PERSISTENT AND TRANSIENT EFFICIENCY OF CONTAINER PORT
TERMINALS IN EUROPE
Jordi Rosella (junior scholar)
Marta Gonzalez-Aregalla,b (junior scholar)
aUniversitat
de Barcelona, GiM-IREA
of Gothenburg
bUniversity
Abstract
Port authorities and local container terminal operators are increasingly having to compete
with the entrance of multinational terminal operators and vertically integrated shipping
lines. This article analyzes the respective efficiency of these different types of terminal
operator by using stochastic frontier analysis. It distinguishes between two types of
inefficiency in the production function: the persistent term, related to the presence of
structural problems in the organization of the production process of a container port
terminal, and the transient term, related to temporary events or short-run managerial
problems. To do so, 92 port container terminals belonging to 50 port authorities in Europe,
between 1991 and 2010, are analyzed. The main findings indicate that the differences
between transient and persistent efficiency are significant, implying different efficiency
behavior. Terminals operated by multinational and vertically integrated operators are
more efficient in allocating resources than are those operated by national firms or port
authorities. Moreover, automated terminals present higher persistent efficiency levels. On
the management side, no differences are found.
Keywords: Container port terminals; Efficiency; Stochastic Frontier, Port authorities,
Shipping lines
JEL Codes:
L91; R40; R49
______________
a Jordi Rosell. GiM-IREA. Department of Econometrics, Statistics and Applied Economics. University of
Barcelona. Avinguda Diagonal 690,08034 Barcelona (Spain). [email protected] website:
www.jordirosell.com Tel: (+34)-934021946.
a,b Marta Gonzalez-Aregall. Department of Econometrics, Statistics and Applied Economics. University
of Barcelona, Barcelona (Spain). [email protected]
Logistics and Transport Research Group, Department of Business Administration, School of Business,
Economics and Law at the University of Gothenburg, Göteborg (Sweden).
1
1. INTRODUCTION
Since the 1990s, container terminal operators have expanded their networks globally in
search of cost savings through economies of scale and network economies. As Soppé et
al. (2009) report, worldwide, contracts with independent third parties are the most
common strategy (74%), followed by joint-ventures with local or international partners
(11%), while partial or full ownership of a subsidiary container port terminal is the least
used (11%). In these years, traditional terminal operators that dominate their regional
markets have expanded their operations to other ports in the same country or
internationally. In Europe, the top five leading container port terminal operators in 1998
handled 50% of total containers, while ten years later this share had risen to 75%
(Notteboom and Rodrigue, 2012). In this process, governments have become more
flexible and prepared to see the foreign private sector as a viable route for achieving
international competitive advantage, furthering the country’s investment flow and
boosting management efficiency. Indeed, mergers, acquisitions or new concessions of
existing terminals or the construction or expansion of new terminals are common
occurrences today throughout the industry.1
But these are not the only actors to have expanded their market: shipping lines have
expanded vertically to safeguard their control of container handling activities. Thus, while
between 1990 and 1999 only seven shipping lines entered Europe’s container terminals,
between 2000 and 2011 this number rose sharply to fifty-seven (OECD, 2015). In this
regard, Rodriguez et al. (2011) reports that EU states have been more protectionist,
awarding new terminals to incumbents when their existing terminals were not so
lucrative.
In regulated industries, governments seek to ensure that container terminal operators
operate as efficiently as possible. For the industry’s regulators and for the industry too,
inefficiency can be attributed to a range of factors, not all of which are dependent upon
the container terminal operators. Most models have sought to estimate terminal efficiency
as a time-invariant component, supposing that the terminal operator cannot in fact
improve its performance over time. This case may be realistic for terminals with a low
1
For a detailed analysis of the factors underpinning the foreign entry strategies of terminal operators, see
Parola et al. (2013).
2
level of management or when operating out of an inappropriate geographical location.
Other models, though, attribute this inefficiency to the firms’ heterogeneity, and so
underestimate their levels of inefficiency. However, what is missing from all these studies
is any attempt to control for systematic differences between container port terminals.
While the possibilities for increasing terminal efficiency in the short run might be few
and far between (given the potentially important role being played by certain timeinvariant factors), manager capabilities can play a non-negligible role in terminal
efficiency. Disentangling this inefficiency in order to provide an accurate analysis of the
container terminal industry has yet to be attempted. The importance though of being able
to undertake accurate measurements has significant policy implications both as regards
regulation and economics.
This paper contributes to the literature by analyzing both the persistent and transient
efficiency of different types of container terminal operators. No previous empirical study,
to the best of our knowledge, has attempted to determine port terminal efficiency using
these two types of efficiency simultaneously. We conduct our study on 92 port container
terminals belonging to 50 port authorities in Europe between 1991 and 2010.
The rest of the paper is organized as follows: Section 2 outlines the main features of port
governance and the efficiency literature; Section 3 describes the empirical model used
and Section 4 provides a description and justification of the explanatory variables
selected. Section 5 addresses some econometric issues and explains the results of the
estimates. Finally, the last section is devoted to summarizing the main findings and
discussing policy implications.
2. PORT GOVERNANCE AND EFFICIENCY
Since the 1980s, almost all port authorities have undergone a process of devolution with
increased private involvement (Brooks, 2004) and the emergence of different governance
approaches.2 This process has had an influence on port terminal efficiency, so that Cheon
et al. (2010) are able to report that the world’s leading ports have increased their
efficiency thanks to better management of terminals, appropriate adjustments in their
2
For a fuller description of port governance models (Service Port, Tool Port, Landlord Port, Private Port),
see World Bank (2007).
3
scales of production and rapid technological progress. Interestingly, Tongzon and Heng
(2005) conclude that private sector participation is positive for improving port operation
efficiency; yet, paradoxically, they believe it is better for port authorities to limit this
participation. More specifically, in the case of Europe’s port system, Cullinane and Wang
(2006) claim that different systems of port governance in Europe might be a key
determinant of container terminal efficiency.
Until the 1990s, Europe continued to operate a more protectionist policy when it came to
awarding new terminals to entrants, while in North America container terminals were
operated by shipping lines (Rodrigue et al., 2011). However, the process of port terminal
reorganization led to the domination of specialized global and international container
terminal operators. According to Farrell (2012), these firms are more successful in
managing terminals and modernizing facilities than are smaller operators, a situation that
led to the raising of entry barriers to new competitors. At the same time, container
terminals represent substantial, capital-intensive, real estate assets, controlled by terminal
operators, indicating that the financial sector is playing a growing role in the industry
(Rodrigue, 2010). As a result, terminal operators have undergone a horizontal integration
which has allowed companies to increase their scale and scope (Slack and Fremont,
2005).
Since the mid-1990s there has been a growing trend towards mergers and alliances
between shipping companies and terminal operators, with a strong concentration at the
global level. This process of increased internationalization and globalization has led to
rising levels of competition and has given operators greater direct bargaining power with
port authorities (Acosta Seró et al., 2012). The growth in alliances and mergers in the
maritime sector, especially among carriers, has been intensified by several circumstances
that have resulted in the formation of megacarriers and global strategic alliances in the
industry. However, this competition for obtaining larger terminals, combined with
regulators’ interests for larger terminals to increase their port traffic and subsidies to
terminals, results in an overcapacity mix that threatens to be an ‘explosive cocktail’
(Haralambides, 2002).
Various authors claim that that this increase in dedicated terminals is an advantage for
shipping lines, enabling them to offer better transport chains, to enhance their load centers
(Notteboom, 2002) and to offer global services (Parola and Veenstra, 2008). A shipping
4
line interested in diverting part of its traffic to a new terminal facility will also find it
profitable to divert traffic from other shipping lines to this terminal (Álvarez-SanJaime et
al., 2013). Finally, while Song and Panayides (2008) suggest that port operators need to
implement strategies to increase port and terminal integration in their supply chains so as
to achieve a competitive advantage, Frémont (2009) argues that the vertical integration
between shipping lines and the transport chain places the emphasis on vessel and
container logistics but less so on freight logistics. However, bearing in mind that some
companies that manage terminals are affiliated to shipping line groups, specialized
companies and shipping lines are likely to cooperate in the future (Slack and Fremont,
2005). Indeed, the development of different forms of co-operative agreements has led to
conflicts of interest between port authorities and terminal management companies. As a
result, the large shipping companies have increased their market power at the expense of
the port authorities (Heaver et al., 2000), while consortia and alliances have acquired a
more powerful negotiating position (Heaver et al., 2001).
Attempts at estimating port and terminal efficiency have been widely developed over the
last two decades. Here, the two main approaches taken have been stochastic frontier
analysis (SFA) and data envelopment analysis (DEA), where the quality of data, the
functional forms, and the possibilities for making behavioral assumptions about
efficiency heavily influence the appropriateness of the method adopted. The main
advantage of SFA is the main disadvantage of DEA, and vice versa. SFA can distinguish
between terminal performance, on the one hand, and noise and measurement error, on the
other, thanks to its stochastic form; DEA is deterministic, which implies some kind of
impact on frontier estimation. However, DEA’s main advantage is that it does not impose
a prior structure on the frontier. Ultimately, SFA is more attractive for analyzing the level
of cost efficiency in container port terminals because it enables us to deal with the
presence of unobserved heterogeneity. Odeck and Brathen (2012) report that SFA studies
present lower technical efficiency scores than those using non-parametric models; that
panel data models report lower efficiency scores than those that employ cross-sectional
models; and, that efficiency is also lower when using European data.
The first SFA of the production side of European terminals was carried out by Notteboom
et al. (2000) using a Bayesian Stochastic Frontier Model. Later, Cullinane et al. (2006)
compared the outcomes of a DEA and SFA and reported differences between them when
applying a cross-sectional approach. Tongzon and Heng (2005) use Battese and Coelli’s
5
(1995) model to test the effect of large terminals and privatization on efficiency, also
applying a cross-sectional approach to large container ports. Estache et al. (2002)
compare Cobb-Douglas and translog production forms and conclude that the former
performs as well as the flexible form.
A time-invariant inefficiency model is also estimated by Trujillo and Tovar (2007) using
Battese and Coelli’s (1988) cross-sectional analysis of European ports and Gonzalez and
Trujillo’s (2008) analysis of Spanish ports. On recent article by Pérez et al. (2016), using
Latin American and Caribbean container terminals from 2000 to 2010, a translog
stochastic frontier is estimated using Battese and Coelli (1995) to test the impact of
transshipment terminals and multicontainer terminal ports, among others. The
predominance of estimating time-invariant inefficiency to rank the terminals or ports, and
the Battese and Coelli (1995) predominance to test different variables on inefficiency
term predominated the efficiency of container terminal estimation. Interestingly
Badunenko and Kumbhakar (2016), citing Yip, Sun and Liu (2011), stress that
unobserved heterogeneity needs to be separated from inefficiency.3
4
Container port
terminals studies to date have estimated either persistent or time-varying inefficiency,
with a tendency to focus their efforts on the persistent part, but rarely on both. The
hypothesis we propound is that part of the inefficiency is likely to be persistent while
another part is likely to be transient or time-varying. In the next section, we focus on the
importance of disentangling these inefficiencies to estimate the efficiency of the different
port terminal operators.
3. MODEL
The production function assumes that each company seeks to maximize its output from a
given set of inputs. As such, the container terminal operator’s objective is to handle the
maximum number of containers. We suppose that a container terminal firm’s production
can be described as:
Y𝑖𝑡 = 𝑓(𝐾𝑖𝑡 , 𝐿𝑖𝑡 , 𝑡𝑖𝑚𝑒_𝑡𝑟𝑒𝑛𝑑𝑡 )
(1)
where subscripts i = 1, 2, . . . , N refers to the container terminal firm and t = 1, 2, . . . , T
to the year. The total output of container terminal firm Y is assumed to be a function of
3
Note that they only estimate time-varying inefficiency.
Coto-Millán et al. (2016) use a tTrue Fixed Effect model for an input distance function to analyze Spanish
regulatory changes in port efficiency.
4
6
the terminal’s inputs. On the production frontier, capital (𝐾) and labor (𝐿) are two of the
main inputs. The container port industry allocates capital when a terminal is built and
faces subsequent difficulties to change this capital allocation. Lengthening the berths or
extending the terminal area are options for increasing container capacity before having to
resort to the building of a new terminal. Thus, good capital proxies are the terminal area
and quay lengths, related also to that of the land area, whereas the labor force can be
proxied by the number of terminal cranes. The latter is more adjustable than capital, but
the proxy presents greater difficulties than those experienced in other industries.5 On the
output side, we focus only on container terminals and, here, by employing the total TEU
capacity measure we avoid multi-output terminal estimation problems.
Using panel data allows us to disentangle persistent and time-varying inefficiency for
firms, and to determine whether the firms’ effects are fixed parameters or realizations of
a random variable. A suitable transformation of the production function (1) is the CobbDouglas production function. Indeed, this and translog models overwhelmingly dominate
the applications literature on stochastic frontier and econometric inefficiency estimations
(Greene, 2008). A translog functional form may also be suitable, but after several
attempts, no convergence is achieved or the coefficient parameters obtained are
counterintuitive, such as a negative and significant sign on the output variable. This
problem, described by Farsi et al. (2005), is perhaps caused by a multicollinearity issue
between the several interaction and second-order coefficients. Indeed, imposing an
appropriate curvature on a translog model is generally a challenging problem at the
production stochastic frontier.
Thus, the stochastic production frontier can be described as:
ln Yit = β0 + βL lnLit + βA lnAit + βD Dit + βSS ln SSit + βMC ln MCit +βCLA ln YCit +
βT Time_trendt -uit + υit
with 𝑖 = 1, 2, … , 92
𝑎𝑛𝑑 𝑡 = 1991, 1992, … , 2010
where subscripts i and t denote the container terminal firm and year, respectively.
Lit , Ait , Dit , SSit , MCit and YCit denote, respectively, the terminal quay length, the
terminal area, the water depth, the number of ship-shore cranes, the number of mobile
5
This is because workforce requirements can vary depending on the type of cranes and over time.
7
(2)
cranes and the number of yard cranes. The SFA production function estimation reveals
that infrastructure inputs (especially, berth length, mobile cranes, and gantry cranes) are
important for predicting the level of container throughput (Suárez-Alemán et al., 2016).
An assumption of the Cobb-Douglas function is that all the observations share the same
production technology. Thus, in the production function we only have a technological
relation, whereas in the cost function, economic behavior arises. A production firm is
technically inefficient if a higher level of output is technically achievable with the same
inputs (output-oriented measure) or if the output level observed can be produced with
fewer inputs (input-oriented measure). In our analysis, we do not consider allocative
efficiency, that is, whether the observed combination of inputs is optimum. One of the
assumptions made by this model is that the type of terminal operator does not affect the
production technology. The composite error term is formed by uit and υit . The random
variable υit is the idiosyncratic error component and is assumed to be identically and
independently distributed 𝑁(0, 𝜎𝑣2 ), being either positive or negative. This term is
independent of uit , the one-sided, non-negative random variable.
The first application of panel data models to stochastic frontier analysis was undertaken
by Pitt and Lee (1981) as a random effect model. To estimate the inefficiency term – one
of the main purposes for estimating this model – a two stage-approach is carried out. The
authors assume that the inefficiency term ui is constant through time and captures firm
inefficiency; terminal operator specific inefficiency is the same in every time period. For
a long panel this could be a strong assumption, although it could be plausible when the
firm operates in a non-competitive environment or input allocations are quite stable. This
is not the case of container terminal operators, where strong competition is found. Another
limitation of this model is that no correlation between the explanatory variables and
inefficiency is assumed. In these models, any individual-specific or unobserved
heterogeneity is captured by the inefficiency term ui or 𝛼: the Pitt and Lee model (1981)
underestimates the level of efficiency.
Pitt and Lee (1981) model cannot disentangle a firm’s inefficiency from cost differences
due to unobserved characteristics of the terminal. Usually, these companies cannot
control for concession characteristics such as the terminal area or those of the port, which
cannot simply be attributed to concessionaire performance. To overcome this problem,
Greene (2005a and 2005b) proposes a model that captures invariant, unmeasured,
unobserved heterogeneity in a specific term, besides a firm-specific inefficiency term and
8
a random noise term. In the true random effects (TRE) specification, unobserved cost
differences across firms that remain constant over time are driven by unobserved
characteristics rather than by inefficiency. This time invariant unobserved characteristic
not absorbed by the inefficiency term is partly beyond the control of the terminal operator.
Recently, models have focused on separating productive efficiency into its persistent and
transient terms. The initial implementation difficulties in the seminal estimation
procedure were addressed in Colombi et al. (2014), Tsionas and Kumbhakar (2014),
Kumbhakar, Lien and Hardaker (2014) and in Filippini and Greene (2016). The persistent
term is related to the presence of structural problems in the organization of the production
process of a container terminal operator or to the presence of systematic shortfalls in
managerial capabilities. This inefficiency does not vary over time, and can be caused by
structural problems in the terminal concession, or alternatively by structural factors that
have not been well allocated or by long-term management errors, among others. In
contrast, the transient term is related to the presence of non-systematic management
problems that can be solved in the short term. This is a more plausible assumption
regarding terminal operators’ capabilities to reduce inefficiency. In contrast, a persistent
inefficiency due to input allocations is difficult to remove, whereas organizational
changes or the elimination of short-run rigidities improve a concessionaire’s transient
efficiency. This part is time varying, reflecting temporary management mistakes or
temporary events affecting the concession. The Pitt and Lee (1981) model tends to reflect
the persistent part of the time-invariant values. In Greene’s TRE specification, any
persistent component of the inefficiency is absorbed in the individual-specific constant
term. In industries in which certain sources of efficiency result in time-invariant excess
of inputs, the estimated inefficiency could be relatively small. Filippini and Greene (2015)
find that the TRE model tends to estimate the transient part of efficiency, whereas the Pitt
and Lee (1981) specification captures persistent efficiency well. Kumbhakar, Lien and
Hardaker (2014) propose a model that splits the error term in four components in order
to overcome these problems.6 A remaining issue is how terminal expansion should be
treated. A transient and a persistent efficiency is obtained simultaneously with this model.
6
This model can be implemented following Kumbhakar et al. (2015)
9
In Table 1, we summarize the econometric specifications of the total production stochastic
frontier. The firm’s inefficiency is estimated using the conditional mean of the
inefficiency term proposed by Jondrow et al. (1982) adapted to each model. The terminal
operator’s efficiency is then evaluated in a second-stage analysis.
Table 1: Econometric specifications of the Stochastic Production Frontier
Pitt
and
Lee
(1981)
Model
𝑦𝑖𝑡
Hardaker (KLH, 2014)
𝑦𝑖𝑡
= 𝛼 + 𝑤𝑖 + 𝛽 𝑥𝑖𝑡
= 𝛼 + 𝑤𝑖 + 𝛽 ′ 𝑥𝑖𝑡
+ 𝑢𝑖 + 𝑣𝑖𝑡
+ 𝑢𝑖𝑡 + 𝑣𝑖𝑡
+ 𝜇𝑖 + 𝑢𝑖𝑡 + 𝑣𝑖𝑡
′
𝛼 + 𝑤𝑖
𝛼 + 𝑤𝑖
𝑤𝑖 ~𝑁(0, 𝜎𝑤2 )
𝑤𝑖 ~𝑁(0, 𝜎𝑤2 )
𝜀𝑖𝑡 = 𝑢𝑖 + 𝑣𝑖𝑡
𝜀𝑖𝑡 = 𝑢𝑖𝑡 + 𝑣𝑖𝑡
𝜀𝑖𝑡 = 𝜇𝑖 + 𝑢𝑖𝑡 + 𝑣𝑖𝑡
𝑢𝑖 ~𝑁 + (0, 𝜎𝑢2 )
𝑢𝑖𝑡 ~𝑁 + (0, 𝜎𝑢2 )
𝜇𝑖 ~ 𝑁 + (0, 𝜎𝜇2 )
𝑣𝑖𝑡 ~𝑁(0, 𝜎𝑣2 )
𝑣𝑖𝑡 ~𝑁(0, 𝜎𝑣2 )
𝑢𝑖𝑡 ~𝑁 + (0, 𝜎𝑢2 )
None (𝛼)
component
error
(TRE)
= 𝛼 + 𝛽 𝑥𝑖𝑡
terminal
Composed
Kumbhakar, Lien and
𝑦𝑖𝑡
′
Container
True Random Effects
𝑣𝑖𝑡 ~𝑁(0, 𝜎𝑣2 )
Efficiency
𝐸[−𝑢𝑖 |𝜀𝑖𝑡 ]
𝐸[−𝑢𝑖𝑡 |𝜀𝑖𝑡 ]
Transient 𝐸[−𝑢𝑖 |𝑢𝑖 +
𝑣𝑖𝑡 ]
Persistent 𝐸[−𝜇𝑖 |𝜇𝑖 +
𝑢𝑖 + 𝑣𝑖𝑡 ]
4. DATA
This section describes an exploratory analysis of the factors that account for port terminal
efficiency among the European port authorities considered in this analysis. Europe’s is
the second largest container port system in the world after Asia’s. Our unit of analysis is
the container terminal rather than the port, given that each port has many terminals. The
latter may be operated by several operators, who independently seek to maximize the total
output in terms of the capital and labor inputs they each allocate. We examine a total of
92 terminals, our selection criterion being to include as many observations as possible.
10
Only residual terminals with less than tens of thousands of TEUs moved in the last few
years are omitted. The Containerization International (CI) Yearbook is our main source,
and we complement and check these data using the Port Authorities’ annual reports. The
time period covered is from 1991 to 2010, but three years (1993, 1994 and 1995) are
unavailable. From the World Container Port Traffic League, we consider 50 European
port authorities and 92 port container terminals for which data are available. Recent data
scarcity in this industry constitutes a research problem, making 2010 the last year
available. Nevertheless, to the best of our knowledge, this paper covers one of the largest
time periods in the literature.
To analyze the efficiency of container terminal operators, we consider four different types
of port terminal ownership. First, we include port terminals operated by their own port
authority. Generally, port authorities are public entities, except for ports located in the
United Kingdom that have private equity ownership (Baird, 2013). Second, we consider
those port terminal operators that concentrate most of their activity in the same country
and refer to them as national terminal operators. We assume these companies face high
competitive pressure from the port authorities. Third, we consider port terminal operators
managed by multinational companies. Note that around 75% of total European Container
throughput in 2008 was handled by just five terminal operators: PSA, APM Terminals,
Hutchison Port Holdings, DP World and Eurogate (Notteboom and Rodrigue, 2012). We
assume that these companies have greater knowledge about operating a terminal and
greater market liquidity than the other two categories. Finally, we consider cases of
vertical integration of port terminals. In line with the definition provided by Slack and
Fremont (2005), these “hybrid” firms are either managed jointly by shipping line groups
and terminal operator companies or by a subsidiary of a shipping line company. Although
these companies might also be considered as being multinationals, we opted to separate
them due to their foundational differences.
Table 2 shows the mean and standard deviation for all the variables considered in our
model by terminal operator type. Based on these descriptive statistics, we can consider
three types of terminal. The largest, with few differences to distinguish them, are the
vertically integrated and the multinational terminal operators. The smallest terminals are
those operated by the port authorities, while the terminals managed by national operators
lie somewhere between the two categories. The terminals that have been operating
11
longest, such as those operated by the port authorities and, to a lesser degree, those
managed by national operators, tend to be smaller owing to space restrictions.
Table 2: Mean value (and standard deviation) of empirical analysis variables by
type of terminal operator
Port
National
Multinational
Vertically
authority
terminal
terminal
integrated
(n=79)
operator
operator
operator
(n=129)
(n=36)
Total
(n=435)
(n=191)
Output
(TEU)
Quay length
(m)
Terminal
area
(m2)
157,046
414,719
841,082
783,119
530,016
(146,517)
(545,334)
(1,183,045)
(685,340)
(816,549)
639.0
1072.5
1405.4
1525.8
1133.6
(337.7)
(709.5)
(1,177.3)
(928.7)
(901.6)
200,162
346,778
568,128
521,016
404,548
(174,579)
(441,868)
(538,722)
(317,944)
(454,202)
2.654
5.644
7.915
8.324
5.986
(3.321)
(5.475)
(5.521)
(6.811)
(5.610)
30.173
52.995
67.705
76.973
55.132
(25.356)
(56.519)
(72.346)
(53.954)
(59.025)
1.852
9.257
8.233
5.054
7.231
(2.637)
(20.736)
(10.682)
(12.445)
(15.564)
10.74
12.62
13.60
15.19
12.79
(3.83)
(3.07)
(2.20)
(1.72)
(3.14)
Ship-shores
Mobile
cranes
Yard cranes
Depth (m)
Source: Based on different sources of information.
12
5. RESULTS
The regression results of the model specified in Equation 2 are presented in Table 3. Since
all variables are expressed in logarithms and normalized on the mean, the coefficients can
be interpreted as elasticities.7 We analyzed 92 terminals between 1991 and 2010. Most of
the terminal data are only available for certain periods of time, which means that the panel
is unbalanced for 435 observations.
Table 3: Stochastic production function parameter estimates
VARIABLES
Pitt and Lee
(1981)
True Random
Effects
L(Quay length)
0.154***
(0.0437)
0.422***
(0.0571)
-0.0054
(0.0472)
0.0658*
(0.0367)
-0.0113
(0.0348)
-0.0039
(0.0772)
0.0446***
(0.0049)
0.365***
(0.0893)
1.136
0.361
3.147***
(0.1182)
-294.33
435
0.0887***
(0.0186)
0.223***
(0.0178)
0.0596***
(0.0169)
0.0149
(0.0122)
0.0754***
(0.0144)
0.0314**
(0.0139)
0.0350***
(0.0026)
-0.1753***
(0.0463)
0.2882
0.0398
7.248***
(0.0266)
-152.93
435
L(Terminal area)
L(Ship-shores)
L(Mobile cranes)
L(Gantry cranes)
L(Depth)
Time trend
Constant
𝜎𝑢
𝜎𝑣
λ= 𝜎𝑢2 /𝜎𝑣2
Log likelihood
Observations
Kumbhakar,
Lien and
Hardaker
(2015)
0.147***
(0.0490)
0.374***
(0.0595)
0.0681
(0.0466)
0.0762
(0.0362)
0.0178
(0.0404)
0.0118
(0.0613)
0.0396***
(0.00486)
-0.494***
(0.0902)
435
Results are significant and relatively stable across the specifications. Quay length is
significant and positive for all specifications, although the impact is different in the TRE
specification. In the Pitt and Lee specification, the coefficient interpretation is that a 1%
increase in quay length increases the number of TEUs by 0.15%. Terminal area is positive
7
Some terminals operate non-gantry cranes and a Taylor approximation is taken to avoid an observation
exclusion.
13
and significant across all models, and a 1% increase in the terminal area has a larger
impact on terminal TEUs of between 0.42 and 0.22%. The impact of this variable is lower
in the TRE specification. The terminal ship-shore interface only positively impacts output
in the TRE model. The mobile crane variable is slightly significant in the Pitt and Lee
specification so that a 1% increase in the number of these cranes increases total output by
0.06%. The yard gantry cranes and depth variables are only significant in the TRE model.
The time trend is positive and significant across all specifications, there having been an
advance in technology of around 3.5-4.5% per year between 1991 and 2010.
One of the reasons for estimating a stochastic production frontier is to obtain the
inefficiency parameters. The parameter lambda indicates the ratio of the inefficiency
terms to the random noise term. The value of 𝑢𝑖𝑡 has to be positive in order to calculate
the inefficiency term. Likewise, if 𝜆 is statistically significant, there is evidence of
inefficiency in the data, while a smaller part of this variation is due to random factors.
Badunenko and Kumbhakar (2016) point out that when lambda values are relatively
small, little confidence should be placed in either the transient or the persistent
estimations. For all models, this parameter is highly significant and positive. Table 4
presents the descriptive statistics of the inefficiency estimates obtained from the different
models. The efficiency descriptive statistics do not present many differences between the
estimates. We would have expected higher efficiency values in the TRE model than in
the random effects model since part of the constant inefficiency over time should be
captured by the container terminal component. Only the transient part from the
Kumbhakar, Lien and Hardaker (2014) specification (KLH model) presents greater
efficiency values. In Figure 1, the four kernel density estimators present a similar pattern
and similar magnitudes of the estimated values, except for the transient term of the KLH
model, which is higher than the others.
Table 4. Production efficiency measures
Model
Mean
Pitt and Lee (1981)
True Random Effects
KLH (2014) transient
KLH (2014) persistent
.6615
.6644
.7782
.6425
Standard
Minimum Maximum
Deviation
.1866
.2119
.9605
.2260
.0019
.9596
.0943
.1998
.9491
.1561
.1445
.9229
Figure 1. Kernel density on efficiency estimates
14
8
6
4
2
0
0
.2
.4
.6
.8
1
x
True Random Effects
Pitt and Lee (1981)
KLH (2014) residual
KLH (2014) persistent
We perform a Spearman correlation to measure the strength and direction of association
between four ranked efficiency estimates and the number of TEUs (Table 5). As expected,
in most cases, the correlation coefficients between transient and persistent inefficiency
are rather low. This weak correlation suggests that container terminals receive completely
different evaluations depending on the model adopted. However, the intragroup
correlation is 0.7 for persistent estimates and 0.59 for transient estimates, which
corroborates our hypothesis that the efficiency estimation of different models differs
(indeed, even the descriptive statistics suggest largely the same pattern). As for the
number of TEUs, persistent efficiency is related positively to the number of containers
operated, while the transient efficiency shows a weak or non-relation.
Table 5. Efficiency Spearman correlation
Output
(TEU)
Output (TEU)
Pitt and Lee
(1981)
True Random
Effects
1.000
0.430
-0.096
Pitt and
Lee (1981)
True
Random
Effects
1.000
-0.2155
1.000
15
KLH (2014)
transient
KLH (2014)
persistent
KLH (2014)
transient
KLH (2014)
persistent
0.192
0.500
0.0970
0.5890
1.000
0.7001
-0.3015
0.2216
1.000
The fact that these efficiencies differ in terms of their absolute values, and given the
negative correlations between them, it is clear that we are measuring two distinct kinds
of efficiency. The non- or negative correlation between them explains that their
interpretation and their regulatory implications are likely to differ. On the one hand, the
persistent term captures cost inefficiencies that are constant over time, for example,
investments in terminal facilities that cannot be changed, the relation between ship-shore
cranes and berths or the terminal surface area, the geographical location of the terminal
or terminal mismanagement throughout the period analyzed. On the other hand, the
transient term captures time-variant inefficiencies, for example, short-run management
mistakes, mobile input misallocations (i.e. mobile cranes) or specific problems relating
to terminal operation (strikes, adverse weather conditions, etc.). Therefore, efficiency
improvements can be expected as far as the transient term is concerned, that is, unless the
terminal is extended or the terminal operator is replaced, among others.
One aim of this study is to determine inefficiency by terminal operator type. Given that
the inefficiency measures differ across models, we can test the impact of terminal operator
type on the inefficiency measures. Recall, we classify our observations according to
whether the container port terminal is operated by the port authority, by a national
terminal operator (that is, one that only operates that or another terminal in the same
country), by a multinational firm operating in several countries or by a vertically
integrated firm (that is, a shipping line that owns, or closely collaborates with, a terminal
operator which has expanded its operations into various countries). Table 6 shows the
inefficiency estimates by type of terminal operator and model specification. In the case
of the persistent efficiency estimations, according to the Pitt and Lee (1981) and
Kumbhakar, Lien and Hardaker (2014) models, port authorities are the most inefficient
terminal mangers, followed by national firms. In contrast, mixed and international firms
are the most efficient managers. In the case of the transient efficiency estimations, all four
terminal manager categories present approximately the same efficiency estimations on
both the TRE and KLH specifications, albeit ranked differently.
16
Table 6. Mean efficiency (and standard deviation) by model and terminal manager
Terminal
manager
Port authority
National
operator
Multinational
Vertically
integrated
operator
Pitt and Lee
(1981)
0.550
(0.2285)
0.635
(0.1556)
0.734
(0.1713)
True Random
Effects
0.681
(0.2442)
0.682
(0.2353)
0.644
(0.2080)
0.752
(0.1499)
0.644
(0.2338)
KLH (2014)
transient
0.760
(0.1631)
KLH (2014)
persistent
0.524
(0.1879)
0.778 (0.075
0.638
0.784
(0.0737)
0.699
(0.1444)
0.788
(0.0670)
0.703
(0.1343)
To test whether there are differences in the inefficiency values related to the
characteristics of the various container port terminal managers or not, we apply the
Kruskal-Wallis test. This is a rank-based nonparametric that can be used to determine if
there are statistically significant differences between two or more groups of a variable.
Table 7 shows the results of the Kruskal-Wallis test for the equality of mean inefficiency
between different types of terminal manager for all the inefficiency estimate models. In
the first four rows, we only test two groups: the type of terminal operator vs. all others.
In the last four rows, we only test two specific groups.
In the case of the persistent estimations, there is a statistical difference in mean
inefficiency between the groups, confirming the results in Table 6. We test whether
vertically integrated and multinational firms perform differently or not. The KruskalWallis test shows no mean inefficiency differences, implying that they perform at the
same level. In the case of the persistent term, the managers are ranked in descending order
of inefficiency as follows: the port authorities, followed by national firms, followed by
vertically integrated and multinational firms (there being no differences between the last
two types). There is clear evidence that terminal managers operating in more than one
country are more efficient than companies (especially port authorities) that operate in just
one country. The former allocate their resources better during the construction phase and
are able to attract more containers. Furthermore, the concentration of ownership and the
availability of finance for capital-intensive projects for terminal operators and shipping
companies (Rodrigue, 2010) allow them to invest in terminal facilities. Likewise,
according to the literature, factors such as geographical location are clearly relevant when
shipping lines select their ports (Tongzon, 2002; Ng, 2006; Tongzon and Sawant, 2007).
17
In this regard, we might deduce that the factor that captures cost inefficiency and which
is constant over time is the most relevant for vertically integrated firms. Finally, Seo and
Park (2016) report that the minimum efficient scale for Korean ports is 753,000 TEUs,
suggesting that larger plants are likely to be more efficient than smaller plants in a port
terminal. According to our data, 16% of national operators, 33% of multinational
operators and 38% of vertical integrated firms operate above this threshold, whereas not
one single port authority operator reaches 753,000 TEU. Although this minimum efficient
scale may vary according to location and the associated costs (Kaselimi et al., 2011), it
can be used as an approximation for descriptive purposes.
In the case of the transient estimations, time-variant inefficiencies are found. The rankbased nonparametric test shows no differences between the four groups on the KLH
(2014) model of transient efficiency. However, the TRE inefficiency model provides
statistical evidence that international firms perform worse than their national
counterparts. Figure 2 shows the evolution in transient efficiency of the four terminal
operator types. Taking into consideration that for some years fewer than five observations
per terminal operator are available (which accounts for the large yearly variations),
generally speaking and taking a long-term perspective, the port authorities have improved
their efficiency over time. However, since 2007, there has been a fall in efficiency due,
in the main, to the reduction in containerized traffic and the persisting overcapacity
presented by most terminals. Indeed, the better performance attributed to port authorities
and national operators by the TRE model between 2007 and 2010 can be associated with
the sharp fall in efficiency during those years experienced by the vertically integrated and
multinational terminal operators. Overall, however, the differences in transient efficiency
between operator types are not as great as those recorded in the persistent term.
The effects of mergers and alliances between shipping lines in the industry have been
partly offset by the traditional fundamentals of economies of scale, scope and density in
maritime shipping markets. Indeed, economies of scale have been closely related to
increases in ship size over the last two decades (Cullinane and Khanna, 1999).
Consequently, large containerships have a negative impact on the excess operating
capacity (Haralambides, 2012), which is an unavoidable cost. Multinational and vertically
integrated container terminal operators opted to expand their facilities, based on future
cargo handling forecasts, but without apparently perceiving problems of overcapacity
(Slack, 1993). For firms, expanding their capacity is a key strategic decision that poses a
18
challenge in terms of capital requirements and the general complexity of the decisionmaking problem. Clearly, it is critical that they establish future demand expectations and
make accurate forecasts of their competitors’ future behavior before expanding operations
(Porter, 1998). Indeed, Chang et al. (2012) warn that overcapacity will result in the
inefficient use of port infrastructure and this is confirmed by the results of our transient
efficiency estimations.
Table 7. Kruskal-Wallis test on type of container terminal operator (p-values)
Type of
True
Pitt and Lee
terminal
Random
(1981)
operator
Effects
Port
22.008
1.124
authority
(0.0001)
(0.2892)
(79)
National
14.386
5.038
operator
(0.0001)
(0.0248)
(191)
Multinational
34.826
9.770
operator
(0.0001)
(0.0018)
(129)
Vertically
7.655
0.202
integrated
(0.0057)
(0.6529)
operator (36)
Port
authority vs.
7.835
0.047
National (79 (0.0051)
(0.8283)
vs. 191)
Port
authority vs.
0.932
Vertically
(0.3342)
integrated(79
vs. 36)
National vs.
41.164
Multinational
(0.0001)
(191 vs. 129)
Vertically
integrated vs.
0.321
0.842
Multinational (0.5707)
(0.3588)
(36 vs. 129)
KLH
(2014)
transient
KLH (2014)
persistent
0.072
(0.7882)
39.333
(0.0001)
1.036
(0.3089)
3.276
(0.0703)
0.181
(0.6707)
31.175
(0.0001)
0.299
(0.5848)
6.178
(0.0129)
0.276
(0.5991)
22.976
(0.0001)
0.062
(0.8032)
22.265
(0.0001)
0.101
(0.7506)
0.051
(0.8209)
Figure 2. Temporal transient efficiency evolution
19
From our database, 22 terminals (corresponding to 24% of total observations) have
undergone expansion (that is, they have lengthened their berths or extended their surface
area). Estimating the time-variant inefficiency allows us to check the impact of extending
a terminal (Figure 3). In the case of transient inefficiency, the most frequently obtained
result is a non-inefficiency change in the TRE and KLH specifications. The most notable
result is that the number of container terminals increasing their efficiency according to
the TRE model is eight, whereas according to the KLH model only three of them increase
their efficiency.
20
Figure 3. Efficiency on extended container terminal
Abra Term. Marit. Bilbao
Alcantara-Sul (Lisboa)
Baltic Container Terminal
DP World Constantza South
Darsena Toscana (Leghorn)
Estibadora ponent (Barcelona)
Fos CT (Marseille)
La Luz (Las Palmas)
La Spezia CT
Leon y Castillo (Las Palmas)
Maritima Valencia
Medcenter Gioia Tauro
Messina Terminal (Genoa)
Muuga CT (Tallinn)
Northfleet Hope (Tilbury)
Salerno CT
Santa Apolonia CT (Lisboa)
Santurce Terminal (Bilbao)
Southern European (Genoa)
Tollerort CT (Hamburg)
WienCont CT
0
.5
1
0
.5
1
0
.5
1
0
.5
1
APM-Maersk (Algeciras)
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
0
.5
1
1990 1995 2000 2005 2010
1990 1995 2000 2005 2010 1990 1995 2000 2005 2010
True Random Effects
KLH (2014) residual
Pitt and Lee (1981)
KLH (2014) persistent
As an additional test of inefficiency, we examine efficiency levels in automated terminals.
In these terminals, some of the container handling equipment is used without any direct
human interaction, which results in higher productivity and increases quay use and yard
density. Our dataset, however, contains just four automated terminals from a total of 92
terminals.8 This number is extremely low but might reflect difficulties in obtaining data
on automated terminals. In an efficiency comparison between both groups, the
performance of automated terminals differs from that of their non-automated
counterparts. A Kruskal-Wallis test (see Table 8) shows that automated terminals are
more efficient than non-automated terminals in terms of persistent efficiency. The former,
promoted by both national and multinational terminal operators, achieve better input
allocations, which allow them to perform better over time. However, better management
abilities or the short-term mismanagement of these terminals have little impact, since
these abilities are captured in the terminal parameter and not in that measuring efficiency.
8
The automated terminals are those operated by the national operator HHLA (Hamburg Burchardkai and
Altenwerder) and the multinational operators, Hutchinson (Delta, Rotterdam) and DP World (Antwerp
Gateway).
21
Table 8. Kruskal-Wallis test on automated and non-automated container terminals (pvalues)
True
Pitt and Lee
Random
(1981)
Effects
Efficiency
non0.725
automated
(89)
Efficiency
automated 0.837
(4)
Automated
9.484
vs Non(0.0001)
automated
KLH (2014) KLH (2014)
transient
persistent
0.644
0.768
0.636
0.607
0.790
0.724
1.321
(0.2504)
0.263
(0.6079)
7.504
(0.0062)
6. CONCLUSIONS
Container port terminal operators have undergone a series of changes in recent decades.
The forces of globalization, privatization and competition have spurred them to move to
other ports in search of greater global profit margins. At the same time, shipping lines, in
an attempt at guaranteeing key port ranges, schedule reliability, and competitiveness,
have integrated vertically with the terminal operators. The efficiency of container
terminal operators has typically been analyzed using three stochastic frontier panel data
models: a random effect model (Pitt and Lee, 1981), which captures time-invariant
inefficiency; a True Random Effects model (Greene, 2005a and 2005b), which captures
time-variant inefficiency separately from unmeasured (unobserved) heterogeneity; and,
the Kumbhakar, Lien, and Hardaker model (2014), which estimates both inefficiencies.
For the first time in this sector, this last model allows the persistent and transient
efficiency of container port terminals to be estimated. Here, we have analyzed the
different types of efficiency characterizing different port terminal operators at 92 port
container terminals belonging to 50 port authorities in Europe between 1991 and to 2010.
The transient and persistent efficiency estimates obtained differ, highlighting the
importance of separating them when examining container port efficiency. The persistent
element captures the inefficiency associated with investments in terminal facilities that
22
cannot be readily modified, such as on surface units and berths, the geographical location,
long-term terminal mismanagement issues and any other factor that does not change over
time. Here, our results suggest that the least efficient terminals are operated by port
authorities, followed by national terminal operators. The most efficient operators are
vertically integrated firms and the multinationals. The transient element captures the
inefficiency associated with short-run management mistakes, temporary problems of
terminal congestion or terminal operation. We find no statistical differences between the
four types of terminal operator.
Considering the different effects of persistent and transient efficiency, the analysis of port
traffic capacity and its impact on port efficiency acquires great relevance. Here, various
factors can be seen to influence terminal investment and, hence, terminal size. First, in a
context of increasing globalization, forecasts about future port traffic prospects can be
made to justify capacity investment. Second, the increase in ship size leads to the largest
terminals attracting these new ships. In this regard, vertically integrated terminals are able
to promote shipping line traffic through alliances made at these terminals. Third, shipping
lines invest in port terminals that enjoy advantageous geographical locations. Finally,
terminal operators and shipping lines retain capital in order to invest in port facilities, but
in practice, this result in a lower ROI. Difficulties in expanding port authority terminals
may account for these results in the case of persistent efficiency. Multinational and
vertically integrated operators began operating at Europe’s larger terminals because they
predicted that the minimum efficient scale was lower than that offered by older terminals.
Since 2007, the increase in transient inefficiency points to a problem of overcapacity
throughout this industry. The Hanjin bankruptcy and the absence of bids in the Corozal
Container Terminal in the Panama Canal are recent examples of how overcapacity are
hurting this industry.
A limited sample of automated terminals has allowed us to check whether there are any
differences between non-automated and automated terminals. We find evidence of greater
persistent efficiency in the case of automated terminal, while transient efficiency does not
vary between the two types of terminal. Here, the most plausible explanation is that of
better initial input allocation when these terminals were built.
In conclusion, these results are a clear indication of the important role to be played by the
port terminal manager in guaranteeing terminal efficiency. However, the effects of longand short-term efficiency clearly differ here and disentangling them is essential for
23
ensuring sound strategic decision making as firms seek to expand their capacity and
safeguard their capital investments. One limitation of the analysis reported here is the lack
of data related to changes in terminal management. Indeed, future research could usefully
examine the influence of such changes on the development of new terminals.
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