Broadband Deployment in the United States: Examining the Impacts of Platform Competition

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1 S. Platform Lee Competition Broadband Deployment in the United States: Examining the Impacts of Platform Competition Sangwon Lee University of Florida, USA Emerging broadband communication technologies are providing an infrastructure for a unifying platform for 3 converging industry sectors: computing, telecommunications, and broadcasting. Despite the steady growth of broadband access in the United States (with 14.5% broadband penetration), the country ranks only 12th among 30 Organisation for Economic Co-operation and Development (OECD) countries (OECD, 2005). For rapid growth in broadband diffusion, the Federal Communications Commission (FCC) has considered access-based competition and facilities-based competition as important policy tools. Through 2 different econometric analyses (time series analysis and multiple regression analysis), this study examines whether platform competition, access-based competition, and other factors have influenced broadband deployment. The result of the time series analysis shows that platform competition has been a key driver of broadband deployment in the United States. The multiple regression analysis suggests the availability of different broadband platforms and the level of income have influenced broadband diffusion. The main findings of this study imply that regulation across platforms should be competitively neutral and that Congress and the FCC should embrace further platform competition through new technologies like broadband over power-line and wireless broadband. Address correspondence to Sangwon Lee, Department of Telecommunication, College of Journalism and Communications, University of Florida, Gainesville, FL sangwon@ufl.edu Technological convergence of mobile and Internet technologies are expected to provide a major future driver for growth in the telecommunication industry. Emerging broadband communication technologies are providing an infrastructure for a unifying platform for three converging industry sectors: computing, telecommunications, and broadcasting (International Telecommunications Union [ITU], 2003b). Communication technologies that provide high-speed, always-on connections to the Internet for large numbers of residential and small-business subscribers are commonly referred to as broadband technologies (Crandall, 2005). Compared with narrowband, the increased speed and always-on nature of broadband enables the exchange of richer content, improved and expanded facilities, and faster communication and allows for sharing of a connection with multiple users (ITU, 2003a, 2004). Broadband technologies can significantly support a variety of applications, including e-commerce, education, health care, entertainment, and e-government. Widespread and affordable broadband access encourages innovation, contributes to productivity and growth in an economy, and attracts foreign investment (ITU, 2003a). Ferguson (2002) noted that failure of broadband performance could reduce U.S. productivity growth by 1% per year or more as well as reducing public safety, military preparedness, and energy security. Crandall and Jackson (2001) estimated that broadband, acting through changes to consumer shopping, commuting, home entertainment, and health care habits, would contribute an extra $500 billion in gross domestic product by For the rapid growth in broadband diffusion, many countries have considered intramodal competition regulation and facilities based competition as important policy tools. In the United States, as in other countries, since legislation of the 1996 Telecommunication Act, the Federal Communication Commission (FCC) ruled for unbundling policy to promote broadband deployment and broadband infrastructure investment. It is widely held that intermodal competition through platform competition (competition among several different broadband platforms) is crucial for reducing prices, improving quality of service, increasing customers, and promoting investment and innovation (DotEcon & Crite- The International Journal on Media Management, 8(4),

2 rion Economics, 2003). However, there are only a few empirical studies about whether platform competition has been a main driver for broadband deployment in the United States. Through empirical research, this study assesses the impacts of platform competition and other determinants on broadband deployment. Through two different econometric analyses, this study examines whether platform competition, access-based entry, and other factors have been key drivers for broadband deployment. From the result of the empirical research, this article suggests policy implications for rapid broadband diffusion. Broadband Deployment in the United States There has been a steady growth of high-speed lines and advanced services lines in the United States. 1 According to the FCC (June 2005), there were about million high-speed lines in service (FCC, 2006). As of June 30, 2005, there were million high-speed lines serving residential and small business subscribers (FCC, 2006). Of the million high-speed lines in service to residential and small business subscribers, it was estimated that million provided advanced services (FCC, 2006). According to the FCC official data (June 2005), the two dominant broadband access platforms in the United States were cable modem (56%) and direct service lines(dsl; 38%). Other platforms, such as fiber, high-speed wireless access, and other wire lines served the remaining 6%(FCC, 2006). Despite the steady growth of broadband access in the United States, the United States ranked only 12th among 30 OECD countries with 14.5% penetration (OECD, 2005). Korea, the Netherlands, Denmark, and other countries lead the United States in broadband penetration among households. There have been a lot of discussions about whether broadband deployment in the United States is reasonable and timely and whether the pace of investment is appropriate (Faulhaber, 2002; FCC, 2004; Owen, 2002). As a result, a policy debate has erupted over how to facilitate consumer adoption of broadband technology to access the Internet. To promote broadband deployment and infrastructure investment, the 1996 Telecommunication Act embraced the concept of unbundling network telecommunication facilities that are needed by new entrants to compete and offering them to entrants at cost-based rates, but it did so without the specific use of the phrase essential facilities (Crandall, 2005). Several studies have argued that facilities-based competition might increase broadband uptake. A report from DotEcon & Criterion Economics (2003) argued that there is strong evidence that platform competition drives broadband adoption. From the analysis of European Union membership countries data, the report argued that competition between platforms rather than access-based entry speeds up the penetration of broadband. The report suggested that broadband penetration tends to be higher in European countries where DSL and non-dsl platforms have more similar market share (DotEcon & Criterion Economics, 2003). Aron and Burnstein (2003) found that broadband availability in a state is driven by intermodal competition and the demand and cost factors but not by raw availability of broadband services. Specifically, using government data from 2000, they estimated a reduced form logit model of broadband deployment. The authors found that the independent effect of direct, intermodal competition is associated with increased household subscription to broadband services in the United States (Aron & Burnstein, 2003). Through statistical analysis of data from 14 European countries, Distaso, Lupi, and Maneti (2006) argued that interplatform competition drives broadband adoption but that competition in the DSL market does not play a significant role. An ITU Internet report determined that intramodal and platform competition, innovation, applications, procompetitive regulation, price, speed, high ICT usage, and urban demographic variables might influence broadband deployment (ITU, 2003b). There is growing body of literature about broadband demand. Using a national sample of U.S. households, Rappoport, Kridel, Taylor, and Alleman (2001) found that the price elasticity of demand for broadband service is much greater than the demand elasticity for narrowband. Using estimation of an economic model based on statistical data from 2000 to 2001, Crandall, Sidak, and Singer (2002) showed that the decision to use a broadband connection depends on the opportunity cost of time for the user and the intensity of Internet use. In a nationwide survey of U.S. residences, Savage and Waldman (2005) found that preference for high-speed access is apparent among households with higher income and college education. Using national survey data from 2002 to 2005, Horrigan (2005) found the intensity of online use is the critical factor in understanding the home broadband adoption decision and suggested that the intensity of Internet use is a function of connection speed and years of online experience. There have been many debates on the effects of the FCC s local loop unbundling (LLU) regulation. Hausman (2001, 2002) contended that the LLU regulation has impeded the incumbents deployments of the network facilities required for DSL, conveying market on the cable operators who control two-thirds of the U.S. broadband market. Faulhaber (2002) argued that line sharing will probably have no future effect on broadband deployment, either positive or negative. Glassman and Lehr (2001) claimed that any reduction of network unbundling for broadband deployment places downward pressure on the competitive carriers equity prices, thereby reducing investment by en- 174 S. Lee

3 trants in network facilities. Through statistical analysis of approximately 100 countries, Garcia-Murillo(2005) argued that unbundling an incumbent s infrastructure only results in a substantial improvement in broadband deployment for middle-income countries, but not for their high-income counterparts. FCC data (2006) have shown that independent market entrants who have tried to provide DSL service by leasing a portion of the incumbents lines have not been successful. The data have shown also that cable television companies have provided competition for the incumbents DSL broadband services in the United States (see Table 1). On February 2003, the FCC ruled that incumbent local carriers no longer had to offer last-mile access to competitors over their networks (FCC, 2003). It appears that the ruling was an attempt to spur investment in next-generation networks such as fiber. From the discussions and the results of previous studies, this article examines the following research questions (RQs) by using two different econometric analyses: RQ1: Have platform competition and access-based entry in the broadband access market significantly influenced broadband deployment in the United States? RQ2: Have the availability of different platforms in the broadband access market and other important factors such as income, population density, and LLU regulation price significantly influenced broadband deployment in the United States? RQ3: If platform competition has been a main driver of broadband deployment, how can we measure the performance of platform competition in the broadband services market, and has the United States been successful in promoting platform competition by this measurement? The Empirical Model, Method, and Data Table 1. High-Speed Lines by Type of Provider as of June 30, 2005 Types of Technology RBOC Other ILEC Non-ILEC Total ADSL 13,436,360 2,134, ,589 16,182,076 Lines (%) Cable modem 23,888,785 23,938,908 Lines (%) 99.8 Note. Data were derived from the Federal Communication Commission (2006) report. High-speed services are for Internet access status as of June 30, RBOC = Regional Bell Operating Company; ILEC = incumbent local exchange carrier; ADSL = asymetrical digital subscriber line. For explaining determinants of broadband deployment pattern, this article uses time series analysis and regression analysis. Time series analysis was used for analyzing RQ1, and regression analysis was used for RQ2. The empirical model, methodology, variables, measurement, and data are as follows. Time Series Analysis The model and method. From the literature review, an empirical model for time series analysis was identified. There were limitations in choosing diverse independent variables because of the paucity of time series data related to broadband deployment and demographic related variables. Equation 1 is a multivariate autoregressive integrated moving average (MARIMA) model for time series analysis. Y t (BPR) = (Platform Competition) + 2(Access-based Entry) + 3 (Internet Use) + 4(Availability) t In the empirical model (Equation 1), the dependent variable Y t is the broadband penetration rate (BPR). From previous studies of broadband deployment, independent variables such as platform competition (facility-based competition), access-based entry, Internet usage, and availability of broadband service were identified. These explanatory variables were important, quantifiable variables that might influence broadband diffusion. However, some independent variables such as income, level of education, and population density were not included in the model because time series data (every 6 months) was not available. Through the analysis of time series data (every 6 months from December 1999 to June 2005: 12 observations), we identified MARIMA (1, 0, 0) model. 2 Measurement and data sources. One motivation for writing this article was to determine whether platform competition and access-based entry have influenced BPR. In the empirical model, the BPR was measured by the number of broadband subscribers per 100 inhabitants. Platform competition is an important variable by which the broadband market is served by competing platforms. Previous studies proposed that broadband penetration tends to be higher in European countries where DSL and non-dsl platforms have more similar market share (DotEcon & Criterion Economics, 2003). This research measures the platform competition by (100 DSL market share Non-DSL market share). How can we measure the access-based entry in the DSL market? Market shares of new entrants in the DSL market can be an indicator of the access-based competition. Non-incumbent market share (Non-ILEC) in the DSL service market was used for the measurement of the access-based entry in the DSL market. If there was an ade- (1) Platform Competition 175

4 quate access-based competition, there might have been sustainable entry from new market entrants in the DSL. Internet use was measured by Internet use by percentage of U.S. population, and availability of broadband service was measured by percentage of zip codes with broadband service available. Table 2 shows variables, measurement, and data sources of the time series analysis. Data was collected from the FCC statistics (2006), U.S. Census Bureau (2005), ITU (2005), and OECD (2005). Time series data from December 1999 to December 2005 (12 observations) was used for the analysis. Regression Analysis The model and method. To capture other determinants for explaining broadband deployment patterns, a multiple regression analysis was also implemented. 3 To examine the influences of other variables on the diffusion patterns of broadband, we formulated the following multiple regression model. Y t (BPR) = (Platform Competition Availability) + 2 (Income) + 3 (Education) + 4(LLU Regulation Price) + 5 (Internet) + 6(Population Density) + t The empirical model of Equation 2 for multivariate analysis was a composite model from previous empirical studies. In the empirical model, the dependent variable (Y t ) is BPR in 40 U.S. states. From the previous studies of broadband uptake, independent variables were identified. Platform competition availability, income, education, LLU regulation price, Internet use, and population density are important quantifiable variables and were included in the multiple regression model. Measurement and data sources. In the regression model, the dependent variable BPR was measured by the number of broadband subscribers per 100 inhabitants. (2) Platform competition availability (INTER) was measured by percentage of end-user premises with access to both DSL and cable modem services available in a given State. For the measurement of income per capita personal income by state was used. Level of education was measured by percentage of people who have bachelor s degrees or higher by state. For reflecting cost conditions of broadband market, state regulated price for a Regional Bell Operating Carrier (RBOC) unbundled network element was used as an indicator of the LLU regulation price, and estimates of population density was included in the model. Table 3 shows variables, measurement, and data sources of the multiple regression analysis. Data was mostly collected from the FCC statistics (2005) and from the U.S. Census Bureau. Forty samples were available. 4 Results and Analysis Results of Time Series Analysis Figure 1 shows an initial plot of the trend of the BPR as a dependent variable. It illustrates a steady increase of broadband deployment in the United States. The modeling strategy used to analyze this dependent variable for time series analysis was the identification, estimation, diagnosis, and hypothesis test. 5 In the identification stage, an autocorrelation function (ACF) and partial autocorrelation function (PACF) were computed from time-series observations. In the estimation stage, the parameter θ was calculated for the tentative model. In the diagnosis stage, an ACF and PACF were computed from the residuals of the estimated tentative model, and these statistics were used to decide whether the tentative model was adequate. In the hypothesis test stage, the effects of platform competition, access-based entry, Internet use, and the availability of broadband service on broadband deployment were analyzed. Model identification. Figure 1 shows the result of autocorrelations analysis and ACF and PACF plots for model identification. Because the time series have an ACF Table 2. Variables, Measurement, and Data Sources for Time Series Analysis a Variable Measurement Data Sources Broadband penetration rate Broadband subscribers per 100 inhabitants (December 1999 June 2005) FCC (2006) Platform competition 100 (DSL market share non-dsl market share) (December 1999 June 2005) FCC (2006) Access-based competition Market share of non-ilec in DSL market (June 2000 June 2005) FCC (2006) Internet Use Internet use by percentage of population (December 1999 December 2005) U.S. Census Bureau (2005), ITU (2005), OECD (2005) Availability Percentage of zip codes with broadband service available (December 1999 June 2005) FCC (2006) Note. FCC = Federal Communication Commission; DSL = digital subscriber line; ILEC = incumbent local exchange carrier; ITU = International Telecommunication Union; OECD = Organisation for Economic Co-operation and Development. a Specific references of time-series data sources are as follows: FCC (2006) and Gregg (August 2005). 176 S. Lee

5 Table 3. Variables, Measurement, and Data Sources for Regression Analysis Variable Measurement Data Sources Broadband penetration rate Broadband subscribers per 100 inhabitants by state FCC (2006) Platform competition availability Percentage of end-user premises with access to both DSL and cable FCC (2006) modem services available in a state Income Per capita personal income by state U.S. Bureau of Economic Analysis and Bureau of the Census (2005) Education Percentage of bachelor s degree or higher by state U.S. Census Bureau (2005) LLU regulation price The regulated price for a RBOC carrier UNE in Zone 1 by state A survey of UNE prices in the United States (2005) Internet use Presence of the Internet for households by state U.S. Census Bureau (2005) Population density Estimates of population density by state U.S. Census Bureau (2005) Note. FCC = Federal Communications Commission; DSL = digital subscriber line; LLU = local loop unbundling; RBOC = Regional Bell Operating Company; UNE = unbundled network. Figure 1. Autocorrelation function (ACF) and partial ACF plot (identification of auto-regressive integrated moving average model [MARIMA; 1, 0, 0]). Note. BPR = broadband penetration rate. that is quickly declined to zero, they are stationary. So we did not need to difference the time series. From the examination of patterns of ACF and PACF, the MARIMA (1, 0, 0) model was identified. Table 4. The Results of Time Series Analysis (Dependent Variable: Broadband Penetration Rate) Variable Estimates SE t p AR1 (Parameter) a Platform competition 1,397, , a Access-based competition 890, , Internet use Availability Constant 1E+008 2E a a Significant at 95%. Estimation and diagnosis. In the estimation stage, the parameter AR1 (θ) was calculated (see Table 4: -.990). The actual coefficients appear in the table along with their estimated standard errors, t ratios, and significance levels (Table 4). In the diagnosis stage, an ACF and PACF were computed from the residuals of the estimated tentative model. Figure 2 illustrates the computation of ACF and PACF for the residuals of the tentative MARIMA model. These statistics met two basic criteria: (1) no significant spikes at early lags and (2) a pattern of white noise. These statistics were used to decide whether the tentative model was adequate. The Box-Ljung statistic for the ACF function was not statistically significant at any lag (Table 5). Therefore the tentative model is adequate for the hypothesis test. Hypothesis test. We conduction correlation analysis to check for signs of multicollinearity. The independent variables were not highly correlated (Pearson correlation coefficient was below 65%). Table 5 shows the result of the hypothesis test. The result shows that Internet use and the availability of broadband service have not influenced the broadband deployment (ps = Platform Competition 177

6 Figure 2. Autocorrelation function (ACF) and partial ACF plot (diagnosis: residuals of tentative model). Note. BPR = broadband penetration rate..927 and.301, respectively). Access-based entry was not statistically significant (p =.255). However, at a 95% significance level, platform competition was statistically significant (p =.041). This means that a main driver of broadband deployment has been the platform competition in the initial U.S. broadband access market. This result may mean that the effects of facility-based competition on broadband diffusion are more significant than those of access-based entry. Results of Regression Analysis The empirical results of the multiple regression analysis allowed the identification of other important factors that influence broadband uptake in the United States. Multicollinearity can occur when independent variables are highly correlated. Therefore, a correlation analysis was used to yield valid results from the empirical study. The independent variables education and Internet use were highly correlated to other variables (Pearson correlation coefficient was over 65%), and they were removed Table 5. Autocorrelations and Box-Ljung Statistic for Residuals Lag Autocorrelations SE Box-Ljung Statistic p from the initial model. Table 6 shows the results of the multiple regression analysis (reduced model). In the model, R =.838, R 2 =.702, and the model was significant (p <.001). The table shows that LLU regulation price and population density were not significant variables in explaining the broadband diffusion patterns. Availability of platform competition and income were significant variables that can explain broadband deployment. Internet use was statistically significant at a 95% significance level (p =.032) and income was statistically significant at a 99% significance level (P =.001). This result means that availability of different platforms in the broadband access market and the level of income has significantly influenced the broadband deployment in the United States. Platform Competition and Broadband Deployment in the United States The results of this empirical study show that platform competition has been a key driver of broadband deployment in the United States. Now how can we measure the performance of platform competition in the U.S. broadband access market? Has the United States been successful in promoting platform competition by the measurement? Figure 3 shows the performance measurement of platform competition in the United States. The market share of nondominant platforms in the broadband access market could be one of the indicators of the performance of platform competition. The market share of nondominant platforms in the broadband market may assess whether there have been diverse choices for customers in the broadband services market. It also 178 S. Lee

7 Table 6. Results of Multiple Regression Analysis (Coefficients) Variable Unstandardized Coefficients B SE Standardized Coefficients B t p Constant Regulation price Income b Population density Platform competition availability a Note. N = 40, R =.838, R 2 =.702, F = , p <.001. a Significant at 95%. b Significant at 99%. shows the degree of competition among different technologies in the broadband service market. In the United States, the dominant platform in the broadband access market has been cable modems. Figure 3 shows the trend of noncable modems (DSL, fiber, satellite, fixed wireless, and other wire-line) market share. It shows that the noncable modem market share has decreased from December 1999 to December The noncable modem market share has decreased from 48.7% to 43.6%. Specifically, from December 2000 to June 2003, the performance of platform competition has fallen sharply. Interestingly, in 2000 the United States was ranked third in the broadband penetration among OECD countries, but in 2003 the United States was ranked tenth in the broadband penetration. The performance measurements of platform competition may imply that the improvement in the performance of the platform competition in the broadband access market could drive faster broadband diffusion in the United States. Main Findings Main findings from the results of the empirical study are as follows. First, the effects of platform competition and the availability of different platforms on broadband penetration were significant. The results of the time series analysis show that platform competition was statistically significant. The regression study also suggests the availability of different platforms has influenced broadband deployment. These results of the empirical study suggest that intermodal competition has been a main driver of broadband deployment in the United States. The intermodal competition has led to facilities-based competition in the broadband access market, which has brought diverse choices for broadband customers. Second, the effects of access-based entry on broadband deployment were not significant. Despite its very aggressive unbundling and line sharing policy since the 1996 Telecommunication Act, the FCC has not been successful in stimulating sustainable entry from independent providers of DSL services (Crandall, 2005). There is no corresponding statistical evidence that access-based entry has driven broadband take-up. Third, income level has influenced the broadband deployment. The result of the multiple regression study shows that income is a significant variable. This result is consistent with Savage s study that found preference for high-speed access is apparent among households with higher incomes (Savage & Waldman, 2005). However, other variables such as population density, unbundled network prices, and Internet use were not significant in explaining broadband deployment in the United States. Fourth, the performance measurement of platform competition in the United States shows that the noncable modem market share has decreased from 1999 to It may suggest that the improvement of the performance of the platform competition could drive faster broadband deployment in the United States. Discussion Figure 3. Noncable modem market share. Source: Federal Communication Commission (2005). High-Speed services for Internet access status as of December 31, Washington, DC: FCC. This article examines whether platform competition, access-based entry, and other factors have had any real effect on broadband diffusion. The result of this empirical study shows that, on the supply side of broadband services, platform competition rather than access-based entry has been a main driver of broadband diffusion. Facilities-based competition with strong platform competition may bring real choice for customers, downward pressure on costs, and incentives for service innovation Platform Competition 179

8 (DotEcon & Criterion Economics, 2003). In addition to competition within a sector, prices fall when several broadband technologies compete for broadband customers. The existence of strong platform competition among DSL, cable modem, fiber, and wireless broadband in a market should ensure that prices remain low (ITU, 2003b). In this context, regulation across platforms should be competitively neutral. Policies that discriminate across platforms by encouraging investment in one (or more) but discouraging investment in another could frustrate policy objectives to increase broadband adoption (Aron & Burnstein, 2003). In particular, considering the innovation of broadband technology, Congress and the FCC should embrace further platform competition through new technologies like broadband over power-line and wireless broadband. The results of this regression study and other previous empirical studies suggest that on the demand side of broadband services, level of income is an influential factor of broadband diffusion. It appears that both income elasticity of demand and price elasticity of demand for broadband service is much greater than the demand elasticity for narrowband service. Availability of time series data with small observations may have limited the conclusions of this article. Other more important factors such as innovative broadband rollouts, applications, speed, and marketing strategy were not included in the time series and regression model. Impacts of other policy factors such as licensing, right of way, and ownership regulation should be examined in future research. Currently, there are only a few empirical studies that estimate the effects of platform competition on broadband diffusion. This study is only an initial one to understand the market dynamics of broadband services efforts through an econometric approach. More useful empirical research such as a longitudinal study that involves observations of the same items over long periods of time will be possible when more data about broadband deployment and platform competition are available. Acknowledgments An earlier version of this article was presented at the National Cable and Telecommunication Association academic seminar, April 2006, Atlanta, GA. I appreciate comments from Dr Justin Brown at the University of Florida and two anonymous reviewers. Notes 1. High-speed lines were defined as those that provide services at speeds exceeding 200 kilobits per second (kbps) in at least one direction, whereas advanced services lines are those that provide services at speeds exceeding 200 kbps in both directions. 2. This empirical study is the first trial to explain broadband deployment in the United States that uses time series data and MARIMA model. 3. For the empirical study, the time series analysis may have a limitation, because only 12 observations were available for the time series analysis. By using multiple regression analysis, this study could include other independent variables with more observations (40 states data). There was an empirical study, which used states data for broadband deployment research and it was a useful reference for this empirical study. However, there are some differences between the previous study and this empirical study in regression model, sample size, variables, data, and measurement (see Aron, & Burnstein, 2003). 4. For the regression analysis, 10 states data were not available for all independent variables, and these states were excluded from the analysis. 5. 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