Waiting for Broadband: Local Competition and the Spatial Distribution ...

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

Waiting for Broadband: Local Competition and 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 wireless 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 Communications Commission is analyzed using a geographic information system and spatial statistical techniques. Results suggest significant spatial variation in broadband Internet access as a function of provider competition in the United States.

Introduction he Telecommunications Act of 1996 (TA96) was the first major overhaul in federal telecommunications policy in nearly six decades. The primary purpose of TA96 was to create a free and competitive market where commercial telecommunication providers would compete for both residential and commercial accounts. An important component of TA96 is Section 706. In this section, Congress directed the Federal Communications Commission (FCC) and the fifty states to encourage the deployment of advanced telecommunications capability to all residents of the U.S. in a reasonable and timely manner. What is advanced telecommunications capability? According to Section 706 of TA96, it refers to “high-speed, switched broadband telecommunications capability that enables users to

T

Tony H. Grubesic is an assistant professor of geography at the University of Cincinnati, Cincinnati, OH. His email address is [email protected]. 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 US and 9600 Garsington Road, Oxford OX4, 2DQ, UK.

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GROWTH AND CHANGE, SPRING 2004

originate and receive high-quality voice, data, graphics, and video telecommunications using any technology” (TA96 1996). In more concrete terms, the FCC (2001) defines advanced services as 200 Kbps (kilobytes per second) transmission speeds both downstream and upstream from provider to subscriber. The FCC also uses the term “highspeed” when referring to services with 200 Kbps capabilities in at least one direction. Currently, several different technologies (in addition to fiber) are able to meet FCC standards for advanced services: digital subscriber lines (xDSL), cable modems, and fixed wireless. In other words, all three of these platforms have the ability to provide high-speed access to residential and business consumers. Where infrastructure is concerned, the FCC’s definition of advanced telecommunication technologies is important for several reasons. First, as mentioned earlier, 200 Kbps is enough bandwidth to provide access to the essential dimensions of Internet use, such as graphic intensive Web pages, streaming audio, video, and teleconferencing. Second, the 200 Kbps benchmark put forth by TA96 disqualifies Integrated Services Digital Network (ISDN) connections, which operate at 144 Kbps, as advanced or high-speed service. Although this does not render ISDN obsolete, it does serve as motivation to Bell Operating Companies to reevaluate their existing wireline infrastructure and begin the process of upgrading network elements in certain areas. This was further motivated by comprehensive assessments of statewide telecommunication infrastructure, such as the FCC’s recent Report on the Availability of HighSpeed and Advanced Telecommunications Services (2002), E-Com Ohio (1999), and the State of Tennessee Digital Divide Report (2000). All three of these reports found advanced telecommunication services were not readily available to many low-income or minority consumers, 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 rural perspectives. For example, Egan (1996) and Malecki (1996) examined the difficulties associated with infrastructure development and its associated costs for rural telecommunication providers. In part, the high cost of network upgrades has significantly slowed the diffusion of advanced services. In a report issued by the National Telecommunications and Information Administration (NTIA) and Rural Utilities Service (RUS) (2000), broadband deployment was widely available in urban areas, but not rural ones. In fact, one nationwide survey found that less than 5 percent of towns with a population below 10,000 had broadband access (NTIA and RUS 2000). In a similar vein, Grubesic (2003) and Grubesic and Murray (2002) examined the inequities in broadband service provision in Ohio by documenting the availability of xDSL and cable modem technologies throughout the state. This work suggests that the level of market demand in many rural areas fails to attract large-scale network investments from telecommunication providers. Similarly, Strover (2001) argues that many Internet service providers (ISP) would be interested in expanding their service to rural customers if the cost structure were more favorable. In many cases, it is too expensive, and economies of scale in rural areas are difficult to achieve. Strover et al. (2000) outline a variety of initiatives that many states, particularly those in rural Appalachia, are pursuing to help resolve issues of broadband access and quality. For example, the state of Virginia has instituted a program where Internet service

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demand is aggregated among a variety of government agencies, yielding a price discount from providers because of the increased volume of traffic along certain links. Other states, including Maryland and West Virginia, share their resources with telecommunication companies (this includes statewide fiber networks) to help maximize the efficiency of new and existing 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 the state and local levels, the overall picture of broadband accessibility and the influence of the Telecommunications Act of 1996 remains incomplete. Given these basic concerns associated with upgrading telecommunication infrastructure in many locations, it would be worthwhile to concretely identify the factors spurring or deterring network upgrades. Is the rollout of advanced services linked to population, income, location, or local economic structure? More importantly, given the provisions of Section 706 and the deregulation of the telecommunication market, how will this likely impact the deployment of advanced telecommunication capability? This article provides a longitudinal analysis of advanced telecommunication service deployment and provider competition in the United States. By documenting the changing spatial distribution of advanced telecommunication service and its competitive environment, one can begin to assess and evaluate the factors spurring telecommunication infrastructure growth, investment, and the overall impact of the Telecommunications Act of 1996 on broadband deployment. Is it working? Does it work better in some places than others? If so, why? Results suggest that although major metropolitan areas such as New York, Los Angeles, and Chicago witnessed significant competition early in the process of deregulation—providing more broadband choices for consumers, smaller metropolitan areas such as Austin, Salt Lake City, San Diego, and Tampa are now beginning to benefit from competition in local broadband markets. The remainder of this article is organized as follows. The second section details the spatial and economic factors associated with the deployment of advanced telecommunication services, exploring the differences between urban and rural markets and provider competition. This section also highlights many of the problems ISPs face when seeking to provide high-speed data services such as xDSL or cable broadband in certain market areas. The third section describes the longitudinal dataset and the methods used for analysis. The fourth section presents results and the last provides a brief discussion and concluding remarks.

Advanced Telecommunication Service and Competition As 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 deployment of advanced telecommunication infrastructure in a reasonable and timely manner to all residents of the United States. However, there are no specific requirements or instructions within TA96 on which advanced service platform to use or how to provide these advanced capabilities. As a result, there are a wide variety of technologies that are being deployed within the residential and commercial broadband markets, with three distinct

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industries competing in the broadband marketplace: cable, telephone, and wireless. More importantly, the pace at which these technologies are being deployed in certain areas is somewhat suspect. The following subsection details the basic technical characteristics of each platform and identifies several regulatory and competitive issues associated with their deployment. Digital subscriber lines (DSL). DSL is the generic name for a family of broadband technologies being provided by local telephone companies to their subscribers. Recent estimates suggest that digital subscriber lines are the second most popular broadband access 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 the next four years, increasing to 16.3 million by 2007 (Yankee Group 2003) (Figure 1). The most popular version of the DSL technologies, particularly in the residential broadband market, are asymmetric digital subscriber lines (ADSL).1 ADSL operates on existing telephone lines (twisted copper pair) and provides an “always on” Internet connection to the subscriber. In addition to digital data transfer, ADSL technology also allows for the passive transmission 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. An interesting aspect of ADSL technology is the wide range of connection speeds that are available. 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 often approach 1.5 Mbps, while upstream speeds range from 64 Kbps to 800 Kbps.2 Therefore, under FCC guidelines, ADSL technologies can qualify as both a high-speed service (trans-

35

Households (Millions)

30 25 20

Cable Modem DSL Wireless

15 10 5 0

2003

2004

2005 Year

2006

2007

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


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mission speed of 200 Kbps in at least one direction) and an advanced telecommunication technology (transmission speed of 200 Kbps in both directions). One caveat with DSL technology is the distance constraint on service areas. In many cases, residential service is only available within 18,000 feet of a central office (locations where DSL hardware and switches are located) (Abe 2000; Grubesic and Murray 2002; Grubesic 2003). In many circumstances, this constraint can limit the number of subscribers within range of DSL service from a telecommunications provider.3 Cable. Cable TV networks are shared, wired networks that have historically been used for one-way television transmission. In this context, a shared network means that multiple households connect to a common piece of coaxial, copper wire. Today, many cable networks have been upgraded for two-way transit of digital information. These upgrades have 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 a household or business. Currently, cable is the most popular broadband platform in the United States, with over 14.6 million subscribers (Figure 1) (Yankee Group 2003). In providing digital data service, cable operators divide their service area into geographic subsets—with each subset composed of several thousand homes. The homes in each subset typically share a single 30 Mbps downstream channel provided by the cable operator— which is often called a “trunk.” Does this mean that 300 cable data subscribers sharing a 30 Mbps line receive a 100 Kbps connection? Not necessarily. Rather than allocating a fixed amount of bandwidth to each subscriber, cable broadband allocates bandwidth according to network load. Therefore, although two or three users might need significant bandwidth at a given moment, once their Internet activity (downloading or uploading) is completed, that bandwidth is again free for any user to access. As a result, cable broadband speeds often range from 1-10 Mbps (Abe 2000). Although the cable platform does not suffer from the identical types of distance constraints that DSL providers are concerned with, there are problems with the return path on cable systems. Specifically, the nature of the return path and cable systems structure creates a funneling effect for noise in the frequency range of 5 to 42 MHz (Abe 2000). In other words, because of the cable system’s tree and branch topology, noise gets increasingly louder as data flow upstream into larger cable trunklines. As a result, data routed upstream can suffer from signal degradation or complete loss if ingress noise is a problem (Steinke 2000). For a more thorough discussion on coaxial and hybrid fiber coaxial cable systems, see Abe (2000) or Patterson and Rolland (2002). Fixed wireless. Table 1 illustrates the wide variety of wireless technologies available in the United States and abroad. Although each technology is able to facilitate communication 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 broadband fixed wireless, or BBFW. Currently, BBFW is the third most popular broadband platform in the United States, with an estimated 310,000 subscribers (Figure 1) (Yankee Group 2003). Similar to cable and xDSL, BBFW access networks provide sufficient bandwidth for applications such as Web browsing, email, and real-time audio and video. The struc-

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TABLE 1. WIRELESS TECHNOLOGIES. Type

Typical Use

U.S. Frequency

Competitive To

Mobile

Cell phones, PDA, pagers Public broadcast

800 MHz/1.9 GHz

CB, wireline telephone

—

Public broadcast

—

Hobby/rescue

— —

Telemetry

Transportation, recreation, hobby Industrial

Satellite, television, Internet broadcast Satellite, television, Internet broadcast None, wireline telephone, mail, fax Mobile, wireline telephone

Satellite WLAN BBFW-UNII BBFW-MMDS

TV LAN Data/voice Data/voice

— 2.4 GHz 5.7 GHz 2.5 GHz

Commercial AM Radio Commercial FM Radio Ham Citizens Band

300/900 MHz

WLAN, wireline monitor/measurement BBFW, mobile BBFW, mobile WLAN, satellite, wireline WLAN, satellite, wireline

Source: Broadband Fixed Wireless Networks (Reid 2001).

ture of BBFW networks can vary, but they basically consist of a transmission station (connected to a local area network and mounted on a roof or utility pole) and a group of receivers (antennas mounted on subscriber dwellings).4 Two of the most common BBFW products in the United States are multichannel multipoint distribution service (MMDS) and unlicensed national information infrastructure (U-NII). MMDS and U-NII are very similar in speed, both providing approximately 1-2 Mbps downstream and 256 Kbps upstream (Reid 2001). Competition and advanced telecommunication service deployment. Considering the promise of advanced telecommunication technologies like cable, xDSL, and BBFW, why has 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 are many other studies that suggest the exact opposite. The paper begins by examining the factors that influence the rollout of advanced services such as xDSL and cable. In order to provide xDSL service to residential and business customers, incumbent local exchange carriers (ILEC) such as Verizon, BellSouth, and SBC must install several pieces of relatively expensive equipment in their central office. Perhaps the most important component is the digital subscriber line access multiplexer, or DSLAM. This is the hardware that serves as the interface between a number of subscriber premises and the carrier network (Newton 2000). Ferguson (2002, p. 6) notes that ILECs failed to move promptly on this front (i.e., installing next generation equipment like the DSLAM) and responded


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to TA96 by “resisting, litigating against, delaying, and allegedly even sabotaging FCC regulations.” In part, ILECs are worried that the deployment of advanced telecommunication services will cannibalize their existing voice and business services, which they currently monopolize, leading to a decline in market share and profits (Ferguson 2002). Thus, ILECs continue to display serious anticompetitive behavior. Kushnick (2001) notes that ILECs 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 complete loop installation work on time. As a result, ILECs retain over 80 percent market share 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 Congress. Recently, two of the more successful competitive local exchange carriers (McLeod USA and Allegiance Telecom) testified at “Competition in the Local Telephone Marketplace,” a Senate Commerce Committee hearing in 2001. They implicated local Bell monopolies as anticompetitive. Clark McLeod (2001), Chairman and CEO of McLeod USA 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 market is a difficult one, with a handful of incumbent local exchange carriers dominating the business. Similarly, the cable industry is composed of regional monopolies that appear to be avoiding competition, fighting to keep their systems closed, and attempting to protect their largest revenue stream—residential video. Ferguson (2002) notes that the two biggest threats to the cable industry are an open architecture that could permit independent content providers to use Internet services to deliver high-definition video, and symmetric highspeed Internet service that allows for peer-to-peer sharing of video and music. Where cable 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. This deal creates a cable company with more than 21 million subscribers and access to approximately 38 million households. This is substantially larger than the nearest competitor, Time Warner, which has only 12.8 million subscribers. Further, despite the Telecommunications Act of 1996, competition is sparse at the local level. For example, a recent study by Kurth (2002), found that 65 percent of the 120 communities in the Detroit metropolitan area have only one choice for cable.5 This means that 2.4 million cable customers live in areas where only one cable provider offers service. Not surprisingly, the dominant cable provider in the Detroit area, Comcast, charges 7.6 percent more for television service in municipalities where it has a monopoly (Kurth 2002). Therefore, similar to basic cable service, the price for broadband Internet service can be contingent on the level of competition (whether cable, xDSL, or BBFW) in a particular municipality (Borland and Heskett 2001). In addition, analysts suggest that innovation in the broadband market will be slower to develop without vibrant competition (Borland and Heskett 2001).

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Although the supply-side issues of telecommunication provision are important, there are also problems on the demand side of the advanced services equation. As mentioned previously, when ILECs decide to upgrade local infrastructure and provide advanced telecommunication services, they are very selective as to which of their local markets to enter—particularly where xDSL is concerned (Grubesic and Murray 2002). Because xDSL is a premium service, with monthly access fees ranging between $50 and $200, only a limited number of customers can afford it.6 As a result, providers target small or mediumsized businesses, and neighborhoods with a dense population of relatively affluent, welleducated residents—the prime demographic for subscription-based, high-speed Internet services (Grubesic 2003; Grubesic and Murray 2002).7 Although this does not necessarily exclude rural areas, the cost of upgrading infrastructure and providing high-speed service does increase in more remote locations while the opportunity to build economies of scale decreases (Egan 1996; Malecki 2003; Strover 2001; Grubesic 2003). Consequently, research by Nielsen NetRatings (2002) suggests that the fastest growing broadband 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 additional exploration and clarification. First, where are advanced telecommunication services currently available? Moreover, is there a significant spatial bias in availability of advanced services between rural and urban areas? If so, what factors are fueling the uneven growth of telecommunication infrastructure? Is the diffusion of information access technologies 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 competition playing a role in the deployment of advanced services? Is there consumer choice in both urban and rural areas? If not, why has deregulation failed to motivate competition in certain places? The following sections provide a thorough examination of advanced telecommunication service growth between December of 1999 and June of 2001. By considering the limitations of existing high-speed platforms, problems associated with their deployment, and the TA96, a spatially-based analysis of advanced telecommunication service availability will provide additional insight into the inequities of broadband Internet access and lack of competition in the United States.

Advanced Services Data and Methodology This 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 Commission. Specifically, these data were retrieved from FCC Form 477, which requires that any facilities-based firm providing 250 or more high-speed service lines or wireless channels report basic information about its service offerings and customers twice yearly.9 The most important piece of information collected from Form 477 is the subscriber data, which are collected at the zip code level.10 The FCC requires that providers identify


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TABLE 2. SUMMARY STATISTICS FOR BROADBAND PROVIDERS BY ZIP CODE FOR JUNE 2001. n Minimum Maximum Mean Standard Deviation

31,583 0 18 2.18 2.914

the zip codes in which they had at least one high-speed service subscriber.11 More importantly, the FCC data do not differentiate between cable, xDSL, and BBFW at the zip-code level—all are simply considered an advanced or high-speed service. Therefore, the resulting database is a nationwide list that documents the number of companies offering broadband 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 contiguous U.S. states.12 These data were then converted to dBASE format and imported into SPSS, a commercial statistics package, and ArcView, a commercial geographic information 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 be mentioned. 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 number of customers at the zip code level. Second, the FCC does not collect data on firms with fewer than 250 high-speed lines in a given state. Therefore, the actual information provided by the FCC is somewhat conservative and may slightly underestimate broadband deployment. 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 complicate such matters. For a more thorough explanation, see Grubesic and Murray (2002).

Broadband Competition and Access Cartographic analysis. Figure 2 illustrates the dramatic changes in broadband availability and competition in the United States between December 1999 and June 2001. Table 3 supports Figure 2 by listing the number of zip codes (including urban) that have at least one broadband provider. There are several trends in these data worth exploring. The most notable feature of Figure 2a (December 1999) is the lack of broadband competition in many of the major U.S. cities. Nationally, 55.46 percent (of the zip codes in the contiguous forty-eight states) have at least one broadband provider. However, as Figure 2a illus-

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TABLE 3. ZIP CODES WITH BROADBAND PROVIDERS.

December-99 June-00 December-00 June-01

Zip Codes

Zip Codes with Providers

Urban Zip Codes with Providers

Rural Zip Codes with Providers

Zip Codes with Ten or More Providers

31,583 31,583 31,583 31,583

17,513 19,594 21,356 22,758

11,884 12,638 13,195 13,489

5,629 6,956 8,161 9,269

10 120 (+1,200%) 707 (+489%) 1,183 (+67%)

(55.46%) (62.04%) (67.62%) (72.06%)

(67.85%) (64.49%) (61.78%) (59.27%)

(32.15%) (35.51%) (38.22%) (40.73%)

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 evidence closely parallels previous research on telecommunication infrastructure access and availability for the United States, where first-tier metropolitan areas, such as New York and Chicago, have significant levels of local infrastructure, while the second- and third-tier U.S. metropolitan areas (e.g., Cincinnati and Milwaukee) have much less (Grubesic and O’Kelly 2002; O’Kelly and Grubesic 2002; Townsend 2001). In part, this disparate land-

A

C

B

D

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


149

scape of competition illustrated in Figure 2a is due to the varying levels of demand for broadband service. Many of the most competitive metropolitan areas are traditional centers for telecommunication access and development. For example, Denver has a particularly high level of specialization in telecommunication technology and is home to several of the largest cable and telecommunication companies in the United States, including Qwest Communications and AT&T Broadband. Many of the other locations on the list are home to major aggregation points for national backbone providers (network access points [NAP] and metropolitan area ethernets [MAE]). Not surprisingly, this is reflected in 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 additional locations begin to display significant levels of broadband competition. Nationally, 62.03 percent of the zip codes had at least one broadband provider—a percentage point increase of 6.58, reflecting a total increase of 10.62 percent from December 1999. However, there was a 1,200 percent increase in the number of zip codes with ten or more broadband providers (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 ten or more providers were found in California, Texas, and Massachusetts. In fact, of the 120 zip 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 spatial patterns associated with these increases are even more interesting. As Figure 2b illustrates, the majority of the emerging, highly competitive zip codes appear to be located in 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 broadband competition. Many of the same trends appear in December 2000 (Figure 2c). Nationally, 67.61 percent of the zip codes have at least one broadband provider. This is a percentage point increase of 5.58, reflecting a total increase of 8.25 percent from June 2000. Competition in urban markets also continues to expand. In fact, some of the most highly competitive zip codes in the city of New York have eighteen different broadband providers. Perhaps the most surprising increase is the number of zip codes with ten or more providers. Recall that this number was 120 in June 2000; by December 2000 it increased 489 percent to 707. Interestingly, Figure 2c displays this increased competition in urban areas. For example, in addition to the larger metropolitan areas already displaying high levels of competition (e.g., New York, Chicago, Washington, etc.), the metropolitan areas of Seattle, San Diego, Sacramento, Pittsburgh, Buffalo, Richmond, Tampa, Philadelphia, and Austin made remarkable gains in December 2000. Conversely, the smaller and more geographically isolated metropolitan areas in the Great Plains and Midwest (e.g., Oklahoma City, Omaha, Topeka, Des Moines) have fewer zip codes with multiple providers.

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Figure 2d illustrates competition by zip code for June 2001. Nationally, the number of zip codes with at least one broadband provider increased 4.44 percentage points for a total of 72.05 percent. This continues the trend of decelerating growth in broadband competition in the United States. Further, although the number of zip codes with ten or more providers 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 Northeastern Corridor (Washington-Boston). Given the modest increases in broadband competition across the United States between December 1999 and June 2001, it is important to determine which locations displayed the largest levels of growth. These areas will be indicative of relatively robust markets where providers are competing for residential and commercial accounts. It is similarly important to determine the locations where competition is decreasing (i.e., the number of broadband providers is declining). These areas will be indicative of markets where broadband choices are limited and competition is sparse. In both cases, such analysis will provide additional insight to the effectiveness of the Telecommunications Act of 1996 by providing 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 level between December 1999 and June 2001. Figure 3a illustrates the significant increase in broadband competition for much of the United States. As mentioned previously, the majority of the growth in broadband competition occurred in metropolitan areas, with several of 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, such as Austin, Indianapolis, Minneapolis, Buffalo, Richmond, Tampa, and Orlando, exhibited increased levels of broadband competition as well. However, growth in competition is occurring at different rates in different places. More importantly, Figure 3b illustrates two distinct pockets of decreased competition: portions of the Northern Great Plains (North Dakota, South Dakota, and Minnesota) and Appalachia (West Virginia and Pennsylvania). In addition, Figure 3 suggests there are several relatively large metropolitan areas, including Albuquerque, Pittsburgh, and Las Vegas, where broadband competition is quite sparse. Competition indices. Statistical evidence corroborating the patterns illustrated in Figure 3 is provided in Table 4. In order to estimate the spatial variation in competition at the consolidated metropolitan statistical area (CMSA) and metropolitan statistical area (MSA) levels, two statistical measures were implemented. The first is an IntraMetropolitan Competition Index (F). This index measures the degree to which broadband competition in a metropolitan area has increased or decreased between December 1999 and June 2001. The formulation of is as follows:

( )

t t F i = ÈÍÊË gi t ˆ¯ - li t ˘˙ *100, mi mi ˚ Î t

(1)

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


151

A

B

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

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TABLE 4. COMPETITION INDICES. CMSA Miami-Fort Lauderdale Los Angeles-Riverside-Orange County San Francisco-Oakland-San Jose Chicago-Gary-Kenosha Detroit-Ann Arbor-Flint Milwaukee-Racine Boston-Worcester-Lawrence Dallas-Fort Worth New York-Northern New Jersey-Long Island Houston-Galveston-Brazoria Seattle-Tacoma-Bremerton Washington-Baltimore Cleveland-Akron Philadelphia-Wilmington-Atlantic City Portland-Salem Denver-Boulder-Greeley Cincinnati-Hamilton MSA Atlanta San Diego Salt Lake City-Ogden Austin-San Marcos Tampa Indianapolis Phoenix-Mesa St. Louis Columbus Minneapolis-St. Paul Kansas City Nashville Albuquerque Las Vegas Pittsburgh

F

Y

ˆ Y

88.81 86.61 84.91 82.26 81.72 80.43 77.83 77.52 77.17 76.65 64.52 63.47 59.62 52.83 51.08 42.25 28.47

1.52 1.47 1.44 1.40 1.38 1.36 1.33 1.31 1.32 1.30 1.17 1.13 1.03 0.96 0.90 0.81 0.53

0.37 0.18 0.00 0.13 0.00 0.00 0.37 0.00 0.28 0.00 2.13 1.52 0.63 1.98 1.07 2.79 1.38

F

Y

ˆ Y

90.22 88.54 84.21 72.94 72.50 66.67 64.75 60.51 59.57 56.04 51.38 51.25 39.53 34.33 22.22

1.53 1.50 1.43 1.26 1.23 1.15 1.16 1.03 1.01 1.03 0.88 0.89 0.71 0.58 0.57

0.00 0.00 0.00 0.58 0.00 0.46 1.78 0.25 0.00 2.39 0.27 0.62 1.15 0.00 5.65


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TABLE 4. (CONTINUED). F

Small MSA Odessa-Midland Chico-Paradise Boise Spokane Tuscaloosa Utica-Rome Sioux Falls Huntington-Ashland Charleston, WV Fargo-Moorehead

80.00 76.47 65.38 56.76 50.00 47.27 32.00 24.49 -18.00 -25.00

Y

ˆ Y

1.36 1.30 1.11 0.96 0.85 0.80 0.54 0.52 0.03 0.14

0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.03 9.90 16.50

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 broadband providers will approach 100. Metropolitan areas with relatively significant losses in broadband providers will approach -100. Those areas with identical gains or losses in broadband providers will have a F of 0.0. By comparing values of F for a select set of MSAs and CMSAs, the overall level of local competition between areas can begin to be assessed. The second measure is an inter-metropolitan competition index (Y). In essence, this index is a slight variation of the location quotient. Originally developed by Hildebrand and Mace (1950), the location quotient is a basic measure of association. Traditionally, the location quotient estimates basic employment in each industry by relating an industry’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 or CMSAs have more or less than their share of broadband competition between December 1999 and June 2001. The formulation for the inter-metropolitan competition index is as follows: t Ê gi ˆ mt Yi = Á t i ˜ ÁÁ GT ˜˜ Ë MTt ¯ t

(2)

where gi is the number of zip codes in a metropolitan area that gained a broadband provider in time-frame t; mit is the total number of zip codes in a metropolitan area during t; GTt is the set of zip codes in all metropolitan areas that gained a broadband provider during t; and MTt is the total number of zip codes in the CMSAs and MSAs set during t. The interpretation 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 share than expected of competitive zip codes, Y is < 1. For metropolitan areas with an average share 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: t Ê li ˆ t ˆ i = Á mi ˜ Y Á LtT ˜ Ë MTt ¯

(3)

where lit is the number of zip codes in a metropolitan area that lost a broadband provider in time-frame t and LtT is the reference set of zip codes in all metropolitan areas that lost a broadband provider during t. As Table 4 indicates, there are significant differences in the levels of broadband competition between this select set of metropolitan areas. The most competitive metropolitan area between December 1999 and June 2001 is Atlanta. At the local, intra-metropolitan level, 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 broadband competition was 1.53. Therefore, Atlanta not only experienced a significant increase in competition at the local level (F), it was the most competitive metropolitan area in Table 4 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 prime segment/target for broadband providers (Grubesic and Murray 2002). In addition, because the physical geography of Atlanta is relatively uncomplicated (i.e., no ocean, lake, or major river), 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), fueling broadband competition (Walcott and Wheeler 2001). The second most competitive city at the local level was Miami-Fort Lauderdale. With a F of 88.81, 120 of 134 zip codes gained additional broadband providers. Miami-Fort Lauderdale was also the second most competitive 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 only 68 of 142 zip codes gaining additional providers. Similarly, the Denver metropolitan area ranks relatively low on the inter-metropolitan competition index for gains, with a score of ˆ for losses, with a score of 2.79. This suggests that the 0.81 and relatively high on the Y market for broadband in Denver might be nearing saturation and that market entry for broadband providers is becoming more difficult and perhaps less profitable. As mentioned previously, there were two areas in the United States with relatively well-defined pockets of decreased competition, namely Appalachia and the Northern Great Plains. This trend is reflected in Table 4 by Charleston, WV, and Fargo-Moorhead, ND-MN. Both


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metropolitan areas have a negative F, which suggests that more broadband providers vacated zip codes than entered. For example, in the case of Charleston, only 1 of 50 zip codes gained a provider, while 10 zip codes lost providers. This suggests that market demand for broadband in these areas is decreasing, and is possibly lower than providers estimated 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 over the 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 nationwide network in 2001, leaving thousands of customers without service (Krause 2001; Green 2001). Considering that all three of these providers were CLECs, it is certainly possible that the anticompetitive behavior of ILECs (outlined in “Competition and Advanced Telecommunications Service Deployment”) can adversely impact the broadband market.

Exploring Competition Through Regression Earlier sections, “Broadband Competition” and “Access,” highlight that broadband competition at the local level is relatively complex and dynamic. While competition increased in some metropolitan areas between December 1999 and June 2000, it decreased in others. More importantly, these sections suggest that the size of metropolitan areas may not be the only contributor to growth in broadband provision and access; otherwise, smaller areas such as Tampa and Salt Lake City would not have had such large increases. This section will explore the factors fueling both increases and decreases in competition through basic spatial statistical analysis and regression modeling. The results provide a more comprehensive profile of competition and access at both the local and metropolitan area levels. Base regression model. The ordinary least squares (OLS) regression model used for analysis is relatively simple, and includes a limited number of explanatory variables. The dependent variable for this base model is the number of broadband providers located in each zip code for June 2001. The independent variables reflect basic demographic, socioeconomic, and geographic indicators that are hypothesized to influence broadband Internet competition. Population density is used as a proxy for broadband market density. Median income is used as a measure of socioeconomic status. Percent white population is used as a measure of demographic composition. Previous work has indicated that demographic composition can be a key factor in broadband availability (Grubesic and Murray 2002). It is hypothesized that the “percent white population” variable in this model will help differentiate areas with a larger minority population (e.g., downtown or adjacent to a central business districts—which often display higher levels of business demand) from those that have larger Caucasian populations (e.g., suburban and exurban neighborhoods— i.e., lower business demand). Percent rent helps reflect more densely populated areas such as the downtown core, where much of the housing is rental based. It also helps capture

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those suburban areas where multi-family housing and large apartment complexes have flourished over the past decade. Grubesic and Murray (2002) suggest that densely populated markets with relatively affluent residents are often good targets for broadband providers. Urbanized area is a categorical variable that better accounts for the location of each 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 center was located in a Census defined urbanized area or urbanized cluster.14 A second categorical variable is business district. Districts with a residential population of fewer than 100 people and a significant daytime population are classified as business districts.15 The last independent variable used in the OLS model is a log transformed computer expenditures measure. This tracks the total household expenditures, by zip code, on computers and computer peripherals for 2000.16 Because broadband Internet access requires a computer, it is hypothesized that zip codes with higher total expenditures on computer products represent better potential markets for broadband providers. As a result, these are the locations where more broadband competition could be taking place, particularly for residential and small business accounts. Table 5 displays the results of this regression model for all zip codes in the United States.17 Both population density and median income were significant and positive factors in explaining broadband Internet competition.18 Interestingly, percent white population

TABLE 5. NATIONAL OLS MODEL. OLS Model Variable

Coefficient*

Constant Pop. Density Median Inc. % White % Rent Urbanized Area Business District Computer Exp.

— 0.104 0.23 -.149 0.108 0.216 0.047 0.519

R Adjusted R-Square Standard Error Moran’s I Moran’s I (p-value)

0.792 0.627 1.779 0.4031 0

t-value

Significance

Tolerance

VIF

-31.836 26.386 51.191 37.560 24.792 48.577 13.325 82.571

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

— 0.761 0.583 0.748 0.621 0.594 0.953 0.54

— 1.313 1.716 1.338 1.609 1.682 1.049 1.853

* Beta coefficients are standardized.


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was significant but the coefficient was negative in the regression model. It is possible that this reflects an increased level of competition in the more urbanized, central city locations of the United States, where larger minority populations reside, and where business demand is greater. The rental variable was also determined to be positive and significant in the OLS model. This further supports the hypothesis that higher levels of broadband competition are linked to urbanized areas (both urban centers and high-density developments on the suburban fringe). Not surprisingly, the categorical variable for urban or rural location had a positive coefficient and significant p-value. This suggests that broadband competition is more strongly associated with urban areas than rural or geographically remote locations. Further, the model suggests that broadband competition is positively linked to business districts with high daytime populations. Finally, the computer expenditure measure 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 yielded a Moran’s I of 0.4031 with a p-value of 0.0.19 This suggests that a moderate amount of spatial autocorrelation is resident in the residuals of the OLS model and that a more intricate 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 of broadband competition, the final portion of this analysis focuses on four different metropolitan areas, Nashville, St. Louis, Indianapolis, and San Francisco. As Table 4 indicates, there are significant differences between levels of intra and inter-metropolitan competition for 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) and Y (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 metropolitan areas is twofold. First, although the national-level OLS model did a relatively good job in helping explain broadband competition, there are always local variations that cannot be accounted for in such a large model. This is particularly true where the provision of broadband services is concerned, because the quality of local infrastructure can influence a provider’s decision for market entry (Grubesic and Murray 2002; Grubesic 2003; Strover 2001; Malecki 2002). Second, one of the major problems with the national level analysis is the spatial autocorrelation of the OLS residual values. A more local analysis will provide the opportunity to take steps in adjusting the model to account for this problem. It will also deepen understanding of the similarities and differences in the local patterns of competition in these metropolitan areas. Table 6 illustrates that all four of the metropolitan area OLS models have similar adjusted R-square and standard residual error values. However, there are substantial variations between the metropolitan area OLS models and the national level OLS model. For example, at the national level, population density was a significant and positive variable. This reflected the dramatic differences in competition and broadband providers between urban and rural areas. However, at the local level, population density was a significant

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TABLE 6. METRO OLS AND SAR MODELS. n = 195

St. Louis OLS

St. Louis SAR

Coefficient*

t-value

Significance

Coefficient*

t-value

Significance


— 0.181 0.179 -.057 0.068 0.111 -.010 0.518

-5.400 2.687 2.624 -.875 0.979 1.792 -.189 7.898

0.000 0.008 0.009 0.383 0.329 0.075 0.000 0.850

— 0.0002 0.0121 0.0001 0.0313 -.0270 -.1351 0.8211

-3.1801 1.6270 0.9665 5.9071 2.9511 -.0924 -.1604 2.8892

0.0017 0.1054 0.3350 0.0000 0.0036 0.9264 0.8728 0.0043


0.784 0.60 1.823 0.2339 0

n = 80

Moran’s I Moran’s I (p-value) Nashville OLS

-.04651 0.3635 Nashville SAR

Variable

Coefficient*

t-value

Significance

Coefficient*

t-value

Significance


— -.056 0.241 -.072 0.489 0.233 0.017 0.401

-3.197 -.505 2.276 -.683 4.266 2.407 0.237 4.156

0.002 0.615 0.026 0.497 0.000 0.019 0.813 0.000

— 0.0001 0.0321 -.0061 0.0543 1.0773 0.2003 1.2924

-3.0656 -.4552 1.8289 -.5628 4.2280 2.3376 0.1899 4.2599

0.003 0.650 0.072 0.575 0.000 0.022 0.850 0.000


0.81 0.623 1.409 0.02584 0.5416

Moran’s I Moran’s I (p-value)

-.003655 0.1266


Variable

n = 108

Indianapolis OLS

Indianapolis SAR

Variable

Coefficient*

t-value

Significance

Coefficient*

t-value

Significance


— -.005 0.242 -.286 0.22 0.273 0.15 0.353

-.984 -.067 2.851 -3.944 2.431 3.727 2.705 3.715

0.328 0.946 0.005 0.000 0.017 0.000 0.008 0.000

— 0.0000 0.0570 -.0397 0.0482 1.8860 2.5372 1.4021

-1.0574 0.0032 3.1301 -4.0056 2.4831 3.7677 2.8183 3.6667

0.293 0.998 0.002 0.000 0.015 0.000 0.006 0.000


0.84 0.686 1.914 -.03277 0.635

n = 407

Moran’s I Moran’s I (p-value) San Francisco OLS

0.001083 0.1788 San Francisco SAR

t-value

Significance

Coefficient*

t-value

Significance

Constant Pop. Density Median Inc. % White % Rent Urbanized Area Business Districts Computer Exp.

— -.056 0.152 -.268 0.188 0.124 0.064 0.594

-2.232 -1.562 4.228 -8.629 5.160 3.750 2.293 16.701

0.026 0.002 0.000 0.000 0.000 0.000 0.022 0.000

— 0.0000 0.0028 -.0400 0.0298 0.6462 1.0058 2.6566

-.4320 -1.3289 0.3960 -6.1235 3.8406 2.4342 1.2962 16.8356

0.666 0.185 0.692 0.000 0.000 0.015 0.196 0.000


0.846 0.71 2.292 0.2881 0

Moran’s I Moran’s I (p-value)

-.01779 0.6289

159

Coefficient*


Variable

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and negative component of the San Francisco OLS model. It is possible that a high level of broadband competition takes place in the suburban communities of the Bay Area. These suburban markets are not only full of well-educated, relatively affluent, and technologically savvy working professionals, they are also home to large numbers of small to medium-sized businesses which often demand higher bandwidth connections—but cannot afford a dedicated fiber-optic line (Grubesic and O’Kelly 2002). This is further supported by 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 United States for broadband subscribers, with over 1.1 million (Nielsen NetRatings 2002). That said, it appears that broadband competition in the suburban areas is so strong that it partially offsets the population density variable of this model. The remaining OLS models display moderately mixed sets of coefficients. For example, although percent white population was a significant and negative variable for San Francisco 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 significantly from the national scale and that local market conditions can impact the spatial distribution 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 for any spatial effects in the data and help obtain a truer picture of the relationship between broadband Internet competition and the significance of the independent variables. In all four metropolitan area SAR models, the addition of a spatial lag in the regression parameters helped accomodate the local geographic relationships between competition and the independent variables (Table 6). Results for the St. Louis SAR model suggest that percent white population, percent rent, and computer expenditures are the most significant variables. These results seem to indicate that residential broadband competition (suburban and exurban) is particularly healthy in the St. Louis area. Conversely, the results of the SAR model for Indianapolis suggest that business demand in more urban locations plays a larger role in broadband competition for the area. For example, the variables for urbanized 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 previous hypothesis that broadband Internet competition is not restricted to the urban centers of the Bay Area, but is also occurring in less-dense suburban and exurban locations.

Discussion The results of this study indicate several trends emerging in a deregulated and competitive market for broadband Internet provision and competition in the United States. First, the Telecommunications Act of 1996, particularly Section 706, has been a moderate success. Broadband competition in many metropolitan areas has increased significantly


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between December 1999 and June 2001. In fact, this growth is not strictly limited to larger metropolitan areas such as New York, San Francisco, and Atlanta; mid-sized metropolitan areas like San Diego, Salt Lake City, Austin, Tampa, Indianapolis, and Phoenix are also highly competitive. Smaller metropolitan areas such as Odessa-Midland, Chico-Paradise, and Boise have also seen significant growth in broadband competition. However, the results of this study do indicate that the competitive landscape in the United States is quite varied. 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 this study also suggest that a significant, competitive divide exists between urban and rural areas. 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, where the number of broadband Internet providers actually decreased between December 1999 and June 2001. As a result, it is clear that stronger legislative measures need to be taken to ensure consumer choice in a deregulated environment, particularly in markets which are not immediately profitable or enticing for broadband providers. For example, the Charleston, WV, and Fargo-Moorehead, SD, MSAs rank far below average in both intrametropolitan and inter-metropolitan competition indices, with both MSAs losing significant numbers of broadband providers between December 1999 and June 2001. Is this a result of existing broadband providers’ attempts to cherry-pick profitable urban markets and ignore those which have problems generating revenues? Or is this simply part of an organic 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 combination of both. It does appear that broadband access is gradually diffusing from larger, firsttier metropolitan areas to smaller second- and third-tier metropolitan areas in the United States (Compaine 2001). In fact, many rural locations in the U.S. also have some form of broadband access available (NTIA and RUS 2000). It is important to note, however, that as of June 2001, the spatial distribution of broadband competition remains remarkably uneven. At the very least, it is important that the FCC continues to monitor and promote facilities-based competition across all three broadband platforms. In addition, both federal and state-level agencies can help competition by reviewing and revaluating current regulatory 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.

Conclusion This paper has provided a comprehensive, longitudinal examination of broadband Internet competition in the United States between December 1999 and June 2001. Results indicate that although competition continues to increase at the national level, it is doing so at a decreasing rate. Competitive growth fell from a high of 6.5 percent between December 1999 and June 2000 to a low of 4.3 percent between December 2000 and June

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

NOTES 1. In addition to ADSL, there are several other versions of digital subscriber line technology, including 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).


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