metadata: authors: 0: author: Arvis,Jean Francois
1: author: Rastogi,Cordula
2: author: Rodrigue,Jean Paul
3: author: Ulybina,Daria
count: World
docna: 0: docna: A Metric of Global Maritime Supply Chain
            Disruptions: The Global Supply Chain Stress Index (GSCSI)
docty: Policy Research Working Paper
abstracts: cdata!: Global supply chains recently faced
            widespread disruptions. The COVID-19 pandemic caused major
            disruptions in 2021 and 2022, while in late 2023,
            geopolitical incidents in the Red Sea and water shortages in
            the Panama Canal disrupted global shipping routes.
            Regardless of the cause, delays, or rerouting mean that
            disruption diffuses at a global scale. To quantify and
            assess the magnitude of disruptions globally or locally, in
            2021, the World Bank developed a proposed metric, the Global
            Supply Chain Stress Index. The index derives from Automatic
            Identification System tracking data. It calculates the
            equivalent stalled ship capacity measured in twenty-foot
            equivalent units), providing data at the port, country,
            regional, and global levels. This granular information can
            inform targeted interventions and contingency planning,
            improving the resilience of maritime infrastructure and
            networks. The index explains the observed surges in shipping
            rates during disruptions, assuming shippers&#x2019;
            willingness to pay for scarcer shipping slots. An increase
            of 1 million twenty-foot equivalent units in global stress
            pushes the Shanghai Containerized Freight Index up by
            US$2,300 per twenty-foot equivalent unit.
display_title: A Metric of Global Maritime Supply
            Chain Disruptions: The Global Supply Chain Stress Index (GSCSI)
listing_relative_url: /research/2024/07/34360500/metric-global-maritime-supply-chain-disruptions-global-supply-chain-stress-index-gscsi
topicv3: Trade Facilitation and Logistics m1327174 772,Supply Chain and Logistics m1503591 2605
publishtoextweb_dt: 2024-07-03T00:00:00Z
dois: 10.1596/1813-9450-10839
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 28
author_keys: 0: author_key: 452197
1: author_key: 626402
2: author_key: 1520666
3: author_key: 1814225
model_extractions: Policy Research Working Paper 10839

# A Metric of Global Maritime Supply Chain Disruptions

The Global Supply Chain Stress Index (GSCSI) _Jean-François Arvis Cordula Rastogi Jean-Paul Rodrigue Daria Ulybina_ Macroeconomics, Trade and Investment Global Practice July 2024
Policy Research Working Paper 10839

# **Abstract**

Global supply chains recently faced widespread disruptions. The COVID-19 pandemic caused major disruptions in 2021 and 2022, while in late 2023, geopolitical incidents in the Red Sea and water shortages in the Panama Canal disrupted global shipping routes. Regardless of the cause, delays, or rerouting mean that disruption diffuses at a global scale. To quantify and assess the magnitude of disruptions globally or locally, in 2021, the World Bank developed a proposed metric, the Global Supply Chain Stress Index. The index derives from Automatic Identification System tracking data. It calculates the equivalent stalled ship capacity measured in twenty-foot equivalent units), providing data at the port, country, regional, and global levels. This granular information can inform targeted interventions and contingency planning, improving the resilience of maritime infrastructure and networks. The index explains the observed surges in shipping rates during disruptions, assuming shippers’ willingness to pay for scarcer shipping slots. An increase of 1 million twenty-foot equivalent units in global stress pushes the Shanghai Containerized Freight Index up by US$2,300 per twenty-foot equivalent unit.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jarvis1@worldbank.org, crastogi@worldbank.org, ecojpr@tamug.edu and dulybina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
A Metric of Global Maritime Supply Chain Disruptions: The Global Supply Chain Stress Index (GSCSI) Jean-François Arvis, Cordula Rastogi, Jean-Paul Rodrigue, Daria Ulybina JEL: F10, F14, L13, R40, R41 Keywords: Maritime Transport, Trade Facilitation and Logistics, Supply chain and logistics
# **1. Introduction**

In recent years, containerized trade, the backbone of global value chains, has experienced unprecedented disruptions. For instance, the COVID-19 pandemic created unforeseen consequences in a far-ranging array of sectors on a global scale, triggering an unprecedented supply chain crisis from late 2020 to mid-2022. Surging trade demand surpassed shipping capacity, itself affected by massive operational disruptions in key ports. In 2023, there were two events of global relevance. First, a severe drought affected the operation of the locks in the Panama Canal, resulting in a reduction in throughput and restricting the size of vessels able to transit the canal. Later in the year, militant groups carried out attacks in the Red Sea, forcing shipping lines to reroute ships servicing the Asia-Europe and Asia-US East Coast trade routes through the Cape of Good Hope.

In contrast to typical localized events impacting supply and logistics chains, the recent disruptions have been geographically extensive, long-lasting in duration, and multi-faceted – impacting demand patterns, manufacturing, maritime logistics, port operations, and freight distribution in complex ways. For decades, the container shipping network has operated loop services according to a regular schedule, like passenger rail or air services. Schedule predictability helps deal with trade imbalances, where surpluses in some regions offset deficits in others. This container supply/demand mismatch means that about 20 percent of total container movements are empty repositioning voyages. When disruptions hit, delays in repositioning containers may amplify the effect of ship delays (section 4).

A key challenge lies in quantifying the stress levels global container ports are facing through a unified metric that can be precisely measured and utilized as a meaningful indicator. To fill this gap, the Global Supply Chain Stress Index (GSCSI) is proposed as a measure capturing the delayed container shipping capacity across global maritime ports. This stress metric, which has been published in the World Bank’s Trade Watch since late 2021, is inherently port-centric and derived from analyzing container shipping traffic flows. The index is computed monthly and can be aggregated from port-level data into regional or global indices. It focuses specifically on container vessels of the Panamax class and larger operating on intercontinental liner services. Feeder loops exhibit, by nature, higher volatility and have not been included in the initial scope. Therefore, the index does not yet fully capture port stress across the entire shipping network as ports being solely called by ships of lower capacity than Panamax are not included.

Measuring and understanding the impacts and propagation of supply chain disruptions has gained significant attention, especially since the 2021-2022 supply chain crisis. Most efforts in this domain have originated from private sector initiatives and publications (e.g., shipping lines and operators, as well as maritime research consultancies), with only a few predating the crisis. The increasing availability of vessel tracking data and schedule information has facilitated these endeavors since this type of information recently became available. However, in contrast, economic literature tackling these topics or providing an empirically testable theoretical framework remains limited, with few exceptions (e.g. Bai, et al. 2024). Notably, a fundamental question as central as the evident connection between freight rates and supply chain disruptions has not been comprehensively covered in prior literature (Notteboom et al., 2021; Rodrigue, 2022).

Indicators quantifying supply chain disruptions generally fall into three broad categories (Table 1): (1) surveys of supply chain professionals, (2) indicators derived from tracking or schedule data, and (3) metaindicators aggregating existing data series, based on expert knowledge. Other noteworthy tools are those visualizing vessel movements, including the IMF Port Watch and similar tools by maritime consultancies.

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Table 1. Indicators Quantifying Supply Chain Disruptions

|Surveys of supply chain<br>professionals|Purchasing Managers’ Index and components (IHS Markit, S&P), see section 5|
|---|---|
|Tracking, Schedule Data|**Global Supply Chain Stress Index (GSCSI)**<br>Schedule reliability (Sea Intelligence), see section 5<br>Ocean TimelinessIndicator<sup>1</sup> (Flexport)|
|Meta indicators|Global Supply Chain Pressure Index (FED New York), see section 5<br>SupplyChain StabilityIndex,<sup>2</sup>KPMG|

Source: Authors.

The current working paper informs the underlying methodology and use cases for the GSCSI, which belongs to the second category of indicators. The rest of the working paper is organized as follows: The next section (2) expands the stress index's conceptual framework. Section 3 explains how the index is derived from AIS tracking data, while Section 4 describes real use cases. Section 5 compares the proposed stress index with other indicators developed by the private sector or governmental institutions. Section 6 proposes a rate model in accordance with observed patterns of rate hikes in times of disruptions, and Section 7 concludes.

# **2. Conceptual Framework and Data Sources**

A stress index attempts to quantify deviations from a norm, which is the typical steady state in which a system operates. The greater the deviation, the more likely the system is to experience stress. However, some level of deviation is expected due to unforeseen events such as a storm or a break in equipment, so only large deviations are considered indicative of stress. Others are a variability in regular operations. A specific threshold for the standard deviation needs to be defined to determine when a deviation is significant enough to indicate stress. Global demand for container shipping has seasonal patterns, with a marked low in February. While disruptions typically have no seasonal patterns, the industry may have more capacity to absorb them in the first quarter of the year.

In this conceptual framework, global container shipping is schematized as a directed network, where the nodes represent ports, and the edges represent the shipping connections between ports used by the shipping lines' loop, or inter-range, services (Figure 1). Under normal circumstances, these loop services are highly predictable, with consistency in:

- (i) Lead times: The transit time between the departure port (d) and the subsequent arrival port (a).

- (ii) Turnaround times: The time spent at each port, especially for loading and unloading operations.

Network data at a given time is therefore characterized by the moving trade capacity (expressed in TEUs) between ports, as well as the consistent transit times, which tend to be relatively stable within the same ship class. Therefore, the mobilized trade capacity (flowing capacity) on a given connection can be calculated as: Mobilized trade capacity = Flowing capacity per unit of time × Transit time However, operational disruptions such as congestion, around the destination port can lead to:

> 1 <u>https://www.flexport.com/research/ocean-timeliness-indicator/</u>

> 2 <u>https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html</u>

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<!-- Start of picture text --> Deviation from median<br>Turnaround time Stress<br>vesd®ne © Leayting,. Ho }-------------t(d,a) ---5-<br>Port (d)day1a) (a) TRANSIT TIME = 0 - Time<br>OS Median<br>-lo0 ---- eee ee ee ee ee<br>(d) oe(da) = eTEU©) x t(d)Sey (a) MOBILIZED CAPACITY = TEU per day de(dc(d,a)<br>O Flowing capacity per unit of time x Transit time ¢(d,a)<br>s®SN) © a7%<br>@) de(a.a)= dTEUx t(da) (@) DELAYED CAPACITY<br>O Flowing capacity per unit of time x Delayed transit time 0 Time<br><!-- End of picture text -->
The starting point is to take a pair of subsequent port _d_ (departure) and _a_ (arrival) in a shipping sequence. The time span between departure at _d_ and the next departure T( _da_ ) (or d ) captures most possible disruptions affecting port _a_ (Figure 2), whether at _a_ or before. These include disruptions _en route_ from _a_ to _d_ , or disruptions at the _a_ (arrival) port. Therefore, looking at the lead time statistics of possible inbound TT connections reveals potential disruptions affecting _a_ .

Figure 2. Lead Time Between a Pair of Subsequent Ports Source: Authors.

For each pair of ports ( _d,a_ ), a dataframe with the following variables was constructed from vessel tracking data:

- Period _t_ (in practice monthly data from February 2016 until most recent).

- Port arrival _a_ .

- • Previous port _d_ . • Median shipping time for period _t:_ d _(t)._ Using median as opposed to mean avoids problems with outliers (vessels staying in port for noncommercial reasons, such as repairs or crew changes).

- • A reference transit time for the edge TT _(d,a):_ d is representative of normal shipping and port conditions on lane _d_ to _a_ above which vessels are considered delayed (see below).

- • Sum of vessel capacity moving from _a_ to _b_ for the period TT d _(t)._ • The same concept with vessel count d _(t)._ • Delay for period or transit time above the reference. CC NN

<!-- Start of picture text --> i d d<br>d 0 i d d<br>(tt) −TTd<br> TT (tt) ≥TT<br>ΔΔ (tt) = � TTd<br> TT (tt) < TT<br><!-- End of picture text -->

# **Estimating Reference Transit Time**

The stress index aims to capture large systemic disruptions. A too small value of the reference transit time ( d )) will capture too much noise in the form of small but frequent deviations, such as in a bell curve TT 5 5
<!-- Start of picture text --> 392 425 451<br>i ot 4<br>1 I<br>17.5 \ !<br>!t<br>II<br>II<br>1I<br>15.0 | H<br>1I<br>1'<br>1I<br>1257 1H<br>7, (quartile) | ' Tua (median)<br>1 H<br>~U<br>&10.0<br>2 1<br>32 f1<br>ue 1<br>75 !<br>5.0<br>25<br>mS MP DD yn<br>FFF FT K KF FT<br>0.0<br>300 350 400 450 500 550 600 650<br>Median transit time (hours)<br><!-- End of picture text -->

~~-~~ (T (quartile))
d d (m d (m d (q Which provides a simple rule to filter episodes of excessive lead time. TT = TT mmiim ) + 2 ∗σσ≈3 ∗TT mmiim ) −2 ∗TT mmqqttiiqqmm) Which provides a simple rule to filter episodes of excessive lead time.

## **Construction of the Index**

Conversely, the contribution of a region or a port to global capacity stress can be directly identified.

# **4. Findings and Applications of the Index**

The application of this analytical approach to global shipping data from 2016 to 2024 yields a global supply chain stress metric consistent with the general understanding and expert knowledge of current and past events. Globally, the GSCSI has risen consistently from early 2020 to March 2022, as expressed in terms of trade capacity (measured in TEUs, Figure 5). This indicates a considerable level of stress across the global supply chain over this period. The index went up in late 2023 in response to the Red Sea crisis.

Figure 5. ~~<u>Glo</u>~~ bal Supply Chain Stress Index (MTEU) January 2016- February 2024 (Monthly) 7
<!-- Start of picture text --> 2.0<br>1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>2016-01 2016-04 2016-07 2016-10 2017-01 2017-04 2017-07 2017-10 2018-01 2018-04 2018-07 2018-10 2019-01 2019-04 2019-07 2019-10 2020-01 2020-04 2020-07 2020-10 2021-01 2021-04 2021-07 2021-10 2022-01 2022-04 2022-07 2022-10 2023-01 2023-04 2023-07 2023-10 2024-01<br><!-- End of picture text --> Source: World Bank.

By converting the supply chain disruptions into their impact on trade capacity, the GSCSI offers a tangible representation of the scale of the global supply chain challenges faced over the 2020-2022 period and beyond. This supply chain stress metric can serve as a valuable tool for tracking, analyzing, and contextualizing the significant operational disruptions that have reverberated through global maritime and logistics networks in recent years. It underlines that shipping lines and terminal operators must contend with a notable unpredictable variability, in addition to more predictable seasonal and economic cycles.

# **The COVID-19 Pandemic and the 2021-22 Supply Chain Disruption**

The 2021-22 supply chain crisis resulted from two trends: First, containerized trade rebounded rapidly from the initial pandemic drop, overshooting pre-pandemic levels by late 2020. This surge was driven by a shift in consumer spending toward goods needed to accommodate sudden changes in lifestyles (e.g., workfrom-home) in high-income countries. While the shipping industry aimed to meet growing demand, supply was hampered by pandemic-induced port closure (notably in Asia), and operational constraints at other ports that caused multi-week vessel queues (especially on the US West Coast).

By mid-2021, severe pier and yard congestion had become prevalent among the world's major port gateways (Map 1), exacerbating the availability of containers and straining shipping lines’ capacity to move containerized cargo. This widespread congestion resulted from delays concentrating on a limited number of key international gateway ports.

Ship delays were amplified in the first half of 2021 by repositioning containers to where they were needed for exports (notably East Asia). At the start of the pandemic, shipping lines often cut capacity through canceled (blank) sailings. When demand bounced back rapidly, the industry struggles to promptly reassign capacity, and reposition containers, leading to bottlenecks. Even after restoring capacity, overloaded networks suffering slower operations due to high demand and landside congestion can face perceived capacity shortages, triggering soaring freight rates and container shortages.

Map 1. Delayed capacity at World’s Major Port Gateways (August 2021) 8
<!-- Start of picture text --> ee<br>; i. et Me OS Delayed Capacity (TEU per Hour)<br>C o wg s/ * Less than 5,000<br>ndPeo,eas f YL¢ }—f @© 5,00015,000toto 15,030, 00 0<br>\" ; ne r . a. @ 30,000to 60,000<br>meat a @ Worethan 60,000 :<br>| cy 2 PG ROTTERDAM MAASVLAKTE :<br>(WW 4 ’ Ye oe a K =o} ‘<br>Bla he “ o % (7 ty (SAVANNAH > ee<br>P yee rm pow 3 , - _LosfNGELES<br>_ g@SINGAPORE@PORTKLANGPs> - - : sc oSinsLONG|BEACH)<br>} f & pie TeeG\YANTANGuness.| —_—s A J J . ” a Distribution of Delayed Capacity<br>NX Cipe r<br>mS 400<br>.<br>, 5 — Mean : 8,287<br>\ : oO 50 — Median : 840<br>F 25 ports account forj8587%of{delayedicapacity) — StdDev: 23,808<br>a! IF 10 ports account fori65!4%yof{delayed 0<br>Note [Delayed Capacityy is the number of TEU per hourjoficontainership|capacity/delayed or stalled Vv e eehe ENG &SEOeveLHPge SS OeOPPS csOePee©<br>Smee monthly median capacity.<br><!-- End of picture text -->
<!-- Start of picture text --> 1% - .<br>Philadelphia IlTEU1.1% (2020) yFYa a ‘ ‘ x 5<br>Lazaro CardenasPort Everglades Mil 1.7%Mill 1.9% or% mcs Ripert f » Fr :<br>Prince Miami Mi 1.9% @ cs . oY<br>JacksonvilleRupert MlMl 2.3%2.0% we,Ae ‘ ef/ x,4q Cw% —~- ) , D<br>CharlestonOakland MiNN 4.1%4.4% @vancouverU \ x f .<br>Norfolk Sm 5.0% Ges tec ars & 8 oa<br>Manzanillo ME 5.2% * a<br>Seatac Houston Mi 5.3% [@Montreal :<br>Alliance MMMM 5.9% hee<br>Vancouver ME 6.2% . fe<br>Savannah 8.4% ey (ra) wr.<br>LongBeachNew York NNI13.5% 15% ~~ Peod JSa ee.<br>Los Angeles ET 16 5: @oariana Fi Rat {@Baltimore<br>0 4,000,000 8,000,000 Py oe<br>PhiladelphiaDelayed Capacity (TEU per hour) ~~\ a EAyi 2OA@ Hampton Roads)Distribution Hub<br>LazaroPort Everglades Cardenas || 0.6% 02%0.0% (2leos-AnaelCanerong Angeles Beach (2)| rad7 CharlestonL) U First Tier<br>Miami 0.1% » f ‘Savannah oO Second Tier<br>Prince Rupert 0.1% ’ / \ x } (@vacksonville ©) Third Tier<br>JacksonvilleCharleston 0.0% we eiDe~ @iouston'ix s \C}A\<br>Oxkiand | 0.2% / fal\<br>Norfolk NN 14.9% Hl s \ oe<br>Manzanillo | 0.4%0.1% . Q F \ / OrrMiami Everglades<br>Houston i 1.8% ’ N yo<br>Seatac Alliance NNN 12.6% XN <= P)<br>VancouverSavannahNew York SNMilM 21%3.0% 16.2% 2 P [\ f C) ad : > = ay =< = toad<br>Long Beach SN 13.5% Owanzaniio ] —@\Veracruze af<br>Los Angeles TT0 50,000 100,000 33150,000 9% Lazaro Cardenas ,<br>. 3enz : > Co—<<br><!-- End of picture text -->
during the pandemic-induced crisis: first, when ports were affected by lockdown and staff availability, then a longer period from the end of 2021, when the pressure from demand challenged handling throughput first. In comparison, a port such as Durban in South Africa experienced bursts of stress prior to the pandemic, which may be related to known systemic in-country infrastructure management issues.

Transshipment hubs such as Algeciras, Singapore, and Tanjung Pelepas appear to exhibit similar erratic fluctuations in their stress patterns compared to gateway ports serving as final vessel destinations or departures. Therefore, port-level stress data can serve as valuable complementary information and provide insights alongside other established indicators and data on port and/or logistics performance (like the World Bank’s Container Port Performance Index (CPPI) and the Logistics Performance Index (LPI)).

A port is heavily dependent on its hinterland connections to facilitate cargo flows. Port congestion has notable impacts on the availability of assets like drayage trucks and rail ramps as connections to inland destinations. A shortage of chassis to haul containers by road can severely limit capacity, as experienced on the United States West Coast during the COVID-19 pandemic for example. These chassis are also utilized for container storage at some rail yards and distribution facilities.

There exists a divergence between the increasingly demanding punctuality and flexibility of modern supply chains, such as e-commerce fulfillment, and the rigidity inherent to maritime shipping networks optimized for economies of scale through post-Panamax vessels. Shippers and cargo owners often respond by increasing inventory holdings and placing additional orders as a buffer, creating a demand-amplifying “bullwhip effect” that propagates backward through supply chains. This surge in demand, driven by actual consumption compounded by precautionary stockpiling, can overload shipping resources, especially the available container equipment pool. Container availability and shortages became the primary propagation and backpropagation mechanism disrupting maritime logistics networks. In this context, containers were spending 20 percent more dwell time immobilized within the logistics system on vessels, chassis, and container yards.

The stress stemming from the declining velocity of container movements initiates a negative feedback loop that occurs with a short time lag (Figure 6). As containers spend more dwell time at terminals or inland locations due to lack of capacity, it progressively impairs velocity and fluidity across all components of the transport chain. Maintaining the same level of service necessitates deploying additional assets, which only further exacerbates prevailing congestion. For instance, if a container yard is congested, it restricts the terminal’s capacity to handle vessel operations since inbound containers cannot be expeditiously unloaded due to a shortage of yard space. As could be seen during the COVID-19 pandemic, localized disruptions near major ports can create significant cargo backups in the hinterland.

Figure 6. Disruptions in Global Maritime Supply Chains and the GSCSI 11
<!-- Start of picture text --> Imbalanced Trade Pier Congestion Chassis Shortages<br>Ship Capacity Shortages Yard Congestion Rail/Drayage Shortages<br>7 oN<br>Shipping Network @ Port Level | Hinterland<br>Propagation and backpropagation aw, Propagation and backpropagation<br>Container Shortages<br>© Positive feedback with a long lag | Stress_] Negative feedback with a short lag @<br>Investments, \/<br>competition, Decline in Velocity/Fluidity = —<br>alternatives Additional capacity tied<br>Higher Shipping Rates<br><!-- End of picture text -->
<!-- Start of picture text --> 2.5 90 2.5 90%<br>80 80%<br>2.0 70 2 70%<br>60 60%<br>1.5<br>1.5 50%<br>50<br>40%<br>40 1<br>1.0<br>30%<br>30<br>0.5 20%<br>0.5 20<br>10%<br>10<br>0 0%<br>0.0 0<br> Global Supply Chain Stress Index (LHS)<br>Stress, MTEU (LHS) Reliability (RHS)<br>U.S. ISM Manufacturing Supplier Deliveries (RHS)<br>Source: S&P and World Bank Source: Sea Intelligence and the World Bank.<br>Note: The PMI suppliers’ delivery time survey question Note: Schedule reliability is a measure of the actual<br>takes 3 positions: faster/slower/same delivery time on-time performance of individual vessel arrivals in<br>compared to last month. The index is the percentage of ports around the world. 4<br>respondents reporting improvement plus 0.5 the<br>percentage responding a stable trend, hence an above 50<br>values report an increase in the Supplier Delivery time.<br>Millions<br>TEU Index<br>Jan-16 Jul-16 Jan-17 Jul-17 Jan-18 Jul-18 Jan-19 Jul-19 Jan-20 Jul-20 Jan-21 Jul-21 Jan-22 Jul-22 Jan-23 Jul-23 Jan-24 1/1/2016 8/1/2016 3/1/2017 5/1/2018 7/1/2019 2/1/2020 9/1/2020 4/1/2021 6/1/2022 1/1/2023 8/1/2023 3/1/2024<br>10/1/2017 12/1/2018 11/1/2021<br><!-- End of picture text -->

# **Sea Intelligence Schedule Reliability Index**

Sea-Intelligence consultancy<sup>5</sup> publishes a Schedule Reliability Index benchmarking carrier on-time performance within an 8-hour window. Despite being available globally and by shipping line, this metric unsurprisingly correlates with the Stress Index, as both leverage the same underlying vessel movement data. However, the Stress Index is port-centric and scalable, while the reliability measure focuses on services, trade lanes, and carriers.

# **Global Supply Chain Pressure Index**

The Federal Reserve Bank of New York’s Global Supply Chain Pressure Index<sup>6</sup> (GSCPI) is a meta-indicator that integrates several existing series to compound into supply chain disruption indicators. Global transportation costs are measured by employing data from the Baltic Dry Index (BDI) and the Harpex index,<sup>7</sup>

> 4 Each month, Sea-Intelligence measures schedule reliability across more than 11,000 vessel arrivals on average, in more than 270 ports, which is the underlying data for the monthly global on-time performance, as well as the individual carrier, trade lane and service on-time performance. The trade lane and service schedule reliability are based on a two-month rolling averages. In other words, February trade lane on-time performance is based on the average on-time performance of vessel arrivals in both January and February. The definition of “on-time” has in accordance with the widely used calendar-day definition been settled as arrival within plus or minus 1 calendar day from the proforma schedule.

> 5 https://www.sea-intelligence.com

> 6 <u>https://www.newyorkfed.org/research/policy/gscpi#/overview</u>

> 7 <u>https://www.harperpetersen.com/harpex</u> This index reports the chartering rate for container shipping.

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as well as airfreight cost indices from the U.S. Bureau of Labor Statistics. The GSCPI also uses supply chainrelated components from the PMI. The index is normalized, so zero represents the historical average, while negative values are representative of a very low stress situation and positive values express a level of stress.

The Pressure and Stress indices reflect major events, but the latter is more granular regarding containerized shipping specifically. Conversely, the GSCPI US-centric composition may explain its relative insensitivity to the Red Sea crisis as compared to the Stress Index.

Figure 9. Global Supply Chain Pressure Index and Stress Index (MTEU), 2016-2024 <!-- Start of picture text --> 2.5 5<br>4<br>2<br>3<br>1.5<br>2<br>1<br>1<br>0<br>0.5<br>-1<br>0 -2<br>Stress, MTEU (LHS) Pressure Index (RHS)<br>1/1/2016 4/1/2016 7/1/2016 10/1/2016 1/1/2017 4/1/2017 7/1/2017 10/1/2017 1/1/2018 4/1/2018 7/1/2018 10/1/2018 1/1/2019 4/1/2019 7/1/2019 10/1/2019 1/1/2020 4/1/2020 7/1/2020 10/1/2020 1/1/2021 4/1/2021 7/1/2021 10/1/2021 1/1/2022 4/1/2022 7/1/2022 10/1/2022 1/1/2023 4/1/2023 7/1/2023 10/1/2023 1/1/2024<br><!-- End of picture text --> Source: Federal Reserve Bank of New York and the World Bank.

# **6. Stress and Rates: Competing for Scarce Capacity**

## **Convergence between GSCSI and Freight Rates**

The striking parallelism between global stress and freight rate trends, while expected, merits further elaboration (Figures 10 and 11). It should be noted that prevailing freight rate indices reflect _spot_ rates, imperfectly capturing the actual shipping costs faced by large trade volumes under annual contracts negotiated between carriers and major shippers. They may imperfectly represent the actual shipping cost of trade, as spot rates correspond to rates paid by ad-hoc shippers competing for remaining slots, which can be limited. As such, they dynamically reflect real-time supply/demand tension in terms of their marginal cost.

The following factors help explain the boom/bust rate cycles:

1. Freight demand exhibits low rate elasticity, as freight cost represents a relatively small share of the landed price at destinations. The Free On Board (FOB) value of the content of a 40-foot container of manufactured goods from China ranges from USD 50,000 to several million. An increase in spot freight rates amounts to an ad valorem markup of only a few percent of the cargo value. Even at elevated rates, shippers willingly pay premiums to expedite deliveries and cut lead time. However, there are notable variations by commodity sector (standard international trade classification (SITC) categories), implying that sectors such as furniture have less elasticity than others such as office machines.

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2. Investments in shipping capacity occur across long cycles. Carriers must carefully anticipate demand to avoid costly overcapacity yet still meet upswings. Therefore, capacity-to-demand adjustments are imperfect. The 2021-22 combination of booming demand and congestion-induced capacity losses was unforeseeable and sparked a surge in orders for new (large) container vessels to be delivered in 202324 amid slowing demand.

<!-- Start of picture text --> Figure 10. Coevolution of Global Stress (MTEU) and Figure 11: Shanghai Freight Index and Stress<br>Freight Rates (USD/TEU), 2019-2024<br>2 6000 6,000<br>y = 2237.1x + 349.35<br>5000<br>1.5 5,000 R2 = 0.743<br>4000<br>4,000<br>1 3000<br>2000 3,000<br>0.5<br>1000<br>2,000<br>0 0<br>1,000<br>0<br>Global Supply Chain Stress Index (LHS) 0 0.5 1 1.5 2 2.5<br>Stress MTEU<br>Shanghai Containerized Freight Index (RHS)<br>Source: The World Bank, Shanghai Shipping Exchange Source: The World Bank, Shanghai Shipping Exchange<br>(https://en.sse.net.cn/). The Shanghai Containerized index (https://en.sse.net.cn/).<br>is a weighted average of Chinese Shipping rates (per TEU)<br>across global destinations.<br>Millions<br>TEU<br>USD/TEU<br>Rate USD<br>2019-01 2019-07 2020-01 2020-07 2021-01 2021-07 2022-01 2022-07 2023-01 2023-07 2024-01<br><!-- End of picture text --> Figure 12. Baltic Dry Index, 1985-2024 <!-- Start of picture text --> 14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>1/1/1985 2/1/1986 3/1/1987 4/1/1988 5/1/1989 6/1/1990 7/1/1991 8/1/1992 9/1/1993 10/1/1994 11/1/1995 12/1/1996 1/1/1998 2/1/1999 3/1/2000 4/1/2001 5/1/2002 6/1/2003 7/1/2004 8/1/2005 9/1/2006 10/1/2007 11/1/2008 12/1/2009 1/1/2011 2/1/2012 3/1/2013 4/1/2014 5/1/2015 6/1/2016 7/1/2017 8/1/2018 9/1/2019 10/1/2020 11/1/2021 12/1/2022 1/1/2024<br><!-- End of picture text --> Source: Baltic Exchange, the Baltic Dry Index is a global average of observed rates over a series of major routes.

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While unprecedented for containerized shipping, freight rate spikes have periodically occurred in other shipping segments like dry bulk prior to the 2008 financial crisis when soaring commodity demand over a multi-year span quintupled the Baltic Dry Index (Figure 12). This historical pattern suggests that shipping rates have a “low normal” state, interrupted by bursts of high rates when capacity cannot match demand surges.

# **Conceptual Framework of Stress and Rate Formation**

Historical freight rate patterns suggest two market regimes: Under “normal” conditions, shipping capacity accommodates shipper demand. Complex price mechanisms exist, which have, in some regions, replaced systems such as freight rate conferences, where prices were fixed. The industry structure is concentrated with six major carriers providing the bulk of the capacity in global container shipping. The existing global operational alliance system, accepted by regulatory authorities, allows carriers to consolidate and optimize vessel utilization with partners while alliance members remain commercially independent.

The nature of the business and investment size limits the number of entrants impacting the competitive environment in oligopolistic behavior. However, in a normal regime and in the absence of price collusion, lines are price takers, and rates correspond to the marginal price considering the imbalance in demand (full containers) across directions. The conventional wisdom in the industry is that shipping lines cannot recover their investment but for the period of tensions when rate spikes happen. Classical models of price formation such as Bertrand’s apply to this regime.

In times of capacity tensions, shippers compete for scarce remaining slots on vessels, becoming rate-takers rather than setters. This is especially true for shippers not engaged in longer-term contracts. Willingnessto-pay dynamics shapes the markets: price formation à la Ramsey-Boiteux-Dessus hinging on freight’s ad valorem markup over product value. Lower value goods suffer more acute increases proportional to cargo value. This first manifests for export loads out of origin regions (e.g., China) before affecting import legs.

Table 2: Shippers’ Willingness to Pay: Mechanism, Rate Formation and Theoretical Reference

|**_Regime_**|**_Unstressed “Normal”_**|**_Stress and rate bursts_**|
|---|---|---|
|Mechanism|Shipping capacity supply clears<br>demand|Shippers are competing for<br>shipping slots|
|Rate formation|Oligopolistic competition: Marginal<br>price|Shippers' willingness to pay|
|Theoretical reference|Bertrand|Ramsey-Boiteux|
|Source: Authors.|||

# **Empirical Connection of Stress and Rate**

Quantitative rate-setting models remain an underexplored topic. Rate impacts happen at varying stress thresholds depending on demand levels. The 2021-22 period saw the conjunction of high demand (pandemic-induced consumer demand for goods) with disruptions. The Red Sea crisis (starting November 2023), while increasing stress, faced a comparatively weaker demand backdrop.

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One complication in this model is that the impact of capacity stress on the market depends on the number of vessels available. The 2021-22 episode caught the industry at the bottom of an investment cycle, where few vessels were available to compensate for the lack of velocity in shipping due to port delays. Since then, more vessels have been put in circulation and the disruptions in the Red Sea happened with more capacity slack than in 2021-22, hence the apparent reduced impact on rates.

Therefore, the connection between rates and stress is non-linear such as: ) Where Slack represents the reserve capacity of the industry at the time of the disruption. RRmmttmmiim mmRR ≃bbmmbbmmqqiim qqmmttmm+ αα∗RRm RR(SSttqqmmb −SSqqmmS Hence rates increase above this threshold of maximum capacity absorption. The RRm RR(RR) = Scattered data points (Figure 12), illustrate that in this regime the relationship is linear. RR i RR≥0, mmqqbbmm 0 The capacity slack prior to a disruption is expected to change slowly over time unlike disruption and stress ΔRRmmttmmiim mmRR (UUSSDD) ≃2300 ∗ΔSSttqqmmb (MMiiq iiMMmm T UU) index, which see fast changes. Unfortunately, there is no good proxy for this threshold: slack is expectedly higher in 2024 than in 2020 due to ship delivered since. The empirical analysis will try explaining the slope α of the connection between stress and rate.

Annex 3 provides a model by which shippers' willingness to pay depends, inversely, on the value of their goods per container. According to this model, stress, and rate changes are proportional. M<sup>mmbb vvmmqmm RRmmqq TUU</sup> Typical value of a container is computed as an average mean across commodities, hence representative of ΔRRmmttmmiim mmRR (UUSSDD) ≃<sup>TTyyRRiiSSmmqq ggM</sup> ∗ΔSSttqqmmb (MMiiq iiMMmm T UU) vviimmgg SSmmRRmmSSiittyy (MM T UU) Typical value of a container is computed as an average mean across commodities, hence representative of lower end values.

With the moving capacity for vessels during the period of interest being 18MTEU, the typical value of goods implied by the observed connection is about **40,000 USD per TEU** . This value is indeed at the lower end of the value per TEU statistics (Annex 4). Therefore, capacity constraints and willingness to pay may explain the very high-rate bursts observed in recent periods of disruptions.

# **7. Conclusion**

The pandemic-induced crisis and other disruptions from 2020 onward significantly impacted shipping capacity and freight rates. While many regions experienced disruptions in their supply chains, the proposed metric and stress index effectively capture relative maritime supply chain disruptions despite their simplicity. Beyond measuring global trends, localized stress signals highlight chokepoints warranting a level of policy interventions. This framework could be extended to other scales (e.g., feeder loops or domestic shipping) and supply chains, where visibility data exists. Systematically producing the Stress Index, including disaggregated data at the regional or port level, could automatically inform productivity and capacity bottlenecks, flagging needs for infrastructure or operational improvements. Port productivity benchmarks like the World Bank’s CPPI, or dwell time made available in the World Bank’s Logistics Performance Index (2023) could be complemented with recurrent localized stress series, with ‘bursts’ flagging port investment 17
needs or operational constraints. This would require a partnership between AIS data providers and an international organization.

The connection between rate spikes and disruptions warrants deeper investigation across sectors, regions, and transport modes (bulk shipping or other modes, e.g., air cargo). The primary takeaway from the proposed framework is that market mechanisms explain the observed significant increases in rates. The latter should not be immediately interpreted as a sign of monopolistic behavior.

Mitigating maritime supply chain disruptions requires multi-faceted support:

1. Enhancing port-hinterland infrastructure resilience including IT systems, contingency planning for infrastructure services and customs, and other initiatives (as outlined in existing toolkits by the World Bank and others (World Bank, 2003)).

2. Increasing end-to-end shipment visibility through real-time tracking to empower shippers and supply chain actors to make agile decisions. Visibility solutions are generally led by the private sector, but public-private collaboration unlocking data-sharing (e.g., API to port community system or customs) could facilitate visibility solution development.

3. Ensuring more flexibility in the utilization of available capacity is crucial for mitigating impacts during periods of supply chain stress. Systems that facilitate capacity sharing across operators can yield substantial benefits, including positive benefits for end consumers. This principle extends to the alliance framework in global container shipping, which most industry experts see as having played a constructive role in the recent crisis. However, the European Commission’s stated intent<sup>8</sup> to reexamine exemptions granted to shipping alliances under competition policy may mean that new forms of operational collaboration may be required in the future.

> 8 <u>https://ec.europa.eu/commission/presscorner/detail/en/ip\_23\_4742</u>

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# **References and resources**

Akinci, Ozge, Gianluca Benigno, Ruth Cesar Heymann, Julian di Giovanni, Jan J.J. Groen, Lawrence Lin, and Adam I. Noble. 2022. “The Global Supply Side of Inflationary Pressures.” Liberty Street Economics, FRB New York, https://libertystreeteconomics.newyorkfed.org/2022/01/the-global-supply-side-of-inflationary-pressures/ Arslanalp, S.; R. Koepke ; J. Verschuur. 2021. “Tracking Trade from Space: An Application to Pacific Island Countries”, IMF Working Paper No. 2021/225 (August 2021) https://www.imf.org/en/Publications/WP/Issues/2021/08/20/TrackingTrade-from-Space-An-Application-to-Pacific-Island-Countries-464345 Baltic Exchange. 2024. "BDI Baltic Exchange Dry Index"; London, United Kingdom Retrieved from Trading Economics: https://tradingeconomics.com/commodity/baltic Accessed: May 2, 2024 Flexport Research. 2024. "Ocean Timeliness Indicator", January 8, 2024 https://www.flexport.com/research/oceantimeliness-indicator/ Accessed: April 25, 2024 KPMG LLP and Association for Supply Chain Management (ASCM). 2022. "The KPMG Supply Chain Stability Index" 2022 https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html Accessed: April 25, 2024 Marine Traffic. 2023. MarineTraffic: Global Ship Tracking Intelligence. https://www.marinetraffic.com/en/ais/home/ Notteboom, T., A. Pallis and J-P Rodrigue (2021) “Disruptions and Resilience in Global Container Shipping and Ports: The COVID-19 Pandemic vs the 2008-2009 Financial Crisis”, Maritime Economics and Logistics. https://doi.org/10.1057/s41278-020-00180-5.

Rodrigue, J-P. 2020. “The Geography of Transport Systems”, Fifth Edition, London: Routledge. 456 pages. ISBN: 978-0367-36463-2. https://doi.org/10.4324/9780429346323.

Sea-Intelligence, "Press Release: Schedule reliability improves M/M in February 2024" (April 2024), Global Liner Performance (GLP) report https://www.sea-intelligence.com/press-room/254-schedule-reliability-drops-again-injanuary-2024 Shanghai Shipping Exchange. 2024. "Shanghai Containerized Freight Index" https://en.sse.net.cn/indices/scfinew.jsp, Accessed: April 25, 2024 S&P Global and Institute for Supply Management (ISM), 2024. US Composite Purchasing Managers’ Index (PMI) Retrieved on April 25, 2024 from https://tradingeconomics.com/united-states/composite-pmi S&P Global & Global eProcure (GEP). 2024. "Global Supply Chain Volatility Index" https://www.gep.com/newsroom/demand-at-asian-factories-rises-at-strongest-rate-in-over-2-years-improving-nearterm-growth-outlook-for-manufacturing-worldwide-gep-global-supply-chain-volatility-index , Accessed April 25, 2024 UNCTAD. 2024. “Liner Shipping Connectivity Index (LSCI)”, Geneva Switzerland, March 2024, https://unctadstat.unctad.org/datacentre/dataviewer/shared-report/8df8f52c-de5e-4090-84ea-5d428b94011f Verschuur, J., Koks, E.E. & Hall, J.W. 2022. “Ports’ criticality in international trade and global supply-chains”. Nat Commun 13, 4351 (2022). https://doi.org/10.1038/s41467-022-32070-0 19
World Bank. 2020-2024. Trade Watch series (May 2020 - First Quarter 2024) (English). Washington, D.C. : World Bank Group. https://www.worldbank.org/en/topic/trade/brief/trade-watch World Bank. 2023. “Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy - The Logistics Performance Index and its Indicators” (English). The International Bank for Reconstruction and Development/The World Bank, Washington, DC (2023) https://lpi.worldbank.org/ World Bank. 2022. “The Container Port Performance Index 2022: A Comparable Assessment of Performance Based on Vessel Time in Port“ (English). Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/099051723134019182/P1758330d05f3607f09690076fedcf4e71a World Bank. 2021. “The Container Port Performance Index 2021: A Comparable Assessment of Container Port Performance” (English). Container Port Performance Index Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/099125006072255739/P1758330dccd270a70b31e0619fee32eb41 World Bank. 2007. Port Reform Toolkit. Washington, DC: World Bank. World Bank. 2010. “Trade and Transport Facilitation Assessment: A Practical Toolkit for Country Implementation.” Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/2490 World Bank. 2003. Port Reform Toolkit. Washington, DC: World Bank. https://elibrary.worldbank.org/doi/epdf/10.1596/0-8213-5046-3 Bai, Xiwen; Jesús Fernández-Villaverde, Yiliang Li, and Francesco Zanetti, 2024. “The Causal Effects of Global Supply Chain Disruptions on Macroeconomic Outcomes: Evidence and Theory” (NBER Working Paper 32098), https://www.nber.org/system/files/working_papers/w32098/w32098.pdf 20
# **List of acronyms**

|AIS|Automatic Identification System|
|---|---|
|API|Application Programming Interface|
|BDI|Baltic Dry Index|
|CES|Constant Elasticity of Substitution|
|CPPI|Container Port Performance Index|
|FOB|Free on Board|
|GSCPI|Global Supply Chain Pressure Index (FED New York)|
|GSCSI|Global Supply Chain Stress Index|
|IT|Information Technology|
|LPI|Logistics Performance Index|
|MDST|MDS Transmodal|
|PMI|Purchasing Managers’ Index (IHS Markit (S & P))|
|SITC|Standard International Trade Classification|
|TEU/MTEU|Twenty-foot Equivalent Units / 1 million Twenty-foot Equivalent Units|
|UNLOCODE|United Nations Code for Trade and Transport Locations|
|USD|United States Dollar|

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# **Annex 1: Global Supply Chain Stress index for selected maritime regions (in TEUs)**

<!-- Start of picture text --> Black Sea China Sea<br>200000 800000<br>150000 600000<br>100000 400000<br>50000 200000<br>0 0<br>West Med Europe North America West Coast<br>200000 1200000<br>1000000<br>150000<br>800000<br>100000 600000<br>400000<br>50000<br>200000<br>0 0<br>Persian Gulf East Med<br>250000 250000<br>200000 200000<br>150000 150000<br>100000 100000<br>50000 50000<br>0 0<br>Red Sea North America East Coast<br>30000 600000<br>25000 500000<br>20000 400000<br>15000 300000<br>10000 200000<br>5000 100000<br>0 0<br>Source: World Bank.<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br>2016-01 2016-08 2017-03 2017-10 2018-05 2018-12 2019-07 2020-02 2020-09 2021-04 2021-11 2022-06 2023-01 2023-08 2024-03 2016-01 2016-09 2017-05 2018-01 2018-09 2019-05 2020-01 2020-09 2021-05 2022-01 2022-09 2023-05 2024-01<br><!-- End of picture text --> 22
<!-- Start of picture text --> DJIBOUTI<br>7000<br>6000<br>S000<br>4000<br>3000<br>2000<br>1000<br>Oo<br>coeegee?eee?792??7?92?2?82893838°AAosooosoNNNnHxo DaaoASGNNotSoN ogoasNooo:soNNNDS ganuaradagGH NSNHHNNNMAMA[2XNooNN=|a oodNnoooNNNad woaNSoN oxoMNNnooNN8STa<br><!-- End of picture text -->

<!-- Start of picture text --> DURBAN<br>30000<br>80000<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>fe}<br>osoeegezegeeseeee?e?e?9e?eee8eAAsooooNNNNwow DtstcoccricrieoscrcosococrieoscsSADCPatiaeadaweavrieadadvieawrieatiellHYNNNDWetioO HHHHazeNN NNNANNwnaai leNSN oNodaoooNNNMAMAWO GD ST=soNN<br><!-- End of picture text --> <!-- Start of picture text --> HAMBURG<br>300000<br>250000<br>200000<br>150000<br>100000<br>50000<br>fe} saooArkaiooNNNNNuwnogdaetoooclcUc;w~wmCcUcUOWCUcUOCUUccoccocococrcrecoocooceosceseaA DSC wnAagawaeeedo NNNdo HHAuwgagxzFwAaadxzwAee OCUceeNNHANAOCUcCee OUUCUCUOUCOUUCOUUCUCOUCOeeNNee eeNNNMAAwasxz£Hee ASee eegSNSN|<br><!-- End of picture text -->

<!-- Start of picture text --> ALGECIRAS<br>50000<br>45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>fe} osooAadcaaAnoo gdaetoO oOoFS uwgdzwFtuwngawz#¥9focOlUmUODlUCUCUOUCUCOUCUCOUCUCOUCUCOlUCUCODdP dG AH HAAwnaA aoooAMAAwo DGDfo ST=S&<br>sooooNNNNtptcoccricocaetaedaweagNNN e aqgeaqueeanrcmcrhNeoscosocNN NNsc CNN&NSN NoNNN o odasoNN<br><!-- End of picture text --> <!-- Start of picture text --> LONG BEACH<br>400000<br>350000<br>300000<br>250000<br>200000<br>150000<br>100000<br>50000<br>S000fe} S8Sseessseseeeeagnag<br>SSSSSSSRESRSSSESSFA ASS SG AHH ANANAAAS<br>NNNN NNN NNN NN NN NN<br><!-- End of picture text --> <!-- Start of picture text --> NINGBO<br>250000<br>200000<br>150000<br>100000<br>50000<br>fe} S8sesseseeseseseaag<br>soosAraANNNNNNSSSSHSSRESSESSSSSADS So SFG HAHANN AAKAANMARMANNN NNN NNAS<br><!-- End of picture text --> <!-- Start of picture text --> SINGAPORE<br>250000<br>200000<br>150000<br>100000<br>50000<br>fe}<br>PSSSSSSSSSSSOnan SSS<br>oa =00 00 =FEESSSSSSSSSRSEREcsc cont wwwt HH ananannmn m<br><!-- End of picture text --> <!-- Start of picture text --> TANJUNG PELEPAS<br>50000<br>45000<br>40000<br>35000<br>30000<br>25000<br>20000<br>15000<br>10000<br>5000<br>wosfe} SSSSSSSSSSSSSRs<br>NAnaneoseoecesceartiaAtSAGASERESSSSSSSSSSSSSEaaqqegqggnugegagaagaagaqgaqggqaAt NNN mo Oo MO<br><!-- End of picture text -->
S=AC = ~~——~~ Rateindex = >. ~~—~~
Averaging over routes, the model yields an approximate linear relationship between stress and freight rate index.

0 + RRmmttmmiim mmRR= RRmmttmmiim mmRR CC Rate index increase= (representative value per container) X (share of fleet idled by disruption) Since values per TEU are in the tens of thousands of dollars or more, this simple rule explains that shipper’s competition for shipping slots may explain the burst observed.

The harmonic mean is lower than the arithmetic mean. Therefore, K is more representative of products with low FOB value or high elasticity, which conforms to intuition.

# **Empirical implementation**

Value per container is taken from MDST World Cargo database (commercial source). Values are estimated at SITC 2 digits as median and quartiles of observation along share of the commodity in containerized trade. (Annex 3).

Ship global capacity entering the stress calculation is about 18 MTEU, taken from the fleet of ships in Marine Traffic, used in the estimate of the Stress index.

The empirical value of K is K=2,300*18=41,400 USD.

Harmonic mean of value per container is estimated at 25,500 USD. The lower value implies an average elasticity of supply across products of 0.6 to match the value of _K._ 25
# **Annex 4: Value per TEU at STIC 2 digits (median observed value)**

|**Product (SITC)**|**USD**|**Product (SITC)**|**USD**|
|---|---|---|---|
|Live animals|47,394|Plastics in primary forms|34,798|
|Meat & meatpreparations|30,625|Plastics in non-primaryforms|82,455|
|Dairy products & eggs|33,225|Other chemicals|74,007|
|Fish & fishpreparations|35,403|Leather & manufactures|56,019|
|Cereals & cerealpreparations|10,870|Rubber manufactures|17,012|
|Vegetables & fruit|20,146|Cork & wood manufactures|9,441|
|Sugar & sugar preparations|23,046|Paper & paperboard|22,984|
|Tea/coffee/cocoa/spices|29,800|Textiles & made-uparticles|36,633|
|Animal Feeding stuffs|20,933|Mineral manufactures|29,143|
|Miscellaneous Food Products|26,032|Iron & steel|45,940|
|Beverages|31,240|Nonferrous metals|125,516|
|Tobacco & tobacco manufactures|41,245|Metal manufactures - other|45,831|
|Hides/skins/furs - raw|10,526|Powergeneratingmachinery|93,009|
|Oilseeds and oleaginous fruits|10,967|Specialized machinery|74,989|
|Crude rubber|20,333|Metalworkingmachinery|90,225|
|Cork & wood|7,178|General industrial machinery|130,064|
|Pulp& wastepaper|13,049|Office machines & adp|210,078|
|Textile fibers|7,351|Telecom & recordingequipment|193,155|
|Animal oils & fats|19,020|Electrical machinery|93,828|
|Fixed vegetable fats and oils|21,795|Sanitary/plumbing/heating/lighting|22,755|
|Other animal & vegetable|17,857|Furniture|24,749|
|Organic chemicals|68,917|Travelgoods & handbags|37,538|
|Inorganic chemicals|42,898|Clothing& accessories|138,951|
|Dyeing/tanning/coloring materials|77,134|Footwear|84,885|
|Medicinal/pharmaceutical products|875,391|Professional, scientific instruments.|125,350|
|Essential oils/cleansing/toilet|115,930|Photographic/optical/watches|77,754|
|Manufactured fertilizers|20,512|Miscellaneous Manufactures|40,768|

Source: MDS Transmodal World Cargo Database and World Bank.

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