MODELING REGULATORY REGIMES FOR LAST-MILE BROADBAND CONNECTIONS IN A SINGLE-PROVIDER MARKET: A MULTI-MODEL APPROACH

MODELING REGULATORY REGIMES FOR LAST-MILE BROADBAND CONNECTIONS IN A SINGLE-PROVIDER MARKET: A MULTI-MODEL APPROACH Richard Curry, PhD Candidae London Business School, Regen s Park, London NW1 4SA, Unied Kingdom. Phone: +44 (0) 207 262 5050, Email: rcurry@london.edu. Kiriakos Vlahos, Associae Professor Ahens Laboraory of Business Adminisraion, Ahinas Ave and Areos Sr. 2A Vouliagmeni 166 71, Ahens, Greece, Email: kvlahos@alba.edu.gr Keywords: Telecommunicaions, Broadband, Regulaion, Simulaion, Muli-Model JEL Codes: L1, L5, L9, O3. INTRODUCTION In his paper, we develop a muli-paradigm model of broadband elecommunicaions ha is used o explore he impac of differen regulaory policies. The model creaes a sable benchmark for regulaory policies in he case where he incumben remains he main provider of broadband elecommunicaions. The model also formalizes he relaionships beween he major acors in broadband. Finally, he model ess he effec of various regulaory regimes based on price caps and minimum invesmens have on broadband adopion. We evaluae he resuls of he model agains he UK regulaor s (OFTEL) goals of encouraging universal access, widespread adopion, and a reasonable price for broadband by 2005 (/1/). The model consiss of hree major sub-sysems: consumers, broadband providers, and a regulaor. We model hese sysems using a modeling framework ha allows he descripion of each sub-sysem using a differen mehodology. For example, he model uses sysem dynamics o model he diffusion of dial-up and broadband services hrough he consumer populaion. The sub-model for broadband providers deermines he opimal pricing and invesmen sraegy using opimizaion. The regulaor s model execues a policy using an exper sysem conaining decision rules for he policy. The modeling framework provides a pracical and heoreical basis for connecing he differen sub-models.

MARKET TRANSFORMATION One of he difficulies in forecasing broadband adopion is ha here have no been many opporuniies o sudy how esablished markes ransform in order o mee changing cusomer needs wih new producs and services (/2/). The cause for his ransformaion may be due o changes in demand or advances in echnology. We address his issue using he produc life cycle concep in conjuncion wih produc diffusion models. The produc life cycle consiss of four phases inroducion, growh, mauriy, and decline (/3/, /4/, and ohers). In he inroducory phase, early adopers buy he produc in small numbers ofen for reasons unrelaed o he price and benefis of he produc (hese consumers are innovaors). During he growh phase, he produc becomes available o mainsream consumers who rapidly adop he produc. Following he growh phase, he produc maures o a plaeau where all consumers have access o i. In final sage, decline, he produc becomes obsolee due o changes in echnology, ase, or he availabiliy of subsiues. Cusomers Mauriy Growh Decline Inroducion Time Figure 1: Produc Life Cycle Our model simulaes he life cycles of hree echnologies currenly available in he elecommunicaions marke. I racks he inroducion and growh of broadband services, he growh and mauriy of narrowband daa services, and he decline of voice-only service. Figure 2 shows schemaically a sample pah for number of users of each echnology. Voice-Only Cusomers Broadband Narrow-Band Figure 2: Overlapping Life Cycle in Telecommunicaions Time

Many models for elecommunicaions adopion use growh curves o model he early porion of he produc life cycle. Basic growh curve models follow from he Bass model of produc peneraion (/5/). The basic form of he Bass model saes ha hen number of cusomers using a services a a ime,, is relaed o he innovaion of he produc and he imiaion by Q P e non-cusomers. This can be represened by he following equaion: F Q 1 e P Q P Q, given ha F is he number of consumers using he produc, Q is he coefficien of imiaion, P is he coefficien of innovaion, and is he ime since inroducion. Many sudies ino he adopion of elecommunicaions have used he Bass and oher growh curve models wih varying degrees of success. Ou of he various ypes of growh curve models, he simple models such as he logisic and Gomperz models seem o be superior when fiing hisorical produc inroducion (/6/). The major difficuly in using hese models for forecasing is esimaing heir parameers. The parameers are difficul o obain because new services lack hisorical preceden, herefore many models rely on primary markeing research mehods, such as surveys, subjecive approaches, primarily based on inuiive findings, and comparaive sudies based on relaed services. In hese echniques, he goal is o obain undersandable model, because i is ofen more imporan o have a persuasive model raher han an accurae one (/7/). However, hese ypes of markeing models do no have a good record of accomplishmen on predicing he adopion of new echnologies. For example, he marke for saellie phones, WAP, and he early inroducion of 3G cellular echnologies were vasly oversaed, while he adopion of oher echnologies such as mobile phones, were significanly underesimaed. REGULATORY AIMS The regulaor in our model has wo policy levers: price caps for dial-up and broadband services and minimum invesmen sandards. The price caps make each service more aracive o consumers, hereby encouraging adopion. Minimum invesmen forces he provider o cover consumers ha i migh oherwise overlook due o high coss and low profiabiliy. These policy opions are represenaive of he curren hinking in elecommunicaions regulaion, which focuses on price caps o conrols he cos of service and he provision of universal service.

I is of ineres o examine how differen regulaions would affec broadband because curren regulaion regimes may no be appropriae for he ask of encouraging he adopion of broadband. For example, curren regulaions focus on radiional neworks, are specific o he ype of service provided, and are policy-differeniaed (/8/). Regulaory polices ha focus on saic cos efficiency of he marke may be unable o keep up wih changes in echnology and marke srucure. Furher, i has been shown ha hisorical regulaions can resul in large losses of consumer welfare (/9/). While he need for new regulaions is compelling, he form hey should ake has been he subjec of much debae. Hisorically, rae-of-reurn and more recenly price caps (/10/) formed he basis of governmenal regulaions. Currenly, regulaion is undergoing a shif from he radiional rae-of-reurn regulaions, which are commonly used o regulae monopolies, owards price caps. This shif is occurring for wo reasons: firs, price caps seem o be more flexible for regulaing marke srucures oher han public monopolies and second, rae-of-reurn regulaions can lead o inefficien behavior. For example, some regulaors consider price caps superior o rae-of-reurn price seing because hey promoe cos-saving invesmen (/11/) and provide higher social welfare, paricularly consumer surplus (/12/).. In addiion o conrolling access o he marke and prices, regulaors wan o encourage he universal availabiliy of elecommunicaions. For example, many regulaors perceive universal service o be of benefi o he enire sociey. Crandall and Waverman (/13/) suppor his viewpoin by demonsraing ha economic growh is greaer (by 0.59%) in counries wih higher levels of elecommunicaions infrasrucure developmen and his difference is almos wice as large (1.2%) where here is universal service (~40% of households). While his does no indicae causaliy, i suggess a relaionship beween growh and universal service, jusifying incenives o induce profi-maximizing firms o provide universal service in order o keep he firms from focusing exclusively on high margin cusomers. In he pas, regulaors, while concerned wih he welfare of he consumer, have ofen made heir decisions based on poliical, raher han economic, reasons. This has on occasion led o regulaions ha have been couner-producive and in he long-erm, couner o he public s ineres. Given he recen preference for price-cap regulaion and he concern for universal services, our model examines he impac various price-caps migh have on he adopion of dial-up and broadband and considers universal service obligaions as a form of required minimum invesmen.

SERVICES IN THE TELECOMMUNICATIONS MARKET We group he elecommunicaions marke ino hree ypes of services: Tradiional, Advanced, and Inensive. Tradiional services are broadcas, fixed and mobile voice elephone, and fax services operaing on he legacy circui-swiched nework. Advanced services include all radiional services plus a daa connecion o he Inerne for e-commerce, on-line banking, and cusomized offerings using he radiional nework for las-mile conneciviy. Inensive services include advanced offerings plus services requiring a broadband connecion o he backbone nework. We define broadband as any daa connecion carrying more han 200 Kbyes/Second. In his model, we assume ha broadband cusomers use one a wised-pair copper line wih ADSL, providing a connecion speed of beween 400 o 1000 Kbyes/Second. Furher, we assume ha he consumer uiliy for elecommunicaion services follows he relaionship T A I and disuiliy of higher prices he relaionshipt A I. For consisency, all measuremens of coss, revenues, and prices are relaive o he monhly fixed price of radiional elecommunicaions services. In he case of advanced services, he price for advanced services includes he cos of radiional services. For example, if he monhly sanding charge for voice calls is 10 / monh, a monhly price for dial-up (advanced) of wo is equal o he 10 for radiional plus an addiional 10 for connecion o he Inerne via an ISP. Unlike advanced services, a broadband (inensive) connecion wih a price of hree would only include a fee of 30 for he broadband supplier who would bundle an ineroperable voice-only service. METHODOLOGY To model he effec regulaory policies have on he adopion of consumer broadband, we use a muli-modeling mehodology. A muli-model is a model composed of sub-models each of which may use a disinc modeling paradigm (/14/, /15/, and ohers). We chose his approach in order o deal wih he dynamic naure of he elecommunicaions indusry, he disparae naure of he various sub-sysems, and he complex ineracions resuling from regulaory policies. Hisorically, when modeling policies, saic raher han dynamic models were he predominan mehodology. Saic models are effecive in explaining equilibrium condiions, however, hey may no be appropriae in markes ha have no ye reached equilibrium. As a resul, saic models are someimes no reliable for quaniaive guidance in he real, dynamic world. A modeler ough o deal wih demand growh and disinguish beween policies, which

have dynamic elemens (/16/). We use a dynamic model o accoun for he ransformaional naure of he elecommunicaions marke and because he price hisory of each service will affec is adopion rae. Tradiional modeling echniques assume ha an enire sysem is describable using a single paradigm. However, he naure of he sub-sysems in our elecommunicaions model and he complexiy and richness of heir ineracions has led us o adop a muli-paradigm modeling approach. For example, he pricing decisions of a monopolis can be represened effecively by an opimizaion model, whils he ransiion of consumers from one echnology o anoher is bes modeled using sysem dynamics. We use he mos appropriae echnique for each subsysem, so ha we can focus on he ineracions beween hem. The challenge in muli-modeling is in joining models having widely differen requiremens and represenaional schemes, which calls for dedicaed sofware. To implemen he model, we use a new muli-modeling sysem developed by he auhors called he Hierarchal Decision Policy Simulaion (HDPS) (/17/). HDPS uses a widely acceped and indusry sandard such as XML o represen and solve a wide variey modeling paradigms, such as simulaion, opimizaion (linear, quadraic, and general non-linear) or exper sysems, and suppors a generic programming model. TOP-LEVEL MODEL For his model, we divide he elecommunicaions sysem ino five major sub-sysems: Regulaory Policy, Indusry Pricing, Nework Capaciy, Demand, and Consumer Adopion. The Regulaory Policy sub-sysem uses an exper sysem o imiae a regulaor seing price caps and invesmen requiremens according o a fixed plan. The Indusry sub-sysem represens a monopoly elecommunicaions provider s profi seeking behavior wih a mahemaical (quadraic) program. The Demand sub-sysem deermines he demand for differen services based on he curren price, funcionaliy, and he marke share of each service. The Consumer Adopion sub-sysem represens he ransiion of consumers from radiional o broadband elecommunicaion services wih a sysem dynamics model. Finally, he Nework Capaciy sub-sysem uses a se of inegral equaions o esimae he availabiliy of broadband services. The sub-models inerac by exchanging informaion for each service caegory as defined in Table 1.

Table 1: Inerfacing Vecors for Tradiional, Advanced, and Inensive Services X Q P D I MAZ PZA MINP MAXP MI Percenage of all cusomers using each ype of service Capaciy for each ype of service as a % of oal consumers Price for each service ype as a muliple of he price for radiional services Demand for each service as a % of oal consumers Invesmen in a he nex period as a % of oal consumers Maximum adopion rae for each echnology given a price of zero Price a which here will be effecively no adopion Minimum price allowed for a service as a % of he price of radiional services Maximum price allowed for a service as a % of he price of radiional services Minimum invesmen as a % of he oal number of consumers Consumer X D Q Demand Capaciy MAZ, PZA I Regulaory Policy MINP, MAXP, MI Indusry P Figure 3: Sub-Model Inerfaces Figure 3 illusraes he connecions beween he five primary sub-models wihin he BTMM. The consumer sub-model use a vecor of demands (D) and a vecor of capaciies for each service (Q) o deermine he percenage of consumers using each service (X). The capaciy sub-model uses new invesmen (I) o deermine he curren capaciy for each service (Q). The price sub-model akes informaion abou he demand curve for each echnology (PZA and MAZ), regulaory consrains (MINP, MAXP, and MI), and he curren sae of consumers (X) o deermine new invesmen in capaciy (I) and he price (P) for each service. The regulaory policy sub-model uses he vecor of curren consumer levels (X) and curren prices (P) o develop regulaions (MINP, MAXP, and MI). The demand model uses he curren consumer sae (X) and he vecor of service prices (P) o deermine he demand (D) for each service. We simulae a six-year period wih a given regulaory policy using a ime-sep of one monh (see Figure 4). The flow of he simulaion is as follows: Given a se of iniial condiions, he op-level model invokes he regulaory policy sub-model o deermine he minimum and

maximum prices and he minimum invesmen ha consrain he choices of he incumben. Then, he op-level model calls he indusry model o deermine he price and invesmen levels chosen by he incumben. The op-level model hen concurrenly evaluaes he oher hree models (adopion, demand, and capaciy) for one ime-sep o updae he curren sae of hese sub-models. Regulaory Policy Pricing YES Adopion Demand Capaciy T > 5 years NO 6 Monhs YES YES Done NO NO 1 Year YES Figure 4: Top-Level Model Flow A each ime sep, he model coninues concurrenly evaluaing he adopion, demand, and capaciy models unil i has run pas six years. A six-monhly inervals, he op-level model calls he pricing sub-model. Addiionally, when he model reaches he beginning of a year, he op-level model invokes he regulaory model using he curren sae. CONSUMER ADOPTION MODEL The consumer adopion model capures he ransiion of consumers from radiional elecommunicaions owards services ha require more bandwidh. The paradigm employed for he consumer adopion sub-model is a coninuous ime simulaion or Sysem Dynamics model (/18/). The model is a simulaion of he diffusion of radiional consumers o he wo new echnologies and is similar o he model used by Homer (/19/). In he model, he socks are consumers using each service and he flows are consumers ransiioning beween services. The curren proporion of consumers using he services, he demand for each ype of service and he nework capaciy for inensive services deermine he flows beween services. The demand used for each service comes from he demand model shown in he nex secion. To simplify he model, we assume ha no consumers will ransiion beween radiional and inensive services wihou passing hrough advanced services.

We describe he consumer adopion sub-model as a sock and flow diagram in Figure 5 and as a collecion of difference equaions (1) o (7). Tradiional Demand for Advanced Migraion Advanced o Tradiional Migraion Tradiional o Advanced Advanced Demand for Inensive Migraion Inensive o Advanced Inensive Capaciy Migraion Advanced o Inensive Inensive Figure 5: Sock and Flow Diagram for Consumer Model. x x A T, T A, (1) x x (2) T, 1 T, xt A, xi A, xa T, xa I, xa, 1 xa, x A I, xi A, xi, 1 xi, (3) (4) x T A, max 0, d A, x I, x A, (5) max 0, min x x d x x A T, T,, A, A, I, x A I, max 0, min xa, qi, xi,, d I, xi, x I A, max 0, x I, d I, (6) (7) Where: A delay facor he % of consumers who wan o change ha acually do so in one period x i, The number of consumers using service i a ime. d i, The demand for service i a ime. q,i, Capaciy for service i a ime. Equaion (1) o (3) are balance equaions saing ha he percenage of consumers in each caegory a ime, is equal o he number in he pervious period plus he unresriced flow in consumers divided by a delay facor (). The delay facor is he percenage of consumers willing o ransiion who acually change service level in he curren period. Equaions (11)- (14) specify he unresriced flow from Tradiional services o Advanced services, from Advanced o Tradiional, from Advanced o Inensive and from Inensive o Advanced respecively.

DEMAND MODEL In he demand sub-model, we use wo facors, he price and curren number of consumers for a given service, o esimae he demand for elecommunicaions services. Price affecs he number of consumers adoping he service, while nework effecs (based on he number of cusomers using a service) deermine he number who wan he service. In his model, we assume ha he adopion of a service is highly correlaed wih he price for he service. This agrees wih he empirical daa ha indicaes ha he price of Inerne access (an advanced service) is one of he major facors ha describe he difference in peneraion raes across markes (/20/). Furher, we assume ha inensive services include all he funcionaliy of advanced service. Because of his subsiuabiliy, we assume he marke for inensive services will be a leas as price sensiive as he marke for advanced services. We model he demand for each service using an insananeous demand curve. The demand curve is linear and specified by wo values: Max Adopion Zero Price (MAZ) and Price Zero Adopion (PZA). The MAZ value is he number of consumers who would adop a echnology a no cos and is similar o imiaion parameer in diffusion models. PZA is he price where effecively no one is willing o adop he service and represens he innovaion presen in he service. These erms follow from demand heory (/21/) and our approach is similar o ha used by Hausman (/9/) wih he PZA acing as a virual or reservaion price for each new service and he demand curve esimaed using he MAZ. Equaion (8) shows he demand as a percenage of he oal populaion of elecommunicaions consumers for each echnology, given a price for each service. (8) d i, i MAZ i, pi, 1 PAZ i, Tradiional, Advanced, Inensive Where: PZA i, MAZ i, d i, p i, Price Zero Adopion for service i a ime Maximum Adopion Zero price for service i a ime Demand for service i a ime (as a percenage of he oal marke) Price for service i a ime

Price PZA Price = PZA (PZA/MZA)*Demand MAZ Demand Figure 6: Demand Funcion While a linear approximaion for he demand curve may no seem realisic, by adjusing he values for MAZ and PZA we generae an insananeous demand curve a each ime sep ha approximaes he rue demand curve near he curren demand. We model he evoluion of MAZ assuming nework effecs and PZA wih decreasing funcion of ime. Noe he using a linear demand curve will end o underesimae demand given MAZ and PZA. The nework effecs change he shape of he shor-run demand curve over ime. We define he MAZ for each echnology as he number of consumers currenly using ha service or a more advanced one plus a fixed percenage of hose using he nex lower level of service. For example, MAZ for advanced services, (10), is he number using advanced services plus hose using inensive services plus a fracion ( A, ) of he consumers using radiional services. (9) MAZ 1 T, A, xa, 1 xi, 1 A, xt, MAZ (10) 1 MAZ x x (11) I, I, 1 I, A, 1 Where: i, Percenage of consumers wihou echnology i who wan he service a ime.

Figure 7 shows a diffusion curve for advanced and inensive services, if he PZA, price, and remain consan. 1 0.9 % of Consumers 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 A, 0.5 I, 0.25 PZA A, 5 PZA I, 7 p A, 3 p I, 6 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 Time AMAZ IMAZ Figure 7: MAZ Trajecory The willingness o pay, as measured by he PZA, is a funcion of ime wih a uning facor, i,, an early adopion adjusmen, i,, and a long-run willingness o pay, PZA i,8, for each service, i. The difference beween i, and PZA i,8 simulaes he acions of early adopers. If i is posiive, early adopers value he produc more while if i is negaive i indicaes ha here may be significan nework effecs. Equaions (12) o (14) show he formulaion for he price for zero adopion for each service. (12) (13) (14) Where: PZA, PZA PZA T A, A, PZAA, A, e I, I, PZAI, I, e i, A shape parameer ha deermines how fas he value of PZA will drop for each i. i, Maximum PZA a he ime of inroducion of he service (early adoper s premium) PZA i,8 Long-erm PZA for service I Figure 8 shows an example PZA rajecory wih equal o 0.04, equal o 3, and PZA i,8 equal o 4 over a 72 monh period. 7 6.5 6 PZA 5.5 5 4.5 4 0 10 20 30 40 50 60 70 Time (Monhs) Figure 8: PZA Trajecory for 72 Monhs Based on hisorical raes, we expec amoun consumers are willing o pay (PZA) for advanced and inensive services o decrease 7% per year during he iniial sages of adopion.

Due o he near universal adopion of radiional elecommunicaions services, we model he PZA for radiional services as approximaely infinie. NETWORK CAPACITY The nework capaciy sub-model deermines he maximum number of consumers a service can accommodae. Capaciy represens he percenage of he populaion having a las-mile connecion o he nework backbone a a given service level. We model changes in he capaciy using a simulaion where he capaciy of a service changes due o he reiremen of old equipmen and he inroducion of new capaciy hrough invesmen. Equaion (15) shows he equaion for changes in he capaciy. (15) Where: q i, 1 i q i, n i, l i, Tradiional, Advanced, Inensive q i, n i, l i, Capaciy for service i a ime in he % of he oal populaion Invesmen in service i a ime as a % of he oal populaion Loss of capaciy for service i a ime as a % of he oal populaion In his analysis, we assume zero invesmen in radiional or advanced services (n T, and n A, = 0) and negligible nework losses (l I, 0). INDUSTRY PRICING MODEL The indusry-pricing model represens a single firm wih an effecive monopoly on he local loop. This mirrors many siuaions where an incumben local exchange carrier conrols he lines from he nework backbone o he consumer. Experience in deregulaion of elecommunicaions seems o suppor he view ha compeiion in he local loop will no be significan in he shor run. For example, radiional providers have mainained he majoriy of business in he local loop hrough aggressive enry deerrence sraegies during he iniial years afer he adopion of 1996 Telecommunicaions Ac in he US (/22/). In his sub-model, we neglec non-infrasrucure compeiion because he monopoly firm sill conrols (subjec o regulaion) he price of he leased line, which provides he majoriy of he cos base for service provision. Given an expeced consumer demand funcion, he incumben ac as an opimizing agen, making pricing decisions for he hree levels of services and invesmen decisions regarding nework capaciy, so ha hey maximize heir expeced shor-erm ne revenue. Shor-run ne revenue is he revenue from cusomers of all services minus he incremenal cos of invesmen (neglecing sunk coss on previous

invesmen and assuming consan nework mainenance coss, regardless of he ype of neworks). Pricing and invesmen decisions are consrained by curren marke condiions and by price conrols and required invesmens (such as universal service obligaions) se by a naional regulaor. We developed he mahemaical program shown in equaions (16)-(25) o approximae he opimal pricing behavior for he monopolis. (16) Max :Ne Revenue Where J Subjec To : (17) x I n q j J (18) xi di j J (19) xa xi da j J (20) x j 1 j J 1- x j pt j J i Advanced, Inensive MAZ j (21) d j MAZ j p j PZA (22) MINP j p j MAXPj (23) 0 x j 1 j J (24) MINI i AI (25) p 1 Where: T j J p x j j C K n p j x j d j MINP j MAXP j PZA j MAZ j q n MINI AI K C Price for echnology j relaive o he price of radiional services Expeced number of consumers for service j Demand for service j Minimum price allowed for service j as a % of he price for radiional services Maximum price allowed for service j as a % of he price for radiional services Price for zero adopion for service j as a % of he price for radiional services Maximum adopion a zero price for service j as a % of he oal number of consumers Curren capaciy for Inensive service in % of oal cusomers New invesmen in Inensive services as a % of he oal number of consumers Minimum inves in Inensive services as a % of he oal number of consumers Available invesmen as a % of cusomers The payback period for new invesmen in service j, in years Cos o provide service j for one consumer, as a muliple of p T The objecive funcion, equaion (16), maximizes shor-erm ne revenue; i breaks down ino four pars. The firs, 1 - x j p T, represens he fracion of cusomers no using advanced or j J inensive services, which is equal o he percenage using radiional services. We conver his number o he revenue from radiional services by muliplying i by p T. The second porion of he objecive funcion, j J p j x j, gives he revenue from consumers adoping service j. The final par of he objecive funcion subracs he cos for providing new

capaciy, C n, which is allocaed o curren year using a simple payback period formula. K Equaion (17) specifies ha he number of inensive consumers is less han he curren capaciy of he inensive nework plus any addiional capaciy resuling from new invesmen. Equaion (18) specifies ha he number of consumers adoping Inensive services is less han he demand for inensive services. Equaion (19) requires he oal number of Advanced and Inensive consumers o be less han he demand for advanced services. Equaion (20) requires he sum of advanced and inensive users o be less han or equal o one. Equaion (21) says ha he quaniy demanded mus be less han or equal o ha indicaed by he demand curve specified by MAZ and PZA. Equaion (22) consrains he prices from above and below wih a regulaed maximum and minimum price. Consrain (23) requires he percenage of consumers using each service be beween 0 and 100% of p T. Consrain (24) requires addiional invesmen o be beween he minimum regulaed invesmen and he available funds. Equaion (25) specifies ha he price for radiional services, p T, is equal o one, since all oher prices and coss are relaive o his price. REGULATORY POLICY MODEL Based on hisorical rends, we allow a regulaor o se price caps and minimum invesmens for services. Price caps are he primary regulaion in his model because of heir sraighforward formulaion as compared rae-of-reurn regulaion, anoher key mehod of regulaion. In addiion o price caps, he regulaor specifies a minimum level of invesmen, which may be viewed a proxy for universal service obligaions as well as a mehod o spur adopion. The price caps and minimum invesmens are se according o he following funcions. (26) MAXPj, MAXPj (27) MINI MINI j, j Figure 9 provides hree examples of MAXP p () for boh a igh and a loose regulaory policy: he firs shows a consan policy, he second a decreasing regulaed price wih ime, and he final example shows a policy ha allows suppliers o increase heir price wih ime.

Max Price Max Price Max Price Time Time Time Figure 9: Three Policies for MAXP Funcions for MINI P () would be similar. MODEL SUMMARY When combined, he elemens conained in he various sub-models exhibi a feedback srucure shown in Figure 10. Regulaor Decisions Tradional Migraion o Advanced Advanced Migraion o inensive Inensive Inensive Capaciy Advanced Demand Inensive Demand Advanced Price Inensive Price Invesmen Pricing and Invesmen Decision Figure 10: Overall Model Feedback Srucure The feedback srucure has four socks, he number of consumers using each service (x j ) and he capaciy for Inensive services (broadband). The number of consumers in each sock changes due o migraion beween he services, which is a funcion of he demand for he more advanced services (d j ). The demand is a consequence of he curren number of consumers and he price (p j ) for he service. The Indusry sub-model deermines he price for each service based on heir expecaion of he number of consumers who will use he service and he consrains imposed by he regulaor. The regulaor makes decisions based on he price of a service and number of consumers using i. Finally, invesmen (n) in inensive services deermines he capaciy of he broadband infrasrucure ha limis he adopion of inensive services. The Indusry sub-model makes decisions abou capaciy invesmen based on he inpu of he regulaor (price caps and invesmen arges) and demand curve esimaes. CONDITIONS The model uses marke condiions similar o hose in he UK in 2000 o iniialize parameers. In he UK, according o OFTEL (/23/), 98% of all households are elecommunicaions

subscribers of which 93% have fixed lines and 5% rely on mobiles exclusively. Beyond radiional voice elephony services, he adopion of he Inerne and oher advanced services has occurred a a slower pace wih ~ 30% of households conneced by November 2000. In addiion, here is much ineres in broadband conneciviy for example; 75% of cusomers in he UK are ineresed in using ADSL or oher broadband echnology. However a he expeced price of GBP 40 per monh, here will be lile movemen owards broadband as only one in four cusomers would consider acquiring broadband service wihin he nex year because he price is well above he amoun (GBP 13 per monh) he average cusomer is willing o pay. Based on surveys of consumers (/23/), we calculae ha he percenage of he populaion who would use advanced services was approximaely 50% and he percenage ha would use inensive services was abou 25% in 2000. Table 2: Model Parameers POLICIES A, I, A, I, A, A, 2 3 9 12 0.4 0.5 3 We simulae differen regulaory policies by specifying equaions for MAXP j () and MINI () exogenously. In paricular, we consider four saic and wo dynamic regulaory policies simulaing a range of marke condiions and regulaory responses. We consider a wide specrum of regulaory policies saring from unregulaed monopoly pricing for boh advanced and inensive services. We hen consider a mixed marke srucure having a compeiive narrowband secor and a regulaed broadband secor. Three saic and wo dynamic policies are examined wih various price cap regimes. Finally, a saic policy is examined wih compeiive condiions in boh advanced and inensive markes, which assume prices reflec he marginal cos of providing he services. To specify hese policies, we use equaion, (28), requiring hree parameers, iniial and long run price caps and a rae of change beween he wo. rj (28) Where: MAXP j MAXPj, long run MAXPj, iniial MAXPj, long run MAXP j, long run The iniial price cap for service j MAXP j, iniial The long-run price cap for service j MAXP r j The rae of decrease in he price cap for service j Table 3 shows he values for he iniial and long run values for he advanced and inensive price caps in he monopoly, compeiive, mixed, and saic policies. e MAXP

Table 3: Saic Policy Parameers Service MAXP MAXP, j, long run j iniial Advanced MAX A MAX A Inensive MAX I MAX I Noice we do no specify he rae parameer because any value will have he same effec because he iniial and long run price caps are idenical. Table 4 shows he price cap for advanced and inensive for each of he six saic models, monopoly, mixed, compeiive, and compeiive advanced wih price caps of four, five, and six for inensive services. Table 4: Policy Parameers Policy MAX A MAX I Monopoly 8 8 Compeiive 2 3 Monopoly Inensive 2 8 Cap 6 2 6 Cap 5 2 5 Cap 4 2 4 In addiion o he saic policies, we have sudied wo dynamic policies having non-consan price caps for inensive services wih a compeiive advanced services marke. The firs policy has an increasing price cap, reflecing an assumpion ha given enough demand, a monopoly will have greaer incenive o lower price as he demand for he produc increases. Table 5 provides he parameers for his policy. Table 5: Increasing Price Pap Parameers Service MAXP MAXP, j, long run j iniial MAXP r j Advanced 2 2 0 Inensive 3 8 0.3 The second dynamic policy has a price cap for inensive services ha decreases wih ime. This could reflec an assumpion ha a monopoly should be given a period o recoup is invesmen by allowing i o charge more during he iniial rollou period. Table 6 provides he parameers for he decreasing price cap policy. Table 6: Decreasing Price Cap Parameers Service MAXP MAXP, j, long run j iniial MAXP r j Advanced 2 2 0 Inensive 8 3 0.3

We evaluae each of he eigh policies wih a specified minimum invesmen of 0%, 6%, 50%, and 100% per year, providing 36 es cases. Noe ha here will be no invesmen if he service is already available o 100% of he populaion. RESULTS Each of he 36 policies creaes a wealh of daa abou he performance of he broadband marke including pricing, adopion, demand, and invesmen informaion. Since OFTEL saes ha adopion as a primary goal, he hisory of adopion raes provides a good iniial indicaion of he performance of a policy. For each policy, we show a race of he adopion rae of consumers o show he progress of migraion. In he compeiive case (wih marginal coss for advanced and inensive services equal o 2 and 3 respecively), he adopion of advanced and inensive services unfolds as in Figure 11. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 70 80 TRADITIONAL CONSUMERS ADVANCED CONSUMERS INTENSIVE CONSUMERS Figure 11: Adopion race for Compeiive Marke From he regulaor s poin of view, his is he bes oucome. We can see ha using he chosen parameers, abou 2/3 rd of he populaion will adop eiher advanced or inensive services, spliing he marke equally. From he shape of he adopion curves, we see ha he radiional marke is decaying, alhough i appears o have sabilized by he end of he sixh year. We also noice ha advanced services have peaked and enered a decline while inensive services coninue o arac cusomers. Based he simulaion of his policy, we conclude ha in a compeiive marke, he number of advanced and inensive consumers will grow only slowly afer he end of he sixh year. We noice ha his falls shor of universal adopion, for which we offer wo possible explanaions. The firs is ha universal adopion may no be possible because he price consumers are willing o pay is oo low o suppor he service. The second is ha modeling bias, paricularly he assumpion of linear demand, disors he model near is boundary condiions. However, if we assume ha ha all consumers wan (a zero price) advanced and inensive services here would be a higher adopion rae of boh services, alhough he rae never approaches 100% of he populaion.

If he compeiive case is he bes possible oucome, he case where boh advanced and inensive services are monopolies is he wors. Figure 12 shows he oucome of he simulaion of his policy. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 70 80 TRADITIONAL CONSUMERS ADVANCED CONSUMERS INTENSIVE CONSUMERS Figure 12: Trace for Monopoly Advanced and Inensive We noice ha in he monopoly case, here is no a large adopion of eiher advanced or inensive services because high prices sifle demand. We noice ha all services have reached equilibrium wih radiional consumers remaining a large majoriy. While he monopoly case provides a wors case for exploring he model, acual experience indicaes ha due o he large number of ISP s available for dial-up connecions he marke for advanced services is compeiive, despie he fac ha he cos of he service largely depends on a regulaed access charge. Given ha neiher he compeiive nor he monopoly cases represen realiy well, for balance of our policies we assume ha he marke for advanced services is compeiive, i.e. prices are close o marginal cos. Furher, because he price for a dialup connecion is abou he same as cos as a phone line, we assume ha he marginal cos, and herefore price, for advanced services is equal o wo. Figure 13 shows an adopion race for a compeiive advanced marke and a monopoly in he inensive marke. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 70 80 TRADITIONAL CONSUMERS ADVANCED CONSUMERS INTENSIVE CONSUMERS Figure 13: Adopion Compeiive Advanced, Monopoly Inensive

We noice ha in his case, he oal adopion of advanced and inensive services is he same as ha in he compeiive case. However, he rae of adopion for inensive services is much lower (approximaely 1/3 rd of he compeiive rae). In his case, we noice ha boh advanced and inensive services have maured while radiional services have decayed o 1/3 rd of heir original values. In order o judge he efficacy of a policy, we use hree measuremens from he sixh year of he simulaion. The firs is he profile of he final adopion a he end of he simulaion (corresponding o 2005, he year when he regulaor wishes o have near-universal availabiliy for broadband). The second measure is price for inensive service as a muliple of ha of radiional service a he end of he simulaion. The final measuremen is he ne revenue from all services during he six-year run of he simulaion as a muliple of radiional-only revenue (i.e. a ne revenue of 2 indicaes ha he regulaory regime gives he provider wice as much revenue han if only radiional services exied). Because he adopion rae is he primary concern of he regulaor, we firs look a he final prices and adopion raes for inensive services. Price Cap 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 12.0 10.0 8.0 6.0 4.0 2.0 0.0 7 6 5 4 3 None Increasing Decreasing Monopoly Percen Adopion Advanced Inensive Final Price Figure 14: Marke Share Figure 14 shows wo elemens; he firs is ha once he simulaion has reached equilibrium, he oal number of advanced and inensive consumers is consan. This is he resul because demand for Inerne services (advanced + inensive) conrols he oal adopion for advanced and inensive services and does no depend on he service level. Secondly, i shows ha he number of users adoping inensive services increases as he price cap decreases. We can also noice ha he effec of a dynamic price cap is very close o ha of he saic policy a he long-run price cap. While he analysis is no complee, his may mean ha i is possible o fine une prices o allow a monopoly ime o recover is invesmen hrough high prices while no affecing he long run adopion of inensive services.

0.45 0.4 % of Inensive Consumers 0.35 0.3 0.25 0.2 0.15 0.1 No Invesmen ~ 6% / year ~ 50% / year Full Invesmen 0.05 0 3 None Monopoly 4 5 6 7 Inensive Price Cap Figure 15: Effec of Invesmen Figure 15 compares he final adopion rae wih he level of invesmen for each of he esed policies. We noice ha he effec of a minimum invesmen depends on he amoun of invesmen and ha invesmen has a greaer effec in cases where prices are low han when hey are high. While his may appear somewha couner-inuiive, he demand for producs shifs a a greaer rae a lower prices due o he greaer number of adopers. In effec, forcing invesmen above he opimal rae can lead o faser adopion, which allows faser increases in he long-run demand for he service. This creaes a posiive feedback loop for demand, more cusomers more demand more cusomers. Because of his effec, a regulaor should eiher require 100% rollou in he firs wo years or allow indusry o inves a heir opimal rae. While we have focused on he effec of regulaion on he consumer, is effec on indusry may also influence he course of regulaory acion. Figure 16 shows he ne revenue for differen levels of invesmen. 2.5 2 Ne Revenue 1.5 1 0.5 0 3 4 5 6 7 Decreasing Increasing Monopoly None Price Cap No Invesmen ~ 6% / year ~ 50% / year Full Invesmen Figure 16: Indusry Ne Revenue Toal revenue from elecommunicaions drops as he price cap decreases, showing ha revenue from addiional consumers does no ouweigh he decrease in revenue per consumer. The seady decline in revenue wih igher price caps shows ha indusry as currenly srucured will have lile incenive o lower prices, because i will decrease he oal revenue.

The policy implicaion is ha for he regulaor o mee is goal of universal access i mus change he srucure of he marke by forcing compeiion, preferably infrasrucure based, or regulae he maximum price and accep he disorions his will cause in he marke. MODEL CALIBRATION Because we have evaluaed he model saring from year 2000 iniial condiions, we can calibrae he model using marke from he pas hree years. Over his period, he mixed case, compeiive advanced and monopoly inensive, maches he acual pah of adopion in he UK. This case fis boh pricing and adopion (alhough our model does over-sae he effec of early adopion due o linear demand). However, during he summer of 2002, here was a change in policy, which compelled BT o lower he access charges for ADSL lines. To compare he resuls of he simulaion wih he curren siuaion, we use figures from he January 2003, OFTEL Inerne and Broadband Brief. The curren average ADSL fee is 29 per monh ( 14.75 for wholesale raes plus 14.25 in ISP fees) in addiion o he sandard line renal, approximaely 8 per monh. Translaing his ino a muliple of he cos of radiional services gives inensive services a cos of five. Based on his informaion, we believe ha he mos likely pah will be he one raced by he case wih a compeiive advanced marke and a price cap of five. Assuming no radical changes in policy over he nex hree years, we would expec ha 68% of he populaion o have Inerne access by 2005, wih 25% of he populaion subscribing o broadband. CONCLUSIONS In his paper, we have explored how he rollou of broadband elecommunicaions migh evolve under various regulaory regimes. The broadband adopion muli-model provides some imporan insighs. The firs confirms he assumpion ha, properly implemened price caps resul in an increase in adopion wihou hindering invesmen. Broadband adopion doubles (from 13% o 26%) under a price cap of five (approximaely he curren case) when compared o he case where here is no price cap. Currenly, abou 4% of UK homes connec o he Inerne wih broadband, which is well below wha we would expec (15%) had he recen drop in prices occurred earlier. Judging from es runs wih decreasing price caps, he number of consumers should come ino line wih he expeced number as ime progresses. The second resul is ha increasing he marke for services is very beneficial. For example, assuming a compeiive ISP marke (lowering he final price of advanced from 5.4 o 2.0

yields a 10-percenage poin gain in broadband adopion wih only a small decrease in inensive prices (9.8 o 8.6). Finally, changing price caps wih ime does no appear o have a large effec on long-run adopion when compared o a consan price cap wih he same final price, alhough i does appear o hur he provider by decreasing ne revenue. We creaed our model using a modular modeling framework ha will enable fuure work hrough he exension and subsiuion of alernaive represenaions for various pars of he model. In addiion o he benefis of exensibiliy, modeling he elecommunicaions marke as a hierarchy of possibly heerogeneous models faciliaes a beer specificaion by adoping an appropriae mehodology for each sub-model. REFERENCES (1) OFTEL. Delivering a compeiive broadband marke Ofel's regulaory sraegy for broadband. (2) Bores, C.; Saurina, C.; Torres, R. Technological Convergence: a sraegic perspecive. Technovaion, 2003 23, 1, 1-31 (3) Levi, T. Exploi he Produc Life Cycle. Harvard Business Review 1965, Nov-Dec, 81-94 (4) Teece, D. J. Capuring Value from Technological Innovaion - Inegraion, Sraegic Parnering, and Licensing Decisions. Inerfaces 1988, 18, 46-61 (5) Bass, F. M. A New Produc Growh Model for Consumer Durables. Managemen Science 1969, 15, 215-227 (6) Meade, N.; Islam, T. Forecasing wih growh curves: An empirical comparison. Inernaional Journal of Forecasing 1995, 11, 199-215 (7) McBurney, P.; Parsons, S.; Green, J. Forecasing marke demand for new elecommunicaions services: an inroducion. Telemaics and Informaics 2002, 19, 225-249 (8) Zhang, B. Undersanding he impac of convergence on broadband indusry regulaion: a case sudy of he Unied Saes. Telemaics and informaics 2002, 19, 37-59 (9) Hausman, J. A. Valuing he Effec of Regulaion on New Services in Telecommunicaions. Brookings Papers on Economic Aciviy. Microeconomics 1997, 1997, 1-38 (10) Vog, G. J. Cap-Sized: How he Promise of he Price Cap Voyage o Compeiion Was Los in a Sea of Good Inenions. Federal Communicaions Law Journal 1998, 51, 349-401 (11) Cabral, L. M. B.; Riordan, M. H. Incenives for cos reducion under price cap regulaion. Journal of Regulaory Economics 1989, 1, 93-102 (12) Clemenz, G. Opimal Price-Cap Regulaion. The Journal of Indusrial Economics 1991, 39, 391-408 (13) Crandall, R. W.; Wavermann, L. Who Pays for Universal Service When elephone subsidies become ransparen; Brookings Insiuion: Washingon, DC, 2000

(14) Oeren. Dynamic emplaes and semanic rules for simulaion advisors and cerifiers. In Knowledge-based simulaion: mehodology and applicaion; Fishwick, P.A., Modjewski, Eds.; Springer-Verlag: 1991; pp 53-76 (15) Fishwick, PA. A Taxonomy for Simulaion Modeling Based on Programming," IIE Transacions on IE Research, 1995, 23-29 (16) Schmalensee, R. Good regulaory regimes. RAND Journal of Economics 1991, 20, 417-436 (17) Curry, R. Hierarchical Decision Policy Simulaion: A Mehod for Inegraing Complex Policies in Simulaion Models, wih a Model for Telecommunicaions. London Business School Ph.D. Disseraion Proposal 2001 (18) Forreser, J. W. Indusrial Dynamics; Produciviy Press: Cambridge, MA, 1961 (19) Homer, Jack B. A Diffusion Model wih Applicaion o Evolving Medical Technologies, Technological Forecasing and Social Change 1987, 31, 197-218 (20) Kiiski, S; Pohjola, M. Cross-counry diffusion of he Inerne. Informaion Economics and Policy, 2002, 14, 297-310 (21) Hicks, J. A Revision of Demand Theory; Oxford Universiy Press: Oxford, 1956 (22) OFTEL. Consumers' use of fixed elecoms services: Summary of Ofel residenial survey. 2000-3. (23) OFTEL. Consumers' use of Inerne: Summary of Ofel residenial survey. 2000-3.