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Pre-announcements of price increase intentions in liner shipping spot markets

Author

Listed:
  • Chen, Gang
  • Rytter, Niels G.M.
  • Jiang, Liping
  • Nielsen, Peter
  • Jensen, Lars

Abstract

Carriers in liner shipping markets frequently make public announcements of general rate increase (GRI) intentions, based on which EU authorities have concerns as to whether this harms market competition. This paper aims to empirically investigate how well the GRI system works from an industrial competition perspective, which will indirectly indicate whether carriers are able to manipulate spot rates following GRI announcements. Taking the Far East–North Europe trade between 2009 and 2013 as an example, the paper first reveals the gradual increase of GRI frequency and size, which reflects carriers’ attempts to restore profitability against overcapacity. However, out of all the GRI events only 28.6% were observed to be successful. Since these GRI successes must be the results of either price collusion (if any) and/or normal rate change by carriers in response to fundamental market developments, the effective collusion, if it exists, is actually lower than 28.6%. Next, we identify eight factors influencing GRI successes. To further assess their impact, we applied an ordered logit regression analysis, which, based on four of the factors involved, yields good predictability for GRI success. The four factors, in sequence of explanation power, are the total capacity of GRI carriers, the idling fleet size, the spot rate level, and the average ship-loading factor. Clearly the latter three factors are market fundamentals, which are unlikely to be influenced by an individual carrier in the short term. In actual fact, the conclusion reached is that there is little evidence that carriers can manipulate and distort spot rates through GRIs.

Suggested Citation

  • Chen, Gang & Rytter, Niels G.M. & Jiang, Liping & Nielsen, Peter & Jensen, Lars, 2017. "Pre-announcements of price increase intentions in liner shipping spot markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 109-125.
    • Handle: RePEc:eee:transa:v:95:y:2017:i:c:p:109-125
    DOI: 10.1016/j.tra.2016.11.004
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    References listed on IDEAS

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    Cited by:

    1. Ziaul Haque Munim & Hans-Joachim Schramm, 0. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 0, pages 1-18.
    2. Wang, Kelly Yujie & Wen, Yuan & Yip, Tsz Leung & Fan, Zuojun, 2021. "Carrier-shipper risk management and coordination in the presence of spot freight market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    3. Ziaul Haque Munim & Hans-Joachim Schramm, 2021. "Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 310-327, June.
    4. Pierre Cariou & Patrice Guillotreau, 2022. "Capacity management by global shipping alliances: findings from a game experiment," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(1), pages 41-66, March.
    5. Choi, Tsan-Ming & Chung, Sai-Ho & Zhuo, Xiaopo, 2020. "Pricing with risk sensitive competing container shipping lines: Will risk seeking do more good than harm?," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 210-229.
    6. Svetlana Avdasheva & Svetlana Golovanova & Gyuzel Yusupova, 2019. "Advance freight rate announcements (GRI) in liner shipping: European and Russian regulatory settlements compared," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(2), pages 192-206, June.
    7. Zhang, X. & Chen, M.Y. & Wang, M.G. & Ge, Y.E. & Stanley, H.E., 2019. "A novel hybrid approach to Baltic Dry Index forecasting based on a combined dynamic fluctuation network and artificial intelligence method," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 499-516.

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