IMO Market-based measures expert group (MBM-EG)...IMO Market-based measures expert group (MBM-EG)...
Transcript of IMO Market-based measures expert group (MBM-EG)...IMO Market-based measures expert group (MBM-EG)...
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
FINAL REPORT
August 2010
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
The assessment framework integrates impacts on shipping and product markets
3
It represents a product’s shipping, production and consumer markets
overseas producers, which are dependent on shipping to
serve the market in question, and land producers, which do
not use ships and some of which may be imports, compete
the introduction of carbon regulation on shipping increases
the cost of shipping
this affects the costs of overseas producers
the overall price of the good rises and therefore the quantity
demanded falls; the size of the price increase depends on the
extent to which firms are able to pass-on the increased costs
the market shares of overseas firms, land firms and ships are
also affected
changes in overseas producer shipping costs are estimated
information requirements are realistic, limited to market
shares, quantities and prices in each market and ship carbon
intensity characteristics
note it is assumed that there is no change in the cost of
transporting goods overlandship costs
freight rate
overseas
producer price
consumer price
consumer
demand
import market
share
demand for
ships
demand for
importsfeedback #1
feedback #2
CPT #1
CPT #2
=
Shipping
Product
Figure 1 The model structure captures many
market interactions
Source: Vivid Economics
The magnitude and distribution of impacts within and across markets can be described
4
Figure 2 This illustration shows how the burden of costs is distributed
Source: Vivid Economics
revenue
raised
costs borne by overseas
producers due to
loss of profit margin
costs borne by overseas
producers due to loss
of quantity
costs borne by
consumers
gains to overland producers due to
change in profit margin
gains to overland producers due to
change in quantitycosts borne by
ship owners
This report covers a selection of shipping and product markets
The tasks in the terms of reference are to estimate:
— the impact of higher bunker price on freight rates (this is assessed using econometrics)
— the impact of higher freight rates on trade, consumers and producers (assessed using market analysis and modelling)
For each task, analysis is presented for several markets:
— a selection of grain markets with a focus on developing countries
— iron ore, with a focus on the Chinese market
— quantitative modelling is undertaken for the Chinese iron ore market
— container shipping with some illustrative data for important products on the East Asia to EU market route
— VLCC shipping rates with a focus on the South Korean and US markets
— quantitative modelling is undertaken for the South Korean and US crude markets
Factors that determine overall impacts of bunker price increases on individual product markets are:
how freight rates respond to increased bunker price
current shares of maritime transport costs within product prices (in turn dependent on distance and efficiency of transport)
the ability of maritime importers to pass on costs to local consumers
Factors that determine cost pass-through rates are:
the share of imports in consumption
competitiveness of local markets and imports
5
The elasticity of the freight rate to bunker price is found to differ across shipping markets
6
Shipping market Market average
Panamax grain 0.19
Capesize ore 0.96
Containers 0.12
VLCC 0.37
Table 1 The estimated average elasticity of the freight rate with respect to bunker price for each
shipping sector ranges from 0.12 for containers to 0.96 for Capesize ore vessels
Source: Vivid Economics
The cost pass-through of increased freight rates into product prices varies across products and markets
7
Product market Cost pass-through (%) Product market Cost pass-through (%)
Wheat South Africa 10–40 Iron ore China* 52
Wheat Kenya 50–75 Furniture EU 60–90
Wheat Algeria 50–75 Apparel EU 10–40
Barley China 10–25 Crude oil South Korea* 111
Rice Philippines 5–20 Crude Oil US* 73
Maize Saudi Arabia 90–100
Table 2 The estimated cost pass-through can range from around 10 per cent to over 100 per cent
depending on the competitive dynamics in the particular market
Source: Vivid Economics
*the estimates for these markets were made with detailed quantitative model, while the
other estimates are assumed, supported by market share data
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
8
9
Increased fuel prices will lead to higher freight rates
For each market, the price elasticity of the freight rate with respect to bunker price will be calculated;
that is, by what percentage will the freight rate increase for a one per cent increase in the bunker
price?
Data has been assembled for 9 different VLCC crude oil routes, 11 different Capesize ore routes, 5
different grain routes, 6 different container routes and 12 different sources of bunker fuels.
The data is generally available weekly, with the exception of the container data which is available
quarterly.
Data on each route is available over a number of years; for some routes data is available as far back
as 1987, while for others data has only begun to be collected over the last few years.
Data for all freight rates and bunker price, with the exception of VLCCs, is measured in dollars per
tonne over the particular route.
The units of the VLCC freight rates are in ‘WorldScale Units’, and are set relative to a benchmark
price in each year; this means that freight rates are not strictly comparable across years as the
benchmark is reviewed annually;
— this can be taken into account by including year dummy variables in the statistical analysis
so that the results are unaffected by the changing units
These elasticities can be applied to any given percentage increase in the bunker price.
This analysis estimates the magnitude of this for a number of shipping routes
Bunker price exhibits substantial volatility over time
Figure 3 Bunker price generally rose until mid-2008 before falling sharply; bunker price for Singapore
is presented and the relationship is similar for other bunker sources
Source: Vivid Economics and Clarksons data
10
Date
Sin
ga
po
re b
un
ke
r fu
el p
rice
$/to
nn
e
100
200
300
400
500
600
700
1990 1995 2000 2005 2010
11
Data on bunker price and freight rates can be used to derive the elasticity of interest
A number of different statistical techniques will be considered
The most basic approach to estimating the elasticity of the freight rate with respect to bunker price is to use a method known
as ordinary least squares (OLS). This technique is relatively simple, but can only be used when the data satisfy certain
conditions. The equation used in the estimation is as follows: lnSt=α+β1lnBt+u, where S is the spot freight rate, B is the spot
bunker price, u a random error term and the subscript t refers to a particular time period (weekly or quarterly, depending on
the data). The coefficient β1 is an estimate of the elasticity.
For VLCCs, some authors have suggested that the elasticity will be higher for higher bunker price. In this case a slightly
different equation is used: St=α+β1Bt+u. The elasticity, which varies over time, is then given by β1*(Bt/St).
Some series in this analysis may be better analysed using an error correction model (ECM) to account for the dynamic
nature of the relationship between the variables. The equation for this type of model is: ∆lnSt=α+β2lnSt-1+ β3lnBt-1+
β4ΔlnBt+u, where the subscripts t and t-1 refer to time periods and ∆ is the difference operator i.e. ∆xt=xt-xt-1 and ln denotes
natural logarithm. Note it is not possible to use this methodology for VLCC routes because the units are different in each
year, nor for the container routes because there are insufficient data. The ECM estimates should generally be preferred to
the OLS estimates.
The intuition behind this equation considers that there is a long-run relationship between shipping spot rates and bunker
price. There is short-term variation in both variables away from the long-run equilibrium relationship. Including both lagged
bunker price and lagged spot prices accounts for an adjustment towards equilibrium from last period’s shock, and the
inclusion of changes in bunker price allows for an adjustment towards a new equilibrium resulting from the change in bunker
price. The long-run elasticity of spot freight rates with respect to bunker price, which can be obtained by inserting xt=x* for all
t, can be calculated as –β3/β2.
Note that in the results, standard errors are presented along with the estimates to enable the interested reader to conduct
additional statistical tests. To construct a 95 per cent confidence interval, multiply the standard error by 1.96 and add and
subtract this to the estimate to give the upper and lower bounds respectively.
The econometric modelling considers a number of factors affecting freight rates
The equations on the previous slide include only bunker fuels as an explanatory variable for ease of
presentation, but the equations as estimated include a range of other variables:
— global fleet size for the relevant vessel type as a measure of shipping supply;
— total global trade in the cargo as a measure of demand for shipping;
— for container shipping, the volume of trade on connected routes;
— for grain markets, specific effects in each year to allow for climatic shocks.
The elasticity of freight rates with respect to bunker price is estimated separately for each route and
cargo combination, and so any factor which varies by route and cargo combination, such as the
number of competitors or the demand elasticity for the cargo, is taken into account by the model.
The R2 statistic, a measure of the proportion of the variation in freight rates accounted for by the
variables included in the regression, is presented for each model in an appendix.
For each model, the best guide to the overall elasticity for that trade across all global routes is given in
bold.
12
Demand and supply conditions are the most important controls included
Care should be taken when extrapolating these results to other routes
the elasticity of the freight rate, s, with respect to bunker price, b, is given by the
equation (∂s/ ∂b)*(b/s); that is, the elasticity is a function of the ratio of the absolute
bunker price to the freight rate
the estimates of the elasticity derived in this section are specific to the particular route,
and may not be the same on all routes; in particular, for those which have substantially
different cost structure
the ratio of the bunker price to the freight rate would be expected to be lower, for
example, on routes which are shorter and for routes which have higher port charges;
consequently, this means that the elasticity of freight rates to bunker price would also be
expected to be lower on such routes
furthermore, the elasticities should only be applied to the portion of total maritime
transport costs which are accounted for by the freight rate; applying these elasticities to
the total maritime transport cost will produce estimates of cost increases which are too
high
13
Fuel prices represent a different proportion of total freight costs for different
routes and commodities
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of six grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
14
15
A notable feature across many shipping markets is the rapid increase in prices in 2007
Some driving factors are common between markets, while others are particular to
a certain cargo
Strong global economic growth, and the accompanying growth in trade volumes, increased demand for shipping; while the supply
of ships was not able to increase fast enough to cater for the additional demand.
China accounted for a large share of global growth in this period, and imports large quantities of the major bulk commodities.
Vessels can generally carry cargo on any number of routes globally, so the freight rate for a particular commodity and vessel type
tends to be very similar across routes, and events in one region can have a global impact on the freight rate.
A drought in Australia caused a large reduction in that country’s output of grain, causing nearby Asian countries to source imports
from further afield; this increased the demand for shipping and drove the freight rate higher.
Australia also played a role in the rising prices of iron ore freight: port congestion there meant that rising Asian demand had to be
met from other sources, such as from Brazil; again, the greater distances involved increased the demand for shipping for
Capesize iron ore vessels.
A challenge in the econometric estimation is to ensure that factors which affect both bunker price and freight rates, such as the
level of economic activity, do not bias the estimate of the elasticities: in order to control for this, the volume of cargo moved
worldwide and the size of the relevant shipping fleet are included as control variables in the regressions (for grain shipping
routes, year dummy variables are also included to account for fluctuations caused by annual climatic variation).
After controlling for these demand and supply factors, there is no evidence that the elasticity varies meaningfully over time for any
route:
— this was tested for by allowing the relevant coefficients to differ post-2001 or post-2005;
— in most cases, this did not produce a statistically significant change; where the change was statistically significant, it
was very small in magnitude such that accounting for it would make no discernible impact upon the results.
Panamax grain freight rates were relatively stable until the 2002 to 2007 period when they rose by a factor of ten
Figure 4 Grain freight rates are highly correlated across routes, suggesting a global market for
Panamax grain vessels
Source: Vivid Economics and Clarksons data 16
Pa
na
ma
xg
rain
fre
igh
t ra
te $
/to
nn
e
20
40
60
80
100
US to EU
Vancouver to Japan
1990 1995 2000 2005 2010
Higher bunker price is associated with higher spot freight rates on the US to EU grain route
17
Figure 5 Grain freight rates are sensitive to changes in the bunker price, and this sensitivity appears
to be higher at higher bunker price
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)log
Sp
ot fr
eig
ht r
ate
($
/to
nn
e, P
an
am
ax
US
Gu
lf to
Ro
tte
rda
m)
2.5
3.0
3.5
4.0
4.5
4.0 4.5 5.0 5.5 6.0 6.5
The relationship between grain freight rates and bunker price is similar on the Vancouver to Japan grain route
18
Figure 6 Higher bunker price leads to increased freight rates on the Vancouver to Japan grain route
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)log
Sp
ot fr
eig
ht ra
te (
$/to
nn
e, P
an
am
ax V
an
co
uve
r to
Ja
pa
n)
2.5
3.0
3.5
4.0
4.5
4.0 4.5 5.0 5.5 6.0 6.5
19
Grain freight rates increase by 2.5 per cent when bunker price increases by ten per cent
Origin DestinationCargo and ship
typeData availability
Elasticity (OLS
estimate)
Elasticity (ECM
estimate)
US Gulf RotterdamLight grain,
Panamax1990–2008 0.223 (0.039) 0.293 (0.218)
US Gulf RotterdamHSS grain,
Panamax1990–2008 0.201 (0.039) 0.238 (0.224)
US Gulf JapanHSS grain,
Panamax1990–2008 0.218 (0.031) 0.156 (0.252)
Northern Pacific
(US/Canada)Japan Panamax 1990–2008 0.103 (0.033) 0.314 (0.237)
Average 0.19 0.25
US Gulf JapanHSS grain,
Supramax2007–2010 1.430 (0.052) 1.561 (0.236)
Table 3 The freight rate elasticity is similar across the four Panamax routes but higher for the newer
Supramax route
Source: Vivid Economics and Clarksons dataHSS: heavy grain, sorghums and soyas
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of six grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
20
The spot freight rate on the Brazil to China iron ore route peaked in 2008
Figure 7 Volatility in spot iron ore freight rates has been increasing over time and movements in freight
rates are similar across routes, suggesting a global market for Capesize vessels
Source: Vivid Economics and Clarksons data 21
Date
Ca
pe
siz
e o
re fre
igh
t ra
te $
/to
nn
e
20
40
60
80
100
Brazil to China
Australia to Rotterdam
1990 1995 2000 2005 2010
Higher bunker price is associated with higher spot freight rates on the Brazil to China iron ore route
22
Figure 8 The relationship between spot freight rates and bunker price is positive but there is
substantial volatility
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)
log
Sp
ot fr
eig
ht r
ate
($
/to
nn
e, C
ap
esiz
eB
razil to
Ch
ina
)
1.5
2.0
2.5
3.0
3.5
4.0
4.5
4.0 4.5 5.0 5.5 6.0 6.5
Iron ore freight rates from Australia to Rotterdam appear less sensitive to bunker price
23
Figure 9 The slope of the line is indicative of the elasticity of the freight rate to bunker price
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)
log
Sp
ot fr
eig
ht r
ate
($
/to
nn
e, C
ap
esiz
eA
ustr
alia
to R
otte
rda
m)
1.5
2.0
2.5
3.0
3.5
4.0
4.0 4.5 5.0 5.5 6.0 6.5
24
Iron ore freight rates are more sensitive to changes in bunker price than grain rates
Origin Destination Data availabilityElasticity (OLS
estimate)
Elasticity (ECM
estimate)
Narvik (Norway) Rotterdam (EU) 1990–2010 0.635 (0.038) 0.801 (0.282)
Tubarao (Brazil) Rotterdam 1991–2010 0.934 (0.044) 1.139 (0.312)
Tubarao Japan 1991–2010 1.074 (0.046) 1.307 (0.354)
Tubarao Beilun (China) 1996–2010 1.031 (0.059) 1.373 (0.381)
Nouadhibou
(Mauritania)Rotterdam 1990–2010 0.644 (0.037) 0.577 (0.255)
W. Australia Rotterdam 1990–2010 0.623 (0.035) 0.483 (0.281)
W. Australia Japan 1990–2010 0.716 (0.039) 0.717 (0.325)
Saldanha Bay
(South Africa)Beilun 2001–2010 0.828 (0.097) 0.804 (0.608)
W. Australia Beilun 2001–2010 0.759 (0.101) 1.165 (0.627)
Goa (India) Beilun 2001–2010 0.853 (0.093) 0.829 (0.540)
Port Cartier (Quebec) Rotterdam 2001–2010 0.701 (0.098) 1.358 (0.494)
Average 0.80 0.96
Table 4 Iron ore freight rates increase by 9.6 per cent for a 10 per cent increase in bunker price
Source: Vivid Economics and Clarksons data
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of six grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
25
26
Data on container freight rates is available for six major routes
The relationship between container freight rates and bunker price is less clear
than for the commodity freight rates
The data used is a quarterly index of container freight rates published by UNCTAD; this index is
available for the six container freight routes to and from each pair of the EU, the US and East
Asia.
There are significant differences between routes, including between routes travelling the same
journey in a different direction.
It was not possible to source any data on a container freight route into a small island developing
state.
Accounting for demand and supply conditions is important in the container market
27
On average, a ten per cent increase in bunker price would increase container
freight rates by one per cent
Figure 10 illustrates the complex relationship between bunker price and fronthaul and backhaul container freight rates for the EU-
Asia route.
EU to Asia container freight rates appear more correlated with bunker price, with the exception of the beginning of the sample
period.
Asia to EU freight rates show greater variance, consistent with demand on this route corresponding to high capacity utilisation
and an inelastic segment of the container shipping supply curve
A number of variables are included in the statistical model for container freight rates:
— bunker price;
— the size of the global container fleet in TEU equivalents;
— the volume of trade on route in question;
— the volume of trade leaving the destination region (e.g. the EU to US and EU to Asia trade volumes used in the model of the Asia
to EU route);
— the measure of imbalance on container routes used by UNCTAD (calculated as (q1-q2)/(q1+q2), where q1 is the volume of trade
on the route in question and q2 is the volume of trade on the reverse route) .
Note that either the volume of trade leaving the destination market OR the imbalance measure is included in the regressions as
they are alternative means of capturing the same dynamic, although the former set of controls are more general and so these
estimates are to be preferred; the imbalance measure is included to allow a more direct comparison with the UNCTAD analysis.
Due to the quarterly nature of the data, there are insufficient observations available for more sophisticated statistical modelling,
and so only the results of the OLS method are presented.
Container freight rates show a different pattern to the freight rates for bulk commodities
Figure 10 Container freight rates on front-haul and back-haul routes do not always move in tandem
Source: Vivid Economics and UNCTAD data 28
Conta
iner
freig
ht
rate
($/T
EU
)/B
unker
pri
ce (
$/t
onne)
500
1000
1500
2000
EU to Asia
Singapore bunker price
Asia to EU
1993 1998 2003 2008
Bunker price appears correlated with the EU to Asia container freight rate, but there is a lot of variation
29
Figure 11 Bunker price appears to be a smaller influence on freight rates for containers than for the
bulk commodities
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)
log
Sp
ot co
nta
ine
r fr
eig
ht r
ate
($
pe
r T
EU
, E
U to
Asia
)
6.4
6.6
6.8
7.0
4.5 5.0 5.5 6.0 6.5
Bunker price is weakly associated with freight rates on the Asia to EU route
30
Figure 12 While higher bunker price is associated with higher freight rates, there are other variables
which are having an important impact
Source: Vivid Economics and Clarksons data
log Bunker fuel price ($/tonne, Singapore)
log
Sp
ot co
nta
ine
r fr
eig
ht r
ate
($
pe
r T
EU
, A
sia
to E
U)
6.8
7.0
7.2
7.4
7.6
4.5 5.0 5.5 6.0 6.5
On average, a ten per cent increase in bunker price increases container freight rates by between one and two per cent
Origin DestinationOLS estimate of elasticity
(standard error)
OLS estimate of elasticity
(standard error)
destination/origin control imbalance control
Asia US 0.207 (0.070) 0.252 (0.071)
US Asia 0.041 (0.066) 0.038 (0.069)
EU Asia 0.260 (0.087) 0.338 (0.084)
Asia EU 0.057 (0.074) 0.135 (0.079)
US EU 0.117 (0.093) 0.191 (0.092)
EU US 0.119 (0.051) 0.236 (0.049)
Average 0.12 0.20
31
The impact appears stronger on some routes than others
Table 5 The estimated elasticity of container freight rates to bunker price is 0.124
Source: Vivid Economics calculations from
UNCTAD, Clarksons and Containerisation
International data
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of six grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
32
33
VLCC freight rates generally increase by three to four per cent if bunker price increases by ten per cent
Origin Destination Data availabilityConstant elasticity
estimate (s.e.)
Variable elasticity
median (mean)
Ras Tanura (Saudi
Arabia)Rotterdam (Netherlands) 1990–2010 0.331 (0.079) 0.247 (0.380)
Ras Tanura Ulsan (South Korea) 1990–2010 0.399 (0.097) 0.357 (0.488)
Ras Tanura Chiba (Japan) 1990–2010 0.385 (0.096) 0.321 (0.455)
Ras Tanura Loop (US Gulf) 1997–2010 0.463 (0.124) 0.463 (0.650)
Bonny Offshore (Nigeria) Loop 1997–2010 0.342 (0.121) 0.292 (0.376)
Bonny Offshore Kaohsiung (Taiwan) 1998–2010 0.249 (0.122) 0.123 (0.145)
Ras Tanura Ain Sukhna (Egypt) 1990–2010 0.364 (0.100) 0.345 (0.451)
Sidi Kerir (Egypt) Rotterdam 1990–2010 0.236 (0.074) 0.158 (0.224)
Ras Tanura Singapore 1996–2010 0.534 (0.139) 0.606 (0.795)
Average 0.37 0.32
Table 6 The price elasticity of VLCC freight rates is similar even if the elasticity is allowed to vary with
bunker price
Source: Vivid Economics and Clarksons data
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— Container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of six grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
34
Overview of evidence to be compiled in the analysis
35
Factors that determine overall impacts of bunker price increase on individual product
markets are:
how freight rates respond to increased bunker price;
current shares of maritime transport costs within product prices (in turn dependent on distance
and efficiency of transport); and
the ability of maritime importers to pass on costs to local consumers.
Factors that determine cost pass-through rates are:
the share of imports in consumption; and
competitiveness of local markets and imports.
36
The markets selected for detailed analysis can inform understanding of the impact of higher freight rates on a wider range of markets
One of the key determinants of whether increased freight rates lead to higher
prices is the proportion of supply which arrives by sea
If imports arriving by sea face competition from land-based domestic production or imports via road or
rail, then they will generally be less able to pass on these increased costs to customers.
Instead, in those markets, some of the costs will be absorbed by the foreign producer, and only part
of the increased costs will be passed through to higher prices.
Land-based producers then experience an increase in profitability and will likely increase their market
share; the market share of seaborne imports will fall.
The balance of these effects depends on the market structure in the destination country: in places
where the product market is more competitive, the increase in prices will be lower and land-based
producers will take more market share; where it is less competitive, land-based producers may
choose to keep output fixed and allow prices to rise more to increase profit margins.
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
37
All of the grain markets analysed are in the developing world
38
These routes account for a small proportion of the global grain trade
There has been particular concern from developing countries about the impact of
increased freight rates on local food prices; for this reason all of the six routes chosen
for analysis are developing country destination markets.
It is important to note that these routes are unlikely to be representative of the broader
grain market, because the bulk of the trade in grain is between the wealthier regions of
North America, Japan, Australia and the EU.
In particular, the share of the total cost burden borne by the developing world will almost
certainly be much higher in the six markets analysed here than for all global grain
markets on average.
The grain market analysis is designed to explore a diversity of outcomes from increasing the cost of sea-borne trade
The six markets chosen for detailed analysis are wheat in South Africa, Kenya and
Algeria; barley in China; rice in the Philippines and maize in Saudi Arabia
Source: International Trade Centre (UNCTAD/WTO)
Figure 13 The chosen markets represent a wide range of import dependency (left) as well as a balance
between developed and developing world suppliers (right)
39
62%
31% 32%
73%
85%
6%
38%
69% 68%
27%
15%
94%
0%
20%
40%
60%
80%
100%
South Africa
(wheat)
Kenya (wheat)
Algeria (wheat)
China (barley)
Philippines (rice)
Saudi Arabia (maize)
Imports Local Production
40%
78%
17%
98%
68%
60%
22%
83%
100%
2%
32%
0%
20%
40%
60%
80%
100%
South Africa
(wheat)
Kenya (wheat)
Algeria (wheat)
China (barley)
Philippines (rice)
Saudi Arabia (maize)
Developed Developing
Wheat consumed in South Africa is supplied from a balance of local and international sources
40Source: ITC and FAO and GrainSA estimated grain production costs
Of the total wheat supply of 3 Mt, 2 Mt is locally sourced.
Shipping costs for the main overseas sources are currently around 24 per cent of value, or $0.06 to $0.07 per
kilogram.
The average profit margin of local producers is close to 50 per cent, indicating the market may not be very competitive
and that local producers have a significant cost advantage over importers.
The majority of imports are shipped across the Atlantic Ocean
Figure 14 South African wheat supply is mostly local production with imports being mainly split between
Europe, North America and South America
62%
11%
1%
14%
11%
1%0%
38%
Local production
EEA (m. Germany)
Ukraine
South America (m. Brazil and Argentina)
North America
Australia
Other
Producers and consumers of wheat in South Africa will barely notice the effect of a 10 per cent increase in bunker price
Element Value Element Value
Initial price ($/tonne) 163–353Resulting increase in price: per
tonne and as %$0.08–$0.33 (0.04–0.17%)
Initial total demand (mega-
tonnes)3.1
Reduction in demand due to
price increase (kilo-tonnes and
%)
1–3 (0.02–0.08%)
Market size ($m per annum) 505–1,094Cost to overseas producers
from change in margin $0.7–1.0 million
Market share of sea-borne
importers38%
Gain to land producers from
change in margin$0.1–1.1 million
Freight rate: per tonne and ad
valorem
from N America: $45/tonne (21%)
from S America: $43/tonne (22%)
Cost to consumers from
increase in price $0.21–1.8 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 10–40%
Split in calculable producer
cost between
developed/developing
at most, developing world
producers bear 27%
Increase in freight rates: per
tonne and ad valorem$0.83 (0.42%)
Split in calculable overall cost
between developed/developing
46–64% of cost borne by
developing world overall
41
Bulk wheat prices rise by around 0.1 per cent and producers’ profits change by
less than $1 million p.a.
Table 7 A 10 per cent rise in bunker price has a small effect on the South African wheat market
Source: Vivid Economics calculations
Kenya is heavily reliant on imports to meets its demand for wheat
42
The Former Soviet Union and Argentina are major suppliers
With consumption of 1 Mt, wheat is Kenya’s second most important grain by weight.
The main supply routes are across the south Atlantic and from the Black Sea through the Suez canal.
Figure 15 Local production and supply from the former Soviet Union each account for one-third of
Kenyan wheat supply
31%
6%
33%
9%
19%
2%
69%
Local production
EEA + Switzerland (11 countries)
Former Soviet Union (m. Russia,
Ukraine)
North America
South America (m. Argentina)
Other
Source: ITC and FAO
Although the increase in freight rate is small, more of it is passed through to wheat prices in Kenya than South Africa
Element Value Element Value
Initial price 240–425Resulting increase in price: per
tonne and as %$0.34–$0.52 (0.24–0.37%)
Initial total demand (mega-
tonnes)1
Reduction in demand due to price
increase (kilo-tonnes and as %)1.2–1.8 (0.12–0.18%)
Market size ($m per annum) 240–425Cost to overseas producers from
change in margin $0.4–0.4 million
Market share of sea-borne
importers69%
Gain to land producers from
change in margin$0.2–0.5 million
Freight rate: per tonne and ad
valorem
from Ukraine*: $30/tonne (18%)
from S America*: $42/tonne
(31%)
Cost to consumers from increase
in price $0.59–1.6 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 50–75%Split in calculable producer cost
between developed/developing
developing world producers bear
at least 88% of small cost
Increase in freight rates: per
tonne and ad valorem$0.69 (0.49%)
Split in calculable overall cost
between developed/developing$0.34–$0.52 (0.24–0.37%)
43
The cost pass-through rate is higher for Kenya, at 50 to 75 per cent, because it is
more reliant on overseas imports
Table 8 A 10 per cent increase in bunker price raises bulk wheat prices in Kenya by around 0.4 per cent,
costing consumers around $1 million p.a. and costing overseas producers around $0.3 million p.a.
Source: Vivid Economics calculations
Algeria also relies heavily on imports for its wheat supply
44
However, coming mostly from Europe, the supply route is shorter
Of the 7 Mt of total wheat consumption, almost 5 Mt comes from France and Germany alone, and 1
Mt is shipped across the northern Atlantic.
Shipping costs for the trans-Atlantic route are 20 per cent of value, or $0.07 per kilogram.
Source: ITC and FAO
Figure 16 Most of Algeria’s wheat supply is shipped from Europe
32%
46%
5%
10%
3%3%1%
68%
Local Production
EEA (m. France)
Former Soviet Union (m. Russia)
North America
Mexico
South America (m. Argentina)
Other
Algeria is similar to Kenya in terms of reliance on overseas imports, but the short sea route involves lower freight costs
45
Most of Algeria’s imports come from France, whose producers absorb costs of
around $1.5 million p.a., while Algeria’s consumers pay an extra $4 to 6 million p.a.
Element Value Element Value
Initial price ($/tonne) 245–285Resulting increase in price: per
tonne and as %$0.25–$0.37 (0.16–0.24%)
Initial total demand (mega-
tonnes)7.3
Reduction in demand due to price
increase (kilo-tonnes and as %)6–9 (0.08–0.12%)
Market size ($m per annum) 1,789–2,081Cost to overseas producers from
change in margin $1.1–1.9 million
Market share of sea-borne
importers68%
Gain to land producers from
change in margin$0.9–1.6 million
Freight rate: per tonne and ad
valorem
from EU*: $20/tonne (14%)
from Americas: $45/tonne (25%)
Cost to consumers from increase
in price $2.8–5 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 50–75%Split in calculable producer cost
between developed/developing
gains to developing world
producers overall,
developed world impact of $0.8–1.4
million
Increase in freight rates: per
tonne and ad valorem$0.5 (0.32%)
Split in calculable overall cost
between developed/developing
65–84% of cost borne by
developing world overall
Table 9 A 10 per cent increase in bunker price raises bulk wheat prices in Algeria by around 0.3 per cent
Source: Vivid Economics calculations
46
China produces around three-quarters of its barley needs
There are relatively few foreign suppliers, with only a few countries exporting to
China
Source: ITC and FAO
4 Mt of barley is consumed in China each year
The main foreign supplier is Australia, shipping close to 1 Mt of barley at a cost of $0.05per kilogram or 16 per cent of
value; imported barley is considered to be higher quality than that which is locally produced.
China also exports significant amounts of barley (0.5 Mt) to South Korea.
Figure 17 China and Australia produce around 90 per cent of the Barley consumed in China
73%
17%
7%
3%
27%
Local production
Australia
Canada
EEA (m. France)
Exporters of barley to China face a lot of competition in China, and absorb most of a bunker price increase
47
Most of the cost increase is borne by developed world producers
Element Value Element Value
Initial price ($/tonne) 115–180Resulting increase in price: per
tonne and as %$0.07–$0.18 (0.04–0.09%)
Initial total demand (mega-
tonnes)4.7
Reduction in demand due to price
increase (kilo-tonnes and as %)1–2 (0.02–0.05%)
Market size ($m per annum) 541–846Cost to overseas producers from
change in margin $0.5–0.6 million
Market share of sea-borne
importers27%
Gain to land producers from
change in margin$0.1–0.6 million
Freight rate: per tonne and ad
valorem
from Australia: $30/tonne (17%)
from Canada: $56/tonne (24%)
from EU: $100/tonne (43%)
Cost to consumers from increase
in price $0.2–0.8 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 10–25%Split in calculable producer cost
between developed/developing
gains to developing world
producers, who are nearly all
domestic, developed world impact
of $0.5–0.6 million
Increase in freight rates: per
tonne and ad valorem$0.71(0.36%)
Split in calculable overall cost
between developed/developing
10–25% of cost borne by
developing world overall
Source: Vivid Economics calculations
Table 10 The impact on prices is small and the cost to Chinese consumers is less than $1 million p.a.
Philippine rice supply remains almost exclusively South East Asian in provenance
Viet Nam is the main importer
Source: ITC and FAO
The Philippines require 12 Mt of rice a year and could not significantly increase local production
(International Rice Research Institute).
A rice self-sufficiency policy is being pursued through caps on imports and raising local rice prices.
Figure 18 Viet Nam is the largest importer of rice to the Philippines, but most rice is grown domestically
48
85%
12%
2%
1%
15%
Local Production
Viet Nam
Thailand
Other
The Philippines imports 15 per cent of its rice and its prices hardly change
Consumers bear costs of around $0.5 million p.a. and producers are broadly
unaffected, if domestic rice production can increase output in response
Element Value Element Value
Initial price ($/tonne) 163–244Resulting increase in price: per
tonne and as %$0.04–$0.15 (0.01–0.03%)
Initial total demand (mega-
tonnes)11.8
Reduction in demand due to
price increase (kilo-tonnes and
%)
0–2 (0.0–0.02%)
Market size ($m per annum) 1,923–2,879Cost to overseas producers from
change in margin $0.4–0.6 million
Market share of sea-borne
importers15%
Gain to land producers from
change in margin$0.1–0.8 million
Freight rate: per tonne and ad
valorem
from SE Asia: $49/tonne (7–
10%)
Cost to consumers from increase
in price $0.2–0.9 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 5–20%Split in calculable producer cost
between developed/developingn/a, no developed world producers
Increase in freight rates: per
tonne and as % of freight rate$0.76 (0.16%)
Split in calculable overall cost
between developed/developing
100% of cost borne by developing
world
Table 11 The price of rice in the Philippines rises by around 0.02%
Source: Vivid Economics calculations 49
Saudi Arabia relies heavily on just two exporters to meet its demand for maize
At 92 per cent, the proportion of its supply which is sea-borne is the highest for
any market examined
More than half of the 2 Mt p.a. of maize consumed in Saudi Arabia comes from Argentina and almost
all the rest from the USA.
At $0.08 per kilogram, or 28 per cent of value, transport costs are higher than for the other markets
considered.
Figure 19 The vast majority of Saudi Arabia’s maize is grown in the Americas
50
6%
61%
30%
2%
1%
94%
Local Production
South America (m. Argentina)
USA
Middle East and North Africa (m. Sudan, Yemen)
Other
Source: ITC and FAO
Saudi Arabia imports practically all its maize and so importers pass on the whole cost increase
Element Value Element Value
Initial price ($/tonne) 196–225Resulting increase in price: per
tonne and as %$0.84–$0.93 (0.43–0.48%)
Initial total demand (mega-
tonnes)1.4
Reduction in demand due to
price increase (kilo-tonnes and
%)
3–3.3 (0.21–0.24%)
Market size ($m per annum) 274–315Cost to overseas producers from
change in margin $0–0.1 million
Market share of sea-borne
importers95%
Gain to land producers from
change in margin $0.1–0.1 million
Freight rate: per tonne and ad
valoremfrom USA: $49/tonne (25%)
Cost to consumers from increase
in price $1.17–1.5 million
Elasticity of freight rates w.r.t.
bunker price0.19
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 90–100%Split in calculable producer cost
between developed/developing
at most, developing world
producers bear 39% of small cost
Increase in freight rates: per
tonne and ad valorem$0.93 (0.48%)
Split in calculable overall cost
between developed/developing
97–100% of cost borne by
developing world overall
51
The price of maize rises by around 0.4 per cent, costing consumers around $1
million p.a.Table 12 The Saudi Arabia maize market shows the rate of highest cost pass-through and the highest
increase in price
Source: Vivid Economics calculations
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
0.50%
South Africa Kenya Algeria China Philippines Saudi Arabia
Ch
an
ge
in
bu
lk p
ric
e o
f g
rain
The markets with the highest shares of imports are most exposed to price increases
52
Even so, the price increases are all less than 0.7 per cent
Figure 20 Only countries with import shares above 60 per cent experience price increases above 0.3%
Source: Vivid Economics calculations
maize
rice
barley
wheat
0%
20%
40%
60%
80%
100%
Land Sea
Many developing countries import all their wheat via seaborne routes
53
Figure 21 Wheat prices are likely to increase the most in South and East Asia
Source: Vivid Economics calculations from FAO and ITC data
0%
20%
40%
60%
80%
100%
Land Sea
Fewer countries are dependent upon sea transport for their rice supply
54
Figure 22 Rice prices will rise in South Africa and Libya, but not in India or Brazil
Source: Vivid Economics calculations from FAO and ITC data
0%
20%
40%
60%
80%
100%
Land Sea
Many developing countries will not see an increase in maize prices if sea freight rates rise
55
Figure 23 Maize prices will be unlikely to rise in Brazil, India or the Comoros, but will be likely to rise in
Costa Rica, South Korea and Japan
Source: Vivid Economics calculations from FAO and ITC data
0%
20%
40%
60%
80%
100%
Land Sea
Many countries are reliant on sea transport for supplies of barley
56
Figure 24 Niger, Chile and Fiji are among those countries likely to see higher barley prices, Peru and
Kenya will experience more moderate price rises
Source: Vivid Economics calculations from FAO and ITC data
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
57
21%
12%
12%
2%
50%
3%
Australia
India
Brazil
South Africa
Iran
Russia
China
Other
15%
8%
8%
1%66%
2%
Domestic production accounts for two-thirds of the Chinese crude iron ore market
58
The low iron content of domestic ore means China is nevertheless dependent on
imported ore
Australia, Brazil and India are the world’s top three producers after China
The overall market size is estimated at 880 Mt per annum.
The iron ore content of the top three importers is approximately twice that of Chinese ore.
Figure 25 Proportion of Chinese market for non-agglomerated iron ore by producer country, average
2005–07, (left) gross weight and (right) adjusted for metal content
Source: UNSD COMTRADE database, US Geological Survey
Land producers
14%
7%
9%
30%
16%
24% 25%
12%
15%
48%
Rio Tinto (Australia)
Vale (Brazil)
BHP Billiton (Aus & Brazil)
Chinese state-owned
Chinese collective and private
Other foreign
Large-scale firms produce iron ore in China and import it
The three largest iron ore producers account for about a third of the Chinese market and half of imports. Rio
Tinto is the largest private supplier to the Chinese market.
Rio Tinto and BHP Billiton have just agreed on a deal to combine their Western Australia operations into a
50:50 owned joint venture. This single JV would supply a quarter of Chinese demand if current provenance
patterns persisted.
Chinese state-owned enterprises as a whole hold a larger market share than any foreign
company
Source: InfoMine, UNSD COMTRADE database, US Geological Survey. Note proportions are estimated from aggregate import figures due
to lack of data on destination of production from individual mines and companies.
Figure 26 Proportion of Chinese (left) market and (right) imports for non-agglomerated iron ore (adjusted
for metal content) by controlling interest
59
The model makes a number of assumptions about the structure of the Chinese iron ore market
Near complete output and ownership details is available for Australian and Brazilian mines.
Individual mine output and ownership details are available for at least 25 per cent of Indian production and 41 per cent
of South African production.
Detailed data on output is available for the 9 largest Indian companies, the remainder of India’s production is presumed
to be split equally between 20 small producers.
The rest of the world accounts for 3 per cent of the Chinese market. Due to limited information, we presume this is
produced by 20 equally sized small companies; Russia may be treated separately as a land producer, although its
market share is less than one per cent.
Where an operation is a 50/50 joint venture, it is assumed to have independent operational control; if an operation has
a shareholder with more than a 50 per cent stake, it is assumed to be controlled by that shareholder.
Where a mine is owned by a Chinese or Japanese company, then its output is presumed to all be sold in that country;
otherwise mines are presumed to export to China in line with their share of production.
Individual mine output and ownership details for China are limited; 8 major state-owned producers have been
identified, along with a large number of private and collectively owned mines:
— control of Chinese production is presumed to be split between 8 state-owned producers each with a 3.7 per
cent market share and 40 private producers each with a 0.4 per cent market share;
— it appears that a large proportion of Chinese iron ore mines are controlled by steel mills and so their output
is not traded on the spot market; to account for this, a scenario is run where the output of state-owned
mines is excluded from the model.
The model is calibrated such that the average variable profit margin on a tonne of ore produced by Rio Tinto, BHP and
Vale is 50 per cent. 60
The iron ore market has a small number of large players and a long tail of small
producers
Demand, especially from China, has supported global increases in iron ore prices
Maritime transport costs have increased, although data is limited
Figure 27 (Left) iron ore price data for the markets available from UNCTAD; (right) maritime transport costs
for non-agglomerated iron ore
Source: UNCTAD, OECD MTC database, Portworld.com. Chinese iron ore prices are available from mixed sources and for a
limited temporal range, and are therefore not shown
61
0.00
1.00
2.00
3.00
4.00
2002 2003 2004 2005 2006 2007
Tra
ns
po
rt c
os
t ($
/kilo
ton
ne
-kilo
metr
e)
Australia
Brazil
EU
0
20
40
60
80
100
120
140
2000 2002 2004 2006 2008
Pri
ce
($
/to
nn
e)
Brazil to Europe
Australia to Japan
It costs around twice as much to ship ore to China from Brazil than from India or Australia
62
Chinese iron ore has a much lower metal content than imported ore
Country of origin
Clarksons freight rate to
China ($ per tonne)
(2005 to 2007 average)
Metal content of ore (%)Implied freight rate
($ per tonne of metal)
Australia 16.26 62.5 26.02
Brazil 38.83 65.9 58.92
China - 32.9 -
India 20.18 64.0 31.53
Iran 22 (assumed) 48.2 45.64
Rest of the World 35 (assumed) 58.9 59.44
South Africa 27.12 63.2 42.91
Table 13 Freight rates for iron ore are converted into an equivalent freight rate per tonne of metal
Source: Vivid Economics calculations from Clarksons and the US Geological Survey data
Cost pass-through of increased shipping costs is estimated to be between 50 and 60 per cent
Initial Final Change Initial Final Change
Spot market including Chinese state-owned firms Spot market excluding Chinese state-owned firms
Market Size (million
tonnes p.a.)412.3 407.7 -1.13% 289.1 285.4 -1.28%
Price ($ per tonne) 111.9 113.5 1.42% 111.9 113.7 1.59%
Domestic market
share46.0% 59.6% 13.64% 23.0% 40.8% 17.81%
Land-based market
share46.3% 60.2% 13.92% 23.4% 41.7% 18.25%
Sea-based import
market share53.7% 39.8% -13.92% 76.6% 58.3% -18.25%
Average added cost
for sea importers ($
per tonne)
3.07 3.04
Cost pass-through
for sea importers51.7% 58.7%
63
Chinese producers increase their market share by around 13 per cent
Table 14 A 10 per cent rise in the cost of bunker fuels increases the average freight rate to China by
around $3 per tonne of metal
Source: Vivid Economics calculations
Profit margins for foreign producers fall by up to $3 per tonne of metal, while those for Chinese producers rise by around $1.50
OriginOriginal market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Original market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Spot market including Chinese state-owned firms Spot market excluding Chinese state-owned firms
Australia 29.4% -0.9% -0.9 42.0% -0.9% -0.7
Brazil 8.3% -2.4% -4.1 11.9% -3.3% -3.8
China 46.0% +13.6% +1.6 23.0% +17.8% +1.8
India 11.2% -6.5% -1.4 16.0% -8.4% -1.2
Iran 0.4% -0.4% -2.8 0.6% -0.6% -2.6
Rest of the World 2.7% -2.7% -4.1 3.9% -3.9% -3.9
South Africa 1.6% -0.9% -2.7 2.3% -1.2% -2.4
64
Foreign producers with less production and which are further away suffer greater
falls in export sales
Table 15 Reductions in profit margins are smaller but those in export volumes are larger if state-owned
Chinese firms do not participate in the spot market
Source: Vivid Economics calculations
The modelling results should be interpreted as medium-term equilibrium impacts
These results indicate what would happen in the medium term if market conditions remained constant
and there was no other change except for the increase in bunker price:
— if the Chinese market for iron ore continues to grow, then even overseas producers may see an increase in sales;
— for example, in the scenario where state-owned firms are excluded from the spot market, Brazilian exports to
China would return to their initial levels if the market grew by around twenty per cent;
— Indian firms may respond to their weakened competitive position by consolidating and achieving lower per-unit
costs;
— Indian margins are predicted to fall by around $1 per tonne; by comparison, India recently raised its iron ore
export tax from 10 to 15 per cent (an increase of around $5 per tonne).
Note that these figures refer to sales to China, they are not predictions of production changes in the
source countries:
— for example, Indian firms will now find the Chinese market less profitable and also be in a stronger competitive
position in the domestic market, they will thus choose to focus more on supplying their local market and increased
local sales will compensate somewhat for reduced exports.
Freight costs are a high proportion of the value of the product, and so the iron ore market results are
more sensitive to changes in the freight rate than for crude oil.
65
Companies will take time to adjust their output and export decisions, and in the
meantime there will be other market changes
Producers who are smaller and further away are more affected
Indian firms suffer greater output losses than Australian or Brazilian ones because Indian mines are
smaller and have higher costs.
Australian producers suffer smaller reductions in profit margins and output than Brazilian producers
because of the shorter sea route.
most of the increase in Chinese production is because the small privately owned mines increase
output aggressively as the rise in price improves their competitive position significantly as their
original margins were very small:
— if the small Chinese firms are not able to expand output, or if control of iron ore production in China is less diffuse
than is presumed, then the impact on foreign producers will be less;
— in the extreme case where small Chinese mines could not increase output, then cost pass-through would be
close to 100 per cent and there would be minimal loss of Chinese sales in exporting countries.
66
Australian producers suffer a smaller decrease in export sales and profit margins
than Indian or Brazilian producers
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
67
There are a large variety of products shipped into the EU by container
68
The ability of importers to pass-through additional freight cost differs across
product categories
it was not possible to obtain data on the composition of container trade
— from the OECD Maritime Transport Costs database, it is possible to infer the top 10 product categories by value
for goods transported into the EU15 by container
— for these categories, table 16 lists the value of bilateral trade between China and the EU
wearing apparel and furniture were selected for analysis
each of these product categories is diverse and contains a range of products
— within each of these broad categories, there is a range of high-value and low-value products
— suppliers vary in their capacity to move up and down the value chain in response to price changes
— consumers may not consider all products within the category to be substitutes for each other
The following product categories appear to be the highest value product categories in the China to EU container trade
69
EU imports from China
($US bn 2008)
Total Chinese exports
($US bn 2008)
Total EU imports ($US
bn 2008)
Nuclear reactors, boilers, machinery, etc 73 269 719
Electrical, electronic equipment 93 342 570
Vehicles other than railway, tramway 6 39 559
Plastics and articles thereof 8 30 205
Optical, photo, technical, medical, etc apparatus 7 43 145
Articles of iron or steel 12 48 117
Articles of apparel, accessories, not knit or crochet 25 52 84
Furniture, lighting, signs, prefabricated buildings 16 43 81
Articles of apparel, accessories, knit or crochet 19 61 76
Wood and articles of wood, wood charcoal 3 9 54
Source: ITC and OECD MTC database
Table 16 The EU imports a large value of wearing apparel and furniture from China
EU27 Domestic production, 52.0% China, 21.4%
Turkey, 5.2%
India, 3.0%
Tunisia, 3.0%
Morocco, 2.8%
Bangladesh, 2.6%
Vietnam, 1.2%
Indonesia, 1.1%
Pakistan, 0.8%
Sri Lanka, 0.8%
Macedonia, 0.5%
Thailand, 0.6%
Ukraine, 0.6%
Other, 4.4%
Other, 41.7%
Around 40 per cent of the wearing apparel sold in the EU arrives by ship
70
The high share of land-based production is likely to limit cost pass-through in this
market to 50 per cent
Source: ITC and Eurostat
Figure 28 China is the largest foreign supplier of wearing apparel to the EU, although the majority of the
value of products sold in this category are produced within the EU
Cost pass-through is likely to be higher for lower value products
71
This is under the assumption that EU producers account for a smaller proportion
of the low-value segment of the market
On average cost pass-through in the wearing apparel sector is expected to be between 10 and 40 per
cent:
— land-based producers have a 60 per cent market share;
— overseas suppliers are likely to be concentrated in the low-value end of the market and the capacity of European
producers to be competitive in this band will likely be lower than their market share by value for the entire sector;
— cost pass-through for lower value products will be higher than this estimate if products transported overland or
produced locally are concentrated in the high-value segment and if overseas producers have a large market
share in this segment;
— conversely, cost pass-through for high value products will be lower than this if overseas producers have a small
market share in this segment.
Wearing apparel prices in Europe might rise by around 0.02 per cent
72
The cost to European consumers is around $20 million p.a., in a market worth $89
billion p.a., and producers overall pay up to $30 million p.a.
Element Value Element Value
Initial price ($/tonne) 9,400Increase in freight rates: per tonne
and ad valorem$9.71 (0.1%)
Initial total demand (mega-
tonnes)9.5
Resulting increase in price: per
tonne and as %$0.97–$3.88 (0.01–0.04%)
Market size ($m per annum) 89,000Reduction in demand due to price
increase (kilo-tonnes and as %)1–4 (0.01–0.04%)
Market share of sea-borne
importers42%
Cost to producers from change in
margin $1.8–29.5 million
Freight rate: per tonne and ad
valoremfrom China: $1,280/tonne (8.6%)
Net cost to producers from change
in quantity unknown, but likely to be small
Elasticity of freight rates w.r.t.
bunker price0.12
Cost to consumers from increase
in price $9.23–36.9 million
Cost pass-through rate 10–40%Loss of consumer welfare from
reduction in consumption negligible
Table 17 The increase in bunker price raises freight rates by 0.1%, but little less than half of this increase
is passed on
Source: Vivid Economics calculations
The majority of furniture sold in the EU is produced in East or South-east Asia
73
China, Indonesia and Viet Nam are the largest foreign suppliers
EU27 Domestic production, 16.5%
China, 38.9%Viet Nam, 4.4%
Turkey, 3.9%
Indonesia, 5.6%
Switzerland, 3.4%
United States of America, 3.1%
Norway, 2.1%
Malaysia, 2.7%
India, 1.9%
Chinese Taipei, 2.0%
Thailand, 1.8%
South Africa, 2.3%
Brazil, 2.0%
Croatia, 1.1%
Japan, 0.8%
Rest of Europe, 3.7%
Other, 4.0%
Other, 69.5%
Source: ITC and Eurostat
Figure 29 Around 70 per cent of furniture (by value) sold in the EU arrives by sea
Cost pass-through of increased freight costs will likely be between 60 and 90 per cent for furniture
On average, the cost pass-through in the furniture sector is expected to be between 60 and 90 per
cent:
— land-based producers have only a 30 per cent market share;
— overseas suppliers are likely to be concentrated in the low-value end of the market and the capacity of European
producers to be competitive in this band is likely to be lower;
— cost pass-through for lower value products will be higher than this estimate if products transported overland or
produced locally are concentrated in the high-value segment and if overseas producers have a large market
share in this segment;
— conversely, cost pass-through for high value products will be lower than this if overseas producers have a small
market share in this segment.
74
This will lead to increases in profit margins for land-based producers
Furniture prices in Europe might rise by 0.2 per cent
75
Consumers bear an extra cost of around $30 million p.a. in a market worth £20
billion p.a.
Element Value Element Value
Initial price ($/tonne) 2,700Resulting increase in price: per
tonne and as %$4.13–$6.19 (0.15–0.23%)
Initial total demand (mega-
tonnes)7.2
Reduction in demand due to price
increase (kilo-tonnes and as %)5.5–8.3 (0.15–0.23%)
Market size ($m per annum) 19,500Cost to producers from change in
margin $11.6–16.6 million
Market share of sea-borne
importers69%
Net cost to producers from change
in quantity unknown, but likely to be small
Freight rate: per tonne and ad
valoremfrom China: $430/tonne (16%)
Cost to consumers from increase
in price $10.5–4.5 million
Elasticity of freight rates w.r.t.
bunker price16%
Loss of consumer welfare from
reduction in consumption negligible
Cost pass-through rate 60–90%
Increase in freight rates: per
tonne and ad valorem$6.88 (0.26%)
Source: Vivid Economics calculations
Table 18 The increase in prices is higher than for apparel because of higher ad valorem freight rates and a
higher rate of cost pass-through
IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
introduction to the modelling approach
the impact on freight rates of higher bunker price on selected routes
— Panamax grain shipping
— Capesize iron ore shipping
— container shipping
— VLCC crude oil shipping
the impact of higher freight rates on selected trade and product markets
— a selection of grain markets in developing countries
— iron ore in China
— clothing and furniture in the EU
— crude oil in South Korea and the US
34%
18%12%
22%
5%
4%3%
2%
Saudi Arabia
UAE
Kuwait
Other Middle East
ASEAN
Australia
Russia
Other
South Korea imports 117 MT of crude oil each year and has no domestic production, so cost pass-through will be close to 100 per cent
77
South Korea is the world’s fifth largest net oil importer making the Middle East to
South Korea an important crude oil route
Source: ITC and EIA
Figure 30 The Middle East accounted for 87 per cent of South Korea’s crude oil imports in 2008
The South Korean model is calibrated to 2008, the most recent year for which trade data is available
Each country is presumed to control the volume of oil exported.
Data for 2008 is used and the average price for crude is taken to be $696 per tonne ($95 per barrel),
there are 7.33 barrels of oil per tonne.
The average cost to transport crude from the Middle East to Japan in 2008 was $17 per tonne:
— this figure is applied to South Korean imports sourced from the Middle East, ASEAN, Australia and Russia;
— imports from elsewhere (mainly Africa and small islands) are presumed to have a transport cost of $25 per tonne
to account for the higher costs for sources which are further away and have less developed infrastructure.
The model is calibrated such that the marginal cost of production in 2008 for Saudi Arabian exports
was $30 per barrel.
The elasticity of the freight rate for crude oil to South Korea is 0.4, from the estimate in table 6.
Note that the added cost of freight induced by a market-based measure is constant across both oil
price scenarios as it will likely be a fixed amount per tonne of fuel, rather than being dependent on the
fuel price.
78
The oil price in this year was $95 per barrel, although an additional scenario is
modelled where the price is $60
Cost pass-through of increased shipping costs is estimated at 110 per cent in the South Korean crude market
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 116.7 116.7 -0.02% 116.7 116.7 -0.03%
Price ($ per tonne) 696.6 697.4 0.11% 440.0 440.7 0.17%
Domestic market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Land-based import market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Sea-based import market share 100.0% 100.0% 0.00% 100.0% 100.0% 0.00%
Average added cost for sea
importers per tonne ($ per tonne)0.69 0.69
Cost pass-through for sea
importers110.9% 111.5%
79
There are only small changes in the consumption and price of crude oil in South
Korea, and almost no change in market shares of suppliers
Table 19 The freight rate for crude to South Korea rises by $0.69 per tonne if bunker price increase by 10
per cent
Source: Vivid Economics calculations
37%
13%
21%
14%
4%
12%
51%
Local Production
Canada
Other Americas
Africa
Other
Middle East
Imports accounted for around two-thirds of US crude oil consumption in 2009
80Source: ITC and EIA
However, only half of crude consumption arrives by sea, as imports from Canada
travel via pipeline
Figure 31 Seaborne imports account for around 50 per cent of US crude consumption
The US model is calibrated to 2009 trade data
81
The oil price in this year was $60 per barrel, and a scenario is also considered
where the oil price is $95
Each country is presumed to control the volume of oil exported from it, with the exception of the US and Canada where
EIA data is used to apportion production between firms.
Data for 2009 is used and the average price for crude is taken to be $60 and $95:
— $60 per barrel corresponds to the average price in 2009, while $95 was the average in 2008 as used in the
South Korean model.
The average cost to transport crude from the Middle East to Japan in 2009 was $7.24 per tonne:
— the freight rate in December 2009 on the Middle East to Japan route was around $10 and the WorldScale rate
at that time was 58.125; the average annual WorldScale rate for 2009 was 58.125 giving the average annual
freight rate at $7.24;
— data for other routes are not publicly available due to the confidentiality of the WorldScale conversions;
— the Middle East to Japan route is 6636 nautical miles, while the Middle East to the US route is 12674 nautical
miles; assuming that the per nautical mile freight rate is equal across routes, this implies a freight rate of
around $13.83 for transporting oil from the Middle East to the US in 2009;
— a similar methodology was employed to infer the prices on other routes to the US.
The model is calibrated such that the marginal cost of production for Saudi Arabian exports was $30 per barrel.
The elasticity of the freight rate for crude oil to the US is 0.4, from the estimate in table 6.
Note that the added cost of freight induced by a market-based measure is constant across both oil price scenarios as it
will likely be a fixed amount per tonne of fuel, rather than being dependent on the fuel price.
Cost pass-through of increased shipping costs is estimated at 73 per cent in the US crude market
82
The US sources more crude locally and has shorter sea routes for imports than
South Korea
Table 20 The freight rate for crude to the US rises by $0.24 per tonne if bunker price increases by 10 per cent
Source: Vivid Economics calculations
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 742.8 742.8 -0.01% 742.8 742.8 -0.01%
Price ($ per tonne) 696.6 696.8 0.03% 440.0 440.2 0.04%
Domestic market share 36.9% 37.0% 0.03% 36.9% 37.0% 0.06%
Land-based import market share 49.5% 49.5% 0.04% 49.5% 49.5% 0.08%
Sea-based import market share 50.5% 50.5% -0.04% 50.5% 50.5% -0.08%
Average added cost for sea
importers per tonne ($ per tonne)0.24 0.24
Cost pass-through for sea
importers72.6% 73.4%
Middle Eastern producers lose US sales volumes to US and Canadian producers
Country of origin Original market shareChange in market share
in percentage points
Change in sales in the
US
change in margin
($/tonne)
US 36.9% 0.0 0.2% 0.2
Canada 12.5% 0.0 0.2% 0.2
Other Americas 20.9% 0.0 0.1% 0.1
Middle East 11.7% -0.0 -0.6% -0.3
Africa 13.9% -0.0 -0.2% -0.1
Other 4.0% -0.0 -0.3% 0.0
83
Exports from the Middle East to the US fall by 0.6 per cent, while the profit margin
on such exports falls by $0.30 per tonne; in contrast, the profit margin on imports
from South and Central America increase
Table 21 There is only a small realignment in volumes in the US market induced by a ten per cent increase
in bunker price
Source: Vivid Economics calculationsNote: these figures are for the $60 per barrel scenario
Appendix: IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
a five per cent increase in bunker price
— iron ore in China
— crude oil in South Korea and the US
a fifteen per cent increase in bunker price
— iron ore in China
— crude oil in South Korea and the US
84
The Chinese iron ore market with a 5 per cent bunker price increase
Initial Final Change Initial Final Change
Spot market including Chinese state-owned
firms
Spot market excluding Chinese state-owned
firms
Market Size (million tonnes p.a.) 412.3 409.6 -0.66% 289.1 287.0 -0.72%
Price ($ per tonne) 111.9 112.8 0.82% 111.9 112.9 0.90%
Domestic market share 46.0% 53.9% 7.87% 23.0% 33.0% 10.02%
Land-based market share 46.3% 54.3% 8.03% 23.4% 33.7% 10.27%
Sea-based import market share 53.7% 45.7% -8.03% 76.6% 66.3% -10.27%
Average added cost for sea
importers ($ per tonne)1.57 1.55
Cost pass-through for sea
importers58.8% 65.1%
85
Chinese producers increase their market share by around 8 per cent
Table A1 A 5 per cent rise in the cost of bunker fuel increases the average freight rate to China by around
$1.60 per tonne of metal
Source: Vivid Economics calculations
Profit margins for foreign producers fall by up to $2 per tonne of metal, while those for Chinese producers rise by around $0.90
Original market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Original market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Spot market including Chinese state-owned firms Spot market excluding Chinese state-owned firms
Australia 29.4% -0.3% -0.3 42.0% -0.2% -0.2
Brazil 8.3% -1.5% -1.9 11.9% -2.0% -1.8
China 46.0% +7.9% 0.9 23.0% +10.0% 1.0
India 11.2% -2.9% -0.6 16.0% -3.3% -0.5
Iran 0.4% -0.2% -1.3 0.6% -0.3% -1.2
Rest of the World 2.7% -2.7% -1.9 3.9% -3.9% -1.8
South Africa 1.6% -0.4% -1.2 2.3% -0.5% -1.1
86
Foreign producers with less production and which are further away suffer greater
falls in export sales
Table A2 Changes in market share and margin for foreign suppliers in the Chinese iron ore market with a 5
per cent increase in bunker price
Source: Vivid Economics calculations
The South Korean crude market with a 5 per cent bunker price increase
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 116.7 116.7 -0.01% 116.7 116.7 -0.02%
Price ($ per tonne) 696.6 697.0 0.05% 440.0 440.4 0.09%
Domestic market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Land-based import market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Sea-based import market share 100.0% 100.0% 0.00% 100.0% 100.0% 0.00%
Average added cost for sea
importers per tonne ($ per tonne)0.34 0.34
Cost pass-through for sea
importers110.9% 111.4%
87
There are only small changes in the consumption and price of crude oil in South
Korea, and almost no change in market shares of suppliers
Table A3 The freight rate for crude to South Korea rises by $0.34 per tonne if bunker price increases by 5
per cent
Source: Vivid Economics calculations
The US crude market with a 5 per cent bunker price increase
88
The US sources more crude locally and has shorter sea routes for imports than
South Korea
Table A4 The freight rate for crude to the US rises by $0.12 per tonne if bunker price increases by 5 per cent
Source: Vivid Economics calculations
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 742.8 742.8 0.00% 742.8 742.8 0.00%
Price ($ per tonne) 696.6 696.7 0.01% 440.0 440.07 0.02%
Domestic market share 36.9% 37.0% 0.01% 36.9% 37.0% 0.03%
Land-based import market share 49.5% 49.5% 0.02% 49.5% 49.5% 0.04%
Sea-based import market share 50.5% 50.5% -0.02% 50.5% 50.5% -0.04%
Average added cost for sea
importers ($ per tonne)0.12 0.12
Cost pass-through for sea
importers72.6% 73.3%
The US crude market with a 5 per cent bunker price increase
Supplier Original market shareChange in market share
in percentage points
Change in sales in the
US
change in margin
($/tonne)
US 36.9% 0.0 0.1% 0.1
Canada 12.5% 0.0 0.1% 0.1
Other Americas 20.9% 0.0 0.1% 0.0
Middle East 11.7% 0.0 -0.3% -0.2
Africa 13.9% 0.0 -0.1% 0.0
Other 4.0% 0.0 -0.1% 0.0
89
Exports from the Middle East to the US fall by 0.3 per cent, while the profit margin
on such exports falls by $0.20 per tonne; in contrast, the profit margin on imports
from South and Central America increase
Table A5 There is only a small realignment in volumes in the US market induced by a five per cent increase
in bunker price
Source: Vivid Economics calculationsNote: these figures are for the $60 per barrel scenario
Appendix: IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
a five per cent increase in bunker price
— iron ore in China
— crude oil in South Korea and the US
a fifteen per cent increase in bunker price
— iron ore in China
— crude oil in South Korea and the US
90
The Chinese iron ore market with a 15 per cent bunker price increase
Initial Final Change Initial Final Change
Spot market including Chinese state-owned
firms
Spot market excluding Chinese state-
owned firms
Market Size (million tonnes p.a.) 412.3 406.6 -1.38% 289.1 284.4 -1.60%
Price ($ per tonne) 111.9 113.8 1.73% 111.9 114.1 2.00%
Domestic market share 46.0% 62.7% 16.68% 23.0% 45.4% 22.42%
Land-based market share 46.3% 63.3% 17.02% 23.4% 46.4% 22.98%
Sea-based import market share 53.7% 36.7% -17.02% 76.6% 53.6% -22.98%
Average added cost for sea importers
($ per tonne)4.55 4.50
Cost pass-through for sea importers 42.6% 49.7%
91
Chinese producers increase their market share by around 17 per cent
Table A6 A 15 per cent rise in the bunker price increases the average freight rate to China by around $4.50
per tonne of metal
Source: Vivid Economics calculations
The Chinese iron ore market with a 15 per cent bunker price increase
Original market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Original market
share
Change in market
share in
percentage
points
Change in
margin ($ per
tonne of metal)
Spot market including Chinese state-owned firms Spot market excluding Chinese state-owned firms
Australia 29.4% -1.9% -1.8 42.0% -2.1% -1.5
Brazil 8.3% -3.0% -6.5 11.9% -4.1% -6.2
China 46.0% +16.7% 1.9 23.0% +22.4% 2.2
India 11.2% -7.7% -2.6 16.0% -10.5% -2.3
Iran 0.4% -0.4% -4.6 0.6% -0.6% -4.3
Rest of the World 2.7% -2.7% -6.6 3.9% -3.9% -6.2
South Africa 1.6% -1.3% -4.4 2.3% -1.7% -4.1
92
Foreign producers with less production and which are further away suffer greater
falls in export sales
Table A7 Changes in profit margins and market share for foreign producers in the Chinese iron ore market
with a 15 per cent increase in bunker price
Source: Vivid Economics calculations
The South Korean crude market with a 15 per cent bunker price increase
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 116.7 116.7 -0.03% 116.7 116.7 -0.05%
Price ($ per tonne) 696.6 697.8 0.16% 440.0 441.1 0.26%
Domestic market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Land-based import market share 0.0% 0.0% 0.00% 0.0% 0.0% 0.00%
Sea-based import market share 100.0% 100.0% 0.00% 100.0% 100.0% 0.00%
Average added cost for sea importers per tonne
($ per tonne)1.03 1.03
Cost pass-through for sea importers 110.9% 111.5%
93
There are only small changes in the consumption and price of crude oil in South
Korea, and almost no change in market shares of suppliers
Table A8 The freight rate for crude to South Korea rises by $1.03 per tonne if bunker price increases by 15
per cent
Source: Vivid Economics calculations
The US crude market with a 15 per cent bunker price increase
94
The US sources more crude locally and has shorter sea routes for imports than
South Korea
Table A9 The freight rate for crude to the US rises by $0.36 per tonne if bunker price increases by 15 per cent
Source: Vivid Economics calculations
Initial Final Change Initial Final Change
Oil price $95 per barrel Oil price $60 per barrel
Market Size (million tonnes p.a.) 742.8 742.8 -0.01% 742.8 742.7 -0.01%
Price ($ per tonne) 696.6 696.9 0.04% 439.9 440.3 0.06%
Domestic market share 36.9% 37.0% 0.04% 36.9% 37.0% 0.09%
Land-based import market share 49.5% 49.5% 0.06% 49.5% 49.6% 0.12%
Sea-based import market share 50.5% 50.5% -0.06% 50.5% 50.4% -0.12%
Average added cost for sea importers
($ per tonne)$0.36 $0.36
Cost pass-through for sea importers 72.6% 73.5%
The US crude market with a 15 per cent bunker price increase
Supplier Original market shareChange in market share
in percentage points
Change in sales in the
US
change in margin
($/tonne)
US 36.9% 0.0 0.2% 0.3
Canada 12.5% 0.0 0.3% 0.3
Other Americas 20.9% 0.0 0.2% 0.1
Middle East 11.7% 0.0 -1.0% -0.5
Africa 13.9% 0.0 -0.3% -0.1
Other 4.0% 0.0 -0.4% 0.0
95
Exports from the Middle East to the US fall by one per cent, while the profit margin
on such exports falls by $0.50 per tonne; in contrast, the profit margin on imports
from South and Central America increase
Table A10 There is only a small realignment in volumes in the US market induced by a 15 per cent increase
in bunker price
Source: Vivid Economics calculationsNote: these figures are for the $60 per barrel scenario
Appendix: IMO Market-based measures expert group (MBM-EG)
Assessment of the economic impact of market-based measures
Econometric appendix
Statistical appendix for container shipping regressions
97
RouteOLS
destination/origin control
OLS
imbalance control
Asia to US 0.368 0.358
US to Asia 0.799 0.774
EU to Asia 0.502 0.434
Asia to EU 0.589 0.469
US to EU 0.618 0.571
EU to US 0.807 0.736
Table A11 R2 statistic for the container shipping regressions
Source: Vivid Economics calculations
98
Statistical appendix for VLCC shipping regressions
Table A12 R2 statistic for the VLCC shipping regressions
Route Constant elasticity OLS model Variable elasticity OLS model
Ras Tanura-Rotterdam 0.686 0.613
Ras Tanura-Ulsan 0.640 0.558
Ras Tanura-Chiba 0.627 0.551
Ras Tanura-Loop 0.666 0.577
Bonny Offshore-Loop 0.672 0.615
Bonny offshore-Kaohsiung 0.669 0.599
Ras Tanura-Ain Sukhna 0.648 0.567
Sidi Kerir-Rotterdam 0.712 0.659
Ras Tanura-Singapore 0.599 0.526
Source: Vivid Economics calculations
99
Statistical appendix for grain shipping regressions
Table A13 R2 statistic for the grain shipping regressions
Route OLS model ECM model
US Gulf-Rotterdam 0.873 0.020
US Gulf-Rotterdam (HSS) 0.856 0.021
US Gulf-Japan (HSS) 0.894 0.018
Vancouver-Japan 0.886 0.015
US Gulf-Japan (HSS, supramax) 0.876 0.183
Source: Vivid Economics calculations
100
Statistical appendix for ore shipping regressions
Table A14 R2 statistic for the ore shipping regressions
Source: Vivid Economics calculations
Route OLS model ECM model
Narvik-Rotterdam 0.703 0.024
Tubarao-Rotterdam 0.748 0.035
Tubarao-Japan 0.745 0.032
Tubarao-Beilun 0.770 0.069
Nouadhibou-Rotterdam 0.772 0.030
W. Australia-Rotterdam 0.696 0.025
W. Australia-Japan 0.672 0.021
Saldanha Bay-Beilun 0.579 0.061
W. Australia-Beilun 0.558 0.056
Goa-Beilun 0.587 0.073
Port Cartier-Rotterdam 0.558 0.077