JRAP 38(3): 254-268. 2008 MCRSA. All rghts reserved. Small Broadband Provders and Federal Assstance Programs: Solvng the Dgtal Dvde? Bran E. Whtacre and Phumsth Mahasuweeracha Oklahoma State Unversty - USA Abstract. Recent data from the Federal Communcatons Commsson allows for examnaton of the locaton decsons of small broadband provders,.e. those wth less than 250 subscrbers. Whle anecdotal evdence suggests that small provders may be servng dsadvantaged or underserved areas, the data ndcates that more than two-thrds servce urban areas and seemngly choose areas wth hgh demand potental. Ths paper models the locaton decson of these small provders and analyzes whether they are nfluenced by federal assstance programs such as USDA broadband grants and loans. The results suggest that whle small provders do tend to locate n urban areas wth hgher levels of educaton and ncome, they also favor rural areas wth hgh levels of Hspancs and Afrcan-Amercans. No statstcal mpact s found for the federal-level polces ncluded n the analyss, mplyng that the focus of these programs may be on the wrong areas. 1. Introducton Broadband Internet access has become ncreasngly popular for households and busnesses snce ts ntroducton n the late 1990 s. 1 These hgh-speed connectons allow users to send and receve enormous quanttes of data, audo or vdeo fles; and can also enhance voce communcaton (Horrgan and Rane, 2002; Preger, 2003). Broadband access has the potental to beneft a wde cross-secton of socety, ncludng busnesses, governments, consumers, and communtes. For the prvate sector, broadband access provdes the opportunty to take advantage of new nput and output markets, and allows frms to ncrease ther productvty by mprovng nformaton exchange, value chan transportaton, and process effcency (Thomas, 2005). Broadband access also adds value to publc sector servces such as educaton, health, and local government servces by ncreasng the avalablty of data and speedng feedback to and from consttuents (Bauer et al., 2002). Moreover, broadband access has the potental to enhance the qualty of lfe of consum- 1 Broadband access, also called hgh-speed access or advanced servce, s defned by the Federal Communcatons Commsson as 200 Klobts per second (Kbps) (or 200,000 bts per second) of data throughput n at least one drecton. ers through economc, socal and cultural development (Warren, 2007). Broadband access s partcularly mportant for rural and remote areas. 2 These geographcally solated regons have the most to gan from the dstancenegatng nature of the Internet, ncludng the opportunty to partcpate n the dgtal economy and become part of the nformaton revoluton (Lndroos and Pnkhosov, 2003; Warren, 2007). However, as wth every technologcal revoluton, some people and areas have been slower to adopt than others. The dgtal dvde, or the gap n Internet access between urban and rural areas, has receved a large amount of attenton from researchers, poltcans, and polcy makers (Strover, 2001; Whtacre and Mlls, 2007). Research on the determnants of broadband nfrastructure fnds that rural locaton does n fact have a sgnfcantly negatve mpact on ts avalablty (Maleck, 2003; Preger, 2003; Strover, 2003). Ths s n part due to the proftablty decsons of nfrastructure provders, based on factors such as number of potental adopters, prospectve demand, and cost to provde. Therefore, 2 Ths paper defnes rural and urban based on ZIP code classfcatons of the Rural Urban Commutng Area (RUCA) system. For an overvew of ths classfcaton see WWAMI (2002).
Broadband provders and the dgtal dvde 255 urban areas, wth hgher levels of educaton, ncome, populaton, and lower cost to provde were the frst to receve broadband nfrastructure (Ward, 2007). Anecdotal evdence suggests that small-scale provders may be servcng the broadband needs of some rural communtes (Rchtel and Belson, 2005; Hurley, 2003; Rchtel, 2003; Whtacre, 2007). For example, a local ctzen wth an entrepreneural mnd mght set up a wreless tower to connect hs hometown, or the local telephone or cable company mght upgrade ther systems due to a sense of prde n ther communty. However, untl recently these small provders would not have been accounted for by the most commonly referenced data collecton effort for broadband provders that performed by the Federal Communcaton Commsson (FCC). The FCC has collected data on subscrbers to broadband servce snce 1999 (va a document commonly known as Form 477) n an effort to evaluate the deployment of advanced telecommuncatons capablty. Intally, a lst of the ZIP codes servced was collected twce a year from hgh-speed provders wth at least 250 lnes n a partcular state. Ths led to concern that although some rural and remote areas were beng depcted as unserved n the data, they dd n fact have nfrastructure avalable to them ther provders were smply not large enough (250 subscrbers) to meet the necessary flng requrements. To ths end, the June 2005 Form 477 requred ALL provders of hgh-speed connectons to report. Thus, whle the number of flers reportng under ths new requrement was more than double the prevous amount conducted just sx months earler, the total number of broadband lnes provded followed the hstorcal trend (Fgure 1). The avalablty of ths data allows for some nsght nto those broadband nfrastructure provders who servce a smaller number of subscrbers. 3 Meshng ths data wth secondary demographc nformaton permts dentfcaton of factors mpactng the locaton decsons of these small broadband provders. Ths paper augments the exstng knowledge base on broadband nfrastructure by 1) comparng areas served by small provders to those served by large provders, 2) descrbng the locaton of small broadband provders, ncludng Geographc Informaton System (GIS) mappng technques and demographc comparsons of communtes wth and wthout small provders; and 3) modelng the determnants of where these provders choose to locate partcularly for rural areas that prevously had no access. One unque contrbuton of ths paper s to explore the roles that federal assstance programs (namely Unted States Department of Agrculture (USDA) broadband grants and loans) play n attractng small broadband provders to prevously unconnected areas. These results are of nterest to ndvduals nvolved n communty development, gven the recent fndng that broadband access leads to growth n employment and number of busnesses (Lehr, Osora, Gllett, and Srbu, 2006). Understandng the locaton decsons of small broadband provders and the role of federal polces n where they locate has mplcatons for polcy makers seekng to reduce the nfrastructure gap between rural and urban areas, as well as for rural areas attemptng to brng broadband provders to ther communty. The paper s organzed as follows. Secton 2 dscusses the data used and provdes descrptve statstcs. Secton 3 sets up the econometrc models and secton 4 reports the results from these models. Fnally, secton 5 draws several conclusons and dscusses ther polcy mplcatons. 2. Data and descrptve statstcs The data used n ths study come from a number of publcly avalable secondary sources. The numbers of broadband provders (at the ZIP code level) are obtaned from the Federal Communcatons Commsson va form 477. The man drawback of ths data s that propretary concerns prevent full dsclosure. 4 In par- Fgure 1. Number of broadband provders reportng and broadband lnes n the U.S., December 1999 June 2005 (source: FCC Form 477 date June 2005). 3 A revewer notes that ZIP codes do not necessarly reflect economc areas and questons ther approprateness for evaluatng market entry decsons. However, the FCC data represents the lowest level of detal on broadband subscrbers avalable at the natonal level and has been used n several studes of ths nature (Preger, 2003; Grubesc and Murray, 2004) 4 Another drawback of the FCC data s the fact that a sngle subscrber n a ZIP code mples that the entre ZIP code has broadband
256 Whtacre and Mahasuweeracha tcular, ZIP codes that have between one and three provders are reported by a * symbol n an effort to reduce nsght nto the number of broadband provders n those areas, whch mght be consdered propretary nformaton. Thus, comparng the number of provders between December 2004 and June 2005 provdes lmted nformaton for these ZIP codes. However, most ZIP codes are not under ths propretary concern, allowng for dentfcaton of those ZIP codes that experenced a provder ncrease over ths perod. Data from the June 2005 FCC Form 477 ndcates that over one-thrd of all ZIP codes reported an ncreased number of provders snce December 2004. Ths same report notes that, small provders of hghspeed connectons, many of whom serve rural areas wth relatvely small populatons, were therefore unrepresented n the earler data (FCC, 2006, p.2). However, the majorty of the ZIP codes that saw an ncrease n the number of provders over ths perod were n fact urban (Table 1). 5 Table 1. Overvew of ZIP codes wth provder ncrease, December 2004-June 2005 Area Total ZIP Codes # wth Increased Provders 12/04 6/05 % Total wth Increase Rural 15,036 4,285 34% Urban 16,571 8,299 66% Total 31,607 12,584 Source: FCC Form 477 dated December 2004 and June 2005; WWAMI RUCA classfcatons by ZIP code Ths data on nfrastructure avalablty can be combned wth demographc data from the U.S. Census Bureau. The Census data, also reported by ZIP code, can be used to descrbe household characterstcs that mght affect the avalablty of broadband provders. 6 One queston that mmedately arses for ths daaccess. Ths drawback has been noted by several sources (GAO, 2006, Flamm, 2006, Lehr et al., 2006). 5 Recall that ZIP codes that experenced an ncrease between December 2004 and June 2005, but stll had between 1 and 3 provders n each report, would dsplay a * n both reports and would therefore not show up as havng ncreased numbers of provders. Ths may be a partal cause of the large percentage of provders denoted as urban, snce these areas are less lkely to be under ths propretary concern. 6 Some ZIP codes n the Census data are artfcal ZIP codes (unclassfed areas, or areas consstng of bodes of water) that do not have a correspondng real ZIP code wth a populaton of at least 1 taset s whether there are any obvous dfferences n ZIP codes wth large provders versus those wth small provders. Table 2 compares ZIP codes that had at least one large provder (n 2004) wth those that had at least one small provder (derved from the 2005 data). 7 A smple means test ndcates that both large and small provders tend to focus on ZIP codes wth demand potental. However, dscrepances between served and unserved ZIP codes are larger for small provders than they are for large provders. In partcular, whle the number of households, populaton densty, and number of busnesses are relatvely smlar for ZIP codes both wth and wthout a large broadband provder, ther levels are sgnfcantly hgher for ZIP codes wth at least one small provder than for those wth no small provders. Smlar patterns hold for educaton and ncome / work characterstcs, wth dfferences between ZIP codes served / not served by small provders tendng to be notceably larger than those for large provders. For example, the ncome gap between ZIP codes wth and wthout small broadband provders s over $4,500, compared to only $2,700 for large provders. Perhaps unexpectedly, small provders serve ZIP codes wth hgher levels of Afrcan-Amercan and Hspanc households than the ZIP codes served by larger provders, ndcatng that small provders may be caterng to ths market. Age characterstcs are smlar for ZIP codes wth both large and small provders, although areas wth no small provders are much more lkely to be composed of senor ctzens. Commute tmes for resdents are not statstcally dfferent for areas wth and wthout large provders; however, sgnfcant dfferences do exst for small provders. Areas wth small provders have hgher rates of commuters wth less than 30 mnute drves and lower rates of those wth more than 60 mnute commutes, suggestng that small provders may target households wth a close work-home connecton. Addtonally, only 34 percent of ZIP codes wth small provders are rural, whle 45 percent of those wth a large provder are rural. These patterns suggest that small provders are cherry-pckng ZIP codes wth the best demand potental by focusng on those wth hgher numbers of people and busnesses; person, and were dropped from the study. Further, there s a noted dscrepancy between the ZIP code lst used by the FCC (the propretary geographc mappng system from Dynamap) and the ZIP code lst from the 2000 Census (Flamm, 2006). Any ZIP code ncluded n the Census lst but not n the FCC lst s assumed to have zero broadband provders n ths paper. 7 Whle some ZIP codes that saw an ncrease may have actually attracted a large provder between December 2004 and June 2005, the dramatc ncrease n provders dsplayed n Fgure 1 suggests that the vast majorty were small provders. Data constrants lmt our ablty to dfferentate between large and small provders.
Broadband provders and the dgtal dvde 257 Table 2. ZIP codes wth and wthout large broadband provders (December 2004) and ZIP codes wth and wthout small broadband provders (June 2005) 2004 2005 Varable No large provder At least one large provder No small provder At least one small provder Number of Households 2,063 2,315* 1,422 3,588* Populaton Densty 913 1,181* 788 1,693* Number of Busnesses 198 231* 125 381* Educaton No HS 0.226 0.216* 0.231 0.198* HS Dploma 0.357 0.343* 0.361 0.321* Some College 0.265 0.269* 0.261 0.280* College Degree 0.101 0.111* 0.097 0.130* Graduate Degree 0.048 0.057* 0.048 0.068* Income / Work Medan ncome 37,272 40,067* 37,026 43,803* Percentage below poverty 0.135 0.124* 0.134 0.112* Percentage workng at home 0.050 0.048 0.052 0.041* Percentage unemployed 0.067 0.063* 0.065 0.061* Race / Ethncty Percentage Afrcan-Amercan 0.063 0.073* 0.061 0.087* Percentage Hspanc 0.065 0.061 0.055 0.072* Percentage Other Race 0.049 0.046 0.045 0.047 Age Percentage 16 and under 0.239 0.237* 0.238 0.237 Percentage 17-29 0.154 0.154 0.149 0.161* Percentage 30-64 0.461 0.465* 0.464 0.465 Percentage 65 and over 0.143 0.142 0.147 0.135* Commute tme Less than 30 mnute 0.608 0.608 0.599 0.623* Between 31 and 45 mnute 0.181 0.183 0.183 0.183 Between 46 and 60 mnute 0.074 0.075 0.076 0.073 More than 60 mnute 0.083 0.083 0.086 0.078* Rural 0.637 0.453* 0.565 0.341* Number of ZIP Codes 3,873 27,734 19,023 12,584 Source: Census 2000; U.S. Census Bureau and FCC Form 477 data dated December 2004 and June 2005 Note: * ndcates the means are statstcally dfferent at the p = 0.05 level.
258 Whtacre and Mahasuweeracha Table 3. Demographc Characterstcs of Rural and Urban ZIP Codes wth and wthout an Increase n Provders, December 2004-June 2005 Varable Urban Rural No Increase Increase No Increase Increase Number of Households 2,395 4,544* 645 1,703* Populaton Densty 1,567 2,492* 189 145* Number of Busnesses 223 499* 50 150* Educaton No HS 0.218 0.188* 0.240 0.218* HS Dploma 0.338 0.298* 0.376 0.367* Some College 0.266 0.284* 0.258 0.273* College Degree 0.112 0.147* 0.085 0.097* Graduate Degree 0.060 0.081* 0.039 0.045* Income / Work Medan ncome 41,085 46,963* 33,402 37,395* Percentage below poverty 0.120 0.107* 0.145 0.121* Percentage workng at home 0.041 0.036* 0.061 0.052* Percentage unemployed 0.067 0.064* 0.066 0.059* Race / Ethncty Percentage Afrcan-Amercan 0.083 0.103* 0.046 0.057* Percentage Hspanc 0.076 0.086* 0.040 0.045* Percentage Other Race 0.044 0.054* 0.047 0.054* Age Percentage 16 and under 0.236 0.235 0.237 0.240* Percentage 17-29 0.160 0.168* 0.143 0.149* Percentage 30-64 0.461 0.465 0.463 0.465 Percentage 65 and over 0.137 0.130* 0.155 0.147* Commute tme Less than 30 mnute 0.594 0.620* 0.602 0.627* Between 31 and 45 mnute 0.191 0.187 0.176 0.174 Between 46 and 60 mnute 0.081 0.075 0.072 0.069* More than 60 mnute 0.088 0.080 0.085 0.075* Number of ZIP Codes 8,272 8,299 10,751 4,285 Source: Census 2000; U.S. Census Bureau and FCC Form 477 data dated December 2004 and June 2005. Note: * ndcates the means are statstcally dfferent at the p = 0.05 level. essentally choosng ZIP codes wth levels of educaton, ncome, and age / locaton that mply ncreased probabltes of broadband use. Dscrepances between rural and urban areas can also be observed from ths demographc data. Table 3 dsplays descrptve statstcs of rural and urban ZIP codes both wth and wthout small provders accordng to the December 2004 and June 2005 FCC data. In general, patterns for areas wth a small provder are smlar regardless of rural or urban locaton. Rural and urban ZIP codes that have been served by small broadband provders have sgnfcantly hgher educaton and ncome levels, and more households and busnesses than those areas that have no small broadband provders. Also, rural and urban ZIP codes wth small broadband provders have slghtly younger
Broadband provders and the dgtal dvde 259 populatons (under 30 years old) than those ZIP codes that have no small broadband provders. The percentage of the populaton between 17 and 29 s partcularly hgher n both rural and urban areas. In terms of commutng tme, rural areas dsplay sgnfcant dfferences n three of the four categores, whle urban areas only show a dfference n one. Ths may ndcate that small provders pay more attenton to commutng tmes n rural (as opposed to urban) areas. For race and ethncty, both rural and urban ZIP codes served by small broadband provders are much more racally dverse than those that have no small broadband provders. Overall, the smlar trends ndcate that the mpact of specfc characterstcs on the locaton decson of a small provder may not dffer greatly between rural and urban areas. Ths hypothess s further tested n our econometrc models. Havng looked at the characterstcs of ZIP codes of small provders, we focus now on the geographc locaton of these broadband provders. GIS mappng technques allow for vsualzaton of where these small provders are located. Plottng the locaton of all small provders suggests that they are n exstence throughout the U.S; however, they are not evenly dspersed. Most of the small provders are located n the northeast, north central, and southeast regons whle the central regon seems to lag behnd (Fgure 2). These patterns hold for both rural and urban ZIP codes. Interestngly, hghly rural regons such as the mountan or west south central have very few small broadband provders. The mportance of these patterns s tested through a seres of regonal dummy varables n our models. Fgure 2. Avalablty of small broadband provders (source: FCC Form 477 date June 2005). A separate contrbuton of ths paper s to analyze the mpacts of federal-level polces, namely the Communty Connect Grants and Farmbll Broadband Loans, to ncrease broadband access n rural and remote areas. Communty Connect Grants were provded by the Unted States Department of Agrculture (USDA) to boost broadband access n rural and remote areas by gvng grants to broadband provders servng
260 Whtacre and Mahasuweeracha n such areas. Farmbll Broadband Loans were also awarded by USDA to provde loans and loan guarantees to fund the cost of constructon, mprovement, or acquston of facltes and equpment for the provson of broadband servce n elgble rural communtes. The names of all communtes recevng ether grants or loans between 2002 and 2005 were provded by USDA, and mapped nto relevant ZIP codes. Around 150 grants and loans were awarded that mpacted approxmately 1,300 communtes over ths perod (Fgure 3). Fgure 3. USDA Broadband Grants and Loans, 2002 05 (source USDA Rural Utltes Telecommuncatons Program) The descrptve statstcs dsplayed n Tables 2 and 3 provde some nsght nto the demographc and economc characterstcs that factor nto the small broadband provder locaton decson. The mpact of federal polces on ths decson can also be explored usng data from USDA grants and loans. The followng secton dscusses the econometrc models employed to estmate the sgn and sze of the effect that each varable has on the probablty of attractng a small broadband provder. 3. Method We model the presence of a small broadband provder (less than 250 subscrbers) n each ZIP code as a functon of demographc, economc, and geographc characterstcs. The model s specfed as y * X Z H N L R η D α (1) * y 1 f y 0 * y 0 f y 0 j j
Broadband provders and the dgtal dvde 261 where y * s a latent measure of the relatve benefts to costs perceved by small broadband provders of servng ZIP code, y s the actual observaton of an ncrease n broadband provders between December 2004 and June 2005, X s a vector of household ncome levels, Z s a vector of resdents educaton levels, H s a vector of other demographc characterstcs, N s a vector relatng to market sze, L s a dummy varable ndcatng the presence of a large provder n December 2004, R s a dummy varable ndcatng when a ZIP code s rural n nature;,,,, and are the respectve assocated parameter vectors, and s the statstcal model s error term. In addton, we nclude a seres of dummy varables, D j, where j equals one of nne regons of the U.S., along wth ther correspondng parameters a j. These regons are depcted n Fgure 4. Note that varables for broadband loans and grants are not ncluded n ths model, but are n a later verson. Fgure 4. Nne regons of the U.S. as denoted n the Bureau of Labor Statstcs Current Populaton Survey. Because y * takes on one of two explct values (one f small broadband provder serves the ZIP code, zero otherwse) a bnary choce model such as the lnear probablty, probt or logt may be employed. In ths paper, a logt model s selected because t has benefts over the other bnary choce models namely, restrctng outcomes to the [0, 1] nterval (whch the lnear probablty model does not), and provdng a closed form soluton (unlke the probt model) (Greene, 2003). Although the presence of spatal dependency n the data would lead to heteroskedastcty or autocorrelaton and thus render logt estmates nconsstent and/or neffcent, spatal estmators are not used for several reasons. Frst, our models are estmated wth heteroskedastc-consstent standard errors. Second, the patterns dsplayed n Fgure 2 gve lttle evdence to suspect that the presence of a small provder n one ZIP code would mpact a smlar presence n neghborng ZIPs. In fact, the varaton seems to be more regonal n nature, whch we account for through a seres of regonal dummy varables D j as noted above. Fnally, testng for autocorrelaton among ZIP codes would requre nvertng a 31,607 x 31,607 weghtng matrx that s computatonally nfeasble for the software package used for ths analyss. 8 Economc theory and prevous research provde a bass for the expected sgns of the relatonshps between the presence of broadband provders and the ndependent varables. Small broadband provders lkely take these same varables nto account, although ther attempt to cater to under- or un-served communtes may alter the relatonshps. Thus, whle the assocaton between demographc / economc characterstcs and regular broadband provders has been well documented, the connecton between these characterstcs and small provders s left as an emprcal queston. For example, several studes have noted that ndvduals wth hgher ncome and educaton levels tend to have hgher demand for broadband access (Horrgan, 2006; Strover, 2003). However, the largest recent ncreases n broadband access rates have come from those wth hgh-school dplomas and low-tomedum ncome levels (Horrgan, 2006). Thus, smaller broadband provders may tend to market ther servces towards communtes wth these types of demographcs. Smlarly, whle research suggests that market sze - namely the number of busness and households n a ZIP code - s postvely assocated wth broadband provders (Preger, 2003); small provders may choose to locate n relatvely smaller markets that have a hgher probablty of not beng served by the large telecommuncatons companes. Other demographc characterstcs such as race / ethncty and age are also expected to have an mpact on whether or not a small broadband provder serves the area. In partcular, some racal and ethnc groups (such as Hspancs and Afrcan-Amercans) have been slower to adopt broadband than others; however, adopton among these groups has recently seen dramatc ncreases (Horrgan, 2006). Small provders may target these underserved communtes wth large mnorty groups. Communtes havng a large number of ndvduals workng from home are expected to ncrease the probablty of a small provder, snce most probably requre broadband access to perform ther work. Younger household heads are more lkely to be faml- 8 The complexty ssue assocated wth spatal estmators s rased n Kler and McMllen (2005).
262 Whtacre and Mahasuweeracha ar wth broadband technologes by nteractng wth them at school, and therefore may be more comfortable adoptng them at home and / or work (Rose, 2003). Thus, ZIP codes wth a large percentage of young resdents may attract broadband provders, ncludng smaller ones. We also nclude a dummy varable for the presence of a large broadband provder n an attempt to account for potental competton. Prevous exstence of a broadband provder may deter entry for small provders; however, ZIP codes can be relatvely bg areas and the presence of a large provder does not necessarly mean that the entre ZIP code s served. In terms of place-based characterstcs, we noted prevously that rural areas have been found to sgnfcantly decrease the probablty of broadband areas. Therefore, the expected sgn of the rural dummy varable s negatve. Further, the Mountan regon s used as the base category for the regonal dummy varables gven the relatvely few small provders depcted n ths area n Fgure 2. Snce larger numbers of small provders seem to be n exstence n all other regons, the expected sgn of the remanng regonal dummes are all postve. In addton to the model specfed n (1), a separate model tests for rural and urban dfferences n the effects of demographc and economc characterstcs. By ncludng a rural nteracton term for each characterstc, the mpact s allowed to vary between rural and urban areas. The model s specfed as y X ( ) Z ( ) H ( * U U R L ( ) D ( ) R j U U R R U ) N ( ) where y *, X, Z, H, N, L, and D j are as prevously defned, but the assocated parameter vectors are allowed to vary by rural and urban status. Thus, any statstcally sgnfcant rural parameter denotes a meanngful dfference n the way the assocated varable mpacts rural and urban areas. We also focus on ZIP codes that prevously had no broadband provders at all. Ths model s smlar to model dsplayed n (1), but the data s restrcted to only those ZIP codes that were depcted as havng no provders n 2004. The sgns of ndependent varables are expected to be the same as the model dsplayed n (1). However, the rural dummy varable could be ether negatve or postve. The prevalence of unserved rural ZIP codes (65 percent of ZIP codes n ths restrcted dataset are rural) suggests that many opportuntes exst for them to be served by small provders, possbly resultng n a postve coeffcent. Perhaps the most nterestng component of ths more focused model s the ncluson of federal-level polces to ncrease broadband access n rural and remote areas. R U R (2) These nclude the Communty Connect Grants and Farmbll Broadband Loans, both sponsored by the Unted States Department of Agrculture. The presence of polcy awards n a ZIP code s expected to be postvely assocated wth an ncrease n small broadband provders. 4. Results The pooled parameter estmates for the presence of a small broadband provder between December 2004 and June 2005 are presented n Table 4 (model 1). Most of the results are ntutve, wth parameter estmates havng the expected sgn and statstcal sgnfcance. For example, most of the educaton coeffcents are postve. Ths mples that, relatve to the proporton of the populaton wth no hgh school educaton, an ncrease n the proporton of people who have hgher levels of educaton ncreases the probablty of the presence of small broadband provders. Surprsngly, the graduate degree coeffcent has a negatve sgn and s sgnfcantly dfferent from zero. Ths may be due to the fact that hghly educated people tend to have hgh demand for broadband adopton, so areas wth hgh proporton of these ndvduals have already attracted a large broadband provder (Horrgan, 2006; Strover, 2003). Small broadband provders may try to avod these markets n order to avod competng wth the larger provder. However, the parameter assocated wth the presence of a large provder n 2004 s statstcally nsgnfcant, perhaps due to a hgh correlaton wth other demand proxes such as ncome and educaton. The coeffcent of ncome s postve and sgnfcant, whch means that areas wth hgher medan ncomes are more lkely to have a small broadband provder. Addtonally, the coeffcents of market sze, namely the number of households and number of busnesses n a ZIP code, are postve and sgnfcantly dfferent from zero. Thus, smlar to large broadband provders, small broadband provders are more lkely to locate n areas wth more potental customers. 9 Surprsngly, but consstent wth our descrptve statstcs, a hgh proporton of Afrcan-Amercan resdents rases the probablty of a small broadband provder. Ths result s nterestng, as several results have shown Afrcan-Amercan households to lag behnd other races n term of Internet connectvty (Mlls and Whtacre, 2003; Horrgan, 2006). Ths seems to mply that small provders feel the Afrcan-Amercan popu- 9 A separate model usng populaton densty nstead of number of households dd not show a statstcally sgnfcant mpact for ths varable, smlar to fndngs n Flamm (2006).
Broadband provders and the dgtal dvde 263 Table 4. Model results Independent Varable Hgh school dploma 0.601** (0.283) Some college 1.019*** (0.239) College 0.136 (0.352) Graduate degree -0.980** (0.432) Income (log) 0.418*** (0.087) Number of busnesses (log) 0.392*** (0.021) Number of households (log) 0.179*** (0.021) Afrcan-Amercan 0.498*** (0.104) Hspanc 0.053 (0.144) Other race 0.260 (0.173) Age 16 to 29 0.962*** (0.344) Age 30 to 64 1.409*** (0.343) Age over 65 0.455 (0.313) Poverty 0.499 (0.311) Work at home 1.117*** (0.344) Commute 30 to 45 mnutes 0.488*** (0.163) Commute 45 to 59 mnutes 0.412 (0.263) Commute over 60 mnutes -0.362 (0.235) Rural -0.079*** (0.029) New England 0.377*** (0.090) Mddle Atlantc 0.274*** (0.082) East North Central 0.279*** (0.080) West North Central 0.136* (0.081) Model 1 Model 2 Model 3 Urban Rural Coeffcent Coeffcent Coeffcent Coeffcent 0.005 (0.371) 1.117*** (0.314) -0.146 (0.437) -1.229** (0.522) 0.284** (0.113) 0.067*** (0.026) 0.496** (0.025) 0.164 (0.128) -0.508*** (0.177) 0.452* (0.249) 0.297 (0.441) 0.998** (0.462) 0.110 (0.398) 0.698 (0.423) 1.195** (0.482) 0.450 (0.229)* 0.610 (0.352) -0.441 (0.309) 1.228** (0.584) 0.004 (0.507) 0.561 (0.779) 0.313 (1.004) 0.360* (0.187) -0.310*** (0.044) 0.367*** (0.048) 1.005*** (0.220) 1.761*** (0.295) -0.071 (0.352) 0.647 (0.751) 0.715 (0.716) 0.721 (0.665) -0.404 (0.648) 0.207 (0.695) 0.057 (0.335) -0.290 (0.536) 0.308 (0.481) - - 0.336** (0.137) 0.249** (0.126) 0.212* (0.126) -0.003 (0.131) 0.155 (0.189) 0.037 (0.175) 0.134 (0.168) 0.293* (0.169) 2.005*** (0.697) 1.472** (0.705) 0.924 (1.076) -4.717** (1.861) -0.671*** (0.239) -0.174*** (0.059) 0.848*** (0.074) -0.134 (0.339) -1.160** (0.555) -0.282 (0.387) -1.154 (1.038) 2.436*** (0.828) 1.258 (0.817) -1.133* (0.700) 3.384*** (0.667) 1.343*** (0.398) 1.387** (0.552) 0.281 (0.576) 0.411*** (0.113) -0.687* (0.405) -2.143*** (0.311) -1.295*** (0.252) 0.895*** (0.212)
264 Whtacre and Mahasuweeracha Table 4. Model results (contnued) Independent Varable Model 1 Model 2 Model 3 Urban Rural Coeffcent Coeffcent Coeffcent Coeffcent South Atlantc 0.223*** (0.081) East South Central -0.327*** (0.090) West South Central -0.166** (0.077) Pacfc -0.102 (0.084) 2004 Provder 0.042 (0.040) 0.201 (0.125) -0.446*** (0.138) -0.137 (0.124) -0.246* (0.130) 0.021 (0.055) -0.015 (0.170) 0.165 (0.186) -0.156 (0.163) 0.325* (0.175) 0.035 (0.081) -1.338*** (0.251) -0.134 (0.263) -1.196*** (0.237) -0.635** (0.291) Grant - - - 0.835 (0.980) Broadband loan - - - -0.261 (0.352) Constant -9.343*** -6.920*** -6.359*** -0.226 (1.026) (1.345) (2.159) (2.615) Number of observaton 31,607 31,607 3,792 Pseudo R 2 0.1663 0.1701 0.2708 - Note: Dependent varable for each model s an ncrease/no ncrease n broadband provder over the perod December 2004 June 2005. Robust standard errors are n parentheses. ***, **, and * ndcate sgnfcance at 1%, 5%, and 10% levels, respectvely. laton s a relatvely untapped market. On the other hand, there s no evdence to suggest that hgh proportons of Hspancs and other racal categores affect the exstence of a small broadband provder. Ths result s somewhat counter-ntutve due to recent results suggestng Hspancs are dramatcally ncreasng ther broadband connectvty (Horrgan, 2006). It may ndcate that small provders have recognzed the potental of Afrcan-Amercan adopters but not Hspanc adopters. Addtonally, areas wth a large workng age populaton (16 64) are more lkely to experence an ncrease n small broadband provders when compared to areas that have a large proporton of populaton below 16. Ths mples that people between the ages of 16 and 64 make better potental customers due to ther preferred Internet actvtes when compared to those under 16 (or over 65, whch show no statstcal mpact). Our results also suggest that the relatonshp between where a person lves and works s mportant. In partcular, areas that have hgher a proporton of ther populaton workng at home tend to have a hgher probablty of a small broadband provder ndcatng that broadband access s mportant to these ndvduals, and that small provders may look for such areas. We also fnd a postve mpact for many medum-dstance commutes (between 30 to 45 mnutes) when compared to the default category of under 30 mnutes. Turnng now to the mpact of place-based varables, rural status has a sgnfcant and negatve effect on the ncrease n small broadband provders. Ths mples that even after controllng for dfferences n household and economc characterstcs between rural and urban areas, locaton n rural areas decreases the probablty of the exstence of a small broadband provder. Ths result shows that, even n terms of small broadband provders, the dgtal dvde between urban and rural areas stll exsts. Addtonally, relatve to Mountan regon, areas n the New England, Mddle Atlantc, East North Central, West North Central, and South Atlantc regons have a hgher probablty of the presence of a small broadband provder. The East South Central and West South Central regons tend to have lower probabltes of small broadband provders when compared to the Mountan regon. These hghly sgnfcant regonal varables ndcate that small provder presence vares qute a bt by locaton, renforcng the fndng of the negatve rural coeffcent. Fnal-
Broadband provders and the dgtal dvde 265 ly, as noted prevously, the presence of a large provder n 2004 has no statstcal mpact on the locaton decson. Ths s lkely due to the mnmal amount of varaton for ths characterstc (87 percent of all ZIP codes lsted by the Census had a large provder n 2004) and correlaton wth other demand-orented varables such as educaton, ncome, and number of households. To test the dfferent effects of demographc and economc characterstcs that may exst between urban and rural areas, a rural nteracton term s ncluded for each explanatory varable (as specfed n equaton 2). These rural parameter coeffcents represent a shft on the urban coeffcent caused by rural locaton. Model 2 n Table 4 presents the results of ths specfcaton. The sgns and values of most urban coeffcents n model 2 concde wth those for the entre populaton n model 1. There are several sgnfcant rural shfts, ncludng the proporton of people wth a hgh school dploma, the proporton of Afrcan-Amercan and Hspanc populaton, medan ncome levels, number of busness, number of households, and the West North Central and Pacfc regons dummy varables. These shfts ndcate that multple characterstcs n rural areas do not have the same mpact they would n urban areas. For nstance, a rural area wth a hgh percentage of ndvduals who completed ther schoolng at the hgh school level s more lkely to attract a small broadband provder than s an urban area wth a smlar percentage. Smlarly, the parameters on Afrcan-Amercan and Hspanc populaton varables are postve shfts from ther urban coeffcents. Ths would mply that rural areas wth hgh proportons of Afrcan-Amercan and Hspanc resdents are more attractve to small broadband provders. These results gve valdty to the dea that Afrcan-Amercan and Hspanc populatons are beng targeted by small broadband provders but only n rural areas. As noted prevously, adopton among these groups (ncludng those wth a hgh-school level of educaton) has recently seen dramatc ncreases (Horrgan, 2006), and small provders seem to be sprngng up where these populatons are located. Regardng market sze, the rural shft for the number of households s postve, ndcatng an even stronger propensty for havng small broadband provders n rural areas that have a hgh number of households. Surprsngly, the rural parameter on the number of busnesses s negatve and shfts from a postve urban coeffcent mplyng that, n rural areas at least, small broadband provders are more drven by potental adopters n households as opposed to busnesses. The last sgnfcant rural shfts are the dummes for the West North Central and Pacfc regons. Ther coeffcents are postve and shft from negatve urban coeffcents. Therefore, gven other varables, rural areas n the West North Central and Pacfc regons tend to be more attractve to small broadband provders. We are also nterested to see what factors attract small provders to ZIP codes prevously depcted as havng no provders. To do ths, we estmate model (1) by usng only ZIP codes that were shown as havng no broadband provders n the December 2004 FCC data. Fgure 5 depcts ths nformaton geographcally, breakng out all ZIP codes that were shown as havng no provders n 2004 nto two groups those that contnued to have no provders n the June 2005 report, and those that were actually served by a small provder. We also nclude two addtonal varables to model (1) when usng ths restrcted subset namely, the presence of a USDA broadband grant or loan program. Results from ths model show whether or not small broadband provders enter these areas wth the same crtera as those locatng elsewhere, and whether the USDA programs are mpactng ther locaton decson. The fnal column of Table 4 (model 3) shows these results. The coeffcent of the rural dummy varable s statstcally sgnfcant at the 1% level, and turns from negatve n the pooled data (model 1) to postve when the data s restrcted (model 3). Thus, rural areas wth no access are attractve targets for small provders, even after takng other economc and demographc varables nto account. Ths may be due to some unmeasured attrbute of rural areas, such as prde n the local communty, or smply the fact that rural areas make up the majorty of unserved ZIP codes. Addtonally, the patterns observed n model 2 for the mpact of market sze n rural areas holds true for ths subset of data, wth a postve coeffcent on the number of households but a negatve coeffcent on the number of busnesses. Thus, market sze s stll an mportant factor for small broadband provders to enter the market; however, they may only focus on the household market. Surprsngly, most coeffcents of regonal dummy varables are negatve and statstcally sgnfcant. Ths means that ZIP codes n New England, Mddle Atlantc, East North Central, South Atlantc, West South Central, and Pacfc, whch had no provders n 2004, are less attractve to small broadband provders than the Mountan regon. Ths result s opposte the results from models (1) and (2). The reason may be smply that the Mountan regon has the fewest broadband provders relatve to other regons (Fgure 2). Ths mples that the Mountan regon could be the market wth the best potental for small broadband provders when compared to other regons.
266 Whtacre and Mahasuweeracha Fgure 5. ZIP Codes wth no provder n 2004 (source: FCC Form 477 date June 2005). The fnal, and potentally most ntrgung, group of varables that we nclude s the presence of the most common broadband grants and loans awarded by the federal government. The USDA awarded around 60 grants and 90 loans to nearly 1,300 communtes over the perod 2002-2005. However, the coeffcents of varables for Communty Connect Grants and Farmbll Broadband Loans are not statstcally sgnfcant. Therefore, we fnd no statstcal evdence that these polces have played a role n attractng small broadband provders to prevously unserved areas. We also fnd that just 64 ZIP codes from the 3,729 ZIP codes that had no broadband provders n 2004 receved ether a Communty Connect Grant or a Farmbll Broadband Loan. 10 Hence, whle the man purpose of these polces s to brng broadband access to rural areas, they have not been successful n attractng small 10 Only 6 of the 59 ZIP codes that obtaned Communty Connect grants (and only 58 of 1,276 ZIP codes wth Broadband loans) had no broadband provders n 2004 accordng to the FCC Form 477 data. provders nto areas that prevously had no access. Ths lack of effectveness of federal programs s also suggested by Feser (2007), who ndcates that such top-down polces fal to adequately address the locally-specfc stuatons of most underserved communtes. 5. Summary and Concluson In ths artcle, we look at descrptve characterstcs and develop models that detal the locaton decson of small broadband provders. The frst nterestng fndng s that small broadband provders are predomnantly located n urban areas, wth only 1/3 of all small provders choosng rural locatons. Thus, f small provders are seekng unserved markets, they are not all located n rural areas nstead they may be fndng small patches of unconnected areas n relatvely urban locatons (suburbs or bedroom communtes, for example). The emprcal results show that, to some extent, the determnng factors are very smlar for both large and small provders. In partcular, areas
Broadband provders and the dgtal dvde 267 wth hgh medan ncomes, number of households, and number of busnesses tend to have a hgh probablty of beng served by a small provder smlar to results documented n the exstng lterature for all broadband provders. However, not all varables fall nto ths pattern. For nstance, whle hgh proportons of some educaton levels (hgh school and some college) ncrease the lkelhood of a small provder, others (such as graduate degrees) actually decrease t. Addtonally, small broadband provders are attracted to areas wth a hgh proporton of Afrcan-Amercan resdents. These unexpected sgns may ndcate that small provders are enterng prevously untapped markets. We also fnd that small provders are more lkely to cluster n varous geographc regons, ncludng the relatvely more populated East Coast but also n relatvely sparsely populated regons such as the West North Central. Further, we can document the exstence of a dgtal dvde between rural and urban areas specfcally n terms of small broadband provders. In general, the small provder market seems to be behavng exactly as a neoclasscal economst mght hypothesze by ntally caterng to areas wth hgh demand (ncludng urban areas wth dense populatons and hgher ncomes) and those wth untapped potental. As these areas become saturated wth broadband avalablty, the ndustry would then move nto markets wth less demand potental, such as those n rural areas. The results also show that the mpacts of race and the number of busnesses vary between rural and urban areas. In terms of race, small broadband provders tend to focus more heavly on rural areas wth hgh proportons of Afrcan-Amercan and Hspanc resdents than they do on urban ones. Moreover, small broadband provders stll consder market sze n rural areas, but are more nterested n the number of households (postve mpact) than busnesses (negatve mpact). When our focus turns to ZIP codes prevously depcted as havng no provders, the coeffcent of the rural dummy varable turns from negatve (n pooled data) to postve and sgnfcant. Small provders seem to prefer locatng n rural areas n ths scenaro, even after other economc and demographc varables are controlled. Whle t would be temptng to thnk that federal broadband grants and loans were responsble for attractng provders to these rural areas, our analyss does not suggest that they do. We also fnd that small provders seem to target only the household market when dealng wth ZIP codes that prevously had no provders. Addtonally, regonal varables are hghly sgnfcant n ths model, wth the Mountan and West North Central regons more lkely to attract small provders. These results mply that local governments wthout any type of access may want to fnd ways to support small provders (possbly through tax ncentves or publc / prvate partnershps) snce they are reachng out to prevously unserved areas. The fact that we do not fnd any statstcal sgnfcance for the USDA Communty Connect Grants and Farmbll Broadband Loans s nterestng. Only 64 of the 3,729 unserved ZIP codes were awarded these programs. Ths result seems to mply that these polces may focus on the wrong areas and/or wrong provders. Ths s consstent wth an audt of the program performed n 2005 (USDA OIG, 2005). However, t s mportant to note that ZIP codes can be relatvely large geographc unts and that a provder servng one part of a ZIP code does not necessarly serve all of t (Wallsten, 2005; Flamm, 2006). Many of the USDA grants and loans are undoubtedly gong to unserved portons of ZIP codes that have broadband access somewhere else n ther vcnty. Ths once agan ponts to the problematc nature of usng a relatvely broad geographc classfcaton (ZIP codes) for the FCC form 477 data (also noted by GAO, 2006; Lehr et al, 2006; and Flamm, 2006). Ultmately, small broadband provders are a part of the overall access pcture, and seem to be reachng prevously unserved demographcs although the characterstcs attractng them dffer to some degree between rural and urban areas. If the ultmate goal s to provde unversal broadband access, future research should focus on the dffuson of such access n the market (ncludng small provders) and the role of publc polces n ths dffuson. Whle natonal-level studes are lmted by the data ssues dscussed above, smaller scale studes at the state or even communty level (such as Grubesc, 2003) may provde a more realstc look at the dsperson of broadband access. References Bauer, J. M., G. Png, K. Junghyun, M. A. Thomas, and W. S. Steven. 2002. Broadband: Benefts and Polcy Challenges. Workng paper, The James H. and Mary B. Quello Center for Telecommuncaton Management and Law, Mchgan State Unversty. Federal Communcatons Commsson. 2007. Local Telephone Competton and Broadband Deployment Data. Avalable at www.fcc.gov/wcb/atd/comp.html, accessed 7 January 2007. Federal Communcatons Commsson-Industry Analyss and Technology Dvson. 2006. Hgh-Speed Servces for Internet Access: Status as of June 30, 2005. Washngton DC, Aprl.
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