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Assessing Impact of Concessionaires on Sea Ports

Gunasekara, Harshanee; Bandara, Yapa


Harshanee Gunasekara


Concession has been acknowledged as an important tool for port authorities to retain control of ports by contracts out the management and operation to the private sector for a specific time period [1]. This study focuses on developing a model to measure the concessionaire impact on port performance and efficiency using the pre and post privatization data. Parameters such as Crane rate, Elapsed rate, Total TEU and Ships handled were used as variable for the assessment and the secondary data were collected from a website of Australian government. The statistical report called `Waterline' provides the latest data available on stevedoring productivity and landside performance of five Australian major port terminals for the period of 1990 to 2007. The samples were drawn as panel data by indicating 90 observations to test the model. The collected data was analyzed using Multiple Regression analysis and “Minitab 16” as the analytical software. Analysis revealed a continuous improvement in the developed model and the findings of the research indicated that crane rate of ports with the concessionaire are higher than that of ports without concessionaire while that value of Elapsed rate was lower than that. From observation in this study, a decision can be made that the increased trend in transferring government port operations and asset to the private sector suggests that public ports can benefit from greater private sector participation than fully privatization.

Presentation Conference Type Conference Paper (Published)
Conference Name 2018 Moratuwa Engineering Research Conference (MERCon)
Start Date May 30, 2018
End Date Jun 1, 2018
Online Publication Date Jul 31, 2018
Publication Date 2018
Deposit Date Dec 4, 2021
Publisher Institute of Electrical and Electronics Engineers
Book Title 2018 Moratuwa Engineering Research Conference (MERCon)
Keywords Port Concessions, Port Efficiency & Performance, Panel Data, Log Linear Multiple Regression
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