Original article | Open Access
Acta Natura et Scientia 2024, Vol. 5(2) 136-149
Determining Growth Drivers in Container Shipping: A Causality Analysis Between Container Throughput and Liner Shipping Connectivity
pp. 136 - 149 | DOI: https://doi.org/10.61326/actanatsci.v5i2.287
Publish Date: November 23, 2024 | Single/Total View: 6/18 | Single/Total Download: 8/22
Abstract
Container transportation, facilitated by the development of standardized containers, has revolutionized global trade by increasing efficiency, reducing costs, and enhancing the competitive power of countries. The Liner Shipping Connectivity Index (LSCI) plays a crucial role in measuring the supply side of container transportation, influencing strategic decisions regarding infrastructure investments and policy development to boost global trade integration. Our study aimed to determine whether container throughput drives LSCI or vice versa, using panel data analysis to inform strategic decisions in maritime trade, investment priorities, and policy development. We conduct our analysis using a unique data set covers the years between 2008 and 2021 and consists of 85 countries and 1190 observations. The results obtained revealed that there is a two-way interaction between Container Throughput and LSCI variables, the effects of the variables are positive and reflected after 1 period, and the impact of changes in LSCI on Container Throughput is higher than the opposite situation. This shows that there is a positive feedback loop between the variables and that improvement in any one of them returns as improvement to itself after a certain period.
Keywords: Container shipping, Feedback loop, Panel causality
APA 7th edition
Durmaz, A., & Acik, A. (2024). Determining Growth Drivers in Container Shipping: A Causality Analysis Between Container Throughput and Liner Shipping Connectivity. Acta Natura et Scientia, 5(2), 136-149. https://doi.org/10.61326/actanatsci.v5i2.287
Harvard
Durmaz, A. and Acik, A. (2024). Determining Growth Drivers in Container Shipping: A Causality Analysis Between Container Throughput and Liner Shipping Connectivity. Acta Natura et Scientia, 5(2), pp. 136-149.
Chicago 16th edition
Durmaz, Alaattin and Abdullah Acik (2024). "Determining Growth Drivers in Container Shipping: A Causality Analysis Between Container Throughput and Liner Shipping Connectivity". Acta Natura et Scientia 5 (2):136-149. https://doi.org/10.61326/actanatsci.v5i2.287
- Açık, A. (2021). Does maritime transport network converge? Evidence from EU countries. Acta Natura et Scientia, 2(2), 86-93. https://doi.org/10.29329/actanatsci.2021.350.01 [Google Scholar] [Crossref]
- Açık, A., & Atacan, C. (2023). Analyzing the convergence of transport network connectivity: Case for Türkiye and its neighbors. Oasis, 39, 189-212. https://doi.org/10.18601/16577558.n39.11 [Google Scholar] [Crossref]
- Akpa, E. O. (2022). Global trade: Testing persistence in global shipping based on the liner shipping connectivity index (LSCI). South-Eastern Europe Journal of Economics, 20(2), 127-139. [Google Scholar]
- Atacan, C., Kayıran, B., & Açık, A. (2022). Impact of liner shipping connectivity on container traffic in Turkish ports. Transactions on Maritime Science, 11(2), 1-17. https://doi.org/10.7225/toms.v11.n02.001 [Google Scholar] [Crossref]
- Ayesu, E. K., Sakyi, D., & Darku, A. B. (2023). Seaport efficiency, port throughput, and economic growth in Africa. Maritime Economics & Logistics, 25(3), 479-498. https://doi.org/10.1057/s41278-022-00252-8 [Google Scholar] [Crossref]
- Bai, J., & Kao, C. (2006). On the estimation and inference of a panel cointegration model with cross-sectional dependence. Contributions to Economic Analysis, 274, 3-30. https://doi.org/10.1016/S0573-8555(06)74001-9 [Google Scholar] [Crossref]
- Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170, 164-177. https://doi.org/10.1016/j.jeconom.2012.04.004 [Google Scholar] [Crossref]
- Breusch, T., & Pagan, A. (1980). The Lagrange multiplier test and its application to model specification in econometrics. Review of Economic Studies, 47, 239-253. https://doi.org/10.2307/2297111 [Google Scholar] [Crossref]
- Canbay, Ş. (2024). The analysis of relationships between global shipping networks and foreign trade volumes in developing countries. Case Studies on Transport Policy, 17, 101242. https://doi.org/10.1016/j.cstp.2024.101242 [Google Scholar] [Crossref]
- Chen, Y.-C., & Hasan, M. K. (2023). Impacts of liner shipping connectivity and global competitiveness on logistics performance: The mediating role of the quality of port and infrastructure. Transport, 164(84142), 1-19. https://doi.org/10.1080/00273171.2018.1479629 [Google Scholar] [Crossref]
- Das, P. (2019). Econometrics in theory and practice: Analysis of cross section, time series and panel data with Stata 15.1. Springer. [Google Scholar]
- Del Rosal, I. (2023). Trade effects of liner shipping across world regions. Maritime Business Review, 9(1), 2-16. https://doi.org/10.1108/MABR-06-2023-0040 [Google Scholar] [Crossref]
- Del Rosal, I., & Moura, T. G. Z. (2022). The effect of shipping connectivity on seaborne containerised export flows. Transport Policy, 118, 143-151. https://doi.org/10.1016/j.tranpol.2022.01.020 [Google Scholar] [Crossref]
- Fugazza, M., & Hoffmann, J. (2017). Liner shipping connectivity as determinant of trade. Journal of Shipping and Trade, 2(1), 1-18. https://doi.org/10.1186/s41072-017-0001-5 [Google Scholar] [Crossref]
- Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74. https://doi.org/10.1016/S0304-4076(03)00092-7 [Google Scholar] [Crossref]
- Jouili, T. A. (2019). Determinants of liner shipping connectivity. International Journal of Advanced and Applied Sciences, 6(11), 5-10. https://doi.org/10.21833/ijaas.2019.11.002 [Google Scholar] [Crossref]
- Juodis, A., Karavias, Y., & Sarafidis, V. (2021). A homogeneous approach to testing for Granger non-causality in heterogeneous panels. Empirical Economics, 60, 93-112. https://doi.org/10.1007/s00181-020-01970-9 [Google Scholar] [Crossref]
- Knox, P., Agnew, J., & McCarthy, L. (2014). The geography of the world economy. Routledge. [Google Scholar]
- Miller, R., Gostomski, E., & Nowosielski, T. (2023). Containerization in maritime transport: contemporary trends and challenges. CRC Press. [Google Scholar]
- Mishra, V. K., Dutta, B., Goh, M., Figueira, J. R., & Greco, S. (2021). A robust ranking of maritime connectivity: Revisiting UNCTAD’s liner shipping connectivity index (LSCI). Maritime Economics & Logistics, 23(3), 424-443. https://doi.org/10.1057/s41278-021-00185-8 [Google Scholar] [Crossref]
- Myrdal, G. (1957). Economic theory and underdeveloped regions. University Paperbacks, Methuen. [Google Scholar]
- Nadarajan, D., Ahmed, S. A. M., & Noor, N. F. M. (2023). Seaport network efficiency measurement using triangular and trapezoidal fuzzy data envelopment analyses with liner shipping connectivity index output. Mathematics, 11(6), 1454. https://doi.org/10.3390/math11061454 [Google Scholar] [Crossref]
- Nazlioglu, S., Lebe, F., & Kayhan, S. (2011). Nuclear energy consumption and economic growth in OECD countries: Cross-sectionally dependent heterogeneous panel causality analysis. Energy Policy, 39(10), 6615-6621. https://doi.org/10.1016/j.enpol.2011.08.007 [Google Scholar] [Crossref]
- Notteboom, T., Pallis, A., & Rodrigue, J. P. (2022). Port economics, management and policy. Routledge. [Google Scholar]
- Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics No. 0435. University of Cambridge, Faculty of Economics. [Google Scholar]
- Reza, M., Suthiwartnarueput, K., & Pornchaiwiseskul, P. (2015). Liner shipping connectivity and international trade in maritime Southeast Asian countries. Journal of International Logistics and Trade, 13(3), 43-74. https://doi.org/10.24006/jilt.2015.13.3.43 [Google Scholar] [Crossref]
- Şeker, A. (2020). The impacts of liner shipping connectivity and economic growth on international trade: Case of European countries and Turkey. In G. Ç. Ceyhun (Ed.), Handbook of research on the applications of international transportation and logistics for world trade (pp. 139-150). IGI Global. https://doi.org/10.4018/978-1-7998-1397-2.ch008 [Google Scholar] [Crossref]
- Smith, L. V., Leybourne, S., Kim, T. H., & Newbold, P. (2004). More powerful panel data unit root tests with an application to mean reversion in real exchange rates. Journal of Applied Econometrics, 19(2), 147-170. https://doi.org/10.1002/jae.723 [Google Scholar] [Crossref]
- Söderbom, M., Markus, F. T. W., Quinn, S., & Zeitlin, A. (2015). Empirical development economics. Routledge. [Google Scholar]
- Statista (2024). Capacity of container ships in seaborne trade from 1980 to 2023. Retrieved on October 11, 2024, from https://www.statista.com/statistics/267603/capacity-of-container-ships-in-the-global-seaborne-trade/ [Google Scholar]
- Taşova, U. (2023). The dictionary of maritime. Entropol. [Google Scholar]
- UNCTAD. (2024). Container port throughput, annual. Retrieved on August 10, 2024, from https://unctadstat.unctad.org/datacentre/dataviewer/US.ContPortThroughput [Google Scholar]
- World Bank. (2024a). Container port traffic (TEU: 20-foot equivalent units). Retrieved on August 10, 2024, from https://data.worldbank.org/indicator/IS.SHP.GOOD.TU [Google Scholar]
- World Bank. (2024b). Liner shipping connectivity index (maximum value in 2004 = 100). Retrieved on August 10, 2024, from https://data.worldbank.org/indicator/IS.SHP.GCNW.XQ [Google Scholar]
- Xiao, J., Karavias, Y., Juodis, A., Sarafidis, V., & Ditzen, J. (2023). Improved tests for Granger noncausality in panel data. The Stata Journal, 23(1), 230-242. https://doi.org/10.1177/1536867X231162034 [Google Scholar] [Crossref]
