48(13) 2749–2769, October 2011
The Location and Global Network Structure of Maritime Advanced Producer Services Wouter Jacobs, Hans Koster and Peter Hall [Paper first received, May 2010; in final form, October 2010]
Abstract Within research on world cities, much attention has been paid to advanced producer services (APS) and their role within both global urban hierarchies and network formation between cities. What is largely ignored is that these APS provide services to firms operating in a range of different sectors. Does sector-specific specialisation of advanced producer services influence the economic geography of corporate networks between cities? If so, what factors might explain this geographical pattern? This paper investigates these theoretical questions by empirically focusing on those advanced producer services related to the port and maritime sector. The empirical results show that the location of AMPS is correlated with maritime localisation economies, expressed in the presence of shipowners and port-related industry as well as APS in general, but not by throughput flows of ports. Based upon the findings, policy recommendations are addressed.
1. Introduction Within the literature on world cities and urban networks, considerable empirical attention has been focused on the intrafirm networks of advanced producer services that connect cities, so enabling production of goods and services on a global scale (Sassen,
2002; Taylor, 2004; Derudder et al., 2010). The various advanced producer services (APS), finance, insurance, consultancy and so on, are treated as a distinctive sector that serves global production functions and that tends to agglomerate within ‘world cities’ or ‘global
Wouter Jacobs is in the Department of Economic Geography, Utrecht University, Heidelberglaan 2, PO BOX 80115, Utrecht, NL-3508 TC, The Netherlands. E-mail:
[email protected]. Hans Koster is in the Department of Spatial Economics, VU University Amsterdam, De Boelelaan 1105, Amsterdam, 1081HV, The Netherlands. E-mail:
[email protected]. Peter Hall is in the Urban Studies Program, Simon Fraser University, Vancouver, British Columbia, Canada. E-mail:
[email protected]. 0042-0980 Print/1360-063X Online © 2010 Urban Studies Journal Limited DOI: 10.1177/0042098010391294
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cities’. However, what is largely ignored, both conceptually and empirically, is the fact these firms provide advanced services to other firms that operate within entirely different sectors (see Jacobs et al., 2010). So, while Sassen (2010) reaffirms that APS provide ‘organisational commodities’ to support the command-and-control functions of their global clients whatever their sector of operation, the possibility that such interactions can lead to sector-specific specialisation amongst APS may be underappreciated. This is problematic to the extent that the ‘sector’ in which global flows of finance and information are organised is somehow regarded as monolithic and hence separate from the resource, manufacturing, distribution or consumer services sectors of the ‘real’ economy that it serves. In short, the underlying depiction of the location dynamics of the advanced services sector as relatively independent from the economic sectors it serves bears closer examination. In contrast to the focus on globally sourced and provided advanced services is an alternative, but not necessarily incompatible, perspective which focuses on the regional interaction between so-called knowledge-intensive business services (KIBS) and their (international) clients (Muller and Zenker, 2001; Keeble and Nachum, 2002; Wood, 2002, 2009; Strambach, 2008; Doloreux et al., 2010). These studies have shown that KIBS cluster in large urban centres due to agglomeration benefits shared with their (globally operating) clients. Cognitive proximities, trust and a shared pool of related human capital facilitate the learning process among locality-based KIBS and their clients (Bennett et al., 2000). Although these KIBS have been defined slightly differently from APS, there is considerable overlap between both groups. In addition to these empirical findings, scholars have argued that KIBS can over time specialise in specific clients operating in specific industries (Muller and Zenker, 2001; Strambach, 2008). Referring
to the opportunities of core British cities besides London to develop KIBS clusters, Wood states Among the distinctive inherited strength of the core cities are various forms of specialist ‘technical’ consultancy, built on long-established industrial, mining, maritime and associated engineering and trading traditions (Wood, 2009, p. 1056).
Likewise, Wernerheim and Sharpe (2003), show how ‘high-order’ advanced producer services in Canada are less footloose than expected as they favour proximity to the manufacturing sectors they support. While these studies seem to suggest a degree of sector-specific specialisation of KIBS facilitated by spatial proximity, empirical elaboration remains limited. Agglomeration economics also predict sector-specific specialisation in the location dynamics of APS or KIBS. From urban economic theory, it can be argued that the provision of advanced services through interaction with clients within particular sector-specific ensembles or clusters (see Porter, 1990) or across different sectors (Jacobs, 1969) can result in specialisation and development of specific services. Such sector-specific specialisation of APS raises the possibility of a different location pattern than that predicted by current world city analyses if such services agglomerate in close proximity to their client industry. This alternative perspective may be compatible with that of global city theory to the extent that sector-specific APS are found to emerge, locate and thrive in those places which contain the command-and-control functions of the sector(s) they serve. Hence the central question of this paper: to what degree does sector-specific specialisation influence the location pattern of advanced producer services and the spatial configuration of intercity networks? In this study, we focus on the maritime transport sector for
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three reasons. First, the maritime transport sector is a major facilitator of the process of economic globalisation (Levinson, 2006) as it links distant production clusters and consumer markets. Secondly, there has been neglect of physical flows within world city network research (Derudder, 2006), which has tended instead to focus on intrafirm networks, air links (Derudder and Witlox, 2005) and Internet connections (Choi et al., 2006). Within the maritime transport sector, there is demand for advanced services: to finance ships and port facilities, to insure ships and cargo, to have a legal representation in case of an incident, to have software solutions in supply chain management, to inspect ships and to provide technical expertise on damages. The geography of those specialised and sectorspecific advanced producer services, maritime APS (hereafter AMPS), has up until now not received much attention (but see Jacobs et al., 2010; Hall et al., 2011). A third reason for focusing on the maritime transport sector is the on-going debates about the relationship between port activity and the economies of port cities (Ducruet and Lee, 2006; Grobar, 2008; Hall, 2009). Although the maritime transport sector is by definition highly mobile, its core activities (transhipment, stevedoring, warehousing, logistics) and the global flows of commodities it facilitates concentrate within seaport nodes located in major metropolitan regions. AMPS do not necessarily share the same need for seaport infrastructure as transport firms, but location near nodes of transport activity might be necessary and beneficial to sustain business relations and monitor market demands. The empirical question is thus: to what extent do specia lised maritime advanced producer services agglomerate near major transport nodes such as seaports and/or in proximity to global shipping and port-related localisation economies? The structure of this paper is as follows. In the second section, we deal with the issue of sector-specificity of advanced producer
services in world city networks. We ask to what extent sector-specific specialisation in the maritime transport sector affects the location and network patterns. Based upon this literature review and explorative interviews, we address our hypotheses. In the third section, we introduce our data and our methodology which is followed by a discussion of the results in the fourth section. The conclusion considers the implications of the analysis for urban economic development.
2. Sector Specificity in World City Networks: The Maritime Transport Sector While some world city studies focus on the location pattern and network configuration of the largest multinationals, irrespective of the industry they represent (see Alderson and Beckfield, 2004; Wall, 2009; Alderson et al., 2010), others focus on the corporate networks of the largest advanced producer services in line with Sassen’s (2002) work on global cities, often making use of the database constructed by the ‘Globalisation and World Cities’ (GaWC) research network (Taylor, 2004). The services that are considered as advanced and that are included in the analyses vary, but mostly they include finance, insurance, accountancy, law and advertising. It is argued that it is the very nature of their business—namely, to provide and control knowledge and capital-intensive inputs for global producers—which makes these services the ‘heart and soul’ of the capitalist world economy. Their geographical concentration in certain locations constitutes ‘world cityness’ (Derudder et al., 2010; Taylor, 2004; Sassen, 2002; Friedmann, 1986). However, what is largely ignored in most studies on world cities, is that these advanced services are provided to other firms that are often in entirely different sectors (but see Lambregts, 2008). This omission results because most empirical studies focus
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on the geography of intrafirm networks at the expense of interfirm relationships (Jacobs et al., 2010; Lüthi et al., 2010). The lack of attention to interfirm relationships in the analysis of advanced service provision obscures two fundamental and related issues. First, by focusing on global hierarchical relationships in the organisation of finance and data flows, the potential for sector-specific specialisation of advanced producer services and hence the potential localisation of these services away from the iconic world cities is obscured. Advanced services may be sectorspecific as they are provided to other industries with a specific and distinctive demand for such services. For example, risk management of a fleet of oil tankers is something different from that of a fleet of lease cars. Not every advanced service provider holds the same sector-specific expertise and not every advanced service provider is competing in the same markets or for the same clients. The literature on KIBS on the other hand has emphasised how they can develop both horizontal and vertical knowledge domains through continuous interaction with their clients (see Strambach, 2008). Be that as it may, taking sector specificity into account may provide a more accurate geographical picture of advanced producer services than is provided by existing world city research. Secondly, related to the issue of sector specificity is geographical proximity. Firms, including entrepreneurial start-ups, capture positive economic externalities by being co-located or clustered in space. These externalities include localisation economies that are available to all local firms within the same sector or industry, and urbanisation economies available to all local firms irrespective of sector and which arise as a result of urban size, population density and the location of public services such as universities or government administration. A special, dynamic, form of urbanisation economies highlighted by Jane Jacobs (1969) may be available to
entrepreneurs and established firms in a city or region with a variety or diversity of sectors. Continuous market and non-market (see Storper, 1997) interaction between actors in different sectors within geographical proximity can over time lead to the creation of ‘related variety’ (Frenken et al., 2007). From this argument, it follows that specialised advanced producer services may be co-located in close proximity to the industry to which they provide these services, as well as in places with diverse and vibrant urbanisation economies. Co-location is also more in line with the findings on KIBS which tend to favour and succeed in closer proximity to clients in large urbanised centres (see Wood, 2009; Keeble and Nachum, 2002). What factors might explain the location and network configuration of AMPS in particular? Based upon on the theoretical arguments already discussed and on exploratory interviews with senior managers of AMPS firms (including maritime law firms, surveyors, insurance brokers, P&I insurance companies and correspondents, Lloyd’s agents, banks and shipowners), we distinguish four factors.1 First, AMPS functions have differential requirements for geographical proximity to seaports and commodity flows, global shipping and port-related industry, and to other APS. For some AMPS, direct proximity to seaports and physical goods movement is important: in order to inspect or classify ships when in port, to inspect damaged ships or cargo, legally to represent the ship or cargo owners, etc. Although these activities include highly educated professions, their activity tends to be routine and in real-time demand. Secondly, for some AMPS, geographical proximity to other APS is more important, while direct proximity to seaports and commodity flows is less relevant. An example is the cluster surrounding Lloyd’s market and the Baltic exchange in the City of London where the representatives of ship
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and cargo owners buy and share risk products from underwriters, fixed premium insurance companies, and P&I clubs. These are highly knowledge-intensive products in which proximity to financial services and specialised human capital becomes crucial. Agglomeration based upon this mechanism tends to apply to the global corporate decision-making offices of the AMPS firms. Here we can expect a considerable overlap with the conventional world city hierarchies (Taylor 2004; Derudder et al., 2010). A third form of geographical proximity is with the customers of AMPS, the shipowners and other port-related industries. A high concentration of shipowners at a particular location might attract offices of maritime advanced services, which is exemplified by the relatively large concentration of Londonbased AMPS in the Piraeus/Athens urban region. Likewise, large-fleet shipowners might be inclined to open offices near centres of AMPS to facilitate the finance and insurance of their fleet. We note in passing here, that the factors which account for the location of customers of AMPS themselves, appear to be quite idiosyncratic; for example, Slack and Frémont (2009) have argued that, while individual entrepreneurs have contributed to industry volatility in the first half-century of containerisation, these entrepreneurs do not themselves appear to be deeply embedded in particular local industrial milieus. Fourthly, institutional and historical factors may also influence the location and networks of maritime advanced services. The dominant position of London, for example, can be traced back to the heyday of the British Empire and the port of London in the late 19th century (see Jacobs et al., 2010). Although the empire disintegrated and the port declined, the City has remained the international centre of maritime advanced producer services. This history also helps to explain the strong positions of the former British colonies of Singapore and Hong Kong and
their strong business relationships with London. Such path-dependent institutional evolution is also responsible for the fact that BIMCO contracts (Baltic and International Maritime Council) between shipowners, cargo owners and third parties are based upon English maritime law, with arbitration and hearings more likely to take place in either London or New York (under American maritime law). This provides these places with a considerable ‘jurisdictional advantage’ over all other places to attract and develop expertise (Feldman and Martin, 2005). We also need to consider other explanatory variables that are more related to conventional urbanisation economies. The fact that a location is a capital city might influence the presence of AMPS as they seek proximity to maritime-related public administration. Other urbanisation externalities such as the presence of universities also need to be considered as well as some basic variables such as urban size or ‘pure’ geographical features. 2.1 Hypotheses
Based on the foregoing discussion of literature and exploratory interviews, it seems plausible that sector specificity of advanced producer services influences the location pattern and the spatial configuration of urban networks. In this study, we add such sector specificity by focusing on those APS involved in the maritime transport sector. To the extent that port operations themselves, as well as the global command-and-control functions of the global shipping and port-related industry, agglomerate in specific places, we propose the alternative hypotheses: H1.1: Maritime advanced producer services agglomerate near seaports and transhipment nodes of commodity flows. H1.2: Maritime advanced producer services agglomerate in proximity to localisation economies of the global shipping and port-related industries.
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On the other hand, it might be the case that, while AMPS emerged and historically evolved in the direct spatial proximity of seaports, their direct spatial proximity ceased to be important over time. The spatial and functional-economic relationships between cities and ports have changed fundamentally during the second half of the 20th century (see Bird, 1963; Hoyle, 1989; Hall, 2007). In economic terms, ports have become less and less dependent on the urban labour market due to increased automation and operational rationalisation. Cities have also become less dependent on ‘their’ ports for local economic growth, but often struggle—like many industrial centres in the developed world—to upgrade and diversify their economies. There is, however, recognition that port–city relationships still exist through more sophisticated—albeit less visible—forms within the tertiary sector (O’Connor, 1989; Slack, 1989; Ducruet and Lee, 2006; Jacobs et al., 2010). Some port cities have managed to diversify economically into thriving service-based economies even though their initial advantage of deepwater access ceased to be important for growth (Fujita and Mori, 1996). Indeed, both the world/global city literature and the literature on knowledge-intensive business services emphasise agglomeration effects. The question then becomes whether AMPS favour a location in proximity of other advanced services in world cities. We propose: H2: Specialised maritime advanced service providers agglomerate near other advanced service providers. Note that H1 and H2 are not alternative hypotheses; it is possible that AMPS locate in proximity to both seaports or maritime localisation economies, and other advanced services. For this reason, we maintain each as a separate hypothesis in the analysis. We proceed by first introducing our data to be able to measure the network of AMPS, after
which we model which factors best explain its structure.
3. Dataset, Measuring Networks and Econometric Method 3.1 Data
Our empirical analysis is based upon a powerful dataset from the World Shipping Register (WSR). The WSR provides up-to-date information on companies involved in the shipping industry, including type of firm and the location of its establishments at the city level.2 We make a distinction between AMPS,3 shipowners and port-related industries, based on a detailed classification used by the WSR. We removed double counts and updated information on the location of each AMPS establishment, using companies’ websites and annual reports. Eventually, we had a database with 4999 AMPS firms and 10 782 establishments located in 2569 cities. The 702 firms which have more than one establishment are particularly interesting with respect to our network analysis. In section 4, we will relate the number of AMPS establishments and their connectivity (defined in sub-section 3.2), to port-specific variables (CONTAINERS, COASTAL CITY, ISLAND), localisation variables (SHIPOWNERS, PORT-RELATED INDUSTRIES), and urbanisation variables (POPULATION, GDP per CAPITA, GOVERNANCE, CAPITAL and UNIVERSITIES) by means of a regression analysis. While port-specific variables are closely related to the localisation variables, they describe physical movement characteristics rather than economic relationships. We use a database gathered by Wall (2009) to describe the locations and connectivity of advanced producer services. Wall selected the most important firms, based on the Fortune Global 500 list. Using annual reports and websites, data on locations of subsidiaries were collected. From
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these, we construct an accurate database with the locations of both headquarters and subsidiaries of advanced producer services. This provides a representative sample of 1647 establishments of 1267 firms, of which 243 firms have more than one establishment. Data on port throughput and the number of containers handled are from Eurostat and the American Association of Port Authorities. We obtained the population of cities from a website (http://www.citypopulation. de) that collates data from national statistical offices. National GDP per capita for 2006 was obtained from the World Bank. Kaufman et al. (2006) present six indicators that measure the institutional quality of a country. They also provide evidence on the reliability of expert assessments of governance that are part of the aggregate indicators. Because the indicators such as political stability, voice and accountability and control of corruption are highly correlated, an average governance index is computed for each country. A number of studies show that highly skilled labour attracts multinationals (for example, Becker et al., 2005) and AMPS generally also need
highly skilled employees. The location of 200 top universities provides our proxy for the presence of highly skilled urban labour. Combining these data leads to a dataset of 459 cities consisting of both port cities and cities which do not have a seaport. The descriptive statistics of this sample are presented in Table 1. We ensured that all important world cities (based upon the lists presented in Wall, 2009, and Derudder et al., 2010) as well as the most important port cities (in terms of cargo throughputs of the port) are included in our sample. We also estimate models for only those cities with seaports (reported in section 4) as well as with total throughput measured in million metric tons instead of using container data. This affects the sample composition, as there are a number of ports for which we have only throughput tonnage data and no container data, but also cities for which we do not have container data. Our dataset then consists of 424 cities. It appeared that the results are very similar to those found in section 4. We therefore expect that the sample selection bias is non-existent or small.
Table 1. Descriptive statistics (N = 459) Mean ESTABLISHMENTS AMPS GNC AMPS ESTABLISHMENTS APS GNC APS COASTAL CITY ISLAND CONTAINERS SHIP-OWNERS PORT-RELATED INDUSTRY POPULATION GDP PER CAPITA GOVERNANCE CAPITAL UNIVERSITY
S.D.
Minimum
Maximum
15.620 824.360 2.790 16.050 0.710 0.050 957 818.600 124.250
28.272 1 119.230 7.532 75.559 0.456 0.209 2 885 290.224 458.746
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
385.000 9 335.000 103.000 1 201.000 1.000 1.000 27 935 500.000 6 123.000
13.280
29.937
0.000
418.000
1 847 808.510 28 460.610 0.891 0.200 0.260
3 756 503.958 18 372.373 0.819 0.400 0.700
1.000 130.000 -1.696 0.000 0.000
37 203 122.000 76 040.000 1.937 1.000 6.000
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3.2 Specification of the Network
Using locational information of a large number of AMPS establishments together with information from websites and annual reports, we determined for each firm where the global, regional and country headquarters are located. We then employed the methodology of Taylor (2001) to study the network formation between cities based upon advanced maritime producer services.4 In contrast to other social and behavioural networks, the world city network has three levels (Taylor, 2001). The first level is the world economy; the second consists of cities (nodes) where the production of services takes place. The third sub-nodal level is constituted by the firms that provide the advanced producer services. Although cities have decision-makers that can influence world city formation, the prime actors are the firms at the sub-nodal level (Beaverstock et al., 2002): firms, which range from startups to multinationals, are able to relocate, establish new relations and make decisions. To measure the relations between establishments participating in the global network, Taylor (2001) argued that only the location and hierarchical structure of a firm (and its establishments) are needed in order to study world city network formation. The starting-point of the network analysis is a matrix with the so-called service values of firm j in city i.5 With respect to this service value, we assume the more important the establishment is, the higher the service value v will be. We largely adopt the methodology of Taylor et al. (2002) to determine v, with some adjustments.6 When there is no office of firm j located in city a, j zero is assigned. When there is a normal subsidiary (establishment) of a certain firm located in city a, a score of 1 is given. Headquarters of firms with 15 or more establishments will get a score of 5. Regional headquarters of such firms will score 3 points. Headquarters of a firm with 8–15
establishments will get a score of 4, between 4 and 7 establishments a score of 3 and headquarters of a firm with two or three establishments will score 2.7 Then the ‘elemental interlock link’ r between two cities a and b, for firm j is
rab, j = vaj × vbj
(1)
The aggregate city interlock link is the summed relations between a and b of all establishments located in city a and b . This is defined as
rab = ∑ rab, j ∀j
(2)
We divide this city interlock link by the highest city interlock (in our case Hong Kong–London) to arrive at the relative city interlock link. From this, we can determine the situational status of a city within the network. Derudder and Taylor (2005) call this the global network connectivity (GNC) of city a. For every firm present in a city, we multiply the value of the service value with the value in all other cities where the firm is present. Formally
GNCa =
∑ rai
∀i ≠ a
(3)
We use the relative GNC in our analysis, so we divide each GNC by the maximum GNC—in our case, London. 3.3 Econometric Methodology
The next step is to model the determinants of the number of AMPS and APS establishments (EST.AMPS; EST.APS) and their connectivity (GNC AMPS; GNC APS), using our cross-sectional count data. Count data are often treated as continuous, because then the use of ordinary least squares methods is feasible. However, this may lead to biased and inefficient estimates (Long, 1997). The Poisson regression technique is widely used
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for count data, suitable when the dependent variable is a non-negative integer with a Poisson distribution. However, an important assumption of Poisson regression is equidispersion (Cameron and Trivedi, 1998). In our sample, it appears that all the dependent variables have a significantly higher variance than the mean—in other words, there is overdispersion. We therefore employ a negative binomial regression technique that allows for overdispersion. We also verified that there is no problem of excess zeros (Long, 1997) and, hence, no need for a zero-inflated negative binomial regression. Another important reason to employ negative binomial regression is that it is closely linked with the random utility maximisation framework—i.e. firms optimise their profit by choosing a location (see McFadden, 1974). Often, computationally intensive conditional logit models are estimated, but Guimarães et al. (2003, 2004) showed that firm location decisions can easily be estimated by negative binomial regressions. We estimate the following negative binomial regression specification Η a = β0 + β1COASTAL CITYa + β12 ISLANDa + β13 log(CONTAINERS )a + β14 log((PORT INDUSTRY )a + β15 log(POPULATION )a + (4) β16 log(GDPpC )a + β17GOVERNANCEa + β18CAPITALa + β19UNIVERSITIESa + ε a EST. AMPS, EST . APS, where, Η a = ; the GNC AMPS, GNC APS β s are the coefficients to be estimated; and ε a denotes the city-specific error term. We also investigate whether the presence of general APS is important for AMPS in order to test hypothesis 2
Μ a = β0 + β1 log( EST . APS)I a + β2 log(GNC APS)(1 − I a ) + β3COASTAL CITYa +
β 4 ISLANDa + β5 log(CONTAINERS )a + β6 log(PORT INDUSTRY )a + (5) β7 log(POPULATION )a + β8 log(GDPpC )a + β9GOVERNANCEa + β10CAPITALa + β11UNIVERSITIESa + ε a where, Μ a = {EST . AMPS, GNC AMPS} ; and I a denotes an indicator function; I a = 1 when Μ a = EST . AMPS .
4. Results 4.1 The Locations of Maritime Advanced Producer Services
In Figure 1, we present the spatial representation of the locations of the establishments of maritime advanced producer establishments. It is not surprising that London ranks first with 385 AMPS establishments. Singapore follows with 199 establishments. However, there are a number of cities in the top 10 that are not part of the rankings of different world city studies (see Friedmann, 1995; Beaverstock et al., 1999; Alderson and Beckfield, 2004; Taylor, 2005; Carroll, 2007). Examples are Piraeus (148 establishments), Rotterdam (128 establishments), Hamburg (104 establishments), Houston (96 establishments) and Panama City (95 establishments). All these cities are port cities and accommodate portrelated industries. For example, many Greek shipowners base their commercial operations in Piraeus. In Rotterdam and Houston, a huge number of port-related industries are located, such as large oil-refining and chemical firms. In contrast to its high ranking in other
Figure 1. The locations of AMPS.
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world city studies, Tokyo is only ranked 12th amongst global maritime cities. It is joined by a number of capital cities that do not host a seaport, but nevertheless do contain a substantial number of establishments of AMPS. Examples are Madrid, Paris and Moscow which rank respectively 15th, 16th and 19th. There are major concentrations of AMPS in western Europe and along the coastline of the Mediterranean Sea, as well as along the eastern coast of the US. African and South American cities are underrepresented with respect to the number of establishments of AMPS. 4.2 The Global Network of Maritime Advanced Producer Services
In Figure 2, we map the GNC of cities, displaying key intercity links. We only show the links which are more than 5 per cent of the strength of the most important link. Furthermore, we represent only cities which have a GNC of at least 1 per cent of that of London. The link between London and Hong Kong is the strongest, followed closely by London and Singapore. Firms which have headquarters in London often have subsidiaries in Hong Kong or Singapore, supporting Taylor et al.’s (2002) finding that, for many global firms, Hong Kong is the place to locate subsidiaries. Connectivity of a city is determined by the number of establishments, the size of the firm that is located in a certain city and the importance of an establishment, which is denoted by the service value. Hosting a large number of establishments does therefore not automatically imply a higher GNC. For example Piraeus, Hamburg and Panama City are no longer in the top 10 and are replaced by Paris, Madrid and Sydney. These are cities where a number of large law firms with maritime specialities have important subsidiaries which contribute to higher global network connectivity. Looking at the shape of the world network of maritime advanced producer services, we note that it replicates long-established
patterns of corporate unevenness (see Hymer, 1972). The major links are between cities that are located in developed countries, whereas African and South American cities remain relatively underrepresented in the network of AMPS firms. Figures 1 and 2 allow some interim conclusions. First, we observe that important AMPS cities are not marked as world cities in other studies. Secondly, looking at the constellation of the network, the most important relations are between London, Hong Kong and Singapore. Thirdly, it can be observed that London is very dominant, in terms of connectivity and number of establishments. Sector specificity appears to exert a noticeable influence both on the location of establishments and on intrafirm network formation when compared with conventional world city research which only looks at the largest APS firms, regardless of sectoral specialisation. However, we need to go further to explain what factors influence the location and connectivity of particular specialised advanced producer services. 4.3 Factors Explaining Locations and Connectivity of Advanced Maritime Service Providers
In this section, we investigate whether the variables, outlined in section 3, might explain why some cities host more AMPS establishments than others, as well as the impact of these factors on GNC of AMPS (see model (1) for establishments; model (5) for GNC). We also estimate models with establishments and GNC of APS as dependent variables (see models (2) and (6)), to investigate whether the effects on AMPS differ substantially from general APS. As it has been shown in the previous sub-sections that some locations host a lot of AMPS but do not have a seaport (for example, Paris, Madrid), we reduce our sample to those 274 cities that actually have a seaport (models (3) and (7)). In order to test hypothesis 1.1 (namely, that, unlike general APS, AMPS
Figure 2. The network of AMPS.
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agglomerate near seaports) we removed all the port-related variables in the models for APS and examined whether the results differ significantly either for establishments (model (4)) or GNC (model (8)). Finally, we investigated whether the presence of general APS is important for AMPS (model (9) for all 459 cities and model (10) for the sample of 274 port cities) and GNC (models (11) and (12)) of AMPS in order to test hypothesis 2. Models (1) and (2) reveal that the urbanisation variables (CAPITAL, UNIVERSITIES, GDP per CAPITA, POPULATION) are far more important for APS than for AMPS (see Table 2). Being a capital, for instance, leads to 73.4 per cent more establishments of APS; for AMPS, this factor results in a more modest 47.2 per cent increase in the number of establishments. The factors CONTAINERS, SHIP-OWNERS and PORT-RELATED INDUSTRIES have a somewhat larger effect on the number of establishments of AMPS than on APS, although the differences are small. Possibly, shipowners and port industries are not only demanding specialised AMPS, but also general APS, which makes it attractive for APS to locate near shipowners and portrelated industries. Because the sample of AMPS cities also includes cities that do not have a seaport, we re-estimate model (1), but now for only those 274 cities that have a seaport (model (3)). We observe that the importance of container throughput increases substantially. Surprisingly, the importance of port-related industries and shipowners is not affected much. In addition, we observe that the importance of being a capital city or having a top university decreases. The maritime services which locate in seaports are possibly less-knowledge-intensive routine activities, whereas maritime services that locate in main urban centres are more command-and-control oriented.8 In model (4), the port and transportrelated factors have been removed. In comparison with model (2), we observe an
increase of the coefficients of all factors except for GOVERNANCE. This points to the presence of an omitted variable bias in model (4) (and also in model (8)) resulting from the exclusion of the variables SHIPOWNERS and PORT-RELATED INDUSTRY which significantly influence both the location and GNC of APS as became clear in models (2) and (6). On the other hand, we observe that all the signs of coefficients remain the same. When we define GNC as the dependent variable, the results are largely in accordance with the results of models (1)–(4). However, there are some noteworthy differences. The coastal city variable has a larger (negative) effect, especially on the connectedness by APS while ISLAND has a positive significant impact on network connectivity. This might be because many island locations, such as Hamilton, Bermuda or Limassol (Cyprus), have favourable tax regimes for international shipping. From our data, it is surprising that the presence of SHIP-OWNERS and PORTRELATED INDUSTRIES is more important for the GNC of APS than for AMPS. For instance, a 10 per cent increase in the number of shipowners will lead to an increase in GNC of AMPS of 0.72 per cent and GNC of APS of 1.94 per cent. Furthermore, we observe that GDP per capita does not influence GNC significantly. Being a capital is even more important for the connectivity of APS than for the locations of APS. For AMPS, being a capital is a less influential determinant of connectivity. In Models (9)–(12) (see Table 4), we observe that AMPS tend to agglomerate near APS: an increase of 10 per cent in APS establishments leads to an increase 1.3 per cent of AMPS. However, the influence of APS on AMPS decreases when we look at the sample of those cities that actually have a seaport (model (10)). This suggests that the reason for an AMPS firm to locate in cities that do not have a seaport is due to urbanisation externalities and proximity to APS in general.
(0.159) (0.184)* (0.011) (0.028)*** (0.044)*** (0.025)*** (0.054) (0.080) (0.094)*** (0.058)*** (0.622)**
-1384.775 1183.240
-0.019 0.346 0.008 0.165 0.350 0.114 0.077 -0.058 0.472 0.264 -1.275 (0.293) (0.392) (0.025)*** (0.060)** (0.094)*** (0.071)*** (0.158)*** (0.237) (0.201)*** (0.114)*** (1.658)***
-686.215 266.130
-0.388 0.566 -0.076 0.125 0.300 0.519 0.431 0.212 0.734 0.453 -11.571
Model (2) Est. APS (n = 459) (0.221) (0.278) (0.016)*** (0.040)*** (0.048)*** (0.031)*** (0.065) (0.092) (0.109)*** (0.049)*** (0.759)** -832.048 1011.620
-0.157 0.346 0.108 0.171 0.355 0.102 -0.009 0.117 0.352 0.163 -1.602
Model (3) Est. AMPS (n = 274)
(0.064)*** (0.168)*** (0.252) (0.193)*** (0.109)*** (1.722)***
0.593 0.517 0.125 1.071 0.464 -12.754
-703.506 247.690
(0.172)*** (0.467)*
-0.691 0.836
Model (4) Est. APS (n = 459)
Notes: Coefficients are significant at the * 0.10, ** 0.05 and *** 0.01 levels. Robust standard errors are between parentheses log( pht ).
Log pseudo likelihood Wald c2
COASTAL CITY ISLAND CONTAINERS (log) SHIP-OWNERS (log) PORT-RELATED INDUSTRY (log) POPULATION (log) GDP PER CAPITA (log) GOVERNANCE CAPITAL UNIVERSITIES (1+t) CONSTANT
Model (1) Est. AMPS (n = 459)
Table 2. Results for negative binomial estimates of the number of AMPS and APS establishments
2762 WOUTER JACOBS ET AL.
(0.042)*** (0.093) (0.145) (0.096)*** (0.061)*** (0.955)***
0.167 0.024 0.097 0.392 0.260 3.035
-3312.947 597.250
(0.169)** (0.225) (0.013) (0.032)** (0.047)***
-0.342 0.222 -0.002 0.070 0.383 0.467 1.018 -0.402 0.606 0.813 -14.968
-1.078 0.776 -0.085 0.231 0.407
-976.223 211.900
(0.096)*** (0.239)*** (0.364) (0.267)** (0.175)*** (2.345)***
(0.368)*** (0.532) (0.032)*** (0.089)** (0.155)***
Model (6) GNC APS (n = 459)
0.148 -0.091 0.333 0.457 0.170 2.451
0.090 -0.002 0.097 0.082 0.386
-1945.262 477.240
(0.053)*** (0.116) (0.187)* (0.105)*** (0.077)** (1.244)**
(0.300) (0.303) (0.027)*** (0.046)* (0.058)***
Model (7) GNC AMPS (n = 274)
0.578 1.053 -0.394 0.994 0.840 -15.817
-1.269 1.292
-990.257 180.030
(0.096)*** (0.244)*** (0.394) (0.263)*** (0.175)*** (2.397)***
(0.282)*** (0.637)**
Model (8) GNC APS (n = 459)
Notes: Coefficients are significant at the * 0.10, ** 0.05 and *** 0.01 levels. Robust standard errors are between parentheses log( pht ).
Log pseudo likelihood Wald c2
COASTAL CITY ISLAND CONTAINERS (log) SHIP-OWNERS (log) PORT-RELATED INDUSTRY (log) POPULATION (log) GDP PER CAPITA (log) GOVERNANCE CAPITAL UNIVERSITIES CONSTANT
Model (5) GNC AMPS (n = 459)
Table 3. Results for negative binomial estimates of the global network connectivity of AMPS and APS
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(0.027)*** (0.217) (0.292) (0.015)*** (0.042)*** (0.049)*** (0.030)** (0.067) (0.090) (0.118)* (0.049)** (0.825) -826.388 1107.34
0.090 -0.030 0.452 0.104 0.169 0.333 0.074 -0.049 0.137 0.221 0.099 -0.760
(0.021)***
0.043 (0.157) 0.329 (0.187)* 0.016 (0.011) 0.160 (0.028)*** 0.305 (0.041)*** 0.075 (0.023)*** 0.011 (0.055) -0.034 (0.078) 0.354 (0.093)*** 0.167 (0.050)*** -0.016 (0.630) -1363.552 1338.470
0.130
Model (10) Est. AMPS (n = 274) 0.088 (0.018)*** -0.265 (0.172) 0.215 (0.231) 0.004 (0.012) 0.067 (0.031)** 0.333 (0.046)*** 0.148 (0.042)*** -0.048 (0.095) 0.143 (0.147) 0.320 (0.092)*** 0.146 (0.054)*** 4.007 (1.014)*** -3308.175 657.82
Model (11) GNC AMPS (n = 459)
0.104 (0.026)*** 0.383 (0.318) 0.107 (0.319) 0.098 (0.027)*** 0.091 (0.044)** 0.333 (0.056)*** 0.130 (0.053)** -0.153 (0.123) 0.391 (0.195)** 0.268 (0.114)** 0.020 (0.070) 3.148 (1.323)** -1942.567 583.48
Model (12) GNC AMPS (n = 274)
Notes: Coefficients are significant at the * 0.10, ** 0.05 and *** 0.01 levels. Robust standard errors are between parentheses log( pht ).
ESTABLISHMENTS APS (log) GNC APS (log) COASTAL CITY ISLAND CONTAINERS (log) SHIP-OWNERS (log) PORT-RELATED INDUSTRY (log) POPULATION (log) GDP PER CAPITA (log) GOVERNANCE CAPITAL UNIVERSITIES CONSTANT Log pseudo likelihood Wald c2
Model (9) Est. AMPS (n = 459)
Table 4. Results for Negative Binomial estimates
2764 WOUTER JACOBS ET AL.
MARITIME PRODUCER SERVICES 2765
Furthermore, across all models, maritime localisation economies (SHIP-OWNERS and PORT-RELATED INDUSTRIES) remain significantly correlated with AMPS, but most port-specific variables (CONTAINERS, COASTAL CITY, ISLAND) are not. It is only in port cities that container throughput is correlated with either establishments or connectivity of AMPS (models 10 and 12). It is also striking that the effects of universities are much smaller in these models, probably because some AMPS need specialised localised knowledge, which is probably provided by APS. When we exclude this variable, UNIVERSITIES accounted for this effect, so the coefficients of previous models are probably somewhat overstated. To summarise, we confront these results with hypotheses 1.1, 1.2 and 2. We may conclude that spatial proximity towards transport flows is of less importance (reject H1.1) for the locations of AMPS than is the spatial proximity to customers (accept H1.2), such as shipowners and port-industrial firms. The location of AMPS establishments follows the logic of localisation economies in the sense that they tend to be located near their clients. While we observed that urbanisation externalities are much more important for APS than for AMPS, we also observed that the presence of port-related industry and shipowners is of significant importance for APS as well. This result suggests that APS, and not only AMPS, provide services to these sectors. We also demonstrated that specialised maritime services tend to agglomerate near other services (accept H2). However, we point out that proximity to customers seems to be more important than proximity to these advanced service providers.
5. Conclusions This study has examined the location and network connectivity of those advanced producer services that are specialised in providing
services to the maritime transport industry, in comparison with advanced producer services in general. It is clear that the specialisation of advanced producer services in the maritime transport industry does result in a different global urban hierarchy and network constellation when compared with the findings of conventional world city research. Cities such as Hamburg, Rotterdam, Houston and Piraeus, with large seaports or a maritime industrial profile, rank much higher. On the other hand, we have observed that the global urban network of AMPS, like that of APS generally, is dominated by London, Singapore and Hong Kong. While both south-east Asian cities do host major seaports and handle large volumes of container traffic, they also act as the region’s leading gateway for advanced services in general. Although London might be the leading city for advanced maritime services because of path dependency, it also tops the general world city ranking. Likewise, the sector specificity of advanced producer services does not explain why cities without a seaport rank highly in terms of AMPS establishments and connectivity as they do in the general world city rankings. This raises the question about what factors explain the location and urban network configuration of specialised advanced maritime producer services. Our analysis showed that the location of AMPS is largely determined by the presence of their clients, the shipowners and port-related industry, and not by the port throughput flows. These findings are in line with empirical findings on the location of knowledge-intensive business services that emphasise spatial proximity with client firms. The observed pattern explains why port cities such as Piraeus and Rotterdam rank highly and why pure load centres such as Shenzhen or Qingdao do not. Moreover, the presence of APS in general has a significant positive influence on the location of AMPS, which partly accounts for the high rankings of locations that do not have
2766 WOUTER JACOBS ET AL.
a seaport. The similarities between APS and AMPS in terms of GNC are due to the fact that GNC includes the service values of firm establishments which are themselves a measure of corporate hierarchy. These results are of importance to policymakers in port cities. Although investments in port expansion and related infrastructure might lead to increased cargo flows through the port, there is no evidence that this will lead to more added value to the regional economy in terms of specialised advanced services. Moreover, increased cargo flows, to the extent that they undermine urban amenity (Hall, 2007), may have a negative effect on the attraction of advanced services in general, which in turn are of significant importance for the presence of specialised advanced maritime services. Further investments in hardware infrastructure in seaports to accommodate future growth in commodity flows will therefore not necessarily lead to more advanced service provision in nearby urban centres, with the risk of these centres increasingly becoming locked-in. Instead, strategic policy in port cities should focus on attracting the head offices of shipowners and port-related industry in conjunction with advanced maritime producer services. Beyond the maritime sector, our findings on the sector specialisation of APS in a variety of spaces of control suggest that urban economic development policies need to pay close attention to the actual connections between APS and other sectors in particular places (see Wood, 2009; Wernerheim and Sharpe, 2003). Except in a handful of most dominant global cities which can afford to direct policies towards APS activities in general as an end in themselves, urban economic developers might instead pay more attention to the relationships between specific APS activities and the goods- and services-exporting sectors already in place. Finally, stronger links with research on KIBS, in particular on their regional interaction with client firms,
could yield useful new insights to the literature on world cities.
Notes 1. Twenty-three interviews were conducted with representatives of 18 different companies in the period between June 2008 and March 2009. Most of these interviews were conducted in Rotterdam by the lead author. Other interviews were conducted in London and Vancouver by the lead author and one of the co-authors. The companies were selected from the World Shipping Register database. The interviews were largely open with a semi-structured interview guide and lasted approximately 1 hour each. The purpose of the interviews was to explore which factors influence the locational behaviour of AMPS as well as the other factors influencing their business. 2. The database is compiled by World Shipping Register, a private company, and is based upon information provided by port authorities, shipping companies and classification societies. For more information, see their website: http://e-ships.net/. 3. The AMPS firms included in our study derived from the World Shipping Register are maritime law, P&I clubs, insurance brokers, classification societies, consultancy, surveyors and maritime organisations 4. In Taylor et al. (2002), only 100 firms and 315 cities are selected. It is now clear that this way of data gathering was not sufficient and the criteria of firm selection were too narrow (see Derudder et al., 2010). In our measurement of the world maritime network, we do not exclude start-up, small or single-location firms. The shipping industry is itself highly international, linking distant centres of supply and demand through the transport of goods. This implies that AMPS also need to have an international orientation when providing legal, financial and insurance products. Hence, we do not want to make a priori assumptions about what are ‘globalisation’ and ‘peripheral’ arenas or actors. 5. For a more elaborate discussion of the constitution of this interlocking network model, see Taylor (2001).
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6. Examples of very similar scoring methods are to be found in Verhetsel and Sel (2009) and Lüthi et al. (2010). 7. Taylor et al. (2002) point out that such data creation is imperfect because subjective errors may be introduced during the coding process. However, these problems will probably not lead to too much uncertainty in the data or analysis, as the scoring method is simple and the data are obtained from a large number of firms. 8. When we look only at the locations of headquarters, it appears that the top 10 global command centres are London, Singapore, Houston, New York, Chicago, Sydney, Tokyo Mumbai, Beijing and Oslo. Except for Houston, Singapore and New York, none of these cities is a large port in terms of throughputs. This suggests that some cities that host many AMPS establishments, such as Rotterdam and Pireaus, have relatively less command power.
Acknowledgements The authors would like to thank five anonymous referees for their useful comments on an earlier draft of this paper. The authors would also like to thank Ronald Wall for the use of his database. An earlier draft of this manuscript has been made available as Research Bulletin 342 on the Globalization and World Cities Research Network (GaWC) website. The usual caveats apply.
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