DETERMINANTS OF TRADE AND SPECIALIZATION IN THE ...sergeshikher.com/papers/Shikher_Determinants_of_trade.pdfIN THE ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT COUNTRIES

DETERMINANTS OF TRADE AND SPECIALIZATIONIN THE ORGANISATION FOR ECONOMIC CO-OPERATION

AND DEVELOPMENT COUNTRIES

SERGE SHIKHER∗

This paper empirically investigates the relative importance of productivity, factorendowments, trade costs, and tastes in determining the current pattern of trade andspecialization. The results show that productivity and taste differences are the first andsecond most significant determinants of trade and specialization. Factor endowmentsare the least influential for the average country in the data set, but their effects aremuch greater in the poorer than richer countries. The results also show the substantialrole of trade costs, which is amplified through interactions with other determinantsof trade. Trade costs affect the relative costs of intermediate inputs and final goods,link preferences with specialization, and reduce the geographical range of comparativeadvantages. (JEL F1, F10)

I. INTRODUCTION

Modern trade theory suggests several deter-minants of trade and specialization. They includetechnology, factor endowments, trade costs,tastes, and returns to scale. Various theories existthat explain the effects of these determinants anda number of empirical studies have been per-formed to check if these theories provide goodexplanations of the data.

There is evidence now that all of the abovedeterminants of trade have a significant effecton the pattern of trade and specialization in pro-duction. It would be interesting then to somehowcompare the importance of these determinants.Unfortunately, there is little theoretical guidancethat can help us make this comparison.

To make this comparison empirically requiresa model that incorporates all of these deter-minants of trade, and a methodology for mea-suring and comparing the importance of thesedeterminants. This paper sets out to accomplishthese tasks, with the exception of studying theeffects of the returns to scale. The results pre-sented in this paper will help us sort throughthe various determinants of trade and specializa-tion and contribute to our understanding of how

*The author would like to thank Jonathan Eaton,Jonathan Haughton, and two anonymous referees for helpfulcomments and suggestions.Shikher: Department of Economics, Suffolk University, 8

Ashburton Place, Boston, MA 02108. Phone +1 617-994-4275, Fax +1 617-994-4216, E-mail [email protected]

these determinants create the currently observedpatterns of specialization and trade.1

The model used in this paper includes con-stant returns to scale, fixed factor endowmentsthat are different across countries, industry-levelproductivity differences, and taste differencesacross countries. It also incorporates trade coststhat are different for each industry and each pairof countries. To explain the within-industry two-way trade between countries, this paper allowsfor producer heterogeneity within industriesusing the framework of Eaton and Kortum.2 Thepaper presents the evidence, both on the microand macro levels, that supports this model.

The effects of various determinants of tradeare studied by performing counterfactual

1. By the pattern of trade I mean “who sells what towhom,” also known in the literature as the direction of trade.

2. The model is parametrized using 1989 data for 19countries; a paper that also uses the framework of Eaton andKortum (2002) and is related to this one is Chor (2010)—itinvestigates the effects of various determinants of trade oncountry welfare.

ABBREVIATIONS

CES: Constant Elasticity of SubstitutionGDP: Gross Domestic ProductGL: Grubel-LloydNAFTA: North American Free Trade AgreementOECD: Organisation for Economic Co-operation and

DevelopmentTFP: Total Factor ProductivityUNIDO: United Nations Industrial Development

Organization

138

Economic Inquiry(ISSN 0095-2583)Vol. 51, No. 1, January 2013, 138–158

doi:10.1111/j.1465-7295.2012.00465.xOnline Early publication April 19, 2012© 2012 Western Economic Association International

SHIKHER: DETERMINANTS OF TRADE AND SPECIALIZATION 139

simulations. The importance of the determinantsis measured by the magnitudes of their effectson specialization and pattern of trade. The paperalso checks if the importance of the determi-nants varies with country income and showsthe effects of various determinants of trade onwelfare.

Simulations produce several interestingresults. Productivity differences across countriesand industries are found to be the most influ-ential determinants of trade and specialization.Preferences are found to be second most influ-ential, signifying that the lack of attention paidto them in the trade literature is unwarranted. Atthe same time, factor endowments are found tobe the least influential in the average country ofthe data set.

Trade costs are found to be very importantdeterminants of trade and specialization. Whileprevious literature has demonstrated their impor-tance for the volume of trade, this paper showshow significant they are for specialization andthe pattern of trade. The trade costs’ directeffects on trade, which occur because trade costsdiffer across countries and industries, is foundto be moderate. However, trade costs’ indirecteffect, whereby they interact with other determi-nants of trade, is very significant. For example,trade costs strongly link preferences with spe-cialization and net exports. They also influencetrade by affecting access to intermediate goods.In addition, trade costs limit the geographicalrange of comparative advantage, forcing it to bedetermined within a neighborhood of a countryinstead of the whole world.

This paper also finds that most of the abovedeterminants have a greater effect on trade andspecialization in poorer than richer countries.Interestingly, factor endowments stand out inthis respect, because their influence is manytimes larger in poorer countries than in richerones. Correspondingly, the effects of factorendowments on trade and specialization in richercountries can be called economically insignif-icant. This result gives support to the notionthat the Heckscher–Ohlin explanation of trade ismore relevant to poorer than to richer countries.

II. DETERMINANTS OF TRADEAND SPECIALIZATION

The determinants of trade and specializationexist on both the supply and demand sides. Thesupply-side determinants include productivity,

factors of production, and trade costs.3 On thedemand side, the cross-country differences inpreferences affect trade.

Of all the determinants of trade listed above,productivity and factor endowments have rec-eived the most attention in the trade litera-ture. The effects of productivity differences ontrade were modeled by Ricardo (1817), Dorn-busch, Fischer, and Samuelson (1977), andEaton and Kortum (2002) and shown to beempirically important by MacDougall (1951),Trefler (1995), and Harrigan (1997). Produc-tivity differences across countries give rise toabsolute advantages. Comparative advantagesexist because productivity differences betweencountries vary across industries and goods.

The effects of factor endowments weremodeled by Heckscher (1919), Ohlin (1924),Samuelson (1949), Vanek (1968), and others.The empirical investigations of the effects offactor endowments are summarized in Leamerand Levinsohn (1995), Harrigan (1997), andDavis and Weinstein (2001). Factor endowmentsaffect trade and specialization because theyvary across countries and are used differentiallyacross industries, giving rise to comparativeadvantages.

Trade costs have been empirically shown tobe an important determinant of trade by thegravity literature. Anderson and van Wincoop(2004) summarize various types of trade costsand show how large they can be. The Dorn-busch–Fischer–Samuelson and Eaton–Kortummodels incorporate trade costs into their generalequilibrium models of trade.

Although the importance of trade costs forthe overall volume of trade between countrieshas been demonstrated many times, their effectson the pattern of trade and specialization havebeen hardly investigated.

Trade costs affect the pattern of trade andspecialization in several ways. The first way ismore obvious: trade costs vary by industry, thusaffecting the relative cost of country i’s goods incountry n and, therefore, the comparative advan-tage of country i (for both final and intermedi-ate goods).4 Another way is less obvious: tradecosts shape comparative advantage even if they

3. The returns to scale are also supply-side determinantsof trade, but are not considered in this paper.

4. This effect of trade costs was analyzed by Venablesand Limao (2002). The evidence that trade costs vary sig-nificantly by industry includes Hummels (2001), Andersonand van Wincoop (2004), and the calculations done in thispaper.

140 ECONOMIC INQUIRY

are equal across industries, by affecting the costof intermediate goods. For example, about 15%of the intermediate goods used by the machin-ery industry come from the metals industry. So,being “close” to a country that can producemetal products cheaply is good for the domesticmachinery industry.

Yet another way in which trade costs affectthe pattern of trade and specialization is dis-cussed in Deardorff (2004) who calls it the“local comparative advantage.” With trade costs,the pattern of trade is determined by the com-parative advantage relative to the low-trade-cost(“neighboring”) trade partners, not so much thehigh-trade-cost (“far away”) countries.5 In otherwords, trade costs decrease the geographic rangeof comparative advantage. Again, even if tradecosts were equal across industries, they wouldstill affect the pattern of trade.

Finally, trade costs affect the pattern oftrade and specialization by linking produc-tion with tastes. The demand-side explanationsof trade have received noticeably less atten-tion than the supply-side explanations, withmost trade models assuming identical homo-thetic preferences. On the other hand, Linder(1961) observed that people with similar per-capita incomes have similar consumption pat-terns and that these patterns vary with the levelof income. This of course implies nonhomoth-etic preferences. Markusen (1986) presents evi-dence for nonhomotheticity of demand and amodel that incorporates nonhomothetic demandto explain the observed pattern of trade. Fieler(forthcoming) incorporates nonhomothetic pref-erences into the Eaton and Kortum (2002)model. In line with previous evidence, the dataused in this paper show significant cross-countryvariation of industry shares in consumption.6

Because of trade costs, there is a strongrelationship between tastes and specialization inproduction.7 For most countries and industries,

5. This effect cannot be modeled in the two-countryRicardian or Dornbusch–Fischer–Samuelson models.

6. For example, Sweden spends 1% of its gross domesticproduct (GDP) on the final Textile products, while Austriaspends 3%. Italy spends 10% of its GDP on the finalMachinery products, while Japan spends 20%. There is alsoa relationship between the industry shares in consumptionand income. Similar to other studies, for example, the shareof the Food industry is negatively correlated with per-capitaincome. See Markusen (1986) for evidence on the Foodindustry. Shares of some industries, such as Paper andMachinery, are positively correlated with per-capita incomein the data used in this paper.

7. The correlation in the data between industry value-added shares in GDP and industry consumption shares inGDP is 0.85.

purchases by domestic customers constitute thelargest use of output. Contrast this with theneoclassical trade model in which the productionand consumption decisions are separate becausecountries can freely buy and sell goods onthe world markets at a world price. In thiscase, a country would export according to itscomparative advantage and import according toits preferences, resulting in relationship betweenpreferences and net exports, but not betweenpreferences and specialization in production.

III. MODEL

This section presents the model that will beused to investigate the importance of variousdeterminants of trade. The model is formallydescribed in Section III.A and the parametriza-tion procedure is explained in Section III.B. Themodel has been previously shown to performwell in counterfactual simulations, as describedin Section III.C.

The model uses the neoclassical assump-tions of multiple industries, constant returns toscale, perfectly competitive markets, and severalfactors that are mobile across industries. Eachindustry is characterized by a particular levelof technology, set of factor intensities, and ademand function. Countries differ in their fixedfactor endowments.

To explain the two-way trade between coun-tries, this model relies on the Eaton and Kor-tum’s (2002) framework at the industry level.Within each industry, there is a continuum ofgoods produced with different productivities.These productivity differences mean that thetwo-way trade will consist of different sets ofgoods being traded each way.

The use of the Eaton–Kortum frameworkinstead of the Armington (1969) approach hasseveral important implications. The goods aredifferentiated by their features, not their coun-try of origin. Countries do not have monopolypower over their products. The home bias inconsumption and cross-country price differen-tials are explained by trade costs rather thandemand-side parameters.

A. Description of the Model

There are N countries and J industries ineach country. Subscripts i and n refer to coun-tries, whereas subscripts j and m refer to indus-tries. There are two factors of production: capitaland labor.


Intra-industry production, trade, and pricesare modeled following Eaton and Kortum(2002). Each industry produces many goods.Within an industry, each good is indexed by areal number l on the interval [0, 1] and producedusing a Cobb–Douglas production process. Fac-tor shares are the same for all goods within anindustry, but different across industries. Totalfactor productivities are different across goods lwithin each industry. The total factor productiv-ity of producing good l in industry j of countryi is zij (l).

The description of these productivities is thekey element of the Eaton–Kortum model. Itis assumed that productivities zij (l) are theresult of the R&D process, which is random.Therefore, the productivities zij (l) are random.They are independent realizations of the randomvariable Zij , which has the Frechet distributionwith cdf Fij (z) = e−Tij z−θ

, where Tij > 0 andθ > 1 are the parameters.8

Parameter T governs the average productiv-ity of producers in an industry. Therefore, itdetermines comparative advantage across indus-tries. For example, country n has a comparativeadvantage in industry j if Tnj /Tnm > Tij /Tim.9

Parameter θ determines the comparative advan-tage across goods within an industry. Lowervalue of θ means more dispersion of productivi-ties among producers, leading to stronger forcesof within-industry comparative advantage.

The cost of producing one unit of good l ofindustry j produced in country i is cij /zij (l). Inthat equation, cij is the industry cost function

8. Kortum (1997) and Eaton and Kortum (1999) providemicrofoundations for this approach. Parameter Tij governsthe mean of the distribution, while parameter θ, which iscommon to all countries and industries, governs the variance.The support of the Frechet distribution is (0, ∞). The useof the Frechet distribution is motivated as follows. Thereare many R&D labs that develop new technologies for goodl. The productivities of these new technologies are drawnfrom the Pareto distribution (which is a good distribution todescribe the situation where you have few good outcomesand many mediocre or bad ones). With perfect competition,the technologies are public knowledge, so only the mostproductive technology for making good l will be usedby producers. The productivity of this technology is themaximum of the Pareto draws, which is described by theextreme value type II (also known as Frechet) distribution.

9. The productivity parameter T is different from thetotal factor productivity (TFP). Parameter T determines themean of the Frechet distribution and is exogenous in thismodel. TFP, on the other hand, is endogenous. Finicelli,Pagano, and Sbracia (2009) derive the analytic relationshipbetween the T of an industry and the mean productivityof the firms that actually operate in that industry. Similar toTFP, though, T is potentially affected by technology as wellas social and political factors.

and is given by

cij = rαj

i wβj

i ρ1−αj −βj

ij ,(1)

where ri is the return to capital, wi is the wage,ρij is the price of intermediate inputs, αj > 0is the capital share, βj > 0 is the labor share,and 1 − αj − βj > 0 is the share of intermediateinputs. It is assumed that industries mix inter-mediate inputs in a Cobb–Douglas fashion, sothat the price of inputs ρij is the Cobb–Douglasfunction of industry prices:

ρij =J∏

m=1

pηjm

im .(2)

In this expression, ηjm > 0 is the share of indus-try m goods in the input of industry j , such that∑J

m=1 ηjm = 1, ∀j .The price of good l of industry j pro-

duced in country i and delivered to countryn is pnij(l) = cij dnij/zij (l), where dnij is theSamuelson’s “iceberg” transportation cost ofdelivering industry j goods from country i tocountry n.10 Since zij (l) is a realization ofa random variable, pnij(l) is also a realiza-tion of a random variable (let’s call it Pnij).The cdf of Pnij is Gnij(p) = Pr

(Pnij ≤ p

) =Pr

(cij dnij/Zij ≤ p

) = Pr(cij dnij/p ≤ Zij

) =1 − Fij

(cij dnij/p

) = 1 − e−Tij (cij dnij)−θ

pθ.

In country n, consumers buy from the lowest-cost supplier, so the price of good l in coun-try n is pnj (l) = min

{pnij(l), i = 1, . . . , N

},

which is a realization of a random variable Pnj .The distribution of Pnj is Gnj (p) = Pr(Pnj ≤p)= 1 − Pr(Pnj > p) = 1 − Pr(Pnij > p,∀i) =1 − ∏N

i=1[1 − Gnij(p)] = 1 − e−�nj pθ, where

�nj = ∑Ni=1 Tij (cij dnij)

−θ summarizes technol-ogy, input costs, and transport costs around theworld.

On the demand side, consumers have nestedtwo-tier preferences. They have Cobb–Douglaspreferences over industries, with industry jin country n having a share ψnj (so that∑

j ψnj = 1). Consumers have constant elastic-ity of substitution (CES) preferences over thegoods within an industry with the elasticity ofsubstitution σ > 0.

The exact price index for the within-industryCES objective function is denoted by pnj andcan be calculated as

10. To receive $1 of product in country n requiressending dnij � 1 dollars of product from country i. Bydefinition, domestic transport costs are set to one: dnnj ≡ 1.Trade barriers result in dnij > 1.


pnj =[∫ 1

0pnj (l)

1−σdl

]1/(1−σ)

=[∫ ∞

0p1−σ

nj dGnj (p)

]1/(1−σ)

(3)

= E[P 1−σ

nj

]1/(1−σ) = γ

[∑N

i=1Tij

(cij dnij

)−θ

]−1/θ

= γ�−1/θ

nj ,

where γ ≡ � ((θ + 1 − σ) /θ)1/(1−σ) and � is theGamma function.11 Plugging this price indexinto Equation (1), the cost equation becomes

cij = rαj

i wβj

i

J∏m=1

[γ−θ

N∑n=1

Tnm (dinmcnm)−θ

]−(ηjm(1−αj −βj ))/θ

.(4)

To derive the industry-level bilateral tradeflows, we note that the probability that a pro-ducer from country i has the lowest price incountry n for good l is πnij ≡ Pr

[pnij(l) ≤

min{pnsj (l); s �= i

}] = ∫ ∞0

∏s �=i

[1 − Gnsj (p)

]dGnij(p) = Tij

(γcij dnij/pnj

)−θ. Since there is

a continuum of goods on the interval [0, 1],this probability is also the fraction of industry jgoods that country n buys from i. It is also thefraction of n’s expenditure spent on industry jgoods from i or Xnij/Xnj (this is true becauseconditional on the fact that country i actuallysupplies a particular good, the distribution of theprice of this good is the same regardless of thesource i). So, the industry-level bilateral tradeis given by

πnij = Xnij/Xnj = Tij

(γcij dnij/pnj

)−θ,(5)

where Xnij is the spending of country n onindustry j goods produced in country i and Xnj

is the total spending in country n on industry jgoods.

Industry output Qij is determined as follows.The goods market clearing equation is

Qij =N∑

n=1

Xnij =N∑

n=1

πnijXnj(6)

=N∑

n=1

πnij(Znj + Cnj

),

11. The last equality is obtained by setting x = − ln p,t = σ − 1, and noting that the moment-generating func-tion for x is E

[etx

] = �t/θ� (1 − t/θ) (Johnson and Kotz1970).

where Znj and Cnj are the amounts spent bycountry n on industry j ’s intermediate and con-sumption goods, respectively. The spending on

the intermediate goods is Znj =∑

mηmjwnLnm

(1 − αm − βm) /βm, where Lnm is the stock oflabor employed in industry m of country n.12

As consumers have Cobb–Douglas prefer-ences across industries, Cnj = ψnjYn, where Yn

is the total income (GDP) in country n andψnj is the share of industry j in country n, aspreviously defined.13 Parameters ψnj determinetastes. Plugging the expressions for intermediateand consumption spending into Equation (6), theoutput equation becomes

Qij =N∑

n=1

πnij

(7)

×((

J∑m=1

ηmj (1 − αm − βm)

βm

wnLnm

)+ ψnjYn

).

Since production is Cobb–Douglas, indus-try factor employments are given by Kij =αjQij /ri and Lij = βjQij /wi . Factors of pro-duction can be freely and instantaneously movedacross industries within a country, subject to theconstraints

∑Jj=1 Kij = Ki and

∑Jj=1 Lij = Li ,

where Ki and Li are the factor stocks, which arefixed.

12. It obtains as follows: Znj =∑

mZnmj =∑

mpnjMnmj =

∑m

ηmjρnmMnm, where Znmj is the

amount spent by industry m on intermediate goods fromindustry j , M is the quantity of intermediate goods, andthe last equality follows from Equation (2). Then from (1)ρnmMnm = wnLnm (1 − αm − βm) /βm.

13. Consumption C includes private consumption andgovernment consumption.


Due to data limitations, only the man-ufacturing industries are included in the Jindustries. The nonmanufacturing sector’s priceindex is normalized to 1 and its purchasesof the manufacturing intermediates are treatedas final consumption.14 Country income Yi isthe sum of the manufacturing income YM

i andnonmanufacturing income YO

i :

Yi = YMi + YO

i = wiLi + riKi + YOi ,(8)

where factor stocks Ki and Li are specific tomanufacturing. The model assumes that capitaland labor are not mobile between the manufac-turing and nonmanufacturing sectors. However,this specification is also consistent with factormobility between manufacturing and nonman-ufacturing if the factors are used in the sameproportions in the two sectors. The nonmanufac-turing income is assumed to be a constant pro-portion of the GDP, so that YO

i = ξiYi , whereξi � 0 is a parameter of the model.15

The model is given by Equations (3)–(5),(7)–(8), and the four factor employment andfactor clearing equations. Model parameters areαj , βj , ηjm, θ, ψnj , dnij, Tnj , Ki , Li , and ξi . Themodel solves for all other variables such as allprices (including factor prices), industry factoremployments, output, and trade.16

B. Assigning Parameter Values

The model is parametrized using 1989 datafor 8 two-digit manufacturing industries in 19OECD countries. The included countries andindustries can be seen in Table 3.

The values of shares αj , βj , and ηjm aretaken from the data.17 Table 1 shows the dif-ferences in factor intensities across industries.

14. Following Eaton and Kortum (2002), it is assumedthat (at least some of) nonmanufacturing output can betraded costlessly and used as the numeraire.

15. Note that the nonmanufacturing share in income ξi

is generally not equal to the nonmanufacturing share inconsumption ψi,nonmanuf .

16. The model has N2J + 5NJ + 3N unknowns andthe same number of equations. The unknowns in the modelare Xnij, cnj , pnj , Knj , Lnj , Qnj , Yn, wn, and rn.

17. Labor shares in output are from the UNIDO. I usethe average labor shares of the countries in the sample.Capital shares in output are obtained using ratios of capitalto labor shares from the data set described in Shikher(2004), which carefully calculates capital shares in severalcountries. I multiply the labor shares by these ratios toobtain capital shares. I do not use the value-added datafrom the UNIDO to obtain capital shares because that dataare unreliable. Industry shares ηjm are obtained from theOECD input–output tables. These tables exist only for someof the countries in the data set and only for select years. Iuse the input–output tables for Canada, France, Germany,

TABLE 1Shares of Factors and Intermediate Inputs in

Output

IndustryCapital

(αj )Labor(βj ) Inputsa

Cap. inVAb

Food 0.062 0.103 0.835 0.37Textile 0.058 0.201 0.741 0.22Wood 0.064 0.182 0.755 0.26Paper 0.081 0.185 0.733 0.31Chemicals 0.082 0.115 0.803 0.42Nonmet. 0.106 0.185 0.709 0.36Metals 0.086 0.133 0.781 0.39Machinery 0.071 0.186 0.743 0.28

aThe share of intermediate inputs is (1 − αj − βj ).bThe share of capital in value added is αj /(αj + βj ).

The most capital-intensive industry, Chemicals,uses nearly twice as much capital stock (for thesame amount of value added) as the least capital-intensive industry, Textile. These differences infactor intensities, when combined with the dif-ferences in factor endowments across countries,determine specialization. For example, a countrywith a low capital-labor ratio will specialize inTextiles, whereas a country with a high capital-labor ratio will specialize in Chemicals.

The value of the technology parameter θ istaken from Eaton and Kortum (2002), where itis estimated to be 8.28. This value is within therange of the long-term trade elasticity estimatesin the literature (Ruhl 2008).18

Estimation of the trade costs is discussedin the first subsection of III.B. The values forparameters ψnj , dnij, Tnj , Li , and ξi are obtainedby fitting a subset of the model to data, whichis described in the second subsection of III.B.

Trade Barriers. Bilateral trade barriers are esti-mated by applying the approach of Eaton andKortum (2002) at the industry level. The ratioof n’s spending on i’s goods to its spend-ing on its domestically made goods is obtained

Japan, United Kingdom, and the United States for 1990, andAustralia for 1989. Input–output tables for these countriesresult in very similar shares ηjm. I use the average sharesacross these countries.

18. The other value of θ estimated by Eaton and Kortum(2002), 3.6, is too low even by their own admission and isoutside of the range of values found in the literature. In anycase, Shikher (2011) found that the choice of θ in the range[3.6, 13] has little effect on how factor endowments andtechnology affect specialization (the differences in resultswere second- or third-order). This paper also finds the resultsto be robust to the choice of θ.


from Equation (5):

πnij/πnnj = Xnij/Xnnj(9)

= Tij /Tnjd−θnij

(cij /cnj

)−θ.

To relate the unobservable trade cost to theobservable country-pair characteristics, the fol-lowing trade cost function is used:

log dnij = dphys

kj + bj + lj + fj + mnj + δnij,

(10)

where dphys

kj (k = 1, . . . , 6) is the effect ofphysical distance lying in the kth interval, b isthe effect of common border, l is the effect ofcommon language, f is the effect of belongingto the same free trade area, mn is the overalldestination effect, and δni is an unobservedcomponent of bilateral trade costs.

Then, the gravity-like estimating equationis obtained by taking logs of both sides ofEquation (9):

log Xnij/Xnnj = −θdphys

kj − θbj − θlj − θfj

(11)

+ Dexp

ij + Dimp

nj − θδnij,

where Dexp

ij = log Tij c−θij is the exporter dummy,

Dimp

nj = −θmnj − log Tnj c−θnj is the importer

dummy.19 The destination-industry specificimport barriers are calculated as mnj = − (1/θ)

(Dexp

nj + Dimp

nj ).Bilateral trade data needed to estimate Equa-

tion (11) is from Feenstra (1997) and Feenstra(2000).20 Imports from home Xiij are calculatedas output minus exports, and spending Xij iscalculated as output minus exports plus imports.Industry output and labor compensation are fromthe UNIDO’s statistical database.

Distance measures used on the right-handside of Equation (11) are obtained as follows.I use distance (in miles) between economiccenters of countries from Stewart (1999). This

19. Note that the estimating equation includes the exportand import dummy variables, similarly to the theoreticallyderived gravity equation of Anderson and van Wincoop(2003).

20. For some pairs of countries, trade values are missingfor 1989. Therefore, I cannot estimate δnij for some n, i,and j , which are part of the distance measure. There are19 × 18 × 8 = 2, 736 observations of δnij possible in thedata, of which 105 or 3.8% are missing. I proxy mostmissing observations by the estimates from the neighboringyears. Six observations that cannot be proxied in this mannerare proxied by the estimates of δni for total manufacturing.

TABLE 2Trade Costs

Tariff Equivalentof Trade Cost

Fooda 146.7%Textilea 106.9%Wooda 159.4%Papera 146.9%Chemicalsa 123.7%Nonmetalsa 139.1%Metalsa 109.8%Machinerya 104.7%Averageb 129.6%Maximumb 566.4%Minimumb 0.0%SDb 76.8%

aAverage for all country pairs.bOf all country pairs and industries.

distance is the great circle distance betweenthe population weighted average of the lati-tude and longitude of major cities. FollowingEaton and Kortum (2002), I divide distance intosix intervals: [0,375), [375,750), [750,1500),[1500,3000), [3000,6000), and [6000, maxi-mum). I consider the following free trade agree-ments for the f variable: EC/EU, EFTA, EEA,FTA, NFTA, CER, and a free-trade agreementbetween Turkey and EFTA.

Table 2 summarizes the estimated trade costsdnij. Their magnitude is substantial: the average(across country pairs and industries) estimatedtrade cost is 2.29 (equivalent to a 129% tariff).21

This trade cost includes all costs necessary tomove goods between countries, such as freight,insurance, tariffs, non-tariff barriers, and theftin transit. Trade costs vary across country pairsand industries. For example, the Machinery andTextile products are typically cheaper to movebetween countries than the Wood and Foodproducts.

Technology and Other Fitted Parameters. Theparameters ψnj , dnij, Tnj , Li , and ξi are obtainedby fitting a subset of the model, together witha long-run equilibrium condition, to domestic

21. This number is greater than the 74% rich countryinternational trade cost estimated in Anderson and van Win-coop (2004). There are several reasons for the difference:(1) the lowest-trade cost industry (Machinery) is by far thelargest industry and (2) the sample of this paper includesa number of poor countries that have significantly highertrade costs than the rich countries. For example, the averagetrade cost for importing to the United States is 51% whileto Turkey it is 231%.


data.22 The subset of the model includes the costequation (4), reproduced here:

cij = rαj

i wβj

i

J∏m=1

(12)

×[γ−θ

N∑n=1

Tnm (dinmcnm)−θ

]−(ηjm(1−αj −βj ))/θ

,

where ri is the interest rate and wi is the wage,as well as a simplified version of the outputequation (7):

Qij =N∑

n=1

πnijXnj ,(13)

where import shares πnij are calculated usingEquations (5) and (3).

Equations (12) and (13) can be solved simul-taneously for the technology parameters Tnm

and costs cij given data on the rates of returnri , wages wi , output Qij , spending Xnj , andthe estimated transport costs dnij.23 However,data on the rates of return ri are not avail-able, so their values in the base year arecalculated from the data on manufacturing capi-tal stocks Ki , industry output Qij , and indus-try capital shares αj using the relationshipri = (1/Ki)

∑j

(αjQij

).24

22. This procedure is different from the approachused by Eaton and Kortum (2002) to find the technol-ogy parameters. They calculate technology parameters fromthe estimated importer and exporter dummies and data onwages.

23. The system of equations is solved numerically usingMatlab. Convergence takes only a couple of minutes ona moderately speedy computer, given a set of reasonablestarting values.

24. Labor compensation data is from the UNIDO’sstatistical database. As mentioned earlier, output data isalso from the UNIDO’s statistical database. Spending Xnj

is obtained as output minus exports plus imports (tradedata is from Feenstra (1997) and Feenstra (2000)). Themanufacturing capital stocks Ki are calculated by applyingthe perpetual inventory method to the investment time seriesfrom the UNIDO. The calculated average rates of return ri

are around 20%, plus or minus a couple of percentage points(these are gross rates, so 20% can be explained roughly asa 10% net return and 10% depreciation rate). To check therobustness of the results to a measure of ri , two alternativemeasures of ri are used: (a) constant 20% for all countriesand (b) from Caselli and Feyrer (2007). In (b), that paper’sreported best measure of the net return to capital is usedtogether with the assumption of a 10% depreciation rate tocalculate the gross rates of return. These gross rates also turnout to be very close to 20% (even though Caselli and Feyrer2007 measure the return to capital for the whole economy

The values of the technology parameters Ti

are hard to interpret. On the other hand, themean productivities are easier to understand.The mean productivity in industry j of country i

is equal to E[zij

] = T1/θ

ij � (1 − 1/θ), where �

is the Gamma function. The mean productivityin industry j of country i relative to the meanproductivity of industry j in the United Statesis then

(Tij /TUS,j

)1/θ.

Table 3 presents the relative mean produc-tivities for each country and industry. Thesemean productivities determine the industry-levelRicardian comparative advantage, which influ-ences the pattern of trade and specialization. Forexample, relative to the United States, Japan isshown to have the strongest comparative advan-tage in Nonmetals and Machinery products andthe strongest comparative disadvantage in Foodand Wood products.25

The industry employments of capital andlabor are calculated as Kij = αjQij /ri andLij = βjQij /wi .26 The manufacturing laborstocks Li are calculated as the sum of indus-try factor employments. The last column ofTable 3 shows the capital-labor ratios in dif-ferent countries, relative to the United States,(Ki/Li) / (KUS/LUS). It shows, for example,that Turkey has only about 12% of the cap-ital per worker that the United States has.27

The differences of factor endowments acrosscountries, when combined with the differencesof factor intensities across industries, shown inTable 1, determine the Heckscher–Ohlin com-parative advantage, which in turn influences thepattern of trade and specialization.

while ri is the return to capital in manufacturing). Using anyof the three measures of ri produces substantially the samesimulation results.

25. The table also shows that there are differences inmean productivities across countries. For example, the meanproductivity draw in Mexico is about a half of that in theUnited States. However, it should be remembered that withtrade, the average productivity of firms actually operating ishigher than the mean productivity draw, because goods withlow productivity draws are not produced, but imported.

26. These industry factor employments may not beexactly equal to the actual ones because of the assump-tions of equal factor shares across countries and equal ratesof return across industries. We can check how close theyare by assembling the data for industry capital and laboremployments. I take the industry labor employments fromthe UNIDO and calculate the industry capital employmentsby applying the perpetual inventory method to the invest-ment time series from the UNIDO. The correlation betweenthe two measures of industry-level capital employments is0.99. The same number for labor employments is 0.97.

27. Relative to the United States, the K/L in manydeveloping countries has declined during the 1980s (“thelost decade”).


TABLE 3Technology Parameters and Capital-Labor Ratios, Relative to the United States

Scaled and Normalized Technology Parameters (Ti/TUS )1/θ

Food Textile Wood Paper Chemicals Nonmet. Metals MachineryCapital-Labor

Ratiosa

Australia 0.84 0.76 0.61 0.68 0.68 0.69 0.89 0.70 0.53Austria 0.65 0.80 0.65 0.74 0.72 0.82 0.79 0.73 0.60Canada 0.85 0.86 0.92 0.97 0.80 0.79 0.99 0.79 0.80Finland 0.59 0.74 0.75 0.90 0.71 0.69 0.84 0.73 0.64France 0.89 0.96 0.79 0.85 0.89 0.97 0.94 0.87 0.92Germany 0.83 0.95 0.83 0.88 0.92 0.99 0.96 0.92 0.89Greece 0.68 0.69 0.45 0.49 0.56 0.66 0.68 0.48 0.20Italy 0.81 1.04 0.88 0.83 0.85 1.04 0.89 0.88 0.88Japan 0.74 0.97 0.77 0.87 0.93 1.05 1.00 1.03 0.98Korea 0.66 0.87 0.56 0.61 0.72 0.68 0.79 0.71 0.26Mexico 0.56 0.55 0.42 0.45 0.60 0.57 0.62 0.50 0.13New Zealand 0.88 0.71 0.62 0.68 0.66 0.57 0.71 0.60 0.38Norway 0.76 0.66 0.66 0.78 0.74 0.67 0.87 0.70 0.68Portugal 0.62 0.65 0.51 0.58 0.54 0.64 0.58 0.51 0.17Spain 0.77 0.79 0.64 0.71 0.74 0.83 0.82 0.69 0.43Sweden 0.66 0.72 0.73 0.85 0.75 0.75 0.85 0.79 0.64Turkey 0.57 0.61 0.38 0.35 0.53 0.58 0.63 0.42 0.12United Kingdom 0.84 0.87 0.70 0.81 0.85 0.88 0.88 0.82 0.62United States 1 1 1 1 1 1 1 1 1

aThese ratios are calculated as (Ki/Li )/(KUS /LUS ).

Nonmanufacturing share (in income) ξi iscalculated as 1 − (riKi + wiLi) /Yi , where thetotal income (GDP) Yi is taken from data. Thetaste parameters are calculated as ψij = Cij /Yi ,where the consumption is calculated as Cij =Xij − Zij = Xij − ∑J

m=1 ηmj (1 − αkm − αlm)Qim. The taste parameters for the manufacturingindustries are shown in Table 4. As mentionedin the Introduction, there are significant differ-ences in consumption preferences across coun-tries, which affect the pattern of specializationand trade. For example, while Turkey spendsa somewhat higher fraction of its income onFood and Textile products than Japan, it spendsa much smaller fraction of its income on Paperand Machinery products. In another example,France, Germany, Italy, and the United King-dom spend about 2% of their income on Textileproducts, while Sweden, Norway, and Finlandspend 1%–1.4%.

C. Evaluating the Model

As the above model will be used to studyproperties of the international trade, it isimportant to evaluate it.28 There are several

28. The evidence for the microfoundations of themodel—heterogeneity of productivity across producers

approaches to evaluating a model. One is tofit the model to data and evaluate the fit. Incase of the model of this paper, it is possible tocompare the trade flows implied by the modelwith the actual trade flows.29 The correlationbetween the predicted and actual trade flows is0.99. It is similar if calculated by industry or bycountry.

Although these numbers show an extremelygood fit, it is necessary to keep in mind thatthe model has many degrees of freedom and,therefore, can fit the in-sample data well. A morechallenging evaluation for the model would beto ask it to make predictions outside of thesample used to parametrize it.

Such exercises were performed in Shikher(2011) and Shikher (forthcoming). The firstpaper evaluated the ability of the model topredict changes in specialization, measured byindustry shares in GDP, in response to changesin capital stock. This was accomplished by

within the same industry—is numerous now and reviewedin Eaton, Kortum, and Kramarz (2004).

29. As mentioned earlier, the bilateral trade data is fromFeenstra (1997) and Feenstra (2000). The trade flows pre-dicted by the model are the values of Xnij = πnijXnj =Tij

(γcij dnij/pnj

)−θXnj , calculated given the parameter val-

ues described in Section III.B.


TABLE 4Consumption Shares in Income

Food Textile Wood Paper Chemicals Nonmetals Metals Machinery

Australia 0.058 0.018 0.017 0.022 0.028 0.016 0.027 0.123Austria 0.073 0.030 0.024 0.020 0.044 0.024 0.021 0.158Canada 0.064 0.018 0.014 0.016 0.040 0.010 0.006 0.157Finland 0.080 0.014 0.022 0.040 0.028 0.015 0.021 0.130France 0.070 0.022 0.012 0.022 0.046 0.013 0.014 0.157Germany 0.065 0.021 0.013 0.012 0.061 0.012 0.001 0.175Greece 0.094 0.036 0.011 0.012 0.071 0.021 0.036 0.116Italy 0.048 0.019 0.007 0.012 0.030 0.011 0.026 0.103Japan 0.067 0.019 0.014 0.025 0.038 0.017 0.008 0.199Korea 0.082 0.021 0.011 0.019 0.105 0.029 0.033 0.239Mexico 0.045 0.009 0.001 0.009 0.039 0.009 0.031 0.092New Zealand 0.085 0.024 0.018 0.031 0.031 0.012 0.007 0.139Norway 0.080 0.013 0.021 0.030 0.017 0.008 0.007 0.134Portugal 0.101 0.013 0.016 0.014 0.085 0.024 0.024 0.167Spain 0.097 0.022 0.015 0.016 0.051 0.020 0.021 0.141Sweden 0.060 0.010 0.022 0.022 0.025 0.009 0.012 0.141Turkey 0.071 0.024 0.003 0.003 0.093 0.021 0.049 0.079United Kingdom 0.074 0.021 0.016 0.033 0.050 0.018 0.013 0.170United States 0.058 0.016 0.011 0.028 0.044 0.008 0.008 0.146

Note: The remaining fraction of income in each country goes toward the nonmanufacturing goods.

asking the model to predict the changes inspecialization that occurred during 1975–1995.The model was parametrized with 1989 data,so the model had to make a backcast for1988–1975 and a forecast for 1990–1995.In order to make the predictions, the modelwas fed the data on changes in country cap-ital stocks during 1975–1995. All the otherparameters were unchanged from their 1989values.

The accuracy of the predictions was eval-uated by (a) regressing the actual changesin specialization on the predicted ones and(b) by comparing the semielasticities of spe-cialization with respect to capital in the actualand simulated data. The estimated slope of theregression in (a) was not significantly differ-ent from one and the semielasticities in theactual and simulated data in (b) were verysimilar.

The second paper, also a historical exper-iment, evaluated the ability of the model topredict changes in trade due to the trade lib-eralization in North America. The model wasparametrized with 1989 data and asked to pre-dict the changes in industry-level trade flowsbetween the United States, Canada, and Mexicothat would occur due to NAFTA. In order tomake these predictions, the model was giventhe magnitudes of the 1989 policy-related trade

barriers (tariffs and non-tariff barriers) thatwere to be removed under NAFTA. All otherparameters were unchanged from their 1989values.30

The accuracy of the forecasts was evalu-ated by comparing (a) the predicted changesin total exports and imports of the NAFTAcountries with the actual ones that occurredbetween 1989 and 2000 and (b) the predictedand actual (1989–2000) changes in the industry-level import shares.31 The correlation betweenthe simulated and actual changes in the exportsand imports was found to be 0.95. The correla-tion between the simulated and actual changesin the import shares for the U.S.–Canada andU.S.–Mexico trade was found to be 0.95.32 Theslope and intercept from the regression of theactual on simulated changes were not signifi-cantly different from 1 and 0, respectively.

Although these two studies do not provide anexhaustive evaluation of the model, they providesolid evidence that predictions of the model canbe believed.

30. Tariffs and NTBs are part of total trade costs d.Therefore, removing tariffs τ means reducing d by τ.

31. An industry-level import share is the share ofcountry i in the industry j imports of country n.

32. U.S.–Canada and U.S.–Mexico trade constitutemore than 99% of the North American trade. TheCanada–Mexico trade is hard to evaluate because of datairregularities in several industries.


IV. RELATIVE IMPORTANCEOF THE DETERMINANTS OF TRADE

The model allows us to study the effectsthat the current levels of factor endowments,productivity, trade costs, and tastes have on thepattern of trade and specialization. As men-tioned earlier, by the pattern of trade I mean“who sells what to whom,” which is alsocalled the direction of trade. The pattern oftrade can be measured by exports, imports,and net exports. Specialization in production,also known as industrial structure, is usuallymeasured by industry shares in GDP.

The pattern of trade and specialization arerelated, as a country that specializes in a partic-ular industry tends to export more of that indus-try’s goods. However, in a world with interme-diate goods, trade costs, and taste differencesthis relationship need not be tight. For example,a country separated from others by high tradebarriers and with a strong preference for a par-ticular good will produce more of that good, butwill not necessarily export more of it.33

More formally, net exports are NEnj =EXnj − IMnj while specialization is measuredby Snj = V Anj/V An. An important differencebetween Snj on one hand and EXnj , IMnj , andNEnj on the other is that the former is mea-sured in value-added (net) terms while the lat-ter three are measured in output (gross) terms.Another important difference is that industryshares are measured relative to other industrieswhile exports, imports, and net exports are not.One consequence of this last difference is thatexports, imports, and net exports will increasewith general economic growth, while industryshares will not.

Twelve counterfactual simulations will beperformed in order to find out how the currentlevels of factor endowments, technology, tradecosts, and tastes affect the pattern of trade andspecialization. Each of these simulations willremove the effect of one of these determinantsand note the consequences of this removal forthe pattern of trade and specialization.34 The

33. On the other hand, in a world of small countries andfree trade the production and consumption decisions can bemade separately.

34. Numerical simulations are done in Matlab. Conver-gence (using Matlab’s “dogleg” algorithm) takes only a fewminutes on a moderately speedy computer. There are a cou-ple of simulations where the algorithm does not converge.In those cases, an intermediate solution (i.e., with param-eter changes equal to the half of the desired changes) isfound first and then used as a starting point to find the finalsolution.

determinants will be studied one by one, so thatwhen the effect of one of the determinants isremoved, the effects of all other determinantsstill remain.

Factor endowments affect trade and special-ization because (a) they are different acrosscountries and (b) factor intensities are differ-ent across industries. Therefore, the effects offactor endowments can be found by either (a)removing factor endowment differences acrosscountries or (b) removing factor intensity differ-ences across industries. Both approaches will beused. Simulation #1 will set αnew

j = α(αj + βj

),

βnewj = (1 − α)

(αj + βj

), where α = (1/J )∑

j αj /(αj + βj

), while Simulation #2 will

set Knewn = (

K/L)Ln, where K/L is some

common capital-labor ratio.35

There are two kinds of the productivity dif-ferences in the model that can affect trade andspecialization. One is at the industry level. Sincethe cross-country differences in productivitiesvary across industries, there are industry-levelcomparative advantages.36 For example, coun-try n has a comparative advantage in industry jif Tnj /Tij > Tnm/Tim. The effect of these com-parative advantages can be found by eliminatingthem, that is setting Tnj /Tij to be the same inall industries.

At the same time, we want to preservethe absolute advantage that country n mayhave over country i. This country-level abso-lute advantage τni is measured as the aver-age of industry-level absolute advantages: τni =(1/J )

∑j Tnj /Tij . So, we want to set Tnj /Tij

to be the same in all industries, while keep-ing τni constant. This is accomplished by set-ting T new

nj = τniTij , ∀j . Simulation #3 will usethe United States as the reference country iand will set T new

nj = (TUS,j /J

) ∑j Tnj /TUS,j

in every country n other than the UnitedStates.

Simulation #4, on the other hand, will studythe effects of eliminating cross-country differ-ences in productivities (i.e., absolute advan-tages) on specialization and trade. Thissimulation will set T new

nj = (1/N)∑

n Tnj , ∀j .

35. The level of the common capital-labor ratio has noeffect on the results. The average factor intensity α, aswell as the other averages calculated in this section, canbe weighted or unweighted. The effects of various weighingschemes on the results are minor. Their small size does notjustify their presentation in the paper.

36. To review the industry-level productivity differencesacross countries in the data, see the second subsection ofIII.B and Table 3.


The second kind of the productivity differ-ences in the model exists at the producer level.Within the same industry, there are differencesof productivities across producers. The disper-sion of these productivities is governed by theparameter θ. To find the effect of these produc-tivity differences on the pattern of trade and spe-cialization, Simulation #5 will eliminate them bysetting a very high value of θ, thereby making allproducers within an industry virtually identicalin terms of productivity.37 Note that Simulation#5 is different from the other simulations per-formed in the paper because it does not set aparameter to its in-sample average. This is donebecause the only way to shut down the producer-level productivity differences is to set θ to a veryhigh value.38

Setting a very high θ eliminates the much-more-productive-than-average producers thatnormally engage in exports. There will stillbe trade, of course, because all other deter-minants of trade (such as industry-level pro-ductivity differences) still remain. However,increasing θ decreases intra-industry trade andresults in nearly complete specialization (corner)solutions.

Tastes in the model are determined by param-eters ψnj . To eliminate the effects of tastedifferences on trade and specialization, Simu-lation #6 will set these parameters equal in allindustries (i.e., set ψnew

nj = ψn, ∀j , where ψn =(1/J )

∑j ψnj ), while Simulation #7 will set

them equal in all countries (i.e., set ψnewnj = ψj ,

∀n, where ψj = (1/N)∑

n ψnj ).As mentioned earlier, trade costs affect the

pattern of trade and specialization in severalways. First, trade costs help determine the com-parative advantage because they vary acrossindustries for each country pair and, there-fore, affect the relative cost of one country’sgoods in another. To eliminate this effect oftrade costs on the pattern of trade and spe-cialization, Simulation #8 will set the tradecosts equal across industries for each pair ofcountries. In other words, it will set dnew

nij =(1/J )

∑j dnij.

Second, trade costs affect the pattern oftrade and specialization because some coun-tries are more “remote” (from other countries)than others. To eliminate this effect of trade

37. In practice, it is increased to 99. It is the maximumvalue for which a numerical solution can be obtained.

38. In addition, of course, parameter θ is the same in allcountries and industries.

costs, Simulation #9 will make all countriesequidistant from each other, while preservingthe cross-industry differences in trade costs. Inother words, trade costs will be set to theircross-country pair in-sample averages: dnew

nij =(1/

(N2 − N

)) ∑n�=i

∑i dnij.

Third, trade costs shape the pattern oftrade and specialization by affecting the costsof intermediate goods. Being “close” to acheap source of intermediate goods can bene-fit industries that use these intermediate goods.To gauge this effect of trade costs, Simu-lation #10 will set trade costs equal in allindustries (as in Simulation #8) and will alsoeliminate the cross-industry variation in depen-dence on intermediates by setting 1 − αj −βj and ηjm to their in-sample averagevalues.

Fourth, trade costs make the determination ofcomparative advantage “local.” With trade costs,the nearby countries have a much greater impactin the determination of comparative advantagethan do far-away countries. Greater trade costsdecrease the size of the “neighborhood” withinwhich the comparative advantage is determined.To gauge this effect of trade costs, Simulation#11 will remove both cross-country and cross-industry differences, so that countries do notface any advantage in regards to geographicalproximity.

The fifth way in which trade costs affectstrade and specialization is by firmly linkingtogether preferences in consumption and spe-cialization in production. To gauge this effect oftrade costs, Simulation #12 will set trade costsequal in all industries (as in Simulation #8) andset taste parameters ψnj equal to their cross-industry average values.

The 12 simulations described above will per-mit us to quantify the effects of factor endow-ments, productivity, tastes, and trade costs on thepattern of trade and specialization. The simula-tions, except for Simulation #5 (as mentionedbefore), will gauge the effects of these deter-minants of trade by setting the values of spe-cific parameters equal to their in-sample averagevalues. We will measure the effects of eachdeterminant of trade by the percent changes inindustry shares, exports, imports, and net exportsrelative to their baseline values. The statisticthat will be used to summarize these percentchanges, which are expected to vary acrossindustries and countries, is the average (acrossall industries and countries) of the absolute


TABLE 5Percent Changes of Industry Shares in Value Added and Net Exports

Industry Shares Net Exports

Sim.# Simulationa

Variationremovedb

Av. ofAbs.c Rank Minimum Maximum

Av. ofAbs.c Rank Minimum Maximum

1. Factor shares Cross-industry 3.88 12 −30.71 27.93 58.04 12 −356.57 1552.99

2. Factor endowments Cross-country 4.76 11 −23.29 46.30 175.29 7 −1739.19 8154.17

3. Industry productivity Cross-industry 30.86 5 −72.80 459.10 329.22 4 −3588.33 3678.06

4. Industry productivity Cross-country 35.01 4 −69.17 604.08 631.42 2 −9086.35 6377.78

5. Firm-level productivity N/A 18.49 7 −99.95 88.44 97.39 11 −256.77 157.72

6. Preferences Cross-industry 88.12 1 −70.88 723.56 172.27 8 −923.85 2021.77

7. Preferences Cross-country 20.15 6 −45.77 243.35 158.05 9 −2450.00 990.27

8. Trade costs Cross-industry 14.30 9 −84.35 86.04 258.85 5 −3030.44 3915.09

9. Trade costs Cross-country 13.16 10 −73.83 72.17 134.90 10 −1348.91 1799.73

10. Trade costs—access to intermediatesd 46.14 3 −56.79 301.96 351.84 3 −3303.19 3623.67

11. Trade costs—localizing comparativeadvantagee

15.06 8 −75.41 75.55 176.60 6 −1507.33 2798.84

12. Trade costs—linking preferences tospecializationf

79.67 2 −89.43 658.28 991.57 1 −4255.83 27560.49

aEach simulation removes a particular determinant of trade.bSpecifies whether the cross-industry or cross-country variation has been removed.cThe average of absolute percent changes.dThis simulation removes cross-industry differences in trade costs and eliminates the cross-industry variation in dependence on intermediates.eThis simulation removes cross-industry and cross-country differences in trade costs.fThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average value.

values of the percent changes.39 These averageswill be used to compare the importance of vari-ous determinants of trade. A determinant, whoseremoval causes the highest average absolute per-cent change in net exports and/or specialization,will be deemed the most important for the pat-tern of trade and/or specialization.

A. Results

Tables 5–8 summarize the results of thesimulations. Each simulation entails the removalof the determinant of trade listed in the secondcolumn of each table. The third column of eachtable shows if a relevant parameter has been setto its cross-industry or cross-country in-sampleaverage during the simulation.

Tables 5 and 6 show the effects of Simula-tions #1–12 on four variables: industry share,net exports, exports, and imports. As discussedin the previous section, changes in industryshares measure changes in specialization whilechanges in net exports measure changes in the

39. The average of absolute percent changes is a goodmeasure because we are interested in the magnitude of theeffect, not its direction. The average of percent changes isnot a good measure because the negative changes will offsetthe positive changes resulting in average changes close tozero.

pattern of trade. Exports and imports are shownin addition to net exports in order to betterunderstand the changes in trade that take placeduring simulations.

The first four columns of numbers in Table 5show the effects on the industry shares. The firstcolumn shows the average (across countries andindustries) of the absolute values of the per-cent changes in industry shares. As mentionedearlier, these average absolute percent changesare used to compare the importance of variousdeterminants of trade for the pattern of trade andspecialization.

The second column of numbers shows therank (1 through 12) of each determinant oftrade according to the average absolute per-cent change in industry shares that its removalcauses. Based on the previous discussion, higherranked determinants of trade are deemed moreimportant than the lower ranked ones. The rank-ings based on the four criteria (industry shares,net exports, exports, and imports) are simi-lar (correlations are in the range of 0.6–0.7).The third and fourth columns of numbers showthe range of the percent changes in the fourvariables.

Table 7 summarizes the rankings presentedin Table 5 and calculates the average rank foreach determinant of trade. Table 8 shows the


TABLE 6Percent Changes of (Gross) Exports and Imports

Exports Imports

Sim.# Simulationa

Variationremovedb

Av. ofabs.c Rank Minimum Maximum

Av. ofabs.c Rank Minimum Maximum

1. Factor shares Cross-industry 9.36 12 −42.13 69.02 6.21 12 −38.97 45.48

2. Factor endowments Cross-country 32.75 10 −18.36 336.18 12.18 11 −70.41 41.92

3. Industry productivity Cross-industry 173.80 3 −89.98 6364.68 55.50 9 −98.27 521.26

4. Industry productivity Cross-country 755.20 1 −84.25 38545.36 57.39 8 −98.80 413.40

5. Firm-level productivity N/A 99.41 4 −100.00 −83.16 95.37 4 −100.00 27.77

6. Preferences Cross-industry 82.13 6 −70.15 307.18 108.42 3 −71.21 1113.60

7. Preferences Cross-country 20.30 11 −37.27 92.42 33.76 10 −51.57 552.48

8. Trade costs Cross-industry 53.20 9 −90.18 293.68 67.04 7 −93.43 1112.93

9. Trade costs Cross-country 76.23 7 −98.09 1.77 71.94 6 −99.39 636.17

10. Trade costs—access to intermediatesd 98.71 5 −91.31 581.65 117.91 2 −85.83 1378.41

11. Trade costs—localizing comparativeadvantagee

75.01 8 −97.23 46.74 80.62 5 −98.45 1024.40

12. Trade costs—linking preferences tospecializationf

226.94 2 −94.27 2892.03 358.65 1 −86.87 9344.95

aEach simulation removes a particular determinant of trade.bSpecifies whether the cross-industry or cross-country variation has been removed.cThe average of absolute percent changes.dThis simulation removes cross-industry differences in trade costs and eliminates the cross-industry variation in dependence on intermediates.eThis simulation removes cross-industry and cross-country differences in trade costs.fThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average value.

TABLE 7Summary of Ranks

Sim. # Simulationa Variation Removedb Ind. Shares Net Exp. Av. Rank Av. Rank 1–9c

1. Factor shares Cross-industry 12 12 12 92. Factor endowments Cross-country 11 7 9 63. Industry productivity Cross-industry 5 4 4.5 2.54. Industry productivity Cross-country 4 2 3 1.55. Firm-level productivity N/A 7 11 9 6.56. Preferences Cross-industry 1 8 4.5 37. Preferences Cross-country 6 9 7.5 58. Trade costs Cross-industry 9 5 7 4.59. Trade costs Cross-country 10 10 10 7

10. Trade costs—access to intermediatesd 3 3 311. Trade costs—localizing comparative advantagee 8 6 712. Trade costs—linking preferences to specializationf 2 1 1.5

aEach simulation removes a particular determinant of trade.bSpecifies whether the cross-industry or cross-country variation has been removed.cAverage of ranks based only on Simulations #1–9.dThis simulation removes cross-industry differences in trade costs and the cross-industry variation in dependence on

intermediates.eThis simulation removes cross-industry and cross-country differences in trade costs.fThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average

value.

effects on welfare, measured by real GDP, ofeach determinant of trade.

We first focus on Simulations #1–9 thatstudy the direct effects of various determi-nants of trade. By contrast, Simulations #10–12investigate the indirect effects of trade costs,

whereby trade costs interact with other determi-nants of trade to influence the pattern of tradeand specialization. Simulations #10–12 involveremoving several determinants of trade at once.

The last column of Table 7 shows the averagerank for each determinant of trade based only on


TABLE 8Percent Changes of Welfare

Sim. # Simulationa Variation Removedb Av. of Abs.c Rank Minimum Maximum

1. Factor shares Cross-industry 0.41 12 −1.59 0.912. Factor endowments Cross-country 14.71 2 −8.89 56.583. Industry productivity Cross-industry 13.07 3 −1.73 31.344. Industry productivity Cross-country 105.41 1 −36.86 390.705. Firm-level productivity N/A 6.61 8 −17.27 13.926. Preferences Cross-industry 10.25 5 −23.11 21.477. Preferences Cross-country 10.69 4 −32.74 14.688. Trade costs Cross-industry 1.87 11 −4.83 9.709. Trade costs Cross-country 9.85 6 −22.49 3.46

10. Trade costs—access to intermediatesd 4.19 10 −15.27 7.3911. Trade costs—localizing comparative advantagee 8.98 7 −22.02 3.2312. Trade costs—linking preferences to specializationf 5.83 9 −8.73 26.60

aEach simulation removes a particular determinant of trade.bSpecifies whether the cross-industry or cross-country variation has been removed.cThe average of absolute percent changes.dThis simulation removes cross-industry differences in trade costs and the cross-industry variation in dependence on

intermediates.eThis simulation removes cross-industry and cross-country differences in trade costs.fThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average

value.

Simulations #1–9. According to this ranking,the cross-country differences in productivity,which determine absolute advantages, have thegreatest influence on the pattern of trade andspecialization. Removing these differences (asin Simulation #4) changes industry shares by anaverage of 35%, net exports by 631%, exportsby 755%, and imports by 57%. As we can see,the effects are especially large on exports andnet exports.

The second greatest impact on the pat-tern of trade and specialization (according tothe last column of Table 7) is caused by thecross-industry differences in productivity, whichdetermine Ricardian comparative advantages.Their removal (as in Simulation #3) results inthe industry shares changing by an average of31% and net exports by 329%. Therefore, ofall the determinants of trade studied in thispaper, productivity differences (cross-countryand cross-industry) have the largest effects onthe pattern of trade and specialization.

Differences in tastes also have significanteffects on trade and specialization. The factthat consumers spend different fractions of theirincome on different industries is the third mostimportant determinant of trade. Removing thesedifferences (Simulation #6) has a very largeeffect on specialization and imports, which isexpected, and a smaller effect on exports andnet exports. The fact that consumers in different

countries have different tastes is the fifth mostimportant determinant of trade. The removal ofthese differences (Simulation #7) has moderateeffects on trade and specialization. One of theconclusions obtained from Simulations #6 and#7 is that, compared to other determinantsof trade, preferences have a relatively greatereffect on industry shares than on trade. Thishappens because trade costs link preferenceswith specialization. This link will be furtherinvestigated in the next section.

Simulations #8 and #9 study the effects ofcross-industry and cross-country differences intrade costs on the pattern of trade and specializa-tion. These effects are found to be moderate inmagnitude. The influence of cross-industry dif-ferences in trade costs is ranked fourth whilethe effect of cross-country differences is rankedeighth (according to the last column of Table 7).

The results of Simulation #8 show that someindustries are more sensitive than others to theremoval of cross-industry differences in tradecosts.40 The Food industry is the most sensitiveby far. For example, while the average industryshare and net exports change by 14.3% and258%, respectively (Table 5), the average Foodindustry share and net exports change by 20%and 760%. Wood industry is also sensitive to the

40. “Sensitivity” here is measured by the absolutepercent changes in industry shares and net exports.


removal of cross-industry differences in tradecosts, but much less so than the Food industry.The high sensitivity of the Food industry isexplained by the high trade costs in that industryand by the fact that the trade costs in the Foodindustry are different from the trade costs inother industries (i.e., the correlation between the[country pair] trade costs in the Food industryand any other industry is noticeably lower thanthe correlation between the trade costs of anytwo non-Food industries).41

The effects of the firm-level productiv-ity differences on specialization and trade aremoderate. The main effect of removing theseproductivity differences is to reduce the volumeof trade. The exports and imports are moderatelyaffected, but the net exports and specializationdo not change as much (because exports andimports are reduced by similar proportions).

Finally, the effects of factor endowments ontrade and specialization are found to be small.Simulation #1 measures the effect of factorendowments by removing factor endowment dif-ferences across countries. The results show thatremoving these differences causes the averageindustry share to change only by 4.8% and netexports by 175%. According to the last columnof Table 7, the effects of factor endowments areranked seventh. Table 6 shows that the effects offactor endowments on exports and imports arealso small relative to the other determinants oftrade.

Simulation #2 measures the effect of factorendowments by removing factor intensity dif-ferences across industries. Removing these dif-ferences causes the average industry share tochange by a small 3.9% and net exports by 58%.The effects of factor intensity differences areranked last in Table 7. Therefore, factor endow-ments are found to be much less influential thanproductivity in determining the pattern of tradeand specialization. However, the second subsec-tion of IV.A has something interesting to add tothe factor endowment story.

We now turn to the indirect effects of tradecosts, whereby trade costs interact with otherdeterminants of trade to influence the pattern oftrade and specialization. These indirect effectsare studied in Simulations #10–12.

The results show that trade costs, especiallyby linking preferences to specialization (Simu-lation #12) and affecting access to intermediates

41. Specifically, consider the J × J correlation matrixwhere each element is ρjm = corr

(dnij, dnim

)and n and i

go from 1 to N . In this case, ρFood,m < ρjm for all j and m.

(Simulation #10), have great effects on trade andspecialization. For example, de-linking prefer-ences and specialization, as in Simulation #12,changes industry shares by an average of almost80%, net exports by almost 1000%, exports by226%, and imports by 358%. The third columnof numbers in Table 7 shows that the effectsof Simulation #12 are the greatest among theeffects of all 12 simulations performed in thispaper. The effects of Simulation #10 (whichremoves the trade costs’ impact on the accessto intermediates) have the second highest rankamong the 12 simulations, same as the effects ofSimulation #4 (which removes the cross-countrydifferences in productivity).

Trade costs also affect the pattern of tradeand specialization by localizing comparativeadvantage. This effect of trade costs, studiedin Simulation #11 is of moderate size—theaverage rank of this effect is 7. The next sectionwill further investigate the interaction betweentrade costs and several other determinants oftrade.

Table 8 shows the effects of the determinantsof trade on welfare. Not surprisingly, cross-country industry level productivity differences(i.e., absolute advantages) have by far the greatesteffects on welfare. The rest of trade determinantsare much less important. For example, cross-country factor endowment differences have (onaverage) the second largest effects on welfare, butthey are much smaller than the effects of absolutetechnological advantages.

More on Trade Costs. As mentioned earlier, the-ory predicts that trade costs affect the pattern oftrade and specialization by, among other things,interacting with the determinants of compar-ative advantage and preferences. Specifically,trade costs reduce the geographic range of com-parative advantages and link preferences withproduction. To further investigate these effectsof trade costs, Simulations #1–3 and #7 arerepeated with trade costs set to one (i.e., withfree trade). The results are presented in Table 9.It shows the average absolute percent changes inindustry shares and net exports that occur whenthe simulations are performed with the currentlevels of trade costs and free trade (dnij = 1 forall n, i, and j ).

The results of Simulations #1–3 showthat, as expected, the factor endowment- andproductivity-based comparative advantagesbecome more influential when trade costs areremoved. With the current level of trade costs,


TABLE 9Interaction Between Trade Costs and Some of the Determinants of Comparative Advantage

Effects on Ind. Sharesc Effects on Net Exp.c

Sim. # SimulationaVariationRemovedb

With CurrentTrade Costs

With FreeTrade

With CurrentTrade Costs

With FreeTrade

1. Factor shares Cross-industry 3.88 7.42 58.04 91.682. Factor endowments Cross-country 4.76 6.11 175.29 109.513. Industry productivity Cross-industry 30.86 102.27 329.22 808.457. Preferences Cross-country 20.15 9.98 158.05 1091.34

aEach simulation removes a particular determinant of trade.bSpecifies whether the cross-industry or cross-country variation has been removed.cThe numbers are the averages of absolute percent changes.

removing industry-level productivity differencescauses the average industry share to change by30.8% and net exports by 329%. With free trade,these numbers are about three times as big:102% and 808%, respectively.

Without trade costs, a country’s comparativeadvantages are determined in the whole world,not just its “neighborhood.” Without trade costs,countries serve more destinations according totheir comparative advantages. As the result,countries become more specialized. One wayto see this increased specialization is by look-ing at the Grubel-Lloyd (GL) intra-industrytrade index. The bilateral GL index is widelyused to measure the prevalence of intra-industrytrade between two countries. Given by Gnij =2 min

(Xnij + Xinj

)/(Xnij + Xinj

), where Xnij

is the amount of industry j imports that nreceives from i, it is equal to 0 if all tradeis inter-industry and 1 if all trade is intra-industry. The world GL index is the averageof all bilateral GL indices in all industries:G = (1/J )

(1/

(N2 − N

))∑n�=i

∑j Gnij.

The GL index in the baseline model is 0.44.42

With the current levels of trade costs, remov-ing industry-level productivity differences (asin Simulation #3) increases the GL index by0.044. So, introducing industry-level compara-tive advantages increases inter-industry trade, asexpected. More inter-industry trade means thatcountries are more specialized.

Repeating Simulation #3 with all trade costsset to 1, we find that removing industry-levelproductivity differences increases the GL indexby 0.12. Therefore, industry-level comparativeadvantages cause more specialization with freetrade than with the current level of tradecosts.

42. For comparison, it is 0.45 in the trade data.

Factor endowments also become more influ-ential with free trade, according to most mea-sures, but not as much as the industry-levelproductivity differences. The effects of factorendowment differences and factor intensity dif-ferences on industry shares are about 1.5 timesgreater with free trade. Their effects on netexports are mixed: factor intensity differenceshave a greater effect with free trade, but factorendowment differences have less. Their effectson the GL index are very small.

Finally, we investigate the relationship bet-ween trade costs and the effects of preferenceson the pattern of trade and specialization. Tradecosts link domestic production with preferences.With free trade, on the other hand, the pro-duction and consumption decisions are madeseparately.

Repeating Simulation #7 with free tradeshows this effect. Removing trade costs increa-ses the separation between production and con-sumption decisions, thus decreasing the effectof tastes on specialization (from 20% to 10%)and increasing their effect on net exports (from158% to 1091%).43

Richer Versus Poorer Countries. It is interest-ing to check if the effects of the determi-nants of trade analyzed in this paper vary withcountry income. In order to do it, we dividethe countries in the data set into two groups.One group includes all the countries that had1989 GDP per capita less than half of theU.S. level. This group includes Greece, Korea,Mexico, Portugal, Spain, Turkey, and is calledthe middle-income group. The second group,

43. The effect of preferences on specialization is notzero even with the free trade because countries are “large,”i.e., able to influence their terms of trade.


TABLE 10Poorer Versus Richer Countries: Effects on Industry Shares and Net Exports

Effects on Industry Sharesa Effects on Net Exportsa

Sim.# Simulationb

VariationRemovedc All Poorerd Richerd

Poorer/Richer All Poorerd Richerd

Poorer/Richer

1. Factor shares Cross-industry 3.88 9.23 1.41 6.53 58.04 123.88 27.65 4.482. Factor endowments Cross-country 4.76 9.28 2.67 3.47 175.29 435.48 55.21 7.893. Industry productivity Cross-industry 30.86 45.41 24.14 1.88 329.22 445.76 275.44 1.624. Industry productivity Cross-country 35.01 54.07 26.21 2.06 631.42 1091.80 418.93 2.615. Firm-level productivity N/A 18.49 22.94 16.44 1.40 97.39 93.61 99.13 0.946. Preferences Cross-industry 88.12 102.40 81.53 1.26 172.27 220.08 150.21 1.477. Preferences Cross-country 20.15 29.41 15.88 1.85 158.05 240.08 120.18 2.008. Trade costs Cross-industry 14.30 17.40 12.88 1.35 258.85 324.45 228.58 1.429. Trade costs Cross-country 13.16 17.84 11.00 1.62 134.90 128.32 137.94 0.93

10. Trade costs—access to intermediatese 46.14 47.98 45.29 1.06 351.84 452.28 305.49 1.4811. Trade costs—localizing comparative

advantagef15.06 19.50 13.01 1.50 176.60 152.37 187.79 0.81

12. Trade costs—linking preferences tospecializationg

79.67 85.46 76.99 1.11 991.57 1276.38 860.11 1.48

aThe numbers are the averages of absolute percent changes.bEach simulation removes a particular determinant of trade.cSpecifies whether the cross-industry or cross-country variation has been removed.dPoorer countries are those with 1989 GDP/capita less than half of the United States, richer are all others.eThis simulation removes cross-industry differences in trade costs and the cross-industry variation in dependence on

intermediates.fThis simulation removes cross-industry and cross-country differences in trade costs.gThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average

value.

that includes all the other countries, is the high-income group.

Table 10 shows the average absolute percentchanges in the industry shares and net exportsof all countries (repeated from the correspondingcolumns of Table 5), middle-income countries,and high-income countries. The last columnshows the ratio of the middle-income countries’to high-income countries’ averages. A higherratio means a greater difference in the effect of aparticular determinant of trade on poorer versusricher countries.44

The results clearly show that the greatest rel-ative difference between the middle-income andhigh-income countries is in the effects of factorendowments and factor intensities. The effectsof these determinants in the middle-incomecountries are 3.5 to 7.9 times greater thanin the high-income countries.45 This supportsthe notion that the Heckscher–Ohlin reason

44. Note that all the numbers in each row of Table 10come from the same simulation, that is the numbers for thericher and poorer countries come from the same experiment.

45. There is a strong negative correlation between theincome level and the effect on industry shares and netexports in Simulations #1 and #2 (−0.9 and −0.81, respec-tively). This correlation is much weaker in Simulations #3

for trade is more applicable to poorer than toricher countries. In the high-income counties,the influence of factor endowments is muchweaker than the influence of other determi-nants of trade. Their effects on specializationare in fact small enough to have little eco-nomic significance.46 These conclusions aboutthe Heckscher–Ohlin reason for trade are espe-cially interesting because they obtain not onlywhen factor endowments differences are neutral-ized (Simulation #2), but also when factor inten-sity differences (which are the same in the richerand poorer countries) are eliminated (Simulation#1). It seems that poorer countries rely more ontheir factor endowment advantages (e.g., lowerwages) in their specialization and trade than doricher countries.

and 4 (−0.62 and −0.68, respectively). Given greater rela-tive importance of factor endowments and factor intensitiesin the middle-income than in the high-income countries, itis sensible to expect their relative importance to be evengreater in the low-income countries.

46. Out of 104 industries (8 industries in 13 countries),only 12 change their industry shares by more than 5% andonly one changes it by more than 10% if factor endowmentdifferences are removed. If factor share differences areremoved, only 1 out of 104 industries changes its share bymore than 5% and none by more than 10%.


TABLE 11Poorer Versus Richer Countries: Effects on Exports and Imports

Effects on (Gross) Exportsa Effects on (Gross) Importsa

Sim.# Simulationb

VariationRemovedc All Poorerd Richerd

Poorer/Richer All Poorerd Richerd

Poorer/Richer

1. Factor shares Cross-industry 9.36 21.96 3.55 6.19 6.21 13.34 2.92 4.572. Factor endowments Cross-country 32.75 83.94 9.13 9.20 12.18 24.46 6.51 3.763. Industry productivity Cross-industry 173.80 333.04 100.31 3.32 55.50 57.72 54.47 1.064. Industry productivity Cross-country 755.20 2025.30 169.00 11.98 57.39 54.16 58.88 0.925. Firm-level productivity N/A 99.41 99.65 99.30 1.00 95.37 92.82 96.55 0.966. Preferences Cross-industry 82.13 69.67 87.89 0.79 108.42 152.50 88.08 1.737. Preferences Cross-country 20.30 24.88 18.18 1.37 33.76 51.25 25.69 2.008. Trade costs Cross-industry 53.20 57.10 51.40 1.11 67.04 58.13 71.15 0.829. Trade costs Cross-country 76.23 74.59 76.99 0.97 71.94 59.59 77.63 0.77

10. Trade costs—access to intermediatese 98.71 99.95 98.13 1.02 117.91 134.11 110.43 1.2111. Trade costs—localizing comparative

advantagef75.01 72.96 75.96 0.96 80.62 64.64 87.99 0.73

12. Trade costs—linking preferences tospecializationg

226.94 281.01 201.99 1.39 358.65 580.96 256.05 2.27

aThe numbers are the averages of absolute percent changes.bEach simulation removes a particular determinant of trade.cSpecifies whether the cross-industry or cross-country variation has been removed.dPoorer countries are those with 1989 GDP/capita less than half of the United States, richer are all others.eThis simulation removes cross-industry differences in trade costs and the cross-industry variation in dependence on

intermediates.fThis simulation removes cross-industry and cross-country differences in trade costs.gThis simulation removes cross-industry differences in trade costs and sets the taste parameter to its cross-industry average

value.

Most, but not all, of the other determi-nants of trade are also more influential inthe poorer than richer countries.47 The impactof the cross-country productivity differences(which determine absolute advantages) is about2.1–2.6 times greater in the poorer countries.The influence of the cross-industry productiv-ity differences (which determine comparativeadvantages) is about 1.6–1.9 times greater inthe poorer countries. It appears that richer coun-tries base their specialization and trade moreon their technological advantages than on theircapital abundance advantages.

The effects of the cross-country taste differ-ences are about 1.8–2 times greater in the poorercountries. The effects of trade costs do not seemto vary much with income level. In fact, some

47. All determinants of trade have a greater effect ofspecialization of poorer than richer countries (i.e., all valuesin the fourth column of numbers of Table 10 are greaterthan 1). Most (9 out of 12) determinants also have a greatereffect on the net exports of poorer than richer countries(i.e., 9 out 12 values in the last column of numbers ofTable 10 are greater than 1). As richer countries are moreopen (i.e., have lower trade costs) than poorer countries,some determinants of trade may end up having slightly moreeffect on the net exports of richer than poorer countries, evenif the specialization is more affected in the poorer countries.

simulations (#9 and #11) show a greater influ-ence of trade costs on net exports of the richerthan poorer countries.

Table 11 shows the differential impacts ofvarious determinants of trade on the exportsand imports of the middle-income and high-income countries. The results shown in thistable are generally similar to those shown inTable 10 with one exception. The effects of thecross-country differences in industry-level pro-ductivity on industry shares and net exports aremoderately greater in the middle-income thanhigh-income countries. However, their effectson exports are much greater in the poorer coun-tries. At the same time, the effects on the importsare about the same in the two groups of coun-tries. This is not surprising since eliminatingcross-country productivity differences signifi-cantly increases the relative productivities of thepoorer countries, thus boosting their exports inall industries.

Some of the results presented in this papermay depend on the particular industrial clas-sification system used (this paper uses theSIC industrial classification, the most commonone in the world). Specifically, the relativeimportance of factor endowments, technology,


and tastes may be different for a different clas-sification system. However, the important andmany-sided role of trade costs is not affectedby the classification system. Most of the effectsof trade costs are conditional on their totalmagnitudes, not on their cross-industry differen-tials. The relatively greater importance of factorendowments in the poor countries is also inde-pendent of the industrial classification systemused.

V. CONCLUSION

This paper investigates the importance oftechnology, factor endowments, trade costs, andpreferences for the pattern of trade and special-ization. It starts by reviewing the mechanismsthrough which these determinants of trade affecttrade and specialization. It then presents a modelthat incorporates all of these determinants oftrade and can be used to gauge their importance.

The effects of various determinants of tradeare studied by performing counterfactual simula-tions of the model, which is parametrized using1989 data for 19 OECD countries. Evidence ispresented to support the ability of the model tomake accurate predictions. The importance ofthe determinants is measured by the magnitudesof their effects on specialization and the patternof trade. The effects of various determinants oftrade on welfare are also presented.

The results show that the productivity differ-ences and differences in tastes are the first andsecond most significant determinants of tradeand specialization. Cross-country productivitydifferences are also found to have very largeeffects on welfare. By most measures, factorendowments are found to be the least influen-tial determinant of trade for the average countryin the data set.

The results also show the great importance oftrade costs. While previous literature has demon-strated their significance for the volume of trade,this paper shows their importance for specializa-tion and the pattern of trade. The direct effects oftrade costs, that exist because trade costs are dif-ferent across countries and industries, are foundto be moderate. However, the indirect effectsof trade costs, whereby trade costs interact withother determinants of trade, are found to be verysignificant. For example, trade costs link domes-tic production with domestic preferences andaffect access to intermediate goods. Trade costsalso decrease the geographic range of compar-ative advantage forces, in effect making them

local rather than global. One of the importantimplications of these results is that any anal-ysis of specialization and trade must includetrade costs and preferences in addition to thetraditional duopoly of technology and factorendowments.

This paper also finds that the impacts of thedeterminants of trade vary with country income.Most of the determinants of trade are moreinfluential in poorer countries than in richerones, but the factor endowments especially so.Their effects in poorer countries are many timesgreater than in richer ones. In richer countries,however, their effects are small enough to becalled economically insignificant. This resultsupports the notion that the Heckscher–Ohlinreason for trade is more applicable to poorercountries than richer ones.

REFERENCES

Anderson, J. E., and E. van Wincoop. “Gravity with Gravi-tas: A Solution to the Border Puzzle.” American Eco-nomic Review, 93(1), 2003, 170–92.

. “Trade Costs.” Journal of Economic Literature,42(3), 2004, 691–751.

Armington, P. S. “A Theory of Demand for ProductsDistinguished by Place of Production.” IMF StaffPapers, 16(1), 1969, 159–78.

Caselli, F., and J. Feyrer. “The Marginal Product of Capital.”Quarterly Journal of Economics, 122, 2007, 535–68.

Chor, D. “Unpacking Sources of Comparative Advantage:A Quantitative Approach.” Journal of InternationalEconomics, 82(2), 2010, 152–67.

Davis, D. R., and D. E. Weinstein. “An Account of GlobalFactor Trade.” The American Economic Review, 91(5),2001, 1423–53.

Deardorff, A. V. “Local Comparative Advantage: TradeCosts and the Pattern of Trade.” RSIE DiscussionPaper No. 500, 2004.

Dornbusch, R., S. Fischer, and P. Samuelson. “ComparativeAdvantage, Trade, and Payments in a Ricardian Modelwith a Continuum of Goods.” American EconomicReview, 67, 1977, 823–39.

Eaton, J., and S. Kortum. “International TechnologyDiffusion: Theory and Measurement.” InternationalEconomic Review, 40, 1999, 537–70.

. “Technology, Geography, and Trade.” Economet-rica, 70(5), 2002, 1741–79.

Eaton, J., S. Kortum, and F. Kramarz. “An Anatomy ofInternational Trade: Evidence from French Firms.”NBER Working Paper No. 10344, 2004.

Feenstra, R. C. “World Trade Flows, 1970–1992, withProduction and Tariff Data.” NBER Working Paper5910, 1997.

. “World Trade Flows, 1980–1997, with Productionand Tariff Data.” UC David, Center for InternationalData website, 2000.

Fieler, A. C. Forthcoming. “Non-Homotheticity and Bilat-eral Trade: Evidence and a Quantitative Explanation.”Econometrica, 79(4), 2011, 1069–101.

Finicelli, A., P. Pagano, and M. Sbracia. “Trade-revealedTFP.” Temi di Discussione n. 729, Bank of Italy, 2009.

Harrigan, J. “Technology, Factor Supplies, and InternationalSpecialization: Estimating the Neoclassical Model.”The American Economic Review, 87(4), 1997, 475–94.


Heckscher, E. “The Effects of Foreign Trade on the Dis-tribution of Income.” Ekonomisk Tidskrift, 21, 1919,497–512.

Hummels, D. “Toward a Geography of Trade Costs.”Manuscript, Purdue University, 2001.

Johnson, N. L., and S. Kotz. Continuous UnivariateDistributions, Houghton and Mifflin, 1970.

Kortum, S. “Research, Patenting, and TechnologicalChange.” Econometrica, 65, 1997, 1389–419.

Leamer, E. E., and J. Levinsohn. “International TradeTheory: The Evidence,” in Handbook of InternationalEconomics, edited by G. Grossman and K. Rogoff.Amsterdam, The Netherlands: North-Holland, 1995,pp. 1339–94.

Linder, S. An Essay on Trade and Transformation. Uppsala,Sweden: Almqvist and Wiksell, 1961.

MacDougall, G. D. A. “British and American Exports: AStudy Suggested by the Theory of Comparative Costs.”The Economic Journal, 61(244), 1951, 697–724.

Markusen, J. R. “Explaining the Volume of Trade: AnEclectic Approach.” The American Economic Review,76(5), 1986, 1002–11.

Ohlin, B. Handelns Teori. Stockholm, Sweden: AB NordiskaBokhandeln, 1924.

Ricardo, D. The Principles of Political Economy and Taxa-tion. London: John Murray, 1817.

Ruhl, K. “The International Elasticity Puzzle.” Manuscript,University of Texas, 2008.

Samuelson, P. A. “International Factor-Price EqualizationOnce Again.” The Economic Journal, 59(234), 1949,181–97.

Shikher, S. “An Improved Measure of Industry Value Addedand Factor Shares: Description of a New Datasetof the U.S. and Brazilian Manufacturing Industries.”Manuscript, Suffolk University, 2004.

. Forthcoming. “Predicting the Effects of NAFTA:Now We Can Do It Better!” Journal of Internationaland Global Economic Studies.

. “Capital, Technology, and Specialization inthe Neoclassical Model.” Journal of InternationalEconomics, 83(2), 2011, 229–42.

Stewart, W. A. “World Trade Data Set Code Book,1970–1992.” UBC Department of Economics, Van-couver, BC, Canada, 1999. Accessed March 8, 2012.www.econ.ubc.ca/helliwell/restrict/datasets/wtdata/wtcodebk.pdf

Trefler, D. “The Case of the Missing Trade and OtherMysteries.” The American Economic Review, 85(5),1995, 1029–46.

Vanek, J. “The Factor Proportions Theory: The n-FactorCase.” Kyklos, 21, 1968, 749–54.

Venables, A. J., and N. Limao. “Geographical Disad-vantage: A Heckscher-Ohlin-von Thunen Model ofInternational Specialization.” Journal of InternationalEconomics, 58, 2002, 239–63.

Documents

DETERMINANTS OF TRADE AND SPECIALIZATION IN THE ...sergeshikher.com/papers/Shikher_Determinants_of_trade.pdfIN THE ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT COUNTRIES