Abstract

The inability of carriers to forecast “demand for containerships” led them to order larger ships. Maritime economists were also unable to forecast it. The new-buildings cut cost per TEU, but “estimated economies of scale” are exhausted with ships beyond 21,000 TEUs, higher than the present. As average cost-AC was not at minimum, carriers did not produce at minimum efficient scale (MES). As larger ships are more competitive, smaller ships led to laid-up, and eventually scrapped. This strategy, however, did not bring the desirable balance between demand and supply. Due to falling demand, following the meltdown at the end of 2008, carriers priced their services at marginal cost-MC, and thus they accumulated losses. As a result, carriers resorted to frequent GRIs (freight rate increases). Supply exceeded demand and average distances fell after 2008. Containership market will remain depressed if economies of scale lead carriers to shipyards. Scrapping—the last hope—removed only 1/7 of the oversupply. Revenue, operating profits, and net profits, due to increased financial expenses, were lower than in the past. Aggressive ship-building programs could not be carried-out, because the depression meant that there are available only limited funds. The estimated funds required for new buildings were as high as $4 billion per carrier. So, the sector is in a vicious circle. The only helpful sign was the reduction in fuel prices after 2011 from $800/ton to $278 (2015). We also showed that ports and canals, through their traditional charging policy on size, penalized containerships for their efficiency—if volume discounts are not provided. Port dues and container handling and canal dues account for as much as 40% of the annualized containership cost. Finally, to study the relationship between concentration (market share) and revenue, operating profit and net profit, we ran three regressions; but only one gave a high correlation coefficient (0.97). This suggests that the containership market is purely competitive. We also showed that the Herfindahl index was 683 units (i.e., <1000) and Lerner’s index was 0.55—both indicating oligopolistic trends. Our model shows that containership market is either oligopolistic or purely competitive. This finding shows the double face of containership markets, which so much confused maritime economists.

Highlights

  • This paper combined theory with empirical evidence from the containership market-CM

  • Ducruet et al (2010) argued that port and maritime studies on containerization showed a global traffic concentration8, where a liner services network aims at a best trade-off between cutting operational costs and reducing calls; this led to an even higher concentration9

  • The primary problem of the CM is the imbalance between supply and demand since the end of 2008

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Summary

Introduction

This paper combined theory with empirical evidence from the containership market-CM. After end- 2008, conditions in CM (freight markets) changed from prosperity to depression. CM attracted the consistent research attention of maritime economists They reached no consensus over its market structure. Ships in building in 2017 were of a size over 21,000 TEUs. The meltdown at the end of 2008 caused container freight rates to decline in a steady way, and to reach record low levels. The meltdown at the end of 2008 caused container freight rates to decline in a steady way, and to reach record low levels This was due to a combination of a weakening demand and the entry of ever larger container vessels, even after the end of 2008. One negative development occurred because SS produced longer transit times (inferior service quality of CM)

Aim of the Paper
Structure of Paper
Literature Review
Data Used and Methodology Adopted
An Overall Picture of the CM
Scrapping
Lay-Up and SS
Value of Containerized Trade
Volume of Containerized Trade
Balance of Supply and Demand
Apparent Paradox
Costs Due to Size
Is CM Contestable?
Can “Concentration” Help Containership Companies’ Profits?
The Herfindahl Index51
Lerner’s Index
10. Managing the Costs of Operation
Findings
12. Conclusions

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