a Disaggregate Analysis of Port Selection (2004)

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A disaggregate analysis of port selection

Matthew B. Malchow *, Adib Kanafani 1

Department of Civil Engineering, Institute of Transportation Studies, University of California,

109 McLaughlin Hall, Berkeley, CA 94720, USA

Received 15 January 2003; received in revised form 3 March 2003; accepted 16 May 2003

Abstract

With this article we use an alternative form of the discrete choice model to analyze the distribution of

maritime shipments among US ports. We model the distribution as a function of the characteristics that

describe each shipment and each port. We assume that vessel schedules are fixed in the short-term and

examine the assignment to ports for exports of various commodity-types as a function of geographic

location, port characteristics, and characteristics of vessel schedules. We find that the most significant

characteristic of a port is its location. We show also how the market share predicted for a port can be

expected to vary with each commodity-type and each carrier, and we show how the choice process varies for

discretionary cargo. 2003 Elsevier Ltd. All rights reserved.

Keywords: Port choice; Shipper behavior; Carrier behavior; Shipment routing

1. Introduction

Competition between ports has intensified. As a result of containerization, which standardizedthe transfer process for shipments between ocean and surface transportation, and deregulation,which allowed maritime carriers to set contracts with rail services and establish rates independent

of location, the area considered a ports hinterland disappeared. Apart from their various mar-keting efforts, ports compete primarily through their investment program. Ports are improvingintermodal facilities to minimize the dwell time of shipments, and they are increasing the storage

* Corresponding author. Tel.: +1-510-231-9460; fax: +1-510-231-9565.

E-mail addresses: [email protected] (M.B. Malchow); [email protected] (A. Kanafani).1 Tel.: +1-510-642-3585; fax: +1-510-642-1246.

1366-5545/$ - see front matter

2003 Elsevier Ltd. All rights reserved.doi:10.1016/j.tre.2003.05.001

Transportation Research Part E 40 (2004) 317337

www.elsevier.com/locate/tre
http://mail%20to:%[email protected]/http://mail%20to:%[email protected]/


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space available to terminal operators to allow carriers to concentrate operations. Ports are also

dredging their waters so that carriers may deploy larger vessels.By investing to attract business, ports recognize that carriers make two primary decisions, the

long-term decision being the deployment of vessels to routes and ports, and the short-termdecision being the assignment of shipments to vessels. Thus with the assignment of a shipment to avessel comes the assignment of a shipment to a port. In this work we apply a choice model tomodel the assignment of each shipment to a vessel/port and hence to evaluate the competition

between ports. We answer three questions:

What factors influence a carriers selection of a port for a shipment?

In what manner and across what domain do ports compete? What strategies might a port follow to increase its market share?

Though the choice-model approach used in this research has not previously been applied to

port selection, our approach has been influenced by earlier work. Qualitative analysis of portcompetition has been done by, among others, Bardi (1973), Foster (1978a,b, 1979), Slack (1985),Hanelt and Smith (1987), and DEste and Meyrick (1992a,b). The results have not always been

identical, but the authors often suggest that service-related factors were more important than pricefactors, and that factors within the control of port authorities were often less important than thosebeyond port control. Brooks (1984, 1985) noted the difference between a characteristic s impor-

tance and salience. Quantitative analysis can differ between the two, as the ranking of charac-teristics comes not from word-of-mouth but the results of actions.

The scheduling of carriers vessels has also been the subject of much research. Kenyon (1970)

and Al-Kazily (1979) explored the development of a carriers maritime network. Hayuth (1981)

suggested the formation of a load center by carriers, while Foggin and Dicer (1985) and Slack(1996) evaluated the effects of load centers. Helmick (1994) sought quantitative evidence of theformation of load centers but suggested that other factors, e.g. the presence of tramp lines in

routes abandoned by major carriers, prevented confirmation of carrier rescheduling. Lago et al.(2001) found that the rescheduling of vessels by carriers was not drastic but did differ betweencorridors. They showed how the level at which scale economies were exploited in oceanic transit

differed between corridors.Economic models of carrier behavior have come from three directions. First, linear program-

ming to optimize the assignment of vessels has been advanced by Benford (1981), Perakis (1985),Lane et al. (1987), and Perakis and Jaramillo (1991). These models show how the distribution of

vessels might be affected by the distribution of traffic but deal with simplified scenarios or aredifficult to apply. Second, cost models have been estimated by Jansson and Shneerson (1978),

Talley (1990), and Lim (1998) for the general cargo or container shipping industry. Garrod andMiklius (1985) and Jansson and Shneerson (1985) emphasized the importance of shippers

inventory costs. Griffiths (1976a,b) measured the optimal size of ports for a given level of traffic.de Neufville and Tsunokawa (1981) confirmed that scale economies existed at ports. Bendall andStent (1987, 1988), Talley (1988a,b, 1994), and Tongzon (1995) all suggested, or measured,

characteristics of the cost function faced by terminal operators. Finally, Allen (1977), Daughetyand Inaba (1978) and Daughety (1979) advanced the economic modeling of the decisions made bycarriers, in contexts other than the assignment of shipments to ports. Winston (1981a,b) and Nam

318 M.B. Malchow, A. Kanafani / Transportation Research Part E 40 (2004) 317337


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(1997) applied discrete choice models to freight transportation decisions. Malchow and Kanafani

(2001) made an initial application of the choice model to the assignment of shipments to indi-vidual ports. The present article extends the work of Malchow et al. by incorporating additional

variables and applying a form of the choice model that accounts for the correlation within themultiple decisions made by each decision maker.

2. Methodology

In this research, we apply a choice model to the assignment of shipments to vessels/ports in

order to evaluate the competition between ports. The fundamental assumption is that a shipper,by choosing a carrier, implicitly chooses a port for a particular shipment. But in the competitive

market of shipping it is the attributes of the door-to-door service that a carrier offers thatinfluences the shipper choice. In other words, while a shipper may be deliberately choosing a

particular port for his or her shipments, it is the attributes of the service as offered by the carrier

that influence that choice process. Therefore the assumption is that the shippers preference for aport is wholly subsumed within the preference for and choice of a carrier that offers a service

through that port.To estimate the choice model, we use data that describe shipments exported from the United

States in December 1999. We classify shipments into four commodity-types using the first two

digits of the harmonized commodity code (HS). 2 The four commodity-types are bulk materials

(HS 25, 26), foods (HS 07, 08, 10), fabrics (HS 52, 54), and manufactured goods (HS 85). The datarepresent exports to eight foreign countries: Australia, Brazil, Egypt, Germany, Japan, Saudi

Arabia, South Africa, and the United Kingdom. The commodity classifications provide variations

in the values (and related characteristics) of the shipments, and the destination countries providegeographic distribution. We restrict shipments to those for which the carrier of record had aschedule listed with the Journal of Commerce, since we use a carrier s schedule to measure par-

ticular variables. Table 1 shows the distribution of shipments by country and commodity-type. 3

For each carrier, the choice set consists of eight ports: Charleston, South Carolina; Long Beach,California; Los Angeles, California; New York, New York; Oakland, California; Savannah,Georgia; Seattle, Washington; and Tacoma, Washington.

We select the eight ports in our choice set for two primary characteristics: (i) the volume oftrade moved through the port, and (ii) the proximity of the port to other significant ports. If twoports were geographically close, factors other than location may influence the choice between

them. We want to capture the impact of such factors. Table 2 shows the distribution, amongports, of shipments within our sample set.

2 The Harmonized System is an international six-digit commodity classification developed under the auspices of the

Customs Cooperation Council. Individual countries have extended it to ten digits for customs purposes, and to eight

digits for export purposes. The system classifies goods by what they are, not according to their stage of fabrication, use,

or origin. The first pair of digits represents a chapter, the next pair a heading, the third pair a subheading.3 The data set was reduced to include only shipments that were moved from one of the 48 contiguous United States

through one of the eight ports by one of the carriers whose schedules are available from the Journal of Commerce.

M.B. Malchow, A. Kanafani / Transportation Research Part E 40 (2004) 317337 319


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Many factors affect the choice process being modeled. Before discussing these factors, let us

define the scenario in which a carrier selects a port for a shipment. First, the carrier selects the portand vessel for each shipment simultaneously. Remember that when modeling the short-term

decision, we assume that the long-term fleet assignment has already been established. Thus, wecan represent each port by the vessel distribution rather than the characteristics of a particular

vessel. We also assume that sufficient space exists for each shipment on vessels scheduled alongeach route. Because we analyze exports rather than imports, this situation should hold. Duringthe 1990s, the ratio of imports to exports fluctuated around 1.5, and carriers often transport

empty containers to fill slots on outbound vessels. Empty space suggests that each shipment isexported through the optimal port.

We model the systematic utility, Vnj of each port as a linear function of five variables: 4

Vnjajb1 Onjb2 Inj b3 Hinj b4 Cinjb5 Pinj; 1

Table 1

The distribution of shipments, by destination and commodity-type

HS code Australia Brazil Egypt Germany Japan Saudi

Arabia

South

Africa

United

Kingdom

Total

07 127 2 1 13 722 6 2 57 930

08 160 8 33 169 836 64 6 141 1417

10 7 1 0 7 70 2 0 12 99

25 63 18 7 85 352 6 23 56 610

26 1 4 0 2 13 0 2 7 29

52 33 25 0 26 190 18 21 52 365

54 25 17 2 35 12 7 18 28 144

85 164 61 15 79 254 69 36 162 840

All 580 136 58 416 2449 172 108 515 4434

Table 2

The distribution of shipments among ports

Rank Port Shipments

1 Oakland, CA 1314

2 Los Angeles, CA 1010

3 Charleston, SC 675

4 Long Beach, CA 650

5 New York/New Jersey 618

6 Seattle, WA 515

7 Savannah, GA 462

8 Houston, TX 346

9 Norfolk, VA 29010 Tacoma, WA 254

4 We modeled the decisions with other variables as well. For example, we used the average number of sailing days in

place of oceanic distance or the frequency of voyages in place of the headway between voyages. In each case, the

explanatory power of the model decreased.



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where,Onjis the oceanic distance to the destination of shipmentn from portj (km, 1000s);Inj, theinland distance from the origin of shipment n to port j (km, 1000s); Hinj, the average headway

between voyages by carrier i from portj to the destination of shipment n (days);Cinj, the average

size of vessels sailed by carrier i from port j to the destination of shipment n (TEUs, 1000s), andPinj, the probability that port j would be the last port visited by a vessel sailed by carrier i to the

destination of shipment n; a; b, coefficients estimated in the model.The variablesOnj andInj are of course independent of the carrier, with the remaining variables

being measures of the carrier as well as the port. 5 For the variableOnj, we use the shortest sailing

distance from port j to the destination of shipment n. This is an approximation of the actualsailing distance, which could not be measured due to the complexity and variability of carriers

schedules. For Inj, we use the inland road distance, which should approximate the inland raildistance as well.

We measure the variables Hinj, Cinj, and Pinj for each carrier through an Internet databasemaintained by the Journal of Commerce. 6 For each destination, we use the schedule of allvoyages from one of eight United States ports to any port near the destination. 7 We measure the

variablesHinj andPinj for each carrier directly. We measure the capacity of each vessel scheduledalong a corridor, in TEUs, through the website MaritimeData.com. We calculate the variable Cinjto represent the average capacity of vessels sailing along the corridor for carrier i.

The variables in this choice function are selected to represent the common objective of theshipper and the carrier: to get each shipment from its origin to its destination as efficiently aspossible. This efficiency is ultimately dependent on transit time and cost. For each shipment, four

factors influence the transit time associated with each port:

ii(i) the distance from the origin to the port,

i(ii) the time needed to transfer the shipment from the ground to the vessel,(iii) the time incurred as the vessel calls at other ports in transit, and

(iv) the oceanic distance from the port to the shipments destination.

Likewise, four factors influence the operating cost associated with each port:

ii(i) the inland distance from the origin to the port,i(ii) the charges assessed by the port,

5 In fact, the headway between (or frequency of) voyages was found to be insignificant when included as the average

across all carriers, a result quite different from the model resulting from carrier-specific values. We would intuitivelyexpect the carrier-specific values to have more explanatory power as well.

6 The site at the time of writing was at: http://www.joc.com/scheds/index.shtml. Because data for a vessel is

maintained only until the vessels voyage has been completed, data for December 1999 were no longer available.

Instead, data for March 2000 were used to represent the variables. Comparison was made with the schedule for June

2000 (likewise, separated by three months) and a correlation coefficient of 0.95 existed between the schedules, implying

that carriers schedules did not change much over three months.7 These records were downloaded from the web and analyzed with a spreadsheet. The twelve US ports consisted of

the eight within the choice set, along with Houston, Miami, Norfolk, and Portland. The foreign ports were not

constrained to the country that was the destination of the shipment. For example, shipments destined for Germany

would also be affected by the nearby ports of Rotterdam and Antwerp. A complete list is given in Appendix A.

http://www.joc.com/scheds/index.shtmlhttp://www.joc.com/scheds/index.shtml


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(iii) the oceanic distance from the port to the destination of the shipment, and(iv) the average vessel size, representing economies-of-scale and density.

Before continuing, we must mention those variables that are not included in the model but maybe significant on a disaggregate level. These include port charges, the cost of the transportation

services, and the intermodal transfer process at each port.Increased port charges could make a port less attractive to a decision maker, with the most

prevalent port charge being wharfage. Because of complexities with port tariffs and the prevalence

of service contracts, port charges are difficult to measure accurately on a disaggregate level.Industry representatives have, however, suggested that port charges are relatively insignificant.

Ocean freight rates are no longer publicly disclosed, as of the Ocean Shipping Reform Act(OSRA) of May 1999 (Lewis and Vellenga, 2000). For two reasons, fortunately, rates might not

be significant in port selection. First, inspection of the tariffs and service contracts availablethrough the Federal Maritime Commission showed that the freight rates prior to OSRA variedlittle among West Coast ports. Rates also varied little among East Coast ports, with a slight

difference between the rates for ports on different coasts. Second, a shipper cares little about theintermediary points through which a shipment is moved, so long as the shipment arrives at thedestination at the expected time. Thus, the port should not affect the rate that a shipper is willing

to pay for transportation services. There is also some empirical evidence from Nam (1997), whoanalyzed the selection of mode for shipments. He found that the rate charged was in most casesinsignificant, suggesting that either service characteristics were more important or rates did not

vary by alternative.Finally, data are not available for intermodal transfer time. However, even if data were

available, two factors would complicate their inclusion into the model. First, we would have to

collect data for each terminal operated by each carrier at each port within the choice set, orapproximately 100 terminals. Second, and perhaps more importantly, a carrier could know a

shipments intermodal transfer time prior to a decision only as an expected value, which lacks thevariability that would be necessary for inclusion into a choice model.

3. An alternate formulation of the choice model

Under the traditional choice model, the utility of a port for a shipment is a linear function ofthe variables describing that port. The carrier is observed as selecting one port from among the

alternatives, and we assume that the carrier has selected the port that provided the greatest utilityin the context of that shipment. By observing the decisions for multiple shipments, we can esti-mate the importance of the factors that describe each alternative. For each port, we estimate the

contribution of each factor to its utility, and we estimate a factor (referred to as the port-specificconstant) that represents the average utility of all unobserved factors. These estimates influence

the likelihood of each decision by the carrier, and we estimate each factors contribution tomaximize the likelihood of the observations.

The data that we model represent panel data, with the shipments moved by each carrier rep-

resenting a separate group. Correlation likely exists among the decisions of a given carrier. Forexample, the intermodal transfer process could influence each carriers selection of a port for each



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shipment, and the transfer process at each port likely varies by carrier. This factor could not be

included directly since it does not vary across shipments. It thus affects the constant term asso-ciated with each port, and thus the constants should vary by carrier. 8

To account for the individual carriers, we could estimate a set of port-specific constants foreach carrier. This, however, would require the estimation of 288 constants (36 carriers, 8 ports).For some carriers the number of shipments would be too small for estimation. Chamberlain(1980) introduced an alternative model that accommodates panel data. In this model, rather than

modeling the selection of a port for each shipment, we model a carriers aggregate distribution ofshipments from the set of feasible distributions. The assignment of each shipment to a port woulddefine each distribution.

Two fundamental properties determine the feasibility of each hypothetical distribution. First,each shipment must be transferred through exactly one port. Second, the number of shipments

predicted by the distribution to move through each port must equal the number actually observedas moving through that port. 9 In mathematical notation, let

xinj the vector of attributes discussed earlier that influence the choice by carrieri of portj forshipment n (i.e. Onj, Inj, Hinj, Cinj, and Pinj),

winj 1 if carrier i actually sends shipment n through port j, and 0 otherwise, sij the number of shipments moved by carrier i through port j (Rn winj), and dinj 1 for each feasible distribution, and 0 for all others.

The two constraints specify that

8in, Rj winj1, and

8ij, Rn dinjsij.

A distribution that meets these constraints is considered feasible. Chamberlains method, rather

than maximizing the probability that the carrier selects the chosen port for each shipment,maximizes the probability that the carrier selects the observed distribution from the set of feasibledistributions. The log-likelihood for all observations is then

LXi

ln exp b0Xn;j

xinjwinj

! Xd2D

exp b0Xn;j

xinjdinj

!, #" ; 2

in whichD represents all feasible distributions of the shipments. In relating this model to the logitmodel, we note that the term Vnj has been replaced by b

0Pn:jxinjwinj and Vnk by b

0Pn:jxinjdinj,

summed over all feasible distributions D. With this formulation, the port-specific constants

8 If the alternative-specific constants remain constant across carriers, then the unobserved error term would be

correlated for each carriers shipments and not distributed with a mean of zero, as required for the model estimation.9 The alternative-specific constant is estimated in the logit model such that the predicted share will be equivalent to

the observed share for each alternative.



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disappear from the equation. (More precisely, the sum of the constants across the feasibledistributions does not vary and thus can not influence the choice.)

4. Choice-model estimation

We first estimate a standard multinomial choice model, in which the probability of choosing

port j is given by

Pinj eVinjP

keVink

; 3

with aij, the port-specific constant that is estimated for each carrier-port combination, constantacross carriers. Table 3 shows the results of the estimation.

The estimate for each of the five coefficients is statistically significant at a level beyond 99%.However, only four of the five estimates have the expected sign. The negative coefficient of vesselcapacity is counterintuitive. Why might this be? Perhaps there is no immediate advantage in

placing a shipment aboard a larger vessel if space is available. Given the trade flows in and out ofthe US, space should always be available for exports. Therefore, our expectation about the impactof vessel capacity is not definite. We learn later that the impact of vessel capacity, when modeled

alone, is actually positive.

5. The Chamberlain model

To examine these results further, we estimate the Chamberlain model. The set of feasibledistributions for some carriers would be computationally cumbersome, so we instead use sample

Table 3

Results of the standard multinomial logit model estimation (ignoring panel effects)

Variable Estimate Standard error Z-statistic P-statistic

Oceanic distance (O) )

0.09 0.01 )

12.6 0.00Inland distance (I) )0.67 0.02 )35.8 0.00

Sailing headway (H) )0.04 0.00 )25.3 0.00

Vessel capacity (C) )0.11 0.04 )3.1 0.00

Prob. of last (P) 0.01 0.00 13.9 0.00

A_Charleston 0.05 0.10 0.5 0.64

A_Long Beach 0.07 0.09 0.8 0.45

A_Los Angeles 0.47 0.08 5.8 0.00

A_New York )0.24 0.10 )2.4 0.02

A_Oakland 0.38 0.08 4.8 0.00

A_Savannah )0.16 0.10 )1.5 0.13

A_Seattle )0.23 0.10 )2.4 0.02

A_Tacoma 0

Log-likelihood )6242.2

Log-likelihood, constants only )8650.6

Log-likelihood, no coefficients )9220.2



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sets of the shipments to represent each carrier. 10 We select five-shipment samples for each of thenineteen carriers that had more than 50 shipments, with 100 samples collected for each of thesecarriers. Table 4 shows the coefficients estimated with the 1840 observations that were retained. 11

Each coefficient is significant with the exception of vessel capacity. To understand why, weexamine the significance of each variable individually with the Chamberlain model. Inland dis-tance provides the greatest explanatory power. The sign of each variable s coefficient is consistent

with the sign estimated in the multinomial model for each variable except vessel capacity. Due tothis inconsistency, as well as the fact that the capacity of a vessel might not affect port selection,given that space is available, we remove vessel capacity from further models. Ignoring vessel

capacity, we estimate the model as shown in Table 5. The likelihood-ratio test confirms that vessel

capacity is not a significant variable (Ben-Akiva and Lerman, 1985). We also use Hausmans testto confirm that the Chamberlain model, in which the alternative-specific constants differ between

carriers, describes the shipment-decisions better than the traditional model.

10 For example, Evergreen represented 527 shipments within our dataset. These shipments could be distributed

among the eight ports in one of 8 527 distributions, a number too large for us to begin with for consideration.

Table 4

Results of the multinomial logit model estimation, accounting for panel data characteristics

Variable Coefficient estimate Standard error Z-statistic P-statistic

Oceanic distance (O) )

0.12 0.01 )

15.6 0.00Inland distance (I) )0.77 0.02 )34.7 0.00


Vessel capacity (C) )0.02 0.05 )0.4 0.70

Prob. of last (P) 0.003 0.001 2.5 0.01


Log-likelihood from constants )6525.8

Log-likelihood, no coefficients )7533.6

11 We discarded sixty observations because the number of feasible distribution was too large for LIMDEP to handle.

LIMDEP is the statistical package used to estimate the choice model.

Table 5

Results of the multinomial logit model estimation, accounting for panel data characteristics and ignoring vessel

capacity


Oceanic distance (O) )0.12 0.01 )15.9 0.00

Inland distance (I) )0.78 0.02 )34.9 0.00


Prob. of last (P) 0.003 0.001 2.5 0.01



No coefficients )7533.6



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6. Discretionary cargo

Here we postulate that the decision made for discretionary cargo, cargo originating in a region

that does not contain a port, differs from that made for cargo originating in a ports hinterland.

To see how the decision process might differ for discretionary cargo, we estimate a model usingonly shipments that originated in the central United States. For these shipments, inland distancewould not vary as much among ports, allowing the impact of other variables to increase. At first

glance, the results of this model appear similar to those from the model for all shipments.However, closer examination of the variables reveals that the variables do play different roles forthe discretionary cargo. Table 6 shows the results.

For discretionary cargo, the probability of being the last port visited is significantly moreimportant. We compare the impact of this to the impact of other variables in Table 7 and find that

Table 6The Chamberlain logit model estimated for discretionary cargo


Oceanic distance (O) )0.29 0.03 )11.3 0.00

Inland distance (I) )1.75 0.10 )16.7 0.00


Prob. of last (P) 0.019 0.004 5.0 0.00



No coefficients )681.2

Table 7

The importance of being the last port visited for discretionary cargo

Ratio Meaning Value, all shipments Value, Midwest shipments

bO=bP The increase in the probability of beingthe last port that would be equivalent

to a reduction of 1000 km in oceanic transit

41.0 15.1

bI=bP The increase in the probability of beingthe last port that would be equivalent

to a reduction of 1000 km in inland transit

258.3 91.9

bH=bP The increase in the probability of being

the last port that would be equivalent to areduction of one day, expected headway

11.0 2.8

bO=bI The decrease in inland distance (km) thatwould be equivalent to a reduction of one km,

oceanic transit

0.16 0.16

bO=bH The decrease in headway (days) that would beequivalent to a reduction of 1000 km in

oceanic transit

3.7 5.4

bI=bH The decrease in headway (days) that would beequivalent to a reduction of 1000 km in inland

transit

23.5 32.9



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the relative importance of other variables decreases significantly. Many industry analysts suggest

that discretionary cargo in particular is sent through the port visited last by a vessel to minimizetransit time. This evidence of shifting values supports this idea. The importance of being the last

port visited is three times as large, when compared to other factors, for discretionary shipments asit is for all shipments.

Though we wish to emphasize the magnitude of this variables changing importance, we mustrecognize that uncertainty exists in our estimated values. Each estimated coefficient has a standard

error. A ratio between two estimates has an even larger standard error, and the ratio between tworatios has a still-larger error. We confirm the statistical difference of these probabilities with theconstruction of density functions in Malchow (2001b).

From the results in Table 7 we also see that a ports share of shipments from the Midwest is lessaffected by the headway between voyages, relative to the distances. The additional day or so of

headway becomes less significant when combined with an additional 48 h of inland transit.

7. Commodity-specific models

We now consider the proposition that the importance of attributes varies with commodity-type.

The commodity groups examined and their characteristics are shown in Table 8.The shipment size for each shipment corresponds to that filed with the shipments customs

form. Because the declared value of each shipment is confidential, the Journal of Commerce

estimated the value of each shipment according to the trade route and commodity code asso-ciated with each shipment. We find that the average shipment size for a commodity decreases as

its average value increases, likely to minimize shippers inventory cost. We expect the impor-

tance of different attributes of each port to vary with the characteristics that describe eachshipment.

The negative impacts of distance are the associated transit time and operating costs, and weexpect the transit time to be less important relative to the operating costs for lower-valuedcommodities. Inland distance is covered by modes (rail, truck) that are faster and more expensive

than water-based transportation; thus, shippers of lower-valued goods would place a lower pri-ority on oceanic distance than on inland distance. Carriers would more likely send lower-valuedcommodities through nearby ports. For example, a low-valued commodity being sent from

Table 8

Characteristics of the shipments from different commodity groups

Commodity # Records Shipment size (metric tons) Average value ($/metric ton)

Bulk 610 53.9 285

Fruits and vegetables 2347 30.6 1198

Fabrics 509 27.7 4287

Manufactured 840 9.8 11,087

All 4434 30.5 1885



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California to the United Kingdom would be loaded at a California port and sent on an extended

ocean voyage, whereas a higher-valued commodity might be transshipped via landbridge to a

waiting vessel on the East Coast. Table 9 shows the results of the estimation for each commodity,and the results do agree with our expectations.

Table 10 shows the marginal rate of substitution between oceanic distance and inland distancefor each of the different commodities.

As expected, the marginal rate of substitution between inland and oceanic transit generally

increases with the commodity value, a minor exception perhaps due to the perishability of fruitsand vegetables. This condition becomes important when examining the shares for certain com-modities from carrier-specific models. With carrier-specific models, we can examine how a ports

share is affected by distance and how this impact varies with the value of the commodity. Againusing the likelihood-ratio test, we find that the model estimated for each commodity is signifi-

cantly more explanatory than the generic model. This suggests that traffic forecasting models usecommodity-specific data to enhance accuracy of predictions. 12

Table 9

Results of the Chamberlain model as estimated for the different commodity-types

Commodity Variable Coefficient

estimate

Standard

error

Z-statistic P-statistic

Fruits and vegetables Oceanic distance (O) )0.38 0.04 )10.0 0.00

(HS 07, 08) Inland distance (I) )1.05 0.05 )22.2 0.00

(1500 simulated shipments) Sailing headway (H) )0.02 0.00 )7.4 0.00

Prob. of last (P) )0.01 0.00 )3.3 0.00

Bulk Oceanic distance (O) )0.12 0.01 )8.4 0.00

(HS 25) Inland distance (I) )0.79 0.04 )20.7 0.00


Vessel capacity (C) 0.58 0.16 3.7 0.00

Prob. of last (P) )0.03 0.00 )6.7 0.00

Fabrics Oceanic distance (O) )0.12 0.01 )9.1 0.00

(HS 52, 54) Inland distance (I) )

0.51 0.04 )

13.5 0.00(800 simulated shipments) Sailing headway (H) )0.04 0.01 )6.3 0.00

Prob. of last (P) 0.01 0.00 5.7 0.00

Manufactured Oceanic distance (O) )0.26 0.01 )23.3 0.00

(HS 85) Inland distance (I) )0.47 0.03 )17.5 0.00


Vessel capacity (C) 0.43 0.12 3.5 0.00

Prob. of last (P) 0.00 0.00 )2.7 0.01

12 Certain studies (Heaver, 1972; Bryan, 1974; Brooks and Button, 1996) have used the classification of shipments by

commodity-type to show that the transit freight rate, on a unit basis, tends to vary with the density of each commodity-

type. However, we noted earlier that the rates for commodity-types tend not to vary between individual ports within

coastal ranges.



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8. Carrier-specific models

To measure the effect of each variable, we could use the elasticity of choice, as described inTrain (1986). We first estimate models with port-specific constants for each carrier, subject to theconstraint that the variable-specific coefficients equal those estimated for the combined data set.We estimate the model for two carriers, American President Lines (APL) and Maersk/SeaLand.

The estimated elasticities are given in Table 11. The italicized ports for each carrier are thosethrough which the carrier transported no shipment. The logit model allows infinitesimally smallprobabilities to exist, but the predicted share of these alternatives would be zero.

From these estimates, we see that distance influences port selection most. The probability ofbeing the last port appears to have a very inelastic effect, and the effect of the headway betweenvoyages is largest for ports not visited at all. 13 With the likelihood-ratio test, we find that the

generic model would be rejected for the model estimated for each carrier; in other words, the

behavior of each carrier is not consistent.

9. Discussion

One interview with a carrier suggested that the selection of a port is not entirely predictable,

implying that the group deciding often does so without much evaluation. If this were true then onemight question the worth of models of the type presented here. However, a fundamental beliefunderlying economic analysis is the rational behavior principle. Carriers will in most cases make

rational decisions and it might be that some decisions do not require the same level of analysis.

We should be able to model this process to some extent, if for anything to estimate the impli-cations of rational behavior.

The carrier-specific models allow analysis of the share of traffic for each port. Port managerscould use such models in marketing. Ports can consider marketing to improve their position in an

Table 10

The importance of inland and oceanic transit for each commodity-type

Commodity Average value ($/metric ton) The marginal rate of substitution

between inland transit and oce-anic transit

Bulk 285 0.15

Fruits and vegetables 1198 0.37

Fabrics 4287 0.23

Manufactured 11,087 0.56

All 1885 0.16

13 Recall that to allow the variable to be assigned a finite value, ports for which the observed frequency of sailing

during March 2000 was zero were assigned an arbitrarily high headway of sixty days. Note also that the elasticity with

regard to being the last port visited would likely increase were the cargo of the discretionary sort.



14/21

established market or enter a new market. Estimates of the effect of certain factors could be used

to assess the worth of port investments.To show how this model could be used, we create simpler environment with three of four

ports: 14

(1) Los Angeles or Long Beach,(2) Oakland, and

(3) Seattle or Tacoma,(4) Charleston.

We examine the competition of these ports for a shipment under various scenarios. Onehypothetical shipment would be destined for Japan, to be moved by APL. 15 The independent

variable represents the shipments origin and moves inland from the Port of Oakland, and weestimate the distance to each port geometrically. Fig. 1 shows the market share for each port in

this scenario.

The predicted market share of the Port of Oakland decreases from 64% within its hinterland to53% as the origin of the shipment moves inland. The market share of competing ports increases as

expected to account for this lost share. Thus, each port does hold an advantage for shipmentswithin its hinterland, but the predicted advantage is not sufficient to ignore competition.

Table 11

The choice elasticities for the individual ports, for two carriers

Carrier Port Oceanic distance (O) Inland distance (I) Sailing headway (H) Prob. of last (P)

APL Cha )

1.62 )

1.97 )

0.15 0.01LB )1.43 )1.29 )0.41 0.00

LA )1.02 )1.03 )0.23 0.00

NY )1.73 )2.17 )0.16 0.02

Oak )1.06 )1.08 )0.26 0.12

Sav )1.84 )2.10 )0.48 0.00

Sea )1.38 )1.40 )0.33 0.00

Tac )1.22 )1.29 )0.43 0.00

Maersk Cha )1.18 )1.90 )0.10 0.05

LB )1.24 )1.13 )0.18 0.00

LA )1.53 )1.22 )0.22 0.00

NY )1.35 )2.10 )0.17 0.03

Oak )

1.01 )

1.05 )

0.17 0.04Sav )1.48 )2.14 )1.02 0.00

Sea )1.67 )1.48 )1.11 0.04

Tac )1.52 )1.36 )1.18 0.02

14 The model applies regardless of the available choice set. In addition, because carriers do not operate multiple

terminals within a region, each port competes with ports in other regions for the assignment of a shipment.15 To simplify the analysis, the variable representing the probability of being the last port selected is ignored.



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The impact of inland distance becomes more apparent when comparing market shares of portsalong different coasts. In another hypothetical case, the export of a shipment by Maersk to Japan,

the market share that would be captured by each of three ports is shown in Fig. 2. In this figure,

we replace the Port of Oakland with the Port of Charleston, which is located along the oppositecoast. Initially, market share for the Port of Charleston remains insignificant, while the Port of

Tacoma attracts a small market share away from the Port of Long Beach. 16 The Port ofCharleston does not begin to steal significant market share away from the West Coast ports until

the origin of the shipment has shifted halfway across the country, and Charleston does not stealsignificant market share until the origin has shifted even further.

Further analysis shows that the potential for competition, however, is impacted by the valueof the good being shipped. We suggested earlier that a carrier would transport higher-valuedshipments to a distant port to minimize oceanic distance relative to inland distance. Fig. 3 shows

this, as the share predicted for the Port of Charleston for a shipment bound to Japan is shown todiffer for a manufactured good relative to the share for a generic shipment. For this example, weestimate a model for P&O Nedlloyd. Clearly, a ports competition with a landbridge-alternative

is greater for higher-valued commodities. 17 This confirms that lower-valued goods are more

likely to be loaded at a neighboring port and transited a longer distance via ocean, and higher-valued, time-sensitive goods are more likely to be shipped via landbridge to a port with greater

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1000 1500 2000 2500 3000 3500 4000

Distance inland from Oakland (km)

Share

Oakland Seattle Los Angeles

Fig. 1. The impact of inland origin location on ports market shares for a theoretical shipment moved by American

President Lines to Japan.

16 The Port of Long Beach is closer to Oakland than Tacoma, so with the shifting of the origin, Long Beach s

advantage is lessened.17 The generic commodity would be of lower value than the highest-value manufactured goods.



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access to the shipments destination. Additional models could be evaluated to get more preciseestimates.

In evaluating oceanic distance, we find that its impact is again greatest when comparing portsfrom opposite coasts. Ports adjacent along one coast would have near-equivalent oceanic dis-

tances, such that an advantage could be gained only when linking calls for the scheduling of avessel.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1000 1500 2000 2500 3000 3500 4000


Marketshare

Tacoma Charleston Long Beach

Fig. 2. The impact of inland origin location on ports market shares for a theoretical shipment moved by Maersk to

Japan.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 500 1000 1500 2000 2500 3000 3500 4000


Marketsha

re

All Manufactured

Fig. 3. The market share predicted for an Atlantic port for the shipment of different commodity-types by Maersk toJapan.



17/21

Fig. 4 represents the significance of the headway between voyages for a theoretical shipmentmoved by American President Lines from Oregon to Japan. Though the market share decreases

steadily as headway increases, the actual impact is not as dramatic. To decrease the headway fromsixty days to thirty days, a carrier needs to add only one voyage per month. A second voyagereduces the average headway again from thirty days to fifteen days. Once a carrier has scheduled a

sufficient number of voyages, an incremental voyage adds insignificantly to a ports market share.

The potential impact becomes more apparent when observing the predicted market share as afunction of frequency. 18 The impact of sailing frequency decreases quickly once a minimum

number of voyages (on the magnitude of one per week, or 4+ per month) have been scheduled. Anadditional voyage would reduce the expected headway by an insignificant amount. A second

voyage (if the voyages were spaced evenly) would reduce the headway by fifteen days, and a thirdvoyage by five days, but a fifth sailing would reduce the headway by only one day. Therefore, so

long as a port has voyages scheduled at the frequency of one per week or greater, additionalvoyages do little but increase the capacity available for shipments. Fig. 5 shows the impact ofsailing frequency on the distribution of shipments between ports.

In a final example, we analyze the importance of being the last port visited by a vessel. Recall

that the significance of being visited last is greatest with discretionary cargo. Fig. 6 represents theshare predicted for the Port of Oakland, when competing with Los Angeles and Seattle, forshipments transported by APL from Kansas to Japan. As mentioned earlier, the significance of

being visited last is much greater for a port with discretionary cargo. Simply by convincing APLto make all of its last calls there, the Port of Oakland could increase its predicted market share for

discretionary cargo from 24% to 85%.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60

Headway between voyages at the Port of Oakland (days)

Marketshare

Oakland Los Angeles Seattle

Fig. 4. The predicted impact of headway between voyages on portsmarket shares for theoretical shipments moved by

APL from Oregon to Japan.

18 We used headway in the choice model because headway is more linearly related to the time spent by a shipment in

transit, of which it is the decision makers objective to minimize.



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10. Conclusion

These results have in many ways reaffirmed the results of earlier qualitative analysis. We havefound that the variables furthest from the control of port authorities, the oceanic and inland

distances, have the greatest impact on carriers distribution of shipments. We have found otherfactors to be significant, but more so in the context of discretionary cargo. Port managers couldrecognize these factors when marketing themselves to carriers, with particular regard to the

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30

Voyages at the Port of Oakland per month

Marketshare

Oakland Los Angeles Seattle

Fig. 5. The predicted impact of sailing frequency on portsmarket shares for theoretical shipments moved by APL from

Oregon to Japan.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Probability of being last visited (%)

Marke

tshare

All Discretionary

Fig. 6. The impact of being visited last on the market share predicted for the Port of Oakland, for theoretical shipments

moved by APL from Kansas to Japan.



19/21

location their port holds in a vessels schedule. In another important finding, choice behavior

varies significantly across carriers as well as commodities. When forecasting traffic for a port ormarketing to carriers, analysts should recognize these differences. Managers might recognize that,

in the context of shipment assignment, many of the investments being made might not be nec-essary. Of interest with future research would be changes that have occurred to the choice processthrough time as a result of technological change or the development of linked transportationnetworks.

In addition, the exports analyzed here represent only half of port traffic. We could expect thedecisions to be similar for imports, except for certain factors. One such factor is the desire ofimporters to concentrate traffic around a particular distribution center. Storage space should not

affect the decision for exports, due to imbalances and carriers desire to move shipments quickly,but it could affect imports if shippers are using terminal space for storage. The capacity available

on vessels could be of more importance for imports, due to the trade imbalance between importsand exports.

We must remember also that the distribution of shipments across ports is but one part of thelarger picture. An equally significant (if not more significant) task faced by the carriers is theassignment of vessels to particular routes. Further examination could be made of the carriers

selection of the last port to visit or, in the context of imports, the first port visited. The schedulingof vessels, coupled with our analysis of shipment distribution, could affect the future predictionsfor port traffic.

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Documents

a Disaggregate Analysis of Port Selection (2004)