Waiting for Broadband: Local Competitionand the Spatial Distribution of Advanced

Telecommunication Services in the United States

TONY H. GRUBESIC AND ALAN T. MURRAY

ABSTRACT With the passage of the Telecommunications Act of 1996, Congress directed the

Federal Communications Commission and all fifty U.S. states to encourage the deployment of

advanced telecommunication capability in a reasonable and timely manner. Today, with the rollout

of advanced data services such as digital subscriber lines (xDSL), cable modems, and fixed wire-

less technologies, broadband has become an important component of telecommunication service

and competition. Unfortunately, the deployment of last-mile infrastructure enabling high-speed

access has proceeded more slowly than anticipated and competition in many areas is relatively

sparse. More importantly, there are significant differences in the availability of broadband

services between urban and rural areas. This paper explores aspects of broadband access as a

function of market demand and provider competition. Data collected from the Federal Commu-

nications Commission is analyzed using a geographic information system and spatial statistical

techniques. Results suggest significant spatial variation in broadband Internet access as a func-

tion of provider competition in the United States.

Introduction

T he Telecommunications Act of 1996 (TA96) was the first major overhaul in federaltelecommunications policy in nearly six decades. The primary purpose of TA96 was

to create a free and competitive market where commercial telecommunication providerswould compete for both residential and commercial accounts. An important componentof TA96 is Section 706. In this section, Congress directed the Federal CommunicationsCommission (FCC) and the fifty states to encourage the deployment of advanced telecom-munications capability to all residents of the U.S. in a reasonable and timely manner. Whatis advanced telecommunications capability? According to Section 706 of TA96, it refersto “high-speed, switched broadband telecommunications capability that enables users to

Growth and ChangeVol. 35 No. 2 (Spring 2004), pp. 139-165

Tony H. Grubesic is an assistant professor of geography at the University of Cincinnati, Cincin-

nati, OH. His email address is tony.grubesic@uc.edu. Alan T. Murray is an associate professor of

geography at The Ohio State University, Columbus, OH.

Submitted Nov. 2002; revised April 2003, July 2003.© 2004 Gatton College of Business and Economics, University of Kentucky.Published by Blackwell Publishing, 350 Main Street, Malden MA 02148 USand 9600 Garsington Road, Oxford OX4, 2DQ, UK.

originate and receive high-quality voice, data, graphics, and video telecommunicationsusing any technology” (TA96 1996). In more concrete terms, the FCC (2001) definesadvanced services as 200Kbps (kilobytes per second) transmission speeds both down-stream and upstream from provider to subscriber. The FCC also uses the term “high-speed” when referring to services with 200Kbps capabilities in at least one direction.

Currently, several different technologies (in addition to fiber) are able to meet FCCstandards for advanced services: digital subscriber lines (xDSL), cable modems, and fixedwireless. In other words, all three of these platforms have the ability to provide high-speedaccess to residential and business consumers. Where infrastructure is concerned, the FCC’sdefinition of advanced telecommunication technologies is important for several reasons.First, as mentioned earlier, 200Kbps is enough bandwidth to provide access to the essen-tial dimensions of Internet use, such as graphic intensive Web pages, streaming audio,video, and teleconferencing. Second, the 200Kbps benchmark put forth by TA96 dis-qualifies Integrated Services Digital Network (ISDN) connections, which operate at 144Kbps, as advanced or high-speed service. Although this does not render ISDN obso-lete, it does serve as motivation to Bell Operating Companies to reevaluate their existingwireline infrastructure and begin the process of upgrading network elements in certainareas. This was further motivated by comprehensive assessments of statewide telecom-munication infrastructure, such as the FCC’s recent Report on the Availability of High-Speed and Advanced Telecommunications Services (2002), E-Com Ohio (1999), and theState of Tennessee Digital Divide Report (2000). All three of these reports found advancedtelecommunication services were not readily available to many low-income or minorityconsumers, as well as those living in sparsely populated or rural areas.

This echoes much of the recent work examining the digital divide from urban and ruralperspectives. For example, Egan (1996) and Malecki (1996) examined the difficultiesassociated with infrastructure development and its associated costs for rural telecommu-nication providers. In part, the high cost of network upgrades has significantly slowed thediffusion of advanced services. In a report issued by the National Telecommunicationsand Information Administration (NTIA) and Rural Utilities Service (RUS) (2000), broad-band deployment was widely available in urban areas, but not rural ones. In fact, onenationwide survey found that less than 5 percent of towns with a population below 10,000had broadband access (NTIA and RUS 2000). In a similar vein, Grubesic (2003) andGrubesic and Murray (2002) examined the inequities in broadband service provision inOhio by documenting the availability of xDSL and cable modem technologies throughoutthe state. This work suggests that the level of market demand in many rural areas fails toattract large-scale network investments from telecommunication providers. Similarly,Strover (2001) argues that many Internet service providers (ISP) would be interested inexpanding their service to rural customers if the cost structure were more favorable. Inmany cases, it is too expensive, and economies of scale in rural areas are difficult toachieve. Strover et al. (2000) outline a variety of initiatives that many states, particularlythose in rural Appalachia, are pursuing to help resolve issues of broadband access andquality. For example, the state of Virginia has instituted a program where Internet service

140 GROWTH AND CHANGE, SPRING 2004

demand is aggregated among a variety of government agencies, yielding a price discountfrom providers because of the increased volume of traffic along certain links. Other states,including Maryland and West Virginia, share their resources with telecommunication com-panies (this includes statewide fiber networks) to help maximize the efficiency of new andexisting routes and lower the overall costs of operation for both parties (Strover et al. 2000).Although these efforts underscore the importance of broadband access and equity at thestate and local levels, the overall picture of broadband accessibility and the influence ofthe Telecommunications Act of 1996 remains incomplete.

Given these basic concerns associated with upgrading telecommunication infrastruc-ture in many locations, it would be worthwhile to concretely identify the factors spurringor deterring network upgrades. Is the rollout of advanced services linked to population,income, location, or local economic structure? More importantly, given the provisions ofSection 706 and the deregulation of the telecommunication market, how will this likelyimpact the deployment of advanced telecommunication capability?

This article provides a longitudinal analysis of advanced telecommunication servicedeployment and provider competition in the United States. By documenting the chang-ing spatial distribution of advanced telecommunication service and its competitive envi-ronment, one can begin to assess and evaluate the factors spurring telecommunicationinfrastructure growth, investment, and the overall impact of the Telecommunications Actof 1996 on broadband deployment. Is it working? Does it work better in some placesthan others? If so, why? Results suggest that although major metropolitan areas such asNew York, Los Angeles, and Chicago witnessed significant competition early in the processof deregulation—providing more broadband choices for consumers, smaller metropolitanareas such as Austin, Salt Lake City, San Diego, and Tampa are now beginning to benefitfrom competition in local broadband markets.

The remainder of this article is organized as follows. The second section details thespatial and economic factors associated with the deployment of advanced telecommuni-cation services, exploring the differences between urban and rural markets and providercompetition. This section also highlights many of the problems ISPs face when seekingto provide high-speed data services such as xDSL or cable broadband in certain marketareas. The third section describes the longitudinal dataset and the methods used for analy-sis. The fourth section presents results and the last provides a brief discussion and con-cluding remarks.

Advanced Telecommunication Service and CompetitionAs mentioned in the introduction, Section 706 of the Telecommunications Act of 1996

(TA96) directs the FCC and all fifty state regulatory commissions to encourage the deploy-ment of advanced telecommunication infrastructure in a reasonable and timely manner toall residents of the United States. However, there are no specific requirements or instruc-tions within TA96 on which advanced service platform to use or how to provide theseadvanced capabilities. As a result, there are a wide variety of technologies that are beingdeployed within the residential and commercial broadband markets, with three distinct

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142 GROWTH AND CHANGE, SPRING 2004

industries competing in the broadband marketplace: cable, telephone, and wireless. Moreimportantly, the pace at which these technologies are being deployed in certain areas issomewhat suspect. The following subsection details the basic technical characteristics ofeach platform and identifies several regulatory and competitive issues associated with theirdeployment.

Digital subscriber lines (DSL). DSL is the generic name for a family of broadbandtechnologies being provided by local telephone companies to their subscribers. Recentestimates suggest that digital subscriber lines are the second most popular broadbandaccess platform in the United States, with 7.4 million subscribers (Yankee Group 2003).Estimates also suggest that the number of DSL subscribers will continue to grow over thenext four years, increasing to 16.3 million by 2007 (Yankee Group 2003) (Figure 1). Themost popular version of the DSL technologies, particularly in the residential broadbandmarket, are asymmetric digital subscriber lines (ADSL).1 ADSL operates on existing tele-phone lines (twisted copper pair) and provides an “always on” Internet connection to thesubscriber. In addition to digital data transfer, ADSL technology also allows for the passivetransmission of analog voice signals (Newton 2000). In effect, this means that users can talk on the phone and use the Internet simultaneously with a single connection. Aninteresting aspect of ADSL technology is the wide range of connection speeds that areavailable. The asymmetric nature of this platform refers to the fact that downstream(browsing/retrieving) connection speeds are typically higher than upstream (sending) connection speeds (Newton 2000; Abe 2000). For example, downstream speeds oftenapproach 1.5Mbps, while upstream speeds range from 64Kbps to 800Kbps.2 Therefore,under FCC guidelines, ADSL technologies can qualify as both a high-speed service (trans-

0

5

10

15

20

25

30

35

2003 2004 2005 2006 2007Year

Ho

use

ho

lds

(Mill

ion

s)

Cable ModemDSLWireless

FIGURE 1. U.S. RESIDENTIAL BROADBAND HOUSEHOLD FORECAST.Source: Yankee Group (2003).

WAITING FOR BROADBAND 143

mission speed of 200Kbps in at least one direction) and an advanced telecommunicationtechnology (transmission speed of 200Kbps in both directions). One caveat with DSLtechnology is the distance constraint on service areas. In many cases, residential serviceis only available within 18,000 feet of a central office (locations where DSL hardware andswitches are located) (Abe 2000; Grubesic and Murray 2002; Grubesic 2003). In manycircumstances, this constraint can limit the number of subscribers within range of DSLservice from a telecommunications provider.3

Cable. Cable TV networks are shared, wired networks that have historically been usedfor one-way television transmission. In this context, a shared network means that multi-ple households connect to a common piece of coaxial, copper wire. Today, many cablenetworks have been upgraded for two-way transit of digital information. These upgradeshave allowed for digital television, telephone, and high-speed Internet access (Abe 2000).In fact, a single cable connection can provide both television and Internet service to ahousehold or business. Currently, cable is the most popular broadband platform in theUnited States, with over 14.6 million subscribers (Figure 1) (Yankee Group 2003). In pro-viding digital data service, cable operators divide their service area into geographicsubsets—with each subset composed of several thousand homes. The homes in each subsettypically share a single 30Mbps downstream channel provided by the cable operator—which is often called a “trunk.” Does this mean that 300 cable data subscribers sharing a30Mbps line receive a 100Kbps connection? Not necessarily. Rather than allocating a fixed amount of bandwidth to each subscriber, cable broadband allocates bandwidthaccording to network load. Therefore, although two or three users might need significantbandwidth at a given moment, once their Internet activity (downloading or uploading) iscompleted, that bandwidth is again free for any user to access. As a result, cable broad-band speeds often range from 1-10Mbps (Abe 2000). Although the cable platform doesnot suffer from the identical types of distance constraints that DSL providers are concernedwith, there are problems with the return path on cable systems. Specifically, the nature ofthe return path and cable systems structure creates a funneling effect for noise in the fre-quency range of 5 to 42MHz (Abe 2000). In other words, because of the cable system’stree and branch topology, noise gets increasingly louder as data flow upstream into largercable trunklines. As a result, data routed upstream can suffer from signal degradation orcomplete loss if ingress noise is a problem (Steinke 2000). For a more thorough discus-sion on coaxial and hybrid fiber coaxial cable systems, see Abe (2000) or Patterson andRolland (2002).

Fixed wireless. Table 1 illustrates the wide variety of wireless technologies availablein the United States and abroad. Although each technology is able to facilitate commu-nication between two points, far fewer are able to facilitate the transfer of digital data.Perhaps the most effective wireless technology for transmitting and receiving data is broad-band fixed wireless, or BBFW. Currently, BBFW is the third most popular broadband plat-form in the United States, with an estimated 310,000 subscribers (Figure 1) (Yankee Group2003). Similar to cable and xDSL, BBFW access networks provide sufficient bandwidthfor applications such as Web browsing, email, and real-time audio and video. The struc-

144 GROWTH AND CHANGE, SPRING 2004

ture of BBFW networks can vary, but they basically consist of a transmission station (con-nected to a local area network and mounted on a roof or utility pole) and a group ofreceivers (antennas mounted on subscriber dwellings).4 Two of the most common BBFWproducts in the United States are multichannel multipoint distribution service (MMDS)and unlicensed national information infrastructure (U-NII). MMDS and U-NII are verysimilar in speed, both providing approximately 1-2Mbps downstream and 256Kbpsupstream (Reid 2001).

Competition and advanced telecommunication service deployment. Considering thepromise of advanced telecommunication technologies like cable, xDSL, and BBFW, whyhas the pace of deployment been so slow? Although the most recent FCC report (2002)argues that the deployment of advanced technologies as reasonable and timely, there aremany other studies that suggest the exact opposite. The paper begins by examining thefactors that influence the rollout of advanced services such as xDSL and cable.

In order to provide xDSL service to residential and business customers, incumbent localexchange carriers (ILEC) such as Verizon, BellSouth, and SBC must install several piecesof relatively expensive equipment in their central office. Perhaps the most important com-ponent is the digital subscriber line access multiplexer, or DSLAM. This is the hardwarethat serves as the interface between a number of subscriber premises and the carriernetwork (Newton 2000). Ferguson (2002, p. 6) notes that ILECs failed to move promptlyon this front (i.e., installing next generation equipment like the DSLAM) and responded

TABLE 1. WIRELESS TECHNOLOGIES.

Type Typical Use U.S. Frequency Competitive To

Mobile Cell phones, PDA, 800 MHz/1.9 GHz CB, wireline telephonepagers

Commercial Public broadcast — Satellite, television,AM Radio Internet broadcast

Commercial Public broadcast — Satellite, television,FM Radio Internet broadcast

Ham Hobby/rescue — None, wireline telephone,mail, fax

Citizens Band Transportation, — Mobile, wireline telephonerecreation, hobby

Telemetry Industrial 300/900 MHz WLAN, wirelinemonitor/measurement

Satellite TV — BBFW, mobileWLAN LAN 2.4 GHz BBFW, mobileBBFW-UNII Data/voice 5.7 GHz WLAN, satellite, wirelineBBFW-MMDS Data/voice 2.5 GHz WLAN, satellite, wireline

Source: Broadband Fixed Wireless Networks (Reid 2001).

to TA96 by “resisting, litigating against, delaying, and allegedly even sabotaging FCC regulations.” In part, ILECs are worried that the deployment of advanced telecommuni-cation services will cannibalize their existing voice and business services, which they cur-rently monopolize, leading to a decline in market share and profits (Ferguson 2002). Thus,ILECs continue to display serious anticompetitive behavior. Kushnick (2001) notes thatILECs frequently refuse to lease telephone lines to competitive local exchange carriers,overcharge for rack space in central offices, engage in predatory pricing, and fail to com-plete loop installation work on time. As a result, ILECs retain over 80 percent marketshare in local business voice and data services and a 95 percent share in residential service(Ferguson 2002). ILEC market dominance is of concern to both competitors and Con-gress. Recently, two of the more successful competitive local exchange carriers (McLeodUSA and Allegiance Telecom) testified at “Competition in the Local Telephone Market-place,” a Senate Commerce Committee hearing in 2001. They implicated local Bellmonopolies as anticompetitive. Clark McLeod (2001), Chairman and CEO of McLeodUSA stated:

“Competitors, after spending billions of dollars, have averaged a 1 percent market share gain per year.

. . . Congress needs to finish what was started in 1996 and take action now to mandate equal access

and enforce it.”

Clearly, the competitive situation in the traditional wireline telecommunication marketis a difficult one, with a handful of incumbent local exchange carriers dominating the busi-ness. Similarly, the cable industry is composed of regional monopolies that appear to beavoiding competition, fighting to keep their systems closed, and attempting to protect theirlargest revenue stream—residential video. Ferguson (2002) notes that the two biggestthreats to the cable industry are an open architecture that could permit independent contentproviders to use Internet services to deliver high-definition video, and symmetric high-speed Internet service that allows for peer-to-peer sharing of video and music. Wherecable competition is concerned, provider consolidation is becoming a reality. For example,in late 2001, Comcast announced a $72 billion dollar merger with AT&T Broadband. Thisdeal creates a cable company with more than 21 million subscribers and access to approx-imately 38 million households. This is substantially larger than the nearest competitor,Time Warner, which has only 12.8 million subscribers. Further, despite the Telecommu-nications Act of 1996, competition is sparse at the local level. For example, a recent studyby Kurth (2002), found that 65 percent of the 120 communities in the Detroit metropoli-tan area have only one choice for cable.5 This means that 2.4 million cable customers livein areas where only one cable provider offers service. Not surprisingly, the dominant cableprovider in the Detroit area, Comcast, charges 7.6 percent more for television service inmunicipalities where it has a monopoly (Kurth 2002). Therefore, similar to basic cableservice, the price for broadband Internet service can be contingent on the level of com-petition (whether cable, xDSL, or BBFW) in a particular municipality (Borland andHeskett 2001). In addition, analysts suggest that innovation in the broadband market willbe slower to develop without vibrant competition (Borland and Heskett 2001).

WAITING FOR BROADBAND 145

Although the supply-side issues of telecommunication provision are important, thereare also problems on the demand side of the advanced services equation. As mentionedpreviously, when ILECs decide to upgrade local infrastructure and provide advancedtelecommunication services, they are very selective as to which of their local markets toenter—particularly where xDSL is concerned (Grubesic and Murray 2002). BecausexDSL is a premium service, with monthly access fees ranging between $50 and $200, onlya limited number of customers can afford it.6 As a result, providers target small or medium-sized businesses, and neighborhoods with a dense population of relatively affluent, well-educated residents—the prime demographic for subscription-based, high-speed Internetservices (Grubesic 2003; Grubesic and Murray 2002).7 Although this does not necessar-ily exclude rural areas, the cost of upgrading infrastructure and providing high-speedservice does increase in more remote locations while the opportunity to build economiesof scale decreases (Egan 1996; Malecki 2003; Strover 2001; Grubesic 2003). Conse-quently, research by Nielsen NetRatings (2002) suggests that the fastest growing broad-band markets are large urban areas (e.g., New York, Los Angeles, Boston, San Francisco,and Philadelphia) while many rural or remote areas continue their struggle to get connected.8

Considering the evidence presented in this section, several important issues need addi-tional exploration and clarification. First, where are advanced telecommunication serv-ices currently available? Moreover, is there a significant spatial bias in availability ofadvanced services between rural and urban areas? If so, what factors are fueling the unevengrowth of telecommunication infrastructure? Is the diffusion of information access tech-nologies like the Internet following a similar path to that of cable television, electricity,and the telephone as Compaine (2001) suggests? Second, to what degree is competitionplaying a role in the deployment of advanced services? Is there consumer choice in bothurban and rural areas? If not, why has deregulation failed to motivate competition incertain places? The following sections provide a thorough examination of advancedtelecommunication service growth between December of 1999 and June of 2001. By con-sidering the limitations of existing high-speed platforms, problems associated with theirdeployment, and the TA96, a spatially-based analysis of advanced telecommunicationservice availability will provide additional insight into the inequities of broadband Inter-net access and lack of competition in the United States.

Advanced Services Data and MethodologyThis analysis of advanced services provision will focus on the forty-eight contiguous

U.S. states. The data obtained for analysis are from the Federal Communication Com-mission. Specifically, these data were retrieved from FCC Form 477, which requires thatany facilities-based firm providing 250 or more high-speed service lines or wireless chan-nels report basic information about its service offerings and customers twice yearly.9

The most important piece of information collected from Form 477 is the subscriberdata, which are collected at the zip code level.10 The FCC requires that providers identify

146 GROWTH AND CHANGE, SPRING 2004

WAITING FOR BROADBAND 147

the zip codes in which they had at least one high-speed service subscriber.11 More impor-tantly, the FCC data do not differentiate between cable, xDSL, and BBFW at the zip-codelevel—all are simply considered an advanced or high-speed service. Therefore, the result-ing database is a nationwide list that documents the number of companies offering broad-band Internet (including cable, xDSL and BBFW) services in each zip code.

The FCC zip code data for December 1999 (n = 17,891), June 2000 (n = 20,087),December 2000 (n = 21,937), and June 2001 (n = 23,314) were pre-processed in Excel,primarily to remove large text strings in the databases and to isolate data for the 48 con-tiguous U.S. states.12 These data were then converted to dBASE format and imported intoSPSS, a commercial statistics package, and ArcView, a commercial geographic informa-tion system, for analysis. Descriptive statistics for the variable of interest in this data set,broadband service providers by zip code, are provided in Table 2.

Limitations. There are several limitations associated with these data that need to bementioned. First, some of the providers request non-disclosure for portions of their data.These providers argue that their information contains competitively sensitive information.As a result, the FCC does not provide information on line speed, service type, or numberof customers at the zip code level. Second, the FCC does not collect data on firms withfewer than 250 high-speed lines in a given state. Therefore, the actual information pro-vided by the FCC is somewhat conservative and may slightly underestimate broadbanddeployment. Finally, the presence of a high-speed customer in a zip code does not necessarily guarantee that high-speed access is available throughout the entire zip code.The technical limitations associated with broadband platforms such as xDSL can compli-cate such matters. For a more thorough explanation, see Grubesic and Murray (2002).

Broadband Competition and AccessCartographic analysis. Figure 2 illustrates the dramatic changes in broadband avail-

ability and competition in the United States between December 1999 and June 2001. Table3 supports Figure 2 by listing the number of zip codes (including urban) that have at leastone broadband provider. There are several trends in these data worth exploring. The mostnotable feature of Figure 2a (December 1999) is the lack of broadband competition inmany of the major U.S. cities. Nationally, 55.46 percent (of the zip codes in the contigu-ous forty-eight states) have at least one broadband provider. However, as Figure 2a illus-

TABLE 2. SUMMARY STATISTICS FOR BROADBAND

PROVIDERS BY ZIP CODE FOR JUNE 2001.

n 31,583Minimum 0Maximum 18Mean 2.18Standard Deviation 2.914

148 GROWTH AND CHANGE, SPRING 2004

TABLE 3. ZIP CODES WITH BROADBAND PROVIDERS.

Zip Zip Codes with Urban Zip Codes Rural Zip Codes Zip Codes with TenCodes Providers with Providers with Providers or More Providers

December-99 31,583 17,513 (55.46%) 11,884 (67.85%) 5,629 (32.15%) 10

June-00 31,583 19,594 (62.04%) 12,638 (64.49%) 6,956 (35.51%) 120 (+1,200%)

December-00 31,583 21,356 (67.62%) 13,195 (61.78%) 8,161 (38.22%) 707 (+489%)

June-01 31,583 22,758 (72.06%) 13,489 (59.27%) 9,269 (40.73%) 1,183 (+67%)

A C

B D

FIGURE 2. PROVIDER COMPETITION BY ZIP CODE, DEC. 1999-JUNE 2001.

trates, many of the most competitive zip codes appear to be located in a select set of metropolitan areas (e.g., New York, Chicago, Washington, Atlanta, Los Angeles, San Francisco, and Denver).13 It appears that other major metropolitan areas, such as St. Louis,Kansas City, Portland, Seattle, and Cincinnati, have much less competition. This evidenceclosely parallels previous research on telecommunication infrastructure access and avail-ability for the United States, where first-tier metropolitan areas, such as New York andChicago, have significant levels of local infrastructure, while the second- and third-tierU.S. metropolitan areas (e.g., Cincinnati and Milwaukee) have much less (Grubesic andO’Kelly 2002; O’Kelly and Grubesic 2002; Townsend 2001). In part, this disparate land-

WAITING FOR BROADBAND 149

scape of competition illustrated in Figure 2a is due to the varying levels of demand forbroadband service. Many of the most competitive metropolitan areas are traditionalcenters for telecommunication access and development. For example, Denver has a par-ticularly high level of specialization in telecommunication technology and is home toseveral of the largest cable and telecommunication companies in the United States, includ-ing Qwest Communications and AT&T Broadband. Many of the other locations on thelist are home to major aggregation points for national backbone providers (network accesspoints [NAP] and metropolitan area ethernets [MAE]). Not surprisingly, this is reflectedin the local market, where demand and supply of broadband Internet service are strong(Grubesic and O’Kelly 2002).

Figure 2b displays the number of providers for June 2000, where several additionallocations begin to display significant levels of broadband competition. Nationally, 62.03percent of the zip codes had at least one broadband provider—a percentage point increaseof 6.58, reflecting a total increase of 10.62 percent from December 1999. However, therewas a 1,200 percent increase in the number of zip codes with ten or more broadbandproviders (10 in Dec. 1999 and 120 in June 2000). This is interesting for several reasons.If the focus shifts to the statewide level, the most significant gains for zip codes with tenor more providers were found in California, Texas, and Massachusetts. In fact, of the 120zip codes with ten or more providers nationally, these three states account for 79.1 percent.For example, in December 1999 there were no zip codes in the state of California with ten or more broadband providers. By June 2000, there were 42. The local spatialpatterns associated with these increases are even more interesting. As Figure 2b illustrates, the majority of the emerging, highly competitive zip codes appear to be locatedin the metropolitan areas of Los Angeles, San Francisco, Dallas, Houston, and Boston.This reinforces the concept of an urban/rural divide in telecommunication access and competition. In other words, as urban customers continue to gain more choices for broadband providers, there appears to be relatively little growth in rural market broadbandcompetition.

Many of the same trends appear in December 2000 (Figure 2c). Nationally, 67.61percent of the zip codes have at least one broadband provider. This is a percentage pointincrease of 5.58, reflecting a total increase of 8.25 percent from June 2000. Competitionin urban markets also continues to expand. In fact, some of the most highly competitivezip codes in the city of New York have eighteen different broadband providers. Perhapsthe most surprising increase is the number of zip codes with ten or more providers. Recallthat this number was 120 in June 2000; by December 2000 it increased 489 percent to707. Interestingly, Figure 2c displays this increased competition in urban areas. Forexample, in addition to the larger metropolitan areas already displaying high levels of com-petition (e.g., New York, Chicago, Washington, etc.), the metropolitan areas of Seattle, SanDiego, Sacramento, Pittsburgh, Buffalo, Richmond, Tampa, Philadelphia, and Austin maderemarkable gains in December 2000. Conversely, the smaller and more geographicallyisolated metropolitan areas in the Great Plains and Midwest (e.g., Oklahoma City, Omaha,Topeka, Des Moines) have fewer zip codes with multiple providers.

Figure 2d illustrates competition by zip code for June 2001. Nationally, the numberof zip codes with at least one broadband provider increased 4.44 percentage points for atotal of 72.05 percent. This continues the trend of decelerating growth in broadband com-petition in the United States. Further, although the number of zip codes with ten or moreproviders increased 67 percent to 1,183, the majority of this growth was once again found in the major metropolitan areas of California, Texas, Florida, and the NortheasternCorridor (Washington-Boston).

Given the modest increases in broadband competition across the United States betweenDecember 1999 and June 2001, it is important to determine which locations displayed thelargest levels of growth. These areas will be indicative of relatively robust markets whereproviders are competing for residential and commercial accounts. It is similarly impor-tant to determine the locations where competition is decreasing (i.e., the number of broad-band providers is declining). These areas will be indicative of markets where broadbandchoices are limited and competition is sparse. In both cases, such analysis will provideadditional insight to the effectiveness of the Telecommunications Act of 1996 by provid-ing a more complete picture of the spatial biases (if any) in broadband competition.

Figure 3 illustrates the relative change in broadband competition at the zip code levelbetween December 1999 and June 2001. Figure 3a illustrates the significant increase inbroadband competition for much of the United States. As mentioned previously, the major-ity of the growth in broadband competition occurred in metropolitan areas, with severalof the largest (e.g., New York, Washington, Chicago, San Francisco, Los Angeles, Dallas,Houston, and Atlanta) leading the way. A select set of smaller metropolitan areas, suchas Austin, Indianapolis, Minneapolis, Buffalo, Richmond, Tampa, and Orlando, exhibitedincreased levels of broadband competition as well. However, growth in competition isoccurring at different rates in different places. More importantly, Figure 3b illustrates twodistinct pockets of decreased competition: portions of the Northern Great Plains (NorthDakota, South Dakota, and Minnesota) and Appalachia (West Virginia and Pennsylvania).In addition, Figure 3 suggests there are several relatively large metropolitan areas, includ-ing Albuquerque, Pittsburgh, and Las Vegas, where broadband competition is quite sparse.

Competition indices. Statistical evidence corroborating the patterns illustrated inFigure 3 is provided in Table 4. In order to estimate the spatial variation in competitionat the consolidated metropolitan statistical area (CMSA) and metropolitan statistical area (MSA) levels, two statistical measures were implemented. The first is an Intra-Metropolitan Competition Index (F). This index measures the degree to which broadbandcompetition in a metropolitan area has increased or decreased between December 1999and June 2001. The formulation of is as follows:

(1)

where is the number of zip codes in a metropolitan area that gained a broadband providerin time-frame t; is the total number of zip codes in a metropolitan area during t; and is the number of zip codes in a metropolitan area that lost a broadband provider during t.

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150 GROWTH AND CHANGE, SPRING 2004

WAITING FOR BROADBAND 151

A

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FIGURE 3. RELATIVE CHANGE IN COMPETITION—PROVIDER GAINS/LOSSES BY ZIP

CODE.

152 GROWTH AND CHANGE, SPRING 2004

TABLE 4. COMPETITION INDICES.

CMSA F Y

Miami-Fort Lauderdale 88.81 1.52 0.37Los Angeles-Riverside-Orange County 86.61 1.47 0.18San Francisco-Oakland-San Jose 84.91 1.44 0.00Chicago-Gary-Kenosha 82.26 1.40 0.13Detroit-Ann Arbor-Flint 81.72 1.38 0.00Milwaukee-Racine 80.43 1.36 0.00Boston-Worcester-Lawrence 77.83 1.33 0.37Dallas-Fort Worth 77.52 1.31 0.00New York-Northern New Jersey-Long Island 77.17 1.32 0.28Houston-Galveston-Brazoria 76.65 1.30 0.00Seattle-Tacoma-Bremerton 64.52 1.17 2.13Washington-Baltimore 63.47 1.13 1.52Cleveland-Akron 59.62 1.03 0.63Philadelphia-Wilmington-Atlantic City 52.83 0.96 1.98Portland-Salem 51.08 0.90 1.07Denver-Boulder-Greeley 42.25 0.81 2.79Cincinnati-Hamilton 28.47 0.53 1.38

MSA F Y

Atlanta 90.22 1.53 0.00San Diego 88.54 1.50 0.00Salt Lake City-Ogden 84.21 1.43 0.00Austin-San Marcos 72.94 1.26 0.58Tampa 72.50 1.23 0.00Indianapolis 66.67 1.15 0.46Phoenix-Mesa 64.75 1.16 1.78St. Louis 60.51 1.03 0.25Columbus 59.57 1.01 0.00Minneapolis-St. Paul 56.04 1.03 2.39Kansas City 51.38 0.88 0.27Nashville 51.25 0.89 0.62Albuquerque 39.53 0.71 1.15Las Vegas 34.33 0.58 0.00Pittsburgh 22.22 0.57 5.65

Y

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WAITING FOR BROADBAND 153

Small MSA F Y

Odessa-Midland 80.00 1.36 0.00Chico-Paradise 76.47 1.30 0.00Boise 65.38 1.11 0.00Spokane 56.76 0.96 0.00Tuscaloosa 50.00 0.85 0.00Utica-Rome 47.27 0.80 0.00Sioux Falls 32.00 0.54 0.00Huntington-Ashland 24.49 0.52 3.03Charleston, WV -18.00 0.03 9.90Fargo-Moorehead -25.00 0.14 16.50

Y

TABLE 4. (CONTINUED).

The interpretation of (1) is relatively straightforward. F is bounded on a -100 to 100 scale.In this particular formulation, metropolitan areas with relatively significant gains in broad-band providers will approach 100. Metropolitan areas with relatively significant losses inbroadband providers will approach -100. Those areas with identical gains or losses inbroadband providers will have a F of 0.0. By comparing values of F for a select set ofMSAs and CMSAs, the overall level of local competition between areas can begin to beassessed.

The second measure is an inter-metropolitan competition index (Y). In essence, thisindex is a slight variation of the location quotient. Originally developed by Hildebrandand Mace (1950), the location quotient is a basic measure of association. Traditionally,the location quotient estimates basic employment in each industry by relating an indus-try’s local employment share to its national employment share (Klosterman 1990).However, in this application, the interest is in determining the degree to which MSAs orCMSAs have more or less than their share of broadband competition between December1999 and June 2001. The formulation for the inter-metropolitan competition index is asfollows:

(2)

where is the number of zip codes in a metropolitan area that gained a broadband providerin time-frame t; is the total number of zip codes in a metropolitan area during t; isthe set of zip codes in all metropolitan areas that gained a broadband provider during t;and is the total number of zip codes in the CMSAs and MSAs set during t. The inter-pretation of (2) is also straightforward. In metropolitan areas with a larger share than

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expected of competitive zip codes, Y is > 1. For metropolitan areas with a smaller sharethan expected of competitive zip codes, Y is < 1. For metropolitan areas with an averageshare of competitive zip codes, the Y = 1. In order to evaluate decreases in competition,a slightly different measure, which has the ability to track losses of broadband providers,is implemented. This provider loss index is defined as follows:

(3)

where is the number of zip codes in a metropolitan area that lost a broadband providerin time-frame t and is the reference set of zip codes in all metropolitan areas that losta broadband provider during t.

As Table 4 indicates, there are significant differences in the levels of broadband com-petition between this select set of metropolitan areas. The most competitive metropolitanarea between December 1999 and June 2001 is Atlanta. At the local, intra-metropolitanlevel, broadband competition increased in 166 of 184 zip codes, yielding a F of 90.22.Further, there were no zip codes in the Atlanta area that lost a provider during this time-frame. At the national inter-metropolitan level, Atlanta’s Y for gains in broadbandcompetition was 1.53. Therefore, Atlanta not only experienced a significant increase incompetition at the local level (F), it was the most competitive metropolitan area in Table4 between December 1999 and June 2001 (Y). Why Atlanta? Gong and Wheeler (2002)note that Atlanta is a major business and professional services center, which is a primesegment/target for broadband providers (Grubesic and Murray 2002). In addition, becausethe physical geography of Atlanta is relatively uncomplicated (i.e., no ocean, lake, or majorriver), the spatial distribution of economic activities is less constrained than other places.As a result, significant business and professional services growth has occurred in suburban business centers on the outskirts of Atlanta (Gong and Wheeler 2002), fuelingbroadband competition (Walcott and Wheeler 2001). The second most competitive city atthe local level was Miami-Fort Lauderdale. With a F of 88.81, 120 of 134 zip codesgained additional broadband providers. Miami-Fort Lauderdale was also the second mostcompetitive city at the national level in Table 4, with a Y for gains at 1.52. Interestingly,the Denver-Boulder-Greeley CMSA, which was an early leader in broadband competition,failed to maintain its competitive edge. Instead, the F of 42.25 is relatively low, with only68 of 142 zip codes gaining additional providers. Similarly, the Denver metropolitan arearanks relatively low on the inter-metropolitan competition index for gains, with a score of0.81 and relatively high on the for losses, with a score of 2.79. This suggests that themarket for broadband in Denver might be nearing saturation and that market entry forbroadband providers is becoming more difficult and perhaps less profitable. As mentionedpreviously, there were two areas in the United States with relatively well-defined pocketsof decreased competition, namely Appalachia and the Northern Great Plains. This trendis reflected in Table 4 by Charleston, WV, and Fargo-Moorhead, ND-MN. Both

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154 GROWTH AND CHANGE, SPRING 2004

WAITING FOR BROADBAND 155

metropolitan areas have a negative F, which suggests that more broadband providersvacated zip codes than entered. For example, in the case of Charleston, only 1 of 50 zipcodes gained a provider, while 10 zip codes lost providers. This suggests that marketdemand for broadband in these areas is decreasing, and is possibly lower than providersestimated upon entry. Therefore, it appears that many of the providers were not able to generate profits and were forced to vacate the market. In fact, several of the most aggressive broadband providers in the DSL market filed for Chapter 11 bankruptcy overthe past few years. For example, both Covad Communications Group and Rhythms NetConnections Inc. filed for court protection in 2001, with Rhythms pulling out of nearly 150 central offices and completely disconnecting their customers (Wagner 2001). Similarly, NorthPoint Communications completely shut down their nationwidenetwork in 2001, leaving thousands of customers without service (Krause 2001; Green 2001). Considering that all three of these providers were CLECs, it is certainlypossible that the anticompetitive behavior of ILECs (outlined in “Competition andAdvanced Telecommunications Service Deployment”) can adversely impact the broadbandmarket.

Exploring Competition Through RegressionEarlier sections, “Broadband Competition” and “Access,” highlight that broadband

competition at the local level is relatively complex and dynamic. While competitionincreased in some metropolitan areas between December 1999 and June 2000, it decreasedin others. More importantly, these sections suggest that the size of metropolitan areas maynot be the only contributor to growth in broadband provision and access; otherwise, smallerareas such as Tampa and Salt Lake City would not have had such large increases. Thissection will explore the factors fueling both increases and decreases in competition throughbasic spatial statistical analysis and regression modeling. The results provide a more com-prehensive profile of competition and access at both the local and metropolitan area levels.

Base regression model. The ordinary least squares (OLS) regression model used foranalysis is relatively simple, and includes a limited number of explanatory variables. Thedependent variable for this base model is the number of broadband providers located ineach zip code for June 2001. The independent variables reflect basic demographic, socio-economic, and geographic indicators that are hypothesized to influence broadband Inter-net competition. Population density is used as a proxy for broadband market density.Median income is used as a measure of socioeconomic status. Percent white populationis used as a measure of demographic composition. Previous work has indicated that demo-graphic composition can be a key factor in broadband availability (Grubesic and Murray2002). It is hypothesized that the “percent white population” variable in this model willhelp differentiate areas with a larger minority population (e.g., downtown or adjacent to acentral business districts—which often display higher levels of business demand) fromthose that have larger Caucasian populations (e.g., suburban and exurban neighborhoods—i.e., lower business demand). Percent rent helps reflect more densely populated areas suchas the downtown core, where much of the housing is rental based. It also helps capture

156 GROWTH AND CHANGE, SPRING 2004

TABLE 5. NATIONAL OLS MODEL.

OLS Model

Variable Coefficient* t-value Significance Tolerance VIF

Constant — -31.836 0.00 — —Pop. Density 0.104 26.386 0.00 0.761 1.313Median Inc. 0.23 51.191 0.00 0.583 1.716% White -.149 37.560 0.00 0.748 1.338% Rent 0.108 24.792 0.00 0.621 1.609Urbanized Area 0.216 48.577 0.00 0.594 1.682Business District 0.047 13.325 0.00 0.953 1.049Computer Exp. 0.519 82.571 0.00 0.54 1.853

R 0.792Adjusted R-Square 0.627Standard Error 1.779Moran’s I 0.4031Moran’s I (p-value) 0

* Beta coefficients are standardized.

those suburban areas where multi-family housing and large apartment complexes haveflourished over the past decade. Grubesic and Murray (2002) suggest that densely popu-lated markets with relatively affluent residents are often good targets for broadbandproviders. Urbanized area is a categorical variable that better accounts for the location ofeach U.S. zip code. Specifically, each zip code was classified as urban or rural (1 or 0).This was accomplished through a basic GIS routine that determined if the zip code centerwas located in a Census defined urbanized area or urbanized cluster.14 A second categor-ical variable is business district. Districts with a residential population of fewer than 100people and a significant daytime population are classified as business districts.15 The lastindependent variable used in the OLS model is a log transformed computer expendituresmeasure. This tracks the total household expenditures, by zip code, on computers andcomputer peripherals for 2000.16 Because broadband Internet access requires a computer,it is hypothesized that zip codes with higher total expenditures on computer products rep-resent better potential markets for broadband providers. As a result, these are the loca-tions where more broadband competition could be taking place, particularly for residentialand small business accounts.

Table 5 displays the results of this regression model for all zip codes in the UnitedStates.17 Both population density and median income were significant and positive factorsin explaining broadband Internet competition.18 Interestingly, percent white population

WAITING FOR BROADBAND 157

was significant but the coefficient was negative in the regression model. It is possible thatthis reflects an increased level of competition in the more urbanized, central city locationsof the United States, where larger minority populations reside, and where business demandis greater. The rental variable was also determined to be positive and significant in theOLS model. This further supports the hypothesis that higher levels of broadband compe-tition are linked to urbanized areas (both urban centers and high-density developments onthe suburban fringe). Not surprisingly, the categorical variable for urban or rural locationhad a positive coefficient and significant p-value. This suggests that broadband competi-tion is more strongly associated with urban areas than rural or geographically remote loca-tions. Further, the model suggests that broadband competition is positively linked tobusiness districts with high daytime populations. Finally, the computer expendituremeasure had a strong influence on the model, with a t-value of 82.57.

A basic test of spatial autocorrelation on the residual values for this OLS model yieldeda Moran’s I of 0.4031 with a p-value of 0.0.19 This suggests that a moderate amount ofspatial autocorrelation is resident in the residuals of the OLS model and that a more intri-cate model, perhaps with additional terms, is needed to account for the nature of this correlation.

Metropolitan area analysis. In an effort to more carefully examine the nature ofbroadband competition, the final portion of this analysis focuses on four different metro-politan areas, Nashville, St. Louis, Indianapolis, and San Francisco. As Table 4 indicates,there are significant differences between levels of intra and inter-metropolitan competitionfor these areas. Of the MSAs analyzed, Nashville ranks below average for both F (51.25)and Y (0.89). St. Louis is a metropolitan area that ranks nearly average for F (60.51) andY (1.03). Indianapolis is slightly above average for the two indices (F = 66.67; Y = 1.15),while San Francisco is one of the most competitive metropolitan regions in the country(F = 84.91; Y = 1.44). The purpose of this more focused examination of several metro-politan areas is twofold. First, although the national-level OLS model did a relatively goodjob in helping explain broadband competition, there are always local variations that cannotbe accounted for in such a large model. This is particularly true where the provision ofbroadband services is concerned, because the quality of local infrastructure can influencea provider’s decision for market entry (Grubesic and Murray 2002; Grubesic 2003; Strover2001; Malecki 2002). Second, one of the major problems with the national level analysisis the spatial autocorrelation of the OLS residual values. A more local analysis will providethe opportunity to take steps in adjusting the model to account for this problem. It willalso deepen understanding of the similarities and differences in the local patterns of com-petition in these metropolitan areas.

Table 6 illustrates that all four of the metropolitan area OLS models have similaradjusted R-square and standard residual error values. However, there are substantial vari-ations between the metropolitan area OLS models and the national level OLS model. Forexample, at the national level, population density was a significant and positive variable.This reflected the dramatic differences in competition and broadband providers betweenurban and rural areas. However, at the local level, population density was a significant

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n = 195 St. Louis OLS St. Louis SAR

Variable Coefficient* t-value Significance Coefficient* t-value Significance

Constant — -5.400 0.000 — -3.1801 0.0017

Pop. Density 0.181 2.687 0.008 0.0002 1.6270 0.1054

Median Inc. 0.179 2.624 0.009 0.0121 0.9665 0.3350

% White -.057 -.875 0.383 0.0001 5.9071 0.0000

% Rent 0.068 0.979 0.329 0.0313 2.9511 0.0036

Urbanized Area 0.111 1.792 0.075 -.0270 -.0924 0.9264

Business District -.010 -.189 0.000 -.1351 -.1604 0.8728

Computer Exp. 0.518 7.898 0.850 0.8211 2.8892 0.0043

R 0.784

Adjusted R-Square 0.60

Standard Error 1.823

Moran’s I 0.2339 Moran’s I -.04651

Moran’s I (p-value) 0 Moran’s I (p-value) 0.3635

n = 80 Nashville OLS Nashville SAR

Variable Coefficient* t-value Significance Coefficient* t-value Significance

Constant — -3.197 0.002 — -3.0656 0.003

Pop. Density -.056 -.505 0.615 0.0001 -.4552 0.650

Median Inc. 0.241 2.276 0.026 0.0321 1.8289 0.072

% White -.072 -.683 0.497 -.0061 -.5628 0.575

% Rent 0.489 4.266 0.000 0.0543 4.2280 0.000

Urbanized Area 0.233 2.407 0.019 1.0773 2.3376 0.022

Business District 0.017 0.237 0.813 0.2003 0.1899 0.850

Computer Exp. 0.401 4.156 0.000 1.2924 4.2599 0.000

R 0.81

Adjusted R-Square 0.623

Standard Error 1.409

Moran’s I 0.02584 Moran’s I -.003655

Moran’s I (p-value) 0.5416 Moran’s I (p-value) 0.1266

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Variable Coefficient* t-value Significance Coefficient* t-value Significance

Constant — -.984 0.328 — -1.0574 0.293

Pop. Density -.005 -.067 0.946 0.0000 0.0032 0.998

Median Inc. 0.242 2.851 0.005 0.0570 3.1301 0.002

% White -.286 -3.944 0.000 -.0397 -4.0056 0.000

% Rent 0.22 2.431 0.017 0.0482 2.4831 0.015

Urbanized Area 0.273 3.727 0.000 1.8860 3.7677 0.000

Business District 0.15 2.705 0.008 2.5372 2.8183 0.006

Computer Exp. 0.353 3.715 0.000 1.4021 3.6667 0.000

R 0.84

Adjusted R-Square 0.686

Standard Error 1.914

Moran’s I -.03277 Moran’s I 0.001083

Moran’s I (p-value) 0.635 Moran’s I (p-value) 0.1788

n = 407 San Francisco OLS San Francisco SAR

Variable Coefficient* t-value Significance Coefficient* t-value Significance

Constant — -2.232 0.026 — -.4320 0.666

Pop. Density -.056 -1.562 0.002 0.0000 -1.3289 0.185

Median Inc. 0.152 4.228 0.000 0.0028 0.3960 0.692

% White -.268 -8.629 0.000 -.0400 -6.1235 0.000

% Rent 0.188 5.160 0.000 0.0298 3.8406 0.000

Urbanized Area 0.124 3.750 0.000 0.6462 2.4342 0.015

Business Districts 0.064 2.293 0.022 1.0058 1.2962 0.196

Computer Exp. 0.594 16.701 0.000 2.6566 16.8356 0.000

R 0.846

Adjusted R-Square 0.71

Standard Error 2.292

Moran’s I 0.2881 Moran’s I -.01779

Moran’s I (p-value) 0 Moran’s I (p-value) 0.6289

160 GROWTH AND CHANGE, SPRING 2004

and negative component of the San Francisco OLS model. It is possible that a high levelof broadband competition takes place in the suburban communities of the Bay Area. Thesesuburban markets are not only full of well-educated, relatively affluent, and technologi-cally savvy working professionals, they are also home to large numbers of small tomedium-sized businesses which often demand higher bandwidth connections—but cannotafford a dedicated fiber-optic line (Grubesic and O’Kelly 2002). This is further supportedby a significant and positive coefficient for the business district variable in the San Francisco OLS model. Not surprisingly, the San Francisco area ranks fourth in the UnitedStates for broadband subscribers, with over 1.1 million (Nielsen NetRatings 2002). Thatsaid, it appears that broadband competition in the suburban areas is so strong that it par-tially offsets the population density variable of this model.

The remaining OLS models display moderately mixed sets of coefficients. Forexample, although percent white population was a significant and negative variable for SanFrancisco and Indianapolis, it was not significant in the St. Louis and Nashville models.Similarly, although the percent rent variable was significant and positive in the Nashville,Indianapolis, and San Francisco models, it was not significant in the St. Louis model.Again, this suggests that the local characteristics of broadband competition can vary sig-nificantly from the national scale and that local market conditions can impact the spatialdistribution of broadband provision.

In an effort to better account for these nuances, a simultaneous spatial autoregressive(SAR) model was fit for each of the metropolitan areas. This is a good way to adjust forany spatial effects in the data and help obtain a truer picture of the relationship betweenbroadband Internet competition and the significance of the independent variables. In allfour metropolitan area SAR models, the addition of a spatial lag in the regression para-meters helped accomodate the local geographic relationships between competition and theindependent variables (Table 6). Results for the St. Louis SAR model suggest that percentwhite population, percent rent, and computer expenditures are the most significant vari-ables. These results seem to indicate that residential broadband competition (suburbanand exurban) is particularly healthy in the St. Louis area. Conversely, the results of theSAR model for Indianapolis suggest that business demand in more urban locations playsa larger role in broadband competition for the area. For example, the variables for urban-ized area business district and computer expenditures were all positive and significant.Further, the percent white variable actually had a negative influence on the model. Finally,the San Francisco SAR model remains largely unchanged. This certainly supports the pre-vious hypothesis that broadband Internet competition is not restricted to the urban centersof the Bay Area, but is also occurring in less-dense suburban and exurban locations.

DiscussionThe results of this study indicate several trends emerging in a deregulated and com-

petitive market for broadband Internet provision and competition in the United States.First, the Telecommunications Act of 1996, particularly Section 706, has been a moderatesuccess. Broadband competition in many metropolitan areas has increased significantly

between December 1999 and June 2001. In fact, this growth is not strictly limited to largermetropolitan areas such as New York, San Francisco, and Atlanta; mid-sized metropolitanareas like San Diego, Salt Lake City, Austin, Tampa, Indianapolis, and Phoenix are alsohighly competitive. Smaller metropolitan areas such as Odessa-Midland, Chico-Paradise,and Boise have also seen significant growth in broadband competition. However, theresults of this study do indicate that the competitive landscape in the United States is quitevaried. While some metropolitan areas benefit from intense growth in competition, others have benefited relatively little. For example, the metropolitan areas of Las Vegas,Cincinnati, and Pittsburgh rank very low on the competition indices. The results of thisstudy also suggest that a significant, competitive divide exists between urban and ruralareas. In this case, rural areas have much less broadband competition than urban ones.This is particularly true in portions of Appalachia and the Northern Great Plains, wherethe number of broadband Internet providers actually decreased between December 1999and June 2001. As a result, it is clear that stronger legislative measures need to be takento ensure consumer choice in a deregulated environment, particularly in markets whichare not immediately profitable or enticing for broadband providers. For example, theCharleston, WV, and Fargo-Moorehead, SD, MSAs rank far below average in both intra-metropolitan and inter-metropolitan competition indices, with both MSAs losing signifi-cant numbers of broadband providers between December 1999 and June 2001. Is this aresult of existing broadband providers’ attempts to cherry-pick profitable urban marketsand ignore those which have problems generating revenues? Or is this simply part of anorganic process that will eventually distribute infrastructure and access to these smaller,more rural areas after the larger markets are saturated? In reality, it is likely a combina-tion of both. It does appear that broadband access is gradually diffusing from larger, first-tier metropolitan areas to smaller second- and third-tier metropolitan areas in the UnitedStates (Compaine 2001). In fact, many rural locations in the U.S. also have some form ofbroadband access available (NTIA and RUS 2000). It is important to note, however, thatas of June 2001, the spatial distribution of broadband competition remains remarkablyuneven. At the very least, it is important that the FCC continues to monitor and promotefacilities-based competition across all three broadband platforms. In addition, both federaland state-level agencies can help competition by reviewing and revaluating current regu-latory requirements as broadband markets and technologies evolve (Fusco 2002). These reviews will help highlight additional ways in which CLECs can obtain the necessary facilities for entering broadband markets, competing for consumers, and providing Internet access.

ConclusionThis paper has provided a comprehensive, longitudinal examination of broadband

Internet competition in the United States between December 1999 and June 2001. Resultsindicate that although competition continues to increase at the national level, it is doingso at a decreasing rate. Competitive growth fell from a high of 6.5 percent betweenDecember 1999 and June 2000 to a low of 4.3 percent between December 2000 and June

WAITING FOR BROADBAND 161

2001. Further, although many metropolitan areas benefit from high levels of broadbandcompetition, rural areas and smaller metropolitan localities often fail to attract significantlevels of activity. In fact, results suggest that there is a relatively clear-cut urban-ruralhierarchy in broadband Internet competition. The larger metropolitan areas of Miami, NewYork, Los Angeles, San Francisco, Chicago, and Atlanta exhibit extremely high levels, asdo other mid-sized metropolitan areas such as Austin, Salt Lake, Tampa, and Phoenix.Near the bottom of this urban-rural hierarchy are smaller, geographically remote MSAssuch as Fargo-Moorehead and Sioux Falls. It appears that the size of these markets candeter broadband provider entry or promote the loss of providers through time. Finally,many rural areas remain without any broadband competition. As of June 2001, 27.9percent (n = 8,825) of the 31,583 zip codes in the contiguous forty-eight states remainwithout a single broadband provider. Of those 8,825 zip codes without a broadbandprovider, 80.04 percent (n = 7,064) are located in rural areas, representing nearly 7.7million U.S. residents. The remaining 1,761 zip codes (19.96 percent) without anyproviders are found in urban areas. These gaps in competition, service provision, andaccess are clearly of concern, particularly when one considers the growing economic,social, and cultural importance of Internet access (O’Kelly and Grubesic 2002; Lentz andOden 2001; Strover 2001; Mitchell and Clark 1999).

NOTES1. In addition to ADSL, there are several other versions of digital subscriber line technology, includ-

ing VDSL and HDSL. For a more thorough discussion on these technologies, see Grubesic and

Murray (2002).

2. Actual speeds may vary. Downstream and upstream speeds are contingent on the distance

between the central office and subscriber location. For a more thorough explanation, see Grubesic

and Murray (2002).

3. Advances in DSL technology are now allowing the reach of DSL service to be extended. For

example, remote digital subscriber line access multiplexers (DSLAM) can now collect traffic from

distant locations, routing it to the central office (on fiber) for switching. It should be noted,

however, that this type of local infrastructure is not widespread (Grubesic and Murray 2002).

4. BBFW is not the same as 802.11b (Wi-Fi) technology. BBFW has the ability to operate at

extended distances (20-25 miles) while 802.11b typically operates at distances of 400-1,000 feet.

In addition, the MMDS system requires an FCC license for operation (Reid 2001).

5. Comcast controls 75 percent of the actual market—with 1,000,000 subscribers.

6. Black (2002) notes that providers are constantly gauging the elasticity of demand for high-speed

access. Starting in 2001, the cost of high-speed connections (cable and xDSL) increased for 15

consecutive months. Once the average price for basic service hit approximately $50 (second

quarter of 2002), demand leveled off.

7. AT&T was recently accused of redlining their broadband services in Broward County,

Florida. The lawsuit alleges that 1 percent of eligible black households have access to high-speed

broadband Internet service as opposed to virtually 100 percent of eligible white households

(UPI 2002).

162 GROWTH AND CHANGE, SPRING 2004

8. In essence, the Nielsen NetRatings statistics suggest that the population of high-speed Inter-

net users in these cities continues to grow despite an overall slowdown in Internet growth

nationwide.

9. Facilities-based firms are telecommnications carriers which own most of thier switching equip-

ment and transmission lines. Newton (2000) suggests that there is no 100-percent-facilities-based

carrier in the United States.

10. These data are publicly available at http://www.fcc.gov/wcb/iatd/comp.html.

11. Unfortunately, the FCC does not require providers to furnish the total number of subscribers for

each zip code.

12. These numbers represent the number of zip codes with at least one provider.

13. Several of the zip codes in the most competitive cities have nine or ten different broadband

providers offering access during December 1999.

14. For Census 2000, the Census Bureau classifies as “urban” all territory, population, and housing

units located within an urbanized area (UA) or an urban cluster (UC). It delineates UA and UC

boundaries to encompass densely settled territory, which consists of core census block groups or

blocks that have a population density of at least 1,000 people per square mile and surrounding

census blocks that have an overall density of at least 500 people per square mile. In addition,

under certain conditions, less densely settled territory may be part of each UA or UC. (See

http://www.census.gov/geo/www/ua/ua_2k.html).

15. These data are from the ACORNTM system (A Classification of Residential Neighborhoods),

which divides all U.S. residential areas into forty-three clusters and nine summary groups based

on demographic characteristics.

16. These data are an integration of consumer spending data from the Bureau of Labor Statistics

(BLS) and Consumer Expenditure Survey (CEX).

17. The variance inflation factor (VIF) and tolerance indices indicate that multicollinearity was not

a problem in the national OLS model.

18. All variables were significant at the p = 0.05 level.

19. A binary connectivity matrix (first order) was used for neighborhood definition on tests for spatial

autocorrelation.

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