Econometric analysis to differentiate effects of various ship safety inspections

doi:10.1016/j.marpol.2007.11.006

Marine Policy

Volume 32, Issue 4, July 2008, Pages 653-662

https://doi.org/10.1016/j.marpol.2007.11.006 Get rights and content

Abstract

This article provides a refined technique to measure and interpret variables associated with the quality of an inspection—be it port state control or vetting inspections towards the probability of a very serious, serious or less serious casualty. It concentrates on filtering out the effect of variables such as detention, the port state control regime that inspected the vessel, time in-between inspections, deficiencies found during an inspection and the effect of vetting inspections. The consensus amongst policy makers in the shipping industry is that data cannot be combined to target vessels. While this article does demonstrate that the decrease in the probability of casualty is stronger for the South American Region, the Indian Ocean Region and Australia versus North Europe, North America or the Caribbean, it also demonstrates that the data can be combined to target vessels for inspections. Since the time in-between inspections and detention is mostly not significant towards decreasing the probability of casualty, these results reflect the lack of coordination amongst port state control regimes and industry inspections. Due to this lack of coordination and trust, a ship might be inspected in several regimes during a relative short time period where the benefit of an inspection can be easily saturated. Our recommendation on direct policy implication is to promote the harmonization of inspection databases across port state control regimes, preferably with the coordination of the development of the Global Integrated Ship Information System (GISIS) of the International Maritime Organization (IMO), to review the policy of a release of a vessel from detention and to increase cooperation amongst regimes with respect to the follow up of the rectification of deficiencies.

Introduction

Port State Control is to be understood and seen as the second line of defense to eliminate substandard vessels to operate on the seven seas. Due to the lack of trust between regulators and industry inspections and the lack of sharing data across various Memoranda of understanding, the system generates a substantial amount of inspections besides port state control inspections which are performed in the name of safety. Another common belief amongst port state control regimes is that data cannot be combined across regimes for the purpose of statistical analysis. For this reason, each regime maintains their own database and does not share data on inspection information with each other directly. The only public database which does share information is Equasis³ but the data cannot be used for risk profiling or to determine the effect of inspections.

The article uses a combined data set of port state control inspections, casualty data and industry inspections to demonstrate that the data can be combined for statistical purpose and to measure the effectiveness of inspections to decrease the probability of casualty. Binary logistic regression is used in the analysis but a twin ship data set is constructed which enables to filter out causal effects of variables such as flag, classification society, age, ship types or ownership of a vessel and concentrates on variables which indicates the quality of an inspection such as detention, which port state control regime inspected the vessel, vetting inspections and deficiencies found during a port state control inspection.

The construction of the twin data set is important in order to be able to concentrate on the variables of interest. Only ships that have been inspected are used and are match against ships that had a casualty with ships of similar construction details (e.g. the same ship type, age or the same shipyard country) without a casualty. In addition, other variables are used reflecting the target factor by the regimes (e.g. age, ship type, flag, etc.) besides other events which happened during the commercial life of a vessel (e.g. change of ownership, change of flag, class withdrawal). These events can all have an effect on the safety quality of a vessel. These variables are part of the twin data set and the regression models as correction factors in order to correct for factors other than variables which are associated with the quality of an inspection and which in this case are the variables of interest. In addition, a time frame between inspections and casualties was introduced for the time period on-hand since it was felt that the effect of an inspection prior to a casualty is more relevant to the topic on hand.

First, the article gives a short summary of the data sets used in Section 2 and provides an explanation of the methodology behind the creation of the twin ship database. In Section 3, the regression models are explained and statistical results are provided. Section 4 presents the interpretation of the results and Section 5 presents the conclusions.

Section snippets

Overview of data sets and descriptive statistics of casualty per region

For the analysis, the same data sets as in Knapp and Franses [1] is used with the only difference that a sub-set out of the total world fleet data set is extracted since the article only concentrates only on ships that were inspected. The total inspection data sets comprises of 183,819 inspections from various Memoranda of Understanding (MoU)⁴ for the

Explanation of regression models and evaluation of key statistical results

The starting positions to create the twin ship database was the total inspection data set of 183,819 inspections which aggregated by IMO number accounts for 25,836 ships of which 21,880 did not have any casualty and 3956 had a casualty regardless of the time frame. Later on, the article will further make a distinction to only use observations of vessels that had inspections at least six months or less prior to a casualty but for the time being and in order to match respective twins, this

Interpretation and visualization of results

This section will provide an interpretation of the results, the visualization thereof when applicable and will show their policy implications. Table 6 provides a summary of the variables of interest for each model in question. On can easily see that the coefficients for the variables indicating if a ship has been inspected by one of the industry vetting inspection regimes (Rightship) are all negative and follow the overall ranking of a vessel⁶

Conclusions

Descriptive statistics showed that high risk areas for casualties are to be found within the South and North China Sea, the Arabian Gulf and the Indian Ocean, the West African Coast and the Mediterranean Sea/Black Sea where most life is lost in the South China Sea and West Africa.

Based on the binary regression models, one can conclude that the strongest effect of a port state control inspection in decreasing the probability of casualty can be found in the South American Region, the Indian Ocean

Acknowledgements

The authors thank the Paris Memorandum of Understanding (MoU), the Caribbean MoU, the Viña del Mar Agreement on PSC, the United States Coast Guard (USCG), the Indian Ocean MoU and the Australian Maritime Safety Authority (AMSA) for providing raw data for this analysis. In addition, the authors would like to thank the International Maritime Organization (IMO), Lloyd's Maritime Intelligence Unit and Lloyd's Register Fairplay for providing casualty data. Finally, the authors would like to thank

References (4)

S. Knapp et al.
Econometric analysis on the effect of port state control inspections on the probability of casualty
Marine Policy
(2007)
MSC/Circ. 953, MEPC/Circ. 372, Reports on marine casualties and incidents, revised harmonized reporting procedures....

There are more references available in the full text version of this article.

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¹: The article does not reflect the view of the European Maritime Safety Agency (EMSA).

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