postindustrial manufacturing in a sunbelt metropolis

POSTINDUSTRIAL MANUFACTURING IN A SUNBELT METROPOLIS: WHERE ARE FACTORIES LOCATED IN PHOENIX?

Breandán Ó hUallacháin1 School of Geographical Sciences and Urban Planning Arizona State University Timothy F. Leslie Department of Geography and Geoinformation Science George Mason University

Abstract: Manufacturing establishments are integral to the spatial structure of fast-growing Sunbelt metropolitan areas, but most concepts and theories of intrametropolitan location were largely developed for an earlier technological era and different spatial contexts. This article investigates the location of nine disaggregated manufacturing sectors in Phoenix, Arizona, showing varying degrees of central core concentration and metropolitan-wide clustering. Distinct sectoral co-location patterns are also evident. We interpret our results as evidence that the intrametropolitan location of postindustrial manufacturing is best understood as a series of spatial distributions with varying concentration, centralization, clustering, and other order-based characteristics. There is little evidence that randomly scattered discrete industrial zones have developed nor that spatial patterns are uniform. Enduring lock-in effects tied to transportation infrastructure are pivotal to understanding the locational distribution of manufacturing industries in metropolitan Phoenix. Results do not support a hypothesis that a positive relationship exists between establishment size and distance from sectoral mean centers. [Key words: manufacturing location, intrametropolitan, concentration, clustering, co-location, lock-in.]

The relationship between urban spatial structure and the organization and intrametropolitan location of manufacturing is an enduring theme of economic geography. Fifty years ago, a series of landmark publications sponsored by the New York Metropolitan Region Study contrasted some locational and organizational attributes of externalitydriven small manufacturers producing customized goods near the CBD with those of decentralized vertically integrated large producers (Gustafson, 1959; Hall, 1959; Helfgott, 1959; Hoover and Vernon, 1959; Chinitz, 1960). Some 25 years later, a new generation of scholars extended this theme to the development of high-technology industrial zones in new manufacturing cities dominated by networks of small flexible producers with highly localized external economies of scale (Scott, 1983a; Piore and Sabel, 1984; Saxenian, 1994). This emphasis on control and regulation in specialized industrial zones and the decentralization of large mass producers contrasted sharply with both Dear and Flusty’s

1

Correspondence concerning this article should be addressed to Breandán Ó hUallacháin, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona 85287; email: ohuallachain@ asu.edu; or Timothy F. Leslie, Department of Geography and Geoinformation Science, George Mason University, Fairfax, Virginia 22030; email: [email protected]

898 Urban Geography, 2009, 30, 8, pp. 898–926. DOI: 10.2747/0272-3638.30.8.898 Copyright © 2009 by Bellwether Publishing, Ltd. All rights reserved.

WHERE ARE FACTORIES LOCATED IN PHOENIX?

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(1998) subsequent proposal that highly fragmented, disjointed, and unrelated parcels dominate postmodern urban spatial structure and Fujii and Hartshorn (1995), Gordon and Richardson (1996), and Lang’s (2003) interrelated assertions that scatteration, uniformity, and edgeless development increasingly pervade the distribution of economic activities in Sunbelt metropolises. Advancements in spatial analytic tools allow us to test aspects of these contrasting claims. In particular, we identify and test a series of hypotheses related to establishment concentration, clustering, and co-location in disaggregated manufacturing sectors in metropolitan Phoenix. We also evaluate the enduring hypothesis that industrial organization and location are related through analysis of the relationship between establishment size and distance from each sector’s mean geographic center. In this article, we have several interrelated goals. First, we seek to discern order in the intrametropolitan distribution of manufacturing industries in Phoenix. We assess order in a similar style as Barff (1987), Sweeney and Feeser (1998), Marcon and Puech (2003), Duranton and Overman (2005), Sweeney and Konty (2005), and Ariba et al. (2008), using cartographic analysis and assessments of concentration, spatial clustering, and the co-location of geo-referenced establishments in different sectors. We follow with a disaggregate analysis of the relationship between establishment size and distance from each sector’s spatial core. If small units producing customized goods tend to concentrate in externality-rich central locations, we should observe a positive relationship between establishment size and distance from the core. This conjecture arose in an era when small, centralized plants were distinguishable from mass-production manufacturers on the urban periphery. Finally, we propose an alternative depiction of urban economic form. The intrametropolitan location of manufacturing is best portrayed as a series of interrelated spatial distributions rather than discrete districts, specialized employment centers and subcenters, haphazard collections of disjointed and fragmented zones, or uniform sprawl. This investigation differs from most previous analyses of the intrametropolitan distribution of manufacturing. We employ extensive, disaggregated, georeferenced point data that identifies the sector, location, and employment in most of the metropolitan area’s manufacturing plants. This allows us to advance understanding of complex location patterns without the snags of areal unit analysis. We sidestep boundaries based on noneconomic forces, we do not have to calculate areal averages because we know the employment numbers in the establishments investigated, and we also know the exact location of each plant. Data suppression that riddles census-based data does not occur, and meager response rates that are common in survey research of manufacturers do not arise. Analysis of manufacturing in Sunbelt cities in general, and Phoenix in particular, has several advantages. We start with the proposition that what we know about the intrametropolitan distribution of manufacturing is increasingly dated because of the changing nature of manufacturing and because conceptual frameworks were never systematically applied to fast-growing Sunbelt cities that developed in a different technological era. In Phoenix, exogenous environmental forces have had little influence on urban spatial structure because no port, navigable river, or other physical feature imposed centrality on the system. The metropolitan area continues to grow rapidly, expanding from approximately 375,000 residents in 1950 to more than four million today. Development patterns have consistently centered on automobiles and trucks, enabling flexible road access across the region as well as rapid expansion away from the city center. Expansion brought jobs in a

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Ó HUALLACHÁIN AND LESLIE

wide range of economic sectors including manufacturing, population densities are increasing throughout the area, and traffic congestion has become costly for commuters and industry (Rex, 2000; Gammage, 2003; U.S. Environmental Protection Agency, 2007). In 2006, manufacturing jobs accounted for 8% of the area’s private-sector jobs compared to 11% in the United States (U.S. Department of Commerce, 2006). Whereas manufacturing job growth concentrated in light industries, especially electronics, electrical equipment, and fabricated metals, no distinctive vertically disintegrated clusters of small manufacturing plants developed that neatly contrast with the large mass-production facilities of previous industrial eras (Glasmeier, 1987). A central aspect of our investigation is to determine whether features of the location of manufacturing in older industrial cities still hold in a new economic and technological era as well as a new spatial context. INTRAMETROPOLITAN LOCATION OF MANUFACTURING The Alonso-Mills-Muth accessibility-based models of urban spatial structure propose that firms choose an equilibrium location to balance the cost and consumption of land. These models predict that all production takes place in an exogenously predetermined center, with workers commuting from outlying residential neighborhoods (Mills, 2000). The failure of the models to predict employment decentralization has led to substantial interest in the formation, location, and structure of employment subcenters that compete with the CBD (Giuliano and Small, 1991; Anas et al., 1998; McMillen and McDonald, 1998; Bogart and Ferry, 1999; Craig and Ng, 2001; McMillen, 2001; Leslie and Ó hUallacháin, 2006; Giuliano et al., 2007; Shearmur et al., 2007). This failure has also sustained interest in historical analysis of intrametropolitan locational patterns in individual cities and in the generality of descriptive depictions of urban spatial structure that reflect conditions in particular cities in particular eras (Muller and Groves, 1979; Viehe, 1981; Gad, 1994; Muller, 2001). Through their investigations of Chicago, Harris and Ullman (1945) proposed that accessibility to the CBD and externalities had generated several specialized and discrete land use nuclei. Among these geographical zones, they distinguished between a light manufacturing district near the CBD and a peripheral heavy manufacturing area. While the former provides good access to transportation facilities, labor pools, and markets, the latter allows construction of large facilities on extensive tracts of land near rail belt lines and switching yards. Isard (1956) modified the multiple nuclei approach by characterizing industrial zones as either highly specialized or sectorally diversified. More recently, Dear and Flusty (1998) portrayed Los Angeles as a gridiron of disjointed, fragmented, and unrelated economic, social, and cultural outcomes. Pred (1964) rejected notions of discrete districts and emphasized the distributional characteristics of San Francisco’s manufacturers that varied from spatially concentrated to dispersed. He preferred Hoover’s (1948) stress on truck-dependent manufacturers in Chicago scattering across the metropolis to access labor, cheap land, suppliers, and markets. Hoover also noted that inner city manufacturing plants usually had fewer employees working in generally older facilities compared to larger and newer suburban manufacturers. Chinitz (1960) argued that the reliance of small plants on the services of specialized freight forwarders, and not the intrinsic cost of transportation, propelled their attraction to central locations.


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Gustafson (1959), Hall (1959), and Helfgott (1959) noted that central city externalities included specialized design, maintenance, and repair services as well as easier face-toface interpersonal communication among the small, functionally related, and flexible producers of vertically disintegrated production chains. Hoover and Vernon (1959) and Vernon (1959) also argued that the central city acts as a common resource base for small producers and mitigates uncertainties inherent in their markets, material supplies, labor pools, and services. Frequent subcontracting of small firms in city centers allowed for flexible responses to the uncertainties of customized manufacturing. Firms producing standardized goods in large integrated facilities decentralized to the metropolitan periphery. Moses and Williamson (1967) documented a positive relationship between establishment size and distance moved by relocating manufacturers in metropolitan Chicago. They speculated that large, autonomous producers were less dependent on input–output linkages at a particular location. Small producers, who relied on the externalities of their immediate neighborhood, moved much shorter distances. This emphasis on externalities of central city production in New York and Chicago in the 1950s and 1960s contrasted sharply with Muller and Groves’s (1979) emphasis on the accessibility and land needs that drove the development of specialized industrial zones in central Baltimore during the 19th century. Scott (1983a) reiterated the conjecture that the production of customized goods mostly occurs in centralized small plants that engage in complex and highly variable subcontracting relationships with nearby suppliers and markets. Large vertically integrated capital-intensive plants, specializing in production of standardized goods, locate toward the urban periphery. This dichotomy leads to two interrelated hypotheses: (1) a positive relationship exists between establishment size and distance from the manufacturing core; and (2) small manufacturing plants are more clustered, concentrated, and centralized. He found support for these propositions in a survey of the women’s dress industry in Los Angeles, where responding small establishments were more concentrated and clustered near the CBD (Scott, 1984). His survey of printed circuit manufacturers found no increase in average plant size beyond 12 km (7.5 miles) from the center, although small plants were the most clustered (Scott, 1983b). Moomaw (1980) reported a positive relationship between plant size and distance from the CBD in Tulsa, which he attributed to the willingness of large producers to sacrifice accessibility for cheaper suburban land. However, Barff’s (1987) results for aggregate manufacturing in Cincinnati showed no evidence that plant size—i.e., number of employees—increases with distance from the CBD. He also found no evidence that clustering intensity in manufacturing varies significantly among different establishment size categories. Marcon and Puech (2003) uncovered significant intersectoral variation in plant clustering in Paris. Clustering mainly occurred in low-technology sectors, including clothing and leather, printing and publishing, and textiles. In contrast, more technologically intensive sectors were significantly dispersed. This result corroborates the findings of Ellison and Glaeser (1997), Maurel and Sédillot (1999), Bertinelli and Decrop (2005), Duranton and Overman (2005), and Mori et al. (2005) at the interurban scale that technologically intensive compared with traditional industries are significantly less geographically concentrated in the United States, France, Belgium, Britain, and Japan, respectively. Across the United States, contrasts between the central and noncentral counties of large metropolitan areas showed a noticeable tendency for the former to develop as administrative

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and management hubs as the latter flourish in production activities (Rossi-Hansberg et al., 2005). Beyond the hypothesis that small compared with large establishments cluster in the center of metropolitan areas, several researchers have underscored the tendency for certain manufacturing sectors to co-locate. Large polluting steel and chemical plants, separated from residential areas, dominated Harris and Ullman’s (1945) peripheral heavy manufacturing district. Hoover (1948) noted that labor-intensive clothing and printing industries located on the fringe of the CBD, with petroleum and steel manufacturers concentrating along peripheral transportation corridors. Hall (1959), Helfgott (1959), Gustafson (1959), and Chinitz (1960) termed New York’s apparel and printing manufacturers examples of “external-economy” industries that relied on speedy access to centralized suppliers, subcontractors, service providers, and customers. Vernon (1959) identified 19 disaggregated textile, wood product, printing, and jewelry industries that co-located in the centers of large metropolitan areas in the Manufacturing Belt. Gordon and Richardson (1996) subsequently argued that metropolitan-wide agglomeration economies have superseded locally based externalities, leading to a more uniform dispersal of establishments in all sectors. Leslie and Ó hUallacháin (2006) contested the precision of this claim, showing that the locational distribution of establishments noticeably varied across sectors in Phoenix. They also identified intersectoral co-location patterns—manufacturing with wholesaling, warehousing, and transportation services; finance and insurance with producer services; and retailing with entertainment and accommodation services. In short, uniform dispersal had not occurred in Phoenix. Shearmur (2007) noted that intersectoral spatial associations have many origins, including strong functional input–output linkages and the need to access enduring local external economies of scale generated by labor pools, service externalities, and transportation facilities. Anas et al. (1998) argued that lock-in effects are far less influential in Sunbelt metropolises, especially those whose formative growth occurred in the automobile/truck era. They contended that Phoenix well illustrates the waning influence of railroad routes on urban spatial structure. Most recent analyses of urban spatial structure have shifted focus from the location of individual manufacturing industries to the identification of employment subcenters in large, sprawling metropolitan areas. Although manufacturing is integral to these analysis, their emphasis is on the distribution of aggregate job numbers that retailing, health care, communications, as well as producer, real estate, and financial services dominate (Giuliano and Small, 1991; McMillen and McDonald, 1998; Bogart and Ferry, 1999; Giuliano et al., 2007; Shearmur et al., 2007). Data availability has partly dictated this emphasis on the location of aggregate employment. Popular census-based areal data, including zip code and traffic analysis zones, seldom reveal characteristics of individual industries, and they never disclose establishment information. Our data for Phoenix, which are described below, permit detailed locational analysis of individual establishments in highly disaggregated manufacturing industries. With a focus on disaggregate manufacturing, we can compare the location of jobs and establishments in Phoenix with earlier studies that investigated locational aspects of particular industries in older cities, and with newer approaches that seek to understand general patterns of polycentricity and sprawl.


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METHODOLOGY Urban spatial structure remains an enduring theme in the search for understanding the composition and transformation of North American metropolitan areas. Booming Sunbelt cities are often portrayed as exemplars of a new economic era, but with considerable disagreement about critical aspects of emerging spatial structure. Development discussions highlight specialized districts and the emergence of employment subcenters, placement of green space and mixed-use development, the degree of land use fragmentation and disorder, and uniformity in locational distributions (Garreau, 1991; Lang, 2003). The lasting influence of transportation infrastructure and local externalities tied to capital stocks are recognized in older industrial cities of the Northeast and Midwest, but are considered far less important in Sunbelt cities. It seems appropriate to apply spatial analytic tools to evaluate some of these competing claims. Our central research focus, therefore, is the relevance of established concepts and theories to understanding current manufacturing location in a large, fast-growing, Sunbelt metropolis. Five interrelated themes comprise our empirical analysis of disaggregated intrametropolitan manufacturing location in Phoenix. Cartographic investigation reveals distributional patterns in relationship to the urban core and major transportation infrastructure. It provides a preliminary glimpse into the tendency for manufacturers to form discrete specialized zones, scatter uniformly, or develop spatial distributions with varying degrees of centralization and clustering. We follow with a summary of establishment and employment concentration and density patterns in concentric zones around the manufacturing core. Second-order analysis uses distance-based methods to evaluate sector-specific clustering and tests the null hypothesis that uniformity best depicts the spatial distribution of establishments. To evaluate the claim that land-use patterns are increasingly fragmented, disjointed, and unrelated, we assess co-location among establishments across sectors. Finally, we test the frequently articulated hypothesis that establishment size and distance from each sector’s core is positively related. We suspect that this relationship does not hold in Phoenix, where manufacturing is not organized as either specialized industrial zones nor as large-scale decentralized mass manufacturing, traditional heavy air-polluting manufacturing is absent, and most of the urban area’s growth occurred in the automobile/ truck area. Cartographic and Clustering Analysis Sectoral establishment maps reveal broad locational patterns, identify relationships between establishment distributions and transportation infrastructure, and highlight differences across sectors in core/suburban concentrations. We summarize the establishment characteristics of three concentric rings focused on the unweighted central point of aggregate manufacturing—0–3, 3–8, and 8–18 miles. This aggregate central point is approximately three miles east of the CBD (Fig. 1; all individual sectoral centers are nearby). These rings are an organizational scheme, not a reversal to notions of urban spatial structure singularly configured by CBD accessibility. Using 3 miles to define the inner ring captures the high-density segment of Central Avenue, which is the main north-south axis of the CBD. The second ring incorporates the manufacturing corridors that radiate from

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Fig. 1. General reference map of the greater Phoenix area, showing the location of the core-city CBD, major transportation features, and the downtowns of Scottsdale and Tempe.

the core and constitute Phoenix’s principal manufacturing concentrations (Leslie and Ó hUallacháin, 2006). Few manufacturers sited their facilities beyond 18 miles from the core. We calculate employment and establishment densities—a first-order property—of each industry in each ring to document facility and job concentration. The mean establishment employment by sector and by ring previews the relationship between establishment size and distance from each sector’s center. Second-order analysis is a scale-free approach that calculates the probability of finding a neighbor at a given distance (Venables and Ripley, 1994; Levine, 2004). Because finding that sectors are clustered at the absolute level is unsurprising, we compare clustering intensities across sectors using Ripley’s K (Marcon and Puech, 2003). Ripley’s K is a function that compares the cumulative distribution function of all inter-establishment distances to a reference distribution of complete spatial randomness (CSR) generated by Monte Carlo permutations. We use the standard linear transformation of Ripley’s K known as L(t) because it is easier to interpret and compare with CSR. Each t value is a circle of increasingly greater radii, allowing examination of clustering at a wide range of distances. If L(t) is greater than zero, then establishments are clustered at a given t value;


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values of L(t) less than zero show dispersion. We compare sectoral peak L(t) values, the distance t at which their maximum value occurs, and the overall shape of the L(t) functions. We examine the distribution of establishments as points and functions weighted by employment. Weighting establishments by employee numbers provides a mechanism for investigating the influence of plant size on clustering distributions (Levine, 2004). Sectoral Associations Intersectoral spatial associations are an additional component of order in urban spatial structure. Given common and enduring accessible and externality needs, it is highly likely that the distribution of some disaggregated manufacturing sectors are related. To identify co-location of establishments across sectors, we use the nearest establishment with asymmetrical relationships (NEAR) method (Leslie and Ó hUallacháin, 2006). This method calculates a Proximity Index (PI) from sector x to sector y for each sectoral pair. Establishments y Links x , y PI x , y = ⎛ ------------------------------------------- ⁄ ----------------------------------------------------------------------------------------------⎞ – 1 ⎝ Establishments x ΣEstablishments – Establishments x⎠

(1)

We first calculate the proportion of establishments from sector y that are closest to the plants in sector x. This is accomplished by dividing the number of times an establishment in sector y is the closest neighbor to an establishment in sector x (Linksx,y) by the number of establishments in sector x (Establishmentsx). Owing to variability in the number of establishments across sectors, we normalize this proportion by the corresponding proportion of establishments of sector y (Establishmentsy) in the area (not including those in sector x, the sector of examination). Subtracting unity makes the expected value of PI equal to zero, when the proportion of closest neighbors of a sector’s pair is equal to the overall representation of that paired sector in the region. Interpretation is straightforward. Each sector has a PI for each other sector. A value of PI equal to zero means that sector y’s proportion of sector x’s closest neighbors equals sector y’s proportion of establishments in the region, excluding those in sector x. Values larger than zero indicate greater links between the two sectors than expected, whereas values less than zero show fewer links. The lower bound of the index is –1.0, which occurs when no establishments of a given sector are the closest to those in another sector. The Proximity Index has no upper bound. A PI of unity indicates that sector y is closest to sector x twice as often as expected, a PI of 2 shows three times as often, and so forth. Asymmetry between sectors can occur—sector x may be the closest neighbor to y, but there is no requirement that sector y’s establishments are closest to those in sector x. Negative Binomial Regression Beyond spatial patterns, we explore the relationship between industrial organization and location. By using establishment size to measure scale we determine whether there is a significant relationship between the distance from sectoral centers and the size of individual establishments. We compare the sign and significance of this variable across sectors. A significant positive relationship would support Scott’s (1983a) hypothesis for the

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intrametropolitan distribution of manufacturing. He argued that small establishments engaged in customized production within functionally integrated dense networks mostly locate in central cities. In contrast, suburban manufacturers of standardized goods for national markets are larger. A significant inverse relationship between establishment size and distance implies that large establishments are more willing to pay the higher costs of central land to access labor pools, transportation infrastructure, and services. This would occur if internal economies of scale and scope positively associate with externalities and small, dispersed establishments are less concerned with central city access and external economies of scale. Our conceptualization of the relationship between industrial organization and location uses the number of jobs in each establishment as the dependent variable. Due to the count-based nature of this distribution, we employ alternatives to ordinary least squares. Starting with the Poisson regression model, the dependent variable for observation i (with i = 1, ..., n), yi, is a non-negative integer count with a mean μi that is specified as a function of explanatory variables xi, and matching parameters β. Poisson regression requires that the variance of the dependent variable equals the mean, conditional on the independent variables. If this fairly strong restriction is not met, the negative binomial regression model is needed to provide consistent parameter estimates and valid hypothesis tests (Cameron and Trivedi, 1998). Greene (2002) provides Cameron and Trivedi’s (1998) “alpha” test and a likelihood ratio test based on the chi-square distribution to evaluate overdispersion and the suitability of either the Poisson or the negative binomial regression models. The negative binomial regression model introduces unobserved heterogeneity to the Poisson model by respecifying the mean μi as log μi = log θi + ∈i = β'xi + ∈i

(2)

where θi is both the mean and the variance of yi, the disturbance term ∈i reflects either cross-sectional heterogeneity or specification error, and exp(∈i) has a gamma distribution with mean 1.0 and variance α. Maximum likelihood estimation derives efficient estimates of the β parameters. In our noncausal analysis, we define a single independent variable xi as the distance from an establishment to the unweighted mean geographical center of its sector. We do not include distances to subcenters in this analysis for two reasons. The mean center of each manufacturing sector lies adjacent to Phoenix’s principal manufacturing hub, located along the main rail/interstate/airport corridor near the CBD. Moreover, other large employment subcenters in Phoenix are dominated by varying mixes of service industries and not manufacturing (Leslie and Ó hUallacháin, 2006). DATA The Maricopa Association of Governments (MAG) is a planning alliance of municipal governments in Maricopa County that collects and maintains extensive socioeconomic and demographic data (Maricopa Association of Governments, 2007). MAG’s 2001 survey of nongovernmental employers in Maricopa County generated point data that identifies the location of 2519 manufacturing establishments, excluding those with less than five employees. The number of establishments in each of the nine disaggregated manufacturing sectors that we investigate is shown in Table 1, excluding only the miscellaneous

Minerals and metals

Machinery

Computer, electronic, electrical equipment

Transportation equipment

Furniture

All manufacturing

324,5,6

327,31,32

333

334–335

336

337

31–33

55,697

5,220

2,420

10,258

4,558

14,176

5,524

8,547

2,016

2,978

Employment

2,286

178

96

339

206

594

252

409

102

110

Establishments

24.4

29.3

25.2

30.3

22.1

23.9

21.9

20.9

19.8

27.1

Average establishment size

Sources: U.S. Department of Commerce (2003), Maricopa Association of Governments (2007).

Wood, paper, printing

Petroleum, chemicals, plastics, and rubber

321,2,3

Food, beverages, tobacco

Textiles, apparel, leather

313,4,5,6

Sector

311–312

NAICS

MAG

2,015

154

116

229

154

577

236

378

64

107

Maricopa CBP establishments

47.3

30.1

138.9

91.5

46.1

33.9

62.5

30.8

34.5

54.7

U.S. CBP average wstablishment size

TABLE 1. SECTORAL COMPOSITION OF MARICOPA COUNTY MANUFACTURING

1.13

1.16

0.83

1.48

1.34

1.03

1.07

1.08

1.59

1.03

Establishment ratio MAG/Maricopa CBP

.51

.98

.18

.33

.48

.70

.35

.68

.57

.49

Establishment size ratio MAG/U.S. CBP

Ratio


907

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category. We aggregate related sectors to simplify our analysis and to ensure that each sector has sufficient observations for analysis. The two largest sectors are minerals and metals, and computer, electronic, and electrical equipment. In the 1970s and 1980s, the leading manufacturing plants in Phoenix were semiconductor production facilities of large multinational electronics firms, especially Motorola (Glasmeier, 1987). Table 1 compares the sectoral composition of MAG’s survey data with County Business Patterns (CBP) tallies for Maricopa County in 2001 (U.S. Department of Commerce, 2003). We restrict the CBP numbers to establishments with five or more employees to correspond with the MAG survey. Although the MAG survey aligns reasonably well with the CBP portrayal of the county’s economic structure, it reports fewer establishments in transportation equipment (NAICS 336) and more in all other sectors, especially textiles, apparel, and leather (NAICS 313,4,5,6) as well as computer, electronic, and electrical equipment (NAICS 334–335) and machinery (NAICS 333). Perhaps some establishments were misclassified. In other cases, fluctuations in employment numbers of small establishments with around five employees during 2001 could be responsible. We have no evidence that systematic locational bias exists in the MAG data or if exclusion of establishments with four or fewer employees distorts our results. Table 1 also shows national average establishment size, measured by employment, in each sector and the ratio of that calculation to its equivalent MAG average. Unlike the MAG data, the national average includes establishments with fewer than five employees, which most likely deflates the calculated values. Manufacturing in Phoenix occurs in establishments that are noticeably smaller compared to national averages, particularly in transportation equipment (18% of the national average); computer, electronic, and electrical equipment (33%); and petroleum, chemicals, plastics, and rubber (35%). Furniture (98%) is an exception, with an average size in Phoenix that approximates the national average. Smaller manufacturing establishments in metropolitan Phoenix corroborate Holmes and Stevens’s (2002) finding that manufacturing plant and metropolitan sizes are inversely related across the U.S. urban system. RESULTS Concentration Figures 2A–1I show the spatial distribution of establishments in each disaggregated sector in the study area. We utilize three concentric rings around the mean center of aggregate manufacturing and differentiate between establishments by employment size. Few manufacturing establishments locate in the heart of the CBD, which Leslie and Ó hUallacháin (2006) and Ó hUallacháin and Leslie (2007) identified as a prominent peak in the spatial distribution of producer and financial services. Manufacturing in Phoenix is closely tied to transportation infrastructure—rail, freeways, surface streets, and airports. The main rail/interstate/air corridor located to the east, west, and south of the CBD and adjacent to the main airport (Sky Harbor) is pivotal, especially in minerals and metals (Fig. 2E) and machinery (Fig. 1F). Features of this corridor were evident to Murphy et al. (1955) and McKnight (1962) some 50 years ago and Struyk and James (1975) noted the concentration and rapid growth of manufacturing in the area during the 1960s. Minor secondary concentrations occur in the suburbs, including a clustering of


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Fig. 2. Distribution of disaggregated manufacturing establishments in Phoenix, Arizona: (A) food, beverages, and tobacco; (B) textiles, apparel, and leather.

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Fig. 2 (cont’d). Distribution of disaggregated manufacturing establishments in Phoenix, Arizona: (C) wood, paper, and printing; (D) petroleum, chemicals, plastics, and rubber.


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Fig. 2 (cont’d). Distribution of disaggregated manufacturing establishments in Phoenix, Arizona: (E) minerals and metals; (F) machinery.

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Fig. 2 (cont’d). Distribution of disaggregated manufacturing establishments in Phoenix, Arizona: (G) computer, electronic, and electrical equipment; (H) transportation equipment.


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Fig. 2 (cont’d). Distribution of disaggregated manufacturing establishments in Phoenix, Arizona: (I) Furniture.

wood, paper, and printing (Fig. 2C); petroleum, chemicals, plastics, and rubber (Fig. 2D); computers, electronic, and electrical equipment (Fig. 2G); and furniture (Fig. 2I) producers near Scottsdale Airpark. The sprawling suburbs to the southeast contain abundant computer, electronic, and electrical equipment as well as machinery establishments. Lower technology sectors, particularly furniture as well as wood, paper, and printing flank Grand Avenue and its adjacent rail line that radiate northwest from the CBD. Highly specialized and discrete industrial zones are absent. Detailed distributions of establishments and jobs by ring and sector are shown in Table 2. The inner ring (0–3 miles) contains few of the area’s manufacturing establishments (123) and jobs (2,291). Densities are also low, especially compared with the intermediate ring (3–8 miles). Whereas the largest plants of the inner ring produce computer, electronic, and electrical equipment (30.5 jobs), more numerous establishments in the minerals and metals sector account for approximately 50% more jobs. These two sectors account for 45% and 43% of inner-ring employment and establishments, respectively. Average establishment size varies from 10.8 to 30.5 employees. Transportation equipment manufacturers are particularly small, averaging fewer than 11 employees. Beyond the latter, the inner ring has the smallest establishments in wood, paper, and printing (13.4 jobs); minerals and metals (15.5 jobs); and petroleum, chemicals, plastics, and rubber (19.1 jobs). The inner ring has the largest establishments in machinery (25.3 jobs).

Sector


Minerals and metals

Machinery



Furniture

All manufacturing

321,2,3

324,5,6

327,31,32

333

334–335

336

337

31–33


Petroleum, chemicals, Plastics and rubber

Minerals and metals

Machinery

Computer, electronic, Electrical equipment


Furniture

All manufacturing

324,5,6

327,31,32

333

334–335

336

337

31–33


313,4,5,6

321,2,3

Food, beverages, Tobacco

311–312

Intermediate ring, 3–8 miles



313,4,5,6


311–312

Inner ring, 0–3 miles

NAICS

1,034

87

47

192

98

237

114

157

45

57

123

12

4

14

4

39

8

28

10

4

Establishments

26,338

3,024

1,527

5,474

1,942

6,119

2,614

3,711

704

1,223

2,291

321

43

427

101

606

153

374

175

91

Employment

5.98

0.50

0.27

1.11

0.57

1.37

0.66

0.91

0.26

0.33

4.35

0.42

0.14

0.50

0.14

1.38

0.28

0.99

0.35

0.14

Establishment density

TABLE 2. RING CHARACTERISTICS: ESTABLISHMENTS, EMPLOYMENT, DENSITY, AND AVERAGE SIZE

152.4

17.5

8.8

31.7

11.2

35.4

15.1

21.5

4.1

7.1

81.0

11.4

1.5

15.1

3.6

21.4

5.4

13.2

6.2

3.2

Employment density

25.5

34.8

32.5

28.5

19.8

25.8

22.9

23.6

15.6

21.5

18.6

26.8

10.8

30.5

25.3

15.5

19.1

13.4

17.5

22.8

Employment/ establishment

914 Ó HUALLACHÁIN AND LESLIE

Machinery



Furniture

All manufacturing

334–335

336

337

31–33

Minerals and metals

327,31,32

333



324,5,6


313,4,5,6

321,2,3


311–312

Outer ring, 8–18 miles

1,033

69

42

126

96

293

118

206

43

40

25,301

1,735

809

4,259

2,301

6,972

2,633

4,064

972

1,556

1.26

0.08

0.05

0.15

0.12

0.36

0.14

0.25

0.05

0.05

31.0

2.1

1.0

5.2

2.8

8.5

3.2

5.0

1.2

1.9

24.5

25.1

19.3

33.8

24.0

23.8

22.3

19.7

22.6

38.9


915

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The second ring contains 49% of the area’s manufacturing jobs and 37% of establishments. Minerals and metals; computer, electronic, and electrical equipment; and wood, paper, and printing establishments account for approximately 58% of the ring’s manufacturing jobs and establishments. Other large job generators include furniture, as well as petroleum, chemicals, rubber, and plastics. This second ring has the highest average manufacturing establishment size (25.5 jobs), particularly in furniture (34.8 jobs) and transportation equipment (32.5 jobs). Other sectors with their largest establishments in the intermediate ring include minerals and metals (25.8 jobs); wood, paper, and printing (23.6 jobs); and petroleum, chemicals, plastics, and rubber (22.9 jobs). This middle ring has the smallest establishments in textile, apparel, and leather (15.6 jobs); machinery (19.8 jobs); computer, electronic, and electrical equipment (28.5 jobs); and food, beverages, and tobacco (21.5 jobs). Employment densities in minerals and metals (35.4) and computer, electronics, and electrical equipment manufacturing (31.7) are noticeably higher in the intermediate ring compared to the inner ring. The third or outer ring, positioned from 8 to 18 miles from the mean manufacturing center, encompasses an area of some 820 square miles, and contains slightly fewer manufacturing jobs and establishments than the middle ring. Minerals and metals; computer, electronic, and electrical equipment producers; and wood, paper, and printing dominate employment and establishment numbers. Densities are low because facilities are distributed across a large area that contains extensive residential districts and tracts of undeveloped land. The outer ring’s aggregate average establishment size (24.5 jobs) approximates the average size of those in the intermediate ring (25.5 jobs). However, the average establishment size in food, beverages, and tobacco (38.9 jobs); textiles, apparel, and leather (22.6 jobs); and computer, electronic, and electrical equipment manufacturing (33.8 jobs) is largest in this ring. These preliminary concentric ring results suggest that small as opposed to large establishments are more centralized in the inner and the intermediate rings. In only one sector—machinery—was establishment size highest in the inner ring. However, establishment size mostly peaks in the intermediate ring, which encompasses the enduring rail/ interstate/air manufacturing corridor. In only three of the nine sectoral groupings was establishment size highest in the outer ring. Clustering Figures 3A–3B show the results of the linearly transformed Ripley’s K(t) function for all manufacturing as well as the nine disaggregated sectors. We show results with establishments in each sector treated as equally important and employment weighted functions. Owing to fewer than 100 establishments in transportation equipment, we were unable to calculate clustering levels in that sector. We do not show the maximum and minimum envelopes of 1000 simulations in each sector because all sectors are noticeably more clustered compared with random distributions over all examined distances. The null hypothesis that manufacturing establishments in metropolitan Phoenix are uniformly distributed is soundly rejected. Minerals and metals are the most clustered, with the peak occurring in the radius of 10 to 17 miles. Machinery establishments rank second in clustering. Intermediate clustering levels occur in furniture; petroleum, chemicals, rubber, and plastics; food, beverages, and tobacco; and wood, paper, and printing. Computers,


Fig. 3. (A) Unweighted and (B) employment-weighted Ripley’s L(t) functions.

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electrical and electronic equipment, and textiles and apparel are the least clustered. There is little difference between clustering levels of the employment-weighted and unweighted plants (many sectors overlay almost exactly). Only in food, beverages, and tobacco and wood, paper, and printing does weighting lead to higher peaks. Ó hUallacháin and Leslie’s (2007) comparative clustering of disaggregated producer services sectors in Phoenix have mostly lower peaks. Legal services, the most clustered producer services sector, has a peak L(t) value of around 7.2, which is noticeably less intense compared to clustering levels in minerals and metals; machinery; furniture; and petroleum, chemicals, rubber, and plastics. Ó hUallacháin and Leslie’s (2007) study also showed differences between unweighted and employment-weighted peaks in an analysis of disaggregate producer services, a result not found here for manufacturing sectors. Sectoral Associations The NEAR results are shown in Figure 4. We identify two general patterns of intersectoral association that do not support the hypothesis that economic activities are distributed in unrelated and disjointed patterns. First, locational affinity is evident among several low-technology sectors. Food, beverages, and tobacco as well as textiles, apparel, and leather are closest to furniture establishments. Wood, paper, and printing are closest to textiles, apparel, and leather. Furniture is closest to textiles, apparel, and leather. A second grouping spatially associates more technologically intensive sectors. Petroleum, chemicals, rubber, and plastics; computers, electronic, and electrical equipment; and transportation equipment establishments are closest to those producing machinery goods. Machinery is closest to petroleum, chemicals, rubber, and plastics. Pronounced intersectoral dissimilarities reinforce separating sectors into lower and higher technology intensive groupings. Food, beverages, and tobacco establishments show little mutual affinity with establishments in petroleum, chemicals, rubber, and plastics or machinery. Textiles, apparel, and leather establishments do not locate near transportation equipment or computer, electronic, and electrical equipment facilities. Machinery establishments are not close to wood, paper, and printing establishments or to facilities in food, beverages, and tobacco. Computer, electronic, and electrical equipment establishments and those in furniture are mutually unassociated. Even though transportation equipment’s weakest associations are with computer, electronic, and electrical equipment, these facilities also exhibit little affinity with the location of textiles, apparel, and leather establishments. Affinities within these two diverse sets of lower and higher technology sectors most likely stem from dependence on distinct sets of external economies and accessibility needs. Establishment Size/Center Distance Sensitivity Estimates of the strength of the relationship between establishment size (dependent variable) and distance from each sector’s mean spatial center (independent variable) are shown in Table 3. The alpha and likelihood ratio (chi-squared) tests confirm overdispersion indicating that negative binomial regression estimation is warranted in all sectors. Only two sectors exhibit statistically significant regression coefficients at the .05 level. Establishment size is inversely related to distance from the manufacturing center of food,


Fig. 4. NEAR results of intersectoral associations.

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TABLE 3. NEGATIVE BINOMIAL REGRESSION RESULTSa NAICS 31–33

311, 312

Sector All manufacturing

Intercept

Regression coefficient

3.202

Food, beverages and tobacco

3.737

313, 314 315, 316

Textiles, apparel, and leather

2.847

321, 322 323

Wood, paper, and printing

2.848

324, 325 326

Petroleum, chemicals, plastics, rubber

3.253

327, 331 332

Minerals and metals

3.111

333

Machinery

3.145

Alpha

Chi-squared

–.0000004

0.835

52500.94

(0.619)

(0.000)

(0.000)

–.0000087

0.914

3440.59

(0.003)

(0.000)

(0.000)

.0000030

0.697

1641.54

(0.364)

(0.000)

(0.000)

.0000043

0.717

6721.50

(0.009)

(0.000)

(0.000)

–.0000035

0.663

3682.37

(0.111)

(0.000)

(0.000)

.0000013

0.749

11598.05

(0.311)

(0.000)

(0.000)

–.0000009

0.732

3687.58

(0.561)

(0.000)

(0.000)

1.103 (0.000)

17861.77 (0.000)

334, 335

Computer, electronic, and electrical equipment

3.617

–.0000044 (0.091)

336


2.944

.000006

0.935

2828.14

(0.184)

(0.000)

(0.000)

Furniture

3.605

–.000005

1.098

7591.65

(0.059)

(0.000)

(0.000)

337

a

Dependent variable = establishment size. Numbers in parentheses are two-tailed significance levels.

beverages, and tobacco, which is clearly at odds with the standard hypothesis that bigger establishments decentralize to the periphery. The hypothesis of a positive relationship between plant size and distance to the mean center is only correct for establishments in wood, paper, and printing. Establishment size in furniture and computer, electronic, and electrical equipment declines with distance from each sectoral mean center, but the significance levels of .059 and .09 are less convincing. The remaining sectors show no significant relationship between size and distance from the mean centers. Perhaps these few significant results reflect the general decentralization of large establishments to smaller metropolitan areas. Phoenix’s manufacturing plants are much smaller compared to national averages. Perhaps lock-in to the rail/interstate/airport corridor in the intermediate ring dictated the location of the relatively larger producers in most sectors.


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Moreover, metropolitan Phoenix’s manufacturing does not neatly bifurcate into discernible centralized industrial zones of small producers and decentralized mass producers. Omitting very small manufacturing establishments could have contributed to our finding that plant sizes and distances from sectoral mean centers are seldom related, especially if most establishments with fewer than five employees locate in the inner core. CONCLUSIONS We set out in this article to evaluate the proposition that concepts and theories of the intrametropolitan distribution of manufacturing have become increasingly irrelevant for understanding the manufacturing geography of large Sunbelt cities, because of the changing nature of industrial production and because they were largely developed in different technological eras and spatial contexts. What we found was that while locational patterns in Phoenix are clearly different when compared to findings for New York or Chicago six decades ago, there are some surprising similarities. First and foremost, our results corroborate Pred’s (1964) proposition that the intrametropolitan location of manufacturing is best viewed as a series of spatial distributions with varying degrees of concentration, clustering, and co-location. Although we found some evidence that manufacturing subcenters exist, these are high points in the overall distributions of establishments and not discrete economic agglomerations. We did find a heavy concentration of manufacturing establishments a few miles outside the heart of the CBD, a result not unlike Harris and Ullman’s (1945) central-city light manufacturing zone. Nonetheless, we find little support for Dear and Flusty’s (1998) attempts to revive interest in discrete and specialized land use parcels or Gordon and Richardson’s (1996) claim that uniformity in economic land use pervades large sprawling Sunbelt metropolises. Manufacturing sectors in Phoenix are significantly clustered, decidedly more so than the region’s supportive producer services. Our results corroborate Shearmur’s (2007) claim that evidence of colocation among industries in metropolitan areas undermines notions that urban land use is increasingly chaotic and random. We found, for example, strong evidence that furniture manufacturers locate closest to those producing textiles, apparel, and leather. We also identified mutual affinity in the location of machinery producers and those in petroleum, chemicals, rubber, and plastics. Our results partially corroborate Marcon and Puech’s (2003) findings for Paris of a loose dichotomy between highly clustered plants in low technology industries compared with more scattered high technology establishments. Minerals and metals plants, for example, are markedly more clustered in Phoenix compared to more technologically intensive computer, electronics, and electrical equipment manufacturing. This result provides some intrametropolitan support for Ellison and Glaeser (1997), Maurel and Sédillot (1999), Bertinelli and Decrop (2005), Duranton and Overman (2005), and Mori et al.’s (2005) interurban findings of greater clustering in traditional compared with technologically intensive industries. We confirm the lasting relationship between transportation infrastructure and intrametropolitan manufacturing location. A surprising aspect of this relationship is the enduring role of rail transportation in understanding locational patterns in Phoenix. Phoenix’s land use is not composed of haphazard sprawl driven by ubiquitous automobile and truck transportation. Manufacturing is somewhat locked-in to the rail network, with road and air transportation infrastructure also influential. Yet lock-in is perhaps too simple a

922


depiction of this relationship, because railroads, over time, have added spurs and sidings to serve potential customers, and Scottsdale’s Airpark was consciously developed with an understanding of the transportation needs of modern manufacturing. Nonetheless, we conclude that Fujita and Mori’s (1996, 2005) argument that transportation infrastructure strongly influences the location of cities in national and international systems has intraurban consequences. Manufacturing in large metropolitan areas increasingly occurs in plants that are much smaller than national size averages. Our results for Phoenix’s manufacturing well illustrates this propensity: average plant size across all sectors is smaller compared to the national average. Indeed, in some sectors, average plant size is strikingly smaller than the national average. However, we do not substantiate the hypothesis that small compared with large establishments are more likely to centralize, which undermines attempts to depict the dispersal of large establishments to suburban locations as a response to either the availability of cheaper land or their limited dependence on local externalities. Establishment size in aggregate manufacturing is largest in an area some 3–8 miles from the central manufacturing core, and not immediately adjacent to the CBD or in the suburban periphery. Holmes and Stevens’s (2002) finding that small plants are concentrated in central locations and that bigger plants have moved to smaller peripheral cities does not appear to extend to Phoenix’s intrametropolitan patterns. Only in wood, paper, and printing does average plant size significantly increase with distance from the sector’s mean center. In all other sectors, there is either no significant establishment size/center distance relationship or that relationship is negative. We suspect that lock-in to the main rail/interstate/airport corridor accounts for the general absence of significant positive relationships between distance from sectoral centers and establishment size. In summary, whereas the intrametropolitan distribution of economic activities in sprawling Sunbelt cities bears little resemblance to the predictions of the Alonso-MuthMills models of the monocentric city, we found no support for recent hypotheses that emphasize either disorder or uniformity in intrametropolitan locational patterns. Manufacturing in Phoenix seems far less centralized and clustered when compared to older metropolitan areas of the Manufacturing Belt, but we recognize that recent scholarship is undermining earlier depictions of employment centralization in those cities as well (Muller, 2001). We found little evidence that manufacturing in Phoenix has developed into a series of specialized zones that scholars have identified across a wide range of cities and time periods, from 19th-century Baltimore (Muller and Groves, 1979) to 1980s Los Angeles (Scott, 1983a, 1983b, 1984). The ties between the location of manufacturing and railroad infrastructure in Phoenix suggest that accessibility to distant suppliers and markets is as much a part of Sunbelt urban structure as it was in other industrial eras and geographic settings. REFERENCES Anas, A., Arnott, R., and Small, K. A., 1998, Urban spatial structure. Journal of Economic Literature, Vol. 36, 1426–1464. Ariba, G., Espa, A., and Quah, D., 2008, A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empirical Economics, Vol. 34, 81– 103.


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