Small Broadband Providers: Where and Why?

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1 Small Broadband Provders: Where and Why? Phumsth Mahasuweeracha Graduate Research Assstant Bran E. Whtacre Assstant Professor & Extenson Economst Department of Agrcultural Economcs Oklahoma State Unversty Selected Paper prepared for presentaton at the Amercan Agrcultural Economcs Assocaton Annual Meetng, Portland, OR, July 29 August 1, Copyrght 2007 by Phumsth Mahasuweeracha and Bran E. Whtacre. All rghts reserved. Readers may take verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 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 also have the potental to enhance voce communcaton (Horrgan and Rane 2002; Preger 2003). Broadband access has the potental to beneft busnesses, governments, consumers, and communtes by contrbutng to productvty and effcency ncreases. For the prvate sector, broadband access provdes the opportunty to take advantage of new nput and output markets, allowng frms to ncrease ther productvty by mprovng nformaton exchange, value chan transportaton, and process effcency (Thomas 2005). Broadband access could also mprove the effcency of publc sector servces such as educaton, health, and local government servces by ncreasng the avalablty of data and speedng feedback to and from ther consttuents (Bauer et al. 2002). Moreover, broadband access could enhance the qualty of lfe of consumers through economc, socal and cultural development. For rural and remote areas, broadband access s partcularly mportant. These geographcally solated regons have the most to gan from the dstance-negatng nature of the Internet, ncludng the opportunty to partcpate n the dgtal economy and become part of the nformaton revoluton (Lndroos and Pnkhosov 2003). However, as wth every technologcal revoluton, some people and areas have lagged behnd. The dgtal dvde, or the gap n Internet access between urban and rural areas, has receved 1 Broadband access, also called hgh-speed access or advanced servce, s defned as 200 Klobts per second (Kbps) (or 200,000 bts per second) of data throughput by the Federal Communcatons Commsson. 1

3 a large amount of attenton from researchers, poltcans, and polcy makers (Strover 2001; Mll and Whtacre 2003). 2 Research on the determnants of broadband access 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 populaton densty and potental demand. Therefore, urban areas, wth hgher educaton, ncome, and populaton densty were the frst to receve broadband nfrastructure. Anecdotal evdence suggests that small-scale provders may be servcng the broadband needs of some rural communtes. 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 2 Ths paper uses the 2000 U.S. Census desgnatons of non-metropoltan and metropoltan countres to compare rural-urban area dfferences n resdental Internet access. Metropoltan countes generally have populaton greater than 100,000 or a town or cty of at least 50,000 and are referred to as urban areas. Nonmetropoltan countes are those countes not classfed as metropoltan and are referred to as rural areas. 2

4 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. Meshng ths data wth secondary demographc nformaton allows for dentfcaton of factors mpactng the locaton decsons of these small broadband provders. Ths paper augments the exstng knowledge base on broadband nfrastructure by 1) descrbng the locaton of these small broadband provders, ncludng Geographc Informaton System (GIS) mappng technques and demographc comparsons of communtes wth and wthout small provders; and (2) 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 polces (USDA broadband grants and loans) play n attractng small broadband provders to these prevously unconnected rural areas. These results wll be 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 et al. 2006). A sold understandng of these factors s of nterest to polcy makers seekng to reduce the nfrastructure gap between rural and urban areas, as well as to rural areas attemptng to brng broadband provders to ther communty. 3

5 The paper s organzed as follows. Secton 2 descrbes 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. 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. 3 In partcular, 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 data. Thus, comparng of the number of provders between December 2004 and June 2005 provdes lmted nformaton for these ZIP codes. However, the majorty of the ZIP codes (66 percent) 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 Ths same report notes that, small provders of hgh-speed connectons, many of whom serve rural areas wth relatvely small populatons, were therefore unrepresented n the earler data (FCC 3 Other drawbacks of the FCC data nclude the fact that a sngle subscrber n a ZIP code mples that the entre ZIP code has broadband access. Ths drawback has been noted by several sources (GAO 2006, Flamm 2006, Lehr et al. 2006). 4

6 2005, 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). 4 Ths data on nfrastructure avalablty can be combned wth demographc data from the U.S. Census Bureau. Ths Census data - also reported by ZIP codes - can be used to descrbe household characterstcs that mght affect the avalablty of broadband provders. 5 Table 2 dsplays descrptve statstcs for rural and urban areas that ether have no broadband provder or have at least one broadband provder. On average, rural and urban areas that have at least one broadband provder have sgnfcantly hgher levels of ntutve varables ncludng the number of households and busnesses, and levels of educaton and ncome than those areas that have no broadband provder. Table 2 also ndcates that rural areas wth at least one broadband provder are slghtly less racally dverse when compared to those areas that have no broadband provders, however; for urban areas, those wth at least one broadband provder are much more racally dverse than those wthout any broadband provders. Rural and urban areas that have at least one broadband provder also have a hgher percentage of younger resdents (under 65 years old) than those areas that have no broadband provders. Interestngly, only urban areas wth broadband provders have hgher levels of populaton densty than ther unconnected counterparts. Ths pattern s reversed for rural areas, as those ZIP codes wthout any provder actually have more densely populated areas than those wth provders. Ths s unexpected, as the factors affectng the decson 4 The FCC s use of ZIP codes as the geographc unt of analyss requres that we measure ruralty va Rural-Urban Commutng Area (RUCA) codes as defned by the USDA/ERS. 5 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 n the analyss, so we dropped these ZIP codes from our analyss. 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. 5

7 to nvest n nfrastructure should be the same for provders regardless of locaton namely, how many potental customers can be reached n a gven area. However, t does gve some credence to the dea that small broadband provders take other non-economc factors nto consderaton when makng the nvestment decson. Ths ssue s further explored n our econometrc model. Havng observed the locaton characterstcs of any broadband provders, we turn now to the characterstcs of small broadband provders. GIS mappng technques allows 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 when only rural ZIP codes are observed (fgure 3). Interestngly, hghly rural regons such as the mountan or west south central have very few small broadband provders. Demographc data from the U.S. Census Bureau provdes addtonal nformaton regardng where small broadband provders locate. Table 3 dsplays descrptve statstcs of rural and urban ZIP codes that ether saw an ncrease or dd not see an ncrease n the number of broadband provders between December 2004 and June Several patterns emerge when table 3 s vewed n conjuncton wth table 2. In partcular, 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 smlar to rural and urban ZIP 6 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. 6

8 codes wth at least one provder (table 2). Also, rural and urban ZIP codes wth small broadband provders have slghtly younger populatons (under 65 years old) than those ZIP codes that have no small broadband provders. The percentage of the populaton between 17 and 29 s partcularly hgher. However, n the case of race and ethncty, both rural and urban ZIP codes that have been served by small broadband provders are much more racally dverse than those that have no small broadband provders. Ths dffers from the case for any type of broadband provders (table 2), where rural areas wth any provder were actually less dverse than those wthout any provder. 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 n rural 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. 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 7

9 econometrc models employed to estmate the sgn and sze of effect that each varable has on the probablty of attractng a small broadband provder. Methodology Econometrc Model 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 * (1) y = Xβ + Z δ + H γ + N τ + R η + D jα j + ε y y = 1 f y = 0 f y * * 0 < 0 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, R s a dummy varable ndcatng when a ZIP code s rural n nature; β, δ, γ, τ, and η are the respectve assocated parameter vectors, and ε s the statstcal model s error term. In addton, we nclude a seres of dummy varables, D, where j equals one of nne regons of the U.S., j along wth ther correspondng parameters α j. These regons are depcted n fgure The nne regons are New England (NE), Mddle Atlantc (MA), East North Central (ENC), West North Central (WNC), South Atlantc (SA), East South Central (ESC), West South Central (WSC), Pacfc (PF), and Mountan (MT). The default category for our model s the Mountan regon. Note that varables for broadband loans and grants are not ncluded n ths model, but wll be n a later verson. 8

10 Because * y takes on one of two explct values (one f a 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). 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-to-medum 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 9

11 / ethncty and age are also expected to have an mpact on the whether or not a small broadband provder serves the area. In partcular, some racal and ethnc groups (such as Hspancs and Blacks) have been slower to adopt broadband than others; however, adopton among these groups has recently seen dramatc ncreases (Horrgan 2006). Small provders may have targeted 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 famlar wth broadband technologes by nteractng wth them at school, and therefore may be more comfortable adoptng them at home and / or work. Thus, ZIP codes wth a large percentage of young resdents may attract broadband provders, ncludng smaller ones. 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 ZIP codes n ths area n fgure 4. 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 * (2) y = X ( β U + β R ) + Z ( δu + δ R ) + H ( γ U + γ R ) + N ( τ U + τ R ) + D j ( αu + α R ) + ε 10

12 where y *, X, Z, H, N, 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 (1), but the data s restrcted to only those ZIP codes that were depcted as havng no provders n Most of ZIP codes are rural (65 percent). 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 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. 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. 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 11

13 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. However, 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 regular broadband provders (Horrgan 2006; Strover 2003). Small broadband provders may try to avod these markets n order to avod competng wth the larger provder. 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. 8 Surprsngly, a hgh proporton of Black resdents rases the presence of a small broadband provder. Ths result s nterestng, as several results have shown Black households to lag behnd other races n term of Internet connectvty (Mlls and Whtacare 2003; Horrgan 2006). Ths seems to mply that small provders feel the Black populaton 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 8 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). 12

14 (Horrgan 2006). 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 may mply that people between the ages of 16 and 64 make better potental customers due to ther ncome and preferred 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 mplyng 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 medan-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 ncrease n small broadband provders. Ths mples that even after controllng for dfferences n household characterstcs 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 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 probablty of ncrease n small broadband provders when compared to the Mountan regon. These hghly sgnfcant regonal varables 13

15 ndcate that small provder presence s qute spatal n nature, renforcng the fndng of the negatve rural coeffcent. Further, 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 (model 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. 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 Black 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 Black and Hspanc populaton varables are postve shfts from ther urban coeffcents. Ths would mply that rural areas wth hgh proportons of Black and Hspanc resdents may be more attractve to small broadband provders. These results gve valdty to the dea that Black 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. 14

16 Regardng market sze, the rural shft for the number of households s postve, ndcatng an even stronger propensty for havng small broadband provder for rural areas that have hgh number of household. 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 whether small provders exst n 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 an addtonal varable to model (1) when usng ths restrcted subset namely, the presence of a USDA broadband grant or loan program. Results from ths model wll 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 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 15

17 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 ther local communty. Addtonally, the patterns observed n model 2 on 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 to 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 that, accordng to GIS mappng (fgure 5), the Mountan regon has the fewest broadband provders relatve to other regons. Ths may mply that the Mountan regon s consdered as the market wth the best potental for small broadband provders when compared to other regons. 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 However, the coeffcents of varables for Communty Connect Grants and Farmbll Broadband Loans are not statstcally sgnfcant. Therefore, statstcally, we do not fnd that these polces have played a role 16

18 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. 9 Hence, whle the man purpose of these polces s to brng broadband access to rural areas, they have not been successful n attractng small provders nto areas that prevously had no access. Summary and Concluson Ths artcle looks at descrptve characterstcs and develops 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, the areas wth hgh medan ncomes, number of households, and number of busnesses tend to have 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 9 Only 6 of the 59 ZIP codes that obtaned Communty Connect grants (and only 55 of 1,276 ZIP codes wth Broadband loans) had broadband provders n 2004 accordng to the FCC Form 477 data. 17

19 are attracted to areas wth a hgh proporton of Black 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 sparely 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. 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 not only on rural areas wth a hgh proporton of Black resdents but also areas wth a hgh proporton of Hspanc resdents. Moreover, small broadband provders stll consder market sze, 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 government may want to 18

20 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 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 appear to dffer 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. 19

21 References Bauer, J. M., G. Png, K. Junghyun, M. A. Thomas, and W. S. Steven 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 Local Telephone Competton and Broadband Deployment Data. Avalable at accessed 7 January Federal Communcatons Commsson-Industry Analyss and Technology Dvson Hgh-Speed Servces for Internet Access: Status as of June 30, Washngton DC, Aprl. Flamm, K Dagnosng the Dsconnected: Where and Why s Broadband Access Unavalable n the U.S.? Paper presented at the Telecommuncatons Polcy Research Conference, Farfax VA, 30 September. Government Accountablty Offce (GAO) FCC Needs to Improve Its Ablty to Montor and Determne the Extent of Competton n Dedcated Access Servce. Report GAO-07-80, November. Greene, W.H Econometrc Analyss, 5th. ed. New Jersey: Prentce Hall. Grubesc, T Inequtes n the Broadband Revoluton. The Annals of Regonal Scence. 37:2, Horrgan, J.B Home Broadband Adopton The PEW Internet and Amercan Lfe Project. Avalable at accessed 5 February Horrgan, J.B., and L. Rane The Broadband Dfference: How Onlne Amercans Behavor Changes wth Hgh-Speed Internet Connectons at Home. The PEW Internet and Amercan Lfe Project. Avalable at accessed 5 February Lehr, W. H., C. A. Osoro, S. E. Gllett, and M. A. Srbu Measurng Broadband s Economc Impact. Paper presented at 33rd Research Conference on Communcaton, Informaton, and Internet Polcy (TPRC), Arlngton VA, September. Lndroos P., and M. Pnkhosov Broadband Drvng Growth: Polcy Responses. Unpublshed, Organzaton for Economc Co-operaton and Development. 20

22 Maleck, E.J Dgtal Development n Rural Areas: Potentals and Ptfalls. Journal of Rural Studes 19: Mlls, B.F. and B.E. Whtacre Understandng the Nonmetropoltan - Metropoltan Dgtal Dvde. Growth and Change 34(2): Preger, J.E The Supply Sde of the Dgtal Dvde: Is There Equal Avalablty n the Broadband Internet Access Market? Economc Inqury 41(2): Strover, S Rural Internet Connectvty. Telecommuncatons Polcy 25: Strover, S The prospects for broadband deployment n rural Amerca. Government Informaton Quarterly 20: Thomas, A Broadband: A Key Development Strategy. Unpublshed, Communcatons for the Publc Interest. Wallsten, S Broadband Penetraton: An Emprcal Analyss of State and Federal Polces. Workng Paper, AEI-Brookngs Jont Center for Regulatory Studes. Whtacre, B.E. and B.F. Mlls The Role of Infrastructure n the Rural-Urban Dvde. Paper presented at AAEA annual meetng, Provdence RI, July. Unted States Census Bureau Census 2000 Summary Fle 3 (SF 3). Avalable at accessed 10 January Unted States Department of Agrculture, Offce of Inspector General Audt Report: Rural Utltes Servce Broadband Grant and Loan Programs. Audt Report Te. September. 21

23 Table 1. Overvew of Zp Codes wth Provder Increase, December 2004-June 2005 Area Total Zp Codes Number wth Increased Provders Between December 2004 and June 2005 Percent of Total wth Increase Rural 15,036 4, % Urban 16,571 8, % Total 31,607 12,584 Source: FCC Form 477 dated June 2005; RUCA codes - Economc Research Servce 22

24 Table 2. Rural and Urban Communtes wth and wthout Broadband Provders, June 2005 Varable No provder Urban At least one provder No provder Rural At least one provder Number of Households 841 3, ,070 Populaton Densty 1,423 1, Number of Busness Educaton No HS HS Dploma Some College College Degree Graduate Degree Income Medan ncome 38,571 44,563 31,476 35,024 Percentage below poverty Percentage workng at home Percentage unemployed Race / Ethncty Percentage Black Percentage Hspanc Percentage Other Race Age Percentage 16 and under Percentage Percentage Percentage 65 and over Number of ZIP Codes 1,445 15,126 2,034 13,002 Source: Census 2000; U.S. Census Bureau and FCC Form 477 dated June

25 Table 3. Demographc Table of ZIP Codes that Saw an Increase n Provder, December 2004-June 2005 Urban Rural Varable No No Increase Increase Increase Increase Number of Households 2,395 4, ,703 Populaton Densty 1,567 2, Number of Busness Educaton No HS HS Dploma Some College College Degree Graduate Degree Income Medan ncome 41,085 46,963 33,402 37,395 Percentage below poverty Percentage workng at home Percentage unemployed Race / Ethncty Percentage Black Percentage Hspanc Percentage Other Race Age Percentage 16 and under Percentage Percentage Percentage 65 and over 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

26 Table 4. Model Results Independent Varable Hgh school dploma Some college College Graduate degree Income (log) Number of busness (log) Number of household (log) Black Hspanc Other race Age 16 to 29 Age 30 to 64 Age over 65 Poverty Work at home Commute 30 to 45 mnutes Commute 45 to 59 mnutes Commute over 60 mnutes Model 1 Model 2 Model 3 Urban Rural Coeffcent Coeffcent Coeffcent Coeffcent 0.594** ** 2.005*** (0.280) (0.396) (0.569) (0.763) 1.017*** 1.115*** ** (0.239) (0.325) (0.496) (0.729) (0.360) (0.462) (0.772) (0.987) ** ** *** (0.445) (0.551) (1.011) (1.893) 0.419*** 0.284** 0.362* *** (0.089) (0.118) (0.188) (0.227) 0.392*** 0.496*** *** *** (0.021) (0.027) (0.045) (0.053) 0.179*** 0.067** 0.367*** 0.848*** (0.022) (0.027) (0.048) (0.067) 0.500*** *** (0.103) (0.128) (0.224) (0.305) *** 1.762*** * (0.138) (0.174) (0.291) (0.606) * (0.165) (0.242) (0.339) (0.335) 0.962*** (0.348) (0.449) (0.760) (1.149) 1.407*** 0.997** *** (0.351) (0.481) (0.724) (0.891) * (0.320) (0.416) (0.667) (0.756) * (0.313) (0.445) (0.667) (0.622) 1.118*** 1.196** *** (0.336) (0.534) (0.699) (0.683) 0.488*** 0.450* *** (0.166) (0.238) (0.337) (0.349) *** (0.275) (0.382) (0.557) (0.533) (0.242) (0.327) (0.494) (0.632) 25

27 Table 4. Contnued Model 1 Model 2 Model 3 Independent Varable Urban Rural Coeffcent Coeffcent Coeffcent Coeffcent Rural *** 0.411*** - - (0.030) (0.119) New England 0.383*** 0.339** ** (0.091) (0.137) (0.189) (0.293) Mddle Atlantc 0.277*** 0.250** *** (0.083) (0.126) (0.174) (0.286) East North Central 0.282*** 0.213* *** (0.080) (0.125) (0.167) (0.301) West North Central 0.138* * 0.895*** (0.081) (0.131) (0.169) (0.255) South Atlantc 0.226*** *** (0.080) (0.125) (0.168) (0.328) East South Central *** *** (0.089) (0.139) (0.184) (0.266) West South Central ** *** (0.078) (0.124) (0.164) (0.259) Pacfc * 0.327* * (0.085) (0.130) (0.177) (0.336) Grant (1.104) Broadband loan (0.384) Constant *** *** *** (1.038) (1.394) (2.164) (2.335) Number of observaton 31,607 31,607 3,792 Pseudo R Note: Dependent varable for each model s an ncrease/not ncrease n small broadband provders. Standard errors are n parentheses. Three (***), two (**), and one (*) astersks ndcate sgnfcance at 1%, 5%, and 10% level, respectvely. 26

28 Fgure 1. Number of Broadband Provders Reportng and Broadband Lnes n the U.S., December 1999 June 2005 Number of Provders 1,400 1,200 1, Dec- 99 Jun- 00 Number of Provders Reportng Number of Broadband Lnes Dec- 00 Jun- 01 Dec- 01 Jun- 02 Dec- 02 Jun- 03 Dec- 03 Jun-Dec Jun Number of Broadband Lnes (Mllons) Source: FCC Form 477 dated June 2005 Fgure 2. Avalablty of Small Broadband Provders Source: FCC Form 477 dated June

29 Fgure 3. Avalablty of Small Broadband Provders n Rural Area Source: FCC Form 477 dated June 2005 Fgure 4. Nne Regons of the U.S. Pacfc New England Mountan West North Central East North Central M ddle A tlantc W est South Central East South South Atlantc Central Source: Whtacre and Mlls

30 Fgure 5. ZIP Codes wth no Provder n 2004 Source: FCC Form 477 dated June

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