GaWC Research Bulletin 98

Gateways into GaWC

This Research Bulletin has been published in A Borsdorf and C Parnreiter (eds) (2003) International Research on Metropolises: Milestones and Frontiers. Wien: Verlag der Österreichischen Akademie der Wissenschaften, 29-42.

Please refer to the published version when quoting the paper.

(Z)

Generating Data for Research on Cities in Globalization

P.J. Taylor

Part I THE NEED TO GENERATE DATA

1. Three Classic Evidential Blemishes

I begin by highlighting one small part of three very important books that discuss cities and globalization. Each selection consists of a quite surprising evidential blemish. These otherwise astute commentators on things global each come a cropper when they try and illustrate how world cities constitute a geography of globalization.

Manuel Castells' (1996 and 2000(2^nd ed)) Network Society is possibly the most influential text for providing a spatial framework for world city studies. Following Sassen's (1991 and 2001(2^nd ed)) charactization of global cities as advanced financial and business service centers, Castells' (1996, 415) describes world cities as the 'most direct illustration' of worldwide nodes and hubs in his space of flows. Although it was not part of Castells' (1996, 26) brief to engage in new data generation, his prime use of data to show his space of flows is incredibly broad grained. The evidence he provides on these nodes and hubs is a set of worldwide information from Federal Express originally analyzed by Michelson & Wheeler (1994, 382-3). It consists of one origin (USA) and just nine destinations only 3 of which are actually cities. While we might go along with Castells' conceptualization of a global space of flows we can but note that the evidence he marshals is mightily unimpressive.

Peter Dicken's (1998 (3^rd ed)) Global Shift is the most influential geographical text on contemporary globalization. The value of this key textbook lies to a large extent in its bringing together reams of evidence to describe contemporary transformations in the world economy. But not so with world cities, here he provides minimal evidence. This consists of a diagram that is intended 'to give an impression of a connected network of cities' (p. 209) because, we are told, 'the links shown are diagrammatic only'. He bases his diagram on John Friedmann's (1986) sketch of a 'world city hierarchy' and produces some very odd linkages. For instance, the route from Dusseldorf to London goes first to Brussels and then on to Paris before finally reaching its destination. Why there should be such a three-step connection in this electronic communication age is not explained. But remember, these are only 'diagrammatic links', again mightily unimpressive fare even for just giving an 'impression' of a world city network.

The Open University texts in the Understanding Cities series are the best textbooks available for urban studies and the key book is Unsettling Cities (Allen, Massey and Pryke, 1999). But systematically marshaling empirical evidence is not one of their fortes. The book begins with a chapter on 'cities of connection and disconnection' (Amin and Graham, 1999) that encourages us to think 'relationally' about cities but provides only evidence in the form of a 'virtual single office' linking together just six cities (p. 11). A later chapter by Allen (1999) provides the most thorough recent discussion of power among world cities, but he backs up his argument with figures (Figures 5.3 and 5.5) that are again drawn from Friedmann (1986). The bizarre outcome is that this discussion of power excludes some of the most rapidly globalizing cities of the 1990s - Moscow, Beijing, Shanghai - simply because it uses a 'world city hierarchy' (purportedly) describing the situation before the end of the Cold War. Once again, with respect to evidence, this book is mightily unimpressive.

Two of the three evidential blemish examples use Friedmann's (1986) diagram of the 'world city hierarchy' and therefore a brief note on its veracity is appropriate here. First, the fact that Friedmann's model was immediately criticized for its paucity of supporting evidence (Korff, 1987) seems to have been generally overlooked due to the pedagogic utility of his world city hierarchy diagram. Second, according to Abu-Lughod (1989, 32), the origin of this mapping was 'a base map provided by Japanese Airlines'. No doubt this explains why the three cities with most connections in the original diagram are in the Pacific Rim (Tokyo, Singapore and Los Angeles) and not in the North Atlantic region (e.g. New York and London). That Friedmann's (1986) preliminary framework for world city studies should have had such longevity, as shown above, is clearly an indictment of the literature's lack of focus on measuring inter-city relations.

Conclusion: even in the best books, there seems to be an inherent problem with saying something soundly empirical about inter-city relations at the global scale (Taylor, 1999).

The concept of KBE envisions a link between knowledge 'creators' and 'users', the actors whose ingenious works restructure our contemporary 'global economy'. They comprise the quaternary sector of professionals, including R&D scientists and producers' service experts, and the quinary sector of decision-makers, who control and lead the globally linked KBE. The quaternary and the quinary sectors concur with Florida's (2004) three Ts: talent, namely the creative human capital reflected by the number of people engaged in creative occupations; technology, which measures the level of innovative activity associated with high tech concentration; and tolerance, reflecting the quality of a place as liberal and tolerant, one that is capable of attracting skilled people able to generate new ideas. Atop the creative class and the quaternary and the quinary sectors perch Beaverstock et al.'s (2004) global super-rich and Sklair's (1991; transnational capitalist class (TCC), a wealthy elite whose foremost objective is to secure the smooth functioning of the global economy.

2. The Culprit: Stat-istics

The common term for social data is 'statistics' a term that derives directly from the word state. This is, of course, no accident: large-scale data collection on human activities has its origins in state needs and continues to be dominated by states: hence my portrayal of it as state-istics.

Unlike the natural sciences, within social science there is little or no 'big science' where very large sums of money are committed to solving theoretical problems. The latter enables natural scientists to concentrate on developing measurements specifically designed for their theoretical purposes. In social science, most data that is collected relates to small-scale cumulative scientific activity. To get an evidential handle on big issues, researchers normally rely on the statistics that are available, that is to say, already collected. Collection is carried out usually by a state agency for the particular needs of government policy, not, of course, for social science research. But the problem is much more than the possibility of having to use unsuitable data. Basing 'big social science' on state-istics means that the state defines the basic dimensions of the leading edge 'macro' social research and therefore the framework within which most social research is conducted. This embedded statism within most very large-scale social data sets is a major reason why the information we want for describing inter-city relations is not available.Three characteristics of urban studies stem from embedded statism.

First, there is the dominance of attribute measures over relational measures in social research. Measurement can take one of two forms: attribute measures on objects or relational measures between objects. The needs of social science and the state diverge at this very starting point. All theory about human social activities is basically about relations between individuals, groups and other human collectivities. Therefore the data need is for relational measures, of flows, connections, linkages and other less tangible relations. The prime concern of the state for data has always been accounting, taking stock, finding out numbers of phenomenon within its territory or parts thereof. Thus the vast majority of statistics are lists of attributes by place as any quick browse through a census volume will confirm. Cities are the most important places in which census counts are made, aggregated, and reported. In this process cities are effectively de-networked: they are stat-istically treated as a bounded sub-territory when the essence of all cities is their unbounded connections to other cities.

Second, there is no transnational scale in stat-istics. There are international statistics compiled by the UN from national censuses and which therefore project the attribute proclivity to the larger scale. These data are largely used for comparisons between countries but there are some important relational data sets at this scale because the states have an interest in traverses across their boundaries. However such trade and migration statistics totally neglect cities. Thus, we can find information on certain relations between, say France and the UK, but there is nothing on relations between London and Paris, or between Manchester and Lyon. This information default is made clear by thinking of 'Main Street, World-Economy', the multifarious connections linking London and New York. This massive concentration of flows of information across the North Atlantic is simple not picked up in any 'official statistics'. There is no state that needs such information and therefore there is no publicly available information about the most important inter-city relation - the most significant geographical connection - in the world today.

Third, there is a great temptation to interpret rankings as hierarchies. Since data can be compiled from official statistics on cities to provide quantities of attributes - population totals, employment sector totals, headquarter totals, etc. - cities can be ordered by size in various ways that may look like an urban hierarchy. Of course, it is no such thing: hierarchies can only be defined as relations between objects, mere ranking of cities says nothing about relations between cities. But since this is the only type of evidence available, such rankings have bolstered the original Friedmann hypothesis that world cities form a hierarchy. In a sense it is the easy way out of the data problem: attribute evidence is combined with a simply transfer of the traditional 'national city hierarchies' model up a scale to create a world city hierarchy. The widespread acceptance of such a patterning of world cities is a classic example of how misguided thinking can fill a huge evidential gap. Obviously rankings by attribute, with or without a hierarchical model, are no solution to the lack of information on inter-city relations.

In conclusion: for the large-scale study of the inter-relations between world cities there is no alternative but to generate your own data.

Part II STUMBLING TOWARDS A SOLUTION

The Globalization and World Cities (GaWC) Study Group and Network was set up to contribute to solving the world city data problem. There are two strands to this work: qualitative studies that focus on a small number of cities to assess their relations (i.e. not to compare attributes of cities), and quantitative studies that attempt to measure the whole network (i.e. a global urban analysis). Here I focus on the latter. Normally this is presented as a logical progression from model to measurement but in practice it was an iterative, trial and error, process starting from quite modest beginnings. There are four main stages to reaching the point where we can measure the world city network.

Initial fumblings involved experiments with several methodologies detailed in Beaverstock et al. (2000). Initially the most promising methodology for producing large-scale quantitative relational data for cities involved place content analysis of the financial pages of city newspapers (Taylor, 1997). Although having the advantage of sources that could produce time series data, in fact the method produced rather general data that was too event orientated. Understanding world city network formation required measures that covered the everyday reproduction of the network.

The London project had the task of simply finding out the business connections between London and other world cities. Using Sassen's identification of advanced producer services as critical to identifying the 'global city', we selected London-located service firms and used their web sites to record other cities in which they had offices. An initial problem was simply to decide with which cities we would trace London's connections. Since there were no rosters of world cities in the literature we had to produce our own. This was done using attribute measures of numbers, sizes and functions of offices within cities. A roster of 55 cities was created (Beaverstock et al., 1999) and the distribution of 72 London-located service firms across these cities was recorded. From this data we were able to compute measures of service office connections between London and the other 54 world cities. The three cities with the highest level of service connection to London were New York, Paris and Hong Kong neatly showing the city's global range of connectivity across the three main globalization arenas (northern America, western Europe and Pacific Asia).

The experimental data set was constructed from the London project data. It was readily apparent that any business service firm with global pretensions had an office in London and therefore our 'London-located firms' were actually a reasonable sample of global service firms. We decided, therefore, to create a global service matrix involving the 55 world cities and the 46 London-located service firms with the widest spread of offices. Within the matrix we indicated the importance of an office on a scale from 0 (no presence) to 3 (headquarters). Thus we had a 55 (cities) x 46 (firms) matrix of office values. Each column of the matrix showed a firm's distribution of offices across cities, each row showed a city's service mix across firms. This enabled up to conduct the first multivariate analyses of a world cities to show patterns of firms with similar global location strategies and cities with similar mixes of service firms (Taylor and Walker, 2001).

The interlocking network model was devised to make sense of the experimental data and analysis. What were we actually measuring and how did it hold together? It seemed reasonable to assume that a pair of cities sharing a lot of the same service firms had more flows than a pair of cities sharing few firms but how could this be modeled? The key point in this assumption is that it is the firms that are creating the flows and therefore it is they who define the world city network. This requires a three-level network model: as well as nodal level (city) and network level there needs to be a sub-nodal level for the firms as agents. This is precisely what the interlocking network model provides with firms in the role of 'interlockers' between cities to produce the connectivities that define the network. Specification of this model was the breakthrough in this research (see Taylor, 2001). There were many references to inter-city model forms in the literature - urban systems, city networks, urban hierarchies - but there was no precise specification upon which measurements could be devised. In contrast the interlocking model was specified as the service values matrix (firms x cities) from which formulae for connectivity measures were derived.

Large-scale customized data collection was carried out as required by the interlocking network model. In fact the new matrix was an improved version of the initial experimental data: much more comprehensive covering 100 firms and 316 cities. Size is important because all connectivity measures are derived as aggregates across firms to iron our individual firm idiosyncrasies. But the key point is that now we have a conceptual basis upon which to probe inter-city relations quantitatively.

In conclusion: these data form the basis of all our current work on the world city network and therefore the methodology will now be described in detail (from Taylor et al., 2002).

Part III THE BIG MEASUREMENT EXERCISE

The specification is clear on data needs: the starting point for world city network analysis is a matrix V where v_ij is the 'service value' provided by firm j in city i (Taylor, 2001, page 186). There are three stages. First, the process of gathering the appropriate information is described. The method employed is described as 'scavenging' since any information that can inform the data needs is recorded. Second, the conversion of this multifarious information into comparable data across firms is described. The data are produced by devising a uniform scale of service value that is then applied separately to the specific information gathered on each firm. Third, this data are used to derive specific measures of cities in the world city network. Measures of total service provision in cities and the global connectedness of cities are both computed.

Information gathering about private corporations on a large scale is usually fraught with difficulty. The most obvious problem is confidentiality since, as a general rule, no corporation wants to reveal its strategies, including location decisions, to its competitors. However, advanced producer service firms are the focus of the information gathering here and they depart from this rule in one crucial respect. These firms provide knowledge-based (expert/profession/creative) services to other corporations to facilitate their business activities. Such corporate service firms have benefited immensely from the technological advances in computing and communications that have allowed them to broaden the geographical distribution of their service provision. For instance, law firms have been traditionally associated with a particular city and its local client base - a 'New York law firm', a 'Boston law firm' and so on - but under conditions of contemporary globalization a few firms have chosen to pursue a strategy of providing legal services across the world. In such a situation, locational strategy is an integral part of the firm's public marketing and recruitment policies. For instance, new potential clients from around the world will want to know the geographical range of the services on offer. Also, since these are knowledge-based firms, a global scope is very obviously an important advantage in signing up the best of the next generation of key workers. Hence among producer service firms, locational strategy is perforce quite transparent. Typically the web sites of such firms provide an option to select 'location' giving addresses of offices, often with a world map of their distribution to emphasis their global presence. Advantage is taken of this transparency for information gathering.

The starting point is to find basic information on where major service firms are present in order to select those firms pursuing a global strategy. Using experience from previous experiments in this field, a firm is deemed to be pursuing a global locational strategy when it has offices in at least 15 different cities including one or more cities in each of the prime globalization arenas: northern America, western Europe and Pacific Asia. Having met this condition, selection of firms is quite pragmatic. Starting with rankings showing the top firms in different sectors, firms are selected on the basis of the availability of information on their office network. In addition, since one obvious research interest is comparison across different service sectors, firms are only included in the data in sectors for which at least ten firms can be identified. Using these criteria, 18 accountancy firms, 15 advertising firms, 23 banking/finance firms, 11 insurance firms, 16 law firms, and 17 management consultancy firms have been selected. These constitute the "GaWC 100", the global service firms at the heart of this research exercise

Although the starting point is firms, the information collected defines networks. Many global service firms exist as 'groups'. For instance, in accountancy there are alliances of medium-sized firms constituted as networks in order to compete globally with the very large firms that lead this sector. In other sectors, take-over activity has led to a corporate structure of core firm plus subsidiaries with the latter providing distinctive services as an additional dimension to the main service provision, for instance, as the investment arm of a mainstream bank. Sometimes the latter structure straddles the sector boundary such as banks owning insurance companies. Such firms are treated here as a single network and allocated to the core company's sector. Basically the networks are defined by the world-wide service contacts provided for clients on a firm's web site. Thus the GaWC 100 constitutes a large sample of global service networks.

A few of the larger firms have branches in many hundreds, even thousands, of cities and towns. The data collection has been restricted to the more important cities for two reasons. The first is analytical: the more cities included the more sparse the final matrix will become with nearly all the GaWC 100 networks not present in the smaller cities and towns. The second is theoretical: the interest is in the more important inter-city relations, ultimately the world city network. Nevertheless, it is also important not to omit any possible significant node in the world city network so that a relatively large number of cities need to be selected. Additionally, it is necessary to ensure that all continents are reasonably represented. The final selection of cities is based upon previous experiments and includes the capital cities of all but the smallest states plus numerous other cities of economic importance. The resulting set consists of 316 cities. It is these cities that are used in recording information on the global service networks of firms.

In selecting the cities to be included in the data collection the main concern has been to avoid excluding any city that may have important global service functions. Thus we have selected many more cities than we expect to use in subsequent detailed analysis of the data. The final selection of cities is based upon previous experiments and includes the capital cities of all but the smallest states plus numerous other cities of economic importance from across all continents. The resulting set consists of 316 cities. This is, of course, a very large number of cities and we are satisfied that it is a large enough selection to ensure no major omissions. It is these cities that are used in recording information on the global service networks of firms.

Selecting firms and cities is relatively straightforward, problems arise when attempts are made to gather information on the importance of a given city to a firm's global service provision. There is no simple, consistent set of information available across firms. The prime sources of information are web sites and everyone is different among the 100 firms. It is necessary to scavenge all possible relevant available information, firm by firm, from these sites supplemented by material from any other sources available such as annual reports. For each firm, two types of information have been gathered. First, information about the size of a firm's presence in a city is obtained. Ideally, information on the number of professional practitioners listed as working in the firm's office in a given city is needed. Such information is widely available for law firms but is relatively uncommon in other sectors. Here other information has to be used such as the number of offices the firm has in a city. Second, the extra-locational functions of a firm's office in a city are recorded. Headquarter functions are the obvious example but other features like subsidiary HQs and regional offices are recorded. Any information that informs these two features of a firm's presence in a city is collected in this scavenger method of information gathering. The end result is that for each of the 100 firms, information is available to create service values in each of 316 cities.

Data production based upon the scavenger method confronts a major problem: the type and amount of information varies immensely across the firms. For instance, some firms have geographical jurisdictions of offices that are 'regional' (transnational) in scope, others have 'national offices, or there may be 'area offices' or 'division offices' with wide variation in the geographical meaning of each category. In addition, many firms will have no specified geographical jurisdictions for any of their offices. Some information is quite straightforward as when a hierarchical arrangement is shown through contact with an office being routed through an office in another city. But it is more common to find a confusing range of information indicating the special importance of an office. Here is a list of some such designations: 'key offices', 'main branches', 'global offices', 'international offices', 'hub offices', 'major operation offices', 'competence centres' (for a given function), 'asset management centers', 'global investment service centers', offices with 'international trade contacts' or simply with 'international contacts', offices for 'multinational corporate customers', offices housing 'senior managers' or 'senior partners', and offices of 'core firms' within alliances. This is a rich vein of information but much work is required to convert it into usable data to compare firms across cities.

In conversion from information to data there is always a tension between keeping as much of the original material as possible and creating a credible ordering that accommodates all degrees of information across cases. In this exercise, there is very detailed information for some firms and much less for others. This tension is resolved here by devising a relatively simple scoring system to accommodate the multifarious information gathered. A six-point scale is used³ where two levels are automatically given: obviously zero is scored where there is no presence of a firm in a city, and 5 is scored for the city that houses a firm's headquarters. Hence decision making on scoring focuses upon allocating the middle four scores (1, 2, 3, and 4) to describe the service value of a firm in a city. This means that for each firm three boundary lines have to be specified: between 1 and 2, 2 and 3, and 3 and 4.

The basic strategy of allocation is to begin with the assumption that all cities with a non-HQ presence of a firm score 2. This score represents the 'normal' or typical' service level of the given firm in a city. To determine such normality requires inspection of the distribution of information across all cities for that firm. To alter this score there has to be a specific reason. For instance, a city where contact with its office is referred elsewhere will be scored 1 for that firm. In other firms where there is full information on numbers of practitioners, a city with an office showing very few (perhaps none) professional practitioners would also score 1. The point is that the boundary between 1 and 2 will differ across firms depending on information available. The same is true of the other boundaries. Generally, the boundary between 2 and 3 has been based upon size factors, and that between 3 and 4 on extra-territorial factors. For instance, exceptionally large offices with many practitioners will lead to a city scoring 3 while location of regional headquarters will lead to a city scoring 4. In practice, size and extra-territorial information have been mixed where possible in deciding on the boundaries for each firm. The end result is the service value matrix V, a 316 x 100 data array with v_ij ranging from 0 to 5.

How credible are these data? They are far from perfect largely dependent as they are on what information is available on web sites. But the key issue is the subjectivity inherent in the process of this data creation: the resulting data do not have the key property of inter-subjectivity. That is to say, two people using the same information will not always decide on the same boundaries. Given the nature of the information this is inevitable. One fundamental question arises. Does this issue lead to so much uncertainty in the data that the exercise is irredeemably flawed? There are two answers to counter this concern. First, the means of scoring has been designed to be as simple as possible, pivoting on '2 as normal' and with decision making limited to just three boundaries. Second, the exercise is carried out over a large number of firms so that particular differences will likely be ironed out in the aggregate analyses that the data are designed for. Thus we are satisfied that we have produced credible data for describing the world city network in 2000.

Measuring of firms and cities in terms of their network locations can be easily derived from V. The sums for columns, rows and the total (specified as equations 1 to 3 in Taylor, 2001, page 186) provide initial description of the universe of global services as defined by the GaWC 100. The total service sum is 16,901 and the top 10 firms and cities in terms of quantity of service values are given in Tables 1 and 2. There is nothing surprising in these rankings with both tables, coincidently, showing a gap separating the top two from the rest. In Table 1the dominance of the accountancy sector is expected given the large number of offices the major firms in this sector operate. The cities listed in Table 2 are exactly the same as the ten cities designated as alpha world cities in an earlier study based upon different data (Beaverstock et al. 1999). The obvious plausibility of these first simple measurements provides an initial credibility to the new data matrix.

The total service values given in Table 2 measure the site service status of the cities (Taylor, 2001, 184, 186). This is a measure of the size of cities as service nodes in the world city network. The situational status of a city within the network is a relational measure defined as

N_a = SS v_aj . v_ij where a ¹ i
i j

so that N_a is the nodal connection of city a into the network defined as n cities, i, and m firms, j, with v as the service values in V. (This equation is a combination of equations 4, 5 and 6 in Taylor, 2001, page 187.) Given the range and scope of the data used here, this measure can be reasonably designated as the global connectivity of a city. The sum of all these city connectivities is 4,078,256 (equation 7 in Taylor, 2001, page 187). Individual city values can be expressed as a proportion of this grand total of interlocking connections (equation 8 in Taylor, 2001, page 188).

The top 10 cities ranked in terms of global connectivity are shown as gross and proportional measures as well as proportions of the highest city connectivity (i.e. London's) in Table 3. (The latter is the form of measurement we have found most convenient and is used in all our studies.) Not surprisingly this table is similar to Table 2 but it not exactly the same: Paris jumps ahead of Tokyo and Milan jumps ahead of Los Angeles while Frankfurt drops out to be replaced by Madrid. What this is indicating is that the important firms in the cities that rise in the ranking are relatively more connected than the equivalent firms in cities falling in the rankings; hence the greater global connectivity of, say, Paris over Tokyo. In terms of comparing the relative utilities of the site and situational measures, global connectivity is an aggregate relational measure and therefore is the preferred means of assessing the importance of cities in a network context. In addition, the situational status of cities is the more analytically interesting since it leads on to the creation of connectivity matrices and more sophisticated data analyses (see Taylor, 2001).

In conclusion: we can now do global urban analysis. The world city network is illustrated as a pattern of nodes in Figure 1. The cartogram includes all cities that have at least one fifth of the highest city connectivity (i.e London's) which includes 123 cities. Treating world cities as global service centers, this is the first time global connectivities of cities have been able to be shown: it is the fruit of much labour.

Postscript: the network model, the data generation methods, and the measurements of city connectivities can be used for other builders of networks across world cities. For instance, using data from media conglomerates brings Los Angeles to the fore while focusing on NGO networks highlights the importance of Geneva and Nairobi.

REFERENCES

ABU-LUGHOD, J. (1989) Before European Hegemony: the World System, AD 1250-1350, New York: Oxford University Press

ALLEN, J. (1999) 'Cities of power and influence', in Allen et al. (eds) Unsettling Cities. London: Routledge

ALLEN, J., MASSEY, D. AND PRYKE, S. (eds) (1999) Unsettling Cities. London: Routledge

AMIN, A. AND GRAHAM, S. (1999) 'Cities of connection and disconnection', in Allen et al. (eds) Unsettling Cities. London: Routledge

BEAVERSTOCK. J. V., SMITH, R. G. AND TAYLOR, P. J. (1999) 'A roster of world cities', Cities, 16, 445-58

BEAVERSTOCK. J. V., SMITH, R. G. AND TAYLOR, P. J., WALKER, D.R.F. & LORIMER, H. (2000) 'Globalization and world cities: some measurement methodologies', Applied Geography, 20, 43-63

CASTELLS, M. (1996, 2001) The Rise of Network Society. Oxford: Blackwell

DICKEN, P. (1998) Global Shift. London: Paul Chapman

FRIEDMANN, J. (1986) 'The world city hypothesis', Development and Change, 17, 69-83

KORFF, R. (1987) 'The world city hypothesis: a critique', Development and Change, 17, 483-95

MICHELSON, R. L. AND WHEELER J. O. (1994) 'The flow of information in a global economy: the role of the American urban system in 1990', Annals of the Association of American Geographers, 84, 87-107.

SASSEN, S. (1991, 2001)) The Global City. Princeton, NJ: Princeton University Press

TAYLOR, P. J. (1997) 'Hierarchical tendencies amongst world cities: a global research proposal', Cities, 14, 323-32

TAYLOR, P. J. (1999) '"So-called world cities": the evidential structure within a literature', Environment and Planning A, 30, 1901-04

TAYLOR, P. J. (2001) 'Specification of the world city network', Geographical Analysis, 33, 181-94

TAYLOR, P. J. AND WALKER, D.R.F. (2001) 'World cities: a first multivariate analysis of their service complexes', Urban Studies, 38, 23-47

TAYLOR, P.J., CATALANO, G. AND WALKER, D.R.F. (2002) 'Measurement of the world city network', Urban Studies, 39, 2367-76

Table 1: Top 10 firms ranked by total service value across 316 cities

	The location of main office
The economic function	Tel Aviv City	The rest of the Metropolis	The rest of Israel	Reference year	N	Source of data
Hi-tech firms - total	16	79	5	2001	476	(1)
Communication firms	18	68	14	2001	152	(1)
Information technology	19	72	9	2001	143	(1)
Internet	20	71	9	2001	71	(1)
Electronics and hardware	58		42	2002	NA	(2)
Software	80		20	2002	NA	(2)
Major banks' headquarters	100	0	0	2002	NA	(3)
Small banks' headquarters	86	0	14	2002	NA	(3)
Trust funds' headquarters	100	0	0	200	NA	(3)
Securities brokers	58	11	31	2002	NA	(3)
Lawyers serving the global economy	69	15	16	2004	427	(4)
Advertising firms affiliated with Multinationals	86	14	0	2004	14	(5)
Accounting firms affiliated with Multinationals	100	0	0	2004	8	(6)
Israel 's best firms	13	73	14	2004	15	(7)
Israel 's best financial services	70	14	16	2004	14	(7)

Table 2: Top 10 cities ranked by total service value across 100 firms

RANK

CITY

TOTAL

1

2

3

4

5

6

7

8

9

10

London

New York

Hong Kong

Tokyo

Paris

Singapore

Chicago

Los Angeles

Frankfurt

Milan

368

357

253

244

235

229

213

201

193

191

Table 3: Top 10 cities ranked by global connectivity

RANK

CITIES

Gross Connectivity

Proportional Connectivity

Proportional to highest

1

2

3

4

5

6

7

8

9

10

London

New York

Hong Kong

Paris

Tokyo

Singapore

Chicago

Milan

Los Angeles

Madrid

63399

61895

44817

44323

43781

40909

39025

38265

38009

37698

0.01556

0.01552

0.01100

0.01087

0.01076

0.01003

0.00957

0.00938

0.00932

0.00924

1.00

0.98

0.71

0.70

0.69

0.65

0.62

0.60

0.60

0.59

Edited and posted on the web on 20^thJanuary 2003

Note: This Research Bulletin has been published in A Borsdorf and C Parnreiter (eds) (2003) International Research on Metropolises: Milestones and Frontiers. Wien: Verlag der Österreichischen Akademie der Wissenschaften, 29-42