- Email: [email protected]
Shedding light on the dark side of maritime trade – A new approach for identifying countries as flags of convenience
Marine Policy 99 (2019) 298–303
Contents lists available at ScienceDirect
Marine Policy journal homepage: www.elsevier.com/locate/marpol
Shedding light on the dark side of maritime trade – A new approach for identifying countries as flags of convenience
T
Jessica H. Forda, , Chris Wilcoxb ⁎
a b
CSIRO Data61, Castray Esplanade, Hobart, Tasmania 7000, Australia CSIRO Oceans and Atmosphere, Castray Esplanade, Hobart, Tasmania 7000, Australia
ABSTRACT
In accordance with Article 91 of the United Nations Convention on the Law of the Sea, there should be a ‘genuine link’ between a vessel and its flag State. However, despite this provision and best-case scenario, maritime flags often bear little to no relationship to their flag State – either through the actual nationality of crew, captain, company, or beneficial owner of the vessel. With the advent of international agreements, such as the United Nations Port State Measure Agreement, attempting to address illegal activities at sea, identifying risk factors such as vessels flying flags of convenience (FOC) is essential to assist in addressing the lack of oversight by flag states, and prioritize vessels for further scrutiny. A novel approach to identifying the likelihood that a flag state is being used as a FOC is presented here, using a model-based scoring system that ranks countries according to their vessel ownership, vessel behaviour, and flag control patterns. This approach provides an accurate and transparent metric for ranking countries which can easily be updated as behaviour or information change. The metric compares favourably with the International Transport Federation classification of FOC and provides informative predictions for rates of inspections and detentions based on independent data. Two key reasons vessel owners use FOC are suggested – tax and reduction of oversight, which lead to different flagging preferences. This hypothesis aligns closely with the literature on international tax havens.
1. Introduction In accordance with Article 91 of the United Nations Convention on the Law of the Sea (UNCLOS), there should be a ‘genuine link’ between a vessel and its flag State [18]. However, despite this provision and best-case scenario, there is no clear understanding of the definition of ‘genuine link’ [17]. Maritime vessels often bear little to no relationship to their flag State – either through the actual nationality of crew, captain, company, or beneficial owner of the vessel; indeed some jurisdictions prohibit vessels flagged to them from operating in their national waters [22]. The practice of vessels flagging out or registering through open registers has grown since the Second World War ([16]; Panama was the first country to develop an open register 1916, followed by Honduras in 1943 and Liberia in 1949 [10,16]). Use of open registers by the shipping industry is increasingly dominating global trade; over the last 50 years, shipping by vessels from open registers has been growing at more than 10 times the general world economic growth rate [22]. In 1970 21.6% of vessels were registered in open registries, by 2015 this had grown to 71.3% of the global fleet [16]. Historically, these behaviours appear to have been driven, and largely influenced, by political and military factors dating back several centuries. However, in more recent times, economics appear to underpin the use of open registers [15] as they allow foreigners to avoid regulation, and minimise taxes and labour wages [19]. The ⁎
intricacies of maritime registers are numerous, some registers do not actually operate their registers as a national body, in addition there are added complexities of second registers or international registers which lie between closed (domestic) and open registers [10]. The accessibility and nature of vessels flagging under open registers, and inherent lack of oversight, contributes to the problem of transnational crimes such as illegal, unreported, and unregulated (IUU) fishing [13,14]. IUU fishing itself has been linked with other organized crimes such as money laundering, drug trafficking and trafficking in persons [13,2]. The United Nations Food and Agriculture Organization Port State Measures Agreement (PSMA) is an international agreement that attempts to standardize port inspections across all coastal countries globally. The agreement is designed to close off the opportunities for IUU fishing vessels to land catches and obtain supplies. However, with thousands of vessels at sea, limited information on their activities, and scarce resources to support inspections, identifying priority vessels for inspections is a key need to support the implementation of PSMA. Traditionally an investigative approach has been used to identify illegal or suspicious vessels, however the large amount of data (for example, in excess of 1TB of Automatic Identification System data is recorded daily) can make this approach somewhat prohibitive. An alternative approach for identifying key vessels of interest is to use a risk-based approach, identifying factors that together suggest a vessel is suspicious or high
Corresponding author. E-mail address: [email protected] (J.H. Ford).
https://doi.org/10.1016/j.marpol.2018.10.026 Received 7 May 2018; Received in revised form 28 September 2018; Accepted 9 October 2018 Available online 15 November 2018 0308-597X/ © 2018 Published by Elsevier Ltd.
Marine Policy 99 (2019) 298–303
J.H. Ford, C. Wilcox
risk for IUU fishing (see [4,5]). In this context, a flag level risk indicator for a vessel is an important input to any comprehensive risk assessment. Within the context of IUU, the International Transport Federation's (ITF) ‘flag of convenience’ (FOC) is commonly used in place of ‘open registers’ (see [14] for a comprehensive review). The ITF fair practices committee takes into account several factors when declaring a FOC, including: number of foreign owned vessels; the social record for human and trade union rights; and the safety and environmental record of the state [11]. This judgement based binary classification approach currently declares 32 countries to be FOC. In development of indicators for a comprehensive risk tool for monitoring, control and surveillance of fisheries and in accordance with PSMA, there was a need for a transparent, repeatable, data-driven approach for prioritisation and ranking of all countries, based on flag state and behaviours often associated with FOC. A novel approach to identifying the risk of flag is presented here, using a model-based scoring system that ranks flag countries according to how likely they are to be used as a FOC. A model-based cluster analysis is used to classify and rank all countries (with available data) for likelihood of being a FOC. Three measures from sourced and derived data are used to capture behaviour associated with FOC, including: foreign ownership within a fleet; government control and corruption; and finally, fidelity to the flag state territorial waters. These three key indicators provide solid metrics for ranking countries displaying behaviours associated with FOC, and are used to create a ranking for the likelihood of FOC. The outcome is a continuous metric that is updated in a transparent and reproducible method using both formal trade measures and derived data indicators. Results are evaluated against current literature on tax havens, current ITF FOC classification and Paris Memorandum of Understanding (MOU) data for rates of inspections and detentions.
aggregate indictors which provide an overview scoring for six-dimensions of governance (www.govindicators.org) [24]. The 2015 Control of Corruption score which ‘Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests’ [12]. This 2015 score was used for this Control of Corruption measure (hereinafter Control of Corruption), the estimate of governance performance score has a possible range of −2.5 (indicating weak governance) to 2.5 (indicating strong governance). 2.1.3. Fidelity Six months of global AIS data (May-October 2016) was used to obtain an estimate of the proportion of time vessels flagged to a nation spend in the home exclusive economic zone, including territorial and archipelagic waters. This data derived indicator (hereinafter Fidelity), includes all AIS transmissions, which in comparison to the sourced variables will include fishing vessels, military vessels, and yachts. However, as these vessels make up a smaller percentage of overall vessels [20], and in our experience, vessel class type field in AIS data is unreliable as it is under the control of the vessel operator, so these vessels have not been excluded. Fidelity for land-locked nations, and nations with small EEZs will have fidelity scores reflecting this (zero or very low). 2.2. Analysis Only countries with complete data across all three indicators were included in the analysis. Primarily this was a result of data unavailable through the summaries obtained from UNCTAD STAT, but also as a result of missing scores from the WGI indicator. Seven countries currently identified by the ITF as FOC were not included in the analysis due to incomplete data for the variables sourced from the UNCTAD STAT and WGI databases: Comoros, Faroe Islands, Gibraltar, Jamaica, Moldova, Mongolia, and Sao Tome and Principe. Model-based clustering was used to identify groups of similar countries based on our three metrics, Ownership ratio, Control of Corruption, and Fidelity. This method searches for an unknown number of groups (or clusters) of similar countries, using a distribution to describe each group. A multivariate normal distribution was used, with allowance for variation in the means of each metric within a group, their variance, and their covariance. Several different structures were evaluated for constraints on the distribution parameters to identify the most parsimonious model. The Mclust package in the R statistical language was used to implement the cluster analysis [6,7]. This package uses an EM maximisation model-based approach to allocate clusters, with the optimal choice on number of clusters determined by the Bayesian information criterion (BIC). The analysis here is based on flag level derived variables, so although fishing vessels are not dominant, it is assumed the overall flag level dynamics are considered to be similar for fishing vessels as for the overall pattern of vessels for the flag state – that is, the nature to which any class of vessel chooses a flag (be it for strong tax laws, banking secrecy, or low labour wages) will be relevant for merchant and fishing vessels alike.
2. Methods 2.1. Indicators Three indicators were used to create a ranking of countries for the likelihood of FOC. Of these, two indicators were created from data sourced from the United Nations Conference on Trade and Development (UNCTAD) and World Bank, and one derived from global vessel tracking data. The vessel tracking data is based on the Automatic Identification System (AIS), which is a radio signal transmitted by vessels to each other to reduce collisions. This data covers most large sea-going vessels as AIS is required for all international vessels 300 GT and above and all commercial passenger ships. 2.1.1. Ownership ratio The Maritime Indicators on United Nations Conference on Trade and Development (UNCTAD) database (UNCTAD STAT [21] include seagoing propelled merchant ships of 100 gross tons and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of floating production, storage and offloading (FPSO) vessels and drill ships). Two data sets from the UNCTAD STAT database were used for all countries where data was available: Fleet – National Flag (DWT); and Fleet – Ownership (DWT). These were combined to give a measure of the ratio of the fleet nationally flagged to nationally owned (hereinafter Ownership ratio).
3. Results One hundred and forty countries were included in the analysis. The median Ownership ratio was 0.82 (proportion foreign), with a maximum of 21271 (Antigua and Barbuda). The median Fidelity was 0.32, with a maximum of 0.91 (China) and minimum of zero, corresponding to several
2.1.2. Control of Corruption The World Governance Indicators (WGI) from the World Bank are
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Table 1 Summaries of Control of Corruption, Fidelity and Ownership ratio for each cluster. Cluster classification
Size of cluster (Number of countries)
Ownership ratio (Median)
Fidelity (Median)
Control of Corruption (Median)
FOC group
1 2 3
111 6 23
0.64 1315 46.1
0.372 0.003 0.020
− 0.110 0.645 − 0.210
3 1 2
landlocked countries. The WGI Control of Corruption score has a possible range of −2.5 (indicating weak governance) to 2.5 (strong governance), the median for countries included in the analysis was −0.11, with a minimum of −1.83 (Equatorial Guinea) and a maximum of 2.29 (New Zealand). The best clustering model of the three indicators at the country level has a BIC value of −1280.7, with three components, where each group is described with varying volume, varying shape, but axis-aligned orientation. Table 1 shows the summary data for each of the three components (clusters). The largest cluster was Cluster 1 with 111 member countries (see Appendix A for complete list of countries in each cluster). This cluster has the lowest Ownership ratio, and highest Fidelity and intermediate Control of Corruption score. Cluster 2 has only six member countries – Antigua and Barbuda, Barbados, Cayman Islands, Liberia, St Vincent and Vanuatu. This group has the lowest median Fidelity (two orders of magnitude lower than the other two clusters), and highest Ownership ratio (two orders of magnitude higher), and a positive median Control of Corruption score (0.645, scored on a scale of −-2.5 to 2.5). Cluster 3 has 23 member countries, a relatively high Ownership ratio, low Fidelity, and negative Control of Corruption score (-0.275). Taking the optimal model cluster allocation, and scores on each indicator, a new group label was allocated to indicate likelihood of FOC - where Cluster 2 is most indicative of likelihood of FOC, followed by Cluster 3, and finally Cluster 1 being least indicative of FOC (see FOC group Table 1). There is consistent agreement between ITF FOC and FOC group. Only four countries currently listed by the ITF as FOC were allocated to FOC group 3 (the lowest likelihood of FOC) in the analysis: Bermuda, Democratic People's Republic of Korea, Lebanon, and Mauritius. In comparison to other ITF FOC nations, these four countries appear to have particularly low Ownership ratio, but a comparable range of Fidelity and Control of Corruption scores (Table 2). The cluster model output provides a measure of the associated classification for each country. Some countries have little uncertainty associated with their classification– for example, Antigua and Barbuda, Cayman Islands, and Liberia are strongly associated with FOC group 1 (the cluster most indicative of FOC). In comparison, three countries in FOC group 2 indicate some uncertainty in classification to FOC groups 1 and 2: Belize, Marshall Islands, and Sierra Leone. Although each of these countries have assignment probabilities greater than 0.75 to FOC group 2, the associated uncertainty indicates these countries have
stronger indications of FOC behaviour than other countries in FOC group 2. In comparison, Cyprus has high uncertainty to classification in FOC group 2, with some indication of potential membership to all three groups. An overall rank of all countries for likelihood of FOC (Fig. 1) was created, using FOC group membership as primary the rank, and membership uncertainty as the secondary rank within each group. Shows the top 20 ranked nations using this approach (see Appendix B for the complete list). 3.1. Paris MOU The Paris MOU register (www.parismou.org/) was used to further highlight agreement of FOC rank as a predictor of the lack of control, regulation and oversight historically associated with the use of flags of convenience. The number of inspections, detentions, and Excess Factor for countries with inspection and detention data for the years 2015–2017 was recorded. The Excess Factor from the Paris MOU register is an indicator of risk based on rates of inspection and detentions. An excess factor below zero suggests low risk, while an excess factor above zero indicates high risk. Data used included all countries with data for 2015–2017 and a calculable excess factor, which requires more than 29 inspections across the period. A moderate negative correlation (−0.36) is evident between FOC rank and rate of detentions (that is top ranked countries will have higher rate of detentions than lower ranked countries). The correlation between FOC rank and Excess Factor showed a similar but slightly weaker result (−0.27), indicating top ranked countries on our FOC metric are higher risk than lower ranked countries based on the MOU inspection data. 4. Discussion To date the approach used by ITF to identify FOC is a binary classification – a flag is listed as a FOC, or it is not. Here a novel method for identification and ranking of all countries for likelihood of FOC associated behaviour is presented. This study was approached from the context of risk modelling for prioritisation of flag states in analysis of IUU fishing. Using a risk-based approach to identifying and prioritising vessels for inspection under the PSMA involves quantifying multiple key behaviours. In comparison to previous methods, the approach presented here produces a ranked list of all countries based on likelihood of FOC using several indicators which cross trade, government and vessel behaviour. This data-driven approach has a number of advantages; it makes no a priori assumptions about previous or current listing or FOC classification, and the identification and ranking of a country is purely data-driven, making it transparent and repeatable, and possible to update as soon as more data becomes available. The results of this study highlight the complex network of partnerships and trade that exists within the international maritime industry. The countries clustered in the top group for likelihood of FOC
Table 2 Four countries currently listed flagged as FOC by the ITF, allocated to Cluster 1. Country
Ownership ratio
Fidelity
Control of Corruption
Korea, Dem People's Rep of Mauritius Bermuda Lebanon
1.06 0.88 0.26 0.09
0.08 0.23 0.006 0.123
−1.29 0.40 1.25 −0.88
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Fig. 1. Map of FOC rank – dark purple indicates top FOC rank (high likelihood of FOC). White indicates the country was missing from the analysis. Numeric text highlight approximate location of the top 20 ranked countries (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
are all currently classified as FOC by the ITF, have high levels of maritime trade [20] and banking secrecy laws [1,3]. Several countries not currently declared FOC by the ITF ranked high for likelihood of FOC. Likewise, several countries currently declared FOC by the ITF display behaviour less indicative of FOC compared to other nations. This divergence in classification may be due to some behaviour not capture by our model, or alternatively due to a change in regulations and associated behaviour of these flag states and open registers which is not currently captured by the ITF binary classification. Within the complex network of maritime trade, two key reasons for foreign vessel ownership are proposed: tax and evasion of oversight, leading to two distinct groups of FOC. Owners seeking to minimise tax may prioritize countries with strong legal systems, as a protection from prosecution by the home country of the vessel owner. For example, the Cayman Islands is an autonomous British Overseas Territory which operates independently of the UK, but with much oversight, giving some reassurance of strong legal framework and political stability (See http://www.financialsecrecyindex.com/). In contrast, vessel owners seeking to evade tax and oversight more generally due to illegal activities may flag in countries with high bank secrecy laws or high levels of corruption. Garcia-Bernardo et al. [9] highlight the global corporate network of conduit and sink offshore financial centres (OFCs). They shed light on the intricate nature of OFCs, and tax avoidance, but also highlight that OFCs include not only the traditional exotic small islands, but also many highly developed countries. The top 20 FOC ranked countries and their top two maritime trading partners (from [23]) highlight this pattern within the maritime trade. Eight of the top 20 countries using the FOC rank developed here were listed as sink-OFC, and only three as non-OFCs. The majority of the top maritime trading partners for these nations were labelled as conduit-OFCs in the study by Garcia-Bernardo
et al. [9]. A future sub-study of the maritime corporate structure would no doubt further highlight this network. In addition, a recent study by Galaz et al. [8] on tax havens and environmental degradation also highlight the strong link with tax havens –13 of the top 20 countries for the FOC rank developed here listed as jurisdictions with features normally associated with tax havens or financial secrecy jurisdictions. They highlight three key reasons for the role of tax havens in global fisheries: firstly, tax evasion; second, evasion of regulations as many tax havens qualify as secrecy jurisdictions; and their third point is that the combination of tax havens and FOCs adds another level of secrecy which facilitates illegal fishing activities. These links between the top FOC ranked nations, and their trading partners, emphasises that the continuation of open registries, or FOC, are the responsibility of both the flagging nations and those countries which are the donors of vessels. The ranking produced here is the first ranking of countries for behaviours traditionally associated with open registries, or flags of convenience. Using a data driven approach, a number of countries were identified which displayed behaviour associated with FOC, but which had not previously been listed by the ITF as a FOC. This approach allows for dynamic updating, as flagging and vessel behaviour change, or as new data becomes available. Based on the evaluation against independent inspection data, the risk-based ranking developed here will be useful for predicting the risk of a range of behaviours of interest to safety, fisheries, anti-terrorism and law enforcement agencies. Acknowledgements The authors thank the Paul Allen family Foundation, USA CSIRO Oceans and Atmosphere, Australia for supporting this project.
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Appendix A Complete list of countries in each cluster. Current FOC countries, identified by ITF, are highlighted in bold text. Cluster 1
Countries Albania Algeria Angola Anguilla Argentina Australia Azerbaijan Bangladesh Belgium Bermuda Brazil Bulgaria Cabo Verde Cameroon Canada Chile China China, Hong Kong SAR China, Taiwan Province of Colombia Congo Costa Rica Croatia Cuba Denmark Djibouti Ecuador Egypt Eritrea Antigua and Barbuda Barbados Bahamas Bahrain Belize Bolivia Brunei Darussalam Cambodia
2 3
Estonia Ethiopia Fiji Finland France Gambia Germany Ghana Greece Greenland Grenada Guatemala Guyana Iceland India Indonesia Iran Iraq Ireland Israel Italy Japan Jordan Kazakhstan Kenya Korea, Dem People's Rep of Korea, Republic of Cayman Islands Liberia Cyprus Equatorial Guinea Gabon Georgia Honduras Kiribati
Kuwait Lao Peoples Dem Rep Latvia Lebanon Libya Lithuania Luxembourg Malaysia Maldives Mauritania Mauritius Mexico Montenegro Morocco Mozambique Myanmar Namibia Netherlands New Zealand Nigeria Norway Oman Pakistan Papua New Guinea Paraguay Peru Philippines Poland Saint Vincent and the Grenadines Madagascar Malta Marshall Islands Panama Portugal Saint Kitts and Nevis
Appendix B Final rank and list of all countries for likelihood of FOC. 1 Cayman Islands 2 Antigua and Barbuda 3 Liberia 4 Saint Vincent and the Grenadines 5 Vanuatu 6 Barbados 7 Sierra Leone 8 Marshall Islands 9 Cyprus 10 Belize 11 Cambodia 12 Saint Kitts and Nevis 13 Bolivia 14 Bahamas 15 Panama 16 Malta 17 United Republic of Tanzania 18 Honduras 19 Brunei Darussalam 20 Kiribati 21 Tonga 22 Samoa 23 Equatorial Guinea 24 Georgia 25 Madagascar 26 Bahrain 27 Gabon 28 Portugal 29 Sri Lanka
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
302
Iran Mauritius Sudan Lithuania Bulgaria Thailand Yemen Iraq Italy Ghana Anguilla China, Taiwan Province of Vietnam Croatia Ukraine Israel Russian Federation Mauritania Belgium Egypt Turkey Poland Morocco Malaysia Korea, Republic of Cameroon Oman France India
Qatar Russian Federation Saudi Arabia Senegal Seychelles Singapore Slovenia South Africa Spain Sudan Suriname Sweden Switzerland Syrian Arab Republic Thailand Trinidad and Tobago Tunisia Turkey Turkmenistan Ukraine United Arab Emirates United Kingdom United States Uruguay Venezuela Vietnam Yemen Vanuatu Samoa Sierra Leone Sri Lanka Tonga United Republic of Tanzania
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100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
China, Hong Kong SAR Bermuda Costa Rica Switzerland Ethiopia Paraguay Lao Peoples Dem Rep Luxembourg Syrian Arab Republic Eritrea Libya Montenegro Albania Korea, Dem People's Rep of Suriname Philippines Senegal Cabo Verde Djibouti Fiji Myanmar Lebanon Greece Gambia Mozambique Cuba Saudi Arabia Grenada Seychelles Bangladesh Congo Kuwait Tunisia Pakistan Jordan Maldives Guatemala Algeria Latvia Slovenia Papua New Guinea
Kenya Trinidad and Tobago Colombia Singapore Germany Spain South Africa United Kingdom Kazakhstan Turkmenistan Estonia Indonesia Denmark Ireland Greenland Qatar Azerbaijan Japan Guyana United Arab Emirates Finland Ecuador Norway Netherlands United States Sweden Venezuela Nigeria Brazil Angola Peru Argentina Canada Uruguay Namibia Mexico China Chile New Zealand Australia Iceland
[12] D. Kaufmann, A. Kraay, M. Mastruzzi, The Worldwide Governance Indicators: Methodology and Analytical Issue. World Bank Policy Research Working Paper No. 5430. Available at SSRN: 〈https://ssrn.com/abstract=1682130〉, 2010. [13] D. Liddick, The dimensions of a transnational crime problem: the case of IUU fishing, Trends Organ Crim. 17 (2014) 290–312. [14] D.D. Miller, U.R. Sumaila, Flag use behaviour and IUU activity within the international fishing fleet: refining definitions and identifying areas of concern, Mar. Policy 44 (2014) 204–211. [15] F.J. Montero Llacer, Open registers: past, present and future. Marine Policy 27 (2003) 513-523, 2003. [16] F. Piniella, J.I. Alcaide, E. Rodriguez-Diaz, The Panama Ship Registry: 1917–2017, Mar. Policy 77 (2017) 13–22. [17] E. Powell, Taming the beast: How the International legal regime creates and contains Flags of Convenience, Annu. Surv. Int. Comp. Law 19(1) (2013) 12. [18] United Nations Convention of the Law of the Sea (UNCLOS), 1982. [19] UNCTAD, Review of maritime transport 2009. United Nations, New York, Geneva., 2009. [20] UNCTAD, Review of maritime transport 2014. United Nations, New York, Geneva., 2014. [21] UNCTAD STAT, Country Profiles: Maritime Profiles. URL 〈http://unctadstat. unctad.org/EN/Index.html〉 (Accessed 12 December 2017). [22] A. Van Fossen, Flags of convenience and global capitalism, Int. Crit. Thought 6 (3) (2016) 359–377. [23] The World Factbook, Washington, DC: Central Intelligence Agency, 2017. 〈https:// www.cia.gov/library/publications/the-world-factbook/index.html〉, 2017. [24] Worldwide Governance Indicators 〈www.govindicators.org〉.
References [1] A. Cobham, P. Jansky, M. Meinzer, The Financial Secrecy Index: shedding new light on the geography of secrecy, Econ. Geogr. 91 (3) (2015) 281–303. [2] A. Couer, H.D. Smith, B. Ciceri, Fishers and Plunderers: Theft Slavery and Violence at Sea, Pluto Press, London, 2015. [3] Environmental Justice Foundation, Pirates and profiteers: how pirate fishing fleets are robbing people and oceans. London, 2005. [4] J.H. Ford, D. Peel, D. Kroodsma, B.D. Hardesty, U. Rosebrock, C. Wilcox, Detecting suspicious activities at sea based on anomalies in Automatic Identification Systems transmissions, PLoS One 13 (8) (2018) e0201640. [5] J.H. Ford, D. Peel, B.D. Hardesty, U. Rosebrock, C. Wilcox, Loitering with intent – catching the outlier vessels at sea, PLoS One 13 (7) (2018) e0200189. [6] C. Fraley, A.E. Raftery, MCLUST: software for model-based cluster analysis, J. Classif. 16 (2) (1999) 297–306. [7] C. Fraley, A.E. Raftery, Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST, J. Classif. 20 (2) (2003) 263–286. [8] V. Galaz, B. Crona, A. Dauriach, J.-B. Jouffray, H. Osterblom, J. Fichtner, Tax havens and global environmental degradation, Nat. Ecol. Evol. 2 (2018) 1352–1357. [9] J. Garcia-Bernardo, J. Fichtner, F.W. Takes, E.M. Heemskerk, Uncovering offshore financial centers: conduits and sinks in the global corporate ownership network, Sci. Rep. 7 (2017) 6246. [10] M. Galley, Flagging interest: ship registration, owner anonymity, and sub-standard shipping, Mt. J. Leg. Stud. 14 (1/2) (2013) 87–109. [11] ITF Seafarers. 〈http://www.itfseafarers.org/defining-focs.cfm〉. (Accessed 1 May 2018).
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Contents lists available at ScienceDirect
Marine Policy journal homepage: www.elsevier.com/locate/marpol
Shedding light on the dark side of maritime trade – A new approach for identifying countries as flags of convenience
T
Jessica H. Forda, , Chris Wilcoxb ⁎
a b
CSIRO Data61, Castray Esplanade, Hobart, Tasmania 7000, Australia CSIRO Oceans and Atmosphere, Castray Esplanade, Hobart, Tasmania 7000, Australia
ABSTRACT
In accordance with Article 91 of the United Nations Convention on the Law of the Sea, there should be a ‘genuine link’ between a vessel and its flag State. However, despite this provision and best-case scenario, maritime flags often bear little to no relationship to their flag State – either through the actual nationality of crew, captain, company, or beneficial owner of the vessel. With the advent of international agreements, such as the United Nations Port State Measure Agreement, attempting to address illegal activities at sea, identifying risk factors such as vessels flying flags of convenience (FOC) is essential to assist in addressing the lack of oversight by flag states, and prioritize vessels for further scrutiny. A novel approach to identifying the likelihood that a flag state is being used as a FOC is presented here, using a model-based scoring system that ranks countries according to their vessel ownership, vessel behaviour, and flag control patterns. This approach provides an accurate and transparent metric for ranking countries which can easily be updated as behaviour or information change. The metric compares favourably with the International Transport Federation classification of FOC and provides informative predictions for rates of inspections and detentions based on independent data. Two key reasons vessel owners use FOC are suggested – tax and reduction of oversight, which lead to different flagging preferences. This hypothesis aligns closely with the literature on international tax havens.
1. Introduction In accordance with Article 91 of the United Nations Convention on the Law of the Sea (UNCLOS), there should be a ‘genuine link’ between a vessel and its flag State [18]. However, despite this provision and best-case scenario, there is no clear understanding of the definition of ‘genuine link’ [17]. Maritime vessels often bear little to no relationship to their flag State – either through the actual nationality of crew, captain, company, or beneficial owner of the vessel; indeed some jurisdictions prohibit vessels flagged to them from operating in their national waters [22]. The practice of vessels flagging out or registering through open registers has grown since the Second World War ([16]; Panama was the first country to develop an open register 1916, followed by Honduras in 1943 and Liberia in 1949 [10,16]). Use of open registers by the shipping industry is increasingly dominating global trade; over the last 50 years, shipping by vessels from open registers has been growing at more than 10 times the general world economic growth rate [22]. In 1970 21.6% of vessels were registered in open registries, by 2015 this had grown to 71.3% of the global fleet [16]. Historically, these behaviours appear to have been driven, and largely influenced, by political and military factors dating back several centuries. However, in more recent times, economics appear to underpin the use of open registers [15] as they allow foreigners to avoid regulation, and minimise taxes and labour wages [19]. The ⁎
intricacies of maritime registers are numerous, some registers do not actually operate their registers as a national body, in addition there are added complexities of second registers or international registers which lie between closed (domestic) and open registers [10]. The accessibility and nature of vessels flagging under open registers, and inherent lack of oversight, contributes to the problem of transnational crimes such as illegal, unreported, and unregulated (IUU) fishing [13,14]. IUU fishing itself has been linked with other organized crimes such as money laundering, drug trafficking and trafficking in persons [13,2]. The United Nations Food and Agriculture Organization Port State Measures Agreement (PSMA) is an international agreement that attempts to standardize port inspections across all coastal countries globally. The agreement is designed to close off the opportunities for IUU fishing vessels to land catches and obtain supplies. However, with thousands of vessels at sea, limited information on their activities, and scarce resources to support inspections, identifying priority vessels for inspections is a key need to support the implementation of PSMA. Traditionally an investigative approach has been used to identify illegal or suspicious vessels, however the large amount of data (for example, in excess of 1TB of Automatic Identification System data is recorded daily) can make this approach somewhat prohibitive. An alternative approach for identifying key vessels of interest is to use a risk-based approach, identifying factors that together suggest a vessel is suspicious or high
Corresponding author. E-mail address: [email protected] (J.H. Ford).
https://doi.org/10.1016/j.marpol.2018.10.026 Received 7 May 2018; Received in revised form 28 September 2018; Accepted 9 October 2018 Available online 15 November 2018 0308-597X/ © 2018 Published by Elsevier Ltd.
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risk for IUU fishing (see [4,5]). In this context, a flag level risk indicator for a vessel is an important input to any comprehensive risk assessment. Within the context of IUU, the International Transport Federation's (ITF) ‘flag of convenience’ (FOC) is commonly used in place of ‘open registers’ (see [14] for a comprehensive review). The ITF fair practices committee takes into account several factors when declaring a FOC, including: number of foreign owned vessels; the social record for human and trade union rights; and the safety and environmental record of the state [11]. This judgement based binary classification approach currently declares 32 countries to be FOC. In development of indicators for a comprehensive risk tool for monitoring, control and surveillance of fisheries and in accordance with PSMA, there was a need for a transparent, repeatable, data-driven approach for prioritisation and ranking of all countries, based on flag state and behaviours often associated with FOC. A novel approach to identifying the risk of flag is presented here, using a model-based scoring system that ranks flag countries according to how likely they are to be used as a FOC. A model-based cluster analysis is used to classify and rank all countries (with available data) for likelihood of being a FOC. Three measures from sourced and derived data are used to capture behaviour associated with FOC, including: foreign ownership within a fleet; government control and corruption; and finally, fidelity to the flag state territorial waters. These three key indicators provide solid metrics for ranking countries displaying behaviours associated with FOC, and are used to create a ranking for the likelihood of FOC. The outcome is a continuous metric that is updated in a transparent and reproducible method using both formal trade measures and derived data indicators. Results are evaluated against current literature on tax havens, current ITF FOC classification and Paris Memorandum of Understanding (MOU) data for rates of inspections and detentions.
aggregate indictors which provide an overview scoring for six-dimensions of governance (www.govindicators.org) [24]. The 2015 Control of Corruption score which ‘Reflects perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests’ [12]. This 2015 score was used for this Control of Corruption measure (hereinafter Control of Corruption), the estimate of governance performance score has a possible range of −2.5 (indicating weak governance) to 2.5 (indicating strong governance). 2.1.3. Fidelity Six months of global AIS data (May-October 2016) was used to obtain an estimate of the proportion of time vessels flagged to a nation spend in the home exclusive economic zone, including territorial and archipelagic waters. This data derived indicator (hereinafter Fidelity), includes all AIS transmissions, which in comparison to the sourced variables will include fishing vessels, military vessels, and yachts. However, as these vessels make up a smaller percentage of overall vessels [20], and in our experience, vessel class type field in AIS data is unreliable as it is under the control of the vessel operator, so these vessels have not been excluded. Fidelity for land-locked nations, and nations with small EEZs will have fidelity scores reflecting this (zero or very low). 2.2. Analysis Only countries with complete data across all three indicators were included in the analysis. Primarily this was a result of data unavailable through the summaries obtained from UNCTAD STAT, but also as a result of missing scores from the WGI indicator. Seven countries currently identified by the ITF as FOC were not included in the analysis due to incomplete data for the variables sourced from the UNCTAD STAT and WGI databases: Comoros, Faroe Islands, Gibraltar, Jamaica, Moldova, Mongolia, and Sao Tome and Principe. Model-based clustering was used to identify groups of similar countries based on our three metrics, Ownership ratio, Control of Corruption, and Fidelity. This method searches for an unknown number of groups (or clusters) of similar countries, using a distribution to describe each group. A multivariate normal distribution was used, with allowance for variation in the means of each metric within a group, their variance, and their covariance. Several different structures were evaluated for constraints on the distribution parameters to identify the most parsimonious model. The Mclust package in the R statistical language was used to implement the cluster analysis [6,7]. This package uses an EM maximisation model-based approach to allocate clusters, with the optimal choice on number of clusters determined by the Bayesian information criterion (BIC). The analysis here is based on flag level derived variables, so although fishing vessels are not dominant, it is assumed the overall flag level dynamics are considered to be similar for fishing vessels as for the overall pattern of vessels for the flag state – that is, the nature to which any class of vessel chooses a flag (be it for strong tax laws, banking secrecy, or low labour wages) will be relevant for merchant and fishing vessels alike.
2. Methods 2.1. Indicators Three indicators were used to create a ranking of countries for the likelihood of FOC. Of these, two indicators were created from data sourced from the United Nations Conference on Trade and Development (UNCTAD) and World Bank, and one derived from global vessel tracking data. The vessel tracking data is based on the Automatic Identification System (AIS), which is a radio signal transmitted by vessels to each other to reduce collisions. This data covers most large sea-going vessels as AIS is required for all international vessels 300 GT and above and all commercial passenger ships. 2.1.1. Ownership ratio The Maritime Indicators on United Nations Conference on Trade and Development (UNCTAD) database (UNCTAD STAT [21] include seagoing propelled merchant ships of 100 gross tons and above, excluding inland waterway vessels, fishing vessels, military vessels, yachts, and offshore fixed and mobile platforms and barges (with the exception of floating production, storage and offloading (FPSO) vessels and drill ships). Two data sets from the UNCTAD STAT database were used for all countries where data was available: Fleet – National Flag (DWT); and Fleet – Ownership (DWT). These were combined to give a measure of the ratio of the fleet nationally flagged to nationally owned (hereinafter Ownership ratio).
3. Results One hundred and forty countries were included in the analysis. The median Ownership ratio was 0.82 (proportion foreign), with a maximum of 21271 (Antigua and Barbuda). The median Fidelity was 0.32, with a maximum of 0.91 (China) and minimum of zero, corresponding to several
2.1.2. Control of Corruption The World Governance Indicators (WGI) from the World Bank are
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Table 1 Summaries of Control of Corruption, Fidelity and Ownership ratio for each cluster. Cluster classification
Size of cluster (Number of countries)
Ownership ratio (Median)
Fidelity (Median)
Control of Corruption (Median)
FOC group
1 2 3
111 6 23
0.64 1315 46.1
0.372 0.003 0.020
− 0.110 0.645 − 0.210
3 1 2
landlocked countries. The WGI Control of Corruption score has a possible range of −2.5 (indicating weak governance) to 2.5 (strong governance), the median for countries included in the analysis was −0.11, with a minimum of −1.83 (Equatorial Guinea) and a maximum of 2.29 (New Zealand). The best clustering model of the three indicators at the country level has a BIC value of −1280.7, with three components, where each group is described with varying volume, varying shape, but axis-aligned orientation. Table 1 shows the summary data for each of the three components (clusters). The largest cluster was Cluster 1 with 111 member countries (see Appendix A for complete list of countries in each cluster). This cluster has the lowest Ownership ratio, and highest Fidelity and intermediate Control of Corruption score. Cluster 2 has only six member countries – Antigua and Barbuda, Barbados, Cayman Islands, Liberia, St Vincent and Vanuatu. This group has the lowest median Fidelity (two orders of magnitude lower than the other two clusters), and highest Ownership ratio (two orders of magnitude higher), and a positive median Control of Corruption score (0.645, scored on a scale of −-2.5 to 2.5). Cluster 3 has 23 member countries, a relatively high Ownership ratio, low Fidelity, and negative Control of Corruption score (-0.275). Taking the optimal model cluster allocation, and scores on each indicator, a new group label was allocated to indicate likelihood of FOC - where Cluster 2 is most indicative of likelihood of FOC, followed by Cluster 3, and finally Cluster 1 being least indicative of FOC (see FOC group Table 1). There is consistent agreement between ITF FOC and FOC group. Only four countries currently listed by the ITF as FOC were allocated to FOC group 3 (the lowest likelihood of FOC) in the analysis: Bermuda, Democratic People's Republic of Korea, Lebanon, and Mauritius. In comparison to other ITF FOC nations, these four countries appear to have particularly low Ownership ratio, but a comparable range of Fidelity and Control of Corruption scores (Table 2). The cluster model output provides a measure of the associated classification for each country. Some countries have little uncertainty associated with their classification– for example, Antigua and Barbuda, Cayman Islands, and Liberia are strongly associated with FOC group 1 (the cluster most indicative of FOC). In comparison, three countries in FOC group 2 indicate some uncertainty in classification to FOC groups 1 and 2: Belize, Marshall Islands, and Sierra Leone. Although each of these countries have assignment probabilities greater than 0.75 to FOC group 2, the associated uncertainty indicates these countries have
stronger indications of FOC behaviour than other countries in FOC group 2. In comparison, Cyprus has high uncertainty to classification in FOC group 2, with some indication of potential membership to all three groups. An overall rank of all countries for likelihood of FOC (Fig. 1) was created, using FOC group membership as primary the rank, and membership uncertainty as the secondary rank within each group. Shows the top 20 ranked nations using this approach (see Appendix B for the complete list). 3.1. Paris MOU The Paris MOU register (www.parismou.org/) was used to further highlight agreement of FOC rank as a predictor of the lack of control, regulation and oversight historically associated with the use of flags of convenience. The number of inspections, detentions, and Excess Factor for countries with inspection and detention data for the years 2015–2017 was recorded. The Excess Factor from the Paris MOU register is an indicator of risk based on rates of inspection and detentions. An excess factor below zero suggests low risk, while an excess factor above zero indicates high risk. Data used included all countries with data for 2015–2017 and a calculable excess factor, which requires more than 29 inspections across the period. A moderate negative correlation (−0.36) is evident between FOC rank and rate of detentions (that is top ranked countries will have higher rate of detentions than lower ranked countries). The correlation between FOC rank and Excess Factor showed a similar but slightly weaker result (−0.27), indicating top ranked countries on our FOC metric are higher risk than lower ranked countries based on the MOU inspection data. 4. Discussion To date the approach used by ITF to identify FOC is a binary classification – a flag is listed as a FOC, or it is not. Here a novel method for identification and ranking of all countries for likelihood of FOC associated behaviour is presented. This study was approached from the context of risk modelling for prioritisation of flag states in analysis of IUU fishing. Using a risk-based approach to identifying and prioritising vessels for inspection under the PSMA involves quantifying multiple key behaviours. In comparison to previous methods, the approach presented here produces a ranked list of all countries based on likelihood of FOC using several indicators which cross trade, government and vessel behaviour. This data-driven approach has a number of advantages; it makes no a priori assumptions about previous or current listing or FOC classification, and the identification and ranking of a country is purely data-driven, making it transparent and repeatable, and possible to update as soon as more data becomes available. The results of this study highlight the complex network of partnerships and trade that exists within the international maritime industry. The countries clustered in the top group for likelihood of FOC
Table 2 Four countries currently listed flagged as FOC by the ITF, allocated to Cluster 1. Country
Ownership ratio
Fidelity
Control of Corruption
Korea, Dem People's Rep of Mauritius Bermuda Lebanon
1.06 0.88 0.26 0.09
0.08 0.23 0.006 0.123
−1.29 0.40 1.25 −0.88
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Fig. 1. Map of FOC rank – dark purple indicates top FOC rank (high likelihood of FOC). White indicates the country was missing from the analysis. Numeric text highlight approximate location of the top 20 ranked countries (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
are all currently classified as FOC by the ITF, have high levels of maritime trade [20] and banking secrecy laws [1,3]. Several countries not currently declared FOC by the ITF ranked high for likelihood of FOC. Likewise, several countries currently declared FOC by the ITF display behaviour less indicative of FOC compared to other nations. This divergence in classification may be due to some behaviour not capture by our model, or alternatively due to a change in regulations and associated behaviour of these flag states and open registers which is not currently captured by the ITF binary classification. Within the complex network of maritime trade, two key reasons for foreign vessel ownership are proposed: tax and evasion of oversight, leading to two distinct groups of FOC. Owners seeking to minimise tax may prioritize countries with strong legal systems, as a protection from prosecution by the home country of the vessel owner. For example, the Cayman Islands is an autonomous British Overseas Territory which operates independently of the UK, but with much oversight, giving some reassurance of strong legal framework and political stability (See http://www.financialsecrecyindex.com/). In contrast, vessel owners seeking to evade tax and oversight more generally due to illegal activities may flag in countries with high bank secrecy laws or high levels of corruption. Garcia-Bernardo et al. [9] highlight the global corporate network of conduit and sink offshore financial centres (OFCs). They shed light on the intricate nature of OFCs, and tax avoidance, but also highlight that OFCs include not only the traditional exotic small islands, but also many highly developed countries. The top 20 FOC ranked countries and their top two maritime trading partners (from [23]) highlight this pattern within the maritime trade. Eight of the top 20 countries using the FOC rank developed here were listed as sink-OFC, and only three as non-OFCs. The majority of the top maritime trading partners for these nations were labelled as conduit-OFCs in the study by Garcia-Bernardo
et al. [9]. A future sub-study of the maritime corporate structure would no doubt further highlight this network. In addition, a recent study by Galaz et al. [8] on tax havens and environmental degradation also highlight the strong link with tax havens –13 of the top 20 countries for the FOC rank developed here listed as jurisdictions with features normally associated with tax havens or financial secrecy jurisdictions. They highlight three key reasons for the role of tax havens in global fisheries: firstly, tax evasion; second, evasion of regulations as many tax havens qualify as secrecy jurisdictions; and their third point is that the combination of tax havens and FOCs adds another level of secrecy which facilitates illegal fishing activities. These links between the top FOC ranked nations, and their trading partners, emphasises that the continuation of open registries, or FOC, are the responsibility of both the flagging nations and those countries which are the donors of vessels. The ranking produced here is the first ranking of countries for behaviours traditionally associated with open registries, or flags of convenience. Using a data driven approach, a number of countries were identified which displayed behaviour associated with FOC, but which had not previously been listed by the ITF as a FOC. This approach allows for dynamic updating, as flagging and vessel behaviour change, or as new data becomes available. Based on the evaluation against independent inspection data, the risk-based ranking developed here will be useful for predicting the risk of a range of behaviours of interest to safety, fisheries, anti-terrorism and law enforcement agencies. Acknowledgements The authors thank the Paul Allen family Foundation, USA CSIRO Oceans and Atmosphere, Australia for supporting this project.
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Appendix A Complete list of countries in each cluster. Current FOC countries, identified by ITF, are highlighted in bold text. Cluster 1
Countries Albania Algeria Angola Anguilla Argentina Australia Azerbaijan Bangladesh Belgium Bermuda Brazil Bulgaria Cabo Verde Cameroon Canada Chile China China, Hong Kong SAR China, Taiwan Province of Colombia Congo Costa Rica Croatia Cuba Denmark Djibouti Ecuador Egypt Eritrea Antigua and Barbuda Barbados Bahamas Bahrain Belize Bolivia Brunei Darussalam Cambodia
2 3
Estonia Ethiopia Fiji Finland France Gambia Germany Ghana Greece Greenland Grenada Guatemala Guyana Iceland India Indonesia Iran Iraq Ireland Israel Italy Japan Jordan Kazakhstan Kenya Korea, Dem People's Rep of Korea, Republic of Cayman Islands Liberia Cyprus Equatorial Guinea Gabon Georgia Honduras Kiribati
Kuwait Lao Peoples Dem Rep Latvia Lebanon Libya Lithuania Luxembourg Malaysia Maldives Mauritania Mauritius Mexico Montenegro Morocco Mozambique Myanmar Namibia Netherlands New Zealand Nigeria Norway Oman Pakistan Papua New Guinea Paraguay Peru Philippines Poland Saint Vincent and the Grenadines Madagascar Malta Marshall Islands Panama Portugal Saint Kitts and Nevis
Appendix B Final rank and list of all countries for likelihood of FOC. 1 Cayman Islands 2 Antigua and Barbuda 3 Liberia 4 Saint Vincent and the Grenadines 5 Vanuatu 6 Barbados 7 Sierra Leone 8 Marshall Islands 9 Cyprus 10 Belize 11 Cambodia 12 Saint Kitts and Nevis 13 Bolivia 14 Bahamas 15 Panama 16 Malta 17 United Republic of Tanzania 18 Honduras 19 Brunei Darussalam 20 Kiribati 21 Tonga 22 Samoa 23 Equatorial Guinea 24 Georgia 25 Madagascar 26 Bahrain 27 Gabon 28 Portugal 29 Sri Lanka
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
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Iran Mauritius Sudan Lithuania Bulgaria Thailand Yemen Iraq Italy Ghana Anguilla China, Taiwan Province of Vietnam Croatia Ukraine Israel Russian Federation Mauritania Belgium Egypt Turkey Poland Morocco Malaysia Korea, Republic of Cameroon Oman France India
Qatar Russian Federation Saudi Arabia Senegal Seychelles Singapore Slovenia South Africa Spain Sudan Suriname Sweden Switzerland Syrian Arab Republic Thailand Trinidad and Tobago Tunisia Turkey Turkmenistan Ukraine United Arab Emirates United Kingdom United States Uruguay Venezuela Vietnam Yemen Vanuatu Samoa Sierra Leone Sri Lanka Tonga United Republic of Tanzania
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100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
China, Hong Kong SAR Bermuda Costa Rica Switzerland Ethiopia Paraguay Lao Peoples Dem Rep Luxembourg Syrian Arab Republic Eritrea Libya Montenegro Albania Korea, Dem People's Rep of Suriname Philippines Senegal Cabo Verde Djibouti Fiji Myanmar Lebanon Greece Gambia Mozambique Cuba Saudi Arabia Grenada Seychelles Bangladesh Congo Kuwait Tunisia Pakistan Jordan Maldives Guatemala Algeria Latvia Slovenia Papua New Guinea
Kenya Trinidad and Tobago Colombia Singapore Germany Spain South Africa United Kingdom Kazakhstan Turkmenistan Estonia Indonesia Denmark Ireland Greenland Qatar Azerbaijan Japan Guyana United Arab Emirates Finland Ecuador Norway Netherlands United States Sweden Venezuela Nigeria Brazil Angola Peru Argentina Canada Uruguay Namibia Mexico China Chile New Zealand Australia Iceland
[12] D. Kaufmann, A. Kraay, M. Mastruzzi, The Worldwide Governance Indicators: Methodology and Analytical Issue. World Bank Policy Research Working Paper No. 5430. Available at SSRN: 〈https://ssrn.com/abstract=1682130〉, 2010. [13] D. Liddick, The dimensions of a transnational crime problem: the case of IUU fishing, Trends Organ Crim. 17 (2014) 290–312. [14] D.D. Miller, U.R. Sumaila, Flag use behaviour and IUU activity within the international fishing fleet: refining definitions and identifying areas of concern, Mar. Policy 44 (2014) 204–211. [15] F.J. Montero Llacer, Open registers: past, present and future. Marine Policy 27 (2003) 513-523, 2003. [16] F. Piniella, J.I. Alcaide, E. Rodriguez-Diaz, The Panama Ship Registry: 1917–2017, Mar. Policy 77 (2017) 13–22. [17] E. Powell, Taming the beast: How the International legal regime creates and contains Flags of Convenience, Annu. Surv. Int. Comp. Law 19(1) (2013) 12. [18] United Nations Convention of the Law of the Sea (UNCLOS), 1982. [19] UNCTAD, Review of maritime transport 2009. United Nations, New York, Geneva., 2009. [20] UNCTAD, Review of maritime transport 2014. United Nations, New York, Geneva., 2014. [21] UNCTAD STAT, Country Profiles: Maritime Profiles. URL 〈http://unctadstat. unctad.org/EN/Index.html〉 (Accessed 12 December 2017). [22] A. Van Fossen, Flags of convenience and global capitalism, Int. Crit. Thought 6 (3) (2016) 359–377. [23] The World Factbook, Washington, DC: Central Intelligence Agency, 2017. 〈https:// www.cia.gov/library/publications/the-world-factbook/index.html〉, 2017. [24] Worldwide Governance Indicators 〈www.govindicators.org〉.
References [1] A. Cobham, P. Jansky, M. Meinzer, The Financial Secrecy Index: shedding new light on the geography of secrecy, Econ. Geogr. 91 (3) (2015) 281–303. [2] A. Couer, H.D. Smith, B. Ciceri, Fishers and Plunderers: Theft Slavery and Violence at Sea, Pluto Press, London, 2015. [3] Environmental Justice Foundation, Pirates and profiteers: how pirate fishing fleets are robbing people and oceans. London, 2005. [4] J.H. Ford, D. Peel, D. Kroodsma, B.D. Hardesty, U. Rosebrock, C. Wilcox, Detecting suspicious activities at sea based on anomalies in Automatic Identification Systems transmissions, PLoS One 13 (8) (2018) e0201640. [5] J.H. Ford, D. Peel, B.D. Hardesty, U. Rosebrock, C. Wilcox, Loitering with intent – catching the outlier vessels at sea, PLoS One 13 (7) (2018) e0200189. [6] C. Fraley, A.E. Raftery, MCLUST: software for model-based cluster analysis, J. Classif. 16 (2) (1999) 297–306. [7] C. Fraley, A.E. Raftery, Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST, J. Classif. 20 (2) (2003) 263–286. [8] V. Galaz, B. Crona, A. Dauriach, J.-B. Jouffray, H. Osterblom, J. Fichtner, Tax havens and global environmental degradation, Nat. Ecol. Evol. 2 (2018) 1352–1357. [9] J. Garcia-Bernardo, J. Fichtner, F.W. Takes, E.M. Heemskerk, Uncovering offshore financial centers: conduits and sinks in the global corporate ownership network, Sci. Rep. 7 (2017) 6246. [10] M. Galley, Flagging interest: ship registration, owner anonymity, and sub-standard shipping, Mt. J. Leg. Stud. 14 (1/2) (2013) 87–109. [11] ITF Seafarers. 〈http://www.itfseafarers.org/defining-focs.cfm〉. (Accessed 1 May 2018).
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