GaWC Research Bulletin 273 |
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From ‘Urban Competitiveness’ to ‘Urban Competition’Over past decades, urban studies and planning literature strongly acknowledges that cities compete in product markets, inward investments, firm establishments, population, tourists, hallmark events and government funding (Harvey, 1989; Lever and Turok, 1999). These inter-city ‘place wars’ (cf. Haider, 1992) in various ‘markets’ can take place at local, regional, national, continental, or even at global spatial scales (Gordon, 1999). In a world in which the role of physical distance is apparently declining (Cairncross, 2001; Friedman, 2007), cities have to work on their ‘competitiveness’ – or their ability to successfully compete with other cities in attracting firms and workers – in order to maintain or strengthen their position within in the urban hierarchy and hence increase their standards of living (Porter, 1990; Friedmann, 1995; Storper, 1997). On the face of it, competition between cities is at an all-time high and local authorities have to put ever more effort into enabling and maintaining their cities as attractive locations of residence. Nowadays, not only cost reduction of targeted populations (e.g., tax credits, project financing), but also the maintenance of amenities, physical infrastructure, and public transportation networks are pivotal to attract and retain firms and workers. As a result, city marketing and city branding have become ‘booming business’ (Paddison, 1993; Van de Berg and Braun, 1999), while budgets for place promotion are ever increasing (Hall and Hubbard, 1996; LeRoy, 2005). This increased interest in the concept of ‘urban competitiveness’ has led to a substantial number of urban ranking lists, in which cities are compared on the basis of their economic performance (Kresl and Singh, 1999; Lever, 1999), global connectivity (Beaverstock et al., 1999; Alderson and Beckfield, 2004) creativity and innovativeness (Florida, 2005), access and quality of services (Kaufman et al., 2005), or environmental sustainability (Dutzik et al., 2001). Hence, the benchmarking of cities has not only come to pass in the academia and commercial research, but also engrained within public policy and popular culture. Nowadays, local authorities increasingly publicise their relative competitive stance with that of other places (Malecki, 2002), while at the same time many newspapers and magazines (e.g., Fortune Magazine, Forbes, Money) seem to be obsessed with rankings of how cities compare to each other (McCann, 2004; Fisher, 2005). Despite the contemporary plethora of research and policy empirical evidence on urban competition remains relatively frail. Although most studies on urban competitiveness assume that cities compete vis-à-vis one another, little attention is paid to actually measuring the intensity of competition ‘between’ cities. Yet, in order to validate the urban competitiveness concept, it is important to understand to what extent cities compete and where this competition comes from. Shifting our focus from urban competitiveness to urban competition can enrich the literature on competitive cities by providing a method to deduce the strongest competitors from the rest, herewith loosening the stringent theoretical assumption that all cities compete against each other (see e.g., Haider, 1992; Markusen and Schrock, 2006). Along these lines, we can estimate the strength of competition received from such cities, identify clusters of competitive cities, and analyze the determinants of urban competition. Using insights from niche overlap theory (MacArthur and Levins, 1967; Field and McFarlane, 1968; Hannan and Freeman, 1977), this article aims to set out a straightforward, yet elegant, indicator to measure the intensity of competition between cities, based on the functional linkages that these cities have to other cities. More specifically, competition is operationalized as an attribute of a relationship between two cities, which can be regarded as the lowest unit of analysis at which competition can be measured (Sohn, 2004). In this article, it is argued that cities are in competition to the extent that they serve the same geographical market, for particular functions within the urban system. As there are many dimensions upon which cities can compete (Lever and Turok, 1999), we will predominantly focus on economic competition between cities in terms of attracting and retaining firms, which can be regarded as the one of the most elementary forms of urban competition1. Although we only focus on economic competition between cities, the proposed indicator in this article is not particularly limited to competition between cities and can without any difficulties be applied to other dimensions of urban competition and other forms of territorial competition, such as competition between regions (see e.g., Kitson et al., 2004). The remainder of this article is structured as follows: In Section 2, we focus on the conceptualization of economic competition between cities using different dimensions of urban systems outlined by Gordon (1999). Section 3 is devoted to how economic competition between cities can be measured, using niche overlap theory. Section 4 provides an application of this method to urban economic competition within the world city network. Finally, Section 5 discusses and concludes. Economic Competition between Cities in the Network EconomyChanging urban systems and increasing economic competition between citiesRecently, there has been increased interest in the role and nature of the dynamics of urban systems. In this literature, it is contended that the rise of the network economy is exemplified by recent advances in transport and communication technology, ongoing globalization, rising common markets, individualization of production and the growth of multinational firms – with significant impact on the spatial economic structure of cities and regions (see e.g., Batten, 1995; Anas et al., 1998). This, while simultaneously the monocentric city is transforming into a polycentric urban network, and where social and economic processes are taking place at ever larger geographical scales than those of the ‘traditional’ city itself (Kloosterman and Musterd, 2001; Van Oort et al., 2008). Hence, physical and administrative boundaries have become insufficient to characterize spatial entities, in which cities are no longer confined by territorial delineations, but by patterns of interaction (Friedmann, 1986). Hence, the competitiveness of cities is primarily determined by what flows through them instead of what is fixed within them (Castells, 1996; Derudder et al., 2007). Nowadays, cities are known to gain their privileged status in the global network economy, by virtue of their relational position in a ‘global space of flows’ (Castells, 1996), hereby shifting attention from traditional developments around internal urban properties, towards an understanding of external relations between cities, such as trade or business activities, with the implication that this knowledge will better define a city’s prosperity. Thus, ‘urban competitiveness’ should be considered as a ‘networked phenomenon’ (Beaverstock et al., 2002), dependent on a ‘society of cities’, in which ‘no city develops in isolation’ (Storper, 1997) - but forms part of system of cities (Berry, 1964), where interaction between cities is an essential component of the dynamics of urban systems (Rozenblat and Pumain, 2007). Thus, cities are relatively autonomous entities, whose evolution is highly influenced or disturbed by other cities in the interaction network (Pred, 1977), and where ‘urban development can no longer be understood without considering the networks and systems to which cities belong’ (Rozenblat and Pumain, 2007). Yet, when the networks of cities show an ever-increasing amount of overlap, it will be very likely that urban competition intensifies. When the cities expand the geographic scope of their markets, it is quite plausible that cities will increasingly serve the same geographic markets and thus start to function as substitutes to each other. Nowadays, cities compete to attract businesses, investments, and economic growth, whilst success in these endeavours is largely dependent on the successful exploitation of a city’s competitive advantage. However, in the network economy, even the sources of urban competitiveness have altered (Ordway, 2003), where in the ‘old economy’ competitiveness was mainly based on immobile and controllable factors such as resource availability, labour costs and the institutional context; in the new economy focus is on knowledge, unique skills and maximizing networking opportunities (Porter, 1990; Ordway, 2003). These new sources of competitiveness are not only more ‘footloose’ than their predecessors, but also less controllable by local authorities. According to Gordon (1999), this increased ‘footlooseness’ and uncontrollability of competitive assets further induces economic competition between cities2. Examples of increased urban competition are numerous, but the most well known are unquestionably the urban rat race between the large financial centres of London, New York, Paris and Tokyo (Sassen, 1991; Alderson and Beckfield, 2004), the fierce rivalry between cities in the European Union as a result of the creation of the common market (Lever, 1999), and the tax wars between American states to attract businesses (Enrich, 1996). However, geographic market overlap, itself does not necessarily constitute urban competition. On the contrary, if in an urban system the various cities specialize in different sectors or perform different organizational functions, they in fact complement each other by fulfilling different economic roles (Meijers, 2005; Van Oort et al., 2008). Two cities within the same urban system that each produce different goods or services, for which the other has an effective demand, can lead to exchange between the two places. For example, a city specialized in financial services, can provide these services to a city specialized in manufacturing, and vice versa. Hence, cities do not have to be specialized in all possible sectors, but can benefit from specializations elsewhere in the urban network (Meijers, 2005). Gordon (1999) mentions in this respect the delegation of routine administrative tasks of headquarters to places offering this blue-collar labour at lower pay rates. In such cases, in which networks (or action radiuses) of cities overlap, but where spatial labour division exists; cities are considered to be complementary (Beckham, 1973; Van Oort et al., 2007). Along these lines, two conditions for the existence of economic competition between cities can be identified, broadly covering different dimensions of urban systems distinguished by Gordon (1999): 1) geographic market overlap and 2) functional overlap. A more detailed explanation of these concepts is found in the next section. The Definition and Measurement of Urban CompetitionEnter Urban NicheIn order to formally define urban competition, we introduce the concept of urban niche. The theoretical concept of niche dates back to the first half of the 20th century and mainly concerned descriptive biological studies on the overlap of the habitats of different species (see e.g., Grinell, 1904; Elton, 1927)3. In its original connotation, a niche of species is defined as the set of environmental states in which a species thrives (Popielarz and Neal, 2007) and typically consists of the resources on which a species depends for its survival, such as its natural habitat from which it collects food. From the 1970s onwards, the concept of niche has been introduced in the social sciences, most notably in organization studies (Hannan and Freeman, 1977; Podolny et al., 1996) and social network analysis (Burt and Talmud, 1993; Sohn, 2004). The application of the niche concept in urban studies and spatial planning is relatively new (e.g., Popielarz and Neal, 2007; Neal, 2008). Analogous to its ecological and organizational counterpart, an urban niche can be regarded as the geographic market of a city in which it employs its economic activities, or in which it fulfills its urban functions. In other words, the urban niche can be decomposed into 1) a geographic niche (its market area) and 2) a functional niche (its activities). Dimension 1: Geographic Niche Overlap Cities are in competition to the extent they serve the same geographic market or have at least to a considerable extent overlapping geographic niches. As outlined in the previous section, geographic niche overlap does not necessarily have to be based on physical proximity. On the contrary, cities are in competition to the extent they have linkages related to the physical movement of goods, people, and services with similar cities. In other words, urban competition is defined by overlapping patterns of interaction and in this fashion, competition between cities can take place at various geographical scales, in which contending cities at a local scale do not necessarily have to be in competition at a national or international scale. For example, the Dutch cities Amsterdam and Rotterdam may compete locally, sharing the same hinterland (Randstad Holland), but may differ in their functional linkages to the rest of the world. Likewise, London and Paris may compete on a global scale, but not on a local scale. Hence, the same rules do not necessarily apply to all spatial scales (Martin, 1999)4. Dimension 2: Functional Niche Overlap Cities are in competition to the extent that they perform the same function within the respective urban system. In this sense, we can distinguish between 1) sectoral or product niche overlap and 2) organizational niche overlap. First of all, cities are in competition to the extent they are specialized in the same sectors or produce the same products. Competition is therefore conceptualized as the lack of inter-urban industrial differentiation, in which cities have overlapping sectoral and product niches. In this respect, Markusen and Schrock (2006) explicitly point to the mimicking of the success of legendary cases such as Silicon Valley and the Cambridge cluster, as drivers behind this overlap. Nowadays, most cities endeavour to be clusters of high-tech or creative industries. As a result, cities become less distinctive and competition intensifies. Secondly, cities are in competition to the extent they perform the same organizational function (Gordon, 1999). One can here think of the traditional division between white-collar and blue-collar work, but also of a division between headquarter and subsidiary (production plant) functions. It is in the absence of functional differentiation of labour, that these cities are more likely to be in competition. When both the geographic and functional niches of cities overlap, cities are in competition as they have to share the same ‘part of the pie’. In sum, it is argued here that cities that serve the same hinterland for the same urban functions are expected to compete for the acquisition of the same firms. In other words, cities that are not distinctive and are interdependent are most likely to be in competition (Neal, 2008). This theoretical framework closely follows the (holistic) Durkheimian view on ecological competition in which the characteristics of cities (hinterland, functions) drive urban competition (Durkheim, 1893; McKenzie, 1933). Cities are regarded as competitors if they function as substitutes to each other in the sense that similarity in market and function provokes competition. Measuring Urban Competition using Niche OverlapAlthough the existence of urban competition is widely recognized in the urban studies and spatial planning literature, few attempts have been made to explicitly measure the extent of competition between cities or within a sub-system of cities. By employing the two dimensions of urban competition discussed in the previous section, we will now turn to the measurement of urban competition. Over the years, several indicators of niche overlap have been developed in the field of statistical ecology to measure the intensity of competition between members of a population. Consider the following urban linkage structure for a particular function in Figure 1. In this urban system, for this particular urban function:
Figure 1 Functional linkages in a hypothetical urban system
In line with the theoretical concept of niche overlap, two cities are in competition to the extent they are linked to the same other cities for equal functions. In contrary to the artificial urban system above, real urban systems, usually differ in size, while simultaneously the functional linkages between cities can differ in intensity. Hence, in order to facilitate comparisons of the degree of urban competition between cities, the strength of linkages between two cities should be expressed as the relative dependency of a city on another city. For example, if city A has two linkages with city B and one linkage with city C, the geographical market (niche) of city A for the urban function under consideration consists for 2/3 of city B and for 1/3 of city C. Hence two cities are in competition to the extent they are relatively linked to the same other cities for the urban function under consideration. Over the years, several statistical approaches to formally measure overlap between members of a population have been developed. Amongst others, we find the alpha-coefficient, Euclidean distance, Manhattan distance, cosine, and standardized versions of these similarity indices (e.g., Bray-Curtis, Kulczynski, Gower metric)5. Notwithstanding their computational differences, a central element of these measures is that they look at the dissimilarity or ecological distance between the members of a given population. Approaching competition by looking at the absence of structural equivalence, competition is conceptualized as an attribute of the relationship between cities. Based on comparative research in ecological statistics (e.g., Bloom, 1981; Beals, 1984; Faith et al., 1987; McCune and Grace, 2002) and our interest in compositional overlap (rather than absolute overlap)6, we use in our study the relative Manhattan distance to measure ecological distance, or in our case the absence of overlap between the geographical markets of cities for a particular function. First, the relative Manhattan distance has the desired property that it uses value zero when there is a maximum niche overlap and a constant maximum value (e.g., 1) when there is no niche overlap (Beals, 1984). Second, the relative Manhattan distance shows a low discrepancy between the predicted and observed similarity. Third, the relative Manhattan distance has a robust linear relationship with true ecological distance when tested with simulated data (Faith et al., 1987). The relative Manhattan distance, also known as the relative Sørensen or relative city block distance, measures the relative distance or dissimilarity in niche between two species i and j for a particular urban function k, here expressed in the non-overlapping of geographical markets between two cities i and j. More formally (1):
in which aih,k is the strength of the urban linkage (e.g., the number of business interactions) between city i and h for urban function k, and aih,k the strength of the urban linkages between city j and city h for urban function k. Linkages between city i and j are excluded, as well as linkages that remain within a city in order to measure genuine competition between the cities under consideration and not urban complementarities. The distance measure is relative because it gives the absolute difference between the cities divided by their absolute sum. In other words, by standardizing the absolute difference to sample totals, the total non-overlap of the geographical markets of the two cities i and j is converted into a percentage non-overlap of the geographical markets of two cities. This allows comparison of the cities by the relative distribution of urban linkages across space (Legendre and Legendre, 1998). The degree of similarity between two cities or the competition coefficient can then be expressed as (3):
The competition coefficient COMPETITIONijk typically ranges between 0 and 1. If the competition coefficient equals zero, the geographical markets of cities i and j are totally different and the intensity of competition between the two cities is at minimum. If the competition coefficient equals one, the geographical markets of cities i and j completely overlap and the intensity of competition between the two cities is at maximum. Equations (1)-(3) present a method to estimate the intensity of competition between cities for one particular urban function. This function can range from global command center in the advanced producer services sector (Taylor, 1999; see below) to the production site in the textiles and apparel commodity chain. The total intensity of competition between two cities for a number of urban functions can be estimated by weighting the competition coefficients for the different urban functions k with the overall importance of these urban functions in the two cities (see Wall et al., 2008). Application: Urban Competition in the World city networkEmpirical Setting: Producer Services in the World City NetworkIn order to show how the described techniques in the previous section can be utilized, we use the case of economic competition between leading world cities (cf. Taylor, 1999) as a test case. Literature on world cities typically identifies the multinational enterprise as a central agent in the generation of the world city system and generally their economic and political power symbolizes the predicaments of globalization. These multinational corporations are the primary movers and shapers of the global economy because they have the ability to control and coordinate production network across different countries, so as to take advantage of geographical differences in factor distributions and to switch and re-switch resources globally. In particular, attention is drawn here to the advanced producer services firms (financial and business services) ‘as command points in the organisation of the world economy’ (Sassen, 1991) and ‘the key agents of global city formation’ (Taylor, 2005). In the world cities literature, the assumption is made that the corporate networks of globally operating advanced business services firms translate into knowledge based linkages between cities in which these offices are established (Pain, 2007). Moreover, the corporate network of advanced producer services shows a high correlation with the worldwide network of FDI and trade (Wall et al., 2007). Hence, the competitiveness of world cities is generated through their connections to other cities. As Beaverstock et al. (2002: 111) rightly note: ‘the prosperity of successful world cities is due to their privileged location at the intersection of all that matters in global economic terms - flows of people, goods, capital and ideas’. However, if two cities have exactly the same linkage structure, in the sense that they command the same other cities, this means that the same ‘external’ knowledge can be obtained in both places. World cities linked to the same other cities for advanced producer services are in competition as they serve the same ‘hinterworld’ (cf. Taylor, 2001), draw on the same resources and are hence substitutable. Note that in accordance with our theoretical framework, we focus here on geographic market overlap for the function as global command centre (organizational niche) for advanced producer services (sectoral niche). Urban competition between the global financial centres has been subject of a large literature in the field of geography and urban studies (see e.g., Sassen, 1991; Gordon, 1999; Beaverstock et al., 2002). However, presently no systematic and objective measurement of this competition has been provided. In this section, we exemplify how urban competition can be measured by focusing on economic competition in advanced producer services between 20 world cities (see Table 1). These cities are classified as world cities based on their level of advanced producer services and the number of commanding linkages they have in the corporate inter-city network of advanced producer services (Beaverstock et al., 1999 for the classification). We acknowledge that this scope is rather limited (see also Robinson, 2005). However, the major purpose of this example is to show how the competition coefficient estimation described in the previous section can be utilized. In a subsequent article (Wall et al., 2008), we focus more elaborately on economic competition between a larger sample of world cities across more economic sectors, looking both at commanding (forward) and subsidiary (backward) corporate relations. DataIn order to measure urban competition, we use a dataset on corporate networks of global advanced producer services firms, which has been compiled using Fortune 500, Lexis-Nexus and Reach sources, containing annual reports of large companies. From these sources, information on the top 100 headquarters based on the Fortune 500 listing were selected, because these claim disproportionate shares of revenue. For instance the top 100 of the Fortune 500 firms in 2005 claimed a share of 27% of OECD revenue, while it took the remaining 400 firms to claim an additional 29% (Wall et al., 2007). Furthermore, these firms held over 50% of the total revenue and 40% of the employment of all 500 firms. All subsidiaries of these headquarters were found and classified into five orders of shareholder relations, starting with headquarter to first subsidiary, first subsidiary to second subsidiary, and so fourth. Next, only the advanced producer services that were an origin of a commanding relation were selected and the city location of every firm (headquarter and subsidiary) was identified, where smaller cities within a discontinuity break of 25 km were added to the proximate major city. Figure 2: The Corporate Inter-City Network of Advanced Producer Services
Source: Wall et al. (2007) The derived subset consists of 3150 commanding relations between advanced producer services headquarters and their subsidiaries across 684 different cities. By geographically aggregating the data to the city level, a corporate inter-city network of advanced producer services could be obtained (see Figure 2). From this network it appears that cities in North America and Europe have the strongest advanced producer service relationships with the world. This is not surprising since much of their manufacturing activities take place in Third World countries, but are financed, insured and facilitated by producer services headquarters within these continents (Wall et al., 2007). The 20 world cities in our sample and their outward connectivity to the corporate inter-city network advanced producer services are listed in Table 1. These cities account for over 2/3 of the total number of commanding linkages in the corporate inter-city network of advanced producer services. Most strikingly, the four most prominent cities in the network (New York, London, Paris and Zurich) claim over 1/3 of the total number of linkages. Table 1: Connectivity of World Cities to the Corporate Inter-City Network of Advanced Producer Services
Urban Competition in Advanced Producer Services at the International ScaleIn our analysis, we focus on urban competition between 20 of the most prominent cities in the world city network, by examining to what extent their linkage pattern of commanding relations to all other cities in this network are similar. Applying the competition measure described in section 3, a matrix of the intensity of competition between 20 financial centres in the world city network was obtained using the UCINET software (Borgatti et al., 2002). Overall, the competition coefficient ranged between 0% (e.g., Berlin – Osaka) and 41% (Frankfurt – Zurich). Figure 3 provides a graphical representation of this matrix in terms of a network diagram. The network diagram consists of nodes (vertices) and linkages (edges). The nodes in the network represent the different world cities, where the colour and shape of the node represent the continent on which the city is situated (Europe, North America, or Asia). The size of the nodes represents the position of a city in the corporate inter-city network of advanced producer services based on the total number of outward linkages a city has. This position can range from primary world city (London, New York, Paris) to world cities that have relatively few commanding relations to other cities (Hong Kong, Madrid, Toronto). Figure 3: Competition in the Corporate Inter-City Network of Advanced Producer Services
The linkages between the cities in the network diagram represent the nature of the relationship between cities in the world city network, where the colours of the linkages represent the intensity of competition between the different cities. If there is no linkage drawn between two cities (e.g., Toronto-Madrid), the competition coefficient is lower than 5%.. This means that there is hardly any geographical market overlap between the two cities for the urban function under consideration. In other words, both cities command totally different cities, in the inter-city advanced producer services network. A grey inter-city linkage (e.g., Munich-Amsterdam) indicates that the competition coefficient ranges between 5% and 15%, which means that degree of geographic market overlap between the two cities ranges between low to average for this urban function. A yellow inter-city linkage (e.g., London-Tokyo) indicates an average degree of geographical market overlap between the two cities with a competition coefficient that lies between 15% and 25%. A red linkage (e.g., Amsterdam-Brussels), signifies that the competition coefficient ranges between 25% and 35%, which indicates an average to strong degree of geographical market overlap. Finally, a purple inter-city linkage (e.g., Frankfurt-Paris) indicates that the competition coefficient is over 35%, which points toward a strong degree of geographic market overlap for advanced producer services between these two cities. Both cities command to a large extent similar cities. For this reason, the intensity of competition between these cities is fiercest. Looking at the overall pattern of competitive relations, a number of empirical observations can be made. First of all, competition between cities for advanced producer services has a strong geographical dimension. The intensity of competition between cities that are geographically proximate tends to be stronger than competition between cities that are geographically distant. In general, the intensity of competition between cities situated on different continents is low to average. Moreover, if there is a strong intensity of competition between cities situated on different continents, there is in most instances also at least one primary world city (London or New York) involved. This is not surprising as these primary world cities serve a more diverse geographical market with a larger geographical scope than other cities in the world city network (see also Derudder and Witlox, 2008). Applying a hierarchical cluster analysis (Johnson, 1967) on the competition coefficients, two major clusters of contending cities can be identified (see Figure 4), namely (1) Northern Transatlantic Seaboard (London, Frankfurt, Zurich, Paris, Amsterdam, Brussels, New York) and (2) Pacific Asia (Tokyo, Osaka, Hong Kong, Singapore). From this, it can be inferred that the intensity of competition that Tokyo receives from the other large world cities (London, New York, Paris) as command centre (of advanced producer services) is rather limited. Whereas Tokyo’s commanding inter-city relations are primarily directed to Asian cities (for over 70%), the commanding inter-city relations of New York and Paris are predominantly directed at European and North American cities. London is in this respect the most globally oriented city, as it is not only strongly connected to cities in Europe and North America, but also to cities in Pacific Asia (particularly Hong Kong and Singapore). These findings once more stress that not all world cities serve the same ‘hinterworld’ (cf. Taylor, 2001; Taylor and Walker, 2004). Figure 4: Hierarchical Cluster Analysis of Competition between World Cities
Moreover, it can be observed that competition is fiercer between cities in the top of the urban hierarchy. Smaller world cities, such as Atlanta, Berlin, Toronto, Dallas, and Madrid face relatively little economic competition from the other world cities in the sample. This can be explained by the fact that the commanding relations of these cities have a primarily regional scope. In other words, such cities have a relatively ‘regionally oriented’ hinterworld. For example, over two thirds of the commanding relations of Madrid remains within Southern Europe and goes to cities like Barcelona and Milan. Likewise, over 90% of the commanding linkages of Toronto do not leave Canada. This is in line with the research conducted by Derudder and Witlox (2008), who equally find that the inter-city relations of the most important world cities in terms of network connectivity have predominantly a global scope, while the inter-city relations of the less-well connected cities in the world city network have a more regional scope. Discussion and Directions of Further ResearchUsing niche overlap theory, this article introduces an indicator to measure the intensity of competition between pairs of cities, which can be considered to be the most fine-grained level at which competition can be measured. Cities are considered to be in competition to the extent they are linked to the same other cities, pending the same functions. Using the individual competition coefficients as building blocks, it is possible to derive the amount of competition a city receives from all other cities, identify clusters of competing cities, and define the extent of markets of cities. In addition, the competition coefficient can easily show that not all cities are in competition with each other and that some cities receive more competition than other cities. In this article, we used the example of competition between (commanding) global financial centres using the corporate inter-city network of advanced producer services. Naturally, this is only a small amount of the competition the cities in this network receive from all other cities and preferably the intensity of competition between cities should be measured across a full spectrum of urban functions. Nonetheless, when urban niches are fully specified in terms of geography and functions, the resulting niche overlap measure can accurately indicate the amount of competition a city receives from the other cities in the urban network. The main limitations of measuring urban competition on the basis of flows between cities are the computational demands when including many dimensions of urban competition and – not surprisingly – data availability. First of all, the proposed indicator of urban competition can be further improved by including multiple dimensions of urban competition and by introducing an asymmetric competition coefficient. Such an asymmetric competition coefficient should take into account that city A does not necessarily receive as much competition from city B, as city B receives from city A (see e.g., Pianka, 1983; Sohn, 2004). In the current measure, city size is neutralized by using proportions when estimating the competition coefficient and hence the coefficient is unable to detect unequal patterns of niche overlap. However, most importantly, the present lack of spatially detailed data on economic linkages hampers current empirical research on measuring accurately the intensity of competition between cities. Although over the past 10 years, we have witnessed an increasing availability of geo-coded datasets, the amount of urban network data that has a global or continental coverage is still rather limited (Taylor, 1999). Notable exceptions are corporate networks (e.g., Beaverstock et al., 1999; Alderson and Beckfield, 2004; Wall et al., 2007) and airline data (Derudder et al., 2008). Yet, there is still a lack of spatially and product detailed trade data, which measures tangible economic relations between cities. Future research should not only concentrate on further fine-graining the measurement of urban networks in general and urban competition in particular, but should also invest in spatially detailed data on connections between cities. Furthermore, the competition coefficient is not meant to replace other, more qualitative accounts, of urban competitiveness and urban competition, but rather, the competition coefficient should be perceived as complementary to qualitative approaches to study competition between cities in the sense they should reinforce each other. For example, having identified the most important competitors of a particular city, it becomes easier for e.g. urban planners to recognize which aspects of urban competitiveness a city should concentrate on, in order to surpass its competitors. This clears the path to more goal-directed and effective strategic urban planning and policy making with regards to urban competitiveness (Ho, 2000, Van Dijk, 2006). In this sense planning and policy becomes manifested in an interactive understanding between cities, and not only within cities. On a similar note, city rankings may still be useful as indicators of urban competitiveness. Yet, it is important to recognize that not all cities are in competition and for that reason should not all put be put in the same ranking list. Future research should not only measure the intensity of competition between cities, but also examine determinants of competition. For instance, are cities of similar size and close proximity more likely to be in competition? Besides giving an indication of the intensity of competition between cities, (aggregated) competition coefficients can also be utilized in a regression framework to link competition to urban performance. Accordingly, the focus shifts from urban competition as an independent variable (“causes of urban competition”) to urban competition as an independent variable (“consequence of urban competition”). Naturally, new questions arise. How does urban competition affect urban performance? Are cities that receive less competition from other cities more likely to grow and strengthen their position within the urban system? 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NOTES* Martijn Burger, corresponding author: Erasmus School of Economics, Department of Applied Economics, Erasmus University Rotterdam and ERIM. Email: mburger@few.eur.nl, URL: http://www.mjburger.net ** Ronald Wall, Erasmus School of Economics, Department of Applied Economics, Erasmus University Rotterdam. Email: wall@few.eur.nl *** Bert van der Knaap, Erasmus School of Economics, Department of Applied Economics and ERIM, Erasmus University Rotterdam. Email: vanderknaap@few.eur.nl, URL: http://people.few.eur.nl/vanderknaap 1. In particular, attention is here drawn to firms in basic sectors (manufacturing, wholesale and producer services), which have a non-local export market and are according to Economic Base Theory (Blumenfeld, 1955) considered most important for local economic growth. 2. On a more general note, it can also be argued that cities are in competition because firms are in competition and firms have to make an effort to warrant a favourable balance between costs and benefits in their theoretically free choice of location (cf. Madig, 2004). In other words, urban competition can be perceived as an unintended consequence of goal-directed behaviour of firms. As some locations of residence yield potentially more benefits (in terms of tax benefits, project financing, accessibility, available human capital, access to knowledge) and fewer costs (in terms of housing prices, congestion) than other locations, urban competition emerges, whether cities like it or not (see also Ho, 2000). 3. An overview of the history of the niche concept in the ecological and social sciences can be found in Popielarz and Neal (2007). 4. In other words, one can speak here of non-perfect aggregation across spatial scales. 5. See McCune and Grace (2002) for an overview of all basic measures of niche overlap. 6. For this reason, we do not use the also recommended Bray-Curtis or Kulczynski coefficient to measure ecological distance. However, from a mathematical point of view, both the Bray-Curtis and the Kulczynski coefficient equal the Relativized Manhattan distance when standardized to equal totals (see Faith et al., 1987).
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