Port Infrastructure Pricing: Findings from a Survey of International SeaportsYapa Mahinda Bandara PhD Candidate, Department of Maritime and Logistics Management, Australian Maritime College, University of Tasmania ybandara@amc.edu.au

Dr Hong-Oanh Nguyen

Senior Lecturer, Department of Maritime and Logistics Management, Australian Maritime College, University of Tasmania onguyen@amc.edu.au

Dr Peggy Shu-Ling Chen

Senior Lecturer, Department of Maritime and Logistics Management, Australian Maritime College, University of Tasmania p.chen@amc.edu.au

International Association of Maritime Economists Conference 2014, Norfolk, Virginia

Structure

• Introduction

• Research questions

• Data collection and method

• Analysis results

• Implications

• Conclusions

• Future research

Introduction• Transformation of seaport in to autonomous

business entities

• Emergence of landlord model ports

• Increasing importance of port pricing policy

• Port infrastructure tariffs as one of the major source of revenue for ports

Port infrastructure pricing

• What knowledge port authorities possess and to what extent they are applied in port pricing

• What factors they (need to) consider in designing infrastructure tariffs

• What factors influence the tariff practices of ports

Data collection• Using online questionnaire:

• A sample of 67 seaports

Variables Variables

• Port ownership • Port’s governance modal• Port’s administrative structure• Port competition• Port pricing knowledge of ports

and its applicability• Port’s tariff objectives• Port costs: investment and

maintenance• Port’s financial position

• Port demand and its characteristics• Perceived port user costs• Government policies and regulation• Stakeholder participation in port

infrastructure tariffs• Transparency of port infrastructure

tariffs• Port infrastructure tariffs revision • Institutional arrangement for design

and revision port infrastructure tariffs

Analysis Method

• Exploratory Factor Analysis– Principal Component Analysis (PCA)

method using the varimax criterion – SPSS

• Confirmatory Factor Analysis– Maximum Likelihood method– AMOS

Sample CharacteristicsOwnership Count Percentage Administrative Structure Count Percentage

Central Government 24 36% Statutory Public Port Authority 22 33%State/Provincial Governemnt 16 24% Commercialized Port Authority 12 18%Municipal Governemnt 11 16% Government Department 6 9%Local Government 6 9% Statutory Corporation 2 3%Public-Private/Foreign Partnership 1 1% Government Owned Limited Corporation 7 10%Private/Foreign Company 2 3% Public Limited Company 5 7%Private/Domestic Company 6 9% Private Limited Company 9 13%Private-Public/Community T rust 1 1% Non Profit Public Enterprise 1 1%Other 0 0% Municipal Enterprise 2 3%

Government T rading Enterprise 1 1%

Governance Modal Count Percentage Port Region Count Percentage

Service Port 18 27% European 28 42%Tool Port 4 6% Nother American 13 19%Landlord Port 36 54% Sotuh American 3 4%Privatized Port 6 9% East Asian 6 9%Other 1 1% South East Asian 4 6%

Australasian 13 19%

Source: Authors’ calculation based on survey data

EFA: Factors determining tariff design 

Factors

Port

Demand

Knowledge and

Applicability

Industry

dynamics

 

Tariff

objective

Cost

recovery

D4 Port’s financial position .792 .068 -.041 .086 -.051

D6 Total financial costs to port users .787 .217 .150 .106 .273

D5 Perceived service quality .762 .061 .123 .372 .103

D7 Trade flow .696 .038 .306 .368 -.185

B2.4 Awareness of discriminatory pricing .104 .580 -.035 .165 .117

B1.2 Awareness of market based pricing -.017 .912 .008 .026 .073

B1.1 Awareness of cost based pricing .081 .839 .007 .085 .077

B2.2 Use of market based pricing .391 .671 .288 -.118 .061

B2.1 Use of cost based pricing .510 .644 .009 -.036 .281

D10 Competing ports’ tariffs .289 .072 .853 .082 -.011

C2 Competing with rivals -.178 .068 .852 .117 .031

D9 Variability in ship size .366 -.137 .622 -.097 .211

C4 Increasing port capacity utilization .240 .012 .062 .871 .121

C6 Promoting regional economic development .162 .068 -.059 .783 .185

C3 Attracting specific cargo and port users .042 .247 .520 .669 .045

C1 Recovering investment costs -.014 .076 .019 .112 .902

C5 Covering operational costs .007 .182 .093 .255 .739

D1 Infrastructure investment cost .500 .144 .072 -.038 .685

Cronbach's Alpha 0.848 0.803 0.730 0.784 0.762

EFA: Factor influential to tariff practice  Factors  

  Tariff

policy

Tariff

Regulation

Transparency Stakeholder

participation

E3.5 Revising tariffs as per port’s competitive position .772 -.052 .213 .051

E2.1 Having a policy guideline for tariff design and revision .723 .454 .000 -.147

E2.2 Having a policy on rebates and discounts .707 -.033 .219 .008

E3.4 Revising tariffs as per port’s strategic plan .706 .207 -.065 .029

E4.3 Obtaining inputs from commercial and planning dept. .684 .128 .134 -.015

E3.2 Adjusting tariffs with inflation and input cost .635 -.222 .063 .144

E2.4 Having a tariffs regulatory control .067 .806 .190 .139

E1.3 Needing government approval for revised tariffs -.042 .793 .113 .123

E2.8 Adhering to published tariffs .242 .556 -.267 .339

E2.5 Offering composite tariff .223 -.015 .785 -.093

E2.7 Offering negotiable tariffs .277 -.013 .719 .213

E2.9 Offering a lump sum port fee -.039 .341 .704 .184

E1.4 Obtaining feedback from port users .032 .159 .014 .926

E1.5 Obtaining feedback from port operators .007 .199 .236 .870

Reliability - Cronbach's Alpha 0.807 0.670 0.678 0.873Extraction Method: Principal Component AnalysisRotation Method: Varimax with Kaiser Normalization

 

a. Rotation converged in 5 iterationsKaiser–Meyer–Olkin measure of sampling adequacy is 0.683 

 

CFA: Factors determining tariff design

Source: Amos output

CFA results

Variables   Factors Estimate S.E. C.R.P

value

B1.1 Cost based pricing

knowledge

Pricing Knowledge &

Applicability1

***

B1.2 Market based pricing

knowledge

Pricing Knowledge &

Applicability1.041 0.417 2.493 0.013

C4 Port capacity utilisation Tariff Objective 1 ***C6 Regional economic

development Tariff Objective 0.76 0.14 5.434 ***

C3 Attracting cargo and port

users Tariff Objective 0.696 0.127 5.488 ***

C1 Recovering investment

cost Cost Recovery 1

***

C5 Covering operational cost Cost Recovery 1 ***

D7 Trade flow Port Demand 0.902 0.207 4.367 ***

D5 Perceived service quality Port Demand 1 ***

Implications: Ports• need to account for port demand and its variability• need to consider total trade flow, service quality

when deciding the level of port infrastructure charge

• needs to look at the available knowledge base in the port organization and makes use of them in the tariff design process

• need to align infrastructure tariff design with the type of cargo, in turn which determines the type of port user

Implications: Ports….cont.• aim to recover the cost associated with

investment and operation of port infrastructure• need to establish a port tariff policy in which the

tariff designing and revision process is clearly outlined

• need to obtain the participation of various stakeholders in tariff revision process

• need to lift the level of transparency of port tariffs setting, thus they will not be subjected to criticism

Conclusion

• Factors affecting port infrastructure design: – port demand – port pricing knowledge and applicability,– Port competition and vessel size – tariff objective of the port and – consideration for cost recovery

Conclusion

• Factors influential to port infrastructure tariff practices: – the existence of a port infrastructure tariff

policy, – tariff regulation, – various stakeholders’ participation in tariff

revision and – the level of transparency

Future research

• look into the analysis of infrastructure tariff design in terms of different governance models

• individual port infrastructure charges can be empirically studied separately to identify specific nature of each charge in terms of its design and determination

THANK YOU• Reviewers of the research paper

• Special acknowledgement to port managers/authorities for taking part in the survey

• AusAid-Australia Award for funding