©Anupam Banerjee, Carnegie Mellon Issues in FTTP Industry Structure: Implications of a...

©Anupam Banerjee, Carnegie Mellon

Issues in FTTP Industry Structure:

Implications of a Wholesale-Retail Split

(Pricing in Open Access Networks)

Anupam Banerjee, Marvin SirbuDepartment of Engineering and Public

PolicyCarnegie Mellon University


Some Background Fiber to the Premise (FTTP) exhibits characteristics of a

natural monopoly industry

Probably the only way to have competition, is service level competition – multiple retailers sharing one network

To encourage competition and thereby increase welfare, some states have mandated a Wholesale-Retail split for municipally owned FTTP infrastructure (in order to create a level playing field for retailer service providers): A municipality that owns a network cannot provide retail service – it is free, however, to decide if it wants to sell layer 2 service or dark fiber. Some municipalities choose to be wholesalers by choice.

This work studies the impact of different wholesale-retail arrangements on wholesaler profits and consumer welfare for different wholesaler objectives and compares it to a vertically integrated industry structure, where a network owner can also offer retail voice, video and data services.


Central Office

ServiceProvider A

ServiceProvider B

Home B

Home A

Common Data LinkLayer Equipment

Network

Motivating Question Open Access: Network operator provides wholesale transport to service

providers Industry structure: Do we want to impose structural separation between

infrastructure ownership and service provisioning?– Do sustainable prices exist for an infrastructure-only provider?

Build a supply/demand model and calculate welfare effects for different industry structure models


Two-service model for the 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


X1=Homes taking service1 (data) at price P1 (Area BDP1P3)X2=Homes taking service2 (video) at price P2 (Area ACP2P3)X3=Homes taking service3 (video and data) at price P3

(Area ACDBZ)

Demand Model..

Willingness to Pay

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100 120 140

Data

Vid

eo

P1

P2

A

BC

D

P3

P3

Z


Supply Model 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

Observation: Marginal Cost of Bundle (C0 +C1+C2) is less than the sum of Marginal Cost of Data (C0 +C1) and Marginal Cost of Video(C0 +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

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)– ‘Stockholm’ Model (Wholesaler selling UNEs or Layer2

access only directly to consumers; consumers buy service from retailers)


Notation P0

W = Wholesaler’s price of UNE loop PA

W = Wholesaler’s price of Layer2 access Pi

W = 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, X3 as described earlier


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 + P2

R X2 + P3R X3

‘Verizon’ chooses P1R, P2

R , P3R to maximize Profit = (Revenue

– Cost)P1

R>C0+C1 or, P1R=C0+C1+1 1>0

P2R>C0+C2 or, P2

R=C0+C1+2 2>0

P3R>C0+C1+C2 or, P3

R=C0+C1+C2+3 3>0

Where i is the elasticity of demand for service i

Industry Structure 1: The ‘Verizon’ Model

ii

1


Total Profit from Providing service = {P1

R X1 + P2R X2 + P3

R X3} - {F + C0(X1+X2+X3)+ C1X1 + C2X2}

Social Welfare =

– Where Ki is the set of subscribers taking ith service and Ki ∩Kj =

‘Bristol’ chooses P1R, P2

R , P3R to maximize Social Welfare

subject to a cost recovery constraint (Profit ≥ 0)

P1R>C0+C1 or, P1

R=C0+C1+1 1>0P2

R>C0+C2 or, P2R=C0+C1+2 2>0

P3R>C0+C1+C2 or, P3

R=C0+C1+C2+3 3>0Where i is the elasticity of demand for service i

Industry Structure 2: The ‘Bristol’ Model

ii

1

)(_)()( 332211

321

R

Kk

kR

Kk

kR

Kk

k PWPWPW


What is Service Arbitrage? Verizon/ Bristol can differentiate between data, video

and video & data bundle customers engage in third degree price discrimination.

Grant County Profit/ Welfare can potentially sell data capability, video capability and video & data bundle capability. Since the bandwidth associated with the video capability1 is sufficient to also support data, a retailer can use a video capability to sell a video& data bundle to a subscriber without Grant County Profit/ Welfare being able to discriminate between a only video subscriber and a video & data bundle subscriber – this is service arbitrage.

Therefore a wholesale retail split interferes with the ability of a wholesaler to price discriminate.

1 We assume here that the wholesaler always sells symmetric bandwidth (see caveats at the end)


Implications of Service Arbitrage

VideoC1

P3R=P1

R+P2R-2

P1R=P*1

R

(assume)

P2

Data

P*2 P*3 P3R=P1

R+P2R-2

Grant County

VZ/ Bristol

PiR=Retail price of ith service in vertically

integrated industry structure (VZ/ Bristol)P*i

R=Retail price of ith service in in wholesale/retail split structure (Grant County Profit/ Welfare)

If we assume that the retail price of data remains the same, i.e. P1

R=P*1R, then in

trying to maximize profits, P3Rwill be > P*3

there will be more consumers that consume the bundle at a lower price, leading to an increase in welfare in the W/R split structure. However, the number of consumers taking video only service will decrease, leading to lower welfare in the w/r split structure.


Industry Structure 3: 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 P2=P3

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

Revenue = P1W X1 + P2

W X2 + P3W X3 but, due to arbitrage,

Revenue = P1W X1 + P2

W (X2 + X3)

Grant County’ chooses P1W, P2

W to maximize Profit = (Revenue – Cost)

Where X1, X2, X3 determined by

P1R= P1

W +C1

P2R= P2

W +C2

P3R= P2

W +C1+C2

Notice that in this case, due to service arbitrage, the profit maximizing price P2

W may get set high enough to ‘kill’ the “video only” service (or service 2) – Welfare Implications?


Industry Structure 4: The ‘Grant County Welfare’

Model ‘Grant County’ can sell only two services: (1) a data capability service and (2) a video capability service. Due to service arbitrage, once a video capability service is sold, data is automatic.

Total Cost of Providing service = F + C0(X1+X2+X3) Revenue = P1

W X1 + P2W (X2 + X3)

Social Welfare =

Where X1, X2, X3 determined by P1

R= P1W

+C1 P2

R= P2W

+C2

P3R= P2

W +C1+C2

Grant County’ chooses P1W, P2

W to maximize Social Welfare subject to the cost recovery constraint (Profit ≥0)

)(_)()( 332211

321

R

Kk

kR

Kk

kR

Kk

k PWPWPW


‘Condo’ sells UNE loop (access) at a price P0W

(PAW ) directly to

the customerTotal Profit from Providing wholesale service = P0

W (X1 + X2 + X3) – F

(PAW (X1 + X2 + X3) - F - (X1 + X2 + X3) C0)

Social Welfare = Where,

– P1R= P0

W + C0+ C1 or P1R= PA

W + C1

– P2R= P0

W + C0+ C2 P2R= PA

W + C2

– P3R= P0

W + C0+ C1+ C2 P3R= PA

W + C1+ C2

If ‘Stockholm’ is to maximize social welfare, it has to choose P0W

(PAW ) such that social welfare is maximized subject to the cost

recovery constraintBecause of retail competition, retail price is driven to incremental costNote that result is the same whether Stockholm sells dark fiber, P0

W, or layer 2 service, PAW

Industry Structure 5: The ‘Stockholm’ Model

)(_)()( 332211

321

R

Kk

kR

Kk

kR

Kk

k PWPWPW


Welfare Implications of the different Industry Structures

13.00

14.00

15.00

16.00

17.00

18.00

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

Correlation between willingness to pay for Data & Video (Rho)

To

tal

We

lfa

re (

$ p

er

ho

me

pe

r m

on

th)

Stockholm Welfare

GCW Welfare

Bristol Welfare

VZ Welfare

GCP Welfare

BaseCase

F=5x104

C0=8C1=20C2=301= 35σ1= 102 = 45σ2 = 10

•As expected, welfare maximizing industry structures (Bristol, GCW, Stockholm) create significantly more welfare ($2-$5 per subscriber per month) than their profit maximizing counterparts (Verizon, GCP)•Bristol creates more welfare (up to $0.60 per subscriber per month) than GCW or Stockholm due to its greater ability to price discriminate•Verizon’s profit is marginally higher than GCP’s Profit (up to $0.10 per subscriber per month) because of Verizon’s greater ability to price discriminate


Comparing VZ and GCP..

Both VZ and GCP industry structures produce the same amount of consumer surplus in spite of individual prices being different..

4.50

6.50

8.50

10.50

12.50

14.50

16.50

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

Correlation between willingness to pay for Data & Video (Rho)

$ p

er

ho

me

pe

r m

on

th

GCP Consumer SurplusGCP ProfitVZ ProfitVZ Consumer SurplusVZ Total WelfareGCP Total Welfare

BaseCase

F=5x104

C0=8C1=20C2=301= 35σ1= 102 = 45σ2 = 10


Comparing Verizon and Grant County Profit Prices ..

– VZ sets lower price for Video (by up to $8 per subscriber per month) and has more “only” video subscribers. GCP has a lower Bundle price (by up to $0.50 per sub per month) and has more Video & Data Bundle subscribers. GCP CAN realize sustainable prices.

40.00

45.00

50.00

55.00

60.00

65.00

70.00

75.00

80.00

85.00

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

Correlation between willingness to pay between Data and Video (Rho)

Pri

ce

($

pe

r h

om

e p

er

mo

nth

)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Su

bs

cri

be

rs

Bundle Subscribers(Grant County Profit) Video Subscribers (Grant County Profit)Video Subscriber (Verizon) Data Price (Grant County Profit)Data Price (Verizon) Video Price (Grant County Profit)Video Price (Verizon) Bundle Price (Grant County Profit)Bundle Price (Verizon)

Grant County ConstraintRetail Price of Bundle - Retail Price of Video = Marginal Cost of Data


Comparing VZ and GCP.. VZ/ GCP consumer welfare increases with rho; while

the profits decrease with rho; total welfare decreases with rho

VZ is able to earn higher profits vis-à-vis GCP (of about $0.10 per subscriber per month) only when -0.7<rho<0.7 due to its greater ability to price discriminate, for the above model parameter values

VZ and GCP create almost the same amount of consumer welfare – the increase in welfare due to lower GCP bundle prices trades off with the decrease in welfare that comes from GCP serving fewer video subscribers, for the above model parameter values.

All industry structures create exactly the same amount of consumer welfare and producer profits for rho>0.7, for the above model parameter values


Comparing VZ and GCP for different values for the mean willingness to

pay for data service

Total surplus associated with the video only service increases as the mean willingness to pay for data decreases from 35 to 15,

For the same incremental cost of providing data service (C1=10), the difference between Verizon’s profits and Grant Count’s profits increases from almost zero cents per home per month (1=35), to 10 cents per home per month (1=25) to 30 cents per home per month (1=15)

1.50

3.50

5.50

7.50

9.50

11.50

13.50

15.50

17.50

19.50

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00Coefficient of Correlation (Rho) between willingness to pay for data and video

service

Pro

du

ce

r P

rofi

t p

er

su

bs

cri

be

r p

er

mo

nth

VZ mu1=35

GCP mu1=35

VZ mu=25

GCP mu=25

VZ mu=15

GCP mu=15


Caveats We have considered a simplified 2-service model. The next step

would be to consider a “more realistic” 3-service model 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

We assume that the wholesaler only sells symmetric bandwidth. For example, the wholesaler could sell an asymmetric connection (of 32Kbps upstream and 4Mbps downstream) as the ‘video capability’ making it impossible for the ‘video capability’ to carry both video and data.

Date post:	23-Dec-2015
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