 0.147

0.133 (0.145, 0.15)

(0.0151) (0.0158)hij 0.47

0.479 (0.465, 0.501)

(0.0289) (0.0308)ln(dCijt) 0.072

0.069 (0.072, 0.069)

(0.0068) (0.0074)0 4.57

2.448

(0.644) (0.6366)Ex. fixed effects 104.45

Im. fixed effects 153.22

Ex-Im fixed effects 17.54

Time effects 1886.92

Adj R-squared 0.9562 0.9555Number of observations 15353 15353

Significant at 1%; Significant at 5%; Significant at 10%; Significant at 1, 5, or 10%.

bound, upper bound). If all estimates within a particular bound are signifi-cant (at least at 10%) and have the same sign as that of the correspondingcoefficient in the original model, then the result is referred to as robust for

that variable; otherwise, the result is seen as being fragile.


17/29


To this aim, we first specified several alternatives to Equation 9 by eitherdropping a number of income variables or replacing them by other variables,such as the GDPs per capita and the sum of GDPs from the partner countries.

We get four different alternative specifications in total. At the second step,

each new specification has been separately estimated, resulting, at the end,in four other estimates for each of our variables of primary interest; i.e., tradecost factors. An extreme bound has been subsequently constructed for eachtrade cost variable using the corresponding four estimates (column 3). Allextreme bounds are similar in sign with their counterparts from column 1,and are significant throughout the range. This shows the great robustness ofour results to the choice of control variables.

V. ESTIMATING ICEBERG TRADE COSTS: OECD CASE STUDY

Rewriting Equation 8, bilateral iceberg trade costs between iand j at t (ijt)are obtainable from

ijt= exp

Kk=1kln

zkijt

1

.

Using the estimates fromTable 2(column 1), it is straightforward to generate

the fitted values for the trade cost functionK=14

k=1 kln

zkijt

. To compute ijt,we also need an estimate of . There are several papers providing such anestimate for (see AvW2004, 715717, for some). Following the literaturethat uses similar aggregate (or semi-aggregate) data, we conclude that islikely to be in the range of five to ten. As a result, we use the intermediate

value of = 8 for our subsequent analysis.14 This measure is not far fromthe measures already obtained (or taken) for by several other studies: AvW(2004): = 8; Hummels (2001): = 7.9; Eaton and Kortum (2002): = 9.28;and Chen and Novy (2011): =7.1, on average.

Replacing the above measures, the bilateral iceberg trade costs arecalculable as

ijt= exp

1

7

14k=1

kln

zkijt

. (12)

14 Our sensitivity analyses indicate that changing sigma between 7.5 and 9.5 does not considerablyaffect the forthcoming results, especially in a proportional way. More extreme values for sigma are,

however, unlikely according to the existing measures for this parameter.


18/29

130 J. Abedini

Based on the cross-section and time dimensions of Equation 12, three mainanalyses would be carried out as follows. First, we represent the icebergtrade costs each OECD country faces for exporting towards other OECDcountries (resulting in a 3333 matrix), as well as its overall multilateral

trade resistance with them. Secondly, we show how geographical, admin-istrative, institutional, and cultural trade costs vary over time for the OECDarea as a whole. Thirdly, we study how heterogeneous in iceberg tradecosts are different OECD sub-samples by region and the level of economicdevelopment.

Bilateral Measures

A 3333 matrix in Appendix I provides the average estimate of iceberg tradecosts that every OECD country faces for exporting to other members, includ-

ing itself. Averaging has been done over the three last years (200810) tokeep the comparative measures up-to-date. Besides these bilateral measures,several general conclusions are also drawn:

1. Varying from 0.692 to 3.781, the average bilateral iceberg trade costs areequal to 2.482 for the intra-OECD exchange of goods; i.e., a tax equivalent

of 148%. That is, on average, 60%

2.48 1

2.48

of shipped goods melt

away during the trading process in the OECD area. This measure is lowerthan the tax equivalent of 170% found by AvW (2004) for industrializedcountries at the end of the twentieth century.

2. The lowest trade cost of each member is obtained for its own domes-tic trade. This is not a surprising result given that the shortest distanceof each country is indeed from itself from a geographical, institutional,and cultural point of view. In addition, no administrative trade cost (tariffand non-tariff barriers) restricts domestic exchanges. Austria, Denmark,Finland, Greece, Iceland, Ireland, Israel, Luxembourg, the Netherlands,New Zealand, Portugal, Slovenia, Sweden, and Switzerland especially ben-efit from internal iceberg trade costs which are significantly less than 1.

This reflects an important preference bias in these countries towards thedomestic trade.3. The most expensive sources to import from are Mexico (for 26 part-

ners) and Turkey (for four partners), while the most expensive markets toexport to are New Zealand (for 21 partners) and Mexico (for five partners).

4. Apparently, ij. are not equal to ji.. This questioned the validity of sym-metric bilateral trade costs which is centrally assumed in the AvW (2004)model. A conventional t-statistic like D

SE( D) 1

NT

ijt jitSE( D)would examine this property more precisely, where NT is the effectivenumber of the estimates (NT = 22539). The computed statistic, over the

entire database, is equal to 114, resulting in a sound rejection of the null


19/29


TABLE 3-1 Multilateral Export Resistance to OECD 20082010

USA GBR CAN IRL FRA DEU NLD DNK SWE CHE NOR2.169 2.217 2.228 2.268 2.281 2.288 2.317 2.318 2.372 2.392 2.403

ESP FIN LUX ISL SVN AUT PRT POL EST ITA ISR

2.405 2.409 2.412 2.450 2.475 2.490 2.554 2.567 2.578 2.586 2.725

JPN CZE HUN AUS GRC SVK KOR NZL CHL MEX TUR2.728 2.743 2.765 2.768 2.778 2.785 2.813 2.858 2.868 2.912 2.998

TABLE 3-2 Multilateral Import Resistance from OECD 20082010

USA GBR CAN FRA IRL DEU NLD ESP DNK SWE SVN2.232 2.284 2.334 2.342 2.353 2.364 2.414 2.422 2.440 2.488 2.501

CHE NOR LUX FIN ISL ITA POL PRT AUT EST ISR 2.503 2.507 2.519 2.537 2.540 2.562 2.564 2.588 2.591 2.616 2.693

GRC CZE HUN JPN SVK MEX KOR TUR AUS CHL NZL 2.746 2.761 2.767 2.786 2.787 2.801 2.820 2.882 2.891 2.930 3.005

hypothesis of symmetric bilateral iceberg trade costs, H0 : ijt= jit. Thatis, trade costs are highly asymmetrically distributed across the OECDs.15

5. Multilateral export and import resistance16 of each country could alsobe drawn as the weighted average of its bilateral trade costs, where the

weight is the GDP-share of the trade partner. Equations 13 and 14 describethe procedure while Tables 3-1 and 3-2 illustrate the correspondingresults, respectively.

Ti,20082010 =1

3

2010t=2008

33j=1

yjt

33j=1

yjt

ijt (13)

Tj,20082010 =1

3

2010t=2008

33i=1

yit

33i=1

yit

ijt (14)

As shown, the U.S. has the lowest multilateral trade resistance for exportingto and importing from the OECD set, including itself. On the other hand,Turkey and New Zealand have, respectively, the largest multilateral exportand import resistance among the OECDs. Testing the symmetry of multi-lateral trade resistances reveals once again a pattern of asymmetry, with a

15 Asymmetric bilateral trade costs have been theoretically modelled by some recent papers. Pleaserefer to Bergstrand et al.(2013) and Novy(2013) for some examples.16 As explained previously, this is just an empirical aggregation over bilateral trade costs weightedby the partners incomes. As a result, one should not confuse it with the (theoretical) formulation of

multilateral trade resistances developed by AvW (2003).


20/29

132 J. Abedini

y = 0.0005x2 0.0178x + 2.6362

R

2

= 0.9312

2.44

2.49

2.54

2.59

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

FIGURE 1 Average iceberg trade costs, OECD countries.

t-statistic equal to 9.09, but to a lesser extent compared to what was obtainedfor bilateral measures.

Analyses over Time

Figure 1 shows how iceberg trade costs vary over time for the OECDs asa whole. The trend is downward but with a reducing speed. The last yearsof the period even indicate a slight increase in iceberg trade costs. The taxequivalent of trade costs, which was around 159% in 1988, reduces to around146% at the end of the period. This, however, shows a very small reductionin intra-OECD trade costs of only 8.2% within the 23-year period.

What we find is that the size of the reduction is much less than is

usually considered for the decline in trade costs. For example, Beltramo(2010) found that trade costs have decreased by 49% on average, within theEuropean Union during a nearly similar period of time, 19892006. This mea-sure is found to be about 40% by Novy (2013) for U.S. trade costs between1970 and 2000. It is important to note that none of these studies uses a tradecost function as complete as we employ here, and none of them controls fortime trend in their models. In this case, those results would be biased due tounderspecification and nonstationarity problems. Thus, one can hardly inter-pret such findings as a net reduction in trade costs. In this case, they couldsimply reflect the (nonstationary) time path of trade flows during the corre-sponding periods of study.17 In contrast, our model controls for a larger set

17 According to the WTO (2014 Press Releases), the word trade has been increasing in an exponential

way, except in a few years during 19902013. The export growth rate was usually greater than the GDPgrowth rate with a ratio varying between 1.4 (in 1990) and 2.4 (in 1998). This feature highlights more theproblems that the ignorance of nonstationarity could cause in our regression analysis. In this case, thetime trend of trade flows would be captured by the time varying variables of the model, including thetime varying trade cost variables. Taking into account the negative relationship between trade and tradecosts, this causes an overestimation of the coefficients of decreasing trade costs and an underestimationof the coefficients of increasing trade costs, resulting in an exaggeration of the overall reduction of trade

costs.


21/29


of trade barriers as well as the deterministic time trend which is responsiblefor the nonstationarity. As a result, Figure 1shows a net and detrended dropin trade costs.

We go deeper in this analysis and show how geographical, administra-

tive, institutional, and cultural iceberg trade costs vary over time. To this aim,the fitted values from

14k=1 kln

zkijt

should be decomposed as specified by

Equation 15.

ijt = exp

1

7

14k=1

kln

zkijt

= exp

1

7

4k=1

kln

zkijt

.exp

1

7

7k=5

kln

zkijt

.exp

1

7

10k=8

kln

zkijt

.exp

1

7

14k=11

kln

zkijt

,

(15)

where geographical, administrative, institutional, and cultural components

are, respectively, drawn by exp 1

7

4k=1 kln

zkijt

, exp

1

7

7k=5 kln

zkijt

,

exp

17

10k=8 kln

zkijt

, and exp

1

7

14k=11 kln

zkijt

. The variables keep

the same order as in column 1 ofTable 2.Figure 2(via panels a, b, c, and d) demonstrates the results. The trends

are apparently heterogeneous across the panels. We find that geographical

y = 0.0003x20.0091x + 3.5441

R2= 0.9875

3.46

3.47

3.48

3.49

3.5

3.51

3.52

3.53

3.54

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

(a) Geographical ITC

y = 0.0009x + 0.861

R2= 0.98610.84

0.845

0.85

0.855

0.86

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

(b) Administrative ITC

y = 4E-06x30.0001x2+ 0.0008x + 0.8491

R2= 0.8167

0.846

0.848

0.85

0.852

0.854

0.856

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

(c) Institutional ITC

y = 1E-05x + 0.9849

R2= 0.7439

0.9849

0.98495

0.985

0.98505

0.9851

0.98515

0.9852

0.98525

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

(d) Cultural ITC

FIGURE 2 Time movement of iceberg trade costs (ITC) by component.


22/29

134 J. Abedini

iceberg trade costs were decreasing until 2000, but the trend was reversedafter that by growing geographic barriers (panel a). Administrative icebergtrade costs (tariff and non-tariff barriers) show, in contrast, a continuousdecline throughout the whole period of study (panel b). That is, the weight

of these barriers has continuously decreased in international trade flows. Thisis in line with the general program of reduction in customs restrictions acrossthe globe.

With the exception of a few intermediate years (between 1998 and2004), the institutional trade costs denote an upward trend over 19882010(panel c). A similar, even more stable result is obtained for the culturalcomponents of trade costs (panel d). In other words, we find that theshare of institutional and cultural barriers to trade is increasing, similar togeographical ones, in intra-OECD trade.

Such an increase in international trade costs can be mainly explained

by an increasing trade between geographically, institutionally, and cultur-ally remote countries in the later years. One could, for instance, note thetrade expansion between Western and Eastern European countries as well asbetween the U.S. and Canada on the one hand and Mexico on the other. Forexample, the share of trade with faraway partners, at a distance more than9,000 kilometers, in the overall OECD trade of the U.S. and European Union,

which was respectively 0.0088 and 0.0087 in 2000, increases to 0.166 and0.145 in 2010. This expansion becomes possible, in major part, thanks tothe large economies of scale and comparative advantage gains behind suchtrade. That is, a reducing production cost is effectively compensating an

increasing trade cost.

Analyses across Sub-Samples

In this section, we divide the OECD members into eight major categoriesaccording to the continent and the level of economic development of part-ners.Table 4shows the mean, standard deviation, minimum, maximum, andrank of each group in iceberg trade costs over 19882010.

First, we note that the average iceberg trade costs are lower when both

exporting and importing countries belong to North America (NA-NA) thanwhen they come from Europe (EU-EU). On average, 47%

= 0.895

1.895

of shipped goods melt away in North American transit, against 51%

= 1.021

2.021

in Europe. That is, regional trade costs are 8% lower for an

intra-North American partnership. The ranking remains similar when consid-ering trade cost components separately, except for institutional costs, whichare substantially lower for intra-trade in Europe. Inter-continent costs ofexporting are rather symmetric for exchanges between Europe and North

America. It is, however, found that Europe (EU-NA) benefits from a slight

comparative advantage in institutional iceberg trade costs, while North


23/29


TABLE 4 Average Bilateral Iceberg Trade Costs by Country-Group, 19882010

Trade costsCountry-group

(Exporter-Importer) Mean S.D. Min. Max.

Rank(smallest

to largest)

Total OECD-OECD 2.511 0.603 0.661 4.059 EU-EU 2.021 0.332 0.686 2.709 2NA-NA 1.895 0.581 1.09 2.924 1EU-NA 2.941 0.339 2.314 3.808 3NA-EU 2.98 0.424 2.222 3.969 4DEV- DEV 2.369 0.574 0.661 3.514 1MER-MER 2.637 0.6 0.802 4.059 3DEV-MER 2.627 0.538 1.395 3.822 2MER-DEV 2.709 0.564 1.459 3.902 4

Geographical OECD-OECD 3.489 0.725 1.28 5.059 EU-EU 2.889 0.368 1.327 3.606 2NA-NA 2.823 0.579 1.872 3.589 1EU-NA 4.068 0.235 3.674 4.657 4NA-EU 4.06 0.236 3.674 4.657 3DEV- DEV 3.446 0.737 1.28 4.902 1MER-MER 3.529 0.712 1.347 5.059 2DEV-MER 3.584 0.67 2.063 5.059 3MER-DEV 3.584 0.67 2.063 5.059 4

Official OECD-OECD 0.85 0.015 0.832 0.877 EU-EU 0.841 0.011 0.832 0.877 2NA-NA 0.84 0.01 0.833 0.869 1EU-NA 0.861 0.01 0.834 0.87 4NA-EU 0.86 0.018 0.832 0.877 3

DEV- DEV 0.849 0.014 0.832 0.87 1MER-MER 0.851 0.015 0.832 0.877 3DEV-MER 0.853 0.015 0.833 0.877 4MER-DEV 0.85 0.015 0.832 0.87 2

Institutional OECD-OECD 0.85 0.05 0.755 1.048 EU-EU 0.839 0.039 0.755 0.939 1NA-NA 0.879 0.075 0.786 1.029 2EU-NA 0.854 0.048 0.772 0.972 3NA-EU 0.868 0.07 0.783 1.005 4DEV- DEV 0.821 0.034 0.755 0.94 1MER-MER 0.879 0.046 0.791 1.048 3DEV-MER 0.856 0.037 0.791 0.98 2

MER-DEV 0.889 0.043 0.819 1.021 4

Cultural OECD-OECD 0.985 0.044 0.801 1.024 EU-EU 0.983 0.047 0.801 1.021 2NA-NA 0.885 0.079 0.801 0.989 1EU-NA 0.984 0.036 0.85 1.02 4NA-EU 0.979 0.04 0.85 1.02 3DEV- DEV 0.976 0.05 0.801 1.023 1MER-MER 0.993 0.035 0.801 1.024 2DEV-MER 0.996 0.024 0.846 1.024 3MER-DEV 0.997 0.023 0.846 1.024 4

EU: European members; NA: North American members; DEV: Developed members; MER: Emerging

members.


24/29

136 J. Abedini

y = 0.0017x + 1.1094

1.05

1.06

1.07

1.08

1.09

1.1

1.11

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

FIGURE 3 Ratio of developing-to-developed iceberg trade costs.

America (NA-EU) has the advantage in cultural iceberg trade costs, in orderto export to the other group.

Separating countries by the level of economic development reveals otherfacts. Iceberg trade costs are substantially lower for trade among developedeconomies (DEV-DEV) than among other groups. The gap is significantlylarge as regards institutional and geographical iceberg trade costs. In partic-ular, developing countries face relatively substantial institutional trade costs

when they export to other countries; either the partner is a developed (MER-DEV) or another developing country (MER-MER). The same costs are muchlower when the exporting country is a developed country. Again, this showsthe asymmetric feature of bilateral trade costs which we previously pointedout. In contrast, cultural trade costs seem to be more symmetrically dis-tributed between developed and developing countries, even though theyare still lower for the intra-trade among developed members. We find, how-ever, that the difference between developed and developing countries inexport barriers is reducing over time as the ratio of developing-to-developedexporter iceberg trade costs converges to one over time (Figure 3).

VI. CONCLUDING REMARKS

Departing from the theoretical gravity model of AvW (2004), we develop anaugmented trade cost function which is later used in the article to estimatebilateral iceberg trade costs of 1089 OECD country pairs over 19882010. Theestimates have been provided and analyzed separately for the geographical,administrative, institutional, and cultural components of OECD trade costs.

We find that previous results about the rapid reduction of trade costs,usually more than 40% during a similar time span, might not be as reliableas believed. Taking into account various types of trade impediments andcontrolling for the nonstationarity of certain variables reduce this amount toonly about 8%. We even find an upward trend for geographical, institutional,

and cultural iceberg trade costs in the last years of the sample, mainly due


25/29


to an increasing trade between distant countries. The boost of trade withfaraway countries relies on increasing returns to scale and differentiation ofproducts which mark this new phase of international trade.

In addition, we find that trade costs are still important and melt, on

average, around 60% of shipped goods (a tax equivalent of 148%) duringthe intra-trading process of the OECDs. This is less than the tax equiva-lent of 170% found by AvW (2004) for industrialized countries at the endof the 2000s. We also find strong evidence against the symmetry assump-tion of bilateral iceberg trade costs in the literature. These costs are highlyasymmetrically distributed across the OECDs, particularly as regards theintra-continent trade and trade between OECD developed and developingcountries. Though to a lesser extent, our findings show a similar pattern ofasymmetry for the multilateral trade resistances of OECD members.

Our analyses reveal that regional trade costs are generally 8% lower in

North America than they are in Europe. However, European countries bene-fit from a slight comparative advantage in institutional trade costs and North

American countries in cultural trade costs in order to export to the otherregion. In addition, we find that trade costs are lower when the exportingcountry is a developed member of the OECD, and higher when it is a devel-oping one. Similarly, the lowest level of trade costs is found for the intra-tradeof the developed OECD countries. These costs are, however, convergent forboth groups over time.

ACKNOWLEDGMENTS

The author is indebted to anonymous reviewers for providing insightful com-ments and providing directions for additional work which has resulted in thisarticle.

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Abedini_Iceberg Trade Cost Measures an Application to the OECD Area Over 1988–2010