Maritime Economics & Logistics

, Volume 14, Issue 2, pp 178–203 | Cite as

A quantitative analysis of European port governance

  • Patrick Verhoeven
  • Thomas Vanoutrive
Original Article

Abstract

The ever-changing environment in which ports operate has put strong pressure on the role of port authorities. The evolution of port governance has so far mainly been analysed in qualitative terms, through expert knowledge and case studies. This article fills a research gap in providing a quantitative analysis of port governance in Europe, using data from a major survey, which the European Sea Ports Organisation carried out in 2010 to prepare a new edition of its ‘Fact-Finding Report’. These reports have been monitoring port governance diversity since the 1970s. The 2010 survey was based on a new conceptual background, which takes into account the evolution of ports, as well as new perspectives on the role of port authorities. This article provides a quantitative assessment of the survey results, identifying elements that may explain the governance diversity of European seaports. This is done with the help of factor analysis. The results confirm the existence of different types of port governance models in Europe, which to some extent correspond to the hypothetical typology according to which port authorities can be conservators, facilitators or entrepreneurs. Differences are mainly geographically defined and the subdivision in Hanseatic, Latin, Anglo-Saxon and new Member State port authorities proves to be a valuable one. In addition to this geographical explanation of diversity, the analysis also detects different governance practices between small and large ports.

Keywords

port authorities port governance autonomy port strategies port reform European Union 

Introduction

It is a well-known fact that ports in Europe are diverse. Governance is one of the key elements that determine this diversity. When using the term ‘port governance’, we can distinguish two levels: the governance of the port and the governance of the port authority. The former corresponds to the wide cluster of economic, societal and public policy stakeholders that relate to a port, whereas the latter concerns the internal firm level or ‘corporate governance’ of the port authority (Brooks and Cullinane, 2007; De Langen, 2007). The term ‘port authority’ implies a specific, that is, public, form of port management, but we use it here as the generic term for the body with statutory responsibilities that manages a port's water and land-side domain, regardless of its ownership or legal form (De Monie, 2004).

Port governance is a broad concept, which encompasses several dimensions. Seven distinct groups of parameters can be used when analysing governance practices: (i) devolution, (ii) corporate governance, (iii) operational profile, (iv) functional autonomy, (v) functional pro-activeness, (vi) investment responsibility and (vii) financial autonomy. Furthermore, governance practices are not stable in time and space. While in the past cargo-handling in European ports was traditionally carried out by locally based companies, horizontal and vertical integration of cargo-handling companies have resulted in a market dominated by global players. This evolution attracted the attention of both policymakers and researchers which often refer to the declining influence of port authorities, while global players gained bargaining power (Heaver et al, 2000; Heaver et al, 2001; Slack and Frémont, 2005; Olivier and Slack, 2006; Jacobs and Hall, 2007; Vanelslander, 2011). As a response to this evolution, several port authorities reposition themselves by adopting pro-active strategies and developing activities in other nodes in the logistic chain, outside the own port perimeter. In addition to the changes in port governance over time, differences across space exist. Suykens (1988; Suykens and Van de Voorde, 1998) identified three major port governance traditions in Europe: the ‘Hanseatic’ tradition of local, mostly municipal, governance, which is dominant in ports around the Baltic and North Sea; the ‘Latin’ tradition of central governance, which reigns in France and countries around the Mediterranean; and finally, the ‘Anglo-Saxon’ tradition of independent governance, which is characteristic of ports in the United Kingdom and Ireland. Finally, governance practices may differ for other organisational reasons such as the size of the port and the port authority (for example, number of employees).

Despite the extensive literature on port governance (for an overview see, for example, Verhoeven, 2010 and Pallis et al, 2010), studies generally describe general trends or limit themselves to case studies. To our knowledge, there exists no study which analyses port governance practices of a large number of ports in a systematic way. In the present article, we analyse port governance in Europe, using a rich database containing variables that cover all aforementioned dimensions of port governance. This information was collected by the European Sea Ports Organisation (ESPO). This organisation, which represents the common interests of European port authorities, and its predecessor, the Community Port Working Group, have been monitoring the diversity in port (authority) governance in Europe since the 1970s through a series of ‘Fact-Finding Reports’. Throughout this period, the environment in which ports operate has changed dramatically, putting strong and multiple pressures on the role of port authorities. The Fact-Finding Reports were mainly descriptive in nature and did not allow a profound quantitative analysis. In 2010, ESPO prepared a new edition of the report through a major survey among European port authorities. The survey was based on a new conceptual background that takes into account the evolution of ports, as well as new perspectives on the role of port authorities. Concretely, the survey enquired about the objectives and functions of port authorities, compared institutional frameworks, and analysed financial capabilities. This exercise yielded a rich database of observations and variables, suitable for in-depth quantitative analysis.

In this article, we do not describe the actual findings of the survey, these can be found in the Fact-Finding Report itself (Verhoeven, 2011). We focus instead on a quantitative assessment of the principal elements that may explain the governance diversity of European seaports. This is done with the help of factor analysis, a commonly applied tool to explore data sets with many variables, which are then summarised into a limited number of unobserved factors.

The first two sections of the article introduce the conceptual background of the survey and the survey data. In the following sections, we describe the research methodology for the quantitative assessment and present the results of the factor analysis. A concluding section discusses the main findings and sets out issues that require further research.

Conceptual Background

The 2010 edition of the ESPO Fact-Finding Report builds on the tradition of the original reports, but it is based on a new conceptual background. This was developed by Verhoeven (2010), taking into account the evolution of the port concept, as well as new perspectives on the role of port authorities. The latter are based on an extensive literature review, which revealed that, in recent times, a renewed interest in the role of port authorities has emerged. This role has come under severe and multiple pressures from stakeholders following important socio-economic changes in the port landscape. Scholars have issued various recommendations for a ‘renaissance’ of port authorities, revisiting the traditional landlord, regulator and operator functions, and devising a community manager function that is intrinsically linked to the changing nature of port communities and stakeholders. In addition, scholars have also identified the scope of port authorities as one that ought to extend their activities beyond the local port perimeter, at regional or even at global level. Combining the functional profile and the geographical dimension in a matrix allows one to elaborate on the existential options of port authorities in a hypothetical typology consisting of three basic types: the ‘conservator’, the ‘facilitator’ and the ‘entrepreneur’. The basic features of each type are illustrated in Table 1.
Table 1

Hypothetical typology of port authorities

Type

Functional dimension

Conservator

Facilitator

Entrepreneur

Landlord

Passive real estate ‘manager’:

Active real estate ‘broker’:

Active real estate ‘developer’:

• continuity and maintenance

• continuity, maintenance and   improvement

• continuity, maintenance and   improvement

• development mainly left to others   (government/private sector)

• development broker and co-investor

• direct investor

• financial revenue from real estate   on ‘tariff’ basis

• includes urban and environmental real   estate brokerage

• includes urban and environmental   real estate development

• financial revenue from real estate on   commercial basis

• financial revenue from real estate   on commercial basis

Mediator in commercial B2B relations between service providers and port customers

• financial revenue from non-core   activities

Strategic partnerships with inland ports, dry ports and other seaports

Direct commercial B2B negotiations with port customers – active pursuit of market niches

Direct investments in inland ports, dry ports and other seaports

Regulator

Passive application and enforcement of rules and regulations mainly set by other agencies

Active application and enforcement of rules and regulations through co-operation with local, regional and national regulatory agencies + setting of own rules and regulations

Idem facilitator + selling expertise and tools outside the port

Financial revenue from regulator role on ‘tariff’ basis

Provide assistance to port community to comply with rules and regulations

Financial revenue from regulator role on commercial basis

Financial revenue from regulator role on ‘tariff’ basis with differential charging options for sustainability

Operator

Mechanistic application of concession policy (license-issuing window)

Dynamic use of concession policy, in combination with real estate broker role

Dynamic use of concession policy, in combination with real estate development role

‘Leader in dissatisfaction’ as regards performance of private port services providers

Shareholder in private port service providers

Provide services of general economic interest and specialised commercial services

Provide services of general economic interest and commercial services

Provide services in other ports

Community manager

Not actively developed

Economic dimension:

Idem facilitator type but economic dimension with more direct commercial involvement

• solve hinterland bottlenecks

• provide training and education

• provide IT services

• promotion and marketing

• lobbying

Societal dimension:

• accommodate conflicting interests

• lobbying

• promote positive externalities

Geographical dimension

Local

Local + Regional

Local + Regional + Global

Source: Verhoeven (2010).

A ‘conservator’ port authority concentrates on being a good housekeeper and essentially sticks to a rather passive and mechanistic implementation of the three traditional port authority functions at local level. Because of this low-profile attitude, conservator port authorities may run the highest risk of becoming extinct in the future. A ‘facilitator’ port authority profiles itself as a mediator and matchmaker between economic and societal interests, hence the well-developed community manager function. Facilitator port authorities also look beyond the port perimeter and try to engage in strategic regional partnerships. It is the type of port authority, which so far seems to find most support in literature. The ‘entrepreneur’ port authority combines the main features of the facilitator with a more outspoken commercial attitude as investor, service provider and consultant on all three geographical levels (local, regional and global). Because of this ambitious profile, it is also the type that runs the highest risk of running into problems caused by conflicts between the various functional levels.

The conceptual framework is completed with the exploration of a number of governance-related elements that may influence the extent to which a port authority will be a mere conservator or will be able to take on facilitating and entrepreneurial responsibilities. Four essential elements can be identified: two formal and two informal ones. The two formal elements consist of the legal and statutory framework on the one hand and the financial capability (that is, financial autonomy and investment responsibility) on the other. The informal elements relate to the balance of power with government and the management culture that reigns within the port authority. It should be noted that these four elements are strongly interrelated. The power balance with government will influence the legal and statutory framework, whereas the financial capability of the port authority will determine the room its management has to make pro-active and intelligent use of port governance tools within a given structural framework.

In the Introduction, we presented seven distinct governance dimensions: (i) devolution, (ii) corporate governance, (iii) operational profile, (iv) functional autonomy, (v) functional pro-activeness, (vi) investment responsibility and (vii) financial autonomy. The meaning of these dimensions is explained below.
  1. i)

    The term ‘devolution’ is used here in the broad sense, to identify to which extent port management has been privatised, decentralised and/or corporatised.

     
  2. ii)

    There is a difference between being corporatised in form and actually following principles of corporate governance that are customary in private undertakings. On the basis of the survey, the latter can be assessed from various perspectives, including the economic and non-economic objectives port authorities have, their organisational structure (including the appointment of top management executives and the composition of supervisory bodies), transparency through the use of public selection procedures to contract out land to port operators, and corporate social responsibility (CSR) policies and the use of corporate accounting principles.

     
  3. iii)

    The customary way to classify port authorities in operational terms is to distinguish between ‘landlord ports’, ‘tool ports’ and ‘service ports’, depending on whether, respectively, port authorities are not involved in (cargo-handling) operations at all, operate superstructure and related services or provide full operations in an integrated manner.

     
  4. iv)

    Functional autonomy is analysed from the perspective of the landlord and regulator function. The landlord function can be considered as the principal function of contemporary port authorities. Important issues here are land ownership, as well as the ability and autonomy in contracting land out to third parties. The regulator function is to a large extent performed by the harbour master's office, which can be an integral part of the port authority structure or a separate entity.

     
  5. v)

    Functional pro-activeness can be assessed at the level of the port authority's own port(s) and beyond. The ‘own port’ dimension covers pro-active fulfilment of the traditional landlord and regulatory functions, as well as the community manager one, which is pro-active by nature. The ‘external’ dimension looks at how port authorities transpose their functions beyond their own borders, including investment in hinterland networks, investment in other ports, export of regulatory and other expertise and so on.

     
  6. vi)

    Investment responsibility concerns financial responsibility for the capital investment, administration, operation and maintenance of the capital assets that constitute a port, including maritime access, terminal-related infrastructure, transport infrastructure within the port area and transport infrastructure outside the port area. Also, sources of port authorities’ operating income are covered here, such as general port dues, land lease, services and public funding.

     
  7. vii)

    Financial autonomy concerns first of all the legal nature, calculation basis and autonomy that apply to different categories of port authority income charges, in particular general port dues. Financial autonomy of port authorities is also analysed in terms of decision making regarding new investments in capital assets, setting of wages, terms and service conditions of its own personnel and the requirement to meet certain financial targets.

     

The ESPO Fact-Finding Survey

Survey design

The 2010 Fact-Finding Report of ESPO is the first to be based on a Web-based survey that was addressed directly to individual port authorities in Europe, rather than to national port organisations, as was the case with previous editions. National port organisations were, however, instrumental in encouraging their members to respond to the survey. The survey comprised 108 questions. Apart from a general section profiling the port(s) controlled by the port authority, it consisted of three main sections that were based on the conceptual framework described above: the first enquired about the objectives and functions of the port authority (landlord, regulator, operator, community manager); the second looked into the institutional framework of the port authority (ownership, legal status and form, organisational structure); and the final set of questions addressed the financial capability of the port authority (financial responsibility, financial autonomy).

Response rate

The survey was made available to all port authorities in the 22 maritime Member States of the European Union and port authorities in four neighbouring countries that are represented in ESPO: Iceland, Norway, Croatia and Israel. The survey was online from 1 April to 15 July 2010. One hundred and sixteen port authorities from the 26 countries represented in ESPO responded. Together, these 116 port authorities reported that they manage a total of 216 different ports. The total freight volume handled by these ports in 2008 amounted to 2 770 803.000 tonnes (Eurostat data completed with national statistics for Iceland and Israel).

Figure 1 illustrates the response rate per country, expressed in percentage of the total volume of cargo handled by all ports in each country.
Figure 1

Survey response rates, expressed in percentage of the total volume of cargo handled.

The bottom line of the figure shows that the total sample of ports that responded to the survey handles 66.2 per cent of the volume of cargo handled by the total European population of ports in 2008. The response rate was very high (75–100 per cent) in Belgium, Bulgaria, Cyprus, Estonia, Germany, Israel, Latvia, Lithuania, Malta, the Netherlands, Poland, Portugal, Romania and Slovenia; it was medium to high (50–74 per cent) in France, Iceland, Ireland, Italy, Spain and the United Kingdom; it was low to medium (25–49 per cent) in Denmark, Finland and Sweden. The response rates of Greece and Norway were very low (less than 25 per cent). It should be noted that in countries with lower response rates the ports that replied do form a representative sample of the governance diversity that exists in these countries.

Research Methodology

The research methodology we used to assess the survey results consists of two main steps. The first concerns the selection and clean-up of the data generated by the survey, whereas the second consists of the use of factor analysis, as data reduction technique, to help revealing the underlying factors that may explain port governance diversity in Europe. As an intermediate step, we introduced a series of dummy variables to test the hypothesis that regional characteristics may constitute an important factor that explains port governance diversity in Europe.

Survey data and selection of variables

As mentioned above, the Fact-Finding Survey contained 108 questions. These questions generated 269 individual variables. Most of these variables are of nominal, that is, categorical nature, containing several answer categories. First, we made a selection of variables to make the data set more manageable and, notably, to obtain a workable ratio between the number of variables and the number of observations. Factor analysis requires that there are more observations than variables. Variables that generated no or only few observations were deleted and the most pertinent variables were selected from different questions that were addressing similar issues. In addition, some variables were clustered into new ones.

This resulted in a data set of 67 variables classified according to the thematic groups that we described in the previous section: devolution, corporate governance, operational profile, functional autonomy, functional pro-activeness within the port authority's own port(s), functional pro-activeness beyond the port authority's own port(s), investment and financial autonomy. In addition, a ‘size’ group was created, which includes variables related to the volumes of total cargo, containers and passengers handled in the port(s) managed by the port authority, as well as the number of staff the port authority employs (SZ_CARGO, SZ_CONTR, SZ_PASSG, SZ_STAFF).

The ‘devolution’ (DV) variables measure to what extent responsibility for port management is transferred from central government, through privatisation (DV_PRIVA), decentralisation (DV_DECEN) and corporatisation (DG_CORPT). An additional variable measures whether governance reform took place in 2000 or later (DG_REFYR). The ‘corporate governance’ (CG) variables measure the existence and nature of the port authority's objectives and mission statement (CG_OBJEC, CG_PROFI, CG_VALUE and CG_MISSI), competences and composition of supervisory boards (CG_CEOAP, CG_BORPO, CG_BORSZ), use of public selection procedures to contract land out (CG_SELEC), existence of CSR policy (CG_CSRPO) and accounting practices (CG_ACSEP, CG_ACAUD, CG_ACPUB, CG_ACANL). The ‘operational’ (OP) variables identify whether the port authority directly or indirectly provides operational services, including technical-nautical services (OP_TECNA), ancillary services (OP_ANCIL), cargo-handling services (OP_CARHA), passenger-handling services (OP_PAXHA) and transport services (OP_TRANS). The ‘functional autonomy’ (FA) variables measure to what extent the port authority can autonomously take management decisions as regulator (FA_ENTIT, FA_HMAST and FA_POLIC) and landlord (FA_LANDO, FA_LANDP, FA_LANDD). The ‘functional pro-activeness’ variables are split between those that cover the port authority's own port(s) (PO) and those that go beyond its own port(s) (PB). The first group measures the degree in which the port authority assumes a facilitating or entrepreneurial attitude in its different functions within the area of the port(s) it has directly under its supervision. This relates to its function as landlord (PO_CLAUS, PO_URBAN) and regulator (PO_ENVIR, PO_RULES, PO_SUSTA), as well as the economic (PO_BOTTL, PO_IMPLE, PO_ITSYS, PO_PROMO, PO_TRAIN) and societal (PO_SOCIE) dimension of its community manager function. The second group measures to what extent the port authority is active beyond the port(s) it has directly under its supervision, in terms of relations with other ports (PB_STRAP, PB_DINVE), export of regulatory expertise (PB_REGEX), provision of operational services (PB_SERVI), investment in hinterland networks (PB_HINTE) and provision of training (PB_TRAIN). The ‘investment’ (IR) variables look at the extent to which the port authority bears investment responsibility for the main capital assets that constitute the port (IR_CAPAS) and looks at its main sources of income (IR_INCOM, IR_PDUES, IR_LEASE, IR_SERVI, IR_PUBFU). The last category seeks to measure the financial autonomy (FI) of port authorities through analysis of general port dues (FI_PRICE, FI_NEGOT, FI_PROMO, FI_CROSS, FI_LEVEL, FI_COLLE, FI_BENEF) and other variables (FI_WAGES, FI_RESUL, FI_TARGT).

Table 2 contains a full description of all variables, including the regional dummy variables that are explained in the following subsection.
Table 2

Port governance variables

Code

Description

Category

Type

SZ_CARGO

Total volume of goods handled by the ports managed by the port authority, in 2009, in tons

Size

Continuous

SZ_CONTR

Total volume of containers handled by the ports managed by the port authority, in 2009, in tons

Size

Continuous

SZ_PASSG

Total number of passengers handled by the ports managed by the port authority, in 2009

Size

Continuous

SZ_STAFF

Total staff employed by the port authority, in FTE

Size

Continuous

RG_HANSE

Port authority is located in the Hanse region

Region

Categorical

RG_NWHAN

Port authority is located in the New Hanse region

Region

Categorical

RG_ANGLO

Port authority is located in the Anglo-Saxon region

Region

Categorical

RG_LATIN

Port authority is located in the Latin region

Region

Categorical

RG_NWLAT

Port authority is located in the New Latin region

Region

Categorical

DV_PRIVA

Port authority is predominantly privately owned

Devolution

Categorical

DV_DECEN

Port authority is predominantly owned at local level

Devolution

Categorical

DV_CORPT

Port authority has corporatised form

Devolution

Categorical

DV_REFYR

Governance reform took place in 2000 or later

Devolution

Categorical

CG_OBJEC

Port authority has general formal objectives

Corporate governance

Categorical

CG_PROFI

Economic objective port authority is maximisation of own profit

Corporate governance

Categorical

CG_VALUE

Economic objective port authority is maximisation of added value

Corporate governance

Categorical

CG_MISSI

Port authority has a mission statement

Corporate governance

Categorical

CG_CEOAP

Supervisory body port authority has end responsibility to appoint CEO

Corporate governance

Categorical

CG_BORPO

Supervisory body port authority has significant number of elected politicians

Corporate governance

Categorical

CG_BORSZ

Number of members in the supervisory body of the port authority

Corporate governance

Continuous

CG_SELEC

Port authority uses public selection procedure to contract land out

Corporate governance

Categorical

CG_CSRPO

Port authority has a CSR policy

Corporate governance

Categorical

CG_ACSEP

Port authority maintains separate financial accounts

Corporate governance

Categorical

CG_ACAUD

Port authority has its financial accounts audited by an external auditor

Corporate governance

Categorical

CG_ACPUB

Port authority publishes its financial accounts

Corporate governance

Categorical

CG_ACANL

Port authority has an internal analytical accounting process

Corporate governance

Categorical

OP_TECNA

Port authority provides technical-nautical services

Operational profile

Categorical

OP_ANCIL

Port authority provides ancillary services

Operational profile

Categorical

OP_CARHA

Port authority provides cargo-handling services

Operational profile

Categorical

OP_PAXHA

Port authority provides passenger-handling services

Operational profile

Categorical

OP_TRANS

Port authority provides transport services

Operational profile

Categorical

FA_ENTIT

Port authority is the only entity with statutory responsibilities for the port(s) it manages

Functional autonomy

Categorical

FA_HMAST

Harbour master is fully integrated in the port authority

Functional autonomy

Categorical

FA_POLIC

Port authority employs its own police force

Functional autonomy

Categorical

FA_LANDO

Port authority is the main owner of port land

Functional autonomy

Categorical

FA_LANDS

Port authority is able to sell port land

Functional autonomy

Categorical

FA_LANDP

Contracting of port land to third parties is governed by private law

Functional autonomy

Categorical

FA_LANDD

Port authority is free to set durations of land use contracts

Functional autonomy

Categorical

PO_CLAUS

Port authority actively uses performance clauses in terminal agreements

Functional pro-activeness

Categorical

PO_URBAN

Port authority engages in urban real estate management

Functional pro-activeness

Categorical

PO_ENVIR

Port authority engages in environmental land management

Functional pro-activeness

Categorical

PO_RULES

Port authority sets own regulations that go beyond legal requirements

Functional pro-activeness

Categorical

PO_SUSTA

Port authority generally goes beyond legal requirements in actions to enhance sustainability

Functional pro-activeness

Categorical

PO_BOTTL

Port authority is leader in solving various types of bottlenecks

Functional pro-activeness

Categorical

PO_IMPLE

Port authority assists and facilitates port community with implementation of regulations

Functional pro-activeness

Categorical

PO_ITSYS

Port authority runs IT system for the entire port community

Functional pro-activeness

Categorical

PO_PROMO

Port authority leads the overall promotion and marketing of the port

Functional pro-activeness

Categorical

PO_TRAIN

Port authority provides training/educational programmes for the port community

Functional pro-activeness

Categorical

PO_SOCIE

Port authority is leader in various societal integration initiatives

Functional pro-activeness

Categorical

PB_STRAP

Port authority has strategic partnerships with other ports

F pro-act beyond own port

Categorical

PB_DINVE

Port authority has direct investments in other ports

F pro-act beyond own port

Categorical

PB_REGEX

Port authority exports regulatory expertise to other ports

F pro-act beyond own port

Categorical

PB_SERVI

Port authority provides operational services in other ports

F pro-act beyond own port

Categorical

PB_HINTE

Port authority invests in hinterland networks outside own port

F pro-act beyond own port

Categorical

PB_TRAIN

Port authority process training/educational programmes outside its own port

F pro-act beyond own port

Categorical

IR_CAPAS

Degree of investment responsibility port authority for the main capital assets that constitute the port

Investment

Continuous

IR_INCOM

Total operational income of the port authority, in 2009, in Euro

Investment

Continuous

IR_PDUES

General port dues form highest percentage of income

Investment

Categorical

IR_LEASE

Land lease forms highest percentage of income

Investment

Categorical

IR_SERVI

Services form highest percentage of income

Investment

Categorical

IR_PUBFU

Public funding forms highest percentage of income

Investment

Categorical

FI_PRICE

General port dues are commercial prices

Financial autonomy

Categorical

FI_NEGOT

General port dues are negotiable

Financial autonomy

Categorical

FI_PROMO

Port authority can give commercial promotions on general port dues

Financial autonomy

Categorical

FI_CROSS

Port authority can cross-subsidies between different sources of income

Financial autonomy

Categorical

FI_LEVEL

Port authority autonomously sets the level of general port dues

Financial autonomy

Categorical

FI_COLLE

Port authority autonomously collects general port dues

Financial autonomy

Categorical

FI_BENEF

Port authority is final beneficiary of general port dues

Financial autonomy

Categorical

FI_INVES

Port authority autonomously decides on port investments

Financial autonomy

Categorical

FI_WAGES

Port authority sets wages, terms and conditions of service of its own personnel

Financial autonomy

Categorical

FI_RESUL

Port authority decides autonomously how to allocate the annual financial result

Financial autonomy

Categorical

FI_TARGT

Port authority does not have to meet certain financial targets

Financial autonomy

Categorical

Introduction of regional dummy variables

As an intermediate step, we added five dummy variables, in order to test the hypothesis that the region where the port authority is located determines the governance diversity of European port authorities. These dummy variables were based on the geographical typology that was developed by Suykens (Suykens, 1988; Suykens and Van de Voorde, 1998). His typology, however, does not take into account the fall of the iron curtain, which has brought a number of new ports around the Baltic Sea, the Mediterranean and the Black Sea in the competitive arena. These were under planned economy regimes for almost 50 years and underwent varied liberalisation processes after the political changeover. These ports can be brought together in two additional regions: ‘New Hanse’, consisting of countries around the Baltic Sea; and ‘New Latin’, consisting of countries in the East Mediterranean and the Black Sea.

In this way, we can classify the port authorities in five regional groups:
  • Hanse (RG_HANSE): Belgium, Denmark, Finland, Germany, Iceland, the Netherlands, Norway and Sweden.

  • New Hanse (RG_NWHAN): Estonia, Latvia, Lithuania and Poland.

  • Anglo-Saxon (RG_ANGLO): Ireland and the United Kingdom.

  • Latin (RG_LATIN): Cyprus, France, Greece, Israel, Italy, Malta, Portugal and Spain.

  • New Latin (RG_NWLAT): Bulgaria, Croatia, Romania and Slovenia.

Most port authorities participating in the survey are either to be found in the Hanse (38 per cent) or Latin (35 per cent) region; third comes the Anglo-Saxon region (14 per cent). The two ‘new’ regions contain relatively few port authorities (New Hanse 7 per cent and New Latin 6 per cent).

Factor analysis

After cleaning up the results of the survey and adding the regional dummy variables, the database still contained 72 variables. Applying a data reduction technique may therefore help to reveal the relations between governance practices of port authorities in Europe and explain port governance diversity. Factor analysis is commonly applied to explore data sets with many variables, which are then summarised into a limited number of unobserved factors. Doing this, the analysis tries to keep the number of factors as low as possible while maintaining a maximum of the information, which is present in the original data. For each factor, the factor loadings indicate to which extent they are correlated with each variable. If the factor loadings of two variables show similarities, these variables are related. On the basis of the resulting pattern, factors are often labelled and accordingly, clusters of observations can be detected (Stevens, 2002).

Two problems remain when analysing the ESPO database. First, a considerable amount of observations has missing values for one or more variables. Second, most variables are categorical in nature. Classical factor analysis, however, assumes continuous and normally distributed variables. Among others, Nisenbaum et al (2004) and Vanoutrive et al (2010) applied binary (categorical) factor analysis to get insight in questionnaires containing an extensive list of binary yes/no questions. The software employed in these studies, Mplus (Muthén and Muthén, 2006), allows one to carry out factor analyses with a mix of both continuous and categorical variables. Furthermore, this package can handle missing data without omitting valuable information, as is the case with standard list-wise or pair-wise deleting options in other software.

Although the chosen technique can handle missing data, we deleted 6 from the 116 observations because these six port authorities did not provide data on more than 40 per cent of the variables. Furthermore, the categorical variables were re-coded in binary variables as this did not bring along an important loss of information, that is, some rare categories would not positively contribute to an analysis of the main patterns present in the data. Finally, we attributed the label ‘missing’ for the cargo variable instead of a value of zero to ports, which do not handle any cargo at all. Given the limited number of ports with only passenger traffic, we do not expect that this affects the results in a major way.

We estimate two models, one with and one without the regional dummy variables. These variables have a value of one if the port belongs to the Hanse, New Hanse, Anglo-Saxon, Latin or New Latin region, respectively, and a value of zero if not. As these dummy variables are mutually exclusive, and to avoid that this pre-specified clustering influences the results and their interpretation, we will first look at the model without these regional variables and use the model with the regional variables to check our findings.

Results of the Factor Analysis

Number of factors and factor loadings

As in standard factor analysis, the eigenvalues are used to select the number of factors. Figure 2 pictures the scree plot which shows the eigenvalues. The twists in a scree plot indicate possible values for the number of factors.
Figure 2

Scree plot of the models with and without regional dummy variables.

The second criterion, taking the same number of factors as there are eigenvalues larger than one, could not be applied as this would imply a large number of factors. The scree plot suggests a model with four or five factors. After an examination of both models, we prefer a model with four factors as the results were easier to interpret than those of the five-factor model. The results (Varimax rotated) are given in Table 3. Factor loadings ⩾0.4 are shown in bold as these are considered meaningful. Note that values ⩾0.3 are also large enough to be important.
Table 3

Factor loadings of the 4-factor models without region dummy variables (left) and with region dummy variables (right)

Variable

Factor1

Factor2

Factor3

Factor4

Factor1

Factor2

Factor3

Factor4

SZ_CARGO

0.14

0.54

0.21

0.13

0.11

−0.04

0.10

0.53

SZ_PASSG

−0.03

−0.01

0.15

−0.07

−0.01

0.14

0.04

0.05

SZ_STAFF

−0.04

0.57

0.11

0.40

0.06

0.10

−0.26

0.65

IR_CAPAS

0.12

0.02

−0.27

0.20

0.22

−0.12

−0.32

0.04

IR_INCOM

0.13

0.80

0.37

−0.12

0.08

−0.02

0.32

0.80

CG_BORSZ

0.71

−0.06

0.02

−0.20

0.64

−0.24

0.35

−0.13

RG_HANSE

−0.82

−0.10

0.42

−0.10

RG_NWHAN

−0.02

0.30

−0.16

0.00

RG_ANGLO

−0.18

−0.24

−0.88

−0.28

RG_LATIN

0.95

−0.14

0.21

0.15

RG_NWLAT

0.26

0.67

−0.15

0.03

DV_PRIVA

−0.23

−0.31

−0.45

0.93

0.00

0.01

−0.92

−0.27

DV_DECEN

−0.71

−0.07

−0.04

−0.23

−0.84

−0.13

0.17

−0.18

DV_CORPT

−0.39

0.21

−0.24

0.42

−0.27

0.08

−0.56

0.30

DV_REFYR

−0.52

0.04

0.38

0.01

−0.54

0.32

0.20

0.10

CG_OBJEC

0.43

0.21

0.40

−0.18

0.38

0.19

0.39

0.26

CG_PROFI

−0.48

−0.01

−0.26

0.41

−0.29

−0.01

−0.66

0.04

CG_VALUE

−0.11

0.36

−0.19

−0.24

−0.24

−0.38

0.25

0.23

CG_MISSI

0.09

0.41

−0.04

−0.42

−0.02

−0.22

0.25

0.33

CG_CEOAP

−0.58

0.22

−0.09

−0.27

−0.63

−0.11

−0.01

0.16

CG_BORPO

−0.49

−0.10

−0.22

−0.58

−0.71

−0.30

0.42

−0.25

CG_SELEC

0.54

0.46

0.19

−0.10

0.55

0.05

0.17

0.53

CG_CSRPO

0.30

0.49

−0.37

0.19

0.31

−0.41

−0.18

0.42

CG_ACSEP

−0.22

0.74

0.37

0.02

−0.21

0.19

0.13

0.81

CG_ACAUD

−0.48

0.86

−0.02

0.03

−0.45

−0.10

−0.17

0.88

CG_ACPUB

−0.11

0.53

−0.56

0.03

−0.12

−0.68

−0.22

0.37

CG_ACANL

0.18

0.51

0.10

0.33

0.22

−0.01

−0.04

0.52

OP_TECNA

−0.33

−0.09

−0.73

−0.03

−0.31

−0.51

−0.49

−0.19

OP_ANCIL

−0.37

0.05

−0.68

−0.26

−0.44

−0.65

−0.14

−0.12

OP_CARHA

−0.34

−0.23

−0.55

0.39

−0.19

−0.15

−0.68

−0.20

OP_PAXHA

0.11

0.04

−0.49

0.02

0.14

−0.37

−0.21

−0.02

OP_TRANS

0.35

−0.31

−0.41

0.07

0.32

−0.28

−0.13

−0.38

FA_ENTIT

−0.42

−0.09

−0.21

−0.40

−0.48

−0.23

0.02

−0.19

FA_HMAST

−0.73

0.04

−0.25

−0.15

−0.76

−0.29

−0.18

−0.07

FA_POLIC

0.45

0.24

−0.22

−0.05

0.47

−0.34

−0.07

0.18

FA_LANDO

−0.55

−0.06

−0.07

−0.03

−0.53

−0.03

−0.18

−0.10

FA_LANDS

−0.48

−0.04

−0.11

−0.06

−0.48

−0.18

−0.15

−0.13

FA_LANDP

−0.58

0.13

0.16

0.11

−0.58

0.17

−0.09

0.15

FA_LANDD

−0.23

−0.32

−0.07

0.07

−0.19

0.02

−0.24

−0.33

PO_CLAUS

0.45

0.30

−0.18

0.05

0.40

−0.24

0.06

0.26

PO_URBAN

0.61

0.08

−0.20

−0.19

0.58

−0.29

0.10

0.04

PO_ENVIR

0.23

0.26

0.09

−0.03

0.23

0.06

0.05

0.29

PO_RULES

−0.07

0.33

−0.29

−0.10

−0.11

−0.42

−0.03

0.20

PO_SUSTA

0.40

0.28

−0.32

−0.07

0.34

−0.51

0.09

0.14

PO_BOTTL

0.34

0.18

−0.50

0.20

0.37

−0.51

−0.27

0.07

PO_IMPLE

0.30

0.26

−0.13

−0.16

0.23

−0.23

0.09

0.20

PO_ITSYS

0.25

0.54

−0.25

−0.1

0.22

−0.25

0.01

0.52

PO_PROMO

0.10

0.06

−0.36

−0.12

0.07

−0.25

−0.06

0.01

PO_TRAIN

0.42

0.11

−0.23

−0.04

0.37

−0.37

0.02

0.00

PO_SOCIE

0.33

0.43

−0.29

−0.28

0.19

−0.57

0.27

0.27

PB_STRAP

0.78

0.20

−0.01

−0.08

0.64

−0.29

0.37

0.12

PB_DINVE

0.44

0.29

−0.08

0.00

0.36

−0.23

0.19

0.23

PB_REGEX

0.41

0.66

−0.12

0.02

0.33

−0.43

0.16

0.54

PB_SERVI

0.06

−0.17

−0.45

0.03

0.02

−0.24

−0.16

−0.21

PB_HINTE

0.63

0.32

−0.07

0.19

0.60

−0.16

0.05

0.31

PB_TRAIN

0.52

0.43

−0.19

−0.02

0.47

−0.42

0.11

0.33

IR_PDUES

−0.23

−0.33

0.01

−0.3

−0.24

0.08

−0.04

−0.31

IR_LEASE

0.22

0.44

−0.13

−0.23

0.08

−0.36

0.34

0.32

IR_SERVI

−0.09

0.07

−0.05

0.80

0.13

0.22

−0.54

0.17

IR_PUBFU

0.30

−0.47

0.11

0.17

0.34

0.23

−0.02

−0.44

FI_PRICE

−0.64

0.12

−0.34

0.21

−0.58

−0.28

−0.48

0.07

FI_PROMO

0.05

0.10

0.05

−0.44

−0.06

−0.15

0.35

0.02

FI_CROSS

−0.13

0.07

−0.40

−0.30

−0.21

−0.44

0.01

−0.04

FI_LEVEL

−0.82

0.00

−0.11

−0.17

−0.79

0.04

−0.22

0.00

FI_COLLE

−0.61

0.27

−0.43

−0.38

−0.68

−0.40

−0.07

0.18

FI_BENEF

−0.35

−0.02

0.03

−0.48

−0.38

−0.05

0.18

−0.01

FI_INVES

−0.31

−0.11

−0.47

0.14

−0.23

−0.22

−0.54

−0.14

FI_WAGES

−0.59

−0.16

0.00

−0.06

−0.56

0.18

−0.21

−0.12

FI_RESUL

−0.11

−0.08

−0.30

0.10

−0.05

−0.15

−0.36

−0.10

FI_TARGT

−0.08

−0.12

0.46

0.13

−0.01

0.49

0.05

0.00

Notes: factor loadings are Varimax rotated; values ⩾0.4 in bold; RMSEA model without region dummy variables (left): 0.155; RMSEA model with region dummy variables (right): 0.162.

Note that the presence of missing and binary data and the relatively limited number of observations, together with a rather large number of variables, can explain the low values of test statistics indicated at the bottom of Table 3. In addition to the fact that the first 23 eigenvalues stay above 1, also the Root Mean Square Error of Approximation (RMSEA) stays above 0.1 even for a model with ten factors, while a moderately well-fitting model has an RMSEA <0.10 (Gilbert and Meijer, 2006) or even RMSEA <0.08 (Stevens, 2002, p. 433) (numbers for the model without regional dummy variables). Although fit statistics suggest that the model does not perform well, many factor loadings have values ⩾0.4 and we could detect patterns that correspond with the literature. As a consequence, we did not try to improve the model by omitting variables as this would imply a loss of information.

In general, the results of the models with and without region dummy variables are similar, which is a first indication that this clustering of ports in regions could also reflect differences in governance practices. This will be explored further in the next section, which discusses the results. Table 4 already marks the correspondence between the factors in both models.
Table 4

Correspondence between factors in models with and without dummy variables

Model without dummy variables

Relationship

Model with dummy variables

Factor 1

+

Factor 1

Factor 2

+

Factor 4

Factor 3

+

Factor 2

Factor 4

Factor 3

Description of the factors

In this section, we describe the four factors individually, looking first at the factor in the model without region dummy variables and then comparing it with the corresponding factor in the model with the dummy variables. For each factor, loadings higher than 0.3 are represented in individual tables. In each table, variables with an estimated residual variance lower or equal to 0.5 are highlighted in bold. Estimated residual variances indicate how much of each variable is explained through the entire model, that is, comprising all factors. The annex gives the estimated residual variances for all variables.

Factor 1: Latin – Hanseatic contrasts in autonomy and pro-activeness

Table 5 illustrates that Factor 1 is generally characterised by positive loadings for variables that relate to functional pro-activeness, both within (PO) and beyond (PB) the own port. Negative loadings exist for variables that relate to financial (FI) and functional autonomy (FA), as well as devolution (DV). Variables on corporate governance (CG) demonstrate a mixed picture. Positive loading exists for the size of the supervisory boards (CG_BORSZ), but a negative one on politicians being significantly present in them (CG_BORPO). Negative loadings exist on profit maximisation as the main economic objective (CG_PROFI) and the external audit of financial accounts (CG_ACAUD). A positive loading appears for the use of public selection procedures to land contracts (CG_SELEC). Although the factor loadings are not high for operational variables (OP), they are generally negative (except for transport services (OP_TRANS)).
Table 5

Loadings Factor 1 (with and without dummy variables)

Model without region dummy variables (Factor 1)

Positive factor loading

>0.7

PB_STRAP, CG_BORSZ

 

>0.6

PB_HINTE, PO_URBAN

 

>0.5

CG_SELEC, PB_TRAIN

 

>0.4

FA_POLIC, PO_CLAUS, PB_DINVE, CG_OBJEC, PO_TRAIN, PB_REGEX, PO_SUSTA

 

>0.3

OP_TRANS, PO_BOTTL, PO_SOCIE

Negative factor loading

<−0.8

FI_LEVEL

 

<−0.7

FA_HMAST, DV_DECEN

 

<−0.6

FI_PRICE, FI_COLLE

 

<−0.5

FI_WAGES, CG_CEOAP, FA_LANDP, FA_LANDO, DV_REFYR

 

<−0.4

CG_BORPO, FA_LANDS, CG_PROFI, CG_ACAUD, FA_ENTIT

 

<−0.3

DV_CORPT, OP_ANCIL, FI_BENEF, OP_CARHA, OP_TECNA, FI_INVES

Model with region dummy variables (Factor 1)

Positive factor loading

>0.8

RG_LATIN

 

>0.6

CG_BORSZ, PB_STRAP, PB_HINTE

 

>0.5

PO_URBAN, CG_SELEC

 

>0.4

FA_POLIC, PB_TRAIN

 

>0.3

PO_CLAUS, CG_OBJEC, PO_TRAIN, PO_BOTTL, PB_DINVE, PO_SUSTA, IR_PUBFU, PB_REGEX, OP_TRANS, CG_CSRPO

Negative factor loading

<−0.8

RG_HANSE, DV_DECEN

 

<−0.7

FI_LEVEL, FA_HMAST, CG_BORPO

 

<−0.6

FI_COLLE, CG_CEOAP

 

<−0.5

FI_PRICE, FA_LANDP, FI_WAGES, DV_REFYR, FA_LANDO

 

<−0.4

FA_ENTIT, FA_LANDS, CG_ACAUD, OP_ANCIL

 

<−0.3

FI_BENEF, OP_TECNA

Note: Variables with an estimated residual variance <0.50 are indicated in bold.

In summary, we could say that, somehow paradoxically, Factor 1 matches limited autonomy with a substantial degree of pro-activeness. If we compare this picture with the model that has regional variables included, we find that Factor 1 has a strongly positive loading for the Latin region (RG_LATIN) and a strongly negative one for the Hanse region (RG_HANSE).

Factor 2: Large corporately governed port authorities

Table 6 shows that Factor 2 has positive loadings for size-related variables (income (IR_INCOM), number of staff (SZ_STAFF) and volume of cargo (SZ_CARGO)). Positive loadings also exist for transparency-related variables in the category of corporate governance (for example, where it concerns financial accounts (CG_ACSEP, CG_ACAUD, CG_ACPUB) and the use of public selection procedures for contracting out land to third parties (CG_SELEC)). It furthermore has positive loadings on functional pro-activeness, both within (PO) and beyond the own port (PB). Within the investment category, a positive loading is present for land lease being the highest percentage of operational income (IR_LEASE) and a negative one for public funding being the highest percentage (IR_PUBFU). A negative loading also appears for private ownership of the port authority (DV_PRIVA).
Table 6

Loadings: Factor 2 (without dummy variables) and Factor 4 (with dummy variables)

Model without region dummy variables (Factor 2)

Positive factor loading

>0.8

CG_ACAUD, IR_INCOM

 

>0.7

CG_ACSEP

 

>0.6

PB_REGEX

 

>0.5

SZ_STAFF, PO_ITSYS, SZ_CARGO, CG_ACPUB, CG_ACANL

 

>0.4

CG_CSRPO, CG_SELEC, IR_LEASE, PO_SOCIE, PB_TRAIN, CG_MISSI

 

>0.3

CG_VALUE, PO_RULES, PB_HINTE

Negative factor loading

<−0.4

IR_PUBFU

 

<−0.3

IR_PDUES, FA_LANDD, OP_TRANS, DV_PRIVA

Model with region dummy variables (Factor 4)

Positive factor loading

>0.8

CG_ACAUD, CG_ACSEP, IR_INCOM

 

>0.6

SZ_STAFF

 

>0.5

PB_REGEX, CG_SELEC, SZ_CARGO, PO_ITSYS, CG_ACANL

 

>0.4

CG_CSRPO

 

>0.3

CG_ACPUB, CG_MISSI, PB_TRAIN, IR_LEASE, PB_HINTE, DV_CORPT

Negative factor loading

<−0.4

IR_PUBFU

 

<−0.3

OP_TRANS, FA_LANDD, IR_PDUES

Note: Variables with an estimated residual variance <0.50 are indicated in bold.

In summary, Factor 2 combines the size of the port authority with principles of good corporate governance and functional pro-activeness. Compared with the corresponding Factor 4 in the model with regional dummy variables, we notice that these do not appear in the list of significant variables. The regional adherence does not therefore play a role.

Factor 3: New European public conservators

It appears from Table 7 that Factor 3 shows predominantly negative loadings, especially for variables that relate to operational involvement in port services (OP), corporate governance (transparency) (CG), functional pro-activeness within (PO) and beyond the port (PB), as well as financial autonomy (FI). A negative loading also exists for private ownership of the port authority (DV_PRIVA), whereas a positive loading appears for the variable that indicates whether the port authority obtained its present legal form in the last decade (DV_REFYR).
Table 7

Loadings: Factor 3 (without dummy variables) and Factor 2 (with dummy variables)

Model without region dummy variables (Factor 3)

Positive factor loading

>0.4

CG_OBJEC,

 

>0.3

DV_REFYR, IR_INCOM, CG_ACSEP

Negative factor loading

<−0.7

OP_TECNA

 

<−0.6

OP_ANCIL

 

<−0.5

CG_ACPUB, OP_CARHA

 

<−0.4

PO_BOTTL, OP_PAXHA, FI_INVES, FI_TARGT, PB_SERVI, DV_PRIVA, FI_COLLE, OP_TRANS, FI_CROSS

 

<−0.3

CG_CSRPO, PO_PROMO, FI_PRICE, PO_SUSTA, FI_RESUL

Model with region dummy variables (Factor 2)

Positive factor loading

>0.6

RG_NWLAT

 

>0.4

FI_TARGT

 

>0.3

DV_REFYR, RG_NWHAN (0.299)

Negative factor loading

<−0.6

CG_ACPUB, OP_ANCIL

 

<−0.5

PO_SOCIE, PO_SUSTA, OP_TECNA, PO_BOTTL

 

<−0.4

FI_CROSS, PB_REGEX, PO_RULES, PB_TRAIN, CG_CSRPO

 

<−0.3

FI_COLLE, CG_VALUE, OP_PAXHA, PO_TRAIN, IR_LEASE, FA_POLIC, CG_BORPO

Note: Variables with an estimated residual variance <0.50 are indicated in bold.

Factor 3 bears resemblance to the ‘conservator’ type of port authority that was identified in Table 1. Compared with the corresponding Factor 2 in the model with regional dummy variables, we see a strong positive loading for the New Latin region (RG_NWLAT) and a modest positive loading for the New Hanse region (RG_NWHAN).

Factor 4: Anglo-Saxon private entrepreneurs

Finally, Table 8 shows that Factor 4 has positive loadings for devolution variables (DV), most strongly for privatisation (DV_PRIVA). It also has a strongly positive loading for the variable that indicates that the provision of services forms the highest percentage of income of the port authority (IR_SERVI). This corresponds with the positive loading for the variables that indicate that the port authority provides cargo-handling services (OP_CARHA) and has maximization of its own profit as an economic objective (CG_PROFI). The factor has negative loadings on financial autonomy variables (FI). A negative loading appears on politicians being significantly present in the supervisory board of the port authority (CG_BORPO).
Table 8

Loadings: Factor 4 (without dummy variables) and Factor 3 (with dummy variables)

Model without region dummy variables (Factor 4)

Positive factor loading

>0.9

DV_PRIVA

 

>0.8

IR_SERVI

 

>0.4

DV_CORPT, CG_PROFI

 

>0.3

SZ_STAFF, OP_CARHA, CG_ACANL

Negative factor loading

<−0.5

CG_BORPO

 

<−0.4

FI_BENEF, FI_PROMO, CG_MISSI

 

<−0.3

FA_ENTIT, FI_COLLE, IR_PDUES, FI_CROSS

Model with region dummy variables (Factor 3)

Positive factor loading

>0.4

CG_BORPO, RG_HANSE

 

>0.3

CG_OBJEC, PB_STRAP, FI_PROMO, CG_BORSZ, IR_LEASE, IR_INCOM

Negative factor loading

<−0.9

DV_PRIVA

 

<−0.8

RG_ANGLO

 

<−0.6

OP_CARHA, CG_PROFI

 

<−0.5

DV_CORPT, IR_SERVI, FI_INVES

 

<−0.4

OP_TECNA, FI_PRICE

 

<−0.3

FI_RESUL, IR_CAPAS

Note: Variables with an estimated residual variance <0.50 are indicated in bold.

This factor has elements of the entrepreneurial type indicated in Table 1. The negative loadings on financial autonomy (FI) seem paradoxical, however. Factor 4 relates negatively to Factor 3 in the model with regional dummy variables. Taking this into account, it is obvious that the Anglo-Saxon regional variable (RG_ANGLO) plays a very important role.

Conclusions and Research Agenda

There exists a wide range of studies, which discuss port governance in general or focus on particular aspects. However, up until now, research on port governance practices was limited to case studies or rather descriptive analyses. The present study extends this research by analysing a large number of European port authorities (n=110) in a quantitative manner, using factor analysis. The 2010 ESPO Fact-Finding Survey proved to be a valuable source of information to explore differences in governance practices between European ports.

The results confirm the existence of different types of port governance in Europe, which to some extent correspond with the hypothetical typology according to which port authorities can be conservators, facilitators or entrepreneurs. Differences are mainly geographically defined and the subdivision in Hanseatic, Latin, Anglo-Saxon and new Member State port authorities proves to be a valuable one. Taking into account that, proportionally, most port authorities in Europe belong to either the Hanse or Latin tradition, the difference between them translates itself in a North–South duality which not only involves simple ownership differences, but also covers many other governance elements, especially functional and financial autonomy, which is typically more limited in the south.

In addition to this geographical explanation of diversity, we could also detect differences in terms of governance practices between small and large ports. The latter generally follow a more pro-active approach and score higher on transparency-related variables.

The findings of our analysis invite more in-depth research. The principal factors should be explored further to explain apparent paradoxes, such as the limited functional and financial autonomy that Latin port authorities seem to combine with a pro-active facilitator approach. The same goes for the outspoken entrepreneurial profile of Anglo-Saxon port authorities that seems to be bound by limited financial autonomy. This in-depth research will be done through comparative case study analysis. The most pertinent variables can furthermore be transformed into performance indicators in order to keep track of evolutions in port governance practices over time. Finally, the potentially harmonising influence of EU law and policy on European port governance should be analysed (Verhoeven, 2009).

Notes

Acknowledgements

The authors wish to thank the independent reviewers of this article and Professor Dr Eddy Van de Voorde for reviewing an earlier version.

References

  1. Brooks, M.R. and Cullinane, K. (eds.) (2007) Introduction. In: Devolution, Port Governance and Port Performance. Amsterdam, the Netherlands: Elsevier, pp. 3–28.Google Scholar
  2. De Langen, P.W. (2007) Stakeholders, conflicting interests and governance in port clusters. In: M.R. Brooks and K. Cullinane (eds.) Devolution, Port Governance and Port Performance. Amsterdam, the Netherlands: Elsevier, pp. 457–477.Google Scholar
  3. De Monie, G. (2004) Mission and role of port authorities after privatisation. Paper presented at the ITMMA PPP Seminar; November, Antwerp, Belgium.Google Scholar
  4. Gilbert, P.D. and Meijer, E. (2006) Money and Credit Factors. Ottawa, Canada: Bank of Canada. Bank of Canada Working paper 2006-3.Google Scholar
  5. Heaver, T., Meersman, H., Moglia, F. and Van de Voorde, E. (2000) Do mergers and alliances influence European shipping and port competition? Maritime Policy & Management 27 (4): 363–373.CrossRefGoogle Scholar
  6. Heaver, T., Meersman, H. and Van de Voorde, E. (2001) Co-operation and competition in international container transport: Strategies for ports. Maritime Policy & Management 28 (3): 293–305.CrossRefGoogle Scholar
  7. Jacobs, W. and Hall, P.V. (2007) What conditions supply chain strategies of ports? The case of Dubai. GeoJournal 68 (4): 327–342.CrossRefGoogle Scholar
  8. Muthén, L.K. and Muthén, B.O. (2006) Mplus User's Guide, 4th edn. Los Angeles, CA: Muthén and Muthén.Google Scholar
  9. Nisenbaum, R., Ismail, K., Wessely, S., Unwin, C., Hull, L. and Reeves, W.C. (2004) Dichotomous factor analysis of symptoms reported by UK and US veterans of the 1991 Gulf War. Population Health Metrics 2 (8).Google Scholar
  10. Olivier, D. and Slack, B. (2006) Rethinking the port. Environment and Planning A 38 (8): 1409–1427.CrossRefGoogle Scholar
  11. Pallis, A.A., Vitsounis, T.K. and De Langen, P.W. (2010) Port economics, policy and management: Review of an emerging research field. Transport Reviews 30 (1): 115–161.CrossRefGoogle Scholar
  12. Slack, B. and Frémont, A. (2005) Transformation of port terminal operations: From the local to the global. Transport Reviews 25 (1): 117–130.CrossRefGoogle Scholar
  13. Stevens, J. (2002) Applied Multivariate Statistics for the Social Sciences, 4th edn. Mahwah, NJ and London: Lawrence Erlbaum Associates.Google Scholar
  14. Suykens, F. (1988) Op weg naar een Europese havenpolitiek? In: J. Blockx (ed.) Liber Amicorum Lionel Tricot. Antwerpen, Belgium: Kluwer, pp. 493–498.Google Scholar
  15. Suykens, F. and Van de Voorde, E. (1998) A quarter of a century of port management in Europe: Objectives and tools. Maritime Policy & Management 25 (3): 251–261.CrossRefGoogle Scholar
  16. Vanoutrive, T., Van Malderen, L., Jourquin, B., Thomas, I., Verhetsel, A. and Witlox, F. (2010) Mobility management measures by employers: Overview and exploratory analysis for Belgium. European Journal of Transport and Infrastructure Research 10 (2): 121–141.Google Scholar
  17. Vanelslander, T. (2011) Competition Concerns in Ports and Port Services. Paris, France: Organisation for Economic Co-operation and Development, OECD DAF/COMP/WP2(2011)5.Google Scholar
  18. Verhoeven, P. (2009) European ports policy: Meeting contemporary governance challenges. Maritime Policy & Management 36 (1): 79–101.CrossRefGoogle Scholar
  19. Verhoeven, P. (2010) A review of port authority functions: Towards a renaissance? Maritime Policy & Management 37 (3): 247–270.CrossRefGoogle Scholar
  20. Verhoeven, P. (2011) European Port Governance: Report of an Inquiry into the Current Governance of European Seaports (The ESPO ‘Fact-Finding Report’). Brussels, Belgium: European Sea Ports Organisation.Google Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2012

Authors and Affiliations

  • Patrick Verhoeven
    • 1
    • 2
  • Thomas Vanoutrive
    • 1
    • 3
  1. 1.Department of Transport and Regional EconomicsUniversity of AntwerpAntwerpBelgium
  2. 2.European Sea Ports OrganisationBrusselsBelgium
  3. 3.Department of GeographyGhent UniversityGhentBelgium

Personalised recommendations