WVi 5a. 7?
POLICY RESEARCH WORKING PAPER                                       2781
Maritime Transport Costs                                            Transport costs have
increased their relative
and      Port      Efficiency                                       importance as a barrier to
trade. Distance,
c :r,'lr-,err:><icin, volume
Ximena Clark
David Dollar                                                        exported, and, most
AlejavidrDolr Mrimportant, the level of seaport
Ale jandro Micco                                                    efficiency, are important
determinants of transport
costs, Port ef-,c:IenY is not
only associated with
infrastructure, but also with
the existence of organized
crime and excessive
regulation.
The World Bank
Development Research Group
Macroeconomics and Growth
February 2002



POLiCy RESEARCH WORKING, PAPER 2781
Summary findings
Recent literature has emphasized the importance of              They find that the efficiency of ports is also important.
transport costs and infrastructure in explaining trade,       Improving the efficiency of a port from the 25th to the
access to markets, and increases in per capita income.        75th percentile reduces shipping costs by 12 percent.
For most Latin American countries transport costs are a       (On average, having bad ports is equivalent to being 60
greater barrier to U.S. markets than import tariffs.          percent farther away from markets.) Inefficient ports also
Clark, Dollar, and Micco investigate the determinants      increase handling costs, which are part of shipping costs.
of the costs of shipping to the United States using a large     Finally, the authors try to explain variations in port
database (more than 300,000 observations a year) on           efficiency. They find that the variations are linked to
shipments of products at the six-digit level of the           excessive regulation, the prevalence of organized crime,
Harmonized System of classification from different ports      and the general condition of the country's infrastructure.
around the world. They find that distance and
containerization matter.
This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the
group to understand the link between competitiveness and transport costs. Copies of the paper are available free from the
World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Emily Khine, room MC3-347, telephone 202-
473-747 1, fax 202-522-3518, email address kkhineCa worldbank.org. Policy Research Working Papers are also posted on
the Web at http://econ.worldbank.org. The authors may be contacted at xclarkQa worldbank.org, ddollara wvorldbank.org,
or alejandromia iadb.org. February 2002. (37 pages)
The Policy Research Wzorking Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings ouit quickly, eveni if the presentations are less than fiuly polished. The
papers carry the namnes of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirelv those of the auithors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



Maritime Transport Costs and Port Efficiency
By Ximena Clark, David Dollar, and Alejandro Micco*
JEL number: [F1, L41, L92]
Keywords: maritime transport costs, port efficiency
* World Bank; World Bank; and Inter-American Development Bank, respectively.






I. Introduction
Since the beginning of modem economics the literature concerning the
determination of living standards has been interested in trade.] Despite some lingering
controversy, empirical studies show a positive relationship between trade and growth.
Frankel and Romer (1999) claim that "...trade has a quantitatively large, significant, and
robust positive effect on income."2
The lack of initial consensus among researchers on the relationship between trade
and growth has been mirrored by differences in the actual trade strategies of developing
countries. During the 1960s and into the 1970s, many countries adopted import
substitution policies to protect their infant industries, though a few economies in East
Asia took a different approach. By the 1990s many developing countries, including most
of the large ones, had shifted to an outward-oriented strategy and had seen accelerations
in their growth rates.3
These recent liberalizations have reduced tariff and, in some cases, non-tariff
barriers too. For instance, Asia reduced its average tariff rate from 30% at the beginning
of the 1980s to 14% by the end of the 1990s, and Latin America reduced its average tariff
rate from 31% to 11%.4 These reductions in artificial trade barriers have implied that the
relative importance of transport costs as a determinant of trade has increased.5 As shown
in Figure 1, in 1997 total import freight costs represented 5.25 percent of world imports.
This percentage -which may seem low- is mainly driven by developed countries, which
represent more than 70 percent of world imports and whose proximity to each other is
reflected in a relatively low freight cost (4.2%). When disaggregating these costs by
region, they turn out to be substantially higher. Although Latin America appears to have
low freight costs relative to the other developing regions (7% compared to 8% for Asia
Adams Smith (The Wealth of Nations, 1776), in his discussion of specialization and the extent of the
market stresses the relationship between wealth and trade between nations.
2 Similarly, Ades and Glaeser (1999) find that openness accelerates growth of backward economies. For a
skeptical view of the cross-national evidence, see Rodriguez and Rodrik (1999).
3 See Dollar and Kraay (2001).
4 Central America and the Caribbean reduced its average tariff rate from 21% to 9% between these periods,
and African countries from 30% to 20%. These average tariff rates correspond to simple averages across
countries of their unweighted tariff. If we consider weighted tariffs, the resulting average tariff rates by the
end of the 90s should be smaller. (Source: World Bank)
1



and 11.5% for Africa), the Latin American figure is weighted by Mexico's proximity to
its main trading partner, the United States, and consequently low freight costs. When
Mexico is excluded, Latin American average freight costs rise to 8.3 percent, more
similar to the rest of the developing countries.
As liberalization continues to reduce artificial barriers, the effective rate of
protection provided by transport costs is now in many cases higher than the one provided
by tariffs. Figure 2 presents a comparison of average tariffs and a measure of transport
costs for various countries around the world, and Figure 3 presents an alternative
comparison of transport costs to the US and average tariffs faced in the US market by a
group of Latin American countries. From Figure 3, it is striking to realize that for some
countries, such as Chile and Ecuador, transport costs exceed by more than twenty times
the average tariffs they face in the US market. Consequently, any additional effort to
integrate a country into the trading system should consider and analyze the effect of
transport costs and its determinants.
As a result, some immediate questions arise. How much do these transport costs
affect trade and growth? How much of these costs can be affected by government
policies? The broad literature that applies the gravity approach to the study of
international bilateral trade shows that geographical distance, which is used as proxy for
transport costs, is negatively related to trade.6 In a recent paper, Limao and Venables
(2000, henceforth LV) show that raising transport costs by 10 percent reduces trade
volumes by more than 20 percent. They also show that poor infrastructure accounts for
more than 40% of predicted transport costs. In a different analysis, Radelet and Sachs
(1998) show that shipping costs reduce the rate of growth of both manufactured exports
and GDP per capita. These authors claim that "... doubling the shipping cost (e.g. from an
8% to 16% CIF band) is associated with slower annual growth of slightly more than-half
of one percentage point."
In spite of the relevance of transport costs for trade and growth, there are not
many other studies on transport costs. Moreover, these few studies rely on macro level
data, which is certainly useful but misses the advantages that microdata can have. An
5 See Amjadi and Yeats (1995) and Radelet and Sachs (1998).
6An example of this literature is Bergstrand (1985).
2



exception is a recent study of Fink, Mattoo and Neagu (2000, henceforth FMN), which
analyzes the determinants of maritime transport costs in 1998, focusing on the effect of
non-competitive public and private policies. They find the latter have a significant effect
on transport costs. But, what about other factors influencing transport costs, such as port
efficiency? There is a wide consensus on the crucial importance of port activities for the
transport services. However, there are no measures of how important are inefficiencies at
port level for transport costs. This is one of the objectives of this study. We analyze the
effect of port efficiency on transport costs (in addition to other standard variables), and
then we explore the factors that lie behind port efficiency.
Our analysis departs from FMN (2000) by incorporating port efficiency variables
and by redefining some variables. In addition, we address the problems of endogeneity
and omitted variable bias that their estimations present, and we also extend backward the
period of analysis to 1995. We find that a 100 percent increase in distance raises maritime
transport costs by around 20 percent, a result that is quite consistent with the existent
literature. With respect to port efficiency, we find that improving port efficiency from the
25th to 75th percentiles reduces shipping costs by more than 12%. This result is robust to
different definitions of port efficiency as well as to different years.
In turn, when looking at the determinants of port efficiency, we find that the level
of infrastructure and organized crime exert a significant positive and negative influence
respectively. In addition, policy variables reflecting regulations at seaports affect port
efficiency in a non-linear way. This result suggests that having some level of regulation
increases port efficiency, but an excess of regulation could start to reverse these gains.
The remainder of this paper is structured as follows. Section II presents a
description of factors that may be behind transport costs. Section III describes the
econometric model used to quantify the relative importance of these factors affecting
transport costs. It also contains a description of the data used as well as the results of our
analyses. In Section IV, we analyze how important are infrastructure, regulation and
organized crime in explaining port efficiency. Section V concludes.
3



II.    What Factors Explain Maritime Transport Costs?
As shown, transport costs may be an important barrier to trade and could have an
important effect on income. But why do some countries have higher transport costs than
others? What are the main determinants of these transport costs? Can government
policies affect these costs? Following some previous studies,7 this section addresses these
questions, based on a qualitative and quantitative description of transport cost
determinants. Given its relative importance (and also the availability of data), the main
focus in this paper is on international maritime transport cost.
The nature of services provided by shipping companies forces them to be
transnational companies serving more than one country. In general, these companies have
access to international capital markets and they are able to hire workers from all over the
world8, although under some restrictions sometimes. In any case, we should not expect
differences in capital or labor costs to be the main factors in explaining differences of
transport costs across countries. There are many other important specific factors affecting
transport costs across countries, which we present next.
The obvious and most studied determinant of transport cost is geography,
particularly distance.9 The greater the distance between two markets, the higher the
expected transport cost for their trade. Using shipping company quotes for the cost of
transporting a standard container from Baltimore (USA) to selected worldwide
destinations, LV(2000) find that an extra 1,000 km raises transport costs by $380 (or 8%
for a median shipment). Moreover, breaking the journey into an overland and a sea
component, an extra 1,000 km by sea raises costs by only $190 while the same distance
by land raises costs by $1,380-4 and 30 percent of a median shipment, respectively. In
addition, if a country is landlocked, transport costs rise by $2,170, almost a 50 percent
7 This section follows McConville (1999) Fuchsluger (1999), Limao and Venables (2000), and Fink,
Mattoo and Neagu (2000).
8 Shipping companies prefer to sail their ships under open-registry flags. This explains that Panama,
Liberia, Cyprus and Bahamas account for more than 40 percents of world fleet (measured in dead weight
tons -dwt-) -UNCTAD (1998)-.
9 It has long been recognized that bilateral trade patterns are well described empirically by the so-called
gravity equation, which relates bilateral trade positively to both countries GDP and negatively to the
distance (which is used as proxy for transport cost) between them. See Bergstrand (1985).
4



increase in the average cost.10 In other words, being landlocked is equivalent to being
located 10,000 km farther away from markets.
Trade composition additionally helps to explain transport costs differences across
countries. First of all, due to the insurance component of transport costs, products with
higher unit value have higher charges per unit of weight. On average, insurance fees are
around 2 percent of the traded value and they represent around 15 percent of total
maritime charges. Therefore, high value added exporting countries should have higher
charges per unit weight due to this insurance component. On the other hand, some
products require special transport features and therefore have different freight rates. l l
Directional imbalance in trade between countries implies that many carriers are
forced to haul empty containers back. As a result, either imports or exports become more
expensive. Fuchsluger (2000) shows that this phenomenon is observed in the bilateral
trade between the US and the Caribbean. In 1998, for instance, 72 percent of containers
sent from the Caribbean to the US were empty. This excess of supply in the northbound
route implied that a US exporter paid 83 percent more than a US importer for the same
type of merchandise between Miami and Port of Spain (Trinidad and Tobago).'2 Similar
phenomena occur in the Asian-US and the Asian-European trade routes, where excess of
supply means that Asian exporters end up paying more than 50% of extra charge in
transport costs compared to suppliers in the US and Europe.13
Maritime transport is a classic example of example of an industry that faces
increasing return to scale. Alfred Marshall put it clearly long ago: "... a ship's 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". 14 Besides increasing
10 This result controls by the extra overland distance that must be overcome by landlocked countries to
reach the sea.
'l LSU-National Ports and Waterways Institute (1998) shows that the average freight rates between Central
America and Miami for cooled load merchandise is about twice the transport cost for textiles.
12 The actual freight rates for a 20-feet dry container between Miami and Port of Spain were $1,400 and
$750 for the southbound and northbound route, respectively.
13 Ships going from Asia to the US utilize more than 75 percent of their capacity, while when going back to
Asia the utilization does not even attain a 50 percent rate. The rates from Asia to the US and in the opposite
direction are $1561/TEU (twenty-feet equivalent unit) and $9991TEU respectively. The capacity utilization
of ships from Asia to Europe is 75% and 58% in the opposite direction, while the rates charged by shipping
companies are $1353/TEU and $873/TEU respectively.
14 Quoted by McConville (1999). Additional economies of scale come from both material to build the
vessel and labor to operate it (especially that of navigation).
5



returns at the vessel level, there are economies of scale at the seaport level. For instance,
at the port of Buenos Aires (Argentina) the cost of using the access channel is $70 per
container for a 200 TEU15 vessel but only $14 per container for a 1000 TEU vessel.'6 In
general, even though most of these economies of scale are at the vessel level, in practice
they are related to the total volume of trade between two regions. Maritime routes with
low trade volumes are covered by small vessels and vice versa. 17
In addition, the development of containerized transport has been an important
technological change in the transport sector during the last decades. Containers have
allowed large cost reductions in cargo handling, increasing cargo transshipment and
therefore national and international cabotage.'8 In turn, this increase in cabotage has
induced the creation of hub ports that allow countries or regions to take advantage of
increasing return to scale.19
Commercial routes more liable to competition and less subject to monopoly
power will tend to have lower markups. Monopoly powers can be sustained either by
government restrictive trade policies or by private anti-competitive practices (cartels).
The former includes a variety of cargo reservation schemes, for example the UN Liner
Code.20 Private anti-competitive practices include, among others, the practice of fixing
rates of maritime conferences.2' Some authors have claimed that maritime conferences
have lost power in recent years,22 which has forced shipping companies to merge as a
way to hold their monopoly power.23
15 TEU is a standard container measure and it refers to Twenty Feet Equivalent Unit.
16 See Fuchsluger (2000).
" See PIERS, On Board Review, Spring 1997.
18 Cabotage refers to transshipment of the merchandise before it arrives to its final destination.
19 See Hoffian (2000).
20 This agreement stipulates that conference trade between two economies can allocate cargo according to
the 40:40:20 principle. Forty per cent of tonnage is reserved for the national flag lines of each exporting
and importing economy and the remaining 20 per cent is to be allocated to liner ships from a third
economy.
21 Maritime conferences enjoy an exemption from competition rules in major trading countries, like the US
and the European Union.
22 In the last years there have been some reforms in the regulation affecting international shipping. For
instance, the United States' Ocean Shipping Reform Act of 1998 eroded the power of conferences, creating
greater scope for price competition.
3 See Fink, Mattoo and Neagu (2000) and Hoffman (2000).
6



Similar restrictions and anti-competitive practices can induce inefficiencies and/or
monopoly power in ports. For example, in many countries workers are required to have
special license to be able to provide stevedoring services, and in general these restrictions
imply high fees and low productivity.24
Finally, the quality of onshore infrastructure is an important determinant of
transport costs. LV(2000) find that it accounts for 40 percent of predicted transport costs
for coastal countries, and up to 60% for landlocked ones.25 If a country with a relatively
poor infrastructure, say at the 75th percentile in an international ranking, is able to
upgrade to the 25th percenitile, it will be able to reduce transport costs by between 30 and
50 percent.26
III. Maritime Transport Costs Estimation
Focusing on the factors affecting transport costs already described, this section
attempts to quantify the importance of most27 of them on maritime transport charges paid
by U.S. imports carried by liner companies28 from     countnres all over the world. This
analysis closely follows FMN (2000). However, we add additional variables - notably a
measure of port efficiency - to the analysis, we redefine some of the other relevant
vanables (including the dependent vanrable), and we also extend backward the period of
analysis to 1995. In addition, we address the problems of endogeneity and omitted
24 In 1981 the supply of seaport service were de-regulated in Chile, and the change in legislation induced a
significant fall in seaport cost. See Trujillo and Nombela (1999) and Camara Chilena Maritima (1999) for a
discussion of this case.
25 Their infrastructure index is measured as a simple weighted average of kilometers of road, paved road,
rail and telephone main line (per square Km of country area and population, respectively). In their
regression, the authors us- this index to the power of -.3.
26 LV(2000) use two alternative measures of transport costs: CIF/FOB ratios reported for bilateral trade
between countries bx the IMF and quotes from a shipping company. According to them, an improvement in
own infrastructure from the 75th to the 25dl percentiles reduces transport costs by 30% based on shipping
data (from $6,604 to $4,638) and by more than 50% based on the CIF/FOB ratio (from 1.40 to 1.11). In
addition, an improvement in own and transit countries' infrastructures from the 75b to the 25' percentiles
reduces by more than half the disadvantage associated with being landlocked.
27 We do not analyze the impact of trade imbalance in transport charges because of data unavailability.
28 For most countries, US imports account for a significant share of their exports. For instance, US imports
accounted for 56 percent of Latin American exports in 1999, and they accounted for 31 percent of Japan's
exports this year.
7



variable bias we believe their estimations present, which we explain in the rest of this
section.
The Model
To estimate the importance of each factor in maritime transport costs we use a
standard reduced form approach. Maritime charges are assumed to be equal to the
marginal cost multiplied by shipping companies' markup. Expressed in logarithm, we
have:
Aik= mc(i,j,k) +  (I, J, k)                                    [1]
Where:
pijk: charges per unit of weight, in logarithm, for the product k transported between
locations i andj.
i: corresponds to foreign port, located in country I
j: corresponds to US port, located in district J
k: product, aggregated at the 6 digit of the Harnonized System (HS) Classification
mc: marginal cost, in logarithm.
p: markup, in logarithm.
As expressed in equation [1], both the marginal cost and the markup should be a
function of factors depending on the port or country of origin (i,), the port or district of
destiny in the US (],J) and the type of product (k). In particular, we assume that the
marginal cost has the following form:
MCjk = aJ + Xk + y wvijk + y Tjk + d diJ +  qlJ + co PE,       [2]
Where:
aj: dummy variable referring to US district J.
Ak: dummy variable referring to product k.
8



WVijk: value per weight for product k, transported from foreign port i to US port j, in
logarithm. We also refer to this variable as the weight value.
Tijk: fraction of k goods shipped (from i to j) in containers.
dij: distance between foreign port i and US custom district J, in logarithm.
qfj: volume of imports carried by liner companies between country I and US coasts, in
logarithm.
PEI: ports' efficiency of foreign country I.
The first term (aj) in equation [2] takes into account potential differences in port
efficiencies across US custom districts. The second term (Ak) accounts for different
marginal costs across products. The third term - weight value, (wvijk) - is used as a proxy
for the insurance component of the maritime transport cost (pik). The fourth term (Tijk)
represent a technological effect, and it captures reductions in costs induced by the
utilization of containers. The fifth term (du) refers to the maritime distance between trade
partners. The next variable (qlj) accounts for potential economies of scale, and the last
term (PEI) accounts for port efficiency in the foreign country. Thus, we expect a positive
sign for V/ and 9, and a negative sign for r, z7, and co.
At this stage, we highlight three differences with respect to FMN (2000). The first
two refer to the construction of the dependent variable and the variable measuring
economies of scale. For the first, we use charges per weight (instead of charges per unit,
as they do), because - despite the level of disaggregation of the data - there are still
important differences inside product categories. This heterogeneity inside product
categories is better captured when using our definition. With respect to the economy of
scale variable, we define it as the volume of imports departing from a particular foreign
country and arriving to a particular coast in the US.29 A third and more important
difference lies in the inclusion of the weight-to-value variable. As already stated, this
variable accounts for the effect of the insurance component on the transport cost and, as
we show next, it turns out to be highly significant. FMN (2000) do not include a variable
of this type, probably because of the inclusion of dummies per product. However,
29 FMN (2000) define this variable as the total value of imports departing from a particular foreign port and
arriving to a particular US district.
9



because of the insufficient level of disaggregation of the data, product dummies are not
enough and the exclusion of this variable can cause important omitted variable bias.30
Finally, and here we follow more closely FMN (2000) formulation, we assume
that shipping companies' markups have the following form:
,u(I, J, k)  Pk + V/  A PA +  'CA A CA                             [3]
Where
AIJPA: existence of price-fixing agreements between country I and US district J.
AliCA: existence of cooperative working agreement between country I and US district J.
The first term (pk) in the above equation reflects a product-specific effect that
captures differences in transport demand elasticity across goods (this is a derived demand
from the final demand of good k in the US). The last two terms account for potential
collusive agreements between shipping companies covering a same route. Two types of
agreements are distinguished: price-fixing agreements (which include most maritime
conferences), and cooperative working agreements that do not have binding rate setting
authority. Substituting the second and third equations into the first one, we obtain the
econometric model to be estimated:
Pyk = a + Ak + If wvyk + y Tik +  du + i7qj + co PE, +  /AAIA + YCAIJCA +
[4]
Where:
fik= Lk +Pk
LJk: error term.
30 We replicated FMN (2000) estimations with and without the unit value variable (which is the relevant
variable to add in their specification, given their construction of the dependent variable). The variable
turned out to be highly significant (even after using product dummies), but their results for the rest of the
variables changed dramatically.
10



Data3' and Results
Data on maritime transport costs, value and volume of imports, and shipping
characteristics - like the percentage of the goods transported through containers - come
from the U.S. hnport Waterborne Databank (U.S. Department of Transportation). Our
dependent vanrable -- transport costs - is constructed using imnports charges, defined by
the U.S. Census Bureau as: "...the aggregate cost of all freight, insurance, and other
charges (excluding U.S. import duties) incurred in bringing the merchandise from
alongside the carrier at the port of exportation -in the country of exportation- and placing
it alongside the carrier at the first port of entry in the United States."
The U.S. Import Waterborne Databank covers the period 1995-1999. Even though
this database includes all U.S. imports carried by sea, classified by type of vessel service
(liner, tanker and tramp), we focus only on liner services to be able to estimate the effect
of conferences and agreements in manrtime charges.32 Liner imports account for around
50 percent of total US imports and 65 percent of US maritime imports.33 Given that our
objective is to focus only on maritime transport costs, we also drop all the observations
for which the origin of the import is different from the port of shipment.34
The distance variable and the data on maritime conferences and working
agreement between liners were kindly provided by FMN(2000). The first correspond to
the distance between foreign ports and US custom districts; it is expressed in nautical
miles, and comes in turn from a private service. The data on carrier agreements - used by
FMN to construct their indices- comes from the Federal Maritime Commission, it covers
59 countries and is available only for 1998. Therefore, when estimating for the other
years (1995-97, 1999), we have no choice but to use the same 1998 values for all them.
Unfortunately, there is not much comparable information about port efficiency -at
port level- to be used in a cross-country analysis.35 So, we use an aggregated measure -
31 Appendix A gives a complete description of the data used.
32 This also allow us to compare our results with FMiN (2000) ones. Liner services are scheduled carriers
that advertise in publications advance of sailing. They generally have a fix itinerary and tend to carry mixed
types of containerized, non-bulk cargo. Tramp and tanker services, in turn, are (dry, liquid) bulk carriers
and have no regular scheduled itineraries, but are more depending on momentary demand.
33 The remaining US imports by sea are carried by tramp services.
34 That is, in transit merchandise is not included.
35 The World Bank is launching a program (Global Facilitation Partnership for Transportation and Trade)
to focus on significant improvements in the invisible infrastructure of transport and trade in different
11



per country- of port efficiency, consisting of a one-to-seven index (with 7 being the best
score) from the Global Competitiveness Report (GCR). This annual data is available for
the period 1995-1999.36 As an alternative measure, we also use GDP per capita as a
proxy for port efficiency. Countries' GDP per capita are correlated with their level of
infrastructure. For our particular problem - explaining the cost of shipping the same
product from different ports in the world to the U.S. - it is hard to see why the per capita
GDP of the sending country would matter except to the extent that it is proxying for the
quality of infrastructure. As noted, we will use both this indirect measure and a direct
measure of port efficiency in different specifications.
In addition to the per capita GDP, we construct a second measure of infrastructure
- this time an index a la LV (2000) - based on information at country level on paved
road, paved airports, railways and telephone lines.37 We incorporate this variable based
on the assumption that the infrastructure level in a country is likely highly correlated with
the infrastructure level at their ports, and also because it allows us to compare our results
with LV (2000). We should note that, despite having a somewhat similar infrastructure
index, our formulation differs from that of LV (2000) in many respects. First, one of their
measures of transport costs is the CIF/FOB ratio, which has the disadvantage of being an
aggregate measure for all products, while we use transport cost information at product
level. Also, this measure is well known for having measurement deficiencies (although
they try to control for that). Their second measure of transport costs - shipping rates (for
a homogeneous product) from Baltimore to a group of different countries - tries to
address these problems. However, as the same authors point out, the shipping rates from
Baltimore are not necessarily representative -not even for the rest of the US ports. Our
database, on the other hand, has information from many ports around the world to
different ports in the US.38 An advantage of their second measure, however, is that it
member countries. However, the project is in its first stage and it does not cover all the countries of our
sample yet.
36 The report, in turn, is based on micro-data from annual surveys at firm level, made to a representative
group of enterprises in every country. The particular question for port efficiency is: "Port facilities and
inland waterways are extensive and efficient. (1 = strongly disagree, 7 = strongly agree)". The number of
countries covered has been growing over time (from 44 in the 1996 report to 56 in the 2000 one).
37 See the Appendix for a description of its construction.
38 In addition, we believe their second sample is biased in favor of African countries. The bad infrastructure
and port quality of African countries may be biasing upward the coefficient estimates they obtain.
12



allows them to construct an estimate of inland transport cost, which is not our purpose in
this paper.
Table 1 reports our estimations for equation [4]. We start by presenting the results
only for 1998 because the variables on maritime conferences and working agreement
between liners refer to this particular year. The first three columns show the coefficients
using OLS, while the rest of the estimations use an Instrumental Variable (IV)
technique.39 Columns (1) and (4) report the results using GDP per capita as a proxy for
port efficiency, columns (2) and (5) use the variable port efficiency from the GCR, and
columns (3) and (6) use the index of infrastructure we constructed. As it can be seen, in
both type of estimations most of the variables are highly significant and with the expected
sign.
Distance has a significant (at 1%) positive effect on transport costs. A doubling in
distance, for instance, roughly generates a 20 percent increase in transport costs. This
distance elasticity close to 0.2 is consistent with the existent literature on transport costs.
The value per weight variable is also positive and highly significant, with a t-statistic
around 50. As already stated, these regressions include dummy variables for products at
the six-digit HS level. One might think that unit values would be quite similar across
countries at that level of disaggregation; not so. Feenstra (1996) shows that there is a
large variation in unit values even at the 10-digit HS level. He cites the examples of
men's cotton shirts, which the U.S. imports from fully half of its 162 trading partners.
The unit values range from $56 (Japan) to $1 (Senegal). These differences in unit values
lead to large differences in insurance costs per kilogram, even for "homogeneous"
products. So, it is not surprising that we find that the more expensive the product, per unit
of weight, the higher the insurance and hence the overall transport cost.40
39 In all the estimations (OLS, IV), we allow the observations to be independent across exporting countries,
but not necessarily independent within countries. At the same time, the standard errors presented in the
table correspond to the consistent Huber/White ones.
40 In addition, there is the possibility that the unit weight variable could be capturing some measurement
errors. The argument is as follows. One should expect that the variables charges and (total) import value
were very carefully measured, because the US custom constructs the dutiable value of imports by excluding
the former to the latter (and it should have a special interest in calculating it correctly). However, this could
not be case for the measurement of weight. If so, measurement errors in the weight variable would induce a
positive correlation between charges per weight (our dependent variable) and value per weight.
13



With respect to the two variables referring to agreements between liner
companies, only the first of them (price fixing binding agreements) turns out to be
positive -as expected- but only significant (at 10%) in two specifications.4 This result
seems to suggest that maritime conferences have been exerting some mild monopoly
power - adding an estimated 6% to transport costs in 1998, ceteris paribus. However, as
we will see later, this estimated effect of the price-fixing agreements is not always
significant for other years.
The next variable, the level of containerization, presents a significant negative
effect on transport costs. As explained before, this variable represents technological
change at both vessels and seaport level. The idea behind this result is that
containerization reduces services cost, such as cargo handling, and therefore total
maritime charges.
The variable capturing economies of scale is the level of trade that goes through a
42
particular maritime route.  This variable, calculated in terms of volume, has a significant
negative coefficient (as expected).43 However, the direct incorporation of this variable in
the estimations presents a problem of endogeneity. On one hand, one should expect the
more the trade the lower the transport costs. But, at the same time, lower transport costs
induce more trade. We address this problem in columns (4) to (6).
Finally, the coefficient related to port efficiency is negative and significant (at 1%
in all cases): the greater the efficiency at port level, the lower the transport costs. This
result is robust to the three alternative measures of port efficiency (columns 1 to 3). In
particular, the coefficient for the measure from the Global Competitiveness Report
(column 2), along with the distribution of the port efficiency index among countries,
indicates that an improvement in port efficiency from    the 25th to the 75th percentile
4' FMN (2000) find the price-fixing agreement dummy variable to be significant and much larger in
magnitude: between .4 and .51; that is, the maritime agreements add at least 40% to transport costs. They
also use policy variables referring to cargo reservation policies (not significant), cargo handling services
(significant in one estimation but with wrong sign, and not significant in another), and mandatory port
services (significant, correct sign).
42 Each couple foreign country and US coast is defined as a maritime route. We define three coasts in the
US: East, West and Golf coast.
43 We must note that this variable differs from the one presented by Fink, Mattoo and Neagu (2000) in two
aspects. First, they use the value of imports while we use the volume of imports (in tons). Second, the
definitions of maritime route through which economies of scale arise are different: they use the trade (in
14



reduces transport charges a little more than 12%.44 In tenns of particular countries, if
China, Indonesia and/or Mexico -for instance- improved their port efficiency to levels
observed in countries like France and/or Sweden, their reductions in transport costs
45
would be around 12%. Similar results are obtained for the other measures.
To solve the endogeneity problem mentioned above, we use countnres' GDP as
instrument. We make the identifying assumption that if country size affects transport
costs, it does so through the volume of trade and economies of scale in shipping.
Columns (4) to (6) in Table 1 present the results for the instrumental variable (TV)
estimations. Most coefficients remain stable -with the expected signs- and they continue
to be significant. Using the instrumental variables, the economy of scale vanrable remains
negative and significant, but the magnitude of the coefficient increases in absolute value
when we use the GCR measure of port efficiency (-0.04 v/s -0.02). Thus, we estimate that
doubling the volume of trade between a particular port and the U.S. reduces transport
costs by 3-4%. As we already mentioned, the coefficients for the rest of the variables -in
particular, for the three port efficiency measures- are quite stable.
We perforned similar estimations for the rest of the years for which we have data.
For brevity of space, Table 2 presents the estimated coefficients only for the IV
regressions using the GCR variable for port efficiency.46
For each year, the coefficients on distance and weight-to-value are quite stable
and significant (at 1%).47 Prior to 1999, the first year after the US eroded the power of
Conferences, the pnrce-fixing rate agreement has the expected sign but it is only
significant (at 10%) in 1997. In 1999, the coefficient turns negative, a result that may be
value) between foreign ports and US districts (31), while we use the trade (in volume) between foreign
countries and US coasts (3).
44 Thiat is, when port efficit iacy is measured with the GCR index, an improvement in port efficiency from
25th to 75th percentile (i.e. from a score of 3.4 to 5.6 respectively) generates a maritime transport costs
decline of around 12%.
45 When proxying poll efficiency with the per capita GDP, an increase from the 25h to the 75th percentile
reduces maritime -i. .rt charges in 14%. When using the infrastructure index, the reduction in transport
costs is around 8%. This last variable could be showing a smaller effect because in fact it is measuring the
existence of infrastructure, but not necessarily its quality, while the other measures should capture also
quality.
46 We use the port efficiency index from the 1999 GCR for all years, in order to avoid a drastic decrease in
the number of countries covered by the report.
41 The exception is the coefficient for distance in 1999. which increases to 0.25. One reason why distance
may be having a bigger effect this year could be the increase in oil prices (from an average of $13/barrel in
1998 to $18/barrel in 1999).
15



related to a war in prices between shipping companies that were previously members of
conferences. Cooperative agreement is significant only in 1995 but with the wrong sign.
In addition, when we use the infrastructure index that we created (not shown here) none
of these policy variables is significant. From these results it is difficult to conclude
whether conferences have been exerting some monopoly power or not.
From Table 2 we can see that the coefficient on containerization is significant
only in 1998.48 In the case of Total Liner Volumes, the coefficient is in general
significant and quite stable over time. Finally, the estimated coefficient for port efficiency
is stable and significant from both an economic and statistical point of view. When we
used the infrastructure index from the GCR (not shown here) we obtain similar results in
terms of stability and significance. These results allow us to conclude that port efficiency
is an important determinant of maritime transport costs. For example, if countries like
Ecuador, India or Brazil improved their port efficiency from their current level to the 75th
percentile -that is, to a level attained by France or Sweden- they would reduce their
maritime transport costs by more than 15% each.
A final caveat about these results. Our model assumes that, if inefficiency in a
port raises shipping costs by 10% for a shipment of shirts, it will increase the shipping
costs for a shipment of cars by the same 10%. Suppose, instead, that the "tax equivalent"
of port inefficiency varies by product. Then, products for which the tax is excessively
high will not be exported and we will not observe them in the data. In other words, we
have estimated the effect of port inefficiency for products that are actually shipped. The
effect may be higher for some products, which are then not exported. In this sense our
estimate of the cost of port inefficiency may be conservative.
48 The low variance on the containerization levels in liner transport services may be explaining this non-
significance.
16



IV. Determinants of Port Efficiency
The previous subsection stresses the importance of port efficiency on maritime
transport cost, but what are the factors behind port efficiency? The activities required at
port level are sometimes crucial for international trade transactions. These include not
only activities that depend on port infrastructure, like pilotage, towing and tug assistance,
or cargo handling (among others), but also activities related to customs requirements. It is
often claimed that "...the (in)efficiency, even timing, of many of port operations is
strongly influenced (if not dictated) by customs".49'50
Some legal restrictions can negatively affect port performance. For example, in
many countries workers are required to have special license to be able to provide
stevedoring services, artificially increasing seaport costs. Other deficiencies, associated
with port management itself, are also harmful to country competitiveness. For instance,
some ports still receive cargo without specifying the presentation of a Standard Shipping
Note, which is inconceivable in modern port practice. In many ports, it is quite
impossible to obtain a written and accurate account of the main port procedures, and
sometimes port regulations are not clear about the acceptance of responsibilities (for
cargo in shed or on the quay, for instance). All of this generates unreasonably long
delays, increases the risks of damage and pilferage of the products (in turn raising the
insurance premiums), and as a consequence considerably increases costs associated with
port activities.
Port efficiency varies widely from country to country and, specially, from region
to region. It is well know that some Asian countries (Singapore, Hong Kong) have the
most efficient ports in the world, while some of the most inefficient are located in Africa
(Ethiopia, Nigeria, Malawi) or South America (Colombia, Venezuela, Ecuador). Table 3
presents some estimates of port efficiency, per geographic region.51
49 Thus, any unexpected delay at ports due to extra custom requirements or cargo inspections, for instance,
may increase considerably the associated port costs (due to moving containers and storage of frozen
products, for example) and hence reduce exporters competitiveness.
50 See John Raven (2000), for a description of relevant issues concerning trade and transport facilitation.
51 We must note that these efficiency variables -per regions- are not directly comparable to each other,
because the availability of countries is not the same for each of the variables. Thus, we should think of
these as complement rather than substitute measures.
17



The first column is a subjective index based on surveys reported by the World
Economic Forum's 1999 Global Competitiveness Report. North America and Europe
have the best rankings, followed by the Middle East, and East Asia & the Pacific. Latin
America and South Asia, in turn, are the regions perceived as having the least efficient
ports. The second column indicates the time, in median days, to clear customs (taken
from business surveys performed by the Inter-American Development Bank and World
Bank52). The striking results are the ones for Africa -Southeast Africa and West Africa-
for which the median number of days to clear customs is 12. Among East and South
African countries, Ethiopia (30 days), Kenya, Tanzania and Uganda (14 days each) are
the countries with bigger delays in clearing customs; while Cameroon (20 days), Nigeria
(18 days) and Malawi (17 days) are the West African countries with the biggest delays.53
The second region presenting big problems at custom levels is Latin America, with a
median delay in clearing customs of 7 days. In this group, Ecuador (15 days) and
Venezuela (11 days) appear as the worst performers.
Finally, the third column of Table 3 presents some estimates of the costs of
handling containers inside the ports (in US$/TEU). This variable was constructed based
on information provided by the Transport Division of the World Bank and information
from additional papers.54 Despite the fact that the sample of countries for this variable is a
lot more restricted than for the previous ones, the estimates are quite consistent with the
previous variables. While the efficient ports in East Asia present lower charges, the Latin
American ports have the most expensive handling services. This relationship is even
clearer when we take into account wage differential across countries. Table 4 presents the
regression of handling costs -adjusted by wage or its proxy- on port efficiency and an
index of infrastructure (same as used in table 1). This index -at country level- is included
under the assumption that infrastructure at country level is highly correlated with
infrastructure at port level. In Column (1) handling costs are adjusted by manufacturing
52 The specific question is: "If you import, how long does it typically take from the time your goods arrive
at their port of entry until the time you can claim them from customs?"
53 The African countries' results from this survey are totally consistent with the results presented by the
African Competitiveness Report 2000/2001 (World Economic Forum), which performed the same custom
clearance question (though the average time presented by the latter are slightly higher).
54 Camara Maritima y Portuaria de Chile (1999) and LSU-National Ports and Waterways Institute (1998).
18



wages,55 in Column (2) and (3) we adjust by per capita GDP (as proxy for wages), and in
Column (4) and (5) handling costs are adjusted by PPP GDP per capita.
Port efficiency is an important determinant of handling cost. Countries with
inefficient seaports have higher handling costs. Also, countries with good infrastructure
have lower seaport costs. Figure 4 presents the relationship between handling costs and
port efficiency, controlling for PPP GDP per capita (as a proxy for wages) and
infrastructure level (Colunm 4 specification of Table 4). The clear negative relationship
shows that countries where ports are considered the most efficient (e.g. Singapore and
Belgium, not marked in the figure) are at the same time the ones whose ports charge the
least for their services (in comparable units). In turn, some Latin American countries (e.g.
Brazil, Ecuador, not marked in the figure) are among the worst ranked in term of their
efficiency and also present the highest charges per services (after controlling by the level
of infrastructure).56
Finally, we try to explain which are the factors behind port efficiency. As we
already mentioned in the case of transport costs, it is reasonable to think that the
determinants of port efficiency will not only consist of infrastructure variables, but also
of management and/or policy variables. Therefore, besides a proxy for port
infrastructure,57 we include among the explanatory variables two policy variables, one
referring to Cargo Handling Restrictions and the other to Mandatory Port Services. Both
variables are zero-to-one indices from FMN (2000). The first captures restrictions and
special requirements imposed on foreign suppliers of cargo handling services, where
foreign suppliers refer to local companies with foreign participation.58 The second
captures the extent to which port services are mandatory for incoming ships.59 Both
indices represent restrictions at port level that could limit competition, so we can expect a
55 Manufacturing wages are taken from LNIDO Industrial Statistics Database.
56 A simnilar result is obtained when manufacturing wages (from the UNIDO Industrial Statistics Database)
are used -instead of GDP per capita- to adjust handling costs. Appendix B presents the values used to
construct these series.
57 We use the index of country infrastructure we constructed as proxy for port infrastructure.
58 The index takes a value of 0 if no restriction exists, 0.25 for minor restrictions, 0.5 if a joint venture
condition is imposed, 0.75 if a very high national participation in the company is required, and 1 if foreign
companies are simply forbidden to provide cargo handling services.
59 This variable is constructed adding .125 for each of the following services if they are mandatory:
pilotage, towing, tug assistance, navigation aids, berthing, waste disposal, anchorage and others mandatory
services.
19



negative relationship between them and port efficiency. However, due to some quality
and security considerations, we also have to consider that it may be beneficial to have a
certain level of regulation at the seaports. Thus, we also explore the possibilities of non-
linearities of the effect of each of these indices on port efficiency.
As we already mentioned, we consider the overall level of infrastructure, which
we assume to be positively correlated with a country's level of seaport infrastructure. We
expect the better the infrastructure the higher the probability of an efficient port; that is, a
positive coefficient for this variable. Finally, we also include a Crime Index, taken from
the Global Competitiveness Report, and consisting of a one-to seven index ranking how
severe is organized crime in a particular country (with 7 meaning "not a problem"). The
idea behind the inclusion of this variable is that organized crime constitutes a direct threat
to port operations and merchandise in transit. With all of this in mind, we present in
Table 5 some estimations of the effects of these variables on port efficiency, calculated
for 1998.
As it can be seen, the coefficient on infrastructure is always positive and
significant. The results for the policy variables are somehow mixed, but make some
sense. Cargo handling restrictions are not significant, no matter the specification. The
variable for mandatory port services, on the other hand, is significant both in level and
square level, presenting a positive and negative sign, respectively. This result suggests
that having some level of regulations increases port efficiency, however, an excess of it
can start to reverse these gains. In terms of the countries in our sample, this result
suggests that Argentina is taking advantage of a moderate level of regulation in its
seaports, but instead Brazil is reducing its seaport efficiency because of excess regulation.
Using a non-parametric method (adjusted spline), figure 5 presents this non-linear
relationship between regulation and port efficiency.
Finally, the crime variable also turns out to be highly significant and with the
expected positive sign (remember that the variable is defined as crime "not being a
problem"). In terms of this sample, an increase in organized crime from the 25th to 75th
percentiles implies a reduction in port efficiency from 50th to 25th percentiles. In other
words, if countries like Brazil, China or India (all with indices around the 75th percentile)
reduced their organized crime to levels attained by countries like Australia, New Zealand
20



or the United Kingdom (all around the 25th percentile), then they would be able to
increase their port efficiency index roughly one point. This in turn would generate a
reduction of maritime transport costs of around 6%.
V.     Conclusion
By the 1990s many countries had adopted a development strategy emphasizing
integration with the global economy and therefore had reduced their tariff and non-tariff
barriers to trade. This reduction in artificial trade barriers has raised the importance of
transport costs as a remaining barrier to trade. Therefore, any strategy aimed. at
integrating a country into the trading system has to take transport costs seriously.
Besides distance and other variables that no government can change, an important
determinant of maritime transport costs is seaport efficiency. An improvement in port
efficiency from 25th to 75th percentiles reduces shipping costs by more than 12%, or the
equivalent of 5,000 miles in distance. This result is robust to different definition of port
efficiency as well as to different years. Inefficient ports also increase handling costs.
Seaport efficiency, though, is not just a matter of physical infrastructure.
Organized crime has an important negative effect on port services, increasing transport
costs. In terms of our sample, an increase in organized crime from the 25th to 75th
percentiles implies a reduction in port efficiency from 50th to 25th percentiles. In addition
our results suggest that some level of regulation increases port efficiency, but excessive
regulation can be damaging.
21



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Appendix A. Data Description
Transport Costs Estimation (Tables 1 and 2)
Maritime Transport costs: calculated as import charges divided by weight. Source:
calculated from data of the US Import Waterbome Databank (US Department of
Transportation).
Distance: Correspond to the distance between the foreign port i and the US custom
district J. Data provided by Fink, Mattoo and Neagu (2000).
Unit Weight: Value of total US imports divided by its total weight, and calculated per
maritime route (where we define routes as "from foreign ports to US custom districts").
Calculated from data of the US Import Waterbome Databank (US Department of
Transportation).
Price-Fixing agreement: Dummy variable signaling the presence of carrier agreements
on maritime routes: conferences and other price-fixing agreements. Source: Fink, Mattoo
and Neagu (2000).
Cooperative agreement: Dummy variable signaling the presence of carrier agreements on
maritime routes: cooperative working agreements that do not have a binding rate
authority. Source: Fink, Mattoo and Neagu (2000).
Containerization: Percentage of cargo transported by containers. Source: US Import
Waterborne Databank (US Department of Transportation).
Total Liner Volume: Total volume of imports transported per maritime route (where we
define routes as "from foreign country to US coast"). Source: constructed from data of
US Import Waterborne Databank (US Department of Transportation).
Foreign GDP per capita: GDP per capita of the exporting countries to the US. Source:
World Development Indicators 2000 (The World Bank).
Port Efficiency: one-to-seven index ranking port efficiency, based on surveys performed
to representative firms of each country. The specific question is "Port facilities and inland
waterways are extensive and efficient (1=strongly disagree, 7=stronlgy agree)". Source:
The Global Competitiveness Report, various years (1996-2000)
Port Efficiency Variables (Tables 3 and 4)
Container Handling Charges: Correspond to containers handling charges inside the
ports, expressed in US$ per TEU (Twenty Feet Equivalent Unit). For nineteen countries
we have information from the Transport Division of the World Bank. For twelve
countries, from which eight are in the World Bank sample, we have information (as an
index) from the Camara Maritima y Portuaria de Chile A.G. Finally, for four Central
American countries from which only Panama is in the previous samples, we have
25



information from the LSU- National Ports and Waterways Institute. Using ratios, we put
all samples in the same unit used by the data from the World Bank.
Port Efficiency: same as above.
Custom Clearance: Correspond to time (days, median) to clear customs, based on surveys
performed (by the World Bank) to importers in each country. The specific question is "If
you import, how long does it typically take from the time your goods arrive at their port
of entry until the time you can claim them from customs?" Source: The World Bank.
Manufactures wages: Source: UNIDO Industrial Statistics Database.
Infrastructure Index: Correspond to the simple average of three normalized indices that
take into account the country level of communications (telephones) and its physical
transport infrastructure (paved roads, railroads and airports). The exact definition of the
index is:
INFi = Avg {AIi, TIj, TTIj }   if there are a least two of them
where
PA?                            F R?      RI2
AI,=    ,      TI= =       TTI, -avg    P    ,   2
,Fj PA,,        X Tj PJ             P  Si * 7i PjS/ J
and T1 is the fixed and mobile telephone lines per capita of country i, PA1 is the number of
paved airports, P1 refers to the population, Si refers to the surface area, PRi is paved roads,
and RR1 is railroads. The sources for the variables are: World Development Indicators
2000 (The World Bank) and The World Factbook 2000 (Central Intelligence Agency).
Port Efficiency Estimation (Table 5)
Infrastructure Index: same as above.
Cargo Handling Restrictions: zero-to-one index that captures restrictions and special
requirements imposed to foreign suppliers of cargo handling services. The index takes a
value of 0 if no restriction exists, 0.25 for minor restrictions, 0.5 if a joint venture
condition is imposed, 0.75 if a very high national participation in the company is
required, and 1 if foreign companies are simply forbidden to provide cargo handling
services. Source: Fink, Mattoo and Neagu (2000).
Mandatory Port Services: zero-to-one index that captures the extent to which port
services are mandatory for incoming ships. This variable is constructed adding 0.125 for
each of the following services if they are mandatory: pilotage, towing, tug assistance,
navigation aids, berthing, waste disposal, anchorage and others mandatory services.
Source: Fink, Mattoo and Neagu (2000).
Organized Crime: one-to-seven index ranking "organized crime as not been a problem",
based on surveys performed to representative firms of each country. The specific
26



question is "Organized crime does not impose significant costs on business and is not a
burden (1=strongly disagree, 7=stronlgy agree)". Source: The Global Competitiveness
Report, various years (1996-2000)
27



Appendix B. Data Used
Cargo Hand. Mandatory  Prce Fixed  Cooperative  Median    Port     Crime    Container Handling Charges
Country       Restriction  Services  Agreements Agreements Clearance time  Efficiency  Wort Bank CMPCH    LSU
Index     Index      Index     Index       Days      Index (1-7) Index (1-7) US$ITEU  Index  Index
Argentina       0.00      0.13      0.00       1.00         7.0       3.81      4.52      na      139     na
Armenia          na        na        na         na          4.0        na        Na       na      na      na
Australia       0.00      0.13       1.00      1.00         na        4.79      6.19      199     na      na
Azerbija        na         na        na         na          5.0        na        na       na      na      na
Belarus         na         na        na         na         4.0         na        na       na      na      na
Belgium         0.00      0.06       1.00      0.00         na        6.17      5.73      120     na      na
Belize          na         na        na         na          5.0        na        na       na      na      na
Benin           1.00      0.00      0.00       0.00         na         na        na       na      na      na
Bolivia         na         na        na         na          9.5       1.61      4.38      na      na      na
Botswana        na         na        na         na         4.0         na        na       na      na      na
Brazil          0.50      0.75      0.00       1.00        10.0       2.92      4.45      328     292     na
Brunei          0.00      0.00      0.00       0.00         na         na        na       na      na      na
Bulgaria        na         na        na         na          2.0       3.68      3.23      na      na      na
Cambodia        na         na        na         na          7.0        na        na       na      na      na
Cameroon        na         na        na         na         20.0        na        na       na      na      na
Canada          0.00      0.13      0.00       0.00         2.0       6.42      6.27      190     na      na
CDI             na         na        na         na          8.5        na        na       na      na       na
Chile           0.00      0.25      0.43       1.00         3.0        3.76     6.05      202     100      na
China           0.50      0.00      0.00       0.00         7.0       3.49      4.44      110     na       na
Colombia        0.50      0.13      0.50       1.00         7.0        2.26     1.88      na      na       na
Costa Rica      0.00      0.00      0.00       1.00         4.0        2.46     3.28      na      na       68
Croatia         na         na        na         na          2.0        na        na       na       na      na
Cyprus          1.00      0.31      0.00       0.00         na         na        na       na      na       na
CzechRep        na         na        na         na          2.0        3.27     4.41      na       na      na
Trinidad and T.  na        na        na         na          7.0        na        na       na      na       na
Denmark         0.00      0.06      1.00       0.00         na        6.16      6.71      na      na      na
Dominican R.    0.25      0.25      0.50       1.00         7.0        na        na       na      na      na
Ecuador         0.00      0.00      0.43       1.00        15.0       2.63      3.65      na      139     na
Egypt           0.75      0.75      0.00       0.00         5.5       3.72      6.37      na      na       na
El Salvador     0.00      0.00      0.00       1.00         4.0        2.95     2.30      na       na      61
Estonia         na         na        na         na          1.0        na        na       na      na       na
Ethiopia        na         na        na         na         30.0        na        na       na       na      na
Finland         0.00      0.25      0.00       0.00         na         6.26     6.63      na       na      na
France          0.00      0.38      1.00       0.00         3.0        5.39     6.58      201     na       na
Georgia         na         na        na         na          2.0        na        na       na       na      na
Germany         0.00      0.38      1.00       0.00         5.0        6.38     6.02      163     117      na
Ghana           1.00      0.50      0.00       1.00         5.0        na        na       na       na      na
Greece          1.00      0.19      0.00       0.00         na         4.28     5.60      na       na      na
Guatemala       na         na        na         na          7.0        na        na       na       na      55
Haiti           na         na        na         na         15.0        na        na       na       na      na
28



(continued)
Cargo Hand. Mandatory  Price Fixed  Cooperative  Median  Port     Crime    Container Handling Charges
Country      Restriction  Services  Agreements Agreements Clearance time Efficiency Index (1-7) World Bank CMPCH  LSU
Index     Index      Index     Index       Days    Index (1-7)          US$1TEU   Index  Index
Honduras        na         na        na         na         4.0        na       na        na       na     na
Hong Kong       0.00      0.25      0.00       0.00        na        6.38      5.46      na       na     na
Hungary         na         na        na         na         3.0       2.59      4.14      na       na     na
Iceland         0.00      0.13      0.00       0.00        na        5.78      6.64      na       na      na
India           0.00      0.00      0.00       1.00        na        2.79      4.28      na       na      na
Indonesia       1.00      0.06      0.00       0.38        5.0       3.41      4.06      na       na      na
Ireland         0.00      0.13      1.00       0.00        na        4.28      5.12      na       na      na
Italy           0.25      0.50      0.38       0.00        2.0       4.11      3.29      228      na     na
Ivory Coast     0.00      0.25      0.00       1.00        na         na        na       na       na     na
Jamaica         0.50      0.00      0.00       0.60        na         na        na        na      na      na
Japan           0.75      0.13      0.89       1.00        na        5.16      5.16      250      202     na
Kazakhst        na         na        na         na         9.0        na        na        na      na      na
Kenya           na         na        na         na         14.0       na        na       na       na      na
Korea           0.00      0.38      0.00       0.00        na        4.12      5.22       na      na      na
Kyrghizs        na         na        na         na         10.0       na        na        na      na      na
Lithuani        na         na        na         na         1.0        na        na       na       na      na
Madagascar      na         na        na         na         10.0       na        na        na      na      na
Malawi          na         na        na         na         17.0       na        na        na      na      na
Malaysia       0.00       0.25      0.00       0.38        7.0       4.95      5.76       75      na      na
Mauritius       1.00      0.38      0.00       0.00        na        5.35      5.53       na      na      na
Mexico          0.50      0.38      0.00       1.00        4.0       3.34      2.61       na      na      na
Moldova         na         na        na         na         5.0        na        na        na      na      na
Morocco        0.50       0.13      0.00       0.00        na         na        na        na      na      na
Namibia         na         na        na         na         4.0        na        na        na      na      na
Netherlands     0.00      0.50      1.00       0.00        na        6.64      5.42      156      84      na
New Zealand     0.00      0.38      1.00       1.00        na        5.82      6.14       na      na      na
Nicaragua       0.00      0.00      0.00       1.00        5.0        na        na        na      na      na
Nigeria         0.00      0.50      0.00       1.00        18.0       na        na        na      na      na
Panama          na         na        na         na         5.0        na        na        na      234     100
Papa N.Guinea  0.50       0.00      0.00       0.00        na         na        na        na      na      na
Peru            0.50      0.00      0.50       1.00        7.0       2.88      3.32       na      142     na
Philippines    0.50       0.00      0.00       0.38        7.0       2.79      3.51      118      na      na
Poland         0.25       0.00      0.00       0.00        3.0       3.34      3.41       na      na      na
Portugal       0.00       0.13      1.00       0.00        8.0       3.81      6.50       na5     na      na
Romania        0.00       0.63      0.00       0.00        3.0        na        na        na      na      na
Russia          na         na        na         na         7.0       3.33      2.23       na      na      na
Senegal         0.00      0.00      0.00       1.00        7.0        na        na        na      na      na
Singapore       1.00      0.38      0.00       0.33        2.0       6.76      6.72      117      na      na
Slovakia        na         na        na         na         2.0       3.50      4.35       na      na      na
Slovenia        na         na        na         na         2.0        na        na        na      na      na
29



(continued)
Cargo Hand. Mandatory  Price Fixed  Cooperatve  Median   Port    Crime    Container Handling Charges
Country         Restction  Servia   Ageements Agreements Cearence time Efficency Index (1-7) Worl Bank CMPCH  LSU
Index     Index      Index     Index       Days    Index (1 -7)        US$STEU  Index   Index
South Africa       na        na        na         na         5.0        5.24     2.08      na      na      na
Spain             0.00      0.06       1.00      0.00        4.0        4.88     6.08      200     105     na
Sweden            0.00      0.06       1.00      0.00        2.0        5.73     6.46      na      na      na
Taiwan            0.50      0.00       0.00      0.00         na        5.18     4.49      140     163     na
Tanzania           na        na        na         na         14.0       na       na        na      na      na
Thailand          0.50      0.63       0.00      0.38        4.0        3.98     5.12      93      na      na
Togo              0.00      0.00       0.00      0.00         na        na       na        na      na      na
Tunisia           0.50      0.13       0.00      0.00        5.5        na       na        na      na      na
Turkey            0.00      0.00       OA3       0.00         na        3.81     5.00      na       na     na
Upnda              na        na        na         na         14.0       na       na        na      na      na
Ukraine            na        na        na         na         10.0       3.41     3.28      na      na      na
United Kingdom    0.00      0.31       1.00      0.00        4.0        5.37     6.17      173     na      na
United States      na        na        na         na         5.0        6.27     5.40      259     336     na
Uruguay           0.00      0.00      0.00       1.00        5.0        na       na        na      na      na
Uzbekist           na        na        na         na         7.0        na       na        na      na      na
Venezuela         0.00      0.00       1.00      1.00        11.0       3.28     3.63      na      na      na
Vietnam           0.00      0.00      0.00       0.50         na        3.81     5.02      na      na      na
Zamnbia            na        na        na         na         10.0       na       na        na      na      na
Zimnbabwe          na        na        na         na         10.0       3.29     5.15      na      na      na
na: not available
30



Figure 1
Estimates of Total Imports Freight Costs Relative to Imports (cif),
1997
15%-
12.4%
11.5%
10%-                     8.0%                    8.0%    8.4%
5%   -       20~~~~~1.
0%       .                                                        0% :1
Total    Dev.    Non-           Africa   Asia    Europe   LAC    Oceania
Dev.
Figure 2
Imports Freight Costs (CIF/FOB ratio) and Import Tariffs
relative to Import value, 1996-97
.25
.2  -                          peru
Tanz
.15 -                             Dorila    Papu   Zirnb
Janm
1-                            Sout PaPli    Indo                         rIdi
1.)  WiPit Vene               Bang5d                                        Thai
Japa    Guat Pena              Ce6ihin
EsbD    New           Chil Rorna                                IFtur
Aujst         C(Olud gz                 Tuni
Turk Kare
.05               Czec Urug LebaMexi        Nepa
Unit
Cana
Hung
0
I                   III             I         I         I          I
0        .05        .1       .15         .2       .25        .3        .35        .4
tariff
31



Figure 3
Export Freight Costs and US Tariff,
Latin American Countries, 1998
14%
* 
12%    *albe
t 0% -*Trndl a-d Tobep                                               /
I-
o 8%
0           ad BatLda/
* 
Z 6%- *    -
4%          * IS a         /~~o +4                      * NcS n
2a * 9KsardNis       a                               H
* *~~~*P
40/D-        Boldlaz        >7Fbi       FU%JL
0%
9\^+xille(3ms ~   DmwcEPeki
0%          2%           4%           6%          8%           100/0       12%          14%
TARFF/CUST
Sources: U.S. Census Bureau, Department of Commerce.60
60 The high calculated duty presented by Central American countries are due to textile products (code 6 in
HTSUSA).
32



Figure 4
Handling Costs and Seaport Efficiency, 1998
-3.63966
0L
C,c
0.
0)
2.46                                                     6.76
Seaport Eff. adj. by Inf.
Natural Cubic Spline
Figure 5
Port Efficiency and Level of Regulation (Mandatory Port Services), 1998
2.65104 -
.0
CU
-
CD
Co
sa ~ ~~      ~~~ I  \
1.80829 -
2.4                                                      6.76
Mandatory Serv., adjusting by Inf. and Crime
Natural Cubic Spline
33



Table 1: Determinants of Maritime Transport Costs, 1998
Dependent Variable: TC=(Charges / Weight)
OLS estimations                 IV estimations
Variables                   (1)        (2)        (3)       (4)       (5)       (6)
Distance                    0.18       0.19      0.18       0.18      0.17      0.17
(11.37)***  (9.83)***  (11.41)*** (10.21)*** (8.47)***  (10.37)***
Weight value                0.55       0.55      0.55       0.55      0.55      0.55
(54.77)***  (51.66)*** (48.78)*** (54.75)*** (50.96)*** (48.81)***
Policy variables
Price fixing rate agreement  0.06    0.02      -0.00      0.06      0.02       0.01
(=1 if agreement exist)  (1.88)*    (0.64)     (0.14)    (1.71)*   (0.51)     (0.28)
Cooperative agreement     -0.01      -0.03     -0.02      -0.01     -0.03     -0.02
(=1 if agreement exist)   (0.89)    (1.30)     (0.78)    (0.90)     (1.23)    (0.83)
Containerization            -0.04     -0.03      -0.03     -0.04      -0.03     -0.03
(2.78)***  (2.30)**  (2.22)**   (2.87)***  (2.27)**  (2.28)**
Total liner volume          -0.02     -0.02      -0.03       --        --        --
(foreign country to US coast)  (3.70)***  (2.85)***  (4.47)***  --     --        --
Total liner volume (Instr)  .           _                  -0.02      -0.04     -0.04
(foreign GDP)                 -          -         _      (2.19)**  (2.72)***  (3.85)***
Foreign port efficiency
Foreign GDP per capita    -0.05                           -0.05
(proxy for infrastructure)  (5.78)***  --               (5.16)***     --
Port efficiency             -        -0.05                          -0.05
(Global Conipet. Report)    -      (3.87)***     --        --     (3.53)***     -
Infrastructure Index        --        --       -0.03        --        --      -0.03
(proxy for port infast.)    --        --      (3.36)***     --        -      (3.50)***
Observations               332,593   314,462    289,834   332,593    314,462   289,834
R-squared (adj.)            0.47       0.47       0.47      0.47      0.47       0.47
Notes: Robust t-statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.
All estimations include fixed effects for products (4848 products) and for US districts (31 districts).
Regressions allow the observations to be independent across exporting countries, and interdependent within
each country.
34



Table 2: Determinants of Maritime Transport Costs, 1995-1999
Dependent Variable: TC=(Charges / Weight)
1995       1996       1997       1998        1999
Distance                       0.17        0.16      0.17       0.17        0.25
(6.30)***  (5.61)***   (6.34)***  (8.47)***  (9.86)***
Weight value                   0.55        0.55       0.55      0.55        0.53
(49.87)***  (42.34)***  (56.63)***  (50.96)***  (54.48)***
Policy variables
Price fixing rate agreement   0.02       0.06       0.08       0.02       -0.07
(=1 if agreement exist)      (0.51)     (1.13)     (1.72)*    (0.51)     (1.98)**
Cooperative agreement        -0.05       -0.04      -0.03      -0.03      0.01
(=1 if agreement exist)     (1.67)*     (1.56)     (0.99)     (1.23)      (0.35)
Containerization              -0.03       0.02      -0.002      -0.03      0.003
(1.32)      (0.91)     (0.15)   (2.27)**    (0.12)
Total liner volume (Instr)    -0.03       -0.03      -0.04      -0.04      0.0001
(foreign GDP)                 (1.75)*    (2.05)**   (2.58)**  (2.72)***    (0.01)
Foreign port efficiency
Port efficiency              -0.080     -0.072     -0.068     -0.053      -0.057
(Global Compet. Report)     (4.07)***  (4.07)***  (4.03)***  (3.53)***  (4.41)***
Observations                  273,337    273,063    297,825    314,462    346,868
R-squared                      0.52        0.49       0.49      0.47        0.45
Notes: Robust t-statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at
1%. All estimations include fixed effects for products (4848 products) and for US districts (31).
Regressions allow the observations to be independent across exporting countries, and interdependent
within each country.
35



Table 3: Port Efficiency Variables
Port            Custom       Container Handling
Efficiency        Clearance     Charges in Ports
(7=best, 1=worst)     (days)         (US$/TEU)
North America                    6.35             3.50             261.7
Europe (excl. East)             5.29              4.00             166.7
Middle East                      4.93              na               na
East Asia & the Pacific         4.66              5.57             150.5
East & South Africa             4.63              12.00             na
North Africa                     3.72             5.50              na
Former Soviet Union              3.37             5.42              na
East Europe                      3.28             2.38              na
Latin Am. & the Caribbean       2.90              7.08             251.4
South Asia                      2.79               --               na
West Africa                      na               11.70             na
Sources: Global Competitiveness Report (1999), World Bank Surveys, Camara Maritirna y
Portuaria de Chile. A.G. (1999), and LSU (1998). (na: not available)
Table 4: Handling Costs and Port Efficiency, 1998
Dependent variable: Container Handling Charges, divided by wage or proxy (in logarithm)
(1)         (2)        (3) a       (4)        (5) a
Variables                (adj. by    (adj. by    (adj. by   (adj. by    (adj. by
m. wage)    GDPpc)      GDPpc)    GDPpc PPP) GDPpc PPP)
Port Efficiency           -0.48       -0.37       -0.31      -0.33       -0.31
(Global Compet. Report)  (14.69)***  (7.69)***  (3.95)***   (6.02)***  (5.09)***
Infrastructure Index b    -0.14       -0.36       -0.42      -0.14       -0.14
(proxy for port          (2.04)*   (9.09)***   (12.89)***  (3.81)***   (4.24)***
infrastructure)
Constant                  -2.28       -2.82       -3.20      -3.00       -3.11
(10.42)***  (10.00)***  (6.93)***   (9.38)***   (8.69)***
Observations               12          23          18         23          18
R-squared                 0.95        0.93        0.95        0.88        0.88
Notes: Robust t-statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant
at 1%. a: regression uses handling cost data from the World Bank only. b: the infrastructure index is
in logarithm.
36



Table 5: Determinants of Port Efficiency, 1998
Dependent var.: Port Efficiency (from the Global Competitiveness Report)
Variables                             (1)           (2)           (3)
Infrastructure                        0.27         0.26           0.26
(3.04)***     (2.81)***     (2.86)***
Cargo Handling Restrictions           0.84         0.05            --
(0.72)        (0.16)          --
Cargo Handling Restrictions (sqr.)   -0.85          --             --
(0.70)          --            --
Mandatory Port Services               3.43         3.37           3.41
(2.25)**      (2.19)**      (2.30)**
Mandatory Port Services (sqr.)       -5.20         -4.90         -4.91
(2.42)**      (2.31)**      (2.34)**
Organized Crime                       0.54         0.51           0.51
(Org. crime is not a problem)      (4.66)***     (5.72)***     (5.84)***
Constant                              1.96         2.10           2.11
(2.65)**     (3.37)***      (3.45)***
Nr. Observations                      41            41            41
R sq.                                 0.77          0.77          0.77
Notes: Robust t-statistics in parentheses. * significant at 10%; ** significant at 5%; *
significant at 1%.
37






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