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 introduction to the modelling

IMO Market-based measures expert group (MBM-EG)

Assessment of the economic impact of market-based measures

FINAL REPORT

August 2010



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


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


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


*the estimates for these markets were made with detailed quantitative model, while the

other estimates are assumed, supported by market share data














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










— a selection of six grain markets in developing countries




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


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


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














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


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



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















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



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



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














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








— Container shipping







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.














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


Initial price 240–425Resulting increase in price: per

tonne and as %$0.34–$0.52 (0.24–0.37%)


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


importers69%




valorem

from Ukraine*: $30/tonne (18%)

from S America*: $42/tonne

(31%)

Cost to consumers from increase

in price $0.59–1.6 million


bunker price0.19



Cost pass-through rate 50–75%Split in calculable producer cost


developing world producers bear

at least 88% of small 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.


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


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.



tonne and as %$0.25–$0.37 (0.16–0.24%)


tonnes)7.3


increase (kilo-tonnes and as %)6–9 (0.08–0.12%)

Market size ($m per annum) 1,789–2,081Cost to overseas producers from



importers68%




valorem

from EU*: $20/tonne (14%)

from Americas: $45/tonne (25%)


in price $2.8–5 million


bunker price0.19





gains to developing world

producers overall,

developed world impact of $0.8–1.4

million







Table 9 A 10 per cent increase in bunker price raises bulk wheat prices in Algeria by around 0.3 per cent


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



tonne and as %$0.07–$0.18 (0.04–0.09%)


tonnes)4.7






importers27%




valorem

from Australia: $30/tonne (17%)

from Canada: $56/tonne (24%)

from EU: $100/tonne (43%)




bunker price0.19





gains to developing world

producers, who are nearly all

domestic, developed world impact

of $0.5–0.6 million


tonne and ad valorem$0.71(0.36%)






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



tonne and as %$0.04–$0.15 (0.01–0.03%)


tonnes)11.8



%)

0–2 (0.0–0.02%)

Market size ($m per annum) 1,923–2,879Cost to overseas producers from



importers15%




valorem

from SE Asia: $49/tonne (7–

10%)




bunker price0.19




between developed/developingn/a, no developed world producers


tonne and as % of freight rate$0.76 (0.16%)



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


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



tonne and as %$0.84–$0.93 (0.43–0.48%)


tonnes)1.4



%)

3–3.3 (0.21–0.24%)


change in margin $0–0.1 million


importers95%




valoremfrom USA: $49/tonne (25%)




bunker price0.19





at most, developing world

producers bear 39% of small cost







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


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%


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


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


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















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


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)


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


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














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.


Initial price ($/tonne) 9,400Increase in freight rates: per tonne

and ad valorem$9.71 (0.1%)


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



importers42%

Cost to producers from change in

margin $1.8–29.5 million


valoremfrom China: $1,280/tonne (8.6%)

Net cost to producers from change

in quantity unknown, but likely to be small


bunker price0.12



Cost pass-through rate 10–40%Loss of consumer welfare from


Table 17 The increase in bunker price raises freight rates by 0.1%, but little less than half of this increase

is passed on


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.


Initial price ($/tonne) 2,700Resulting increase in price: per

tonne and as %$4.13–$6.19 (0.15–0.23%)


tonnes)7.2


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


importers69%

Net cost to producers from change

in quantity unknown, but likely to be small


valoremfrom China: $430/tonne (16%)




bunker price16%



Cost pass-through rate 60–90%




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














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


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


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





Price ($ per tonne) 696.6 696.8 0.03% 440.0 440.2 0.04%



Sea-based import market share 50.5% 50.5% -0.04% 50.5% 50.5% -0.08%





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)


a five per cent increase in bunker price



a fifteen per cent increase in bunker price



84

The Chinese iron ore market with a 5 per cent bunker price increase


Spot market including Chinese state-owned

firms

Spot market excluding Chinese state-owned

firms


Price ($ per tonne) 111.9 112.8 0.82% 111.9 112.9 0.90%


Land-based market share 46.3% 54.3% 8.03% 23.4% 33.7% 10.27%



importers ($ per tonne)1.57 1.55



85


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


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)


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



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


The South Korean crude market with a 5 per cent bunker price increase




Price ($ per tonne) 696.6 697.0 0.05% 440.0 440.4 0.09%








87



Table A3 The freight rate for crude to South Korea rises by $0.34 per tonne if bunker price increases by 5

per cent


The US crude market with a 5 per cent bunker price increase

88


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




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%





importers ($ per tonne)0.12 0.12




Supplier Original market shareChange in market share



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



Table A5 There is only a small realignment in volumes in the US market induced by a five per cent increase

in bunker price




a five per cent increase in bunker price



a fifteen per cent increase in bunker price



90



Spot market including Chinese state-owned

firms

Spot market excluding Chinese state-

owned firms


Price ($ per tonne) 111.9 113.8 1.73% 111.9 114.1 2.00%


Land-based market share 46.3% 63.3% 17.02% 23.4% 46.4% 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


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



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)


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



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


The South Korean crude market with a 15 per cent bunker price increase




Price ($ per tonne) 696.6 697.8 0.16% 440.0 441.1 0.26%




Average added cost for sea importers per tonne

($ per tonne)1.03 1.03


93



Table A8 The freight rate for crude to South Korea rises by $1.03 per tonne if bunker price increases by 15

per cent



94


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





Price ($ per tonne) 696.6 696.9 0.04% 439.9 440.3 0.06%




Average added cost for sea importers

($ per tonne)$0.36 $0.36



Supplier Original market shareChange in market share



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



Table A10 There is only a small realignment in volumes in the US market induced by a 15 per cent increase

in bunker price




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


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


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


100

Statistical appendix for ore shipping regressions

Table A14 R2 statistic for the ore shipping regressions


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

Documents

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 introduction to the modelling