The spatial coverage of shipping lines and container terminal operators

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Journal of Transport Geography 16 (2008) 292–299

The spatial coverage of shipping lines and container terminal operators

Francesco Parola a,*, Albert W. Veenstra b

a University of Genoa, Italian Centre of Excellence for Integrated Logistics and Faculty of Economics,

Department of Business Studies, Via Vivaldi 5, 16126 Genoa, Italyb RSM Erasmus University, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, The Netherlands

Abstract

The aim of this paper is to provide a quantitative basis for the discussions about globalization of carriers and container terminal oper-ators. A measuring framework is developed that allows for the comparison of the distribution of ship carrying capacity across variousregions, and the comparison of the distribution of terminal throughput worldwide. This approach also allows the investigation of thedegree to which liner shipping networks match related terminal portfolios.

The main outcomes highlight significant differences between the geographical scope of liner networks, and between the coverage ofcorresponding terminal portfolios. Interesting results emerge from the matching of networks of integrated operators: early-movers suchas Maersk and latecomers such as MSC have very different levels of vertical integration.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Liner shipping; Stevedoring industry; Gini coefficient; Spatial coverage; Vertical integration; Globalization

1. Introduction

The shipping industry is by nature a ‘global’ industry.Ships carry most international trade, and a large part ofit is made up of commodities and products that have tobe carried from one part of the world to the other.

The global nature and especially the dynamics of indus-try concentration receive much attention from academics.This is witnessed by a growing body of very recent work(Olivier et al., 2007; Bichou and Bell, 2007; Olivier, 2005;Slack and Fremont, 2005; Notteboom and Rodrigue,2005) on the development of partnerships and alliances inliner shipping, port operations and between liner shippingcompanies and port operators. In much of this ‘companyoriented’ work, the globalization and concentration ofthe parties offering container transportation and terminaloperations is the main starting point, the recent merger

0966-6923/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jtrangeo.2007.11.001

* Corresponding author. Tel.: +39 010 209 5071; fax: +39 010 209 5088.E-mail address: [email protected] (F. Parola).

between Maersk and P&O Nedlloyd being an often citedexample.

At the same time, however, Fremont and Soppe (2003)showed that there are great differences between the geo-graphical coverage of trade routes by liner shipping com-panies. While many of the European carriers seem to aimfor a full coverage of the major markets in the world, anumber of Asian carriers concentrate a smaller set ofroutes, or on a specific area such as the Pacific. This ‘ser-vice-oriented’ view shows that there is not only a concen-tration of companies, but also a concentration of servicesin certain areas.

There is very little previous work that analyses this sec-ond type of market concentration in shipping. Some papersoffer rather descriptive investigations (see for instance,Slack et al., 2002), but little quantitative analysis. Thispaper aims to develop a measuring framework that willallow for the comparison of the service concentration inliner networks and terminal operations. With this measure-ment framework, we are able to test statistically if certaincompanies offer more concentrated services than others

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F. Parola, A.W. Veenstra / Journal of Transport Geography 16 (2008) 292–299 293

and if the service pattern of certain companies may be sim-ilar or not to the global spread of their terminal portfolio.

The analysis in the present paper can be seen as a con-tribution to the debate on globalization of the servicesoffered by liner shipping companies and terminal operatingcompanies, but it is not a study in company international-isation. The academic literature on internationalisation ofmultinational enterprises (see, for instance, Dicken, 2002;Johanson and Vahlne, 1977; Perlmutter, 1969; Porter,1990; Rugman and Verbeke, 2004; Sullivan, 1994, 1996;UNCTAD, 2002; van Tulder et al., 2001) suggests that amore elaborate set of variables is required to assess theinternationalisation of companies: percentage of foreignsales, international experience of management, number offoreign subsidiaries, percentage of foreign assets, disper-sion of international operations, and so on. Looking atthe shipping industry, it is immediately clear that some ofthese measures may not be so useful: a shipping line mayhave low percentages of foreign sales since all cargo book-ings are done in the country of destination under free-on-board conditions. Also: how should the dispersion of oper-ations be measured with moving assets such as ships?

This paper uses liner and terminal networks as a basisfor the analysis of the variable ‘dispersion of internationaloperations’, an approach that has previously been sug-gested by, for example, Sullivan (1994).

The remainder of the paper is structured as follows. Sec-tion 2 introduces the formal measurement tools that will beemployed in this paper to measure and compare globalcoverage. Section 3 introduces the data. Section 4 reportsthe results for liner shipping companies and terminal oper-ators. This section also shows how the measurement frame-work can be used to compare the spatial coverage of a linernetwork and a terminal portfolio. The final section con-tains concluding remarks and raises questions for futurestudies.

2. Measuring global coverage

A number of authors in the field of maritime and porteconomics have addressed the issue of continuous global-ization of liner shipping and container port businesses overtime.

Slack et al. (2002) present a comparison of the geogra-phy of shipping lines in terms of areas served, number ofservices offered and ports selected, for sampled years. Fre-mont and Soppe’s (2003) analysis has already referred to inSection 1.

Slack et al. (2002) and Notteboom (2004) illustrate thegreat dynamic in the liner shipping industry, which seemsto be in constant re-alignment as a result of mergers, acqui-sitions and takeovers, with subsequent service adjustments,vessel rescheduling and so on.

Turning to the stevedoring industry, some interestingcontributions about the international focus of terminaloperators are Ferrari and Benacchio (2000), Peters (2001)and Notteboom (2004). These authors also observe increas-

ing concentration and the development of ‘global’ players,but say generally little about what this means in terms ofthe presence of these operators in different geographicalareas.

More recently, Slack and Fremont (2005) analyse theport terminal operations industry and conclude that thisindustry is characterised by two dominant business models,one where the terminal operator is the result of a horizontalintegration in the port terminal industry, and the other,where the terminal operator has developed out of a verticalintegration process with a liner shipping company. In prac-tice, a ‘hybrid’ strategy is becoming common: maritimegroups use their terminals to facilitate their own shippingactivities, but also look for additional third-party customersto increase the profitability of the stevedoring business. Thistopic is also discussed by Olivier et al. (2007), who concludethat there is still a strong relationship with local institu-tional conditions and the expansion possibilities of terminaloperators.

Given that the term globalization is strongly associatedwith the development of companies in liner shipping andterminal operations, we use the term ‘global service cover-age’ to indicate the degree of globalization of transportand container handling services. A high global coverageimplies that the liner network or container terminal portfo-lio covers many different geographical areas, but also thatthe distribution of capacity across those areas is in line withglobal demand for container transportation or containerhandling.

All these papers discuss globalization in liner shipping(McCalla et al., 2004) and container handling and, in somecases, some simple measures are used to quantify this glob-alization, such as the number of routes, or the number ofareas served. However, to our knowledge, no formal anal-ysis has been presented so far that allows for the compari-

son of degrees of global coverage in such a way that we cantest statistically if the service network of Maersk Line ismore global than APL or not.

The liner shipping industry is made up of a large numberof geographical areas that are connected by routes. Inter-national trade is carried across these routes, in a mannerthat reflects the distribution of economic developmentacross the globe: much of the cargo flows travel fromindustrialising countries, such as China, to consumptionoriented areas such as Europe and North America. Wemeasure the presence of liner services in different areas bylooking at the distribution of ship carrying capacity thatis offered in these areas over one year. We take this capacitydistribution to reflect, at a global level, the distribution oftrade across the globe.

Given that there is no absolute measure of global ser-vice, we employ a relative measure that compares the distri-bution of shipping capacity across areas to the distributionof ship capacity of the entire industry. As a measure(benchmark) for the entire industry, we use the distributionof the 25 largest liner shipping companies in the world.This admittedly does not cover the entire industry, but in

294 F. Parola, A.W. Veenstra / Journal of Transport Geography 16 (2008) 292–299

such a concentrated industry, it seems an adequate measurethat reflects a large part of the industry.1

This approach reduces the comparison between networksto a comparison of ship capacity distributions. For the com-parison of distributions, many dispersion and concentrationmeasures could be used, including simple measures as themedian and variance, as well as more complex measuressuch as the Herfindahl index and the Gini coefficient. Ouranalysis will be based on the Gini index (see for applicationsin the maritime industry for instance Notteboom, 1997).

Note that the comparison of distributions rules out theimpact of the absolute level of ship capacity in the compar-ison. Obviously, a company cannot have the same amountof ship capacity compared to the world total, but the distri-bution of the capacity can, in theory, be completely similarto the world total. Such a company would be labelled as a‘global player’.

For terminal operators, the analysis is somewhat sim-pler, because terminals are fixed assets. We will thereforelook at the distribution of terminal throughput across areasand compare that to the distribution of terminal through-put in the world. Again, we use the distribution of total ter-minal throughput of the 24 largest international terminaloperators as a benchmark to measure global coverage ofthe individual company terminal portfolios. Finally, a com-parison of terminal operations with liner services should bebased on the same type of data. Given that we have linershipping capacity, and terminal throughput, we use termi-nal utilisation information for different areas in the worldto calculate (approximately) terminal capacity from termi-nal throughput data.

In calculating the Gini index, we follow Giles (2004). Heintroduces an artificial Ordinary Least Squares (OLS) basedcalculation method that has the added benefit that hypoth-esis tests on the Gini coefficients across the data set can beperformed in a Seemingly Unrelated Regression (SUR)framework. The Gini coefficient G can be calculated from

G ¼ 2hn� 1� 1

n; ð1Þ

where n is the number of observations, and h is the leastsquares estimator in

iffiffiffiffi

yip ¼ h

ffiffiffiffi

yip þ ui: ð2Þ

Here yi is the ith data point in y, and i is a rank vector.ui ¼

ffiffiffiffiffiffiffi

yivip

, where vi is a error term with the usual proper-ties. Note that using (2) implies the data have to be enteredin the regression procedure after ranking by the size of thedata elements in y. This procedure directly generates stan-dard errors for h and thus for G.

A drawback of this method is, as Ogwang (2004)remarks, that the usual assumption for the error term ofthe regression in (2) may not be valid in most real-worldcases. The differences between the standard errors resulting

1 The 25 samples shipping lines controlled over 77% of the fully cellularfleet capacity at end-2005.

from different methods are, however, small and alternativemethods, such as the jackknife2 procedure also have theirproblems. In any case, this drawback concerns the stan-dard errors, and not the point estimate of the Gini coeffi-cient itself. The quality of the standard errors alsodepends strongly on the amount of data used. In our case,the size of the data set is sufficient to avoid small sampleproblems.

Comparing Gini coefficients is now fairly straightfor-ward by stacking the relevant data in one vector, and per-forming a SUR (see for details, for instance, Heij et al.,2004, p. 684). This results in two Gini values that can betested for equality with the common Wald test (Heijet al., 2004, p. 232). This test has as its null hypothesis thatthe Gini values are the same.

Rejection of the null hypothesis indicates different distri-butions of capacity across areas. The tests will be per-formed by comparing each company against the worldtotal as explained above. Rejection of the null hypothesiswould indicate that the company has a different spread ofactivities than the industry overall and thus has a differentglobal coverage. Given that we compare the company’s ser-vice offering to the world total, this implies that the globalcoverage of that company is always lower than the worldtotal. In most cases, we therefore expect to find Gini valuesthat will be higher (indicating a more concentrated distri-bution) than that of the world total. A Gini value that islower than the world total indicates the distribution of thatparticular company is probably more uniformly (i.e. moreflat) spread out across a similar number of routes than thedistribution for the world as a whole. We present the out-come of the tests together with data on number of routes orareas as well as the distribution of capacity to be able to seehow well these two variables on their own indicate globalcoverage. To say something about development over time,we present results for two selected years: 2002 and 2005.

3. The data

The analysis in this paper is based on two data sets. Forthe liner shipping industry, a data set is used that was orig-inally collected by Fremont and Soppe from data in theContainerisation International Yearbook (selected years).This data set consists of total container (slot) carryingcapacity allocation across 27 major geographical areas.These data are the result of the ship capacity deployedbetween the 27 regions and include thousands of routes.A list of the operating areas can be found in Table 1.The figures in the basic database (in slots supplied perport), represent a powerful synthesis of service patterns,ship capacity, number of vessels deployed and service fre-quency. In our study we take into account only the opera-tional capacity deployed by the sampled carriers, namely

2 The jackknife procedure is a re-sampling approach to estimatestandard errors of parameter estimates. See for a reference Bissell andFerguson (1975).

Table 1Ship capacity and terminal throughput by area

Areas 2002 2005

Slot allocation per area –carrying capacity (%)

Annual throughputper area (%)


Annual throughputper area (%)

Southern Africa 1.13 0.03 1.13 0.02East Africa 0.85 0.22 0.27 0.19North Africa 0.08 0.00 0.26 0.00West Africa 0.84 0.61 1.07 1.07Central America 1.51 1.72 2.35 1.77East Asia 14.70 24.76 16.60 29.08North East Asia 11.78 6.17 10.15 4.67South East Asia 10.28 17.56 10.79 15.09Australasia 0.86 1.09 0.90 0.57Caribbean 2.82 1.46 1.54 1.36European Atlantic Coast 0.98 0.38 0.74 0.19North America East Coast 3.99 3.14 3.85 3.12South America East Coast 2.22 0.52 1.87 0.52North America West Coast 5.96 10.61 4.35 9.13South America West Coast 0.57 1.02 0.81 0.71East Med/Black Sea 3.66 0.52 4.34 1.60North America Gulf Coast 1.28 0.33 1.39 0.32British Islands 8.49 2.97 8.14 1.94Red Sea 2.15 0.56 4.34 0.74Mid-East 3.47 4.01 3.28 4.15Indian Ocean 0.46 0.00 0.34 0.01West Mediterranean 6.95 7.80 7.49 7.84South Pacific 0.07 0.00 0.10 0.00Northern Range 10.28 12.10 10.74 13.15Scandinavia/Baltic 0.69 0.13 0.44 0.26Indian Subcontinent 3.93 2.32 2.75 2.52

Total 100 100 100 100

Note: Throughput data are refer to 2002 and 2005 year-end (Drewry, 2003 and 2006) while carrier data were collected in late-2002 and late-2005.


owned and chartered vessels. Therefore, slot charter andvessel sharing agreements are not included. This wouldhave led to substantial double counting of the real capacityoffered by major carriers. On the contrary, our aim is totest statistically the global coverage achieved through man-aged and controlled ‘assets’. Feeder services have been con-sidered only if directly operated.

Since container ships follow routings across differentareas, there is still some degree of double counting of con-tainer ship capacity in the data. Eliminating this doublecounting would require information on the exact numberof container liftings in various areas that can be used tocorrect the total capacity offered for capacity that is alreadyreserved for containers lifted in areas previously visited.Such information is not generally available for a largenumber of companies. We therefore use the uncorrected‘total capacity offered’ data.

The data for the international container terminal opera-tors is based on data collected from Drewry reports (2003and 2006) and other public sources3 on the 2002/2005 port-folios of the major terminal operator companies. The col-lected data includes the location and yearly throughputof the individual terminals. The presence of the various ter-

3 Terminal operator and port authority websites, ContainerisationInternational on-line and corporate interviews.

minal handling companies is categorized in the same 27regions as for the liner shipping data. We calculate theworld total terminal throughput as the sum of the 24 inter-national terminal operators in our data set. This represents67% of total terminal throughput in the world, and coversall major terminal operating companies. We use terminalutilisation provided in the same Drewry reports for majoroperating areas to calculate approximate terminal capacityfrom terminal throughput data.

4. Main outcomes

Table 1 presents an overview of the data. The Tableshows that the distribution of carrying capacity and termi-nal throughput per area is very uneven. Furthermore, theport throughput is much concentrated in a few regions(mainly East Asia and South-East Asia) while the shipcapacity is distributed across various ranges. The overalltrend in both markets (2002–2005) shows an increase ofthe weight of a few major geographical areas, particularlyEast Asia (which includes China).

The results from the Gini calculations for the 25 largestliner shipping companies and the 24 largest terminal opera-tors in the world are shown in Tables 2 and 3, respectively.Table 2 only considers the top 25 liner shipping companiesin 2005 and shows a number of features that are also noted

Table 2Top 25 liner shipping companies

Carrier 2002 2005


No. ofareas

Concentrationindex (Gini)


No. ofareas


Wan Hai 1.13 6 0.85 1.14 7 0.85Hyundai MM 3.40 10 0.75 1.67 11 0.80PIL 1.45 11 0.75 1.33 13 0.75CSAV-Norasia 0.00 1.88 12 0.72Hatsu 0.00 0.82 11 0.72COSCON 2.73 13 0.78 2.92 17 0.72Yangming 2.50 11 0.74 1.92 12 0.72Hanjin 5.15 17 0.70 3.12 17 0.72China Shipping 2.73 16 0.69 3.18 18 0.71K Line 4.54 17 0.72 2.47 20 0.71OOCL 2.48 13 0.79 2.40 15 0.71APL 6.23 16 0.67 3.41 18 0.70Lloyd Triestino 0.97 13 0.71 0.92 16 0.69UASC 2.42 10 0.69 1.43 11 0.68MOL 3.57 20 0.65 2.88 22 0.67Delmas 0.91 15 0.70 1.20 15 0.67CMA-CGM 5.68 24 0.59 18.70 25 0.66ZIM 2.47 19 0.68 2.39 19 0.64CSAV 0.70 13 0.66 1.13 15 0.64Evergreen MC 7.21 19 0.65 4.46 18 0.63NYK 2.76 21 0.67 3.20 23 0.61Hamburg Sud 0.00 1.77 20 0.58Hapag-Lloyd 4.38 18 0.62 5.77 22 0.53Maersk Line 19.31 25 0.51 18.11 26 0.51MSC 9.40 24 0.51 11.78 25 0.49PONL 7.87 26 0.50 0.00

Total top 25 100 26 0.54 100 26 0.55

Note: Lloyd Triestino was re-branded as Italia Marittima in March 2006. In mid-2007 Evergreen Marine Corp. (Taiwan) Ltd., Hatsu Marine Ltd. andItalia Marittima S.p.A. adopted a unified common trade name Evergreen Line for international marketing. All three carriers are affiliates of Evergreen ofTaiwan. P&O Nedlloyd was taken over by AP Møller/Maersk in mid-2005.

4 For this analysis we reduced the number of listed terminal operators(from 24 to 15) as many of them reject the test.


by other authors. The industry is characterised by consider-able growth (see for instance the number of routes per com-pany), by integration of companies through mergers andtakeovers, and by industry dynamics. As a result, the top25 in 2005 is different from 2002, with new companiesappearing in 2005 (Hamburg Sud, CSAV Norasia andHatsu) and companies disappearing or having been takenover (P&O Nedlloyd). The takeover of P&O Nedlloyd wasannounced in the course of 2005, while the new integratednetwork of Maersk Line was launched in February 2006.This means that P&O Nedlloyd was included in the analysisin 2002, but not in 2005. The 2005 data originates from afterthe takeover, but before the launch of the new network, andtherefore does not include P&O Nedlloyd separately, whilethe Maersk figures did not include P&O Nedlloyd either.

Some of the largest companies (MSC, Maersk Line,Hapag Lloyd) serve a large number of regions and havea Gini coefficient that is very close to the value for theTop 25. Some other large companies (for instance, Ever-green, COSCON, Hanjin) show a lower number of regionsserved, and a larger Gini coefficient. What is also apparentis that a number of companies (Hapag Lloyd, NYK) showan increase in the number of regions, and a lower Ginicoefficient for 2005 compared to 2002. This indicates that

these companies have increased their global coveragebetween 2002 and 2005.

A slightly different picture emerges from Table 3. Theterminal operators all show a much bigger gap in the num-ber of regions and the Gini index compared to the top 24overall. Where some shipping companies were active inalmost all geographical areas, this is not the case for theterminal operators. APM Terminals is the company withthe widest range of activity but is only operating in 16 areasout of a total of 24, whereas amongst liner shipping com-panies Maersk Line and MSC are active in 26 and 25 areas,respectively, out of a total of 26.

Tables 4 and 5 contain the Gini comparison tests, basedon the SUR testing approach. The tables contain theresults for the 25 largest liner shipping companies and the15 largest terminal operators in 2005.4 Based on a 5% sig-nificance test criterion, we recognise 16 truly global playersin liner shipping, whose service network has a global cov-erage equal to the top 25 total. For other companies, suchas COSCO and China Shipping, the 5% test rejects a global

Table 3Top 24 terminal operating companies

Terminal operator 2002 2005

Throughput(mln TEU)

No. ofareas


Throughput(mln TEU)

No. ofareas


Hyundai 1.1 3 0.89 1.2 2 0.94Yang Ming 1.3 2 0.91 1.5 2 0.93COSCO (Group) 4.8 4 0.91 14.7 5 0.93HHLA 4 4 0.94 6 5 0.93ICTSI 1.3 2 0.95 1.9 4 0.92Eurogate 9.5 4 0.91 12.1 3 0.92K-Line 2 2 0.91 2.7 2 0.92SSA 4.4 3 0.91 7.3 3 0.91Hanjin 3.7 3 0.91 4.9 3 0.91OOCL 3 3 0.91 4.3 3 0.90TCB 1.8 3 0.92 2.8 5 0.90PSA 26.2 7 0.90 40.3 8 0.90APL 4.3 4 0.89 5.7 4 0.89Dragados 2.2 4 0.91 3.6 6 0.89MOL 2.7 3 0.90 3.2 3 0.89P&O Nedlloyd 1 2 0.91 2.5 5 0.88CMA-CGM 3.4 4 0.88DPW 5.3 3 0.94 12.9 9 0.87MSC 2.2 4 0.89 7.8 6 0.85Evergreen 5.8 7 0.81 8.7 6 0.84NYK 3.6 7 0.84 4.2 7 0.84HPH 36.7 11 0.84 51.8 11 0.84P&O Ports 12.8 11 0.72 23.8 13 0.75APM Terminals 17.2 15 0.66 40.4 16 0.69

Total top 24 159.7 23 0.66 267.7 24 0.68

Note: Dubai Ports World took over CSX World Terminals in late-2004 and P&O Ports in early-2006. Therefore CSX is not formally listed in the table, butit is included in the 2002 sample.


coverage equal to the Top 25 overall. The case of HapagLloyd is interesting: in 2002, Hapag Lloyd would havequalified as a global player, but in 2005, it does not. Thetest also confirms our earlier observation that some compa-nies are becoming more global in their service offering:ZIM, UASC, OOCL, YML and K-Line are global playersin 2005, but they were not in 2002. In addition, one of the‘new’ companies that have entered the top 25 is also a glo-bal player: Hamburg Sud. These results clearly show thatto be a global player it is not strictly necessary to be a‘giant’ in the market: the most important factor is thediversity of the service network.

The tests for the international terminal operators inTable 5, on the other hand, shows that there are onlytwo truly global terminal operators: APM Terminals andP&O Ports (in 2006 this set of terminals was included inthe Dubai Ports World network). These are the same twoglobal players as in 2002. For all other operators, the testsdo not recognise a global coverage equal to the Top 24overall. In addition, the tests for 2002 and 2005 do not indi-cate a globalization development among companies that isas apparent as in the liner shipping industry. Only in thecases of MSC and DPW (even before the takeover ofP&O Ports) was there a significant growth in the degreeof globalness. Nevertheless neither are classified in thisresearch as global terminal operators.

In all cases, one can also conclude that the number ofareas in which a company is active is a fairly good indicatorof global coverage. In both industries, the companies witha presence in the largest number of areas also turn out to bethe global players on the basis of the Gini test.

Finally, results are presented of the comparison of glo-bal coverage in liner shipping and terminal operations forthe companies that are active in both industries. One couldexpect that companies that follow this dual strategy strivefor a geographical match between their liner service net-work and their terminal portfolio. In the press and the aca-demic literature, there is some speculation about this, butno clear evidence exists to either negate or confirm thisassumption. Moreover, the strategy of attraction third-party customers to some terminals, for instance by APMterminals, complicates this discussion.

Table 6 contains the results of the Gini tests and showsthat AP Møller (owner of both Maersk Line and APMTerminals) is in fact the only company for which the linernetwork and the terminal portfolio match in terms of glo-bal coverage. This is more surprising because the test forthe industry totals can only be accepted at the 1% signifi-cance level. In all other cases, the test indicates that, eventhough the companies seem to follow a dual strategy ofinvesting in both shipping and ports, the global reach ofboth activities does not match.

Table 4Gini equality test results: liner shipping companies

Carrier 2002 2005

Wald teststatistic

P-value Wald teststatistic

P-value

P&O Nedlloyd 0.42 0.51Hamburg Sud 0.02 0.90NYK Line 2.84 0.09 1.49 0.22CSAV 2.80 0.09 1.49 0.22Maersk 0.20 0.59 1.50 0.22Evergreen 1.50 0.22 1.59 0.21MSC 0.02 0.88 1.95 0.16Delmas 3.96 0.05 2.46 0.12ZIM 4.19 0.04 2.52 0.11MOL 2.97 0.10 2.93 0.09UASC 4.67 0.03 3.23 0.07Lloyd Triestino 4.76 0.03 3.60 0.06OOCL 8.10 0.004 3.66 0.06YML 5.74 0.02 3.86 0.05APL 3.44 0.06 3.87 0.05CMA CGM 1.50 0.22 3.90 0.05K-Line 5.50 0.02 3.91 0.05COSCON 7.25 0.007 4.26 0.04China Shipping 4.10 0.04 4.44 0.04CSAV Norasia 4.53 0.03Hanjin 4.57 0.03 4.78 0.03Hatsu 4.90 0.03PIL 6.36 0.01 5.30 0.02Hapag Lloyd 2.06 0.15 6.87 0.009Hyundai MM 6.89 0.009 8.22 0.004Wan Hai 13.74 0.000 11.94 0.000

In all cases, the Gini was tested for equality with the Gini index for theTop 25 total. The test used is a Wald test, with the null hypothesis that theGini’s are equal. Low P-values indicate rejection of this hypothesis. YMLis Yang Ming Line.

Table 5Gini equality test results: terminal operating companies

Terminal operator 2002 2005

Wald test P-value Wald test P-value

APM Terminals 0.19 0.66 0.23 0.63P&O Ports 1.61 0.20 1.66 0.20HPH 4.44 0.04 4.31 0.04Evergreen 4.40 0.04 5.18 0.02NYK 5.70 0.02 5.53 0.02DPW 15.24 0.000 5.89 0.02MSC 9.27 0.002 6.00 0.01PSA 8.74 0.003 7.89 0.005APL 9.12 0.003 8.45 0.004OOCL 11.24 0.001 9.79 0.002Hanjin 11.43 0.001 10.43 0.001SSA Marine 11.68 0.001 10.83 0.001Eurogate 11.31 0.001 11.60 0.001COSCO 11.33 0.001 11.80 0.001HHLA 15.21 0.000 12.21 0.001

In all cases, the Gini was tested for equality with the Gini index of theworld. The test used is a Wald test, with the null hypothesis that the Ginisare equal. Low P-values indicate rejection of this hypothesis. HPH isHutchison Port Holdings, DPW is Dubai Ports World, COSCO (Group)includes the terminals owned by COSCON and COSCO Pacific, HHLA isHamburg Hafen und Lagerhaus.

Table 6Gini matching results

Combinations Wald test statistic P-value

Carrier Terminal operator

Maersk Line APM Terminals 1.01 0.32Top 25 Top 25 6.25 0.01NYK NYK 7.75 0.005COSCON COSCO Group 8.99 0.003APL APL 11.16 0.001MOL MOL 12.55 0.000MSC MSC 13.34 0.000OOCL OOCL 13.59 0.000Hanjin Hanjin 14.67 0.000K-Line K-Line 16.79 0.000Evergreen Evergreen 18.68 0.000YML YML 19.00 0.000

The test used is a Wald test, with the null hypothesis that the Ginis areequal. Low P-values indicate rejection of this hypothesis. COSCO (Group)includes the terminals owned by COSCON and COSCO Pacific, YML isYang Ming Line.


5. Discussion and conclusions

In this paper, we have suggested a quantitative approachto verify the global coverage of the services offered bycontainer shipping companies and container terminaloperators. We used a relative definition of ‘globalness’that compares the spread of container carrying capacityacross various regions for each player with the distributionof the combined capacity of all the selected large compa-nies in the industry. We have restricted the analysis to ser-vice offerings. Therefore, our study is not an analysis oncompany internationalisation in shipping or terminalmanagement.

The testing procedure based on the calculation and com-parison of Gini coefficients turns out to be simple and effec-tive, and it is based on data that is readily available frompublic sources.

Given our definition of global coverage, in liner ship-ping, the larger part of the top 25 companies turn out tobe global players. In the terminal operator industry, how-ever, only two of the top 24 are marked as global players.In addition, AP Møller is the only company for which theliner service network and the terminal portfolio can be saidto have a similar global coverage.

Our results for liner shipping confirm the findings byFremont and Soppe (2003) that the European based carri-ers seem to have a more global reach than a number of theAsian carriers. The Asian carriers rely more on alliancesand consortia.

The difference between the findings for liner shippingand terminal operations is striking. While liner shippingis an industry offering increasingly global services, ourresults seem to indicate that the container terminal industryis still neither as globally oriented, nor as dynamic despitethe recent efforts in port reforms worldwide.

A future research agenda may proceed in two differentdirections. First, a ‘qualitative’ approach analyzing the


customer–supplier relationships in the terminal industry ona port by port basis would allow a more detailed analysis ofcooperation between liner shipping companies and termi-nal operators even if their ties are not so obvious. Thisanalysis could be based on ship sailings data, which wouldshow which ships regularly visit which terminals in whichparts of the world. Second, further investigations alongthe lines of Olivier et al. (2007) and other authors wouldreveal if services globalization in container transport andport handling is similar to, or different from, enterpriseinternationalisation.

Acknowledgements

Authors are grateful to Dr. Antoine Fremont and Dr.Martin Soppe of the ‘‘Institut National de Recherche surles Transports et leur Securite” (INRETS) for their invalu-able support in providing data concerning carriers net-work. Authors are also grateful to the three anonymousreferees for their useful comments and suggestions duringthe writing of this paper.

References

Bichou, K., Bell, M.G.H., 2007. Internationalisation and consolidation ofthe container port industry: assessment of channel structure andrelationships. Maritime Economics and Logistics 9, 35–51.

Bissell, A.F., Ferguson, R.A., 1975. The jackknife – toy, tool or two-edgedweapon. The Statistician 24, 79–100.

Dicken, P., 2002. ‘Placing’ firms – ‘firming’ places: grounding the debateon the ‘global’ corporation. In: Paper presented at the Conference onResponding to Globalization: Societies, Groups, and Individuals,University of Colorado, Boulder, April.

Drewry Shipping Consultants, (2003, 2006). Annual Review of GlobalContainer Terminal Operators, Report, London, UK.

Ferrari, C., Benacchio, M., 2000. Market structure in container terminaloperators and port services. In: Paper presented at the IAMEConference, September 13–15, Naples, Italy.

Fremont, A., Soppe, M., 2003. Les strategies des armateurs de lignesregulieres en matiere de dessertes maritime. In: Paper presented at theseminar Maritime Transport, Globalisation, Regional Integration andTerritorial Development, June 3–5, Le Havre, France.

Giles, D.E.A., 2004. Calculating a standard error for the Gini coefficient:some further results. Oxford Bulletin of Economics and Statistics 66,425–433.

Heij, C., de Boer, P., Franses, P.H., Kloek, T., van Dijk, H.K., 2004.Econometric Methods with Applications in Business and Economics.Oxford Press, Oxford.

Johanson, J., Vahlne, J.-E., 1977. The internationalization process of thefirm – a model of knowledge development and increasing foreignmarket commitments. Journal of International Business Studies 8, 23–32.

McCalla, R.J., Slack, B., Comtois, C., 2004. Dealing with globalisation atthe regional and local level: the case on contemporary containeriza-tion. The Canadian Geographer 48, 473–487.

Notteboom, T.E., 1997. Concentration and load centre development in theEuropean container port system. Journal of Transport Geography 5(2), 99–115.

Notteboom, T.E., 2004. Container shipping and ports: an overview.Review of Network Economics 3, 86–106.

Notteboom, T., Rodrigue, J.P., 2005. Port regionalization: toward a newphase in port development. Maritime Policy & Management 32, 297–313.

Ogwang, T., 2004. Calculating a standard error for the Gini coefficient:reply. Oxford Bulletin of Economics and Statistics 66, 435–437.

Olivier, D., 2005. Private entry and emerging partnerships in containerterminal operations: evidence from Asia. Maritime Economics &Logistics 7, 87–115.

Olivier, D., Parola, F., Slack, B., Wang, J.J., 2007. The time scale ofinternationalisation: the case of the container port industry. MaritimeEconomics & Logistics 9, 1–34.

Peters, H.J.F., 2001. Developments in global seatrade and containershipping markets: their effects on the port industry and private sectorinvolvement. International Journal of Maritime Economics 3, 3–26.

Porter, M.E., 1990. The Competitive Advantage of Nations. Free Press,New York.

Perlmutter, H.V., 1969. The tortuous evolution of the multinationalcorporation. Columbia Journal of World Business 1, 9–18.

Rugman, A.M., Verbeke, A., 2004. A perspective on regional and globalstrategies of multinational enterprises. Journal of International Busi-ness Studies 35, 3–18.

Slack, B., Comtois, C., McCalla, R., 2002. Strategic alliances in thecontainer shipping industry: a global perspective. Maritime Policy &Management 29, 65–76.

Slack, B., Fremont, A., 2005. Transformation of port terminal operations:from the local to the global. Transport Reviews 25, 117–130.

Sullivan, D., 1994. Measuring the degree of internationalization of a firm.Journal of International Business Studies 25, 325–342.

Sullivan, D., 1996. Measuring the degree of internationalization of a firm:a reply. Journal of International Business Studies 27, 179–192.

Tulder, R., van, Berghe, D., van den, Muller, A., 2001. The ErasmusScoreboard of Core Companies, Report, Department of BusinessSociety Management, Erasmus University Rotterdam.

UNCTAD, 2002. Investment Report – Chapter IV ‘The Largest Trans-national Corporations’ UNCTAD/WIR/2002, United Nations,Switzerland.

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The spatial coverage of shipping lines and container terminal operators