information technology in the 1990s: more footloose or more location-bound?

DOI: 10.1007/s10110-004-0195-1Papers Reg. Sci. 83, 467–485 (2004)

c© RSAI 2004

Information technology in the 1990s:More footloose or more location-bound?�

Jungyul Sohn

National Center for Smart Growth, Research and Education, University of Maryland,1112N Preinkert Field House, College Park, MD 20742, USA (e-mail: [email protected])

Received: 13 August 2002 / Accepted: 10 May 2003

Abstract. This article examines whether the growth of information technology(IT) is associated with a dispersion or concentration of economic activities. Thelocational Gini coefficient and Moran’s I are first applied to ascertain the relation-ship between the growth of information technology and the distribution pattern ofeconomic activities at the metropolitan scale. Next, using the G∗

i statistic as thedependent variable and the level of information infrastructure as the independentvariable, the above relationship is analysed at an intra-metropolitan scale. The re-sults suggest that trends at a metropolitan scale do not necessarily reflect the trendsat an intra-metropolitan scale in association.

JEL classification: R1, R3

Key words: Information technology, spatial distribution, concentration, disper-sion, Washington-Baltimore CMSA

1 Introduction

While information technology (IT) can be defined in a number of different ways, itis generally associated with technology that overcomes distance barriers. People donot have to travel to communicate with one another if they have an alternative wayof interaction that does not require physical movement. Traditionally, the telephoneand fax have been examples of such technology. In the last decade, the internet,email, and wireless technologies have revolutionised the IT sector.

It is not clear, however, if relieving the burden of distance is directly associatedwith mitigating the locational constraints of people and their activities. While it is

� The author is indebted to three anonymous referees for their helpful comments. The author alsothanks the editor for the comprehensive editorial review.

468 J. Sohn

clear that IT has a substantial influence on the locational behaviour and distributionpattern of activities, the extent of this impact still needs to be clarified. During thepast few decades, there has been a significant debate on the spatial implication ofIT as to whether or not it induces a dispersion of economic activities. Supportersof IT’s impact toward dispersion believe that a reduced significance, if not thedemise, of distance will eventually lessen dependence on locational factors, so thatit may work as an alternative to urban congestion problems and/or agglomerationdiseconomies. Other scholars, sceptical of this impact, insist that it is only a mythto believe that IT induces dispersion. They argue that, regardless of the observableinfluence, some locational constraints will still remain significant as before showingthat geography still matters even in the new IT society (Kutay 1988; Drucker 1989;Markusen and Gwisada 1993; Capello 1994; Poole and Samuels 1994; Echeverri-Carroll 1996; Salomon 1996; Gordon and Richardson 1997; Gasper and Glaeser1998; Hajer and Zonneveld 2000; Shen 2000).

Despite a large number of studies addressing this issue at the conceptual level,there have been only a small number of empirical works examining this relation-ship. In many cases, it is due to the difficulty of obtaining adequate data on IT. Thepurpose of this study is to examine at an empirical level, whether the technologyhas worked in favour of dispersion or concentration of economic activities, withspecific focus on some manufacturing sectors. The Washington-Baltimore CMSAis the region of interest here and the time period analysed is 1994 to 1997. TheWashington-Baltimore CMSA has a few advantages to conduct the proposed anal-ysis. First, the locational pattern of manufacturing sectors is in transition; hencethe locational and distributional features of the sectors might be easily capturedfrom the data with a relatively short time span. For example, many traditionalmanufacturing establishments in the area are relocated from Baltimore while thenumber of manufacturing establishments with advanced technology has increasedon the outskirts of Washington D.C. Second, while the CMSA is one of the biggestmetropolitan areas in the U.S., differences in size between bigger cities and smallercities in the area are relatively small. Since the centripetal force of big cities inlocation decision may be less prominent in this area compared to some other largeCMSAs, IT impact on locational patterns can be identified more clearly. Third, thesize of the ZIP Code zone, that is, the unit area of the analysis in the CMSA isrelatively even. This implies that data bias due to the difference in areal size ofobservations may not be a problem.

The next section discusses the spatial implication of the information technologyalong with the dispersion/concentration propositions raised earlier. Section 3 dealswith the main framework of the empirical analysis, and findings are reported inSect. 4. Section 5 concludes and discusses limitations and potential extensions ofthe analysis.

2 Does IT induce dispersion?

While there have been a number of studies that focus on the new technology and itsspatial implications, the fundamental question has been whether IT has any impacton the spatial structure of an urban area or urban form, and if it does, whether or not

Information technology in the 1990s 469

it induces a dispersion of urban activities. There are two major activities of interestthat have a greater influence on the spatial structure of urban areas: economic andresidential. Urban economic and residential spatial structure can be interpreted asthe spatial distribution pattern of urban economic and residential activities. It canbe disaggregated into the location decision patterns of individual urban agents suchas establishments or households. Those factors having an influence on residentiallocation decision are usually different from the ones on industrial location decision.In a similar way, the IT effect on residential location is also different from that onindustrial location. The major focus in this article is limited to the IT impact on urbaneconomic spatial structure, namely, the location and distribution of establishments.

A comprehensive review of IT’s impact on urban form is found in Atkinson(1998). He noted that while the IT influence on urban form is clear, the extent towhich it alters business location is determined by three factors: (1) cost effectivenessin the transformation of functions into electronic flows, (2) dependence on spatialproximity to suppliers and customers, and (3) the significance of current urbanadvantages. Focusing on urban manufacturing, he showed that the overall patternis dispersed due to transportation and communication technologies from the urbancore and inner suburb to outer suburbs and exurban areas. According to the author,however, there are reasons for some manufacturing activities to remain in urbanareas. For these sectors, the IT impact also remains limited. A more theoreticalreview of the relationship may be obtained from Audirac (2002), who identifiedtwo major theories; that is, decentralisation and restructuring schools. Even if theinterpretations are different, both schools agree that IT, along with automobileusage, has been more influential and the expected urban form is polycentric ratherthan monocentric.

As in the case of transportation technology, IT has also been acknowledged asa facilitator towards the dispersion of urban activities by many scholars. Gordonand Richardson (1997) conjectured that such technology leads to a dispersion ofurban activities, possibly up to the stage where “geography is irrelevant”. In a sim-ilar context, Drucker (1989) also noted that office work instead of workers willmove around through information network. Some of the early attempts to establisha theoretical model to explain the IT impact are found in a few studies conductedby Kutay (1986a,b, 1988). While the two earlier papers focused more on the mod-els of office location and the impact of IT, a more generalised approach dealingwith a model of the urban systems was featured in the third paper. The findingssuggested that IT would increase the economic disadvantage of the centre and, asa result, lead to a decentralisation of activities. Such decentralisation of economicactivities is towards the edge cities in an intra-metropolitan context, and towards thelower-tier cities in an inter-metropolitan context where establishments can avoidagglomeration diseconomies. Some recent studies have attempted to combine theo-retical frameworks with empirical evidence. Shen (1999, 2000) devised an adjustedaccessibility measure for employment opportunities incorporating location, trans-portation and telecommunication factors (Shen 1999) as well as other types ofopportunities (Shen 2000). His studies show that geographic location has been lessimportant whereas transportation and, in recent years, telecommunication factorshave been more significant. Focusing on firms as consumers of intermediate goods,

470 J. Sohn

Fujita and Hamaguchi (2001) showed from their theoretical model that firms aremore dispersed with a better-developed transportation and communication infras-tructure as in case of many developed countries. Such decentralisation tendencymay continue in the information society as firms prefer optimal place(s) in terms ofconnectivity rather than proximity (Hajer and Zonneveld 2000). Considering thatIT has both centralising and decentralising pressures (Lusht and Farber 1996), itmay be worthwhile separating and examining the dispersion impact. One exampleis the industrial organisational approach. Many headquarter functions related tocentral control and more routine functions react in a different way to the IT influ-ence. The emergence of back office activities, geographically separated from thecore organisation, provides an example of this difference (Richardson and Gillespie1996).

Some researchers criticise the optimistic view of the new technology. Accord-ing to Capello (1994), there exists a gap between the introduction of new IT, and,changes in the spatial pattern of firms. This misconception is attributed to the over-estimation of technology potential, and to optimistic and superficial analyses ofthe relationship between the new technology and spatial restructuring. Rather, thegeneral expectation that IT induces dispersion cannot be realised due to severalconstraints. Poole and Samuels (1994) identified five possible reasons that hinderthe dispersion of economic activities: (1) industrial inertia, (2) local market orien-tation, (3) advantages of flexible specialisation, (4) proximity to consumers, and (5)proximity to labour force. Echeverri-Carroll (1996) also noted that the technologydoes not neutralise distance decay effects, since it imposes more investment oninter-firm linkages and more stringent restrictions on labour skills and flexibilitythat constrain the freedom of location decision.

The need for face-to-face contact seems to be another reason for some typesof firms to stay close to each other. Gasper and Glaeser (1998) focused on the re-lationship between IT and face-to-face interactions, and/or the cities that facilitatesuch interactions. Using telephone call data, they concluded that IT and face-to-faceinteractions are complements rather than substitutes, reflecting the fact that cen-tripetal forces in cities continue to operate. Markusen and Gwisada (1993) showedthat high-tech and producer service firms prefer to remain geographically close tomanufacturing activities, indicating no dispersion effect of IT on manufacturing.Arguing against such optimistic view of technology, Salomon (1996) suggestedthat there are four assumptions underlying the proposition that cities will dispersedue to IT: (1) the substitutive relationship between transportation and telecommu-nication, (2) the substitution of information for material goods, (3) the ubiquity oftelecommunications, and (4) the recognition that dispersal has been constrained bycongestion and travel costs.

There are also studies in the middle of the spectrum of the above debate that sug-gest a mix of influences on IT. Exploring the effect of a more broadly defined com-puter technology on the location pattern of economic activities, Peitchinis (1992)identified the global dispersion of production process and the spatial concentrationof management. In a work that examines IT’s impact on cities, Moses (1998) alsoargued that both concentration and dispersion are probable in the future. Lusht andFarber (1996) suggested that even if the decentralisation force has gained in impor-


Table 1. Manufacturing sectors considered in the analysis

SIC Description

27 Printing and publishing28 Chemicals and allied products36 and 38 Electronics, electrical and computer equipment37 Transportation equipment

tance with the influence of the new technology, there are several factors favouringa concentration of activities such as: (1) reduced congestion due to flexible workhours, (2) other pollution/congestion-reducing technology, (3) the need for centralcontrol on dispersed production and distribution functions, (4) the uneven distribu-tion of IT facilities, and (5) the need for face-to-face contacts.Yen and Mahmassani(1997) identified two factors that might be considered significant by organisationsin assessing the impact of IT on location decision: (1) access to telecommunicationnetworks, and (2) the IT infrastructure cost compared to costs associated with tradi-tional office locations. These two conditions impose another type of concentrationpressure, under which firms continue to concentrate in some areas in the short-rununtil the technology becomes ubiquitous.

3 Empirical analysis framework

Our main focus is on the spatial distribution pattern of manufacturing sectors.Table 1 shows the list of manufacturing sectors chosen and analysed in the empiricalanalysis. These sectors have a higher level of industrial backward and forwardlinkages with other sectors as well as among themselves,1 and as a result, arethought to have a greater dependency on IT due to higher communication needsthan other sectors.

The first part of the analysis examines the temporal trends of the growth ofIT infrastructure and the overall level of dispersion of the manufacturing activitiesdescribed in Table 1 in the area. By comparing these two trends, some insights maybe provided to the question of whether the expansion of IT is related to the changingdistribution pattern of the above manufacturing activities and, if so, whether it isassociated with either concentration or dispersion.

Since it is difficult to obtain a direct measure of the level of IT infrastructure,a surrogate index, the number of information-intensive establishments (Tofflemire1992, Sohn et al. 2002) is used in the analysis. The logic behind this is that ahigher level of IT infrastructure is expected as the number of information-intensiveestablishments increases. If datasets are available at a more disaggregate level,information-intensive establishments can be defined in a more precise way as in

1 All the sectors are ranked within the top six among the 20 two-digit SIC manufacturing sectors interms of linkage multipliers calculated based on the 1997 input-output table compiled by the Bureau ofEconomic Analysis. While SIC 31 (leather and leather products) and 32 (stone, clay, glass, and concreteproducts) are ranked 4th and 5th in the list, they are not included in the analysis, as it is conceptuallydifficult to link IT impact with those two sectors.

472 J. Sohn

0 1 0 1 1 0 0 0 0 0 0 0

1 0 1 1 1 0 0 1 0 0 2 0

0 1 0 0 0 0 0 0 0 0 0 0

(a) (b) (c) (d)

Fig. 1. Hypothetical distribution pattern

the studies of Sinden (1995) and Moulaert and Djellal (1995) where, for exam-ple, British SIC 7902 (telecommunications) and French NAE 7703 (informationtechnology and organisation consulting sector) and 7704 (computer services) wereadopted. In many cases, however, such detailed information is unavailable, espe-cially when conducting an analysis at a more disaggregate geographic scale (ZIPCode zones as a unit area in this case). Information-intensive establishments aretherefore usually defined in a broader way. Tofflemire (1992), for example, usedUS SIC 6000 (F.I.R.E.), 73 (business services), 81 (legal services), and 87 (en-gineering, accounting and management services) to capture information-intensivesectors. Sohn et al. (2002) applied SIC 73, 81, and 87 to their empirical researchon Chicago. To the extent that many functions in F.I.R.E. industries are also heav-ily dependent on the accessibility of information technology, this analysis followsTofflemire’s (1992) definition of information-intensive sectors; SIC 6000 is alsoconsidered in measuring information infrastructure index as well as professionalservices (SIC 73, 81, and 87).2

The level of dispersion can be measured by two indices, which are comple-mentary to one another: locational Gini and Moran’s I . While both are indicesmeasuring the level of concentration or dispersion, each is specialised in differentways. For example, the former is more focused on the relative distribution patternamong observations while the latter is more devoted to the spatial pattern of the dis-tribution. Figure 1 shows how these two measures are complementary in explainingdistribution patterns.

Figure 1a and b show a clear difference in distribution pattern. This difference isreflected in the Moran’s I , but not in the locational Gini coefficient. Conversely, (c)and (d) are not distinguished by the Moran’s I , but by locational Gini coefficient.(b) is expected to show a higher Moran’s I than (a) while (d) is expected to showa higher locational Gini coefficient than (c), respectively.

The locational Gini coefficient is defined as:

2 We may argue that the establishments in these sectors are consumers of almost ubiquitous IT –telecommunication networks, and the IT infrastructure argument is more about high-speed and high ca-pacity lines. Some functions in such establishments, however, need more than an access to the traditionaltelecommunication network. Examples include network servers for online stock exchange in financialcompanies, real-time access to online listing of real estate market database, local area networks (LAN),as well as wide area networks (WAN) of consulting firms for a fast and easy transfer of huge datasets.


Gk =

1n(n−1)

n∑i=1

n∑j=1

|xi − xj |

4µx(1)

where

n = number of ZIP Code zones

xi(j)=ZIP Code zone i’s (j’s) employment (establishment) share in sector k

ZIP Code zone i’s (j’s) total employment (establishment) share(i�=j)

µx =

n∑i=1

xi

n

Equation (1) was first introduced by Krugman (1991) to examine the relativespatial concentration of the US industries.3 While the traditional Gini coefficientfocuses on the relative concentration pattern of a certain economic sector in relationto other sectors in the same zone, locational Gini considers the relative concentrationpattern of a certain economic sector in a zone in relation to the same sector in otherzones. If activities are evenly distributed among zones (or the share of a certainsector equals to the total share in all zones), the coefficient becomes zero. On theother hand, when all the activities of a certain sector are concentrated in one zone,the coefficient becomes 0.5. Between those two numbers, a higher value implies ahigher level of concentration and a lower value reflects a higher level of dispersion.This index in this analysis is able to provide such information on whether a largenumber of establishments are concentrated in a small number of ZIP Code zonesor not.

One of the drawbacks of the locational Gini coefficient is that it does not provideany information on the geographic distribution pattern of the activities of interest.In other words, with a higher locational Gini coefficient, we know that a certaineconomic activity is concentrated in a limited number of ZIP Code zones ratherthan distributed evenly all over the area. However, it does not tell us whether thosezones are spatially concentrated or just randomly distributed. If the latter is thecase, it may not be considered as a spatial concentration at a multi ZIP Code zonallevel, even if it is so at a single ZIP Code zonal level. In this respect, Moran’s I canbe used as a complementary statistic to check the spatial concentration of activitiesat an interzonal level.4 Moran’s I is defined as:

I = (n/S0)n∑

i=1

n∑j=1

wij(xi − µx)(xj − µx)/n∑

i=1

(xi − µx)2 (2)

where

S0 =n∑

i=1

n∑j=1

wij

wij = element in the spatial weight matrix

corresponding to the observation pair i, j

3 Notations of the equation are borrowed from Kim et al. (2000).4 The equation for Moran’s I is drawn from Anselin (1995b).

474 J. Sohn

Moran’s I and a correlation coefficient have a very similar equation structure.For example, a correlation coefficient equals the covariance divided by the productof variances of each variable. Under a certain condition,5 Moran’s I is reduced tothe product of the deviation of each observation pair from the mean divided by thevariance of the variable. The difference is that the latter deals with one variable (theformer with two variables) and the spatial weight matrix works to assign differentweights to observations based on the distance between the pair of observations inthe latter equation. Similar to the correlation coefficient, the range of the coefficientis approximately between -1 and 1. A positive coefficient implies positive spatialautocorrelation and a negative value indicates negative spatial autocorrelation.6 Amore concentrated pattern is expected with a higher I and a more dispersed patternwith a lower I . The spatial weight matrix is built based on the distance decay effect,rather than using a contiguity relationship. It seems appropriate to assume that thespillover effect of one ZIP Code zone in an urban area might reach farther than theadjacent ZIP Code zones. Each element of the spatial weight matrix is calculatedas the inversed squared distance between a pair of zones and then the matrix isrow-standardised.

The second part of the analysis focuses on the distribution pattern at a microgeographic scale. The aim is to examine if the distribution pattern influenced by ITinfrastructure at a local scale matches the pattern identified from the first part of theanalysis at the global scale. While the global distribution pattern in the first part ofthe analysis can be summarised as a single number, measures of local distributionpatterns need to be associated with individual unit areas in the region: one measure(or statistic) per one unit area. The measures of local distribution patterns then areexamined to check if the local distribution (or location) pattern of establishmentsis significantly associated with the distribution of IT infrastructure. A regressionmodel is established to explain the possible influence of IT infrastructure on thelocation pattern of establishments in the selected manufacturing sectors at a localscale. The dependent variable of the regression model is the measure (or statistic) oflocal distribution pattern. This measure should be able to account for a broader rangeof the impact area of IT infrastructure. The local distribution pattern is typicallymeasured by a local indicator of spatial association (LISA) (Anselin 1995a).

Perhaps the most well-known LISA is the local Moran which is the local versionof the Moran’s I . However, the local Moran is not the best measure in this analysis,since a higher value of local Moran and, as a result, a positive spatial associationpattern does not always mean a cluster of high values (zones with a large number ofestablishments). It could also be a cluster of low values (zones with a small numberof establishments). These two need to be distinguished to examine IT impact onthe distribution pattern (concentration or dispersion) of economic activities. Forthis need, the G∗

i statistic is calculated by each ZIP Code zone and used as the

5 When the spatial weight matrix is row-standardised, the scaling constant S0 equals N , replacingN/S0 with 1.

6 Since the expected value of I is not exactly zero, it should be stated that positive autocorrelationprevails with I greater than the expected value and vice versa. However, as sample size increases, theexpected value of I converges to zero.


Table 2. Independent variables in the regression models

Group Variable Description

IT impact IT Number of establishments in theinformation-intensive sectors

Centrality EDENSITY Total establishment density per square mileAccessibility DCA Distance (mile) from the DCA airport

IAD Distance (mile) from the IAD airportBWI Distance (mile) from the BWI airportHIGHWAY Highway dummy

Market/labour force POPULATION Population

dependent variable in the model.

G∗i =

∑j wijxj − W ∗

i x

s{[(nS∗1i) − W ∗2

i ]/(n − 1)}1/2 , all j (3)

where

xj= observation in j

W ∗i =

∑jwij

x =

∑j xj

n

s =

√∑j (xj−x)2

(n − 1)

n = number of ZIP Code zones

S∗1i =

∑jw2

ij

It is first developed by Getis and Ord (1992) and later revised by Ord andGetis (1995). The uniqueness of this statistic is that a positive value of G∗

i statisticindicates a spatial clustering of high values, whereas a negative value indicatesa spatial clustering of low values (Anselin 1995a, p. 102). The regression modelexamines the relationship between this statistic and the IT variable. The spatialweight matrix is constructed in the same way (distance based) as in the Moran’s Icase for the same reason.

Table 2 lists a set of independent variables to be used in the regression models.The first variable is the level of IT in a certain ZIP Code zone represented by thenumber of establishments in information-intensive sectors. It is the key explanatoryvariable in the regression model. The IT impact on distribution pattern at the localscale can be determined based on this information. For example, a positive (nega-tive) and significant coefficient of IT indicates a higher concentration (dispersion)pattern of establishments around the zone with a higher level of IT infrastruc-ture. Some other important factors considered by establishments in their locationdecision-making process are also included in the model: centrality, accessibilityand the proximity to market and/or labour force.

476 J. Sohn

Table 3. Descriptive statistics of the four manufacturing sec-tors and the information-intensive sector

Year Variable Mean Std. Dev. Min Max

1994 SIC 27 3.8 7.0 0 53SIC 28 0.3 1.1 0 16SIC 3638 0.9 2.1 0 19SIC 37 0.2 0.8 0 11Information 92.4 168.8 0 1452




Unit: Establishment

The centrality of establishments is related to the benefits derived from agglom-eration economies. If a certain zone is located at or closer to the major city centresand subcentres, establishments located in the zone may have a better chance oftaking advantage of the externalities provided by the centers. Establishment den-sity is measured and used as an independent variable representing the centralityof each zone. It is expected that a higher density of establishments is related toa more compact land use of business activities, which is a typical feature of citycentres. A positive sign of the coefficient of this variable implies that more con-centration of establishments is expected near city centres. The second group ofvariables is to examine accessibility. If establishments are located in an area withhigh accessibility, transportation cost is reduced and, as a consequence, goods andservices can be produced at a lower production cost. For accessibility to airports,three variables are included representing the distances to the three major airportsBaltimore-Washington International (BWI), Reagan Washington National (DCA),and Washington Dulles International (IAD), respectively. For the accessibility tothe highway network, a highway dummy is assigned as follows: one if interstatehighway passes a certain ZIP Code zone, and zero otherwise. While the formermeasures how significant the location of airports and air transportation mode areto the location of some manufacturing activities, the latter examines how impor-tant the interstate highway system is to the location of their activities. A negativecoefficient of DCA, IAD or BWI means more concentration of establishments nearthe airports, while a positive HIGHWAY coefficient implies a greater concentra-tion of establishments near interstate highways. The last independent variable is


population to capture both the product market as well as the source of the labourforce. Establishments may benefit if they are near the market area of their productsbecause they can save on the shipping costs of the products to customers in themarket. They may also benefit if they are near the labour market, in that they canreduce the cost to be spent on searching and recruiting qualified potential workers.A positive coefficient of POPULATION shows that establishments tend to stay nearhighly populated zones.

4 Information technology impact in the Washington-Baltimore CMSA

There are 479 ZIP Code zones in the Washington-Baltimore CMSA. All theeconomic-related variables are extracted from the 1994 to 1997 ZIP Code BusinessPatterns. Population is obtained from the 1990 Population Census.

Table 3 lists the descriptive statistics on the 479 ZIP Code zones of the fourmanufacturing sectors to be examined as well as the information-intensive sectors.The units used are the number of establishments.

In the Washington-Baltimore CMSA, SIC 27 (printing and publishing) has thelargest number of establishments, followed by SIC 3638 (electronics, electricaland computer equipment), while SIC 28 (chemicals and allied products) and SIC37 (transportation equipment) have a relatively small number of establishments.Overall, SIC 3638 and 37 indicate a slight increase in the number of establishmentsin the area while the other two have a more stable trend over the period.

Figure 2 shows the change in the locational Gini coefficients for the four sec-tors along with the change in the information infrastructure index as measured bythe total number of establishments in the information-intensive sectors defined inthe previous section. The 1994 number of establishments is set at 100% and thetrend shows that the index has increased over the years. While the graph is notdesigned to provide any direct evidence of the IT impact on the distribution pat-tern of establishments (rather, it shows an association pattern between the growthof IT infrastructure and the level of concentration of establishments), a negative(positive) slope may suggest a possibility that the finding supports with the dis-persion (concentration) hypothesis of IT discussed in the earlier section. In otherwords, establishments are spatially more dispersed (concentrated) in associationwith a higher level of information infrastructure. The locational Gini coefficientsshow that SIC 37 (transportation equipment) and 28 (chemicals and allied prod-ucts) have a relatively high level of concentration whereas SIC 27 (printing andpublishing) has the lowest over the years.

The temporal trends of the coefficient suggest that three of the four sectors,SIC 28, 3638 (electronics, electrical and computer equipment), and 37, show anincrease in the coefficient from 1994 to 1995 and a mild decrease subsequently.SIC 27, on the other hand, shows an increase between 1994 and 1995 and between1995 and 1996, and a decrease in the last period. The negative slope in the trendsof SIC 28, 3638, and 37 between 1995 and 1997, and SIC 27 between 1996 and1997, suggests an association of more dispersion of establishments with a higherlevel of information infrastructure. Nevertheless, a small magnitude of change in

478 J. Sohn

0.3

0.4

0.5

95 100 105 110 115 120

information infrastructure index

loca

tio

nal

Gin

i co

effi

cien

t

SIC27

SIC28

SIC3638

SIC37

94

95 96 97

Fig. 2. Information infrastructure index and locational Gini coefficient

locational Gini coefficients during the period implies that the results need to beinterpreted cautiously.

The results in Fig. 2 do not inform us if a dispersion occurs around neighbour-ing ZIP Code zones, or, toward zones at a farther location. Similarly, it does nottell us if a concentration among observations reflects spatial concentration. If thenumber of establishments is dispersed (concentrated) among observations, but notspatially dispersed (concentrated), it may not be viewed as a substantial dispersion(concentration) even if a decrease (increase) in locational Gini coefficient is ob-served. In other words, while a cluster of ZIP Code zones with a larger numberof establishments may be considered to be a concentration, the coefficient is notable to detect this type of concentration. In addition, considering that there is noreason to expect economic behaviour to conform to arbitrarily-determined arealunits (Anselin and Bera 1998), it is necessary to examine distribution patterns indifferent spatial contexts to complete the analysis. Moran’s I is a complementarystatistic to locational Gini coefficient in this respect.

Figure 3 presents the change of Moran’s I in association with the change in theinfrastructure index. Except for SIC 28 (chemicals and applied products), whichshows a decrease in 1997, all other sectors have remained insensitive to the increasein the information infrastructure index. The stability of the coefficient regardlessof the increase in the information infrastructure index implies that the distributionpattern at a multiple ZIP Code zone level is not influenced by the infrastructureindex. The decreasing coefficient, as in the case of SIC 28 (chemicals and appliedproducts), on the other hand, reflects that more dispersion and as a result, smallerclusters of ZIP Code zones (with a large number of establishments) are expectedacross the area over the years. Overall, SIC 27 (printing and publishing) has main-tained a high level of concentration of activities while the other three sectors showa lower level of concentration.

The results associated with the locational Gini coefficient and the Moran’s Ican be interpreted jointly. Overall, none of the sectors show a clear trend duringthe period even if some sectors reveal a mild decrease (dispersion) either in the


0

0.1

0.2

0.3

95 100 105 110 115 120

information infrastructure index

Mo

ran

's I

SIC27

SIC28

SIC3638

SIC37

Fig. 3. information infrastructure index and Moran’s I

locational Gini coefficient or in the Moran’s I as the information infrastructure indexincreases. More specifically, SIC 28 (chemicals and allied products), for exampledoes not show any clear pattern of change (i.e., more concentration or dispersion)in locational Gini coefficient, but reveals a decrease in Moran’s I , implying thatthe dispersion occurs from one zone to another zone farther away. SIC 27 (printingand publishing), SIC 3638 (electronics, electrical and computer equipment), andSIC 37 (transportation equipment) reveal that the locational Gini coefficient varieswhile the Moran’s I remains relatively stable during the period. It suggests that noclear trend of change may be identified in the distribution pattern of establishmentsat a global spatial scale.7

While Figs. 3 and 4 attempt to associate the distribution pattern of establish-ments with the increase in the level of information infrastructure in an urban area ata global spatial scale, intra-metropolitan location pattern will be examined using aset of regression models and the corresponding IT coefficients. Tables 4 to 7 list theregression results of the four sectors from 1994 to 1997. Briefly summarising othercoefficients than IT, EDENSITY is positive and significant for SIC 27 (printing andpublishing), so that the centre in an urban area as well as subcentres might be anattractive location of establishments in the sector. It is negative and significant forSIC 3638 (electronics, electrical and computer equipment) reflecting the fact thatthe sector tends to stay away from the city centres and subcentres. SIC 28 (chemi-cals and allied products), and 37 (transportation equipment) are indifferent to thisfactor with the exception of SIC 28 in 1994 and 1995 – during which centralityappears to be attractive to the sector.

7 Another interesting finding to be noted is that SIC 27 has the lowest locational Gini coefficient, butthe highest Moran’s I , while SIC 37 has the highest locational Gini, but the lowest Moran’s I . SIC 28and 3638 lie in the middle in both cases. This finding suggests that the spatial scale of concentration maydiffer among economic sectors. For example, SIC 27 might have Fig. 1b type of concentration whereasSIC 37 might have Fig. 1d type of concentration. This reconfirms the complementarity of locationalGini and Moran’s I .

480 J. Sohn

Table 4. Regression results: printing and publishing (SIC 27)

Variables 1994 1995 1996 1997

Constant 2.34678∗∗∗ 2.54425∗∗∗ 2.69006∗∗∗ 2.58316∗∗∗(0.2184) (0.2207) (0.2177) (0.2232)

IT 0.00059 0.00063 0.00055 0.00072∗(0.0005) (0.0005) (0.0005) (0.0004)

EDENSITY 0.00079∗∗∗ 0.00079∗∗∗ 0.00083∗∗∗ 0.00085∗∗∗(0.0001) (0.0001) (0.0001) (0.0001)

DCA 0.05322 0.11928∗ 0.20619∗∗∗ 0.19148∗∗∗(0.0627) (0.0633) (0.0623) (0.0638)

IAD −0.10606∗ −0.17522∗∗∗ −0.25968∗∗∗ −0.24789∗∗∗(0.0619) (0.0625) (0.0615) (0.0631)

BWI −0.01949∗∗∗ −0.02093∗∗∗ −0.02492∗∗∗ −0.02009∗∗∗(0.0041) (0.0042) (0.0041) (0.0042)

HIGHWAY 0.33083∗∗∗ 0.27226∗∗ 0.23705∗∗ 0.22131∗(0.1232) (0.1237) (0.1211) (0.1235)

POPULATION 0.00003∗∗∗ 0.00003∗∗∗ 0.00003∗∗∗ 0.00002∗∗∗(0.000004) (0.000004) (0.000004) (0.000004)

Adjusted R2 0.6605 0.6670 0.6626 0.6533

∗∗∗ 99% ∗∗ 95% ∗ 90%Standard error in parentheses

The estimated results of three airport-related variables can be grouped into twotypes. Both SIC 27 and 3638 show a positive coefficient of DCA and negativecoefficients of IAD and BWI during the period. These two sectors tend to stayaway from the DCA airport, but remain close to the IAD and the BWI airports.

While both SIC 28 and 37 have a positive coefficient for IAD, the former hasa negative coefficient for BWI and is insignificant for DCA, while the latter has anegative coefficient for DCA and is insignificant for BWI. SIC 28 seems to havea preference for the BWI airport, and SIC 37 towards the DCA airport. They bothtend to stay away from the IAD airport. On the one hand, the result implies thatdifferent service characteristics of these airports might have a different influence ondifferent industrial activities. Due to the proximity of DCA and BWI to downtownD.C. and Baltimore respectively, those two airport variables may also be pickingup a centrality impact as well. For example, the result of SIC 27 and 3638 maybe interpreted as a locational shift of economic activities from downtown D.C.to downtown Baltimore. In a similar way, SIC 28 and 37 indicate a preference fordowntown Baltimore and downtown D.C., respectively. The positive and significantsign of HIGHWAY in SIC 27 and 28 shows that the accessibility to the majortransportation systems is an important location factor. The insignificant HIGHWAYof SIC 3638 and 37 implies that the highway accessibility is not an importantlocation factor for these sectors. A similar pattern is found in the coefficients ofPOPULATION. While SIC 27 and 28 show positive and significant coefficients,the coefficients in SIC 3638 and 37 have mostly remained insignificant during theperiod. A proximity to product and labour market seems to be one of the importantlocation factors in SIC 27 and 28, but not in SIC3638 and 37.

Figure 4 summarises the temporal change of the IT coefficients in the regressionmodel for the four sectors. The positive signs of SIC 27 (printing and publishing)


Table 5. Regression results: chemicals and allied products (SIC 28)

Variables 1994 1995 1996 1997

Constant 0.54970∗∗ 0.56833∗∗ 0.66272∗∗∗ 0.61352∗∗∗(0.2389) (0.2317) (0.2236) (0.2214)

IT −0.00119∗∗ −0.00120∗∗ −0.00087∗ −0.00090∗∗(0.0005) (0.0005) (0.0005) (0.0004)

EDENSITY 0.00029∗∗ 0.00029∗∗ 0.00019 0.00017(0.0001) (0.0001) (0.0001) (0.0001)

DCA −0.11569∗ −0.08074 −0.06505 −0.08743(0.0686) (0.0664) (0.0640) (0.0633)

IAD 0.14766∗∗ 0.11069∗ 0.09065 0.11033∗(0.0677) (0.0656) (0.0632) (0.0626)

BWI −0.04316∗∗∗ −0.04177∗∗∗ −0.03948∗∗∗ −0.03613∗∗∗(0.0045) (0.0044) (0.0042) (0.0041)

HIGHWAY 0.35178∗∗∗ 0.34471∗∗∗ 0.28555∗∗ 0.31573∗∗∗(0.1348) (0.1298) (0.1243) (0.1225)

POPULATION 0.00003∗∗∗ 0.00002∗∗∗ 0.00002∗∗∗ 0.00002∗∗∗(0.000005) (0.000005) (0.000004) (0.000004)

Adjusted R2 0.5166 0.5029 0.4782 0.4607


Table 6. Regression results: electronics, electrical and computer equipment(SIC 36 and 38)

Variables 1994 1995 1996 1997

Constant 2.43116 2.44185 2.51783 2.61737(0.2571)∗∗∗ (0.2526)∗∗∗ (0.2634)∗∗∗ (0.2667)∗∗∗

IT 0.00094 0.00093 0.00104 0.00137(0.0006)∗ (0.0005)∗ (0.0005)∗ (0.0005)∗∗∗

EDENSITY −0.00034 −0.00031 −0.00037 −0.00044(0.0001)∗∗ (0.0001)∗∗ (0.0001)∗∗ (0.0001)∗∗∗

DCA 0.42988 0.45927 0.50478 0.52744(0.0738)∗∗∗ (0.0724)∗∗∗ (0.0754)∗∗∗ (0.0763)∗∗∗

IAD −0.42664 −0.45546 −0.50351 −0.53(0.0729)∗∗∗ (0.0715)∗∗∗ (0.0745)∗∗∗ (0.0754)∗∗∗

BWI −0.05625 −0.05731 −0.05530 −0.05407(0.0049)∗∗∗ (0.0048)∗∗∗ (0.0049)∗∗∗ (0.0050)∗∗∗

HIGHWAY 0.12175 0.14930 0.11648 0.07514(0.1450) (0.1415) (0.1465) (0.1476)

POPULATION 0.000004 0.000004 −0.000002 −0.000006(0.000005) (0.000005) (0.000005) (0.000006)

Adjusted R2 0.3311 0.3465 0.2912 0.2871


and SIC 3638 (electronics, electrical, and computer equipment) indicate that alarger number of establishments tend to concentrate as information infrastructureexpands in a certain ZIP Code zone. Considering that these sectors might requireintensive computer technology and network linkage in the operation of their busi-nesses, it is not surprising to observe this pattern of the impact. For example, many

482 J. Sohn

Table 7. Regression results: transportation equipment (SIC 37)

Variables 1994 1995 1996 1997

Constant −0.37846 −0.56643 −0.51119 −0.66759(0.2328) (0.2391)∗∗ (0.2389)∗∗ (0.2278)∗∗∗

IT −0.00076 −0.00056 −0.00054 −0.00006(0.0005) (0.0005) (0.0005) (0.0004)

EDENSITY 0.00014 0.00011 0.00007 −0.00001(0.0001) (0.0001) (0.0001) (0.0001)

DCA −0.39060 −0.48621 −0.41103 −0.44239(0.0669)∗∗∗ (0.0685)∗∗∗ (0.0684)∗∗∗ (0.0652)∗∗∗

IAD 0.40862 0.50305 0.42035 0.45229(0.0660)∗∗∗ (0.0677)∗∗∗ (0.0675)∗∗∗ (0.0644)∗∗∗

BWI −0.00959 −0.00404 0.00067 0.00372(0.0044)∗∗ (0.0045) (0.0045) (0.0043)

HIGHWAY 0.13980 0.12006 0.09923 0.08576(0.1313) (0.1340) (0.1329) (0.1261)

POPULATION 0.00001 0.000006 0.000004 0.000003(0.000005)∗∗ (0.000005) (0.000005) (0.000004)

Adjusted R2 0.3200 0.3173 0.2011 0.2132

∗∗∗ 99% ∗∗95% ∗90%Standard error in parentheses

-0.0015

-0.0005

0.0005

0.0015

1994 1995 1996 1997

year

info

rmat

ion

in

fras

tru

ctu

re c

oef

fici

ent

SIC27

SIC28

SIC3638

SIC37

Fig. 4. Temporal trends of the change of the coefficient on information infrastructure

of printing and publishing jobs are now performed on computers through informa-tion network (internet and e-mail). Similarly, specific advantages derived from ITappeal to the computer manufacturers (for example, Dell and Gateway) who do notuse traditional production systems and marketing strategies, and who adopt onlineorder-based production and shipment systems. The other two sectors, on the otherhand, show a negative sign implying that they prefer to stay away from zones witha higher level of information infrastructure. One explanation is that these activitiesmay be relatively less dependent on the new technology, so that they are able toreduce production costs by locating themselves outside the beneficiary area of suchinfrastructure (for cheaper rent) and avoiding, either directly or indirectly, agglom-


eration diseconomies such as congestion costs and severe competition with otherbusinesses on local product and labour markets.

SIC 3638 (electronics, electrical and computer equipment) and 37 (transporta-tion equipment) show an increase in values of the coefficient over the period, sug-gesting that information infrastructure as a factor of locational attractiveness hasbecome more important over the years. On the other hand, SIC 27 and 28 do notshow any clear trend. In fact, the coefficients in SIC 27 are relatively stable overthe period. While the coefficients for SIC 28 show an increase between 1995 and1996, they decreased during the other periods. In the case of SIC 3638, it is clearthat a bigger and positive coefficient means a higher dependence of the locationpatterns of establishments on information infrastructure. An increase of negativecoefficients (a decrease in magnitude) of SIC 37 over the years is related to a re-duced level of repellence and, as a result, an increased level of attraction to someextent. This result raises an issue that is related to the spatial scale of an analysis.What is implied from this set of analyses is that the distribution pattern measuredat the metropolitan scale does not reflect the corresponding distribution pattern atan intra-metropolitan scale. It is difficult to determine if the concentration or thedispersion hypothesis is correct at the metropolitan scale in the sense that both thelocational Gini coefficient and the Moran’s I do not show a prominent trend ofchange.

However, the concentration effect of IT appears to be detected at an intra-metropolitan scale (in the locational context) among such sectors as SIC 3638 and37 based on the examination of the IT regression coefficients in the Washington-Baltimore CMSA. It suggests that a proper analysis of the IT impact on urbaneconomic spatial structure needs to be associated with the corresponding global(distributional) or local (locational) scale. The interpretation of the trend of the ITcoefficient change, however, is limited in two respects. First, the IT coefficient ofSIC 37 is not significant during the period. Second, since the analysis here has notbeen formally tested for differences between the coefficients across years, even aconsistent increase in the value of the coefficients for SIC 3638 and 37 during theperiod may not be indicative of a persistent trend.

5 Conclusion

This article began by asking if IT has a dispersion/concentration effect on the spatialdistribution of selected manufacturing activities in an urban area. The Washington-Baltimore CMSA with 479 ZIP Code zones was analysed. First, the locational Ginicoefficient and the Moran’s I were used focusing on the distribution pattern ofthe urban area as a whole. No clear trend in the change of the spatial distributionpattern associated with IT may be found. Second, the G∗

i statistic was used as thedependent variable in the regression model in order to examine if the attractionforce of IT on the establishments of some industrial sectors has increased at anintra-metropolitan scale. A discrepancy was identified between the results of thetwo analyses on IT impact on distribution patterns depending on the spatial scaleof analysis: a mild dispersion trend at global scale (in the distributional context)and a mild concentration trend at local scale (in the locational context).

484 J. Sohn

The analysis found that even if more dispersion or concentration of manufactur-ing activities in association with IT infrastructure occurs at a global scale, it does notnecessarily mean that such dispersion or concentration is occurring evenly over thearea. Rather, it might suggest a trend of very uneven distribution patterns at a localscale (more concentration around a higher level of IT infrastructure in this analy-sis). While the analysis has only been applied to the Washington-Baltimore CMSA,other major metropolitan areas might have similar results if IT is not ubiquitouswithin the area. Hence it is also expected that cities with more uneven distribu-tion of IT infrastructure will show a stronger concentration of economic activitiesaround it.

IT may not work as a facilitator for the dispersion of economic activities asthe optimistic view may forecast. This view may only be realised in the long-run if IT becomes ubiquitous and if the economic activity of establishments ishighly dependent on the new technology. As IT infrastructure shows an unevenspatial distribution pattern, it may work as a factor of locational attractiveness thatdraws economic activities. As a result, the location and distribution of economicactivities may be positively associated with the location and distribution of the ITinfrastructure.

One future extension of the present article is to apply the model to areas ofa different spatial scale such as county and/or state in order to understand theinfluence of IT in a different spatial context. Examining a longer time series wouldalso contribute to an understanding of the long-run trends of urban economic spatialstructure associated with IT’s impact. Such research will help shed light on thestructural changes in the relationship between IT infrastructure and the distributionpattern of establishments. Finally, the results need to be interpreted cautiously,given the measures for IT in this article.

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