DETERMINANTS OF CONTAINER FREIGHT RATES : ESTIMATED MODELS FOR SPANISH EXPORTS 1 2007 IMSF ANNUAL MEETING DETERMINANTS OF CONTAINER FREIGHT RATES: ESTIMATED.

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2007 IMSF ANNUAL MEETING2007 IMSF ANNUAL MEETING

DETERMINANTS OF CONTAINER FREIGHT DETERMINANTS OF CONTAINER FREIGHT

RATES: ESTIMATED MODELS FOR SPANISH RATES: ESTIMATED MODELS FOR SPANISH

EXPORTSEXPORTS

Laura Márquez Ramos (Universitat Jaume I, Spain)Inmaculada Martínez Zarzoso (Universität Göttingen, Germany)

Eva Pérez García (Fundación Valenciaport, Spain)Gordon Wilmsmeier (Universität Osnabrück, Germany)

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1. Introduction

2. Objectives

3. Variables and data sources

4. Determinants of container freight rates

5. Conclusions

CONTENTSCONTENTS

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INTRODUCTIONINTRODUCTION

Decreasing role of tariff barriers as an influencing factor on trade

Increasing importance of transport costs as a determinant of trade

Transport costs and international trade

Source: Own elaboration using data of the World Trade Organisation, 2005

% Non-Weighted Tariff Over Import Value

0.00

4.00

8.00

12.00

16.00

20.00

24.00

28.00

32.00

36.00

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

19

96

1997

19

98

1999

20

00

2001

20

02

2003

Year

%

Africa America Asia Europe Oceania

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Importance of maritime transport costs on trade

Source: Own elaboration using UNCTAD data


% Maritime Transport Costs Over Import Value

0%

2%

4%

6%

8%

10%

12%

14%

16%

1980 1990 2000 2002

Africa America Asia Europe Oceania

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Some studies on the determinants of transport costs

Port Infrastructure (Hoffmann, Micco, Pizzolotti, Sánchez, Sgut and Wilmsmeier, 2003)

Supply of maritime services (Pérez and Wilmsmeier, 2005)

Port reforms (Micco and Pérez, 2001; Sánchez et al, 2002)

Trade liberalisation and transport services (Fink et al, 2001)

Trade volume and quality of transport services (Kumar and Hoffmann, 2002)

Distance, infrastructure variables and landlocked dummy (Limão and Venables, 2001)


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Geographical distance as a proxy of transport costs

Some estimated models using gravity equations proved that distance is not an adequate proxy for transport costs of ceramic tiles exports (Martínez Zarzoso et al, 2003)

Anderson and van Wincoop (2004) emphasised the need to obtain better measures of transport costs. These new factors could be used to expand the gravity models and treat the present endogeneity of this variable in this kind of models.


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Identify the determinant variables of maritime transport costs of containerised trade and analyse the role of connectivity

In a second phase of the study, the importance of transport costs for international trade will be analysed

OBJECTIVESOBJECTIVES

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TradeTrans– Spanish Trade and Transport Flows (Fundación Valenciaport)

– Spanish export flows to 17 countries

– Variables related to the commodity and total export flows

– Variables related to the transport chain for containerised trade in 2004

– 36,152 observations

Countries included in the study:

– Algeria, Brazil, Chile, China, Dominican Republic, Greece, Israel, Japan, Mexico, Poland, Russia, South Africa, South Korea, Turkey, United Arab Emirates, United Kingdom and United States of America

VARIABLES AND DATA SOURCESVARIABLES AND DATA SOURCES

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Export flows

2003-2004

22 countries of destination

5 Spanish ports

FrancFrancee

GermanGermanyy

BelgiumBelgiumUKUKRussiaRussiaPolandPoland

PortugalPortugalItalyItalyGreeceGreeceTurkeyTurkeyAlgeriAlgeri

aaIsraelIsraelUAEUAE

KoreaKorea

JapanJapanChinaChina

S. AfricaS. AfricaBrazilBrazil

ChileChile

Dom. Dom. Rep. Rep.

MexicMexicoo

USAUSA

Container traffic

1 port of destination per country

Shipments of less than 1 tm not available


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VARIABLE DESCRIPTIONEXPECTED

SIGNESTIMATE

D SIGNIndex of Unitary

Value (IVU)Ratio value/weight (€/Kg) + +

Exported volume

Total weight (in tonnes) of Spanish exports to a specific country of destination

- -

DistanceAverage distance in kilometres between the Spanish port of departure and the port of destination

+ +

Trade imbalance (absolute

terms)

+ / - +

Negative trade

imbalance*Dummy negative trade imbalance - -

Number of shipping

linesConnectivity measure - -

Vessel capacity

Connectivity measure - -Port throughput

Connectivity measure - -Refrigerated

cargoQuality measure: dummy variable + +

),max(_

jiij

jiij

MX

MXimbalanceTrade


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pijk Freight rate: price of port-to-port maritime transport service

Wk Commodity index of unitary value

QIJ Trade volume

Dij Distance

DqIJ Trade imbalance (absolute terms)

DqnIJ Negative trade imbalance

Connectivityij Connectivity between countries

Qualityij Quality of maritime transport services for containers

DETERMINANTS OF CONTAINER FREIGHT RATESDETERMINANTS OF CONTAINER FREIGHT RATES

(1)ijkijij

IJIJijIJkijk

QualitytyConnectivi

DqnDqDQWp

lnln

lnlnln

86

543210

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Explanatory variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

6.28*** 4.89*** 5.41*** 5.69*** 5.69*** 5.78*** 8.97***(63.65) (49.17) (54.32) (61.25) (63.06) (61.72) (24.76)0.02*** 0.02*** 0.02*** 0.02*** 0.02*** 0.02*** 0.02***(13.24) (15.42) (14.21) (13.03) (16.51) (15.16) (14.48)

-0.17*** -0.11*** -0.09*** -0.02*** -0.02*** -0.03*** -0.23***(-40.28) (-24.51) (-21.93) (-4.58) (-4.62) (-6.44) (-10.42)0.04*** 0.08*** 0.09*** 0.15*** 0.15*** 0.19*** 0.16***(15.39) (27.67) (34.44) (50.57) (53.45) (37.11) (25.55)1.27*** 1.26*** 1.21*** 0.87*** 0.87*** 0.84*** 0.83***(123.99) (122.76) (117.9) (68.41) (70.03) (66.67) (56.99)-0.28*** -0.32*** -0.32*** -0.33*** -0.34*** -0.31*** -0.14***(-37.61) (-40.63) (-40.66) (-43.16) (-45.32) (-33.52) (-6.92)

Connectivity:-0.14*** -0.15*** -0.14*** -0.13*** -0.12*** -0.03***(-35.34) (-35.58) (-36.66) (-34.31) (-23.97) (-3.21)

-0.12*** -0.11*** -0.12*** -0.11*** -0.09***(-34.52) (-36.06) (-37.25) (-32.4) (-22.06)

-0.15*** -0.15*** -0.17*** -0.12***(-42.66) (-44.99) (-46.01) (-20.44)

Quality:0.73*** 0.75*** 0.74***(45.44) (46.66) (40.17)

-0.01** 0.04***(-1.99) (5.01)

-0.07*** -0.11***(-9.22) (-16.12)

Adjusted R-Squared 0.377 0.399 0.421 0.452 0.477 0.479 0.455

Standard Error of Regression 0.397 0.389 0.382 0.372 0.363 0.363 0.371

Number of Observations 36038 36038 36038 36038 36038 35874 35874

-

Number of Calls - - - - -

-

Number of Days between Service Departures

- - - -

-

Dummy Refrigerated Cargo - - -

Vessel Capacity (TEUS) - -

Port Throughput (TEUS) - -

Trade Imbalance (Absolute Terms)

Negative Trade Imbalance

Number of Lines -

Constant Term

Index of Unitary Value

Volume Exported

Distance

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Index of unitary value

Significant at a 1% level

Positive sign:

High value-added commodities tend to pay higher transport costs, possibly due to their choice for quality services.

Small coefficient (0.02):

Scarce influence on container freight rates. The coefficient is expected to have been higher if the transport insurance cost had been included in the port-to-port container transport costs.


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Exported volume


Negative sign:

A larger trade volume would reduce transport costs. Economies of scale applying in the container transport sector

Coefficient of meagre magnitude (-0.03) when the model is estimated using ordinary least squares (Model 6)

Higher coefficient (-0.23) when the estimation is conducted using instrumental variables (Model 7), where the exported volume is incorporated as an endogenous variable: wide margin of negotiation between shipper and shipping line.


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Distance


Positive sign:

Larger distances increase freight rates.

Coefficient in line with other studies (varying between 0.15 and 0.19):

The models with the largest explanatory capacity show that an increase in distance by 10% would raise freight rates between 1.5% and 1.9%.

Although distance remains a determinant factor of freight rates, comparatively its influence explaining freight rates is smaller than the one of trade imbalance, quality variables and the exported volume.


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Trade imbalance


Positive sign for “trade imbalance (absolute terms)”

Negative sign for “negative trade imbalance”


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5,6 Million 5,6 Million TEUsTEUs



Source: Own elaboration using Containerisation International 2005 data.

),max(_

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jiij

MX

MXimbalanceTrade

NEGATIVE TRADE NEGATIVE TRADE IMBALANCE:IMBALANCE:

Imports > ExportsImports > Exports

Exports using trade leg with Exports using trade leg with lowest vessel capacity lowest vessel capacity utilisationutilisation

More competition for the More competition for the cargocargo

Freight rates covered to a Freight rates covered to a certain extent by the busiest certain extent by the busiest legleg

The larger the imbalance, the The larger the imbalance, the lower the freight rates for lower the freight rates for exportsexports

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1,564,000 1,564,000 TEUsTEUs

926,000 926,000 TEUsTEUs

13,9 Mill. 13,9 Mill. TEUsTEUs



Source: Own elaboration using Containerisation International 2005 data.

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Trade imbalance


Positive sign for “trade imbalance (absolute terms)”

Effect of exports to USA over the total sample: (+)

Negative sign for “negative trade imbalance”

Influence of competition for the attraction of cargo.

Large coefficient: important weight of this variable in the process of price fixing for container freight rates

If there is a large trade imbalance between two given origin and destination areas, the freight rates that will be charged for the different legs will vary considerably


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Number of shipping lines


Negative sign:

A larger number of shipping lines offering services between a given pair of ports of origin and destination raises market competition and provokes an effect of price reduction

Coefficient (-0.12):

Notable weight determining freight rates.


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Vessel capacity


Negative sign:

The larger the average capacity of the vessels deployed in a route, the smaller the unitary freight rate applied. Economies of scale at the vessel level


Economies of scale generated by the growing capacity of container vessels have an impact decreasing freight rates.


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1st Generation (1956-1970)

Converted Tanker

2nd Generation (1970-1980)

Cellular Containership

3rd Generation (1980-1988)

Panamax Class

4th Generation (1988-2000)

Post Panamax Plus5th Generation (2000- 2005)

Post Panamax

Converted Cargo Vessel

CapacityDraught

135m – 200m

< 9 metres

500 – 800 TEUs

215 metres

10 metres

1,000 – 2,500 TEUs

250 – 290 metres

11 - 12 metres

3,000 – 4,000 TEUs

275 – 305 metres

11 - 13 metres

4,000 – 5,000 TEUs

352 metres

14 - 15 metres

6,000 – 9,000 TEUs

Source: DPI Terminals (2005)

L.O.A.

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2607

992

289

1289

2951210

2211325

3442

186

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

Capacity ('000s TEUs) No. Vessels

Source: Own elaboration using data from CI online

> 6,000 TEUS5,000-6,000 TEUS

< 3,000 TEUS

4,000-5,000 TEUS3,000-4,000 TEUS

8,258 3,598


DEPLOYED FLEET DECEMBER DEPLOYED FLEET DECEMBER 20052005

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584261

76

658

150

378

691808

964

227

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Source: Own elaboration using data from CI Online

> 6,000 TEUS

5,000 - 6,000 TEUS

< 3,000 TEUS

4,000 - 5,000 TEUS

3,000 - 4,000 TEUS

4,069 1,106

VESSELS UNDER CONSTRUCTION 2006-2009VESSELS UNDER CONSTRUCTION 2006-2009


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2.607

992

289

1.289

2951.210

2211.325

3.442

186

0%

10%20%

30%40%

50%

60%70%

80%90%

100%


Source: Own elaboration using data from CI online

> 6.000 TEUS5.000-6.000 TEUS

< 3.000 TEUS

4.000-5.000 TEUS3.000-4.000 TEUS

8.258 3.598

31911254

365

1947

445

1588

2903133

4406

413

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Source: Own elaboration using data from CI Online

> 6,000 TEUS5,000-6,000 TEUS

< 3,000 TEUS

4,000-5,000 TEUS

3,000-4,000 TEUS

12,327 4,704

FORECAST: FORECAST:

DEPLOYED FLEET IN 2009DEPLOYED FLEET IN 2009


Capacity deployed with vessels > 6,000 TEUS

December 2005: 16.05%

Forecast 2009: 24.51%

DEPLOYED FLEET DEC DEPLOYED FLEET DEC 20052005

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COSTE OPERATIVO DEL BUQUE - TOTAL ANUAL EUROS/TEU

0

50

100

150

200

250

300

350

800 1.500 2.556 5.100 7.500

Capacidad Máxima del Buque (TEUs)

Co

ste

(€/T

EU

)

COSTE OPERATIVO DEL BUQUE - TOTAL ANUAL EUROS/TEU

0

20

40

60

80

100

120

140

160

180

200

6.800 8.800 10.700 12.500

Capacidad Máxima del Buque (TEUs)

Co

ste

(€

/TE

U)

Source: Own elaboration

Source: Own elaboration using data by Tozer and Penfold (2000)


Vessel Operative Cost – Annual Total in Euros / TEU

Vessel Operative Cost – Annual Total in Euros / TEUMax Vessel Capacity in TEUs

Max Vessel Capacity in TEUs

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Port traffic


Negative sign: Existing port economies of scale


Although vessel economies of scale play a notable role on price fixation, their effect is lower than economies of scale at the port level.

Doubling the traffic of a particular port, freight rates of services offered from this port may be reduced between 12% and 17%.

Relevance of having a hub port within reach (for shippers): increased connectivity and lower freight rates.


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Dummy for refrigerated containers


Positive sign:

Cargo that needs to be kept refrigerated or under controlled temperature will pay higher freight rates

Coefficient (0.75):

This large coefficient proves that after trade imbalance, the dummy for refrigerated transport is the most determinant variable of freight rates.


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Number of days between two consecutive departures (headway)

Significant at a 5% level using ordinary least squares (OLS) (Model 6) and at a 1% level estimating with instrumental variables (IV) (Model 7)

Negative sign with OLS: the more frequent the service (the lesser the headway), the higher the quality perception of customers and therefore the larger the freight rate that can be charged

Positive sign: headway acts as a proxy variable of competition within a specific transport market

Coefficient (Model 6: -0.01 Model 7: 0.04):

Little weight on price fixation.


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Number of calls between port of origin and destination


Negative sign:

The larger the number of port calls between the port of origin and destination, the lower the perceived quality of the service (as transit time increases and so does the probability to suffer damages or losses on the cargo). Hence, the shipper will negotiate price reductions for using such services


Scarce influence on price fixation.


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CONCLUSIONSCONCLUSIONS

All the explanatory variables included in the estimated models have been proven significant, both estimating with OLS and with IV.

The adjusted coefficient of determination increases when including measures of connectivity and service quality

The models with the largest explanatory capacity confirm the influence of the following variables on price fixation:

Trade imbalance

Special transport conditions: e.g. refrigerated cargo

Exported volume

Distance

Port economies of scale

Vessel economies of scale

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CONCLUSIONSCONCLUSIONS

Application of obtained results:

Trade imbalance: forecasting value as the effect of future trade trends on freight rates can be foreseen

Exported volume: establishing logistic-oriented associations of shippers to increase bargaining power

Port traffic: fostering the creation of a regional hub

New research line:

Door to door transport costs

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THANKS FOR YOUR ATTENTION!

Eva Pérez García (Fundación Valenciaport, España)

[email protected]

DETERMINANTS OF CONTAINER FREIGHT RATES : ESTIMATED MODELS FOR SPANISH EXPORTS 1 2007 IMSF ANNUAL MEETING DETERMINANTS OF CONTAINER FREIGHT RATES: ESTIMATED.

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