metadata: authors: 0: author: Ardelean,Adina Teodora
1: author: Lugovskyy,Volodymyr
2: author: Skiba,Alexandre
3: author: Terner,David Michael
count: World
docna: 0: docna: Fathoming Shipping Costs : An Exploration of
            Recent Literature, Data, and Patterns
docty: Policy Research Working Paper
abstracts: cdata!: Recent academic research on maritime
            shipping has been affected by two factors: (i) changes in
            shipping technology and market structure and (ii) the
            explosion of new micro data sets in shipping and
            international trade. This paper describes this research,
            focusing on containerized and dry-bulk shipping and
            emphasizing recent trends and determinants of freight rates.
            The findings are presented in a systematic set of stylized
            facts. Some of the important and perhaps underappreciated
            insights include discussion of the scale effects of
            utilizing fuel based on vessel size over different periods
            and comparison of different functional forms of
            transportation costs: per twenty-foot equivalent, per kilo,
            and ad-valorem. The paper also presents several novel
            empirical exercises to further the insights from the
            existing literature. It shows that the effect of sea
            distance on trade costs slowly “dies" over time, but
            its effect on seaborne trade volumes is both significant and
            persistent over time. Moreover, the paper documents the
            presence of substantial country- and product-level
            heterogeneity of the impact of sea distance on international
            maritime freight markets.
display_title: Fathoming Shipping Costs : An
            Exploration of Recent Literature, Data, and Patterns
listing_relative_url: /research/2022/04/33778531/fathoming-shipping-costs-exploration-recent-literature-data-patterns
topicv3: Transport m1326661 135,Trade m1326924 698
publishtoextweb_dt: 2022-04-04T00:00:00Z
dois: 10.1596/1813-9450-9992
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 817129
1: author_key: 687426
2: author_key: 687335
3: author_key: 2019013
model_extractions: Policy Research Working Paper 9992 Fathoming Shipping Costs An Exploration of Recent Literature, Data, and Patterns _Adina Ardelean Volodymyr Lugovskyy Alexandre Skiba David Terner_ Transport Global Practice May 2022
Policy Research Working Paper 9992

# **Abstract**

Recent academic research on maritime shipping has been affected by two factors: (i) changes in shipping technology and market structure and (ii) the explosion of new micro data sets in shipping and international trade. This paper describes this research, focusing on containerized and dry-bulk shipping and emphasizing recent trends and determinants of freight rates. The findings are presented in a systematic set of stylized facts. Some of the important and perhaps underappreciated insights include discussion of the scale effects of utilizing fuel based on vessel size over different periods and comparison of different functional forms of transportation costs: per twenty-foot equivalent, per kilo, and ad-valorem. The paper also presents several novel empirical exercises to further the insights from the existing literature. It shows that the effect of sea distance on trade costs slowly “dies” over time, but its effect on seaborne trade volumes is both significant and persistent over time. Moreover, the paper documents the presence of substantial country- and product-level heterogeneity of the impact of sea distance on international maritime freight markets.

This paper is a product of the Transport 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 atardelean@scu. edu, vlugovsk@indiana.edu, askiba@uwyo.edu, and dmterner@iu.edu.

_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
Fathoming Shipping Costs: An Exploration of Recent Literature, Data, and Patterns<sup>*</sup> Adina Ardelean<sup>†</sup> Volodymyr Lugovskyy<sup>‡</sup> Alexandre Skiba<sup>§</sup> David Terner<sup>¶</sup>

**Keywords:** Maritime Shipping, Freight Rate, Ports **JEL Classification:** L9, F1, R4

> *This paper was prepared as a background paper for the Global Transport Practice Flagship report _Shrinking Economic Distance,_ led by Matias Herrera Dappe. We thank Matias Herrera Dappe, Jan Hoffman, David Hummels, Ahmad Lashkaripour, Mathilde Lebrand, Alejandro Molnar, Aiga Stokenberga, and participants of the World Bank 2022 Workshop for the Transport Flagship Report _Shrinking Economic Distance_ for helpful comments.

> † Department of Economics, Santa Clara University; e-mail: atardelean@scu.edu

> ‡Department of Economics, Indiana University, Wylie Hall Rm 301, 100 S. Woodlawn, Bloomington, IN 47405-7104; e-mail: vlugovsk@indiana.edu

> §Department of Economics and Finance, University of Wyoming, Dept 3985, 1000 E University Ave, Laramie, WY 82071, e-mail: askiba@uwyo.edu

> ¶Department of Economics, Indiana University, Wylie Hall Rm 101, 100 S. Woodlawn, Bloomington, IN 47405-7104; e-mail: dmterner@iu.edu
# **1 Introduction**

Maritime shipping is indispensable for international trade: ships transport 70% of the world’s trade value and 80% of the world’s trade weight (UNCTAD, 2015, 2017). Maritime shipping dates back to at least 5,000 years ago, and its history is well documented, including multiple waves in its development facilitated by technological advancements, wars, trade with colonies, etc. (Stopford, 2009). One of the more recent waves occurred in the 1950s when containerized shipping revolutionized maritime shipping. This technological breakthrough had a pronounced impact on trade volumes, patterns, and infrastructure (Hummels, 2007; Levinson, 2008; Blonigen and Wilson, 2018). The rate of change has further intensified in the last two decades with rapid fleet upgrades and expansions, improvements in infrastructure (port upgrading, expansions of Panama and Suez Canals), and changes in regulation and market structure.

In the last two decades (i.e., 2000-2020), the availability of qualitatively new and much more disaggregated data sets prompted a surge in empirical research of maritime shipping. This paper summarizes this research and complements it with novel data analysis and stylized facts. Understanding of the complexity underlying factors of the maritime shipping is even more important in light of the recent disruptions and high freight rates in maritime shipping caused by the COVID-19 pandemic. While we do not directly explore the most recent events, our review might be instrumental for this research as well.<sup>1</sup> Our focus is on container and dry-bulk maritime shipping.<sup>2</sup> Containerized maritime shipping is the dominant mode of international transportation: it accounts for two-thirds of total world trade. On the other hand, dry bulk shipping is used for commodities, and it accounts for about half of the total seaborne trade by weight.

We start by documenting several vital trends in maritime shipping. First, motivated by recent papers on the back-haul effects in maritime shipping (Wong, 2020; Brancaccio et al., 2020), we show that maritime trade is highly imbalanced across regions and that the magnitudes of the imbalances

> 1Please see the UNCTAD discussions for more information on the recent crisis at https://unctad.org/meeting/adhoc-expert-meeting-maritime-supply-chain-crisis.

> 2Other types of maritime shipping include various types of Tankers, Ro-Ro (Roll-on/roll-off), general cargo, and reefer vessels.
have increased over time. Second, we document the rapid increase in the number of vessels and average vessel size between 1996 and 2021. Specifically, containerized fleet capacity has increased 8 times, and the dry-bulk fleet capacity has increased 3.8 times during this period. In the presence of economies of scale that characterize the shipping industry, these changes should have reduced the cost of shipping. But, how pronounced are these economies of scale? We show that in terms of fuel consumption in containerized shipping they became significant only in the last decade. In terms of the new-build vessel prices, they are the most pronounced when comparing the smallest and the mid-size vessels. Finally, turning to the freight rates, we show that they decreased over time in the dry-bulk shipping, but not in the containerized shipping.

Our understanding of maritime shipping heavily relies on data availability and quality. In Section 3, we discuss how the measures of transport costs evolved with the arrival of new datasets. By way of motivating example, researchers in the past often had to rely on distance as the only available proxy of transport costs. We show that direct distance measures are a poor proxy for transport cost: the distance elasticity of freight rate elasticity is economically small and, further, decreasing over time. Likewise, in many other datasets, the units of goods were typically unavailable; rather, the only way to measure transport costs was in ad-valorem terms as a ratio of the c.i.f. and f.o.b. measures of trade values. We discuss the advantages and disadvantages of this measure.

As the data with the units of traded goods became available, freight rates have been shown to have a pronounced specific (i.e., non-advalorem) component, which shed a new light on our understanding of quality specialization and gains from trade (Hummels and Skiba, 2004; Wong, 2021). Lately, in addition to the per-unit measure of transport costs, we observe per volume and per container freight rates. We provide the summary and the discussion of these rates and their trends. We also discuss datasets which allow to measure port quality and maritime transport policy restrictions.

Our section 4 focuses on the rate determinants in containerized liner The ex- freight shipping. isting literature shows that price formation in these markets is more complex than in most trade models. It crucially depends on the network organization and market structure. Furthermore, liner shipping is split into two markets: the (long-term) contract market and the spot market. Long-term 2
contracts are strictly confidential, and thus we are limited to indirect inference about price formation; an analysis in patterns of the overall distribution of freight rates sheds light on this market’s otherwise opaque price formation. Presumably, the contract market contributes to the within-route freight rate heterogeneity with larger trading firms facing lower freight rates (Ardelean and Lugovskyy, 2021). Unlike for the contract market, the data on the spot markets are available. Using these data, Wong (2020) evaluated the magnitude of the back-haul effects, while Ganapati et al. (2020) demonstrated the effect of network organization on the distribution of freight rates across routes.

Other factors that affect liner freight rates are product types and variety characteristics, distance traveled, and port infrastructure. We discuss all factors in detail. Product variety characteristics are a first-order determinant of freight rates. Failing to account for variety-specific characteristics leads to debilitating omitted variables bias. To this end, we demonstrate that the distance elasticity of the freight rate is heterogeneous across countries and products.

Ports provide essential infrastructure for maritime shipping. Section 4.4 describes the literature evaluating and ranking port efficiency across the globe. We discuss and compare two approaches: (i) based on regressing transport cost on port characteristics or fixed effects, and (ii) the production function approach to estimation port efficiency based on the observable inputs.

We conclude by discussing the gaps on the policy questions that need further research, such as regulation of liner shipping, a better understanding of the determinants of port efficiency, and the effects of environmental regulation on the transportation sector.

# **2 Major Trends in Maritime Shipping**

## **2.1 Containerized Exports, Imports, and Imbalances by Region**

Maritime shipping is primarily determined by the developments in the world economy and trade, with the growth of international trade being among the most critical factors in determining the demand for transportation services (Stopford, 2009). At the same time, containerization has been a 3
key driver of globalization since the 1960s as it allows for (i) faster transportation, loading, and unloading, (ii) reduced cargo damage and theft, and (iii) refrigeration or temperature-controlled environments for perishable goods (Hummels, 2007; Levinson, 2008; Bernhofen et al., 2016). Moreover, the of container has the of world trade and availability shipping changed geography global supply chains (Notteboom and Rodrigue, 2007). These days, 95% of trade in manufactured goods is transported by containerships.

To get a better understanding of the trade trends, we leveraged Clarksons Shipping Intelligence Network (SIN)<sup>3</sup> to calculate maritime containerized trade and trade imbalances for years 2002-2020 for eight regions: Africa, Europe, Far East (including South East Asia), Indian Sub-Continent, Latin America, Middle East, North America, and Oceania.<sup>4</sup> The imports and exports trends are presented in Figure 1 with imports and exports for each region normalized to 100 in the year 2002. We summarize it as follows.

**Stylized Fact 1.** _Between 2002 and 2020, Far East, Indian Sub-Continent, and Middle East faced the most pronounced export and import growth—up to 300%. Africa, Indian Sub-Continent, and Oceania faced much faster imports than exports growth._

> 3See section 3.3 for details on Clarksons SIN database.

4Naturally, even within the same region, the country-specific trade and trade imbalances may differ in terms of magnitudes and even directions. Furthermore, the dry-bulk trade imbalances are different from the containerized trade imbalances.

4
<!-- Start of picture text --> Containerized Trade Growth<br>Africa Europe Far East Indian Sub-Continent<br>8t+<br>co}oOwo<br>=}oOoO<br>Seslo}Nwo<br>Noauoowse<br>se<br>z Latin America Middle East North America Oceania<br>22eosv<br>FO<br>2 ro<br>Fe8<br>co}Nwo<br>oSN<br>gwo<br>8s a oT oo oo oOeoeonon—eTe,S00 oo0.ooOoOoo ner_~oorryoaaga<br>~ 2005 2010 2015 2020 2005 2010 2015 2020 2005 2010 2015 2020 2005 2010 2015 2020<br>Direction -» Imports ~4& Exports<br>Source: Clarksons Shipping Intelligence Network<br><!-- End of picture text --> <!-- Start of picture text --> Containerized Trade Imbalances<br>Imports-Exports (mil TEU) Imports/Exports<br>3<br>_ ae °<br>2 —— a<br>_ 6N<br>o<br>2<br>g<br>3<br>22© °a —_ _. e<br>Eo —_ _f<br># * «<br>8 . ° —<br>§<br>2<br>ee<br>3 ee<br>2005 2010 2015 2020 2005 2010 2015 2020<br>Year<br>Region + africa -* Far East -* Latin America -*- North America<br>-* Europe -* Indian Sub-Continent -* Middle East * Oceania<br>Source: Clarksons Shipping Intelligence Network<br><!-- End of picture text -->
Wong (2020) documented pronounced backhaul effects in the spot freight rates reported by Drewry, especially for the US-China routes, where it costs three times more to ship a container from China to the US than from the US to China.

**Stylized Fact 2.** _Between 2002 and 2020, maritime trade was highly imbalanced. The containerized imports exceeded exports for 7 of 8 regions except for the Far East, for which the net exports increased from 10 to 30 million TEU. The largest relative imbalances are observed in the Middle East and Africa._

# **2.2 Industry Growth, Technological Changes, and Economies of Scale**

The shipping industry has responded to the growing demand for transportation by building larger vessels that presumably improve efficiency through economies of scale. For example, containerships have increased in size from 700-1,000 TEU in the 1960s to more than 23,000 TEU (Fan et al., 2018). Using the Clarksons SIN data, we document that the growth of the containerized maritime fleet has been remarkable in the last 25 years.<sup>6</sup> We plotted the trends in Figures 3 and 4 and summarize them as following.

**Stylized Fact 3.** _The total containerized fleet capacity has increased eight times between 1996 and 2021, with the average vessel size increasing 2.7 times. The total capacity share of the largest ships, of over 8,000 TEU, has increased from 0% in 1996 to 55% in 2021._

Indeed, while the containerized fleet capacity, measured in twenty-foot equivalent units (TEU), has increased eight times, the number of vessels has increased only three times. From Figure 4, we learn that smaller ships, with the capacity of under 3,000 TEU, were rapidly replaced with the larger ones. Specifically, the capacity share of the smaller vessels has decreased from 65% in 1996 to 18% in 2021. Instead, as pointed out above, the capacity of the largest ships, of over 8,000 TEU, has increased from 0% in 1996 to 55% in 2021. The largest ships in the industry these days exceed 23,000 TEUs.<sup>7</sup> 6Clarksons SIN provides a comprehensive list of container and dry-bulk vessel types and their total capacity for each year starting 1996. See section 3.3 for information about SIN.

7For example, the Korean carrier, HMM owns a dozen of 23,000+ TEUs containerships (HMM Vessel Fleet).

6
<!-- Start of picture text --> Fleet Capacity Fleet Count<br>6005<br>_ 6004<br>i Fleet Type<br>a =—® Containership<br>=<br>xe 4004 —* Dry Bulk<br>o<br>a<br>=<br>1995 2000 2005 2070 2015 20201995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
<!-- Start of picture text --> Containership Fleet Composition<br>ee<br>80%<br>70%<br>60%<br>Size Category<br>@ 50% + <3000 TEU<br>oo<br>a 40% -* 3000-8000 TEU<br>40% —s >8000 TEU<br>20% See aaa.<br>100 % bet<br>1995 2000 2005 2010 2015 20201995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
<!-- Start of picture text --> Dry Bulk Fleet Composition<br>re<br>60%<br>50%<br>40% Size Category<br>2 —® Handysize<br>3 30% —® Handymax<br>“ —® Panamax<br>20% ~® = Capesize<br>40% a mn<br>0%<br>1995 2000 2005 2010 «2015 382020 1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
<!-- Start of picture text --> $0000 2019 Containership Newbuild Price/TEU<br>><br>#<br>5ao2 20000<br>Q<br>2<br>ag<br>FS"5® 10000<br>Zz<br>0<br>i 8 A<br>2g 63 63 og og<br>- 6 e ¢€ g 8 g 8<br>>ze*’® 5a- 38®8> 8¢° ry2e (o}888 in22 a38° zBa3@ >pS in6©e5 Oo5aE idg@2s in-E8> 6>ae<br>&8N g&$5 ira§35B @i$8a co2£g$8 2#gs8 &g§eS FES#5 FEB55 &®&a8o Sgs8=: Ss8S8¢ bedF=8 cy8B¢s 8gS&s<br>se8s=> §8©°& ~ % “ zg8gs8 Py88§8 * 8Ss= &os= F 8S2 8aa<br>Sa oO oO<br><!-- End of picture text --> <!-- Start of picture text --> 8e+05 2019 Dry Bulk Newbuild Prices (DWT/USD)<br>a<br>no<br>2 6e+05<br>E<br>a<br>® 4e+05<br>8<br>a<br>z<br>5@Fa 2e+05<br>za<br>Oe+00<br>5<br>i=<br>xe © e 2%<br>28 og 5 Paé *E xg &3 a3 §5<br>2 2 2 5 5 5 a = é<br>5s =5S Ees= é£ EB&= Ebé= fa)Si= 65a 5ax<br>ax8oO ax$2 ax8= 5xio aa83 Fea®2 x&%i bex8é Sao3<br>“ ° © * = = ®g<br>3a<br>6<br><!-- End of picture text --> <!-- Start of picture text --> Fuel Economies in Containerships<br>600<br>><br>oO<br>2<br>oOi<j<br>S<br>= 400<br>fe)<br>ire<br>aod<br>&<br>2<br>S 200<br>2<br>a<br>0<br>° 3 3= 3= g& gN<br>TEU<br>Vessel Construction Period KE 1960-2000 | 2000-2010 A 2010-2020<br><!-- End of picture text -->
**Stylized Fact 6.** _Between 1969 and 2009, the fuel consumption per container was roughly the same for all ship sizes. The fuel consumption economies of scale became more visible only starting in 2010. The overall fuel efficiency for all ship sizes has also visibly improved starting in 2010._

An increase in fuel efficiency in the 2010s has coincided with a rapid increase in the number and capacity share of the eco-ships with electronic engines. As a result, their capacity share in the total liner fleet has increased from 0% in 2004 to 56% in 2020 (Lugovskyy and Terner, 2021).

The data on the share of the fuel costs in the operational and total costs is sparse. Based on the data from Delhaye et al. (2010), highlighted by Bernacki (2021), we report the following.

**Stylized Fact 7.** _In 2010, the fuel costs in the total costs constituted 54% for vessel sizes between 500 and 2,000 TEU and 42% to 46% for vessel sizes between 5,000 and 12,000 TEU. The calculations are based on the speed of 14 knots for the first (smaller) type of vessels and on the speed of 18 knots for the second type._

# **2.3 Freight Rates**

The research on international trade pays special attention to transportation costs as they represent a sizeable part of the overall trade costs. Transport costs affect the trade volume and determine trade patterns, including the organization of just-in-time production and global supply chains (Hummels, 2007; Behar and Venables, 2011). Over time, the contribution of shipping costs to trade costs increased in both developed and developing countries (Clark et al., 2004; Hummels, 2007; Hummels et al., 2009).

The magnitudes and trends differ for the dry-bulkers and containerships, which is not surprising given how different the industries are. Dry-bulkers are used for commodities with lower per tonne value and time sensitivity than manufactured goods transported by containerships. Dry-bulk shipping markets are organized as decentralized spot markets where trading firms hire a ship at the spot market for a specific route—similar to hiring a taxi. The market is characterized by high dispersion 11
in freight rates across destinations. It stems from the fact that trade in commodities is highly imbalanced. As a result, countries with large net imports face much higher ‘incoming’ than ‘outcoming’ freight rates, with many ‘outcoming’ vessels traveling without a cargo, i.e., ballast traveling (Brancaccio et al., 2021).<sup>10</sup> Leveraging the Clarksons SIN spot freight rates, we demonstrate this fact with Table 3 (Column 5) and summarize it as follows.

**Stylized Fact 8.** _There is a pronounced dispersion in the normalized dry-bulk freight rates across destinations._

Using Clarksons Research data from 2010 to 2016, Brancaccio et al. (2020) demonstrated that the asymmetry and endogeneity in dry-bulk freight rates have important implications for international trade and beyond. First, it weakens the strength of comparative advantage and reallocates production from exporters to importers. Second, there is a network effect which can be demonstrated as follows. If one of the large importers experiences a slow-down in its imports, some ships reallocate to other countries and regions, thus reducing their freight rates and increasing trade in those regions.

Over time, the freight rates are affected by the demand volatility and uncertainty and by the sluggish response in supply due to time to build and convex operating costs (Kalouptsidi, 2014).<sup>11</sup> To construct the trend of dry-bulk freight rates, we utilize Jacks and Stuermer (2021) data and estimates, who draw on a bevy of historical sources to construct an impressive 170 year-long time series (see Appendix A in their paper for specifics). We present the resulting trend in Figure 8. When explaining the long-run trend in real dry-bulk freight rates, the authors appeal to Harley (1988), Mohammed and Williamson (2004), and Tenold (2019) who suggested the long-run productivity gains as the major contributing factors.<sup>12</sup> Instead, for the short-run variation, Jacks and Stuermer (2021) found the _demand_ shocks to account for approximately 50% of short-run boom/bust historical freight rate

> 10The magnitude of the effect is very large. According to Brancaccio et al. (2021), a staggering 42% of dry-bulk vessels are travelling without cargo.

11Kalouptsidi (2014) described pronounced “echo effects” in this market: entry spikes are likely to repeat with intervals equal to the ship’s lifetime. Government interventions potentially may smooth the supply and the volatility in this market.

12For example, a significant drop in the freight rates between 1850 and 1914 can be attributed to switching from wind sailing to steam engine shipping and the opening of the Suez Canal in 1869 (Pascali, 2017).

12
<!-- Start of picture text --> Real Dry Bulk Index, 1850-2020 (1850=100)<br>200<br>150<br>& 100<br>50<br>1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->
containerized freight rates to the increasing input costs, such as fuel, ship prices, and port costs. From the market structure perspective, the industry has significant entry lags due to the time it takes to build a new ship. Bridgman (2021) attributes the lack of a dramatic fall in freight rates at least in part to the strong labor unions in ports. Due to their strong bargaining power, port-related labor costs did not fall much even though containerization resulted in enormous labor productivity gains. Finally, the demand for international trade and transportation services is constantly increasing (see Fig. 1), which may allow maintaining high freight rates in the highly concentrated industry with the legalized collusion.

**Stylized Fact 10.** _Between 1970 and 2004, containerized freight rates did not exhibit a decreasing trend. The non-decreasing trend, despite some technological advances, can be blamed on the increasing input costs, market structure, and constantly increasing demand._

# **3 Transport Costs Measures and Data Sources**

over We consider three measures of transportation costs: (i) approximation by distance, (ii) the c.i.f. f.o.b. ratio, and (iii) the direct (either specific or ad-valorem) measures. This section discusses the advantages and disadvantages of each measure in the context. We also describe available datasets 1. and provide their summary in Table

## **3.1 Transportation Costs Approximated by Distance**

Understanding the size and nature of transport costs requires detailed data on freight rates. These data are sparse. Researchers have widely used physical distance as a proxy for transport cost to overcome the paucity of transport cost data.<sup>14</sup> This tendency in empirical literature has been in part motivated by a robust to many specifications and periods ln( _Distance_ ) coefficient of ( _−_ 1) in the _a_ ggregate trade gravity regressions initially introduced by Tinbergen (1962). In these regressions, 14An example of the widely-used bilateral distances is CEPII trade barriers and geography dataset.

14
distance serves as a proxy of all trade costs, including transportation costs. These papers are nicely summarized in two meta-studies: (i) Disdier and Head (2008) reported that between 1985 and 2000, the elasticity persisted at around _−_ 0.90 (it was somewhat lower before that), and (ii) Head and Mayer (2014) report an average elasticity of _−_ 0.93 based on 159 papers with 1,835 elasticity estimates.

Such a strong and robust partial correlation between the distance and trade flow prompted using distance in many trade and growth papers. Notably, Romer and Frankel (1999) use distance as the exogenous shifter of trade flows to disentangle the endogeneity between trade and growth. Redding and Venables (2004) rely on distance as an exogenous determinant of market access and trade to explain income differences across countries. At the same time, this strong correlation is not necessarily an indicator of distance being a good proxy for transportation costs. Many other potential trade barriers are likely to be correlated with distance.<sup>15</sup> Indeed, both the previous empirical work and our own in this document a rather weak correlation between the distance and mar- analysis paper itime transport costs. Martínez-Zarzoso and Nowak-Lehmann (2007) demonstrated that distance is a weak predictor of transportation cost. Hummels and Lugovskyy (2009) report the distance elasticity of the c.i.f./f.o.b. ad-valorem measure of maritime transportation cost to be between 0.003 and 0.15 depending on the dataset and sample selection. Hummels and Skiba (2004) reported the distance elasticity of the per-unit maritime freight rate in the range between 0.11 and 0.26. Hummels et al. (2009) reported this elasticity to be under 0.18. For comparison, they showed that the average product-level prices from different origins explain up to 94 times more variation in freight rates than distance.<sup>16</sup>

**Stylized Fact 11.** _Distance elasticity of freight rate is low: across studies, it varies between_ 0.03 _and_ 0.26 _._

We further extend this analysis by estimating transport costs’ distance transportation using the

> 15Disdier and Head (2008) considered the robust distance coefficient as a puzzle, definitely not explainable by transportation costs alone. Chaney (2013) stated that previous models were not able to explain the distance coefficient; instead, he explained it with the effect of distance on the costs of creating contacts.

> 16For Latin American importers, the 95th percentile f.o.b. price of a given HS6 good demands an 846% greater freight rate than the 5th percentile price; instead, the freight rate is only 9% greater when comparing the 95th and 5th distance percentiles (Hummels et al., 2009, Table 7).

15
1989-2018 US Census imports data and showing that the low elasticity further decreases over time. To this goal, we first estimate the sea-distance elasticity of ad-valorem and unit-cost maritime transport costs _τodkt_ at the HS6 level _k_ : where subscripts _o_ , _d_ , _k_ , _t_ denote country of origin, destination port, product and year, respectively; _Y_ , ( _LANG_ ), ( _FTA_ ) are time-varying gravity equation variables from the “Dynamic Gravity Dataset" denoting GDP, common language, and free trade agreement, respectively; ( _SeaDist_ ) is the CERDI country level bilateral sea distance;<sup>17</sup> _WgtIModktodkt_<sup>istheratioofimportcharges(CIF)toimportweight</sup> measured in kilograms; and _θt_ and _νk_ are the year and HS6 product fixed effects, respectively. To explore how it varies over time, we then added the distance-year interaction terms, ln( _SeaDistod_ ) _× θt_ . The Chow-Test confirmed that it is time-variant since the interaction terms significantly improve the model. The full set of estimates is reported in Appendix Table 6.

Figure 9 illustrates the results with the year-specific elasticities. The left panel demonstrates that the average decline in the distance elasticity of ad-valorem transportation cost is approximately _−_ 0.003 per year or _−_ 0.09 over the entire thirty-year interval. The downward trend was even more pronounced for the distance elasticity of the per-unit transportation cost. As demonstrated by the right panel, this elasticity fell, on average, by _−_ 0.007 per year or _−_ 0.21 over the same three decades. To the best of our knowledge, the decreasing trends in these elasticities and the negative distance elasticity of the transportation costs have not been documented before. This trend is paralleled by the increasing share of low-income and remote countries in global trade. Since low-income and remote countries specialize in lighter goods (Lashkaripour, 2020), their growing share may cause the distance elasticity to decline. A more comprehensive explanation of the decreasing trend requires further investigation. To summarize:

> 17 See https://halshs.archives-ouvertes.fr/halshs-01288748/document.

16
<!-- Start of picture text --> Seadistance Cost Elasticity: US Imports 1989-2018<br>0.2<br>0.1<br>2 is<br>ul<br>1990 2000 2010 1990 2000 2010<br>Year<br><!-- End of picture text -->
COMTRADE database to obtain iceberg transport cost by taking the ratio of the importing country’s c.i.f unit values to exporting country’s f.o.b unit values for each 4-digit SITC product for many countries between 1984 and 2008. While this methodology allows for the measurement of transport costs to vary across countries, product, and time, Feenstra and Romalis (2014) reported that there is a large amount of measurement error in the unit values, and there are many instances when the c.i.f unit value is less than the f.o.b. unit value.

**Stylized Fact 13.** _Matched partner c.i.f./f.o.b. ratios from IMF and UN data are often inaccurate measures of the ad-valorem freight rates._

the for international trade recommend that countries re- Recently, official reporting guidelines port bilateral trade distinguished by five different modes of transport. Thus, the COMTRADE Plus database reports f.o.b. and c.i.f values by mode of transportation. In December 2020, UNCTAD, in collaboration with the World Bank, launched the Global Transport Cost database that has information on transport costs for 105 importing countries and 200 exporting countries. This dataset has the most extensive coverage of any public transport data so far, but it is available only for 2016. The system that has been put in place will allow for more updates and expansion in the future.

# **3.3 Direct Measures of Transportation Costs**

Several data sets contain direct measures of transport costs. We start our discussion of these data with the product-level customs data sets. The United States, New Zealand, and 11 Latin American countries report data on freight expenditures (including insurance) at 10-digit HS product level that are collected using importers’ customs declarations. These data provide a direct measurement of transport costs by mode of transportation together with trade values, quantities, and port of entry (for the US data). Several studies have employed this data to understand the trends in transport costs since the 1950s (Hummels, 2007; Hummels and Schaur, 2010), to model importers’ choice between air and ocean (Hummels and Schaur, 2013), to explore the nature and the determinants of international transport costs and the effect of freight rate on the patterns and quality of trade (Hummels and Skiba, 18
2004; Hummels et al., 2009; Lugovskyy and Skiba, 2015). In addition, the Maritime Transport Costs (MTC) database, prepared by OECD, combines the above-mentioned data on freight expenditures with the COMTRADE dataset and other data sources to provide a comprehensive transport costs database for 43 importing countries from 218 countries at 6-digit HS product level from 1991 to 2007 (Korinek, 2011).

Another useful dataset comes from the U.S. Army Corps of Engineers(ACE). It focuses on the U.S. waterborne transport and includes freight expenditures at HS4-product level and U.S. ports of loading and unloading. These data have been used to study the effect of policy on maritime transport costs (Fink et al., 2002) and to estimate port efficiency and its impact on trade flows (Blonigen and Wilson, 2008, 2018; Clark et al., 2004).

More recently, researchers can obtain customs data from some countries with freight expenditures at the transaction level. For example, Panjiva, a part of S&P Global, provides these data for Chilean and Colombian imports from 2009 onward. These data also contain trade values, quantities, mode of transportation, port of loading and unloading, shipment weight and volume, the identity of the importing firm, information on whether the importing firm or exporting firm is responsible for arranging transportation, and the identity of the carrier or logistic firm arranging transportation. The data on freight expenditures collected from custom declarations or bills of ladings is a direct measurement of transport cost. Therefore, these datasets represent the best sources of data to study the size and nature of maritime transport costs. However, they are available only for a small number of countries.

Panjiva also provides the US import bills of lading. These data scope out a large portion of a shipment’s journey; a typical observation includes where a shipment originated, ports of lading and unlading, vessel-specific IMO Number, where the shipment is ultimately sent within the US (shipment destination), and some product characteristics (e.g., shipment HS8 code, TEU volume, and weight). Unfortunately, Panjiva’s US Imports lack _direct_ measures of freight cost, and the value of the shipments are reported for a striking minority of observations.

Another useful data supplier is Clarksons Research—the world’s leading shipbroker. Its Shipping 19
Intelligence Network database provides data on vessel-specific fixtures, namely time charter and spot rates. Vessel-specific rates are not particularly representative of a given market segment (i.e., containerships, dry bulk, tankers, auto carriers, etc). For example, Brancaccio et al. (2020) report that Clarksons tend to oversample newer and medium-sized vessels in dry bulk freight markets. That said, Clarksons does provide a representative summary of the freight earnings time series (from an annual to a weekly reporting frequency) for main trade routes and vessel benchmarks: e.g., Feeder Containership 2,7500 TEU gearless 6-12 month Timecharter rates ($/day) or SCFI Shangahi-West Coast America (spot) freight rates ($/FEU).

Another readily available data source of transport costs for large cross-sections of countries is the Doing Business (DB) survey, compiled by the World Bank.<sup>18</sup> It contains information on international transport for 189 countries since 2006. The section "Trading across Borders" provides data on the number of documents, time, and import and export costs. More specifically, it reports the US dollar cost container to and The data in the DB is collected a World per import export. survey using Bank questionnaire completed by trade facilitators and freight-forwarders across countries. For the comparability across countries, the survey presents the respondents with a detailed, stylized import or export case. The cost per container is for dry-cargo, 20-foot, full container load, and the products shipped do not require any special standards. Given the description in the survey, the most likely product categories are textile yarn and fabrics (SITC 65), articles of apparel and clothing accessories (SITC 84), and coffee, tea, cocoa, spices (SITC 07). The respondents are also asked to identify the likely port of loading (Djankov et al., 2010). Therefore, the variation in transport costs across products, routes, or firms is rather limited (Blonigen and Wilson, 2018). The advantage of these data is that it allows for a comparison across many countries and some variation over time in the cost of a container and provides a more accurate measure of transport costs than, for example, the previously discussed c.i.f over f.o.b ratio or physical distance.

Finally, some studies use more focused data. For example, Simonovska (2015) used the DHL shipping rates, Limao and Venables (2001) used shipping rate quotes for a standard 20-container from

> 18The survey has been recently discontinued. See WB Statement for more details.

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Baltimore, Kleinert and Spies (2011) use shipping prices from the UPS. These data are not generally available and focus on the cost of shipping for standardized units of cargo instead of the cost of shipping specific products.

# **3.4 Other Useful Data Sources**

In studying the determinants of shipping costs to the U.S. and port efficiency, Clark et al. (2004) used quality of port infrastructure and port handling costs from the Global Competitiveness Report. The Global Competitiveness Report, published annually by the World Economic Forum, provides the quality of port infrastructure index for 140 countries between 2007 and 2017. The index measures business executives’ perception of their country’s port facilities in terms of development and accessibility. These data are collected through online surveys or in-person interviews.

The Service Trade Restrictiveness Index developed by the OECD measures 44 countries’ restrictions on service sectors, including maritime transportation, from 2014 to 2020 (the value of the index has little variation over time). This index allows researchers to study the cross-country variation in maritime sector policy restrictions and their impact on transport costs (Bertho et al., 2016).

Using data from surveys of logistic professionals, the World Bank provides The Logistic Performance Index (LPI) that ranks countries on six dimensions of trade. The index includes information on customs infrastructure and timeliness of The LPI is more use- performance, quality, shipments. ful for a cross-country comparison in trade facilitation. It does not allow researchers to identify the international transport aspects of trade separately from the domestic infrastructure.

1. We end this section by providing basic facts about selected datasets in Table

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Table 1: Databases providing international transport data

|**Database**|**Institution**|**Country**<br>**Coverage**|**Frequency**|**Aggregation**<br>**Level**|**Time**|**Measure of**<br>**Transportation**|
|---|---|---|---|---|---|---|
|BlueWater<br>Reporting|BlueWater<br>Reporting|Global|Biweekly|Liner<br>Rotation|2012-|TEU capacity per<br>rotation/carrier/vessel|
|COMTRADE|United Nations|200+<br>countries|Annual|HS6|1984-|c.i.f and f.o.b values|
|Global<br>Transport<br>Costs Dataset|United Nations,<br>World Bank|105 importing,<br>200 exporting|Annual|HS6|2016|c.i.f and f.o.b values<br>transport cost by mode<br>and countries|
|U.S. Imports of Mer-<br>chandise|U.S. Census Bureau|U.S.|Monthly<br>Annual|HS10|1990-|freight expenditures<br>bymode,entry port|
|International Trans-<br>port Database(BTI)|ECLAC|11 Latin Ameri-<br>can countries|Annual|SITC<br>Rev. 3|1999-|freight expenditures<br>bymode|
|New Zealand Cus-<br>tom Data|New Zealand Cus-<br>toms Service|New Zealand|Monthly,<br>Annual|HS10||freight expenditures|
|U.S.<br>Waterborne<br>Commerce Data<br>2|U.S. Army Corps of<br>Enginneers(ACE)|U.S.|Annual|HS4|2000-<br>2019|freight expenditures by U.S.<br>port of(un)loading|
|Panjiva<br>2|Panjiva|Chile<br>Colombia|Monthly,<br>Annual|HS8,<br>Firm level|2009-|freight expenditures by mode,<br>port of(un)loading|
|Panjiva|Panjiva|U.S.|Monthly,<br>Annual|HS8<br>Firm level|2009-|by mode,<br>port of(un)loading|
|Shipping<br>Intelli-<br>gence Network|Clarksons Research|Global|Annual-<br>Weekly|Aggregate,<br>route,<br>vessel|1989-|Spot/Time<br>Charter Rates|
|Global Competitive-<br>ness Report|World Economic Fo-<br>rum||Annual|Aggregate|2007-<br>2017|port quality|
|Doing Business(DB)|World Bank|189 countries|Annual|Aggregate|2006 -<br>2015|US dollar cost per container|
|Service Trade<br>Restrictiveness<br>Index(STRI)|OECD|44 countries|Annual|Aggregate|2014-|maritime transport policy re-<br>strictions|

_Notes: 11 Latin American countries covered by BTI are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, Venezuela The Waterborne Commerce Data is available at https://www.iwr.usace.army.mil/About/Technical-Centers/WCSC-Waterborne-Commerce-Statistics-Center-2/WCSCWaterborne-Commerce/_
# **3.5 Ad-valorem vs. Specific Transportation Costs**

As discussed above, the cost of transporting goods can be expressed in an ad-valorem (or iceberg) form as a percentage relative to the value of the transported goods: _τ_ = Freight Charges Value of Transported Goods<sup>. Al-</sup> ternatively, it can also be expressed in a specific form, as transportation charges per unit or weight: = Freight Charges _f_ Quantity (or Weight) of Transported Goods<sup>; or as a mix of ad-valorem and specific components of trans-</sup> portation cost. An empirically-relevant way to distinguish between these forms is by evaluating the price elasticity of freight rate, _∂_<sup>_∂_</sup> _p_<sup>_<u>f</u>_</sup> _<u>pf</u>_<sup>, where</sup><sup>_p_is the factory (i.e., exclusive trade costs) price of the</sup> good and f is the per-unit freight rate as defined above. The unitary elasticity corresponds to the ad-valorem form, zero elasticity—to the specific form, and the elasticity between zero and one—to having both components present. This section will discuss the literature utilizing both measures and the importance of the functional form of transportation cost for international trade.

For many decades, the workhorse empirical model to estimate the effect of trade costs on bilateral trade was the gravity equation. A rich line of literature derives the gravity equation from a variety of theoretical models (e.g., Krugman, 1980; Eaton and Kortum, 2002; Melitz, 2003). These models assume a convenient ad-valorem form of transportation costs, also known as the Samuelson iceberg. Therefore, the ad-valorem formulation became a natural default for transportation costs in the empirical literature. In addition, most datasets with the direct measure of transportation charges report the value but not the quantity of the imported goods.<sup>19</sup> This further strengthened the reasons to utilize the ad-valorem transportation cost as the per-unit measure could not be calculated without the information on the units.

The knowledge about the functional form of transportation costs has been greatly enriched by a seminal work by Hummels and Skiba (2004), who utilized the direct measures of transportation costs using the data from six countries (Argentina, Brazil, Chile, Paraguay, Uruguay, the US) importing from the rest of the world. They showed that the data do not support the widely assumed ad-valorem 19In the past, several studies have also employed measures of transportation costs constructed using matched partner c.i.f./f.o.b. ratios from the IMF Direction of Trade Statistics (DOTS) and UN COMTRADE data. Hummels and Lugovskyy (2009) found that the ratios from these sources are subject to significant measurement error and are unrelated to actual variation in shipping costs.

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form of the transportation costs and that the freight rate contains the specific component.<sup>20</sup> They also were the first to point out that the assumption of ad-valorem transport costs is not innocuous as it underplays the effects of transportation costs and tariffs on the quality composition of exports and imports. This paper has instigated the importance of the functional form of the transportation cost for multiple aspects of trade. Feenstra and Romalis (2014) used the specific transport costs to estimate traded quality. Due to the presence of the specific component in transport costs, remote economies were predicted and found to specialize in higher quality by Lugovskyy and Skiba (2015) and in lighter varieties by Harrigan (2010) and Lashkaripour (2020).<sup>21</sup> Wong (2021) showed that specific transport costs reduce welfare and trade by more than an equal-yield ad-valorem transport costs because they are more price-distorting. We summarize the advantages and disadvantages of the ad-valorem measure as follows.

**Stylized Fact 14.** _The advantages of the ad-valorem measure of transport costs are: (i) it is in line with many trade models (ii) it is available for many countries and years; and (iii) it is intuitive and comparable with tariffs._

_At the same time, the functional form of the transportation cost contains a significant specific (per unit) component. This fact is important for estimating the traded quality and welfare gains from trade._ The data on the per-unit measure of transport costs are relatively sparse. Earlier papers on the specific freight rates explored the determinants of the per-weight freight rates. Mainly because in the BTI dataset—the pioneering dataset for measuring specific freight rates—other units were not reported. Lashkaripour (2020) made a point that consumers buy units, not kilos of manufactured goods, and thus the per-unit measure is more relevant from the perspective of trade models. The per-unit freight rates are available only in a handful of datasets: e.g., the US Imports by Census and

> 20They estimated the price elasticity of freight rate to be 0.6. The elasticity consistent with the ad-valorem functional form would have to be equal to one.

> 21Theoretically, remote in this case refers to the higher transportation costs to the destination markets. Due to the lack of transportation costs for many countries, the measure of remoteness is often based on the average distance or tradeweighted average distance. Lugovskyy and Skiba (2015) constructed the MQC (Multilateral Quality Compensation) index that combines the trade-weighted importer’s preferences for quality and the composition of transportation costs at the exporter-HS6 level and discusses their distribution (see their Fig 1).

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**Stylized Fact 15.** _The ranking of freight rates depends on their measure. In ad-valorem terms, the drybulk freight rate is up to 5 times_ greater _than the containerized (non-refrigerated) freight rate. However, the specific (per kilo) dry-bulk freight rate is up to 10 times_ lower _than the containerized one._

**Stylized Fact 16.** _The freight rate trends of the containerized and dry-bulk freight rates are not synchronized, implying that the cost and demand shocks are either asynchronous or have asymmetric effects on transport costs across commodity types._

**Stylized Fact 17.** _The aggregate per-kilo and per-TEU trends for a given commodity type are synchronized._

The last fact is important from a practical standpoint since per-kilo freight rate measures are available in many more datasets. These similarities in trends indicate that, in the absence of the per-TEU measures, the per-kilo trends are good proxies for the per-TEU trends.

Finally, some datasets provide the per-container charges. In some cases, these datasets contain the spot-market, but not the annual-contract charges (e.g., Drewry, as described by Wong, 2020). The US Bills of Lading data provide per-container charges, but only for a limited sample of the shipments.<sup>23</sup> These data limitations impede utilizing the per-container freight rates in the context of international trade.

# **4 Containerized Maritime Shipping: Freight Rate Determinants**

Containerized maritime shipping is a multi-billion industry with complex and sophisticated technology. While an exact functional form of transportation cost is difficult to derive, it includes both fixed and variable cost components. The variable cost depends on the market structure, network organization, distance and geography, volume of trade and directional imbalances, good- and variety-specific imbalance: e.g., empty reefer boxes are needed for the Chilean exports, and therefore the empty returns to Chile are very cheap.

> 23These data have been used by Holmes and Singer (2018) and Terner (2021). The value of the transported goods is also often missing from the data.

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characteristics, shipping technology, fuel costs, and economies of scale at the vessel level. We discuss these components in Sections 4.1–4.3. The fixed cost component is a function of the vessel prices and port infrastructure. We discuss the port infrastructure in Section 4.4.<sup>24</sup>

# **4.1 Market Structure: Network Organization, Industry Concentration, AnnualContract versus Spot Markets**

Containership transportation is organized similar to bus services, with the routes labeled as rotations and ports serving as bus stops. Major rotations include multiple ports, with the total duration of a rotation often exceeding 60 days. Both ports and ships vary in their capacity and cost of transportation. As discussed above, the average containership has become much larger over the last decades. These larger ships require automatic and modern equipment such as engines and cranes to load and unload them. The developments in shipping technology have created the need for ports to develop their infrastructure to handle these larger ships, such as special berths, bigger cranes, more storage space for the increased cargo size, and more developed and efficient port logistics. Since the investment in port infrastructure requires a large amount of capital and human resources, the largest container ports handled a disproportionate volume and value of world trade. Notteboom (2018) calculated that in 2015, the 20 largest container ports handled 312.5 million TEU, which is 45% of world trade. The network structure of the containerized shipping was explored in detail by Heiland et al. (2019) and Ganapati et al. (2020). The latter also showed that a handful of ports play a disproportionately significant role:<sup>25</sup>

**Stylized Fact 18.** _Larger ports serve as hubs accommodating larger ships, making hub-to-hub transportation significantly cheaper and hubs essential for maritime trade._

Most networks in liner focus on cross-sectional data The papers analyzing shipping analysis. literature is scant when it comes to documenting the network evolution of containerized shipping 24The discussion on (i) crewing and registration and (ii) trade and transport facilitation is beyond the scope of this review.

25They also document that, for a given leg, a 1% increase in traffic reduces transportation costs by over 0.05%.

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over time. Ducruet and Notteboom (2012) tackle the evolution of shipping networks using Lloyd’s Marine Intelligence Unit data from 1996 and 2006. They argue that, over time, the shipping network converges to a structure with densely concentrated and connected neighborhoods of ports. These neighborhoods are distant from one another and are connected via hubs (see Appendix 6.1 for more details). Hoffmann and Hoffmann (2020) compare the global 2020 network structure of ports to that of 2006. One of their main findings is that the network center has shifted from Europe to Asia with the major shifts being associated with Chinese ports (e.g., Nansha, Ningbo, and Shekou).

The amount of market power affects the freight rates in two ways. First, the markup over the marginal cost of transportation increases in market power: According to Hummels et al. (2009) it is lower on thick routes with more carriers and greater traffic. Second, larger traffic prompts carriers to use larger vessels, which minimizes the average cost of transportation (Asturias, 2020; Ganapati et al., 2020). Both effects work in the same direction lowering the observed freight rates on thicker routes with larger traffic. Potentially, over-reliance on larger ports may increase the risk of the lack of resilience of global transportation system to various shocks, which should become a topic of future research explorations.

Containerized maritime transportation is very concentrated. In 2018, the top 9 carriers (Maersk, MSC, Cosco-OOCL, GMA CGM, Evergreen, Hapag-Lloyd, ONE, and Yang Ming) organized in three alliances controlled 80% of the market (Merk et al., 2018). Furthermore, there are many “thin” routes, i.e., routes served by either a single or very few carriers. Hummels et al. (2009) reported a high degree of the within-route concentration, with the majority of importer-exporter pairs served by three or fewer ships, often owned by a single carrier. Using BlueWater Research data for years 20122021, Ardelean and Lugovskyy (2021), reported that up to 60% of rotations<sup>26</sup> connecting to the US were served by 4 carriers or less. Naturally, we can expect relatively high markups charged by the carriers, especially on the thin routes (see Hummels et al., 2009, for a discussion of the variation in route-specific markups).

> 26 Rotations are defined as ports on a given schedule, similar to having bus stops on a bus schedule. Rotations often include over two ports in multiple countries.

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**Stylized Fact 19.** _Containerized shipping is very concentrated. Carriers exercise market power by charging higher markups on thinner routes._

From a legal perspective, the industry is unique in that collusive agreements were allowed and enforced by the US government until 1998. Specifically, up until 1998, all carriers within a given conference were expected to post a common freight rate for each route and commodity, with the deviations allowed only in the form of an _independent action_ by a carrier, which had to be publicly announced and offered to all qualified shippers. Any secretive deviation from the common freight rate was subject to a stiff fine by the Federal Maritime Commission (FMC). Naturally, this type of price-fixing agreement, known as conference agreements, severely limited within-route price variation in liner shipping (Clyde and Reitzes, 1995). From the modeling perspective, these agreements justified the assumption of symmetric transportation costs in international trade literature. Indeed, the vast majority of trade models assume transportation costs to be symmetric for a given route. The difference is only in whether they are symmetric in ad-valorem or specific fashion.<sup>27</sup> In 1998, the Ocean Shipping Reform Act (OSRA) abolished the price-fixing requirement and drastically changed the market structure. Specifically, the vast majority of the US and worldwide markets quickly switched to the confidential annual contracts between carriers and trading firms (FMC, 2001; OECD, 2015). The discussion of the recommended freight rates is still allowed among carriers. However, the recommended freight rates are not part of the contracts, so they cannot be legally enforced. Keeping contracts confidential made the detection of these deviations unlikely, which further weakens the possibility of enforcing the recommended rates by other means of repeated interactions.<sup>28</sup> Therefore, the freight rate dispersion within a route became more likely. Unfortunately, the direct empirical analysis of these markets is problematic due to the confidentiality of the annual contracts,

> 27For example, Krugman (1980); Eaton and Kortum (2002); Melitz (2003); Arkolakis et al. (2012) used ad-valorem transport costs, while Hummels and Skiba (2004); Feenstra and Romalis (2014); Wong (2021) used specific transport costs. Importantly, in all these papers, transport costs are symmetric for a given route.

> 28Other non-rate-fixing cooperative agreements between carriers still exist. They include vessel sharing and slot chartering arrangements and global alliances. These agreements are motivated by improving the quality and efficiency of maritime transport due to economies of scale, but they also raise antitrust issues (UNCTAD, 2018).

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which blocks researchers’ attempts to get the direct contract data.<sup>29</sup> In parallel to the annual contract market, there are spot markets for containerized shipping, which serve the residual demand, including smaller firms without annual contracts. In addition, the spot markets are better suited for empirical analysis as the relevant data are available to researchers (e.g., Wong, 2020). In the spot markets, information about the freight rates is widely available to all participants. As a result, the within-route freight rates in these markets tend to be unified, in line with how transport costs are viewed in trade models. Even the literature on the market power in shipping, trade imbalances, and network structure primarily tends to assume uniform within-route pricing. It focuses on explaining the variation in freight rates across the routes (e.g., Hummels et al., 2009; Asturias, 2020; Wong, 2020; Ganapati et al., 2020).

Shipping practitioners and trading firms are vividly aware of the differences between the two markets and closely monitor them. Indeed, while we do not have the exact data on the shares of the spot and annual-contract markets, some studies indicate that the annual-contract market is a dominant one (FMC, 2001; OECD, 2015). The primary role of the annual-contract market is further supported by the anecdotal evidence from the Journal of Commerce (JOC), a reputable source in the transportation and logistics industry. According to the JOC, the Shanghai Containerized Freight Index spot rate has increased by striking 135% between the 4th quarter of 2019 and 2020. Meanwhile, the average corresponding freight rates and earnings of individual carriers increased by much smaller amounts: the ONE’s average Pacific rate increased by 35% (JOC, Jan 29, 2021), while HappagLloyd’s average freight rate has increased by 9.5% in the fourth quarter with its annual freight rate increasing by only 4% (JOC, Jan 27, 2021).

These facts prompt several observations. First, the annual contract market is much larger than the spot market.<sup>30</sup> Second, by design, the annual-contract freight rates are much less volatile than 29For example, Wong (2020) described her unsuccessful attempt to get the contract data. We are aware of only one public reporting of (aggregated) contract freight rates: UNCTAD (2021a) reported average inter-regional contract freight rates for years 2018-2020, obtained from TIM Consult Market Intelligence.

30If the annual-contract ONE has not changed, the difference between the 135% and 35% implies that only 25.9% of the freight rates were determined on the spot market (35/135*100%). For the Happag-Lloyd’s, the share of the spot market is also likely to be low. Still, we do not have an apple-to-apple comparison since a 9.5% increase is an average increase across all of its routes, whereas the spot rates’ dynamics vary across regions.

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the spot market rates—the fact which has not been explored in the trade literature. In practice, importers closely watch annual contract rates as they determine their trade volumes for the entire year.<sup>31</sup> Third, if annual-contract markets dominate containerized shipping, the within-route price dispersion should be detectable in the transaction-level datasets, conditional on them reporting freight rates. Two papers have recently conducted this type of analysis: Holmes and Singer (2018) and Ardelean and Lugovskyy (2021).

**Stylized Fact 20.** _There are two types of containerized shipping markets: long-term contract market and spot market. The rates may differ significantly between the two._

Both document rate and link it to the and papers pronounced freight dispersion firm shipment sizes: larger firms and larger shipments face lower freight rates. The papers use different datasets and provide different interpretations of these findings. Holmes and Singer (2018) use Bills of lading filed with the U.S. Department of Customs and Border Protection (CBP). They attribute the lower freight rates faced by larger trading firms to the container indivisibility and the ability of larger firms to pack shipments fuller than smaller companies.<sup>32</sup> Ardelean and Lugovskyy (2021) use Chilean Imports reported by Panjiva. They explicitly model price-discriminating carriers with trading firms getting quotes from carriers, where each quote is subject to a fixed cost of processing. Larger firms have greater bargaining power in this setting as they have greater incentives to obtain more quotes. This advantage disappears if the number of carriers is limited so that all firms, large and small, obtain the same number of quotes.

Empirically, they find that the 90<sup>_th_</sup> percentile importing firms pay up to 23% lower freight rates than the 10<sup>_th_</sup> percentile firms. In line with their theoretical predictions, they also find that price discrimination disappears in the thin markets—i.e., in markets with three carriers or fewer. These results indicate that reducing market power distortion in the upstream industry (containerized shipping) may amplify the informational friction distortion in the downstream industry (freight rate

> 31See https://www.freightwaves.com/news/importers-face-massive-hike-in-ocean-contract-costs

> 32The main advantage of their dataset is in observing container types and sizes and specifying whether a container is filled to capacity. The disadvantage is in not observing important variety-specific characteristics, such as price and weight per unit.

31
across This of discrimination also results in the ac- asymmetry importers). type price asymmetric cess to imports between larger and smaller importers, increasing the observed firm heterogeneity.<sup>33</sup> Therefore, this result may have important implications for the welfare calculations and policy analysis (e.g., Han, 2021).

**Stylized Fact 21.** _Ceteris paribus, larger firms face lower freight rates than smaller firms._

# **4.2 Product Type and Variety Characteristics**

Different products demand different transportation costs as they vary in volume, units of measurement, dimensions, weight, time sensitivity, and import demand elasticity. Thus, for accurate measurement of transport costs, it is necessary to control for the product type. In the literature on the freight rate determinants, it is typically done with the fixed effects at the HS6, HS8, or HS10 product level.

Even within a product, there is a significant variation in variety-specific characteristics, such as price, weight per unit, time sensitivity, and dimensions. Schott (2004) has shown that across the 1994 U. S. manufacturing imports, the within-product unit value dispersion was on average 24 times. Lashkaripour (2020) found that, for 95% of goods, price is positively correlated with the weight per unit. Not surprisingly, more expensive varieties of the same good are subject to higher transportation costs. Indeed, most studies find the price elasticity of the freight rate to be between 0.4 and 0.6 (Hummels and Skiba, 2004; Hummels et al., 2009; Ardelean and Lugovskyy, 2021). Hummels et al. (2009) found that after controlling for the product type, variety price explains by far the most variation in freight rates. Ardelean and Lugovskyy (2021) demonstrated that omitting product and variety characteristics in freight rate regressions results in the pronounced omitted variables bias of other freight rate determinants.

> 33In many models, that would increase the shape parameter _θ_ in the Frechet productivity distribution of firms.

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**Stylized Fact 22.** _Product-level controls (at least at the HS6 level) and the within-product variety characteristics (e.g., price, weight per unit) are essential for the unbiased estimation of maritime freight rates._

# **4.3 The Effect of Distance on Freight Rates**

As discussed in Section 3.1, distance is often used as a proxy for transport costs, where the distance effect is typically estimated as a single parameter for all goods and destinations elasticity of freight rates. However, there are at least two strong reasons why the effect of distance is likely to be heterogeneous. First, geographic distance between countries affects only a portion of the total transportation costs. This fact alone suggests an essential source of heterogeneity of the distance effects on the total cost of transporting goods because the size of the distance-related portion depends on other factors such as port efficiency (Humphreys et al., 2019; Blonigen and Wilson, 2008) or fuel price (Brancaccio et al., 2021; Behar and Venables, 2011). Precisely, the total cost of delivering goods consists of voyage costs incurred en route (such as the cost of fuel) and terminal costs incurred at the point of departure or destination (such as the cost of unloading). Halaszovich and Kinra (2020) present indirect evidence of such heterogeneity by showing that the effect of geographic distance on trade is affected by the domestic port and road infrastructure. The imports and exports of countries with better port infrastructure are less affected by the geographic distance to trading partners. The road infrastructure weakens the estimated effect of distance on imports but strengthens the negative effect of distance on exports. Taken together, the results in Table 9 of their paper illustrate that the effects of distance on trade vary across countries.

Second, the heterogeneity of the distance effects can also be caused by technology. For example, Behar and Venables (2011) find that effect of fuel costs, which are related to the distance, on the shipping costs is smaller for containerized cargo than that one for bulk cargo. This fact is consistent with the idea that a larger part of the cost of shipping containers is not related to the distance. Another reason for heterogeneity is the varying degree of market power (see our discussion in section 4.1).

33
Thus, the effect of distance on the freight rate will be mixed with the effect of switching to a different route, which potentially has a different degree of market power.

Ignoring the resulting heterogeneity in distance effects is materially important if the research question goes beyond the average effect of distance. The average effect is sufficient to capture the negative impact of distance on trade. However, focusing on the average effect of distance is of less interest if we are interested in the effects of distance on particular industries and countries with specific and observed characteristics. The rest of the section focuses on determining the extent of the distance heterogeneity across countries and products.

# **4.3.1 Heterogeneous Distance Elasticity of Transport Costs: US Imports**

Using the US Census imports data, we explore whether the distance elasticity of transportation costs varies across export origins and products. For the country-specific heterogeneity, we leverage the fact that the US is geographically large, with ports allocated to different Custom Districts.<sup>34</sup> We first calculate the sea distances between various countries of origin and each of the US Custom Districts.<sup>35</sup> Next, we use these distances when employing the following model featuring the most direct approach to the estimation of the heterogeneity in distance elasticity: 34Therefore, the same country-exporter to the US has sufficient variation in sea distances to the US in large part due to the difference in sea distances to the coasts.

> 35These calculations are described in detail in Terner (2021). These distance measurements reflect the average _traveled_ distance of containerships from country _o_ to Custom District _d_ across 2015-2017.

34
<!-- Start of picture text --> Distribution of Transport Cost-Distance Elasticity<br>Country HS6<br>0.6<br>204<br>=<br>ao<br>0.2<br>oo. * i i ols i<br>-0.1 0.0 0.1 0.2 03 -0.1 0.0 0.1 0.2 0.3<br>Distance Elasticity<br>Note: There are 66 and 3,973 Country and HS6 estimates, respectively.<br><!-- End of picture text -->
||CountryModel|HS6 Model|
|---|---|---|
||(1)|(2)|
|(Intercept)|_−_5.904<sup>_∗∗∗_</sup>|_−_5.968<sup>_∗∗∗_</sup>|
||(0.304)|(0.284)|
|log(seadistance)|0.116<sup>_∗∗∗_</sup>|0.069<sup>_∗∗∗_</sup>|
||(0.017)|(0.010)|
|log(shipsize)|0.007|0.001|
||(0.010)|(0.010)|
|log(value-to-weight)|_−_0.463<sup>_∗∗∗_</sup>|_−_0.459<sup>_∗∗∗_</sup>|
||(0.002)|(0.003)|
|AIC|816522.218|804847.066|
|BIC|816835.895|805840.379|
|Log Likelihood|_−_408231.109|_−_402328.533|
|<br>Num. obs.|256,766|256,766|
|Num. groups: country|66||
|sd: country (Intercept)|0.63058||
|<br>sd: country_×_log(seadistance)|0.07421||
|sd: Residual|1.18527|1.14571|
|Num. groups: HS6||3973|
|sd: HS6 (Intercept)||0.32145|
|<br>sd: HS6_×_log(seadistance)||0.01625|
|Included Fixed Effects:|Year,District|Year,District,Country|
|Distance ElasticityCV:|0.466|0.079|

> _∗∗∗ p <_ 0.01; _∗∗ p <_ 0.05; _∗ p <_ 0.1, sd - standard deviation

Table 2: Heterogeneity of Distance Elasticity (based on US imports) maritime transport) for 223 importers and 23 partners annually for 2006 and 2007.

# **4.4 Port Infrastructure**

Ports infrastructure is vital to the integration of countries in the global economy via international trade. Consequently, there is an ongoing discussion of port reform and challenges associated with it (Haralambides, 2017). This discussion requires careful estimation of port efficiency, its sources, and its effect on transportation costs. Blonigen and Wilson (2008) classify the literature on evaluating port efficiency into three major groups: (i) Production function approaches, (ii) Surveys of port efficiencies, and (iii) Estimating port efficiencies from international trade data. In what follows, we will 36
describe these literatures, compare some measures of port efficiency, and discuss the links between port efficiencies and transportation costs.

# **4.4.1 Production Function Measures and Cost Analysis**

This operations research study approach is based on the production function estimation and involves several linear-programming applications. It focuses on (i) identifying an _efficiency frontier_ based on observed inputs and outputs; and then (ii) calculating the distance between a productive-unit (read: port) efficiency and the efficiency frontier. A popular linear-programming application includes the Data Envelopment Analysis (DEA), a non-parametric and deterministic approach sensitive to variable selection and measurement errors. The competing production function estimation approach is represented by the Stochastic Frontier Analysis (SFA)—a parametric and stochastic approach that requires careful decisions over the functional form of the production function.

Blonigen and Wilson (2008) provide an informative literature review of this approach. The main advantage of estimating the port efficiency as the production function is in pinpointing the marginal capacity gained from investing in any particular part of the port infrastructure. This analysis then allows identifying the bottlenecks for any given port and performing an informed cost-benefit analysis when evaluating various investments in ports. However, this type of analysis tends to omit the demand side and thus by itself is not providing a direct link with the transportation costs associated with utilizing the ports.

Recent work by Herrera Dappe and Suárez-Alemán (2016) linked this approach to transportation cost.<sup>36</sup> This estimated individual the DEA for paper first ports efficiency using methodology each year<sup>37</sup> and then merged the average DEA-methodology annual port efficiencies with the OECD Maritime Transport Cost dataset and UN Commtrade US exports from 2000-2007. Next, building on

> 36This paper leverages the data on nearly fifty (50) ports across East Asia, the Middle East, Southeast Asia, and Southern/Eastern Africa. Herrera Dappe et al. (2017) further focused on countries and ports around the Indian Ocean.

> 37It also decomposed productivity changes into technological progress, pure efficiency, and scale efficiency. This decomposition is performed by the Malmquist Total Factor Productivity (TFP) Index a la Caves et al. (1982). Other papers, such as Suárez-Alemán et al. (2016), also use the Malmquist TFP Index to track and decompose sources of productivity gains over longer port-efficiency times series.

37
Fink et al. (2002), they regressed (log) transport price _p_ on the (log) marginal transport cost _mc_ and (log) markup _μ_ with _mc_ being a function of the average country-specific port efficiency and _μ_ being determined by the product fixed effects and liner connectivity index:<sup>38</sup> where subscripts _j_ , _k_ , and _t_ index country-importer, HS2 product, and year, respectively.

Their estimation results indicate that a 0.1 increase in port efficiency reduces the (unit) maritime transport cost of exporting to the US by 2.3 percent. Put differently, moving from the 25th to the 75th percentile reduces transport costs by 3.5%. With similar data sources and empirical strategies, Herrera Dappe et al. (2017) extended this analysis by focusing on the impacts of port efficiency on bilateral trade flows with the US: The same 25th to the 75th percentile efficiency increases the value of exports to the US by roughly 0.56 percent.

Furthermore, Herrera Dappe et al. (2021) find that increasing port efficiency (based on Stochastic Frontier Analysis) from the 25th-75th percentile lowers transport costs by approximately 4 percent. Their point estimate of the unit transportation cost elasticity with respect to efficiency, namely _−_ 0.23, is consistent with the Data Envelopment Analysis efficiency measures of Indian/Western Pa- _− −_ cific Ocean ports which are between 0.22 and 0.24 (Herrera Dappe and Suárez-Alemán, 2016; Herrera Dappe et al., 2017).

# **4.4.2 Surveys of Port Efficiencies and Linking Them to Transportation Costs**

Clark et al. (2004) used the US Import Waterborne Databank alongside the World Economic Forum’s 1999 _Global Competitiveness Report_ (WEF GCR hereafter) to show that moving from the 25th to the 75th percentile of port efficiency reduces transport costs by approximately 12 percent. Their main variables were: (i) liner transportation charges per weight at the HS6 product level and (ii) Country38 Marginal cost is also a function of (i) containerization, (ii) transportation distance multiplied by fuel costs, (iii) economies of scale, (iv) trade imbalances, (v) value per weight, (vi) national income (GDP), (vii) _average_ country-specific port efficiency, and(viii-x) country, commodity, and time fixed effects.

38
level GCR port efficiency scores, indexed from one to seven (7 is the highest score).<sup>39</sup> To illustrate how their analysis may be applied to predict trade flows, the authors developed a country-specific maritime cost index (driven to a large degree by WEF GCR port efficiency) and used it in the standard bilateral gravity model of trade. They concluded that increasing maritime transport costs from the 25th to the 75th percentile would decrease bilateral trade by 22 percent.

# **4.4.3 Transport Costs as Measures of Port Efficiency**

Blonigen and Wilson (2008) expanded upon Clark et al. (2004) by estimating port-specific efficiency rates. Whereas Clark et al. (2004) utilizes _country_ level measures of port efficiency, which are based on survey results that are unsuitable for longitudinal analysis, Blonigen and Wilson (2008) used timevarying port fixed effects to capture an _individual_ port’s contribution to maritime shipping costs. Using US import data sourced from the National Data Center of the Army Corps of Engineers, they produced estimates for the top fifty (50) American ports and top one hundred (100) foreign ports (by import value from the US perspective). Foreign port efficiency measurements are next aggregated into a country-level estimate and then are used in a standard gravity trade model. Without controlling for country-level fixed effects, the elasticity of bilateral trade with respect to port efficiency is 1.27. Since port efficiency estimates are time-varying, country fixed effects can be included. Thus, controlling for unobserved country-specific confounds reduces the bilateral trade port efficiency elasticity to 0.32. Based on these, moving a country from the 25th to 75th percentile of port efficiency increases bilateral trade by a more modest 5 percent.

Feenstra and Ma (2014) used this approach to measure trade facilitation. They used port infrastructure to analyze the extensive margin—or product variety—on both import and export trade. A 10 percent increase in port efficiency increase export variety by 1.5-3.4 percent but only increases the intensive margin by 0.2-1 percent; this result is broadly consistent with a literature, best exemplified by Bergin and Lin (2008), which documents how and why intensive and extensive margins respond 39The scores included a country’s overall level/quality of infrastructure, cargo handling restrictions, mandatory port services, and severity of the organized crime. These four factors can explain more than 77 percent of WEF GCR variation.

39
<!-- Start of picture text --> Country Level Port Efficiency<br>cial SGP°<br>o ° DEU<br>= *HKG NLD e r<br>5 BEL<br>a4 Mys SWE .<br>£ @ SPN © owN ih *<br>g a KOR ® CAN<br>= NZL e<br>=tas gre® ESP Ps GBR<br>a HND | THA U5 e<br>3 bd ISR<br>= LKA, ZAF<br>44 e® y=5.7+7.3x<br>g CHN us JPAK DOM ao<br>a e a e Tun e R*=0.45<br>re GT a<br>wi Mex @ ARG<br>= 34-PH @ON TO ITA<br>* ECU @ BRA<br>+n ® PER<br>BGD * *cri<br>40% -30% -20% -10% o% 10% 20%<br>Blonigen & Wilson Efficiency<br><!-- End of picture text -->
global economy. Shipping services became the first-order part of the overall infrastructure in our economy, comparable to other essential services such as electricity, international transportation, and communication. Providing ocean shipping services proves to be challenging because the demand is much more volatile than the ability of carriers to adjust capacity on the supply side. Therefore, on the one hand, carriers require a sufficient profit margin to absorb negative shocks while providing sufficient capacity during significant demand spikes. On the other hand, research documents significant market carriers which utilize for both across- and within-route discrimination power by they across trading firms. Designing optimal policy becomes even more complicated in the international setting.

Oceanic transportation is directly related to the environmental costs of international trade. Environmental regulation has had a negligible effect on the carriers’ costs and freight rates in the past. Therefore, the literature has mainly focused on estimating the extent of carbon emission by oceanic shipping, the related welfare analysis, and the comparison of emissions across transportation modes (Cristea et al., 2013; Shapiro, 2016). Any significant additional environmental regulations, such as, for example, operational efficiency regulations by IMO, will affect freight rates, fleet size, modes of transportation, technological choices, and eventually patterns of trade (UNCTAD, 2021a,b). New research can leverage new detailed datasets to understand better how changes in maritime freight rates will affect the technological and geographic (routes) choices of carriers and trading firms on the intensive and extensive margins, including the choice of the mode of transportation.

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46
<!-- Start of picture text --> US Container Imports 2007-2019<br>Source: Panjiva<br>Total Number of Shipments Total TEU Shippment volume Total Vessel TEU Capacity<br>30<br>25<br>a)<br>iu<br>Ee<br>#<br>© 20<br>=<br>a<br>a<br>2<br>45<br>oO<br>wo<br>Cc<br>2<br>5<br>2007 2009 2011 20135 2015 2047 20192007 2009 2041 2013 2015 2017 20192007 2009 2011 2013 2015 2017 2019<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> US Share of World Fleet Containership Capacity<br>°<br>2007 2009 2011 2013 2015 2017 2019<br>Year<br><!-- End of picture text -->
Table 3: Dry Bulk Average Spot Rate

|**ISO Code**|**Economy Name**|**TC rate**|**DWT**|**TC rate/DWT**|**Count**|
|---|---|---|---|---|---|
|BR|Brazil|15107.65|70584.86|0.23|605|
|BN|Brunei Darussalam|,<br>10,500.00|,<br>52,223.00|0.20|1|
|CA|Canada|13,144.44|59,302.11|0.23|9|
|CL|Chile|14,250.00|60,656.00|0.24|2|
|CN|China|11,733.57|85,474.21|0.15|2,495|
|CI|Côte d’Ivoire|21,750.00|59,589.50|0.36|2|
|CM|Cameroon|19,375.00|50,629.50|0.41|4|
|CD|Democratic Republic of the Congo|15,750.00|53,413.00|0.29|1|
|CG|Republic of Congo|15,000.00|56,925.00|0.26|1|
|CO|Colombia|16,094.29|62,087.26|0.29|35|
|CV|Cabo Verde|11,583.33|48,251.00|0.24|3|
|CR|Costa Rica|31,000.00|61,384.00|0.51|1|
|CU|Cuba|19,333.33|31,686.67|0.63|3|
|DE|Germany|15,684.12|77,911.42|0.21|149|
|DK|Denmark|11,882.56|51,600.42|0.26|43|
|DO|Dominican Republic|14,750.00|42,179.00|0.36|4|
|DZ|<br>Algeria|14,667.50|56,995.81|0.27|21|
|EC|Ecuador|14,915.69|78,756.74|0.19|838|
|EG|Egypt, Arab Rep.|17,569.03|73,046.89|0.25|88|
|ES|<br>Spain|15,806.75|74,979.96|0.23|126|
|FI|Finland|14,100.00|37,985.00|0.37|1|
|FR|France|14,880.95|68,593.94|0.24|85|
|GA|Gabon|14,020.00|59,442.40|0.24|5|
|GB|United Kingdom|15,205.85|77,411.61|0.21|124|
|GE|<br>Georgia|13,416.67|34,405.33|0.39|3|
|GH|Ghana|18,285.71|58,057.57|0.32|7|
|GI|Gibraltar|15,983.66|88,155.99|0.19|231|
|GN|Guinea|10,501.32|69,028.05|0.16|19|
|GR|Greece|14,387.50|51,635.89|0.31|28|
|GT|Guatemala|16,500.00|38,246.50|0.43|2|
|HK|Hong Kong, SAR China|10,770.85|76,276.70|0.15|293|
|HR|Croatia|14,350.00|81,895.00|0.18|1|
|HT|Haiti|14,000.00|56,982.00|0.28|2|
|ID|Indonesia|11,270.00|64,128.69|0.18|270|
|IN|India|13,881.78|70,522.49|0.20|782|
|IE|Ireland|15,898.53|80,095.97|0.20|34|
|IR|Iran, Islamic Republic of|16,150.00|56,655.00|0.28|2|
|IQ|Iraq|17,958.33|60,391.17|0.30|6|
|IS|Iceland|13,250.00|75,200.00|0.18|1|
|IL|Israel|17,500.00|89,092.67|0.28|3|
|IT|Italy|16,444.26|84,897.43|0.21|122|
|JM|Jamaica|13183.33|79954.83|0.20|12|
|JO|Jordan|,<br>16,325.00|,<br>62,847.25|0.26|4|
|JP|Japan|12,267.62|80,874.57|0.16|421|
|KE|Kenya|12,150.00|60,729.80|0.20|5|
|KH|Cambodia|12,750.00|55,564.10|0.23|10|
|KR|Korea, Rep.|11,234.89|84,478.56|0.14|427|
|KW|Kuwait|13,930.77|55,481.38|0.25|13|
|LY|Libya|45,000.00|174,316.00|0.26|1|
|LK|Sri Lanka|12,237.93|74,459.17|0.17|29|

48
Table 3: Dry Bulk Average Spot Rate

|**ISO Code**|**Economy Name**|**TC rate**|**DWT**<br>**TC rate/DWT**|**Count**|
|---|---|---|---|---|
|LT|Lithuania|11,125.00|63,082.75<br>0.16|4|
|LV|Latvia|12,500.00|37,100.00<br>0.34|1|
|MA|Morocco|15,648.33|69,660.23<br>0.24|75|
|MX|Mexico|16,057.14|43,079.14<br>0.38|7|
|ML|Mali|12,750.00|56,452.00<br>0.23|1|
|MT|Malta|16,875.00|79,612.00<br>0.21|2|
|MM|Myanmar|9,950.00|56,951.57<br>0.18|7|
|MZ|Mozambique|11,725.00|57,209.22<br>0.22|18|
|MR|Mauritania|15,500.00|48,619.20<br>0.33|5|
|MU|Mauritius|11,500.00|81,889.00<br>0.14|1|
|MY|Malaysia|12,612.71|71,957.76<br>0.18|118|
|NA|Namibia|12,000.00|57,945.00<br>0.21|1|
|NC|New Caledonia|10,125.00|69,970.38<br>0.15|8|
|NG|Nigeria|17,035.71|53,050.14<br>0.36|7|
|NL|Netherlands|15,619.89|82,984.95<br>0.20|220|
|NO|Norway|9,375.00|41,154.25<br>0.24|4|
|OM|Oman|18,500.00|71,810.83<br>0.26|6|
|PK|Pakistan|14,894.30|60,602.02<br>0.25|57|
|PE|Peru|14,500.00|59,675.50<br>0.25|2|
|PH|Philippines|12,489.61|69,267.46<br>0.18|308|
|PL|Poland|18,052.56|74,680.23<br>0.25|39|
|PR|Puerto Rico|13,875.00|45,995.50<br>0.32|2|
|PT|Portugal|14,805.00|69,885.77<br>0.27|30|
|QA|Qatar|10,975.00|48,714.83<br>0.23|6|
|RO|Romania|12,931.25|48,255.50<br>0.28|16|
|RU|Russian Federation|12,135.29|59,408.41<br>0.24|17|
|SA|Saudi Arabia|16,093.75|52,346.83<br>0.35|24|
|SD|Sudan|18,227.50|53,586.10<br>0.32|10|
|SN|Senegal|17,068.75|43,760.12<br>0.41|8|
|SG|Singapore|12,591.70|71,002.03<br>0.19|594|
|SL|Sierra Leone|12,750.00|57,200.00<br>0.23|2|
|SV|El Salvador|13,437.50|58,492.50<br>0.25|4|
|SR|Suriname|12,500.00|32,616.00<br>0.38|2|
|SK|Slovak Republic|11,708.33|81,401.75<br>0.15|12|
|SI|Slovenia|31,000.00|90,266.00<br>0.35|2|
|SE|Sweden|21,333.33|66,789.83<br>0.32|6|
|TG|Togo|18,875.00|50,870.00<br>0.38|4|
|TH|Thailand|10,268.98|56,779.10<br>0.19|108|
|TT|Trinidad and Tobago|14,500.00|81,462.00<br>0.18|1|
|TN|<br>Tunisia|12,500.00|48,379.50<br>0.27|2|
|TR|Turkey|15,022.26|57,638.42<br>0.27|266|
|TW|Taiwan, China|11,091.10|81,341.17<br>0.14|292|
|UA|Ukraine|33,062.00|112,093.56<br>0.28|25|
|US|United States|16,065.54|63,978.53<br>0.27|436|
|VE|Venezuela, RB of|15,250.00|53,368.60<br>0.29|5|
|VN|Vietnam|11,816.67|66,780.01<br>0.19|204|
|YE|Yemen, Rep.|13,250.00|56,220.33<br>0.23|3|
|ZA|South Africa|12,783.28|69,796.60<br>0.19|290|

49