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PolicyResearchWorkingPaper10789

Intra-nationalTradeCostsinLow-andMiddle-IncomeCountries

BernardoDíazdeAstarloaNinoPkhikidze

WORLDBANKGROUP

TransportGlobalPracticeJune2024

PolicyResearchWorkingPaper10789

Abstract

Casualobservationsuggeststhatintra-nationaltradecostsremainhighinlow-andmiddle-incomecountries.Preciselyestimatingthemiscrucialforguidingpoliciesaimedatoptimizingeconomicefficiencywithinacountry’sborders.Thispaperestimatesintra-nationaltradecostsforsixlow-andmiddle-incomecountriesinAfricaandEasternEurope:Kenya,Madagascar,Nigeria,Rwanda,Tanzania,andGeor-gia.Theanalysisexploitsunit-levelpricedatacollectedbycountries’nationalstatisticalofficesforconsumerprice

indexcalculationpurposes.Itappliesthepricedifferentialmethodology,whichaimsatestimatingtradecostswhileaccountingforthepossibilityofimperfectcompetitionamongintermediaries,controllingforspatialvariationinmarkups.Thefindingsshowthattheintra-nationaltradecostsinthesampleofcountriesarebetween2.5and14timeslargerthanpreviousestimatesfortheUnitedStatesusingthesamemethodology.

ThispaperisaproductoftheTransportGlobalPractice.ItispartofalargereffortbytheWorldBanktoprovideopenaccesstoitsresearchandmakeacontributiontodevelopmentpolicydiscussionsaroundtheworld.PolicyResearchWorkingPapersarealsopostedontheWebat

/prwp.Theauthorsmaybecontactedatbernardo

.diazastarloa@economicas.uba.arandnpkhikidze@.

ThePolicyResearchWorkingPaperSeriesdisseminatesthefindingsofworkinprogresstoencouragetheexchangeofideasaboutdevelopmentissues.Anobjectiveoftheseriesistogetthefindingsoutquickly,evenifthepresentationsarelessthanfullypolished.Thepaperscarrythenamesoftheauthorsandshouldbecitedaccordingly.Thefindings,interpretations,andconclusionsexpressedinthispaperareentirelythoseoftheauthors.TheydonotnecessarilyrepresenttheviewsoftheInternationalBankforReconstructionandDevelopment/WorldBankanditsaffiliatedorganizations,orthoseoftheExecutiveDirectorsoftheWorldBankorthegovernmentstheyrepresent.

ProducedbytheResearchSupportTeam

Intra-nationalTradeCostsinLow-andMiddle-IncomeCountries*

BernardoD′?azdeAstarloatNinoPkhikidze?

Keywords:Tradecosts,Pricedifferentialmethodology,Traveldistance,Kenya,Madagascar,Nigeria,Rwanda,Tanzania,Georgia.

JELclassiication:R12,F1,O1,D02.

*ThispaperwaspreparedasbackgroundresearchfortheWorldBank’sShrinkingEconomicDistanceFlagshipReport.IgnacioCaroSol′?sprovidedsuperbresearchassistance.Forhelpandassistanceinaccessingpricedata,wethanktheWorldBank’scountryteamsandthenationalstatisticsofficesoftheresearchcountries.WehavebenefitedgreatlyfromdiscussionswithMatiasHerreraDappe,RomanZarate,TheophileBougna,AlejandroMolnar,MathildeLebrand,AigaStokenberga,andtheparticipantsofWorldBank’sWorkshoponShrinkingEconomicDistance.Thefindings,interpretations,andconclusions

exp-ierilley,UniversityofBuenosAires.Email:

bernardo.diazastarloa@economicas.uba.ar.

?WorldBank.Email:

npkhikidze@.

2

1Introduction

Internationaltradecostshavefallendrasticallyinthelastdecades.However,intra-national(i.e.within-country)tradecostsstillremainhighinlow-andmiddle-incomecountries.Quantifyingtradecostsandtheircomponentsiscrucialsincelowertradecostscanincreaseefficiency,improvethewelfareofhouseholdsandfirms,anddrivestructuraltransforma-

tionthroughincreasedtradeopportunities(Sotelo,

2019;

FajgelbaumandRedding,

2014;

CostinotandDonaldson,

2016)

.Byunderstandingthecostsassociatedwithdomestictrade,policymakerscanidentifybarriershinderingmarketaccessandformulatetargetedpoliciestoreducetheseobstacles.

Low-andmiddle-incomecountriesareoftencharacterizedbymanybarriersaffectingthefreeflowofgoodswithintheirborders,suchaspoorroadconditions,oldtransportfleet,andpoorlogistics.Theseaffecttradeflowsandlimitthegainsofreducedinterna-tionaltradecostsandincreasedregionalintegration.Remoteregionstendtobeespeciallyisolatedoftenduetohightravelcosts,includinghightraveltime.Inturn,highertrans-portationcostsareoftenpassedontoconsumersintheformofincreasedprices.Therefore,householdsandfirmsintheseareasfacehigherspatialpricegaps,whichlimitsthevariety

ofgoodstheycanchoosefromandreducesdisposableincome(Aggarwal,

2018;

Martin,

MaynerisandTheophile,

2020)

.

Despitetherecentisolatedattemptstostudytheextentofintra-nationaltradecosts,1

thereisagapintheliteratureonthecross-countrycomparisonofwithin-countrytradecosts.

Inthispaperweestimateintra-nationaltradecostsforsixlow-andmiddle-incomecountriesinAfricaandEasternEurope:Kenya,Madagascar,Nigeria,Rwanda,Tanza-niaandGeorgia.Weexploitmicrodatacollectedbycountries’nationalstatisticaloffices(NSO)forconsumerpriceindex(CPI)calculationpurposes.

Therearetwomainestimation-basedapproachesintheliteraturetoestimatetrade

costs,thegravityapproachandthepricedifferentialapproach(Co?sar,

2022)

.Thegravityapproachlooksattheimpactofvaryingtraveldistanceortraveltimeontradeflows,whichareinverselyproportionaltoeachother

.2

UsinggravityestimationtoestimatetheimpactoftraveldistanceontradebetweenColombiancities,

Duranton

(2015)estimates

thatincreasingthetraveldistancebetweenthemby10%decreasestradeflowsby7%andweightby6%.

Thepricedifferentialmethodcombinesdataonpricesfromdifferentmarketsorcitieswithvariablesthatarethoughttoaffectthecostofshippinggoodsbetweenlocations.Usingthesedata,onecancomputepricedifferencesacrossspace(spatialpricegaps)and

assesshowdifferentcostshiftersaffectthesedifferences.Distancebetweenwherethe

1See,forexample,

AtkinandDonaldson

(2015)ontheestimatesforEthiopia,Nigeria,andtheU.S

.,and

Bougna,Ewane,JonesandKondylis

(2020)onRwanda

.

2HeadandMayer

(2014)summarizetheliteratureongravityequationsandprovideanextensive

overviewofbest-practicemethodsandastep-by-stepcookbook.

3

productisproducedorsourcedfrom(originlocations)andwhereitisfinallysoldbyretailers(destinationlocations)typicallysummarizesthecontributionoftransportcoststooveralltradecosts.However,theremaybeothervariablesthatcanbecorrelatedwithdistancewhichcanalsoaffecttradecosts,suchastheextentofcompetitionbetween

logisticsoperators,distributorsorretailers(AtkinandDonaldson,

2015)orwithin-country

“border-effects”(Borraz,Cavallo,RigobonandZipitria,

2016)

.

Inthispaper,wefollowtheprice-differentialapproachandapplythemethodologydevelopedby

AtkinandDonaldson

(2015),whichaimsatestimatingtradecostswhile

takingintoaccountthepossibilityofimperfectcompetitionamongintermediaries,con-trollingforspatialvariationinmarkups.Wefindthatintra-nationaltradecostsinoursampleofcountriesarebetween2.5and14timeslargerthanpreviousestimatesfortheU.S.by

AtkinandDonaldson

(2015)usingthesamemethodology.

Thispaperrelatestoagrowingliteraturethatattemptstoestimateintra-nationaltradecosts.Givenitshistoricallyhightransportcosts,Africa,andinparticularSub-SaharanAfrica,hasbeenthefocusofseveralpapersstudyingtradeandtransportprices.

Bougnaetal.

(2020)estimateintra-nationaltradecostsinRwandausingdataonrural

marketsandfindthemtobe10timeshigherthanintheU.S.Theyalsofindthatmarkupsarehigherinlocationswithfewertraders.

Mayneris,MartinandTheophile

(2020)doc

-umentspatialdifferencesinthecostoflivingacrossEthiopiancitiesandfindthatmoreremotecitiesshowsignificantlyhigherprices,lessproductavailability,andahighercostofliving.

Porteous

(2019)takesadifferentmethodologicalapproachto

AtkinandDonaldson

(2015)toestimateintra-nationaltradecostsinSub-SaharanAfrica

.Thepaperfocusesonstaplecerealgrainstradedbetween230largeregionalhubsin42countries,andquantifiestradecostsbyestimatingastructuralmodelthatfeaturescompetitivetraders(i.e.,ab-stractingfrommarkups),grainconsumption,storage,andtrade.Theresultsin

Porteous

(2019)suggesttradecoststhatarequalitativelysimilarbuthigherinmagnitudetothose

estimatedin

AtkinandDonaldson

(2015),althoughtheformerpotentiallyincludenon

-distancedependentfactors.Specifically,themediantradecostisover5timeshigherthaninternationalbenchmarkfreightrates.Inacounterfactualwheretradecostsonoverlandlinksarereducedtolevelssimilartotherestoftheworld,grainpricesfall30%onaverageandaggregatewelfareincreasesby1.6%ofGDP.

TeravaninthornandRaballand

(2009)

estimatetradecostsusingtruckersurveysintheU.S.andalongseveralmajortransportcorridorsinSub-SaharanAfricaandfind1.88to3.28timeshighertradecostscomparedtotheU.S.,whicharelowerthantheresultsof

AtkinandDonaldson

(2015)

.Intermsofthetradecostsinhigh-incomecountries,

Agnosteva,AndersonandYotov

(2019)use

gravitymodeltechniquestounderstandtheeffectsofintra-regional,inter-regionalandinternationalfrictionsontradeflowsinCanadianprovinces,andfindthatfurtherandless-developedregionstendtohaverelativelylowinternalfrictionsandlargeborderef-fects,whileeconomicallymoredevelopedandcentralregionsshowrelativelylowborderbarriers.

4

2Data

Oursamplecovers6low-andmiddle-incomecountriesinAfricaandEasternEurope:

Kenya,Madagascar,Nigeria,Rwanda,TanzaniaandGeorgia.Inordertoapplythemethodologydevelopedby

AtkinandDonaldson

(2015),werequiretwosetsofdatafor

eachcountry.First,weneedpricedataonnarrowly-definedproductvarieties,withob-servationsspanningmultiplelocationswithineachcountryatamonthlyfrequencyoverseveralyears.Second,weneedthelocationsinwhichproductvarietieswereproducedorfromwhichtheywereimported.Thelatterallowsfortheidentificationoftradingpairsandthecorrespondingdirectionoftradefromorigintodestinationlocations.Inthissec-tion,webrieflydescribehowweconstructthesedata.Appendix

A

includesadditionaldetailsaboutthedatapreparationprocess.

2.1Pricedata

OurpricedatacomefromCPImicrodataprovidedtousbyNSOsineachcountryin

oursample.NSOscollectpricedatafromapredefinedlistofproductvarieties(includinggoodsandservices),designedtocapturetheconsumptionbasketofatypicalhousehold,atspecifiedlocations.Apartfromthemonthlypricequote,theinformationprovidedtousbyNSOsincludesaproductdescription,insomecasesincludingthebrandandpresentation(e.g.,“Coca-Cola,canned,500ml”or“Bodylotion,Nivea,200ml”),thenameofthelocationwherethepricewascollected,and,occasionally,thenameofaproducerorthecountryfromwheretheproductwasimported.

Werestrictthechoiceofproductstonarrowlydefinedvarieties,i.e.,thoseforwhichwecanobserveadetailedproductdescriptionandabrand.Formostcountriesinoursample,pricesareatthetownorcitylevel,withtheexceptionofRwanda,wherepricesarecollectedatthe(moreaggregated)districtlevel.

Theanalysisinsection

4

usescleansamplesofpricedata,obtainedbyapplyingacleaningalgorithmtotherawdata.Moreover,asin

AtkinandDonaldson

(2015),we

convertallpricestoconstantU.S.dollarsbydeflatingthemusinginflationratesatoriginlocationsandbilateralnominalexchangeratesbetweenlocalcurrenciesandtheU.S.dollar.ThecleaninganddeflatingproceduresaredescribedinAppendix

A.

2.2Products’originlocations

Theestimationstrategyproposedby

AtkinandDonaldson

(2015)dependsonprecisely

identifyinglocationswhereaproductisproducedorimported.Todothis,wesearchovertheInternettodeterminewhichmanufacturersproduceeachbrandandwheretheirfactoriesarelocated.Forimportedgoods,weidentifythemainportsofentryineach

5

countryandtheirlocationstoassignimportedproductstothem

.3

Forlandlockedcoun-tries,weidentifyportsatneighboringcountriesthroughwhichmostimportscome(e.g.,insomecases,countriesareinconflictwithsomeborderingcountriesandimportsdonotcomethroughthem).Then,weidentifythebordercrossingsthatareclosesttothoseports.Finally,wegeocodealllocationstodeterminetheirlatitudeandlongitude.Foreachproduct,wedefinea“tradingpair”asanoriginandadestinationlocation.

Conditionalonbeingnarrowlydefined,weconsiderproductsthatarebroadlyavailableacrosslocationsandperiodsinoursample.Specifically,werestrictsamplestoproductsforwhichpricesareobservedintheproducts’sourceanddestinationlocationsinmorethansixmonths

.4

Table

1

describesthemainfeaturesoftheresultingsamplesweconsiderforeachcountry.Thenumberofproductsrangesfrom9inMadagascarto43inRwanda.Thefulllistofproductsforeachcountry,includingtheirbrands,presentations,andmanufacturers,isincludedintheAppendix

A.2.

Thenumberofdestinationlocations(markets,cities,ordistricts)rangesfrom6inGeorgiato38inNigeria.Figure

1

showsthelocationsforeachcountryinoursample,aswellasthemajorroadsconnectingthem,andindicatesthelocationwhereaproductismanufacturedorimported,onwhichweelaborateinsection

2.2.5

Table

1

alsoshowstheperiodscoveredbyeachcountrysample.WiththeexceptionoftheNigeriasample,whichstartsinJanuary2001,allsamplesstartafterJanuary2010.ThelongestpanelcorrespondstoMadagascarandcoverstheperiodfromJanuary2010toApril2021.TheshortestpanelistheoneforKenya,coveringfromOctober2018toJanuary2022.

Averageoriginprices,pricegapsbetweenoriginanddestinationlocations,anddis-tances,arereportedinTable

2.

Table1:Mainfeaturesandcoverageofthedata

Country

No.ofproducts

Origins

Destinations

Typeoflocations

Startperiod

Endperiod

Kenya

20

10

32

Cities

Oct2018

Jan2022

Madagascar

9

3

7

Majorcities

Jan2010

Apr2021

Nigeria

25

5

38

Cities

Jan2001

Jul2010

Rwanda

43

4

13

Districts

Jan2013

Dec2020

Tanzania

32

5

20

Cities

Jan2012

Apr2021

Georgia

21

4

6

Cities

Jan2012

Dec2020

Notes:thistabledescribesthemainfeaturesofthedatasetsforeachcountryinoursample.Source:

authors’elaborationbasedonCPIdatafromcountries’NSO.

3OurmainsourcesareLogcluster,awikiwithdetailedlogisticsinformationabouteachcountry,andWorldPortSource.

4Asdiscussedinsection

3.2,estimationofpass-throughratesreliesonvariationofpricesovertimefor

eachproduct-destinationpair.

5ThedataunderlyingthemapsaretakenfromtheGlobalRoadsInventoryProject(GRIP),availableat

/download-grip-dataset.

6

(a)Kenya(b)Madagascar

(c)Nigeria(d)Rwanda

(e)Tanzania(f)GeorgiaFigure1:Mapsofmarketlocations

Notes:thisfigureshowsmarketlocationsforeachcountryinoursample,aswellasmajorroadsconnectinglocations,indicatingoriginlocations(productionlocationsorportsofentry).Source:authors’elaborationbasedontheGRIPdataset.

7

2.3Distancebetweenlocations

Thebaselinemeasureofdistanceweemployisgeodesicdistance,thatis,theshortestdis-tancebetweentwolocations.Table

2

reportsthemean,minimumandmaximumgeodesic

distancebetweenoriginanddestinationlocations.

Alternatively,following

AtkinandDonaldson

(2015),weusetwoadditionalmeasures

tocontrolforquantityandqualitydifferencesincountries’roadnetworks.Thefirst,whichaimsatcapturingquantitydifferences,isthedistancebetweentwolocationsalongthequickestroute,ascalculatedbyGoogleMaps.Figure

1

showstheroadnetworkineachcountry.Thesecond,whichshouldcapturequalitydifferences,isthetimeittakestocompleteatripbetweentwolocationsfollowingthequickestroute,alsofromGoogleMaps.

Table2:Descriptivestatistics

KE

MG

NG

RW

TZ

GE

Meanoriginprice

2.306

0.922

0.902

2.328

0.898

2.577

(1.833)

(0.451)

(1.223)

(2.993)

(0.840)

(2.775)

Meandestinationprice

2.357

0.918

0.960

2.302

0.910

2.584

(1.984)

(0.520)

(1.386)

(2.980)

(1.001)

(2.729)

Meanpricegap(tradingpairs)

0.052

-0.004

0.058

-0.025

0.013

0.007

(0.454)

(0.264)

(0.433)

(0.480)

(0.662)

(0.454)

Geodesicdistance(km)

279

414

597

80

493

170

(208)

(224)

(292)

(35)

(276)

(79)

Logdistancetooriginlocation

5.305

5.858

6.214

4.273

5.943

5.001

(0.870)

(0.605)

(0.691)

(0.499)

(0.927)

(0.551)

Min.distance(tradingpairs,km)

20.6

114.3

34.2

7.9

2.2

50.9

Max.distance(tradingpairs,km)

915.8

874.9

1,254.8

152.9

1,362.6

318.5

Numberoftradingpairs

134

18

153

30

85

20

Observations

9,280

5,997

48,379

26,215

50,583

6,173

Notes:Thistablepresentsdescriptivestatisticsforoursampleofcountries.Thefirstandsecondrowsreportthemeanpriceatthefactorylocation(orlocationoftheportofentry)andthemeanpriceatdestinations.Thethirdrowreportspricegapsusingdataformtradingpairs.AllpricesaredeflatedbyinflationattheoriginlocationandthenconvertedtoU.S.dollarsusingtheexchangerateprevailingduringthebaseperiod.Thefourthrowreportsmeangeodesicdistancebetweentradingpairsmeasuredinkilometers.Thefifthrowreportstheaveragedistancetotheoriginlocation(inlogkilometers).Thefifthandsixthrowsreporttheminimumandmaximumdistancesbetweentradingpairs,respectively.Theseventhrowreportsthenumberoftradingpairs.Thelastrowreportsthenumberofobservations.Source:Authors’estimatesbasedonnationalCPIdataanddistancescalculatedusingGoogle.

3Methodologicalframework

Thetheoreticalframeworkunderlyingourestimationstrategyisbasedonthemodelde-velopedby

AtkinandDonaldson

(2015)

.Inthissection,weoutlinethemainelements

8

andintuitionofthetheory.Interestedreaderscanfindadditionaldetailsandproofsinthatpaper.

3.1Theory

Theeconomyischaracterizedbymultiplemarkets(orlocations)indexedbydandasingleconsumptiongood.TheinversedemandforthisproductineachmarketisgivenbyP(Qd,Dd),whereQdisquantitydemandedandDdcharacterizesdemandconditionsinlocationd.Productionofthegoodcantakeplaceathomeorabroadatasinglefactory.Whatmattersforthemodelisthedomesticoriginoftheproduct,denotedo.Ifproducedathome,oindicatesthefactorylocation;ifimported,oindicatestheportorbordercrossingthroughwhichtheproductenteredthecountry.

IdenticaldomesticintermediariesbuytheproductinwholesalemarketsattheoriginlocationatpricePoandsellittoconsumersind,effectivelyactingasretailersaswell.Intermediaries’tradingtechnologyischaracterizedbytheirtotalcosts,Cd(qd),whereqdisthequantitytradedfromotod.Intheextensionofthemodeltomanyproducts,wefurtherassumethateachproductissourcedfromonelocationonly,sothatocanbeomittedfromthesubscriptinqd.TotalcostsincludeafixedcostFdandamarginalcostcd.Themarginalcostincludesthecostofbuyingthegoodattheorigin,givenbyitspricePo,andthecostoftradingthefoodfromotod,givenbyτ(Xd),whereXdisavectorofcostshifters,includingdistanceand,potentially,otherfactorssuchasroadqualityanddestination-specificretailcosts.Totalcostsarethengivenby

C(qd)=Fd+[Po+τ(Xod)].

Anintermediarychoosequantitiesqdtomaximizeprofitsinanimperfectlycompetitivemarket,subjecttotheperceivedresponseofotherintermediaries.Thereisnoentryofintermediariesand,undercertainassumptions,thefirst-orderconditionsoftheintermedi-ary’soptimizationproblemimplythatpricegapsbetweentheoriginandthedestinationlocationsaregivenby:

?Pod≡Pd?Po=τ(Xod)+μd(τod,Dd,?d)(1)

whereμdisthemark-upchargedbytheintermediaryatlocationd,whichdependsontradecosts,demandconditionsatd,andacompetitivenessindex?d,whichinturndependsonthenumberofintermediariesandtheextentofstrategicinteractionsamongthem.

From(1),variationintradecostsshiftersimplies:

whereρod≡1+(?μ/?τod)isthepass-throughrate,thatis,theeffectoftradecostson

9

prices.Itcanbeshownthat,inthisframework,thepass-throughratedependsontheelasticityoftheslopeofthedemandscheduleandcompetitiveconditions(?d).InourempiricalanalysisinSection

4,themaintradecostshifterofinterest

xodisdistance.Tosimplifytheexposition,inwhatfollowswesetXod=xod.

Equation(2)illustratesthreesourcesofbiasthatcanarisewhenaimingatidentifying

tradecostsfromspatialpricegaps:(1)incompletepass-through(ρod1),(2)variationindemandconditionsacrosslocations,and(3)variationintheextentofcompetitionacrosslocations.Underadditionalassumptions,thepass-throughratecancontrolforthissourcesofbias.Inparticular,undertheassumptionthatthepass-throughrateisindependentofquantities

qd,equation(2)simplifiesto

?Pod=ρodτ(xd)+(1?ρod)(ad?Po),(3)

whereadisalocaldemandshifter.Underthisspecification,ρodandthelocaldemandshifteradaresufficienttocontrolforlocalcompetitiveanddemandconditions.

Thefollowingsubsectionexplainshowthistheoreticalframeworkcanguidethespec-ificationofempiricalmodelstoestimatetheeffectofdistanceontradecostsexploitingorigin-destinationpricegaps,controllingforotherfactorsthatcorrelatewithdistanceandcouldaffectpricedifferentialsthroughvariationofmarkupsacrosslocations.Section

4

describesthemethodsusedtoestimatethesemodels.

3.2Estimationstrategy

Theestimationstrategyexploitspricedataonmultipleproductsksourcedfromlocationsoandsellingatlocationsdatdifferenttimeperiodst.Withvariationacrossproducts,

destinations,originlocations,andtime,equation(3)impliesthefollowingrelationship

betweenproductpricesattheoriginandthedestination:

Pt=ρdP+ρdτ(xdt)+(1?ρd)at,(4)

wherewehavefurtherassumedthatthepass-throughrateremainsfixedovertime

.6

Thelatterassumptionholdsiflocaldemandelasticitiesandcompetitiveconditions(e.g.,entryofnewintermediaries)areconstantovertime.

Inequation(4),tradecosts

τ(xdt),pass-throughratesρdanddemandshiftersat

arenotobservable.Thestrategyaimsatidentifyingτ(xdt)andρdbasedonvariation

inpricesovertimeandspace,anddistancebetweenlocations,whiletreatingatasun-

observedheterogeneityandcapturingitthroughasetoffixedeffects.Forthisreason,preciselyidentifyingoriginlocationsiskeyfortheaccuracyoftheestimates.

Theestimationprocedureinvolvestwosteps,asfollows:

6Itisinfeasibletoestimateseparatepass-throughratesforeachperiodsincethenumberofparameterstoestimatewouldequalthenumberofobservations.

10

1.Pass-throughrates.Inthefirststep,weproduceestimatesofpass-throughrates

dbyregressingdestinationpricesonoriginprices,controllingfortradecostsand

demandshifters.Specifically,weestimatetheparametersofthefollowingmodel:

Pt=ρdP+γ+γt+εdt,(5)

whereγareproduct-destinationfixedeffects,γtisaproduct-destinationlinear

timetrend,andεdtisanunobservederrortermthatcapturesshockstotradecosts

andlocaldemandshifters.

2.Tradecosts.Inthesecondstep,werecoverestimatesoftradecosts(xodt)using

pass-throughestimatesfromthefirststep.Given(unbiased)estimatesd,wecan

manipulateequation(4)toderiveandexpressionforan“adjustedpricegap”:

Computingthis“adjustedpricegap”allowsfortheestimationofτ(xdt)when

markupsarepositiveandvaryacrosslocations,i.e.,ρd1.Localdemandcon-

ditionsarecontrolledforbyincludingfixedeffectsinteractedwiththeadjustment

factor,andtradecostsaredecomposedasτ(xdt)=f(xod)+χdt,whereχdtcaptures

unobservabletime-varyingfactorsthataffecttradecosts.Theestimatingmodelthen

becomes(7)

whereαareproduct-timefixedeffects,αdaredestinationfixedeffects,andξdt

capturesunobservedshockstolocaldemandandtradecosts.InSection

4.3,we

specifythefunctionf(xod)thatwetaketothedatatorecovertheeffectofdistanceontradecosts.

4Estimatesofintra-nationaltradecosts

Thissectionpresentsestimationresultstoquantifytherelationshipbetweendistanceandintra-nationaltradecostsinoursampleoflow-andmiddle-incomecountries.Usingthenotationintroducedintheprevioussection,weattempttoestimate?τ(xod)/?xod,wherexodisoneofthedifferentmeasuresofdistancedescribedinsection

2.3.

Ultimately,weapplythestrategydescribedinsection

3.2,whichaimsatestimatingthisrelationship

usingdataaboutpricegapsacrosslocations,controllingforvariationinmarkupsacrosslocations.Beforeturningtothatexercise,forbenchmarkingpurposes,wefirstpresentestimatesthatabstractfromvariationinmarkups.

4.1Spatialpricegaps

Underthetheoreticalframeworkdescribedinsection

3.1,ifm