FEDERAL COMMUNICATIONS COMMISSION

MEDIA BUREAU STAFF RESEARCH PAPER

Most-Favored Customers 

In the Cable Industry

By Nodir Adilov and Peter J. Alexander

September 2002

2002 -

14
Most-Favored-Customers in the Cable

Industry

Nodir Adilov and Peter J. Alexander

September 25, 2002

ABSTRACT

In this paper, we explore the implications of most-favored-customer

clauses in the cable industry. We show that the introduction of

a most-favored-customer clause for large buyers will increase their

profitability, and that the seller’s profits may decrease. We examine the experimental cable bargaining results of Bykowsky, Kwasnica, and Sharkey (2002), and compare these results to our model.

We find that the results of the Bykowsky-Kwasnica-Sharkey experiments regarding the effect of a most-favored-customer agreement are

consistent with our findings.

I Introduction

In this paper, we explore the use of ‘most-favored-customer’ clauses (hereafter, MFC) in the cable industry.1 We examine the impact of MFC clauses

on bargaining outcomes between buyers and sellers, and show that these

outcomes depend on the market share of the larger buyers and the relative

valuation of the seller’s programming to different buyers.

The paper is organized as follows. We begin with the general case

with many buyers and sellers, and show that in the absence of capacity

constraints and MFC arrangements the competitive outcome obtains. We

then introduce channel capacity constraints, and demonstrate that the

competitive outcome still obtains. Next, we explore the case of large firms

and MFC clauses. We show that the introduction of MFC clauses can disadvantage sellers and small buyers. We find that as the market share of the

large buyer increases, smaller buyers are more likely to be disadvantaged.

∗Adilov: Department of Economics, Cornell University, email: na47@cornell.edu; Alexander: Federal Communications Commission, email: palexand@fcc.gov. We thank David Sappington and William Sharkey for their

many thoughtful and useful comments. Any errors are our own. The views expressed in this paper are those of

the authors, and do not necessarily represent the views of the Federal Communications Commission, any of its

Commissioners, or other staff.

1The MFC represents a formal or quasi-formal arrangement by which the larger buyer pays no more than the

highest amount of any smaller buyer.

1
Specifically, we find that if there are differences in the relative valuation

of programming among buyers such that the larger buyer has a greater

per-customer valuation, smaller buyers may be precluded from access to

the programming because of its relative expense. In the penultimate section, we extend our model to accommodate the methodology utilized in

the experimental studies conducted by Bykowsky, Kwasnica, and Sharkey

(2002).2 Our prediction that an MFC arrangement yields market power is

supported by their data.3 Finally, we make some concluding remarks.

II The General Case of Multiple Buyers and

Sellers

Assume that risk neutral content providers(also known as cable networks)have

positive fixed (sunk) costs of producing and zero marginal costs of distributing their product. These content providers will be referred to as

sellers (of programming). There are I sellers. The sellers earn revenue by

selling their product to cable owners. The cable owners will be referred to

as buyers.

For simplicity, we begin by assuming that sellers make a ‘take it or leave

it’ offer to each prospective buyer and denote by T1,i, T2,i, ..., TM,i the total

payments to seller i from buyers 1, 2, ..., M respectively, if the product is

sold. There are M buyers, each of whom has N1, N2, ..., NM subscribers,

where PM

m=1 Nm = N.

We assume that buyer m has positive fixed costs Fm and zero program

provision costs (an assumption we relax later in the paper). We note that

given I sellers with I products, every buyer has 2I possible programming

choices. We denote a programming choice of buying only seller i’s program

by E

i

1

, where subscript 1 denotes the program package consisting of only

one program and the superscript i denotes seller i. The programming

package consisting of 2 products, e.g., products from seller k and seller l,

is given by E

k,l

2 ≡ E

k

1 + E

l

1 ≡ E

k

1 ∪ E

l

1

.

The program package that includes all programs from all sellers is denoted by EI or E

1,2,..,I

I

. The revenue that buyer m can derive from programming package E ̃ is denoted by Vm(E ̃). Buyer m’s objective is to

maximize profits

πm = Vm(E ̃) −

X

i:Ei

1∈E ̃

Tm,i (1)

2Bykowsky, Mark, Anthony Kwasnica, and William Sharkey, ”Horizontal Concentration in the Cable Television

Industry: An Experimental Analysis,” Federal Communications Commission, Office of Plans and Policy, Working

Paper Series, Number 35, June, 2002.

3Bykowsky, Kwasnica, and Sharkey use the term ‘most-favored-nation’ which follows the tradition in the

experimental literature. We prefer to use the term ‘most-favored-customer’ for the sake of precision. Both terms

as used refer to the same thing.

2
by choice of programming package E.  ̃ We assume that the value of any

combination of programs is positive, and that the ‘value correspondence’

satisfies decreasing marginal returns. More formally, we assume that for

any buyer m, any two programming packages Eˆ and E ̃, and for any seller

i’s program such that E

i

1 6⊆ E ̃ ∪ Eˆ, the following inequality holds:

Vm(E ̃ + E

i

1

) − Vm(E ̃) ≥ Vm(Eˆ + E ̃ + E

i

1

) − Vm(Eˆ + E ̃) > 0 (2)

i.e., Vm is sub-modular.

Claim 1: With M buyers and I sellers, the unique Nash Equilibrium

transfer price for each seller k to buyer m is:

Tm,k = Vm(EI ) − Vm(EI − E

k

1

) (3)

and all buyers buy programs from all sellers.

Proof of Claim 1: First, we show that if there is a Nash Equilibrium,

it is an equilibrium where all buyers buy from all sellers. Second, we show

that in the equilibrium where all buyers buy from all sellers, (3) must

hold. Finally, we prove by induction that the transfer price Tm,i is in fact

a unique Nash Equilibrium transfer price.

By contradiction, assume that in some Nash Equilibrium, buyer m did

not buy the program from seller i. Then, seller i’s payoffs from buyer

m are zero. Now, denote by E

∗

the value of the set of programs bought

by buyer m. Since V (E

∗ + Ei

1

) > V (E

∗

), seller i is strictly better off

(i.e., obtains positive payoffs) by charging any transfer price in the set

T ∈ [0, V (E

∗ + Ei

1

) − V (E

∗

)], and buyer m finds it optimal to buy from

seller i.

Next, assume that there is a Nash Equilibrium where all buyers buy

from all sellers. Then, it must be the case that buyer m prefers buying

from all sellers to buying from any set of (I − 1) sellers; i.e., the following

condition holds for all m and k:

Vm(EI ) −

X

I

i=1

Tm,k ≥ Vm(EI − E

k

1

) −

X

I

i=1

Tm,i − Tm,k (4)

Assume (4) holds with a strict inequality for any seller l. Then, seller l

can increase it’s payoffs by increasing the transfer price by an epsilon small

amount, while condition (4) still holds for all k = 1, ..., I. This is a contradiction. Therefore, (4) must hold with equality Vm(EI ) −

PI

i=1 Tm,i =

Vm(EI − E

k

1

) −

PI

i=1 Tm,i − Tm,k, which simplifies to (3).

We have shown that for all sellers it is optimal to charge Tm,k. In

order to ensure that this is in fact a Nash Equilibrium, we must check

that for any buyer m the value of buying from all sellers is greater than or

equal to the value of any programming package from the remaining 2I − 1

possibilities. To begin, denote by T

n

m,k the transfer price defined in (3)

when there are a total of I = n sellers. Clearly, when I = 1,

T

1

m,k = Vm(E

1

1

) (5)

3
is a Nash Equilibrium of the game, and all buyers buy from the seller.

Now, assume that T

n

m,k is a Nash Equilibrium outcome for some I =

n ≥ 1. Then, it suffices to show that T

n+1

m,k is also a Nash Equilibrium,

which we do by showing that buyer m’s benefit from buying all available

n + 1 programs is positive. We note that Vm(En+1) −

Pn+1

i=1 T

n+1

m,i equals

Vm(En+1 − E

n+1

1

) −

Pn

i=1 T

n+1

m,i . We then note that Vm(En+1 − E

n+1

1

) − Pn

i=1 T

n+1

m,i ≥ Vm(En+1 − E

n+1

1

) −

Pn

i=1 T

n

m,i ≥ Vm(En) −

Pn

i=1 T

n

m,i ≥ 0

where the last inequality holds due to our assumption that T

n+1

m,i = T

n

m,i

Any buyer m’s payoffs are positive when there are n+1 sellers charging

T

n+1

m,i , and this buyer is better off buying n+1 programs than any program

package consisting of n programs. But, we know from our induction assumption for I = n, that when there are n sellers, buying from all sellers

is preferred to all other choices. Therefore, with n + 1 sellers, buying from

all n + 1 sellers is preferred to any other programming package. Then, for

I = n + 1, a Nash Equilibrium consists of sellers charging T

n+1

m,i and all

buyers buying from all sellers. By construction this Nash Equilibrium is

unique. Q.E.D.

One simple interpretation of Claim 1 is straightforward: when there are

no capacity restraints, cable operators buy all network programs. However,

in practice, cable operators do not buy from all sellers. We offer several

explanations which we explore in the next two sections. First, we argue

that there may exist capacity constraints on cable operators. Second, we

explore the possible effects on program carriage in the presence of socalled ’most-favored-customer’ clauses. In these cases, larger buyers are

able to obtain prices that are at least as favorable as the prices secured

by the smaller buyers, i.e., smaller buyers do not obtain asymmetric price

discounts.

III The General Case of Multiple Buyers

and Sellers with Capacity Constraints

We introduce the idea of capacity constraints by noting that the total cost

of any given cable operator m, excluding the payments to cable networks,

is:

T Cm = Fm +

X

k

i=1

Cm(i) (6)

where Fm are the fixed costs and Cm(i) is the marginal cost of introducing

i’s program. We assume that 0 ≤ Fm and Cm(i) ≤ Cm(i + 1) for all i

and all m. These assumptions capture all possible cost structures with

non-decreasing marginal costs.

We also assume that for any buyer m, any two programs E

i

1

and E

k

1

,

and Eˆ such that (E

i

1∪E

k

1

)∩Eˆ = ∅ where Vm(E

i

1

) ≤ Vm(E

k

1

), the inequality

4
Vm(E

i

1 + Eˆ) ≤ Vm(E

k

1 + Eˆ) holds. Simply put, we are assuming that if

a buyer prefers one program to another, the buyer will always prefer this

program to the other, regardless of the combination of other programs.

We are now able to show that under these conditions, if buyers cannot

influence the bargaining outcomes between other buyers, there is unique

Nash Equilibrium outcome. Furthermore, this outcome is efficient.

Since, by assumption, any given buyer cannot influence bargaining outcomes among other buyers, it suffices to show the result for only one buyer.

We begin with any buyer m. Without loss of generality, we assume that

for this buyer Vm(E

1

1

) ≥ Vm(E

2

1

) ≥ ... ≥ Vm(E

I−1

1

) ≥ Vm(E

I

1

) > 0. If our

assumptions hold, there is a unique Nash Equilibrium solution such that,

if

Cm(I) ≤ Vm(EI ) − Vm(EI − E

I

1

) (7)

then,

Tm,k = Vm(EI ) − Vm(EI − E

k

1

) − Cm(I) (8)

and the buyer buys from all sellers.

This is a direct extension of Claim 1. The condition on the cost function implies that there is a positive value to be obtained by including

an additional program regardless of the current combination of programs.

Therefore, all programs will be bought in the unique Nash Equilibrium.

The transfer price charged by a seller will be such that the buyer is indifferent between buying and not buying this additional program. Also, if

our assumptions hold, there is a second unique Nash Equilibrium solution

such that if:

Cm(1) ≥ Vm(E

1

1

) (9)

then buyer m does not buy from any seller regardless of the transfer price.

The condition placed on the cost structure implies that the net benefit

from buying any program is negative. Clearly, no programs will be bought

in this equilibrium.

Finally, if our assumptions hold, there is a third unique Nash Equilibrium solution such that if:

Cm(I) > Vm(EI ) − Vm(EI − E

I

1

) (10)

and

Cm(1) < Vm(E

1

1

) (11)

then there exists a k ∈ {1, 2, ..., I −1} such that Vm(E

1,2,..,k

k

)−Vm(E

1,2,..,k

k −

E

k

1

) ≥ Cm(k) and Cm(k + 1) > Vm(E

1,2,..,k,k+1

k+1 ) − Vm(E

1,2,..,k,k+1

k+1 − E

k+1

1

)

The transfer price is given by:

Tm,i = Vm(E

1,2,..,k

k

) − Vm(E

1,2,..,k

k − E

i

1

)−

max{Cm(k), Vm(E

1,2,..,k

k − E

i

1 + E

k+1

1

) −

Vm(E

1,2,..,k

k − E

i

1

)} (12)

5
for all ≤ i ≤ k, and Tm,i ≥ 0 for k + 1 ≤ i ≤ I. In this case, buyer m

buys from the first k sellers.

This condition states that the net value of buying just one program is

positive, and the net value of buying the last program after buying all other

I−1 programs in negative. Clearly, there exists a k between 1 and I−1 such

that the net value of buying from first k sellers (ignoring transfer prices)

is positive and the net value of buying from the (k + 1)’s seller (ignoring

transfer prices) is negative. Thus, the buyer will buy, at most, k programs.

Since the value of seller i’s program is never less than the value of seller

(i + 1)’s program, it is straightforward to see that if seller i is served then

seller i+1 should also be served in any Nash Equilibrium. This implies that

sellers k + 1, ..., I are not served in any Nash Equilibrium. Seller k must

be served in any Nash Equilibrium, since it can always charge Tm,k = 0

and the buyer buys from k, either by replacing some of its programs by

program k or by keeping all other programs.

Therefore, if there is a Nash Equilibrium, then all k programs will be

bought. If there is a Nash Equilibrium with k sellers served, then it should

be the case that the buyer is indifferent between buying from any seller

i as compared to not buying from that seller, and to replacing it with

any other program from any of remaining I − k sellers’ programs i.e., for

1 ≤ i ≤ k, (7) holds. Just as in Claim 1,

Tm,i ≥ 0 (13)

and

Vm(E

1,2,..,k

k

) −

X

k

i=1

Cm(i) −

X

k

i=1

Tm,i ≥ 0 (14)

and both buyers and sellers accept these transfer prices. Q.E.D.

Optimality implies that all programs that have a marginal value above

marginal cost will be broadcast. The claim above shows that under our

assumption of constrained capacity, the market outcome is efficient.

IV Most-Favored-Customer Clauses

Assume there are two sellers and two types (sizes) of buyers. Buyer one

is large, and is able to obtain MFC concessions from both sellers. Denote

v1(1) as buyer one’s per customer valuation of seller one’s product, v1(1+2)

as buyer one’s valuation of having both sellers’ products, and v2(2) as buyer

two’s valuation of seller two’s product.

We also assume that assumption one, given in equation (Section 1,

Equation 2) still holds, i.e., v1(1) + v1(2) > v1(1 + 2) and v2(1) + v2(2) >

v2(1 + 2). We know that the Nash Equilibrium prices under the nonMFC provisions are t

∗

11 = v1(1 + 2) − v1(2), t

∗

12 = v1(1 + 2) − v1(1),

t

∗

21 = v2(1 + 2) − v2(2), and t

∗

22 = v2(1 + 2) − v2(1), where the t

∗ are

6
the equilibrium non-MFC transfer prices. Using these assumptions, we

consider the following four cases.

First, we consider the case where t

∗

11 ≤ t

∗

21 and t

∗

12 ≤ t

∗

22. In this

case, both the MFC and non-MFC treatments give the same prices and

outcomes since the MFC provisions do not restrict the sellers behavior in

any fashion.

Second, we explore the case where t

∗

11 > t∗21 and t

∗

12 ≤ t

∗

22. In this case,

the MFC clause only affects the first seller, and the seller has two options.

Seller 1 could charge (A) t11 = t21 = t

∗

21 in which case both buyers buy

from seller one. Seller one’s revenue in this case is N ·t

∗

21 = (PM

m=1 Nm)·t

∗

21

and seller two’s best response to seller one’s price is to charge t12 = t

∗

12

and t22 = t

∗

22. Or, seller 1 could charge (B) t11 = t21 = t

∗

11 and sell only

to buyer one. In this case, seller one’s revenue is N1 · t

∗

11 and seller two’s

best response is to charge t12 = t

∗

12 and t22 = v2(2) if v2(1) − t

∗

11 < 0

and t12 = t

∗

12 and t22 = v2(2) − v2(1) + t

∗

11 if v2(1) − t

∗

11 ≥ 0. Seller

one prefers B to A if N · t

∗

21 < N1 · t

∗

11 which we write equivalently as

N1

N

· (v1(1 + 2) − v1(2)) > v2(1 + 2) − v2(2) where N1

N

is firm one’s market

share.

Third, we have the case where t

∗

11 ≤ t

∗

21 and t

∗

12 > t∗22. We notice

immediately that this case is symmetric to case two and therefore the

results are the same.

Fourth, we have the case where t

∗

11 > t∗

21 and t

∗

12 > t∗

22. In this case, the

MFC arrangements restrict both sellers, and each seller has three choices:

(1) provide the product only to buyer one, (2) provide the product to only

buyer two, or (3) provide the product to both buyers.

In the table that follows, we have listed each of the possible combinations for the sellers.

Seller One

SellerTwo

Buyer One Buyer Two Both Buyers

Buyer One a b c

Buyer Two d e f

Both Buyers g h i

As we shall demonstrate, (b), (d), (e), (f), and (h) can never be part

of a Nash Equilibrium, while (a), (i), (c), and (g), can be part of a Nash

Equilibrium.

We note immediately that (e) cannot be a Nash Equilibrium. If both

sellers serve only buyer two, then t21 = t

∗

21 and t22 = t

∗

22, and then t11 = t

∗

21

and t12 = t

∗

22. But at these transfer prices, buyer one finds it optimal to buy

from both sellers. It is also clear that (f) and (h) cannot be Nash for the

same reasons given for (e). Next, assume (b) is a Nash Equilibrium. Then,

7
buyer one buys only from seller one, and buyer two buys only from seller

two. However, this is not incentive compatible for seller two. Seller two

can always charge a positive price to buyer one (that buyer one accepts)

and increase it’s profits. Given the symmetry of (d) and (b), (d) cannot

be a Nash Equilibrium.

Next, we explore the conditions under which (a), (i), (c), and (g) are

Nash Equilibria.

In the first case, (a) is a Nash Equilibrium if t

∗

11 ·

N1

N1+N2

≥ V2(1) > t∗

21

and t

∗

12 ·

N1

N1+N2

≥ V2(2) > t∗

22. In this case, buyer one buys both products,

and buyer two does not buy any product. Seller one’s profits are t

∗

11, and

seller two’s profits are t

∗

12.

In the second case, (g) is a Nash Equilibrium if t

∗

11 ·

N1

N1+N2

≤ t

∗

21 and

t

∗

12 ·

N1

N1+N2

> t∗

22 or V2(1) > t∗

11 ·

N1

N1+N2

> t∗

21 and V2(2) > t∗

11 ·

N1

N1+N2

> t∗

22

and N1 ·(t

∗

12 − t

∗

11) ≤ [V2(2) − V2(1)](N1 + N2). In this case, seller one sells

to buyer one only, while seller two sells to both buyers.

In the third case, (c) is a Nash Equilibrium if t

∗

11 ·

N1

N1+N2

> t∗

21 and

t

∗

12 ·

N1

N1+N2

≤ t

∗

22 or V2(1) > t∗11 ·

N1

N1+N2

> t∗

21 and V2(2) > t∗11 ·

N1

N1+N2

> t∗

22

and N1 ·(t

∗

12 − t

∗

11) ≤ [V2(2) − V2(1)](N1 + N2). In this case, seller one sells

to both buyers, and seller two sells to buyer one only.

Finally, (i) is a Nash Equilibrium if t

∗

11·

N1

N1+N2

≤ t

∗

21 and t

∗

12·

N1

N1+N2

≤ t

∗

22.

In this case, both sellers sell to both buyers.

When the MFC affects both sellers, it is optimal for the sellers to

always sell to buyer one. In this case, only buyer two’s profits potentially

decrease, while buyer one’s profits are never decreasing. The higher the

valuation of the program for the large buyer as compared to the smaller

buyer, the more likely that the smaller buyers will not be able to buy the

”MFC” program. This effect depends on two basic factors: (1) the large

buyer’s market share, and (2) the relative per-customer valuation of the

programs to different buyers.

V The Bykowsky-Kwasnica-Sharkey Results

Bykowsky, Kwasnica, and Sharkey (2002), report results of experimental

studies that explore bargaining among buyers and sellers in the cable industry. These results give us an opportunity to evaluate the predictive

power of our model. However, in order to evaluate the results of these

experiments in the context of our MFC model, we must first extend the

model given in Section 4 to accommodate multiple buyers and a sequential

bargaining process. In the context of this extended model, we can then

show that the Bykowsky-Kwasnica-Sharkey experimental results relating

to MFC treatments are broadly consistent with our theory.

We start by modelling a bargaining process with one seller and multiple buyers, and then extend our MFC model to include multiple buyers

8
and sellers. We model this bargaining process as one in which the seller’s

choices are independent, which implies that a model with a single seller is

reasonable. The assumption of independence among buyers is consistent

with the experimental framework employed by Bykowsky, Kwasnica, and

Sharkey (2002). Finally, we extend our model to accommodate informational asymmetries.

We begin by assuming that without a most-favored-customer provision,

seller i is charging t

∗

1

, t∗

2

, t∗

3

, ..., t∗

M per customer transfer prices to buyers

1, 2, 3, ..., M respectively. Assume that buyer one has the most customers,

i.e., N1 > Nm for all m ≥ 2. Now, assume that buyer one is able to obtain

‘most-favored-customer’ terms requiring the seller to charge a per customer

price no more than the minimum of prices charged to other buyers, i.e.,

t1 ≤ min{t2, t3, ..., tM}. We note that if t

∗

m ≥ t

∗

1

for all m ≥ 2, then the

MFC provision will have no effect on a seller’s decision.

For simplicity, assume that t

∗

takes four possible values 0 = t

∗

4 < t∗3 <

t

∗ < t∗

2

. In fact, this analysis applies to any finite number of buyers. In the

present case, there are some buyers with (non-MFC) transfer prices above

t

∗

1

, there are some buyers with (non-MFC) transfer prices below t

∗

1

, and

there are some buyers who do not buy from seller i, denoted by t

∗

4 = 0. We

denote customers served by different transfer prices t

∗

P

k byn1 = N1; n2 =

t

∗m=t

∗

2

Nm; n3 =

P

t

∗m=t

∗

3

Nm; and n4 =

P

t

∗m=t

∗

4

Nm where P4

k=1 nk = N.

The MFC arrangements do not affect the buyers who are paying above

buyer one’s price. Given the MFC constraint, the seller has two options.

First, the seller could charge t1 = t3 = t

∗

1

and t2 = t

∗

2

. In this case, the seller

serves only the first and second type of buyers, and the seller’s revenue is

r1 = n1 ·t

∗

1 + n2 ·t

∗

2

. Or, the seller could charge t1 = t3 = t

∗

3

and t2 = t

∗

2

. In

this case, the seller serves all the buyers that it would serve without the

MFC and the seller’s revenue is r2 = (n1 + n3) · t

∗

3 + n2 · t

∗

2

. We note that

only the first and second buyer types are served if r1 > r2 ⇔ n1

n1+n3

>

t

∗

3

t

∗

1

.

Notice the higher n1 (the market share of buyer one), the more likely

it is that smaller buyers will not buy programming. Also, note that buyer

one always buys the product and pays, at most, the price under the nonMFC provision. These results are consistent with our findings in Section

4.

As noted above, the model we have constructed must be amended

to accommodate the information asymmetries embedded in the sequential

bargaining framework of Bykowsky, Kwasnica, and Sharkey (2002). Specifically, in the Bykowsky-Kwasnica-Sharkey model, the sellers do not know

the buyers’ valuation, and thus must form some expectation regarding the

willingness-to-pay on the part of each individual buyer. Moreover, the

seller must determine an optimal trading sequence. Amending our model

to accommodate these conditions is a simple exercise in straightforward

logic, as we demonstrate next.

Assume that we have two buyers and single seller where the seller does

9
not know the buyer’s valuation of the seller’s product. As we showed in

Section 4 (equilibria a,c,g,i), it is always optimal for the seller to trade

with the larger buyer, but not the smaller buyer. Thus, the seller will

always want to trade with the biggest buyer first and hence the outcome

of the game is the same as if the seller knew, with certainty, the outcome of

negotiations with other buyers. Since trading with the smaller buyer first

would lock the seller into equilibrium i, if we extend the analysis to the case

with more than two buyers, we conclude that the seller would always want

to trade with the biggest buyer first. The determination of a particular

equilibrium will depend on the biggest buyer’s market share, the relative

valuation of of programming by different buyers, and the uncertainty of

the bargaining outcome with the remaining buyers.

Four of the results of the Bykowsky-Kwasnica-Sharkey (2002) experiments are germane to our model. First, Bykowsky, Kwasnica, and Sharkey

find that with no channel capacity constraints and no MFC clauses, all of

the sellers were able to conduct profitable trades, which is precisely the

result our model predicts in Section 2. Second, Bykowsky, Kwasnica, and

Sharkey find that with capacity constraints and no MFC clauses, a seller’s

bargaining power decreased, while a buyer’s bargaining power increased

relative to the case of no capacity constraints. This result is consistent

with our model, as can be seen by comparing (3) in Section 2, with (3)

and (7) in Section 3, and noting the extra negative terms in Section 3.

Third, Bykowsky, Kwasnica, and Sharkey find that the existence of an

MFC clause increases the profitability of MFC buyers, a result our (extended) Section 4 and 5 model predicts. Finally, note that in our model

(where the sellers can make take-it-or-leave-it offers, by assumption), the

presence of an MFC arrangement is the only source by which large firms

exhibit greater market power. This is exactly paralleled by the results of

the Bykowsky-Kwasnica-Sharkey study.

VI Conclusion

In this paper, we explored the use of ‘most-favored-customer’ clauses in the

cable industry. We examined the impact of MFC clauses on bargaining

outcomes between buyers and sellers, and showed that these outcomes

depended on the market share of the larger buyers and the relative percustomer valuation of the seller’s programming to different buyers.

We showed that both with and without channel capacity constraints, in

the absence of MFC clauses, the market outcome is efficient. However, the

introduction of MFC clauses can disadvantage sellers and small buyers. We

found that as the market share of the large buyer increases, smaller buyers

are more likely to be disadvantaged. Specifically, we found that if there is

a disparity in the relative valuation of programming among buyers, in the

case where the large buyer has a greater per-customer valuation, smaller

10
buyers may be precluded from access to the programming because of its

relative expense.

We extended our model to accommodate the methodology utilized in

the experimental studies conducted by Bykowsky, Kwasnica, and Sharkey

(2002) and demonstrated that our prediction that an MFC arrangement

yields market power is supported by their data. Bykowsky, Kwasnica,

and Sharkey find that with no channel capacity constraints and no MFC

clauses, all of the sellers were able to conduct profitable trades, which is

precisely the result our model predicts in Section 2. Consistent with the

experimental results, our model predicts that under capacity constraints

and no MFC clauses, a seller’s bargaining power decreases, while a buyer’s

bargaining power increases relative to the case of no capacity constraints.

Bykowsky, Kwasnica, and Sharkey’s findings that the existence of an MFC

clause increases the profitability of MFC buyers is a prediction of our

(extended) Section 4 and 5 model. In our model, the presence of an MFC

arrangement is the only source by which large firms exhibit greater market

power. This is exactly paralleled by the results of the Bykowsky-KwasnicaSharkey study.

11