Journals / Journal of Transportation and Logistics / 2026 / Volume: 11 Issue: 1

The Relationship Between Shipping Demand and Macroeconomic Determinants in Türkiye: An ARDL Approach

Pages
—
Publication Date
2026-05-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study provides empirical evidence related to maritime policy development through macroeconomic shifts to enhance the understanding of how fluctuations in the macroeconomy affect.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to identify the short- and long-run macroeconomic determinants of shipping demand in Türkiye and examine whether structural breaks alter these relationships.
📊
Key Findings & Data:Monthly data spanning January 2012 to June 2025 are analyzed using an autoregressive distributed lag model and an error correction model.
💡
Conclusion & Impact:This study provides empirical evidence related to maritime policy development through macroeconomic shifts to enhance the understanding of how fluctuations in the macroeconomy affect.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Shipping demand · maritime resilience · economic policy uncertainty

Abstract

This study aims to identify the short- and long-run macroeconomic determinants of shipping demand in Türkiye and examine whether structural breaks alter these relationships. Monthly data spanning January 2012 to June 2025 are analyzed using an autoregressive distributed lag model and an error correction model. In addition, Augmented Dickey-Fuller, Zivot-Andrews, and Bai-Perron tests were incorporated to identify any structural breaks and assess stationarity. Based on the analysis of empirical results, the Industrial Production Index was determined to be the most significant long-run variable in predicting long-term shipping demand. Conversely, there is a significant negative impact of crude oil prices on shipping activity. The Baltic Dry Index and Global Economic Policy Uncertainty were both determined to be insignificant in predicting long-term shipping demand. However, they each had an immediate negative impact on shipping demand in the short run, exhibiting lagged response effects. The model showed that stable long-run equilibrium exists, which indicates that the model accommodates relatively rapid adjustments. There was evidence of a regime shift occurring in March 2019, which indicates a structural break in the market. This study provides empirical evidence related to maritime policy development through macroeconomic shifts to enhance the understanding of how fluctuations in the macroeconomy affect.

🔖

Cite This Article

Genç, U., Toygar, A., Fışkın, C. S., Tehci, A. (2026). The Relationship Between Shipping Demand and Macroeconomic Determinants in Türkiye: An ARDL Approach. Journal of Transportation and Logistics, Volume: 11 Issue: 1. https://doi.org/10.26650/JTL.2026.1862286

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (56)

  1. Akpinar, H., & Özer-Çaylan, D. (2023). Organizational resilience in maritime business: a systematic literature review. Management Research Review, 46(2), 245–267. https://doi.org/10.1108/MRR-12-2021-0866 google scholar
  2. Alizadeh, A. H., & Talley, W. K. (2011). Microeconomic determinants of dry bulk shipping freight rates and contract times. Transportation, 38, 561–579. https://doi.org/10.1007/s11116-010-9308-7 google scholar
  3. Anh, T. P., Tran, H. T., & Hung, N. M. (2019). The impact of exchange rates on Vietnam-China bilateral trade. JATI-Journal of Southeast Asian Studies, 24(1), 70–96. https://doi.org/10.22452/jati.vol24no1.4 google scholar
  4. Aslan, C., & Acikgoz, S. (2023a). Are the global economic policy uncertainties blocking the export flows of emerging markets? A heterogeneous panel SVAR analysis. Economic Journal of Emerging Markets, 15(1), 87–100. https://doi.org/10.20885/ejem.vol15.iss1.art7 google scholar
  5. Aslan, Ç., & Acikgoz, S. (2023b). Analysis of global economic policy uncertainty and export flows for emerging markets with panel VAR modeling. International Journal of Emerging Markets, 18(9), 2790–2815. https://doi.org/10.1108/IJOEM-12-2020-1570google scholar
  6. Aytekin, A., Korucuk, S., & Görçün, Ö. F. (2024). Determining the factors affecting transportation demand management and selecting the best strategy: A case study. Transport Policy, 146, 150–166. https://doi.org/10.1016/j.tranpol.2023.11.003 google scholar
  7. Bai, X., & Lam, J. S. L. (2021). Freight rate co-movement and risk spillovers in the product tanker shipping market: a copula analysis. Transportation Research Part E: Logistics and Transportation Review, 149, 102315. https://doi.org/10.1016/j.tre.2021.102315 google scholar
  8. Baker, S. R., Bloom, N., & Davis, S. J. (2024). Economic Policy Uncertainty Index. https://policyuncertainty.com/ google scholar
  9. Chang, C. W., Hsueh, M. H., Wang, C. N., & Huang, C. C. (2023). Exploring the factors influencing the impact of the COVID-19 pandemic on global shipping: A case study of the baltic dry index. Sustainability, 15(14), 11367. https://doi.org/10.3390/su151411367 google scholar
  10. Coto-Millán, P., Baños-Pino, J., & Castro, J. V. (2005). Determinants of the demand for maritime imports and exports. Transportation Research Part E: Logistics and Transportation Review, 41(4), 357–372. https://doi.org/10.1016/j.tre.2004.05.002 google scholar
  11. Daly, H., Abdulrahman, B. M. A., Ahmed, S. A. K., Abdallah, A. E. Y., Elkarim, S. H. E. H., Sahal, M. S. G., & Elshaabany, M. M. (2024). The dynamic relationships between oil products consumption and economic growth in Saudi Arabia: Using ARDL cointegration and Toda-Yamamoto Granger causality analysis. Energy Strategy Reviews, 54, 101470. https://doi.org/10.1016/j.esr.2024.101470 google scholar
  12. Dhanak, M., Parr, S., Kaisar, E. I., Goulianou, P., Russell, H., & Kristiansson, F. (2021). Resilience assessment tool for port planning. Environment and Planning B: Urban Analytics and City Science, 48(5), 1126–1143. https://doi.org/10.1177/2399808321997824 google scholar
  13. Drobetz, W., Gavriilidis, K., Krokida, S. I., & Tsouknidis, D. (2021). The effects of geopolitical risk and economic policy uncertainty on dry bulk shipping freight rates. Applied Economics, 53(19), 2218–2229. https://doi.org/10.1080/00036846.2020.1857329 google scholar
  14. Drobetz, W., Richter, T., & Wambach, M. (2012). Dynamics of time-varying volatility in the dry bulk and tanker freight markets. Applied Financial Economics, 22(16), 1367–1384. https://doi.org/10.1080/09603107.2012.657349 google scholar
  15. Gu, B., & Liu, J. (2022). Determinants of dry bulk shipping freight rates: Considering Chinese manufacturing industry and economic policy uncertainty. Transport Policy, 129, 66–77. https://doi.org/10.1016/j.tranpol.2022.10.006 google scholar
  16. Gu, B., & Liu, J. (2025). A systematic review of resilience in the maritime transport. International Journal of Logistics Research and Applications, 28(3), 257–278. https://doi.org/10.1080/13675567.2023.2165051 google scholar
  17. Ibrahim, K. H., Handoyo, R. D., Kristianto, F. D., Kusumawardani, D., Ogawa, K., Zaidi, M. A. S., & Sarmidi, T. (2024). Exchange rate volatility and COVID-19 effects on Indonesia’s food products’ trade: Symmetric and asymmetric approach. Heliyon, 10(12), e32611. https://doi.org/10.1016/j.heliyon.2024. e32611google scholar
  18. Index Mundi. (2024). Commodity Prices. https://www.indexmundi.com/commodities/?commodity=crude-oil&months=240 google scholar
  19. Iqbal, J., Jabeen, S., Nosheen, M., & Wohar, M. (2024). The asymmetric effects of exchange rate volatility on Pakistan–Japan commodity trade: Evidence from non-linear ARDL approach. Asia-Pacific Financial Markets, 31(3), 657–732. https://doi.org/10.1007/s10690-023-09427-6 google scholar
  20. Jugović, A., Komadina, N., & Perić Hadžić, A. (2015). Factors influencing the formation of freight rates on maritime shipping markets. Pomorstvo, 29(1), 23–29.google scholar
  21. Kavussanos, M. G., & Alizadeh-M, A. H. (2001). Seasonality patterns in dry bulk shipping spot and time charter freight rates. Transportation Research Part E: Logistics and Transportation Review, 37(6), 443–467. https://doi.org/10.1016/S1366-5545(01)00004-7google scholar
  22. Kawasaki, T., Matsuda, T., Lau, Y. Y., & Fu, X. (2022). The durability of economic indicators in container shipping demand: a case study of East Asia–US container transport. Maritime Business Review, 7(4), 288–304. https://doi.org/10.1108/MABR-12-2020-0075google scholar
  23. Ke, L., Liu, Q., Ng, A. K., & Shi, W. (2024). Quantitative modelling of shipping freight rates: developments in the past 20 years. Maritime Policy & Management, 51(3), 442–460. https://doi.org/10.1080/03088839.2022.2138595 google scholar
  24. Khalid, W., Civcir, I., & Özdeşer, H. (2024). Asymmetric effects of exchange rate volatility on Turkish-German commodity trade. The Journal of International Trade & Economic Development, 1–44. https://doi.org/10.1080/09638199.2024.2356780 google scholar
  25. Khan, K., Su, C. W., Tao, R., & Umar, M. (2021). How do geopolitical risks affect oil prices and freight rates? Ocean & Coastal Management, 215, 105955. https://doi.org/10.1016/j.ocecoaman.2021.105955 google scholar
  26. Koray, M., Kaya, E., & Keskin, M. H. (2025). Determining logistical strategies to mitigate supply chain disruptions in maritime shipping for a resilient and sustainable global economy. Sustainability, 17(12), 5261. https://doi.org/10.3390/su17125261 google scholar
  27. Kripfganz, S., & Schneider, D. C. (2023). ardl: Estimating autoregressive distributed lag and equilibrium correction models. The Stata Journal, 23(4), 983– 1019. https://doi.org/10.1177/1536867X231212434 google scholar
  28. Lau, Y. Y., Chen, Q., Poo, M. C. P., Ng, A. K., & Ying, C. C. (2024). Maritime transport resilience: A systematic literature review on the current state of the art, research agenda and future research directions. Ocean & Coastal Management, 251, 107086. https://doi.org/10.1016/j.ocecoaman.2024.107086google scholar
  29. Li, W., Asadabadi, A., & Miller-Hooks, E. (2022). Enhancing resilience through port coalitions in maritime freight networks. Transportation Research Part A: Policy and Practice, 157, 1–23. https://doi.org/10.1016/j.tra.2022.01.015 google scholar
  30. Lun, Y. V., Lai, K. H., Wong, C. W., & Cheng, T. C. E. (2013). Demand chain management in the container shipping service industry. International Journal of Production Economics, 141(2), 485–492. https://doi.org/10.1016/j.ijpe.2011.12.009 google scholar
  31. McIntosh, R. D., & Becker, A. (2019). Expert evaluation of open-data indicators of seaport vulnerability to climate and extreme weather impacts for US North Atlantic ports. Ocean & Coastal Management, 180, 104911. https://doi.org/10.1016/j.ocecoaman.2019.104911google scholar
  32. Michail, N. A. (2020). World economic growth and seaborne trade volume: quantifying the relationship. Transportation Research Interdisciplinary Perspectives, 4, 100108. https://doi.org/10.1016/j.trip.2020.100108 google scholar
  33. Ministry of Trade of the Republic of Türkiye. (2024). Foreign Trade Logistics. https://ticaret.gov.tr/data/5b87bf9113b8761160fa1258/Dış Ticaret Lojistiği 2024.pdf google scholar
  34. Mou, N., Xie, Y., Yang, T., Zhang, H., & Kim, Y. R. (2019). The impact of slumping oil price on the situation of tanker shipping along the maritime silk road. Sustainability, 11(17), 4796. https://doi.org/10.3390/su11174796 google scholar
  35. MTI-GDMA. (2021a). The amount of cargo handled in our ports increased by 2.6% compared to last year. https://denizcilik.uab.gov.tr/uploads/pages/yayinlar/denizcilik-istatistikleri-bulteni-2020-02-02-2021-6114cee0349da.pdf google scholar
  36. MTI-GDMA. (2021b). Cargo Statistics, December 2020: The amount of cargo handled at our ports increased by 7.0%. https://denizcilik.uab.gov.tr/uploads/pages/aylik-yayinlar-2022/dgm-yuk-istatistikleri-haber-bulteni-aralik-2020.pdf google scholar
  37. MTI-GDMA. (2025). Cargo Statistics - 2024. Minister of Transport and Infrastructure of the Republic of Türkiye. https://denizcilikistatistikleri.uab.gov. tr/yuk-istatistikleri-2024 google scholar
  38. Murshed, M. (2021). LPG consumption and environmental Kuznets curve hypothesis in South Asia: a time-series ARDL analysis with multiple structural breaks. Environmental Science and Pollution Research, 28(7), 8337–8372. https://doi.org/10.1007/s11356-020-10701-7 google scholar
  39. Nomikos, N. K., & Tsouknidis, D. A. (2023). Disentangling demand and supply shocks in the shipping freight market: their impact on shipping investments. Maritime Policy & Management, 50(5), 563–581. https://doi.org/10.1080/03088839.2021.2017041google scholar
  40. Notteboom, T., Pallis, T., & Rodrigue, J. P. (2021). Disruptions and resilience in global container shipping and ports: the COVID-19 pandemic versus the 2008–2009 financial crisis. Maritime Economics & Logistics, 23(2), 179. https://doi.org/10.1057/s41278-020-00180-5 google scholar
  41. Öçal, B. (2022). Covid-19′un Türkiye’nin dış ticaret taşıma türlerine etkisinin incelenmesi. Journal of Interdisciplinary Entrepreneurship and Innovation Studies (JINENIS), 1(2), 65–77. https://doi.org/10.5281/zenodo.7487537 google scholar
  42. Özen, M., Fayyaz, M., & Yaman, H. T. (2020). Factors affecting the capacity utilization of road freight transport in Turkey. Teknik Dergi, 31(2), 9813–9832. https://doi.org/10.18400/tekderg.413807 google scholar
  43. Pesaran, M. H., & Shin, Y. (1995). An autoregressive distributed lag modelling approach to cointegration analysis (Vol. 9514, Issues 371–413). UK: Department of Applied Economics, University of Cambridge. google scholar
  44. Rožić, T., Naletina, D., & Zając, M. (2022). Volatile freight rates in maritime container industry in times of crises. Applied Sciences, 12(17), 8452. https:// doi.org/10.3390/app12178452 google scholar
  45. Suárez-de Vivero, J. L., & Mateos, J. C. R. (2017). Forecasting geopolitical risks: oceans as source of instability. Marine Policy, 75, 19–28. https://doi.org/10.1016/j.marpol.2016.10.009 google scholar
  46. Sugiharti, L., Esquivias, M. A., & Setyorani, B. (2020). The impact of exchange rate volatility on Indonesia’s top exports to the five main export markets. Heliyon, 6(1). https://doi.org/10.1016/j.heliyon.2019.e03141 google scholar
  47. Sun, L., Zhang, W., Hu, Z., & Ning, Z. (2024). Food security under global economic policy uncertainty: fresh insights from the ocean transportation of food. Maritime Economics & Logistics, 26, 414–435. https://doi.org/10.1057/s41278-023-00282-w google scholar
  48. Toygar, A., & Yıldırım, U. (2024). Strategic approaches to crisis management: Global challenges in the Turkish container shipping sector due to the COVID-19. Case Studies on Transport Policy, 18, 101283. https://doi.org/10.1016/j.cstp.2024.101283 google scholar
  49. Trading Economics. (2024). Baltic Exchange Dry Index. https://tradingeconomics.com/commodity/balticgoogle scholar
  50. Tsouknidis, D. A. (2016). Dynamic volatility spillovers across shipping freight markets. Transportation Research Part E: Logistics and Transportation Review, 91, 90–111. https://doi.org/10.1016/j.tre.2016.04.001google scholar
  51. Turkish Chamber of Shipping. (2024). Maritime Sector Report. Turkish Chamber of Shipping. https://www.denizticaretodasi.org.tr/media/SharedDocu ments/sektorraporu/2024/maritime sector report_2023_web2.pdf google scholar
  52. TURKSTAT. (2024). Industrial Production Index. https://data.tuik.gov.tr/Kategori/GetKategori?p=sanayi-114 google scholar
  53. Wang, Y., & Meng, Q. (2021). Optimizing freight rate of spot market containers with uncertainties in shipping demand and available ship capacity. Transportation Research Part B: Methodological, 146, 314–332. https://doi.org/10.1016/j.trb.2021.02.008 google scholar
  54. Wendler-Bosco, V., & Nicholson, C. (2020). Port disruption impact on the maritime supply chain: a literature review. Sustainable and Resilient Infrastructure, 5(6), 378–394. https://doi.org/10.1080/23789689.2019.1600961 google scholar
  55. Xu, L., Luo, Y., Chen, J., & Zhou, S. (2024). Capacity prioritization allocation and credit financing option in shipping freight forwarding market. Computers & Industrial Engineering, 189, 109987. https://doi.org/10.1016/j.cie.2024.109987 google scholar
  56. Yin, J., & Shi, J. (2018). Seasonality patterns in the container shipping freight rate market. Maritime Policy & Management, 45(2), 159–173. https://doi.org/10.1080/03088839.2017.1420260 google scholar