Investigating sea shift in international freight transport:a case between Southeast Asia and the U.S.
Tomoaki KATO
Graduate Student
International Development Engineering
Tokyo Institute of Technology
TEL/FAX +81-3-5734-3468
E-mail: katou@tp.ide.titech.ac.jp

Anthony TH CHIN
Associate Professor
Department of Economics
National University of Singapore
TEL/FAX +65-6516-3959/+65-6775-2646
E-mail: anthonychin@nus.edu.sg

Shinya HANAOKA
Associate Professor
International Development Engineering
Tokyo Institute of Technology
TEL/FAX +81-3-5734-3468
E-mail: hanaoka@ide.titech.ac.jp

Tomoya KAWASAKI
Researcher
Planning and Research Department
Japan Maritime Center
TEL/FAX +81-3-3263-9421/+81-3-3264-5565
E-mail: t-kawasaki@jpmac.or.jp

ABSTRACTOne observes a shift from air freight to maritime freight in recent years due to primarily
increase in fuel costs and improvements in container and shipping technology. The area ofcontestability has grown larger and few commodities remain exclusively air or sea. There arefew studies which look into this phenomena and this study utilizes freight data between four
Southeast Asian countries to the U.S. in an attempt to study the “air-sea shift”. Stratifying thedata according to sea transport ratio, transition of values and the number of commodity
groups are investigated in the preliminary analysis. The result suggests that a shift of freightin favor of sea carriers is, though not obviously, observed in trade value. The binary logit
model on choice between air versus sea suggest that the value per weight of commodities andshipping cost are important in addition to commodity characteristics such as unfinishedproducts, components and finished products. The relatively low Rho square indicates theabsence of other factors which this study is not able to gather.

1

INTRODUCTION
Maritime and air transport are major modes in transporting international freight transport.
Relative to maritime freight, air freight was usually confined to high value, low weight, time
sensitive, customer and destination specific due to its high costs. This is changing. Maritime
transport is beginning to compete in shipping commodities such as documents,
pharmaceuticals, fashion garments, electronics consumer goods, and perishable food products
and etc (Coyle et al, 2010, DC Velocity Staff, 2007 and MLIT Japan Civil Aviation Bureau,2009). As part of combating climate change, DELL is progressively using sea transportationin shipping computer equipment (Green Transportation and Logistics, DELL). In recent years,
pharmaceutical products such as Ventolin and blood products are shipped as chilled or frozen(UK P&I CLUB, 2002). This modal shift of international freight is referred to as “sea shift”.
Worldwide growth in ocean import tonnage has grown faster than air import tonnage since2004. In Asia, this trend has been observed since 2003. A survey concluded that 16 out of 21
shippers in Japan had shifted shipments from air to sea (MLIT Japan Civil Aviation Bureau,2009) The major drivers of sea shift are: (i) the rise in air freight rates; (ii) enhanced andefficient sea transport service; (iii) changes in global supply chain and logistics; and (iv)desire for environmentally friendly transportation. Thus the demarcation between
commodities ‘captured’ by sea and air begin to blur creating a large area of contestability. Few
studies have addressed this phenomenon as this shift unfolds. Against this backdrop, thisstudy attempts to understand the ‘sea shift’ phenomenon, more specifically, to discover towhat extent ‘sea shift’ is true and to ascertain the factors which affect mode choice. The
analysis utilizes freight transport data between the U.S. and four major Southeast Asian
countries drawn from TradeViewTM, by Zepol (http://www.zepol.com/).

LITERATURE REVIEW
Remarkably fewer studies have been done on freight mode choice than on passenger travel
mode choice mainly due to data availability issue. Studies on freight mode choice models
originally began in 1970s. At that time logistics operations started to become increasingly
complex and gather more attention among researchers. From 1980s onwards, modeling basedon random utility theory began to prevail in the field of discrete choice modeling and logit
model became mainstream in freight/passenger mode choice modeling, although some studies
which do not adopt logit model are found such as Abdelwahab (1998) and Tsuboi et al (2010).
Logit model has been widely applied and used due to its theoretical consistency andsuperiority in accuracy and handling. Picard and Gaudry (1998) studied the superiority of a
Box-Cox logit model over the linear one using the example of freight mode choice between
truck and rail in Canada. Another good example of developed logit mode used for freightmode choice is nested logit model (Jiang et al, 1999). Weighted logit freight mode-choice2

model attempted by Rich et al (2009) is also derived from the nested logit model.
When it comes to the modes examined in previous studies, rail, truck and their combination
often appear (Nam, 1997; Abdelwahab, 1998; Jiang et al, 1999 and Norojono et al, 2003).
There have been not so many studies done which includes maritime transport in its choice set(Rich et al, 2009 and Hayakawa et al, 2011) partly because data for shipping industry is
relatively inaccessible. As far as we know, Tsuboi et al (2010) is the only research which deals
with mode choice between sea and air. It focuses on increasing competition between sea andair transport and considers a mode choice model taking into account time, cost, inventory costand obsolescence cost. But other than Tsuboi et al (2010), no attempt has been made with afocus on air-sea mode choice and influential attributes regardless of overall sea shift trend andpopularization of logit freight mode choice modeling.

METHODOLOGYThis study consists of two major analytical parts. As stated in the introduction, it is said thatthe trend of sea shift has been globally progressing since the mid-2000s. Firstly, an attempt
was made to reveal whether the sea shift trend is true of freight transport between the U.S.
and Southeast Asia by investigating container traffic statistics for certain commodity groups(10 digits HTS code) between four selected ASEAN countries (Malaysia, Thailand, Singapore
and Indonesia) and all the U.S. districts. More specifically sea transport ratio, which
represents how many percent in value was shipped by sea and is hereafter written as STR intables and figures, was calculated as an indicator of sea shift. Dividing data into fivecategories: air dependent (STR 0 to 10%), slightly air dependent (STR 10 to 40%), highmodal competition (STR 40 to 60%), slightly sea dependent (STR 60 to 90%), sea dependent(STR 90 to 100%), an exploration on the transition of the number and the value ofcommodities was done. The data of commodities with less than 100,000 USD value were
eliminated beforehand in order to avoid the effect of minor commodities.
An analysis was done to clarify causes and attributes determining mode choice. Modal choiceproblems were set as simple binary choice problem between sea and air. Logit model was
employed as modeling methodology; due to its wide acceptance in the field of modal choicemodeling and ease in handling. Under random utility theory, the probability for n to choose
alternative i is written as follows (Ben-Akiva and Lerman, 1985)

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𝑃𝑛 (𝑖) = Pr(𝑈𝑖𝑛 ≥ 𝑈𝑗𝑛 )= Pr(𝑉𝑖𝑛 + 𝜀𝑖𝑛 ≥ 𝑉𝑗𝑛 + 𝜀𝑗𝑛 )= Pr(𝜀𝑗𝑛 − 𝜀𝑖𝑛 ≤ 𝑉𝑖𝑛 − 𝑉𝑗𝑛 )

(1)

Here
𝑈𝑖𝑛 : Random variables representing utility for n choosing alternative i.
𝑉𝑖𝑛 : Systematic components of 𝑈𝑖𝑛
𝜀𝑖𝑛 : Disturbance components of 𝑈𝑖𝑛
In logit model, 𝜀𝑛 = 𝜀𝑗𝑛 − 𝜀𝑖𝑛 is assumed to be logistically distributed. Hence theprobability is written as follows.
𝑃𝑛 (𝑖) =exp(𝑉𝑖 )⁄∑𝑁 exp(𝑉 )
𝑘
𝑘=1

(2)

Many models were estimated with changing variables in order to discover critical variables.
Their utility function will be shown in the following section along with their estimation
results. Unlike the preliminary analysis, destinations were confined to two districts: Los
Angeles and New York. Eliminating commodities having less than 100,000 USD value
similarly in the preliminary analysis, we used the year 2011 data for estimating parameters ofutility function. Out of the data gathered, 10 to 40% STR commodities and 60 to 90% STR
commodities were used as choice sets. The former commodities were seen as alternative 0: by
air, and the latter as alternative 1:by sea. In other words, one HTS 10 digits commodity acts as
one sample, even though it is, in fact, accumulated data for a year and does not correspond to
one single mode choice. As for software for estimation, BIOGEME (Bierlaire, 2003) was
used.

DATA
TradeViewTM, which is provided by Zepol, was used for data source in the present study.
TradeViewTM is based on “trade data released by the U.S. Census Bureau’s Foreign Trade
Division” (TradeViewTM Website, Zepol). In this database, various items, such as air/vessel
value and air/vessel weight, are provided for each commodity groups. At the most precise
level, HTS code 10 digits data can be obtained at the level of the U.S. districts although it
does not mention districts or ports for the other country.
Two different types of datasets were examined for different purposes in the present study. The
U.S. imports data from four selected ASEAN countries to all the U.S. districts were used forthe preliminary analysis whereas data of logit modeling has only two districts as destination.

4

Names of items
Value

Definitions
Value of general imports assessed by U.C. customs, or the price actually
paid or payable, excluding U.S. import duties, freight, insurance and othercharges.

Vessel/Air Value

Value of goods (in dollars) transported by waterborne vessels/airborne
carriers.

Vessel/Air Weight

Weight of goods (in kilograms) transported by waterborne/airborne carriers.

Consumption
Charges

Import Total cost of all freight, insurance and other charges (excluding U.S. importduties) incurred in bringing the merchandise from the carriers at the foreign
port to the carrier at the first port of entry in the U.S.

Air Import Charges

Total cost of all freight, insurance and other charges (excluding U.S. importduties) incurred in transporting the merchandise via air from the carriers at
the foreign port to the carrier at the first port of entry in the U.S.

Table 1. Definitions of collected data
Source: Zepol TradeViewTM Glossary
As for commodity types, fresh and processed foods (start with 02, 03, 04, 05, 06, 07, 08, 09
and 10 in HTS codes), pharmaceutical and organic chemicals (starts with 29 and 30), clothing(starts with 61 and 62) and manufacturing goods (starts with 84, 85 and 90) were chosen forboth datasets because they are generally perceived as commodities in area of contestability
according to some reports. The data dating back to 2007 is available and annual data from2007 to 2011 was collected and analyzed; even though monthly data is also available. The
items we referred to and their definitions are shown in Table 1.

RESULTS OF ANALYSIS
The result of preliminary analysis is summarized in Table 2. First of all it should be noted thatthe total trade value shows sudden drop in 2009. Similar trend can also be seen in STR 60 to90%. It is thought to be due to worldwide financial crisis triggered by Lehman’s fall. Possible
explanation is that the demand in the U.S. dramatically dropped in the financial crisis andgradually picked up in following years. Turning to other groups, we can see that sea
dependent commodities (STR 90 to 100%) and commodities in high competition zone (STR
40 to 60%) have been increasing at slower pace. On the other hand STR 0 to 10% shows
consistent decreasing trend. Thus, it suggests that dependence on sea transport has been
increasing. In other words, ‘sea shift’ seems to be taking place in terms of traded value. Whenit comes to the number of commodity groups in each stratum, it is hard to see any consistentincreasing or decreasing trend. It is enough to mention that similar trend can be observed in
the total, STR 0 to 10% and STR 60 to 90%. STR 10 to 40% and 40 to 60% show gradual riseafter 2009. But these results cannot be sufficient evidence of ‘sea shift’. From the5aforementioned results, we can reach the tentative conclusion that ‘sea shift’ is progressing invalue, but not in commodity variety. The authors speculate that this ‘sea shift’ results from theimprovement of service quality in sea transport.
Table 3 shows that manufacturing goods (HTS 84 and 85) are predominant in terms of bothquantity and varieties. There is no clear consistent trend seen in commodity group breakdown
other than that STR 10 to 60% commodities have been slightly increasing in manufacturing
goods.
As for the modal choice modeling, the estimation results of three different models, out of anumber of models tried, are presented and discussed for comparison. The utility functions andthe estimation results for respective model are shown in table 4 and 5. The logarithm of value# of commodity
groupsValue [Million USD]
(Brackets) show share (%)

per weight was incorporated into the models because it showed better fits than without
logarithm. It is consistent with a result of previous study (Rich et al, 2009). The observation
number is 669 which is not identical to sum of STR 10 to 40% and 60 to 90%. This is because
all the datasets had to have more than 100,000 USD in single OD pairs. Consequently thecommodities used in logit models were mostly from HTS 61, HTS 62, HTS 84, HTS 85 and
HTS 90.

Total
STR 0 to 10 %STR 10 to 40 %STR 40 to 60 %STR 60 to 90 %STR 90 to 100%
Total
STR
STR
STR
STR
STR

0 to 10 %10 to 40 %40 to 60 %60 to 90 %90 to 100 %Year 2007
63,98839,528(61.77)4,978(7.78)
1,018(1.59)
8,063(12.60)10,401(16.25)
2,079
432
216
124
436
871

Year 2008
59,74332,966(55.18)5,167(8.65)
1,785(2.99)
8,962(15.00)10,862(18.18)
2,076
423
209
122
423
899

Year 2009
50,64727,881(55.05)3,548(7.00)
2,536(5.01)
4,755(9.39)
11,927(23.55)
1,966
404
215
118
390
839

Year 2010
55,652
31,074(55.84)4,260(7.65)
2,412(4.33)
6,332(11.38)
11,573(20.80)
2,017
459
230
133
414
781

Year 2011
57,54429,801(51.79)4.695(8.16)
4,191(7.28)
6,595(11.46)
12,262(21.31)
2,077
426
241
148
446
816

Table 2. Value and the number of commodity groups in stratified categories

6

FoodsChemical andPharmaceutical
Clothing
Manufacturing
Goods

Total
STR
STR
STR
STR
STR
Total
STR
STR
STR
STR
STR
Total
STR
STR
STR
STR
STR
Total
STR
STR
STR
STR
STR

0 to 10%10 to 40%
40 to 60%
60 to 90%
90 to 100%
0 to 10%10 to 40%
40 to 60%
60 to 90%
90 to 100%
0 to 10%10 to 40%
40 to 60%
60 to 90%
90 to 100%
0 to 10%10 to 40%
40 to 60%
60 to 90%
90 to 100%

Year 2007
170
14
5
3
11
137
93
25
3
4
5
56
543
9
26
25
207
276
1,273
384
182
92
213
402

Year 2008
185
18
4
2
8
153
93
24
5
4
3
57
523
7
15
22
176
303
1,275
374
185
94
236
386

Year 2009
181
17
2
5
4
153
91
22
3
3
3
60
488
6
14
17
150
301
1,207
359
196
93
233
326

Year 2010
180
14
5
2
7
152
106
27
1
5
3
70
484
7
18
22
189
248
1,247
411
206
104
215
311

Year 2011
184
15
4
2
12
151
110
31
3
4
7
65
512
7
15
27
195
268
1,271
373
219
115
232
332

Table 3. The number of commodity groups in stratified categories by commodity types

Model 1Model 2

Utility function
𝑉 = 𝛽𝐶𝑃𝑊 𝐴𝑖𝑟 ∗ 𝐶𝑃𝑊 + 𝐴𝑆𝐶…. (same throughout three models)
𝑉 𝑠𝑒𝑎 = 𝛽𝐶𝑃𝑊𝑆𝑒𝑎 ∗ 𝐶𝑃𝑊 𝑆𝑒𝑎 + 𝛽𝑉𝑃𝑊 ∗ 𝐿𝑜𝑔(𝑉𝑃𝑊 𝑇𝑜𝑡𝑎𝑙 )
𝑎𝑖𝑟

𝐴𝑖𝑟

𝑉 𝑠𝑒𝑎 = 𝛽𝐶𝑃𝑊 𝑆𝑒𝑎 ∗ 𝐶𝑃𝑊 𝑆𝑒𝑎 + 𝛽𝑉𝑃𝑊 ∗ 𝐿𝑜𝑔(𝑉𝑃𝑊 𝑇𝑜𝑡𝑎𝑙 ) + 𝛽𝐻𝑇𝑆61𝑑𝑢𝑚𝑚𝑦 ∗𝐻𝑇𝑆61𝑑𝑢𝑚𝑚𝑦 + 𝛽𝐻𝑇𝑆62𝑑𝑢𝑚𝑚𝑦 ∗ 𝐻𝑇𝑆62𝑑𝑢𝑚𝑚𝑦 + 𝛽𝐻𝑇𝑆84𝑑𝑢𝑚𝑚𝑦 ∗𝐻𝑇𝑆84𝑑𝑢𝑚𝑚𝑦 + 𝛽𝐻𝑇𝑆90𝑑𝑢𝑚𝑚𝑦 ∗ 𝐻𝑇𝑆90𝑑𝑢𝑚𝑚𝑦

𝑉 𝑠𝑒𝑎 = 𝛽𝐶𝑃𝑊 𝑆𝑒𝑎 ∗ 𝐶𝑃𝑊 𝑆𝑒𝑎 + 𝛽𝑉𝑃𝑊 ∗ 𝐿𝑜𝑔(𝑉𝑃𝑊 𝑇𝑜𝑡𝑎𝑙 ) + 𝛽𝐷𝑑𝑢𝑚𝑚𝑦∗ 𝐷𝑑𝑢𝑚𝑚𝑦 + 𝛽𝑃𝑎𝑟𝑡𝑠𝑑𝑢𝑚𝑚𝑦 ∗ 𝑃𝑎𝑟𝑡𝑠𝑑𝑢𝑚𝑚𝑦
ASC: Alternative specific constant, CPW: Cost per weight (Air/Vessel import charges divided by
air/vessel weight), VPW: Value per weight (Air/Vessel value divided by air/vessel weight),
HTS61/62/84/90dummy: a dummy variable which takes value 1 if the commodity’s HTS code starts
with 61/62/84/90. Ddummy: a dummy variable which takes value 1 if its destination is New York,
Partsdummy: a dummy variable which takes 1 if the commodity is not finished product but parts.
Model 3

Table 4. Utility functions of the estimated models

7Model 1669-5.17(***-11.10)-0.0175(-0.89)-0.295(**-2.56)-1.11(***-9.02)# of observations
𝐴𝑆𝐶
𝛽𝐶𝑃𝑊 𝐴𝑖𝑟𝛽𝐶𝑃𝑊 𝑆𝑒𝑎𝛽𝑉𝑃𝑊𝛽𝐻𝑇𝑆61𝑑𝑢𝑚𝑚𝑦𝛽𝐻𝑇𝑆62𝑑𝑢𝑚𝑚𝑦
𝛽𝐻𝑇𝑆84𝑑𝑢𝑚𝑚𝑦
𝛽𝐻𝑇𝑆90𝑑𝑢𝑚𝑚𝑦
𝛽𝐷𝑑𝑢𝑚𝑚𝑦𝛽𝑃𝑎𝑟𝑡𝑠𝑑𝑢𝑚𝑚𝑦# of parameters
Likelihood Ratio Test
Adjusted Rho square

Model 2669
-4.63(***-8.10)-0.0196(-0.99)-0.285(**-2.42)-0.999(***-7.36)
0.353(1.06)
0.407(1.54)-0.164(-0.61)-0.358(-1.00)Model 3669
-6.42(***-11.33)-0.0279(-1.21)-0.208(*-1.88)-1.31(***-9.49)-0.943(***-4.48)-0.931(***-3.50)
48
6
234.879241.657
265.512
0.245
0.243
0.273
* 10 % significant, ** 5% significant, *** 1% significant

Table 5. Results of estimation, (Brackets) show t-value

In model 1, only four parameters were estimated and among them only one coefficient,𝛽𝐶𝑃𝑊𝐴𝑖𝑟 , is not statistically significant. Although three variables are significant at 5% and themodel itself is 1% significant in likelihood ratio test, adjusted rho square is only 0.243 which
is not high compared to other studies using logit model (for example, Rho square isapproximately 0.5 in Nam (1997) and approximately 0.4 in Hayakawa et al (2011)). 𝛽𝐶𝑃𝑊𝐴𝑖𝑟is unalterably insignificant in every specification. That is possibly because air transportcommodities normally have high added value or/and time-sensitivity and shipping rate does
not have remarkable influence. The signs of significant parameters are consistent with the
expectation. They can be interpreted that high sea transport cost and high value for unit
weight decrease the utility of sea transport - i.e. those commodities are more likely to be
shipped via air. On the other hand, ASC, which is in the function for air, shows negative signand large value of coefficient. It implies that there are some important backgrounds which are8not taken into account in the model and act to put down the utility of air transport.
Alternatively we can also interpret it as meaning that many of shippers select sea transport,
just following custom and without any consideration. In one model tried, which is notpresented in table 5 and had seven ODdummy variables standing for eight OD pairs, five outof seven dummy variables was 5% significant. Ideally OD pairs should have been more
specifically identified and actual transit time should have been used as variables for moreaccurate estimation. In model 2, four of HTSdummy variables were added to represent
commodity characteristics. But none of them passes 5% significant criteria possibly because
categorization by two digits HTS code is too crude to represent their properties. Those trial
and errors led to find model 3 where two of dummy variables are incorporated: Partsdummy
and Ddummy. Considering their signs, it can be said that shipment to LA and finishedproducts tend to be shipped by sea transport. As for destinations, it makes sense in that LA isgeographically closer to Southeast Asia and air transport accordingly cannot show its speed
advantage. On the other hand, it was unexpected that finished product more tend to be
shipped by sea because it is viewed finished product had higher added value and deserved airtransport. This may be explained by the fact that parts commodities tend to require highpunctuality and swiftness.

CONCLUSION
This study examined the sea shift from air transport and ascertains factors that influence themode choice in selected commodities between four Southeast Asian countries and the U.S.
Preliminary results partly confirmed this trend. Sea shift, however, does not seem to be taking
place in terms of commodity variety. Possible explanation is that Southeast Asia and the U.S.
are not geographically as close as drivers of sea shift exceed the superiority of air transport inspeed. The results of the binary logit modeling indicate that variables representing commodity
characteristic, route and cost significantly affect mode choice except for air transport costwhose coefficient are extremely small. The relatively low explanatory power of the models
may be due to omission of important variables.
One limitation of the present study is that each observation in the logit modeling is aggregateddata and does not reflect mode choice of a single shipment. Further, route attributes wererepresented by dummy variables due to unavailability of route specific data. Accurate route
attributes should give better results. The inclusion of more origin and destination coveringlager and longer distances areas such as Europe, Middle East and East Asia could bring forth
the advantage of speed over cost. A more precise classification on commodity characteristics,
which was not achieved in this study due to data availability, is also encouraged to be
attempted.
9

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