Determinants of Cargo Handling Performance in Türkiye Ports: Time Series Analysis of Foreign Trade, Price Dynamics, and Global Uncertainties

Abstract

This study aims to investigate the interactions between the cargo handling performance of Turkish ports and macroeconomic as well as global risk variables through multivariate time series analyses. The objective is to reveal the structural dynamics of port operations, long-term cointegration relationships, and the system’s responses to external shocks. Using Principal Component Analysis, the variables identified in the study were reduced to four structural factors: foreign trade and logistics capacity, pricing structures, global economic uncertainties, and operational factors related to transit transportation. The findings indicate the existence of at least three cointegration relationships within the system. Accordingly, a Vector Error Correction Model was constructed to analyze how deviations from the equilibrium are corrected in the short run. Furthermore, Impulse Response Functions were employed to evaluate the temporal effects of external shocks on the identified components. The results demonstrate that cargo handling performance in Turkish ports is shaped predominantly by factors representing foreign trade volume, logistics capacity, and structures sensitive to international price movements. These factors exhibit strong and rapid automatic adjustment mechanisms in response to short-term shocks. It was also observed that the factor representing global economic uncertainty tends to increase port activity during periods of heightened uncertainty, with this effect extending into the long term. Overall, the study presents an analytically grounded framework that structurally models the responses of port cargo handling performance to external shocks, offering a scientific basis for the formulation of port policies, infrastructure investments, and logistics strategies.

Keywords

• Cargo Handling Performance
• Port Efficiency
• Global Uncertainty
• The Time Series Analysis

Türkiye Limanlarında Yük Elleçleme Performansının Belirleyicileri: Dış Ticaret, Fiyat Dinamikleri ve Küresel Belirsizliklerin Zaman Serisi Analizi

Abstract

Bu çalışma, Türkiye’deki limanların yük elleçleme performansları ile makroekonomik ve küresel risk değişkenleri arasındaki etkileşimleri çok değişkenli zaman serisi analizleri yoluyla inceleyerek, limancılık faaliyetlerinin yapısal dinamiklerini, uzun dönemli eşbütünleşme ilişkilerini ve dışsal şoklara karşı verilen tepkileri ortaya koymayı amaçlamaktadır. Çalışma kapsamında belirlenen değişkenler, Temel Bileşenler Analizi yardımıyla dış ticaret ve lojistik kapasite, fiyat düzeylerine ilişkin yapılar, küresel ekonomik belirsizlikler ve transit taşımacılığa ilişkin operasyonel unsurlar olarak dört yapısal faktöre indirgenmiştir. Elde edilen bulgular, modelde en az dört eşbütünleşme ilişkisinin varlığına işaret etmektedir. Bu doğrultuda kurulan Vektör Hata Düzeltme Modeli (VHDM) aracılığıyla, sistemdeki denge sapmalarının kısa vadede nasıl düzeltildiği analiz edilmiş; ayrıca Etki-Tepki Fonksiyonları (ETF) ile dışsal şokların zaman içinde bileşenler üzerindeki etkileri değerlendirilmiştir. Sonuçlar, Türkiye limanlarındaki yük elleçleme performansının, dış ticaret hacmi ve lojistik kapasiteyi temsil eden faktörler ile uluslararası fiyat hareketlerine duyarlı yapıların etkisi altında şekillendiğini göstermektedir. Bu faktörlerin, kısa dönemli şoklara karşı güçlü ve hızlı tepki veren otomatik düzeltme mekanizmalarına sahip olduğu tespit edilmiştir. Küresel ekonomik belirsizlikleri temsil eden faktörün ise, belirsizlik artış dönemlerinde liman faaliyetlerinde artış eğilimi yarattığı ve bu etkinin uzun döneme yayıldığı görülmüştür. Çalışma, liman yük elleçleme performansının dışsal şoklara karşı verdiği tepkilerin yapısal olarak modellendiği analitik bir çerçeve sunarak, liman politikaları, altyapı yatırımları ve lojistik stratejilerin şekillendirilmesi açısından bilimsel bir zemin sağlamaktadır.

Keywords

• Yük Elleçleme Performansı
• Liman Verimliliği
• Küresel Belirsizlik
• Zaman Serisi Analizi

References

1. Abdi. H., & Williams. L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4). 433-459.
2. Açıkgöz, T., & Sezgin Alp, Ö. (2023). The impact of oil prices on the transportation industry stock returns: The case of the Turkish equity market. Ekonomi Politika ve Finans Araştırmaları Dergisi, 8(3).
3. Aggarwal, R., Akhigbe, A., & Mohanty, S. K. (2012). Oil price shocks and transportation firm asset prices. Energy Economics, 34(5), 1370-1379.
4. Ateş, A. (2014). Türkiye’de liman özelleştirmeleri İskenderun liman örneği. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(25).
5. Baker, S. R., Bloom, N., & Davis, S. J. (2025). Türkiye ekonomik politika belirsizlik endeksi. Economic Policy Uncertainty. https://policyuncertainty.com/turkiye_index.html (Erişim Tarihi: 08.04.2025)
6. Caris, A., Macharis, C., & Janssens, G. K. (2011). Network analysis of container barge transport in the port of Antwerp by means of simulation. Journal of Transport Geography, 19(1), 125-133.
7. Chen, J., Zhao, R., Xiong, W., Wan, Z., Xu, L., & Zhang, W. (2023). Influencing factors of crude oil maritime shipping freight fluctuations: a case of Suezmax tankers in Europe–Africa routes. Maritime Business Review, 8(1), 48-64.
8. Chi, J. (2016). Exchange rate and transport cost sensitivities of bilateral freight flows between the US and China. Transportation Research Part A: Policy and Practice, 89, 1-13.

9. Dieaconescu, R. I., Belu, M. G., & Gheorghe, M. (2022). Impact of oil price evolution on logistics industry. The Romanian Economic Journal, (84).
10. Dickey. D. A., & Fuller. W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a). 427-431.
11. Doğru, E. (2024). Belirsizliklerin finansal piyasalara simetrik ve asimetrik etkisi: BIST ulaştırma endeksi üzerine bir araştırma. Journal of Transportation and Logistics, 9(1), 97-111.
12. Engle. R. F., & Granger. C. W. (1987). Co-integration and error correction: representation. estimation. and testing. Econometrica: journal of the Econometric Society. 251-276.
13. Feng, M., Mangan, J., & Lalwani, C. (2012). Comparing port performance: Western European versus eastern Asian ports. International Journal of Physical Distribution & Logistics Management, 42(5), 490-512.
14. Haezendonck, E., & Moeremans, B. (2020). Measuring value tonnes based on direct value added: a new weighted analysis for the port of Antwerp. Maritime Economics & Logistics, 22, 661-673.
15. Hamilton. J. D. (2020). Time series analysis. Princeton university press.
16. Hofmann, E., Solakivi, T., Töyli, J., & Zinn, M. (2018). Oil price shocks and the financial performance patterns of logistics service providers. Energy Economics, 72, 290-306.
17. International Monetary Fund (IMF). (2025). IMF Data - World Economic Outlook Databases. https://data.imf.org/?sk=471dddf8-d8a7-499a-81ba-5b332c01f8b9 (Erişim Tarihi: 08.04.2025)
18. Intihar, M., Kramberger, T., & Dragan, D. (2015). The relationship between the economic indicators and the accuracy of container throughput forecasting. IAME 2015 Conference Kuala Lumpur, Malaysia.
19. İpekçi, E. (2025). Düzenli hat bağlantı endeksi kullanılarak liman etkinliğinin VZA ile incelenmesi. Mersin Üniversitesi Denizcilik Ve Lojistik Araştırmaları Dergisi, 7(1), 65-77.
20. Johansen. S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3). 231-254.
21. Johansen. S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: journal of the Econometric Society, 1551-1580.
22. Jolliffe. I. T. (2002). Principal component analysis for special types of data. Springer New York.
23. Kishore, L., Pai, Y. P., Ghosh, B. K., & Pakkan, S. (2024). Maritime shipping ports performance: a systematic literature review. Discover Sustainability, 5(1), 108.
24. Konings, R. (2007). Opportunities to improve container barge handling in the port of Rotterdam from a transport network perspective. Journal of Transport Geography, 15(6), 443-454.
25. Lütkepohl. H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.
26. Nwaogbe, O. R., Pius, A., Abduljelil, A., & Alharahsheh, H. H. (2019). An empirical study of Nigerian seaports operational performance. Transport & Logistics: The International Journal, 20(48), 73-87.
27. Pham, H. T., & Nguyen, L. H. (2022). Empirical performance measurement of cargo handling equipment in Vietnam container terminals. Logistics, 6(3), 44.
28. Porzio, F. (2014). Improving handling operations at port of Antwerp. Development of a new stacking method and application at a deep sea container terminal.
29. Ohakwe, C. R., & Wu, J. (2025). The impact of macroeconomic indicators on logistics performance: A comparative analysis using simulated scenarios. Sustainable Futures, 9, 100567.
30. Okere, C. C. (2022). Cargo handling equipment and ports performance in Nigeria. Journal of Procurement & Supply Chain, 6(2), 40-51.
31. Pérez, I., González, M. M., & Trujillo, L. (2020). Do specialisation and port size affect port efficiency? Evidence from cargo handling service in Spanish ports. Transportation Research Part A: Policy and Practice, 138, 234-249.
32. Rencher. A. C., & Christensen. W. F. (2002). Méthods of multivariate analysis. John Wiley & Sons. Inc. Publication. 727. 2218-0230.
33. Riaz, A., Hongbing, O., Hashmi, S. H., & Khan, M. A. (2018). The impact of economic policy uncertainty on US transportation sector stock returns. International Journal of Academic Research in Accounting, Finance & Management Sciences, 8(4), 163-170.
34. Said. S. E., & Dickey. D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika. 71(3).
35. Sims. C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
36. Sun, J., & Yu, S. (2019). Research on relationship between port logistics and economic growth based on VAR: A case of Shanghai. American Journal of Industrial and Business Management, 9(07), 1557.
37. Ticaret Bakanlığı. (2024). Dış Ticaret Lojistiği. https://ticaret.gov.tr/data/5b87bf9113b8761160fa1258/D%C4%B1%C5%9F%20Ticaret%20Lojisti%C4%9Fi%202024.pdf. (Erişim Tarihi: 09.04.2025)
38. Trading Economics. (2025). Containerized Freight Index. https://tradingeconomics.com/commodity/containerized-freight-index (Erişim Tarihi: 08.04.2025)
39. TÜİK. (2025). Dış Ticaret İstatistikleri Aralık 2024. https://data.tuik.gov.tr/Bulten/Index?p=Foreign-Trade-Statistics-December-2024-53538&dil (Erişim Tarihi: 08.04.2025)
40. TÜRKLİM. (2025). Türkiye Limancılık Sektörü 2024 Raporu. https://www.turklim.org/pdf/Turklim-2024-Sekt%C3%B6r-Raporu-4-Haziran.pdf (Erişim Tarihi: 08.04.2025)
41. Ulaştırma Bakanlığı. (2025). Yük İstatistikleri - 2024. https://denizcilikistatistikleri.uab.gov.tr/yuk-istatistikleri-2024 (Erişim Tarihi: 09.04.2025)
42. UNCTAD. (2025). Review of Maritime Transport. https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime- transport#:~:text=Around%2080%25%20of%20the%20volume, Emerging%20trends%20affecting%20maritime%20transport (Erişim Tarihi: 12.04.2025)
43. van der Horst, M., Kort, M., Kuipers, B., & Geerlings, H. (2019). Coordination problems in container barging in the port of Rotterdam: An institutional analysis. Transportation Planning and Technology, 42(2), 187-199.
44. World Bank. (2024). The container port performance index 2023: A comparable assessment of performance based on vessel time in port. Washington, DC: World Bank Group. https://openknowledge.worldbank.org/handle/10986/41067