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Applied Economics LettersPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/rael20

An application of the Ricardian trade model with tradecostsAndrew J. Cassey aa School of Economic Sciences, Washington State University, PO Box 646210, Pullman, WA,99164, USA

Version of record first published: 22 Nov 2011

To cite this article: Andrew J. Cassey (2012): An application of the Ricardian trade model with trade costs, AppliedEconomics Letters, 19:13, 1227-1230

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An application of the Ricardian trade

model with trade costs

Andrew J. Cassey

School of Economic Sciences, Washington State University, PO Box 646210,Pullman, WA 99164, USAE-mail: cassey@wsu.edu

Deardorffs (2004) broad definition of technology in Ricardian trademodels is useful for extending the explanatory power of comparativeadvantage to account for a fact on firm level exporter clusteringunexplained under the standard definition.

Keywords: international trade; Ricardian; technology; agglomeration

JEL Classification: F10; B12

I. Introduction

The Ricardian trade theory is a cornerstone of eco-nomics. Its lesson on the efficiency of allocatingresources according to comparative advantage istaught from high school to graduate courses.Principle classes illustrate this lesson using a two-region, two-goods model. Regions differ in the tech-nology for turning labour, the unique input, into twogoods, which are traded without cost. Jones (1961),among others, generalized the original Ricardian the-ory to the multi-region, multi-goods case. Shiozawa(2007) generalized the theory further, to include inter-mediate goods and technology choice.Deardorff (2004) extended the Ricardian model by

including trade costs in the technology of producingand delivering a good from one country to another. Iuse Deardorffs extension to account for a recentlydocumented fact: agglomeration of exporters by des-tination of shipments. Using Russian customs data,Cassey and Schmeiser (2010) showed that exportingfirms are physically clustered by the destination ofshipments in addition to clustering around ports.This fact is also documented by Koenig (2009) usingother data. I show the Ricardian model can account

for this fact if trade costs are included in technologybut not otherwise.1

II. Facts on Exporter Agglomeration

Glejser et al. (1980) showed exporting firms are phy-sically clustered around ports but Cassey andSchmeiser (2010) went further and showed that inaddition, exporters are clustered by the destinationof their shipments. Exporting firms in the same regionare more likely to send shipments to the same countrythan exporting firms in another region identical insize, distance and industrial composition.The evidence is presented in Table 1. The table

contains the Morans (1950) I statistic for spatialcorrelation:

I NPi

P

j

wij

P

i

P

j

wijXi XcXj XdP

i

Xi Xc21

whereN is the number of regions indexed by i and j,Xcis the number of exporters shipping to country c, Xc is

1Cassey and Schmeiser (2010) accounted for the agglomeration of exporters by destination by positing an externality inshipping costs. An externality is consistent with the Ricardian theory given here, but it is just one story possible under aRicardian framework. Koenig (2009) used a logit estimator to document this fact, but does not account for it.

Applied Economics Letters ISSN 13504851 print/ISSN 14664291 online# 2012 Taylor & Francishttp://www.tandfonline.com

http://dx.doi.org/10.1080/13504851.2011.617871

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Applied Economics Letters, 2012, 19, 12271230

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the mean across regions,Xd is the number of exportersshipping to country d and wij is a spatial weight. (I usean inverse Euclidean distance weight so that regionswhose centroids are closer are weighted more heavily.)The statistic ranges from -1 to 1, with positives indi-cating clustering. Under no spatial correlation,EI 1=N 1.The Morans I is univariate or bivariate. For the

univariate version, the statistic differences the numberof exporters in each region i shipping to country cfrom the average number of exporters also shippingto c and compares it to the difference between thenumber of exporters in another region j shipping tocountry c from the average. A statistic different fromE(I ) is evidence of spatial autocorrelation, clusteringif positive. The bivariate version is similar except cd,so the statistic differences the number of exporters ineach region i shipping to country c from the averagenumber of exporters also shipping to c and comparesit to the difference between the number of exporters inanother region j shipping to country d from the aver-age also shipping to d. Because of its construction withrespect to the other country d, the bivariate Morans Iis not symmetric. A statistic different from E(I ) sug-gests a spatial pattern, clustering if postitive, betweenexporters shipping to country c and those shipping tocountry d.The data to calculate the statistic are the number of

manufacturing firms exporting from each of 89Russian regions to eight countries and are fromCassey and Schmeiser (2010). The disadvantage ofthese data is that exporter location is known only tothe regional level.Observations on the diagonal of Table 1 are

the univariate Morans I for the location of expor-ters shipping to that country. Stars indicate statis-tically significant autocorrelation or clustering.

Off-diagonals are the bivariate Morans I indicating

the strength of spatial correlation between exporters

shipping to the two countries. If Russian exporters are

clustered around two destinations, such as Canada

and the United States (indicated by stars on the diag-

onal), the lack of off-diagonal stars indicates the pat-

tern of clustering is not the same for exporters

shipping to those countries. If Russian exporters are

not clustered around either of the two destinations,

such as China and Poland (indicated by no stars on the

diagonal), off-diagonal stars indicate the randomness

in exporter location cannot be distinguished between

exporters shipping to China and exporters shipping to

Poland. (In this case, the negative sign indicates dis-

persion.) If there is a diagonal star for one country but

not the other and the off-diagonal is starred, such as

Japan and the United States, this indicates spatial

correlation in that exporters to Japan tend to cluster

about the exporters to the United States though

exporters to Japan do not exhibit spatial

autocorrelation.Table 1 shows that Russian exporting firms cluster

by destination for four of the eight countries in the

sample: Canada, Germany, Great Britain and the

United States. Furthermore, the pattern of clustering

is not the same for seven of twelve possibilities.

Therefore, there is evidence of firm-level clustering

by destination of shipments.

III. A Ricardian Model and an Example

In a two-region, two-goods, zero trade costs Ricardian

model, if acg is the exogenous unit labour requirement

in region c to produce good g, then region 1 produces

and exports good 1 if and only if

Table 1. Morans I for location of firms exporting to select countries

Canada China Germany Great Britain Japan Poland United States Ukraine

Canada 0.0345* -0.0012 0.0243 0.0313 0.0300 0.0196 0.0194 0.0340China 0.0089 0.0023 -0.0448* -0.0458* -0.0147 -0.0450* -0.0375* -0.0408*Germany 0.0249 -0.0472* 0.0324* 0.0610** 0.0248 0.0547* 0.0406 0.0637**Great Britain 0.0215 -0.5160* 0.0677** 0.0886*** 0.0676** 0.0277 0.0783** 0.0916***Japan 0.0221 -0.0164 0.0319 0.0533* 0.0227 0.0337 0.0593** 0.0677**Poland 0.0146 -0.0514* 0.0581* 0.0370 0.0446 0.0115 0.0450 0.0658**United States 0.0189 -0.0367* 0.0413 0.0571* 0.0525** 0.0307 0.0369* 0.0751**Ukraine 0.0258 -0.0426* 0.0593* 0.0801** 0.0626** 0.0520 0.0773** 0.0220

Source: Authors calculation using data from Cassey and Schmeiser (2010).Notes: Observations on the diagonal are the Morans I for the location of exporters shipping to that country. Off-diagonals arethe bivariate Morans I representing the degree of spatial correlation betweenXc andXd. The statistic has been normalized withrespect to regional Gross Domestic Product to account for the geographic lumpiness of Russian economy activity. Columns arethe c countries and the rows are the d countries. Bold observations are the bivariate statistic where both countries have astatistically significant univariate Morans I.*, ** and *** Indicate p-values less than 0.1, 0.05 and 0.01, respectively.

1228 A. J. Cassey

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a11a12

a21a22

2

In Deardorffs (2004) model with trade costs fromshipping good g from region c to country d, tcgd, region1 exports good 1 to country 1 if and only if

a11 t111a12 t122

a21 t211a22 t222 3

The data in Section II show that firms in differentRussian regions export to Canada and the UnitedStates despite that all Russian firms face the sameimporter tariffs, language and cultural barriers, andthat these countries are roughly the same distance andbuy the same products from Russia. The standardRicardian model cannot account for this because itpredicts whichever region has the lowest relativelabour cost will export the good to both countries.Now consider a model with two regions with access

to the only resource, labour. They produce the samegood, but they do not consume it. Instead there ispotential sales to Canada and the United States.There is a technology that converts labour into exportsdeliverable to the United States and there is a differenttechnology that converts labour into exports deliver-able to Canada. Table 2 shows the units of labourrequired to deliver one unit of exports to each country.The standard Ricardian definition of technology is

the transformation of labour into goods traded with-out cost. But the definition in this example is thetransformation of labour into a good deliverable to aspecific destination. It may take more labour forregion 1 to produce and deliver exports to the UnitedStates than it does to Canada. This expanded inter-pretation of technology incorporates variable tradecosts such as physical distance and fixed trade costssuch as getting permits.By reducing the number of goods from two to one,

Equation 3 simplifies to a1 t11=a1 t12 a2 t21=a2 t22. A change of notation ofac tcd xcd reduces this example to Equation 2where x replaces a and d replaces g.In Table 2, the cost of region 1 delivering a unit of

exports to Canada is two exports to the United States

whereas region 2 can deliver a unit of exports to

Canada for one US export. Therefore, this theory

predicts that region 1 will produce exports to be deliv-

ered in the United States and region 2 will produce

exports to be delivered in Canada. This theory gener-

ates specialization of destinations by region: the firms

in region 1 specialize in exportingmanufactured goods

to the United States instead of Canada because they

have a relative cost advantage in producing and deli-

vering goods there.Why does technology for producing and delivering

exports differ across regions? Why does technology

differ in Ricardos example of England and Portugal

producing wine and cloth? These are the assumptions

of the model.The replacement of goods with countries in this

example from the standard Ricardian story yields an

interpretation of goods differentiated by place of con-

sumption. This contrasts with Armington (1969)

because in that work exports are unique goods to the

producing country whereas here exports are unique

goods to the consuming country. British tea is tea

that is consumed in Britain, no matter where it is

made, rather than tea produced in Britain and con-

sumed anywhere. The point, however, is this uncom-

fortable interpretation from redefining goods is not

necessary. Rather a broader interpretation of technol-

ogy is acceptable and more digestible.

IV. Conclusion

David Ricardo described his model of trade using a

thought experiment. The theory has developed since

then, and though both theoretical and empirical chal-

lenges confront it, the Ricardian model has been enor-

mously influential and persistent. Increasingly, the

international trade literature has been using customs-

level data to identify new facts about international

trade. Cassey and Schmeiser (2010) used customs

data from Russia to show that exporters agglomerate

around the destination of exported shipments. I pre-

sent more evidence of this exporter agglomeration by

destination.My contribution is to show that Deardorffs (2004)

broader theoretical interpretation of technology can

account for exporter clustering by destination.

Technology transforms labour into a commodity

delivered to a destination and thus regions specialize

if they have sufficiently low delivery costs relative to

others. This is useful to extend the idea of the com-

parative advantage to account for facts that at first

seem inconsistent with standard Ricardian theory.

Table 2. An example

Canada United States

Region 1 10 5Region 2 6 6

Note: Units of labour required to deliver one export fromeach region to each country.

Application of Ricardian model with trade costs 1229

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Acknowledgements

Thanks to Jeremy Sage for ArcGIS and Geoda help,Katherine Schmeiser for the data and Julian Diaz andMark Gibson for comments. Partial support for thiswork by the Agricultural Research Center Project#0540 at Washington State University.

References

Armington, P. (1969) A theory of demand for productsdistinguished by place of production, InternationalMonetary Fund Staff Papers, 16, 15978.

Cassey, A. and Schmeiser, K. (2010) The agglomerationof exporters by destination, Forum for Researchon Empirical International Trade Working PaperNo. 233.

Deardorff, A. (2004) Local comparative advantage: tradecosts and the pattern of trade, University of Michigan

Research Seminar in International Economics Dis-cussion Paper No. 500, University of Michigan, AnnArbor, MI.

Glejser, H., Jacquemin, A. and Petit, J. (1980) Exports inan imperfect competition framework: an analysis of1,446 exporters, Quarterly Journal of Economics, 94,50724.

Jones, R. (1961) Comparative advantage and the theory oftariffs: a multi-country, multi-commodity model,Review of Economic Studies, 28, 16175.

Koenig, P. (2009) Agglomeration and the export decisionsof French firms, Journal of Urban Economics, 66,18695.

Moran, P. (1950) Notes on continuous stochastic phenom-ena, Biometrika, 37, 1723.

Shiozawa, Y. (2007) A new construction of Ricardian tradetheory a many-country, many-commodity case withintermediate goods and choice of production techni-ques, Evolutionary and Institutional Economics Review,3, 14187.

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