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2007 IMSF ANNUAL MEETING

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

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  1. DETERMINANTS OF CONTAINER FREIGHT RATES: ESTIMATED MODELS FOR SPANISH EXPORTS Laura Márquez Ramos (Universitat Jaume I, Spain)Inmaculada Martínez Zarzoso (Universität Göttingen, Germany)Eva Pérez García (Fundación Valenciaport, Spain)Gordon Wilmsmeier (Universität Osnabrück, Germany) 2007 IMSF ANNUAL MEETING

  2. CONTENTS • Introduction • Objectives • Variables and data sources • Determinants of container freight rates • Conclusions

  3. INTRODUCTION Transport costs and international trade • Decreasing role of tariff barriers as an influencing factor on trade • Increasing importance of transport costs as a determinant of trade Source: Own elaboration using data of the World Trade Organisation, 2005

  4. INTRODUCTION Importance of maritime transport costs on trade Source: Own elaboration using UNCTAD data

  5. INTRODUCTION 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)

  6. INTRODUCTION 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.

  7. OBJECTIVES • 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

  8. VARIABLES AND DATA SOURCES • 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

  9. VARIABLES AND DATA SOURCES • Export flows • 2003-2004 • 22 countries of destination • 5 Spanish ports Korea Russia USA Poland Belgium UK France Germany Turkey Japan Italy Mexico Greece Algeria Israel Portugal China Dom. Rep. UAE Brazil S. Africa Chile • Container traffic • 1 port of destination per country • Shipments of less than 1 tm not available

  10. VARIABLES AND DATA SOURCES

  11. DETERMINANTS OF CONTAINER FREIGHT RATES (1) ln denotes natural logarithms 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

  12. DETERMINANTS OF CONTAINER FREIGHT RATES

  13. DETERMINANTS OF CONTAINER FREIGHT RATES 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.

  14. DETERMINANTS OF CONTAINER FREIGHT RATES Exported volume • Significant at a 1% level • 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.

  15. DETERMINANTS OF CONTAINER FREIGHT RATES Distance • Significant at a 1% level • 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.

  16. DETERMINANTS OF CONTAINER FREIGHT RATES Trade imbalance • Significant at a 1% level • Positive sign for “trade imbalance (absolute terms)” • Negative sign for “negative trade imbalance”

  17. DETERMINANTS OF CONTAINER FREIGHT RATES NEGATIVE TRADE IMBALANCE: Imports > Exports Exports using trade leg with lowest vessel capacity utilisation More competition for the cargo Freight rates covered to a certain extent by the busiest leg The larger the imbalance, the lower the freight rates for exports 5,6 Million TEUs 9,9 Million TEUs Source: Own elaboration using Containerisation International 2005 data.

  18. DETERMINANTS OF CONTAINER FREIGHT RATES 1,8 Million TEUs 3,3 Million TEUs 4,3 Million TEUs 13,9 Mill. TEUs 1,564,000 TEUs 5,6 Million TEUs 926,000 TEUs 9,9 Million TEUs Source: Own elaboration using Containerisation International 2005 data.

  19. DETERMINANTS OF CONTAINER FREIGHT RATES Trade imbalance • Significant at a 1% level • 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

  20. DETERMINANTS OF CONTAINER FREIGHT RATES Number of shipping lines • Significant at a 1% level • 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.

  21. DETERMINANTS OF CONTAINER FREIGHT RATES Vessel capacity • Significant at a 1% level • 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 • Coefficient (-0.11): Economies of scale generated by the growing capacity of container vessels have an impact decreasing freight rates.

  22. DETERMINANTS OF CONTAINER FREIGHT RATES L.O.A. Capacity Draught 1st Generation (1956-1970) Converted Cargo Vessel Converted Tanker 2nd Generation (1970-1980) Cellular Containership 3rd Generation (1980-1988) Panamax Class 4th Generation (1988-2000) Post Panamax Post Panamax Plus 5th Generation (2000- 2005) Source: DPI Terminals (2005)

  23. DETERMINANTS OF CONTAINER FREIGHT RATES DEPLOYED FLEET DECEMBER 2005 8,258 3,598 > 6,000 TEUS 5,000-6,000 TEUS 4,000-5,000 TEUS 3,000-4,000 TEUS < 3,000 TEUS

  24. DETERMINANTS OF CONTAINER FREIGHT RATES VESSELS UNDER CONSTRUCTION 2006-2009 4,069 1,106 > 6,000 TEUS 5,000 - 6,000 TEUS 4,000 - 5,000 TEUS 3,000 - 4,000 TEUS < 3,000 TEUS

  25. DETERMINANTS OF CONTAINER FREIGHT RATES FORECAST: DEPLOYED FLEET IN 2009 12,327 4,704 > 6,000 TEUS 5,000-6,000 TEUS 4,000-5,000 TEUS DEPLOYED FLEET DEC 2005 3,000-4,000 TEUS Capacity deployed with vessels > 6,000 TEUS December 2005:16.05% Forecast 2009:24.51% < 3,000 TEUS

  26. DETERMINANTS OF CONTAINER FREIGHT RATES Vessel Operative Cost – Annual Total in Euros / TEU Vessel Operative Cost – Annual Total in Euros / TEU Max Vessel Capacity in TEUs Source: Own elaboration Max Vessel Capacity in TEUs Source: Own elaboration using data by Tozer and Penfold (2000)

  27. DETERMINANTS OF CONTAINER FREIGHT RATES Port traffic • Significant at a 1% level • Negative sign: Existing port economies of scale • Coefficient (-0.17): 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.

  28. DETERMINANTS OF CONTAINER FREIGHT RATES Dummy for refrigerated containers • Significant at a 1% level • 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.

  29. DETERMINANTS OF CONTAINER FREIGHT RATES 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.

  30. DETERMINANTS OF CONTAINER FREIGHT RATES Number of calls between port of origin and destination • Significant at a 1% level • 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 • Coefficient (-0.07): Scarce influence on price fixation.

  31. CONCLUSIONS • 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

  32. CONCLUSIONS 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

  33. THANKS FOR YOUR ATTENTION! Eva Pérez García (Fundación Valenciaport, España)eperez@fundacion.valenciaport.com

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