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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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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
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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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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.
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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.
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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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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
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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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