Posted: April 24th, 2023

Effect and Improvement Areas for Port State Control Inspections to Decrease the Probability of Casualty

Effect and Improvement Areas for Port
State Control Inspections to Decrease
the Probability of Casualty

Econometric Institute Report 2006-32
Abstract
This report is the fourth part of a PhD project called “The Econometrics of
Maritime Safety – Recommendations to Enhance Safety at Sea” and is based on
183,000 port state control inspections2 and 11,700 casualties from various data
sources. Its overall objective is to provide recommendations to improve safety at
sea. The fourth part looks into measuring the effect of inspections on the
probability of casualty on either seriousness or casualty first event to show the
differences across the regimes. It further gives a link of casualties that were found
during inspections with either the seriousness of casualties and casualty first
events which reveals three areas of improvement possibilities to potentially
decrease the probability of a casualty – the ISM code, machinery and equipment
and ship and cargo operations.

1 Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR, Rotterdam,
The Netherlands, email: s.knapp@vienna.at or franses@few.eur.nl 2 The authors would like to thank the following secretariats for their kind co-operations: Paris
MoU, Indian Ocean MoU, Viña del Mar Agreement on PSC, Caribbean MoU, Australian Maritime
Safety Authority, the United States Coast Guard, Lloyd’s Register Fairplay, Lloyd’s Maritime
Intelligence Unit, the International Maritime Organization (IMO), Right Ship and the
Greenaward Foundation.
2
1. Overview of Datasets and Variables Used
Chapter 1 of this report are extracts from Knapp (2006)3 which are necessary in order to
explain the datasets and variable preparation as a basis for the analysis explained in
chapter 3.
Three datasets have been used for the analysis and their relation can be seen in Figure 1.
Set A consists of the inspection database of 183,819 inspections from various Memoranda
of Understanding (MoU4) for the time period January 1999 to December 2004 where the
time period is not fully covered by all regimes. This total dataset is a combination of six
individual inspection datasets and when aggregated, it accounts for approx. 26,020 ships5
where the average amount of inspections per vessel is by 7 per ship or 1.7 inspections per
ship per year.6
Figure 1: Overview of Datasets Used
Set C represents an approximation of the total ships in existence7. Out of these vessels,
ships below 400 gt8 and ship types which are not eligible for port state control inspection

3 Knapp, S. (2006), The Econometrics of Maritime Safety – Recommendations to Enhance Safety at
Sea, Doctoral Thesis (to be published), Econometric Institute, Erasmus University, Rotterdam
4 A memorandum of understanding (MOU) is a legal document describing an agreement between
parties but is less formal than a contract.
5 25,836 exact ships plus 184 estimated ships. Since there are 1,288 ships with missing IMO
numbers out of the total port state control dataset and the average number of inspections per ship
lies by 7, the unidentified ships can be aggregated to another 184 inspected ships.
6 Based on an average of 4 inspection years which is the average of the total months per regime to
bring the different years of data to the same level for all regimes. The total time period Jan. 1999
to Dec. 2004 therefore represents a total of full 4 inspection years instead of 6 years.
7 As per data received from Lloyd’s Register Fairplay.
8 As per Marpol 73/78, Annex I, Regulation 4 which identifies the vessels subject to mandatory
surveys (page 51)
Total
Ships in Service
= 93,719 ships
where 47% are
eligible for
PSC inspection
= 43,817 ships
PSC eligible
(43,817 ships)
Set A
Ships
Inspected
183,819 insp.
= 26,020 ships
59% of Set C Set B
11,701
Casualties
= 9,598 ships
10 % of Set C
Set C
Industry
Data
3
such as fishing vessels, government ships, yachts and ferries (for the Paris MoU) have
been eliminated from this dataset which leaves approx. 43,817 ships (46,75% of the total)
for inspection. Since the amount of inspections from the Paris MoU is the dominating part
of this dataset and ferries are treated separately in the EU, ferries have been excluded
from PSC eligible ships. The total estimated inspection coverage by the regimes in
question of eligible ships is 59.4% between set A and the eligible ships of Set C for the
time period in question (1999-2004).
Besides the port state control inspection dataset, a small industry inspection dataset has
been collected and comprises of vetting inspection information9 of vetting inspections
performed on oil tankers and dry bulk carriers from Rightship. In addition, oil tankers
which are certified by Greenaward have also been identified. The casualty and industry
data is linked to the port state control data by the IMO number and within the same time
frame.
This total dataset is a combination of six individual inspection datasets and when
aggregated, it accounts for approx. 26,020 ships10 where the average amount of
inspections per vessel is 7 per ship or 1.7 inspections per year.11 Set C represents an
approximation of the total ships in existence12. Out of these vessels, ships below 400 gt13
and ship types which are not eligible for port state control inspection such as fishing
vessels, government ships, yachts and ferries have been eliminated from this dataset
which leaves approx. 44,047 ships (47% of the total) for inspection. The total estimated
inspection coverage by the regimes in question of eligible ships lies therefore by slightly
above 59% between set A and the eligible ships of Set C.
Set B is the casualty dataset which consists of 11,701 records for time period 1993 to 2004
and is a combination of data received from Lloyd’s Register Fairplay, LMIU14 and the
IMO (International Maritime Organization). The time period 2000 to 2004 is the most
complete casualty dataset since it draws from all three datasets. Aggregated, this dataset
accounts for approx. 9,598 ships or 10% of the total ships in existence from Set C where
the average amount of casualties per ship is by 1.2. Port State relevant casualties without
the fishing fleet aggregate to 6005 ships for the time period 1999 to 2004 or 13.7% of the
total PSC eligible ships.
The sets are used in various ways depending on the kind of analysis which is conducted.
In essence the combination of these datasets gives insight into the amount of ships that
are inspected/not inspected, detained/not detained and have/do not have a casualty with
their respective combinations. Figure 2 gives an overview of the variables used for all
types of analysis for port state control and casualties where the link between the two
datasets is given by the IMO number and the dates of inspection/casualty respectively.

9 Rightship Rating Data (48,834 records of which 37,080 are used) and Greenaward Data on
certified ships (244 records)
10 25,838 exact ships plus 184 estimated ships. Since there are 1,288 ships with missing IMO
numbers out of the total port state control dataset and the average number of inspections per ship
lies by 7, the unidentified ships can be aggregated to another 184 inspected ships. 11 Based on an average of 4 inspection years which is the average of the total months per regime to
bring the different years of data to the same level for all regimes. The total time period Jan. 1999
to Dec. 2004 therefore represents a total of full 4 inspection years instead of 6 years. 12 As per data received from Lloyd’s Register Fairplay.
13 As per Marpol 73/78, Annex I, Regulation 4 which identifies the vessels subject to mandatory
surveys (page 51)
14 Lloyds Maritime Intelligence Unit
4
This short introduction to the research questions, the methods and datasets used to
conduct the analysis should provide enough evidence that the subject is covered from
various angles and that great care was placed on the selection of the datasets and the
data preparation.
Given the datasets used for the quantitative part, it can be assumed that with almost
60% of coverage of port state control data, a sensible interpretation can be made even
with the lack of data from one of the major safety regimes – the Tokyo MoU where
cooperation for this analysis unfortunately 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
are used or the data is coded into groups (e.g. flag states can be used individually or
grouped into black, grey or white listed flag states). The incorporation of the ownership of
a vessel is not a straight forward task in shipping and requires some careful thinking.
Two types of variable groups have therefore been used. The first one is information
concerning the Document of Compliance Company (DoC) of a vessel based on information
received from Lloyd’s Register Fairplay and the second one and due to the lack of the
completeness of information on the DoC Company is the addition on the ownership of a
company which represents the “beneficial owner”15. Variable transformation and
regrouping was performed for port state control data and casualty data. Transformation
tables were used to re-code all of the following variables:
1) Flag States (Black, Grey, White, Undefined) – Paris MoU
2) Classification Societies – IACS and Not IACS recognized
3) Ownership of a vessel as per Alderton & Winchester or technical management as
per LR Fairplay (DoC Company)
4) Ship Types

15 based on Lloyd’s Register Fairplay data of the “World Shipping Encyclopedia CD” and Lloyd’s
“Maritime Database CD”
At time of construction
At time of inspection/casualty
PORT STATE CONTROL CASUALTIES
Construction Information
Vessel Particulars (Age, Size, Ship Type)
Classification Society
Vessel Registration (Flag State)
Beneficial Owner
DoC Company
Date of Inspection
Location of Inspection
(either country or port)
Deficiencies
(main deficiency coding)
Detention
Date of Casualty
Location of Casualty
Casualty First Events
Seriousness
Pollution
Loss of Life, Loss of Vessel
Link:
IMO Number
Industry Data
Rightship Ranking
Greenaward Cert.
5
Variables were recoded using a transformation table for each MoU and the casualty
datasets into standard codes for each variable group (flag, class, owner, ship type). The
standard coding used for the total datasets were then transferred into dummy variables
for the regressions or descriptive statistics.
Flag States
Flag States were coded individually or grouped into four major groups according to the
Paris MoU Black, Grey and White List16 where white listed flag states are performing
well followed by grey. Black listed flag states are performing worst. Flag states in the
group “undefined” are flag states that do not have enough inspections for the Paris MoU
or do not trade in the Paris MoU area.
Classification Societies (RO)
Classification Societies have been coded individually or grouped into two groups – either
they are a member of the International Association of Classification Society or not which
serves as a kind of quality indicator. There are currently ten members as follows:17
1) American Bureau of Shipping
2) Bureau Veritas
3) China Classification Society
4) Det Norske Veritas
5) Germanischer Lloyd
6) Korean Register of Shipping
7) Lloyd’s Register
8) Nippon Kaiji Kyokai (ClassNK)
9) Registro Italiano Navale
10) 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 of Compliance18 The datasets were merged with data from Lloyds Register
Fairplay in order to identify the ownership of a certain vessel for both variables. For the
true ownership, the country of location was then grouped according to Alderton and
Winchester (1999)19 to reflect the 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.

16 Paris Memorandum of Understanding Annual Reports for 2000 – 2004.
17 As per IACS, https://monkessays.com/write-my-essay/iacs.org.uk
18 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 identified on this document. Sometimes for smaller companies, this can be the owner; otherwise
it is contracted out to manager who runs the vessel on behalf of the owner.
19 Alderton T. and Winchester N (2002). “Flag States and Safety: 1997-1999”. Maritime Policy and
Management, Vol 29, No. 2, pp 151-162
6
The Selection of Ship Types
The selection of ship types for the analyses is important and therefore considerable
amount of time was spent to find the best possible grouping. This provides a more
accurate analysis of the probability of detention. The decision was based on five points as
follows:
o Point 1: Legal Base including the major conventions and related codes
distinguishing different applications based on ship types and the deriving
differences in conducting a port state control inspection.
o Point 2: 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
commercial characteristics of each segment
o Point 3: Analysis of Casualties per ship type and their severity
o Point 4: Analysis of Regression Results of port state control data for each ship type
and in aggregated version
o Point 5: Correspondence Analysis based on port state control data in order to
visualize the effects on aggregating the data and to provide an overall
confirmation on the selection of the grouping of ship types.
Taking the decision points listed above into account where the detailed analyses involved
to derive at the grouping is shown in Knapp (2006) in detail, the following ship types have
been aggregated out of the 19 original ship types:
1. General Cargo & Multipurpose (General Cargo, Ro-Ro Cargo, Reefer Cargo,
Heavy Load)
2. Dry Bulk
3. Container
4. Tanker (Tanker, Oil Tanker, Chemical Tankers, Gas Carriers, OBO)
5. Passenger Vessel (Passenger Ships, Ro-Ro Passenger, HS Passenger)
6. Other (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
casualty dataset 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
very heavy storms
2. Ships attacked by pirates or ships lost due to war
3. Ships involved in a collision with no identified fault20
4. Any other miscellaneous items not relevant to PSC such as drugs found, virus
outbreaks of passengers or accidents which happened in dry docks
5. Not PSC relevant ships types such as ferries, the fishing fleet, tugs or government
vessels. The fishing fleet cases were kept separate and a separate analysis was
performed based only on the fishing fleet above 400gt.

20 The identification of “no fault” in this case was not straight forward and some cases still
included in the dataset might be ships with no fault and were not eliminated due to lack of
exactness of data.
7
The remaining 6291 cases concern 6,005 ships when aggregated by IMO number and
were then reviewed and re-grouped into the three groups of seriousness as per IMO MSC
Circular 953 of December 2000:21
1. Very serious casualties: casualties to ships which involve total loss of the ship,
loss of life or severe pollution
2. Serious casualties are casualties to ships which do not qualify as “very serious
casualties” and which involve fire, explosion, collision, grounding, contact, heavy
weather damage, ice damage, hull cracking, or suspected hull defect, etc. resulting
in: immobilization of main engines, extensive accommodation damage, severe
structural damage, such as penetration of the hull under water, etc. rendering the
ship unfit to proceed, or pollution (regardless of quantity); and/or a breakdown
necessitation towage or shore Helpance.
3. Less serious casualties are casualties to ships which do not qualify as “very
serious casualties” or “serious casualties” and for the purpose of recording useful
information also include “marine incidents” which themselves include “hazardous
incidents” and “near misses”.
In addition to the classification of seriousness of casualties, the cases were also examined
and re-classified according to casualty first events. The casualty first events are classified
as follows:
o Deck and Hull related casualties: Deck and hull related items such as maintenance
items (cracks, holes, fractures, hatch cover problems, cargo equipment failure,
lifeboat gear failure, anchor and mooring ropes problems), stability related items
such as capsizing, listing, cargo shifts and flooding
o Fire/Explosion: Fire and Explosion anywhere on the vessel (main areas are engine
room)
o Engine or machinery related casualties: Engine related items including engine
breakdown, black outs, steering gear failure and propulsion failure
o Wrecked/Stranded/Grounded: Wrecked, Stranded, Grounded where a large
portion of the ships in this category are stranded or grounded. 112 ships in this
category are ships that were lost and therefore could probably be classified as
wrecked. Nevertheless, for the purpose of the analysis, this category is to be
interpreted primarily for stranded and grounded vessels.
o Collision and Contact: Collision and Contact
Figure 3 then gives an overview of the split up of the casualty first events. The graph is
not detailed but can be understood as a first attempt to break up the casualty types into
relevant categories.
The lack of information and fragmentation of the data does not permit a better split up.
Interesting to see is the high amount of engine and machinery related events of about
32% (engine breakdown, engine black out, steering gear failure and propulsion failure)
while the probability of detention has shown a relative low probability of detention based
on deficiencies in the area of propulsion and auxiliary machinery (code 1400).

21 as per IMO MSC Circular 953, 14th December 2000
8
Figure 3: Casualty First Events per Ship Type (1999 to 2004)
Engine
Breakdown
25%
Fire/Explosion
10%
Contact
19%
Collision
8%
Deck & Hull
Related
12%
Wrecked/
Stranded/
Grounded
19%
Engine
Black Out
2%
Propulsion
Failure
2%
Stearing Gear
Failure
3%
PSC Relevant Cases, compiled by author
2.2. Overview of Deficiencies and Casualties
Figure 4 gives an overview of the mean amount of deficiencies found six month prior to a
casualty per flag state group while Figure 5 shows the split up for IACS recognized
classification societies and non IACS recognized classification societies.
Figure 4: Mean Amount of Deficiencies per Flag State: 6 months prior to casualty
4.3
2.7
1.7
2.2
2.8
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
Black Grey White Undefined Average
Mean amount of deficiencies
Note: based on ships that were inspected six month prior to casualty
Black listed flag states have an average of 4.3 deficiencies versus 1.7 deficiencies for
white listed flag states. Ships of Non-IACS classification societies have an average of 6.5
deficiencies versus 2.4 for ships with ICAS classification in an inspection at least six
9
months prior to a casualty. Figure 6 shows the mean amount of deficiencies that are
found previous to a casualty per seriousness of casualty and detention. Ships that have
been detained show a significant higher amount of deficiencies than ships that have not
been detained prior to a casualty.
Figure 5: Mean Amount of Deficiencies per Class: 6 months prior to casualty
6.5
2.4
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Non IACS IACS
Mean amount of deficiencies
Note: based on ships that were inspected six month prior to casualty
Figure 6: Mean Amount of Deficiencies per Seriousness of Casualty
16.9
14.9
16.1
10.0
8.3 9.0
3.0
1.7 1.9
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
very serious serious less serious
Mean Amount of Deficiencies
detained average not detained
Note: based on ships that were inspected six month prior to casualty
The next chapter will provide the probability of casualty as refined view and based on
either seriousness or casualty first event. It further provides models which link the
deficiencies found during and inspection with the casualty first events to identify room for
improvements of inspections.
10
3. The Probability of Casualty – Refined View
3.1. Description of Model and Methodology
This model will provide the estimated probability (P) of a ship having a casualty based on
each ship type defined previously for each safety regime. The dependent variable (y) in
this case is “casualty” or “no casualty”. In a binary regression, a latent variable y* gets
mapped onto a binominal variable y which can be 1 (casualty) or 0 (no casualty). When
this latent variable exceeds a threshold, which is typically equal to 0, it gets mapped onto
1, other wise onto 0. The latent variable itself can be expressed as a standard linear
regression model
y*i = xiβ + εi
where i denotes ship i. The xi contains independent variables such as age, size, flag,
classification society or owner, and β represents a column vector of unknown parameters
(the coefficients). The binary regression model can be derived as follows:22
P (yi = 1|xi) = P (y*i > 0| xi) = P (xiβ + εi > 0|xi) = P (εi > – xiβ|xi) = P (εi ≤ xiβ|xi)
The last term is equal to the cumulative distribution function of εi evaluated in xiβ, or in
short:
P (yi = 1|xi) = F (xiβ)
This function F can take many forms and for this study two were considered, namely the
cumulative distribution function of the normal distribution (probit model) and the
cumulative distribution function of the logistic function (logit model). The general model
can therefore be written in the form of Equation 1 where the term xiβ changes according
to the model in question.
Equation 1: Probability of Casualty (per seriousness)
x β)
x β)
i
i
P (
(
i 1 e
e
+ =
To estimate the coefficients, quasi-maximum likelihood (QML)23 is used as method of
estimation in order to give some allowance for a possible misspecification of the assumed
underlying distribution function. For the final models, logit and probit models are
compared to see if there are any significant differences and logit models are used for the
visualization part.
Figure 7 provides an overview of the steps that were taken in order to perform the
analysis which will be described in the next chapter.

22 for further reference, refer to Franses, P.H. and Paap, R. (2001). Quantitative Models in
Marketing Research. Cambridge University Press, Cambridge, Chapter 4
23 for further details on QML, refer to Greene H.W. (2000), Econometric Analysis, Fourth Edition,
page 823ff
11
The first step is the same as mentioned in the previous chapter and will therefore not be
explained again in detail. The bases for the casualties are all ships with port state control
relevant casualties – a total of 6,005 ships.
Figure 7: Description of Methodology Used
3.2. Explanation of Relevant Datasets and Procedure to Match Ships
The second step was then to link the ships with a casualty to the ships without casualty
and 3,956 ships emerged to show a connection based on the IMO number and were taken
into further consideration. These vessels provide the basis for several merges within a
certain time frame in order to gain a better overview of the connection between casualties
and port state control inspections. The following results are listed in Table 1 below.
Table 1: Datasets for Port State Control and Casualty Merges Performed
Datasets
Criteria Used for Data
Links
Total
Inspections
Total
Casualties
Total
Records
Total
Ships
Set 1 Inspections with no casualties 148,557 Nil 148,557 21,880
Set 2A Time Frame: 6 months 23,401 2,921 26,322 2,321
Set 2B No Time Frame 33,974 4,737 38,711 3,956
The datasets show three scenarios where set 2B does not contain any particular time
frame between an inspection and a casualty. The remaining set is based on a minimum of
at least one connection of an inspection prior to a casualty of 6 months. After this merge
was performed, the remaining inspections and if applicable casualties of a particular
vessel were added in order to account for the whole inspection history of a vessel.
Step 3 is the starting point to create a dataset which is then used in the analysis. The
idea is to match ships from the datasets described earlier, and which had casualties, with
ships that did not have casualties. In order to match ships from each of the groups, a set
of variables had to be identified to guarantee the best possible match.
Step 1
Step 2
Step 3
The selection of PSC relevant casualties and the
classification of the cases by seriousness according to
IMO classification
The selection of relevant datasets of inspections and
casualties based on a time frame of six months and no
time frame
The selection of the variables that are used to match
ships for the datasets identified under step 2 and the
execution of the matching.
Step 4 The preparation and calculation of the variables used in
the regression and based on the matches found under
step 3.
12
These variables are assumed not to have a direct impact on the seriousness of a casualty
and are listed in order of importance given the fact that the difference of observations in
the datasets is quiet large. In doing the match, the first three variables are the most
important ones followed by the country the ship was constructed and the owner and then
the remaining variables such as class, flag and hull details for tankers.
Table 2: List of Variables used to Match Ships
1. Ship Type at the time of construction
2. Year Built (in 11 ranges)
3. Gross Tonnage (in 44 ranges)
4. Country of Owner at time of construction
5. Country where Ship was primarily built
6. Class at construction
7. Flag at construction
8. Double Hull
Ship type is found to be the most important variable for determining the construction
quality and operating environment of a ship. Out of the total 25,836 ships from the
inspection dataset, 1546 ships were converted or alternated since construction and 290
ships changed their ship type completely.
The matching was performed between Set 1 and Set2B using Oracle24 and following the
methodology which is visualized in Figure 8. Set 2A is a subset of Set 2B and is then
extracted from the result of the basic match performed on Set 2B. The match was
performed in two rounds where double matches for ships with casualties are allowed out
of the dataset of ships with inspections only. The first round matches the vessels on the
three basic criteria listed in the figure namely: ship type, age and tonnage. From the
21,880 available ships, 17,727 were used to match and 4,153 records were not used.
Figure 8: Visualization of Matching Methodology (per Ship)

24 Credit is given here to Ratan Singh Ratore who Helped the author in performing this match by
providing the necessary software (Oracle) and SQL statements to execute the queries.
+
3 Main Criteria
for Basic Match:
Ship Type
Age Group
Tonnage Group
Round 1: 297,532 matches with 3,916 casualties
Degree 8: 122,295 matches with 3769 casualties: 187 lost
Round 2: 2,491 matches with 147 casualties: 40 lost
Set 1: inspections only
21,880 ships
(17,727 used)
Set 2B: with casualties
3,956 ships
(3,916 used)
5 Secondary Criteria: points Rd1 points Rd2
Owner at construction 5 1
Shipyard Country 4 1
Class at construction 3 1
Flag at construction 2 1
Double Hull 1 1
13
This is then followed by a refinement of the matches using the remaining criteria in order
of importance with weighted points. The basic match comes up with 297,532 matches for
3,916 ships with casualties. The refinement match based on ownership (5 points), the
ship yard country (4 points), the class at construction (3 points) and the flag state at
construction (2 points) reduces the basic matches to 122,295 hits with 3,769 ships with
casualties by using a degree 8 to match ships. The decision to allocate points is not based
on any empirical evidence of the impact of the variables on the construction quality. It is
based on the author’s understanding of the shipping industry and partly derived from
interviews25 with surveyors who have experience with new buildings, naval architects
and one of the ship owner’s associations26.
Degree 8 means that the matching ships have both eight points out of the total of 15
points which would be a perfect twin. Several scenarios were run using various degrees of
matching before a decision was made to use degree eight for the analysis and the results
are shown in Table 3. The table lists the degrees of matches (as total points of the point
allocated per additional criteria), the matches of ships with inspections, the number of
ships with casualties that were used and not used and the % of casualties that are lost to
the total casualties (3,956).
The last column indicates which of the scenarios is then accepted for the analysis. Degree
eight was chosen because it provides a balanced result of loosing 4.7% of the cases with
casualties by having a matching degree of 8 points out of 15 total points where eight
points means that at least three out of the five additional criteria are matched besides the
three basic criteria.
With degree 8, 187 ships did not have a corresponding ship. Since 23 cases are very
serious casualties and 115 cases serious casualties, a second round of matching was
performed on these ships by using a simplified point system for the remaining criteria (1
point instead of the weighted point system). The resulting basic match based on degree 1
(at least two more variables match besides the three basic ones) reveals 2,491 hits with
147 ships with casualties.
Table 3: Summary of Matches by Degrees for Round 1(by Ship)
Degrees
of Match
Ships with
Inspections
Matched
Ships with
Casualties
Used
Ships with
Casualties
Not Used
% Ships Lost
to
Total (3,956)
Dataset
Used for
Analysis
15 6,840 1,757 2,199 55.5% Yes
14 n/a 1,788 2,168 54.8% No
13 n/a 2,829 1,127 28.5% No
12 n/a 3,021 935 23.6% No
11 n/a 3,166 790 19.9% No
10 47,768 3,394 562 14.2% No
9 106,658 3,720 236 5.9% No
8 122,295 3,769 187 4.7% Yes
7 124,172 3,781 175 4.4% No
A total of 40 ships do not have any corresponding vessel out of the inspection dataset.
From these 40 ships, 6 had a very serious casualty, 24 a serious casualty and 10 a less
serious casualty. These 40 ships are not used in the analysis. The final results of the
match are presented in Table 4 and are based on ship counts (a ship can have several

25 For detailed list of interviews performed by the author can be found in the Bibliography.
26 Dutch Royal Ship Owner’s Association
14
casualties). The column indicating the ships with casualties lost is based on the number
of ships with casualties as listed in Table 1 and the actual number of ships that found
matches. This match provides the basis for the models that will follow. In addition to the
six months time frame, a separate match was performed based on perfect twins instead of
degree 8 in order to see if enough observations are available to use this dataset. From the
total amount of observations of 8,597 cases in comparison to 77,599 cases, one can easily
see that the amount of observations is limited. It is therefore decided to only use the
dataset based on degree 8 and with the time frame of six months prior to a casualty.
Table 4: Summary of Matched Datasets (by Ship)
Final Datasets
Ships with
Inspections
Round 1 & 2
Ships with
Casualties
Round 1 & 2
Ships with
Casualties
Lost
Total
Dataset
Cases
No Time Frame 124,786 3,916 40 128,702
6 months 75,302 2,297 24 77,599
Perfect Twins 6,840 1,757 n/a 8,597
Table 5 gives an overview of the types of models that are used. The table lists the types of
models with the total amount of regressions in each series, the datasets on which the
models are based on and the variables that are of interest in each of the regressions. Type
I model is based on the seriousness of casualty and should give an overview of the
effectiveness of an inspection (per regime) on the probability of either a very serious,
serious or less serious casualty. Type II is an aggregated model which uses multiplicative
dummy variables for the deficiencies and ship types in order to see if there are any
significant differences with respect to deficiencies found before an inspection and the ship
types. It can be seen as a further refinement of the type I models but only for very serious
and serious casualties.
Table 5: List of Twin Models and their Variables of Interest
Model Name Types of Model/Data based on Variables of interest
Type I Models
(Total Models: 3)
Based on twins of ships inspected six
months prior to a casualty
Types are as follows*):
o Very serious (5,826)
o Serious (45,486)
o Less serious (27,411)
Time in-between inspection
Detention
Deficiencies (less serious)
Regimes (overall view)
Vetting Inspections
Type II Model
(Total Model: 1)
Based on twins of ships inspected six
months prior to a casualty but combined of
very serious and serious casualties in order
to increase the amount of observations. The
deficiencies are multiplied by ship types
and used as multiplicative dummies
Total # of observations: (52,150)
Time in-between inspection
Detention
Deficiencies per ship type
Regimes (overall view)
Type III Models
(Total Models: 5)
Based on casualties of a respective first
event in relation to the deficiencies found
during a PSC inspection. Types are as
follows:
o Fire & Explosion (6,218)
o Wrecked/Stranded/Grounded (19,131)
o Collision/Contact (23,254)
o Deck Related First Events (8,357)
o Engine Related First Events (27,079)
Time in-between inspection
Detention
Deficiencies (refined view)
Regimes (overall view)
*) Note: the numbers in brackets are number of observations in the model
15
The type III models are based on the casualty first events identified at the beginning of
this report and are a first attempt to link the deficiencies with casualties. The next
section will explain the variables and the base model itself which is then applied
according to the type I, II or III models.
3.3. Explanation of Variables used in the Models
The variables listed in Table 6 are a summary of the variables that are used in the
regressions. The variables are split into two blocks where block 1 contains the variables
which are normally used to target vessels such as the ship type, the classification society,
the flag state and the ownership of a vessel and block 2 provides a summary of the
inspection history of a particular vessel including information on industry inspections
(vetting inspections of Rightship and Greenaward).
Table 6: Variables Used in the Twin Regressions (Type I, II and III models)
Dependent Variable 1: Casualty:
This can be either per seriousness or by casualty first event 1/0

Independent Variables
Number of
Variable nℓ
Remark on
Variable
Expected
Sign
ℓ Block 1: Ship Particulars: included to account for target factors
Ln(Age) 1 Average age at Inspection 1 C
Ln(SIZE) 2 Gross Tonnage 1 C
ST 3 Ship Type at present 6 D
STInd 4 Ship Type Changed 1 D
CL 5 Classification Society at inspection 33 D
CLInd 6 Classification Society changed 1 D
CLWdr 7 Class Withdrawn 1 D
FS 8 Flag State at inspection 81 D
FSInd 9 Flag State Changed 1 D
OWN 10 Owner of vessel 6 D
OWNInd 11 Ownership changed 1 D
LIOWN 12 Legal Instruments Rectified (Owner) 1 C
LIFS 13 Legal Instruments Rectified (Flag) 1 C
DH 14 Double Hull 1 D
Block 2: Inspection History: variables of interest
RS 15 Rightship Inspected (5 Star Rating or indicator) 5 D neg
GR 16 Greenaward Certified 1 D neg
ln(TIME) 17 Time in between inspections (days) 1 C neg
PSC 18 Inspections Frequency per Regime (Fractions) 6 D neg
DETPS 19 Detention Frequency per Regime 6 D neg
CODE 20 Deficiency main codes (also multiplied by ST) 26 (156) C und
Total Variables*) 181(311)
*) in brackets indicates number of multiplicative dummy variables
C= Continuous, D= Dummy
Within block 1, changes in any of the variables since the construction of the vessel and
during the years of inspection history are identified (e.g. the ship type was converted,
flag, class or ownership changed). This block also includes information the number of
legal instruments a certain flag or country of residence of an owner has rectified.
16
Since the whole inspection and casualty history of a particular vessel is taken into
consideration, average percentage fractions over all records of one particular vessel
(aggregated by IMO number) are used in the regressions for the inspections and the
detentions while the deficiencies are aggregated and represent a total sum. In addition
and depending on the final model, the variables indicating where the ship was inspected
can be replaced by the actual port states and the deficiencies can be multiplied by ship
types and therefore increases the amount of variables accordingly. The increased amount
of variables is shown in brackets in the table.
Figure 9 visualizes the variable structure of the twin models by following a time line of a
pair of vessels over its course of life and the model can be written in the form of Equation
1 where the term xiβ can change accordingly to the casualty model in question (either per
seriousness of casualty or by casualty first event) and is given in Equation 2 and its
variables are further explained in Table 6.
Figure 9: Visualization of Variable Structure: Twin Models
Note: Variables of interest are in italic
Different Life Events:
Change of ST
Change of Flag
Change of Owner
Change of Class
Class Withdrawal
Vettings Inspections
PSC Inspections (Fraction)
Greenaward certified
Detention
Deficiencies
Time-inbetween inspections
Particulars over Time
Average Class
Average Flag
Average Owner
Legal Framework:
Number of LI ratified
(Flag and Owner)
Different Life Events:
Change of ST
Change of Flag
Change of Owner
Change of Class
Class Withdrawal
Vetting Inspection
PSC Inspection (Fraction)
Greenaward certified
Detention
Deficiencies
Time-inbetween inspections
Particulars over Time
Average Class
Average Flag
Average Owner
Legal Framework:
Number of LI ratified
(Flag and Owner)
Ship 1 Ship 2 (Twin of Ship 1)
Casualty No Casualty
Same Initial Events:
Age Group, Tonnage Group, Construction Details (Ship Yard
Country, Owner country, Classification Society, DH)
17
Equation 2: Detailed Effect of Inspections (Seriousness or First Event)
k k,i
n
k k k,i
n
k k k,i
n
k
k k,i i i
n
k i i
k k,i i i
n
k i
k k,i
n
k k k,i i i
n
k
k k,i i
n
k i i
β β β
β β β β β
β β β β
β β β β
x β β β β β β
Σ PSC Σ DETPS Σ CODE
LIFS DH Σ RS GR ln(TIME )
FSInd Σ OWN OwnInd LIOWN
Σ CL CLInd CLWdr Σ FS
ln(AGE ) ln(SIZE ) Σ ST STInd
20, 1 19,
1
1 18,
1
1
15, 16 17
1
1 13 14
10, 11 12
1
1 9
8,
1
1 5, 6 7
1
1
3, 4
1
1 i 0 1 2
18 19 20
15
10
5 8
3
=
−
=
−
=
−
=
−
=
−
=
−
=
−
=
+ + +
+ + + + +
+ + + +
+ + + +
= + + + +
The model produces probabilities on an individual ship level (i). The rest of the notation is
as follows: ℓ represents the variable groups, nℓ is the total number of variables within
each group of ℓ and k is an index from 1 to nℓ
3.4. Model Assessment and Final Results (type I, II and III models)
Table 7 provides a split up of the ships with casualties into their seriousness which is the
basis for the type I and II models.
Table 7: Summary of Matched Dataset by Seriousness of Casualty (by Ship)
Final Datasets Very Serious Serious Less Serious
By Seriousness Casualties Total Cases Casualties Total Cases Casualties
Total
Cases
No Time Frame 306 10,778 2,345 77,980 1,457 44,882
6 months 167*) 6,007 1,387 46,522 881 28,008
Perfect Twins 156 958 1,033 4,824 641 3,091
*) Note: figures are with passenger vessels and the Caribbean MoU while final models are without
these variables due to lack of data
Type II is based on a combined dataset for very serious and serious casualties. The
figures are based on the number of ships and since a ship can have multiple casualties,
some ships are counted in each of the casualty categories. The table provides the number
of ships with a certain type of casualty and the corresponding total cases (ships with
casualties and inspections).
The type III models are a series of five regressions which link casualty first events with
deficiencies that are found previously. The basis for these regressions is all ships that
were inspected six month prior to a casualty, a total of 2321 ships. The corresponding
datasets are listed in Table 8 below.
18
Table 8: Summary of Matched Dataset by Casualty First Events (by Ship)
Casualty First Event Casualties Twins Total Obs
Fire/Explosion 213 6005 6218
Wrecked/Stranded/Grounded 526 18605 19131
Collision/Contact 713 22541 23254
Deck Related 253 8104 8357
Engine Related 819 26260 27079
*) Figures are with the Caribbean MoU and all ship types while final model can vary accordingly
All models were tested for the presence of heteroscedasticity using the LM test as
described by Davidson and McKinnon (1993)27. The null hypothesis (ho) assumes
homoscedasticity and the alternative hypothesis assumes heteroscedasticity in the
following form where γ is unknown and z are a number of variables which are assumed to
be the cause of heteroscedasticity:
Variance = exp (2z’γ)
A summary of the findings can be seen in Table 9 for the type I and type II models where
ho is rejected only for tonnage in the type II model. Based on Knapp (2006, chapter 6)28
where probabilities were calculated based on a model developed by Greene29 and based on
Harvey (1976) and no significant difference was found between the normal and the
corrected model, it is assumed that the presence of heteroscedasticity for the variable
tonnage does not have a serious effect of the estimated probabilities.
Table 9: Test Statistics for LM-Test: Type I and II models
Type of Model Variable
Tested
LM-Statistic p-value
6m very serious (type I) Age 4.261 0.0389 – do not reject ho
Tonnage 4.061 0.0438 – do not reject ho
Combined model (type II) Age 5.900 0.0268 – do not reject ho
Tonnage 20.998 0.0000 – reject ho
Note: 1% significance level used
The results for the type III models are shown in Table 10 where no presence of
heteroscedasticity could be identified for the variables in question. The remaining
statistics of the final models for type I and type II are then presented in Table 11 and for
the type III models in Table 12. The table lists the number of observations in each model,
the number of twin outliers that were identified and eliminated, the cut off rate and a
summary of other relevant statistics.
One cannot see any major difference between probit and logit. The models were reduced
using a 1% significance level. Only two variables (deficiency codes) in the less serious
casualty model are left in the model at a 5 % significance level. The results are acceptable
for the amount of observations in each model. For the type I models, the hit rate and the

27 Davidson and McKinnon (1993), Estimation and Inference in Econometrics, New York: Oxford
University Press, 1993, page 526ff
28 Knapp, S. (2006), The Econometrics of Maritime Safety – Recommendations to Enhance Safety
at Sea, Doctoral Thesis (to be published), Econometric Institute, Erasmus University, Rotterdam
29 Greene H.W. (2000), Econometric Analysis, Fourth Edition, Econometric Analysis, Prentice Hall,
New Jersey; page 518ff; Furthermore, recognition is to be given to Richard Paap from the
Econometric Institute for pointing this program out to me and for making it available to me.
19
McFadden R2 is higher for the very serious models compared to the other two model types
and the HL-statistic indicates a better fit. Logit models are used for the visualization
part.
Table 10: Test Statistics for LM-Test: Type III models
Model Variable Tested LMStatistic
p-value
Fire/Explosion Age n/a n/a
Tonnage 1.255 0.2625 – do not reject ho
Wrecked/Stranded/Grounded Age 0.279 0.5967 – do not reject ho
Tonnage 0.398 0.5276 – do not reject ho
Collision/Contact Age 1.019 0.3126 – do not reject ho
Tonnage 2.448 0.1176 – do not reject ho
Deck Related First Events Age 0.194 0.6600 – do not reject ho
Tonnage 0.302 0.5823 – do not reject ho
Engine Related First Events Age 0.176 0.6744 – do not reject ho
Tonnage 0.261 0.6091 – do not reject ho
Note: 1% significance level was used
Table 11: Summary of Statistics – Type I and II Model
6 months Time Frame
Type I Models very serious serious less serious
0 = 5665 0 = 44124 0 = 26551
1 = 161 1 = 1362 1 = 860
# observations in
final model
Total = 5826 Total = 45486 Total = 27411
# outliers (twins) none none none
Cut Off 0.0276 0.0299 0.0314
LOG PRO LOG PRO LOG PRO
Mc Fadden R2 0.166 0.162 0.139 0.139 0.077 0.077
% Hit Rate y=0 73.93 72.22 70.00 68.28 66.95 66.00
% Hit Rate y=1 71.43 72.67 73.35 75.18 64.07 65.23
% Hit Rate Tot 73.86 72.23 70.10 68.49 66.86 65.97
HL-Stat. (df=8) 9.41 19.54 3.00 16.60 9.75 9.62
p-value 0.3088 0.0120 0.9343 0.0345 0.2832 0.2927
Remarks
w/o passenger vessels
and Caribbean MoU
with passenger vessels but without Caribbean
MoU
Type II Model (VS and Serious)
0 = 50610
1 = 1540
# observations in
final model
Total = 52150
# outliers (twins) none
Cut Off 0.0295
LOG PRO
Mc Fadden R2 0.130 0.1292
% Hit Rate y=0 68.88 67.09
% Hit Rate y=1 72.53 74.09
% Hit Rate Tot 68.98 67.29
HL-Stat. (df=8) 11.749 24.39
p-value 0.1633 0.0020
20
Table 12: Summary of Statistics – Type III Models
6 months Time Frame
Fire/Explosion
Wrecked/
Stranded/Grounded Collision/Contact
0 = 5484 0 = 18098 0 = 21641
1 = 191 1 = 502 1 = 688
# observations in final
model
Total = 5675 Total = 18600 Total = 22329
# outliers (twins) none none none
Cut Off 0.0337 0.02698 0.0308
LOG PRO LOG PRO LOG PRO
Mc Fadden R2 0.088 0.088 0.070 0.0698 0.068 0.068
% Hit Rate y=0 66.63 65.24 67.05 66.07 67.89 66.74
% Hit Rate y=1 66.49 67.54 64.34 65.94 61.48 62.35
% Hit Rate Tot 66.63 65.32 66.98 66.06 67.70 66.61
HL-Stat. (df=8) 9.08 5.00 13.78 15.28 6.72 4.74
p-value 0.3360 0.7574 0.0878 0.0539 0.5666 0.7849

Deck Related Engine Related Remarks
0 = 7538 0 = 26260 All Models except
1 = 233 1 = 819 Engine rel. casualties
# observations in final
model
Total = 7771 Total = 27079 are w/o the Caribbean
# outliers (twins) none none plus:
Cut Off 0.0300 0.0310 Fire: w/o container
LOG PRO LOG PRO WSG: w/o passenger
Mc Fadden R2 0.079 0.079 0.084 0.085 and other ST
% Hit Rate y=0 65.46 64.13 69.72 68.74 COCO: w/o other ST
% Hit Rate y=1 67.81 69.96 63.00 64.22 Deck: w/o passenger
% Hit Rate Tot 65.53 64.30 69.52 68.60 and other ST
HL-Stat. (df=8) 4.80 4.15 6.32 3.90
p-value 0.7789 0.8438 0.6111 0.8664
Note: WSG = Wrecked, Stranded, Grounded, COCO = Collision/Contact
The results of the type III models are not as good as the models based on the seriousness
of casualty which is expected to be the case. The results are still acceptable and the hit
rate is still above 65% for all models. The models had to be adapted according to the
number of observations and for most models (except engine related), the Caribbean MoU
had to be taken out as well as the passenger vessels and other ship types due to lack of
data. Not much difference can be seen between the logit and the probit models and logit
models are used for the visualization part.
3.5. Visualization of Refined Results – Effect of Inspections
Table 13 lists a summary of the coefficients of the variables of interest for the type I, type
II and type III series of models. The main findings from this table can be summarized as
follows and will be visualized in the sections to come.
Table 13: Summary of Main Variables and their Significance: All Twin Models
Type I Models Type II Model Type III Models
Variables of Interest
very
serious serious less serious
combined model
very serious & serious
Fire
Expl. W/S/G
Collision
Contact
Deck
Related
Engine
Related
Industry Inspections Coef. Coef. Coef. Coef. Coef 2nd ST Coef. Coef. Coef. Coef. Coef.
Rightship 1 star (RS inspected) benchmark benchmark n/s n/s n/s -1.4344 n/s
Rightship 2 star -1.0013 -0.9613 -0.2872 -0.9796 n/a n/a n/a n/a n/a
Rightship 3 star -1.3447 -1.1532 -0.5105 -1.1838 n/a n/a n/a n/a n/a
Rightship 4 star -2.4995 -2.7490 -0.5970 -2.7714 n/a n/a n/a n/a n/a
Rightship 5 star -3.0056 -3.9050 -0.7596 -3.8793 n/a n/a n/a n/a n/a
Greenaward Certified n/s n/s n/s n/s n/s n/s n/s n/s n/s
Port State Control Inspected/Detained
Time in-between inspections n/s 0.1526 0.1169 0.1107 0.1503 n/s n/s n/s 0.0938
Paris MoU inspected n/s n/s n/s n/s n/s n/s n/s n/s 0.8225
Caribbean MoU inspected not in model n/s not in model n/s
Viña del Mar MoU inspected -1.3812 -0.6322 -0.6322 -0.8266 -1.0950 n/s -0.9150 -1.2067 n/s
Indian Ocean MoU inspected n/s -1.5607 -1.5607 -1.4198 -1.5103 -1.2964 -1.1704 -2.3361 n/s
USCG inspected n/s n/s n/s -0.3184 n/s n/s n/s n/s 0.8545
AMSA inspected n/s -0.5662 -0.5662 -0.5182 n/s -1.3373 -0.8534 n/s n/s
Paris MoU detained n/s n/s n/s n/s n/s n/s n/s n/s n/s
Caribbean MoU detained not in model n/s not in model n/s
Viña del Mar MoU detained n/s n/s n/s n/s n/s n/s n/s n/s n/s
Indian Ocean MoU detained n/s n/s n/s n/s n/s n/s n/s n/s n/s
USCG detained n/s n/s n/s n/s n/s n/s n/s n/s -0.3169
AMSA detained n/s n/s 0.7553 n/s n/s 0.6844 n/s n/s 0.5202
Deficiencies Found (only significant ones are listed) per ship type
C0400 Food and catering n/s n/a n/s -0.6149 (ST3) n/s n/s n/s n/s -0.1245
C0500 Working spaces & acc.prev. n/s n/s n/s n/s n/s n/s n/s n/s n/s
C0700 Fire Safety measures n/s 0.0335 n/s 0.0495 (ST1) n/s 0.02376 n/s n/s 0.0432
C0900 Structural Safety n/s n/s 0.0252 0.0657 (ST3) n/s n/s 0.0315 n/s n/s
C1000 Alarm Signals n/s n/s n/s n/s n/s 0.2636 n/s n/s n/s
C1200 Load lines n/s n/s n/s -0.0527 (ST1) 0.1261 (ST4) n/s n/s n/s 0.0701 n/s
C1400 Propulsion & aux.engine n/s 0.0318 0.0241 0.0467 (ST2) 0.0486 0.02231 n/s n/s 0.0645
C1700 MARPOL A. I (Oil Pollution) n/s n/s 0.0573 n/s n/s n/s n/s n/s 0.0449
C1800 Gas and chemical carriers 0.5037 n/s n/s n/s 0.2849 n/s n/s n/s n/s
C1900 MARPOL A.II (Noxious L.) n/s n/s n/s n/s n/s n/s n/s 0.5146 n/s
C2000 SOLAS Operational def. n/s -0.0724 n/s -0.1117 (ST1) n/s n/s n/s n/s -0.0801
C2100 MARPOL relat. oper. def. n/s n/s n/s n/s n/s n/s -0.3094 n/s n/s
C2200 MARPOL A.III n/s n/s n/s n/s n/s n/s n/s n/s n/s
C2300 MARPOL A.V n/s n/s -0.1578 n/s n/s n/s n/s n/s n/s
C2500 ISM related deficiencies n/s 0.0392 n/s 0.04147 (ST1) 0.1193 (ST4) n/s n/s 0.0624 n/s n/s
Note: n/s= not significant, n/a=not applicable, W/S/G=Wrecked, Stranded, Grounded; ST1=general cargo, ST2=dry bulk, ST3=container, ST4=tanker, ST5=passenger
22
Overall Summary
o The coefficients for the variables indicating if a ship has been inspected by one of the
industry vetting inspection regimes (Rightship) for bulk carriers and oil tankers are all
negative and follow the overall ranking of a vessel30 while the coefficient of the variable
indicating if the ship is Greenaward certified is not significant which might be just due
to lack of data31. For the type III models, this parameter is only significant for deck
related first events. Given the fact that those inspections are primarily carried out on
bulk carriers, this finding is found to be in line with the general expectation.
o The parameter of the variable indicating time in-between inspections is not significant for
very serious casualties but is positive for all other categories of the type I and type II
models. For the casualty first events, it is only significant for fire & explosion and engine
related first events.
o The coefficients of the variables indicating where the ship was inspected is mostly
negative for serious and less serious casualties and only one regime remains significant
for very serious casualties (Viña del Mar Agreement on PSC). For the casualty first
events, these parameters are mostly negative or not significant with the exception for
engine related first events where it is positive for two regimes.
o The coefficients of the variables indicating if the ship was detained are mostly not
significant with the exception of less serious casualties and for the categories wrecked,
stranded or grounded and engine related first events. For engine related first events, the
parameter is negative for one regime (USCG).
Deficiencies per seriousness and ship type (type I and II models)
o For very serious casualties, a positive effect can be found with code 1800 (gas and
chemical carriers) and not other code remains significant.
o For serious casualties, a negative effect can be found for codes 2000 (SOLAS operational
deficiencies) while a positive effect can be seen with codes 700 (fire safety measures),
codes 1400 (propulsion and aux. engine) and code 2500 (ISM code).
o For less serious casualties, only one code shows a negative effect – code 2300 (Marpol
Annex V). Codes with positive effect are code 900 (structural safety), code 1400
(propulsion and aux. engines) and code 1700 (Marpol Annex I).
o With respect to the deficiencies per ship type (type II model), codes that remain
significant for general cargo vessels are code 700 (fire safety measures: positive), code
1200 (load line: negative), code 2000 (SOLAS oper. safety: negative) and code 2500 (ISM
related def.: positive).
o For dry bulk, only one code remains significant and positive which is code 1400
(propulsion and aux. engine). For the container vessel, codes 400 (food and catering:
negative), code 900 (structural safety: positive) remain significant. For tankers, two
codes remain in the model and are code 1200 (load lines: positive) and code 2500 (ISM
related deficiencies: positive).
Deficiencies per casualty first event (type III models)
o Looking at the deficiency codes and the significance of their parameters with respect to
the casualty first events, for fire and explosion, codes 1400 (propulsion and aux. engines)
and code 1800 (gas and chemical carriers) are significant and positive.

30 For Rightship, the risk associated with a vessel is ranked by stars where a 1 star vessel shows
highest risk and a 5 star vessel shows lowest risk
31 the total amount of Greenaward certified vessels incorporated into the dataset was only about 240
records for the time span 2000 to 2004
23
o For the category wrecked/stranded/grounded, the remaining codes are code 700 (fire
safety measures), code 1000 (alarm signals) and code 1400 (propulsion and aux. engines)
are significant and positive32.
o For collision/contact, two codes are positive as – code 900 (structural safety) and code
2500 (ISM related def.) and one codes is negative – code 2100 (Marpol relat. operat.
deficiencies).
o For deck related first events, codes 1200 (load lines) and code 1900 (Marpol Annex II) are
significant and positive.
o For engine related first events, four codes remain of which three are positive and two are
negative. Positive effects are with codes 700 (fire safety measures), 1400 (propulsion and
aux. engines) and 1700 (Marpol Annex I) while negative effects are with codes 400 (food
and catering) and code 2000 (Solas operat. deficiencies).
Summary of Findings in relation to deficiencies
It is difficult to interpret the significance and the sign of the parameters of the deficiency
codes towards either the seriousness and with respect to the ship types or the casualty first
events since the variable is not based on the last inspection only but also contains
information from previous inspections. Sometimes, this means an accumulation of
inspections and sometimes this means, only the last inspection which was performed at
least six months or less before the casualty. The analysis is therefore only being seen as a
first attempt to look at both aspects closer.
What can be concluded from this portion of the analysis is that some inspections and the
fact that deficiencies are found are effective towards decreasing the probability of having a
casualty. This effect varies across ship types, seriousness of casualty and casualty first
events. Code 1400 (propulsion & aux. engine) seems to be an important deficiency code
which in the probability of detention does not come out to be very important in all regimes.
It has a positive effect for serious and less serious casualties, in particular for dry bulk
carriers and for casualty first events such as fire & explosion, wrecked/stranded/grounded
and engine related casualty first events. This could indicate that there is room for
improvement. It seems that deficiencies are found but due to lack of enforcement
(detention) or follow up on deficiencies, the effect is positive rather than negative. It is
important to notice though that when the vessel is in port and port state control is
performed, the engines are not under full operation and it is therefore difficult to inspect
some aspects of the main engine.
Another code where its parameter shows a positive effect is the ISM code (code 2500) which
captures the whole safety management system onboard a vessel. It is positive for serious
casualties, general cargo vessels and tankers and for casualty first event collision and
contact. On the other hand, code 2000 (Solas operational related deficiencies) is negative for
serious casualties and engine related first events for the ship type general cargo. This could
be interpreted as the effectiveness in rectifying the deficiencies or drills and therefore
having a negative effect for general cargo ships.
Another clear example is code 1800 (gas and chemical carriers) which is positive and
significant for very serious casualties and casualty first events fire and explosion. This
could be further identified as an area that should be looked at and has already been
discussed at IMO during MSC (81) in May 200633. This finding confirms that there is
problem with enforcing the legal conventions on chemical carriers. The next area will

32 code 700 and code 1400 are significant at the 5% level only
33 The author attended as observer MSC (81) in May 2006, IMO, London
24
present some graphs to visualize the findings. It will first look at the time in-between
inspections, the effect of inspections and detention and the deficiencies themselves.
Table 14 is based on the type I models where restrictions34 using the Wald Test were tested
for the variables indicating where the vessel was inspected to see if the means differ. The
null hypothesis in this case states that there is no significant difference across the regimes
(ho= coefficients do not vary). The results indicate that while there is no significant
difference with relation to very serious casualties (only one variable remains significant), for
serious casualties, AMSA and the Viña del Mar MoU are apart from the IMOU. No
difference can be seen for the Paris MoU and the USCG since as a benchmark, the Paris
MoU was used and the USCG does not remain to be significant.
Table 14: Testing of Restrictions (Wald Test) – Inspection Variables: Type I Models
Very
Serious
Serious
Restrictions/p-value
Less Serious
Restrictions/p-value
AMSA=IMOU=VMOU
(0.0017) – reject ho
AMSA=IMOU=VMOU
(0.0286) – do not reject ho
AMSA=IMOU
(0.0007)- reject ho
AMSA=IMOU
(0.1054)- do not reject ho
IMOU=VMOU
(0.0013) – reject ho
IMOU=VMOU
(0.0086)- reject ho
Only the VMOU
remains significant
and shows a negative
effect. There is no
significant difference
amongst the other
regimes.
AMSA=VMOU
(0.07607) – do not reject ho
AMSA=VMOU
(0.2040) – do not reject ho
Note: Figure in bracket is the p-value of the test, 1% significance level
For less serious casualties, AMSA, the Indian Ocean MoU and the Viña del Mar Agreement
do not show a difference (at the 1% significance level) while the USCG and the Paris MoU
are not significant. Figure 10 to Figure 12 both visualize these effects for a particular ship
for a particular vessel but not in a combined format. The variables indicating where a ship
was inspected is constructed as a percentage fraction of each vessel to the total inspection a
vessel had previously and not as a total sum of inspections which was used in the normal
casualty models. What the variables give is a capture of the total inspection fraction of all of
the regimes of a particular vessel where the Caribbean MoU had to be excluded from the
models due to lack of data.
In order to visualize the differences, a particular ship is chosen and its associated
probability of casualty is calculated. In order to see the effect over time, the variable in
question is increased by certain percentage fractions (10%) which can also be seen as an
increase in the frequency of inspections. The interesting part in these graphs is to see how
the regimes differ with respect to the probability of casualty. As mentioned before, only the
Viña del Mar Agreement on PSC is significantly different from the other regimes for very
serious casualties. The average is to be understood as the average of all regimes.
For serious casualties, AMSA and the Indian Ocean MoU are very close and apart from the
Indian Ocean MoU and the other regimes. For less serious casualties, AMSA, the Indian
Ocean MoU and the Viña del Mar Agreement on PSC are similar (also confirmed
previously) and below the average. They are different from the Paris MoU and the USCG.
At first sight, the order of the regimes is not as one would have expected them to be.
Regimes below the average seem to show a larger effect. On the other hand, it might also

34 based on Wald Test for Testing Coefficient Restrictions, a standard procedure in Eviews
25
reflect the learning stage of a regime over the time period covered by the inspection data.
By the end of 2004, the Paris MoU has been in existence since 1982 while the Indian Ocean
MoU only exists since 7 years and the Viña del Mar since 13 years.
Figure 10: Inspection Effect across Regimes: Very Serious
Very Serious Casualty
Inspection Effect – Difference Across Regimes
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Percentage change in Frequency of Inspection
Vina del Mar Average
Ship Type: Dry Bulk
Age: 14 yrs, Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Vina del Mar
average
Figure 11: Inspection Effect across Regimes: Serious
Serious Casualty
Inspection Effect – Difference across Regimes
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Vina del Mar Indian MoU
AMSA Average
Ship Type: Dry Bulk
Age: 14 yrs, Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Percentage Change in Frequency of Inspections
average
Indian Ocean MoU
Vina del Mar
AMSA
26
Figure 12: Inspection Effect across Regimes: Less Serious
Less Serious Casualty
Inspection Effect – Difference across Regimes
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Vina del Mar Indian MoU
AMSA Average
Ship Type: Dry Bulk
Age: 14 yrs, Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Percentage Change in Frequency of Inspections
average
AMSA
Vina del Mar
Indian Ocean MoU
The Indian Ocean MoU region has more local trade with ships that might show more
obvious signs of being sub-standard and the effect of inspections are therefore to be
expected to be higher than in other regions with better ships. In addition, the Paris MoU
area has maintained the 25% target factor previously which could have led to the inspection
of good ships in the past in order to fill the quota versus sub-standard ships when they have
not been available in the area needed for inspection for the last six years. As for the Viña
del Mar region, the region might show more of the sub-standard ships that have been
driven out of the Paris MoU or the USCG over the last six years.
The next section will give an overview in relation to the casualty first events based on the
type III models and similar to the procedure described for the type II models. The results
can be seen in Table 15.
Table 15: Testing of Restrictions (Wald Test) – Inspection Variables: Type III Models
Model Type Variables Tested p-value
Fire/Explosion VMOU=IMOU 0.5819 – do not reject ho
Wrecked/Stranded/Grounded AMSA=IMOU 0.9359 – do not reject ho
Collision/Contact AMSA=VMOU=IMOU 0.7131 – do not reject ho
Deck Related First Events VMOU=IMOU 0.1136 – do not reject ho
Engine Related First Events USCG=PMOU 0.0406 – do not reject ho
Note: 1% significance level used
One can see that there are no significant differences for the variables that are left in the
models across the regimes and that the null hypothesis (ho= coefficients do not vary) can be
rejected in all cases at a 1% significance level. The results are visualized with a combined
27
graph (Figure 13) which shows the average effect of inspection on the probability of casualty
per first event for a dry bulk carrier. The average is based on an average of all regimes and
is therefore more averaged out in comparison to each of the individual variables.
Figure 13: Average Inspection Effect per Casualty First Event
Average Inspection Effect per Casualty First Event
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Percentage Change in Frequency of Inspection
Probability of Casualt
y
Fire/Explosion Wrecked/Stranded/Grounded
Collision/Contact Deck Related
Engine Related
ST: Dry Bulk
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Fire/Explosion Wrecked/Stranded/Grounded
Engine Related
Collision/Contact
Deck Related
The graph shows that the strongest effect can be seen for deck related first events followed
by collision/contact similar to the category wrecked/stranded/grounded and fire/explosion.
For engine related first events, the effect is slightly positive. Linking this graph back to the
probability of detention and deficiency code 1400 (propulsion and aux. machinery) where
the contribution weight of this code was found not to be very high across all regimes, one
could conclude that there is room for improvement in this area.
3.6. PSC Deficiencies and the Probability of Casualty
The next area will provide a closer look at deficiencies in relation to seriousness and first
events of a casualty and is based on the type I, type II and type III models. It visualizes the
findings stated in Table 13 previously in order to facilitate the interpretation of the
coefficients.
The first set of graphs are based on the type II model (the combined model) where the
deficiencies are multiplicative dummy variables of the ship types and combine very serious
and serious casualties. Figure 14 and Figure 15 show the results for codes with negative
effects and codes with positive effects.
The negative effect of food and catering for container vessels cannot really be explained
other than that an improvement in working and living conditions for crew members
onboard have an overall positive effect on the performance of the crew.
28
Figure 14: Very Serious and Serious Casualties (Negative Effects)
Very Serious and Serious per Ship Type (Negative Effects)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Food & Catering (5) – container
Load Lines (6) – general
Solas oper. Safety (4) – general
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type II models
The significance of the other two codes for general cargo ships are easier to interpret.
According to the Paris MoU Manual for PSC Officers35 deficiencies in the area of load lines
include overloading, freeboard markings, conditions of railings, cargo hatches, doors,
ventilation pipes and lashings. It seems that general cargo vessel seems to show deficiencies
in this area prior to a casualty and that these deficiencies are rectified. For load line,
rectification might not be so easy and immediate while for deficiencies in the area of SOLAS
related deficiencies, it can be rectified easier and therefore have an immediate effect.
SOLAS related operational deficiencies include deficiencies such as the muster list, fire
drills, abandon ship drills, the level of communication onboard, bridge operations, the
operation of GMDSS and cargo operations. It shows that if port state control identifies these
deficiencies and if they are rectified, they can have a negative effect. In addition, the drills
might help in this respect to. The only regime who requires drills during an annual exam is
the USCG.
Figure 15 shows the deficiency codes that have a positive effect towards the probability of
casualty. ISM appears twice (for tankers and general cargo) and codes associated with
stability and structure (load lines and structural safety) are relevant for containers and
tankers. It might be more difficult to rectify deficiencies in this area since it might take
more time to do so. In the case of lack of follow up, it seems that ships with deficiencies in
this area show a higher probability of having a very serious or serious casualty.

35 Paris MoU, Manual for PSC Officers, Revision 8
29
Figure 15: Very Serious and Serious Casualties (Positive Effects)
Very Serious and Serious per Ship Type (Positive Effects)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Structural Safety (6) – container
Propulsion & Machinery (3) – dry bulk
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
ISM (8) – tanker
Load Line (6) – tanker
ISM (8) – general cargo
Fire Safety M. (2) – general cargo
Note: based on type II models
Fire safety measures are relevant for general cargo and show that general cargo ships
might show more problems with the actual equipment related to fire prevention while the
drills and the performance of drills reflects the operational side which can have a negative
effect. Very little effect can be seen by deficiencies in the area of propulsion and machinery
for dry bulk vessels.
What is not visualized here but clearly shown in Table 13 is the positive effect of code 1800
(gas and chemical carriers) towards the probability of a very serious casualty. This code is
associated with deficiencies in the area of cargo segregation, cargo transfers and ventilation
systems, the cargo pump room, temperature controls, and fire protection of cargo deck
areas, personal protection and emergency towing arrangements. It applies to chemical
tankers, gas carrier and oil tankers and shows that this is an area port state control can
improve in not only detecting the deficiencies but also in ensuring that they are rectified
and that the ISM system onboard is implemented onboard which is further confirmed by
the positive effect of the ISM code with tankers. This is somehow surprisingly given the fact
that tankers undergo a significant amount of vetting inspections which also looks closely at
the implementation of the ISM code.
Figure 16 shows the deficiency codes which are left to be significant for less serious
casualties for all ship types. It is less accurate than the previous models but confirms two
areas – structural safety and propulsion and machinery with a moderate positive effect and
Marpol Annex I (oil pollution) with a stronger positive effect.
30
Figure 16: Less Serious Casualties and Deficiencies
Less Serious Casualty-all ship types
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0 1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Certificates (1) Working Conditions (2)
Safety & Fire (2) Stability & Structure (4)
Equipment & Machinery (5) Navigation & Commun (6)
Ship & Cargo Operations (7) Management (8)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Structural Safety (4)
Propulsion & Machinery (5)
All other codes
Marpol Annex I (7)
Marpol Annex V (7)
Note: based on type I model – less serious
Deficiencies in this area contain for instance the SOPEP (Ship oil emergency plan), the oil
record book, the 15 ppm alarm and oil filtering equipment, the segregation of ballast tanks,
the operation of COW (crude oil washing). The probability of detention is very strong in this
area for the USCG especially for tankers but in aggregated form, for all ship types
(contribution weight is about 21%), this code is not significant for very serious or less
serious casualties but might play a role for less serious casualties and could be an area of
potential problems.
This last section takes a closer look at the various types of casualties and the deficiencies
that were found during an inspection. A separate graph per casualty first event is produced
and is shown in Figure 17 through Figure 21.
Figure 17 shows the results for fire and explosion identified as first event. Fire and
explosion in this case means a fire and explosion anywhere on the vessel where the main
area of fire has been identified to be in the engine room. Code 1800 (oil, chemical tankers
and gas carriers) and code 1400 (propulsion and machinery) both show a positive effect
towards the probability of having a fire or explosion. This finding is interesting as it
confirms a problem that is already known in the industry and which has been an agenda
item during MSC36 (81) in May 2006 where a study conducted by an inter-industry
workgroup identified 35 cases of fire and explosions on chemical and product tankers over
the last 25 years.

36 Maritime Safety Committee Meeting at IMO (10th to 19th May 2006)
31
Figure 17: Fire and Explosion and Deficiencies
Fire and Explosion
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Propulsion and Machinery (3)
Oil, Chemical Tankers &
Gas Carriers (4)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type III model
The group concluded that technical failure could not be identified but that the prime
contributor was lack of following the proper operational guidelines which is partly reflected
in the ISM system (onboard and shore side) and might also explain the positive contribution
of the ISM code for tankers in Figure 15. From the port state control point of view, it shows
that a certain lack of compliance has been detected but that the system lacks in
enforcement and implementation. The same applies to propulsion and machinery where the
effect is much less.
Figure 18 shows the probability of engine related first events and Figure 19 gives an insight
into the probability of deck related first events in relation to deficiencies previously found in
port state control inspections.
Engine related first events contain engine breakdowns, black outs, steering gear failure and
propulsion failure. It is therefore not surprising that deficiencies associated with code 1400
(Propulsion and machinery) shows the strongest positive contribution followed by Marpol
Annex I (code 1700) and fire safety measures (code 700). Marpol Annex I has also been
identified with a positive effect for less serious casualties which might also be reflected
here. The code is not unrelated to the engine room but somehow not directly related to the
events listed above since it deals with environmental issues (oil pollution) and all
procedures connected to it. The fact that this code is positive can also just indicate the lack
of the implementation of operational procedures in the engine room and that ships that do
have a high probability of engine related casualties, also do have a problem in the area of
pollution prevention and fire & safety measures.
32
Figure 18: Engine Related First Events and Deficiencies
Engine Related First Events
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Food & Catering (5)
SOLAS oper. def. (4)
Fire Safety Meas. (2)
Marpol Annex I (4)
Propulsion & Machinery (3)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type III model
On the other hand, two codes show a negative effect which are code 2000 (SOLAS
operational related deficiencies) and code 400 (food and catering). Both codes also show a
negative effect for very serious and serious casualties for general cargo ships and container
vessels. It seems that drills and other operational related items do have a negative effect on
the probability of having an engine related casualty. The code food and catering might just
reflect the human factor such as living and working conditions in general which are also
associated with food.
Figure 19 shows the probability of deck related first events and two deficiency codes which
remain significant – Marpol Annex II (code 1900) and load lines (code 1200). Deck related
first events contain items such as deck maintenance and stability related items (capsizing,
listing, cargo shifts and flooding).
According to the Paris MoU PSC Manual for PSC Officers37, deficiencies associated with
Marpol Annex II (Noxious Liquids in Bulk) are deficiencies such as the cargo record book,
the P&A (Procedure & Arrangement Manual) manual, stripping and tank washing
equipment, cargo heating systems and ventilation equipment. At first sight, this code does
not seem to be directly associated with deck related first events but by taking a closer look,
one can identify a connection, especially when it comes to cargo handling which might also
be reflected in deficiencies associated with load lines where cargo shifts or flooding might be
more relevant. Interesting to see is that for instance the ISM code is not relevant which is
somehow unexpected. Overall, one can conclude that the lack of following proper cargo
operation procedures (in what form ever) do have a positive influence on the probability of
having a casualty. For port state control, this could mean that deficiencies in this area have

37 Paris MoU, Manual for PSC Officers, Revision 8
33
been identified but that there is lack of ability to ensure that these procedures are followed
in the future.
Figure 19: Deck Related First Events and Deficiencies
Deck Related First Events
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Load Lines (6)
Marpol Annex II (4)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type III model
Figure 20 shows the effect of deficiencies on casualty first events associated with ships that
were wrecked, stranded or grounded. This category is dominated by stranded and grounded
ships versus wrecked ships. Three codes are significant and show a positive effect – code
1000 (Alarm Signals), code 700 (fire safety measures) and code 1400 (propulsion and
machinery) where the last two are only slightly significant. The more interesting group of
deficiencies are the groups of alarm signals which contains deficiencies related to the
general alarm, crew and fire alarm, steering gear alarm, engineer’s and other machinery
alarms, inert gas alarm, UMS (unmanned machinery spaces) and boiler alarms. The types
of alarms which can be brought into relation with the first event are probably the alarms
associated with the steering gear and other machinery alarm. It seems that deficiencies are
identified in this area by port state control and that the positive effect is rather strong.
Figure 21 shows the last graph in this series and shows that deficiencies found in the area
of ISM (code 2500) have a positive effect on the probability of having a collision or contact.
The same applies for structural safety (code 900). Deficiencies in the 900 range contain
closing devices (such as watertight doors), stability and loading information and
instruments, steering gear, hull damage, the condition of ballast tanks, any kind of hull and
bulkhead corrosion and cracking.
34
Figure 20: Wrecked/Stranded/Grounded and Deficiencies
Wrecked/Stranded/Grounded
0.00
0.10
0.20
0.30
0.40
0.50
0.60
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Fire Safety Meas. (2) and
Alarm Signals (2)
Propulsion & Machinery (3)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type III model
Marpol related operational deficiencies (code 2100) are associated with oily mixtures in
cargo spaces and other related items for cargo spaces of tankers. This code is not related to
collisions and contacts and therefore cannot be interpreted. For code 900, items related to
the steering gear might be very relevant in this category.
The ISM code is certainly relevant as it might show lack of enforcement of safety
procedures. ISM related deficiencies contain items such the safety and environmental
policy, the definition of company responsibilities and the master’s responsibility,
deficiencies in the area of shipboard operations, emergency preparedness, reporting and
analysis of non-conformities, accidents and near misses, the maintenance of the vessel and
company audits. Another area deals with resources and personnel. Violations against
working and resting hours are not a separate code in the ISM group but are included in the
deficiency group 200 – crew certificates.
It is difficult to interpret this graph with certainty but the strong positive effect of ISM
related deficiencies shows that vessels in this category have a higher probability of having a
collision or contact. It might reflect fatigue or lack of bridge procedures as well as lack of
overall onboard maintenance (as reflected in code 900). From a port state control
perspective, this might also mean that the deficiencies are identified but that the
enforcement onboard is lacking as is the rectification of such deficiencies. Especially ISM
audits (if required) cannot be done immediately unless the ship is detained. When under
pressure to keep schedules, ships might proceed and ignore some of the recommendations
from port state control.
35
Figure 21: Collision and Contact and Deficiencies
Collision and Contact
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
1 2 3 4 5 6 7 8 9 10
Number of Deficiencies
Probability of Casualty
Marpol related oper. Def. (4)
ISM (8)
Structural Safety (6)
Age: 14 yrs
Tonnage: 30419 gt
Flag: Panama
Class: GL
Owner: TMN
Note: based on type III model
On a short notice on ISM, the code’s origins go back as far as the late 1980’s when more
concern arose as to the poor safety management of the industry. It was adopted in 199338
and had to be implemented by 1 July 2002. The last Maritime Safety Committee (MSC 81)
in December 2005 presented an impact assessment of the ISM code on the industry in
which several areas of improvement could be identified as follows39:
o More systematic training
o Having an ISM Code performance measurement scheme
o More monitoring of compliance
o Integrating into employment requirements; and
o Involving more people, especially seafarers in writing ISM manuals.
Given the findings in this section of this analysis, the author can fully support these
recommendations. Especially the last recommendation is very relevant. From the 25
inspections and one ISM audit the author could observe during the course of this project, it
has become apparent that very few management companies allow proper input from
seafarers on the design and continuous improvement of the safety management system
which is of direct impact of daily shipboard operations.

38 MSC 81/17/1, Role of the Human Element, Assessment of the impact and effectiveness of
implementation of the ISM Code, 21 December 2005, page 2
39 MSC 81/17/1/ Role fo the Human Element, Assessment of the impact and effectivness of
implementation of the ISM Code, 21 December 2005, page 14
36
On oil and chemical tankers, it has been observed that on many occasions, the system has
been designed to only serve one purpose – which is to meet the requirements of the vetting
inspection questionnaires and not the overall perspective which is to improve the safety
level onboard a ship by taking into consideration the particular working environment
onboard a vessel. This reduces the ISM code to a paper exercise rather than a workable
system for the industry. The latest addition of the oil industry’s Tanker Management Self
Assessment (TMSA) system in addition to ISM further proofs that ISM has reduced to a
paper exercise. In theory, one safety management system should be sufficient and adaptable
to the various industries within the shipping industry. TMSA allows compliance to four
levels where the first level is seen to be the minimum requirement and meets the
requirements of ISM.
4. Conclusions on Casualties – Refined View
The parameters of the variables indicating if a ship has been inspected by one of the
industry vetting inspection regimes are all negative. The coefficients of the variable
indicating time in-between inspections are not significant for very serious casualties but are
positive for all other categories. For the casualty first events, it is only significant for fire &
explosion and engine related first events.
The variables indicating where the ship was inspected is mostly negative for serious and
less serious casualties and only one regime remains significant for very serious casualties
(Viña del Mar Agreement on PSC) while several other regimes appear to be significant for
serious and less serious casualties.
For the casualty first events, the parameters are mostly negative or not significant with the
exception for engine related first events. Testing of restrictions shows that there is no
significant difference with respect to the coefficients of the variables indicating where the
ship was inspected and casualty first events at the 1% significance level. The strongest
negative effect can be found on the probability of deck related first events (about 3%) and a
slightly positive effect can be found for engine related casualty first events.
The time span in-between inspection is not significant for very serious casualties but is for
less serious and serious casualties. On average and regardless of the seriousness of
casualty, the probability increases by 2.3% within the time frame of one year. For fire and
explosion, this increase can be 2.7% and 0.5% for engine related casualties.
With respect to the probability of casualty and frequency of inspection and detention, the
probability of a casualty decreases on average while on the contrary, the probability of
serious and less serious casualties increases with the frequency of inspection. The picture is
similar for multiple detentions. The coefficients of the variables indicating if the ship has
been detained are mostly not significant with the exception of less serious casualties and for
the categories wrecked, stranded or grounded and engine related first events. For engine
related first events, this variable is negative for one regime (USCG).
It is difficult to interpret the significance and the signs of the parameters of the deficiency
codes towards either the seriousness and with respect to the ship types or the casualty first
events since the variable is not based on the last inspection only but is a summary of all
inspections that were performed prior to a casualty. Sometimes, this means an
accumulation of inspections and sometimes this means, only the last inspection which was
performed at least six months or less before the casualty. The analysis is therefore only
being seen as a first attempt to look at both aspects closer.
37
What can be concluded from this portion of the analysis is that some inspections and the
fact that deficiencies are found are effective towards decreasing the probability of having a
casualty. This effect varies across ship types, seriousness of casualty and casualty first
events. Code 1400 (propulsion & aux. engine) seems to be an important deficiency code
while in the probability of detention does not come out to be very important in all regimes.
It has a positive effect for serious and less serious casualties, in particular for dry bulk
carriers and for casualty first events such as fire & explosion, wrecked/stranded/grounded
and engine related casualty first events. This could indicate that there is room for
improvement. It seems that deficiencies are found but due to lack of enforcement
(detention) or follow up on deficiencies (detentions), the effect is positive rather than
negative.
The same applies for another important code – the ISM code (code 2500) which captures the
whole safety management system onboard a vessel. The parameter of this variable is
positive for serious casualties, general cargo vessels and tankers and for casualty first event
collision and contact. The ISM code captures the whole safety management onboard and its
effect is only positive. It could mean that even though port state control detects deficiencies
in the implementation of the ISM code onboard, the rectification or follow up on the
deficiencies it not very successful and the lack of proper implementation onboard leads to an
increase in the probability of having a casualty which can be very serious and serious and is
more likely to be associated with a collision.
On the other hand, code 2000 (Solas operational related deficiencies) is negative for serious
casualties and engine related first events for the ship type general cargo. This could be
interpreted as the effectiveness in rectifying the deficiencies and therefore having a
negative effect for general cargo ships. It can also be interpreted that for instance increased
drills can help in decreasing the probability of having a very serious casualty.
Another clear example is code 1800 (gas and chemical carriers) which is positive and
significant for very serious casualties and casualty first events fire and explosion. This
could be further identified as an area that should be looked at and has already been on the
agenda of MSC (81) in May 2006. This finding confirms that there is problem with enforcing
the legal conventions on chemical carriers but that the main contributor was identified to be
of human error by not following the proper procedures.
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Conference Attendances
Mare Forum: Shipping in a Responsible Society, Quo Vadis? 12th and 13th Sept. 2005, Rome,
Italy
Royal Institution of Naval Architects: Learning from Marine Incidents III, 25th and 26th
January 2006, London, UK
Connecticut Maritime Association: Shipping 2006: 20th to 22nd March 2006, Stamford,
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39
IMO Legislative Resources including IMO Proceedings (as Observer)
IMO Proceedings (Attendance as Observer):
Sub-Committee Meeting on Flag State Implementation – FSI (13), 7th to 11th March 2005,
IMO, London
Committee Meeting on Maritime Safety – MSC (80), 17th to 20th May 2005, IMO, London
General Assembly – 24th Session, 21st November to 2nd December 2005, IMO, London
Sub-Committee on Standards of Training and Watchkeeping – STCW (37), 23rd January to
27th January 2006, IMO, London
Committee Meeting on Maritime Safety – MSC (81), 9th to 15th May 2006, IMO, London
Sub-Committee Meeting on Flag State Implementation – FSI (14), 6th to 9th June 2006,
IMO, London
Conventions:
International Convention on Load Lines (LL), 1966 and Protocol 1988, adopted 5th April
1966, IMO, London
International Convention on Civil Liability for Oil Pollution Damage (CLC),1969 and
Protocols 1976/1992, adopted 1969, IMO, London
International Convention of 1996 on Liability and Compensation for Damage in Connection
with the Carriage of Harzardous and Noxious Substances by Sea (not in force yet), IMO,
London
International Convention on Civil Liability for Bunker Oil Pollution Damage, 2001 (Bunker
Oil Convention), adopted March 2001, IMO, London
International Maritime Dangerous Goods Code, IMO Publication, London, 2004
International Convention for the Prevention of Pollution from Ships with Annexes I to VI,
1973/1978 (MARPOL), Consolidated Edition, IMO Publication, London, 2001
International Convention for the Safety of Life at Sea (SOLAS), 1974 and Protocols 1978
and 1988, Consolidated Edition, IMO Publication, London, 2004
International Convention on Standards of Training, Certification and Watchkeeping for
Seafarers (STCW), 1978, IMO Publication, London
The Torremolinos International Convention for the Safety of Fishing Vessels, adopted 1977
and superseded by the 1993 Protocol, adopted April 1993 (not yet in force)
Codes and Resolutions:
Code of safe practice for solid bulk cargoes (BC Code) and Code for the construction and
equipment of ships carrying dangerous chemicals in bulk (IBC Code) adopted December 85
by resolution MEPC 19(22), IMO, London
40
Code of practice for the safe loading and unloading of bulk carriers, adopted November 1997
by Assembly Resolution A862 (20), IMO, London
Code of safe practice for cargo stowage and securing, adopted November 1991 by Assembly
Resolution A 714(17), IMO, London
Fire Safety Systems Code, adopted December 2000 by MSC 98(73), IMO, London
International Code of safety for high-speed craft, adopted May 1994 at MSC 36 (63), IMO,
London
International Code for the construction and Equipment of Ships Carrying Liquefied Gas in
Bulk and Old Gas Carrier Code for ships constructed before 1st October 1994 as per
resolution MSC 5 (48), IMO, London
International Code for the Construction and Equipment of ships carrying dangerous
chemicals in bulk, adopted 1993, IMO, London
International Code for security of ships and of port facilities, adopted 2002, IMO, London
International Life Saving Appliance Code, adopted June 1996 by MSC 48(66), IMO, London
International Safety Management Code and Guidelines on Implementation of the ISM
Code, IMO Publication, London, 2002
IMO Resolution A.682 (17): Regional Co-Operation in the Control of Ships and Discharges,
adopted November 1991, IMO, London
IMO Resolution A.746 (18): Survey Guidelines under the Harmonized System of Survey &
Certification, IMO, London
IMO Resolution A.973 (24): Code for the Implementation of Mandatory IMO Instruments,
IMO, London
IMO Resolution A.974 (24): Framework and Procedures for the Voluntary IMO Member
State Audit Scheme, IMO, London
IMO Resolution A.739 (18): Guidelines for the Authorization of Organizations acting on
behalf of the Administration, IMO, London
IMO Resolution A.787(19) and A.822(21), Procedures for Port State Control, IMO
Publication, London, 2001
Circulars, Committee Working Papers and any Other Documents:
FSI/14/WP.3, Harmonization of Port State Control Activities, PSC on Seafarer’s Working
Hours, Development of Guidelines on Port State Control Under the 2004 BWM Convention,
Report of the Working Group, Flag State Implementation Sub-Committee Meeting, IMO,
London, 8th June 2006
41
FSI/14/WP.5 Add.1, Draft Report to the Maritime Safety Committee and the Marine
Environment Protection Committee, Sub-Committee on Flag State Implementation, IMO,
London, 9th June 2006
MSC/Circ. 1023, MEPC/Circ. 392, Guidelines for Formal Safety Assessment (FSA) for use in
the IMO Rule-Making Process, IMO, London, 4th April 2002
MSC81/18, Formal Safety Assessment, Report of the correspondence group, submitted by
the Netherlands, MSC 81/18, Maritime Safety Committee, IMO, London, 7th February 2006
MSC 81/17/1, The Role of the Human Element, Assessment of the impact and effectiveness
of implementation of the ISM Code, IMO Secretariat, Maritime Safety Committee Meeting
81, London, 21st December 2005
MSC/Circ. 953, MEPC/Circ. 372, Reports on Marine Casualties and Incidents, Revised
harmonized reporting procedures, 14th December 2000, IMO, London
MSC/Circ. 1014, Guidance on Fatigue Mitigation and Management, IMO, London, 12th June
2001
National Legislative Resources including EU Law
Australian Navigation Act of 1912, The Power of Inspections of Surveyors and Detentions of
Ships not registered in Australia: http://scaletext.law.gov.au/html/pasteact/1/516/top.htm
Bahamas Maritime Authority, Flag State Inspection Report, received from the Bahamas
Maritime Authority directly, London, March 2006
Canadian Marine Insurance Act of 1993, c.22 – Loss and Abandonment,
https://monkessays.com/write-my-essay/canlii.org/ca/sta/m-0.6/sec57.html
Code of Safety for Caribbean Cargo Ships (CCSS Code), Cargo Ships less than 500 gt,
adopted 1996, Barbados
https://monkessays.com/write-my-essay/uscg.mil/hq/g-m/pscweb/code_of_safety_for_caribbean_car.htm
Directive 106/2001/EC of the European Parliament and of the Council of 19th December
2001 amending Council Directive 95/21/EC concerning the enforcement, in respect of
shipping using Community ports and sailing in the waters under the jurisdiction of the
Member States, of international standards for ship safety, pollution prevention and
shipboard living and working conditions (port State control)
http://europa.eu.int/eur-lex/en/index.html)
Proposal for a Directive on Port State Control, COM (2005), 588 final version of 23rd
November 2005
http://europa.eu.int/comm/transport/maritime/safety/2005_package_3_en.htm
Proposal for a Directive on the civil liability and financial securities of shipowners, COM
(2005), 593 final version of 23rd November 2005
http://europa.eu.int/comm/transport/maritime/safety/2005_package_3_en.htm
42
Proposal for a Directive establishing the fundamental principles governing the investigation
of accidents in the maritime transport sector and amending Directive 1999/35/EC and
2002/59/EC, COM (2005). 590 final version of 23rd November 2005
http://europa.eu.int/comm/transport/maritime/safety/2005_package_3_en.htm
Council Framework Decision 2005/667/JHA of 12th July 2005 to strengthen the criminal-law
framework for the enforcement of the law against ship-source pollution.
http://europa.eu.int/comm/transport/maritime/safety/2005_package_3_en.htm
Directive 2005/35/EC of 7th September 2005 on ship-source pollution and on the introduction
of penalties for infringements.
http://europa.eu.int/comm/transport/maritime/safety/2005_package_3_en.htm
Final Regulatory Impact Assessment – Draft Merchant Shipping (Port State Control
Amendment) Regulations 2003, MCA
International Labor Organization, Maritime Labor Convention 2006,
https://monkessays.com/write-my-essay/ilo.org/public/english/dialogue/sector/papers/maritime/consolcd/overview.htm
Malta Maritime Authority, Flag State Inspection Report, received from Inspector directly
Memorandum of Understanding on Port State Control in the Caribbean Region, received
from IMO Regional Maritime Adviser (Caribbean) at IMO, London.
Memorandum of Understanding on Port State Control for the Indian Ocean Region as of 1st
Oct. 2000, https://monkessays.com/write-my-essay/indianmou.org
Memorandum of Understanding on Port State Control in the Asia-Pacific Region containing
8th Amendment, 23rd Nov. 2004, https://monkessays.com/write-my-essay/tokyomou.org
Paris Memorandum of Understanding on Port State Control Including 27th Amendment,
adopted 13th May 2005, https://monkessays.com/write-my-essay/parismou.org
Paris Memorandum of Understanding on Port State Control, Annual Reports 2002, 2003
and 2004, https://monkessays.com/write-my-essay/parismou.org
Paris Memorandum of Understanding on Port State Control, Manual for PSC Officers,
Revision 8
USCG Marine Safety Manual, Vol. II, Section D: Port State Control
https://monkessays.com/write-my-essay/uscg.mil/hq/g-m/pscweb/Publication.htm
USCG Port State Control Speech
https://monkessays.com/write-my-essay/uscg.mil/hq/g-m/pscweb/psc_speech.pdf
United Nations Conventions on the Law of the Sea:
https://monkessays.com/write-my-essay/un.org/Depts/los/convention_agreements/texts/unclos/closindx.htm
Wikipedia, Legal Definitions:
http://en.wikipedia.org/wiki/List_of_legal_terms
http://en.wikipedia.org/wiki/General_average
43
Other Accessible Resources
BIMCO/ISF, Manpower 2005 Update, The worldwide demand for and supply of seafarers,
Institute for Employment Research, Coventry, 2005
Chemical Distribution Institute (CDI), Ship Inspection Report, Chemical Tanker, 5th
Edition, 2003, London
Fearnley’s Review 2004, Fearnresearch, Oslo, 2005
Fearnley’s World Bulk Trades 2003, Fearnresearch, Oslo, 2003
IACS, Class – What, Why and How, www.iacs.org.uk/_pdf/Class_WhatWhy&How.PDF
International Transport Worker’s Federation, Seafarer fatigue: Wake up to the dangers, ITF,
London
ISL Shipping Statistics and Market Review (SSMR), Volume 49 (2005), Institute of
Shipping Economics and Logistics, Bremen
ISM Code Training Manual for DNV Auditors, DNV ISM Code Auditor Course, Part I-V
Lloyd’s Maritime Atlas of World Ports and Shipping Places, Lloyd’s Marine Intelligence
Unit, T&F Informa, UK, 2004
Marine Accident Investigation Branch, Bridge Watchkeeping Safety Study, July 2004,
Southampton
Main Characteristics of CAP and CAS Compared, DNV Presentation, 2005
OCIMF (Oil Companies International Marine Forum), Vessel Inspection Questionnaire for
Bulk Oil Tankers, Combination Carriers and Shuttle Tankers, 3rd Edition, May 2005
OCIMF (Oil Companies International Marine Forum), Tanker Management and Self
Assessement, A Best-Practice Guide for Ship Operators, First Edition 2004, London
Reyner, L. and Baulk S, (1998), Fatigue in Ferry Crews: A Pilot Study, Seafarers
International Research Center, Cardiff, 1998
Rules and Regulations for the Classification of Ships, Part 1, Edition July 2003, Lloyd’s
Register, London 2003
Rules for Classification and Construction/Ship Technology, Edition 2005, Germanischer
Lloyd, Hamburg, 2005
Selection & Accreditation of Rightship Tanker Inspectors, received from Rightship directly,
Australia
SeaCure for Operations 2004, 9th Edition, Rev. O, Greenaward Foundation, Rotterdam, The
Netherlands
Ship Inspection System, Final Inspection Report for Dry Cargo Ships, received from
Rightship directly, Australia
44
United Nations Conference on Trade and Development, Review of Maritime Transport,
2004, https://monkessays.com/write-my-essay/unctad.org/Templates/StartPage.asp?intItemID=2614&lang=1
Interviews
Bergot, G., and Barbeira-Gordon, S. (2004). Interview by author, European Commission,
Directorate-General for Energy and Transport, Brussels, June 2004
Bergot, G. and Gonzalez-Gil, J. (2005), Interview by author, European Commission,
Directorate-General for Energy and Transport, Brussels, October 2005
Castex, B. M. (2005 & 2006), Interview by Author, IMO Secretariat, during proceedings of
General Assembly, STCW and MSC, IMO, London, 22nd -23rd November 2005, 23rd January
2006 and 14th May 2006
Davidson, C. and Rimington D. (2005), Interview by Author, Australian Maritime Safety
Authority, during proceedings of General Assembly, IMO, London, 22nd -23rd November
2005
De Graeve, W. (2004), Interview by Author, Federal Public Service Mobility and Transport,
Maritime Transport, Maritime Inspectorate, Antwerp, July 2004
Dudley, J. Capt. (2005), Interview by Author, Koch Supply & Trading Ltd, Rotterdam,
October 2005
Downs Tim J. Capt. and George, D. Capt.,(2005) Interview by Author, Shell Trading and
Shipping Company Ltd, London, November 2005
Fransen, J. and Capt. Den Heijer, R. (2004). Interview by Author, Green Award
Foundation, Rotterdam
Gardiner, C.R. D. (2005), Interview by Author, Office of the Permanent Representative to
IMO (Antigua & Barbuda), during proceedings of MSC, IMO, London, 11th -20th May 2005
Groves, B. (2006), Interview by Author, Australian Maritime Safety Authority, during
proceedings of STCW, IMO, London, 23rd – 27th January 2006
Harts, P. (2004). Interview by Author, Inspectorate Transport and Water Management,
Netherlands Shipping Inspectorate, Rotterdam
Hassing, S. (2006), Interview by Author, Dutch Directorate for Transport Safety, during
proceedings of STCW, IMO, London, 23rd – 27th January 2006
Huisink, G.J. (2005), Interview by Author, Royal Association of Netherlands’ Shipowners,
Rotterdam, October 2005
Hutchinson, D. Capt. (2005), Interview by Author, Bahamas Maritime Authority, during
proceedings of General Assembly, IMO, London, 22nd -23rd November 2005
Jansen, P. Capt. (2005), Interview by Author, Ministry of Transport and Infrastructure,
Antwerp, November 2005
45
Kamstra, P.C. (2004), Interview by Author, Inspectorate Transport and Water
Management, Netherlands Shipping Inspectorate, Rotterdam.
Kinley, M. and Evans, B. (2005), Interview by Author, Australian Maritime Safety
Authority, during proceedings of FSI , IMO, London, 7th -11th March 2005
Koorneef, C.W. (2004). Interview by Author, Department of Noxious and Dangerous Goods,
Port of Rotterdam, Rotterdam
Koert, C. (2004), Interview by Author, Department of Noxious and Dangerous Goods, Port of
Rotterdam, Rotterdam
Mansell, J. (2006), Interview by Author, New Zealand Maritime Authority, during
proceedings of FSI, IMO, London, 6th June 2006
Monzon, A.M. (2005), Interview by Author, Prefectura Naval Argentina, during proceedings
of FSI, IMO, London, 7th -11th March 2005
Morton, L. (2006), Interview by Author, Exxon Mobile, June 2006, Rotterdam
Norman, W. Capt. (2005), Interview by Author, RightShip, Mare Forum Conference, Rome,
12th -13th September 2005
Parr, P. and Dolby, P. (2006), Interview by Author, UK Shipping Policy Unit and MCA,
London, January 2006
Pas, D. (2005), Interview by Author, Directorate for Transport Safety (former Senior Policy
Advisor), Erasmus University, Rotterdam, November 2005
Salwegter, A. (2004), Interview by Author, Inspectorate Transport and Water Management,
Netherlands Shipping Inspectorate, Rotterdam
Sakurada, Y. (2005), Interview by Author, DNV Senior Surveyor, Rotterdam, October 2005
Scheres, G. (2004), Interview by Author, Inspectorate Transport and Water Management,
Netherlands Shipping Inspectorate, Rotterdam
Schiferli, R. (2005), Interview by Author, Paris MoU Secretariat, Den Hague, November
2005
Snow, G. (2006), Interview by Author, Oil Companies International Marine Forum, London,
May 2006
Thorne Paul Cdr. and E.J. Terminella Cdr. (2005), Interview by Author, USCG Foreign &
Offshore Compliance Division, during proceedings of FSI, IMO, London, 7th -11th March
2005
Turenhout, H., van der Veer G.J. and Kreuze, A. (2006), Interview by Author, Jo Tankers,
Rotterdam, June 2006
Whittle, M. A., Interview by Author, Chemical Distribution Institute, London, November
2005
46
Wright, C. (2006), Interview by Author, Permanent Secretary of IACS, London, January
2006
Zecchin, L. A. (2005), Interview by Author, Prefectura Naval Argentina, during proceedings
at General Assembly, IMO, London, 22nd -23rd November 2005
Ship Visits, Inspections, Surveys
The ship names and IMO numbers are not disclosed as per the request of some of the ship
owners/operators.
PSC Inspection: Flag: Luxembourg, Ship Type: Containership, Surveyor: Aarnout
Salwegter, Rotterdam, June 2004
PSC Inspection: Flag: Syria, Ship Type: General Cargo, Surveyor: Walter De Graeve,
Antwerp, July 2004
PSC Inspection: Flag: Cyprus, Ship Type: General Cargo, Surveyor: Walter De Graeve,
Antwerp, July 2004
PSC Expanded Inspection: Flag: Grand Caymans, Ship Type: Bulk Carrier, Surveyor:
Aarnout Salwegter, Amsterdam, August 2005
PSC inspection/Detention: Flag: Ukraine, Ship Type: General Cargo, Inspector: J. P. Van
Byten, Antwerp, October 2005.
PSC safety inspection: Flag: Hong Kong, Ship Type: Dry Bulk, Inspector in charge: Ralph
Savercool, New York, March 2006
PSC security inspection: Flag: Liberia, Ship Type: Container, Inspector in charge: Diane R.
Semmling, New York, March 2006
PSC security inspection: Flag: Panama, Ship Type: Container, Inspector in charge: Diane R.
Semmling, New York, March 2006
Flag State Inspection: Flag: Malta, Ship Type: Container, Surveyor: Henk Engelsman,
Rotterdam, August 2005
Flag State Inspection: Flag: Malta, Ship Types: Bulk Carrier, Surveyor: Henk Engelsman,
Rotterdam, October 2005,
Class Annual Survey and Underwater Diving Inspection: Flag: Norwegian International
Register, Ship Type: Oil/Bulk Carrier, Surveyor: Yuri Sakurada, DNV, Rotterdam, March
2005
Class Annual Survey: Flag: Norwegian International Register, Ship Type: Chemical
Tanker, Surveyor: Yuri Sakurada, DNV, Rotterdam, May 2005
Class Annual Survey: Flag: Malta, Ship Type: Crude Oil Tanker, Surveyor: Rob Pijper,
Lloyd’s Register, Rotterdam, November 2005.
47
Class Annual Survey: Flag: Barbados, Ship Type: General Cargo Ship, Surveyor: Pieter
Andringa, Germanischer Lloyd, Rotterdam, October 2005.
Class Renewal Survey: Ship Name: Flag: Dutch, Ship Type: Chemical/Oil Product Tanker,
Surveyor: Rob Pijper, Lloyd’s Register, Rotterdam Damen Shipyard, August 2005
Class Follow Up: Flag: Cyprus, Ship Type: Bulk Carrier, Surveyor: Rob Pijper, Lloyd’s
Register, Rotterdam, September 2005
ISM Audit: Flag: Liberia, Ship Type: Juice Carrier, Surveyor: Rob Pijper, Lloyd’s Register,
Rotterdam, October 2005
Vetting Inspection (CDI): Flag: Dutch, Ship Type: Chemical Tanker, Inspector (CDI): Henk
Engelsman, Rotterdam, August 2005
Vetting Inspection (CDI): Flag: Bahamas, Ship Type: Chemical/Oil Tanker, Inspector (CDI):
Henk Engelsman, Rotterdam, October 2005;
Vetting Inspection (SIRE, Kuwait Oil): Flag: Sweden, Ship Type: Oil Tanker, Inspector
(OCIMF): Henk Engelsman, Rotterdam, September 2005
Vetting Inspection (SIRE, Eni Oil): Flag: Saudi Arabia, Ship Type: Chemical Tanker,
Inspector (OCIMF): Henk Engelsman, Rotterdam, October 2005;
Vetting Inspection (SIRE, Statoil): Flag: Sweden, Ship Type: Tanker, Inspector (OCIMF):
Henk Engelsman, Rotterdam, June 2006
Vetting Inspection (SIRE, Statoil): Flag: Liberia, Ship Type: Oil Tanker, Inspector (OCIMF):
Henk Engelsman, Rotterdam, June 2006
Vetting Inspection (Rightship): Flag: Hong Kong, Ship Type: Dry Bulk Carrier, Inspector
(Rightship): Dennis Barber, Ijmuiden, March 2006
P&I Club Inspection: Flag: Greece, Ship Type: Bulk Carrier, Inspector: Walter Vervloesem,
Ghent, October 2005;
Marpol Inspection: Flag: Norway, Ship Type: Oil Tanker, Port Superindendent: Mr. CeesWillem Koorneef, Rotterdam, August 2004
Marpol Inspection: Flag: Panama, Ship Type:OBO, Port Superindendent: Mr. Cees-Willem
Koorneef, Rotterdam, August 2004
Ship Visit (VLCC): Flag: Bahamas, Ship Type: Oil Tanker, Class: ABS, Rotterdam, October
2005
48
Appendix
Appendix 1: Grouping of Countries of Ownership
The grouping of ownership of a vessel was made according to Alderton and Winchester
(1999) and is as follows:
1. Old Open Registries: Antigua and Barbuda, Bahamas, Bermuda, Cyprus, Honduras,
Liberia, Malta, Marshall Islands, Panama, St. Vincent & the Grenadines
2. New Open Registries: Barbados, Belize, Bolivia, Cambodia, Canary Islands, Cayman
Islands, Cook Islands, Equatorial Guinea, Gibraltar, Lebanon, Luxembourg, Mauritius,
Myanmar, Sri Lanka, Tuvalu and Vanuatu
3. International Registries: Anguila, British Virgin Islands, Channel Islands, DIS,
Falklands, Faeroes, Hong Kong, Isle of Man, Kerguelen Islands, Macao, Madeira, NIS,
Philippines, Sao Tome and Principe, Singapore, Turks and Caicos, Ukraine, Wallis and
Fortuna, Netherlands Antilles
4. Traditional Maritime Nations: Argentina, Australia, Austria, Belgium, Brazil, Canada,
Chile, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan,
Mexico, Netherlands, New Zealand, Norway, Portugal, Russia, South Africa, Spain,
Sweden, Switzerland, UK, Uruguay, USA, Venezuela.
5. Emerging Maritime Nations: Albania, Algeria, Angola, Azerbaijan, Bahrain,
Bangladesh, Benin, Brunei, Bulgaria, Cameroon, Cape Verde, China, Colombia, Comoro,
Congo, Costa Rica, Croatia, Cuba, Djibouti, Dominica, Dominican Republic, Egypt, El
Salvador, Ecuador, Eritrea, Estonia, Ethiopia, Fiji, Gabon, Gambia, Georgia, Ghana,
Grenada, Guatemala, Guinea, Guyana, Haiti, Hungary, India, Indonesia, Iran, Iraq,
Israel, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, North Korea, South Korea,
Kuwait, Laos, Latvia, Libya, Lithuania, Madagascar, Malaysia, Maldives, Mauritania,
Micronesia, Morocco, Mozambique, Namibia, Nicaragua, Nigeria, Oman, Pakistan,
Papua New Guinea, Paraguay, Peru, Poland, Qatar, Romania, St. Helena, St. Kitts &
Nevis, Samoa, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Slovakia, Slovenia,
Solomon Islands, Somalia Republic, Sudan, Surinam, Syria, Taiwan, Tanzania,
Thailand, Togo, Trinidad, Tunisia, Turkey, Turkmenistan, UAE, Vietnam, Yemen
6. Other/Unknown: Undefined by dataset, Unknown (Fairplay), Azores, Cameroon,
Greenland, Monaco, Puerto Rico, Serbia & Montenegro, St. Pierre & Miquel

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