predicting freight flows in a globalising world

8132019 Predicting freight flows in a globalising world

httpslidepdfcomreaderfullpredicting-freight-flows-in-a-globalising-world 18

Predicting freight 1047298ows in a globalising world

Johannes Broumlcker a Artem Korzhenevych b Marie-Catherine Riekhof c

a University of Kiel Institute for Regional Research and Department of Economics D-24098 Kiel Germanyb Kiel Institute for the World Economy D-24100 Kiel Germanyc University of Kiel Institute for Regional Research D-24098 Kiel Germany

a r t i c l e i n f o

Article history

Available online 15 December 2010

Keywords

Interregional tradeTransport 1047298owsGlobalizationGravity model

a b s t r a c t

In this paper we suggest a methodology to predict commodity speci1047297c transportation 1047298ows that bringstogether data in value and in quantity terms in a consistent way The approach is based on the moderngravity formulation There are three driving forces of the transport 1047298ows dynamics economic growththe ongoing globalization (reduction of trade barriers) and the changing commodity composition of trade whereby the evolution of value-to-weight ratios is explicitly taken into account The methodologyis applied to forecast the interregional trade 1047298ows in Europe

2010 Elsevier Ltd All rights reserved

1 Introduction

This paper is about predicting transport 1047298ows for a system of regions covering the entirearea of Europe andin additionincludingtherestof theworld on a higherlevel of aggregation There arethreeprocesses driving the dynamics of transportation 1047298ows

The 1047297rst is economic growth at varying paces in different partsof the world Growth in the developed world slowed down tomoderate rates that will likely prevail in the future while Asiancountries in particular China catch up enjoying growth rateswell above historical averages At the same time growthperspectives in some parts of the world in particular in Africaare dismal or even devastating

The second is the structural change accompanying this growthprocess Knowledge is becoming the decisive production factorand increasing quality and sophistication instead of expandingquantity is becoming a dominant dimension of growth This isthe more so the higher the level of development

The third process is a global tendency towards decliningphysical as well as institutional trade barriers This is theprocess usually called globalization Improving transportationand communication technology makes the physical 1047298ow of goods easier and greatly facilitates the matching of producersand customers worldwide the provision of services thewriting of contracts and the building up of trust As

a consequence we observe international trade 1047298ows to growmuch faster than output in the last decades

These processes are not independent of course Growth andstructural change are two sides of a coin growth makes economiesshift from mere quantitative expansion to more innovation baseddevelopment and knowledge accumulation is a main driver of growth Innovation and growth are also related to opennessThough the literature does not seem to support the claim thatopenness unambiguously favors growth (Helpman 2004 Ch 5) atleast there seems to be a correlation high level of developmentgoes hand in hand with integration into the world market andthere seem to be no examples of countries having experiencedrapid growth while keeping themselves isolated over a long timeThe three processes change the pattern of transportation ina fundamental way Long distance and cross-border 1047298ows growfaster than local and regional 1047298ows value-to-weight ratios formanufactures tend to increase and qualityas well as frequency andreliability of transport are becoming more important

Wetrytocapturethesedrivingprocessesinapredictionmodeltobe presented in the following sections This model was designed asa part of TRANS-TOOLS a larger system of models for transportanalysis developed for the European Commission (Petersen et al2009) One of the dif 1047297culties we are facing is that we have to relyon two types of data of a fundamentally different character quan-titiesand values The transportation1047298owsin ourbenchmark data setas well as the 1047298ows to be predicted are quantities measured intonnes Data inputs providing information on growth and global-ization however are in values such as GDP or foreign trade Evendata on real GDP are not quantities in the literal sense they arede1047298ated values which is something entirely different from

Corresponding author Tel thorn49 (0) 431 880 1724 fax thorn49 (0) 431 880 3366E-mail addresses broeckereconomicsuni-kielde (J Broumlcker) korzhenevych

economicsuni-kielde (A Korzhenevych) riekhofeconomicsuni-kielde (M-CRiekhof)

Contents lists available at ScienceDirect

Research in Transportation Economics

j o u r n a l h o m e p a g e w w w e l s e v i e r c o m l o c a t e r e t r e c

0739-8859$ e see front matter 2010 Elsevier Ltd All rights reserved

doi101016jretrec201011006

Research in Transportation Economics 31 (2011) 37e44



quantities measured in tonnes One of our methodological innova-tions is to bring these different types of data together in a consistentway

The rest of the paper is organized as follows Section 2 explainsthe prediction model One of its key elements is to merge value andquantity data which makes it necessary to estimate and predictvalue-to-weight ratios for commodity groups This is described inSection 3 Another key element is to incorporate the impact of globalization by making use of international trade predictionsThese are outlined in Section 4 Finally we need elasticitiesmeasuring the impact of growth on the change in the sectoralcomposition of transportation 1047298ows Their estimation is docu-mented in Section 5 Section 6 highlights a sample of predictionresults and Section 7 concludes

2 The prediction model

Our aim is to predictthe transport 1047298ow x[rst (in quantity terms)of

commodity type [ from production region r to consumption regions in a future year t gt 0 The set of regions covers the whole worldRegions can be parts of countries entire countries or aggregates of

countries Most European countries are subdivided according to theNUTS21 system or a comparable subdivision in case of countriesthat do not have an of 1047297cial NUTS2 subdivision Outside Europelarge aggregates of countries represent the rest of the worldMiddle East and North Africa enter the system country-wise Forthe details on the regional system see Appendix A A key feature of the array of 1047298ows is thus that it covers intranational as well asinternational 1047298ows at the same time Flows are sectorally sub-divided according to 10 commodity types following the NSTR classi1047297cation see Appendix B

Year t frac14 0 is the reference year (2005 in our case) that we havedata for ie x[rs

o is known The data come from the ETIS-BASE dataset (ETIS-BASE Consortium 2005) This data set is compiled frommany national and international sources and 1047298ows are partlyestimated There are manygapsthathad to be1047297lled before applyingour procedure Thus this data is far from representing hard factsbut it is the best we have and work is going on to update andimprove this data basis

The prediction starts from a modi1047297ed gravity model reading

xt [rs frac14 a[

yt

r

h[ yt

s

z[ ht r g t

s f t [rs (1)

a[ is a multiplier representing the overall scale of commodity [ yr t is

real GDP per capita in region r at time t hr t is a region of origin effect

that is common to all commodities It represents the size of theregion of origin its factor stocks and productivity g s

t is a region of destination effect It represents the level of overall demand in thedestination region f [rs

t is the distance decay function representingthe trade impeding effect of transport costs as well as other

barriers in particular those erected by national borders Finally h[

and z[ are commodity speci1047297c elasticities to be estimated (seeSection 5)

As Eq (1) is the basis forall to follow some discussion is in orderFirst note that the GDP terms are irrelevant for the 1047298ow totalsoriginating in r or arriving in s These totals are controlled by themultipliers hr

t and g st While varying across regions and over time

these multipliers have no commodity subscript Thus they repre-sent level effects that are common to all commodities Instead theelasticities vary across commodities but not across regions andthey are constant over time They control the structural change The

richer a region of origin the more it specializes in goods that havecomparatively high elasticities h[ The richer a destination regionthe more its demand concentrates on goods with comparativelyhigh elasticities z[

Compare this with a traditional gravity speci1047297cation

xt [rs frac14 a[

Y t

r h[

Y t

sz[ f t [rs (2)

One deviation of (1) from (2) is that GDP per capita yr t appears in (1)instead of total GDP Y r

t This is because we want the term eth yt r THORNh[ to

determine the structural composition not the level of 1047298ows origi-nating in r while in (2) the term ethY t

r THORNh[ has both tasks at the same

time determining the level as well as the structural compositionThe other deviation is that in addition to the GDP effects Eq (1)also has country of origin and country of destination effects thatare common to all commodities thus not bearing an index [ This isessential to avoid a fundamental de1047297ciency of speci1047297cation (2)except for the unlikely case that the two elasticities just add up toone a prediction by Eq (2) will e in the long run e either generatea trade explosion (in case of h[thorn z[gt 1) or a trade collapse (in caseof h[thorn z[lt 1) Take the former case Imagine a world in a steadystate where GDP in all regions grows at the same rate 2 per

annum say Then keeping distance and barrier effects constanttrade grows at 2(h[thorn z[) per annum which is obviously incon-sistent with the steady state assumption This is why we need themultipliers hr

t and g st they allow for keeping consistency of the

trade prediction with the prediction of the overall level of activityrepresented by the regional GDP

As a further illustration we may also compare speci1047297cation (1)with a traditional gravity equation containing GDP as well aspopulation as explanatory variables written as

xt [rs frac14 a[

P t

r

a[

Y t r

h[

P t s

x[Y t s

z[ f t [rs (3)

with population P r t and corresponding elasticities a[ and x[ This

speci1047297cation frequently appears in the literature (Anderson 1979

Eaton amp Tamura 1994 Frankel amp Rose 2005) If we restrict theparameters in the way that neither a[thorn h[ nor x[thorn z[ vary acrosscommodities then we are back at (1) with

yt r frac14 Y t

r =P t r ht

r frac14

P t r

a[thornh[ and g t

s frac14

P t s

x[thornz[

Speci1047297cation (1) thus turns out to be on the one hand morerestrictive than (3) in that it imposes a restriction on the elasticitieswhile on the other hand it is more general as the multipliers hr

t and g s

t may represent all kinds of determinants associated with regionsof origin and destination respectively not just population

Dividing the1047298ow for some future year t through the 1047298ow for thereference year t frac14 0 yields the updating formula

xt [rs frac14 x

o[rs yt

r

yor h

[ yt s

yosz[

~h

t

r ~ g

t

s dt ij (4)

where region r belongs to country i and region s to country j2 Themultipliers with a tilde are de1047297ned as ~h

t

r frac14 ht r =ho

r and ~ g t s frac14 g t

s= g os In (4) we assume that f [rs

t remains constant except for cross-border1047298ows The barriers for cross-border 1047298ows are assumed to declinedue to globalization This reduction is represented by the factor dij

t which we call the ldquoglobalization factorrdquo The factor equals one fort frac14 0 and is increasing for is j over time if the globalization processis going to continue Stating it in formal terms we assume

1 NUTS stands for ldquoNomenclature of territorial units for statisticsrdquo this is the

of 1047297

cial EU delineation of regions for statistical purposes

2 If we speak about countries this is always meant to include country aggregates

As already mentioned a country may cover just one region

J Broumlcker et al Research in Transportation Economics 31 (2011) 37 e4438



f t [rs= f o

[rs frac14

dt

ij if r andsbelongtocountries iand j is jrespectively

1 otherwise

Inthelightofwhathasbeensaidaboveaboutimprovingtransportandcommunication technologies it may appear awkward to assume thedistancefactornottobesmallerinthefuturethanitistodayexceptforinternational 1047298ows In fact a general tendency of declining transport

costs could easily be incorporated Analyzing international trade1047298ows however we found overwhelmingevidence of declining borderbarriers but virtually no sign of a declining distance effect in generalThis issue deserves more scrutiny using time series data on transport1047298ows but this is beyond the scope of this paper For the moment wework under theassumption that a declineof borderbarriersis theonlyprocess affecting the distance factors f [rs

t in the course of timeFor applying the updating formula (4) we need GDP predictions

the multipliers ~ht

r and ~ g t s the globalization factor d ij

t and elasticityestimates Regarding GDP predictions we rely on external sources(Mantzos and Capros (2006) summary is given in Appendix C)Section 4 deals with the globalization factor and Section 5 explainshow to estimate the elasticities What remains is to show how toobtain the multipliers ~h

t

r and ~ g t s The idea is to 1047297x the multipliers

such that the predictions on transport 1047298

ows are made consistentwith aggregate predictions on regional supply and demand At thispoint we encounter the problem announced above namely to bringthe two types of data together quantities measured in tonnes andvalues measured in dollars Aggregate supply and demand that wewant our predictions to be consistent with are in values We thustranslate quantities into values by the predicted value-to-weightratio p[rs

t Flows in values are thus

vt [rs frac14 pt

[rs xt [rs

The values must add up to the predicted total supply S r t in the

region of origin and to the predicted total demand Dst in the

destination region

X[X

sv

t [rs frac14 S

t r cr (5)X

[

Xr

vt [rs frac14 Dt

scr (6)

This provides us with equations just suf 1047297cient to 1047297x theunknown multipliers ~h

t

r and ~ g t sBut this raises a couple of new

problems namely how to estimate and predict value-to-weightratios and how to predict regional supply and demand We devoteSection 3 to the estimation and prediction of value-to-weightratios Predicting the regional supply and demand is relativelysimple We start on the national level by keeping ratios betweenmanufacturing supply and demand on the one hand and GDP onthe other hand constant over the prediction period The respective

ratios are taken from the GTAP database (Dimaranan 2006) Thenwe distribute thus obtained future national 1047297gures for supply anddemand across regions according to regional GDP and populationrespectively

3 Value-to-weight ratios

For the benchmark calibration we need value-to-weight ratiosfor the reference year 2005 These are found in different ways forinternational and domestic 1047298ows respectively

The Eurostat COMEXT database3 provides information oninternational trade 1047298ows in value (Euros) and quantity (tonnes)

terms by NSTR commodity group Thus it is possible to calculatethe value-to-weight ratios (eg thousand Euros per tonne) forinternational 1047298ows directly from these data Trade 1047298ows inside theEU are reported from two sources the importer side and theexporter side In these cases we 1047297rst took averages of 1047298ows acrossthese two data sources and then calculated the respective value-to-weight ratios

In order to 1047297nd the value-to-weight ratios for the domestic1047298ows we use the following estimation procedure We collecteda time series (1991e2005) of trade 1047298ows in value and in quantityterms from the COMEXT database and calculate value-to-weightratios p[ij

t for international 1047298ows by commodity group The log of this ratio is then traced back to commodity speci1047297c origin anddestination effects and a commodity speci1047297c time trend in an OLSregression

log pt [ij frac14 a[i thorn b[ j thorn 4[t thorn 3t

[ij

As before subscripts i and j denote countries of origin and desti-nation respectively a[i and b[ j are the country of origin and countryof destination effects respectively 4[ is the time trend t frac14 0 standsfor the reference year 2005 The natural assumption is that the

same equation holds for the unknown within-country ratios For2005 they are thus estimated as (hats denoting estimates)

b po

[ii frac14 exp ba[i thorn

bb[i

for each country i

Let us denote the value-to-weight ratios for 2005 obtained sofar though partly observed and partly estimated simply as p[ij

o withno hat We take them only as a preliminary 1047297rst step estimatehowever because the value estimates obtained by multiplying theknown 1047298ows x[rs

o with the respective ratios obtained so far gener-ates value 1047298ows generally inconsistent with what we know aboutregional supply regional demand and international trade We thuscorrect the preliminary 1047297rst step estimates in a second step toadjust them to the known totals of regional production valuesregional values of intermediate and 1047297nal demand and values of international trade

vo[rs frac14 xo

[rs po[ija

or b

os c oij

frac14 xo[rsp

o[rs r fig s˛f jg

v[rso is the estimated trade value p[rs

o is the corrected value-to-weight ratio The multipliers ar

o bso and c ij

o are obtained from therestrictionsX[

Xr

vo[rs frac14 Do

scr (7)

X[ Xs

vo[rs frac14 S or cr (8)

X[

Xr fig

Xs˛f jg

vo[rs frac14 V oijci j (9)

i and j denote the sets of regions belonging to countries i and jrespectively V ij

o is the value of trade 1047298ow from country i to country jPutting it differently v[rs

o is the minimum information estimateunder the restrictions (7)e(9) given the prior x[rs

o p[rso It makes best

use of the information supplied by the constraints that macrovalues have to add up to the known totals of sales and purchases ineach region and of international trade Data sources for supply anddemand are as described in Section 2 in the context of prediction

Next we need to project the estimated ratio p[rso to the future

years This is done by using the estimated commodity speci1047297c3

httpeppeurostateceuropaeunewxtweb

J Broumlcker et al Research in Transportation Economics 31 (2011) 37 e44 39



trends in the value-to-weight ratio with trend rates 4l by NSTR commodity group summarized in Table 1

The estimates say that eg the value per tonne of agriculturalproducts is predicted to decline at a rate of 06 per year while thatof oil and gas is predicted to increase at a rate of 33 per year onaverage Values are measured in terms of the commodity basketunderlying the GDP de1047298ator

4 Predicting the globalization factor

The globalization factor is obtained from estimating a gravitymodel for trade between and within countries in a style similar tothe transportation 1047298ow model reading

V t ij frac14 exp

at

i thorn bt j s

t ij thorn ft

Dc ij thorn 4t D

lij gt log z t

ij

thorn 3t

ij (10)

ait and b j

t are 1047297xed effects for the countries of origin and destinationrespectively sij

t is a trade barrier for imports from i to j By de1047297ni-tion barriers are zero for within-country trade ie sij

t frac14 0 for i frac14 jFor is j we assume a barrier l j

t applying to all countries exportingto country j that is however reduced if countries i and j belong tosome common free trade area k Hence

st ij frac14 lt

jDb

j X

k

mt kD

kij (11)

The Ds are several kinds of dummies D jb is the import barrier

dummy for country j attaining one for imports to country j and zerofor the intranational 1047298ow Dij

k is a dummy representing the freetrade area k attaining one if countries i and j is j both belong tosome free trade area k and zero otherwise Dij

c is a proximitydummy attaining one if countries i and j share a common border orif i frac14 j and zero otherwise Finally Dij

l is a language dummyattaining one if countries i and j speak the same language or if i frac14 jand zero otherwise l j

t 4t 4t and mkt are the respective parameters

all expected to be positive z ijt is distance as the crow 1047298ies and gt is

the respective elasticity also expected to be positive Distances are

population weighted great-circle distances For populationweighting we use the CIESIN (2005) database offering populationof the world geographically assigned to a very 1047297ne grid(25 25 min) of the land map of the world Note that this methodimplies that the distances are time varying

Inserting the barrier (11) into (10) yields the gravity equation

V t ij frac14 exp

at

i thorn bt

j lt jD

b j thorn f

t D

c ij thorn 4t

Dlij thorn

Xk

mt kD

kij gt log z t

ij

thorn 3t

ij

(12)

It is fairly standard except that it applies to intranational as well as

international1047298

ows alike This is essential for being able to estimate

border barriers whose tendency to decline is a key element in ourtrade prediction Recent research brought about ample evidencethat these barriers are high This was for the 1047297rst time demon-strated by Broumlcker (1984) using within-country and betweencountry transport 1047298ows for the EU6 The most often cited referencenowadays is McCallum (1995) Helliwell (1998) and Helliwell andSchembri (2005) review lots of literature showing that recentestimates of border barriers turn out to be somewhat lower thanMcCullumrsquos estimates but similar to Broumlckerrsquos estimates

We estimate the equation for trade between 187 countries of theworld for each year between 1993 and 2004 by the method of Broumlcker (1984) (see also Broumlcker amp Rohweder 1990) The approachhas been reinvented by Silva and Tenreyro (2006) under the nameldquoPoisson pseudo-maximum-likelihoodrdquo (PPML) The original sourceof trade data is the UN COMTRADE database4 This way we generateestimated time series bl

t

j for the trade barriers among others While

the other parameters ( bft b4t bmt k bgt ) are fairly stable over time

trade barriers tend to decline rapidly Hence we base our predictionon the assumption that besides the 1047297xed effects it is only thesebarriers that will change in the course of time

To project the barriers to the years beyond 2005 we lay a lineartrend through the series As one would expect however the trendlines have ratherheterogeneous slopes across countries with manyextreme values To rule out outliers we use average slopes forcountry groups We group together countries with similar institu-tional historical and geographical properties For each of theresulting 15 groups named in Table 2 we calculate a GDP weightedaverage slope

The second column of Table 2 shows barrier estimates

bl

t

j for2004 averaged (GDP weighted) over all countries within the

respective group As expected barriers are positive and highlysigni1047297cantly different from zero the standard errors (not shown)are extremely small As frequently noted in the literature despiteongoing globalization barriers are surprisingly large On averageacross all country groups border barriers are around 4 meaningthat within-country trade exceeds cross-border trade by a factorlarger than 50 other things equal

The third column shows average slopes of the linear trend lineie the per annum changes of the barriers As expected averageslopes are all negative Barriers tend to decline some of themsharply As a case in point look at the new EU members if the trend

Table 1

Trend rates of value-to-weight ratios

NSTR co mmodit y gro up ( short name) Trend rat e p aa

Agriculture 060 (016)Foodstuffs 077 (011)Solid fuels 067 (027)Oil and Gas 330 (020)Ores 062 (038)

Metals 050 (012)Minerals 071 (029)Fertilizers 213 (032)Chemicals 221 (020)Manufactures 044 (014)

a Standard errors in parentheses

Table 2

Barriers and per annum barrier changes GDP weighted averages for country groups

Country group Barrier level2004

Barrier changeper annum

Old EU members and EFTA countries 20 030New EU members ( aft er 2004) 37 113Balkan States 54 144Ex-Soviet Uniona 42 068

Non-European Mediterranean Area 46 030Arabian Peninsula 52 042Southeast Asian Nations 24 056Remaining Asian Countries 27 051South African Countriesb 42 068Remaining African Countries 72 017North America 15 001Central America 51 038South America 36 049Oceania 16 002Rest of the world 74 011

a Estimation based on the years 1995e2005b Estimation based on the years 2000e2005

4

httpcomtradeunorgdb






representing the log of the distance decay function including anykind of constant barrier Representing distance and other barriersby a 1047297xed effect instead of a distance function and a set of dummiesavoids misspeci1047297cations of the barriers to bias the elasticity esti-mates the only parameters we are interested in This comes at thecost of losing lots of degrees of freedom of course and thereforerenders the standard errors of the elasticities rather high

The parameters are not fully identi1047297ed This is however onlya concern with regard to the elasticities They are both identi1047297edup to an arbitrary additive constant Note that adding a constantk say to h[ and subtracting (klog yi) from a i

t obviously leaves thebracketed expression unchanged (similarly for z[) Fortunatelythat is all we need for the prediction model It is immediate fromEq (4) and the way we determine the multipliers ~h

t

r and ~ g t s that

adding a constant to the h[s and some other constant to the z[sdoes not change the prediction because it would be exactlycompensated by a corresponding change of ~hr and ~ g s The levelof the elasticities does not matter only the differences acrosscommodities matter For identi1047297cation we restrict the average of the elasticities across commodities to zero This is natural butwe emphasize that any other way of restricting the level woulddo

As before the error is assumed to have zero expectation to beuncorrelated across different observations and to have a varianceproportional to the expected 1047298ow The estimation methods are theones described in Section 4

Table 3 shows the elasticity estimates heteroscedasticityrobust standard errors are in brackets As the row averages arezero positive (negative) 1047297gures indicate above (below) averageelasticities Commodities with positive (negative) h-elasticities arethose whose share in production increases (decreases) withincreasing income per capita For example if income per capitadoubles the ratio of chemical products (the category with thehighest elasticity) over fertilizers (the category with the lowestelasticity) increases by the factor 2051thorn060 frac14 216 Similarlycommodities with positive (negative) z-elasticities are those

whose share in demand increases (decreases) with increasingincome per capita The range of these elasticities is smaller thanthat of the h-elasticities meaning that in a growing countrydemand ratios change less than production ratios ceteris paribusOne should keep in mind however that the evidence forsystematic tendencies in structural change revealed by the esti-mates is not very strong no more than about one half of theestimates are signi1047297cantly different from zero

6 Selected results

The results presented below aremeant to illustrate the impact of the three driving forces de1047297ned in Section 1 e economic growth

structural change and ongoing globalizatione on the magnitudeof the predicted transport 1047298ows We will mostly concentrate on theEU countries as they are characterized by the most detailed and

reliable dataFig 2 illustrates the 1047297rst point countries with higher projectedrates of economic growth are also expected to expand their trademore Germany characterized by one of the lowest growth rates inEurope will expand external trade by the smallest share (althoughit would still be the largest increase in absolute terms) The newestEU members Romania and Bulgaria may roughly double theirtrade 1047298ows if the projected high growth persists

Moreover Fig 2 also gives a hint on the impact of ourassumptions concerning the further reduction of the internationaltrade barriers For the countries with low projected economicgrowth rates (like Germany or Italy) the reduction of the tradebarriers leads to the decoupling of the rates of growth of interna-tional and domestic 1047298ows The domestic 1047298ows may grow muchslower or even decline Further the trade barriers within the EU arealready quite low and for most of the EU members the expansionof trade with the rest of the world will be larger than the expansionof trade with the European partners

Fig 3 illustrates the overall tendencies in the commoditystructure of the global trade 1047298ows In correspondence to the trendrates for value-to-weight ratios reported in Table 1 the growth of trade in value terms will fall short of the growth of trade in quantityterms for two commodity groups agricultural goods and minerals

Table 3

Parameters of the transport prediction model

NSTR commoditygroup

Elasticity of commoditystructure with respectto exporterrsquos GDP a

Elasticity of commoditystructure with respectto importerrsquos GDP a

Agriculture 001 (007) 012 (007)Foodstuffs 006 (005) 031 (005)Solid fuels 014 (010) 020 (013)Oil and Gas 019 (011) 002 (009)Ores 010 (010) 005 (012)Metals 014 (007) 034 (006)Minerals 002 (009) 034 (008)Fertilizers 060 (013) 001 (012)Chemicals 051 (006) 018 (005)Manufactures 027 (005) 015 (005)

a

Standard errors in parentheses

Fig 2 Total growth of transportation 1047298ows for selected countries 2005e2030

Fig 3 Total growth of worldwide transportation 1047298ows by commodity group

2005e

2030




The estimated trend rates for these two groups are negative Thegrowth of trade 1047298ows in value terms is highest for petroleumproducts for which the estimated increase in the value-to-weightratio is equal to 33 per annum

In addition Fig 4 presents details on the development of the

commodity structure of the EU imports until the year 2030 Forfourcommodity groups solid fuels ores chemicals and metals theindividuals shares in total 1047298ows are predicted to remain at roughlythe same level as in 2005 (13 for four groups together) and we donot show separate trend curves for these groups

As of year 2005 minerals (including building materials)account for 40 of total transportation volume This share isexpected to increase to 45 by 2030 Another group for whichthe share is predicted to increase contains the manufacturingproducts (rise from 17 to 19) All other commodity groups aregoing to shrink in terms of the share in transportation volumethe largest decrease being in the group of foodstuffs from above10 to under 8

7 Conclusions

In this paper we described the data-intensive methodology toforecast interregional trade and transportation1047298owsby commoditygroup This approach allowed us to take account of three majordriving forces underlying the dynamics of trade economic growthglobalization and changing commodity composition of trade 1047298owsFor the 1047297nal prediction a doubly-constrained gravity model wasspeci1047297ed that combined all these factors The impact of globaliza-tion was taken into account by estimating the evolution of borderbarriers between a set of 187 countries The time trends incommodity speci1047297c value-to-weight ratios and in the commoditycomposition of trade1047298ows were estimated based on the time series

of transportation 1047298

ows A distinguishing feature of the model isthat it is speci1047297ed for a large system of regions covering the entirearea of Europe on a 1047297ne scale and in addition including the rest of the world on a higher level of aggregation

Appendix A List of NUTS2 regions

The 27 EU and 4 EFTA countries (Norway SwitzerlandIceland and Liechtenstein) are divided according to the of 1047297cialNUTS2 classi1047297cation5 In addition the following regions arede1047297ned

Fig 4 Dynamics of the commodity structure of the EU imports (in quantity terms)

Code Name of the region Country or area

AL Albania AlbaniaBA Bosnia and Herzegovina Bosnia and HerzegovinaBY Republic of Belarus Republic of BelarusMK Macedonia The Former

Yugoslav Republic of Macedonia FYR

HR Croatia Croatia

MD Moldova MoldovaRU Russian Federation Russian FederationTR Turkey TurkeyUA Ukraine UkraineYU Serbia Montenegro Serbia MontenegroAUNZ Australia and New Zealand Australia and New ZealandDZ Algeria AlgeriaEG Egypt EgyptGAA Georgia Armenia and

AzerbaijanGeorgia Armenia and Azerbaijan

IL Israel Israel JP Japan JapanLB Lebanon LebanonLY Libya LibyaMA Morocco MoroccoMIAS Middle Asia Middle AsiaMSAM Middle and South America Middle and South AmericaRAFR Rest of Africa Rest of Africa

RASI Rest of Asia Rest of AsiaREUR Rest of Europe Rest of EuropeRNAM Rest of Northern America Rest of Northern AmericaRWOL Rest of the World Rest of the WorldSY Syria SyriaTN Tunisia TunisiaUSA USA USA

Appendix B List of NSTR commodity groups

Group Description Short name

0 Ag ricultural products and liv e animals Agriculture1 Foodstuffs and animal fodder Foodstuffs2 Solid mineral fuels Solid fuels

3 Petroleum products and crude oil Oil and Gas4 Ores and metal waste Ores5 Metal products Metals6 Crude and manufact ured minerals

building materialsMinerals

7 Fertilizers Fertilizers8 Chemicals Chemicals9 Machine ry transpo rt equipment

manufactured articlesand miscellaneous articles

Manufactures

Appendix C Assumed GDP growth rates

Country or country group GDP growth rate 2005e2030 pa

Austria 21

Belgium and Luxembourg 21Germany 15Denmark 20Spain 27Finland 22France 20Greece 22Ireland 48Italy 17Netherlands 16Portugal 22Sweden 27United Kingdom 27Cyprus 41Czech Republic 32Estonia 46Hungary 30

(continued on next page)

5

httpeceuropaeueurostatramonnutshome_regions_enhtml




References

Anderson J E (1979) A theoretical foundation for the gravity equation The American Economic Review 69(1) 106e116

Broumlcker J (1984) How do international trade barriers affect interregional trade InAring E Andersson W Isard amp W Puu (Eds) Regional and industrial development theories Models and empirical evidence (pp 219e239) Amsterdam North Holland

Broumlcker J amp Rohweder H (1990) Barriers to international trade methods of measurement and empirical evidence Annals of Regional Science 4289e305

Center for International Earth Science Information Network (CIESIN) and CentroInternacional de Agricultura Tropical (CIAT) (2005) Gridded population of theworld version 3 (GPWv3) Internet Palisades NY Socioeconomic Data andApplications Center ( SEDAC) Columbia University httpsedacciesincolumbiaedugpw

Dimaranan B V (Ed) (2006) Global Trade Assistance and Production The GTAP 6 Data Base Center for Global Trade Analysis Purdue University

Eaton J amp Tamura A (1994) Bilateralism and regionalism in Japanese and US tradeand direct foreign investment patterns Journal of the Japanese and InternationalEconomies 8(4) 478e510

ETIS-BASE Consortium (2005) Complete ETIS database on CD thorn transport referenceinformation database in electronic and aggregated paper version thorn user manual of database access ETIS-BASE Deliverable D8 Rotterdam NEA Funded by EC 5thFramework Transport RTD Programme

Frankel J A amp Rose A K (2005) Is trade good or bad for the environment Sortingout the causality The Review of Economics and Statistics 87 (1) 85e91

Helliwell J (1998) How much do national borders matter Washington DCBrookings Institution Press

Helliwell J F amp Schembri L L (2005) Borders common currencies trade andwelfare what can we learn from the evidence Bank of Canada Review Internet httpwwwbankofcanadacaenreviewrev_spring2005html

Helpman E (2004) The mystery of economic growth Cambridge MA HarvardUniversity Press

McCallum J (1995) National borders matter Canada-US regional trade patterns American Economic Review 85 615e623

Mantzos L amp Capros P (2006) European energy and transport Trends to 2030 e

Update 2005 Luxembourg Of 1047297ce for Of 1047297cial Publications of the EuropeanCommunities

Petersen M S Broumlcker J Enei R Gohkale R Granberg T Hansen C O et al(2009) Report on scenario traf 1047297c forecast and analysis of traf 1047297c on the TEN-Ttaking into consideration the external dimension of the union e Final report Funded by DG TREN Copenhagen Denmark

Silva J M C S amp Tenreyro S (2006) The log of Gravity Review of Economics andStatistics 88(4) 641e658

Appendix C (continued )


Lithuania 50Latvia 56Malta 22Poland 40Slovenia 30Slovak Republic 30

Albania 16Bosnia 45Bulgaria 53Belarus 53Switzerland and Liechtenstein 14Macedonia 29Croatia 45Iceland 26Moldavia 29Norway 32Romania 45Russia 46Turkey 47Ukraine 50Se rbia Montenegro Ko sovo 40Other areas 40




quantities measured in tonnes One of our methodological innova-tions is to bring these different types of data together in a consistentway

The rest of the paper is organized as follows Section 2 explainsthe prediction model One of its key elements is to merge value andquantity data which makes it necessary to estimate and predictvalue-to-weight ratios for commodity groups This is described inSection 3 Another key element is to incorporate the impact of globalization by making use of international trade predictionsThese are outlined in Section 4 Finally we need elasticitiesmeasuring the impact of growth on the change in the sectoralcomposition of transportation 1047298ows Their estimation is docu-mented in Section 5 Section 6 highlights a sample of predictionresults and Section 7 concludes

2 The prediction model

Our aim is to predictthe transport 1047298ow x[rst (in quantity terms)of

commodity type [ from production region r to consumption regions in a future year t gt 0 The set of regions covers the whole worldRegions can be parts of countries entire countries or aggregates of

countries Most European countries are subdivided according to theNUTS21 system or a comparable subdivision in case of countriesthat do not have an of 1047297cial NUTS2 subdivision Outside Europelarge aggregates of countries represent the rest of the worldMiddle East and North Africa enter the system country-wise Forthe details on the regional system see Appendix A A key feature of the array of 1047298ows is thus that it covers intranational as well asinternational 1047298ows at the same time Flows are sectorally sub-divided according to 10 commodity types following the NSTR classi1047297cation see Appendix B

Year t frac14 0 is the reference year (2005 in our case) that we havedata for ie x[rs

o is known The data come from the ETIS-BASE dataset (ETIS-BASE Consortium 2005) This data set is compiled frommany national and international sources and 1047298ows are partlyestimated There are manygapsthathad to be1047297lled before applyingour procedure Thus this data is far from representing hard factsbut it is the best we have and work is going on to update andimprove this data basis

The prediction starts from a modi1047297ed gravity model reading

xt [rs frac14 a[

yt

r

h[ yt

s

z[ ht r g t

s f t [rs (1)

a[ is a multiplier representing the overall scale of commodity [ yr t is

real GDP per capita in region r at time t hr t is a region of origin effect

that is common to all commodities It represents the size of theregion of origin its factor stocks and productivity g s

t is a region of destination effect It represents the level of overall demand in thedestination region f [rs

t is the distance decay function representingthe trade impeding effect of transport costs as well as other

barriers in particular those erected by national borders Finally h[

and z[ are commodity speci1047297c elasticities to be estimated (seeSection 5)

As Eq (1) is the basis forall to follow some discussion is in orderFirst note that the GDP terms are irrelevant for the 1047298ow totalsoriginating in r or arriving in s These totals are controlled by themultipliers hr

t and g st While varying across regions and over time

these multipliers have no commodity subscript Thus they repre-sent level effects that are common to all commodities Instead theelasticities vary across commodities but not across regions andthey are constant over time They control the structural change The

richer a region of origin the more it specializes in goods that havecomparatively high elasticities h[ The richer a destination regionthe more its demand concentrates on goods with comparativelyhigh elasticities z[

Compare this with a traditional gravity speci1047297cation

xt [rs frac14 a[

Y t

r h[

Y t

sz[ f t [rs (2)

One deviation of (1) from (2) is that GDP per capita yr t appears in (1)instead of total GDP Y r

t This is because we want the term eth yt r THORNh[ to

determine the structural composition not the level of 1047298ows origi-nating in r while in (2) the term ethY t

r THORNh[ has both tasks at the same

time determining the level as well as the structural compositionThe other deviation is that in addition to the GDP effects Eq (1)also has country of origin and country of destination effects thatare common to all commodities thus not bearing an index [ This isessential to avoid a fundamental de1047297ciency of speci1047297cation (2)except for the unlikely case that the two elasticities just add up toone a prediction by Eq (2) will e in the long run e either generatea trade explosion (in case of h[thorn z[gt 1) or a trade collapse (in caseof h[thorn z[lt 1) Take the former case Imagine a world in a steadystate where GDP in all regions grows at the same rate 2 per

annum say Then keeping distance and barrier effects constanttrade grows at 2(h[thorn z[) per annum which is obviously incon-sistent with the steady state assumption This is why we need themultipliers hr

t and g st they allow for keeping consistency of the

trade prediction with the prediction of the overall level of activityrepresented by the regional GDP

As a further illustration we may also compare speci1047297cation (1)with a traditional gravity equation containing GDP as well aspopulation as explanatory variables written as

xt [rs frac14 a[

P t

r

a[

Y t r

h[

P t s

x[Y t s

z[ f t [rs (3)

with population P r t and corresponding elasticities a[ and x[ This

speci1047297cation frequently appears in the literature (Anderson 1979

Eaton amp Tamura 1994 Frankel amp Rose 2005) If we restrict theparameters in the way that neither a[thorn h[ nor x[thorn z[ vary acrosscommodities then we are back at (1) with

yt r frac14 Y t

r =P t r ht

r frac14

P t r

a[thornh[ and g t

s frac14

P t s

x[thornz[

Speci1047297cation (1) thus turns out to be on the one hand morerestrictive than (3) in that it imposes a restriction on the elasticitieswhile on the other hand it is more general as the multipliers hr

t and g s

t may represent all kinds of determinants associated with regionsof origin and destination respectively not just population

Dividing the1047298ow for some future year t through the 1047298ow for thereference year t frac14 0 yields the updating formula

xt [rs frac14 x

o[rs yt

r

yor h

[ yt s

yosz[

~h

t

r ~ g

t

s dt ij (4)

where region r belongs to country i and region s to country j2 Themultipliers with a tilde are de1047297ned as ~h

t

r frac14 ht r =ho

r and ~ g t s frac14 g t

s= g os In (4) we assume that f [rs

t remains constant except for cross-border1047298ows The barriers for cross-border 1047298ows are assumed to declinedue to globalization This reduction is represented by the factor dij

t which we call the ldquoglobalization factorrdquo The factor equals one fort frac14 0 and is increasing for is j over time if the globalization processis going to continue Stating it in formal terms we assume

1 NUTS stands for ldquoNomenclature of territorial units for statisticsrdquo this is the

of 1047297

cial EU delineation of regions for statistical purposes

2 If we speak about countries this is always meant to include country aggregates

As already mentioned a country may cover just one region




f t [rs= f o

[rs frac14

dt


1 otherwise




the multipliers ~ht



t





vt [rs frac14 pt

[rs xt [rs



destination region

X[X

sv

t [rs frac14 S

t r cr (5)X

[

Xr

vt [rs frac14 Dt

scr (6)


t











[ij



b po


bb[i

for each country i




vo[rs frac14 xo

[rs po[ija

or b

os c oij

frac14 xo[rsp

o[rs r fig s˛f jg



o bso and c ij


Xr

vo[rs frac14 Do

scr (7)

X[ Xs


X[

Xr fig

Xs˛f jg

















V t ij frac14 exp

at

i thorn bt j s

t ij thorn ft

Dc ij thorn 4t D

lij gt log z t

ij

thorn 3t

ij (10)

ait and b j





st ij frac14 lt

jDb

j X

k

mt kD

kij (11)











V t ij frac14 exp

at

i thorn bt

j lt jD

b j thorn f

t D

c ij thorn 4t

Dlij thorn

Xk

mt kD

kij gt log z t

ij

thorn 3t

ij

(12)






t






bl

t




Table 1






Table 2







4

httpcomtradeunorgdb









t

r and ~ g t s that





6 Selected results






Table 3


NSTR commoditygroup




a




2005e

2030








7 Conclusions










HR Croatia Croatia












Manufactures



Austria 21



5





References
























f t [rs= f o

[rs frac14

dt


1 otherwise




the multipliers ~ht



t





vt [rs frac14 pt

[rs xt [rs



destination region

X[X

sv

t [rs frac14 S

t r cr (5)X

[

Xr

vt [rs frac14 Dt

scr (6)


t











[ij



b po


bb[i

for each country i




vo[rs frac14 xo

[rs po[ija

or b

os c oij

frac14 xo[rsp

o[rs r fig s˛f jg



o bso and c ij


Xr

vo[rs frac14 Do

scr (7)

X[ Xs


X[

Xr fig

Xs˛f jg

















V t ij frac14 exp

at

i thorn bt j s

t ij thorn ft

Dc ij thorn 4t D

lij gt log z t

ij

thorn 3t

ij (10)

ait and b j





st ij frac14 lt

jDb

j X

k

mt kD

kij (11)











V t ij frac14 exp

at

i thorn bt

j lt jD

b j thorn f

t D

c ij thorn 4t

Dlij thorn

Xk

mt kD

kij gt log z t

ij

thorn 3t

ij

(12)






t






bl

t




Table 1






Table 2







4

httpcomtradeunorgdb









t

r and ~ g t s that





6 Selected results






Table 3


NSTR commoditygroup




a




2005e

2030








7 Conclusions










HR Croatia Croatia












Manufactures



Austria 21



5





References




























V t ij frac14 exp

at

i thorn bt j s

t ij thorn ft

Dc ij thorn 4t D

lij gt log z t

ij

thorn 3t

ij (10)

ait and b j





st ij frac14 lt

jDb

j X

k

mt kD

kij (11)











V t ij frac14 exp

at

i thorn bt

j lt jD

b j thorn f

t D

c ij thorn 4t

Dlij thorn

Xk

mt kD

kij gt log z t

ij

thorn 3t

ij

(12)






t






bl

t




Table 1






Table 2







4

httpcomtradeunorgdb









t

r and ~ g t s that





6 Selected results






Table 3


NSTR commoditygroup




a




2005e

2030








7 Conclusions










HR Croatia Croatia












Manufactures



Austria 21



5





References





























t

r and ~ g t s that





6 Selected results






Table 3


NSTR commoditygroup




a




2005e

2030








7 Conclusions










HR Croatia Croatia












Manufactures



Austria 21



5





References




























7 Conclusions










HR Croatia Croatia












Manufactures



Austria 21



5





References
























References






















predicting freight flows in a globalising world

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