the impact of port characteristics on international transport costs - wilmsmeier - hoffmann and...

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CONTENTS

LIST OF CONTRIBUTORS vii

INTRODUCTIONKevin Cullinane and Wayne K. Talley 1

THE EVOLUTION AND CHALLENGES OF PORTECONOMICS

Trevor Heaver 11

AN ECONOMIC THEORY OF THE PORTWayne K. Talley 43

MULTIPLE OUTPUTS IN PORT COST FUNCTIONSSergio R. Jara-D az, Eduardo Martinez-Budria andJuan Jose Diaz-Hernandez

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ESTIMATING THE RELATIVE EFFICIENCY OF

EUROPEAN CONTAINER PORTS: A STOCHASTICFRONTIER ANALYSIS

Kevin Cullinane and Dong-Wook Song 87

THE IMPACT OF PORT CHARACTERISTICS ONINTERNATIONAL MARITIME TRANSPORT COSTS

Gordon Wilmsmeier, Jan Hoffmann and RicardoSanchez

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STRATEGIC POSITIONING ANALYSIS FORSEAPORTSElvira Haezendonck, Alain Verbeke and Chris Coeck 143

PORT INVESTMENT: PROFITABILITY, ECONOMICIMPACT, FINANCING

Musso Enrico, Ferrari Claudio and Benacchio Marco 173

SHIPPING DEREGULATIONS WAGE EFFECT ONLOW AND HIGH WAGE DOCKWORKERS

James Peoples, Wayne K. Talley and PithoonThanabordeekij

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CONTENTSvi


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LIST OF CONTRIBUTORS

Marco Benacchio Autorita Garante della Concorrenza e del

Mercato, Rome, Italy

Chris Coeck Antwerp Port Authority, Antwerp,Belgium

Kevin Cullinane University of Newcastle upon Tyne, Newcastle,

UK

Juan Jose

Diaz-Hernandez

Universidad de La Laguna, La Laguna,

Spain

Claudio Ferrari Universita di Genova, Genova, Italy

Elvira Haezendonck University of Antwerp, Antwerp, Belgium

Trevor Heaver University of British Columbia, Vancouver,

Canada

Jan Hoffman UNCTAD, Geneva, Switzerland

Sergio R. Jara-Diaz Universidad de Chile, Santiago, Chile

Eduardo Martinez-

Budria

Universidad de La Laguna, La Laguna, Spain

Enrico Musso Universita di Genova, Genova, Italy

James Peoples University of Wisconsin-Milwaukee,

Milwaukee, WI, USA

Ricardo Sanchez Austral University, Buenos Aires,

Argentina

Dong-Wook Song The University of Hong Kong, Hong KongWayne K. Talley Old Dominion University, Norfolk,

VA, USA

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Pithoon Thanabordeekij University of Wisconsin-Milwaukee,Milwaukee, WI, USA

Alain Verbeke University of Calgary, Calgary, Canada

Gordon Wilmsmeier University of Osnabruck, Osnabruck, Germany

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LIST OF CONTRIBUTORSviii


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THE IMPACT OF PORT

CHARACTERISTICS ON

INTERNATIONAL MARITIME

TRANSPORT COSTS

Gordon Wilmsmeier, Jan Hoffmann and

Ricardo Sanchez

ABSTRACT

The chapter provides empirical evidence that indicators for different port

characteristics have a statistically significant and strong impact on

international maritime transport costs. It reports on empirical work on

trade among 16 Latin-American countries. The database incorporates

75,928 observations, which comprise practically all maritime trade

transactions in containerizable goods on most intra-Latin-American trade

routes for the year 2002. The regressions incorporate the main classical

explanatory variables of maritime transport costs, such as unit cargo

value, volume per transaction, geographical distance, bilateral tradevolume, and trade balances. It further looks at six indicators for different

port characteristics as possible additional determinants of international

transport costs. It is found that indicators for port efficiency, port

infrastructure, private sector participation, and inter-port connectivity

have significant impacts on international maritime transport costs. The

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Port Economics

Research in Transportation Economics, Volume 16, 119142

Copyright r 2006 by Elsevier Ltd.All rights of reproduction in any form reserved

ISSN: 0739-8859/doi:10.1016/S0739-8859(06)16006-0

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estimated elasticity for port efficiency is the highest of all port-relatedvariables; doubling port efficiency in a pair of ports has the same impact

on international transport costs as halving the distance between them

would have.

1. BACKGROUND

Determinants of international transport costs are the topic of a growing

recent literature. Interest in the topic arises from the desire to better explain

economic development and international trade patterns, as well as to

identify possibilities to reduce transaction costs. Most international trade

continues to be transported by sea, and ports are crucial nodes in global

shipping networks.

Transport costs are a major component of overall trade costs.

Anderson and van Wincoop (2004) provide an extensive review of trade

costs, which are estimated to amount to a 170% ad valorem tax-equivalent,

including all transport, border-related, and local distribution costs from the

foreign producer to the domestic user. Initial work on the determinants of

international transport costs, for example by Radelet and Sachs (1998), uses

mainly explanatory variables that are related to distance and geographical

characteristics, such as if countries are land locked, or if trading partners are

neighbours, and to country characteristics such as GDP per capita.

Martinez-Zarzoso, Garcia Menendez, and Suarez-Burguet (2003) suggest

that greater distance and poor trade partner infrastructure notably increases

maritime transport costs. Inclusion of infrastructure measures improves the

fit of the regression, corroborating the importance of infrastructure in

determining transport costs. Hummels (1999, 2000, 2001) assesses whether

international transport costs have declined, and introduces time as a trade

barrier. Wilson, Mann, and Otsuki (2003) find that port efficiency has a

strong and significant impact on bilateral trade flows in the Asia-Pacific

region. This positive impact of port efficiency on trade flows is most likely

due to both its effects on the quality of maritime transport services and,

also, on the international maritime transport costs.

The present chapter is about the role of port characteristics as

determinants of international maritime transport costs. It follows up the

work of Fuchsluger (1999), Hoffmann (2002), Kumar and Hoffmann

(2002), Sanchez et al. (2003), Wilmsmeier (2003), and Ma rquez Ramos,

Martnez Zarzoso, Pe rez Garca, and Wilmsmeier (2006). We analyse

GORDON WILMSMEIER ET AL.120


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international freight charges as captured by customs declarations. Foreach maritime trade transaction, the CIF (Cost, Insurance, Freight) value

declared to customs is the sum of the cargos FOB (Free on Board) value,

the insurance costs, and the freight charges. It is these freight charges

alone, i.e. not including the insurance, which we use for the empirical

research presented in this chapter. Also, it is important to emphasize that we

do not look at average CIF/FOB ratios, as have been used in some early

cross-country studies (e.g. Gallup, Sachs, & Mellinger, 1998; Radelet &

Sachs, 1998; Limao & Venables, 2000), but rather at data for individual

transactions.We present empirical results from trade between 7 importing and 16

exporting Latin-American countries. The database incorporates 75,928

observations. They comprise practically all maritime trade transactions on

105 intra-Latin-American trade routes for containerizable goods in the year

2002; containerizable meaning here a high likelihood of being contain-

erized (see Annex C).

The presented research incorporates the main classical explanatory

variables of cargo value, volume per transaction, geographical distance,

bilateral trade volume, and trade balances. It generally confirms previousresults as regards the impact of these variables. It further looks at six

different indicators for port characteristics as possible additional determi-

nants of international transport costs; the indicators are for port

infrastructure, port efficiency, port privatization, general transport infra-

structure, customs delay, and port connectivity.

The relationships between such port characteristics, port costs, and

international transport costs are not at all straightforward (see, e.g. Tovar,

Jara-Daz, & Trujillo, 2003 for an overview of the literature on cost

functions in the port sector; Cullinane & Song, 2002 on private sectorparticipation in ports; Hoffmann, 2001 on ports in Latin America; de

Langen, 2004 on maritime clusters and seaports; Beresford & Dubey, 1990

on the competitiveness of trade corridors; Bichou & Gray, 2005 on port

terminology). Better port infrastructure may improve efficiency, but this

may be at a cost, i.e. it might actually increase port charges and

consequently, also the overall transport costs. Port privatization may lead

to new investment, but it may also coincide with reduced public subsidies,

leading to higher charges to port users. Shippers may be prepared to pay

more for a faster and more reliable service, because overall transaction costsare not identical to international transport costs. In spite of these diverse

relationships, the empirical results presented in this chapter are quite clear

and straightforward: increases in port efficiency, port infrastructure, private

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International Maritime Transport Costs 121


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sector participation, and inter-port connectivity all help to reduce theoverall international maritime transport costs.

Our results confirm those presented by Sanchez et al. (2003), who develop

a complex measure for port efficiency based on quantitative port

performance indicators. Their work provides evidence that port efficiency

has the equivalent impact on international maritime transport costs as

geographical distance. Sanchez et al. used data for US imports from Latin-

American countries. They concluded, Given the large pertaining differ-

ences in productivity among Latin-American ports, these conclusions are

relevant for policy makers, for the ports, and for researchers. Unlikedistance, economies of scale, and most other determinants of transport

costs, port efficiency is within the scope of national policies.

This chapter attempts to analyse if different port characteristics have a

measurable impact on international maritime transport costs, and to

quantify these impacts. The different indicators for these characteristics

themselves are not being discussed, and neither do we attempt to provide an

in-depth analysis of the specific mechanisms through which they might

influence maritime transport costs.

2. MODEL

The log of the maritime freight costs (FREIGHTij) per ton of import cargo

to country ifrom country jis assumed to depend on

the type and value of the commodity, distance, volume,

the trade balance, and port characteristics.

These basic variables are chosen because they have been shown to be

relevant in the previous research mentioned above. Some other variables,

such as being land-locked, that have proven to be significant in other

work, are not relevant for our group of countries. Again other variables that

have been included in previous research were excluded here because they did

not appear to have a significant impact; examples are the flag of the vessel or

whether the trading countries belong to the same political block. Manyother variables with an impact on transport costs, such as fuel prices or

vessel charter rates, are not relevant for our analysis because they vary over

time and do not depend on the chosen port or trade route. As has been

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common practice in the prevailing literature, the log was chosen for mostnon-binary variables; this has been shown to result in better econometric

fits, and it also allows for the interpretation of the results as elasticities.

In order to capture different types and values of the commodity, we include

different constants for different commodity groups, and we include the

USD value per ton of cargo. In order to capture the effect of distance, we include the distance in

kilometer between the two main ports of the importing and the exporting

country. In order to capture economies of scale we include the volume (tons) of

each individual transaction as well as the total volume of the contain-

erizable trade between the two countries. In order to capture the trade balance, we include the balance of

containerizable trade between the two countries. In order to capture port characteristics, we include six different indicators

of which port infrastructure, port efficiency, overall transport infra-

structure, and private sector participation are qualitative; and average

customs delay and port connectivity are quantitative indicators.

The data is described in more detail in Section 3. The resulting model is

given in Eq. (1).

FREIGHTi;j;c;kb0;c b1TONSk

b2 VALUEPERTONk

b3 DISTANCEij

b4 BILATERSLVOLUMEij

b5 BALENCEROUTEijb6 PORTINFRAib7PORTINFRAj

b8 PORTEFFICib9PORTEFFICj

b10 TRANSPORTINFRAi b11 TRANSPORTINEFRAj

b12 PORTPRIVATi b13 PORTPRIVATj

b14 CUSTOMSDELAYib15 CUSTOMSDELAYj

b16 PORTCNNECTij 1

y

where b0,c is the constant term, which is different for each commoditygroupc, TONSkthe total weight in tons of the individual trade transactionk

in natural logarithm, VALUEPERTONk the US dollar value of the

individual trade transaction k in natural logarithm, DISTANCEij the

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distance in kilometer between the main ports of country iand country j innatural logarithm, BILATERALVOLUMEij the total volume of contain-

erizable trade between country i and country j in natural logarithm,

BALANCEROUTEijthe coefficient of the imports of containerizable cargo

of country i from country jdivided by the exports of containerizable cargo

from country i to country j, PORTINFRAi an indicator for port

infrastructure in the importing country i, PORTINFRAj the equivalent

for the exporting country j, PORTINFRAij the log of the sum of the two

indicators, PORTEFFICi an indicator for port efficiency in the importing

country i, PORTEFFICj the equivalent for the exporting country j,PORTEFFICij the natural log of the sum of the two indicators,

TRANSPORTINFRAian indicator for general transport infrastructure in

the importing country i, TRANSPORTINFRAj the equivalent for the

exporting country j, PORTPRIVATi an indicator for successful advances

with private sector participation in the importing countrys main common

user port, PORTPRIVATjthe equivalent for the exporting countrys main

common user port, CUSTOMSDELAYi the average delay of customs

clearance in the importing country in natural logarithm, CUSTOMSDE-

LAYjthe equivalent for the exporting countryj, and PORTCONNECTijthemonthly frequency of direct liner services between the ports of countryiand

country jin natural logarithm.

3. DATA

3.1. Observations and Variables

After filtering out observations with incomplete or extreme data andselecting only commodity groups that are containerizable, the database

includesn 75,928 observations.

Each observation corresponds to a transaction k; hence there are 75,928

values for the variables FREIGHTk, TONSk, and VALUEPERTONk.

There are seven importing countries i which lead to seven different values

for PORTEFFICi, PORTPRIVATi, CUSTOMSDELAYi, PORTINFRAi,

and TRANSPORTINFRAi. There are 16 exporting countries j, which lead

to 16 different values for PORTEFFICj, PORTPRIVATj, CUSTOMSDE-

LAYj, PORTINFRAj, and TRANSPORTINFRAj. There are 7 times16 minus 7 pairs of countries (the 16 exporting countries include the 7

importing countries), which lead to 105 different values for DISTANCEij,

BILATERALVOLUMEij, BALANCEROUTEij, and PORTCONNECTij.

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3.2. The Dependent Variable FREIGHT

The international maritime transport costs are those recorded by the

customs authorities of seven Latin-American importing countries, as

reported in the International Trade Data Base (BTI), which is maintained

by the United Nations Economic Commission for Latin America and the

Caribbean (ECLAC).1 FREIGHT is the log of the maritime transport costs,

without insurance costs, of one trade transaction. For this chapter, we use

the data for all imports of containerizable cargo of Argentina, Brazil, Chile,

Colombia, Ecuador, Peru, and Uruguay coming from the exportingcountries of Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, El

Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay,

Peru, Uruguay, and Venezuela.

The BTI distinguishes between the country of origin, which is where the

good is made, and the country of departure, which is the country from

where the good is exported during this particular trade transaction. We use

the country of departure, which is of relevance for transport costs. The

country of origin would be of relevance if, for example, the level of

customs duties had to be determined.The BTI does not provide information on whether the cargo was actually

containerized or not. For the purposes of this chapter, we selected a group

of Standardized International Trade Classification (SITC) codes at the

three-digit level that are assumed to be in principle containerizable. Above

all, those commodities that are usually transported as liquid or dry bulk are

thus excluded from this research. See Annex C for the list of SITC codes

included in the regressions. See also Annex A QA :3for a description of the data

with respect to the number of observations, means, maximum and minimum

values, and the standard deviation.

3.3. Explanatory Variables

3.3.1. Type and Value of the Commodity

The type of commodity and its value per ton might, in theory, not be related

to the pure freight rate. It could be assumed that for a container-shipping

operator it is irrelevant what is inside the box, as it does not affect his costs,

and, hence, neither the freight rate nor the box-handling charge. In practice,however, traditional tariffs of liner-shipping companies do strongly

distinguish between different commodities. In particular, if goods are of

very high value, the freight may effectively be charged irrespective of weight

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and measurement on an ad valorem basis. Different charges are also appliedto less-than-container-loads (LCL) and full-container-loads (FCL). In

the former case, the rates are usually the same as those charged for non-

containerized shipments. For FCL containers, charges are either for

freight-all-kinds or the carrier may apply commodity box rates.

Apart from possibly applying different freight rates for different types of

commodities, there may also exist different price elasticities for different

commodities. In particular, for higher valued goods, a shipper is likely to be

prepared to pay a higher freight. It must also be noted that our data does

not distinguish between different types of services. Some shippers may bewilling to pay a premium for a direct fast service, whereas others might

choose to pay less, accepting perhaps a later delivery, transshipment or a less

reliable itinerary. A higher FREIGHT may in this case simply be an

indicator of a better service.

We attempt to capture the possible effect of different commodities and

unit values by, first, introducing different CONSTANTs for different

commodities, and second, by including the value per ton of cargo

VALUEPERTON as an explanatory variable in the regressions.

CONSTANT b0,c assumes different values for the different codes of theSITC system at the one-digit level. Goods belonging to SITC codes 3, 4, and

9 are excluded from our research, which does not cover bulk cargo. Hence,

we have six different constant terms, reflecting the SITC codes 1, 2, 5, 6, 7,

and 8.

The variable VALUEPERTON is the log of the value in USD per ton of

cargo. It is computed by the authors based on the customs declarations as

regards the FOB value and the weight of the traded goods. The mean value

per ton is USD 11,048, with a standard deviation of 51,870.

3.3.2. Distance

Ceteris paribus, freight increases with distance as it implies more fuel and

use of vessels and working hours. The variable DISTANCE is the log of the

maritime distance in kilometers. The mean distance is 4,874 km and the

standard deviation is 3,162. The source for the distances is Fairplay Ports

Guide and www.distances.com.

3.3.3. Economies of Scale

Maritime transport is a traditional prime example of economies of scale. Aships carrying power varies as the cube of her dimensions, while the

resistance offered by the water increases only a little faster than the square

of her dimensions (Marshall, 1890). Economies of scale can be found in

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ports as well as in shipping. We attempt to capture the effect of economiesof scale on the freight by including variables for the total volume of bilateral

trade between the two countries and the volume of the individual

transaction.

The variable TONS is the log of the volume of the individual trade

transaction for which the freight is being paid, as reported by the BTI. The

mean for the volume is 3.49 ton, with a standard deviation of 6.35.

The variable BILATERALVOLUME is the log of the total annual

containerizable trade between the two countries in 2002. It is calculated

from the BTI data. The mean total volume of the containerizable trade is9,58,587 metric tons, with a standard deviation of 1,570,857.

3.3.4. Trade Balance

If a ship or a container has to return empty from the importing country, the

freight paid for this import cargo will also have to bear the repositioning

costs. We attempt to capture this effect by including the trade balance of

containerizable goods between the two countries, based on the BTI data.

BALANCEROUTE is calculated by dividing the volume of imports of

countryifrom countryjby the volume of exports from country ito countryj. The mean value of the balance (imports/exports) is 9.27, with a standard

deviation of 25.68.

3.3.5. Port Characteristics

A ports efficiency, its private sector participation, delays at customs

clearance, the port infrastructure, the countrys general transport infra-

structure, and inter-port connectivity may possibly have an impact on the

international maritime transport costs. Inter-port connectivity, i.e. liner-

shipping services that connect two ports, itself will depend strongly on otherport characteristics that affect the services provided to the shipping lines, as

well as on trade volumes and inter-modal connections.

PORTINFRAi and PORTINFRAj are the indices for the perceived

quality of the importing and exporting countries port infrastructure in

2002. The means and standard deviations of the indices are 3.09 and 0.64,

respectively, for the importing, and 3.79 and 0.96 for the exporting country.

PORTINFRAijis the log of the sum of the two indices. The source for the

indices is the World Economic & Forum (2004).

PORTEFFICi and PORTEFFICj are the indices for the perceivedefficiency of the importing and exporting countries main ports. The mean

and standard deviation are 3.39 and 0.57 for the importing country, and

3.74 and 0.80 for exporting country, respectively. PORTEFFICij is the log

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of the sum of the two indicators. The source for the indices is the WorldEconomic Forum (2004).

TRANSPORTINFRAi and TRANSPORTINFRAj are the indices for

the perceived quality of the importing and exporting countries general

transport infrastructure. The means and standard deviations of the indices

are 3.28 and 0.70, and 3.71 and 0.49 for the importing and exporting

country, respectively. The source for the indices is the World Economic

Forum (2004).

PORTPRIVATi and PORTPRIVATj are the indices for the perceived

success of private sector participation in common user ports for theimporting country and for the exporting country, respectively. Data is taken

from Hoffmann (2001) and reflects the results of a poll among Latin-

American port specialists who attached values between 1 (very poor) and 10

(highly successful) for the introduction of private sector participation in the

main common user port of each of the 16 non-Caribbean Latin-American

countries in 2000. The highest index was computed for Panama (8.4) and the

lowest for El Salvador (1.9). The mean and standard deviation for

PORTPRIVATi and PORTPRIVATj are 4.79 and 1.81, and 6.25 and

1.75, respectively.CUSTOMSDELAYiand CUSTOMSDELAYjare the logs of the average

delay in customs clearance for the importing and the exporting country. The

mean customs delay for the importing country is 6.08 days, with a standard

deviation of 1.63. The mean customs delay for the exporting country is 6.56

days, with a standard deviation of 2.70. Note that the delay refers to

clearance of imports; it is included here also for the exporting country as a

possible indicator of the efficiency of customs as regards export procedures.

The source of the data is the World Bank (2004).

PORTCONNECTij is the log of the number of direct liner services permonth between the two countries ports, i.e. an indicator of inter-port

connectivity.2 The mean of the number of services per month is 68.15, with a

standard deviation of 103.11. Source for the data is Containerization

International on-line, July 2002. Inter-port connectivity is not so much a

characteristic of a single port, but rather an indicator for the level of

services, and possibly liner-shipping competition, between a pair of ports.

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4. EMPIRICAL RESULTS

4.1. The Basic Model

In the basic model, we introduce five variables considered to reflect the

major determinants of international maritime transport costs, i.e. TONS,

VALUEPERTON, DISTANCE, BILATERALVOLUME, and BAL-

ANCEROUTE. We differentiate between three groups of cargo. Models 1

and 4 include all goods that are considered containerizable; Models 2 and

5 include only those goods with a medium to high likelihood ofcontainerization; and Models 3 and 6 only those with a high likelihood of

containerization (see Annex C for the list of SITC codes). The observations

included in Model 2 are thus a sub-set of those included in Model 1; and the

observations included in Model 3 are a sub-set of those included in Model 2.

We further differentiate between the case where different constants are

included for the main commodity groups by one-digit SITC code (Models 1,

2, and 3), and the case where one single constant is included (Models 4, 5,

and 6). The results are presented in Table 1.

For all six models, the estimated parameters have the expected signs andare statistically significant at the 95% level (except for BILATERALVO-

LUME in Model 3). The estimated parameter values for TONS,

VALUEPERTON, and DISTANCE are very stable, with double-digit t-

values. The estimated parameter values for BILATERALVOLUME and

BALANCEROUTE are slightly less stable, with single-digit t-values.

All six models can be considered adequate to serve as a basic model, upon

which to build and expand the analysis to include additional variables. We

chose to continue with Model 1 for two reasons. First, it provides for a

larger number of observations and a larger variance among the explanatoryvariables. Second, it allows for different constants for different commodity

groups, given that different commodities are often traded by different

countries and on different routes. AnF-test confirms the hypothesis that the

CONSTANTSITC are the same has to be rejected. In any case, most of the

subsequent results have been tested against the other models, and no

significant change occurred as regards the sign and magnitude of the

estimated parameters.

In Table 2, we present further empirical results, based on Model 1, i.e.

including all containerizable cargo, and allowing for different constants fordifferent SITC code commodities. Given that a high correlation exists

between most of the variables that aim to measure different port

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Table1.

RegressionResults,BasicModel.

Variable

Model1

Model2

Model3

Model4

Model5

Model6

Observations

N

75,9

28.

All

Containerizable

Cargoes

N

73,536.

Medium+High

Containerization

N

72,319.Only

Hig

h

Containerization

N

75,9

28.

All

Containerizable

Cargoes

N

73536.

M

edium+High

C

ontainerization

N

72319.On

ly

High

Containerizatio

n

CONSTAN

T

0.5317

0.5260

0.5121

CONSTAN

TSITC1

0.7

849

0.8

522

0.8530

CONSTAN

TSITC2

0.6

389

0.6

179

0.6143

CONSTAN

TSITC5

0.7

020

0.7

046

0.6968

CONSTAN

TSITC6

0.6

196

0.6

144

0.5926

CONSTAN

TSITC7

0.4

815

0.4

835

0.4725

CONSTAN

TSITC8

0.4

710

0.4

708

0.4589

TONS

k

0.0847(56.71)

0.0837(55.2

2)

0.0823(53.8

5)

0.0

948(64.7

6)

0.0942(63.30)

0.0933(62.2

5)

VALUEPERTON

k

0.3408(128.38)

0.3392(125.7

5)

0.3362(123.49)

0.3

586(139.75)

0.3

563(135.84)

0.3

533(133.42)

DISTANCE

ij

0.3716(97.6

7)

0.3710(96.08)

0.3700(95.1

8)

0.3

623(94.9

6)

0.3

618(93.3

8)

0.3

609(92.49

)

BILATERA

LVOLUME

ij

0.0065(3.3

1)

0.0048(2.45)

0.0029(1.47)

0.0

161(8.38)

0.0145(7.4

6)

0.0128(6.51

)

BALANCE

ROUTE

ij

0.00049(4.3

8)

0.00042(3.6

5)

0.00040

(3.4

7)

0.0

0069(6.1

5)

0.0

0063(5.4

7)

0.0

0062(5.33

)

AdjustedR

2

0.431

0.424

0.417

0.4

23

0.4

14

0.4

08

F

5760

5403

517

6

11132

10409

9955

Note:t-Va

luesinparentheses.



17/28

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35

37

39

Table2.

RegressionResults,Expanded

ModelwithPortCharacteristics.

Variable

Model7

Model8

Model9

Model10

Model11

Model12

Model13

Observations

N

75,928

N

75,928

N

75,9

28

N

75,9

28

N

75,9

28

N

35,4

38

N

73,818

TONS

k

0.0

863(57.65)0.0

863(57.6

7)0.0

869(58.1

1)

0.0846(56.51)0.0874(58.85

)0.0

632(29.1

5)0.0

857(57.0

0)

VALUEPER

TON

k

0.3

422(128.74)

0.3

416(128.82)

0.3

416(128.94)

0.3408(128.3

8)

0.3374(127.73

)

0.4

665(113.1

9)

0.3

447(129.16)

DISTANCE

ij

0.3

716(95.8

0)

0.3

698(97.2

6)

0.3

542(90.3

1)

0.3716(92.47)

0.3890(96.81)

0.3

380(55.36)

0.1

769(30.28)

BILATERALVOLUMEij

0.0

100(4.4

6)

0.0

109(5.53)

0.0

161(7.97)

0.0075(3.3

1)

0.0322(13.70

)0.0

794(23.7

4)

0.0

256(10.91)

BALANCER

OUTE

ij

0.0

0020(1.7

3)

0.0

0027(2.4

0)

0.0

0047(4.2

5)

0.00051(4.3

1)

0.00022(1.80

)

0.0

0082(5.0

6)

0.0

0228(14.31)

PORTINFR

Ai

0.0

333(9.9

2)

PORTINFR

Aj

0.0

497(10.76)

PORTINFR

Aij

0.2

444(13.5

1)

PORTEFIC

ij

0.3

835(17.6

5)

0.3

786(17.0

3)

TRANSPOR

TINFRA

i

0.0056(1.1

9)

TRANSPOR

TINFRA

j

0.0011(0.1

9)

PORTPRIVAT

i

0.0038(2.0

0)

PORTPRIVAT

j

0.0562(32.00

)

CUSTOMSD

ELAY

i

0.0

512(4.3

2)

CUSTOMSD

ELAY

j

0.0

074(0.8

0)

PORTCONN

ECT

ij

0.1

129(32.6

0)

AdjustedR2

0.4

33

0.4

33

0.4

34

0.432

0.439

0.5

01

0.4

45

F

4832

5265

5286

5160

4953

2971

4933

Notes:t-Valuesinparentheses.

Constants

notreported.

ThenumberofobservationsforModel12issm

aller,becauseinformationabout

averagecustomsdelayswasnotavailableforallcountriesinthesample.



18/28

characteristics, we present only the results with one or two port variablesincluded at a time.

5. INTERPRETATION OF RESULTS

5.1. The Base Model

5.1.1. Type and Value of the Commodity

Despite looking only at containerizable cargo, different types and values ofcommodities continue to lead to a significant variation of freight rates. The

estimated elasticity for VALUEPERTON is 0.34 (Model 1), i.e. a 1 per cent

increase in the unit value of the goods leads to an increase of 0.34 per cent in

the freight charged. Given the high variance of this variable, the overall

impact on the variance of the freight is the highest among all variables taken

into account in our model. An increase in the value per ton by 469 per cent

(this is equivalent to the standard deviation divided by the mean) leads to an

increase of the freight per ton by 80.91 per cent. Or, to take a simpler

example, doubling the unit value leads to an increase in the freight chargedof 26.6 per cent. Note that our data does not include payments for insurance

by the shipper.

5.1.2. Distance

The estimated elasticity for DISTANCE coincides with the results of other

research. An increase of the distance by 1 per cent leads to an increase of the

freight by 0.37 per cent. Although this is a high elasticity if compared to

other variables, it is actually quite low if compared to the traditional

assumption made in classical gravity trade models that distance could beused as a proxy for transport costs. Distance is certainly not proportional to

transport costs. Doubling the distance does not double the freight, but leads

to an increase of just 29.4 per cent, and an increase of the distance of 65 per

cent (i.e. the standard deviation in our sample) increases the freight by only

20.4 per cent (Model 1).

5.1.3. Economies of Scale

The elasticity for TONS is 0.0847, i.e. an increase in the volume of a

transaction of 1 per cent leads to a reduction of the freight by 0.0847 percent. Although this may not seem high, it makes an important contribution

to the variation of FREIGHT, because TONS itself has a high variance. If,

by way of example, we ship 1,000 ton from country jto country iwith one

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single transaction, instead of shipping the same goods with 10 shipments of100 ton each, we will, on average, achieve a saving of 8.04 per cent on the

international maritime transport costs (Model 1).

The impact of BILATERALVOLUME has the expected negative sign,

and is statistically significant in most regressions. The estimated parameter

value is quite low, however. An increase of the bilateral containerizable

trade of 1 per cent leads to a reduction of the freight charges by only 0.0065

per cent (Model 1). If, by way of example, two countries have bilateral trade

of 10 million tons instead of 1 million tons, the FREIGHT (per ton) for this

bilateral trade will be 1.5 per cent lower.As regards the specification of FREIGHT as the dependent variable, and

the volume of trade as an explanatory variable, it could be argued that trade

volumes are also being explained by transport costs. FREIGHT is basically

a price, which depends on supply and demand, and it would have to be

estimated by using, for example, instrumental variables. However, for our

case, on a given route, the total volume of bilateral trade per year can be

assumed to be given for a transport service provider, who adjusts their

price for a given transaction at short notice in view of costs and the market

environment. In fact, we believe that the effect of economies of scale onfreight rates may be stronger than the effect of lower transport costs on

trade volumes. The elasticities for transport costs as a determinant of trade

volumes as estimated, for example, by Limao and Venables (2000) may be

too high. The dynamic relation between transport costs and trade volumes

will require further research.

5.1.4. Trade Balance

BALANCEROUTE has the expected positive sign, i.e. if a country imports

more than what it exports, the FREIGHT for the imports will go up. Eachincrease of the coefficient imports/exports by one point will lead to an

increase in the freight costs by 0.00049 per cent. If a country iimports twice

as much from country jas it exports to country j, its freight will go up by

0.034 per cent (Model 1). Although the parameter is statistically significant

in all models, and has the expected sign, the estimated parameter value is far

too low and does not reflect the real impact of trade imbalances on freight

rates. As any container-shipping company knows, on many major liner-

shipping routes, the freight rates in one direction may be twice as high as in

the other direction, and one main reason for the difference is the unbalancedtrade.

The variables BILATERALTRADE and BALANCEROUTE are both

computed only for the trade between countries iand j. This appears to be

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inadequate to capture the effect of economies of scale and trade imbalances,both of which need to be applied to broader trade routes. By way of

example, the bilateral trade volumes and the trade imbalances between

Guatemala and Chile have only a minor effect on the freight rates between

these two countries. What really matters is the total trade volume along the

South American Pacific coast and South Americas trade balance with

North America and Asia. Future research will have to attempt to capture

the broader trade volumes and imbalances.

5.2. Expanded Models Incorporating Port Characteristics

5.2.1. Port Infrastructure

The index PORTINFRAi for the importing countrys port infrastructure

has a negative impact on FREIGHT, i.e. it leads to a reduction of transport

costs. If an importing country with the lowest index of the sample (2.3)

could improve its port infrastructure to the level of the best index of the

sample (4.6), it would be expected to reduce the maritime transport costs for

its imports by 7.4 per cent (Model 7).As regards the index PORTINFRAj for the exporting countrys port

infrastructure, the impact on FREIGHT is larger than for the importing

countrys port infrastructure. PORTINFRAjhas a larger variation, and the

value of the estimated parameter is higher. If a country with the worst index

(1.4) could improve its port infrastructure to the level of the best index (5.4),

it would be expected to reduce the maritime transport costs for its exports

by 18 per cent (Model 7).

In a different approach of including port infrastructure into our model,

we generated the log of the sum of the two indices for the importing andexporting countries PORTINFRAij. This allows for an easier interpretation

of the estimated elasticity, i.e. a 1 per cent increase of the combined port

infrastructure index leads to a reduction of the freight by 0.24 per cent

(Model 8). If the two countries of the sample with the worst port

infrastructure improved theirs to the level of the two countries with the best

port infrastructure, the maritime transport costs on the route between them

would be expected to decrease by 21.6 per cent.

5.2.2. Port EfficiencyThe combined port efficiency of the importing and exporting countries

ports PORTEFFICij has the highest estimated elasticity of all variables

included in our regressions. Increasing the indicator for port efficiency by 1

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per cent reduces freight charges by 0.38 per cent (Model 9). If the twocountries of the sample with the lowest port efficiency improved their

efficiency to the level of the two countries of the sample with the highest

indices, the freight charges on the route between them would be expected to

decrease by 25.9 per cent.

5.2.3. General Transport Infrastructure

The general transport infrastructure of a country has practically no bearing

on the international maritime freight (Model 10). The estimated parameter

values for TRANSPORTINFRAi and TRANSPORTINFRAjare statisti-cally not significant. This does not mean that general transport infra-

structure would not be relevant for overall trade efficiency, just that it has

no effect on the international maritime portion of the trade costs.

5.2.4. Port Privatization

The private sector participation in the main container ports of the countries

of the sample, as measured by an index derived from a poll taken in 2000,

leads to somewhat ambiguous results. The impact of PORTPRIVATi for

the importing country is very small, and positive, i.e., it leads to a minor

increase of the freight (Model 11). The difference between the best and the

worst case of our sample leads to a difference in the freight of less than 2 per

cent.

For the private sector participation on the exporting countrys side,

PORTPRIVATj, the impact is far stronger. The difference between the best

and the worst case of our sample leads to a difference in the freight of 30.6

per cent (Model 11). In other words, if the country with the lowest indicator

had advanced as much as the country with the highest indicator, the

maritime freights for its exports would be expected to be 30.6 per cent lower.

5.2.5. Customs Delay

The delay of cargo during customs procedures has a minor, positive, impact

on freight. On the importing countrys side, CUSTOMSDELAYi, a 1 per

cent reduction of the time it takes to clear customs implies a reduction of the

maritime freight of 0.051 per cent (Model 12). For the exporting country,

CUSTOMSDELAYjis statistically not significant.

The speed of customs operations may be correlated to other aspects ofport efficiency and thus just be an indicator of the latter. It may also be that

carriers charge higher freights if their containers are expected to spend more

time in the importing country due to delayed customs clearance.

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5.2.6. Inter-Port ConnectivityIncreasing the frequency of liner services between a pair of ports by 1 per

cent leads to a reduction of freight by 0.113 per cent (Model 13). Given the

high variability of this variable, the impact on the freight is quite large. If

two ports increase their connectivity by 150 per cent (i.e. the standard

deviation in our sample), the freight between them can be expected to go

down by almost 10 per cent.

PORTCONNECT is closely correlated with BILATERALVOLUME;

ships follow the cargo. In fact, the estimated parameter for BILATER-

ALVOLUME in Model 13 becomes positive, suggesting a differentinterpretation of the parameters. The number of liner services per ton of

cargo could be interpreted as an indicator of competition as shown in Eq.

(2).

LINERCOMPETITION PORTCONNECTBILATERALVOLUME

(2)

Note that the variables are defined as logarithms, i.e. LINERCOMPETI-

TION is the logarithm of the coefficient (number of liner services)/(total

bilateral trade volume). Hence, a reformulation of Model 13, wherePORTCONNECT is replaced with LINERCOMPETITION would yield

the estimated parameters 0.1129 for LINERCOMPETITION and 0.0873

for BILATERALVOLUME (0.08730.02560.1129). The interpretation

of these parameters would be as follows: A 1 per cent increase in the level of

competition between liner services (number of services per ton of cargo)

leads to a decrease of freight by 0.1129 per cent. At the same time, an

increase in the volume of bilateral trade leads to a decrease of freight by

0.0873 per cent (Model 13).

6. DISCUSSION AND CONCLUSIONS

A more efficient port does not necessarily need to be less expensive. On the

contrary, it may charge higher prices to the shipper and the carrier if it

provides faster and more reliable services, or if it allows the shipper or the

carrier to achieve savings elsewhere. Installing ship-to-shore gantries, for

example, may well lead to higher port charges to the shipping line. The linemay still achieve an overall saving, because its ships spends less time in the

port, or because it can change from geared to gearless vessels. This, in turn,

will also lead to lower freight rates.

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The empirical results of our research suggest that this is effectively thecase. We do not know if port improvements lead to lower freights because of

lower port costs charged to the carrier, better services provided to the

carrier, or both. What is clear, however, is that there is a clear measurable

impact on international maritime transport costs. Increases in port

infrastructure and private sector participation, too, lead to reduced

maritime transport costs. Inter-port connectivity, too, reduces transport

costs, most likely because it allows for economies of scale, and also more

competition among carriers. The elasticity for port efficiency is higher than

the elasticity for distance; in fact, it is the highest of all the variables includedin our research. Unlike distance, port efficiency can be influenced by policy

makers. Doubling port efficiency at both ends has the same effect on

international maritime transport costs as would a move of the two ports

50 per cent closer to each other, i.e. reducing the distance between them by

half.

Port improvements appear to have a stronger impact on the maritime

freight of a countrys exports than on the freight of its imports. The

exception is average customs delay, which as might be expected has a

stronger bearing on the maritime freight charged on imports. The generalland transport infrastructure has as expected no significant bearing on

maritime transport costs.

Our models explain between around 40 and 50 per cent of the variance of

FREIGHT. The remaining part of the variance may partly be due to the

fluctuations of freight rates throughout a year (see also Stopford, 2002;

Sanchez, 2004; Hoffmann, 2005). The BTI does not tell us in which month a

transaction took place and the aspect of time could thus not be incorporated

into our model. It also appears that additional or different measures to

cover economies of scale as well as trade imbalances might further improvethe regression fit. Finally, the R2 can be improved significantly if regressions

are undertaken for individual commodity groups, reaching values of up to

0.8. The main results regarding port characteristics as presented in this

chapter, however, remain unchanged.

The overall impact of port efficiency on trade costs goes beyond the

measurable impact on international maritime transport costs. Almost all

trade uses more than one mode of transport, and not all port costs are

charged to the maritime transport operator. Some port costs may be

charged to the trader prior to determining the goods FOB value, and othersmay be charged to the trader after the CIF value has been determined and

declared to customs. In addition, port improvements will not only lead to

lower freight rates, but by providing better services ports can also attract

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additional liner services and additional cargo. Both more liner services andhigher cargo volumes lead to a further reduction of freight rates. Lower

transport costs, in turn, will stimulate increased trade volumes, which lead

to further economies of scale and lower freight charges. These dynamic

effects of port improvements will thus lead to further reductions of transport

costs that go beyond those measured by our research.

The international leg of most international trade transactions continues to

be maritime, and most determinants of international maritime transport

costs are beyond the control of policy makers. It is through improvements in

the ports that cost savings and increased trade competitiveness can beachieved.

NOTES

1. The International Transport Data Base (BTI, Base de datos de TransporteInternacional) was created by the United Nations Economic Commission for LatinAmerica and the Caribbean (ECLAC) in 2000 in order to facilitate research in theareas of trade and international transport. It was the result of a research project

(Fuchsluger, 1999) and is described in more detail in Hoffmann, Pe rez, andWilmsmeier (2002). It contains trade data for 11 Latin American countries. Inaddition to the typical trade data that is commonly published for example byCOMTRADE (http://unstats.un.org/unsd/comtrade/), the BTI includes, inter alia,information about the mode of transport, the country of departure, the freight, andthe insurance paid for international transport. Further information is available onthe ECLAC Maritime Profile www.eclac.cl/transporte/perfil/bti.asp.

2. Given that between some pairs of countries there are no direct liner services, weadded one to all observations. This avoids the problem of having to take logarithmof zero values, and it can be justified to represent the option of using an indirectservice, with transshipment.

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ANNEX A. DESCRIPTION OF DATA

Variable Obs. Mean Std. Dev. Min. Max.

BALANCEROUTEij 75953 9.272659 25.68117 0.0000787 518.24

BILATERALVOLUMEij 75953 12.75741 1.478283 6.536532 15.52339

CUSTOMSDELAYi 35518 1.755342 0.3437897 1.098612 1.94591

CUSTOMSDELAYj 75857 1.798404 0.410935 1.098612 2.70805

DISTANCEij 75954 8.23266 0.7561608 5.47 9.39FREIGHT 75929 5.282098 1.008404 0.97 9.98

PORTCONNECTij 73843 3.363312 1.433481 0.3074847 6.089476

PORTEFFICi 75954 3.390797 0.5738745 2.5 4.3

PORTEFFICj 75954 3.737947 0.8003441 1.8 5

PORTEFFICij 75954 1.950333 0.1398182 1.45 2.23

PORTINFRAi 75954 3.090664 0.6368681 2.3 4.6

PORTINFRAj 75954 1.301688 0.2498696 0.3364722 1.686399

PORTINFRAij 75954 1.916256 0.1604536 1.308333 2.302585

PORTPRIVATi 75954 4.786635 1.813817 2.7 7.5

PORTPRIVATj 75954 6.249979 1.750989 1.9 8.4TONS 75954 0.5805068 2.269712 5.3 3.6

TRANSPORTINFRAi 75954 3.284014 0.7008314 2.5 4.8

VALUEPERTON 75954 8.297586 1.289257 3.066657 15.14848

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1

3

5

7

9

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13

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17

19

21

23

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27

29

31

33

35

37

39

ANNEX

B.PARTIALCOR

RELATIONCOEFF

ICIENTSBETWEE

NTHEVARIABLE

S

FREIGH

T

TONS

VALUE-

PERTON

DISTANCE

BILA

TERAL-

VO

LUME

BALANCE-

ROUTE

PORT-

INFRAi

PORT-

INFR

Aj

PORT-

EFFICi

PORTE-

FFICj

PORTE-

FFICij

TRAN-

SPORT

-

INFj

PORT-

PRIVATi

PORT-

PRIVATj

CUSTOMS-

DELAYi

CU

STOMS-

D

ELAYj

PORT-

CONNECTij

FREIGHT

1

0.53

0.64

0.34

0.26

0.13

0.10

0.0

7

0.1

7

0.09

0.10

0.08

0.12

0.15

0.04

0.0

5

0.37

TONS

0.53

1

0.58

0.02

0.08

0.03

0.02

0.0

7

0.03

0.0

7

0.0

6

0.02

0.01

0.08

0.02

0.0

5

0.0

4

VALUEPERTON

0.64

0.58

1

0.08

0.02

0.0

2

0.04

0.0

0

0.04

0.0

0

0.0

2

0.03

0.01

0.07

0.03

0.01

0.0

7

DISTANCE

0.34

0.0

2

0.08

1

0.38

0.3

1

0.03

0.0

3

0.19

0.01

0.1

3

0.00

0.0

9

0.29

0.1

8

0.06

0.76

BILATERALVOLUME

0.26

0.08

0.0

2

0.3

8

1

0.20

0.3

2

0.2

9

0.49

0.39

0.23

0.09

0.39

0.3

0

0.0

4

0.34

0.59

BALANCEROUTE

0.13

0.03

0.0

2

0.3

1

0.20

1

0.2

9

0.0

8

0.33

0.29

0.11

0.25

0.06

0.1

9

0.24

0.06

0.41

PORTINFRAi

0.10

0.02

0.04

0.03

0.34

0.29

1

0.1

7

0.8

8

0.16

0.79

0.99

0.35

0.01

0.84

0.0

5

0.17

PORTINFRA

j

0.07

0.07

0.00

0.03

0.29

0.08

0.17

1

0.1

3

0.8

9

0.41

0.18

0.13

0.43

0.14

0.4

1

0.00

PORTEFFIC

i

0.17

0.03

0.04

0.19

0.49

0.33

0.88

0.1

3

1

0.13

0.81

0.18

0.26

0.06

0.56

0.1

3

0.41

PORTEFFIC

j

0.09

0.07

0.00

0.01

0.39

0.29

0.16

0.8

9

0.1

3

1

0.47

0.60

0.15

0.41

0.11

0.3

6

0.11

PORTEFFICij

0.10

0.06

0.04

0.15

0.23

0.11

0.69

0.4

1

0.8

1

0.47

1

0.22

0.14

0.19

0.43

0.1

1

0.30

TRANSPORTINFj

0.08

0.02

0.02

0.13

0.09

0.15

0.25

0.7

9

0.1

8

0.6

0

0.22

1

0.06

0.33

0.27

0.3

1

0.01

PORTPRIVAT

i

0.12

0.01

0.01

0.09

0.39

0.06

0.35

0.1

3

0.2

6

0.15

0.14

0.06

1

0.11

0.02

0.09

0.16

PORTPRIVATj

0.15

0.08

0.07

0.29

0.30

0.19

0.01

0.4

3

0.0

6

0.4

1

0.19

0.33

0.11

1

0.08

0.7

1

0.15

CUSTOMSDELAY

i

0.04

0.02

0.03

0.18

0.04

0.24

0.84

0.1

4

0.5

6

0.1

1

0.43

0.27

0.02

0.08

1

0.0

6

0.13

CUSTOMSDELAYj

0.05

0.05

0.01

0.06

0.34

0.06

0.05

0.4

1

0.1

3

0.36

0.11

0.31

0.09

0.71

0.06

1

0.06

PORTCONNECTij

0.38

0.04

0.07

0.76

0.59

0.41

0.17

0.0

0

0.4

1

0.11

0.30

0.01

0.15

0.15

0.13

0.06

1


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ANNEX C. LIST OF SITC CODES INCLUDED IN THEDATA

The following commodities as defined by the United Nations Standard

International Trade Classification, Revision 3, code (SITC rev. 3) are

included in the empirical research. See http://unstats.un.org/unsd/cr/

registry/regcst.asp?Cl=14 for more details on the individual codes.

High probability of containerizations. 111, 112, 12, 12, 121, 122, 16, 17, 212,

22, 261, 263, 264, 266, 267, 268, 289, 35, 37, 48, 515, 525, 531, 532, 533, 541,

542, 551, 553, 554, 56, 57, 571, 572, 573, 574, 575, 58, 581, 582, 583, 59, 593,

597, 598, 611, 612, 613, 62, 621, 625, 629, 633, 64, 641, 642, 651, 652, 653,

654, 655, 656, 657, 658, 659, 664, 665, 666, 667, 681, 683, 684, 685, 686, 687,

689, 694, 695, 696, 697, 733, 735, 737, 74, 74, 741, 742, 743, 744, 745, 746,

747, 748, 749, 75, 751, 752, 759, 76, 761, 762, 763, 764, 77, 771, 772, 773, 774,

775, 776, 778, 784, 785, 811, 812, 813, 821, 831, 841, 842, 843, 844, 845, 846,

848, 851, 871, 872, 873, 874, 881, 882, 883, 884, 885, 891, 892, 893, 894, 895,896, 897, 898, 899, 98.

Medium probability of containerization. 211, 222, 223, 231, 232, 244, 245,

265, 269, 277, 284, 285, 286, 287, 288, 291, 292, 42, 431, 46, 47, 512, 513, 514,

516, 522, 523, 524, 591, 592, 634, 635, 663, 675, 676, 678, 679, 692, 699, 711,

712, 713, 714, 716, 718, 72, 721, 723, 724, 725, 726, 727, 728.

Low probability of containerization. 511, 579, 662, 671, 672, 673, 674, 677,

691, 693, 722, 731, 791, 792, 793.

All other SITC codes are considered not to be containerizable and are

excluded from the regressions.

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the impact of port characteristics on international transport costs - wilmsmeier - hoffmann and...

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