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Some issues on FTTP Competition and Industry Structure for Access Networks Marvin Sirbu Anupam Banerjee Carnegie Mellon University Joint work in part with Sharon Gillett, William Lehr, of M.I.T. Outline Models of Competition What is “Open Access” Models of Municipal Open Access

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Some issues on fttp competition and industry structure for access networks l.jpg

Some issues on FTTP Competition and Industry Structure for Access Networks

Marvin Sirbu

Anupam Banerjee

Carnegie Mellon University

Joint work in part with Sharon Gillett, William Lehr, of M.I.T.


Outline l.jpg
Outline Access Networks

  • Models of Competition

  • What is “Open Access”

  • Models of Municipal Open Access

  • Economics of A Wholesale/Retail split


Models of competition in telecommunications l.jpg
Models of Competition in Telecommunications Access Networks

  • Facilities Based Competition


Models of competition in telecommunications4 l.jpg
Models of Competition in Telecommunications Access Networks

  • UNE Based Competition (made possible by Physical Plant Unbundling)


Models of competition in telecommunications5 l.jpg
Models of Competition in Telecommunications Access Networks

  • Open Access Based Competition (made possible by Logical Layer Unbundling)


Models of competition in telecommunications6 l.jpg
Models of Competition in Telecommunications Access Networks

  • UNE Based Competition AND Open Access


Open access l.jpg
Open Access Access Networks

  • What is “open access”

    • Multiple competitors use a common shared infrastructure

    • Customers can elect services from alternative suppliers

  • One third of FTTH systems in the U.S. have been built by municipalities

  • Some municipalities have opted (or been required by state law) to operate Open Access systems


Open access and layering l.jpg
Open Access and Layering Access Networks


Open access decision points for a municipality l.jpg
Open Access Decision Points For a Municipality Access Networks

  • To which services?

    • Voice telephony

    • Data (ISP): Internet access

    • Data (transport): broadband circuits, dark fiber

    • Video: broadcast TV, VoD

  • At what layer?

  • With what partnership model

    • Network operator also competes at retail?

    • What control over identity and number of service providers?

    • Who bills customer? Who pays whom on what basis?

    • Wholesale prices negotiated or regulated?


Open access decision points l.jpg
Open Access Decision Points Access Networks

  • What shared facilities beyond “last-mile” distribution?

    • Shared middle mile backhaul to tier 1 ISPs

    • Shared ISP peering point (NAP)

    • Shared telephony gateway

    • Shared video head end


Grant county zippnet l.jpg
Grant County Zippnet Access Networks

  • Architecture: FTTH Active Star, IP video

  • Open for:

    • Voice: open at layer 2 VLAN

      • Handoff is circuits from shared VoIP gateway

    • Data (ISP): open at layer 2 VLAN

      • Shared middle mile via NOAANet

    • Data (xport): SONET and Ethernet services

    • Video: Open at layer 2 VLAN

      • Shared headend available for video providers

  • Partnership model: wholesale retail split mandated by state law

  • Pricing: ZippNet posts wholesale prices. Retailer bills customer


Jackson tn e plus network l.jpg
Jackson, TN, E-Plus Network Access Networks

  • Architecture: FTTH Active Star + PON, video overlay

  • Open for:

    • Voice: open at layer 2

      • Handoff is VoIP packets

    • Data (ISP): open at layer 2

      • No middle mile sharing

    • Data (xport): Ethernet services

    • Video: closed

  • Partnership model: voluntary split to settle a lawsuit

  • Pricing: negotiated pricing. Charge for wholesale service plus percent of retail revenues. JEA bills customer


Kutztown pa hometown utilities hu l.jpg
Kutztown Pa Hometown Utilities (HU) Access Networks

  • Architecture: FTTH ATM PON

  • Open for:

    • Voice: open at layer 2

    • Data (ISP): closed

    • Data (xport): closed

    • Video: closed

  • Partnership model: wholesale retail split

  • Pricing: Negotiated prices

  • Kutztown would have preferred to be open for data and video but could not find any service providers who would enter the market so decided to offer services itself


Spencer iowa municipal utility smu l.jpg
Spencer, Iowa Municipal Utility (SMU) Access Networks

  • Architecture: HFC

  • Open for:

    • Voice: closed

    • Data (ISP): open at network layer

      • No shared backhaul

    • Data (xport): closed until 3/2004; now open

    • Video: closed

  • Partnership model: voluntarily opened to ISPs to gain political support; SMU recently began own retail ISP service

  • Pricing: SMU bills customer for bandwidth, ISP bills for retail service


Amsterdam netherlands proposed l.jpg
Amsterdam, Netherlands (proposed) Access Networks

  • Architecture: FTTH (bids currently being reviewed)

  • Open for:

    • Voice: open at layer 2

    • Data (ISP): open at layer 2 VLAN

      • Shared middle mile via NOAANet

    • Data (xport): SONET, Ethernet services and dark fiber

    • Video: Open at layer 2

    • Partnership model: City owns fiber. An exclusive contract at layer 1 to company that will light fiber at layer 2 and manage dark fiber leasing. This company will wholesale at layer 2 service to competitive service retailers

  • Pricing: not determined


Amsterdam netherlands l.jpg
Amsterdam, Netherlands Access Networks

Source: http://www.citynet.nl/upload/Wholesale-bandwidth-Amsterdam-Citynet.pdf


Observations role of government l.jpg
Observations: Role of Government Access Networks

  • Open Access strongly motivated by State policy

    • Mandatory wholesale retail split in Washington state

    • Onerous burdens in Utah for a muni to offer retail services

  • Voluntary adoption of open access remains rare


Observations legacy business models and service providers l.jpg
Observations: Legacy Business Models and Service Providers Access Networks

  • Historically many ISPs did not own underlying physical network

    • Dialup, DSL

    • there were many ISPs willing to be service providers over a muni infrastructure

  • Rise of CLECs in late 90’s and VoIP providers more recently created group of companies willing to provide voice service over network they didn’t own

  • Video providers (e.g. cable) used to owning the physical network

    • Few video SPs

  • Result: open access for ISP service found many willing SPs compared to voice or video


Observations technology and open access l.jpg
Observations: Technology and Open Access Access Networks

  • Technology choice and open access policy must be aligned

    • e.g. Can’t provide open access video on a video overlay PON

      • Open access video systems are all using IP video

    • If technology chosen first, it constrains business models

    • If business model chosen first, technologies will be chosen to enable it

  • Impact of Everything-Over-IP

    • “Sufficient” IP service enables unrelated SPs to provide voice or video over IP—e.g. Vonage, Movielink

    • So if ISP service is open, all services are open

    • Not that simple:

      • QoS

      • Multicast

      • CPE

      • Business relationship


Observations beyond the last mile l.jpg
Observations: Beyond the Last Mile Access Networks

  • In rural areas, the costs of middle mile services may discourage retail entrants

  • In order to facilitate open access, muni must provide shared services beyond last mile distribution

    • Backhaul

    • Peering point

    • Video headend


Observations open access and pricing l.jpg
Observations: Open Access and Pricing Access Networks

  • A wholesale retail split reduces the ability of the infrastructure owner to price discriminate

  • May make more difficult the recovery of high fixed costs of infrastructure

  • Cost-based pricing of access favors triple play service providers

    • High fixed cost requires multiple services to recover cost

    • May limit number of entrants

  • No consensus to date on how to price open access


A theoretical model of the wholesale retail split l.jpg
A Theoretical Model of the Access Networks Wholesale-Retail Split

  • Demand Model

    • Consumers have different willingness to pay for voice, video and data services: Willingness to pay for a particular service can be modeled by a statistical distribution for a particular market

    • There is correlation between the willingness to pay for voice, video and data for one particular consumer: One can imagine a 3-space where the coordinates of each point give her willingness to pay for voice, video and data services

    • For simplicity, here we assume everyone wants voice – so our demand model is 2-space, where the coordinates of each point give the willingness to pay for data and video


Slide23 l.jpg

Assume a bi-variate Normal distribution to model willingness to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:


Slide24 l.jpg

X to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:1=Homes taking service1 (data) at price P1 (Area BDP1F)X2=Homes taking service2 (video) at price P2 (Area ACP2E)X3=Homes taking service3 (video and data) at price P3 (Area ACDBZ)

A

E

Z

P3

P2

C

B

D

F

P1

P3

Since the willingness to pay is modeled as a bi-variate Normal, each of these areas can be evaluated with definite double integrals


Supply ftth incudes fixed plus variable costs l.jpg
Supply: FTTH Incudes to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:Fixed Plus Variable Costs

  • e.g. for Jackson, TN

    • Fixed=$1059

    • Variable=$1219

Cost = Fixed + R * Variable

$

Slope = avg cost

Fixed

costs

0%

100%

Take Rate

(R = customers / homes passed)

Adapted from Friogo, et.al.


Supply model l.jpg
Supply Model to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

  • Annualized Fixed cost for wiring up the entire market consisting of X homes = F

  • Annualized Fixed Cost of installing CPE and drop loop = C0

  • Annual incremental cost of providing data service (Service 1) per home = C1

  • Annual incremental cost of providing video service (Service 2) per home = C2

  • If X1 homes take data service, X2 homes take video service and X3 take both, annual cost of providing service =

  • F + C0(X1+X2+X3)+ C1X1+ C2X2+ (C1+C2)X3


Possible industry structures l.jpg
Possible Industry Structures to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

  • Vertically Integrated entity (Network owner provides retail service)

    • ‘Verizon’ Model (Profit Maximizing)

    • ‘Bristol’ Model (Welfare Maximizing)

  • Structurally Separated entities (Network owner, either by regulation or choice, is only a wholesaler. The retail market is assumed to be competitive/contestable)

    • ‘Grant County Welfare’ (Welfare Maximizing Wholesaler selling layer 2 service)

    • ‘Grant County Profit’ (Profit Maximizing Wholesaler selling layer 2 service)

    • ‘Greenfield Condo’ Model (Wholesaler selling UNEs or Layer2 access only directly to consumers; consumers buy service from retailers)


Notation l.jpg
Notation to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

  • P0W= Wholesaler’s price of UNE loop

  • PAW= Wholesaler’s price of layer 2 access

  • PiW= Wholesaler’s price of (layer 2) service i;i= 1, 2, 3

  • PjR= Retailer’s price of service j;j= 1, 2, 3

    • j= 1 => Data Service

    • j = 2 => Video Service

    • j= 3 => Data-Video Bundle

  • Wik= Willingness of pay for kth consumer for ith service

  • F, C0, C1, C2, X1, X2, X3as described earlier


  • Industry structure 1 the verizon model l.jpg
    Industry Structure 1: to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:The ‘Verizon’ Model

    • Total Cost of Providing service =F + C0(X1+X2+X3)+ C1X1+ C2X2 +(C1+C2)X3

      • MC1 (data service)=C0+C1

      • MC2 (video service)=C0+C2

      • MC3 (data and video)=C0+C1+C2

      • MC1+MC2>MC3

    • Revenue = P1R X1+ P2R X2+ P3R X3

    • ‘Verizon’ chooses P1R,P2R , P3Rto maximize Profit = (Revenue – Cost)

    • P1R>C0+C1or,P1R=C0+C1+g1 g1>0

    • P2R>C0+C2or,P2R=C0+C1+g2 g2>0

    • P3R>C0+C1+C2or,P3R=C0+C1+C2+g3 g3>0

    • Where ei is the elasticity of demand for service i


    Industry structure 2 the bristol model l.jpg
    Industry Structure 2: to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:The ‘Bristol’ Model

    • Total Profit from Providing service =

    • {P1R X1+P2R X2+P3R X3} - {F+C0(X1+X2+X3)+C1X1+C2X2}

    • Consumer Welfare =

      • Where Kiis the set of subscribers taking ith service and Ki ∩Kj = f

    • ‘Bristol’ chooses P1R,P2R, P3Rto maximize Consumer Welfare subject to a cost recovery constraint (Profit ≥ 0)

    • P1R>C0+C1or,P1R=C0+C1+g1g1>0

    • P2R>C0+C2or,P2R=C0+C1+g2g2>0

    • P3R>C0+C1+C2or,P3R=C0+C1+C2+g3 g3>0

    • Where ei is the elasticity of demand for service i


    Industry structure 4 the grant county profit model l.jpg
    Industry Structure 4: to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:The ‘Grant County Profit’ Model

    • ‘Grant County’ can sell only two services:

    • (1) a data capability service; and (2) a video capability service. The wholesale video service provides sufficient bandwidth to also offer data. Service arbitrage forces P2W= P3W

    • Total Cost of Providing service =F + C0(X1+X2+X3)

    • Revenue = P1W X1+ P2W X2+ P3W X3but, due to arbitrage,

    • Revenue = P1W X1+ P2W (X2+ X3)

    • Grant County chooses P1W,P2Wto maximize Profit = (Revenue – Cost)

    • Where X1, X2, X3determined by

    • P1R= P1W+C1

    • P2R= P2W+C2

    • P3R= P2W+C1+C2

    • Notice that in this case, due to service arbitrage, the profit maximizing price P2W may get set high enough to ‘kill’ the “video only” service (or service 2) –Welfare Implications?


    Industry structure 5 the grant county welfare model l.jpg
    Industry Structure 5: to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:The ‘Grant County Welfare’ Model

    • ‘Grant County’ can sell only two services: (1) a data capability service and (2) a video capability service. The wholesale video service provides sufficient bandwidth to also offer data. Service arbitrage forces P2W= P3W

    • Total Cost of Providing service =F + C0(X1+X2+X3)

    • Revenue = P1W X1+ P2W (X2+ X3)

    • Consumer Welfare =

    • Where X1, X2, X3determined by

    • P1R= P1W+C1

    • P2R= P2W+C2

    • P3R= P2W+C1+C2

    • Grant County’ chooses P1W,P2Wto maximize Consumer Welfare subject to the cost recovery constraint (Profit ≥0)


    Industry structure 3 the greenfield condo model l.jpg
    Industry Structure 3: to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:The ‘Greenfield Condo’ Model

    • ‘Condo’ sells UNE loop (access) at a price P0W (PAW) directlyto the customer

    • Total Profit from Providing wholesale service =P0W (X1+X2+X3) – F (PAW(X1+X2+X3) - F - (X1+X2+X3) C0)

    • Consumer Welfare =

    • Where,

      • P1R= P0W + C0+ C1 orP1R= PAW + C1

      • P2R= P0W + C0+ C2 P2R= PAW + C2

      • P3R= P0W + C0+ C1+ C2 P3R= PAW + C1+ C2

    • If the ‘Condo’ is to maximize Consumer welfare, it has to choose P0W (PAW) such that Consumer welfare is maximized subject to the cost recovery constraint

    • Because of retail competition, retail price is driven to incremental cost

    • Note that result is the same whether Condo sells dark fiber, P0W, or layer 2 service, PAW


    Research questions l.jpg
    Research Questions to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

    • Is Verizon profitable when ‘Grant County Profit’ would not be?

      • Wholesale/retail split reduces ability to price discriminate

      • Could ‘Verizon’ be sustainable where ‘Grant County Profit’ is not?

    • Are there demand scenarios under which ‘Verizon’ leads to more welfare than ‘Grant County Profit’?

    • Does ‘Bristol’ lead to more welfare than ‘Grant County Welfare’?

    • Does ‘Greenfield Condo’ lead to less welfare than ‘Grant County Welfare’?

      • Condo can set only a single Access (UNE) price

    • Answers to these questions will vary depending upon assumed parameters of the WTP distribution. We will explore this space.


    Caveats l.jpg
    Caveats to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

    • We assume incremental costs, Ci , are the same in both vertically integrated and competitive retail cases

      • Competition should drive down incremental costs of services

    • We assume layer 2 costs, C0, are the same whether supplied competitively or by wholesaler

      • See above

    • Double Marginalization: We have assumed a competitive retail industry – how does the above change when it isn’t?

      • Retailers offer differentiated, imperfectly substitutable products

      • Retail entry barriers lead to oligopolistic competition

      • In either case, retail price can be above marginal cost


    Conclusions l.jpg
    Conclusions to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

    • Municipalities are leading the way in experimenting with open access business models

    • Open access is becoming easier as more facility-less service providers emerge and technology matures

    • Government has played a major role in inducing open access models

    • A decision to build an open access system has implications for technology and architecture

      • It may be costly or difficult to retrofit open access on a system originally designed to be closed

    • The viability--and welfare implications--of these models remains unproven, though it is still early and there is much experimentation to find the right formula


    Conclusions37 l.jpg
    Conclusions to pay for data and video: For example, if for data we assume N (35,10) and for video we assume N (45,10) and we assume a correlation coefficient = (-0.5), then the distribution of willingness of pay for 1000 subscribers looks as follows:

    • Benefits of municipal networks (whether open or closed) go beyond the lower prices they may charge to customers

    • As a new competitor, they may force price reductions by incumbents, providing savings even to customers who do not subscribe to the muni system.

      • E.g. in Kutztown, cable competitor charges 40% less in Kutztown than in neighboring communities served by the same headend

    • Cross subsidization by MSOs of communities with municipal competition poses threat to viability of muni operations

      • Unlike DBS, or ILECs, muni systems cannot dry up the cross subsidy by competing across the MSO’s territory