The spatial distribution of economic activity in Melbourne, 1971–2006

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The spatial distribution of economicactivity in Melbourne, 1971–2006Andrew Robert Watkinsa

a Department of Transport, Planning and Local Infrastructure,Level 13, 1 Spring Street, Melbourne, Victoria 3000, AustraliaPublished online: 24 Jul 2014.

To cite this article: Andrew Robert Watkins (2014): The spatial distribution of economic activity inMelbourne, 1971–2006, Urban Geography

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The spatial distribution of economic activity in Melbourne, 1971–2006

Andrew Robert Watkins*

Department of Transport, Planning and Local Infrastructure, Level 13, 1 Spring Street, Melbourne,Victoria 3000, Australia

(Received 5 June 2013; accepted 10 February 2014)

This paper reports an empirical analysis of the distribution of employment by industryin Melbourne and how this changes over time. Depending on the nature of the industrybeing considered, two main patterns of spatial behavior are found, population-relateddispersion and centralization. A methodology for estimating the benefits of centraliza-tion is developed and applied. Centralization benefits are highest for business servicesand other high-level services, supporting the view that the benefits of concentrating inthe central business district are related to knowledge creation and exchange. Otherforces, such as competitive pressure, land prices, and colocation with other industries,play only a minor role in determining the spatial distribution of industries, at least atthe spatial levels used here. The implications of these findings are discussed.

Keywords: employment; spatial economics; polycentricity

Introduction

Over the past half-century, urban scholarship has transformed our understanding of thedynamics of urban spatial economies. Cities were previously understood as populationconcentrations fulfilling societal functions—administration, defense, basic trade, culturalactivities (Richardson, 1978)—and it was only through the work of pioneers such asChristaller (1966) and Lösch (1954; also Parr, 2002b) that the economics of spatiallocation and transportation costs were given a solid theoretical foundation. Alonso(1964) built on this foundation to formalize a monocentric city model, the simplestform of which entailed a supply of workers distributed across an urban area who commuteto a single central business district (CBD) containing all regional employment. “Bid-rent”economic competition yielded a pattern of land prices declining with distance from thecenter, as workers’ and households’ trade-offs between location rent and commuting costsshaped the spatial distribution of the population (Mills, 1972; Muth, 1969).

Krugman’s “New Economic Geography” further advanced theoretical understandingof urban spatial economies, by incorporating the concept of increasing returns (Fujita,2007; Krugman, 1991, 1996). Insights derived from the economies of nation-states(Krugman, 1991) and subnational states (Santolaria, Cuartero, & Garcia, 2012) weresubsequently applied to the economic structure of cities (Mori, 2006). Krugman’sapproach theorizes the spatial structure of economies as determined by the balancebetween agglomerative forces (positive interactions among firms) and dispersive forces(land costs, transportation costs, congestion). The approach accounts for a wide array ofcontemporary urban processes, such as the tendency for certain types of firms to locate

*Email: [email protected]

Urban Geography, 2014http://dx.doi.org/10.1080/02723638.2014.930574

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farther from the city center (Muller, 1981) to exploit the improved access to regionaltransport networks offered by suburbs (Hughes, 1974; Schmenner, 1978) and the forma-tion of the so-called “edge cities” (Krugman, 1996).

Clustering and dispersion in urban environments

However, a number of questions remain unanswered, in particular the nature of theagglomerative forces which form an integral part of the model. At the regional scale thenature of such forces is clear: faced with relatively large distances, economic agentscluster so that consumers are close to suppliers of goods, and services and firms are closeto their workforces. Yet the causal relations are more ambiguous in the urban context,where many different points in the city may be more or less equally accessible, and wherethe constraints of congestion and limited land supply become significant factors in thelocation decisions of firms and their customers. Under these conditions, agglomerationand clustering—whether in the CBD or across the broader metropolitan fabric—is moredifficult to explain (Shearmur & Coffey, 2002). Varied factors have been suggested,including knowledge spillovers between firms (Fujita, 2007), access to cheaper land andto clients (Coffey, Drolet, & Polèse, 1996), labor specialization and new firm formation(Lyons, 1995), business cycle dynamics (Waddell & Shukla, 1993), specific commutingand shopping patterns (Fuji & Hartshorn, 1995) and inter-industry linkages and planningregulations (Shearmur, 2007). Yet there is no consensus on any of these factors as theunambiguous determinants of agglomeration in urban economies.

Uncertainties also surround the nature of the dispersive forces which operate in urbaneconomies, in particular why it is that only some industries are spread throughoutmetropolises, while others remain largely concentrated in the CBD (Coffey, Polèse, &Drolet, 1996; Shearmur & Alvergne, 2002). In most older cities, business and professionalservices generally remain centrally located, while lower level services such as retail andmanufacturing tend to disperse (Coffey & Polèse, 1988; Erickson, 1983; Esparza, 1992;Gilli, 2009; Guillain, Le Gallo, & Boiteux-Orain, 2006; Kneebone, 2009; O’Connor &Rapson, 2003; Searle, 1998; Shearmur & Coffey, 2002). In addition, many industries formclusters or subcenters outside the CBD, as has been found for manufacturing (ÓhUallacháin & Leslie, 2009) and financial, health care, producer services and retailindustries (Leslie & Ó hUallacháin, 2006). Nor are these patterns consistent, showingconsiderable variation within industry groups and between cities (Shearmur, 2007).

Melbourne’s spatial economy

The lack of any satisfactory model able to explain these aspects of urban spatial eco-nomies, together with inconsistencies between the various empirical investigations ofurban industry location, provide an important justification for undertaking further workin this area. The study reported here is perhaps the first to examine the evolution of a cityeconomy—Melbourne, Australia—across the entire range of industry sectors over severaldecades, during a time of far-reaching economic change at the state, national and globalscales. Melbourne was founded in the nineteenth century, and is Australia’s second largestcity. Initially it owed its economic prosperity to the discovery of gold, but later became amajor financial center. In the early twentieth century Melbourne played an important rolein the growth of agriculture, with wool and then later wheat and dairy products becomingimportant export earners for the State. After World War II manufacturing expanded,

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particularly in the areas of motor vehicle production, metal processing, chemicals andtextiles.

In the latter decades of the twentieth century, manufacturing employment declinedsharply, while employment expanded both in low-level services such as retail trade andhigher level functions such as finance and insurance (Birrell & Healy, 2010; O’Connor &Stimson, 1997; O’Connor, Stimson, & Daly, 2001; Searle, 1998). These trends mirroredthe broader economic transformation of Australia (Connolly & Lewis, 2010; Tonts &Taylor, 2010) and other parts of the developed world, where manufacturing declineproceeded amidst the growth of high-level producer services (Berube & Katz, 2006;Borel-Saladin & Crankshaw, 2009; Fournier & Axelsson, 1993; Schettkat & Yocarini,2006; Turok & Edge, 1999).

The impact of these changes in Melbourne can be seen in Table 1, where the decline inmanufacturing and the high growth rates for service industries—including personalservices such as education and health care—can be seen clearly. Overlaying these changeswas a rapid outward movement of population, which increased from 2.54 million in 1971to 3.68 million in 2006 as new suburbs were settled on the expanding perimeter of themetropolis. The past generation has brought significant changes to Melbourne’s spatialeconomy. In 1971, 48.5% of all manufacturing employment was concentrated in just fivecentral municipalities (Melbourne, Yarra, Maribyrnong, Moreland and Darebin; seeFigure 1). This share fell to 27.9% by 1991 and to 17.9% by 2006, with the outermunicipalities Hume, Kingston, Monash and Greater Dandenong (with a total of 36.2%

Table 1. Growth rates and shares of industry employment in metropolitan Melbourne (1-digitindustry divisions).

Industry sector

Average annualgrowth rate inemployment

1971–2006 (%)

Employment by industry(persons)

Share of totalemployment (%)

1971 2006 1971 2006

Agriculture, forestry andfishing

0.4 8,429 9,726 0.8 0.6

Mining −1.1 3,662 2,515 0.3 0.2Manufacturing −1.1 350,309 239,570 32.7 14.0Electricity gas and watersupply

−1.7 17,407 9,699 1.6 0.6

Construction 1.9 58,273 110,930 5.4 6.5Wholesale trade 1.3 65,818 104,524 6.2 6.1Retail trade 1.6 144,692 249,494 13.5 14.6Accommodation, cafes andrestaurants

3.0 24,915 70,242 2.3 4.1

Transport and storage 0.2 70,657 75,882 6.6 4.4Communication services 1.0 24,314 34,631 2.3 2.0Finance and insurance 2.2 39,673 85,925 3.7 5.0Property and business services 3.5 69,184 22,7882 6.5 13.3Government administration anddefense

0.6 55,885 69,752 5.2 4.1

Education 3.1 45,119 13,1749 4.2 7.7Health and community services 3.2 59,962 18,1364 5.6 10.6Cultural and recreationalservices

3.8 12,833 46,840 1.2 2.7

Personal and other services 3.3 19,345 59,720 1.8 3.5

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of employment) taking over the role of manufacturing centers for the metropolis. Thismassive spatial reorganization can be contrasted with other industries where little changeoccurred; the CBD share of employment in finance and insurance, for example, declinedminimally between 1971 and 2006 (Table 2).

Melbourne—along with the State of Victoria—was also the scene of considerablegovernment-initiated changes to its economic and social structure in the 1980s and 1990s.Privatization of the electricity and gas utilities, public transport services, prisons andambulance services was pushed through, in many cases resulting in the fragmentation anddispersion of large quasi-government organizations (Argy, 2001; King & Pitchford, 1998).

The work reported here was made possible by the availability of spatial employmentdata sets for Melbourne going back to the early 1970s, giving the study a 35-year timespan. The analysis is divided into three sections. The first outlines the data sources andanalytical techniques used, while the second presents the results. In the third section thefindings are discussed in the context of the evolution of city economies generally. Itconcludes with a short discussion of the implications for future policy directions.

Data and methodology

Data sources

The data presented here are based on two types of spatial units: (1) municipalities or LocalGovernment Areas (LGAs), and (2) Statistical Local Areas (SLAs), which are

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15

1618

19

20

2122

23

24

25 26

27

28

29

30

25

km

0

PORT PHILLIP BAY

BASS STRAIT

Figure 1. Location of Local Government Areas (LGAs; heavy boundaries) and Statistical LocalAreas (light boundaries) in metropolitan Melbourne. The numbers refer to the LGAs listed inTable 3.

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subdivisions of LGAs. LGAs are typically divided into two to five SLAs; theMetropolitan area comprises 31 LGAs and 76 SLAs (see Figure 1 and Table 3).

Two data sets were used in this study, both derived from the quinquennial Census ofPopulation and Housing carried out by the Australian Bureau of Statistics. One consists ofemployment by municipality or LGA in Melbourne, for each of 17 industries at the 1-digitindustry division level (ABS, 1993), spanning the period 1971 to 2006 in 5-year intervals.The second, more detailed, database provides employment numbers for a more restrictedperiod (from 1996 to 2006) also at 5-year intervals, but at a more disaggregated industry

Table 2. Proportion of industry employment in Melbourne LGA (1-digit industry divisions).

Industry sector 1971 1976 1981 1986 1991 1996 2001 2006

Agriculture, forestry and fishing 4.0% 2.8% 5.7% 2.7% 3.8% 2.6% 5.0% 3.5%Mining 48.8% 38.2% 35.3% 38.1% 35.0% 55.9% 53.1% 45.0%Manufacturing 17.7% 14.8% 14.1% 12.4% 7.5% 6.3% 7.0% 7.6%Electricity gas and water supply 45.9% 49.8% 45.7% 42.2% 35.3% 22.3% 32.0% 33.5%Construction 14.1% 10.5% 11.0% 6.9% 8.8% 7.0% 8.8% 7.8%Wholesale trade 26.8% 19.8% 16.4% 12.8% 11.5% 10.0% 8.6% 7.7%Retail trade 26.7% 19.7% 16.3% 12.7% 11.4% 9.0% 8.6% 7.9%Accommodation, cafes andrestaurants

29.9% 27.6% 26.8% 23.6% 21.9% 26.9% 26.3% 26.6%

Transport and storage 54.4% 47.9% 44.5% 41.3% 39.8% 24.1% 21.6% 16.8%Communication services 54.4% 56.6% 50.9% 37.1% 42.8% 40.4% 44.2% 44.5%Finance and insurance 60.7% 52.6% 37.8% 41.8% 39.9% 56.8% 59.7% 55.7%Property and business services 54.7% 47.0% 34.3% 37.9% 36.4% 31.1% 34.5% 34.0%Government administration anddefense

48.5% 50.3% 44.9% 40.2% 46.0% 46.5% 37.5% 39.6%

Education 31.8% 28.1% 25.1% 23.5% 20.3% 13.9% 13.6% 13.9%Health and community services 31.8% 28.1% 25.2% 23.5% 20.3% 15.8% 15.1% 14.3%Cultural and recreational services 30.1% 27.7% 26.5% 23.6% 21.7% 28.0% 25.5% 30.2%Personal and other services 28.7% 25.9% 24.0% 22.1% 19.8% 20.4% 16.7% 17.2%

Table 3. Guide to location of LGAs in metropolitan Melbourne (see Figure 1).

LGA no. Local Government Area LGA no. Local Government Area

1 Banyule 17 Maroondah2 Bayside 18 Melbourne3 Boroondara 19 Melton4 Brimbank 20 Monash5 Cardinia 21 Moonee Valley6 Casey 22 Moreland7 Darebin 23 Mornington Peninsula8 Frankston 24 Nillumbik9 Glen Eira 25 Port Phillip10 Greater Dandenong 26 Stonnington11 Hobsons Bay 27 Whitehorse12 Hume 28 Whittlesea13 Kingston 29 Wyndham14 Knox 30 Yarra15 Manningham 31 Yarra Ranges16 Maribyrnong

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level (2-digit industry subdivision level, comprising 53 industries) and a finer level ofspatial detail (SLA). Earlier data at the 1-digit (industry division) level based on the olderAustralian Standard Industrial Classification (ASIC) were converted to the Australian andNew Zealand Standard Industrial Classification (ANZSIC) using published concordances(ABS, 1993). Data at the 2-digit (industry subdivision) level were all based on theANZSIC classification and needed no further conversion (Watkins, 2009). As with moststudies of this type, employment numbers serve as a proxy for economic activity; expliciteconomic measures are unavailable at the spatial scales considered here. While thisapproach is not able to distinguish between a small number of large establishments anda large number of small establishments in any particular area of the metropolis, it can beargued that such distinctions are less important than having a gauge of overall economicactivity regardless of establishment structure. The estimated resident population figuresused in this study are from the Australian Bureau of Statistics (ABS, 2003).

Census data in Australia are available based only on administrative (LGA) and census(SLA) boundaries, and are not available in geocoded form. This unavoidably makes astudy such as this vulnerable to the modifiable areal unit problem (MAUP), where theresults of analyses of area-based data can change with the choice and size of areal unit(Horner & Murray, 2002). In this work the impact of this problem could be minimized byusing the smallest units (SLAs) for which data are available, by working with density-based measures (dispersion index, Moran’s I) and by basing analyses on comparisons ofarea-based measures made using the same spatial structures (centralization benefit)(Giuliano & Small, 1991).

Measures of dispersion

The dispersion index Δj is based on the widely used locational Gini index (Alonso-Villar,2011; Dewhurst & McCann, 2002; Kim, Barkley, & Henry, 2000; Marshall, 1981;Suedekum, 2006) and uses area rather than population as the yardstick for the share ofa particular industry in a LGA or SLA. It is obtained by calculating, for a particularindustry j, the share of total employment

SEij ¼Eij

E0i

and the share of total area

SAi ¼ Ai

A0

of each LGA or SLA i. Here Eij denotes the number of jobs in LGA or SLA i and industryj, E0j the number of jobs in the whole metropolis and industry j, Ai the area of LGA orSLA i and A0 the area of the entire metropolis. All LGAs (or SLAs) are then ranked inorder of their employment shares SEij and the cumulative employment shares CE

nj:

CEnj ¼

Xni¼1

SEij

and cumulative area shares CAn

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CAn ¼

Xni¼1

SAi

are calculated. In this way one ends up with pairs of values of CEnj and CA

n for each LGA orSLA, for n successively increasing from n ¼ 1 to n ¼ N , where N is the total number ofLGAs or SLAs. The area under the curve formed by plotting CE

nj against CAn , when divided

by the total area (0.5) corresponding to the triangle bounded by the axes and the linecorresponding to CE

nj ¼ CAn , gives Δj, which measures the spatial dispersion of industry j:

Δj ¼ 1

2

XNn¼1

CAnþ1 � CA

n

� �CEnþ1;j þ CE

n;j

� �(1)

If employment in industry j were spread over all available LGAs with uniform density(i.e., complete dispersal throughout the metropolis), then one would observe values of Δj

equal to unity, while values of Δj close to zero would indicate the opposite.

Clustering of economic activity

Spatial correlation of economic activity was tested using Moran’s I , which measures thedegree to which regions of economic activity are spatially dependent (Griffith, 1987).Moran’s I is defined as

I ¼ Nz0Wz

Sz0z(2)

where N is equal to the total number of regions and z is a vector, each element of which isthe departure of the employment density ei for a particular region i from the average for allregions �e:

z ¼ ei � �e

W is a proximity matrix specifying whether or not any pair of regions is contiguous. Anelement of W is set to unity if the pair of regions in question have any part of their bordersin common; otherwise it is equal to zero. All diagonal elements in W are set to zero. S isthe sum of all elements of W . Values of Moran’s I were calculated for 1996, 2001 and2006 using the more detailed SLA-based data, which is disaggregated into industrysubdivisions.

Firms in many industries tend to concentrate in the CBDs of cities when it wouldappear to be advantageous for them to disperse throughout the metropolis, and in doing solocate closer to their employees. As White (1999, p. 1378) explains, “firms have anincentive to suburbanize because they can pay lower wages, which workers are willing toaccept because they commute less. Since wages are the largest single cost for many firms,this is likely to be an important consideration.” That these firms do not suburbanizesuggests that there are benefits to the firm in remaining centrally located, and that thesebenefits are at least sufficient to outweigh the higher salaries the firm must pay itsemployees in order to compensate them for the longer commute distances associatedwith a centrally located workplace (Felsenstein, 1994; Scott, 1981; White, 1976).

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A lower bound for these benefits can be obtained by estimating the extra costsinvolved in commuting to the CBD. Consider the case where employees of the firmcommute to the CBD from an annulus of width dr (where dr is small) located at a distancer from the CBD on which it is centered (Figure 2). The number of employees dn residentwithin this annulus is

dn ¼ δπ r þ drÞ2 � r2� �h i

where δ is the density of employees per square kilometer at distance r. If travel costs tothe CBD are α per kilometer for each commuter (α is assumed to be independent of theindustry in which the employee works), the cost of commuting for all these employeeswill be

dC ¼ αrδπ r þ drð Þ2 � r2h i

¼ 2αδπr2dr(3)

as dr becomes very small. The residential density of employees δ working in the CBD in aparticular industry is not constant but decreases with increasing distance from the CBD,and follows to a close approximation an exponential function of the form

δ ¼ ke�mr (4)

where k and m are constants (Figure 3).From Equations (3) and (4)

dC ¼ 2αkπr2e�mrdr

¼ 2αkπm2

x2e�xdr(5)

CBD

dr

r

Figure 2. Diagrammatic representation of commuting to Melbourne CBD.

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where

x ¼ mr

To obtain the total commuting costs of employees from all parts of the metropolis, wenote that δ falls rapidly to zero with increasing distance from the CBD (Figure 3).Equation (5) can therefore be integrated over the entire space without loss of accuracy,giving

C ¼ 4αkπm2

(6)

where the identity

Z1

0

xne�xdx ¼ n!

has been used (Courant, 1957).In order to retain its employees, this cost C is met by the firm (whether in the form of

higher salaries and bonuses, prestige, travel, superior working conditions or in some otherway), which only does so because the benefit β it gains from being centrally located is atleast equal to the higher commuting cost C. If there is a total of NE employees whocommute from the entire metropolis to the centrally located firm, then the minimumbenefit per employee will be

15

20

25

30

0

5

10

0 10 20 30 40 50 60 70 80

Res

iden

tial

den

sity

(p

erso

ns/

squ

are

km)

Distance from Melbourne CBD (km)

Figure 3. Distance dependence of residential density for employees working in the CBD inTransport and storage. Points are the observed densities; the solid line represents the best fitprovided by Equation (4) with a ¼ 22:0 and b ¼ �0:087.

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β ¼ C

NE

¼ 4αkπNEm2

(7)

An estimate of the centralization benefit β can be obtained if values of α, k, m and NE areavailable for each industry. The parameters k and m in Equation (4) were derived byfitting journey to work data (ABS, 2009b) to Equation (4), and NE was obtained from theemployment data described above. However, no published commuting costs α forMelbourne are available. The only available estimates of such data appear to be thoseof a recent study in the Netherlands, which established a relation between marginalcommuting costs and hourly wage rates, using job search and job mobility data (vanOmmeren & Fosgerau, 2009). They found that the ratio of marginal commuting cost towages was 2.0; it should be noted that there were substantial differences between malesand females which, however, could not be incorporated into the analysis presented herebecause of the lack of gender-specific data. Hourly wage rates were calculated frompublished average weekly earnings by 1-digit industry division (ABS, 2009a) (availablefor all industries except agriculture, forestry and fishing), which, after deflating by theconsumer price index for all groups in Melbourne (ABS, 2009c), were used to calculatecentralization benefits for the two census years 2001 and 2006. In the absence of any dataspecifically for Melbourne, an average commuting speed of 50 km/h has been assumed incalculating β, in line with data published for major United States cities (Santos,McGuckin, Nakamoto, Gray, & Liss, 2011).

Other spatial measures

The spatial stability of industries—the tendency of industries to remain in the samelocation over time—was measured by estimating the Pearson correlation coefficient rbetween the spatial distribution of employment across the metropolis at different times(Snedecor & Cochran, 1980). Correlation coefficients R were also used to measure theinteraction between industry employment and population, and between different industriesat the same point in time to estimate the degree to which they colocate. It should be notedthat values of r, Δj, j or j derived using different spatial units or different levels of industryaggregation are not comparable.

Results and discussion

Population-related dispersion

The results indicate marked differences in the way industries relocated as Melbourneexpanded and suburbanized (Table 2). One group of industries was distinguished by sharpdeclines in the CBD share between 1971 and 2006, indicative of movement into themiddle and outward zones of the metropolis. These industries include manufacturing,construction, wholesale trade, retail trade, education, health and community services andpersonal and other services; the latter consists of various hiring services, interest groupsand public safety organizations and various other activities such as photographic andfuneral services. These industries are much more mobile than the others (note the smallvalues of the correlation coefficient r in Table 4) and become increasingly spread out withtime (note the rising dispersion index Δj in Table 5).

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Outside the CBD, these industries show strong correlations with population distribu-tion throughout the study period, in most cases with R highly significant throughout theperiod 1971 to 2006 (Table 6).

This behavior is replicated at the finer SLA level (Tables 7 and 8). Most of thesubdivisions of the industries in this group with high values of Δj (above 0.15 in 2006)again tend to be low-level service industries, such as various branches of retailing,transport and educational and community-related services. These industries also display

Table 4. Spatial correlation by LGA in metropolitan Melbourne, 1971–2006(excluding Melbourne LGA).

Industry r (Excluding Melbourne LGA)

Agriculture, forestry and fishing 0.76Mining 0.35Manufacturing 0.37Electricity gas and water supply 0.21Construction 0.25Wholesale trade 0.36Retail trade 0.42Accommodation, cafes and restaurants 0.79Transport and storage 0.43Communication services 0.46Finance and insurance 0.91Property and business services 0.90Government administration and defense 0.20Education 0.53Health and community services 0.66Cultural and recreational services 0.79Personal and other services 0.72

Table 5. Dispersion index Δj for metropolitan Melbourne LGAs, 1-digit industry divisions(excluding Melbourne LGA).

Industry sector 1971 1976 1981 1986 1991 1996 2001 2006

Agriculture, forestry and fishing 0.94 0.86 0.85 0.97 0.99 1.01 1.05 1.04Mining 0.56 0.59 0.55 0.41 0.54 0.61 0.87 0.96Manufacturing 0.14 0.16 0.18 0.21 0.25 0.35 0.42 0.42Electricity gas and water supply 0.17 0.29 0.39 0.37 0.33 0.42 0.37 0.36Construction 0.21 0.34 0.36 0.52 0.68 0.71 0.78 0.85Wholesale trade 0.14 0.18 0.21 0.29 0.31 0.29 0.40 0.44Retail trade 0.14 0.18 0.21 0.29 0.31 0.41 0.56 0.56Accommodation, cafes and restaurants 0.19 0.25 0.36 0.44 0.40 0.61 0.64 0.66Transport and storage 0.24 0.27 0.27 0.27 0.29 0.36 0.46 0.46Communication services 0.24 0.22 0.22 0.23 0.21 0.19 0.30 0.30Finance and insurance 0.13 0.17 0.19 0.21 0.22 0.24 0.24 0.21Property and business services 0.13 0.17 0.21 0.24 0.24 0.23 0.26 0.28Government administration and defense 0.31 0.35 0.41 0.51 0.51 0.59 0.51 0.57Education 0.16 0.21 0.29 0.33 0.28 0.65 0.77 0.76Health and community services 0.15 0.19 0.28 0.32 0.28 0.27 0.32 0.34Cultural and recreational services 0.20 0.24 0.36 0.40 0.38 0.39 0.46 0.48Personal and other services 0.17 0.22 0.30 0.34 0.29 0.37 0.51 0.54

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significant spatial correlations with population distribution R, which vary markedly withinbroad industry divisions.

The magnitude of R corresponds to the ratio of employment to population and can beinterpreted as a measure of each industry’s labor intensity. Within the retail trade sector,for instance, R for food retail trade is much higher than for personal and householdretailing (primarily clothing, furniture and household equipment) as well as vehicleretailing, where a smaller number of establishments each serving a larger catchmentarea are needed for the less frequent interactions with consumers. For the majority ofindustries, R declines over time as a more mobile population gives rise to larger establish-ments with labor-saving economies of scale (such as in wholesale trade and health andcommunity services) or the introduction of new technologies lowering the labor intensityof production processes (manufacturing).

Centralizing industries

A second group of industries stands out because of its strong and (in spite of somedeclines) relatively stable presence in the center of the metropolis (Table 2). In the CBD,the main activity of most of these industries (mining, electricity gas and water supply,communication services, finance and insurance, parts of property and business servicesand government administration and defense) concerns administration (legal, accounting,marketing, communications, IT and so on) or creative activities (cultural and recreational

Table 6. Correlation between population and employment, based on LGAs and 1-digit industrydivisions (excluding Melbourne LGA).

Correlation coefficient R

Industry 1971 1976 1981 1986 1991 1996 2001 2006

Agriculture, forestry andfishing

−0.38 −0.39 – – – – – –

Mining – – – – – – – –Manufacturing 0.59 0.53 0.52 0.50 0.40 0.40 0.38 0.37Electricity gas and watersupply

– – – – – 0.39 – –

Construction 0.81 0.79 0.77 0.76 0.77 0.76 0.74 0.71Wholesale trade 0.70 0.72 0.69 0.65 0.60 0.38 0.43 0.44Retail trade 0.75 0.72 0.69 0.65 0.60 0.68 0.69 0.63Accommodation, cafes andrestaurants

0.71 0.57 0.45 0.42 – – – –

Transport and storage 0.50 – – – – – – –Communication services 0.50 0.67 0.67 0.53 0.44 – – –Finance and insurance 0.55 0.44 0.38 – – – – –Property and businessservices

0.63 0.53 0.46 0.43 – – – –

Government administrationand defense

– – – – – – 0.40 0.47

Education 0.86 0.80 0.74 0.64 0.57 0.69 0.69 0.67Health and communityservices

0.87 0.80 0.74 0.65 0.57 0.41 0.36 –

Cultural and recreationalservices

0.74 0.62 0.52 0.48 – – – –

Personal and other services 0.82 0.76 0.70 0.63 0.51 0.43 0.40 –

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services) rather than physical production. Consequently these firms employ a highproportion of highly educated and highly skilled specialists. Outside the CBD theindustries in this second group generally become increasingly dispersed between 1971

Table 7. Industry dispersion Δj, based on SLAs and 2-digit industry subdivisions (excludingMelbourne LGA).

Industry sector 1996 2001 2006 Industry sector 1996 2001 2006

Agriculture 0.64 0.69 0.66 Vehicle retailing 0.30 0.31 0.25Services to agriculture,hunting and trapping

0.68 0.74 0.64 Accommodation cafesand restaurants

0.31 0.36 0.24

Forestry and logging 0.45 0.48 0.35 Road transport 0.29 0.32 0.27Commercial fishing 0.30 0.26 0.19 Rail transport 0.09 0.20 0.05Coal mining 0.01 0.01 0.01 Water transport 0.05 0.10 0.06Oil and gas extraction 0.03 0.08 0.03 Air and space transport 0.04 0.06 0.05Metal ore mining 0.08 0.11 0.05 Other transport 0.08 0.16 0.04Other mining 0.53 0.64 0.42 Services to transport 0.15 0.16 0.12Services to mining 0.06 0.17 0.08 Storage 0.16 0.14 0.13Food, beverages andtobacco

0.26 0.23 0.23 Communicationservices

0.14 0.18 0.10

Textiles, clothing,footwear and leather

0.10 0.14 0.15 Finance 0.22 0.21 0.07

Wood and paperproducts

0.20 0.23 0.19 Insurance 0.10 0.12 0.03

Printing, publishingand recorded media

0.13 0.16 0.12 Services to finance andinsurance

0.15 0.14 0.06

Petroleum, coal andchemical products

0.13 0.15 0.14 Property services 0.29 0.32 0.23

Nonmetallic mineralproducts

0.19 0.24 0.19 Business services 0.16 0.18 0.10

Metal products 0.21 0.24 0.21 Governmentadministration

0.27 0.29 0.17

Machinery andequipment

0.16 0.19 0.14 Defense 0.10 0.09 0.05

Other manufacturing 0.20 0.22 0.19 Education 0.32 0.36 0.25Electricity and gassupply

0.17 0.21 0.10 Health services 0.18 0.20 0.15

Water supply andsewerage services

0.18 0.15 0.12 Community services 0.28 0.31 0.26

General construction 0.36 0.41 0.31 Motion picture, radioand televisionservices

0.07 0.09 0.06

Construction tradeservices

0.38 0.44 0.42 Library, music and arts 0.30 0.34 0.19

Basic materialwholesaling

0.30 0.34 0.34 Sport and recreation 0.32 0.34 0.23

Machinery and vehiclewholesaling

0.16 0.17 0.14 Personal services 0.28 0.32 0.26

Personal andhouseholdwholesaling

0.14 0.20 0.17 Other services 0.23 0.28 0.17

Food retail trade 0.36 0.41 0.33 Private householdemployed staff

0.19 0.07 0.01

Personal andhousehold retailing

0.24 0.27 0.21

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Table 8. Correlation between population and employment, based on SLAs and 2-digit industrysubdivisions (excluding Melbourne LGA).

Correlation coefficient R Correlation coefficient R


Agriculture – – −0.40 Vehicle retailing 0.69 0.67 0.65Services to agriculture,hunting andtrapping

– – – Accommodation cafesand restaurants

0.45 0.41 0.38

Forestry and logging 0.25 – – Road transport 0.52 0.52 0.50Commercial fishing 0.28 – – Rail transport 0.24 0.35 –Coal mining – – – Water transport – – –Oil and gas extraction – – – Air and space

transport– – –

Metal ore mining – – – Other transport 0.36 0.26 –Other mining 0.24 – – Services to transport – – 0.25Services to mining – – – Storage 0.31 0.31 0.38Food, beverages andtobacco


0.30 – 0.36


0.39 0.43 0.48 Finance 0.51 0.40 –


0.51 0.52 0.47 Insurance – – –


0.32 0.33 0.39 Services to financeand insurance

– – –




0.40 0.47 0.43 Business services – – –


0.56 0.46 0.55


0.47 0.40 0.42 Defense – – –


0.37 – – Health services 0.46 0.46 0.43


– – 0.27 Community services 0.64 0.68 0.71


– – –


0.77 0.80 0.72 Library, music andarts

0.26 – –






0.37 0.42 0.46 Other services 0.42 0.46 0.42


0.33 – –


0.65 0.65 0.61

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and 2006 (note the rising Δj values in Table 5), and any links to population which mayhave been present in the early part of the study period rapidly disappear (Table 6). Thesame general pattern can be observed at the 2-digit level (Tables 7 and 8). It should benoted that the two components of property and business services behave differently withregard to their presence in the CBD, making it something of a hybrid. Property servicesserve the general population and in 2006 only 17.9% of employment was located in theCBD; business services, by contrast, have as their clients chiefly other firms, a largenumber of which are located in the CBD, and in 2006 had 35.9% of employment inthe CBD.

An estimate of tangible and intangible benefits gained by firms locating in the centerof the metropolis is provided by the centralization benefit β (Table 9). Centralizationbenefits tend to be higher for industries in this second group, and are particularly high forthe high-level business service sectors (communication services and finance and insur-ance) as well as for the CBD-located branches of electricity, gas and water supply, as wellas mining. While there are a number of reasons why firms might wish to centralize, thelarger centralization benefits found for high-level service industries suggest that thebenefits are in some way linked to knowledge creation or exchange.

Other industries

Several industries cannot readily be assigned to either of the two groups discussed earlier:these include agriculture, accommodation, cafes and restaurants and transport and storage.These sectors exhibit spatial distributions that are driven by factors unique to eachindustry. Agriculture, tied to land availability which is by definition the greatest in areasof low population, is spatially evenly dispersed (note the high Δj in Table 5) and has onlyweak or negative links to population (R not significantly different from zero, Table 6).Accommodation, cafes and restaurants (similar to property and business services) iscomprised of two distinct components—one with strong links to local population (cafes

Table 9. Centralization benefit β by industry.

Industry group

β (2006 $/day per employee)

2001 2006

Mining $4.56 $5.00Manufacturing $2.97 $3.46Electricity gas and water supply $3.98 $4.41Construction $3.28 $3.75Wholesale trade $3.18 $3.48Retail trade $2.42 $2.63Accommodation, cafes and restaurants $2.36 $2.30Transport and storage $3.48 $3.71Communication services $3.89 $4.12Finance and insurance $3.93 $4.64Property and business services $3.39 $3.76Government administration and defense $3.54 $3.87Education $3.27 $3.53Health and community services $3.07 $3.30Cultural and recreational services $3.22 $3.56Personal and other services $3.41 $3.66

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and restaurants) and a second (accommodation) highly concentrated in the CBD nexus ofregional, national and transnational travel flows that sustain large hotels. The availabledata, however, do not provide separate information for these two groups, even at thesubdivision level. Transport and storage moved out of the CBD between 1971 and 2006(Table 2) as freight and passenger travel operations dispersed into the suburbs, andwarehouses moved out of the city seeking cheaper land. As a result this industry wasquite mobile between 1971 and 2006 (low r, Table 4) with transport headquarters andwarehouses distributed in a “lumpy” fashion rather than being evenly dispersed (low Δj,Table 5) and with few if any links to population distribution (R not significantly differentfrom zero, Table 6).

Competition and clustering

If competitive forces caused firms to disperse in order to find exclusive markets, a highconcentration of a particular industry in a particular LGA or SLA should deter newentrants (Alsleben, 2005). This was tested, for 1-digit industries for the 5 year periodsbetween 1971 and 2006 and for 2-digit industries between 1996 and 2006, by measuringfor each industry the correlation between growth in employment numbers and existingemployment levels across all LGAs and SLAs, both as absolute values and as a ratio ofthe employment in the industry to the total number of jobs in the LGA or SLA. In no casecould any evidence for such an effect be found. The implication is that industry growth inany particular locality is largely independent of the amount of industry that is alreadythere; competitive forces, at the spatial scale of the SLAs or LGAs used here, are too weakto act as deterrents to other firms. The impact of land prices on manufacturing location(Curran, 2007; Dodson & Berry, 2004) was assessed briefly, by testing for a negativecorrelation between the proportion of manufacturing employment in each LGA and themedian industrial land price; none was found. However, the variability of land prices overshort distances and the lags between land prices and occupancy by industries, as well asthe complex commercial arrangements operating in many cases, make it difficult to drawany firm conclusion from this observation.

Spatial autocorrelation at the SLA level measured using Moran’s I is reported inTable 10 for the ex-CBD part of the metropolis. High values of I (>0.4) occur for adiverse range of industries. Insofar as any pattern can be discerned, these appear to bemainly those which provide services directly to the population—construction, retail,accommodation, cafes and restaurants, property services, education, health services,community services, libraries, music and arts and personal services. This finding, alongwith the observed lack of any correlation between Moran’s I and the tendency ofindustries to concentrate in the center (measured by the proportion of employment locatedin the central Melbourne LGA), suggests that the factors causing industries to agglomeratein the urban core play little part in industry clustering elsewhere. Rather, at the SLA levelindustries tend to colocate where there are concentrations of population, as suggested bythe high degree of autocorrelation for population density (Table 10) and the high degree ofcorrelation between employment and population (Table 8) for most of the industries withlarge values of Moran’s I .

It might be expected that manufacturing firms would locate close to other industrieswhich occupy different stages in the same production process, or to firms to which theyhave outsourced various parts of their operations (Esparza, 1992). To investigate whetherthis was the case with manufacturing in Melbourne, the spatial association (which was

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regarded as being important when the spatial correlation coefficient had a value greaterthan 0.8) was measured on an SLA basis between different 2-digit manufacturing indus-tries. No strong pattern of colocation of manufacturing subdivisions was found in 1996,

Table 10. Values of Moran’s I based on SLAs and 2-digit industry subdivisions (excludingMelbourne LGA).


Agriculture 0.180 0.229 0.136 Vehicle retailing 0.462 0.418 0.351Services to agriculture,hunting and trapping

0.014 0.062 0.079 Accommodation cafesand restaurants

0.498 0.513 0.523

Forestry and logging 0.044 0.107 0.013 Road transport 0.232 0.263 0.238Commercial fishing 0.036 0.113 0.098 Rail transport 0.250 0.146 0.187Coal mining −0.016 0.106 0.000 Water transport 0.024 0.025 0.059Oil and gas extraction 0.110 0.115 0.019 Air and space transport 0.073 0.013 0.063Metal ore mining 0.006 0.132 0.045 Other transport 0.027 0.028 −0.001Other mining −0.024 0.110 −0.155 Services to transport 0.127 0.152 0.187Services to mining 0.108 0.120 0.216 Storage 0.170 0.195 0.208Food, beverages andtobacco


0.191 0.149 0.231


0.369 0.346 0.372 Finance 0.395 0.302 0.184


0.201 0.151 0.136 Insurance 0.199 0.226 0.178


0.280 0.331 0.271 Services to finance andinsurance

0.276 0.385 0.320




0.092 0.086 0.181 Business services 0.367 0.365 0.364


0.276 0.237 0.280


0.163 0.174 0.152 Defense 0.000 0.029 −0.030


0.217 0.027 0.026 Health services 0.597 0.511 0.485


−0.006 −0.003 −0.017 Community services 0.664 0.617 0.621


0.220 0.238 0.276


0.413 0.487 0.412 Library, music and arts 0.419 0.448 0.401






0.385 0.387 0.394 Other services 0.296 0.265 0.197


0.621 0.213 −0.020


0.552 0.522 0.455 Population density 0.697 0.692 0.691

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2001 or 2006, with the exception of a few cases—petroleum, coal and chemical productson the one hand, and on the other hand nonmetallic mineral products, metal products,machinery and equipment and other manufacturing. These results may reflect planningrestrictions confining heavy industries to particular areas, rather than direct interactionsbetween these industries.

Significant differences in growth rates are apparent for the inner and outer parts of themetropolis between 1996 and 2006. Employment grew strongly within 5 km of the citycenter for high-level services, including communication services, finance and insurance,business services, education, health and community services and culture and recreationalservices; beyond the 5 km threshold, however, growth rates fell dramatically. The oppositeprevails for low-level services, including wholesale trade, retail trade, accommodation,cafes and restaurants, transport and storage, property services, and personal and otherservices. Employment in all of these activities grew slowly in the inner part of Melbourne,rising outside the 5 km radius and then gradually increasing to the periphery of themetropolis (Figure 4). This suggests that employment in Melbourne is becoming increas-ingly partitioned, with high-level services (which generally command high salaries)moving to the center, and lower paid jobs in low-level services moving to the middleand outer parts of the metropolis.

Conclusions

The key finding of this study is that over several decades of city expansion, industries inMelbourne can be categorized (within the limits imposed by the data) into two broadlydistinct patterns of spatial reorganization. One group of industries is characterized byclose spatial interdependencies with residential population; these industries tend to relo-cate in tandem with local populations as the metropolis suburbanizes. The precise natureof these ties varies by industry. It is imperative for certain kinds of firms to be close toconsumers: this applies to firms in the retail and wholesale trade sectors, which supplyeverything from food to white goods and automobiles, and to education, health and

10,000

15,000

20,000

25,000

30,000

35,000

–5,000

0

5,000

0 10 20 30 40 50 60 70 80 90

Ch

ang

e in

em

plo

ymen

t n

um

ber

s b

y S

LA

(per

son

s)

Distance from Melbourne CBD (km)

High-level service industries Low-level service industries

Figure 4. Location-specific growth of employment in industry groups, Melbourne SLAs, 1996–2006.

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community services as well as personal and other services. In other cases the relationshipis more complex: population tends to follow construction where it provides infrastructuresuch as roads and water, while the reverse is largely true for residential and nonresidentialbuilding construction. Similarly, manufacturing firms can follow the movement of popu-lation in order to be close to their workforce, or population can follow manufacturingwhere land costs take precedence to favor cheap locations on the outskirts of the city(Alkay & Hewings, 2012). The decay of these links with time observed in the presentstudy is probably a result of the increasing mobility of the population, which makesdistance a less important consideration in transactions with these industries. In addition tothese economic links, other factors, such as proximity to main roads and highways,cheaper land or better access to freight transport in the outer parts of a city, can alsoplay a role (Maoh & Kanaroglou, 2009; Thurston & Yezer, 1994), and it cannot be ruledout that, for at least some firms in this group, they work in tandem with economic links toproduce the spatial behavior observed here.

Industries in the second group, by contrast, remain mostly but not exclusively con-centrated at the center of the metropolis over the 35 years of the study period; this appliesto mining, electricity, gas and water supply, communication services, finance and insur-ance, property and business services, government services and defense and cultural andrecreational services (Table 2). Nearly all of the centrally located firms in this secondgroup provide high-level services to their clients, such as administration, accounting,research, investment advice and IT services. They do not have physical outputs that needto be transported to their points of use, rather they deal in intangible outputs (information,knowledge) with minimal transport costs; distance to the final consumers is of relativelylittle importance in these firms’ locational decisions. This makes the preference of high-level service industries for expensive central locations somewhat puzzling. The clusteringof these industries in city centers has been observed in other Australian cities (Searle,1998, 2009), as well as in Phoenix, Arizona (Ó hUallacháin & Leslie, 2007), Montreal(Coffey, Drolet, et al., 1996) and in a number of Spanish cities (Arauzo-Carod &Viladecans-Marsal, 2009). This phenomenon is generally ascribed to the need for theseindustries to create and exchange knowledge, which can only occur if they are in closeproximity to one another (Cook, Pandit, Beaverstock, Taylor, & Pain, 2007; Huber, 2012;Storper & Venables, 2004). The study reported here provides some support for this view,particularly in the larger centralization benefits observed here for many of these industries.The findings of other studies—that high-level service firms place an unusually high valueon city-center locations (White, 1999), and that agglomeration in urban environmentsleads to enhanced earnings in high-skill occupations but has little effect on low-skilloccupations (Gabe & Abel, 2011)—also bear out this interpretation. However, otherreasons for firms in these industries preferring a central location cannot be ruled out.Examples are the need to service other centrally located industries (Holl, 2008; Schwartz,1992), to minimize transaction costs (Iammarino & McCann, 2006) or to exploit theeconomies of scale to be gained from concentrating economic activity in one place (Calem& Carlino, 1991). The latter consideration, for example, is a prime reason for large,centrally located sporting venues and cultural and entertainment centers in the “culturaland recreational services” category. Generally, the exact nature of the forces bringingabout centralization is still unclear (Henderson, 2007; Parr, 2002a), and it may be that newapproaches and more detailed data will be necessary if definitive answers are to beobtained. In this respect survey-based studies of firms’ behavior (Esparza, 1992;Stolarick & Florida, 2006) show considerable promise.

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Outside the city center, firms in this group show significant links to population in afew cases which, however, decay rapidly over time (Tables 6 and 8). With the exceptionof cultural and recreational services, these links to population arise to a significant extentfrom the changing nature of what these firms do outside the city center. Banking in thesuburban environment, for instance, is heavily biased towards branch operations (over-the-counter transactions with depositors and borrowers) which require close contact withconsumers rather than the headquarters-related activities of the center. The weakening ofthese links with time is at least partly due to the increased mobility of the population, aswell as the introduction of labor-saving and remote access technologies (e.g., automaticteller machines and internet banking).

The spatial distributions of some of these industries reflect the privatization wavesimposed by Victoria in the 1990s, which replaced large centrally located institutions witha number of smaller enterprises. This process has been particularly important for publicutilities. For example, the main electricity utility (the State Electricity Commission ofVictoria) was divided into five distribution and retail companies, five generation compa-nies and a transmission company; the gas utility (the Gas and Fuel Corporation ofVictoria) was replaced by four producers, six transmission pipeline owners/operators,two gas distributors and four retailers. This has been a contributing factor in the dramaticdeclines of both electricity gas and water supply and transport and storage (publictransport, a significant part of the total transport activity in Victoria, was moved intothe private sector during these years) in the Melbourne LGA during the study period(Table 2), and in the sharp reductions in spatial autocorrelation between 1996 and 2001for electricity and gas supply and rail transport (Table 10).

In a number of other areas this study returned less conclusive results. Little evidencecould be found for intra-industry competition or land prices having any influence onindustry location. The spatial autocorrelation results, which follow the same spatial patternas clustering of population, suggest that any association of firms occurs as a by-product ofdirect contact with the population, rather than interactions between the firms themselves.There is certainly nothing to suggest that the spatial autocorrelation observed here is dueto the same forces which cause some industries to concentrate in the center of the city,even though other studies have found such clustering patterns, particularly for high-levelservice industries and specialized manufacturing (Boiteux-Orain & Guillain, 2004;Chakravorty, Koo, & Lall, 2005; Coffey & Shearmur, 2002; Guillain et al., 2006; Han& Qin, 2009; Shearmur & Alvergne, 2002).

Results provide little support for the occurrence of colocation of manufacturingsubdivisions (the only industry sector for which this investigation was carried out) linkedby supply chains or outsourcing. This finding is somewhat surprising, given that coloca-tion of industries linked through different stages of the same production process (e.g., autoparts suppliers) has been observed at the regional level in the United States (Sohn, 2004),as well as within cities such as Los Angeles (Agarwal, Giuliano, & Redfearn, 2012;Giuliano & Small, 1991), Cleveland (Bogart & Ferry, 1999), Atlanta (Fuji & Hartshorn,1995) and Paris (Gilli, 2009).

There are a number of possible reasons why the results here differ from those ofprevious studies. One reason is that the small scale of a city such as Melbourne (comparedto regions or states) and the existence of efficient transport systems makes the location offirms within the metropolis largely immaterial to their economic interests. Another reasonis that Melbourne, like many cities in the world, is constantly evolving and therefore,being a relatively young city in an earlier phase of suburban expansion, may have had lesstime to develop the urban structures characteristic of more mature cities (Erickson, 1983;

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Rodriguez-Gamez & Dallerba, 2012), where the decline in the importance of the CBD andthe spread of subcenters is more advanced (Chakravorty et al., 2005; Guillain et al., 2006).A contributing factor, however, is very likely the nature of the data available for thisstudy, which are at a much coarser level of spatial detail than most of the studies citedabove.

Despite these limitations, empirical studies such as this are valuable in revealingpatterns of industry location which can provide clues to the underlying forces at workin urban economies. The extended time period used here—unusual in studies of this kind—brings out clearly the importance of population shifts and centralizing forces in con-trolling industry location, as well as the complexity of the mechanisms at work. Clearly,simple models of cities are no longer sufficient to provide adequate explanations of urbaneconomies, a view which has been summed up in a study of Phoenix, Arizona: “intrame-tropolitan distribution. . . bears little resemblance to the predictions of the Alonso-Muth-Mills models of the monocentric city; we found no support for . . . either disorder oruniformity in intrametropolitan location patterns” (Ó hUallacháin & Leslie, 2009, p. 922).Disentangling these and other factors will only be possible with data at a finer spatialresolution and a more disaggregated breakdown of industry categories.

The industry-specific spatial behavior observed here also has important consequencesfor the way cities operate. Population-dependent industries such as manufacturing, con-struction and retail trade are labor intensive and employ a large proportion of city work-forces, making them prime drivers in changing the spatial structure of cities’ economies(Kneebone, 2009). With the lateral spread of cities and the increasing dispersal ofpopulation, many of these industries can be expected to move outwards, with the resultingsprawl increasing car dependence and leading to greater fossil fuel consumption andpollution (Newman & Kenworthy, 1999).

The same process has led to increasing partitioning of employment in the metropolis(Figure 4) with employment growth in the knowledge industries far stronger in the centerand growth in low-skill service industries such as retail strongest at the suburban fringe.From this it appears that Melbourne has become increasingly differentiated into a knowl-edge, cultural and entertainment center with a high concentration of well-paid, high-levelknowledge-based jobs and a region outside this—most strongly marked on the peripheryof the metropolis—where most employment growth is in low-level service industries. Thishas intensified the spatial divide of job types and hence of incomes, bringing about anincreased economic polarization of the metropolis (O’Connor & Healy, 2002). It also hintsat the possibility of the evolution of parts of the outer metropolis into the so-called “edgecities”—satellite centers of employment and shopping located away from, and to a largedegree independent of, the CBD (Marlay & Gardner, 2010). Edge cities were firstproposed for United States cities (Garreau, 1991) and later extended to cities in Europe(Bontje & Burdack, 2005). Edge cities may well become a characteristic of Melbourne inthe future, particularly if it follows the trend established by older cities, and experiencesincreasing formation of clusters of producer services and other high-paying knowledge-based industries outside the CBD (Coffey, Polèse, et al., 1996; Shearmur & Alvergne,2002).

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Documents

The spatial distribution of economic activity in Melbourne, 1971–2006