impact of factor caracterizing port on performance feb2010 (in revision)

8/14/2019 Impact of Factor Caracterizing Port on Performance Feb2010 (in Revision)

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1.INTRODUCTION

The efficiency and performance of the ports are becoming increasingly importantworldwide. This phenomenon is related to the privatization of management of ports an port terminals, joining as vital links of the logistics of global operating companies, whic

leads to the greater importance of efficiency and performance of ports, for countries toreach international competitive advantage (Tongzon, 2002).Moreover, the containerization and intermodal transport are decisive changes in the ports irecent years.The containerization has led to two major changes in the ports (Cullinane2005): (a) the globalization of service coverage, achieved through numerous alliances anacquisitions (horizontal integration) in the liner industry, (b) provision of logistical servicein the extended international context, increasing the supply of owners door-to-door by setransport companies and offering added value in terms of the supply chain (verticalintegration).In another case, the extension of transport infrastructures, the creation of large logisticareas, ports and inland, interconnected and forming bipolar systems, and the increasing sizof ships with the selection to only the large-scale hub - ports , serving increasing hintelandled to greater competition between ports, where the hinterlands intersect more and mor(Wang and Cullinane, 2006).Sea lines have a greater bargaining power, taking into account the wide offer of ports, anmore in a position to demand higher performance and efficiency, higher speed and qualitof service (Wang and Cullinane, 2006) and sometimes ports have to deal with the departurof a container line to another port, as was the case of the port of Singapore.Thus, the performance, efficiency and competitiveness of the ports are currently topicsamong academic researchers and users of the ports, which demonstrates the relevance othis study.Measure and maximize the performance of the ports is now essential to the fulfillment o port role in the logistics chain, in a context of increasing competition between ports anincreasing among logistics networks, in the hinterland and in the foreland.Given that improvements in accessibility by land, the internationalization of economies anthe growth of intermodal ports allow more straightforward and more competitive in largehinterlands, increasing the power of choice of customer, the goal of ports changed toincrease your traffic beyond the normal economic growth (Haralambides, 2002 and Notteboom, 2005), with the objective of increasing the maximum output for the same inpufactors.Being efficient is a necessity of modern container terminals in a competitive environmenas this clearly has a strong impact on unit costs and then in the price and competitivenes(Nottebom and Winkemans, 2001 and Robinson, 2002). But not only in the containemarket, as well terminals for general cargo and bulk begin to enter in the race forefficiency.Europe is facing greater competition in the ports due to the nearness of their ports, thamost of the rest of the world and is therefore essential to study this phenomenon in the olcontinent, so there is particular interest in studying European ports as a target populationthis study.Moreover, it is noted that the ports are a major determinant of the cost of shipping, witemphasis on port efficiency, as verified Sanchez et al. (2003) in a sample of ports in LatiAmerica, demonstrating the importance of this factor to sea-based logistics.The cost o


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shipping depends so heavily on the cost of ports and on the time of ships in port, since thescosts are usually proportional to the time in port.In fact, if we understand that a port usually has the dual purpose of, first, to developcommercially, generating more business value of their business cluster port and alsocontribute to the development of the region, creating jobs and attracting investment

business and industry to its proximity, working as a pole of development, any of thesobjectives will certainly need to improve performance, either in terms of efficiency, and iterms of operation.To be able to achieve these objectives of the ports and modern logistics chains, it isnecessary to increase their performance and it is essential to try to understand the extent twhich intervention is possible in particular characteristics of the various ports, and it itherefore necessary to identify the factors characterization of the port that determine it performance and to determine its importance.

Recent theoretical approaches and Gaps Chang and Lee (2007), made an extensive review of existing studies with regard to po performance and inter-port competition and concluded that they are to study issues such awhat is the hinterland where the ports compete ?Privatization becomes even morecompetitive ports? How to measure differences between the relative efficiency of ports incompetition? As the "hinterlands" are changing the face of restructuring of supply chains?Studies on the ports can be divided into these major categories, according to Chang and Le(2007): selection of input ports, competitiveness policies, governance, ownership and privatization, measures of efficiency, performance and productivity and cooperationalliances and acquisitions.Studies of factors for selection of ports have establishment models of choice of ports foships and cargo: Slack (1985), Murphy and Daley (1994) and Buckman and Veldman(2003), cited by Chang and Lee (2007), which attempted to analyze the weight of thefactors influencing the choice of ports.With regard to competitiveness policies, Robinson (2002), refers to the need to analyze th port rather than a single point, but as a part of the transport chain. Second, Mak and Ta(2001) and McCalla (1999) cited by Chang and Lee (2007), who studied the best way t build new ports and terminals to be competitive.Regarding the study of cooperation, alliances and acquisitions, there are several exampleaccording to Chang and Lee (2007), namely: Heaver et al., (2001), which refers to allianceand cooperation that have taken place in ports worldwide and its influence on por performance, Song, (2002), which shows that the capital structures of the ports affect thstrategies of cooperation and competition between ports; Yap and Lam (2006), who studiethe competition between ports in Asia during several years, and Christidis (2001), whicshows the trends of change in the transportation industry and globalization havetransformed the operating environment of ports and forced the ports to adopt cooperativstrategies and alliances globally to improve their efficiency and competitiveness.According to these authors, with regard to measures of efficiency, performance and productivity, have been many studies addressing this issue, and comparing the performancof ports at different levels.Examples, Cullinane, (2005), Tongzon, (1995) and (2005)Estache (2002), Song and Yeo (2004), and the DEA, "Data Envelopment Analysis", asource of great use for many authors in recent years.


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Finally, in its study of governance, ownership and privatization, many of them have beecarried out on the influence of trends in the world to include private companies in the portBaird, (2002), studied in detail the processes of privatization that occurred in ports, Bir(1963) and Rodrigue and Notteboom (2005), defined the model of development of ports iits various forms of location, infrastructure and governance and ownership.Cullinane

(2002) studied the influence of ownership of the terminals in their performance, and Slacand Frmont (2005) who distinguished between terminals operated by internationacompanies and terminals operated by national companies, assessing the influence of thifactor in the performance of ports.In this context it is important to cluster sets of characterization factors, which have beenstudied by researchers, and which affect the performance of ports, seeking to understantheir relationship, proximity or affinity, trying to determine what are the real factors that ar behind the groups factors and found what is its contribution to the performance of ports.Cullinane et al. (2005) carried out the measure of the efficiency of a set of port terminaland concluded that the determinants of efficiency are the location, the governance andownership of the port, indicating that in a second phase, it was necessary to apply aregression model to try to explain the determinants of port inefficiency, which is considerethat it is this point a gap in the existing literature on the performance of ports.In fact, it appears that many of the studies address the issue of port performance juslooking for new ways to measure and compare ports and port terminals, but it is noexplained the differences and why a port is more or less efficient than another, or why it ha better or worse performance.In recent years researchers have focused mainly on measuring the efficiency of ports, anwhy a port has a higher output for the same amount of inputs used. But it is also importantto see what are the characteristics of the port's that influence efficiency.This is a "Gap" thawas identified in this study.This is reinforced by Tovar and Trujillo (2007) that compared the efficiency of a widerange of European ports and conclude that their work fails to explain the factors thadetermine the different levels of port efficiency, which would be very important to helpimprove efficiency and to be a real alternative to the road in Europe.Estache et al. (2005) state that in addition to partial studies on productivity, the academiliterature on the subject of the ports is very limited and that there are essentially testsstudying the link between efficiency and ownership of ports and comparison rankings oefficiency, failing to study the link between efficiency and other characteristics of the portat a broader level.Moreover, the authors note that few studies focus on aggregate output variables, and almosdisaggregate by type of cargo, not identifying the other output variables, and few studierely on environmental control variables.The port is in a estuary? Nearby is the city? It is near the Mediterranean? What is the GDP per capita in the region where the port is inserted and that influence the characteristics othe port and it performance? These are issues that should be evaluated.One of the shortcomings of the literature is to understand the importance of each feature othe port on performance, so you can improve the performance of ports.In structural terms, for example, concludes Cullinane, (2002) that the size influences thefficiency and Notteboom, Coeck and Broeck (2000), the location of the Hub terminaallows greater efficiency then feeder ports. However, there are many factors that determin


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the efficiency of ports, but few studies attempt to examine systematically all the sources ochange in efficiency based on the characteristics of ports.Another gap that was detected by the author is the need of studies using the DEA (Datenvelopment Analisys) efficiency figures as output of the ports characteristics, excepTurner, Windle and Dresner (2004). In fact it seems to be a Gap important that there are no

studies that make use, for example, of the rate of port efficiency DEA as na output variablin explanatory models of the factors that influence port performance.

Study Objective The purpose of this study is to identify and analyze the impact of factors characterizing th ports that determine its performance, both for the level of activity, and in terms oefficiency and financial performance.As a control model, is still considered the question: to what extent the performance of th port is influenced by the performance of the region, while environmental factor, given ththis factor has a direct bearing on the factors that characterize the ports and indirectly otheir performance.The aim of the study is to analyze the European ports, and for this purpose was used asample of the population of European ports, taking into account the great diversity in termof characteristics of ports, efficiency levels and in different regions where are located.The author carry out this study is to ascertain the factors that characterize the port, whichhave influence on their performance, which is essential for the construction of new ports fothe adaptation of existing ports and development of competitiveness policies ports andterminals, in order to try to have a port industry more competitive.The study is based on a sample of European ports with figures relating to constructs such asize, governance, location, infrastructure, logistical integration, maritime servicesspecialization and performance of the region where the port is situated, then proceeds to aanalysis of the multiple regression model with output of various performance indicators o ports, including cargo handling for different types of financial performance of the porauthority and port efficiency, whose indices are calculated from the DEA methodology. Iaims to know the importance of each factor and sets of factors for various dimensions o performance of ports.

2. LITERATURE REVISION Multidimensionality Ports The academic works that analyze the efficiency, productivity or performance of the portare scarce and according Tujillo Gonzalez (2007), the port size, location and governancare key determinants of efficiency, which affect the capacity of the port itself, and the sizof the market in the hinterland of the port that can make use of that capacity and alsodetermine how this capacity is managed, and how the port interacts in the market, incompetition with other ports.But the literature review of the ports is also scarce and have started by Estache et al. (2001Cullinane, (2002) and Wang et al. (2006), which gave a strong contribution.The economic study of the port began in the '60s with the study of the structure of porcharges, skills and investment.The first studies on port efficiency have emerged in the 90s.Recently, the efficiency and productivity have been major themes for port researcherssince there have been major changes with the expansion and deepening of ports, with


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improved technology, organizational change, privatization and specialization of ports inpuand terminals , with impacts on efficiency and productivity that caused obvious diference between ports.The studies on port efficiency can be classified into three major groups (Gonzalez andTujillo, 2007): The first includes studies with one-dimensional or partial indicators o

productivity of the port system and did not reflect the multidimensional reality of portsThe second group of studies includes those with only a vision of the engineering side, usinterminal operational simulations and the queues theory; The third group, the most recencovers the frontier production using multivariate approaches in the inputs and outputs ansupport the political and economic port decision.The disadvantage of one-dimensional ports view, as it only compares a variable input wita variable output, do not cover the special multidimensional and multivariate nature o ports, which handle various types of cargo and have several inputs related to labor, capitaland, among others.This problem was only solved through the analysis of TFP (total factor produtivity), whicreflects the overall contribution of all factors relevant input and all outputs. PoitrasTongzon and Li (1996) made one of the first studies with the ports on the DEA model, DatEnvelopment Analysis, which reflects this multidimensional nature of the ports, analyzinits performance.In 90 years, new methodologies for measuring efficiency were used in studies of the ports but there was a lot of discussion about which method best defined the complex reality othe ports.Studies have focused on the relationship between efficiency and reforms in th ports, port ownership, size, transhipment, investment, hub ports (Noteboom et al., 2000and the efficiency and time (Cullinane et al., 2004).

Governance and Logistical IntegrationThe ownership and management of ports, namely governance, is considered one of thcharacterization factors that influence port performance and efficiency (Liu, 1995), since believes that when a management or ownership of ports is on the public side, there is nincentive to be held constant improvement of management efficiency of the ports, inopposition to what occur in ports run by private companies which aim to profit.The model of port management has evolved over the past few years, having been madconcessions for many port terminals around the world, with the spread of the traditionamodel of Northern Europe, given their success in terms of performance.For example, Estache, Gonzalez and Tujillo (2001), there efficiency gains from porreforms in Mexico and using the methodology of the production frontier, demonstrated thoccurrence of gains 6 to 8% in the efficient use of infrastructure port with the concession toperate the terminals.At the end of the twentieth century, the vast majority of ports that were previously manage by port authorities, have suffered various forms of privatization of their management, eithe by granting long-term licensing of new projects or BOT, Build Operate and Transfer.The weight of the terminals operated by private companies have grow and thus havimportant influence on the performance of ports, contributing to their integration into newsupply chains and global operators of the terminals, adding new expertise used by terminoperators in other terminals in the world and customer satisfaction and performance levelsBarros and Athanassiou (2004) report that privatization is the best way to dramaticallincrease the efficiency of ports and Lui et al. (2005) reported that the Chinese port


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terminals with Sino-foreign partnerships (private) have higher levels of performance. Thilatest study shows that management by private companies linked to international groupenhances the performance of the terminals to allow the integration of innovative technicaknowledge developed within the group and raise standards of performance through thcomparison in the group, or in order to meet the standards customers of the group.

Port Size The size of the port has been, for many years, considered another factor that influence po performance (Liu, 1995, Wingmans, 2003), one of the key variables taking into accounthat affects economies of scale and agglomeration.The productivity of ports increases with the size and there are significant economies oscale, which led to a recommendation to invest more in bigger ports and be cautious insmall ports, (De Neufville and Tsunokawa, 1981).However, small ports have also played a part, with economic impact in the region, despitits poor performance in absolute terms.It is interesting to know if a port can become larger, with better performance, if increase thinvestment.The question usually arises more on the terminals than the ports, and yet thterminals at larger ports may also benefit from this in its performance, in the result osynergies with other terminals.In 2005 Estache et al. decomposed the change in the efficiency of technical changes anchanges of scale, showing the importance of the performance of ports (like Turner, Windland Dresner, 2004, Gonzalez and Trujillo, 2007), checking the learning effect of the large ports that contributes to its better performance.The learning effect is cited by many authors as an explanation for the difference in performance between large ports and small ports, because larger ports are required to adopsystems and processes to achieve more efficient to move a large cargo amounts, to be morefficient and productive. The labor force of larger ports have access to more training resutooIn large ports, the problems occur more frequently, solutions are rebuilt several timesauthorities have created greater coordination and the systems are optimized.The effect of scale and dilution of indirect costs, and fixed administrative costs are usuallindicated as the main contributors to the effect of size on the performance of ports.Ports have, from time to time, to invest large amounts in infrastructure, which can lead tsome periods of inefficient, aiming to increase their size and have a better performance ithe future.

Location of Port and Region PerformanceThe location of the port seems to be another factor of Port performance (Lui, 1995) and perhaps the most important, since the port does not exist by itself, with the exception o ports exclusively "transhipment", but is dependent on development and performance of ihinterland, despite the fact that the improvement of accessibility and land development orailways have extended the hinterland of the ports to large distances, increasing competitio between ports, the basis for development of a port remains in its its closer hinterland.In a study on the competitiveness of Chinese ports, using the Analytic Hierarchy Procesmethodology and using a wide range of factors, Song and Yeo (2004) reported that thevolume of cargo from ports is strongly associated with the location, which can not bchanged regularly but location is a variable that, once built the port, can not easily b


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changed, other than with the relocation of the port itself or specific terminals in order totake advantage of any location.In 2005, Rodrigue Notteboom and identified a new phase in the life of the ports in generathey call regionalization, stressing the importance of the relationship between thedevelopment of the port and development of the region where the port is located.

Ports have a pattern of change over time, appearing usually associated with a city, veryclose to it, but gradually, with growth, going more and more away from the town in ordeto obtain larger areas, deeper depth and avoid congestion on land accessibility and lanconflicts with their areas of expansion.The very concept of location of the port is associated with various dimensions such adistance to urban area, population density and richness of the region of influence, th physical location on the coast, in a river or estuary, the location and accessibility in the facof the industry, the location in a island, the mainland or in the peripheral zone, its distancfrom the center of Europe, the cultural influences on organizational models, the situatiofacing the major shipping routes and inland. No doubt the performance of the regioninfluence the characteristics of the port, except that the location factor contain elements othe region.The importance of the hinterlands of ports as their own extensions was also analyzed bGuthed (2005), determining its performance.Tongzon stated in 2002, the location is one of the main reasons for choosing the port oBangkok, which increases its operational performance, the location near small economieaffects performance of the port and points out that the demand for port services derive fromthe size of cargo flow and consumption in the region where the port is located.

Infrastructure and Accessibility The investment in port infrastructure and capital intensity in the ports has often been aexplanatory factor for differences in performance and efficiency in ports (Liu, 1995), sincein fact, without the infrastructure and supply capacity, would not be possible to have higher movement of vessels or cargo.A large movement of vessels is only possible withquay and equipment sufficient to allow not have high waiting times, unbearable for ships.Moreover, a high level of efficiency in the port, which would provide a competitive position in the port sector, need adequate infrastructure and superstructure exploiteintensively, to ensure the use of investment with high standards of performance.In 1996, S. Achish concluded that capital investment was a major factor affecting the productivity of ports in Israel. Founded that the extent of activity and capital investmenwere the main influences on productivity. Not only should interest the amount of capitainvested, but also the quality of these investments, training and suitability of suchinvestments to market needs and demand.Moreover, Goss (1990) states that the competition can lead to increased efficiency, but alscan lead to excessive investment in capacity of the port infrastructure.Although excess pocapacity is essential to maintain competition between ports, thereby maintaining its performance in the customer point of view.Sachish and Kim (1986), assessed the impact of labor and capital investment in the performance of the port of Ashdod (Israel), and also found a relationship betweeinvestment in container technology and improved performance.The capacity of the quay is a variable input important for the efficiency that has beenstudied by Park and R.K. P. De (2004), as input which is related to the results output.


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In 2007, Garcia-Alonso and Martin-Bofarull, in a study on the evolution of the relativefficiency and the hinterlands of the ports of Valencia and Bilbao, during a period of heavinvestment in both infrastructure, stands that there not always the same level of investmenin infrastructure leads to similar improvements in performance, it is necessary to studyother factors related to the location, integration into supply chains, the hinterlands, amon

others.Shipping Services and Logistical IntegrationIn 2002, Tongzon, studied the determinants of port performance and the ports choice todetermine that port efficiency is the most important factor in choosing a port, and hasaddressed the equipment, the frequency of ships, infrastructure, location, rates and productivity indicators, transit time and waiting time for ships in port. In its 1995 studyreported the frequency of ships of the line and routes that pass through the port as importafactors in choice of port and its performance as well as the importance of economies oscale of larger vessels to carry port.Furthermore, the lines determine the ports that call based on the partnerships that have anlogistics networks that integrate (Tongzon and Heng, 2005), and the important issue ointegration of ports and maritime services, including the links of global operators to majo ports worldwide and the level of integration in the logistics networks.In 2003, Veldmen Buckmann to explain the market share of the northern ports of Europeand its performance using factors such as frequency and transit time of vessels and freigh prices, the prices of the terminal and inland transport.Turner, Windle and Dresner (200 4studied the impact of the type of shipping services and port facilities in the performance o ports.The frequency of liners and size of vessels calling at the port are very importantdeterminants for the efficiency of the port itself, since that characterize the service that th port provides to its customers and cargo, and the value it adds for customers.A liner requires pre-determined schedules, ports of origin and destination, freight predefined integration with chains of land and sea transportation, higher number of weekllines is important to attract more cargo at to thet port, increasing the performance of th port, minimizing wait times and costs for transportation sea, offering a wider range odestinations to lower costs and with low "transit times".A port with regular lines has usually better performance and higher levels of efficiency anis required to maintain these levels to keep the lines and attract new regular lines that hava very high requirement for quality of service and schedules .Basically, it can be said that the maritime services and the integration of the ports in globamaritime logistics, or even regional levels allows better performance to ports, making imore attractive.

Specialization Specialization, including the level of containerization, are reported by Tovar and Trujillo2007, Medda and Carbonaro, 2007 and Laxe, 2005, but no less important is the rate ounitization cargo (general cargo/ total cargo of the port), as reflecting the degree ofdevelopment of the port, to the phase for modern industrial and commercial port.In a study of Caldeirinha, 2007, it was found that the Iberian ports are divided into threquadrants according to the crossing of two variables, the rate of unitization of cargo and icomposition, which explains a good part of their characteristics and thus the performance.


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In fact, the ports with greater specialization in containers usually have higher levels oincome per ton, and often more efficient use of their infrastructure platform. A portspecialized in bulk has usually a very high performance in number of tons, but low performance in terms of revenue per ton and per employee.A not specialized port, do not usually have high levels of efficiency, because it

infrastructure is flexible and suitable for all types of cargo, not getting the most out of eactype of load, because there are no specialized equipment and there is a constant adaptatioof the equipment to each type of cargo. This ports are usually more expensive.Specialization is thus an important variable to consider in relation to the performance o ports, which may serve to classify groups of ports.

Performance of ports As previously stated, the performance of ports is now essential for all stakeholders in itoperations, including managers, customers, traders, industries and governments.But the performance can be measured in various ways, may be a synonym for efficiencydoing more with less. A port may only do more in absolute terms, irrespective of theresources spent up, but others may prefer the performance level of costs, guaranteeing minimum service.For others it may be possible to earn more for each ton moved, providinmore services.So consider the authors studied a range of performance indicators that vary according to th purpose they want to, or the target audience they have in mind, or the phenomenon ianalysis.The performance indicator most used is the absolute cargo in tons or TEUs (twenty-fooequivalent unit) in the case of containers. Some authors use the distinction betweenabsolute tonnes by different type of cargo and number of ships served by the port in a give period. This indicator shows the breadth of choice of port for their customers, ie, morcargo, shows that more customers have chose the port and shows better operationa performance.In this case, each ton has one vote in the choice of port.Some prefer to use indicators of absolute income of the port authorities or per tonne ocargo. The cost of the port to the client, related to the revenue of the port, is sometimeused as a factor that characterizes the port and as a choice factor of the port. In fact, this ian important factor in choice of port by cargo and vessels, but is also an indicator o performance resulting from the features of the port itself, so it is vital understand which arthe determinants of this variable. Now, with the greatest concern for the efficiency of ports, the efficiency indicatomultidimensional DEA has been widely used in the comparison between ports, althougfew studies have used this indicator in regression models which can explain the factors thadetermine the values found in the ports. It should be noted that the efficiency is considere by many authors as one of the factors influencing the choice of the a port.How to choose the performance indicators of the port? We must choose those related to itinternal efficiency, with its size and market success? These indicators are certainly related but are sometimes conflicting.Ships do not choose the ports with the worst performance in terms of waiting times andspeed of operation, due to the time of detention of ships, because that have a significancost in shipping and it must be added to price charged by the port to ships.And in the case of general cargo liner market, cargo often follow the choices of shipswhich is not the case in the tramp market of bulk, where the ships follow the preferences o


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the cargos in terms of ports. What audience to choose, as this influences the indicators tselect?If we consider how the performance qualities that lead to attract more ships and morecargo, we have for example Sanchez et al. (2003) determined the three main components o port efficiency: the time in port, efficiency in the terminal and the turnaround time o

vessels in port.But the main performance indicators used in ports in almost studies are the movement ocargo, either in tons or TEU, or by type of cargo, roll-on roll-off, breakbulk, containerizecargo, solid bulk and liquid bulk, since this is the end result of any port, move more anmore cargo and ships.Just to name a few authors who used the throughput of the ports in absolute and variabloutput model of performance analysis, refer to Song and Yeo, 2004, Poitras, Tongzon andLi, 1996 Barros, 2003 and Trujillo Tovar, 2007 Garcia -Alonso and Martin-Bofarull, 200Park and De, 2004 Herrera and Pang, 2006.Another indicator of performance that seems appropriate is the level of revenue per tonnor employee of the Port Authority in, the public perspective, since it reflects the outpuvalue of the range of services offered and what customers are willing to pay in terms orates to call the port, given its condition or location.Just to name a few authors who choosthe performance indicators of revenue we have Barros, 2003, Park and De, 2004, Kent anAshar, 2001, Gonzalez and Trujillo, 200 7, Turner et al, 2004.Basically, the performance of a port is a multivariable reality, and in this study used a battery of performance indicators that go through the operational performance in thhandling of cargo and a year, the financial performance of port authorities and theeconomic performance or efficiency the port in its relationship between output and inputin terms of DEA indicator.

3. THE CONCEPTUAL MODEL Research model The test model is based on the hypothesis of the relationship between the characteristics o ports and their performance. It is considered that the performance of ports is explainelargely by the characteristics of ports, which determine its level. In other words, it isconsidered that the ports with different characteristics will have different performancerelated with this characteristics, in the various levels at which performance can bemeasured.The performance of the region also influences the performance of the port directly orindirectly, through its influance the characteristics of ports. In this model, the performancof the region in which the port is located and to which it relates serve as an environmenvariable or control.The explanatory model of port performance based on their characteristics makes use oconstructs already developed, setting variables that act as drivers.Basically, we identify, in the literature review, a set of constructs based on factors thacharacterize the ports that have emerged as influencing the performance of ports.To characterize the ports were considered seven constructs which appear to us as somehowseparate and characterize the ports, seems to contribute to their performance in a relevanway.


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In terms of performance of ports, we used three constructs covering multidiversidade po performance, taking into account the objectives of port in operational and financiaefficiency.

Figure 1Model

Region Performance

Location

Size Operational Performance

Infrastructure

Specialization Port Caracteristics Port Performance Financial Performance

Maritime Se rvices

Logistical Integration Port Eficiency

Governance

The constructs of the input model considered for the characteristics of the ports are thlocation of the port, the port size, port infrastructure, port specialization, shipping servicethe degree of integration into global logistics and governance model.The constructs of the output model considered for the performance of the port, areoperating performance, financial performance and performance in terms of efficiency of th port.It is also important to better explain what are the variables drivers considered as proxies othe constructs to operationalize the model.To simplify the processing of data was defined a simple classification for each variable:

Table 12008 variables Construct DROTERD2 Distance to Rotterdam in a straight line

in kmLOCATION

DMEDIT3 Distance from to the Mediterranean Seaaxis east-west in km

LOCATION

SEAPORT4 Sea Port (1) or River / Estuary (0) LOCATIONDCITY5 Distance to nearest city in Km LOCATIONQUAYL6 Total length of quay in meters SIZECRAINSKM7 Number of cranes / km of quays INFRASTRUCTURETERMSIZE8 Average size if terminals in tons INFRASTRUCTUREMAXDRAFT9 Maximum depth of quay INFRASTRUCTURETXUNIT10 Unitization rate (general cargo in

tons/total cargo in tons)SPECIALIZATION


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TXHORIZ11 Horizontalization rate (roro cargo intons/general cargo in tons)

SPECIALIZATION

TXCONT12 Containerization rate (container cargo intons/general cargo in tons)

SPECIALIZATION

REGULARLSHIPS13 Number of regular lines / total number of ship calls

MARITIME SERVICES

SHIPSIZE14 Average Ship size in tons of GrossTonnage

MARITIME SERVICES

BIGSHIPO15 Number of regular lines of the seven bigship operators / Total number of regular lines

LOGISTICAL INTEGRATION

PORTPRIV16 Percentage of private terminals in the port: (1)> = 50%, (0)


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vessel, reducing the cost of stay, maximizing the quay use and the return on heavyinvestments.The ports with large terminals may have important effects on it performance the result oeconomies of scale of these terminals and their learning effect and return on fixedinvestments and overall management costs, facilitating the logistics between lines, th

transhipment and the relations of interface with the hinterland, creating benefits for clientincluding logistics and value added to the cargo, without further handling operations.The ports with the largest depth access may receive larger vessels, generating positiveeffects on the performance of the crane and the terminal itself, reducing the time of shipmaneuver by reducing freight costs in the port and increasing the productivity ofoperations.The ports can be classified as ports specialized in certain types of cargos.The kind ospecialization of the port determines its physical characteristics, its infrastructure, its traffivessels, helping to explain differences in performance between some ports.A port with a higher rate of unitization, ie, with a greater movement of general cargo itons thenbulk cargo, tends to be a port with better results in terms of revenue per tonne, hamore added value in the port.A bulk specialized port tends to be a port with higher productivity, since the spacialized systems in bulk get higher speeds, because thhomogeneity of cargo and discharge in a continuous process.A port specialized in the horizontal cargo, roll-on roll-off, cars and heavy vehicles, traileramong others, tend to have lower berth productivity due to the nature of the operationalthough operations do not require investment in equipment.The importance of regular lines in the port and the size of vessels calling the port are verimportant determinants for the efficiency of the port itself, since that characterize theservice that the port provides to its customers and cargo, as well as adds value to customerIt is not enough to have private management at the port, the question is what is the degreof integration of private companies that operate the port in the regional and global logisticsince this may affect the efficiency of the port.In fact, some ports have been privatized under the operation of local small companies thado not allow raising its performance globally, keeping roughly the same rates of growth.Since the ports and terminals that have been licensed to major international operatorsincluding global ship operators, seem more prone to develop their performance, as they arintegrated into strings of international ports, drinking their technical knowledge and bein"pulled" by international group to levels of much higher efficiency, and integrate group'international supply chains.The integration of ports in chains allows performance much higher than the ports that dnot have it, and that is a factor which characterizes the port and its services.The model of governance may be related to the performance of ports, it is important tostudy additional evidence, seeking to identify the benefits of private management of portand what is the public and private mix that maximizes the output port.In fact, this is one of the main variables studied by several authors who address the issue o ports, comparing the privatization impact on ports performance.The performance of the region has a significant impact on the performance of the port. Ifact, the region determines its infrastructure and facilities, and determines it hinterland an perfomance. A larger performance of the region, determine a port with higher performancthan others.


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Performance The performance is the result of the economic operation and the existence of the portwhich can be approached from different perspectives, depending on your point of view. Wwill consider the performance operationally, financially and in terms of relative efficiency.The level of annual turnover of the port is commonly used by researchers as an indicator o

operating performance of the port can be introduced in studies such as the total movemenof the port where tons of cargo, or more particularly in TEU.However, it do not compare ports that do not have any containers, so it may not be helpfuexcept for comparison exclusive of specialized container terminals.The basic variables of a port such as the movement of goods total (tonnes) and theMovement decomposed (general cargo and bulk) are used as variables to the performance.In terms of financial performance, the variables total revenue for the Port Authority euro per tonne and total revenue of the Port Authority per employee are used reflecting thadded value achieved by the port with its activity as continuous variables monetary greatethan zero.This type of variable is used by Ugboma, 2006, Poitras, 2005, Kent, 2001; Tongzon, 20052002, Lee, 2006 and is defined as the total revenue of the Port Authority (PA) in the yeaon the total number of tons of cargo moved on the year, in euros, and total revenue of thPort Authority (PA) with the port in the year, the total number of employees in euros.Finally in the case of performance in terms of efficiency, we use two variables related tothe Index of Relative Efficiency DEA, one for the model of constant returns to scale (CCRand one for the model of increasing returns to scale (BCC).Both variables are calculated based on the use of two output variables - tonnes of generacargo and tonnes of bulk - and the three input variables - the sum of the lengths in meters oquay, the sum of area of terminals in square meters and total number of cranes in the port.This type of variables is used by Ugboma (2006) and Turner (2004) as output variable iregression models of performance of ports.The efficiency measure consist in to compare the level of input of productive factors odifferent nature for the production of one unit of output port.For example, Liu et al., 2005, defined as DEA model inputs to ports, the land factors (baseon the variable size of the area), capital (based on variable length of quays, emulator oinvestment in port) and work (based on variable number of cranes, which is proportional tthe number of workers).These indicators are described in the literature review of Gonzalez and Trujillo, 2007, anare frequently used by many researchers. The novelty is to use this s indicators as output oa regression model for ports.

4. RESEARCH METHODS Data According to the 2007 annual report of ESPO, European Sea Ports Organization, Europhas 350 ports according to information from Eurostat, and from those 230 ports have morthan 500,000 tons of bulk annually and 200 ports have over 200,000 tons of general cargoThe list of ports highlighted in the annual report for 2007, ESPO, was crossed with thinformation databases of emails from port authorities contained in the following siteshttp://portfocus.com/index.htmlandhttp://www.worldportsource.com/countries.php.Then was sent the e-mail surveys of the port authorities of 230 ports, and during the monthof April to June 2009 were received 43 complete responses to the survey.
http://portfocus.com/index.htmlhttp://www.worldportsource.com/countries.phphttp://www.worldportsource.com/countries.phphttp://portfocus.com/index.html


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Methodologies

DEA - Data Envelopment Analysis DEA (Data Envelopment Analysis) was originally developed by Charnes, Cooper and

Rhodes (1978), and allows to compare the relative efficiency of complex production unitsuch as ports, schools, hospitals, banks, among others.Since its first publication in 1978, the DEA has developed dramatically, and that manyapplications have shown how this technique can be considered an important tool foassessing efficiency.Ahn, Charnes and Cooper (1988) evaluated the relative efficiency of public and privatuniversities. Morey, Fine and Loree (1989) used DEA to determine the relative performance of public hospitals in California.In banking sectors, DEA has been used tidentify efficient units (Di Giokas, 1990).This methodology measures the relative efficiency of units of decision making (DMUDecision Making Units), which perform tasks with multiple inputs and multiple outputs.The DEA involves the task of selecting inputs and outputs to produce an empirica production function that is based on optimal behavior observed.The DEA model compareeach DMU with the best practice observed, to obtain a measure of relative efficiency. EacDMU is then classified as efficient or inefficient (Moita, 1995).The DEA has been structured along two different models, known as CCR developed bCharnes, Cooper and Rhodes and BCC developed by Banker, Charnes and Cooper.The CCR, which was used in this study is to evaluate the overall technical efficiency, buadmits the possibility of constant returns to scale, that is, if a unit assessed increaseresources at a certain level, its production should increase at the same proportion, as if thiunit to reduce the resources, their production should be reduced proportionately. The BCCmodel allows increasing returns to scale, and seems to apply to the ports, due to economieof scale and learning effect.DEA analysis is a technique of Operational Research, which is based on linear programming, and aims to analyze comparatively independent units.Because it is a nonparametric tool, DEA differs from parametric approaches, and optimizeeach individual observation in order to calculate an efficient frontier, determined by unitthat are Pareto efficient.This technique has been applied in several studies relating to transportation systems Novaes (1997 and 2001) and Chu and Friefding (1992) and Odeck And Halmarsson J(1996). In the port sector, studies developed by Bendall and Stent (1987) Tabernacle(1995), Ashar (1997) and De Monie (1987). According to Cullinane et al (2004), the DEAis one of the most important techniques to measure efficiency. The author also stresses thathere are many applications of DEA in the classic seaport industry like the examples oTongzon (2001), Valentine and Gray (2001) and Martinez et al (1999).As mentioned above, the DEA technique to classical two angles of analysis, the CCRmodel which determines a border CRS (constant returns to scale) indicating that growth oinputs will produce proportional increases proportional outputs.The BCC model determines a border VRS (Variable Returns to Scale) and differs from theCRS model by considering the possibility of increasing returns or decreasing returns toscale on the efficient frontier.


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It is assumed that there are n decision making units (DMUs) to be evaluated. Each DMUconsumes varying amounts of m different inputs to produces different products.Specifically, DMUj (j = 1 ,..., n) consumes an amount Xj = (xij) of inputs (i = 1 ,..., m) and produces an amount Yj = (Yrs) product (r = 1 ,..., s).It is assumed that xij> 0 and Yrj> 0.

The array of productssxn is represented byY and themxn matrix of inputs is denoted byX.

Figure 2 CCR Models

Badin (1997)

Figure 3 BCC Models

Badin, 1998

In this study, the CCR and BCC models, maximizing production, are based on the Frontie

Analyst software version 4.Linear Regression "The analysis using regression models is used as a statistical tool that seeks to find threlationship between two or more variables so that a variable can be calculated from onanother or others."(Neter & Wasserman, 1985).This form of a relationship through a regression model is different from that found by causal role, as a function and has a perfect relationship between the variables, therelationship found by regression models is not exactly perfect, and have distortions in thestimated parameters.The purpose of a regression model is to determine a relationship between the data so that

variable can be defined in relation to another or others.This relationship is not perfect as function has errors in the estimated values, called errors of dispersion.There are several regression models, and the choice of a model depends on thecharacteristics of the data and the objective to achieve with the regression.According Neter & Wasserman, 1985, the regression model is a methodology that seeks tshow two essential ingredients of the statistical relationship.On the one hand, the trend othe dependent variable Y vary with one or more independent variables X, a system, ansecond, show the set of observations around the curve of the statistical determined.


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5. DATA ANALISYS Descriptive Statistics In the following we find the main descriptive statistics of the variables that are used in th

model.Table 2

N Minimum Maximum Sum Mean Std. Deviation Var iance Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic

DROTERD2 43 80 2970 49392 1148,65 670,777 449941,42 0,246 -0,45

DMEDIT3 43 0 3120 52741 1226,53 848,101 719275,26 0,345 -0,816

SEAPORT4 43 0 1 31 0,72 0,454 0,206 -1,021 -1,006

DCITY5 43 0 32 154 3,59 5,865 34,395 3,385 13,744

QUAYL6 43 400 80000 365938 8510,19 13246,584 1,76E+08 4,193 20,815

CRAINSKM7 43 0 12,7 156,001 3,62793 3,033383 9,201 1,231 1,09

TERMSIZE 8 4 3 8 67 65 1 ,24 E+0 7 1 ,07 E+ 08 2 ,48 E+ 06 2,7 2E +0 6 7 ,41 E+ 12 2 ,1 77 5,0 39

MAXDRAFT9 43 7 26 590 13,73 4,525 20,475 0,823 0,801TXUNIT10 43 0,02528 1 18,86606 0,4387456 0,30757091 0,095 0,442 -1,093

TXHORIZ11 43 0 0,98731 9,09449 0,2114998 0,29902372 0,089 1,58 1,521

TXCONT12 43 0 0,98116 15,14615 0,352236 0,31767226 0,101 0,514 -0,901

REGULARLSHIPS13 43 0 0,01408 0,1686 0,0039209 0,0033917 0 1,16 0,837

SHIPSIZE14 43 59 20026 243189 5655,55814 5,33E+03 2,84E+07 1,266 1,092

BIGSHIPO15 43 0 1 7,4344432 0,17289403 0,280520875 0,079 1,742 2,397

PORTPRIV16 43 0 1 24 0,56 0,502 0,252 -0,243 -2,038

GDPCAP17 43 50 127 3872 90,05 25,548 652,712 -0,308 -1,118

TOTALTON18 43 209000 178675809 1001260463 23285127,1 3,61E+07 1,30E+15 2,977 9,644

GENERALTON19 43 145000 117322134 421787594 9809013,81 2,27E+07 5,16E+14 4,105 16,951

BULKTON2 0 43 0 8 10 51 000 57 89 718 10 13 464460,7 1,84E +0 7 3,38 E+14 1 ,9 55 3,9 37

EURPERS ON21 43 17,8 931 7733,1 179,84 165,4261 27365,78 2,962 10,827

EURTON22 43 0,0005 9,6 126,3005 2,937221 2,1714463 4,715 1,437 1,932DEABCC23 43 3,5 100 2115,5 49,198 36,8929 1361,087 0,417 -1,53

DEACCR24 43 2,4 100 1311,9 30,509 31,8945 1017,258 1,442 0,751

Descriptive Statistics

The normality of the variables used in the model was tested with the Kolmogorov-Smirnoand Shapiro-Wilk, and found that only not rejects the hypothesis H0, hypothesis that thvariable distribution is normal for the parameters of average and the variance, for thvariables DROTERD2, DMEDIT3, MAXDRAFT9, TXUNIT10, TXCONT12 andSHIPSIZE14.This happens due to small sample size.Thus, it should be some caution in the analysis of model results, given this constraintwhich may have significant effects on the power of parametric tests, and for non-parametri

(Maroc, 2007).Correlation An important stage prior to the application of variables to the model is the correlation odata, for which, given the large number of variables involved, we used the Pearsoncorrelation between variables. Note that all the statistical calculations of this study wer performed with the use of SPSS software, version 1 7.


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In the analysis of the contents of the Pearson correlation between variables, it was possiblto draw some important conclusions. The correlations between factors and dependenvariables are mostly significant at the 1% to at least one of the dependent variables, excepfor the variables DMEDIT3, DCITY5, TERMSIZE8, BIGSHIPO15 and PORTPRV16,although this not mean that these variables, combined with others, do not contribut

significantly to the explanation of the evolution of some dependent variables.There is significant correlation between the dependent variables TOTALTON18,GENERALTON19, BULKTON20 and EURTON22, and there is significant correlation between the dependent variables DEABCC23 and DEACCR24, which seems normconsidering that these are performance variables resulting from the operation of the port oits efficiency .Just outside is the dependent variable EURPERSON21, since it is a variable that is alsdependent on the efficiency of the port authority.The variables used as inputs have several intense relations between them, which lead tconcern with issues of multicollinearity when the regression model, which is the objectivof this study.

Table 3

D R O T E R D 2

D M E D I T 3

S E A P O R T 4

D C I T Y 5

Q U A Y L 6

C R A I N S K M 7

T E R M S I Z E 8

M A X D R A F T 9

T X U N I T 1 0

T X H O R I Z 1 1

T X C O N T 1 2

R E G U L A R L S H I P S 1 3

S H I P S I Z E 1 4

B I G S H I P O 1 5

P O R T P R I V 1 6

G D P C A P 1 7

T O T A L T O N 1 8

G E N E R A L T O N 1 9

B U L K T O N 2 0

E U R P E R S O N 2 1

E U R T O N 2 2

D E A B C C 2 3

D E A C C R 2 4

1 -,372 * 0,079 -0,01 -,335 * -0 -0,1 0,023 0,108 -0,25 0,053 0,117 -0,09 0,019 -,391 ** -,464 ** -,420 ** -,379 * -,354 * -0,03 -0,07 -0,12 -0,13

-,372 * 1 -0,09 -0,01 0,126 0,09 0,192 -,363 * 0,214 ,306 * -0,26 -,311 * -0,24 0,043 -0,04 0,274 0,043 0,129 -0,08 0,185 0,256 -0,05 -0,04

0,079 -0,09 1 -0,1 - ,351 * -,321 * 0,066 0,232 -0,08 0,256 -0,07 -0,11 0,048 0,084 -0,24 0,044 -,320 * -,335 * -0,22 0,283 0,149 -0,13 0,006

-0,01 -0,01 -0,1 1 -0,01 0,072 -0,11 0,283 -0,21 0,004 -0,04 -0,01 0,257 0,031 0,238 0,031 0,083 -0,05 0,22 0,273 -0,23 0,093 -0,03

-,335 * 0,126 -,351 * -0,01 1 0,004 0,121 0,23 0,088 -0,11 ,328 * ,312 * 0,219 -0,01 ,309 * ,303 * ,829 ** ,889 ** ,528 ** 0,062 -0,21 0,18 -0,04

-0 0,09 -,321 * 0,072 0,004 1 0,217 0,076 -,353 * -,420 ** 0,277 0,136 0,177 ,352 * 0,019 -,385 * 0,173 0,069 0,254 -0,14 -,342 * -0,06 -0,19

-0,1 0,192 0,066 -0,11 0,121 0,217 1 0,021 -0,03 0,11 0,063 -0,17 0,089 0,176 0,196 -0,06 0,164 0,131 0,161 -0,06 -0,03 0,076 0,232

0,023 -,363 * 0,232 0,283 0,23 0,076 0,021 1 -,430 ** -0,24 ,391 ** 0,264 ,703 ** 0,17 ,364 * 0,069 ,319 * 0,18 ,403 ** ,384 * -0,3 -0,05 -0,06

0,108 0,214 -0,08 -0,21 0,088 -,353 * -0,03 -,430 ** 1 ,544 ** -0,2 -0,07 -,580 ** -,435 ** -0,15 0,198 -0,04 0,23 -,358 * -0,05 0,258 -0,05 -0,1

-0,25 ,306 * 0,256 0,004 -0,11 -,420 ** 0,11 -0,24 ,544 ** 1 -0,3 -,315 * -,373 * -0,22 0,008 ,371 * -0,12 -0,01 -0,22 0,119 ,533 ** 0,018 0,023

0,053 -0,26 -0,07 -0,04 ,328 * 0,277 0,063 ,391 ** - 0,2 - 0,3 1 ,619 ** ,321 * 0,285 0,109 0,04 ,435 ** ,375 * ,391 ** -0,05 -0,23 0,039 -0,12

0,117 -,311 * -0,11 -0,01 ,312 * 0,136 -0,17 0,264 -0,07 -,315 * ,619 ** 1 ,312 * -0,05 0,015 0,239 ,399 ** ,384 * ,309 * 0,002 -0,28 0,156 0,031

-0,09 -0,24 0,048 0,257 0,219 0,177 0,089 ,703 ** -,580 ** -,373 * ,321 * ,312 * 1 0,182 0,208 0,025 ,438 ** 0,217 ,591 ** ,403 ** -,468 ** -0,01 -0,02

0,019 0,043 0,084 0,031 -0,01 ,352 * 0,176 0,17 -,435 ** -0,22 0,285 -0,05 0,182 1 -0,16 -0,28 0,062 -0,05 0,186 -0,04 -0,21 0,184 0,142

-,391 ** -0,04 -0,24 0,238 ,309 * 0,019 0,196 ,364 * -0,15 0,008 0,109 0,015 0,208 -0,16 1 ,328 * 0,287 0,256 0,247 -0,04 -0,06 0,13 0,25

-,464 ** 0,274 0,044 0,031 ,303 * -,385 * -0,06 0,069 0,198 ,371 * 0,04 0,239 0,025 -0,28 ,328 * 1 ,313 * ,376 * 0,149 0,122 0,186 0,208 0,188

-,420 ** 0,043 -,320 * 0,083 ,829 ** 0,173 0,164 ,319 * -0,04 -0,12 ,435 ** ,399 ** ,438 ** 0,062 0,287 ,313 * 1 ,903 ** ,847 ** 0,092 -,314 * 0,129 -0,08

-,379 * 0,129 -,335 * -0,05 ,889 ** 0,069 0,131 0,18 0,23 -0,01 ,375 * ,384 * 0,217 -0,05 0,256 ,376 * ,903 ** 1 ,537 ** 0,048 -0,19 0,154 -0,04

-,354 * -0,08 -0,22 0,22 ,528 ** 0,254 0,161 ,403 ** -,358 * -0,22 ,391 ** ,309 * ,591 ** 0,186 0,247 0,149 ,847 ** ,537 ** 1 0,12 -,387 * 0,063 -0,11

-0,03 0,185 0,283 0,273 0,062 -0,14 -0,06 ,384 * -0,05 0,119 -0,05 0,002 ,403 ** -0,04 -0,04 0,122 0,092 0,048 0,12 1 -0,09 -0,1 -0,1

-0,07 0,256 0,149 -0,23 -0,21 -,342*

-0,03 -0,3 0,258 ,533**

-0,23 -0,28 -,468**

-0,21 -0,06 0,186 -,314*

-0,19 -,387*

-0,09 1 -0,01 0,084-0,12 -0,05 -0,13 0,093 0,18 -0,06 0,076 -0,05 -0,05 0,018 0,039 0,156 -0,01 0,184 0,13 0,208 0,129 0,154 0,063 -0,1 -0,01 1 ,791 **

-0,13 -0,04 0,006 -0,03 -0,04 -0,19 0,232 -0,06 -0,1 0,023 -0,12 0,031 -0,02 0,142 0,25 0,188 -0,08 -0,04 -0,11 -0,1 0,084 ,791 ** 1

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

GENERALTON19

BULKTON20

EURPERSON21

EURTON22DEABCC23

DEACCR24

REGULARLSHIPS13

SHIPSIZE14

BIGSHIPO15

PORTPRIV16

GDPCAP17

TOTALTON18

CRAINSKM7

TERMSIZE8

MAXDRAFT9

TXUNIT10

TXHORIZ11

TXCONT12

Pearson Correlations

DROTERD2

DMEDIT3

SEAPORT4

DCITY5

QUAYL6


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Factorial Data Analysis

In a factorial analysis of all data, we founded that the ports can by classified in the followgroups:Group 1 Port with BULKTON20 5 Miotons;Group 4 Port with BULKTON20 5 Mio tonsTo simplify, we call:Group 1 SP Small Ports; Group 2 BP Bulk Ports; Group 3 BMP Big Multifuntional Ports; Group 4 GCP - General cargo Ports;

Figure 4

Since some of the variables did not pass the normality test, we used the application of nonparametric test to compare the behavior of the averages of all the dependent andindependent variables for each of the four groups of ports defined, the test-Krustkal Wallis


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Table 4

GRUPESPEC25 N

D

R O T E R D 2

D

M E D I T 3

S

E A P O R T 4

D

C I T Y 5

Q

U A Y L 6

C

R A I N S K M 7

T

E R M S I Z E 8

M

A X D R A F T 9

T

X U N I T 1 0

T

X H O R I Z 1 1

T

X C O N T 1 2

R

E G U L A R L S H I P

S

1 3

S

H I P S I Z E 1 4

B

I G S H I P O 1 5

P

O R T P R I V 1 6

G

D P C A P 1 7

T

O T A L T O N 1 8

G

E N E R A L T O N 1 9

B

U L K T O N 2 0

E

U R P E R S O N 2 1

E

U R T O N 2 2

D

E A B C C 2 3

D

E A C C R 2 4

1,00 19 27,29 21,95 22,34 22,16 12,97 21,21 16,00 16,21 25,18 19,92 16,34 19,63 14,53 20,32 17,92 19,58 10,00 12,68 12,08 19,03 26,76 21,29 21,13

2,00 7 21,57 11,14 24,93 22,57 25,21 21,71 24,14 33,43 5,57 17,07 27,00 29,14 36,43 23,93 22,29 20,71 29,29 15,71 32,14 24,36 13,00 22,71 23,93

3,00 11 14,18 27,00 18,23 21,73 33,55 25,68 27,73 27,23 20,91 23,45 26,55 21,95 29,55 25,23 29,55 24,14 37,00 35,91 36,00 24,73 13,41 22,36 20,68

4,00 6 20,08 25,67 24,42 21,33 25,67 18,08 28,00 17,42 33,08 31,67 25,75 21,25 15,00 19,17 20,75 27,25 24,00 33,33 15,92 23,67 33,17 22,75 24,92

Total 43

7,786 7,492 2,666 ,041 20,091 1,609 8,201 12,575 17,964 5,420 7,032 2,964 21,811 1,976 8,160 2,267 35,560 30,598 31,514 1,938 16,245 ,117 ,703

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

,051 ,058 ,446 ,998 ,000 ,657 ,042 ,006 ,000 ,144 ,071 ,397 ,000 ,577 ,043 ,519 ,000 ,000 ,000 ,585 ,001 ,990 ,873

Chi-Square

df

Asymp. Sig.

Mean Rank Krustkal-Wallis Test

The test result does not reject the hypothesis H0, and at least one group has an averagvalue different from others, in the variables BULKTON20, GENERALTON19,TOTALTON18 and TXUNIT10 also for following variables: DROTERD2, DMEDIT3,QUAYL6, TERMSIZE8, MAXDRAFT9, TXCONT12, SHIPSIZE14, PORTPRIV16 andEURTON22.For these variables we can see that the averages are lower the rest for the following groupGroup 1 SP Small Ports in QUAYL6, TERMSIZE8, MAXDRAFT9, TXCONT12 eSHIPSIZE14; Group 2 BP Bulk Ports in DMEDIT3, TXUNIT10 e EURTON22Group 3 BMP Big Multifuntional Ports in DROTERD2, e EURTON22, bem comovalores elevados em POTRPRIV16; Group 4 GCP - General cargo Ports inMAXDRAFT9 e SHIPSIZE14;that is, seems to have obtained a model to classify a general European ports on theispecialization and size, with the definition of groups of ports that have differentcharacteristics with each other.This could imply that certain features of the ports are connected, determine or bedetermined by the specialization of the port and its size, which can be reveled withintersection of variables BULKTON20 and GENERALTON19.

Figure 5Groups of Ports

Group 2 BP Bulk Ports Group 3 BMP Big Multifuntional Port

Ports near Mediterran Sea, deapacess channel, low prices per tonne

Privatized ports near Rotherdam, deap acesschannel, long quays, beg terminals and lowprices per tonne

Group 1 SP Small Ports Group 4 GCP - General Cargo Ports

public ports with short quay, shortterminals, acess channel forregional ships, high prices pertonne and low containerization rate

Ports with long quay and big terminals, acesschannel for regional ships, High weigth of general and roro cargo and high prices pertonne -

B U

L K T O N 2 0

+

- GENERALTON19 +


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Phases of the Model Equation The application of the model equation was carried out in phases for each of the dependenvariables by multiple linear regression with all factors of the model embodied in thevariables that were presented.To obtain a parsimonious model that explains the results of the ports in terms of its variou

dimensions of financial performance and operational efficiency, we used the stepwise an backward, allowing successively eliminate the variables, analyzing even the hypothesis onormality , homogeneity and independence of errors, the Durbin-Watson values near 2.The values Variance Inflation Factor (VIF) for each independet variable of the modelsthere are no serious problems of multicollinearity between the factors of the regressions.To test the hypothesis of a relationship in non-linear format between dependent variableand some of the explanatory variables were also carried out tests on the logarithmicvariables, with very significant results for the dependent variables BULKTON20 DEACCR24, DEABCC23.

Linear Regression Model for TOTALTON18 In the case of linear regression model for TOTALTON18, the 11th multiple linear regression, with a Durbin-Watson value of 1.849, helped to identify the variablesDROTERD2, DMEDIT3, QUAYL6, CRAINSKM7, TXUNIT10 and SHIPSIZE14, assignificant predictors of TOTALTON18.The total cargo of ports depends on the proximity of Rotterdam and the Mediterranean Sefrom, on size, on intensity of cranes per km of quays, on ships size, as well onspecialization in general cargo .The resulting model is then adjusted TOTALTON18 ^ = -13,189 * DEROTERD2 -5,393.56 * DEMEDIT3 + 1,833.123 * QUAYL6 + 2,213,756 * CRAINSKM7 +22,500,000 * TXUNIT10 + 2,086.998 * SHIPSIZE14.This model is highly significant, with p value


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The port general cargo, depends on the proximity of Rotterdam or the Mediterranean Seon size, on intensity of cranes per km of quays, on regular lines, as well on specialization igeneral cargo.The resulting model is then adjusted GENERALTON19 ^ = = -8,683.23 * DEROTERD2-3,233.52 * DEMEDIT3 + 1,278.675 * QUAYL6 + 830,430.8 * CRAINSKM7 +

16,900,000 * TXUNIT10 + 772,000,000 * REGULARSHIPS13.This model is highly significant with a p value


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Table 8

StandardizedCoefficients

B Std. Error Beta t Sig. Tolerance VIF

QUAYL6 1,293 0,144 0,723 8,958 0 0,41 2,438TXUNIT10 -2,711 0,822 -0,266 -3,297 0,002 0,41 2,438

Coefficients a,b

Model

UnstandardizedCoefficients Collinearity Statis tics

Linear Regression Model for EURTON22 In the case of linear regression model for EURTON22 the 13th multiple linear regressionwith a Durbin-Watson value of 2.028, helped to identify the variables TXHORIZ11 andGPDCAP17, as significant predictors of EURTON22.That is, the Port Authority revenue per ton depends on the specialization in Roro and regioGPD.The resulting model is then adjusted EURTON22 ^ = 3.538 * TXHORIZ11 + 0.023 *GPDCAP17.This model is highly significant with a p value


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The revenue per employee of the port Authority depends on the distance from theMediterranean Sea, intensity of cranes per km of quays, depth in the maritime access to th port and vessels size.The resulting model is then adjusted EURPERSON21 ^ = + 0.065 * DMEDIT3 - 15.642 CRAINSKM7 + 0.012 * MAXDRAFT9 + 6.903 SHIPSIZE14

This model is highly significant with a p value


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In the case of linear regression model with logarithmic variables DEACCR24 for the 14tregression, with a Durbin-Watson value of 2.22, identified the variable with the valueTERMSIZE8 B, , as significant predictors of DEACCR24.The efficiency of ports depends on the average size of the port terminals.However, recallthat many of the authors claim that the indicator DEACCR24 should not apply to ports

because ports have increasing returns to scale.The resulting model is then adjusted DEACCR24 = TERMSIZE8 ^ (0.204)This model is highly significant with a p value


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In the case of linear regression model with logarithmic variables DEABCC23 for the 14tregression, with a Durbin-Watson value of 2.291, has identified the variable with the valuQUAYL6 B, , as significant predictor of DEABCC23. That is, the more efficient portssimply depends on port size.The resulting model is then adjusted DEABCC23 ^ = QUAYL6 ^ (0.41)

This model is highly significant with a p value


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The larger is the size of the port, the better is the performance in terms of port operations aregards the total port traffic, the general cargo and bulk and the better is the performance othe port in terms of port efficiency , and even it is the only explanatory factor for theefficiency indicator on DEABCC23 in the logarithmic model.The greater the number of cranes per km of quay at the port, the better is the performanc

of port operations as regards the total port traffic, the general cargo and bulk, but the worsis the performance in financial terms of revenue per employee of the Port Authority.It also was found that the larger depth of access to the port, the better is the performance othe port in financial terms in respect of revenue per employee of the Port Authority.The larger is the average size of the port terminals, the better is the performance of the porin terms of efficiency DEACCR24.It was found that the higher is the rate of unitization in the port, the better is the performance in terms of port operations as regards the total port traffic, and general cargo.The higher is the horizontalization rate at the port, the better is the performance of the poin financial terms in respect of earnings per ton and the better is the performance in termof efficiency with regard to the indicator DEBCC23.Although the rate of containerization of the port have a significant correlation with th performance of the port operation, was not considered as an explanatory variable in thmodels of port performance, considering that its effect is integrated with other mosimportant explanatory variables, such as unitization rate, liner and integration with majoglobal shipping lines.The larger the average size of the vessels calling at the port, the better the performance interms of port operations as regards the total port traffic and bulk.It was found that the greater the weight of the liner ships to port, the better the performancin terms of port operations as regards the movement of general cargo, and the better it performance in terms of port efficiency with indicator DEABCC23.It was found that the greater the weight of the major global players lines in the regular lineat the port, the better its performance in terms of port efficiency indicator withDEABCC23.It was found that the greater the weight of cargo movement through terminals operated bthe private, the better is the performance in terms of port efficiency indicators forDEACCR24 and DEABCC23.The higher the GPD per capita in the region where the port is situated, the better is th performance in financial terms in relation to revenue per ton and in terms of port efficiencindicators for DEACCR24 and DEABCC23. However, this effect is indirect, since it ifound that is reflected in the characteristics of the port that have a direct effect on thei performance.

7. CONCLUSION Implications for Management This study brings its findings a series of new implications for the management of portsincluding the coordination role of the port authorities, of governments and port terminaloperators.Firstly it is important to the decision maker to accurately define what exactly is the mainobjective of the port or terminal, if if it is to have more total cargo with all theconsequences this has for the region's economy, whether it is to have greater efficiency anthus have lower costs, making the port an element of support to the competitiveness o


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companies in the region or whether it simply wants to increase revenue per tonne oemployee of Port Authority and of all service providers that usually have commissions.The location of the port or terminal is a basic element to it performance, as found in thmodel. But in most cases it is a fact that already exists and is not easy to change, includinthe distance to Rotterdam and the Mediterranean Sea. In this case, only the rulers who

decide to create new ports can take these variables into account.The size of the port is an essential aspect of its operational performance, which is obviouto any manager. Without a quay there is no cargo and usually without cargo will not becreated a new quay. Seems to be preferable to invest in a large port, which in manysmall.The problem is not the understanding of this relationship, but the "timing" in whicthey must build a new quay, so that there it will not pass not too many years withoutmovement to amortize the investment.The simple increase of the size of the port is not a guarantee of increased port traffic, aevidenced by several cases. But it appears only if the quay exists. If the relationship between the size of the port and its cargo is elementary, is more important it relationshiwith efficiency due to economies of scale, and its relation to the revenue per tonne andemployee, as a major port should be able to practice lower rates due to economies of scaleAlready the average size of the port terminals is a key variable for the efficiency of the porThe intensity of use of the quay, with the largest number of cranes, is a decisive factor fothe performance of the ports at the operational level and contributes to lower port costs peemployee, contributing to their competitiveness.Depth of port access, allowing the port to receive larger vessels, is a key variable for th performance of ports, including total cargo and bulk, but also for the container markeHowever, the large investment required can make highest port charges per ton and peemployee, making the port less competitive, which is certainly offset by the increasedcompetitiveness of the freight of the larger ships that can reach the port and have a loweconsumption per tonne / km in its travels, giving the advantage to port.Specialization in general cargo is also an issue that is important for ports that want to hava higher performance in the total cargo and general cargo.Attracting more regular lines for the port requires the establishment of managemenconditions and appropriate infrastructure, but ensure great performance in terms of thgeneral cargo, including containers, and requires the port to adjust the degree of relativefficiency in order to have lower costs.The study demonstrated the importance of the privatization of port management of the poterminals, since such a measure contributes significantly to the improvement of porefficiency and thus its competitiveness, which associated with integration the port ininternational networks of major container shipping operators, facilitates the attainment o performance targets of the port.Finally, it is surprising conclusions about the importance of specialization rate in rorocargo, involving performances in terms of higher revenue per tonne, making the port lescompetitive, but are advantageous in terms of less need for investment in equipment cranemaking the port more efficient, with advantages in the speed with which cargo is loadedand unloaded of the ship.

Research for the Future Despite the ambition of this study, the result leaves more questions than answers to the problems of the ports. It is to know the precise extent to which the characteristics of th


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ports influence their performance and to measure accurately how the performance of porinfluences the characteristics, since it is certainly a reciprocal relationship over time, whicgradually characteristics of ports and the environmental variables will influence the performance of ports and this is reflected in contributing to the change of environment othe port and the characteristics of the port.

Secondly, to verify whether the constructs indicated in this study can be classified asfeatures of ports, or if some are environmental variables or dependent on other results. Iwould be important to define the extent to which factor is determined to each other, howthey relate, and how they contribute directly and indirectly to the performance of the porWe proceed with a possible model for this, drawn from the correlation coefficients ofPearson and the results of the study.

Figure 7

Location Infrastructure

Governance

Specialization

Port Perfomance

Region Perfomance Maritime Service

Size

n+1

Finally, several issues may still be investigated and further developed, such as the type omanagement of terminal, the port terminals characteristics that determine then performancwhat determines the performance of the port at the level of service to ships and waitintimes.Also in the present study, may also be asked several questions such as, for example: Thlargest ports with more private management have better performance?The ports with largeterminals and larger depths have better performance?The relationship between the variables that characterize the port and between them andtheir performance could be assessed by a survey of the opinion of major European playerin the port sector, custmers and operators.

Strengths and Limitations of This Analysis This study's main limitation is the number of responses that was obtained from european port authorities, who collaborated with difficulty, with partial responses, which wercompleted with great effort and persistence.Nevertheless, it was not possible to feed thdatabase of the sample with all variables initially intended, as some of the issues have noreceived a response from some ports, making it impractical to use, despite the possiblimportance for the model.The conclusions of such studies are importante for ports, so it would be important that th port authorities collaborate more actively in the responses to surveys.


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An alternative is ESPO to promote a annual survey to collect a wide range of informatioon European ports and make available to all researchers wishing to study the ports. Nevertheless, given that the vast majority of studies on the ports are restricted to the porof certain countries or to very simple and basic quantitative data or to ESPO and publishe by Eurostat, one of the strengths of this study was able is to cover a wide range o

quantitative variables in a large population of European ports, which allows a betteunderstanding of diversity of characteristics that determine the performance of ports.


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