Abstract
Maritime connectivity is becoming increasingly important due to its inextricable relation to maritime trade, especially in the global economy. This paper presents a robust framework to rank countries on their maritime connectivity. Typically, in most index creation models, the arithmetic mean is used to build a composite index. In this paper, we propose a more reliable ranking approach, by applying the method of stochastic multicriteria acceptability analysis (SMAA). In this, we take into account the variability in the weights assigned to the connectivity factors, thus producing a probabilistic ranking for each country (i.e., the probability to achieve a certain rank). UNCTAD’s ranking, based simply on the arithmetic mean, where all the weights are equal, becomes a special case of our approach. Our results show that China achieves the highest level of connectivity, followed by Singapore.
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Notes
Recently, LSCI has been improved further by adding another factor, namely, the number of directly connected trading partners (UNCTAD 2019).
i.e., ranking obtained by considering one particular weight vector.
We illustrate the phrase “category of preferences” by the following example. Suppose a group of individuals interested in assigning a higher weight to LinerServices than MaxShipSize. Then, the preference of this group, or all the weight vectors that represent this group’s preferences, are referred to as a “category of preference.”
Note that South Korea could also occupy the second ranking position with particular type of weight vectors that assign very high weight to NumShips. But the probability of occurring such scenarios are extremely low (< 0.002).
The term “degree of robustness” is used to emphasize the fact the ranking index is computed by taking all possible factor weights, and it does not depend on a particular choice of a weight vector. Thus, it has a higher robustness in comparison to the ranking index computed with a single weight vector.
Note that Taiwan was chosen at random for no specific reason, and any other country could have been chosen instead.
References
Anderson, J.E. 1979. A theoretical foundation for the gravity equation. The American Economic Review 69 (1): 106–116.
Angilella, S., S. Corrente, and S. Greco. 2015. Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem. European Journal of Operational Research 240: 172–182.
Angilella, S., P. Catalfo, S. Corrente, A. Giarlotta, S. Greco, and M. Rizzo. 2018. Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems 158: 136–153.
Bartholdi, J.J., P. Jarumaneeroj, and A. Ramudhin. 2016. A new connectivity index for container ports. Maritime Economics & Logistics 18 (3): 231–249.
Bird, J., and G. Bland. 1988. Freight forwarders speak: The perception of route competition via seaports in the European Communities Research Project. Part 1. Maritime Policy & Management 15 (1): 35–55.
Chaney, T. 2018. The gravity equation in international trade: An explanation. Journal of Political Economy 126 (1): 150–177.
Corrente, S., S. Greco, M. Nicotra, M. Romano, and C.E. Schillaci. 2018. Evaluating and comparing entrepreneurial ecosystems using SMAA and SMAA-S. The Journal of Technology Transfer 1–35.
Demirel, B., K. Cullinane, and H. Haralambides. 2012. Container terminal efficiency and private sector participation. In The Blackwell companion to maritime economics, edited by W. K. Talley, 571–598.
Diestel, R. 2010. Graph theory. Graduate Texts in mathematics, 4th ed. Heidelberg: Springer.
Ducruet, C. 2015. Global maritime connectivity: A long-term perspective. Port Technology International 65: 34–36.
Figueira, J., S. Greco, and M. Ehrgott, eds. 2005. Multiple criteria decision analysis: State of the art surveys. Berlin: Springer.
Fugazza, M. 2015. Maritime Connectivity and Trade. UNCTAD policy issues in international trade and commodities research study series. No. 70.
Fugazza, M., and J. Hoffmann. 2016. Bilateral Liner Shipping Connectivity since 2006. UNCTAD policy issues in international trade and commodities research study series. No. 72.
Fugazza, M., and J. Hoffmann. 2017. Liner shipping connectivity as determinant of trade. Journal of Shipping and Trade 2: 1.
Fugazza, M., J. Hoffmann, and R. Razafinombana. 2013. Building a dataset for bilateral maritime connectivity. New York and Geneva: United Nations.
Greco, S., A. Ishizaka, B. Matarazzo, and G. Torrisi. 2018. Stochastic multi-attribute acceptability analysis (SMAA): AN application to the ranking of Italian regions. Regional Studies 52 (4): 585–600.
Greco, S., A. Ishizaka, M. Tasiou, and G. Torrisi. 2019. On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research 141: 61–94.
Haralambides, H.E. 2019. Gigantism in container shipping, ports and global logistics: A time-lapse into the future. Maritime Economics & Logistics 21: 1–60.
Helliwell, J.F. 2003. How’s life? Combining individual and national variables to explain subjective well-being. Economic Modelling 20: 331–360.
Helliwell, J.F., and C.P. Barrington-Leigh. 2010. Viewpoint: Measuring and understanding subjective well-being. Canadian Journal of Economics/Revue canadienne d’economique 43: 729–753.
Hoffmann, J. 2005. Liner shipping connectivity. UNCTAD Transport Newsletter 27: 4–12.
Hoffmann, J., B. Cui, and G. Wilmsmeier. 2013. Bilateral Liner Shipping Connectivity. Proceedings of the 2013 International Association of Maritime Economics (IAME) Conference, Marseille, Marseille.
Hoffmann, J., N. Saeed, and S. Sødal. 2019. Liner shipping bilateral connectivity and its impact on South Africa’s bilateral trade flows. Maritime Economics & Logistics 1–27.
Hoffmann J, J.-W. Van Hoogenhuizen, and G. Wilmsmeier. 2014. Developing an index for bilateral liner shipping connectivity. In IAME 2014 Conference Proceedings. Presented at the International Association of Maritime Economists (IAME), Norfolk, United States.
Ishizaka, A., and P. Nemery. 2013. Multi-criteria decision analysis: Methods and software. Chichester: Wiley.
Jiang, J., L.H. Lee, E.P. Chew, and C.C. Gan. 2015. Port connectivity study: An analysis framework from a global container liner shipping network perspective. Transportation Research Part E: Logistics and Transportation Review 73: 47–64.
Keeney, R.L., and H. Raiffa. 1976. Decisions with multiple objectives: Preferences, and value tradeoffs. New York: Wiley.
Lagravinese, R., P. Liberati, and G. Resce. 2019. Exploring health outcomes by stochastic multicriteria acceptability analysis: An application to Italian regions. European Journal of Operational Research 274 (3): 1168–1179.
Lahdelma, R., and P. Salminen. 2001. SMAA-2: Stochastic multicriteria acceptability analysis for group decision making. Operations Research 49 (3): 444–454.
Lahdelma, R., J. Hokkanen, and P. Salminen. 1998. SMAA-stochastic multiobjective acceptability analysis. European Journal of Operational Research 106 (1): 137–143.
Low, J.M., S.W. Lam, and L.C. Tang. 2009. Assessment of hub status among Asian ports from a network perspective. Transportation Research Part A: Policy and Practice 43 (6): 593–606.
McCalla, R., B. Slack, and C. Comtois. 2005. The Caribbean basin: Adjusting to global trends in containerization. Maritime Policy & Management 32 (3): 245–261.
Niérat, P., and D. Guerrero. 2019. UNCTAD maritime connectivity indicators: review, critique and proposal. UNCTAD Transport and Trade Facilitation Newsletter N°84—Fourth Quarter. Article No. 42.
Notteboom, T.E. 2006. The time factor in liner shipping services. Maritime Economics & Logistics 8 (1): 19–39.
Rodrigue, J.P. 1999. Globalization and the synchronization of transport terminals. Journal of transport geography 7 (4): 255–261.
Roy, B. 1996. Multicriteria methodology for decision aiding. Dordrecht: Kluwer Academic Publishers.
Saeed, N., K. Cullinane, and S. Sødal. 2020. Exploring the relationships between maritime connectivity, international trade and domestic production. Maritime Policy & Management 1–15.
Stopford, M. 2008. Maritime economics, 3rd ed., 2008. New York: Taylor & Francis.
Talley, W.K. 2013. Maritime transportation research: Topics and methodologies. Maritime Policy & Management 40 (7): 709–725.
Tang, L.C., J.M. Low, and S.W. Lam. 2011. Understanding port choice behavior—a network perspective. Networks and Spatial Economics 11 (1): 65–82.
UNCTAD. 2019. Liner shipping connectivity index, quarterly. https://unctadstat.unctad.org/wds/TableViewer/summary.aspx. Accessed December 2020.
UNCTADSTAT http://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=13321.
Wang, Y., and K. Cullinane. 2008. Measuring container port accessibility: An application of the Principal Eigenvector Method (PEM). Maritime Economics & Logistics 10 (1–2): 75–89.
Wilmsmeier, G., and J. Hoffmann. 2008. Liner shipping connectivity and port infrastructure as determinants of freight rates in the Caribbean. Maritime Economics & Logistics 10 (1–2): 130–151.
Wilmsmeier, G., J. Hoffmann, and R.J. Sanchez. 2006. The impact of port characteristics on international maritime transport costs. Research in Transportation Economics 16: 117–140.
Acknowledgements
V.K.M. thanks Dr. Jan Hoffmann, Chief of UNCTAD’s Trade Logistics Branch for providing the necessary data to carry out this research and for several fruitful discussions during the preparation of this article. The authors would like to thank the anonymous reviewers, Editor Dr. Hoffmann, and Editor in Chief Prof. Haralambides for their constructive comments that led to improvements in the quality and presentation of the paper. JRF acknowledges the support from the FCT grant SFRH/BSAB/139892/2018 under the POCH Program. VKM acknowledges the support from the Fond for Scientific Research Vlaanderen (FWO) projects G028015N and G090117N and the FNRS-FWO under Excellence of Science (EOS) project no. 30468160 “Structured low-rank matrix/tensor approximation: numerical optimization-based algorithms and applications”.
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Appendix: SMAA
Appendix: SMAA
Denote the set of alternatives by \(A = \left\{ {a_{1} , a_{2} , \ldots ,a_{m} } \right\}\) and the set of criteria by \(G = \left\{ {g_{1} , g_{2} , \ldots ,g_{n} } \right\}\). Then, after assigning nonnegative weights \(w_{i}\) to each criterion \(g_{i}\), with \(w_{1} + w_{2} + ... + w_{n} = 1\), the overall evaluation for alternative \(a_{k}\) is set as follows:
There may be imprecisions with respect to the weights assigned to the criteria and the resulting evaluations. To address these issues, SMAA considers two probability distributions \(f_{W} \left( w \right)\) and \(f_{X} \left( \xi \right)\) on the spaces \(W\) and \(X,\) respectively. These spaces are defined as follows:
\(X\) is the evaluation space, i.e., the space of the values that a criterion \(g_{i} \in G\) can take.
Using SMAA, we first introduce a ranking function relative to alternative \(a_{k}\):
where \(\rho \left( {{\text{false}}} \right) = 0\) and \(\rho \left( {{\text{true}}} \right) = 1.\) Then, for every alternative \(a_{h} ,\) for every evaluation of the alternatives \(\xi \in X\) and for every rank \(r = 1,2,...,l,\) the SMAA computes the set of weights of criteria for which alternative \(a_{k}\) achieves rank \(r\):
The SMAA is used for the computation of the following indices:
Rank acceptability index: This is defined as the measure of the set of weight vectors and evaluations on the underlined criteria for which alternative \(a_{k}\) achieves rank \(r\):
where \(b_{k}^{r}\) is the probability of \(a_{k}\) getting rank r in the preference ranking.
The central weight vector: This is the barycenter of the set of weight vectors for which alternative \(a_{k}\) has the best rank and it exhibits the preferences of the average individual weight vector, giving to \(a_{k}\) the best rank. Mathematically, it can be represented as follows:
The confidence factor: It represents the frequency with which an alternative is the most preferred outcome using its central weight vector. It is formulated as follows:
Pairwise winning index: Another important index in SMAA is the pairwise winning index, defined as the frequency that alternative \(a_{h}\) is preferred over alternative \(a_{k}\) in the space of possible weight vectors and possible evaluations on a single criterion:
Next, we recall the multidimensional generalization of the Gini index, defined using the rank acceptability index \(b_{k}^{r}\). For this purpose, we first consider the upward cumulative rank acceptability index of position \(l,\) where \(l = 1, 2, \ldots , n - 1,\) for every \(r = 1, 2, \ldots , n - 1\), as the probability that alternative \(a_{k }\) has rank \(l\) or better, that is:
We can compute the Gini index of the upward cumulative rank acceptability index of position \(l,\) as follows:
which, in view of the fact that \(\sum\nolimits_{h = 1}^{n} {b_{r}^{h} = 1}\) for \(r = 1, 2, \ldots , n\), and, in turn, \(\sum\nolimits_{h = 1}^{n} {b_{ \ge l}^{h} } = l,\) gives:
\(G^{ \ge l}\) is the concentration of probability to obtain a rank position \(l\) or better among the considered alternatives. Analogously, we can define the index \(G^{ \le l}\) that measure the concentration of probability to obtain a rank position \(l\) or worse, where \(l = 2, 3, \ldots , n,\) as:
define the downward cumulative rank acceptability index of position \(l\) for alternative \(a_{k}\). The above concepts are found in Lahdelma et al. (1998), Lahdelma and Salminen (2001), and Greco et al. (2018).
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Mishra, V.K., Dutta, B., Goh, M. et al. A robust ranking of maritime connectivity: revisiting UNCTAD’s liner shipping connectivity index (LSCI). Marit Econ Logist 23, 424–443 (2021). https://doi.org/10.1057/s41278-021-00185-8
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DOI: https://doi.org/10.1057/s41278-021-00185-8