The Overall View of the Effect of Inspections and Evaluation of the Target Factor to target substandard vessels.
This report is the third part of a PhD project entitled
“The Econometrics of MaritimeSafety – Recommendations to Enhance Safety at Sea”
which is based on 183,000 portstate control inspections
and 11,700 casualties from various data sources. Its overallobjective is to provide recommendations to improve safety at sea. The second part looksinto the differences across port state control regimes based on the probability ofdetention while the third part is measuring the effect of inspections on the probabilityof casualty which can be measured for very serious casualties but not for serious or lessserious casualties. It further determines the magnitude of improvement areas fortargeting substandard vessels and gives an evaluation of the target factor.
data from one of the major safety regimes – the Tokyo MoU where cooperation for this analysisunfortunately could not be obtained.
Figure 2: Overview of Variables Used
Note: DoC = Document of Compliance Company, an ISM requirement
Depending on the type and method of analysis, either dummy variables for each variable areused or the data is coded into groups (e.g. flag states can be used individually or grouped intoblack, grey or white listed flag states). The incorporation of the ownership of a vessel is not astraight forward task in shipping and requires some careful thinking. Two types of variablegroups have therefore been used. The first one is information concerning the Document ofCompliance Company (DoC) of a vessel based on information received from Lloyd’s RegisterFairplay and the second one and due to the lack of the completeness of information on the DoCCompany is the addition on the ownership of a company which represents the “beneficialowner”
. Variable transformation and regrouping was performed for port state control dataand casualty data. Transformation tables were used to re-code all of the following variables:1)
Flag States (Black, Grey, White, Undefined) – Paris MoU2)
Classification Societies – IACS and Not IACS recognized3)
Ownership of a vessel as per Alderton & Winchester or technical management as per LRFairplay (DoC Company)4)
Ship Types Variables were recoded using a transformation table for each MoU and the casualty datasetsinto standard codes for each variable group (flag, class, owner, ship type). The standard codingused for the total datasets were then transferred into dummy variables for the regressions ordescriptive statistics.
Flag States were coded individually or grouped into four major groups according to the ParisMoU Black, Grey and White List
where white listed flag states are performing well followed
based on Lloyd’s Register Fairplay data of the “World Shipping Encyclopedia CD” and Lloyd’s“Maritime Database CD”
Paris Memorandum of Understanding Annual Reports for 2000 – 2004.
PORT STATE CONTROL CASUALTIES
Construction InformationVessel Particulars (Age, Size, Ship Type)Classification SocietyVessel Registration (Flag State)Beneficial OwnerDoC Company
Date of Inspection
Location of Inspection(either country or port)Deficiencies(main deficiency coding)Detention
Date of Casualty
Location of CasualtyCasualty First EventsSeriousnessPollutionLoss of Life, Loss of Vessel
Link:IMO NumberIndustry Data
Rightship RankingGreenaward Cert.
by grey. Black listed flag states are performing worst. Flag states in the group “undefined” areflag states that do not have enough inspections for the Paris MoU or do not trade in the ParisMoU area.
Classification Societies (RO)
Classification Societies have been coded individually or grouped into two groups – either theyare a member of the International Association of Classification Society or not which serves as akind of quality indicator. There are currently ten members as follows:
American Bureau of Shipping2)
China Classification Society4)
Det Norske Veritas5)
Korean Register of Shipping7)
Nippon Kaiji Kyokai (ClassNK)9)
Registro Italiano Navale10)
Russian Maritime Register of Shipping
Ownership or Technical Management
Ownership is represented by two variables. It is either the “true owner” (not the registered one)who has the financial benefit or it is the technical manager on the ISM Document ofCompliance
The datasets were merged with data from Lloyds Register Fairplay in order toidentify the ownership of a certain vessel for both variables. For the true ownership, thecountry of location was then grouped according to Alderton and Winchester (1999)
to reflectthe safety culture onboard. The grouping of the countries into six main groups is found in Appendix 1 for further reference but is as follows:
traditional maritime nations
emerging maritime nations
new open registries
old open registries
international open registries
“unknown” for unknown or missing entries.
The Selection of Ship Types
The selection of ship types for the analyses is important and therefore considerable amount oftime was spent to find the best possible grouping. This provides a more accurate analysis of theprobability of detention. The decision was based on five points as follows:
Legal Base including the major conventions and related codes distinguishingdifferent applications based on ship types and the deriving differences in conducting aport state control inspection.
World Trade Flows to capture exposure of the regimes in connection with the %of ship types that were inspected/detained by each regime and the special commercialcharacteristics of each segment
As per IACS, http://www.iacs.org.uk
The Document of Compliance is a requirement by the ISM (International Safety Management Code)Code. The technical manager responsible for the safety management of the vessel needs to be identifiedon this document. Sometimes for smaller companies, this can be the owner; otherwise it
Alderton T. and Winchester N (2002). “Flag States and Safety: 1997-1999”.
Maritime Policy andManagement
, Vol 29, No. 2, pp 151-162
Analysis of Casualties per ship type and their severity
Analysis of Regression Results of port state control data for each ship type andin aggregated version
Correspondence Analysis based on port state control data in order to visualizethe effects on aggregating the data and to provide an overall confirmation on theselection of the grouping of ship types.Taking the decision points listed above into account where the detailed analyses involvedderiving at the grouping is shown in detail in Knapp (2006), the following ship types have beenaggregated out of the 19 original ship types:1.
General Cargo & Multipurpose
(General Cargo, Ro-Ro Cargo, Reefer Cargo, HeavyLoad)
(Tanker, Oil Tanker, Chemical Tankers, Gas Carriers, OBO)5.
(Passenger Ships, Ro-Ro Passenger, HS Passenger)6.
(Offshore, Special Purpose, Factory Ship, Mobile Offshore, Other Ship Types)
2. Descriptive Statistics and Key Figures for Casualties
2.1. Selection of Port State Control Relevant Casualties
Considerate care was given on the selection of casualties for the analysis. From the casualtydataset within the time period 1999 to 2004 of 9,851 cases, the following cases were eliminated.1.
Cases due to extreme weather conditions such as hurricanes, typhoons, gales and veryheavy storms2.
Ships attacked by pirates or ships lost due to war3.
Ships involved in a collision with no identified fault
Any other miscellaneous items not relevant to PSC such as drugs found, virus outbreaksof passengers or accidents which happened in dry docks5.
Not PSC relevant ships types such as ferries, the fishing fleet, tugs or governmentvessels. The fishing fleet cases were kept separate and a separate analysis wasperformed based only on the fishing fleet above 400gt.The remaining 6291 cases concern 6,005 ships when aggregated by IMO number and were thenreviewed and re-grouped into the three groups of seriousness