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Determinants of maritime transport costs. Importance of connectivity measures

Laura Márquez Ramos

(Universitat Jaume I, Spain) Inmaculada Martínez Zarzoso

(Universitat Jaume I, Spain) Eva Pérez García

(Fundación Valenciaport, Spain) Gordon Wilmsmeier

(Osnabrück University, Germany)

Abstract This paper aims at identifying the determinant variables of maritime transport costs1 of Spanish exports as well as estimating the effect of these transport costs on trade. Primarily the dependency of the freight rate on different factors is measured, focusing on the effect of the maritime network and services structures and the port infrastructure variables. The relevance of these variables as well as the importance of the traditional factors determining trade will then be included in trade equations. The empirical analysis is based on the use of TradeTrans, a database developed by Fundación Valenciaport. The sample includes all Spanish export operations to 17 countries in 2003. The content of this database is highly representative of the Spanish trade and transport flows as it incorporates the information on trade flows from the official Spanish Trade Flows Database published by the Spanish Customs Department and it complements these data with a simulation of the transport route most likely followed by every shipment. Transport data to complete this simulation have been obtained from exhaustive fieldwork with specialists in transport: freight forwarders and shipping agents, as well as with logistics directors of a representative sample of Spanish exporters. The main findings of this study are the key variables that determine maritime freight rates, transport conditions and supply and demand factors having been found relevant. Countries can reduce transport costs by improving port terminals’ efficiency. However, the obtained results show that the embeddedness in the liner shipping network has a significant impact on transport costs. In this regard, the stakeholder’s (shipping lines) activities in the maritime network and their strategies can influence considerably transport costs to a country. Finally, the estimated trade equation proves that income, population, geographical distance and a shared language are still significant explanatory variables of trade, even after including transport costs as a determinant variable in the equation. Maritime transport costs are indeed found to be an important explanatory factor of trade, and the effect of this variable is proven to be larger than geographical distance when considered endogenous.

1 The literature on transport economics has consolidated the expression “maritime transport costs” as the term to designate the rate or price to pay for a maritime transport service.

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Résumé

Cet article a pour objectif d’identifier les déterminants des coûts de transport

maritime afférents aux exportations espagnoles et d’estimer leurs effets sur le

commerce extérieur. Tout d’abord, la dépendance des tarifs de fret envers différents

facteurs est évaluée, en prenant en compte l’impact du réseau maritime, des structures

des services et de l’infrastructure portuaire. La pertinence de ces variables et

l’importance des facteurs traditionnels qui expliquent le commerce sont ensuite prises

en compte dans la modélisation du commerce. Pour mener à bien l’analyse empirique,

nous utilisons la base de données TradeTrans, procurée par la Fundacion Valenciaport.

L’échantillon est composé de toutes les opérations d’exportation de l’Espagne vers 17

pays tiers en 2003. Le contenu de cette base de données est hautement représentatif du

commerce de l’Espagne et des flux de transport dans la mesure où elle contient des

informations relatives aux flux d’échanges issues de la base de données officielle

Spanish Trade Flows Database publiée par le Spanish Customs Department et, en

outre, elle complète ces données par une simulation des itinéraires de transports

empruntés par la plupart des chargements. Les données sur le transport qui complètent

cette simulation ont été obtenues de manière exhaustive auprès de spécialistes du

transport : transitaires, compagnies de navigation, responsables logistiques d’un

échantillon représentatif d’exportateurs espagnols. Les principaux résultats de cette

étude établissent les variables-clés qui déterminent les taux de fret maritime, les

conditions de transport et les facteurs d’offre et de demande les plus pertinents. Les

pays peuvent réduire les coûts de transport en améliorant l’efficacité des terminaux

dans les ports. Toutefois, les résultats obtenus montrent que l’implication dans le

réseau de transport maritime a un impact significatif sur les coûts de transport. A cet

égard, les activités et les stratégies des parties prenantes (compagnies de navigation)

au réseau maritime peuvent influencer considérablement les coûts de transport. Enfin,

le modèle d’estimation du commerce montre que le revenu, la population, la distance

géographique et une langue commune sont toujours des variables explicatives

significatives du commerce, même après avoir inclus les coûts de transport comme

variable explicative dans le modèle. Il est ainsi établi que les coûts de transport

maritime sont un facteur explicatif important du commerce et que leur impact est plus

important que celui de la distance géographique, dès lors qu’on les considère comme

endogènes.

1. Introduction Growing trade liberalisation, trends towards geographical regionalisation and globalisation have motivated the decreasing role of tariff barriers as an influencing factor of trade (Figure A.1. Appendix). Anderson and van Wincoop (2004) show how

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tariff and non-tariff barriers differ depending on the sector, and in general both of them are found to be lower between developed countries. Antidumping practices and other non-tariff barriers apply overall to trade on sensitive commodities (food products, textiles, wood and other manufactures). Nonetheless, even though non-tariff barriers have spread over the last years, the relative importance of transport costs has increased, these costs having become a relevant determinant of trade patterns. Figure A.2 (Appendix) shows the decreasing evolution line of maritime transport costs. This evolution line can be compared with the steeper decreasing slope displayed in the tariff evolution graph. Depending on the continent, transport costs vary between an 8% and a 13% of the import values. Despite their importance, there are not many studies focusing on transport costs, and the existing researches are mainly carried out at an aggregate level. In fact, a wide range of articles consider only proxies to transport costs in their estimated models. For instance, gravity models use distance between country capital

cities as a proxy of transport costs, assuming that jiijtt = (Deardorff, 1995; Bergstrand,

1985, 1989; Anderson and van Wincoop , 2003). The latter studies have proven that geographical distance is a crucial determinant of trade costs. However, this paper’s authors’ belief that geographical distance may be representing a series of factors such as cultural proximity, a shared history, a perception of closeness and information costs rather than being a proxy of maritime freight rates, since the last ones tend to be fixed according to supply and demand conditions applying in the market. Amongst the studies on transport costs Limao and Venables’ (2001) should be highlighted. This work analyses the dependency of transport costs on geographical and infrastructural variables. The authors prove that distance and being landlocked affect positively transport costs. Although the geographical location of countries cannot be modified, the effect of distance can be lessened by improving the infrastructure of the origin, transit and destination countries (Limao and Venables, 2001; Martínez-Zarzoso, Pérez-García, San Juan-Lucas and Suárez, 2004). Clark, Dollar and Micco (2004) focus on the determinants of maritime transport costs. Geographical factors, transport insurance, whether the cargo requires special transport conditions (i.e. refrigerated transport), trade imbalance, economies of scale, the development of containerised transport, number of maritime lines, port efficiency and anti-competition legal and practical restrictions, all determine maritime transport costs. Hoffmann, Micco, Pizzolotti, Sánchez, Sgut and Wilmsmeier (2003) demonstrate that port infrastructure in terms of efficiency affect transport costs. They base their analysis on quantitative port performance measures (turnaround time etc.). Hoffmann (2001) and Wilmsmeier (2003) prove the effect of institutional factors, analyzing the effect of the port operator model on transport costs for the case of South America. The last mentioned studies analyse the explanatory variables of port efficiency and prove that the latter does not only depend on infrastructure, but on a series of variables related to administrative and political issues. Although a certain degree of regulation increases port efficiency and reduces maritime transport costs, over a threshold of high regulation levels, this effect works on the opposite direction, hence decreasing port efficiency and raising maritime transport costs. Wilmsmeier and Pérez (2005) analyse the effect of liner shipping network conditions on transport costs from different regions to South America. They show the reducing effect of maritime services supply on transport cost and how the structure of the deployed fleet for directly connected regions contributes to the level of transport costs. From a sectorial perspective, Martínez-Zarzoso, García-Menéndez and Suárez-Burguet (2003) study the explanatory factors of both maritime and road transport costs for the Spanish exports of ceramic tiles. In this paper, transport costs are found to increase with longer distances and poor infrastructure. The cost of exporting a

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commodity depends on the selected port of departure at the country of origin, transport costs increasing when the cargo is not loaded at the most efficient port. Estimations of gravity equations carried out prove that distance is not an appropriate proxy of transport costs for the ceramic tiles sector. Anderson and van Wincoop (2004) emphasise the need to obtain better transport costs measures and to use these measures in order to expand gravity models and treat the endogeneity of the transport costs variable in this kind of equations. The present paper deals with the above mentioned issues from an empirical point of view. Firstly, the determinants of maritime transport costs are estimated, paying particular interest to the importance of connectivity measures, as two countries may be far away from a geographical perspective but may be very well connected, the high degree of connectivity influencing transport costs. Following this, a trade equation is estimated, maritime transport costs being included as an explanatory variable and the estimated results being analysed when this latter factor is considered endogenous. The structure of the paper is as follows: section 2 discusses on transport costs and trade equation determinants. Section 3 describes the data used and defines the variables to be included in the empirical analysis. The relevance of incorporating connectivity measures as a explanatory factor of maritime transport costs is studied and a connectivity index is constructed. Section 4 presents the empirical analysis carried out, concerning the estimation of the determinants of transport costs and modelling a trade equation. Finally, section 5 concludes. 2. Transport costs and international trade A maritime transport costs model derived from previous existing studies has been estimated and its results presented in this paper. Maritime transport costs are considered to depend on supply and demand factors, as they are the result of adding up marginal costs and a profit margin of the company offering a transport service. Marginal costs and profit margin are a function of variables representing the transport service conditions, infrastructural variables of origin, transit and destination countries, factors inherent to the characteristics of the commodity to be transported and the degree of competition existing in the market. In a second stage, maritime transport costs will be included as an explanatory variable in the estimation of a trade equation derived from traditional gravity models.

According to Limao and Venables (2001), the variable ijT denotes the unitary costs to

transport a commodity shipped from the country of origin i to the destination country j,as defined in equation (1):

),,,(ijjiijij

XXxTT µ= (1) where

ijx is a vector of the itinerary characteristics between i y j,

iX is a vector of the

features of the country of origin, j

X is a vector of the characteristics of the destination country, and

ijµ represents non-observable variables.

Martínez-Zarzoso and Suárez-Burguet (2003) include variables that take into account the characteristics of the country of origin, country of destination, type of commodity (xijk) and shipment k (!k). Proceeding this way, the specific effect of the transported commodity is studied and the different demand elasticities of transported goods are incorporated in the analysis, as it can be observed in equation (2):

),,,,,(ijkijkkjiijij

xXXxTT µ!= (2)

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Model (3) shows the linear equation of transport costs derived by the last mentioned authors. Transport costs depend on the unitary value of the commodity (

ijkW ),distance

(ij

D ), trade volume (ij

Q ), infrastructure at the countries of origin and destination (

iInf and

jInf ), whether the origin and destination countries are landlocked (

iLand

and j

Land ), and whether these countries share a common language (ij

Lang ).

ijkkijji

jiijijijkijk

xLangLandLand

InfInfQDWT

++++++++++=

!"""#####

321

54321 lnlnlnlnlnln(3)

Various researchers estimate transport costs as a function of marginal costs of transport plus profit margin of the transport company. Clark et al. (2004) express this equation in logarithms:

),,(),,( kJIkjimcpijk

µ+= (4)

where ijkp are the unitary costs of maritime transport, in logarithms, for commodity k to

be transported from the port of origin i (in country I) to the port of destination j (in country J). mc and µ denote marginal costs and profit margin respectively. According to Clark et al. (2004), marginal costs and profit margin must be a function of variables representing the characteristics of ports, country of origin and destination and type of commodity. The explanatory variables considered in the present study can be divided in three groups:

� Variables related to the itinerary and service between i and j: geographical distance between port of origin and destination, connectivity and quality of transport service.

� Factors related to characteristics of country of origin (I) and destination (J): trade volume, trade imbalance and port infrastructural variables.

� Variables related to the features of the commodity (k): unitary value of the commodity.

The specification of the transport costs model to be estimated in the empirical analysis section is as follows:

ijkijij

IJIJijIJkijk

QualitytyConnectivi

DqnDqDQWp

µ########

+++

++++++=

lnln

lnlnln

86

543210(5)

where ln denotes natural logarithms. According to equation (5), maritime transport costs (in natural logarithms) depend on: the unitary value index of the commodity (

ijkW ), distance (

ijD ), trade volume (

ijQ ), trade imbalance, included in absolute terms

(IJ

Dq ) and as a separate variable accounting for the components of trade imbalance that show a negative sign (

IJDqn ), connectivity between countries (

ijtyConnectivi ), and on

transport service quality (ij

Quality ).ijk

µ is the regression error term. In a second part of the present study a trade equation is estimated. This equation has been derived from gravity models that in their most basic form include explanatory variables such as income of the country of origin and destination, population of both countries and geographical distance between both points as a proxy of transport costs (Martínez-Zarzoso and Nowak-Lehman, 2003). In this paper, maritime transport costs are incorporated into the trade equation and a socio-cultural variable, sharing a common

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language is also included, as in Frankel, Stein and Wei (1995) and Martínez-Zarzoso, Pérez-García, San Juan-Lucas and Suárez (2004). This specification differs from the one used in Martínez-Zarzoso and Suárez-Burguet (2003) as sharing a language is considered an explanatory variable within the trade equation but not as a determinant of transport costs. This equation only includes income and population of the country of destination, since this paper concerns the Spanish exports to 17 destination countries and therefore the income and population of the origin country do not vary. The specified equation is:

ijijijkijjjijLangpDPYHX $"""""" ++++++= 654310 lnlnln (6)

where i is the exporting country and j the importing country.j

YH andj

P are GDP per capita and population of the importing country,

ijD is the distance between both

countries, ijk

p is the variable representing maritime transport costs in natural logarithms and

ijLang is a dummy variables that takes value 1 if the origin and destination country

share a common language and 0 otherwise. In section 4, equation (6) will be estimated by ordinary least squares (OLS) and using Instrumental Variables (IV) in order to correct for the endogeneity presented by maritime transport costs. 3. Reference data and definition of variables 3.1. Data, sources and variables

The data used in this study have been extracted from the database TradeTrans – Spanish

Trade and Transport Flows, developed by Fundación Valenciaport. TradeTrans compiles export declaration forms and completes them with a series of variables providing information about the mode of transport, transport route followed by each export shipment and the costs and time associated to that transport service for each shipment leaving Spain with destination in 23 countries. Data for each of 36,152 shipments exported as containerised cargo by sea from the 51 Spanish provinces to 17 countries during 2003 have been used in the model. These represent all the Spanish export maritime shipments to those 17 destination countries, cases with a large proportion of missing values having been excluded (these excluded shipments represent only a 0,3% of the total population). The 17 countries representing destinations of shipments included in the study are: 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 (see Table A.1, Appendix). Countries have not been selected at random, but chosen as an exogenous sample to provide for variation in the variables describing shipments to them. The selection of countries includes all the major trade partners of Spain for maritime cargoes. Large trade counterparts such as France, Germany, Italy and other European countries have not been included in this study as the percentage of shipments transported by sea is not representative of the Spanish exports flows destined to these countries. The United States of America is the most represented amongst the previous list of sample countries, being the destination for 18,70% of the total weight of the considered export flows. The dependent variable in the estimated model is the freight rate between the port of origin in Spain and the port of destination. This variable expresses the total tariff in euros that the exporter or the importer, depending on the INCOTERM, had to pay for the export shipment to be transported in container/s by sea from on-board the vessel

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moored at the port of origin to the port of destination (on-board the vessel). This value is referred to the total weight exported for every specific shipment. For every pair of port of origin and destination 10 different quotes of the freight rates charged in order to transport a TEU (twenty equivalent unit container), a FEU (forty equivalent unit container), refrigerated or not, and needing consolidation or not, were obtained. The dependent variable has been constructed using as a basis the average of the quotes obtained from at least 10 shipping agents representing shipping lines offering the service between every specific port of origin and destination, and taking into account the weight to be transported for each shipment and the ratio tonnes per TEU or FEU, depending on the type of merchandise exported. The variables inserted in the estimated model and their a priori expected signs are: Index of Unitary Value (€/Kg): ratio of value/weight (in euros/kilograms) calculated for each specific export shipment. It provides a consistent indicator of the value of the exported merchandise, being this index comparable for different types of commodities. The expected sign of this variable as a determinant of maritime transport costs is positive, as even if the type of commodity has been accounted for with the inclusion of dummy variables and a reefer cargo dummy has also been inserted in the model, the effect of the transport insurance will be larger the higher the value of the specific good. Volume exported: total weight in tonnes of the Spanish export flows shipped in containers to each specific country of destination. The effect that a growth in the exported volume is expected to have on the maritime transport costs is negative, as a larger volume would generate further economies of scale at the exporter level, and at ports and vessels, therefore producing the expected impact of decreasing the freight rate applied. The relationship between trade and transport costs however works both ways, as a decrease in transport costs would also influence an increase in trade. Distance: average distance in kilometres between the Spanish port of origin and the port of destination. This variable has been built as an average of the real distances travelled by the different lines offering that transport service. The source of this data is the Fundacion Valenciaport’s database Lineport. This database compiles information of every call made at one of the five Spanish ports under study of lines accepting cargo for the 22 countries of destination (for those lines publicised at the port's webpage or port community journals). Lineport has collected vessel calls at Spanish ports for all lines operating between Spain and Europe since 2003. Due to the labour-insensitivity involved in this task, for non-European countries, the same compiling procedure is conducted, but only for three months along the year: March, July and October, and this information is then extrapolated to the rest of the year. The average distance between the Spanish port of origin and the port of destination is drawn as the statistical mean of all the values of distances travelled by the different lines offering the service, taking into account the port calls included in their itineraries. The expected sign of this variable is positive, as the longer the distance to be travelled the larger the amount of costs the shipping line will incur into and therefore the higher the tariff they will be tending to charge. Although the effect of distance on freight rates is inevitable and undeniable, the authors of this paper believe the validity of using distance as a proxy variable for transport costs has been overrated, as proven by both the information obtained from interviews with managers in charge of pricing their company’s shipping services and this paper’s results.

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Trade imbalance: international trade flows are heavily imbalanced between areas, a disequilibrium that applies both to general world trade and to containerised seaborne trade. Figure 1 shows the major trade flows of containerised cargoes transported by sea to and from Europe in TEUs for year 2004. A glance at this figure suffices for us to see the size of the disequilibrium, close to 50% in most cases. Consequently, the percentage of capacity utilisation of containerships deployed between two areas will be on average close to 50% in one of the two legs of the trip. Figure 1. Imbalance in Containerised Seaborne Trade to and from Europe (TEUs, 2004)

Source: Own elaboration The influence of trade imbalance on maritime transport costs depends on the sign of the disequilibrium when calculated according to the following expression:

When trade imbalance is negative, calculated according to the above formula, Spanish imports are larger than Spanish exports, and in this case, the larger the imbalance the lower the freight rates; whilst if exports are larger than imports, the larger the imbalance the higher the freight rates to be expected. This divergence depending on the sign of trade imbalance occurs given the price fixing mechanisms applying in the liner market for freight rates. The liner company knows that one of the legs of the turnaround trip will present a reduced percentage of vessel capacity utilisation and therefore adapts their pricing scheme to the direction of the trip and its corresponding expected cargo. Freight rates will be higher for the shipments transported in the leg of the trip with the larger amount of traffic, as the total amount charged in this leg must compensate the relatively reduced income to be raised on the return trip, when part of the capacity of the vessel will be inevitably used repositioning empty containers. An excess of capacity supplied on the return trip will increase the competition for the different liner services and as a result freight rates will tend to be lower.

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Aiming at capturing the influence that a change in this variable sign has on the freight rate level, trade imbalance has been introduced in our models as two separate variables: first of all, trade imbalance has been included in absolute terms, calculated according to the following formula:

),max(jiij

jiij

MX

MXimbalanceTrade

%= (7)

Secondly, a dummy accounting for negative trade imbalance has been constructed. This dummy variable takes the value of one when there is a negative imbalance, zero otherwise. We interact this dummy with the variable calculated in equation (7). The expected sign is negative when there is a negative imbalance and either positive or negative for the imbalance in absolute terms, as the final sign will depend on whether the positive or negative disequilibrium predominates. Number of lines: as a proxy of the degree of competition between lines offering the same maritime transport service at a specific port, an increase in this variable would cause a decrease in transport costs and vice versa, the sign of this variable hence expected to be negative. The source of this data is the Fundacion Valenciaport’s database Lineport. For every observation, the value assigned to this variable is the calculation of the number of maritime regular lines offering a transport service from the port of origin to the port of destination of the observation considered. All shipping lines that publicise their services between the two ports under study have been included.

Vessel capacity: economies of scale at vessel level still apply in the market, as it can be judged by the continuous increase in vessel size over the last years as well as by most of the research papers found in the literature on this issue (Jansson and Shneerson, 1982; Talley, 1990; Lim, 1998; Tozer and Penfold, 2000; and Lloyd’s Register Technical Association, 2002). Whilst the largest container vessel built in 1968 had capacity to carry 1,700 TEUs, in 1980 this capacity had increased to 2,900 TEUs, in 1990 the figure of 4,000 TEUs had been reached and by 2004 8,000 TEUs container vessels were deployed. An analysis of the operating costs per TEU of differed sized vessels has drawn the results presented in Figure 2.

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Figure 2. Operating costs for vessels with capacity under 7,500 TEUs

Source: Own elaboration

Note: This figure has been obtained according to the following assumptions: the average vessel life has been fixed in 18 years, the purchase vessel price has been extracted from Containerisation International July and August 2004 for 5 vessels constructed in the same Korean shipyard and for similar shipbuilding orders of 2 to 3 vessels with speeds varying from 22 to 24 knots, linear amortisation has been applied, a 3.5% annual maintenance cost over the purchasing price has been assumed, and direct costs have been calculated according to the vessel size and the average costs of fuel, crew wages and US$ to € exchange rate applying in August 2004. The 5 vessels under study have been assumed to be deployed on the same route, entailing 15 sailing days per trip, 270 sailing days per year and the percentage of vessel capacity used has been fixed in 65% on average.

The previous graph shows the presence of scale economies at the vessel size level. Although the optimal containership size will depend on the company’s market and strategy, for the same volume of traffic, number of port calls and time per port call, optimal containership size increases for the same route and also as the route distance increases (Talley, 1990). Tozer and Penfold (2000) indicate that even if there are limits to scale economies, where further increases in vessel size provide only limited unit cost reductions, this inflexion point has not yet been reached. Various studies have tackled the calculation of the optimum vessel capacity. Tozer and Penfold (2000) find their optimal configuration of a 12,500 TEUs capacity vessel. The dimensions of this vessel are compatible with maximum permissible draught restrictions through the Suez Canal, together with current and planned infrastructure developments at key port terminals –a number of container terminals being already in a position to handle such ships. The operating costs of the different configurations studied by Tozer and Penfold (2000) can be observed in the following graph, where the 10,700 and 12,500 solutions are twin engine vessels and the navigational speed has been kept constant at 25 knots.

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Figure 3. Operating costs for vessels with capacity over 6,800 TEUs

Source: Tozer and Penfold, 2000

The expected sign of this variable is therefore negative, as larger vessel sizes would mean decreases in maritime transport costs. Port container throughput: In recent studies container port traffic (container throughput) has been regarded as an appropriate variable involving economies of scale and port’s production and efficiency (Wang, Cullinane and Song, 2005). The appropriateness is justified as a more effective terminal can be expected to induce less unit transport costs. Furthermore, economies of scale are also presented at the port level, as larger volumes of containerised cargo to be loaded and unloaded at a port will enable the shipping lines to use larger containerships as well as permitting the terminal operator to optimise the use of terminal equipment, infrastructure and stevedoring shifts. Large port cargo volumes will also tend to attract more liner services, thus increasing the degree of competition between services at that specific port. The expected sign of this variable will hence be negative, and raising port container throughput would imply a likely reduction in container freight rates. Refrigerated cargo: commodities that require special conditions for their transport, such as refrigerated cargo, would bear an increased price to be transported. A positive sign is therefore expected for this variable. Number of days between service departures: this variable reports the average of the time in days between two consecutive calls of vessels deployed in services between the port of origin and the port of destination (according to the dates publicised by the different lines). The source for the calculation of this variable has been Lineport database. As it has been defined, this variable is therefore negative and directly related to frequency. The effect of the number of days between service departures on the average service freight rate can be twofold: on the one side, frequency can be seen as a proxy of the

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service quality, as a more frequent port to port service decreases overall the shipper’s transit time from door to door on average and increases the flexibility of the latter one to program its shipments. The impact of frequency perceived as quality on the freight rate will then be positive, as the shorter the time lag between shipping opportunities, the higher the transport price. Hence, the effect of an increase in the number of days between service departures would decrease the maritime service price. On the other side, a lack of frequency or consequently an increase in the number of days between service departures in the offered transport services connecting the port of origin to the port of destination is also indicating a lack of competition between different shipping lines. In this case, a longer time interval between departures will mean less competition and increased freight rates. The two described effects provoke impacts of contrary directions, the estimation of a model being relevant in this case in order to find out the prevailing effect. Number of calls: This variable informs about the average number of ports where a shipping line calls at between the port of origin and destination. It is therefore a proxy of the service quality, its expected sign being negative as a higher number of calls would imply a reduction in the service quality and a decrease in the freight rate the shipping line will be able to charge the shipper. A further set are infrastructure supply variables, number of cranes, maximum draught and storage area for origin and destination ports. De Neufville and Tsunokawa (1981) use a measurement of the number of cranes and the quay length as inputs to port performance. In the context of this study the authors consider the interaction of the three before mentioned variables as an appropriate proxy for quayside operation performance, where a greater fit of these three variables contributes to a reduction in transport costs. Following the ideas from Bendall and Stent (1987) and Wilmsmeier and Perez (2005) the study introduces vessel specific variables, age of the youngest vessel in service x, average capacity (in TEUs) and speed (in knots). These variables are expected to explain the positive effect of fleet performance of a service x between two ports. It is expected that a greater performance, higher speed, larger capacities and younger vessels will contribute to lower transport costs, due to higher productivity. 3.2. Importance of connectivity measures on transport costs

Interconnectivity is an attribute of networks that refers to the quality and costs to move freight between two points in space (Greenhuizen, 2000). In earlier studies, infrastructure density indicators have been used for the comparison of infrastructure development levels (Micco and Pérez, 2002). However, this comparison cannot be used for maritime transport, because waterborne transport along its relation (routes) is not bound to any physical infrastructure and therefore must be described as rather discrete. Therefore, the study uses the performance and structure of the mobile entities (ships) in maritime transport to define the connectivity between origin and destination. Moreover, the performance of ports is essential for the efficiency and effectiveness of the maritime network. The functioning of the network and its structure involve complex interaction patterns that subsequently influence the cost of transport in the relation between two countries. Indicators of infrastructure use in terms of transport services offered and transport volumes provide a simple form of connectivity index (described above), but do not reflect the system inherent interrelations in the supply of transport services.

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Interconnectivity can be addressed in two ways. In a broad sense, it encompasses physical characteristics of the network, features of the modes and co-ordination of various operators, as well as integration of services. In a narrow sense, interconnectivity is limited to the physical properties of the network. Attempting to comprise connectivity in our study, the paper analyses the following aspects and indicators and tries to evaluate the potential importance of these variables in the configuration of transport costs:

Table1. Configuration of variables of transport costs

Transport infrastructure supply � No. ports in a region, � Network distance (Euclidean distance v.

network distance)

Infrastructure capacity � Port infrastructure endowment

Transport services � Average capacity of vessels deployed in regular services,

� No. of services, � Monthly frequency of services, � Minimum transit time

Traffic volume indicators � Transport volume, � Number of shipping lines

The influence of infrastructure endowment and services on transport costs has gained significant attention on the last years. By including these variables in our analysis, the goal is to portray the physical availability of port infrastructure and maritime services, revealing the impact of both conditions on transport costs. Table 2 shows a list of conditions that influence transport costs. The construction of complex connectivity measures is expected to fill the gap of determinants of infrastructure and services structure and the conditions of services on their routes. Table 2. Conditions and determinants of transport costs

Condition Determinants

Geography Distance

Type of product Containerised, type of packaging, weight, value

Economies of scale Shipment size

Trade imbalance Empty movements

Infrastructure and services Connectivity measures

Services/route Capacity, limitations, operational conditions

Competition and regulation ???

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3.3. Construction of a connectivity measure

We attempt to address the complex measure of connectivity and the influence of connectivity on maritime transport costs by introducing these variables into our transport cost model. Considering the variety and quantity of variables known to explain the multiplicity and ambiguity of the system, we intend to construct more comprehensive connectivity measures. The measures are intended to provide what is often suggestively referred to as a “breakdown” of a more complex structure. This association should not obscure the process, but rather help to clarify it. The new measures offer a variety of interpretations far beyond the apparent dimensionality of the data. Factor analysis (FA), of which principle component analysis (PCA) is a variant, one among a number of multivariate techniques, is designed to derive information from a set of variables without specifying any of the variables as either dependent of independent. PCA will not bring about variables explaining a particular phenomenon, but it is seen as a statistical manner to isolate the variables most likely to provide fruitful contributions to the study. Creating a set of factors to be treated as uncorrelated measures, we also use them as an approach to handling multicollinearity in such processes as multiple regressions. Conceptually, the process to calculate principle components from a set of variables merely consists of another measurement designed to capture the correlations among the correlations themselves. Correlation values can be shown to fall into certain broad clusters that can be explained as the manifestations of an abstract underlying dimension. As a result, a set of i.e. 100 variables, might be aggregated to a much smaller set of significant principle components. The extraction of factors is itself a ranking process. The significance of factors is expressed as the percentage of the variance that they explain, resulting that a small number of latent variables is defined to account for most of the variation in the original set of variables. The extracted factors reflect the common and unique variance of the variables and may be seen as a variance with all components to reproduce correlations. PCA seeks a linear combination of variables such that the maximum variance is extracted from the variables. In a following step it then removes this variance and searches for the following linear combination which explains the maximum proportion of the remaining variance. This is repeated until 100% of the variance is explained by the sum of factors extracted. The factor loadings are the correlation coefficients between the variables (rows) and factors (columns). Factor loadings are the basis for assigning labels to the different factors. In order to determine the number of factors relevant different tests and algorithms can be used. One of the most common is the so called Kaiser criterion, dropping all components with eigenvalues below 1.0. The cattel scree test plots the components on the X axis and the corresponding eigenvalues on the Y axis. When the drop ceases and the curve makes an elbow toward a less steep decline, the Cattle’s scree test advises to drop all further components after the one starting the elbow. Another way to proceed is by using the rule of keeping enough factors to account for a certain percentage of the variation. Since the obtained factor loadings are often hard to interpret, rotation methods serve to make the output more understandable. The sum of eigenvalues is not affected, but

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rotation will alter the eigenvalues of particular factors. In the following analysis, Varimax rotation will be used. This is an orthogonal rotation of the factor axes to maximise the variance of the squared loadings of a factor on all the variables in a factor matrix, which has the effect of differentiating the original variables by extracted factor. This process minimises the number of variables which have high loadings of particular loadings. This rotation type makes it was to interpret the results. Other rotation methods are quartimax rotation, equimax rotation, direct oblimin rotations and promax rotations. The variables introduced in the PCA analysis to explain the connectivity presented below are the following: percentage of transhipment lines on that route, transit time, maximum and minimum number of calls on the route, distance between ports, number of services, time lag between shipping opportunities, vessel speed, fleet age, average capacity; and for the ports of origin and destination: number of cranes, maximum water depth and storage area. In the first PCA model we introduced all variables. The correlation matrix and the Kaiser-Meyer-Olkin (KMO) test (0.664) proved to be significant, which indicates the sampling adequacy of the chosen variables. The PCA extracted five factors, which explained 75.4% of the intrinsic variance of the data fulfilling the Kaiser Criterion with Eigenvalues over 1. The first factor, which accounted for 22.8% of the total variance, loaded high on transit time, minimum and maximum number of calls, distance between ports and percentage of transhipment lines. Based on the theoretical framework of connectivity we interpret this factor to represent the route structure in the maritime network. Table 3. PCA Connectivity Model A

Initial Eigenvalues Extraction Sums of Squared LoadingsComponent Total % of Variance Cumulative % Total % of Variance Cumulative

%1 - Route Structure 3.648 22.801 22.801 3.648 22.801 22.801

2 – Port of Origin Infrastructure Supply

3.505 21.905 44.705 3.505 21.905 44.705

3 - Port of Destination

Infrastructure Supply

2.129 13.305 58.010 2.129 13.305 58.010

4 - Equipment Structure

1.652 10.323 68.333 1.652 10.323 68.333

5 - Service Structure 1.127 7.042 75.375 1.127 7.042 75.375Source: Own elaboration

Infrastructure supply variables in the port of origin loaded high on the second factor, explaining 21.9% of the variance, while infrastructure supply variables of the port of

destination loaded high on the third factor (13.3% of the variance). Age of ships, their average capacity (TEUs) and speed determined the fourth factor (10.3% of the variance), the factor will be regarded as equipment structure hereafter. Finally, minimum frequency, number of liner services and maximum number of ports of call construct the fifth factor, which can be regarded as service structure.

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Table 4. PCA Connectivity rotated component matrix Model A

Component1 – Route Structure

2 – Infrastructure Supply -

Port of Origin

3 - Infrastructure Supply - Port of

Destination

4 – Equipment Structure

5 – Service Structure

Transit time .888Minimum number of calls .874

Distance between ports .831Maximum number of calls .728 .474

Percentage of transhipment lines

.575

Port of origin: number of cranes

.911

Port of origin: max. draught -.887Port of origin: storage area .772Port of destination: number

of cranes.850

Port of destination: max draught

.824

Port of destination: storage area

.779

Age of the youngest vessel .817Average capacity (TEUs) .426 .744

Speed .401 .681Number of lines .826

Time lag between shipping opportunities

-.767

Source: Own elaboration Notes: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. A Rotation converged in 5 iterations.

In a second modelling approach we left out port infrastructure variables in order to construct a pure connectivity measure based on the mobile elements of the maritime transport network. As in the first case the correlation matrix and the Kaiser-Meyer-Olkin (KMO) test (0.718) proved to be significant, which indicates the sampling adequacy of the chosen variables. The PCA extracted five factors, which explained for 71.3% of the intrinsic variance of the data fulfilling the Kaiser Criterion with Eigenvalues over 1.

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Table 5. PCA Connectivity Model B

Initial Eigenvalues Extraction Sums of Squared LoadingsComponent Total % of Variance Cumulative % Total % of Variance Cumulative

%1 – Route Structure

B3,397 33,969 33,969 3,397 33,969 33,969

2 – Equipment Structure B

2,169 21,692 55,662 2,169 21,692 55,662

3 – Services Structure B

1,565 15,653 71,315 1,565 15,653 71,315

Source: Own elaboration

Transit time, minimum and maximum port calls, distance between ports and percentage of transhipment lines loaded high on the first factor (34.0% of the variance). The component will be referred to as route structure hereon. The second component (21.7% of the variance) reflects the equipment structure on a certain route and as in model A constructs from the same variables. Finally the third factor can be described as service structure, with the high loadings from the following variables: number of liner services, minimum frequency and with a relatively high loading on the maximum number of port calls. Table 6. PCA Connectivity rotated component matrix Model B

Component1 – Route Structure B 2 – Equipment

Structure B3 – Services Structure B

Transit time .909Minimum number of calls .861

Distance between ports .835Maximum number of calls .736 .491

Percentage of transhipment lines

.634

Average capacity (TEUs) .909Speed .871

Age of the youngest vessel .620Number of lines .826

Time lag between shipping opportunities

-.772

Source: Own elaboration. Notes: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. A Rotation converged in 5 iterations.

In a third modelling approach we also dropped the distance variable, because distance has been proved to be a significant single determinant in transport costs regressions in

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prior studies (Hoffmann 2002, Martínez-Zarzoso et al. 2002, Wilmsmeier 2003). In order to be able to use this measure as a single variable in the latter regression we tried to generate the components and as in the two prior models the correlation matrix and the Kaiser-Meyer-Olkin (KMO) test (0.593) proved to be significant, which indicates the sampling adequacy of the chosen variables. The PCA extracted five factors, which explained for 75.4% of the intrinsic variance of the data fulfilling the Kaiser Criterion with Eigenvalues over 1. Table 7. PCA Connectivity Model C

Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative

%1 - Equipment

Structure C2.351 29.390 29.390 2.351 29.390 29.390

2 – Route Structure C

1.952 24.398 53.788 1.952 24.398 53.788

3 – Services Structure C

1.410 17.621 71.409 1.410 17.621 71.409

Source: Own elaboration

We can observe a similar structure of components like in model A. In this model the equipment structure, determined from the same variables as before, explains 29.4% of the variance. The route structure evolves from the model a the second component explaining 24.4% of the variance and including the same variables as in model A. The third component reflects the services structure and in difference to the models A and B next to the variables number of liner services and minimum frequency the variables maximum number of ports of call and the percentage of transhipment lines have relatively high loadings. Table 8. PCA Connectivity rotated component matrix Model C

Component1 – Equipment Structure

C2 – Route Structure C 3 – Services Structure

CAverage capacity (TEUs) .915

Speed .881Age of the youngest vessel .612

Transit time .901Maximum number of calls .769 .476

Percentage of transhipment lines

.704 -.439

Number of lines .794Time lag between shipping

opportunities-.788

Source: Own elaboration. Notes: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 5 iterations. As the connectivity varies significantly between the different trade relations under study. The PCA analysis shows that the structure of variables has a certain continuum. Model A and B set the measure of distance in a more complex context. Moreover, it

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clearly explains the complex dimension and interconnectedness of the connectivity variables. The results from this exploratory phase are analysed for their specific impact on transport costs in the following section. 4. Empirical framework 4.1. Estimation of transport costs

As explained in Section 2, we estimate equation (5), where the dependent variable is the maritime transport cost of exports from Spain to 17 market destinations (see Table A.1, Appendix) for the year 2003. Commodities have been defined using the combined nomenclature at a 8-digit disaggregation level. Only containerised transport costs are considered. In order to understand better the effect of explanatory variables on transport costs, the different factors explaining the dependent variable have been included progressively in regressions. Table 9 shows our final results. Table 9. Determinants of maritime transport costs

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

Constant Term 6.28*** (63.65)

4.89*** (49.17)

5.41***(54.32)

5.69***(61.25)

5.69***(63.06)

5.78***(61.72)

8.97***(24.76)

Index of Unitary Value 0.02*** (13.24)

0.02*** (15.42)

0.02*** (14.21)

0.02*** (13.03)

0.02*** (16.51)

0.02*** (15.16)

0.02*** (14.48)

Volume Exported -0.17*** (-40.28)

-0.11*** (-24.51)

-0.09*** (-21.93)

-0.02*** (-4.58)

-0.02*** (-4.62)

-0.03*** (-6.44)

-0.23*** (-10.42)

Distance 0.04*** (15.39)

0.08*** (27.67)

0.09*** (34.44)

0.15*** (50.57)

0.15*** (53.45)

0.19*** (37.11)

0.16*** (25.55)

Trade Imbalance (Absolute Terms)

1.27*** (123.99)

1.26*** (122.76)

1.21*** (117.9)

0.87*** (68.41)

0.87*** (70.03)

0.84*** (66.67)

0.83*** (56.99)

Negative Trade Imbalance -0.28*** (-37.61)

-0.32*** (-40.63)

-0.32*** (-40.66)

-0.33*** (-43.16)

-0.34*** (-45.32)

-0.31*** (-33.52)

-0.14*** (-6.92)

Connectivity:

Number of Lines - -0.14***

(-35.34) -0.15*** (-35.58)

-0.14*** (-36.66)

-0.13*** (-34.31)

-0.12*** (-23.97)

-0.03*** (-3.21)

Vessel Capacity (TEUS) - - -0.12*** (-34.52)

-0.11*** (-36.06)

-0.12*** (-37.25)

-0.11*** (-32.4)

-0.09*** (-22.06)

Port Throughput (TEUS) - - - -0.15*** (-42.66)

-0.15*** (-44.99)

-0.17*** (-46.01)

-0.12*** (-20.44)

Quality:

Dummy Refrigerated Cargo - - - - 0.73*** (45.44)

0.75*** (46.66)

0.74*** (40.17)

Number of Days between

Service Departures - - - - - -0.01**

(-1.99) 0.04*** (5.01)

Number of Calls - - - - - -0.07*** (-9.22)

-0.11*** (-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 Notes: ***, **, * indicate significance at 1%, 5% and 10%, respectively. T-statistics are in brackets. The dependent variable is the freight rate of transporting good k from the exporting country i (Spain) to the importing country j in natural logarithms. All explanatory variables, excluding trade imbalance and dummies are also in natural logarithms. Models 1-6 have been estimated by OLS, whereas Model 7 has been estimated by Instrumental Variables. Volume exported is considered as endogenous and the instrument used is the population in the importing country. The estimation uses White’s heteroscedasticity-consistent standard errors. Data are for the year 2003.

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Results in Model 1 show that the index of unitary value has the expected sign and is significant. A higher value of the transported good increases transport costs. Its coefficient is low (0.02) since the value of this variable depends on the transported good and its effect can be compensated when products of different sectors are included. The exported volume has the expected sign and is significant, indicating that a larger trade volume has a negative effect on transport costs, due to scale economies. Average distance from port to port according to liner services itineraries between two ports, is significant and has the expected sign. Longer distances increase maritime transport costs, although this effect does not seem very important, since price fixing processes of maritime freight rates depend more on supply and demand conditions. Trade imbalance variables are significant and have the expected sign. As expected, negative trade imbalance displays a negative sign, implying that in a situation where Spain exports less than imports from a trade partner (trade imbalance will be negative), freight rates will tend to be lower due to the low percentages of capacity used of the vessels deployed in the services linking both countries and the high degree of competition amongst lines to attract that traffic. Although not shown in Table 9, other regressions have been carried out and the positive sign displayed by positive trade imbalance has been estimated. The estimated sign of trade imbalance in absolute terms is positive, hence the effect of the positive trade disequilibrium being larger than the negative trade imbalance. Indeed, the country accumulating the larger percentage of the total amount of exported tonnes is the United States of America, Spain exporting more than importing from this trade partner country. Trade imbalance in absolute terms is highly significant and constitutes the variable will the larger coefficient, thus its high influence on the freight rate fixing processes being justified in our regressions. Models 2, 3 and 4 include connectivity measures. A better connectivity (a higher number of lines, vessel capacity and port throughput) between the origin and destination ports decreases transport costs. We return to the importance of connectivity variables below, where the connectivity indexes are estimated. Model 5 includes a dummy variable to consider whether goods are transported in refrigerated containers. A higher percentage of refrigerated cargo has a positive effect on the dependent variable. Therefore, the variable has the expected sign, since freight rates are higher for products that have special transport requirements (i.e. cooling conditions). Model 6 includes two additional variables representing service quality in maritime transport: number of days between two consecutive departures for the same destination country, and number of calls. A higher number of days between service departures decreases the number of opportunities per month or year for the exporting company demanding transport to ship their exports, hence reducing the perception of quality associated to the service. As a result, the higher the number of days between service departures, the lower the maritime transport costs that liner companies operating that particular route are able to charge their customers. A higher number of calls also indicates a lower service quality. Therefore, this variable decreases transport costs since the transit time it takes goods to reach their final destination is higher. The R-squared in Model 6 is 47.9%, similar to the obtained values in other papers where transport cost functions are estimated (see Clark et al., 2004). In Model 7, trade volume is considered as endogenous. Results are similar to those obtained in Model 6, although the effect of the exported volume on transport costs is considerably higher (-0.23). Moreover, in Model 7 the sign of the number of days between service departures changes. In this case, the longer the number of days between

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two consecutive departures of vessels, the larger the transport costs. This variable is thus now serving as a proxy of the degree of competition present in the market. Table 10 introduces the calculated complex connectivity component variables. Table 10. Determinants of maritime transport costs: estimation of the importance of connectivity measures

Model 8 Model 9 Model 10

Constant Term 5.93*** (63.11)

5.21*** (55.85)

4.5*** (47.89)

Index of Unitary Value 0.03*** (20.37)

0.03*** (20.26)

0.03*** (18.01)

Volume Exported -0.12*** (-25.66)

-0.09*** (-20.65)

-0.09*** (-21.39)

Distance - - 0.07*** (16.89)

Trade Imbalance (Absolute Terms) 0.71*** (37.99)

1.21*** (114.95)

1.22*** (118.26)

Negative Trade Imbalance -0.24*** (-25.76)

-0.37*** (-43.98)

-0.34*** (-41.32)

Connectivity Index:

Route Structure 0.03*** (10.18)

0.06*** (21.15)

0.03*** (7.2)

Port of Origin Infrastructure Supply -0.07*** (-33.75) - -

Port of Destination Infrastructure Supply -0.14*** (-33.88) - -

Equipment Structure -0.06*** (-25.81)

-0.07*** (-30.59)

-0.07*** (-32.25)

Service Structure -0.07*** (-30.99)

-0.08*** (-31.48)

-0.09*** (-36.1)

Quality:

Dummy Refrigerated Cargo 0.71*** (42.83)

0.69*** (42.62)

0.69*** (40.58)

Adjusted R-Squared 0.451 0.436 0.451 Standard Error of Regression 0.372 0.377 0.372

Number of Observations 36038 36038 36038

Notes: ***, **, * indicate significance at 1%, 5% and 10%, respectively. T-statistics are in brackets. The dependent variable is the freight rate of transporting good k from the exporting country i (Spain) to the importing country j in natural logarithms. Excluding trade imbalance, all explanatory variables, dummies and connectivity measures are also in natural logarithms. Models 8, 9 and 10 have been estimated by OLS. The estimation uses White’s heteroscedasticity-consistent standard errors. Data are for the year 2003. Model 8 displays that the interaction between a greater number of ports of call, a longer total distance and the need for transhipment lead to higher transport costs. The model strengthens the hypothesis that a greater port productivity and a greater fit between port production variables leads to reduced transport costs. In case the equipment structure operating on a certain route implies a younger fleet with a higher average ship speed and greater capacity per vessel, transport costs will be lower. Furthermore, a high degree of competition on a certain route combined with a greater number of opportunities to ship cargo lead to a reduction of transport costs. Model 9 not including port infrastructure measures confirms the before mentioned results. Model 10 incorporates the distance between port of origin and destination and the important factor of transit time into the model. Transit time is considered as an important determinant for the complex route structure variable. Model 10’s results

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provide evidence that a high transit time on a longer route with a greater number of ports of call contributes to a rise in transport costs. The obtained results are especially interesting when compared with the main findings on the impact of connectivity measures on transport costs in other regions. Wilmsmeier and Pérez (2005) and Wilmsmeier et al. (2005) demonstrate similar results on the importance of connectivity in maritime networks for trade with South America and particularly for intra South American trade. 4.2. Estimation of a trade equation

In previous sections, the increasingly important role of transport costs on international trade has been remarked. From an empirical point of view, several authors have shown that the effect of transport costs on trade flows is negative (e.g.. Martínez-Zarzoso y Suárez- Burguet, 2003). In this line, we also consider necessary to include transport costs on trade models. Moreover, this effect can be higher when this variable is considered as endogenous. Table 11 shows the estimation results of equation (6) by OLS and Instrumental Variables. Table 11. Determinants of international trade

Model 11 Model 12

Constant Term 20.91*** (470.76)

23.03*** (372.11)

Importer’s Income per Capita -0.11*** (-52.49)

-0.13*** (-55.81)

Importer’s Population 0.22*** (125.45)

0.19*** (96.99)

Distance -0.38*** (-110.85)

-0.38*** (-106.23)

Maritime Transport Costs -0.25*** (-51.95)

-0.61*** (-68.86)

Dummy Language 0.42*** (66.92)

0.59*** (77.62)

Adjusted R-Squared 0.436 0.368 Standard Error of Regression 0.445 0.471

Number of Observations 34151 34151 Notes: ***, **, * indicate significance at 1%, 5% and 10%, respectively. T-statistics are in brackets. The dependent variable is the exported volume in natural logarithms. All explanatory variables, excluding dummies are also in natural logarithms. Model 11 has been estimated by OLS, whereas Model 12 has been estimated by Instrumental Variables. Maritime transport cost is considered as endogenous and the instruments used are the variables included in Model 8 (excluding the exported volume). The estimation uses White’s heteroscedasticity-consistent standard errors. Data are for the year 2003.

In Models 11 and 12 trade equations are estimated. Variables traditionally included in this kind of models are considered (income, population, geographical distance and sharing a language) and a new variable, freight rates, usually approximated in the literature has been innovatively incorporated in the models. Results show that all variables included are significant and have the expected sign. A higher negative coefficient of income per capita in the importing country indicates that the 17 sample countries prefer to import products of low added value and that are not highly differentiated. This result is confirmed by the sign displayed by the variable importing country’s population. The larger the population of the importing country, the larger the volume exported by Spain, hence the existence of economies of scale being corroborated. This antithesis can be explained by the following line of reasoning:

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although most Spanish manufacturing industries are nowadays adopting or intending to adopt a strategy towards the differentiation of their products, most Spanish manufactures have been exported over the last decades to markets where these commodities would compete in price rather than in quality. The estimated results are consistent with this argument: a larger population in the importing country will mean a larger volume of Spanish exports being sold to that country, whilst if the importing country’s income per capita rises, the population will tend to buy high added-value goods with a higher degree of differentiation, rather than purchasing the usual low to medium-value mass-production commodity. This, subsequently, translates into a decrease of Spanish exports to this high income populated country. Geographical distance has the expected sign and could be reflecting trade costs different from maritime transport costs (e.g. cultural similarities, common history and information barriers). The positive coefficient of the dummy variable “sharing a language” confirms the strong trade relations between Spain and Latin-American countries, where the population speaks the same Spanish language (Chile, Mexico and the Dominican Republic). Finally, as expected, the estimated results prove that lower freight rates foster international trade. In Model 12 equation (6) has been estimated by IV. All variables included in Model 8 have been used as freight rates instruments. Results show a higher elasticity of this variable and therefore, a higher effect on the volume exported. Moreover, this influence is larger than the one obtained with the geographical distance variable. Judging from these results it could be argued that the negative impact of maritime transport costs on trade flows has been traditionally underestimated by the economic literature. 5. Conclusions In this paper, we aim to analyse the determinants of maritime transport costs of Spanish exports and their effect on international trade flows. In order to do so, we use data from TradeTrans, a database developed by Fundación Valenciaport. We focus on the importance of connectivity measures and we use the PCA methodology, a variant of factor analysis, to build three complex connectivity component variables (Model A, B and C). Results show that all the variables included in the estimated transport cost equation are significant and have the expected signs, in both OLS and IV estimations. The explanatory power increases after including connectivity and quality measures in the model specifications. Moreover, our results emphasise the importance of connectivity in maritime networks for Spanish trade. These results have also been found by other authors (Wilmsmeier and Pérez, 2005; Wilmsmeier, et al. 2005) who obtain similar empirical evidence for Latin American countries. Secondly, we estimate a trade equation. All variables included in the estimated models are significant and have the expected sign. A negative coefficient of the income per capita in the destination market indicates that importers have a preference for Spanish low added-value goods with a low level of differentiation. This result shows that Spanish exports continue to depend on price competitiveness in order to be sold at foreign markets, transport costs becoming consequently a crucial determinant of the Spanish exporting patterns. Geographical distance is an important factor explaining the exported volume. This variable has been traditionally included as a proxy of transport costs in gravity models. In our estimated trade equation we however incorporate maritime transport costs, and

24

thus distance is considering other trade barriers different from transport costs, such as cultural similarities, common history, proximity perception and information barriers. Results show that transport costs are an important determinant of trade flows. Their relevance is higher when this variable is considered as endogenous, then having a larger impact than distance on explaining exporting patterns. In relation to connectivity measures, shipping lines are actively seeking out ways of reducing their costs, a process that frequently results in changed levels of service at particular ports. Ports attempt to counteract this effect by providing incentives to ocean carriers and by making substantial infrastructure investments along the waterfront, by doing so intending to increase their productivity. The result is a network that is extremely dynamic; reinforcing processes of agglomeration in some places, but at the same time providing an economic advantage that can improve the position of regions in the global hierarchy. The relationship between ports and carriers in the context of this dynamic network can leave little doubt that the structure of transportation networks is an important variable for the structure of transport costs. This structure is not a fixed locational advantage, but a dynamic and changing force capable of shifting dramatically with changes initiated through global economic developments, but also capable of shifting those networks. These findings highlight the importance of implementing proactive policies for port infrastructure development and management in order to create a suitable environment for the demands and requirements of shipping lines. Moreover, the vertical integration presently taking place in the maritime transport network, can contribute to a better fit between nodes (ports) and services. Finally, as containerisation expands further, liner transportation is becoming a point to point or door to door business rather than port-to-port, and freight rates are established covering the entire movement. Further research on this issue would therefore be desirable, where the study would be conducted using door to door costs.

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APPENDIX Figure A.1

Source: World Bank (2005)

Figure A.2

% Maritime Transport Costs Over Import Value

0%

2%

4%

6%

8%

10%

12%

14%

16%

1980 1990 2000 2002

Africa America Asia Europe Oceania

Source: Review of Maritime Transport, 2004. UNCTAD, and Own Elaboration

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Table A.1. Sample of selected 17 importing countries and their corresponding destination port for Spanish exports

Algeria: Algiers Brazil: Santos

Chile: Valparaiso China: Shangai

Dominican Republic: Rio Haina Greece: Pireo Israel: Haifa Japan: Kobe

Mexico: Veracruz Poland: Gdynia

Russia: Saint Petersbourg South Africa: Durban

South Korea: Busán/Pusán Turkey: Istanbul

United Arab Emirates: Dubai United Kingdom: Felixstowe

United States of America: New York