Modeling the Risks of the Global Customs Space
Abstract
:1. Introduction
2. Literature Review
2.1. Analysis of the “Customs Space” Concept
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- Openness to interaction and interpenetration with other subspaces of the geo-economic space. Diffusion of subspaces can be accompanied by migration of basic elements between them, changing the concentration;
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- Anisotropy, which manifests in the different reactions of structural elements of space to the action of factors external to them. The anisotropy of the customs space is explained by its structural asymmetries, the internal contradictions of the architecture of the space, the heterogeneity of the global environment, and the differentiation of the characteristics of the space;
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- Heterogeneity of space in both temporal, territorial, and functional dimensions;
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- Zonal and sectoral differences;
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- Dynamism (the customs space can be considered as the Minkowski space, which is three-dimensional, but supplemented by a non-spatial variable, time).
2.2. Global Sustainable Development and Balancing the National Customs Space
2.3. Definition of the “Customs Risk”
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- The probability/likelihood of violations of customs rules, implementation of customs threats, non-compliance with customs legislation, etc. (Hammadi et al. 2017);
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- Loss/threat of loss (González García and Mateos Caballero 2021);
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- A combination of the probability of violations and negative consequences (Chalendard et al. 2019).
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- Risks associated with the activities of customs authorities (caused by risks in the management of the customs service). It is advisable to consider them regarding the main components and areas of management support, such as legal, financial, personnel, informational, and logistical ones.
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- Risks associated with the activities of foreign economic activity entities, customs brokers, carriers, owners of customs warehouses, temporary storage warehouses, and the behavior of citizens when crossing the border.
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- Risks of documentary sources associated with incompleteness, inconsistency, or unreliability of information specified in documents submitted to the customs authority.
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- Risks of physical sources associated with goods and means of transport by which they are moved.
3. Materials and Methods
4. Results
5. Discussion
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- Considering the foreign economic nature of the origin of customs risks, taking into account in their profiles the specifics and structure of foreign economic activity in terms of its participants, the geography of its implementation, type of transport, volume and direction of cargo flows, as well as their structure (Chan et al. 2015).
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- Development of an institutional platform for the customs space, which will combine all state bodies, institutions, and organizations involved in ensuring the security of space and its balanced development (Pourakbar and Zuidwijk 2018).
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- Development of international cooperation and use of transit potential of the customs space of a country (Afontsev 2014).
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- Intensification of professional development of specialists who will ensure the operation of the customs space institutional platform, including through distance learning (Tsirekidze 2019).
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- Introduction of innovative technologies in the activities of customs institutions, taking into account the challenges of the 4.0 industrial revolution for national customs spaces and the possibility of future physical movement of goods produced using 4D and 5D printers across the border, which significantly distorts the organizational and institutional structure of customs (Darling 2015).
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
1 | Afghanistan | 59 | Gambia | 124 | Peru |
2 | Albania | 60 | Georgia | 125 | Philippines |
3 | Algeria | 61 | Germany | 126 | Poland |
+4 | Angola | 62 | Ghana | 127 | Portugal |
5 | Antigua and Barbuda | 63 | Greece | 128 | Qatar |
6 | Argentina | 64 | Grenada | 129 | Romania |
7 | Armenia | 65 | Guatemala | 130 | Russian Federation |
8 | Australia | 66 | Guinea | 131 | Rwanda |
9 | Austria | 67 | Guinea-Bissau | 132 | Samoa |
10 | Azerbaijan | 68 | Guyana | 133 | Saint Vincent and the Grenadines |
11 | Bahamas | 69 | Haiti | 134 | Sao Tome and Principe |
12 | Bahrain | 70 | Honduras | 135 | Saudi Arabia |
13 | Bangladesh | 71 | Hong Kong | 136 | Senegal |
14 | Barbados | 72 | Hungary | 137 | Serbia |
15 | Belarus | 73 | Iceland | 138 | Seychelles |
16 | Belize | 74 | India | 139 | Sierra Leone |
17 | Belgium | 75 | Indonesia | 140 | Singapore |
18 | Benin | 83 | Kenya | 141 | Slovak Republic |
19 | Bhutan | 84 | Republic of Korea (South Korea) | 142 | Slovenia |
20 | Bolivia | 85 | Kuwait | 143 | Solomon Islands |
21 | Bosnia and Herzegovina | 86 | Kyrgyz Republic | 144 | South Africa |
22 | Botswana | 87 | Lao PDR | 145 | Spain |
23 | Brazil | 88 | Latvia | 146 | Sri Lanka |
24 | Brunei Darussalam | 89 | Lebanon | 147 | Saint Lucia |
25 | Bulgaria | 90 | Lesotho | 148 | Saint Kitts and Nevis |
26 | Burkina Faso | 91 | Liberia | 149 | Suriname |
27 | Burundi | 92 | Lithuania | 150 | Swaziland |
28 | Cape Verde | 93 | Luxembourg | 151 | Sweden |
29 | Cambodia | 94 | Macau SAR, China | 152 | Switzerland |
30 | Cameroon | 95 | Madagascar | 153 | Tajikistan |
31 | Canada | 96 | Malawi | 154 | Tanzania |
32 | Central African Republic (CAR) | 97 | Malaysia | 155 | Thailand |
33 | Chad | 98 | Maldives | 156 | Timor-Leste |
34 | Chile | 99 | Mali | 157 | Togo |
35 | China | 100 | Malta | 158 | Tonga |
36 | Colombia | 101 | Mauritania | 159 | Trinidad and Tobago |
37 | Comoros | 102 | Mauritius | 160 | Tunisia |
38 | Democratic Republic of the Congo | 103 | Myanmar | 161 | Turkey |
39 | Republic of the Congo | 104 | Mexico | 162 | Uganda |
40 | Costa Rica | 105 | Moldova | 163 | Ukraine |
41 | Côte d’Ivoire | 106 | Mongolia | 164 | United Arab Emirates |
42 | Croatia | 107 | Montenegro | 165 | United Kingdom |
43 | Cuba | 108 | Morocco | 166 | USA |
44 | Cyprus | 109 | Mozambique | 167 | Uruguay |
45 | Czech Republic | 110 | Namibia | 168 | Uzbekistan |
46 | Denmark | 111 | Nauru | 169 | Vanuatu |
47 | Djibouti | 112 | Nepal | 170 | Venezuela |
48 | Dominica | 113 | Netherlands | 171 | Vietnam |
49 | Dominican Republic | 114 | New Zealand | 172 | Yemen |
50 | Ecuador | 115 | Nicaragua | 173 | Zambia |
51 | Egypt | 116 | Niger | 174 | Zimbabwe |
52 | El Salvador | 117 | Norway | ||
53 | Ethiopia | 118 | Oman | ||
54 | Estonia | 119 | Pakistan | ||
55 | Finland | 120 | Palau | ||
56 | Fiji | 121 | Panama | ||
57 | France | 122 | Papua New Guinea | ||
58 | Gabon | 123 | Paraguay |
Appendix B
N = 98 | Canonical Analysis Summary Canonical R: 0.89735 Chi? (200) = 444.55 p = 0.0000 | |
Left Set | Right Set | |
No. of variables | 20 | 10 |
Variance extracted | 61.5842% | 100.000% |
Total redundancy | 32.6775% | 60.9344% |
Variables: 1 | Net barter terms of trade | Human Development Index |
2 | Imports of goods and services (% of GDP) | GDP per capita at purchasing power parity, US dollars |
3 | Customs duty and other import taxes (% of tax revenues) | Terrorist activity level |
+4 | Logistics performance index | Corruption Perception Index |
5 | Time of import, border crossing (hours) | Global Enabling Trade Index |
6 | Time of export, border crossing (hours) | Environmental Performance Index |
7 | The efficiency of the customs clearance process | Social Progress Index |
8 | Duty-free, most-favored-nation treatment, % | Global Competitiveness Index |
9 | Non-ad valorem duty, bound tariff, % | Business Freedom Index |
10 | Non-ad valorem duty, most-favored-nation treatment, % | Global Peace Index |
11 | Customs duty > 15%, bound tariff, % | X |
12 | Customs duty > 15%, most-favored-nation treatment, % | X |
13 | Customs duty > 3 arithmetic mean of bound tariff, % | X |
14 | Customs duty > 3 arithmetic mean of the most-favored-nation treatment, % | X |
15 | Preferential terms that have not yet been enacted in 2019, % | X |
16 | Number of individual customs rates, bound tariff | X |
17 | Number of individual customs rates, most-favored-nation treatment | X |
18 | Coefficient of variation, bound tariff | X |
19 | Coefficient of variation, most-favored-nation treatment | X |
20 | Number of tariff rates, most-favored-nation treatment | X |
Appendix C
Root Variable | Factor Structure, the Left Set | ||
---|---|---|---|
Root 1 | Root 2 | Root 3 | |
Net barter terms of trade | –0.076167 | –0.019435 | –0.304932 |
Imports of goods and services (% of GDP) | 0.087883 | 0.383995 | –0.287508 |
Customs duty and other import taxes (% of tax revenues) | –0.016848 | 0.219997 | 0.181722 |
Logistics performance index | –0.106327 | 0.118739 | –0.119042 |
Time of import, border crossing (hours) | –0.607252 | –0.406659 | 0.144996 |
Time of export, border crossing (hours) | –0.635549 | –0.393813 | 0.095065 |
The efficiency of the customs clearance process | –0.107956 | 0.123080 | –0.121570 |
Duty-free, most-favored-nation treatment, % | 0.225458 | 0.092618 | –0.019574 |
Non-ad valorem duty, bound tariff, % | –0.708766 | 0.055926 | –0.229487 |
Non-ad valorem duty, most-favored-nation treatment, % | –0.400056 | –0.010045 | 0.136845 |
Customs duty > 15%, bound tariff, % | –0.387540 | –0.239892 | 0.139522 |
Customs duty > 15%, most-favored-nation treatment, % | –0.602225 | 0.114348 | 0.182434 |
Customs duty >3* arithmetic mean of bound tariff, % | –0.600790 | 0.225441 | 0.196453 |
Customs duty > 3* arithmetic mean of the most-favored-nation treatment, % | –0.657668 | 0.287198 | 0.015687 |
Preferential terms that have not yet been enacted, % | –0.067391 | 0.421803 | 0.422655 |
Number of individual customs rates, bound tariff | 0.749929 | 0.240568 | 0.287653 |
Number of individual customs rates, most-favored-nation treatment | 0.721224 | 0.347976 | 0.303215 |
Coefficient of variation, bound tariff | 0.673200 | 0.134107 | 0.044361 |
Coefficient of variation, most-favored-nation treatment | 0.398936 | 0.031500 | 0.109919 |
Number of tariff rates, most-favored-nation treatment | 0.564657 | –0.370910 | 0.048979 |
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Root | Eigenvalues | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Root 1 | Root 2 | Root 3 | Root 4 | Root 5 | Root 6 | Root 7 | Root 8 | Root 9 | Root 10 | |
Value | 0.805240 | 0.684724 | 0.486463 | 0.427290 | 0.379699 | 0.297511 | 0.247794 | 0.119538 | 0.113054 | 0.074826 |
Root Removed | Chi-Square Tests with Successive Root Removed | |||||
---|---|---|---|---|---|---|
Canonicl R | Canonicl R-sqr. | Cgi-sqr. | df | p | Lambda Prime | |
0 | 0.897352 | 0.805240 | 444.5482 | 200 | 0.000000 | 0.004277 |
1 | 0.827480 | 0.684724 | 311.2151 | 171 | 0.000000 | 0.021959 |
2 | 0.697469 | 0.486463 | 217.1392 | 144 | 0.000082 | 0.069649 |
3 | 0.653674 | 0.427290 | 162.8248 | 119 | 0.004829 | 0.135627 |
4 | 0.616197 | 0.379699 | 117.3987 | 96 | 0.068492 | 0.236815 |
5 | 0.545446 | 0.297511 | 78.4784 | 75 | 0.369393 | 0.381775 |
6 | 0.497789 | 0.247794 | 49.6986 | 56 | 0.710673 | 0.543460 |
7 | 0.345743 | 0.119538 | 26.4919 | 39 | 0.936488 | 0.722489 |
8 | 0.336235 | 0.113054 | 16.1162 | 24 | 0.883782 | 0.820579 |
9 | 0.273544 | 0.074826 | 6.3386 | 11 | 0.849828 | 0.925174 |
Root Variable | Factor Structure, the Right Set | |
---|---|---|
Root 1 | Root 2 | |
Human Development Index | 0.920447 | 0.007432 |
GDP per capita at purchasing power parity, US dollars | 0.743164 | 0.208457 |
Terrorist activity level | –0.117864 | –0.442463 |
Corruption Perception Index | 0.676567 | 0.458954 |
Global Enabling Trade Index | 0.847085 | 0.418918 |
Environmental Performance Index | 0.896309 | 0.006174 |
Social Progress Index | 0.864605 | 0.229713 |
Global Competitiveness Index | 0.836657 | 0.030461 |
Business Freedom Index | 0.570014 | 0.366788 |
Global Peace Index | –0.407824 | –0.626864 |
Root Factor | Variance Extracted (Proportions), Left Set | |
---|---|---|
Variance Extracted | Reddncy. | |
Root 1 | 0.242365 | 0.195162 |
Root 2 | 0.063755 | 0.043655 |
Root 3 | 0.040041 | 0.019478 |
Root 4 | 0.058681 | 0.025074 |
Root 5 | 0.027646 | 0.010497 |
Root 6 | 0.034425 | 0.010242 |
Root 7 | 0.047888 | 0.011866 |
Root 8 | 0.040625 | 0.004856 |
Root 9 | 0.037245 | 0.004211 |
Root 10 | 0.023172 | 0.001734 |
Root Variable | Variance Extracted (Proportions), The Right Set | |
---|---|---|
Variance Extracted | Reddncy. | |
Root 1 | 0.533085 | 0.429261 |
Root 2 | 0.120664 | 0.082621 |
Root 3 | 0.039860 | 0.019391 |
Root 4 | 0.040195 | 0.017175 |
Root 5 | 0.045964 | 0.017452 |
Root 6 | 0.057180 | 0.017112 |
Root 7 | 0.068696 | 0.017022 |
Root 8 | 0.034120 | 0.004079 |
Root 9 | 0.021529 | 0.002434 |
Root 10 | 0.038708 | 0.002896 |
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Borysenko, O.; Vasyl’yeva, O.; Katerna, O.; Masiuk, I.; Panakhi, O. Modeling the Risks of the Global Customs Space. J. Risk Financial Manag. 2022, 15, 598. https://doi.org/10.3390/jrfm15120598
Borysenko O, Vasyl’yeva O, Katerna O, Masiuk I, Panakhi O. Modeling the Risks of the Global Customs Space. Journal of Risk and Financial Management. 2022; 15(12):598. https://doi.org/10.3390/jrfm15120598
Chicago/Turabian StyleBorysenko, Olha, Olena Vasyl’yeva, Olga Katerna, Iuliia Masiuk, and Oleg Panakhi. 2022. "Modeling the Risks of the Global Customs Space" Journal of Risk and Financial Management 15, no. 12: 598. https://doi.org/10.3390/jrfm15120598
APA StyleBorysenko, O., Vasyl’yeva, O., Katerna, O., Masiuk, I., & Panakhi, O. (2022). Modeling the Risks of the Global Customs Space. Journal of Risk and Financial Management, 15(12), 598. https://doi.org/10.3390/jrfm15120598